eLife Assessment

This fundamental work has the potential to advance our understanding of brain activity using electrophysiological data, by proposing a completely new approach to reconstructing EEG data that challenges the assumptions typically made in the solutions to Maxwell’s equations. Convincing evidence for the superior spatio-temporal resolution of this method is provided through a number of experiments, including simultaneous FMRI/EEG acquisitions. This work will be of broad interest to neuroscientists and neuroimaging.

eLife Assessment

The authors investigated the mechanisms underlying the pause in striatal cholinergic interneurons (SCINs) induced by thalamic input, identifying that Kv1 channels play a key role in this burst-dependent pause. The experimental evidence is convincing.<br /> The study provides important mechanistic insights into how burst activity in SCINs leads to a subsequent pause, highlighting the involvement of D1/D5 receptors.

Reviewer #1 (Public review):

Summary:<br /> Tubert C. et al. investigated the role of dopamine D5 receptors (D5R) and their downstream potassium channel, Kv1, in the striatal cholinergic neuron pause response induced by thalamic excitatory input. Using slice electrophysiological analysis combined with pharmacological approaches, the authors tested which receptors and channels contribute to the cholinergic interneuron pause response in both control and dyskinetic mice (in the L-DOPA off state). They found that activation of Kv1 was necessary for the pause response, while activation of D5R blocked the pause response in control mice. Furthermore, in the L-DOPA off state of dyskinetic mice, the absence of the pause response was restored by the application of clozapine. The authors claimed that 1) the D5R-Kv1 pathway contributes to the cholinergic interneuron pause response in a phasic dopamine concentration-dependent manner, and 2) clozapine inhibits D5R in the L-DOPA off state, which restores the pause response.

Strengths:<br /> The electrophysiological and pharmacological approaches used in this study are powerful tools for testing channel properties and functions. The authors' group has well-established these methodologies and analysis pipelines. Indeed, the data presented were robust and reliable.

The authors addressed all concerns I raised. Presented data are convincing and support their claims.

Reviewer #2 (Public review):

Summary:<br /> This manuscript by Tubert et al. presents the role of D5 receptors (D5R) in regulating the striatal cholinergic interneuron (CIN) pause response through D5R-cAMP-Kv1 inhibitory signaling. Their findings provide a compelling model explaining the "on/off" switch of the CIN pause, driven by the distinct dopamine affinities and the balance of D2R and D5R. Furthermore, the study bridges their previous finding of CIN hyperexcitability (Paz et al., Movement Disorder 2022) with the loss of the pause response in LID mice and demonstrates the restore of the pause through D1/D5 inverse agonist clozapine.

Strengths:<br /> The study presents solid findings, and the writing is logically structured and easy to follow. The experiments are well-designed, properly combining ex vivo electrophysiology recording, optogenetics, and pharmacological treatment to dissect / rule out most, if not all, alternative mechanisms in their model.

Weaknesses (fixed in this revision):<br /> In this round of revision, the authors have included additional experiments examining the role of D2R, and the possible clozapine effects on serotonin receptors in the LID off -L-DOPA ex vivo slices. Although, to our surprise, D2R agonism using quinpirole and sumanirole failed to restore the CIN pause, this study still provides new insights into the balance between D2R and D5R in modulating CIN pause.

Overall, the authors' response adequately addressed concerns raised in the previous revision.

Reviewer #3 (Public review):

The authors have addressed all of my concerns. Congratulations!

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Tubert C. et al. investigated the role of dopamine D5 receptors (D5R) and their downstream potassium channel, Kv1, in the striatal cholinergic neuron pause response induced by thalamic excitatory input. Using slice electrophysiological analysis combined with pharmacological approaches, the authors tested which receptors and channels contribute to the cholinergic interneuron pause response in both control and dyskinetic mice (in the L-DOPA off state). They found that activation of Kv1 was necessary for the pause response, while activation of D5R blocked the pause response in control mice. Furthermore, in the L-DOPA off state of dyskinetic mice, the absence of the pause response was restored by the application of clozapine. The authors claimed that 1) the D5R-Kv1 pathway contributes to the cholinergic interneuron pause response in a phasic dopamine concentration-dependent manner, and 2) clozapine inhibits D5R in the L-DOPA off state, which restores the pause response.

Strengths

The electrophysiological and pharmacological approaches used in this study are powerful tools for testing channel properties and functions. The authors' group has well-established these methodologies and analysis pipelines. Indeed, the data presented were robust and reliable.

Weaknesses:

Although the paper has strengths in its methodological approaches, there is a significant gap between the presented data and the authors' claims.

The authors answered the most of concerns I raised. However, the critical issue remains unresolved.

I am still not convinced by the results presented in Fig. 6 and their interpretation. Since Clozapine acts as an agonist in the absence of an endogenous agonist, it may stimulate the D5R-cAMP-Kv1 pathway. Stimulation of this pathway should abolish the pause response mediated by thalamic stimulation in SCINs, rather than restoring the pause response. Clarification is needed regarding how Clozapine reduces D5R-ligand-independent activity in the absence of dopamine (the endogenous agonist). In addition, the author's argued that D5R antagonist does not work in the absence of dopamine, therefore solely D5R antagonist didn't restore the pause response. However, if D5R-cAMP-Kv1 pathway is already active in L-DOPA off state, why D5R antagonist didn't contribute to inhibition of D5R pathway? Since Clozapine is not D5 specific and Clozapine experiments were not concrete, I recommend testing whether other receptors, such as the D2 receptor, contribute to the Clozapine-induced pause response in the L-DOPA-off state.

Thank you for the opportunity to clarify this point. It seems there may have been a misunderstanding regarding our proposal about clozapine's mechanism of action. We are not suggesting that clozapine acts as an agonist, but rather as an “inverse agonist”. Unlike classical agonists, inverse agonists produce a pharmacological effect opposite to that of an agonist. Although clozapine is best known for its antagonistic effects on dopamine and serotonin receptors, under conditions where no endogenous agonist is present, it has been shown to reduce the constitutive activity of D1 and D5 receptors (PMID: 24931197). This is explained in lines 240-254 in the Results section.

In contrast, the prototypical and selective D1/D5 receptor antagonist SCH23390 does not exhibit inverse agonist properties and would not be expected to produce effects in the absence of an agonist (PMID: 7525564). The observation that SCH23390 blocks the effects of clozapine in dopamine-depleted animals strongly supports the idea that clozapine acts through D1/D5 receptors. This is now clarified in lines 257264.

To further address your comments, we now include a new figure (Figure 6) presenting experiments that show D2-type receptor agonists do not restore the pause response in dyskinetic mice in the off-L-DOPA condition. These results are described in a new subsection of the Results section and discussed in a newly added paragraph in the Discussion (lines 369-380).

Finally, to exclude a potential contribution of serotonin receptors to clozapine’s effects, we have expanded what is now Figure 7 (formerly Figure 6) to show that clozapine continues to restore the pause response even in the presence of a serotonin receptor antagonist in the bath.

All these results are further discussed in lines 342-360.

Reviewer #2 (Public review):

Summary:

This manuscript by Tubert et al. presents the role of D5 receptors (D5R) in regulating the striatal cholinergic interneuron (CIN) pause response through D5R-cAMP-Kv1 inhibitory signaling. Their findings provide a compelling model explaining the "on/off" switch of the CIN pause, driven by the distinct dopamine affinities of D2R and D5R. This mechanism, coupled with varying dopamine states, is likely critical for modulating synaptic plasticity in cortico-striatal circuits during motor learning and execution. Furthermore, the study bridges their previous finding of CIN hyperexcitability (Paz et al., Movement Disorder 2022) with the loss of the pause response in LID mice and demonstrates the restore of the pause through D1/D5 inverse agonism.

Strengths:

The study presents solid findings, and the writing is logically structured and easy to follow. The experiments are well-designed, properly combining ex vivo electrophysiology recording, optogenetics, and pharmacological treatment to dissect / rule out most, if not all, alternative mechanisms in their model.

Weaknesses:

While the manuscript is overall satisfying, one conceptual gap needs to be further addressed or discussed: the potential "imbalance" between D2R and D5R signaling due to the ligand-independent activity of D5R in LID. Given that D2R and D5R oppositely regulate CIN pause responses through cAMP signaling, investigating the role of D2R under LID off L-DOPA (e.g., by applying D2 agonists or antagonists, even together with intracellular cAMP analogs or inhibitors) could provide critical insights. Addressing this aspect would strengthen the manuscript in understanding CIN pause loss under pathological conditions.

Thank you for your comments. Although our primary focus is on the role of D5 receptors, we have also investigated the effects of two D2-type receptor agonists in dyskinetic mice in the off-L-DOPA condition. We found that neither quinpirole nor sumanirole was able to restore the pause response. These results are presented in Figure 6 and related text in the Results and Discussion sections.

Understanding why D2 agonists fail to restore the pause response—despite their expected effect of reducing cAMP levels—is an important question that warrants further investigation. Interestingly, previous studies have reported paradoxical effects of D2 receptor stimulation in SCINs in animal models of dystonia (PMID: 16934985, PMID: 21912682), as well as under conditions where the SCIN’s constitutively active integrated stress response is diminished (PMID: 33888613). This is now discussed in lines 369-380.

Reviewer #3 (Public review):

Summary:

Tubert et al. investigate the mechanisms underlying the pause response in striatal cholinergic interneurons (SCINs). The authors demonstrate that optogenetic activation of thalamic axons in the striatum induces burst activity in SCINs, followed by a brief pause in firing. They show that the duration of this pause correlates with the number of elicited action potentials, suggesting a burst-dependent pause mechanism. The authors demonstrated this burst-dependent pause relied on Kv1 channels. The pause is blocked by a SKF81297 and partially by sulpiride and mecamylamine, implicating D1/D5 receptor involvement. The study also shows that the ZD7288 does not reduce the duration of the pause, and that lesioning dopamine neurons abolishes this response, which can be restored by clozapine.

Weaknesses:

While this study presents an interesting mechanism for SCIN pausing after burst activity, there are several major concerns that should be addressed:

(1) Scope of the Mechanism: It is important to clarify that the proposed mechanism may apply specifically to the pause in SCINs following burst activity. The manuscript does not provide clear evidence that this mechanism contributes to the pause response observed in behavioral animals. While the thalamus is crucial for SCIN pauses in behavioral contexts, the exact mechanism remains unclear. Activating thalamic input triggers burst activity in SCINs, leading to a subsequent pause, but this mechanism may not be generalizable across different scenarios. For instance, approximately half of TANs do not exhibit initial excitation but still pause during behavior, suggesting that the burstdependent pause mechanism is unlikely to explain this phenomenon. Furthermore, in behavioral animals, the duration of the pause seems consistent, whereas the proposed mechanism suggests it depends on the prior burst, which is not aligned with in vivo observations. Additionally, many in vivo recordings show that the pause response is a reduction in firing rate, not complete silence, which the mechanism described here does not explain. Please address these in the manuscript.

Thank you for the opportunity to clarify these points. We acknowledge that the response of SCINs to optogenetic stimulation of thalamic afferents in brain slices represents a model system that may not capture all aspects of TAN responses to behaviorally salient events. Nevertheless, this approach allows us to test mechanistic hypotheses that are difficult to address in behaving animals with current technologies. This is now stated in lines 311-314.

Importantly, our ex vivo preparation reproduces, for the first time, the loss of TAN responses observed in non-human primates with parkinsonism, enabling investigation of the underlying mechanisms. In line with your suggestion, we have expanded the Discussion (third and fourth paragraphs) to address the sources of variability in pause responses.

(2) Terminology: The use of "pause response" throughout the manuscript is misleading. The pause induced by thalamic input in brain slices is distinct from the pause observed in behavioral animals. Given the lack of a clear link between these two phenomena in the manuscript, it is essential to use more precise terminology throughout, including in the title, bullet points, and body of the manuscript.

Thank you for raising this important point. We agree that it is essential to be precise in describing the nature of the pause observed in our ex vivo model. While we believe that readers would recognize from the abstract and methods that our study focuses on a model of the pause response, we understand your concern about potential confusion. In response, we have revised the terminology in the abstract, bullet points, and throughout the manuscript to more clearly reflect that we are describing an ex vivo model of the pause observed in behaving animals.

(3) Kv1 Blocker Specificity: It is unclear how the authors ruled out the possibility that the Kv1 blocker did not act directly on SCINs. Could there be an indirect effect contributing to the burst-dependent pause?

Clarification on this point would strengthen the interpretation of the results.

This issue is addressed in lines 147-150.

(4) Role of D1 Receptors: While it is well-established that activating thalamic input to SCINs triggers dopamine release, contributing to SCIN pausing (as shown in Figure 3), it would be helpful to assess the extent to which D1 receptors contribute to this burst-dependent pause. This could be achieved by applying the D1 agonist SKF81297 after blocking nAChRs and D2 receptors.

Figure 3C shows that the D1/D5 receptor antagonist SCH23390 does not modify the pause, while the full D1/D5 agonist SKF81297 abolishes it, indicating that in our slice preparation, baseline dopamine levels are not contributing to the pause through D1/D5 receptor stimulation.

(5) Clozapine's Mechanism of Action: The restoration of the burst-dependent pause by clozapine following dopamine neuron lesioning is interesting, but clozapine acts on multiple receptors beyond D1 and D5. Although it may be challenging to find a specific D5 antagonist or inverse agonist, it would be more accurate to state that clozapine restores the burst-dependent pause without conclusively attributing this effect to D5 receptors.

As explained in our response to Reviewer #1, the effect of clozapine is blocked by the D1/D5-selective antagonist SCH23390. Additionally, new data presented in Figure 7C show that clozapine's ability to restore the pause response is maintained even in the presence of a broad-spectrum serotonin receptor antagonist. Since SCINs do not significantly express D1 receptors, we believe that these findings strongly support a role for D5 receptors in SCINs.

Comments on revisions:

The authors have addressed many of my concerns. However, I remain unconvinced that adding an 'ex vivo' experiment fully resolves the fundamental differences between the burst-dependent pause observed in slices - defined by the duration of a single AHP - and the pause response in CHINs observed in vivo, which may involve contributions from more than one prolonged AHP. In vivo, neurons can still fire action potentials during the pause, albeit at a lower frequency. Moreover, in behaving animals, pause duration does not vary with or without initial excitation. The mechanism proposed demonstrates that the pause duration, defined by the length of a single AHP, is positively correlated with preceding burst activity.

As discussed in paragraphs 3 and 4 of the Discussion (starting at line 285), and illustrated in Figure 1J–K, our data show that the duration of the pause can be modulated by rebound excitation from thalamic input. The absence of this rebound allows us to observe a longer pause when more spikes are elicited during the initial excitatory phase, providing a clearer readout of the contribution of intrinsic membrane mechanisms. We do not claim that intrinsic mechanisms alone account for the entire phasic response of SCINs in behaving animals (lines 295-303 in Discussion).

To improve clarity, I recommend using the term 'SCIN pause' to describe the ex vivo findings, distinguishing them more explicitly from the 'pause response' observed in behaving animals. This distinction would help contextualize the ex vivo findings as potentially contributing to, but not fully representing, the pause response in vivo.

We did changes in the abstract, bullet points, and main text to clarify that we are not studying the in vivo response.

Again, it would be helpful to present raw data for pause durations rather than relying solely on ratios. This approach would provide the audience with a clearer understanding of the absolute duration of the burst-dependent pause and allow for better comparison to the ~200 ms pause observed in behaving animals.

Thank you for your comment. Following your suggestion, we provide the average pause durations for the data shown in Figure 1H (lines 127–130). We opted not to include raw pause durations in the main text for all figures, as this would make the manuscript more difficult to read and, in our view, is unnecessary. The figures already allow readers to estimate absolute durations: in each case, pause durations are shown relative to baseline ISIs in one panel, while the corresponding absolute ISIs are shown side-by-side. This provides a clearer understanding of pause magnitude relative to the cell’s spontaneous firing, which is more informative than absolute values alone, since one would expect a pause to be longer than the average ISI. Please note that baseline ISI are significantly shorter in dyskinetic mice (Figure 5l). Showing the pause duration relative to baseline ISI allows readers to readily compare results across figures regardless of changes in SCIN baseline firing rate.

Additionally, it is important to note that, in vivo, pause durations are typically inferred from perievent time histograms (PETHs), which represent population averages across many trials. In contrast, in our ex vivo studies, we measured pause duration on a trial-by-trial basis. This approach enables us to analyze how the pause duration varies as a function of the initial burst size in the same neuron—something not typically reported in in vivo studies. As described in the first two paragraphs of the Results, the same SCIN may respond with a different number of spikes in successive trials, and this variability is influenced by factors such as the timing of the last spontaneous spike relative to stimulation onset (Figure 1D–F). We are not aware of studies reporting trial-by-trial analyses of pause duration in behaving animals, particularly in relation to the strength of initial excitation. Therefore, while our slice preparation may yield pause durations that are longer than those observed in vivo, direct comparison to PETH-derived pause durations from behaving animals is not straightforward.

eLife Assessment

This timely and important study used functional near-infrared spectroscopy hyperscanning to examine the neural correlates of how group identification influences collective behavior. The work provides solid evidence to indicate that the synchronization of brain activity between different people underlies collective performance and that changes in brain activity patterns within individuals may, in turn, underlie this between-person synchrony, although the order in which different task stages were completed could not be counter-balanced. This study will be of interest to researchers investigating the neuroscience of social behaviour.

Reviewer #1 (Public review):

The article provides a timely and well-written examination of how group identification influences collective behaviors and performance using fNIRs and behavioral data.

Strengths:

(1) Timeliness and Relevance:<br /> The topic is highly relevant, particularly in today's interconnected and team-oriented work environments. Triadic hyperscanning is important to understand group dynamics, but most previous work has been limited to dyadic work.

(2) Comprehensive Analysis:<br /> The authors have conducted extensive analyses, offering valuable insights into how group identification affects collective behaviors.

(3) Clear Writing:<br /> The manuscript is well-written and easy to follow, making complex concepts accessible.

Comments on previous revisions:

Most reviewer concerns have been addressed in the revised manuscript, but some limitations persist with respect to core aspects of study design, such as the long block durations and lack of counter-balancing.

Author response:

The following is the authors’ response to the previous reviews

We are appreciative of the reviewers’ and editors’ constructive suggestions of manuscript, which have helped us to improve our manuscript. We have made considerable revisions to our details of data analyses.

The reason that the reviews did not change is that there were really three central points that led to the "incomplete". These were (1) the fact that there was potentially a selection bias due to double dipping, and (2) there was potentially a time-confound due to the lack of counterbalancing (3) There is confusion about how the modeling was done, but it seems like the modelling was of the complete block (rather than tied to specific events in that block).

(1) Double dipping

We appreciate the opportunity to explain our robust safeguards against double-dipping and have provided detailed clarifications regarding the data analyses (pp.11-14).Our study ensures statistical independence between task-related region selection and hypothesis testing through three orthogonal mechanisms:

(1) Regressor Orthogonality:Statistical Independence Between Selection and Testing

The selection regressor (group mean activation) was mathematically independent from test regressors (group differences, behavioral scores). This was confirmed through our GLM implementation: First-level: Task vs. rest contrast (β values) for each participant; Second-level: One-sample t-tests (selection) vs. independent group/behavioral tests.

(2) Multimodal Validation: Complementary Neural and Behavioral Measures

We employed multiple distinct metrics to provide convergent yet independent validation of effects.

Neural Measures: Three orthogonal indices assessed different neural dimensions.

A. Single-brain activation examines neural activity patterns within individual decision-makers,

B. while within-group neural synchronization (GNS) quantifies the temporal alignment of neural activity across interacting group members during shared decision processes.

C. Functional connectivity (FC) analyses, by contrast, measure correlated activity between different brain regions within individual participants.

Behavioral Safeguards: Behavioral metrics were analyzed in independent regressions, avoiding circularity.

A. Individual performance was based on personal accuracy,

B. collective performance represented the group-level average accuracy across raters, and

C. their similarity was quantified as the Euclidean distance between individual and collective scores.

(3) Statistical Safeguards

We further ensured independence by applying strict FDR correction at both selection (p < 0.05) and testing stages (p < 0.05). Besides, permutation test was conducted, we tested 1,000 pseudo-group iterations for GNS null distributions.

Drawing on both classic and latest NIRS (e.g., Jiang et al., 2015; Liu et al., 2023; Stolk et al., 2016; Xie et al., 2023) and NIRS hyperscanning studies (e.g., Liu et al., 2019; P’arnamets et al., 2020; Reinero et al., 2021; Számadó et al., 2021; Solansky, 2011), we performed the data analyses. Below, we provide the details of our data analysis:

Single-brain activation. To identify task-related brain regions (channels), we used a one-sample t-test based on brain activation data from all participants during the task compared to the baseline (resting state).

(1)  Data Collection: Each participant had brain activation data (HbO signals measured by fNIRS) during the task (the entire process of reading, sharing, discussing, and decision-making) and the resting state (baseline).

(2)  Pre-processing: We sought to explore the neural mechanisms that manipulated group identification and its effect on collective performance. Data were preprocessed using the Homer2 package in MATLAB 2020b (Mathworks Inc., Natick, MA, USA). First, motion artifacts were detected and corrected using a discrete wavelet transformation filter procedure. After that, the raw intensity data were converted to optical density (OD) changes. Then, kurtosis-based wavelet filtering (Wav Kurt) was applied to remove motion artifacts with a kurtosis threshold of 3.3 (Chiarelli, Maclin, Fabiani, & Gratton, 2015). Based on a prior multi-brain study of social interactions (Cheng et al., 2022), the output was bandpass filtered using a Butterworth filter with order 5 and cut-offs at 0.01 and 0.5 Hz to remove longitudinal signal drift and instrument noise. Finally, OD data were converted to HbO concentrations.

(3) Individual-Level Analysis: First, a GLM was used to compute the "task vs. rest" brain activation contrast for each participant [0,1], obtaining each individual's "task effect" value (β value, representing task activation strength).

(4) Group-Level Analysis: These "task effect" values from all participants were then aggregated, and a one-sample t-test was performed for each brain region (or channel) to determine whether the average activation in that region was significantly greater than 0 (i.e., significantly more active during the task compared to the resting state).

(5) Task-Related Regions: If the t-test result for a brain region was significant (p < 0.05, FDR-corrected), we considered that region "task-related" and suitable for further analysis.

(6) Subsequent Tests:

- Group Comparisons: We examined differences in activation between groups (e.g., high vs. low group identification) using independent t-tests on the same task vs. baseline contrast.

- Behavioral Correlations: We analyzed relationships between task-related activation (β values) and behavioral scores (e.g., individual performance) using Pearson analyses.

- Mediation model: We examined the relationship between an individual's perceived group identification and individual performance, which was mediated by task-related activation (β values).

Within-Group Neural Synchronization (GNS).

(1) Data Collection and Pre-processing as above

(2) Calculation: WTC was applied to generate the brain-to-brain coupling of each pair in each triad (Coherence1&2, Coherence 1&3, and Coherence 2&3). Then, three coherence values from three pairs were averaged as the GNS for each triad, that is, GNS = (Coherence 1&2 + Coherence 1&3 + Coherence 2&3) / 3.

(3) Task-Related Regions: Time-averaged GNS (also averaged across channels in each group) was compared between the baseline session (i.e., the resting phase) and the task session (from reading information to making decisions) using a series of one-sample t-tests. When determining the frequency band of interest, the time-averaged GNS was also averaged across channels. After that, we analyzed the time-averaged GNS of each channel. Then, channels showing significant GNS were regarded as regions of interest and included in subsequent analyses.

(4) Permutation test: The nonparametric permutation test was conducted on the observed interaction effects on GNS of the real group against the 1,000 permutation samples.

(5) Subsequent Tests:

- Group Comparisons: We examined differences in activation between groups (e.g., high vs. low group identification) using independent t-tests on the same task vs. baseline contrast.

- Behavioral Correlations: The Pearson’s correlation between GNS and collective performance (i.e., calculated by averaging the individual scores assigned by the three raters for each group) was performed.

-  Mediation model: We examined how GNS mediated the relationship between group identification and collective performance.

The brain activation connectivity.

(1) Data Collection and Pre-processing as above

(2) Calculation: Exploratory Pearson’s correlations between individual performance related HbO and collective performance-related HbO.

(3) Moderation analysis: Single-brain activation × connectivity → GNS.

(2) Counterbalancing.

We sincerely appreciate this valuable methodological insight. Building on prior group decision-making research (De Wilde et al., 2017; Stasser et al., 1992), we refined all stages to enhance experimental control and procedural clarity throughout the process (i.e., a. Reading information, b. Sharing private information, c. Discussing information, d. Decision) (Xie et al., 2023). Importantly, we maintained a fixed task sequence to preserve ecological validity, as this progression mirrors natural group decision-making dynamics.

While this design choice precludes sequential counterbalancing, several factors mitigate potential temporal confounds: (1) random assignment and uniform task timing across conditions minimize systematic between-group differences; (2) our whole-block GLM approach captures sustained decision-related neural activity rather than phase-specific effects; and (3) We fully acknowledge this limitation and will incorporate a detailed discussion of temporal considerations in the revised manuscript, while noting that our design provides unique advantages for studying naturalistic decision-making processes.

(3) The modelling was of the complete block

In our revised manuscript, we have explicitly stated that the analysis was performed at the block level rather than the event level, for the following reasons:

(1) The hidden profile task is inherently a “group decision-making process” that unfolds dynamically across multiple stages (reading, sharing, discussing, and deciding). Prior research in this paradigm (De Wilde et al., 2017; Stasser & Titus, 1985; Xie et al., 2023) has consistently treated these phases as integrated blocks because the key cognitive and social processes (e.g., information integration, deliberation, and consensus formation) occur over extended interactions rather than discrete events.

(2) Methodologically, our fNIRS hyperscanning approach requires longer blocks to reliably capture the slow hemodynamic response and the gradual emergence of inter-brain neural synchronization during naturalistic social exchanges (Cui et al., 2012; Liu et al., 2019). Event-related designs, while useful for transient stimuli, are less suited for studying prolonged, interactive decision-making where neural coupling develops over time. Thus, our block-based analysis aligns with both the cognitive demands of the task and the neuroimaging constraints, ensuring robust detection of group-level neural dynamics.

eLife Assessment

This manuscript presents a clever and powerful approach to examining differential roles of Nav1.2 and Nav1.6 channels in excitability of neocortical pyramidal neurons, by engineering mice in which a sulfonamide inhibitor of both channels has reduced affinity for one or the other channels. Overall, the results in the manuscript are compelling and give important information about differential roles of Nav1.6 and Nav1.2 channels. Activity-dependent inactivation of NaV1.6 was also found to attenuate seizure-like activity in cells, demonstrating the promise of activity-dependent NaV1.6-specific pharmacotherapy for epilepsy.

Reviewer #1 (Public review):

Summary:

Prior research indicates that NaV1.2 and NaV1.6 have different compartmental distributions, expression timelines in development, and roles in neuron function. The lack of subtype-specific tools to control Nav1.2 and Nav1.6 activity however has hampered efforts to define the role of each channel in neuronal behavior. The authors attempt to address the problem of subtype specificity here by using aryl sulfonamides (ASCs) to stabilize channels in the inactivated state in combination with mice carrying a mutation that renders NaV1.2 and/or NaV1.6 genetically resistant to the drug. Using this innovative approach, the authors find that action potential initiation is controlled by NaV1.6 while both NaV1.2 and NaV1.6 are involved in back-propagation of the action potential to the soma, corroborating previous findings. Additionally, NaV1.2 inhibition paradoxically increases firing rate, as has also been observed in genetic knockout models. Finally, the potential anticonvulsant properties of ASCs were tested. NaV1.6 inhibition but not NaV1.2 inhibition was found to decrease action potential firing in prefrontal cortex layer 5b pyramidal neurons in response to current injections designed to mimic inputs during seizure. This result is consistent with studies of loss-of-function Nav1.6 models and knockdown studies showing that these animals are resistant to certain seizure types. These results lend further support for the therapeutic promise of activity-dependent, NaV1.6-selective, inhibitors for epilepsy.

Strengths:

(1) The chemogenetic approaches used to achieve selective inhibition of NaV1.2 and NaV1.6 are innovative and help to resolve long-standing questions regarding the role of Nav1.2 and Nav1.6 in neuronal electrogenesis.

(2) The experimental design is overall rigorous, with appropriate controls included.

(3) The assays to elucidate the effects of channel inactivation on typical and seizure-like activity were well selected.

Weaknesses:

(1) As discussed in the revised manuscript, the fact that channels are only partially blocked by the ASC and that ASCs act in a use-dependent manner complicates the interpretation of the effects of NaV1.2 versus NaV1.6 on neuronal activity.

(2) The idea that use-dependent VGSC-acting drugs may be effective antiseizure medications is well established. Additional discussion of the existing, widely used, use-dependent VGSC drugs (e.g. Carbamazepine, Lamotrigine, Phenytoin) would improve the manuscript. Also, the idea that targeting NaV1.6 may be effective for seizures is established by studies using genetic models, knockdown, and partially selective pharmacology (e.g. NBI-921352). Additional discussion of how the results reported here are consistent with or differ from studies using these alternative approaches would improve the discussion.

Reviewer #2 (Public review):

The authors used a clever and powerful approach to explore how Nav1.2 and Nav1.6 channels, which are both present in neocortical pyramidal neurons, differentially control firing properties of the neurons. Overall, the approach worked very well, and the results show very interesting differences when one or the other channel is partially inhibited. The experimental data is solid and the experimental data is very nicely complemented by a computational model incorporating the different localization of the two types of sodium channels.

The revised manuscript has re-organized figures that make the results and interpretation easier to follow.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Prior research indicates that NaV1.2 and NaV1.6 have different compartmental distributions, expression timelines in development, and roles in neuron function. The lack of subtype-specific tools to control Nav1.2 and Nav1.6 activity however has hampered efforts to define the role of each channel in neuronal behavior. The authors attempt to address the problem of subtype specificity here by using aryl sulfonamides (ASCs) to stabilize channels in the inactivated state in combination with mice carrying a mutation that renders NaV1.2 and/or NaV1.6 genetically resistant to the drug. Using this innovative approach, the authors find that action potential initiation is controlled by NaV1.6 while both NaV1.2 and NaV1.6 are involved in backpropagation of the action potential to the soma, corroborating previous findings. Additionally, NaV1.2 inhibition paradoxically increases the firing rate, as has also been observed in genetic knockout models. Finally, the potential anticonvulsant properties of ASCs were tested. NaV1.6 inhibition but not NaV1.2 inhibition was found to decrease action potential firing in prefrontal cortex layer 5b pyramidal neurons in response to current injections designed to mimic inputs during seizure. This result is consistent with studies of loss-of-function Nav1.6 models and knockdown studies showing that these animals are resistant to certain seizure types. These results lend further support for the therapeutic promise of activity-dependent, NaV1.6-selective, inhibitors for epilepsy.

Strengths:

(1) The chemogenetic approaches used to achieve selective inhibition of NaV1.2 and NaV1.6 are innovative and help resolve long-standing questions regarding the role of Nav1.2 and Nav1.6 in neuronal electrogenesis.

(2) The experimental design is overall rigorous, with appropriate controls included.

(3) The assays to elucidate the effects of channel inactivation on typical and seizure-like activity were well selected.

Weaknesses:

(1) The potential impact of the YW->SR mutation in the voltage sensor does not appear to have been sufficiently assessed. The activation/inactivation curves in Figure 1E show differences in both activation and inactivation at physiologically relevant membrane voltages, which may be significant even though the V1/2 and slope factors are roughly similar.

We have performed new experiments testing how YW->SR mutations affect spiking on their own. The reviewer’s intuition was correct; the small changes in voltage-dependence in NaV1.6 identified in heterologous expression systems translated into a ~2 mV hyperpolarization in threshold in neurons.

(2) Additional discussion of the fact that channels are only partially blocked by the ASC and that ASCs act in a use-dependent manner would improve the manuscript and help readers interpret these results.

We have updated text extensively to address this concern. Details are found in the author suggestions below.

(3) NaV1.6 was described as being exclusively responsible for the change in action potential threshold, but when NaV1.6 alone was inactivated, the effect was significantly reduced from the condition in which both channels were inactivated (Figure 4E). Similarly, Figure 6C shows that blockade of both channels causes threshold depolarization prior to the seizure-like event, but selective inactivation of NaV1.6 does not. As NaV1.2 does not appear to be involved in action potential initiation and threshold change, what is the mechanism of this dissimilarity between the NaV1.6 inactivation and combined NaV1.6/ NaV1.2 inactivation?

We believe the dissimilarity is due to interactions between NaV1.2 and other channel classes (e.g., potassium channels) throughout the cell, including the somatodendritic domain. NaV1.6 that initiates APs, localized to the AIS, do not live in isolation, and AP threshold can be affected by the recent membrane potential history. Loss of NaV1.2-mediated depolarization in the dendrites begets less potassium channel-mediated repolarization, as described in Figure 4.

(4) The idea that use-dependent VGSC-acting drugs may be effective antiseizure medications is well established. Additional discussion or at least acknowledgement of the existing, widely used, use-dependent VGSC drugs should be included (e.g. Carbamazepine, Lamotrigine, Phenytoin). Also, the idea that targeting NaV1.6 may be effective for seizures is established by studies using genetic models, knockdown, and partially selective pharmacology (e.g. NBI-921352). Additional discussion of how the results reported here are consistent with or differ from studies using these alternative approaches would improve the discussion

We agree; the concept of use-dependent block as a means to treat seizure is not new, and we have updated the discussion to include commentary on other medications currently in use. What is new here is our ability to explore the role of NaV1.2 and NaV1.6 in electrogenesis with a level of drug selectivity that could not be achieved without the addition of the YW->SR mutations. This approach in itself will not be useful in the clinic, but it may help guide drug design in the future. One major interpretation of this work is that NaV1.6 block is more effective than NaV1.2 block in general, and may even be effective for non-SCN8A genetic conditions. This is indeed one of the reasons that we believe that drugs like NBI-921352, itself an aryl-sulfonamide, is being tested in seizure models.

Reviewer #2 (Public review):

The authors used a clever and powerful approach to explore how Nav1.2 and Nav1.6 channels, which are both present in neocortical pyramidal neurons, differentially control firing properties of the neurons. Overall, the approach worked very well, and the results show very interesting differences when one or the other channel is partially inhibited. The experimental data is solid and the experimental data is very nicely complemented by a computational model incorporating the different localization of the two types of sodium channels.

In my opinion the presentation and interpretation of the results could be improved by a more thorough discussion of the fact that only incomplete inhibition of the channels can be achieved by the inhibitor under physiological recording conditions and I thought the paper could be easier to digest if the figures were re-organized. However, the key results are well-documented.

This is a concern raised by multiple reviewers, and we thank you all for your help in improving the way in which we discuss the results. We have revised the manuscript extensively, moving figures around per your advice and the advice of R1 in their comments to authors.

Reviewer #3 (Public review):

Summary:

The authors used powerful and novel reagents to carefully assess the roles of the voltage gated sodium channel (NaV) isoforms in regulating the neural excitability of principal neurons of the cerebral cortex. Using this approach, they were able to confirm that two different isoforms, NaV1.2 and NaV1.6 have distinct roles in electrogenesis of neocortical pyramidal neurons.

Strengths:

Development of very powerful transgenic mice in which NaV1.2 and/or NaV1.6 were modified to be insensitive to ASCs, a particular class of NaV blocker. This allowed them to test for roles of the two isoforms in an acute setting, without concerns of genetic or functional compensation that might result from a NaV channel knockout.

Careful biophysical analysis of ASC effects on different NaV isoforms.

Extensive and rigorous analysis of electrogenesis - action potential production - under conditions of blockade of either NaV1.2 or NaV1 or both.

Weaknesses:

Some results are overstated in that the representative example records provided do not directly support the conclusions.

We have swapped out example records to better capture the median effect observed and to better capture our discussion of these results. Please see below, in recommendations for authors, for details.

Results from a computational model are provided to make predictions of outcomes, but the computational approach is highly underdeveloped.

Modeling has been elaborated upon extensively, with more detail in methods, a new sensitivity analysis supplemental figure, and a deposition into ModelDB.  Please see below, in recommendations for authors, for details.

Reviewer #1 (Recommendations for the authors):

Regarding the concern about the potential impact of the YWàSR mutation: All results in Figures 2-6 report only within-subject changes before and after drug-activating protocols. These results show that the drug has no effect on the mutant channel, but whether the mutant channel itself has any effect on neuronal properties is not clear. This deficiency could be rectified by reporting raw values for AP threshold, spike rate, etc. in the pre-drug condition and statistically analyzing the apparent differences in the activation/inactivation curves.

Data in our original submission only included data in the presence of GNE-4076. We now present new data showing how the YWàSR mutation affects baseline activity of neurons. These data are in Supplemental Figure 1. Compared to wildtype (no drug control) neurons, we observe no change in peak dV/dt. However, threshold is hyperpolarized by approximately 2 mV in dual knockin neurons (median values: -57.4 mV for dual knockin and -55 mV for wildtype). This is consistent with measures from heterologously expressed channels, where we observed somewhat subtle shifts in voltage-dependence of inactivation and activation in NaV1.6 as a result of YWàSR incorporation. 

In addition to these data, we also include the baseline dataset from Figure 3, where GNE-4076 is present throughout recording, and report that neither threshold nor peak dV/dt are influenced by the presence of GNE at baseline. This suggests that any drug binding at baseline (i.e., before firing APs via somatic current injection) is negligible, consistent with the concept that GNE-4076 has low affinity for the closed channel state.

Minor Comments:

While the single-cell response to "seizure-like" input aptly demonstrates the change in action potential threshold and firing rate induced by NaV1.6 inhibition, this component of the paper could be enhanced by a network-level assay that assesses the impact of this drug on an actual seizure-like event in acute slices or on seizure susceptibility in vivo.

This is an excellent thought, and the work near the end of this manuscript is an effort to mimic network-like activity in a controlled way in single cells. To expand this to bona fide seizure-like activity in acute slices or in vivo is something that we are considering for future studies. To do this properly requires extensive validation of dosing and seizure induction that will require several years’ effort.

Fig 1e caption says "circles" but the markers are squares

This has been corrected, thank you for catching it.

Color scheme in S2B is not intuitive to me

We’ve now updated the caption to better describe the color scheme used within.

Fig S2: graph or show change in threshold

Empirical threshold data are in main figure 3D. Changes in threshold related to modeling are now included in a new sensitivity analysis that is in a new Supplemental Figure 2.

Fig 3A example of NaV1.6 inhibition does not show change in AP threshold apparent in the aggregate data

We have updated the representative example to better illustrate the change in AP threshold for NaV1.6 inhibition.

"AP initiation is mediated exclusively by NaV1.6" not corroborated by data; APs still occur when NaV1.6 is inhibited

This was an over-interpretation of our data, indeed. We have updated the language to be more accurate to the following: “AP threshold and AP initiation appears to be initiated in an NaV1.6-rich region in control conditions; when NaV1.6 is inhibited, APs can occur at more depolarized potentials, likely mediated predominately by NaV1.2.”

Fig S3C missing WT/Scn8aSR/SR significance marking. Chosen example makes it look like there is a small decrease.

Please note that there is no difference between these two conditions when in delta dV/dt for AIS inflection point (p = 0.4344).

Reviewer #2 (Recommendations for the authors):

This manuscript presents a clever and powerful approach to examining differential roles of Nav1.2 and Nav1.6 channels in excitability of pyramidal cell excitability, by engineering mice in which a sulfonamide inhibitor of both channels has reduced affinity for one or the other. Overall, the results in the manuscript are interesting and give important information about differential roles of Nav1.6 and Nav1.2 channels.

The paper makes an important contribution to better understanding distinct roles of Nav1.2 and Nav1.6 channels. This improved understanding could help guide design of anti-seizure drugs targeted to sodium channels.

Having made it clear that I think this is an important and impressive piece of work for which the authors should be congratulated, I found reading and interpreting the manuscript a frustrating experience. I will be blunt about the ways in which I found the presentation and discussion to be frustrating and even annoying, in the spirit of frank feedback by one interested and appreciative reader that the authors can consider or reject as they wish.

From the start, I had the feeling that the authors were presenting and discussing the results in a sanitized "never-mind-about the details" fashion such as might be appropriate for a seminar to a general audience not interested in details, but not appropriate for a research paper.

Our intent certainly was not to frustrate or annoy readers. We are very grateful that you have provided these comments, which have certainly improved the manuscript, hopefully mitigating some of the frustration for future readers. We appreciate that there are complex drug and voltage effects occurring within these studies, and in an effort to distill these effects into digestible prose, we appear to have been too earnest. We have expanded on the requested topics below and please note that, for the aficionados, every figure displays individual data. Further, we have made a special effort to ensure that features of excitability are presented throughout the drug and manipulation timecourse, including time-points before and after periods subject to statistical comparison, so that the reader may draw their own conclusions.

General:

There were two major ways in which I found the presentation and discussion frustrating and even annoying: First, not clearly discussing early in the presentation the fact that it is impossible to achieve complete inhibition with this agent during measurements of physiological firing and second, presenting so much of the effects as deltas of various parameters rather than showing effects on absolute values of the parameters.

Our response to the first issue will follow the next comment, as it relates to this statement. Regarding use of deltas and absolute values for changes in threshold and dV/dt across figures. Every cell has a unique AP threshold and peak dV/dt, and we found that displaying data zeroed to baseline values best illustrated the effects of GNE-4076. Without this, GNE-based effect could be buried within the cell-to-cell variability. This helped most when trying to make the case that threshold was unaffected in 2a/8a YWàSR knockin animals. We continue to believe that this is the best way to display the data in the primary figures, but to provide a more complete account, we now present absolute values in supplemental tables and supplemental figures.

The first issue, the incomplete inhibition by the agent, was the most annoying because the authors obviously thought a lot about this and even closed the paper by proposing this as a positive feature of this class of inhibitors, yet discussed it only piecemeal - and with most of the key experimental data in the Supplement. There are two fundamental characteristics of this (and other) sulfonamide inhibitors that complicate interpretation of experiments, especially when applied in a slice experiment: they only bind to the channel when the channel is depolarized, and even when the channel is depolarized for many seconds, bind very slowly to the channel.

That makes it almost impossible to know exactly what fraction of channels is being inhibited during measurements of firing. Obviously, the authors are well-aware of this issue and they allude to it and even make use of it in some of the protocols, but they never really discuss it in a very clear manner.

We agree that it is impossible to know the precise fraction of channels inhibited in acute slice preparations. But the reason for this is likely different than what has been interpreted by this reviewer. To state that ASMs “only bind to the channel when the channel is depolarized, and even when the channel is depolarized for many seconds, bind very slowly to the channel.” is not consistent with prior data on ASM–channel interactions. Clarification on these points may help the reviewer and a broader audience better understand the effects occurring here, and we appreciate being able to both address this concept here and by revising the manuscript.

First, ASMs bind activated channels and stabilize the inactivated state. It is correct that channels are more likely to enter these states when subject to voltage depolarization, but channel state is stochastic and can enter activated states near resting membrane potentials. The on-rate is fast enough that channels are blocked immediately in recordings in heterologous systems (Figure 1C). It is more likely that channel biophysical state stochasticity, along with drug concentration used herein, are likely dictating the rate at which channels accumulate block during repetitive spiking.

To address this in text, we have revised the 3rd paragraph of the introduction to better incorporate these ideas. This also helps with comments in the reviewer paragraph below.

The key experimental data on this is relegated to the Supplemental Figures. When the reader is first shown results of the effects of the inhibitor on firing in Fig 2, the presentation has been set up as if everything is perfect, and the inhibitor will be completely inhibiting either both or only one channel according to the mouse. With this presentation, it is then exceptionally striking that the cell in the middle panel of Fig 2A, labeled "Nav1.2/1.6 Inhibited" is firing action potentials very nicely even with both channels "inhibited". For a reader not already aware that there is likely only partial inhibition of each channel, the reaction will be "Huh? Shouldn't blocking both channels simply completely block excitability?". The authors do preface Fig 2 by a very brief allusion to the incomplete inhibition: "In spiking neurons, ASCs would therefore be predicted to exhibit use-dependence, progressively blocking channels in proportion to a neuron's activity rate" but this comes out of nowhere after the over-simplified picture of complete inhibition up to that point, and without any estimation of how much inhibition there is likely to be before activity, or how much induction of inhibition there is likely to be during the activity. Without this, interpreting the data in Fig 2 is basically impossible.

The key experimental data on this issue is really in Supplemental Figures 1-2 and Fig 4, and I found myself immediately ping-ponging back and forth between the Supplemental figures and the main text trying to understand what is going on with the partial inhibition. This was frustrating.

Thank you for these suggestions; they help with readability appreciably. We have re-organized the figures presented in the manuscript and emphasized details about ASCs to ensure readers can discern between near-complete blockade of channels (Figures 1-4) and activity-dependent ASC onboarding (Figures 5-7). We now present near-complete block experiments first, detailing the current clamp-> voltage clamp (-12 mV)-> current clamp experiments. We incorporated Supp. Fig. 1 into main Figure 1 and moved Supp. Fig. 2 into main Fig. 2.

As the reviewer notes, there are clear time-dependent effects on channel function when stepping to -12 mV, independent of GNE-4076 block. As stated previously, “We therefore focused on the 12-20 sec after voltage-clamp offset for subsequent analysis, as it is a period in which most channel-intrinsic recovery has occurred, but also a period in which we would still expect significant block from GNE-4076.” We hope that reordering the manuscript as suggested and placing these results near the beginning will help with discerning between near-complete block and activity depending onboarding. By beginning with these experiments, which underscore that 100% block cannot be studied without “contamination” from native slow inactivation, we hope that the readers can better understand why data was done as presented.

In my opinion, the paper would be greatly improved by a detailed discussion of the voltage- and time-dependence of the inhibitor at the very beginning of the paper. For me, reading and digesting the paper would have been far easier if Fig 1 included a discussion of the voltage- and time-dependence of inhibition, and next Figs were then Supplemental Figs 1-2, and main Fig 4. The key questions are: how much inhibition is there before a 10-s current injection from the resting potential, and how much additional inhibition is there produced during either the 10-s bout of firing or the "on-boarding" depolarization protocol, and how long does that additional inhibition last? The most direct information on that is in the plots in Fig. 4D and Fig 4F in combination with Supplemental Fig 1, which shows that the on-boarding depolarization reduces current to about 30% of current before on-boarding. This is so central to the interpretation of all the results that I think Supp Fig 1 should be in the main paper as the first piece of data in neurons.

We originally had the nucleated patch data in supplement due to space constraints in an already large figure 1. Based on your recommendation we have moved it to the main figure. We have also changed the ordering of the paper and related figures to present data as suggested. Hopefully this better guides readers through the questions you are raising above, which are addressed in the (now reordered) figures mentioned above.

Specific:

(1) Fig.1 I can find no information on the voltage protocol used to generate the dose-response curves. In the literature characterizing sulfonamide blockers, most protocols use very unphysiological strong, long depolarization to induce inhibition, usually with equally unphysiological short hyperpolarizations to produce recovery from inactivation. One assumes something like that was used here. Obviously, the protocol needs to be explained.

We updated the methods section to better describe the voltage protocol used to generate the dose response curves. In contrast to the literature characterizing sulfonamide blockers, we used pulses that closely mimic physiological activation from -80 mV (rest) to 0 mV (depolarized) for 20 msec. GNE-4076 was perfused onto cells at increasing concentrations throughout the experiment. At each successive dose, cells were held at 0 mV to allow adequate GNE-4076 onboarding.

(2) Supp Fig1. This shows the effect of depolarization to enhance inhibition, but not how much inhibition there was before the depolarization. Presumably, there were measurements during the application of drug? How much inhibition is there before the depolarization? Why does the time only go to 20-s, when the times in Figs 4 go to 10 minutes?

Nucleated patch recordings are notoriously difficult to maintain for long durations, especially when subjecting the patch to large voltage deflections. These recordings extend to 20s recovery periods because that is the duration for which we maintained all recordings, though some exhibited rather impressive longevity and allowed for several minutes of recording thereafter. Regardless, the goal here was to assess block within the 12-20 sec recovery window we utilized in current clamp recordings from intact neurons. This was achieved.

Please note that GNE-4076 was present throughout all recordings. This was in part due to time constraints, as we could not maintain patches long enough to also perform wash-in. The degree of inhibition can be inferred by comparing peak dV/dt and threshold of cells in the absence and presence of GNE-4076. These data are presented in a new Supplemental figure 1, showing no difference in threshold or peak dV/dt.

(3) Fig. 4. Similar question here - this is a very nice and informative figure, but we see only the delta in threshold and dv/dt, but how were the initial absolute values different in the drug compared to control?

These data are presented in a new Supplemental Figure 1, showing no difference in threshold or peak dV/dt.

(4) Fig 2. As far as I can tell, we have no idea how much inhibition there is at rest, before the current injection -what is the dv/dt in the drug compared to in the control? Were there experiments in which the current injections were delivered before and after applying drug? If not, at least it would be useful to see population data on dv/dt of the first spike in control and with drug.

These data are presented in a new Supplemental Figure 1, showing no difference in threshold or peak dV/dt.

(5). Fig. 2. Do the authors have any quantitative information on how much extra inhibition would be produced at 200 nM drug using physiological waveforms of firing?

These types of analyses are part of later figures using EPSC-like waveforms to evoke spiking.

I was unconvinced that the changes in threshold and dv/dt during the firing in the drug necessarily represent time-dependent use-dependent effects of drug. Partial inhibition by TTX would probably produce greater progressive changes in spike shape and reduced ability to fire robustly.

TTX is not use-dependent, so it is a good contrast to GNE-4076. We experimented with a few cells at 2 and 10 nM TTX concentrations and found that concentrations required to mimic the block of spiking that occurs with 200 nM GNE-4076 in WT cells was associated with a marked use-independent elevation in AP threshold, with an inability to maintain ~10 Hz spiking rates with the baseline EPSC-like stimulation pattern. These effects are very different from those produced by GNE-4076, but were expected given the use-independence of TTX. We did not pursue this line of inquiry fully, so we present these data only as individual examples in the reviewer figure below:

Author response image 1.

Data from Figure 6B, D, E are replicated here with individual lines of 2 nM and 10 nM TTX shown in dashed lines. Note marked changes in threshold not observed with GNE-4076. TTX sourced from Alomone Labs.

Minor:

p. 5 and elsewhere: it seems unnecessary to give values of threshold and dv/dt to three decimal places, especially when the precision is not better than a single decimal place.

We have reduced unnecessary precision throughout.

Reviewer #3 (Recommendations for the authors):

The computational model is highly underdeveloped. Without more rigorous development the results of the computational model appear to provides little additional insight beyond that expected from the known axodendritic localizations of NaV 1.2 and 1.6. If the authors wish to use the computational results to make rigorous predictions, then this section needs to be either be expanded to be more complete and promoted to a regular figure, with full details of the model, and how it was evaluated for accuracy. Alternatively, this point regarding computational insight could be de-emphasized and or removed from the paper.

Modeling:

(1) I don't see any methods describing the precise model parameters that were used.

Apologies, this is a model that we have built and tested extensively over the years (PMID: 38290518, 35417922, 34348157, 31995133, 31230762, 28256214), though there have been some small updates over these works. We have deposited this model at ModelDB and provide data there regarding model construction (access #2019342).

(2) There appears to be no robustness test to assess whether the particular results/conclusions were unduly dependent on particular model construction decisions.

We have now generated a new supplemental figure 2 that explores the robustness of these observations to changes in NaV1.2 and NaV1.6 position within the AIS and changes in relative density of NaV1.2 and NaV1.6. As shown there, the model is tolerant to all but extreme, non-physiological manipulations to these parameters.

(3) Figure S2 does not really provide convincing evidence of a biologically relevant model. Probably the model itself needs to be redesigned to better replicate the biological response and be validated by testing parameter sensitivity.

a) All of the results in S2C show that there is a huge reduction in the first action potential (black?) followed by relatively little change in subsequent spikes. This is not seen in any of the models. The progressive changes in threshold as predicted by the model for dual and NaV1.6 block are not at all evident in the results of C, except perhaps for the the very first and the very last spikes.

b) The baseline action potential in B is different than the recorded action potentials. In particular, the somatic depolarization occurs much later and over a more extended time frame than the real neuron, and the phase plot shows an actual dip in depolarization at the transition to the somatic spike, which is not representative of naturally occurring action potentials.

To address both (a) and (b), please note that in empirical experiments there are two parallel processes occurring: block by GNE-4076 and channel recovery from inactivation. In the model we can isolate the effects of block to test that parameter fully and in isolation. This is something that we could never achieve biologically. The important take home here in both cases is to observe that with NaV1.6 block there is a change in threshold, whereas with NaV1.2 block there is none.

(4) The one finding that seems to be robust is that the changes in NaV1.2 have little effect on threshold.

Yes! This is a major take-home message from both the model and the use of these knockin mice in combination with GNE-4076. In mature pyramidal cells, NaV1.6 is the major determinant of AP threshold. And to editorialize on this observation, changes in threshold are a useful metric to test if other pharmacology are truly selective for NaV1.2 over NaV1.6. We note that phrixotoxin-3, which is described as NaV1.2 specific in multiple papers, was never tested for specificity over NaV1.6 in its original description, and we find that it fails this test in our hands.

Data presentation:

(1) The phase plots in Figure 3B (left and right) appear to be visually identical, and as such don't strongly support any particular conclusion.

We changed the representative example record (specifically for Fig. 3A-B) to more directly support the conclusions.

(2) It is unclear to me what is meant by AP speed (title of Figure 3 legend). Do the authors mean propagation speed along the axon, or perhaps the rate of action potential firing?

Apologies, we are referencing dV/dt when we mention AP speed. We updated AP speed to AP velocity throughout the manuscript.

eLife Assessment

This study presents a valuable investigation into how heavy metal stress may have influenced the domestication of maize from its wild ancestor, teosinte parviglumis, focusing on specific ATPase genes with proposed roles in heavy metal homeostasis. The evidence supporting the main claims is incomplete, with suggestive but not definitive data linking gene function to domestication traits, and limited environmental context for the hypothesized selection pressures. While the work introduces an interesting model connecting environmental stress responses to evolutionary transitions and highlights underexplored aspects of teosinte plasticity, the conclusions would benefit from more comprehensive analyses such as transcriptomics, a broader survey of loci, and stronger paleoenvironmental validation. The study will be of interest to researchers in plant evolution and domestication, but currently lacks the analytical depth to fully support its central hypothesis.

Reviewer #1 (Public review):

In this study, Acosta-Bayona et al. aim to better understand how environmental conditions could have influenced specific gene functions that may have been selected for during the domestication of teosinte parviglumis into domesticated maize. The authors are particularly interested in identifying the initial phenotypic changes that led to the original divergence of these two subspecies. They selected heavy metal (HM) stress as the condition to investigate. While the justification for this choice remains speculative, paleoenvironmental data would add value; the authors hypothesize that volcanic activity near the region of origin could have played a role.

The authors exposed both maize and teosinte parviglumis to a fixed dose of copper and cadmium, representing an essential and a non-essential element, respectively. They assessed shoot and root phenotypic traits at a defined developmental stage in plants exposed to HM stress versus controls. They then focused on three genes already known to help plants manage HM stress: ZmHMA1, ZmHMA7, and ZmSKUs5. Two of these genes are located in a genomic region linked to traits selected during domestication. A closer examination of nucleotide variability in the coding and flanking regions of these genes provided evidence of selective pressure among teosinte parviglumis, maize, and the outgroup Tripsacum dactyloides.

They further generated a null mutant for ZmHMA1 and showed, for the first time in maize, a pleiotropic phenotype reminiscent of traits associated with the domestication syndrome. Finally, using qPCR, they reported increased expression of the domestication gene Teosinte branched1 (tb1) in teosinte parviglumis under HM stress. Comparative studies focusing on teosinte parviglumis and the genes ZmHMA1, ZmHMA7, and ZmSKUs5 under HM stress are limited; thus, this phenotypic characterization provides a promising starting point for further understanding the genetic basis of the response.

The dataset is of good quality, but the conclusions are not sufficiently supported by the data. Analyses should be expanded, and additional experiments included to strengthen the findings.

(1) Although the paper presents some interesting findings, it is difficult to distinguish which observations are novel versus already known in the literature regarding maize HM stress responses. The rationale behind focusing on specific loci is often lacking. For example, a statistically significant region identified via LOD score on chromosome 5 contains over 50 genes, yet the authors focus on three known HM-related genes without discussing others in the region. It is unclear why ZmHMA1 was selected for mutagenesis over ZmHMA7 or ZmSKUs5.

(2) The idea that HM stress impacted gene function and influenced human selection during domestication is of interest. However, the data presented do not convincingly link environmental factors with human-driven selection or the paleoenvironmental context of the transition. While lower nucleotide diversity values in maize could suggest selective pressure, it is not sufficient to infer human selection and could be due to other evolutionary processes. It is also unclear whether the statistical analysis was robust enough to rule out bias from a narrow locus selection. Furthermore, the addition of paleoclimate records (Paleoenvironmental Data Sources as a starting point) or conducting ecological niche modeling or crop growth models incorporating climate and soil scenarios would strengthen the arguments.

(3) Despite the interest in examining HM stress in maize and the presence of a pleiotropic phenotype, the assessment of the impact of gene expression is limited. The authors rely on qPCR for two ZmHMA genes and the locus tb1, known to be associated with maize architecture. A transcriptomic analysis would be necessary to 1- strengthen the proposed connection and 2- identify other genes with linked QTLs, such as those in the short arm of chromosome 5.

Reviewer #2 (Public review):

Summary:

This work explores the phenotypic developmental traits associated with Cu and Cd responses in teosinte parviglumis, a species evolutionary related to extant maize crops. Cu and Cd could serve as a proxy for heavy metals present in the soils. The manuscript explores potential genetic loci associated with heavy metal responses and domestication identified in previous studies. This includes heavy metal transporters, which are unregulated during stress. To study that, the authors compare the plant architecture of maize defective in ZmHMA1 and speculate on its association with domestication.

Strengths:

Very few studies covered the responses of teosintes to heavy metal stress. The physiological function of ZmHMA1 in maize also gives some novelty in this study. The idea and speculation section is interesting and well-implemented.

Weaknesses:

The authors explored Cu/Cd stress but not a more comprehensive panel of heavy metals, making the implications of this study quite narrow. Some techniques used, such as end-point RT-PCR and qPCR, are substandard for the field. The phenotypic changes explored are not clearly connected with the potential genetic mechanisms associated with them, with the exception of nodal roots. If teosintes in response to heavy metal have phenotypic similarity with modern landraces of maize, then heavy metal stress might have been a confounding factor in the selection of maize and not a potential driving factor. Similar to the positive selection of ZmHMA1 and its phenotypic traits. In that sense, there is no clear hypothesis of what the authors are looking for in this study, and it is hard to make conclusions based on the provided results to understand its importance. The authors do not provide any clear data on the potential influence of heavy metals in the field during the domestication of maize. The potential role of Tb-1 is not very clear either.

Author response:

Reviewer 1:

The selection of heavy metal stress as the condition to investigate is not speculative. The elucidation of the genome from the Palomero toluqueño maize landrace revealed heavy metal effects during domestication (Vielle-Calzada et al., 2009). Differences concordant with its ancient origin identified chromosomal regions of low nucleotide variability that contained the three domestication loci included in this study; all three are involved in heavy-metal detoxification. Results presented in Vielle-Calzada et al 2009 indicated that environmental changes related to heavy metal stress were important selective forces acting on maize domestication. Our study expands those results by starting to elucidate the function of these heavy metal response genes and their role in the evolutionary transition from teosinte parviglumis to maize.

Although the paper presents some interesting findings, it is difficult to distinguish which observations are novel versus already known in the literature regarding maize HM stress responses. The rationale behind focusing on specific loci is often lacking. For example, a statistically significant region identified via LOD score on chromosome 5 contains over 50 genes, yet the authors focus on three known HM-related genes without discussing others in the region. It is unclear why ZmHMA1 was selected for mutagenesis over ZmHMA7 or ZmSKUs5.

We appreciate the value of this comment. We will modify the manuscript to clearly show which phenotypic observations are novel and which were previously reported for maize grown under HM stress. The rationale for focusing on three specific loci is related to results from Vielle-Calzada et al. 2009 (see comment above). Although we demonstrated that these three loci show unusual reduction in genetic variability when compared to the rest of chromosome 5 – including a separate class of genes previously identified as being affected by domestication (Hufford et al., 2012) -, we will expand the genetic and expression analysis to all genes included in a region precisely defined via LOD scores of five QTL 1.5-LOD support intervals that overlap with ZmHMA1.Within this region of 1.5 to 2 Mb, we will compare nucleotide variability and gene expression in response to HMs. Contrary to major domestication loci showing a single highly pleiotropic gene responsible for important domestication traits, in this chr.5 genomic region phenotypic effects are due to multiple linked QTLs (Lemmon and Doebley, 2014). The mutagenic analysis of ZmHMA7 and ZmSKUs5 will be included in a different publication; we can anticipate that the results reinforce the conclusions of this study.

The idea that HM stress impacted gene function and influenced human selection during domestication is of interest. However, the data presented do not convincingly link environmental factors with human-driven selection or the paleoenvironmental context of the transition. While lower nucleotide diversity values in maize could suggest selective pressure, it is not sufficient to infer human selection and could be due to other evolutionary processes. It is also unclear whether the statistical analysis was robust enough to rule out bias from a narrow locus selection. Furthermore, the addition of paleoclimate records (Paleoenvironmental Data Sources as a starting point) or conducting ecological niche modeling or crop growth models incorporating climate and soil scenarios would strengthen the arguments.

We agree that lower nucleotide diversity values in maize are not sufficient to infer human selection and could be due to other evolutionary processes. As a matter of fact, since these same HM response loci also show unusually low nucleotide variability in teosinte parviglumis (Fig 2), we cannot discard the possibility that natural selection forces related to environmental changes could have affected native teosinte parviglumis populations in the early Holocene, before maize emergence. This possibility supports a speculative model suggesting that phenotypic changes caused by HM stress could have preceded human selection and its consequences, contributing to initial subspeciation; the model is proposed in the “Ideas and Speculation” section of the manuscript. Fortunately, as suggested by the reviewer, a large body of paleoclimatic records and paleoenvironmental data is available for the Trans-Mexican Volcanic Belt  in the Holocene, including geographic regions where the emergence of maize presumably occurred. We will include an extensive analysis of available paleoenvironmental data and discuss it at the light of our current results regarding the effects of HM stress. We are also expanding the physical range of our statistical analysis to cover at least 60 Kb per locus - including neighboring genes for all three loci - to determine if our results could be due to narrow locus selection.

Despite the interest in examining HM stress in maize and the presence of a pleiotropic phenotype, the assessment of the impact of gene expression is limited. The authors rely on qPCR for two ZmHMA genes and the locus tb1, known to be associated with maize architecture. A transcriptomic analysis would be necessary to 1- strengthen the proposed connection and 2- identify other genes with linked QTLs, such as those in the short arm of chromosome 5.

Although real-time qPCR is an accurate and reliable approach to assess the expression of specific genes such as ZMHMA1 and Tb1, we will explore the possibility of complementing our analysis with available RNA-seq results that are pertinent for this study (see for example Li et al., 2022 and Zhang et al., 2024) and further explore causative effects between HM stress, Tb1 and ZmHMA1 expression. As also pointed by Reviewer#1, TEs are known to influence gene expression under abiotic stress and RNA-Seq analysis would allow to determine if TE activity could lead to similar outcomes.

Reviewer #2:

The authors explored Cu/Cd stress but not a more comprehensive panel of heavy metals, making the implications of this study quite narrow. Some techniques used, such as end-point RT-PCR and qPCR, are substandard for the field. The phenotypic changes explored are not clearly connected with the potential genetic mechanisms associated with them, with the exception of nodal roots. If teosintes in response to heavy metal have phenotypic similarity with modern landraces of maize, then heavy metal stress might have been a confounding factor in the selection of maize and not a potential driving factor. Similar to the positive selection of ZmHMA1 and its phenotypic traits. In that sense, there is no clear hypothesis of what the authors are looking for in this study, and it is hard to make conclusions based on the provided results to understand its importance. The authors do not provide any clear data on the potential influence of heavy metals in the field during the domestication of maize. The potential role of Tb-1 is not very clear either.

Thank you for these comments. We will clearly emphasize our hypothesis that HM stress was an important factor driving the emergence of maize from teosinte parvglumis through action of HM response genes. A comprehensive panel of heavy metals would not be more accurate in terms of simulating the composition of volcanic soils evolving across 9,000 years in the region where maize presumably emerged. Copper (Cu) and cadmium (Cu) correspond each to a different affinity group for proteins of the ZmHMA family. ZmHMA1 has preferential affinity for Cu and Ag (silver), whereas ZmHMA7 has preferential affinity to Cd, Zn (zinc), Co (cobalt), and Pb (lead). Since these P1b-ATPase transporters mediate the movement of divalent cations, their function remains consistent regardless of the specific metal tested, provided it belongs to the respective affinity group. By applying sublethal concentrations of Cd (16 mg/kg) and Cu (400 mg/kg), we caused a measurable physiological response while allowing plants to complete their life cycle, including the reproductive phase, facilitating a comprehensive analysis of metal stress adaptation.

Although real-time qPCR is an accurate and reliable approach to assess gene expression, we agree that RNA-Seq results would improve the scope of the analysis and better assess the role of Tb1 in relation to HM response (see comments for Reviewer#1). There are two phenotypic changes clearly connected with the genetic mechanisms involved in the parviglumis to maize transition: plant height and the number of seminal roots (not nodal roots). We will emphasize these phenotypic changes in a modified version of the manuscript. There is a possibility for HM stress to represent a confounding factor in the selection of maize and not a driving factor; however, if such is the case, we think it is rather unlikely that the real driving factor could have acted through mechanisms not related to abiotic stress or HM response. To address the possibility that HM stress was a cofounding factor, we will extensively analyze genetic diversity and gene expression in all loci containing genes mapping in close proximity to peak LOD scores of all 1.5-LOD support intervals located in chromosome 5 and showing pleiotropic effects on domestication traits (Lemmon and Doebley, 2014). These will also include those mapping in close proximity to ZmHMA1. The potential influence of heavy metals in the field is being investigated through the analysis of paleoenvironmental data (see response to Reviewer#1); we will include our results in a modified version of the manuscript.

We thank both reviewers for their detailed revision the manuscript and their pertinent recommendations to improve its presentation and reading.

References:

Hufford, Matthew B., Xun Xu, Joost Van Heerwaarden, Tanja Pyhäjärvi, Jer-Ming Chia, Reed A. Cartwright, Robert J. Elshire, et al. 2012. Comparative population genomics of maize domestication and improvement. Nature Genetics 44(7): 808-11.

Lemmon Zachary H., Doebley John F. 2014. Genetic dissection of a genomic region with pleiotropic effects on domestication traits in maize reveals multiple linked QTL. Genetics 198(1): 345-353.

Lin Kaina, Zeng Meng, Williams Darron V., Hu Weimin, Shabala Sergey, Zhou Meixue, Cao Fangbin, et al. 2022. Integration of transcriptome and metabolome analyses reveals the mechanictic basis for cadmium accumulation in maize. iScience 25(12): 105484.

Vielle-Calzada JP, De La Vega OM, Hernández-Guzmán G, Ibarra-LacLette E, Alvarez-Mejía C, Vega-Arreguín JC, Jiménez-Moraila B, Fernández-Cortés A, Corona-Armenta G, Herrera-Estrella L, Herrera-Estrella A. 2009. The Palomero genome suggests metal effects on domestication. Science 326: 1078.

Zhang Mengyan, Zhao Lin, Yun Zhenyu, Wu Xi, Wu Qi, et al. 2024. Comparative transcriptome analysis of maize (Zea mays L.) seedlings in response to copper stress. Open Life Sciences 19(1): 20220953.

eLife Assessment

This study presents valuable findings about daily rhythm changes of the Drosophila melanogaster adult gut metabolome, which is shown to be dependent on the circadian clock genotype, dietary regime and composition, and gut microbiota. The phenomena observed are supported by convincing experimental evidence. The general descriptive approach limits the power of the proposed conclusions. The work will be of interest to a broad range of physiology specialists

Reviewer #1 (Public review):

The authors build on their previous study that showed the midgut microbiome does not oscillate in Drosophila. Here, they focus on metabolites and find that these rhythms are in fact microbiome-dependent. Tests of time-restricted feeding, a clock gene mutant, and diet reveal additional regulatory roles for factors that dictate the timing and rhythmicity of metabolites. The study is well-written and straightforward, adding to a growing body of literature that shows the time of food consumption affects microbial metabolism which in turn could affect the host.

Some additional questions and considerations remain:

(1) The main finding that the microbiome promotes metabolite rhythms is very interesting. Which microbiota are likely to be responsible for these effects? Future work could be done to link specific microbiota linked to some of the metabolic pathways investigated.

(2) TF increases the number of rhythmic metabolites in both microbiome-containing and abiotic flies. This is somewhat surprising given that flies typically eat during the daytime rather than at night, very similar to TF conditions. Future work could be done to restrict feeding to other times of day to see if there is a subsequent shift in the timing of metabolites.

(3) Along these lines, the authors show that Per loss of function reveals a change in the phase of rhythmic metabolites. The authors note that these changes are not due to altered daily feeding rhythms in per mutants. This data suggest Per itself is responsible for these changes. Future work could be done to characterize the mechanisms responsible for these effects.

(4) The calorie content of each diet - normal vs high protein vs high-sugar are different. Future work in this area could consider the possibility of a calorie effect rather than difference in nutrition (protein/carbohydrate) or an effect of high protein/sugar on the microbiome itself.

(5) The supplementary table provided outlining the specific metabolites will be useful for future research in this area.

Reviewer #2 (Public review):

The revised version of the paper clarifies the authors' discoveries regarding daily changes in metabolite concentrations in the gut of adult female Drosophila melanogaster. The authors have addressed all the questions and made the necessary changes, thereby strengthening the value of the article. They demonstrate that various factors influence metabolite oscillations: circadian clock genotype, dietary regime and composition, and gut microbiota.<br /> The notable strengths of this research article remain unchanged: the originality of the experimental design with multiple conditions tested, the variety of detected metabolites, and the clarity in data presentation.

Among the weaknesses, one may consider the following:<br /> Limitations of potential reproducibility: It is unclear whether another research team would identify the same set of cycling metabolites, although similar conclusions appear robust.<br /> Limitations of generalisation: While the conclusions regarding the influence of microbiota, circadian genotype, and dietary regime may be valid, the specific metabolic pathways affected might differ, whereas specific mechanistic explanations remain elusive.<br /> Accuracy of data interpretation: Addressed in comments to the authors. This point corresponds to interpretations discussed by the authors in the text of the manuscript, including beneficial effects of cycling metabolites and phenomenon of oscillation as a whole, its physiological relevance and lack of proofs for existence of any compensative effects, their relevance to metabolism in the gut.<br /> Nevertheless, the authors have clearly and thoroughly addressed all the reviewers' concerns, enabling a better interpretation of the entire study.

Reviewer #3 (Public review):

Summary:

Zhang et al sought to quantify the influence of the gut microbiome on metabolite cycling in a Drosophila model with extensive metabolomic profiling in 4 time points over a 24 hour period. The authors report that the microbiome enhances metabolite cycling in a context-dependent manner. The metabolomics data presented are comprehensive and complex, and they open up may new questions. The major strength of the work is the production of a large dataset of metabolites that can be the basis for hypothesis generation for more specific experiments. There are several weaknesses that make some of the conclusions speculative.

Strengths:

The revised manuscript is significantly improved due to the inclusion of new data and expanded analyses, particularly of time-resolved food intake. The dataset is comprehensive and of high value to the community. The experimental design includes multiple metabolomic comparisons across genetic and dietary conditions, specifically, germ-free versus microbially-colonized flies, time-restricted versus ad libitum feeding, high-sugar versus high protein diets, and wildtype genotype versus the per01 clock mutant. Additionally, the cycling of individual metabolites is presented, allowing readers to examine metabolites of interest. The datasets are made publicly available, allowing this resource to benefit the community.

Weaknesses

Many of the statistically significant differences, e.g. the effects of the microbiome on lipids and biogenic amines in Fig S5A, are quite small in magnitude, and, thus, it is difficult to believe that they are of biological significance without more mechanistic studies. Key conclusions, such as those pertaining to regulation or compensation by the microbiome, are not fully supported by mechanistic experiments. The manuscript uses terms like "regulate" or "compensate," which imply causality or a purpose of the microbiome that is not yet demonstrated, but this type of study opens up many important questions for which new hypotheses can be formed.

A minor limitation is the modest temporal resolution (only four time points in 24 hours), which constrains interpretation of rhythmicity and phase. Additional experimental controls and targeted perturbation experiments are needed to support conclusions about functional impacts of metabolite oscillations. However, these types of limitations are expected from an early study in the field such as this one. Overall, the data are valuable, and the findings demonstrate the promise of the model for studying the interplay between the microbiome, metabolome, and circadian rhythm.

Assessment of Aims

The authors explore how the microbiome interacts with host circadian rhythms and diet to shape metabolite cycling. They largely succeed in characterizing broad trends and generating a valuable resource dataset. However, the conclusion that the microbiome actively regulates or compensates for cycling under specific conditions is not convincingly demonstrated with the current data.

Impact and Utility

The dataset will be a useful reference for researchers interested in microbiome-host interactions, metabolomics, and circadian biology. Its primary value lies in descriptive insight rather than mechanistic resolution. An alternative perspective is that per01 mutants serve as a useful negative control for rhythmicity detection, providing a baseline for distinguishing signal from experimental noise ---an idea that could be emphasized more in the interpretation.

Contextual Considerations

Metabolomics datasets are valuable for understanding the influence of the microbiome. Future follow-up work using higher resolution sampling and functional perturbations (e.g., more extensive genetic or microbial manipulations) will be essential to test hypotheses about the roles of specific metabolites, regulatory pathways, and microbiota members in circadian modulation. This paper lays a strong foundation for such studies.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

The authors build on their previous study that showed the midgut microbiome does not oscillate in Drosophila. Here, they focus on metabolites and find that these rhythms are in fact microbiome-dependent. Tests of time-restricted feeding, a clock gene mutant, and diet reveal additional regulatory roles for factors that dictate the timing and rhythmicity of metabolites. The study is well-written and straightforward, adding to a growing body of literature that shows the time of food consumption affects microbial metabolism which in turn could affect the host.

We thank the reviewer for the positive comments.

Some additional questions and considerations remain:

(1) The main finding that the microbiome promotes metabolite rhythms is very interesting. Which microbiota are likely to be responsible for these effects? The author's previous work in this area may shed light on this question. Are specific microbiota linked to some of the metabolic pathways investigated in Figure 5?

This is a good question. Although the Drosophila microbiome shows limited diversity, comprised largely of two major families (Acetobacteraceae and Lactobacillaceae), effects on the host could arise from just a subset of species within these families. However, identifying these would require inoculating microbiome-free flies with single and mixed combinations of species and conducting metabolomics to examine cycling of each of the three categories of metabolites we studied-- primary, lipids and biogenic amines (each of these may respond differently to different species). We believe this is beyond the scope of this manuscript, which is focused on how cycles in these different types of metabolites change in the context of the microbiome, the circadian clock and different diets.

(2) TF increases the number of rhythmic metabolites in both microbiome-containing and abiotic flies in Figure 1. This is somewhat surprising given that flies typically eat during the daytime rather than at night, very similar to TF conditions. I would have assumed that in a clock-functioning animal, the effect of restricting food availability should not make a huge difference in the time of food consumption, and thus downstream impacts on metabolism and microbiome. Can the authors measure food intake directly to compare the ad-lib vs TF flies to see if there are changes in food intake? Would restricting feeding to other times of day shift the timing of metabolites accordingly?

Previous studies have indicated that there is no significant difference in food consumption between ad-lib and TF flies (Gill et al., 2015; Villaneuva et al 2019). We also found that the presence of a microbiome does not alter total food consumption when compared with germ-free flies (Zhang et al, 2023, and current manuscript). Though flies primarily feed during the day, some food consumption occurs at night (i.e the feeding rhythm is not tight) and so restricting food to the daytime can increase metabolite cycling. Restricting feeding to other times of day is expected to shift metabolite cycling. We previously showed that this shifts transcript cycling (Xu et al, Cell Metabolism 2011)

(3) In Figure 2, Per loss of function reveals a change in the phase of rhythmic metabolites. In addition, the effect of the microbiome on these is very different = The per mutants show increased numbers of rhythmic metabolites when the microbiome is absent, unlike the controls. Is it possible that these changes are due to altered daily feeding rhythms in per mutants? Testing the time and amount of food consumed by the per mutant flies would address this question. Would TF in the per mutants rescue their metabolite rhythms and make them resemble clock-functioning controls?

We previously showed that per⁰¹ flies lose feeding rhythms in DD and LD conditions, but consume a lot more food (Barber et al, 2021). Given that locomotor rhythms are maintained in per⁰¹ in LD (Konopka and Benzer 1971), these rhythms or other rhythms driven by LD cues likely account for the maintenance of metabolite rhythms. And the increased food consumption may contribute to the changes observed. To address the reviewer’s question about the microbiome, we assayed feeding rhythms in per⁰¹ in the absence/presence of a microbiome on the diets that haven’t been tested before (high sugar and high protein diet). Surprisingly, feeding was rhythmic on a high protein diet, regardless of whether a microbiome was present (new Figure S10). On a high sugar diet, feeding appears to be somewhat rhythmic in the presence of a microbiome (although not significant) and not when the microbiome is absent. The same is true in iso31 controls, and in all cases, the phase is the same. Despite the similar effect of the microbiome on feeding rhythms in wild type and per⁰¹, the effect on cycling is very different. Thus, feeding rhythms do not appear to explain the effects of the microbiome on metabolite cycling in per⁰¹.

(4) The calorie content of each diet-normal vs high protein vs high-sugar are different. The possibility of a calorie effect rather than a difference in nutrition (protein/carbohydrate) should be discussed. Another issue worth considering is the effect of high protein/sugar on the microbiome itself. While the microbiome doesn't seem to affect rhythms in the high-protein diet, the high-sugar diet seems highly microbiome-dependent in Supplementary Fig 8C vs D. Does the diet impact the microbiome and thus metabolite rhythmicity downstream?

Thank you for these good suggestions. We have added to the discussion the possibility that caloric content, rather than nutrition (protein/carbohydrate), affects metabolite cycling in flies fed normal vs. high-protein vs. high-sugar diets. We have also discussed the possibility that effects of different diets on metabolite cycling are mediated by changes in the microbiome. We cite papers that show effects of diet on microbiomes.

(5) It would be good if a supplementary table was provided outlining the specific metabolites that are shown in the radial plots. It is not clear if the rhythms shown in the figures refer to the same metabolites peaking at the same time, or rather the overall abundance of completely different metabolites. This information would be useful for future research in this area.

We have added a supplementary Table 1-21 which includes all the raw metabolites.

Reviewer #2 (Public Review):

Summary:

The paper addresses several factors that influence daily changes in concentration of metabolites in the Drosophila melanogaster gut. The authors describe metabolomes extracted from fly guts at four time-points during a 24-hour period, comparing profiles of primary metabolites, lipids, and biogenic amines. The study elucidates that the percentage of metabolites that exhibit a circadian cycle, peak phases of their increased appearance, and the cycling amplitude depends on the combination of factors (microbiome status, composition or timing of the diet, circadian clock genotype). Multiple general conclusions based on the data obtained with modern metabolomics techniques are provided in each part of the article. Descriptive analysis of the data supports the finding that microbiome increases the number of metabolites for which concentration oscillates during the day period. Results of the experiments show that timed feeding significantly enhanced metabolite cycling and changed its phase regardless of the presence of a microbiome. The authors suggest that the host circadian rhythm modifies both metabolite cycling period and the number of such metabolites.

Strengths:

The obvious strength of the study is the data on circadian cycling of the detected 843, 4510, and 4330 total primary metabolites, lipids, and biogenic amines respectively in iso31 flies and 623, 2245, and 2791 respective metabolites in per⁰¹ mutants. The comparison of the abundance of these metabolites, their cycling phase, and the ratio of cycling/non-cycling metabolites is well described and illustrated. The conditions tested represent significant experimental interest for contemporary chronobiology: with/without microbiota, wild-type/mutant period gene, ad libitum/time-restricted feeding, and high-sugar/high-protein diet. The authors conclude that the complex interaction between these factors exists, and some metabolic implications of combinations of these factors can be perceived as reminiscent of metabolic implications of another combination ("...the microbiome and time-restricted feeding paradigms can compensate for each other, suggesting that different strategies can be leveraged to serve organismal health"). Enrichment analysis of cycling metabolites leads to an interesting suggestion that oscillation of metabolites related to amino acids is promoted by the absence of microbiota, alteration of circadian clock, and time-restricted feeding. In contrast, association with microbiota induces oscillation of alpha-linolenic acid-related metabolites. These results provide the initial step for hypothesising about functional explanations of the uncovered observations.

We thank the reviewer for summarizing the contributions made by this manuscript.

Weaknesses:

Among the weaknesses of the study, one might point out too generalist interpretations of the results, which propose hypothetical conclusions without their mechanistic proof. The quantitative indices analysed are obviously of particular interest, however are not self-explaining and exhaustive. More specific biological examples would add valuable insights into the results of this study, making conclusions clearer. More specific comments on the weaknesses are listed below:

(1) The criterion of the percentage of cycling metabolites used for comparisons has its own limitations. It is not clear, whether the cycling metabolites are the same in the guts with/without microbiota, or whether there are totally different groups of metabolites that cycle in each condition. GO enrichment analysis gives only a partial assessment, but is still not quantitative enough.

Microbiome-containing flies and germ-free flies do share some cycling metabolites. Figure 6 provides GO analysis for the pathways enriched in each condition, and Figure S6 shows quantitative data on the number that overlap between different conditions. We have also expanded discussion of specific cycling groups (e.g. amino acid metabolism) to indicate that different metabolites of the same pathway may cycle under different conditions. In addition, we have added detailed information for all cycling metabolites in Supplemental Tables 1-21.

(2) The period of cycling data is based on only 4 time points during 24 hours in 4 replicates (>200 guts per replicate) on the fifth day of the experiment (10-12-day-old adults). It does not convincingly prove that these metabolites cycle the following days or more finely within the day. Moreover, it is not clear how peaks in polar histogram plots were detected in between the timepoints of ZT0, ZT6, ZT12, and ZT18.

We acknowledge these limitations, but note that these experiments are very challenging because of the amount of tissue/guts needed for each data point and the time it takes to dissect each gut. Thus, getting more closely spaced time points is difficult. And we believe the detection of daily peaks with four biological replicates provides good evidence for cycling. The peaks in polar histogram plots are based on the parameter of JTK_adjphase when conducting JTK cycle analysis; as the data are averaged across replicates, the average can sometimes fall in between two assayed time points. Details can be found in the attached Supplementary Tables.

(3) Average expression and amplitude are analysed for groups of many metabolites, whereas the data on distinct metabolites is hidden behind these general comparisons. This kind of loss of information can be misleading, making interpretation of the mentioned parameters quite complicated for authors and their readers. Probably more particular datasets can be extracted to be discussed more thoroughly, rather than those general descriptions.

We analyzed groups of metabolites, dividing them into primary metabolites, lipids and biogenic amines, to extract general take-home messages and also to simplify the presentation. Figure 6 demonstrates specific pathways whose cycling is affected in each condition assayed. And Figure S11 shows examples of cycling metabolites under different conditions. To highlight a dataset that is altered under different conditions, we expanded our discussion of amino acid metabolism, and show how the specific metabolites that cycle in this pathway may vary from one condition to another (Figure S11). For more quantitative data on individual metabolites, we now provide supplementary tables that display all the cycling metabolites. These include those uniquely cycling in one group, those shared between both two groups, and those uniquely cycling in the other group.

(4) The metabolites' preservation is crucial for this type of analysis, and both proper sampling plus normalisation require more attention. More details about measures taken to avoid different degradation rates, different sizes of intestines, and different amounts of microbes inside them will be beneficial for data interpretation.

We were careful to control for gut size and to preserve the samples so as to minimize degradation (We placed all the fly samples on ice during collection, and the entire dissection process was also conducted on ice. Once the gut sample collection was completed, we immediately transferred the samples to dry ice for storage. After we finished collecting all the samples, we stored them at -80°C). In general, gut sizes varied in the following order: females fed high-protein diets >females fed normal chow diets> female flies fed high-sugar diets. As the metabolomic facility suggested 10mg samples for each biological repeat, we dissected at least 180 female guts from flies fed high-protein diets, 200 female guts from flies fed normal chow diets, and at least 250 female guts from flies fed high-sugar diets. Also, as gut sizes were smaller in sterile flies, relative to microbiome-containing flies, on a high protein diet, we collected 200 guts from sterile flies under these conditions. Finally, the service that conducted the metabolomics (UC Davis) provided three detailed files to describe the extraction process for primary metabolites, lipids, and biogenic amines, respectively. We have submitted these files as supplemental materials in the revised manuscript.

(5) The data in the article describes formal phenomena, not directly connected with organism physiology. The parameters discussed obviously depend on the behavior of flies. Food consumption, sleep, and locomotor activity could be additionally taken into account.

These are very interesting suggestions. Previous results indicated that microbiome-containing flies do not change their total food consumption or exhibit changes in feeding rhythms when compared with germ-free flies (Zhang et al., 2023), which indicates that microbiome-mediated metabolite cycling is independent of feeding rhythms. As noted above, we examined the contribution of feeding to metabolite cycling in per⁰¹ flies, and did not see an obvious link. We also assayed feeding rhythms and overall food consumption in wild type under AS and AM conditions and on different diets, and likewise could not account for changes in metabolite cycling based on altered food intake (new Figure S10). We acknowledge that behavior, including locomotor activity and sleep, could indeed influence metabolite cycling. We have added discussion of this.

(6) Division of metabolites into three classes limits functional discussion of found differences. Since the enrichment analysis provided insights into groups of metabolites of particular interest (for example, amino acid metabolism), a comparison of their cycling characteristics can be shown separately and discussed.

The intent of this work was to provide an overall account of changes in metabolite cycling that occur under different conditions/diets/genotypes. We have expanded the discussion of amino acid metabolism and show how different metabolites of this pathway cycle under different conditions (Figure S11). We believe that discussion/analysis of other specific groups would be good follow-up studies, which can build upon this work. Detailed datasets about all cycling metabolites are provided in Table S1-12.

Reviewer #3 (Public Review):

Summary:

The authors. sought to quantify the influence of the gut microbiome on metabolite cycling in a Drosophila model with extensive metabolomic profiling over a 24-hour period. The major strength of the work is the production of a large dataset of metabolites that can be the basis for hypothesis generation for more specific experiments. There are several weaknesses that make the conclusions difficult to evaluate. Additional experiments to quantify food intake over time will be required to determine the direct role of the microbiome in metabolite cycling.

Strengths:

An extensive metabolomic dataset was collected with treatments designed to determine the influence of the gut microbiome on metabolite circadian cycling.

Weaknesses:

(1) The major strength of this study is the extensive metabolomic data, but as far as I can tell, the raw data is not made publicly available to the community. The presentation of highly processed data in the figures further underscores the need to provide the raw data (see comment 3).

The raw data have been submitted to the public metabolite database. https://www.ebi.ac.uk/metabolights/. (ID: MTBLS8819)

In addition, the normalized metabolite data have been added in the supplemental materials.

(2) Feeding times heavily influence the metabolome. The authors use timed feeding to constrain when flies can eat, but there is no measurement of how much they ate and when. That needs to be addressed.

Since food is the major source of metabolites, the timing of feeding needs to be measured for each of the treatment groups. In the previous paper (Zhang et al 2023 PNAS), the feeding activity of groups of 4 male flies was measured for the wildtype flies. That is not sufficient to determine to what extent feeding controls the metabolic profile of the flies. Additionally, timed feeding opportunities do not equate to the precise time of feeding. They may also result in dietary restriction, leading to the loss of stress resistance in the TF flies. The authors need to measure food consumption over time in the exact conditions under which transcriptomic and metabolomic cycling are measured. I suggest using the EX-Q assay as it is much less effort than the CAFE assay and can be more easily adapted to the rearing conditions of the experiments.

As noted above, we have now added considerable additional data on feeding and feeding rhythms in microbiome-containing and sterile wild type and per⁰¹ flies on different diets (Figure S10). Our previous study, using the EX-Q assay method, found no differences in either total food consumption or feeding rhythms between microbiome-containing flies and germ-free flies (Zhang et al., 2023). Also, previous work has demonstrated that there is no significant difference in food consumption between ad-lib and TF flies (Villaneuva et al 2019).

(3) The data on the cycling of metabolites is presented in a heavily analyzed form, making it difficult to evaluate the validity of the findings, particularly when a lack of cycling is detected. The normalization to calculate the change in cycling due to particular treatments is particularly unclear and makes me question whether it is affecting the conclusions. More presentation of the raw data to show when cycling is occurring versus not would help address this concern, as would a more thorough explanation of how the normalization is calculated - the brief description in the methods is not sufficient.

For instance, the authors state that "timed feeding had less effect on flies containing a microbiome relative to germ-free flies." One alternative interpretation of that result is that both treatments are cycling but that the normalization of one treatment to the other removes the apparent effect. This concern should be straightforward to address by showing the raw data for individual metabolites rather than the group.

We have added Supplement Table1-21 that includes detailed information on metabolite identity and data processing. Also, we have included the raw data, encompassing all the cycling metabolites, in the Supplement Table1-21.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The abstract could be rewritten to clarify. I found the last part of the introduction better but struggled to understand the abstract.

We apologize for this. The abstract was indeed quite dense; we have revised it for clarity.

(2) Supplementary Figure 8 could be moved to the main text. Since all the comparisons are on one page it is much easier to see the similarities and differences in the conditions tested.

We have moved Supplementary Figure 8 to main Figure 5.

(3) The sex and age of the flies used in all experiments should be clarified. The authors mention female guts are collected in the methods (line 111) but it is not clear if this is throughout.

All guts used in this study were female. We have clarified this in the manuscript.

Reviewer #2 (Recommendations For The Authors):

Some minor notes that might be improved:

(1) The order of obtaining eggs without microbiota might be different (first - bleaching, second - sterilisation with ethanol). Otherwise, it is not clear why dechorionating is needed after sterilisation.

Protocols for generating axenic flies vary. We used the method Feltzin et al reported in 2019: “For newborn fly embryos (<12 hours). First, cleanse and sterilize any leftover agar from collection plates using 100% ethanol, second, dechorionate the fly embryos with 10% bleach, and then immediately rinse three times in germ-free PBS”.

(2) References for the resources used might be provided (MetaboAnalyst5.0, JTK_CYCLEv3.1).

We have added the reference for MetaboAnalyst5.0, JTK_CYCLEv3.1 (Pang et al., 2022)

(3) References or justification for the chosen composition of the diets might be useful (standard diet, high-protein diet, high-sugar diet).

We have added the references (Bedont et al, 2021, Morris et al, 2021).

(4) Justification of the choice of iso31 line and per⁰¹ mutant might be important.

iso31 is the standard wild type line we use in the laboratory. To understand the role of the endogenous clock in microbiome-mediated metabolite cycling, we chose the classical canonical clock mutant per⁰¹ as this displays fewer non-circadian phenotypes seen. For instance, loss of transcriptional activators of the clock produces additional effects (e.g. hyperactivity), likely because of the effect it has on overall expression of many genes. We have added this explanation to the manuscript.  

(5) Abbreviation decoding might be introduced when it is used for the first time in the text (line 240 - TM, TS).

We apologize for this omission and have rectified it. Thanks

TM (timed feeding microbiome-containing flies)

TS (timed feeding germ-free flies)

(6) The term "germ-free" is recommended to be avoided in the context of the paper (germ-free = infertile for animals). It might be replaced with the terms "without microbiota" or "germ-free" for example.

Given that the reviewer recommends use of the word “germ-free” in the second sentence, we assume that the first sentence was intended to say we should avoid “sterile” (and not “germ-free”). We have edited to “germ-free” in the manuscript.

(7) When only one diet is assumed, it might be better to say so (line 324 - "the protein diet" instead of "protein diets").

Sorry, we have edited accordingly.

(8) Too many speculative conclusions are confusing (line 476 - what does it mean for "just as” - how exactly similar; line 477 - what kind of "compensation"; line 503 - how exactly it is related to "metabolic homeostasis" and to which kind of homeostasis).

“just as” was not intended to refer to any degree of similarity but only to the fact that amino acid cycling occurs in the absence of a clock, as it does in the absence of a microbiome. We speculate that this “compensates” for something that is normally conferred by the clock and the microbiome, for instance maybe the clock drives cycling of a microbiome component that is important for protein metabolism. In the absence of either the clock or the microbiome, this is compensated for by amino acid cycling. We have clarified in the text.

We used the term "metabolic homeostasis" to reflect steady maintenance of metabolic health via interaction and modulation of different factors. As in the case of the example given above for amino acid metabolism, a perturbation of one process might produce a change in another to optimize metabolism. We have changed the wording in the text to better convey our message (lines 576-579)

(9) Particular examples of metabolites might be beneficial for supporting conclusions (a figure which shows, for instance, the specific data on linolenic acid: in which conditions it cycles, in which not, what is the period of cycling, what are the exact expression and JTK_amplitude values).

All cycling metabolites, including linolenic acid, are now included in the supplemental tables.

Reviewer #3 (Recommendations For The Authors):

(1) The level of biological replication is unclear for the metabolomic experiments. I see that 200 guts per sample were collected and 4 repeat samples were made at each timepoint. Are these 4 biological replicates for each treatment (AS, AM, TS, TM) at each timepoint? 5 replicates are standard in metabolomics. Please be more explicit in the methods.

There are 4 biological replicates for each time point of each of the 4 treatments. The metabolomics service recommended 4-6 replicates, so we prepared 4 replicates for each sample. As noted above, these preparations are quite difficult. We found that in general the biological replicates do not differ significantly from each other.

(2) Wolbachia can have a significant influence on fly physiology. How was this variable addressed? Were flies checked for Wolbachia?

All the flies are Wolbachia-free, as in our previous study (Zhang et al., 2023). Initially, we treated the flies with 1 mM kanamycin (11815024, ThermoFisher) to remove bacteria. Afterwards, we repopulated the flies with a Wolbachia-free microbiome containing Lactobacillus and Acetobacter bacteria from a medium previously occupied by other flies.

(3) In Results section 1, the authors report changes in the percentages of metabolites that are cycling, but no statistical test is presented to show that these changes are indeed significant. The authors need to report statistics on the percentages of cycling metabolites.

We used statistical tests, specifically JTK cycle, to determine cycling of each metabolite. The P value for cycling of each metabolite in this test is computed on the basis of all the biological replicates and all time points. Metabolites that showed a significant P value contribute to the percent cycling. As a result, there is only one value for the percentage cycling in each condition. Thus, statistical analysis cannot be done.

(4) The authors report that the species proportions in the gut microbiome don't cycle, but do absolute CFU counts? By many accounts (see e.g. Blum et al 2013 mBio), the gut microbiome in lab flies is what they recently ate from the food. The abundance of bacteria in the gut would then be directly tied to the timing of feeding. Timed feeding should produce oscillations in individual flies, so individual flies should be analyzed.

We assume the reviewer is suggesting that rhythmic feeding could result in rhythmic abundance of the microbiome, which could contribute to cycling. This is indeed a possibility and one we now discuss in the manuscript. Thanks! Analysis of the gut microbiome in individual flies would require quantitation of CFUs from single guts. We do not believe a single gut would yield enough material.

(5) Line 252: the ZT9 peak could just be due to feeding and digestion.

This is possible. We now acknowledge this

(6) What is the expectation for metabolite cycling in per mutant flies? Shouldn't per mutant flies have no cycling on average? Does the cycling suggest there is an external factor causing cycling?

Under light-dark conditions, metabolite cycling in per mutant flies may be driven by light: dark cues, either directly or through other light-driven rhythms e,g. locomotor activity is rhythmic in per⁰¹ flies maintained in LD.

(7) Performing food intake analysis on each of the treatments would provide critical data to address the direct role of the microbiome in metabolite cycling.

As noted above, we now provide considerable additional data on food intake at different times of day in microbiome-containing and germ-free wild type and per⁰¹ flies on different diets (Figure S11). Overall, our data indicate that food intake or feeding rhythms do not account for the effects we report here.

(8) Please be more explicit about replication in the methods and figure legends.

We have added n=4 for each condition in the methods and figure legends.

(9) There are numerous minor grammatical errors such as incorrect verb tenses and usage of articles. Additional proofreading could correct these.

Sorry! We have done a thorough proofreading and made corrections.

eLife Assessment

This study introduces a novel and broadly applicable metric-phenological lag-to partition the effects of spring warming from other abiotic constraints on plant phenology. While the dataset is extensive and the analytical framework is valuable conceptually, the manuscript lacks clarity in its aims and justification for the new metric, and key results are underdeveloped or poorly visualized. The strength of evidence is moderate to solid, but revisions are needed to clarify the study's contribution and improve interpretability.

Reviewer #1 (Public review):

Summary:

Jiang et al. present a measure of phenological lag by quantifying the effects of abiotic constraints on the differences between observed and expected phenological changes, using a combination of previously published phenology change data for 980 species, and associated climate data for study sites. They found that, across all samples, observed phenological responses to climate warming were smaller than expected responses for both leafing and flowering spring events. They also show that data from experimental studies included in their analysis exhibited increased phenological lag compared to observational studies, possibly as a result of reduced sensitivity to climatic changes. Furthermore, the authors present compelling evidence that spatial trends in phenological responses to warming may differ from what would be expected from phenological sensitivity, due to the seasonal timing of when warming occurs. Thus, climate change may not result in geographic convergences of phenological responses. This study presents an interesting way to separate the individual effects of climate change and other abiotic changes on the phenological responses across sites and species.

Strengths:

A clearly defined and straightforward mathematical definition of phenological lag allows for this method to be applied in different scientific contexts. Where data exists, other researchers can partition the effects of various abiotic forcings on phenological responses that differ from those expected from warming sensitivity alone.

Identifying phenological lag and associated contributing factors provides a method by which more nuanced predictions of phenological responses to climate change can be made. Thus, this study could improve ecological forecasting models.

Weaknesses:

The authors include very few data visualizations, and instead report results and model statistics in tables. This is difficult to interpret and may obscure underlying patterns in the data. Including visual representations of variable distributions and between-variable relationships, in addition to model statistics, provides stronger evidence than model statistics alone.

The use of stepwise, automated regression may be less suitable than a hypothesis-driven approach to model selection, combined with expanded data visualization. The use of stepwise regression may produce inappropriate models based on factors of the sample data that may preclude or require different variable selection.

Reviewer #2 (Public review):

Summary:

This is a meta-analysis of the relative contributions of spring forcing temperature, winter chilling, photoperiod and environmental variables in explaining plant flowering and leafing phenology. The authors develop a new summary variable called phenology lag to describe why species might have different responses than predicted by spring temperature.

Strengths:

The summary statistic is used to make a variety of comparisons, such as between observational studies and experimental studies.

Weaknesses:

By combining winter chilling effects, photoperiod effects, and environmental stresses that might affect phenology, the authors create a new variable that is hard to interpret. The authors do not provide information in the abstract about new insights that this variable provides.

Comments:

It would be useful to have a map showing the sites of the studies.

The authors should provide a section in which the strengths and weaknesses of the approach are discussed. Is it possible that mixing different types of data, studies, sample sizes, number of years, experimental set-ups, and growth habits results in artifacts that influence the results?

Now that the authors have created this new variable, phenological lag, which of the components that contribute to it has the most influence on it? Or which components are most influential in which circumstances? For example, what are some examples where photoperiod causes a phenological lag?

eLife Assessment

This is a potentially important study that explores the relevant range of parameter values for calibration and validation of cardiac electromechanics in ventricular models. Although much of the work presented is solid, the evidence provided to support the authors' key scientific claims is incomplete, especially as it relates to the emphasis on standardized validation and verification approaches. Notably, the level of model personalization presented in this work falls short of the threshold for what could reasonably be called a "digital twin", even by the relatively relaxed standards that have emerged in computational physiology and related fields in recent years.

Reviewer #1 (Public review):

Summary:

The study by Wang et al. investigates cardiac electromechanical modeling and simulation techniques, focusing on the calibration and validation of ventricular models according to ASME V&V40 standards. The researchers aim to calibrate model parameters to align with key biomarkers such as QRS duration and left ventricular ejection fraction, and validate the model against independent measurements such as displacement and strain metrics. The authors also examine the impact of parameter variations on deformation, ejection fraction, strains, and other biomarkers. The overarching aim of the study is to give "credibility to the underlying computational electromechanics framework" and to "pave the way towards credible cardiacelectromechanical Digital Twins."

Strengths:

(1) The study presents a solid validation strategy for cardiac models based on independent data.

(2) It integrates electrophysiological, mechanical, and hemodynamic biomarkers for sensitivity analysis and calibration.

Weaknesses and Limitations:

(1) Model Assumptions: The study employs simplified modeling assumptions that are not state-of-the-art, e.g.,<br /> a) Isotropic scaling of the mesh to generate an unloaded reference geometry.<br /> b) Simple afterload and preload models that fail to produce physiological results.<br /> c) Simplified epicardial boundary conditions.

(2) Numerical Framework:<br /> a) The mesh resolution and/or the numerical framework used for the mechanical part appears to suffer from known numerical artifacts (locking effects), leading to overly stiff or inaccurate behavior in finite element analysis. This results in an artificially stiff response to deformation, which is compensated by setting active contraction to ten times the value reported in the literature. The authors attribute this to limitations in using ex vivo tissue measurements to represent in vivo function, although similar issues were not observed in previous works.<br /> b) Further, the authors employ the monodomain model for the simulation of the electrical excitation and relaxation on a relatively coarse grid with an approximate edge length of 1mm. This resolution is known to be insufficient for reliable results in organ-scale electrophysiology modeling.

(3) Geometrical model and digital twin: The geometrical model, taken from a public cohort and calibrated to an ECG of another individual along with population-averaged values from a databank (UK Biobank), and unrelated measurements from surgical procedures, can hardly be considered a digital twin. Further, validation of the model was then performed against data from yet another cohort.

(4) Calibration procedure: There are apparent flaws in the calibration procedure, or it is not described in sufficient detail. The authors dedicate significant effort to motivating parameter ranges, but in the end they use mostly other parameters for the calibration process, aiming to maximize left ventricular ejection fraction. It is not clear whether the chosen parameters result in, e.g., physiological calcium traces or calibrated parameters that are within physiological ranges.

(5) Goodness of fits, e.g., a direct comparison of the measured and the simulated ECG, are not provided to assess calibration quality.

(6) Due to these limitations and weaknesses, the authors fall short of achieving some of their goals, particularly establishing credibility for the underlying computational framework and in reproducing healthy pressure-volume loops, and in achieving physiological simulations while using physiological or reported ranges for the calibrated parameters.

For example, a key physiological requirement is that the right and left ventricular stroke volumes are approximately equal in a heart beating at a limit cycle, as the blood pumped by the right ventricle into the pulmonary circulation must match the amount pumped by the left ventricle into the systemic circulation. This balance is not achieved in this study.

(7) The conclusive claim that "the study paves the way towards credible electromechanical cardiac Digital Twins" is not supported. The model exhibits non-physiological behavior, requires unsupported parameter alterations (such as a 10-fold active stress scaling), and does not represent a digital twin, as model data are drawn from various unrelated, non-patient-specific sources.

Conclusion:

Overall, this reviewer considers that the study requires a major revision, including improvements in numerical methods, modeling choices, and checks for physiological behavior. Nevertheless, the provided tables with averaged values from the UK Biobank and the presented validation strategy could be valuable to the research community.

Reviewer #2 (Public review):

The authors present an interesting study on calibrating and validating a biventricular cardiac electromechanical model. This is an important contribution, but some questions remain about the quantitative validation and verification aspects of the study.

Major comments:

(1) The title and paper stress the importance of validation on several occasions. However, the actual validation performed is limited to the section in lines 427-439. Furthermore, it is entirely qualitative, making assessing the model's quality difficult. Most of the paper is focused on sensitivity analysis, which is also interesting but unrelated to validation. Can you include a quantitative comparison with deformation biomarkers? E.g., spatially quantify strain differences between simulation and in vivo data, or overlay the current configuration of the geometry with MRI in various views, and calculate a displacement error norm.

(2) You mention the ASME V&V40 standards throughout your paper. Yet, you only address the "second V" validation, ignoring the "first V" verification. How did you ensure that your computational models are implemented correctly?

(3) All parameters discussed in this publication are physical parameters. What is the sensitivity of your model outputs concerning computational parameters?

eLife Assessment

This study presents significant and novel insights into the roles of zinc in mammalian meiosis/fertilization events. These findings are useful to our understanding of these processes. The evidence presented is solid, with experiments being well-designed, carefully described, and interpreted with appropriate rigor.

Reviewer #1 (Public review):

The authors investigated the role of the zinc transporter ZIP10 in regulating zinc sparks during fertilization in mice. By utilizing oocyte-specific Zip6 and Zip10 conditional knockout mice, the authors effectively demonstrate the importance of ZIP10 in zinc homeostasis, zinc spark generation, and early embryonic development. The study is overall useful as it identifies ZIP10 as an important component of oocyte processes that support embryo development, thus opening the door for further investigations. While the study provides solid evidence for the requirement of ZIP10 in the regulation of zinc sparks and zinc homeostasis, it falls short of revealing the underlying mechanism of how ZIP10 exerts this important function.

(1) The zinc transporters the authors are knocking out are expressed in mouse oocytes through follicular development, and the Gdf9-cre driver used means these oocytes were grown in the absence of appropriate Zinc signaling. Thus, it would be difficult to assert that the lack of fertilization associated with zinc sparks is solely responsible for the failure of embryo development. Spindle morphology and other meiotic parameters do not necessarily report oocyte health, so normalcy of these features may not be a strong argument when it comes to metabolic issues.

(2) While comparing ZIP6 and ZIP10 in the abstract provides context, focusing more on ZIP10 would improve reader comprehension, as ZIP10 is the primary focus of the study. Emphasizing the specific role of ZIP10 will help the reader grasp the core findings more clearly.

(3) Zinc transporters ZIP6 and ZIP10 are expressed during follicular development, but the biological significance of the observation is not clearly addressed. The authors should investigate whether the ZIP6 and ZIP10 knockout affects follicular development and discuss the potential implications.

(4) In Figure 3, the zinc fluorescence images are unclear, making it difficult for readers to interpret the data. Including snapshot images of calcium and zinc spikes as part of the main figure would improve clarity. Moreover, adding more comparative statements and a deeper explanation of why Zip10 KO mice exhibit normal calcium oscillations but lack zinc sparks would strengthen the manuscript.

(5) While the study identifies the role of ZIP10 in zinc spark generation, it lacks a clear mechanistic insight. The topic itself is interesting, but without providing a more detailed explanation of the underlying mechanisms, the study leaves an important gap. Further discussion on the signaling pathways potentially involved in zinc spark regulation would add depth to the findings.

Reviewer #2 (Public review):

Summary:

In this important study, the authors examine the role of two zinc uptake transporters, Zip6 and Zip10, which are important during the maturation of oocytes, and are critical for both successful fertilization and early embryogenesis.

Strengths:

The authors report that oocytes from Zip10 knockout mice exhibit lower labile zinc content during oocyte maturation, decreased amounts of zinc exocytosis during fertilization, and affect the rate of blastocyst generation in fertilized eggs relative to a control strain. They do not observe these changes in their Zip6 knockout animals. The authors present clear and well-documented results from a broad range of experimental modalities in support of their conclusions.

Weaknesses:

(1) The authors' statement that Zip10 is not expressed in the oocyte nuclei (line 252). Furthermore, in that study, ZIP10 was detected in the nuclear/nucleolar positions of oocytes of all follicular stages (Chen et al., 2023), which we did not observe. This is not supported by Figure 1, where some Zip10 signal is apparent in the primordial, primary, and secondary follicle oocytes. This statement should be corrected.

(2) Based on the FluoZin-3AM data, there appears to be less labile zinc in the Zip10d/d oocyte, eggs, and embryos; however, FluoZin-3AM has a number of well-known artifacts and does not accurately capture the localization of labile zinc pools. The patterns do not correspond to the well-documented zinc-containing cortical vesicles. Another zinc probe, such as ZinPyr-4 or ZincBY-1 should be used to visualize the zinc vesicles and confirm that there is less labile zinc in these locations as well.

(3) Line 268 The results indicate that ZIP10 is mostly responsible for the uptake of zinc ions in mouse oocytes. The situation seems a bit more complicated given that the differences in labile zinc content between oocytes from the WT and Zip10d/d animals are small (only 20-30 %) and that the zinc spark is diminished but still apparent at a low level in the Zip10d/d oocytes. Clearly, other factors are involved in zinc uptake at these stages. A variety of studies have suggested that Zip6 and Zip10 work together, perhaps even functioning as a heterodimer in some systems. The double KO would address this more clearly, but if it is not available, it might be more prudent to state that Zip10 plays some role in uptake of zinc in mouse oocytes while the role of Zip6 remains uncertain.

(4) Zip6d/d oocytes did not have changes in labile zinc, nor did the lack of Zip6 have an impact on the zinc spark. However, Figure S1 does show a small amount of detectable Zip6 in the western blot. It is possible that this small amount could compensate for the complete lack of Zip6. Can ZIP6 be found in immunofluorescence of GV oocytes or MII eggs from the Zip6d/d animals? Additionally, it is possible that Zip6's role is only supplementary to that of Zip10. The authors should discuss this possibility. It would also be interesting to see if the Zip6/Zip10 double knockout displays greater defects compared to the Zip10 knockout when considering previous studies.

eLife Assessment

Inspired by bee's visual behavior, the goal of the manuscript is to develop a model of visual scanning, visual processing and learning to recognize visual patterns. In this model, pre-training with natural images leads to the formation of spatiotemporal receptive fields that can support associative learning. Due to an incomplete test of the necessity and sufficiency of the features included in the model, it cannot be concluded that the model is either the "minimal circuit" or the most biologically plausible circuit of this system. With a more in-depth analysis, the work has the potential of being important and very valuable to both experimental and computational neurobiologists.

Reviewer #1 (Public Review):

Insects, such as bees, are surprisingly good at recognizing visual patterns. How they achieve this challenging task with limited computational resources is not fully understood. Based on the actual bee's behaviour and visual circuit structure, MaBouDi et al. constructed a biologically plausible model where the circuit extracts essential visual features from scanned natural scenes. The model successfully discriminated a variety set of visual patterns as the actual bee does. By implementing a type of Hebb's rule for non-associative learning, an early layer of the model extracted orientational information from natural scenes essential to pattern recognition. Throughout the paper, the authors provided intuitive logic for how the relatively simple circuit could achieve pattern recognition. This work could draw broad attention not only in visual neuroscience but also in computer vision.

However, there are a number of weaknesses in the manuscript. 1) The authors claim that the model is inspired by micromorphology, yet it does not rigorously follow the detailed anatomy of the insect brain revealed as of now. 2) Some claims sound a bit too strong compared to what the authors demonstrated with the model. For example, when the authors say the model is minimal, the authors simply investigated how many lobula neurons are required for pattern discrimination in the model. However, the manuscript appears to use this to claim that the presented model is the minimal one required for visual tasks. 3) It lacks explanations of what mechanisms in the model could discriminate some patterns but not others, making the descriptions very qualitative. 4) The authors did not provide compelling evidence that the algorithm is particularly tuned to natural scenes.

Reviewer #2 (Public Review):

This study is inspired by the scanning movements observed in bees when performing visual recognition tasks. It uses a multilayered network, representing stages of processing in the visual lobes (lamina, medulla, lobula), and uses the lobula output as input to a model of associative learning in the mushroom body (MB). The network is first trained with short "scanning" sequences of natural images, in a non-associative adaptation process, and then several experimental paradigms where images are rewarded or punished are simulated, with the output of the MB able to provide the appropriate discriminative decisions (in some but not all cases). The lobula receptive fields formed by the initial adaptation process show spatiotemporal tuning to edges moving at particular orientations and speeds that are comparable to recorded responses of such neurons in the insect brain.

There are two main limitations to the study in my view. First, although described (caption fig 1) as a model "inspired by the micromorphology" of the insect brain, implying a significant degree of accuracy and detail, there are many arbitrary features (unsupported by current connectomics). For example, the strongly constrained delay line structure from medulla to­ lobula neurons, and the use of a single MB0N that has input synapses that undergo facilitation and decay according to different neuromodulators. Second, while it is reasonable to explore some arbitrary architectural features, given that not everything is yet known about these pathways, the presented work does not sufficiently assess the necessity and sufficiency of the different components, given the repeated claims that this is the "minimal circuit" required for the visual tasks explored.

Regarding the mushroom body (MB) learning model, it is strange that no reference is made to recent models closely tied to connectomic and other data in fruit flies, which suggests separate MBONS encode positive vs. negative value; that learning is not dependent on MB0N activity (so is not STDP); that feedback from MBONs to dopaminergic signalling plays an important role, etc. Possibly the MB of the bee operates in a completely different way to the fly, but the presented model relies on relatively old data about MB function, mostly from insects other than bees (e.g. locust) so its relationship to the increasingly comprehensive understanding emerging for the fly MB needs to be clarified. It is implied that the complex interaction of the differential effects of dopamine and octopamine, as modelled here, are required to learn the more complex visual paradigms, but it is not actually tested if simpler rules might suffice. Also, given previous work on models of view recognition in the MB, inspired by bees and ants, it seems plausible that simply using static 25×25 medulla activity as input to produce sparse activity in the KCs would be sufficient for MB0N output to discriminate the patterns used in training, including the face stimulus. Thus it is not clear whether the spatiotemporal input and the lobula encoding are necessary to solve these tasks.

It is also difficult to interpret the range of results in fig 3. The network sometimes learns well, sometimes just adequately (perhaps comparable to bees), and sometimes fails. The presentation of these results does not seem to identify any coherent pattern underlying success or failure, other than that the ability to generalise seems limited. That is, recognition (in most cases) requires the presentation of exactly the same stimulus in exactly the same way (same scanning pattern, distance and speed). In particular, it is hard to know what to conclude when the network appears able to learn some "complex patterns" (spirals, faces) but fails to learn the apparently simple plus vs. multiplication symbol discrimination if it is trained and tested with a scan passing across the whole pattern instead of just the lower half.

In summary, although it is certainly interesting to explore how active vision (scanning a visual pattern) might affect the encoding of stimuli and the ability to learn to discriminate rewarding stimuli, some claims in the paper need to be tempered or better supported by the demonstration that alternative, equally plausible, models of the visual and mushroom body circuits are not sufficient to solve the given tasks.

Reviewer #3 (Public Review):

In this manuscript, the authors use the data collected and observations made on bees' scanning behaviour during visual learning to design a bio-inspired artificial neural network. The network follows the architecture of bees visual systems, where photoreceptors project into the lamina, then the medulla, medulla neurons connect to a set of spiking neurons in the lobula. Lobula neurons project to kenyon cells and then to MBON, which controls reward and punishment. The authors then test the performance of the network in comparison with real bee data, finding it to perform well in all tasks. The paper attempts to reproduce a living organism network with a practical application in mind, and it is quite impressive! I appreciate both the potential implications for the understanding of biological systems and the applications in the development of autonomous agents, making the paper absolutely worth reading.

However, I believe that the current version somewhat lacks in clarity regarding the methodology and in some of the keywords used to describe the model.

Definitions:

Throughout the manuscript, the authors use some key terminology that I believe would benefit from some clarification.

The generated model is described in the title and once in the introduction as "neuromorphic". The model is definitely bio-inspired, but at least in some layers of the neural network, the model is built very differently from actual brain connectivity. Generally, when we use the term neuromorphic we imply many advantages of neural tissue, like energy efficiency, that I am not sure the current model is achieving. I absolutely see how this work is going in that direction, and I also fundamentally agree with the choice of terminology, but this should be clearly explained to not risk over-implications

The authors describe this as a model of "active vision". This is done in the title of the article, and in the many paragraph headings (methods, results). In the introduction, however, the term active vision is reserved to the description of bees' behavior. Indeed, the developed model is not a model of active vision, as this would require for the model to control the movement of the "camera". Here instead the stimuli display is given to the model in a fixed progression. What I suspect is that the authors' aim is to describe a model that supports the bees' active vision, not a model of active vision. I believe this should be very clear from the paper, and it may be appropriate to remove the term from the title.

In the short title, it said that this network is minimal. This is then characterized in the introduction as the minimal network capable of enabling active vision in bees. The authors, however, in their experiment only vary the number of lobula neurons, without changing other parts of the architecture. Given this, we can only say that 16 lobula neurons is the minimal number required to solve the experimental task with the given model. I don't believe that this is generalizable to bees, nor that this network is minimal, as there may be different architectures (for the other layers especially) that require overall less neurons. Moreover, the tasks attempted in the minimal network experiment did not include any of the complex stimuli presented in figure 3, like faces. It may be that 16 lobula neurons are sufficient for the X vs + and clockwise vs counter-clockwise spirals, but we do not know if increasing stimuli complexity would result in a failure of the model with 16 neurons.

Methodology:

The current explanation of the model is currently a bit lacking in clarity and details. This risks impacting negatively on the relevance of the whole work which is interesting and worth reading! This issue affects also the interpretation of the results, as it is not clear to what extent each part of the network could affect the results shown. This is especially the case when the network under-performs with respect to the best performing scenario (e.g., when varying the speed and part of the pattern that is observed, such as in Fig 2C). Adding a detailed technical scheme/drawing specific to the network architecture could have been a way of significantly increasing the clarity of the Methods section and the interpretation of the results.

On a similar note, the authors make some comparisons between the model and real bees. However, it remains unclear whether these similarities are actually indicative of an optimality in the bees visual scanning strategy, or just deriving from the authors design. This is for me particularly important in the experiments aimed at finding the best scanning procedure. If the initial model training is based on natural images it is performed by presenting left to right moving frames, the highest efficiency of lower-half scanning may be due to how the weights in the initial layers are structured and a low generalizability of the model, rather than to the strategy optimality

Author response:

Reviewer #1 (Public Review):

Insects, such as bees, are surprisingly good at recognizing visual patterns. How they achieve this challenging task with limited computational resources is not fully understood. Based on the actual bee's behaviour and visual circuit structure, MaBouDi et al. constructed a biologically plausible model where the circuit extracts essential visual features from scanned natural scenes. The model successfully discriminated a variety set of visual patterns as the actual bee does. By implementing a type of Hebb's rule for non-associative learning, an early layer of the model extracted orientational information from natural scenes essential to pattern recognition. Throughout the paper, the authors provided intuitive logic for how the relatively simple circuit could achieve pattern recognition. This work could draw broad attention not only in visual neuroscience but also in computer vision.

We appreciate your positive feedback.

However, there are a number of weaknesses in the manuscript. 1) The authors claim that the model is inspired by micromorphology, yet it does not rigorously follow the detailed anatomy of the insect brain revealed as of now. 2) Some claims sound a bit too strong compared to what the authors demonstrated with the model. For example, when the authors say the model is minimal, the authors simply investigated how many lobula neurons are required for pattern discrimination in the model. However, the manuscript appears to use this to claim that the presented model is the minimal one required for visual tasks. 3) It lacks explanations of what mechanisms in the model could discriminate some patterns but not others, making the descriptions very qualitative. 4) The authors did not provide compelling evidence that the algorithm is particularly tuned to natural scenes.

We appreciate the reviewer's constructive feedback and have revised the manuscript to clarify and strengthen our claims. Below, we address each of the concerns raised:

(1) The model does not rigorously follow the detailed anatomy of the insect brain

We acknowledge that our model is an abstraction rather than a direct reproduction of the full micromorphology of the insect brain. The goal of our study was not to replicate every anatomical feature but rather to extract the core computational principles underlying active vision, based on the functional activity of insect brain. Although the recent connectome studies provide detailed structural maps, they do not fully capture the functional dynamics of sensory processing and behavioural outcomes. Our model integrates key neurobiological insights, including the hierarchical structure of the optic lobes, lateral inhibition in the lobula, and non-associative learning mechanisms shaping spatiotemporal receptive fields.

However, to address this concern, we have revised the introduction and discussion to explicitly acknowledge the model’s level of abstraction and its relationship to the known anatomy of the insect visual system. Furthermore, we highlight future directions in which connectomic data could refine our model.

(2) Strength of claims regarding minimality of the model

We appreciate the reviewer’s concern regarding the definition of a "minimal" model. Our intention was not to claim that this model represents the absolute minimal neural architecture for visual learning task but rather that it identifies a minimal set of necessary computational elements that enable pattern discrimination in insects. To clarify this, we have refined the text to ensure that our conclusions about minimality are explicitly tied to the specific constraints and assumptions of our model. For instance, in the revised manuscript, we emphasise that our findings demonstrate how the number of lobula neurons, inhibitory lateral connection, non-associative learning model, affect neural representation and discrimination performance, rather than establishing an absolute lower bound on the complexity required for visual processing in insects.

(3) Mechanistic explanations for pattern discrimination

Thank you for highlighting this point. We have conducted a more detailed analysis of the model’s response to different patterns and expanded our discussion of the underlying mechanisms. To address this, we have refined our explanation of how different scanning strategies and temporal integration mechanisms contribute to neural selectivity in the lobula and overall discrimination performance. Specifically:

- Figure 3 illustrates how the model benefits from generating sparse coding in the visual network, leading to improved performance in pattern recognition tasks.

- Figure 5 now includes a more detailed explanation of how different scanning strategies influence the selectivity and separability of lobula neuron responses. Additionally, we provide further analysis of why the model successfully discriminates certain patterns (e.g., simple oriented bars) but struggles with more complex spatially structured quadrant-based patterns.

- We elaborate on how sequential sampling, temporal coding, and lateral inhibition collectively shape neural representations, enabling the model to distinguish between visual stimuli effectively.

These refinements provide a clearer mechanistic explanation of the model’s strengths and limitations, ensuring a more comprehensive understanding of its function.

(4) Evidence that the model is tuned to natural scenes

We have revised the manuscript to provide stronger support for the claim that the model is particularly adapted to natural scenes. Specifically:

- Figure 3 demonstrates that training on natural images leads to sparse, decorrelated responses in the lobula, a hallmark of efficient coding observed in biological systems.

- Supplementary Figure 2-1B shows that training with shuffled images fails to produce structured receptive fields, reinforcing that the statistical structure of natural images is crucial for efficient learning.

- We now explicitly discuss how the receptive fields emerging from non-associative learning align with known orientation-selective responses in insect visual neurons, supporting the idea that the model is optimised for processing natural visual inputs (Figures 3, 6) and discussion section.

Taken together, these revisions clarify how the model captures fundamental principles of insect vision without making overly strong claims about biological fidelity. We thank the reviewer for these insightful comments; addressing them has significantly strengthened the clarity and depth of our manuscript.

Reviewer #2 (Public Review):

This study is inspired by the scanning movements observed in bees when performing visual recognition tasks. It uses a multilayered network, representing stages of processing in the visual lobes (lamina, medulla, lobula), and uses the lobula output as input to a model of associative learning in the mushroom body (MB). The network is first trained with short "scanning" sequences of natural images, in a non-associative adaptation process, and then several experimental paradigms where images are rewarded or punished are simulated, with the output of the MB able to provide the appropriate discriminative decisions (in some but not all cases). The lobula receptive fields formed by the initial adaptation process show spatiotemporal tuning to edges moving at particular orientations and speeds that are comparable to recorded responses of such neurons in the insect brain.

There are two main limitations to the study in my view. First, although described (caption fig 1) as a model "inspired by the micromorphology" of the insect brain, implying a significant degree of accuracy and detail, there are many arbitrary features (unsupported by current connectomics). For example, the strongly constrained delay line structure from medulla to­ lobula neurons, and the use of a single MB0N that has input synapses that undergo facilitation and decay according to different neuromodulators. Second, while it is reasonable to explore some arbitrary architectural features, given that not everything is yet known about these pathways, the presented work does not sufficiently assess the necessity and sufficiency of the different components, given the repeated claims that this is the "minimal circuit" required for the visual tasks explored.

We appreciate your feedback and have refined the manuscript to clarify model design choices and address concerns regarding minimality.

(1) Model Architecture and Functional Simplifications<br /> While our model is inspired by insect visual system, it is not intended as an exact anatomical reconstruction but rather a functional abstraction to uncover key computational principles of active vision and visual learning. The delay-line structure and simplified MBON implementation were deliberate choices to enable spatiotemporal encoding and associative learning without overcomplicating the model. As connectome data alone do not fully reveal functional relationships, our approach serves as a hypothesis-generating tool for future neurobiological studies.

(2) Necessity and Sufficiency of Model Components<br /> We have removed overstatements about minimality and now clarify that our model represents a functional circuit rather than the absolute minimal configuration. Additionally, we conducted new control experiments assessing the influence of different model components, and further justifying key mechanisms such as spatiotemporal encoding and lateral inhibition.

For a more detailed discussion of these revisions and improvements, please refer to our response to the Journal, above.

Regarding the mushroom body (MB) learning model, it is strange that no reference is made to recent models closely tied to connectomic and other data in fruit flies, which suggests separate MBONS encode positive vs. negative value; that learning is not dependent on MB0N activity (so is not STDP); that feedback from MBONs to dopaminergic signalling plays an important role, etc. Possibly the MB of the bee operates in a completely different way to the fly, but the presented model relies on relatively old data about MB function, mostly from insects other than bees (e.g. locust) so its relationship to the increasingly comprehensive understanding emerging for the fly MB needs to be clarified. It is implied that the complex interaction of the differential effects of dopamine and octopamine, as modelled here, are required to learn the more complex visual paradigms, but it is not actually tested if simpler rules might suffice. Also, given previous work on models of view recognition in the MB, inspired by bees and ants, it seems plausible that simply using static 25×25 medulla activity as input to produce sparse activity in the KCs would be sufficient for MB0N output to discriminate the patterns used in training, including the face stimulus. Thus it is not clear whether the spatiotemporal input and the lobula encoding are necessary to solve these tasks.

Thank you for your suggestion. The primary focus of this study was not to uncover the exact mechanisms of associative learning in the mushroom body (MB) but rather to evaluate the role of lobula output activity in active vision. The associative learning component was included as a simplified mechanism to assess how the spatiotemporal encoding in the lobula contributes to visual pattern learning.

We conducted a detailed analysis of lobula neuron activity, focusing on sparsity, decorrelation, and selectivity to demonstrate how the visual system extracts compact yet relevant signals before reaching the learning centre (see Figure 5). Theoretical predictions based on these findings suggest that such encoding enhances pattern recognition performance. While selecting this possible associative learning mechanism allowed us to explicitly evaluate this capability, it also facilitated comparison with previous active vision experiments and assessed the influence of different components on bee behaviour.

We acknowledge that recent Drosophila connectomics studies suggest alternative MB architectures, including separate MBONs encoding positive vs. negative values, learning mechanisms independent of MBON activity, and feedback from MBONs to dopaminergic pathways. However, visual learning mechanisms in the MB remain poorly characterised, especially in bees, where the functional relevance of different MBON configurations is still unclear. The decision to simplify the MB learning process was intentional, allowing us to prioritise model interpretability over anatomical replication.

These simplifications have been explicitly discussed in the revised manuscript, where we suggest future directions for integrating more biologically detailed MB models to enhance our understanding of active visual learning in insects. For a broader discussion of our rationale for prioritising computational simplifications over direct neurobiological replication, please refer to our response to the Journal, above.

It is also difficult to interpret the range of results in fig 3. The network sometimes learns well, sometimes just adequately (perhaps comparable to bees), and sometimes fails. The presentation of these results does not seem to identify any coherent pattern underlying success or failure, other than that the ability to generalise seems limited. That is, recognition (in most cases) requires the presentation of exactly the same stimulus in exactly the same way (same scanning pattern, distance and speed). In particular, it is hard to know what to conclude when the network appears able to learn some "complex patterns" (spirals, faces) but fails to learn the apparently simple plus vs. multiplication symbol discrimination if it is trained and tested with a scan passing across the whole pattern instead of just the lower half.

We acknowledge that the variability in the model’s performance across different tasks and conditions required a clearer explanation. In the revised manuscript, we have analysed the underlying factors influencing success and failure in greater detail and have expanded the discussion on the model’s generalisation limitations.

To address this, we have conducted new control experiments and deeper analyses, now presented in Figure 5, Figure 6F, which illustrate how scanning conditions impact recognition performance. Specifically, we examine why the model can successfully learn complex patterns (e.g., spirals, faces) but struggles with apparently simpler tasks, such as distinguishing between a plus and multiplication symbol when scanning the entire pattern rather than just the lower half. Our results suggest that spatially constrained scanning enhances discriminability, while whole-pattern scanning reduces selectivity due to weaker and less sparse feature encoding in lobula neurons.

We have also clarified in the Discussion section that while the model demonstrates robust pattern learning under specific conditions, its ability to generalise remains limited when tested with compex patterns (Figure 6F. Further investigation is needed to explore how adaptive scanning strategies or hierarchical processing might improve generalisation.

In summary, although it is certainly interesting to explore how active vision (scanning a visual pattern) might affect the encoding of stimuli and the ability to learn to discriminate rewarding stimuli, some claims in the paper need to be tempered or better supported by the demonstration that alternative, equally plausible, models of the visual and mushroom body circuits are not sufficient to solve the given tasks.

There is limited knowledge in the literature regarding the neural correlates of visual-related plasticity in the mushroom body (MB). The majority of our current understanding of the MB is derived from studies on olfactory learning, particularly in Drosophila, which does not provide sufficient data to directly implement or comprehensively compare alternative models for visual learning.

However, the primary focus of our study is on active vision and how spatiotemporal signals are encoded in the insect visual system. Rather than aiming to replicate a detailed biological model of MB function, we intentionally employed a simplified associative learning network to investigate how neural activity emerging from our visual processing model can support pattern recognition. This approach also allows us to compare model performance with bee behaviour, drawing on insights from previous experimental work on active vision in bees.

We now discuss the limitations of our approach and the rationale for selectively incorporating specific neural network components in lines 652-677. Additionally, we have provided further justification (see responses above) for prioritising a simplified model, rather than attempting to mimic a highly detailed, yet currently unverified, alternative learning circuit. These clarifications help ensure that our claims are appropriately tempered while still demonstrating the functional relevance of our model.

Reviewer #3 (Public Review):

In this manuscript, the authors use the data collected and observations made on bees' scanning behaviour during visual learning to design a bio-inspired artificial neural network. The network follows the architecture of bees visual systems, where photoreceptors project into the lamina, then the medulla, medulla neurons connect to a set of spiking neurons in the lobula. Lobula neurons project to kenyon cells and then to MBON, which controls reward and punishment. The authors then test the performance of the network in comparison with real bee data, finding it to perform well in all tasks. The paper attempts to reproduce a living organism network with a practical application in mind, and it is quite impressive! I appreciate both the potential implications for the understanding of biological systems and the applications in the development of autonomous agents, making the paper absolutely worth reading.

Thank you for your positive feedback and appreciation of our work.

However, I believe that the current version somewhat lacks in clarity regarding the methodology and in some of the keywords used to describe the model.

Definitions:<br /> Throughout the manuscript, the authors use some key terminology that I believe would benefit from some clarification.<br /> The generated model is described in the title and once in the introduction as "neuromorphic". The model is definitely bio-inspired, but at least in some layers of the neural network, the model is built very differently from actual brain connectivity. Generally, when we use the term neuromorphic we imply many advantages of neural tissue, like energy efficiency, that I am not sure the current model is achieving. I absolutely see how this work is going in that direction, and I also fundamentally agree with the choice of terminology, but this should be clearly explained to not risk over-implications

We appreciate the reviewer’s feedback and acknowledge the importance of clarifying key terminology in our manuscript. As outlined in our response to the Journal, we intentionally simplified the model to focus on understanding the core computational processes involved in active vision rather than precisely replicating the full complexity of insect neural circuits (see other reasons for simplification in the manuscript). This simplification allows us to systematically analyse the influence of specific components underlying active vision mechanisms.

Despite these simplifications, our model incorporates key neuromorphic principles, including the use of a recurrent neural network architecture and a spiking neuron model at multiple processing levels. These elements enable biologically inspired information processing, aligning with the fundamental characteristics of neuromorphic computing, even if the model does not explicitly focus on hardware efficiency or energy constraints.

The authors describe this as a model of "active vision". This is done in the title of the article, and in the many paragraph headings (methods, results). In the introduction, however, the term active vision is reserved to the description of bees' behavior. Indeed, the developed model is not a model of active vision, as this would require for the model to control the movement of the "camera". Here instead the stimuli display is given to the model in a fixed progression. What I suspect is that the authors' aim is to describe a model that supports the bees' active vision, not a model of active vision. I believe this should be very clear from the paper, and it may be appropriate to remove the term from the title.

While our model does not actively control camera movement in the environment, it does simulate the effects of active vision by incorporating scanning dynamics. Our results demonstrate that model responses change significantly with variations in scanning speed and restricted scanning areas, highlighting the importance of movement in shaping visual encoding. However, we acknowledge that true active vision would involve adaptive, real-time control of gaze or trajectory, which the step after the current implementation for make more realistic model of active vison. To address your concern, we have discussed the potential for incorporating dynamic flight behaviours in future studies, allowing the model to actively adjust its scanning strategy based on learned visual cues.

In the short title, it said that this network is minimal. This is then characterized in the introduction as the minimal network capable of enabling active vision in bees. The authors, however, in their experiment only vary the number of lobula neurons, without changing other parts of the architecture. Given this, we can only say that 16 lobula neurons is the minimal number required to solve the experimental task with the given model. I don't believe that this is generalizable to bees, nor that this network is minimal, as there may be different architectures (for the other layers especially) that require overall less neurons. Moreover, the tasks attempted in the minimal network experiment did not include any of the complex stimuli presented in figure 3, like faces. It may be that 16 lobula neurons are sufficient for the X vs + and clockwise vs counter-clockwise spirals, but we do not know if increasing stimuli complexity would result in a failure of the model with 16 neurons.

We agree that analysing only the number of lobula neurons is not sufficient to establish a truly minimal model for active vision. To address this, we conducted further control experiments to evaluate the influence of other key components, including non-associative learning, scanning behaviour, and lateral connectivity, on model performance. Our results suggest that the proposed model represents a computationally minimal network capable of implementing a basic active vision process, but a more complex model would be required for higher-order visual tasks.

However, to avoid potential misinterpretation, we have revised the short title and updated the manuscript to clarify that our model identifies a possible minimal functional circuit rather than the absolute minimal network for active vision. Additionally, we have added further discussion on the simplifications made in the model and emphasised the need for future studies to explore alternative architectures and assess their relevance for understanding active vision in insects.

Methodology:

The current explanation of the model is currently a bit lacking in clarity and details. This risks impacting negatively on the relevance of the whole work which is interesting and worth reading! This issue affects also the interpretation of the results, as it is not clear to what extent each part of the network could affect the results shown. This is especially the case when the network under-performs with respect to the best performing scenario (e.g., when varying the speed and part of the pattern that is observed, such as in Fig 2C). Adding a detailed technical scheme/drawing specific to the network architecture could have been a way of significantly increasing the clarity of the Methods section and the interpretation of the results.<br /> On a similar note, the authors make some comparisons between the model and real bees. However, it remains unclear whether these similarities are actually indicative of an optimality in the bees visual scanning strategy, or just deriving from the authors design. This is for me particularly important in the experiments aimed at finding the best scanning procedure. If the initial model training is based on natural images it is performed by presenting left to right moving frames, the highest efficiency of lower-half scanning may be due to how the weights in the initial layers are structured and a low generalizability of the model, rather than to the strategy optimality

We appreciate the reviewer’s constructive feedback and have taken steps to enhance the clarity, interpretability, and transparency of our model description and results. Below, we address the concerns regarding model explanation, performance interpretation, and the comparison with real bee behaviour.

(1) Improved Model Explanation and Network Clarity: We apologise that the previous version of the manuscript did not fully detail the architecture and functioning of the model. To address this, we have expanded the Methods section with a more detailed breakdown of the network components, their roles, and their contribution to active vision processing. Additionally, we have summarised the network architecture and its implementation for visual learning tasks at the beginning of the Results section, providing a clearer overview of the information flow from visual input to associative learning. Furthermore, we have explicitly analysed and discussed the role of key model components, including scanning strategies, lateral connectivity, and non-associative learning mechanisms, clarifying how each contributes to the observed results.

(2) Interpretation of Model Performance Variability: Understanding the factors influencing performance variability is crucial, and to improve clarity, we have conducted further analysis of model performance across different conditions, particularly examining the effects of scanning speed, spatial constraints, and feature encoding (see Figure 2C). Additionally, we have expanded the discussion on how scanning conditions impact performance, providing explanations for why some conditions lead to higher or lower discrimination success. Furthermore, we have clarified why certain stimuli present greater challenges for the model, linking these difficulties to receptive field properties and scanning dynamics.

(3) Comparison Between Model Behaviour and Real Bees: To address your concern regarding the link between scanning preferences and true biological optimality, we have included further analysis discussing the influence of training conditions on the model’s learned behaviours. Additionally, we propose future experiments to test alternative scanning strategies, including adaptive scanning mechanisms that adjust based on visual task demands. Furthermore, we have expanded the discussion on the simplifications made in this study, explicitly stating the limitations of the model and emphasising the need for future research to explore more flexible and biologically plausible scanning mechanisms.

We believe these revisions significantly enhance the clarity and interpretability of the study, ensuring that the model’s findings are well contextualised within both computational and biological frameworks.

Author Response

eLife assessment

This potentially valuable study uses classic neuroanatomical techniques and synchrotron X-ray tomography to investigate the mapping of the trunk within the brainstem nuclei of the elephant brain. Given its unique specializations, understanding the somatosensory projections from the elephant trunk would be of general interest to evolutionary neurobiologists, comparative neuroscientists, and animal behavior scientists. However, the anatomical analysis is inadequate to support the authors' conclusion that they have identified the elephant trigeminal sensory nuclei rather than a different brain region, specifically the inferior olive.

Comment: We are happy that our paper is considered to be potentially valuable. Also, the editors highlight the potential interest of our work for evolutionary neurobiologists, comparative neuroscientists, and animal behavior scientists. The editors are more negative when it comes to our evidence on the identification of the trigeminal nucleus vs the inferior olive. We have five comments on this assessment. (i) We think this assessment is heavily biased by the comments of referee 2. We will show that the referee’s comments are more about us than about our paper. Hence, the referee failed to do their job (refereeing our paper) and should not have succeeded in leveling our paper. (ii) We have no ad hoc knock-out experiments to distinguish the trigeminal nucleus vs the inferior olive. Such experiments (extracellular recording & electrolytic lesions, viral tracing would be done in a week in mice, but they cannot and should not be done in elephants. (iii) We have extraordinary evidence. Nobody has ever described a similarly astonishing match of body (trunk folds) and myeloarchitecture in the trigeminal system before. (iv) We will show that our assignment of the trigeminal nucleus vs the inferior olive is more plausible than the current hypothesis about the assignment of the trigeminal nucleus vs the inferior olive as defended by referee 2. We think this is why it is important to publish our paper. (v) We think eLife is the perfect place for our publication because the deviating views of referee 2 are published along.

Change: We performed additional peripherin-antibody staining to differentiate the inferior olive and trigeminal nucleus. Peripherin is a cytoskeletal protein that is found in peripheral nerves and climbing fibers. Specifically, climbing fibers of various species (mouse, rabbit, pig, cow, and human; Errante et al., 1998) are stained intensely with peripherin-antibodies. What is tricky for our purposes is that there is also some peripherin-antibody reactivity in the trigeminal nuclei (Errante et al., 1998). Such peripherin-antibody reactivity is weaker, however, and lacks the distinct axonal bundle signature that stems from the strong climbing fiber peripherin-reactivity as seen in the inferior olive (Errante et al., 1998). As can be seen in Author response image 1, we observe peripherin-reactivity in axonal bundles (i.e. in putative climbing fibers), in what we think is the inferior olive. We also observe weak peripherin-reactivity, in what we think is the trigeminal nucleus, but not the distinct and strong labeling of axonal bundles. These observations are in line with our ideas but are difficult to reconcile with the views of the referee. Specifically, the lack of peripherin-reactive axon bundles suggests that there are no climbing fibres in what the referee thinks is the inferior olive.

Errante, L., Tang, D., Gardon, M., Sekerkova, G., Mugnaini, E., & Shaw, G. (1998). The intermediate filament protein peripherin is a marker for cerebellar climbing fibres. Journal of neurocytology, 27, 69-84.

Author response image 1.

The putative inferior olive but not the putative trigeminal nucleus contains peripherin-positive axon bundles (presumptive climbing fibers). (A) Overview picture of a brainstem section stained with anti-peripherin-antibodies (white color). Anti-peripherin-antibodies stain climbing fibers in a wide variety of mammals. The section comes from the posterior brainstem of African elephant cow Bibi; in this posterior region, both putative inferior olive and trigeminal nucleus are visible. Note the bright staining of the dorsolateral nucleus, the putative inferior olive according to Reveyaz et al., and the trigeminal nucleus according to Maseko et al., 2013. (B) High magnification view of the dorsolateral nucleus (corresponding to the upper red rectangle in A). Anti-peripherin-positive axon bundles (putative climbing fibers) are seen in support of the inferior olive hypothesis of Reveyaz et al. (C) High magnification view of the ventromedial nucleus (corresponding to the lower red rectangle in A). The ventromedial nucleus is weakly positive for peripherin but contains no anti-peripherin-positive axon bundles (i.e. no putative climbing fibers) in support of the trigeminal nucleus hypothesis of Reveyaz et al. Note that myelin stripes – weakly visible as dark omissions – are clearly anti-peripherin-negative.

Reviewer #1:

Summary:

This fundamental study provides compelling neuroanatomical evidence underscoring the sensory function of the trunk in African and Asian elephants. Whereas myelinated tracts are classically appreciated as mediating neuronal connections, the authors speculate that myelinated bundles provide functional separation of trunk folds and display elaboration related to the "finger" projections. The authors avail themselves of many classical neuroanatomical techniques (including cytochrome oxidase stains, Golgi stains, and myelin stains) along with modern synchrotron X-ray tomography. This work will be of interest to evolutionary neurobiologists, comparative neuroscientists, and the general public, with its fascinating exploration of the brainstem of an icon sensory specialist.

Comment: We are incredibly grateful for this positive assessment.

Changes: None.

Strengths:

• The authors made excellent use of the precious sample materials from 9 captive elephants.

• The authors adopt a battery of neuroanatomical techniques to comprehensively characterize the structure of the trigeminal subnuclei and properly re-examine the "inferior olive".

• Based on their exceptional histological preparation, the authors reveal broadly segregated patterns of metabolic activity, similar to the classical "barrel" organization related to rodent whiskers.

Comment: The referee provides a concise summary of our findings.

Changes: None.

Weaknesses:

• As the authors acknowledge, somewhat limited functional description can be provided using histological analysis (compared to more invasive techniques).

• The correlation between myelinated stripes and trunk fold patterns is intriguing, and Figure 4 presents this idea beautifully. I wonder - is the number of stripes consistent with the number of trunk folds? Does this hold for both species?

Comment: We agree with the referee’s assessment. We note that cytochrome-oxidase staining is an at least partially functional stain, as it reveals constitutive metabolic activity. A significant problem of the work in elephants is that our recording possibilities are limited, which in turn limits functional analysis. As indicated in Figure 4 for the African elephant Indra, there was an excellent match of trunk folds and myelin stripes. Asian elephants have more, and less conspicuous trunk folds than African elephants. As illustrated in Figure 6, Asian elephants have more, and less conspicuous myelin stripes. Thus, species differences in myelin stripes correlate with species differences in trunk folds.

Changes: We clarify the relation of myelin stripe and trunk fold patterns in our discussion of Figure 6.  

Reviewer #2 (Public Review):

The authors describe what they assert to be a very unusual trigeminal nuclear complex in the brainstem of elephants, and based on this, follow with many speculations about how the trigeminal nuclear complex, as identified by them, might be organized in terms of the sensory capacity of the elephant trunk.

Comment: We agree with the referee’s assessment that the putative trigeminal nucleus described in our paper is highly unusual in size, position, vascularization, and myeloarchitecture. This is why we wrote this paper. We think these unusual features reflect the unique facial specializations of elephants, i.e. their highly derived trunk. Because we have no access to recordings from the elephant brainstem, we cannot back up all our functional interpretations with electrophysiological evidence; it is therefore fair to call them speculative.

Changes: None.

The identification of the trigeminal nuclear complex/inferior olivary nuclear complex in the elephant brainstem is the central pillar of this manuscript from which everything else follows, and if this is incorrect, then the entire manuscript fails, and all the associated speculations become completely unsupported.

Comment: We agree.

Changes: None.

The authors note that what they identify as the trigeminal nuclear complex has been identified as the inferior olivary nuclear complex by other authors, citing Shoshani et al. (2006; 10.1016/j.brainresbull.2006.03.016) and Maseko et al (2013; 10.1159/000352004), but fail to cite either Verhaart and Kramer (1958; PMID 13841799) or Verhaart (1962; 10.1515/9783112519882-001). These four studies are in agreement, but the current study differs.

Comment & Change: We were not aware of the papers of Verhaart and included them in the revised ms.

Let's assume for the moment that the four previous studies are all incorrect and the current study is correct. This would mean that the entire architecture and organization of the elephant brainstem is significantly rearranged in comparison to ALL other mammals, including humans, previously studied (e.g. Kappers et al. 1965, The Comparative Anatomy of the Nervous System of Vertebrates, Including Man, Volume 1 pp. 668-695) and the closely related manatee (10.1002/ar.20573). This rearrangement necessitates that the trigeminal nuclei would have had to "migrate" and shorten rostrocaudally, specifically and only, from the lateral aspect of the brainstem where these nuclei extend from the pons through to the cervical spinal cord (e.g. the Paxinos and Watson rat brain atlases), the to the spatially restricted ventromedial region of specifically and only the rostral medulla oblongata. According to the current paper, the inferior olivary complex of the elephant is very small and located lateral to their trigeminal nuclear complex, and the region from where the trigeminal nuclei are located by others appears to be just "lateral nuclei" with no suggestion of what might be there instead.

Comment: We have three comments here:

1) The referee correctly notes that we argue the elephant brainstem underwent fairly major rearrangements. In particular, we argue that the elephant inferior olive was displaced laterally, by a very large cell mass, which we argue is an unusually large trigeminal nucleus. To our knowledge, such a large compact cell mass is not seen in the ventral brain stem of any other mammal.

2) The referee makes it sound as if it is our private idea that the elephant brainstem underwent major rearrangements and that the rest of the evidence points to a conventional ‘rodent-like’ architecture. This is far from the truth, however. Already from the outside appearance (see our Figure 1B and Figure 6A) it is clear that the elephant brainstem has huge ventral bumps not seen in any other mammal. An extraordinary architecture also holds at the organizational level of nuclei. Specifically, the facial nucleus – the most carefully investigated nucleus in the elephant brainstem – has an appearance distinct from that of the facial nuclei of all other mammals (Maseko et al., 2013; Kaufmann et al., 2022). If both the overall shape and the constituting nuclei of the brainstem are very different from other mammals, it is very unlikely if not impossible that the elephant brainstem follows in all regards a conventional ‘rodent-like’ architecture.

3) The inferior olive is an impressive nucleus in the partitioning scheme we propose (Author response image 1). In fact – together with the putative trigeminal nucleus we describe – it’s the most distinctive nucleus in the elephant brainstem. We have not done volumetric measurements and cell counts here, but think this is an important direction for future work. What has informed our work is that the inferior olive nucleus we describe has the serrated organization seen in the inferior olive of all mammals. We will discuss these matters in depth below.

Changes: None.

Such an extraordinary rearrangement of brainstem nuclei would require a major transformation in the manner in which the mutations, patterning, and expression of genes and associated molecules during development occur. Such a major change is likely to lead to lethal phenotypes, making such a transformation extremely unlikely. Variations in mammalian brainstem anatomy are most commonly associated with quantitative changes rather than qualitative changes (10.1016/B978-0-12-804042-3.00045-2).

Comment: We have two comments here:

1) The referee claims that it is impossible that the elephant brainstem differs from a conventional brainstem architecture because this would lead to lethal phenotypes etc. Following our previous response, this argument does not hold. It is out of the question that the elephant brainstem looks very different from the brainstem of other mammals. Yet, it is also evident that elephants live. The debate we need to have is not if the elephant brainstem differs from other mammals, but how it differs from other mammals.

2). In principle we agree with the referee’s thinking that the model of the elephant brainstem that is most likely correct is the one that requires the least amount of rearrangements to other mammals. We therefore prepared a comparison of the model the referee is proposing (Maseko et al., 2013; see Author response table 1 below) with our proposition. We scored these models on their similarity to other mammals. We find that the referee’s ideas (Maseko et al., 2013) require more rearrangements relative to other mammals than our suggestion.

Changes: Inclusion of Author response table 1, which we discuss in depth below.

The impetus for the identification of the unusual brainstem trigeminal nuclei in the current study rests upon a previous study from the same laboratory (10.1016/j.cub.2021.12.051) that estimated that the number of axons contained in the infraorbital branch of the trigeminal nerve that innervate the sensory surfaces of the trunk is approximately 400 000. Is this number unusual? In a much smaller mammal with a highly specialized trigeminal system, the platypus, the number of axons innervating the sensory surface of the platypus bill skin comes to 1 344 000 (10.1159/000113185). Yet, there is no complex rearrangement of the brainstem trigeminal nuclei in the brain of the developing or adult platypus (Ashwell, 2013, Neurobiology of Monotremes), despite the brainstem trigeminal nuclei being very large in the platypus (10.1159/000067195). Even in other large-brained mammals, such as large whales that do not have a trunk, the number of axons in the trigeminal nerve ranges between 400,000 and 500,000 (10.1007/978-3-319-47829-6_988-1). The lack of comparative support for the argument forwarded in the previous and current study from this laboratory, and that the comparative data indicates that the brainstem nuclei do not change in the manner suggested in the elephant, argues against the identification of the trigeminal nuclei as outlined in the current study. Moreover, the comparative studies undermine the prior claim of the authors, informing the current study, that "the elephant trigeminal ganglion ... point to a high degree of tactile specialization in elephants" (10.1016/j.cub.2021.12.051). While clearly, the elephant has tactile sensitivity in the trunk, it is questionable as to whether what has been observed in elephants is indeed "truly extraordinary".

Comment: These comments made us think that the referee is not talking about the paper we submitted, but that the referee is talking about us and our work in general. Specifically, the referee refers to the platypus and other animals dismissing our earlier work, which argued for a high degree of tactile specialization in elephants. We think the referee’s intuitions are wrong and our earlier work is valid.

Changes: We prepared a Author response image 2 (below) that puts the platypus brain, a monkey brain, and the elephant trigeminal ganglion (which contains a large part of the trunk innervating cells) in perspective.

Author response image 2.

The elephant trigeminal ganglion is comparatively large. Platypus brain, monkey brain, and elephant ganglion. The elephant has two trigeminal ganglia, which contain the first-order somatosensory neurons. They serve mainly for tactile processing and are large compared to a platypus brain (from the comparative brain collection) and are similar in size to a monkey brain. The idea that elephants might be highly specialized for trunk touch is also supported by the analysis of the sensory nerves of these animals (Purkart et al., 2022). Specifically, we find that the infraorbital nerve (which innervates the trunk) is much thicker than the optic nerve (which mediates vision) and the vestibulocochlear nerve (which mediates hearing). Thus, not everything is large about elephants; instead, the data argue that these animals are heavily specialized for trunk touch.

But let's look more specifically at the justification outlined in the current study to support their identification of the unusually located trigeminal sensory nuclei of the brainstem.

(1) Intense cytochrome oxidase reactivity.

(2) Large size of the putative trunk module.

(3) Elongation of the putative trunk module.

(4) The arrangement of these putative modules corresponds to elephant head anatomy.

(5) Myelin stripes within the putative trunk module that apparently match trunk folds.

(6) Location apparently matches other mammals.

(7) Repetitive modular organization apparently similar to other mammals.

(8) The inferior olive described by other authors lacks the lamellated appearance of this structure in other mammals.

Comment: We agree those are key issues.

Changes: None.

Let's examine these justifications more closely.

(1) Cytochrome oxidase histochemistry is typically used as an indicative marker of neuronal energy metabolism. The authors indicate, based on the "truly extraordinary" somatosensory capacities of the elephant trunk, that any nuclei processing this tactile information should be highly metabolically active, and thus should react intensely when stained for cytochrome oxidase. We are told in the methods section that the protocols used are described by Purkart et al (2022) and Kaufmann et al (2022). In neither of these cited papers is there any description, nor mention, of the cytochrome oxidase histochemistry methodology, thus we have no idea of how this histochemical staining was done. To obtain the best results for cytochrome oxidase histochemistry, the tissue is either processed very rapidly after buffer perfusion to remove blood or in recently perfusion-fixed tissue (e.g., 10.1016/0165-0270(93)90122-8). Given: (1) the presumably long post-mortem interval between death and fixation - "it often takes days to dissect elephants"; (2) subsequent fixation of the brains in 4% paraformaldehyde for "several weeks"; (3) The intense cytochrome oxidase reactivity in the inferior olivary complex of the laboratory rat (Gonzalez-Lima, 1998, Cytochrome oxidase in neuronal metabolism and Alzheimer's diseases); and (4) The lack of any comparative images from other stained portions of the elephant brainstem; it is difficult to support the justification as forwarded by the authors. The histochemical staining observed is likely background reactivity from the use of diaminobenzidine in the staining protocol. Thus, this first justification is unsupported.

Comment: The referee correctly notes the description of our cytochrome-oxidase reactivity staining was lacking. This is a serious mistake of ours for which we apologize very much. The referee then makes it sound as if we messed up our cytochrome-oxidase staining, which is not the case. All successful (n = 3; please see our technical comments in the recommendation section) cytochrome-oxidase stainings were done with elephants with short post-mortem times (≤ 2 days) to brain removal/cooling and only brief immersion fixation (≤ 1 day). Cytochrome-oxidase reactivity in elephant brains appears to be more sensitive to quenching by fixation than is the case for rodent brains. We think it is a good idea to include a cytochrome-oxidase staining overview picture because we understood from the referee’s comments that we need to compare our partitioning scheme of the brainstem with that of other authors. To this end, we add a cytochrome-oxidase staining overview picture (Author response image 3) along with an alternative interpretation from Maseko et al., 2013.

Changes: 1) We added details on our cytochrome-oxidase reactivity staining protocol and the cytochrome-oxidase reactivity in the elephant brain in general recommendation.

2) We provide a detailed discussion of the technicalities of cytochrome-oxidase staining below in the recommendation section, where the referee raised further criticisms.

3) We include a cytochrome-oxidase staining overview picture (Author response image 2) along with an alternative interpretation from Maseko et al., 2013.

Author response image 3.

Cytochrome-oxidase staining overview along with the Maseko et al. (2013) scheme Left, coronal cytochrome-oxidase staining overview from African elephant cow Indra; the section is taken a few millimeters posterior to the facial nucleus. Brown is putatively neural cytochrome-reactivity, and white is the background. Black is myelin diffraction and (seen at higher resolution, when you zoom in) erythrocyte cytochrome-reactivity in blood vessels (see our Figure 1E-G); such blood vessel cytochrome-reactivity is seen, because we could not perfuse the animal. There appears to be a minimal outside-in-fixation artifact (i.e. a more whitish/non-brownish appearance of the section toward the borders of the brain). This artifact is not seen in sections from Indra that we processed earlier or in other elephant brains processed at shorter post-mortem/fixation delays (see our Figure 1C). Right, coronal partitioning scheme of Maseko et al. (2013) for the elephant brainstem at an approximately similar anterior-posterior level.

The same structures can be recognized left and right. The section is taken at an anterior-posterior level, where we encounter the trigeminal nuclei in pretty much all mammals. Note that the neural cytochrome reactivity is very high, in what we refer to as the trigeminal-nuclei-trunk-module and what Maseko et al. refer to as inferior olive. Myelin stripes can be recognized here as white omissions.

At the same time, the cytochrome-oxidase-reactivity is very low in what Maseko et al. refer to as trigeminal nuclei. The indistinct appearance and low cytochrome-oxidase-reactivity of the trigeminal nuclei in the scheme of Maseko et al. (2013) is unexpected because trigeminal nuclei stain intensely for cytochrome-oxidase-reactivity in most mammals and because the trigeminal nuclei represent the elephant’s most important body part, the trunk. Staining patterns of the trigeminal nuclei as identified by Maseko et al. (2013) are very different at more posterior levels; we will discuss this matter below.

Justifications (2), (3), and (4) are sequelae from justification (1). In this sense, they do not count as justifications, but rather unsupported extensions.

Comment: These are key points of our paper that the referee does not discuss.

Changes: None.

(4) and (5) These are interesting justifications, as the paper has clear internal contradictions, and (5) is a sequelae of (4). The reader is led to the concept that the myelin tracts divide the nuclei into sub-modules that match the folding of the skin on the elephant trunk. One would then readily presume that these myelin tracts are in the incoming sensory axons from the trigeminal nerve. However, the authors note that this is not the case: "Our observations on trunk module myelin stripes are at odds with this view of myelin. Specifically, myelin stripes show no tapering (which we would expect if axons divert off into the tissue). More than that, there is no correlation between myelin stripe thickness (which presumably correlates with axon numbers) and trigeminal module neuron numbers. Thus, there are numerous myelinated axons, where we observe few or no trigeminal neurons. These observations are incompatible with the idea that myelin stripes form an axonal 'supply' system or that their prime function is to connect neurons. What do myelin stripe axons do, if they do not connect neurons? We suggest that myelin stripes serve to separate rather than connect neurons." So, we are left with the observation that the myelin stripes do not pass afferent trigeminal sensory information from the "truly extraordinary" trunk skin somatic sensory system, and rather function as units that separate neurons - but to what end? It appears that the myelin stripes are more likely to be efferent axonal bundles leaving the nuclei (to form the olivocerebellar tract). This justification is unsupported.

Comment: The referee cites some of our observations on myelin stripes, which we find unusual. We stand by the observations and comments. The referee does not discuss the most crucial finding we report on myelin stripes, namely that they correspond remarkably well to trunk folds.

Changes: None.

(6) The authors indicate that the location of these nuclei matches that of the trigeminal nuclei in other mammals. This is not supported in any way. In ALL other mammals in which the trigeminal nuclei of the brainstem have been reported they are found in the lateral aspect of the brainstem, bordered laterally by the spinal trigeminal tract. This is most readily seen and accessible in the Paxinos and Watson rat brain atlases. The authors indicate that the trigeminal nuclei are medial to the facial nerve nucleus, but in every other species, the trigeminal sensory nuclei are found lateral to the facial nerve nucleus. This is most salient when examining a close relative, the manatee (10.1002/ar.20573), where the location of the inferior olive and the trigeminal nuclei matches that described by Maseko et al (2013) for the African elephant. This justification is not supported.

Comment: The referee notes that we incorrectly state that the position of the trigeminal nuclei matches that of other mammals. We think this criticism is justified.

Changes: We prepared a comparison of the Maseko et al. (2013) scheme of the elephant brainstem with our scheme of the elephant brainstem (see Author response table 1). Here we acknowledge the referee’s argument and we also changed the manuscript accordingly.

(7) The dual to quadruple repetition of rostrocaudal modules within the putative trigeminal nucleus as identified by the authors relies on the fact that in the neurotypical mammal, there are several trigeminal sensory nuclei arranged in a column running from the pons to the cervical spinal cord, these include (nomenclature from Paxinos and Watson in roughly rostral to caudal order) the Pr5VL, Pr5DM, Sp5O, Sp5I, and Sp5C. However, these nuclei are all located far from the midline and lateral to the facial nerve nucleus, unlike what the authors describe in the elephants. These rostrocaudal modules are expanded upon in Figure 2, and it is apparent from what is shown that the authors are attributing other brainstem nuclei to the putative trigeminal nuclei to confirm their conclusion. For example, what they identify as the inferior olive in Figure 2D is likely the lateral reticular nucleus as identified by Maseko et al (2013). This justification is not supported.

Comment: The referee again compares our findings to the scheme of Maseko et al. (2013) and rejects our conclusions on those grounds. We think such a comparison of our scheme is needed, indeed.

Changes: We prepared a comparison of the Maseko et al. (2013) scheme of the elephant brainstem with our scheme of the elephant brainstem (see Author response table 1).

(8) In primates and related species, there is a distinct banded appearance of the inferior olive, but what has been termed the inferior olive in the elephant by other authors does not have this appearance, rather, and specifically, the largest nuclear mass in the region (termed the principal nucleus of the inferior olive by Maseko et al, 2013, but Pr5, the principal trigeminal nucleus in the current paper) overshadows the partial banded appearance of the remaining nuclei in the region (but also drawn by the authors of the current paper). Thus, what is at debate here is whether the principal nucleus of the inferior olive can take on a nuclear shape rather than evince a banded appearance. The authors of this paper use this variance as justification that this cluster of nuclei could not possibly be the inferior olive. Such a "semi-nuclear/banded" arrangement of the inferior olive is seen in, for example, giraffe (10.1016/j.jchemneu.2007.05.003), domestic dog, polar bear, and most specifically the manatee (a close relative of the elephant) (brainmuseum.org; 10.1002/ar.20573). This justification is not supported.

Comment: We carefully looked at the brain sections referred to by the referee in the brainmuseum.org collection. We found contrary to the referee’s claims that dogs, polar bears, and manatees have a perfectly serrated (a cellular arrangement in curved bands) appearance of the inferior olive. Accordingly, we think the referee is not reporting the comparative evidence fairly and we wonder why this is the case.

Changes: None.

Thus, all the justifications forwarded by the authors are unsupported. Based on methodological concerns, prior comparative mammalian neuroanatomy, and prior studies in the elephant and closely related species, the authors fail to support their notion that what was previously termed the inferior olive in the elephant is actually the trigeminal sensory nuclei. Given this failure, the justifications provided above that are sequelae also fail. In this sense, the entire manuscript and all the sequelae are not supported.

Comment: We disagree. To summarize:

(1) Our description of the cytochrome oxidase staining lacked methodological detail, which we have now added; the cytochrome oxidase reactivity data are great and support our conclusions.

(2)–(5)The referee does not really discuss our evidence on these points.

(6) We were wrong and have now fixed this mistake.

(7) The referee asks for a comparison to the Maseko et al. (2013) scheme (agreed, see Author response image 4 4 and Author response table 1).

(8) The referee bends the comparative evidence against us.

Changes: None.

A comparison of the elephant brainstem partitioning schemes put forward by Maseko et al 2013 and by Reveyaz et al.

To start with, we would like to express our admiration for the work of Maseko et al. (2013). These authors did pioneering work on obtaining high-quality histology samples from elephants. Moreover, they made a heroic neuroanatomical effort, in which they assigned 147 brain structures to putative anatomical entities. Most of their data appear to refer to staining in a single elephant and one coronal sectioning plane. The data quality and the illustration of results are excellent.

We studied mainly two large nuclei in six (now 7) elephants in three (coronal, parasagittal, and horizontal) sectioning planes. The two nuclei in question are the two most distinct nuclei in the elephant brainstem, namely an anterior ventromedial nucleus (the trigeminal trunk module in our terminology; the inferior olive in the terminology of Maseko et al., 2013) and a more posterior lateral nucleus (the inferior olive in our terminology; the posterior part of the trigeminal nuclei in the terminology of Maseko et al., 2013).

Author response image 4 gives an overview of the two partitioning schemes for inferior olive/trigeminal nuclei along with the rodent organization (see below).

Author response image 4.

Overview of the brainstem organization in rodents & elephants according to Maseko et. (2013) and Reveyaz et al. (this paper).

The strength of the Maseko et al. (2013) scheme is the excellent match of the position of elephant nuclei to the position of nuclei in the rodent (Author response image 4). We think this positional match reflects the fact that Maseko et al. (2013) mapped a rodent partitioning scheme on the elephant brainstem. To us, this is a perfectly reasonable mapping approach. As the referee correctly points out, the positional similarity of both elephant inferior olive and trigeminal nuclei to the rodent strongly argues in favor of the Maseko et al. (2013), because brainstem nuclei are positionally very conservative.

Other features of the Maseko et al. (2013) scheme are less favorable. The scheme marries two cyto-architectonically very distinct divisions (an anterior indistinct part) and a super-distinct serrated posterior part to be the trigeminal nuclei. We think merging entirely distinct subdivisions into one nucleus is a byproduct of mapping a rodent partitioning scheme on the elephant brainstem. Neither of the two subdivisions resemble the trigeminal nuclei of other mammals. The cytochrome oxidase staining patterns differ markedly across the anterior indistinct part (see our Author response image 4) and the posterior part of the trigeminal nuclei and do not match with the intense cytochrome oxidase reactivity of other mammalian trigeminal nuclei (Referee Figure 3). Our anti-peripherin staining indicates that there probably no climbing fibers, in what Maseko et al. think. is inferior olive; this is a potentially fatal problem for the hypothesis. The posterior part of Maseko et al. (2013) trigeminal nuclei has a distinct serrated appearance that is characteristic of the inferior olive in other mammals. Moreover, the inferior olive of Maseko et al. (2013) lacks the serrated appearance of the inferior olive seen in pretty much all mammals; this is a serious problem.

The partitioning scheme of Reveyaz et al. comes with poor positional similarity but avoids the other problems of the Maseko et al. (2013) scheme. Our explanation for the positionally deviating location of trigeminal nuclei is that the elephant grew one of the if not the largest trigeminal systems of all mammals. As a result, the trigeminal nuclei grew through the floor of the brainstem. We understand this is a post hoc just-so explanation, but at least it is an explanation.

The scheme of Reveyaz et al. was derived in an entirely different way from the Maseko model. Specifically, we were convinced that the elephant trigeminal nuclei ought to be very special because of the gigantic trigeminal ganglia (Purkart et al., 2022). Cytochrome-oxidase staining revealed a large distinct nucleus with an elongated shape. Initially, we were freaked out by the position of the nucleus and the fact that it was referred to as inferior olive by other authors. When we found an inferior-olive-like nucleus at a nearby (although at an admittedly unusual) location, we were less worried. We then optimized the visualization of myelin stripes (brightfield imaging etc.) and were able to collect an entire elephant trunk along with the brain (African elephant cow Indra). When we made the one-to-one match of Indra’s trunk folds and myelin stripes (Figure 4) we were certain that we had identified the trunk module of the trigeminal nuclei. We already noted at the outset of our rebuttal that we now consider such certainty a fallacy of overconfidence. In light of the comments of Referee 2, we feel that a further discussion of our ideas is warranted. A strength of the Reveyaz model is that nuclei look like single anatomical entities. The trigeminal nuclei look like trigeminal nuclei of other mammals, the trunk module has a striking resemblance to the trunk and the inferior olive looks like the inferior olive of other mammals.

We evaluated the fit of the two models in the form of a table (Author response table 1; below). Unsurprisingly, Author response table 1 aligns with our views of elephant brainstem partitioning.

Author response table 1.

Qualitative evaluation of elephant brainstem partitioning schemes

++ = Very attractive; + = attractive; - = unattractive; -- = very unattractive We scored features that are clear and shared by all mammals – as far as we know them – as very attractive. We scored features that are clear and are not shared by all mammals – as far as we know them – as very unattractive. Attractive features are either less clear or less well-shared features. Unattractive features are either less clear or less clearly not shared features.

Author response table 1 suggests two conclusions to us. (i) The Reveyaz et al. model has mainly favorable properties. The Maseko et al. (2013) model has mainly unfavorable properties. Hence, the Reveyaz et al. model is more likely to be true. (ii) The outcome is not black and white, i.e., both models have favorable and unfavorable properties. Accordingly, we overstated our case in our initial submission and toned down our claims in the revised manuscript.

What the authors have not done is to trace the pathway of the large trigeminal nerve in the elephant brainstem, as was done by Maseko et al (2013), which clearly shows the internal pathways of this nerve, from the branch that leads to the fifth mesencephalic nucleus adjacent to the periventricular grey matter, through to the spinal trigeminal tract that extends from the pons to the spinal cord in a manner very similar to all other mammals. Nor have they shown how the supposed trigeminal information reaches the putative trigeminal nuclei in the ventromedial rostral medulla oblongata. These are but two examples of many specific lines of evidence that would be required to support their conclusions. Clearly, tract tracing methods, such as cholera toxin tracing of peripheral nerves cannot be done in elephants, thus the neuroanatomy must be done properly and with attention to detail to support the major changes indicated by the authors.

Comment: The referee claims that Maseko et al. (2013) showed by ‘tract tracing’ that the structures they refer to trigeminal nuclei receive trigeminal input. This statement is at least slightly misleading. There is nothing of what amounts to proper ‘tract tracing’ in the Maseko et al. (2013) paper, i.e. tracing of tracts with post-mortem tracers. We tried proper post-mortem tracing but failed (no tracer transport) probably as a result of the limitations of our elephant material. What Maseko et al. (2013) actually did is look a bit for putative trigeminal fibers and where they might go. We also used this approach. In our hands, such ‘pseudo tract tracing’ works best in unstained material under bright field illumination, because myelin is very well visualized. In such material, we find: (i) massive fiber tracts descending dorsoventrally roughly from where both Maseko et al. 2013 and we think the trigeminal tract runs. (ii) These fiber tracts run dorsoventrally and approach, what we think is the trigeminal nuclei from lateral.

Changes: Ad hoc tract tracing see above.

So what are these "bumps" in the elephant brainstem?

Four previous authors indicate that these bumps are the inferior olivary nuclear complex. Can this be supported?

The inferior olivary nuclear complex acts "as a relay station between the spinal cord (n.b. trigeminal input does reach the spinal cord via the spinal trigeminal tract) and the cerebellum, integrating motor and sensory information to provide feedback and training to cerebellar neurons" (https://www.ncbi.nlm.nih.gov/books/NBK542242/). The inferior olivary nuclear complex is located dorsal and medial to the pyramidal tracts (which were not labeled in the current study by the authors but are clearly present in Fig. 1C and 2A) in the ventromedial aspect of the rostral medulla oblongata. This is precisely where previous authors have identified the inferior olivary nuclear complex and what the current authors assign to their putative trigeminal nuclei. The neurons of the inferior olivary nuclei project, via the olivocerebellar tract to the cerebellum to terminate in the climbing fibres of the cerebellar cortex.

Comment: We agree with the referee that in the Maseko et al. (2013) scheme the inferior olive is exactly where we expect it from pretty much all other mammals. Hence, this is a strong argument in favor of the Maseko et al. (2013) scheme and a strong argument against the partitioning scheme suggested by us.

Changes: Please see our discussion above.

Elephants have the largest (relative and absolute) cerebellum of all mammals (10.1002/ar.22425), this cerebellum contains 257 x109 neurons (10.3389/fnana.2014.00046; three times more than the entire human brain, 10.3389/neuro.09.031.2009). Each of these neurons appears to be more structurally complex than the homologous neurons in other mammals (10.1159/000345565; 10.1007/s00429-010-0288-3). In the African elephant, the neurons of the inferior olivary nuclear complex are described by Maseko et al (2013) as being both calbindin and calretinin immunoreactive. Climbing fibres in the cerebellar cortex of the African elephant are clearly calretinin immunopositive and also are likely to contain calbindin (10.1159/000345565). Given this, would it be surprising that the inferior olivary nuclear complex of the elephant is enlarged enough to create a very distinct bump in exactly the same place where these nuclei are identified in other mammals?

Comment: We agree with the referee that it is possible and even expected from other mammals that there is an enlargement of the inferior olive in elephants. Hence, a priori one might expect the ventral brain stem bumps to the inferior olive, this is perfectly reasonable and is what was done by previous authors. The referee also refers to calbindin and calretinin antibody reactivity. Such antibody reactivity is indeed in line with the referee’s ideas and we considered these findings in our Referee Table 1. The problem is, however, that neither calbindin nor calretinin antibody reactivity are highly specific and indeed both nuclei in discussion (trigeminal nuclei and inferior olive) show such reactivity. Unlike the peripherin-antibody staining advanced by us, calbindin nor calretinin antibody reactivity cannot distinguish the two hypotheses debated.

Changes: Please see our discussion above.

What about the myelin stripes? These are most likely to be the origin of the olivocerebellar tract and probably only have a coincidental relationship with the trunk. Thus, given what we know, the inferior olivary nuclear complex as described in other studies, and the putative trigeminal nuclear complex as described in the current study, is the elephant inferior olivary nuclear complex. It is not what the authors believe it to be, and they do not provide any evidence that discounts the previous studies. The authors are quite simply put, wrong. All the speculations that flow from this major neuroanatomical error are therefore science fiction rather than useful additions to the scientific literature.

Comment: It is unlikely that the myelin stripes are the origin of the olivocerebellar tract as suggested by the referee. Specifically, the lack of peripherin-reactivity indicates that these fibers are not climbing fibers (Referee Figure 1). In general, we feel the referee does not want to discuss the myelin stripes and obviously thinks we made up the strange correspondence of myelin stripes and trunk folds.

Changes: Please see our discussion above.

What do the authors actually have?

The authors have interesting data, based on their Golgi staining and analysis, of the inferior olivary nuclear complex in the elephant.

Comment: The referee reiterates their views.

Changes: None.

Reviewer #3 (Public Review):

Summary:

The study claims to investigate trunk representations in elephant trigeminal nuclei located in the brainstem. The researchers identified large protrusions visible from the ventral surface of the brainstem, which they examined using a range of histological methods. However, this ventral location is usually where the inferior olivary complex is found, which challenges the author's assertions about the nucleus under analysis. They find that this brainstem nucleus of elephants contains repeating modules, with a focus on the anterior and largest unit which they define as the putative nucleus principalis trunk module of the trigeminal. The nucleus exhibits low neuron density, with glia outnumbering neurons significantly. The study also utilizes synchrotron X-ray phase contrast tomography to suggest that myelin-stripe-axons traverse this module. The analysis maps myelin-rich stripes in several specimens and concludes that based on their number and patterning they likely correspond with trunk folds; however, this conclusion is not well supported if the nucleus has been misidentified.

Comment: The referee gives a concise summary of our findings. The referee acknowledges the depth of our analysis and also notes our cellular results. The referee – in line with the comments of Referee 2 – also points out that a misidentification of the nucleus under study is potentially fatal for our analysis. We thank the referee for this fair assessment.

Changes: We feel that we need to alert the reader more broadly to the misidentification concern. We think the critical comments of Referee 2, which will be published along with our manuscript, will go a long way in doing so. We think the eLife publishing format is fantastic in this regard. We will also include pointers to these concerns in the revised manuscript.

Strengths:

The strength of this research lies in its comprehensive use of various anatomical methods, including Nissl staining, myelin staining, Golgi staining, cytochrome oxidase labeling, and synchrotron X-ray phase contrast tomography. The inclusion of quantitative data on cell numbers and sizes, dendritic orientation and morphology, and blood vessel density across the nucleus adds a quantitative dimension. Furthermore, the research is commendable for its high-quality and abundant images and figures, effectively illustrating the anatomy under investigation.

Comment: Again, a very fair and balanced set of comments. We are thankful for these comments.

Changes: None.

Weaknesses:

While the research provides potentially valuable insights if revised to focus on the structure that appears to be the inferior olivary nucleus, there are certain additional weaknesses that warrant further consideration. First, the suggestion that myelin stripes solely serve to separate sensory or motor modules rather than functioning as an "axonal supply system" lacks substantial support due to the absence of information about the neuronal origins and the termination targets of the axons. Postmortem fixed brain tissue limits the ability to trace full axon projections. While the study acknowledges these limitations, it is important to exercise caution in drawing conclusions about the precise role of myelin stripes without a more comprehensive understanding of their neural connections.

Comment: The referee points out a significant weakness of our study, namely our limited understanding of the origin and targets of the axons constituting the myelin stripes. We are very much aware of this problem and this is also why we directed high-powered methodology like synchrotron X-ray tomograms to elucidate the structure of myelin stripes. Such analysis led to advances, i.e., we now think, what looks like stripes are bundles and we understand the constituting axons tend to transverse the module. Such advances are insufficient, however, to provide a clear picture of myelin stripe connectivity.

Changes: We think solving the problems raised by the referee will require long-term methodological advances and hence we will not be able to solve these problems in the current revision. Our long-term plans for confronting these issues are the following: (i) Improving our understanding of long-range connectivity by post-mortem tracing and MR-based techniques such as Diffusion-Tensor-Imaging. (ii) Improving our understanding of mid and short-range connectivity by applying even larger synchrotron X-ray tomograms and possible serial EM.

Second, the quantification presented in the study lacks comparison to other species or other relevant variables within the elephant specimens (i.e., whole brain or brainstem volume). The absence of comparative data for different species limits the ability to fully evaluate the significance of the findings. Comparative analyses could provide a broader context for understanding whether the observed features are unique to elephants or more common across species. This limitation in comparative data hinders a more comprehensive assessment of the implications of the research within the broader field of neuroanatomy. Furthermore, the quantitative comparisons between African and Asian elephant specimens should include some measure of overall brain size as a covariate in the analyses. Addressing these weaknesses would enable a richer interpretation of the study's findings.

Comment: The referee suggests another series of topics, which include the analysis of brain parts volumes or overall brain size. We agree these are important issues, but we also think such questions are beyond the scope of our study.

Changes: We hope to publish comparative data on elephant brain size and shape later this year.  

Author response:

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

We are thankful for the handling of our manuscript. The following is a summary of our response and what we have done:

(1) We are most thankful for the very thorough evaluation of our manuscript.

(2) We were a bit shocked by the very negative commentary of referee 2.

(3) We think, what put referee 2 off so much is that we were overconfident in the strength of our conclusions. We consider such overconfidence a big mistake. We have revised the manuscript to fix this problem.

(4) We respond in great depth to all criticism and also go into technicalities.

(5) We consider the possibility of a mistake. Yet, we carefully weighed the evidence advanced by referee 2 and by us and found that a systematic review supports our conclusions. Hence, we also resist the various attempts to crush our paper.

(6) We added evidence (peripherin-antibody staining; our novel Figure 2) that suggests we correctly identified the inferior olive.

(7) The eLife format – in which critical commentary is published along with the paper – is a fantastic venue to publish, what appears to be a surprisingly controversial issue.

eLife assessment

This potentially valuable study uses classic neuroanatomical techniques and synchrotron X-ray tomography to investigate the mapping of the trunk within the brainstem nuclei of the elephant brain. Given its unique specializations, understanding the somatosensory projections from the elephant trunk would be of general interest to evolutionary neurobiologists, comparative neuroscientists, and animal behavior scientists. However, the anatomical analysis is inadequate to support the authors' conclusion that they have identified the elephant trigeminal sensory nuclei rather than a different brain region, specifically the inferior olive.

Comment: We are happy that our paper is considered to be potentially valuable. Also, the editors highlight the potential interest of our work for evolutionary neurobiologists, comparative neuroscientists, and animal behavior scientists. The editors are more negative when it comes to our evidence on the identification of the trigeminal nucleus vs the inferior olive. We have five comments on this assessment. (i) We think this assessment is heavily biased by the comments of referee 2. We show that the referee’s comments are more about us than about our paper. Hence, the referee failed to do their job (refereeing our paper) and should not have succeeded in leveling our paper. (ii) We have no ad hoc knock-out experiments to distinguish the trigeminal nucleus vs the inferior olive. Such experiments (extracellular recording & electrolytic lesions, viral tracing would be done in a week in mice, but they cannot and should not be done in elephants. (iii) We have extraordinary evidence. Nobody has ever described a similarly astonishing match of body (trunk folds) and myeloarchitecture in the brain before. (iv) We show that our assignment of the trigeminal nucleus vs the inferior olive is more plausible than the current hypothesis about the assignment of the trigeminal nucleus vs the inferior olive as defended by referee 2. We think this is why it is important to publish our paper. (v) We think eLife is the perfect place for our publication because the deviating views of referee 2 are published along.

Change: We performed additional peripherin-antibody staining to differentiate the inferior olive and trigeminal nucleus. Peripherin is a cytoskeletal protein that is found in peripheral nerves and climbing fibers. Specifically, climbing fibers of various species (mouse, rabbit, pig, cow, and human; Errante et al., 1998) are stained intensely with peripherin-antibodies. What is tricky for our purposes is that there is also some peripherin-antibody reactivity in the trigeminal nuclei (Errante et al., 1998). Such peripherin-antibody reactivity is weaker, however, and lacks the distinct axonal bundle signature that stems from the strong climbing fiber peripherin-reactivity as seen in the inferior olive (Errante et al., 1998). As can be seen in our novel Figure 2, we observe peripherin-reactivity in axonal bundles (i.e. in putative climbing fibers), in what we think is the inferior olive. We also observe weak peripherin-reactivity, in what we think is the trigeminal nucleus, but not the distinct and strong labeling of axonal bundles. These observations are in line with our ideas but are difficult to reconcile with the views of the referee. Specifically, the lack of peripherin-reactive axon bundles suggests that there are no climbing fibers in what the referee thinks is the inferior olive.

Errante, L., Tang, D., Gardon, M., Sekerkova, G., Mugnaini, E., & Shaw, G. (1998). The intermediate filament protein peripherin is a marker for cerebellar climbing fibres. Journal of neurocytology, 27, 69-84.

Reviewer #1 :

Summary:

This fundamental study provides compelling neuroanatomical evidence underscoring the sensory function of the trunk in African and Asian elephants. Whereas myelinated tracts are classically appreciated as mediating neuronal connections, the authors speculate that myelinated bundles provide functional separation of trunk folds and display elaboration related to the "finger" projections. The authors avail themselves of many classical neuroanatomical techniques (including cytochrome oxidase stains, Golgi stains, and myelin stains) along with modern synchrotron X-ray tomography. This work will be of interest to evolutionary neurobiologists, comparative neuroscientists, and the general public, with its fascinating exploration of the brainstem of an icon sensory specialist. 

Comment: We are incredibly grateful for this positive assessment.

Changes: None.

Strengths: 

- The authors made excellent use of the precious sample materials from 9 captive elephants. 

- The authors adopt a battery of neuroanatomical techniques to comprehensively characterize the structure of the trigeminal subnuclei and properly re-examine the "inferior olive".

- Based on their exceptional histological preparation, the authors reveal broadly segregated patterns of metabolic activity, similar to the classical "barrel" organization related to rodent whiskers. 

Comment: The referee provides a concise summary of our findings.

Changes: None.

Weaknesses: 

- As the authors acknowledge, somewhat limited functional description can be provided using histological analysis (compared to more invasive techniques). 

- The correlation between myelinated stripes and trunk fold patterns is intriguing, and Figure 4 presents this idea beautifully. I wonder - is the number of stripes consistent with the number of trunk folds? Does this hold for both species? 

Comment: We agree with the referee’s assessment. We note that cytochrome-oxidase staining is an at least partially functional stain, as it reveals constitutive metabolic activity. A significant problem of the work in elephants is that our recording possibilities are limited, which in turn limits functional analysis. As indicated in Figure 5 (our former Figure 4) for the African elephant Indra, there was an excellent match of trunk folds and myelin stripes. Asian elephants have more, and less conspicuous trunk folds than African elephants. As illustrated in Figure 7, Asian elephants have more, and less conspicuous myelin stripes. Thus, species differences in myelin stripes correlate with species differences in trunk folds.

Changes: We clarify the relation of myelin stripe and trunk fold patterns in our description of Figure 7.

Reviewer #2 (Public Review): 

The authors describe what they assert to be a very unusual trigeminal nuclear complex in the brainstem of elephants, and based on this, follow with many speculations about how the trigeminal nuclear complex, as identified by them, might be organized in terms of the sensory capacity of the elephant trunk.

Comment: We agree with the referee’s assessment that the putative trigeminal nucleus described in our paper is highly unusual in size, position, vascularization, and myeloarchitecture. This is why we wrote this paper. We think these unusual features reflect the unique facial specializations of elephants, i.e. their highly derived trunk. Because we have no access to recordings from the elephant brainstem, we cannot back up all our functional interpretations with electrophysiological evidence; it is therefore fair to call them speculative.

Changes: None.

The identification of the trigeminal nuclear complex/inferior olivary nuclear complex in the elephant brainstem is the central pillar of this manuscript from which everything else follows, and if this is incorrect, then the entire manuscript fails, and all the associated speculations become completely unsupported. 

Comment: We agree.

Changes: None.

The authors note that what they identify as the trigeminal nuclear complex has been identified as the inferior olivary nuclear complex by other authors, citing Shoshani et al. (2006; 10.1016/j.brainresbull.2006.03.016) and Maseko et al (2013; 10.1159/000352004), but fail to cite either Verhaart and Kramer (1958; PMID 13841799) or Verhaart (1962; 10.1515/9783112519882-001). These four studies are in agreement, but the current study differs.

Comment & Change: We were not aware of the papers of Verhaart and included them in the revised manusript.

Let's assume for the moment that the four previous studies are all incorrect and the current study is correct. This would mean that the entire architecture and organization of the elephant brainstem is significantly rearranged in comparison to ALL other mammals, including humans, previously studied (e.g. Kappers et al. 1965, The Comparative Anatomy of the Nervous System of Vertebrates, Including Man, Volume 1 pp. 668-695) and the closely related manatee (10.1002/ar.20573). This rearrangement necessitates that the trigeminal nuclei would have had to "migrate" and shorten rostrocaudally, specifically and only, from the lateral aspect of the brainstem where these nuclei extend from the pons through to the cervical spinal cord (e.g. the Paxinos and Watson rat brain atlases), the to the spatially restricted ventromedial region of specifically and only the rostral medulla oblongata. According to the current paper, the inferior olivary complex of the elephant is very small and located lateral to their trigeminal nuclear complex, and the region from where the trigeminal nuclei are located by others appears to be just "lateral nuclei" with no suggestion of what might be there instead.

Comment: We have three comments here:

(1) The referee correctly notes that we argue the elephant brainstem underwent fairly major rearrangements. In particular, we argue that the elephant inferior olive was displaced laterally, by a very large cell mass, which we argue is an unusually large trigeminal nucleus. To our knowledge, such a large compact cell mass is not seen in the ventral brain stem of any other mammal.

(2) The referee makes it sound as if it is our private idea that the elephant brainstem underwent major rearrangements and that the rest of the evidence points to a conventional ‘rodent-like’ architecture. This is far from the truth, however. Already from the outside appearance (see our Figure 1B and Figure 7A) it is clear that the elephant brainstem has huge ventral bumps not seen in any other mammal. An extraordinary architecture also holds at the organizational level of nuclei. Specifically, the facial nucleus – the most carefully investigated nucleus in the elephant brainstem – has an appearance distinct from that of the facial nuclei of all other mammals (Maseko et al., 2013; Kaufmann et al., 2022). If both the overall shape and the constituting nuclei of the brainstem are very different from other mammals, it is very unlikely if not impossible that the elephant brainstem follows in all regards a conventional ‘rodent-like’ architecture.

(3) The inferior olive is an impressive nucleus in the partitioning scheme we propose (Figure 2). In fact – together with the putative trigeminal nucleus we describe – it’s the most distinctive nucleus in the elephant brainstem. We have not done volumetric measurements and cell counts here, but think this is an important direction for future work. What has informed our work is that the inferior olive nucleus we describe has the serrated organization seen in the inferior olive of all mammals. We will discuss these matters in depth below.

Changes: None.

Such an extraordinary rearrangement of brainstem nuclei would require a major transformation in the manner in which the mutations, patterning, and expression of genes and associated molecules during development occur. Such a major change is likely to lead to lethal phenotypes, making such a transformation extremely unlikely. Variations in mammalian brainstem anatomy are most commonly associated with quantitative changes rather than qualitative changes (10.1016/B978-0-12-804042-3.00045-2). 

Comment: We have two comments here:

(1) The referee claims that it is impossible that the elephant brainstem differs from a conventional brainstem architecture because this would lead to lethal phenotypes etc. Following our previous response, this argument does not hold. It is out of the question that the elephant brainstem looks very different from the brainstem of other mammals. Yet, it is also evident that elephants live. The debate we need to have is not if the elephant brainstem differs from other mammals, but how it differs from other mammals.

(2) In principle we agree with the referee’s thinking that the model of the elephant brainstem that is most likely to be correct is the one that requires the least amount of rearrangements to other mammals. We therefore prepared a comparison of the model the referee is proposing (Maseko et al., 2013; see Referee Table 1 below) with our proposition. We scored these models on their similarity to other mammals. We find that the referee’s ideas (Maseko et al., 2013) require more rearrangements relative to other mammals than our suggestion.

Changes: Inclusion of Referee Table 1, which we discuss in depth below.

The impetus for the identification of the unusual brainstem trigeminal nuclei in the current study rests upon a previous study from the same laboratory (10.1016/j.cub.2021.12.051) that estimated that the number of axons contained in the infraorbital branch of the trigeminal nerve that innervate the sensory surfaces of the trunk is approximately 400 000. Is this number unusual? In a much smaller mammal with a highly specialized trigeminal system, the platypus, the number of axons innervating the sensory surface of the platypus bill skin comes to 1 344 000 (10.1159. Yet, there is no complex rearrangement of the brainstem trigeminal nuclei in the brain of the developing or adult platypus (Ashwell, 2013, Neurobiology of Monotremes), despite the brainstem trigeminal nuclei being very large in the platypus (10.1159/000067195). Even in other large-brained mammals, such as large whales that do not have a trunk, the number of axons in the trigeminal nerve ranges between 400,000 and 500,000 (10.1007. The lack of comparative support for the argument forwarded in the previous and current study from this laboratory, and that the comparative data indicates that the brainstem nuclei do not change in the manner suggested in the elephant, argues against the identification of the trigeminal nuclei as outlined in the current study. Moreover, the comparative studies undermine the prior claim of the authors, informing the current study, that "the elephant trigeminal ganglion ... point to a high degree of tactile specialization in elephants" (10.1016/j.cub.2021.12.051). While clearly, the elephant has tactile sensitivity in the trunk, it is questionable as to whether what has been observed in elephants is indeed "truly extraordinary".

Comment: These comments made us think that the referee is not talking about the paper we submitted, but that the referee is talking about us and our work in general. Specifically, the referee refers to the platypus and other animals dismissing our earlier work, which argued for a high degree of tactile specialization in elephants. We think the referee’s intuitions are wrong and our earlier work is valid.

Changes: We prepared a Author response image 1 (below) that puts the platypus brain, a monkey brain, and the elephant trigeminal ganglion (which contains a large part of the trunk innervating cells) in perspective.

Author response image 1.

The elephant trigeminal ganglion is comparatively large. Platypus brain, monkey brain, and elephant ganglion. The elephant has two trigeminal ganglia, which contain the first-order somatosensory neurons. They serve mainly for tactile processing and are large compared to a platypus brain (from the comparative brain collection) and are similar in size to a monkey brain. The idea that elephants might be highly specialized for trunk touch is also supported by the analysis of the sensory nerves of these animals (Purkart et al., 2022). Specifically, we find that the infraorbital nerve (which innervates the trunk) is much thicker than the optic nerve (which mediates vision) and the vestibulocochlear nerve (which mediates hearing). Thus, not everything is large about elephants; instead, the data argue that these animals are heavily specialized for trunk touch.

But let's look more specifically at the justification outlined in the current study to support their identification of the unusually located trigeminal sensory nuclei of the brainstem. 

(1) Intense cytochrome oxidase reactivity.

(2) Large size of the putative trunk module.

(3) Elongation of the putative trunk module.

(4) The arrangement of these putative modules corresponds to elephant head

anatomy. 

(5) Myelin stripes within the putative trunk module that apparently match trunk folds. <br /> (6) Location apparently matches other mammals.

(7) Repetitive modular organization apparently similar to other mammals. <br /> (8) The inferior olive described by other authors lacks the lamellated appearance of this structure in other mammals.

Comment: We agree those are key issues.

Changes: None.

Let's examine these justifications more closely.

(1) Cytochrome oxidase histochemistry is typically used as an indicative marker of neuronal energy metabolism. The authors indicate, based on the "truly extraordinary" somatosensory capacities of the elephant trunk, that any nuclei processing this tactile information should be highly metabolically active, and thus should react intensely when stained for cytochrome oxidase. We are told in the methods section that the protocols used are described by Purkart et al (2022) and Kaufmann et al (2022). In neither of these cited papers is there any description, nor mention, of the cytochrome oxidase histochemistry methodology, thus we have no idea of how this histochemical staining was done. To obtain the best results for cytochrome oxidase histochemistry, the tissue is either processed very rapidly after buffer perfusion to remove blood or in recently perfusion-fixed tissue (e.g., 10.1016/0165-0270(93)90122-8). Given: (1) the presumably long post-mortem interval between death and fixation - "it often takes days to dissect elephants"; (2) subsequent fixation of the brains in 4% paraformaldehyde for "several weeks"; (3) The intense cytochrome oxidase reactivity in the inferior olivary complex of the laboratory rat (Gonzalez-Lima, 1998, Cytochrome oxidase in neuronal metabolism and Alzheimer's diseases); and (4) The lack of any comparative images from other stained portions of the elephant brainstem; it is difficult to support the justification as forwarded by the authors. The histochemical staining observed is likely background reactivity from the use of diaminobenzidine in the staining protocol. Thus, this first justification is unsupported. 

Comment: The referee correctly notes the description of our cytochrome-oxidase reactivity staining was lacking. This is a serious mistake of ours for which we apologize very much. The referee then makes it sound as if we messed up our cytochrome-oxidase staining, which is not the case. All successful (n = 3; please see our technical comments in the recommendation section) cytochrome-oxidase stainings were done with elephants with short post-mortem times (≤ 2 days) to brain removal/cooling and only brief immersion fixation (≤ 1 day). Cytochrome-oxidase reactivity in elephant brains appears to be more sensitive to quenching by fixation than is the case for rodent brains. We think it is a good idea to include a cytochrome-oxidase staining overview picture because we understood from the referee’s comments that we need to compare our partitioning scheme of the brainstem with that of other authors. To this end, we add a cytochrome-oxidase staining overview picture (Author response image 3) along with an alternative interpretation from Maseko et al., 2013.

Changes: (1) We added details on our cytochrome-oxidase reactivity staining protocol and the cytochrome-oxidase reactivity in the elephant brain in the manuscript and in our response to the general recommendations.

(2) We provide a detailed discussion of the technicalities of cytochrome-oxidase staining below in the recommendation section, where the referee raised further criticisms.

(3) We include a cytochrome-oxidase staining overview picture (Author response image 2) along with an alternative interpretation from Maseko et al., 2013.

Author response image 2.

Cytochrome-oxidase staining overview. Coronal cytochrome-oxidase staining overview from African elephant cow Indra; the section is taken a few millimeters posterior to the facial nucleus. Brown is putatively neural cytochrome-reactivity, and white is the background. Black is myelin diffraction and (seen at higher resolution, when you zoom in) erythrocyte cytochrome-reactivity in blood vessels (see our Figure 1E-G); such blood vessel cytochrome-reactivity is seen, because we could not perfuse the animal. There appears to be a minimal outside-in-fixation artifact (i.e. a more whitish/non-brownish appearance of the section toward the borders of the brain). This artifact is not seen in sections from Indra that we processed earlier or in other elephant brains processed at shorter post-mortem/fixation delays (see our Figure 1C).

The same structures can be recognized in Author response image 2 and Supplememntary figure 36 of Maseko et al. (2013). The section is taken at an anterior-posterior level, where we encounter the trigeminal nuclei in pretty much all mammals. Note that the neural cytochrome reactivity is very high, in what we refer to as the trigeminal-nuclei-trunk-module and what Maseko et al. refer to as inferior olive. Myelin stripes can be recognized here as white omissions.

At the same time, the cytochrome-oxidase-reactivity is very low in what Maseko et al. refer to as trigeminal nuclei. The indistinct appearance and low cytochrome-oxidase-reactivity of the trigeminal nuclei in the scheme of Maseko et al. (2013) is unexpected because trigeminal nuclei stain intensely for cytochrome-oxidase-reactivity in most mammals and because the trigeminal nuclei represent the elephant’s most important body part, the trunk. Staining patterns of the trigeminal nuclei as identified by Maseko et al. (2013) are very different at more posterior levels; we will discuss this matter below.

Justifications (2), (3), and (4) are sequelae from justification (1). In this sense, they do not count as justifications, but rather unsupported extensions. 

Comment: These are key points of our paper that the referee does not discuss.

Changes: None.

(4) and (5) These are interesting justifications, as the paper has clear internal contradictions, and (5) is a sequelae of (4). The reader is led to the concept that the myelin tracts divide the nuclei into sub-modules that match the folding of the skin on the elephant trunk. One would then readily presume that these myelin tracts are in the incoming sensory axons from the trigeminal nerve. However, the authors note that this is not the case: "Our observations on trunk module myelin stripes are at odds with this view of myelin. Specifically, myelin stripes show no tapering (which we would expect if axons divert off into the tissue). More than that, there is no correlation between myelin stripe thickness (which presumably correlates with axon numbers) and trigeminal module neuron numbers. Thus, there are numerous myelinated axons, where we observe few or no trigeminal neurons. These observations are incompatible with the idea that myelin stripes form an axonal 'supply' system or that their prime function is to connect neurons. What do myelin stripe axons do, if they do not connect neurons? We suggest that myelin stripes serve to separate rather than connect neurons." So, we are left with the observation that the myelin stripes do not pass afferent trigeminal sensory information from the "truly extraordinary" trunk skin somatic sensory system, and rather function as units that separate neurons - but to what end? It appears that the myelin stripes are more likely to be efferent axonal bundles leaving the nuclei (to form the olivocerebellar tract). This justification is unsupported.

Comment: The referee cites some of our observations on myelin stripes, which we find unusual. We stand by the observations and comments. The referee does not discuss the most crucial finding we report on myelin stripes, namely that they correspond remarkably well to trunk folds.

Changes: None.

(6) The authors indicate that the location of these nuclei matches that of the trigeminal nuclei in other mammals. This is not supported in any way. In ALL other mammals in which the trigeminal nuclei of the brainstem have been reported they are found in the lateral aspect of the brainstem, bordered laterally by the spinal trigeminal tract. This is most readily seen and accessible in the Paxinos and Watson rat brain atlases. The authors indicate that the trigeminal nuclei are medial to the facial nerve nucleus, but in every other species, the trigeminal sensory nuclei are found lateral to the facial nerve nucleus. This is most salient when examining a close relative, the manatee (10.1002/ar.20573), where the location of the inferior olive and the trigeminal nuclei matches that described by Maseko et al (2013) for the African elephant. This justification is not supported. 

Comment: The referee notes that we incorrectly state that the position of the trigeminal nuclei matches that of other mammals. We think this criticism is justified.

Changes: We prepared a comparison of the Maseko et al. (2013) scheme of the elephant brainstem with our scheme of the elephant brainstem (see below Referee Table 1). Here we acknowledge the referee’s argument and we also changed the manuscript accordingly.

(7) The dual to quadruple repetition of rostrocaudal modules within the putative trigeminal nucleus as identified by the authors relies on the fact that in the neurotypical mammal, there are several trigeminal sensory nuclei arranged in a column running from the pons to the cervical spinal cord, these include (nomenclature from Paxinos and Watson in roughly rostral to caudal order) the Pr5VL, Pr5DM, Sp5O, Sp5I, and Sp5C. However, these nuclei are all located far from the midline and lateral to the facial nerve nucleus, unlike what the authors describe in the elephants. These rostrocaudal modules are expanded upon in Figure 2, and it is apparent from what is shown that the authors are attributing other brainstem nuclei to the putative trigeminal nuclei to confirm their conclusion. For example, what they identify as the inferior olive in Figure 2D is likely the lateral reticular nucleus as identified by Maseko et al (2013). This justification is not supported.

Comment: The referee again compares our findings to the scheme of Maseko et al. (2013) and rejects our conclusions on those grounds. We think such a comparison of our scheme is needed, indeed.

Changes: We prepared a comparison of the Maseko et al. (2013) scheme of the elephant brainstem with our scheme of the elephant brainstem (see below Referee Table 1).

(8) In primates and related species, there is a distinct banded appearance of the inferior olive, but what has been termed the inferior olive in the elephant by other authors does not have this appearance, rather, and specifically, the largest nuclear mass in the region (termed the principal nucleus of the inferior olive by Maseko et al, 2013, but Pr5, the principal trigeminal nucleus in the current paper) overshadows the partial banded appearance of the remaining nuclei in the region (but also drawn by the authors of the current paper). Thus, what is at debate here is whether the principal nucleus of the inferior olive can take on a nuclear shape rather than evince a banded appearance. The authors of this paper use this variance as justification that this cluster of nuclei could not possibly be the inferior olive. Such a "semi-nuclear/banded" arrangement of the inferior olive is seen in, for example, giraffe (10.1016/j.jchemneu.2007.05.003), domestic dog, polar bear, and most specifically the manatee (a close relative of the elephant) (brainmuseum.org; 10.1002/ar.20573). This justification is not supported. 

Comment: We carefully looked at the brain sections referred to by the referee in the brainmuseum.org collection. We found contrary to the referee’s claims that dogs, polar bears, and manatees have a perfectly serrated (a cellular arrangement in curved bands) appearance of the inferior olive. Accordingly, we think the referee is not reporting the comparative evidence fairly and we wonder why this is the case.

Changes: None.

Thus, all the justifications forwarded by the authors are unsupported. Based on methodological concerns, prior comparative mammalian neuroanatomy, and prior studies in the elephant and closely related species, the authors fail to support their notion that what was previously termed the inferior olive in the elephant is actually the trigeminal sensory nuclei. Given this failure, the justifications provided above that are sequelae also fail. In this sense, the entire manuscript and all the sequelae are not supported.

Comment: We disagree. To summarize:

(1) Our description of the cytochrome oxidase staining lacked methodological detail, which we have now added; the cytochrome oxidase reactivity data are great and support our conclusions.

(2)–(5)The referee does not really discuss our evidence on these points.

(6) We were wrong and have now fixed this mistake.

(7) The referee asks for a comparison to the Maseko et al. (2013) scheme (agreed, see Referee Table 1).

(8) The referee bends the comparative evidence against us.

Changes: None.

A comparison of the elephant brainstem partitioning schemes put forward by Maseko et al 2013 and by Reveyaz et al.

To start with, we would like to express our admiration for the work of Maseko et al. (2013). These authors did pioneering work on obtaining high-quality histology samples from elephants. Moreover, they made a heroic neuroanatomical effort, in which they assigned 147 brain structures to putative anatomical entities. Most of their data appear to refer to staining in a single elephant and one coronal sectioning plane. The data quality and the illustration of results are excellent.

We studied mainly two large nuclei in six (now 7) elephants in three (coronal, parasagittal, and horizontal) sectioning planes. The two nuclei in question are the two most distinct nuclei in the elephant brainstem, namely an anterior ventromedial nucleus (the trigeminal trunk module in our terminology; the inferior olive in the terminology of Maseko et al., 2013) and a more posterior lateral nucleus (the inferior olive in our terminology; the posterior part of the trigeminal nuclei in the terminology of Maseko et al., 2013).

Author response image 3 gives an overview of the two partitioning schemes for inferior olive/trigeminal nuclei along with the rodent organization (see below).

Author response image 3.

Overview of the brainstem organization in rodents & elephants

The strength of the Maseko et al. (2013) scheme is the excellent match of the position of elephant nuclei to the position of nuclei in the rodent (Author response image 3). We think this positional match reflects the fact that Maseko et al. (2013) mapped a rodent partitioning scheme on the elephant brainstem. To us, this is a perfectly reasonable mapping approach. As the referee correctly points out, the positional similarity of both elephant inferior olive and trigeminal nuclei to the rodent strongly argues in favor of the Maseko et al. (2013), because brainstem nuclei are positionally very conservative.

Other features of the Maseko et al. (2013) scheme are less favorable. The scheme marries two cyto-architectonically very distinct divisions (an anterior indistinct part) and a super-distinct serrated posterior part to be the trigeminal nuclei. We think merging entirely distinct subdivisions into one nucleus is a byproduct of mapping a rodent partitioning scheme on the elephant brainstem. Neither of the two subdivisions resemble the trigeminal nuclei of other mammals. The cytochrome oxidase staining patterns differ markedly across the anterior indistinct part (see our Author response image 3) and the posterior part of the trigeminal nuclei and do not match with the intense cytochrome oxidase reactivity of other mammalian trigeminal nuclei (Author response image 2). Our anti-peripherin staining (the novel Figure 2 of our manuscript) indicates that there probably no climbing fibers, in what Maseko et al. think. is inferior olive; this is a potentially fatal problem for the hypothesis. The posterior part of Maseko et al. (2013) trigeminal nuclei has a distinct serrated appearance that is characteristic of the inferior olive in other mammals. Moreover, the inferior olive of Maseko et al. (2013) lacks the serrated appearance of the inferior olive seen in pretty much all mammals; this is a serious problem.

The partitioning scheme of Reveyaz et al. comes with poor positional similarity but avoids the other problems of the Maseko et al. (2013) scheme. Our explanation for the positionally deviating location of trigeminal nuclei is that the elephant grew one of the if not the largest trigeminal systems of all mammals. As a result, the trigeminal nuclei grew through the floor of the brainstem. We understand this is a post hoc just-so explanation, but at least it is an explanation.

The scheme of Reveyaz et al. was derived in an entirely different way from the Maseko model. Specifically, we were convinced that the elephant trigeminal nuclei ought to be very special because of the gigantic trigeminal ganglia (Purkart et al., 2022). Cytochrome-oxidase staining revealed a large distinct nucleus with an elongated shape. Initially, we were freaked out by the position of the nucleus and the fact that it was referred to as inferior olive by other authors. When we found an inferior-olive-like nucleus at a nearby (although at an admittedly unusual) location, we were less worried. We then optimized the visualization of myelin stripes (brightfield imaging etc.) and were able to collect an entire elephant trunk along with the brain (African elephant cow Indra). When we made the one-to-one match of Indra’s trunk folds and myelin stripes (former Figure 4, now Figure 5) we were certain that we had identified the trunk module of the trigeminal nuclei. We already noted at the outset of our rebuttal that we now consider such certainty a fallacy of overconfidence. In light of the comments of Referee 2, we feel that a further discussion of our ideas is warranted.

A strength of the Reveyaz model is that nuclei look like single anatomical entities. The trigeminal nuclei look like trigeminal nuclei of other mammals, the trunk module has a striking resemblance to the trunk and the inferior olive looks like the inferior olive of other mammals.

We evaluated the fit of the two models in the form of a table (Author response table 1; below). Unsurprisingly, Author response table 1 aligns with our views of elephant brainstem partitioning.

Author response table 1

Qualitative evaluation of elephant brainstem partitioning schemes

++ = Very attractive; + = attractive; - = unattractive; -- = very unattractive

We scored features that are clear and shared by all mammals – as far as we know them – as very attractive.

We scored features that are clear and are not shared by all mammals – as far as we know them – as very unattractive.

Attractive features are either less clear or less well-shared features.

Unattractive features are either less clear or less clearly not shared features.

Author response table 1 suggests two conclusions to us. (i) The Reveyaz et al. model has mainly favorable properties. The Maseko et al. (2013) model has mainly unfavorable properties. Hence, the Reveyaz et al. model is more likely to be true. (ii) The outcome is not black and white, i.e., both models have favorable and unfavorable properties. Accordingly, we overstated our case in our initial submission and toned down our claims in the revised manuscript.

What the authors have not done is to trace the pathway of the large trigeminal nerve in the elephant brainstem, as was done by Maseko et al (2013), which clearly shows the internal pathways of this nerve, from the branch that leads to the fifth mesencephalic nucleus adjacent to the periventricular grey matter, through to the spinal trigeminal tract that extends from the pons to the spinal cord in a manner very similar to all other mammals. Nor have they shown how the supposed trigeminal information reaches the putative trigeminal nuclei in the ventromedial rostral medulla oblongata. These are but two examples of many specific lines of evidence that would be required to support their conclusions. Clearly, tract tracing methods, such as cholera toxin tracing of peripheral nerves cannot be done in elephants, thus the neuroanatomy must be done properly and with attention to detail to support the major changes indicated by the authors. 

Comment: The referee claims that Maseko et al. (2013) showed by ‘tract tracing’ that the structures they refer to trigeminal nuclei receive trigeminal input. This statement is at least slightly misleading. There is nothing of what amounts to proper ‘tract tracing’ in the Maseko et al. (2013) paper, i.e. tracing of tracts with post-mortem tracers. We tried proper post-mortem tracing but failed (no tracer transport) probably as a result of the limitations of our elephant material. What Maseko et al. (2013) actually did is look a bit for putative trigeminal fibers and where they might go. We also used this approach. In our hands, such ‘pseudo tract tracing’ works best in unstained material under bright field illumination, because myelin is very well visualized. In such material, we find: (i) massive fiber tracts descending dorsoventrally roughly from where both Maseko et al. 2013 and we think the trigeminal tract runs. (ii) These fiber tracts run dorsoventrally and approach, what we think is the trigeminal nuclei from lateral.

Changes: Ad hoc tract tracing see above.

So what are these "bumps" in the elephant brainstem? 

Four previous authors indicate that these bumps are the inferior olivary nuclear complex. Can this be supported?

The inferior olivary nuclear complex acts "as a relay station between the spinal cord (n.b. trigeminal input does reach the spinal cord via the spinal trigeminal tract) and the cerebellum, integrating motor and sensory information to provide feedback and training to cerebellar neurons" (https://www.ncbi.nlm.nih.gov/books/NBK542242/). The inferior olivary nuclear complex is located dorsal and medial to the pyramidal tracts (which were not labeled in the current study by the authors but are clearly present in Fig. 1C and 2A) in the ventromedial aspect of the rostral medulla oblongata. This is precisely where previous authors have identified the inferior olivary nuclear complex and what the current authors assign to their putative trigeminal nuclei. The neurons of the inferior olivary nuclei project, via the olivocerebellar tract to the cerebellum to terminate in the climbing fibres of the cerebellar cortex.

Comment: We agree with the referee that in the Maseko et al. (2013) scheme the inferior olive is exactly where we expect it from pretty much all other mammals. Hence, this is a strong argument in favor of the Maseko et al. (2013) scheme and a strong argument against the partitioning scheme suggested by us.

Changes: Please see our discussion above.

Elephants have the largest (relative and absolute) cerebellum of all mammals (10.1002/ar.22425), this cerebellum contains 257 x109 neurons (10.3389/fnana.2014.00046; three times more than the entire human brain, 10.3389/neuro.09.031.2009). Each of these neurons appears to be more structurally complex than the homologous neurons in other mammals (10.1159/000345565; 10.1007/s00429-010-0288-3). In the African elephant, the neurons of the inferior olivary nuclear complex are described by Maseko et al (2013) as being both calbindin and calretinin immunoreactive. Climbing fibres in the cerebellar cortex of the African elephant are clearly calretinin immunopositive and also are likely to contain calbindin (10.1159/000345565). Given this, would it be surprising that the inferior olivary nuclear complex of the elephant is enlarged enough to create a very distinct bump in exactly the same place where these nuclei are identified in other mammals? 

Comment: We agree with the referee that it is possible and even expected from other mammals that there is an enlargement of the inferior olive in elephants. Hence, a priori one might expect the ventral brain stem bumps to the inferior olive, this is perfectly reasonable and is what was done by previous authors. The referee also refers to calbindin and calretinin antibody reactivity. Such antibody reactivity is indeed in line with the referee’s ideas and we considered these findings in our Referee Table 1. The problem is, however, that neither calbindin nor calretinin antibody reactivity are highly specific and indeed both nuclei in discussion (trigeminal nuclei and inferior olive) show such reactivity. Unlike the peripherin-antibody staining advanced by us, calbindin nor calretinin antibody reactivity cannot distinguish the two hypotheses debated.

Changes: Please see our discussion above.

What about the myelin stripes? These are most likely to be the origin of the olivocerebellar tract and probably only have a coincidental relationship with the trunk. Thus, given what we know, the inferior olivary nuclear complex as described in other studies, and the putative trigeminal nuclear complex as described in the current study, is the elephant inferior olivary nuclear complex. It is not what the authors believe it to be, and they do not provide any evidence that discounts the previous studies. The authors are quite simply put, wrong. All the speculations that flow from this major neuroanatomical error are therefore science fiction rather than useful additions to the scientific literature. 

Comment: It is unlikely that the myelin stripes are the origin of the olivocerebellar tract as suggested by the referee. Specifically, the lack of peripherin-reactivity indicates that these fibers are not climbing fibers (our novel Figure 2). In general, we feel the referee does not want to discuss the myelin stripes and obviously thinks we made up the strange correspondence of myelin stripes and trunk folds.

Changes: Please see our discussion above.

What do the authors actually have? 

The authors have interesting data, based on their Golgi staining and analysis, of the inferior olivary nuclear complex in the elephant.

Comment: The referee reiterates their views.

Changes: None.

Reviewer #3 (Public Review):

Summary: 

The study claims to investigate trunk representations in elephant trigeminal nuclei located in the brainstem. The researchers identified large protrusions visible from the ventral surface of the brainstem, which they examined using a range of histological methods. However, this ventral location is usually where the inferior olivary complex is found, which challenges the author's assertions about the nucleus under analysis. They find that this brainstem nucleus of elephants contains repeating modules, with a focus on the anterior and largest unit which they define as the putative nucleus principalis trunk module of the trigeminal. The nucleus exhibits low neuron density, with glia outnumbering neurons significantly. The study also utilizes synchrotron X-ray phase contrast tomography to suggest that myelin-stripe-axons traverse this module. The analysis maps myelin-rich stripes in several specimens and concludes that based on their number and patterning they likely correspond with trunk folds; however, this conclusion is not well supported if the nucleus has been misidentified.

Comment: The referee gives a concise summary of our findings. The referee acknowledges the depth of our analysis and also notes our cellular results. The referee – in line with the comments of Referee 2 – also points out that a misidentification of the nucleus under study is potentially fatal for our analysis. We thank the referee for this fair assessment.

Changes: We feel that we need to alert the reader more broadly to the misidentification concern. We think the critical comments of Referee 2, which will be published along with our manuscript, will go a long way in doing so. We think the eLife publishing format is fantastic in this regard. We will also include pointers to these concerns in the revised manuscript.

Strengths: 

The strength of this research lies in its comprehensive use of various anatomical methods, including Nissl staining, myelin staining, Golgi staining, cytochrome oxidase labeling, and synchrotron X-ray phase contrast tomography. The inclusion of quantitative data on cell numbers and sizes, dendritic orientation and morphology, and blood vessel density across the nucleus adds a quantitative dimension. Furthermore, the research is commendable for its high-quality and abundant images and figures, effectively illustrating the anatomy under investigation.

Comment: Again, a very fair and balanced set of comments. We are thankful for these comments.

Changes: None.

Weaknesses: 

While the research provides potentially valuable insights if revised to focus on the structure that appears to be the inferior olivary nucleus, there are certain additional weaknesses that warrant further consideration. First, the suggestion that myelin stripes solely serve to separate sensory or motor modules rather than functioning as an "axonal supply system" lacks substantial support due to the absence of information about the neuronal origins and the termination targets of the axons. Postmortem fixed brain tissue limits the ability to trace full axon projections. While the study acknowledges these limitations, it is important to exercise caution in drawing conclusions about the precise role of myelin stripes without a more comprehensive understanding of their neural connections.

Comment: The referee points out a significant weakness of our study, namely our limited understanding of the origin and targets of the axons constituting the myelin stripes. We are very much aware of this problem and this is also why we directed high-powered methodology like synchrotron X-ray tomograms to elucidate the structure of myelin stripes. Such analysis led to advances, i.e., we now think, what looks like stripes are bundles and we understand the constituting axons tend to transverse the module. Such advances are insufficient, however, to provide a clear picture of myelin stripe connectivity.

Changes: We think solving the problems raised by the referee will require long-term methodological advances and hence we will not be able to solve these problems in the current revision. Our long-term plans for confronting these issues are the following: (i) Improving our understanding of long-range connectivity by post-mortem tracing and MR-based techniques such as Diffusion-Tensor-Imaging. (ii) Improving our understanding of mid and short-range connectivity by applying even larger synchrotron X-ray tomograms and possible serial EM.

Second, the quantification presented in the study lacks comparison to other species or other relevant variables within the elephant specimens (i.e., whole brain or brainstem volume). The absence of comparative data for different species limits the ability to fully evaluate the significance of the findings. Comparative analyses could provide a broader context for understanding whether the observed features are unique to elephants or more common across species. This limitation in comparative data hinders a more comprehensive assessment of the implications of the research within the broader field of neuroanatomy. Furthermore, the quantitative comparisons between African and Asian elephant specimens should include some measure of overall brain size as a covariate in the analyses. Addressing these weaknesses would enable a richer interpretation of the study's findings.

Comment: The referee suggests another series of topics, which include the analysis of brain parts volumes or overall brain size. We agree these are important issues, but we also think such questions are beyond the scope of our study.

Changes: We hope to publish comparative data on elephant brain size and shape later this year.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I realize that elephant brains are a limiting resource in this project, along with the ability to perform functional investigations. However, I believe that Prof. Jon Kaas (Vanderbilt University) has one or more series of Nissl-stained brainstems from elephants. These might be of potential interest, as they were previously used to explore general patterns of trigeminal brainstem organization in a comparative manner (see Sawyer and Sarko, 2017, "Comparative Anatomy and Evolution of the Somatosensory Brain Stem" in the Evolution of Nervous System series) and might shed light on the positioning of the trigeminal complex and IO, with parts of the trigeminal nerve itself still attached to these sections.

Comment: The referee suggests adding data from more elephants and we think this is a great suggestion because our ns are small. We followed this advice. We agree we need more comparative neuroanatomy of elephants and the urgency of this matter is palpable in the heated debate we have with Referee 2. Specifically, we need more long-range and short-range analysis of elephant brains.

Changes: We plan to include data in the revised manuscript about cytoarchitectonics (Nissl), cytochrome-oxidase reactivity, and possibly also antibody reactivity from an additional animal, i.e., from the African elephant cow Bibi. The quality of this specimen is excellent and the post-mortem time to brain extraction was very short.

We also have further plans for connectivity analysis (see our response above), but such data will not become available fast enough for the revision.

Other recommendations: 

- A general schematic showing input from trunk to PrV to the trigeminal subnuclei (as well as possibly ascending connections) might be informative to the reader, in terms of showing which neural relay is being examined.

Comment: We think this is a very good suggestion in principle, but we were not satisfied with the schematics we came up with.

Changes: None.

- Perhaps a few more sentences described the significance of synchrotron tomography for those who may be unfamiliar.

Comment & Change: We agree and implement this suggestion.

- "Belly-shaped" trunk module description is unclear on page 9. 

Comment & Change: We clarified this matter.

- Typo on the last sentence of page 9. 

Comment & Change: We fixed this mistake.

Reviewer #2 (Recommendations For The Authors): 

The data is only appropriate a specialized journal and is limited to the Golgi analysis of neurons within the inferior olivary complex of the elephant. This reviewer considers that the remainder of the work is speculation and that the paper in its current version is not salvageable.

Comment: Rather than suggesting changes, the referee makes it clear that the referee does not want to see our paper published. We think this desire to reject is not rooted in a lack of quality of our work. In fact, we did an immense amount of work (detailed cytoarchitectonic analysis of six (now seven) elephant brainstems rather than one as in the case of our predecessors), cell counts, and X-ray tomography. Instead, we think the problem is rooted in the fact that we contradict the referee. To us, such suppression of diverging opinions – provided they are backed up with data – is a scientifically deeply unhealthy attitude. Science lives from the debate and this is why we did not exclude any referees even though we knew that our results do not align with the views of all of the few actors in the field.

Changes: We think the novel eLife publishing scheme was developed to prevent such abuse. We look forward to having our data published along with the harsh comments of the referee. The readers and subsequent scientific work will determine who’s right and who’s wrong.

In order to convince readers of the grand changes to the organization of the brainstem in a species suggested by the authors the data presented needs to be supported. It is not. 

Comment: Again, this looks to us like more of the ‘total-rejection-commentary’ than like an actual recommendation.

Changes: None.

The protocol for the cytochrome oxidase histochemistry is not available in the locations indicated by the authors, and it is very necessary to provide this, as I fully believe that the staining obtained is not real, given the state of the tissue used. 

Comment: We apologize again for not including the necessary details on our cytochrome-oxidase staining.

From these comments (and the initial comments above) it appears that the referee is uncertain about the validity of cytochrome-oxidase staining. We (M.B., the senior author) have been doing this particular stain for approximately three decades. The referee being unfamiliar with cytochrome-oxidase staining is fine, but we can’t comprehend how the referee then comes to the ‘full belief’ that our staining patterns are ‘not real’ when the visual evidence indicates the opposite. We feel the referee does not want to believe our data.

From hundreds of permutations, we can assure the referee that cytochrome-oxidase staining can go wrong in many ways. The most common failure outcome in elephants is a uniform light brown stain after hours or days of the cytochrome-oxidase reaction. This outcome is closely associated with long ≥2 days post-mortem/fixation times and reflects the quenching of cytochrome-oxidases by fixation. Interestingly, cytochrome-oxidase staining in elephant brains is distinctly more sensitive to quenching by fixation than cytochrome-oxidase staining in rodent brains. Another, more rare failure of cytochrome-oxidase staining comes as entirely white or barely colored sections; this outcome is usually associated with a bad reagent (most commonly old DAB, but occasionally also old or bad catalase, in case you are using a staining protocol with catalase). Another nasty cytochrome-oxidase staining outcome is smeary all-black sections. In this case, a black precipitate sticks to sections and screws up the staining (filtering and more gradual heating of the staining solution usually solve this problem). Thus, you can get uniformly white, uniformly light brown, and smeary black sections as cytochrome-oxidase staining failures. What you never get from cytochrome-oxidase staining as an artifact are sections with a strong brown to lighter brown differential contrast. All sections with strong brown to lighter brown differential contrast (staining successes) show one and the same staining pattern in a given brain area, i.e., brownish barrels in the rodent cortex, brownish barrelettes (trigeminal nuclei) in the rodent brainstem, brownish putative trunk modules/inferior olives (if we believe the referee) in the elephant brainstem. Cytochrome-oxidase reactivity is in this regard remarkably different from antibody staining. In antibody staining you can get all kinds of interesting differential contrast staining patterns, which mean nothing. Such differential contrast artifacts in antibody staining arise as a result of insufficient primary antibody specificity, the secondary antibody binding non-specifically, and of what have you not reasons. The reason that the brown differential contrast of cytochrome-oxidase reaction is pretty much fool-proof, relates to the histochemical staining mechanism, which is based on the supply of specific substrates to a universal mitochondrial enzyme. The ability to reveal mitochondrial metabolism and the universal and ‘fool-proof’ staining qualities make the cytochrome-oxidase reactivity a fantastic tool for comparative neuroscience, where you always struggle with insufficient information about antigen reactivity.

We also note that the contrast of cytochrome-oxidase reactivity seen in the elephant brainstem is spectacular. As the Referee can see in our Figure 1C we observe a dark brown color in the putative trunk module, with the rest of the brain being close to white. Such striking cytochrome-oxidase reactivity contrast has been observed only very rarely in neuroanatomy: (i) In the rest of the elephant brain (brainstem, thalamus cortex) we did not observe as striking contrast as in the putative trunk module (the inferior olive according to the referee). (ii) In decades of work with rodents, we have rarely seen such differential activity. For example, cortical whisker-barrels (a classic CO-staining target) in rodents usually come out as dark brown against a light brown background.

What all of this commentary means is that patterns revealed by differential cytochrome-oxidase staining in the elephant brain stem are real.

Changes: We added details on our cytochrome-oxidase reactivity staining protocol and commented on cytochrome-oxidase reactivity in the elephant brain in general.

The authors need to recognize that the work done in Africa on elephant brains is of high quality and should not be blithely dismissed by the authors - this stinks of past colonial "glory", especially as the primary author on these papers is an African female.

Comment: The referee notes that we unfairly dismiss the work of African scientists and that our paper reflects a continuation of our horrific colonial past because we contradict the work of an African woman. We think such commentary is meant to be insulting and prefer to return to the scientific discourse. We are staunch supporters of diversity in science. It is simply untrue, that we do not acknowledge African scientists or the excellent work done in Africa on elephant brains. For example, we cite no less than four papers from the Manger group. We refer countless times in the manuscript to these papers, because these papers are highly relevant to our work. We indeed disagree with two anatomical assignments made by Maseko et al., 2013. Such differences should not be overrated, however. As we noted before, such differences relate to only 2 out of 147 anatomical assignments made by these authors. More generally, discussing and even contradicting papers is the appropriate way to acknowledge scientists. We already expressed we greatly admire the pioneering work of the Manger group. In our view, the perfusion of elephants in the field is a landmark experiment in comparative neuroanatomy. We closely work with colleagues in Africa and find them fantastic collaborators. When the referee is accusing us of contradicting the work of an African woman, the referee is unfairly and wrongly accusing us of attacking a scientist’s identity. More generally, we feel the discussion should focus on the data presented.

Changes: None.

In addition, perfusing elephants in the field with paraformaldehyde shortly after death is not a problem "partially solved" when it comes to collecting elephant tissue (n.b., with the right tools the brain of the elephant can be removed in under 2 hours). It means the problem IS solved. This is evidenced by the quality of the basic anatomical, immuno-, and Golgi-staining of the elephant tissue collected in Africa.

Comment: This is not a recommendation. We repeat: In our view, the perfusion of elephants in the field by the Manger group is a landmark experiment in comparative neuroanatomy. Apart, from that, we think the referee got our ‘partially solved comment’ the wrong way. It is perhaps worthwhile to recall the context of this quote. We first describe the numerous limitations of our elephant material; admitting these limitations is about honesty. Then, we wanted to acknowledge previous authors who either paved the way for elephant neuroanatomy (Shoshani) or did a better job than we did (Manger; see the above landmark experiment). These citations were meant as an appreciation of our predecessors’ work and by far not meant to diminish their work. Why did we say that the problems of dealing with elephant material are only partially solved? Because elephant neuroanatomy is hard and the problems associated with it are by no means solved. Many previous studies rely on single specimen and our possibilities of accessing, removing, processing, and preserving elephant brains are limited and inferior to the conditions elsewhere. Doing a mouse brain is orders of magnitude easier than doing an elephant brain (because the problems of doing mouse anatomy are largely solved), yet it is hard to publish a paper with six elephant brains because the referees expect evidence at least half as good as what you get in mice.

Changes: We replaced the ‘partially solved’ sentence.

The authors need to give credit where credit is due - the elephant cerebellum is clearly at the core of controlling trunk movement, and as much as primary sensory and final stage motor processing is important, the complexity required for the neural programs needed to move the trunk either voluntarily or in response to stimuli, is being achieved by the cerebellum. The inferior olive is part of this circuit and is accordingly larger than one would expect.

Comment: We think it is very much possible that the elephant cerebellum is important in trunk control.

Changes: We added a reference to the elephant cerebellum in the introduction of our manuscript.

eLife Assessment

In their valuable study, Bracey et al. investigate how microtubule organization within pancreatic islet beta cells supports optimal insulin secretion. Using a combination of live imaging and photo-kinetic assays in an in vitro culture system, they provide compelling evidence that kinesin-1-mediated microtubule sliding, which plays critical roles in neurons and embryos, also plays a critical role in forming the sub-membranous microtubule band in response to glucose in beta cells. This work will be of interest to cell biologists studying cytoskeletal dynamics and organelle trafficking, as well as to translational biologists focused on diabetes.

Joint Public Review:

This elegant study provides important insights into the organization of sub-membrane microtubules in pancreatic β-cells, highlighting a key role for the motor protein KIF5B. The authors propose that KIF5B drives microtubule sliding and alignment along the plasma membrane, a process enhanced by high glucose levels. This precise microtubule arrangement is essential for regulated secretion in β-cells. Supporting this model, the authors show that KIF5B is more highly expressed than other kinesins in MIN6 cells, and its depletion via shRNA disrupts sub-membrane microtubule density and organization. In contrast, KIF5A knockdown alters overall microtubule architecture. Using a dominant-negative approach, they further demonstrate that KIF5B-mediated microtubule sliding relies on its tail domain and is stimulated by glucose, paralleling known glucose-dependent increases in kinesin-1 activity.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Specific comments:

(1) It is difficult to appreciate that there is a "peripheral sub-membrane microtubule array" as it is not well defined in the manuscript. This reviewer assumes that this is in the respective field clear. Yet, while it is appreciated that there is an increased amount of MTs close to the cytoplasmic membrane, the densities appear very variable along the membrane. Please provide a clear description in the Introduction what is meant with "peripheral sub-membrane microtubule array".

A definition has been added to the Introduction.

(2) The authors described a "consistent presence of a significant peripheral array in the C57BL/ 6J control mice, while the KO counterparts exhibited a partial loss of this peripheral bundle.

Specifically, the measured tubulin intensity at the cell periphery was significantly reduced in the KO mice compared to their wild-type counterparts". In vitro "control cells had convoluted nonradial MTs with a prominent sub-membrane array, typical for β cells (Fig. 2A), KIF5B-depleted cells featured extra-dense MTs in the cell center and sparse receding MTs at the periphery (Fig. 2B,C)". Please comment/discuss why in vivo there are no "extra-dense MTs in the cell center".

We now add a discussion of this point, which we believe could be a manifestation of 3D shape of a beta cell in tissue and/or compensatory mechanisms in organisms.

(3) Authors should include in the Discussion a paragraph discussing the fact that small changes in MT configuration can have strong effects.

A paragraph added to the discussion.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 1: Even though the reviewer appreciates that minor changes of MT configuration have severe effects, still the overall effects appear minor (40 vs. <50% or 35% vs. around 28%). Notably, there are no statistically significant differences in the different groups in Fig. 1Suppl-Fig.1 D. This reviewer is not sure if the combination of many not significantly different data points can result in significant changes and this should be checked by a statistician. Authors should include in the Discussion a paragraph discussing the fact that small changes in MT configuration can have strong effects.

We have now added the requested paragraph to the discussion. Indeed, the differences are small, and the significance is only detected in a data set with a large sample size in Fig. 1J,K (combined data sets with smaller sizes from Fig. 1-Suppl-Fig.1 D), consistent with the fact that a larger sample size generally provides more power to detect an effect.

(2) Unfortunately, the authors cannot block kinesin-1 resulting in microtubule accumulation in the cell center and then release the block (best inhibiting microtubule formation), to show that the MTs accumulated in the cell center will be transported to the periphery.

This is indeed the case at the moment, yes.

Minor comments:

- Abstract: β-cells vs. β cells (and throughout the manuscript)

- Page 4: "MTOC, the Golgi, (Trogden et al. 2019), and"

- Page 5: "β-cell specific"

- MT-sliding vs. MT sliding

- Kinesin 1 vs. kinesin-1

- Page 6, line 1: "β cells. actively"

- Page 7: "a microtubule probe", should be "MT"

- Page 9: "1μm" vs. "1 μm"

- Page 10: "demonstrate a dramatic effect" recommended is: "demonstrate a marked effect"

- Page 13, line 1: dramatically vs. markedly

- Page 13, line 5: "50μm" vs. "50 μm" (in general, there should be a space between number and unit?)

- "37 degrees C" vs. "37{degree sign}C"

- Animal protocol number?

- "Mice were euthanized by isoflurane inhalation"? What concentration? How long? More details are needed (no cervical dislocation?).

- Antibodies: more identifiers are needed.

- Antibody information in Key reagents and under 5. Reagents and antibodies do not fit (1:500 and 1:1000).

Thank you, we corrected all relevant information now.

eLife Assessment

This fundamental study provides new insights into the maturation of ribbon synapses in zebrafish neuromast hair cells. Live-cell imaging and pharmacological and genetic manipulations together provide compelling evidence that the formation of this synaptic organelle is a dynamic process involving the fusion of presynaptic elements and microtubule transport, though the evidence that ribbon precursors move in a directed motion toward the active zone is less persuasive. These findings will be of interest to neuroscientists studying synapse formation and function and should inspire further research into the molecular basis for synaptic ribbon maturation.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors set out to resolve a long-standing mystery in the field of sensory biology - how large, presynaptic bodies called "ribbon synapses" migrate to the basolateral end of hair cells. The ribbon synapse is found in sensory hair cells and photoreceptors, and is a critical structural feature of a readily releasable pool of glutamate that excites postsynaptic afferent neurons. For decades, we have known these structures exist, but the mechanisms that control how ribbon synapses coalesce at the bottom of hair cells is not well understood. The authors addressed this question by leveraging the highly-tractable zebrafish lateral line neuromast, which exhibits a small number of visible hair cells, easily observed in time-lapse imaging. The approach combined genetics, pharmacological manipulations, high-resolution imaging and careful quantifications. The manuscript commences with a developmental time course of ribbon synapse development, characterizing both immature and mature ribbon bodies (defined by position in the hair cell, apical vs. basal). Next, the authors show convincing (and frankly mesmerizing) imaging data of plus end-directed microtubule trafficking toward the basal end of the hair cells, and data highlighting the directed motion of ribbon bodies. The authors then use a series of pharmacological and genetic manipulations showing the role of microtubule stability and one particular kinesin (Kif1aa) in the transport and fusion of ribbon bodies, which is presumably all prerequisite for hair cell synaptic transmission. The data suggest that microtubules and their stability is necessary for normal numbers of mature ribbons, and that Kif1aa is likely required for fusion events associated with ribbon maturation. Overall, the data provide a new and interesting story on ribbon synapse dynamics.

Strengths:

(1) The manuscript offers comprehensive Introduction and Discussion sections that will inform generalists and specialists.<br /> (2) The use of Airyscan imaging in living samples to view and measure microtubule and ribbon dynamics in vivo represents a strength. With the rigorous quantification and thoughtful analyses, the authors generate datasets often only gotten in cultured cells or more diminutive animal models (e.g., C. elegans).<br /> (3) The number of biological replicates and the statistical analyses are strong. The combination of pharmacology and genetic manipulations also represents strong rigor.<br /> (4) One of the most important strengths is that the manuscript and data spur on other questions - namely, do (or how do) ribbon bodies attach to Kinesin proteins? Also, and as noted in the Discussion, do hair cell activity and subsequent intracellular calcium rises facilitate ribbon transport/fusion.

Reviewer #3 (Public review):

Summary:

The manuscript uses live imaging to study the role of microtubules in the movement of ribeye aggregates in neuromast hair cells in zebrafish. The main findings are that

(1) Ribeye aggregates, assumed to be ribbon precursors, move in a directed motion toward the active zone;<br /> (2) Disruption of microtubules and kif1aa increases the number of ribeye aggregates and decreases the number of mature synapses.

The evidence for point 2 is compelling, while the evidence for point 1 is less convincing. In particular, the directed motion conclusion is dependent upon fitting of mean squared displacement that can be prone to error and variance to do stochasticity, which is not accounted for in the analysis. Only a small subset of the aggregates meet this criteria and one wonders whether the focus on this subset misses the bigger picture of what is happening with the majority of spots.

Strengths:

(1) The effects of Kif1aa removal and nocodozole on ribbon precursor number and size is convincing and novel.<br /> (2) The live imaging of Ribeye aggregate dynamics provides interesting insight into ribbon formation. The movies showing fusion of ribeye spots are convincing and the demonstrated effects of nocodozole and kif1aa removal on the frequency of these events is novel.<br /> (3) The effect of nocodozole and kif1aa removal on precursor fusion is novel and interesting.<br /> (4) The quality of the data is extremely high and the results are interesting.

Weaknesses:

(1) To image ribeye aggregates, the investigators overexpressed Ribeye-a TAGRFP under control of a MyoVI promoter. While it is understandable why they chose to do the experiments this way, expression is not under the same transcriptional regulation as the native protein and some caution is warranted in drawing some conclusions. For example, the reduction in the number of puncta with maturity may partially reflect regulation of the MyoVI promoter with hair cell maturity. Similarly, it is unknown whether overexpression has the potential to saturate binding sites (for example to motors), which could influence mobility. In the revised manuscript, the authors provide evidence to suggest that overexpression is not at unreasonably high levels, which is reasonable. However, I think it remains important to think of these caveats while reading the paper--especially keeping in mind that expression timing is undoubtedly influenced by the transcriptional control of the exogenous promoter .<br /> (2) The examples of punctae colocalizing with microtubules look clear (fig 1 F-G), but the presentation is anecdotal. It would be better and more informative, if quantified.<br /> (3) It appears that any directed transport may be rare. Simply having an alpha >1 is not sufficient to declare movement to be directed (motor driven transport typically has an alpha approaching 2). Due to randomness of a random walk and errors in fits in imperfect data will yield some spread in movement driven by Brownian motion. Many of the tracks in figure 3H look as thought they might be reasonably fit by a straight line (i.e. alpha = 1).<br /> (4) The "directed motion" shown here does not really resemble motor driven transport observed in other systems (axonal transport, for example) even in the subset that have been picked out as examples here. While the role for microtubules and kif1aa in synapse maturation is strong, it seems likely that this role may be something non-canonical (which would be interesting). In the revision, the authors do an excellent job of considering the issues brought up in point 3 and 4. While perhaps no longer a weakness, I am leaving the critiques here for context for the readers to consider. The added taxol results may not completely settle the issue, but are interesting and provide important information.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript by Hussain and collaborators aims at deciphering the microtubule-dependent ribbon formation in zebrafish hair cells. By using confocal imaging, pharmacology tools, and zebrafish mutants, the group of Katie Kindt convincingly demonstrated that ribbon, the organelle that concentrates glutamate-filled vesicles at the hair cell synapse, originates from the fusion of precursors that move along the microtubule network. This study goes hand in hand with a complementary paper (Voorn et al.) showing similar results in mouse hair cells.

Strengths:

This study clearly tracked the dynamics of the microtubules, and those of the microtubule-associated ribbons and demonstrated fusion ribbon events. In addition, the authors have identified the critical role of kinesin Kif1aa in the fusion events. The results are compelling and the images and movies are magnificent.

Weaknesses:

The lack of functional data regarding the role of Kif1aa. Although it is difficult to probe and interpret the behavior of zebrafish after nocodazole treatment, I wonder whether deletion of kif1aa in hair cells may result in a functional deficit that could be easily tested in zebrafish?

We have examined functional deficits in kif1aa mutants in another paper that was recently accepted: David et al. 2024. https://pubmed.ncbi.nlm.nih.gov/39373584/

In David et al., we found that in addition to a subtle role in ribbon fusion during development, Kif1aa plays a major role in enriching glutamate-filled synaptic vesicles at the presynaptic active zone of mature hair cells. In kif1aa mutants, synaptic vesicles are no longer enriched at the hair cell base, and there is a reduction in the number of synaptic vesicles associated with presynaptic ribbons. Further, we demonstrated that kif1aa mutants also have functional defects including reductions in spontaneous vesicle release (from hair cells) and evoked postsynaptic calcium responses. Behaviorally, kif1aa mutants exhibit impaired rheotaxis, indicating defects in the lateral-line system and an inability to accurately detect water flow. Because our current paper focuses on microtubule-associated ribbon movement and dynamics early in hair-cell development, we have only discussed the effects of Kif1aa directly related to ribbon dynamics during this time window. In our revision, we have referenced this recent work. Currently it is challenging to disentangle how the subtle defects in ribbon formation in kif1aa mutants contribute to the defects we observe in ribbon-synapse function.

Added to results:

“Recent work in our lab using this mutant has shown that Kif1aa is responsible for enriching glutamate-filled vesicles at the base of hair cells. In addition this work demonstrated that loss of Kif1aa results in functional defects in mature hair cells including a reduction in evoked post-synaptic calcium responses (David et al., 2024). We hypothesized that Kif1aa may also be playing an earlier role in ribbon formation.”

Impact:

The synaptogenesis in the auditory sensory cell remains still elusive. Here, this study indicates that the formation of the synaptic organelle is a dynamic process involving the fusion of presynaptic elements. This study will undoubtedly boost a new line of research aimed at identifying the specific molecular determinants that target ribbon precursors to the synapse and govern the fusion process.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors set out to resolve a long-standing mystery in the field of sensory biology - how large, presynaptic bodies called "ribbon synapses" migrate to the basolateral end of hair cells. The ribbon synapse is found in sensory hair cells and photoreceptors, and is a critical structural feature of a readily-releasable pool of glutamate that excites postsynaptic afferent neurons. For decades, we have known these structures exist, but the mechanisms that control how ribbon synapses coalesce at the bottom of hair cells are not well understood. The authors addressed this question by leveraging the highly-tractable zebrafish lateral line neuromast, which exhibits a small number of visible hair cells, easily observed in time-lapse imaging. The approach combined genetics, pharmacological manipulations, high-resolution imaging, and careful quantifications. The manuscript commences with a developmental time course of ribbon synapse development, characterizing both immature and mature ribbon bodies (defined by position in the hair cell, apical vs. basal). Next, the authors show convincing (and frankly mesmerizing) imaging data of plus end-directed microtubule trafficking toward the basal end of the hair cells, and data highlighting the directed motion of ribbon bodies. The authors then use a series of pharmacological and genetic manipulations showing the role of microtubule stability and one particular kinesin (Kif1aa) in the transport and fusion of ribbon bodies, which is presumably a prerequisite for hair cell synaptic transmission. The data suggest that microtubules and their stability are necessary for normal numbers of mature ribbons and that Kif1aa is likely required for fusion events associated with ribbon maturation. Overall, the data provide a new and interesting story on ribbon synapse dynamics.

Strengths:

(1) The manuscript offers a comprehensive Introduction and Discussion sections that will inform generalists and specialists.

(2) The use of Airyscan imaging in living samples to view and measure microtubule and ribbon dynamics in vivo represents a strength. With rigorous quantification and thoughtful analyses, the authors generate datasets often only obtained in cultured cells or more diminutive animal models (e.g., C. elegans).

(3) The number of biological replicates and the statistical analyses are strong. The combination of pharmacology and genetic manipulations also represents strong rigor.

(4) One of the most important strengths is that the manuscript and data spur on other questions - namely, do (or how do) ribbon bodies attach to Kinesin proteins? Also, and as noted in the Discussion, do hair cell activity and subsequent intracellular calcium rises facilitate ribbon transport/fusion?

These are important strengths and as stated we are currently investigating what other kinesins and adaptors and adaptor’s transport ribbons. We have ongoing work examining how hair-cell activity impacts ribbon fusion and transport!

Weaknesses:

(1) Neither the data or the Discussion address a direct or indirect link between Kinesins and ribbon bodies. Showing Kif1aa protein in proximity to the ribbon bodies would add strength.

This is a great point. Previous immunohistochemistry work in mice demonstrated that ribbons and Kif1a colocalize in mouse hair cells (Michanski et al, 2019). Unfortunately, the antibody used in study work did not work in zebrafish. To further investigate this interaction, we also attempted to create a transgenic line expressing a fluorescently tagged Kif1aa to directly visualize its association with ribbons in vivo. At present, we were unable to detect transient expression of Kif1aa-GFP or establish a transgenic line using this approach. While we will continue to work towards understanding whether Kif1aa and ribbons colocalize in live hair cells, currently this goal is beyond the scope of this paper. In our revision we discuss this caveat.

Added to discussion:

“In addition, it will be useful to visualize these kinesins by fluorescently tagging them in live hair cells to observe whether they associate with ribbons.”

(2) Neither the data or Discussion address the functional consequences of loss of Kif1aa or ribbon transport. Presumably, both manipulations would reduce afferent excitation.

Excellent point. Please see the response above to Reviewer #1 public response weaknesses.

(3) It is unknown whether the drug treatments or genetic manipulations are specific to hair cells, so we can't know for certain whether any phenotypic defects are secondary.

This is correct and a caveat of our Kif1aa and drug experiments. In our recently published work, we confirmed that Kif1aa is expressed in hair cells and neurons, while kif1ab is present just is neurons. Therefore, it is likely that the ribbon formation defects in kif1aa mutants are restricted to hair cells. We added this expression information to our results:

“ScRNA-seq in zebrafish has demonstrated widespread co-expression of kif1ab and kif1aa mRNA in the nervous system. Additionally, both scRNA-seq and fluorescent in situ hybridization have revealed that pLL hair cells exclusively express kif1aa mRNA (David et al., 2024; Lush et al., 2019; Sur et al., 2023).”

Non-hair cell effects are a real concern in our pharmacology experiments. To mitigate this in our pharmacological experiments, we have performed drug treatments at 3 different timescales: long-term (overnight), short-term (4 hr) and fast (30 min) treatments. The fast experiments were done after 30 min nocodazole drug treatment, and after this treatment we observed reduced directional motion and fusions. This fast drug treatment should not incur any long-term changes or developmental defects as hair-cell development occurs over 12-16 hrs. However, we acknowledge that drug treatments could have secondary phenotypic effects or effects that are not hair-cell specific. In our revision, we discuss these issues.

Added to discussion:

“Another important consideration is the potential off-target effects of nocodazole. Even at non-cytotoxic doses, nocodazole toxicity may impact ribbons and synapses independently of its effects on microtubules. While this is less of a concern in the short- and medium-term experiments (30-70 min and 4 hr), long-term treatments (16 hrs) could introduce confounding effects. Additionally, nocodazole treatment is not hair cell-specific and could disrupt microtubule organization within afferent terminals as well. Thus, the reduction in ribbon-synapse formation following prolonged nocodazole treatment may result from microtubule disruption in hair cells, afferent terminals, or a combination of the two.”

Reviewer #3 (Public Review):

Summary:

The manuscript uses live imaging to study the role of microtubules in the movement of ribeye aggregates in neuromast hair cells in zebrafish. The main findings are that

(1) Ribeye aggregates, assumed to be ribbon precursors, move in a directed motion toward the active zone;

(2) Disruption of microtubules and kif1aa increases the number of ribeye aggregates and decreases the number of mature synapses.

The evidence for point 2 is compelling, while the evidence for point 1 is less convincing. In particular, the directed motion conclusion is dependent upon fitting of mean squared displacement that can be prone to error and variance to do stochasticity, which is not accounted for in the analysis. Only a small subset of the aggregates meet this criteria and one wonders whether the focus on this subset misses the bigger picture of what is happening with the majority of spots.

Strengths:

(1) The effects of Kif1aa removal and nocodozole on ribbon precursor number and size are convincing and novel.

(2) The live imaging of Ribeye aggregate dynamics provides interesting insight into ribbon formation. The movies showing the fusion of ribeye spots are convincing and the demonstrated effects of nocodozole and kif1aa removal on the frequency of these events is novel.

(3) The effect of nocodozole and kif1aa removal on precursor fusion is novel and interesting.

(4) The quality of the data is extremely high and the results are interesting.

Weaknesses:

(1) To image ribeye aggregates, the investigators overexpressed Ribeye-a TAGRFP under the control of a MyoVI promoter. While it is understandable why they chose to do the experiments this way, expression is not under the same transcriptional regulation as the native protein, and some caution is warranted in drawing some conclusions. For example, the reduction in the number of puncta with maturity may partially reflect the regulation of the MyoVI promoter with hair cell maturity. Similarly, it is unknown whether overexpression has the potential to saturate binding sites (for example motors), which could influence mobility.

We agree that overexpression of transgenes under using a non-endogenous promoter in transgenic lines is an important consideration. Ideally, we would do these experiments with endogenously expressed fluorescent proteins under a native promoter. However, this was not technically possible for us. The decrease in precursors is likely not due to regulation by the myo6a promoter. Although the myo6a promoter comes on early in hair cell development, the promoter only gets stronger as the hair cells mature. This would lead to a continued increase rather than a decrease in puncta numbers with development.

Protein tags such as tagRFP always have the caveat of impacting protein function. This is in partly why we complemented our live imaging with analyses in fixed tissue without transgenes (kif1aa mutants and nocodazole/taxol treatments).

In our revision, we did perform an immunolabel on myo6b:riba-tagRFP transgenic fish and found that Riba-tagRFP expression did not impact ribbon synapse numbers or ribbon size. This analysis argues that the transgene is expressed at a level that does not impact ribbon synapses. This data is summarized in Figure 1-S1.

Added to the results:

“Although this latter transgene expresses Riba-TagRFP under a non-endogenous promoter, neither the tag nor the promoter ultimately impacts cell numbers, synapse counts, or ribbon size (Figure 1-S1A-E).”

Added to methods:

“Tg(myo6b:ctbp2a-TagRFP)^idc11Tg reliably labels mature ribbons, similar to a pan-CTBP immunolabel at 5 dpf (Figure 1-S1B). This transgenic line does not alter the number of hair cells or complete synapses per hair cell (Figure 1-S1A-D). In addition, myo6b:ctbp2a-TagRFP does not alter the size of ribbons (Figure 1-S1E).”

(2) The examples of punctae colocalizing with microtubules look clear (Figures 1 F-G), but the presentation is anecdotal. It would be better and more informative, if quantified.

We did attempt a co-localization analysis between microtubules and ribbons but did not move forward with it due to several issues:

(1) Hair cells have an extremely crowded environment, especially since the nucleus occupies the majority of the cell. All proteins are pushed together in the small space surrounding the nucleus and ultimately, we found that co-localization analyses were not meaningful because the distances were too small.

(2) We also attempted to segment microtubules in these images and quantify how many ribbons were associated with microtubules, but 3D microtubule segmentation was not accurate in hair cells due to highly varying filament intensities, filament dynamics and the presence of diffuse cytoplasmic tubulin signal.

Because of these challenges we concluded the best evidence of ribbon-microtubule association is through visualization of ribbons and their association with microtubules over time (in our timelapses). We see that ribbons localize to microtubules in all our timelapses, including the examples shown (Movies S2-S10). The only instance of ribbon dissociation it when ribbons switch from one filament to another. We did not observe free-floating ribbons in our study.

(3) It appears that any directed transport may be rare. Simply having an alpha >1 is not sufficient to declare movement to be directed (motor-driven transport typically has an alpha approaching 2). Due to the randomness of a random walk and errors in fits in imperfect data will yield some spread in movement driven by Brownian motion. Many of the tracks in Figure 3H look as though they might be reasonably fit by a straight line (i.e. alpha = 1).

(4) The "directed motion" shown here does not really resemble motor-driven transport observed in other systems (axonal transport, for example) even in the subset that has been picked out as examples here. While the role of microtubules and kif1aa in synapse maturation is strong, it seems likely that this role may be something non-canonical (which would be interesting).

Yes, it is true, that directed transport of ribbon precursors is relatively rare. Only a small subset of the ribbon precursors moves directionally (α > 1, 20 %) or have a displacement distance > 1 µm (36 %) during the time windows we are imaging. The majority of the ribbons are stationary. To emphasize this result we have added bar graphs to Figure 3I,K to illustrate this result and state the numbers behind this result more clearly.

“Upon quantification, 20.2 % of ribbon tracks show α > 1, indicative of directional motion, but the majority of ribbon tracks (79.8 %) show α < 1, indicating confinement on microtubules (Figure 3I, n = 10 neuromasts, 40 hair cells, and 203 tracks).

To provide a more comprehensive analysis of precursor movement, we also examined displacement distance (Figure 3J). Here, as an additional measure of directed motion, we calculated the percent of tracks with a cumulative displacement > 1 µm. We found 35.6 % of tracks had a displacement > 1 µm (Figure 3K; n = 10 neuromasts, 40 hair cells, and 203 tracks).”

We cannot say for certain what is happening with the stationary ribbons, but our hypothesis is that these ribbons eventually exhibit directed motion sufficient to reach the active zone. This idea is supported by the fact that we see ribbons that are stationary begin movement, and ribbons that are moving come to a stop during the acquisition of our timelapses (Movies S4 and S5). It is possible that ribbons that are stationary may not have enough motors attached, or there may be a ‘seeding’ phase where Ribeye aggregates are condensing on the ribbon.

We also reexamined our MSD a values as the a values we observed in hair cells were lower than those seen canonical motor-driven transport (where a approaches 2). One reason for this difference may arise from the dynamic microtubule network in developing hair cells, which could affect directional ribbon movement. In our revision we plotted the distribution of a values which confirmed that in control hair cells, the majority of the a values we see are typically less than 2 (Figure 7-S1A). Interestingly we also compared the distribution a values between control and taxol-treated hair cells, where the microtubule network is more stable, and found that the distribution shifted towards higher a values (Figure 7-S1A). We also plotted only ‘directional’ tracks (with a > 1) and observed significantly higher a values in taxol-treated hair cells (Figure 7-S1B). This is an interesting result which indicates that although the proportion of directional tracks (with a > 1) is not significantly different between control and taxol-treated hair cells (which could be limited by the number of motor/adapter proteins), the ribbons that move directionally do so with greater velocities when the microtubules are more stable. This supports our idea that the stability of the microtubule network could be why ribbon movement does not resemble canonical motor transport. This analysis is presented as a new figure (Figure 7-S1A-B) and is referred to in the text in the results and the discussion.

Results:

“Interestingly, when we examined the distribution of α values, we observed that taxol treatment shifted the overall distribution towards higher α a values (Figure 7-S1A). In addition, when we plotted only tracks with directional motion (α > 1), we found significantly higher α values in hair cells treated with taxol compared to controls (Figure 7-S1B). This indicates that in taxol-treated hair cells, where the microtubule network is stabilized, ribbons with directional motion have higher velocities.”

Discussion:

“Our findings indicate that ribbons and precursors show directed motion indicative of motor-mediated transport (Figure 3 and 7). While a subset of ribbons moves directionally with α values > 1, canonical motor-driven transport in other systems, such as axonal transport, can achieve even higher α values approaching 2 (Bellotti et al., 2021; Corradi et al., 2020). We suggest that relatively lower α values arise from the highly dynamic nature of microtubules in hair cells. In axons, microtubules form stable, linear tracks that allow kinesins to transport cargo with high velocity. In contrast, the microtubule network in hair cells is highly dynamic, particularly near the cell base. Within a single time frame (50-100 s), we observe continuous movement and branching of these networks. This dynamic behavior adds complexity to ribbon motion, leading to frequent stalling, filament switching, and reversals in direction. As a result, ribbon transport appears less directional than the movement of traditional motor cargoes along stable axonal filaments, resulting in lower α values compared to canonical motor-mediated transport. Notably, treatment with taxol, which stabilizes microtubules, increased α values to levels closer to those observed in canonical motor-driven transport (Figure 7-S1). This finding supports the idea that the relatively lower α values in hair cells are a consequence of a more dynamic microtubule network. Overall, this dynamic network gives rise to a slower, non-canonical mode of transport.”

(5) The effect of acute treatment with nocodozole on microtubules in movie 7 and Figure 6 is not obvious to me and it is clear that whatever effect it has on microtubules is incomplete.

When using nocodazole, we worked to optimize the concentration of the drug to minimize cytotoxicity, while still being effective. While the more stable filaments at the cell apex remain largely intact after nocodazole treatment, there are almost no filaments at the hair cell base, which is different from the wild-type hair cells. In addition, nocodazole-treated hair cells have more cytoplasmic YFP-tubulin signal compared to wild type. We have clarified this in our results. To better illustrate the effect of nocodazole and taxol we have also added additional side-view images of hair cells expressing YFP-tubulin (Figure 4-S1F-G), that highlight cytoplasmic YFP-tubulin and long, stabilized microtubules after 3-4 hr treatment with nocodazole and taxol respectively. In these images we also point out microtubules at the apical region of hair cells that are very stable and do not completely destabilize with nocodazole treatment at concentrations that are tolerable to hair cells.

“We verified the effectiveness of our in vivo pharmacological treatments using either 500 nM nocodazole or 25 µM taxol by imaging microtubule dynamics in pLL hair cells (myo6b:YFP-tubulin). After a 30-min pharmacological treatment, we used Airyscan confocal microscopy to acquire timelapses of YFP-tubulin (3 µm z-stacks, every 50-100 s for 30-70 min, Movie S8). Compared to controls, 500 nM nocodazole destabilized microtubules (presence of depolymerized YFP-tubulin in the cytosol, see arrows in Figure 4-S1F-G) and 25 µM taxol dramatically stabilized microtubules (indicated by long, rigid microtubules, see arrowheads in Figure 4-S1F,H) in pLL hair cells. We did still observe a subset of apical microtubules after nocodazole treatment, indicating that this population is particularly stable (see asterisks in Figure 4-S1F-H).”

To further address concerns about verifying the efficacy of nocodazole and taxol treatment on microtubules, we added a quantification of our immunostaining data comparing the mean acetylated-a-tubulin intensities between control, nocodazole and taxol-treated hair cells. Our results show that nocodazole treatment reduces the mean acetylated-a-tubulin intensity in hair cells. This is included as a new figure (Figure 4-S1D-E) and this result is referred to in the text. To better illustrate the effect of nocodazole and taxol we have also added additional side-view images of hair cells after overnight treatment with nocodazole and taxol (Figure 4-S1A-C).

“After a 16-hr treatment with 250 nM nocodazole we observed a decrease in acetylated-a-tubulin label (qualitative examples: Figure 4A,C, Figure 4-S1A-B). Quantification revealed significantly less mean acetylated-a-tubulin label in hair cells after nocodazole treatment (Figure 4-S1D). Less acetylated-a-tubulin label indicates that our nocodazole treatment successfully destabilized microtubules.”

“Qualitatively more acetylated-a-tubulin label was observed after treatment, indicating that our taxol treatment successfully stabilized microtubules (qualitative examples: Figure 4-S1A,C). Quantification revealed an overall increase in mean acetylated-a-tubulin label in hair cells after taxol treatment, but this increase did not reach significance (Figure 4-S1E).”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The manuscript is fairly dense. For instance, some information is repeated (page 3 ribbon synapses form along a condensed timeline in zebrafish hair cells: 12-18 hrs, and on .page 5. These hair cells form 3-4 ribbon synapses in just 12-18 hrs). Perhaps, the authors could condense some of the ideas? The introduction could be shortened.

We have eliminated this repeated text in our revision. We have shortened the introduction 1275 to 1038 words (with references)

(2) The mechanosensory structure on page 5 is not defined for readers outside the field.

Great point, we have added addition information to define this structure in the results:

“We staged hair cells based on the development of the apical, mechanosensory hair bundle. The hair bundle is composed of actin-based stereocilia and a tubulin-based kinocilium. We used the height of the kinocilium (see schematic in Figure 1B), the tallest part of the hair bundle, to estimate the developmental stage of hair cells as described previously…”

(3) Figure 1E is quite interesting but I'd rather show Figure S1 B/C as they provide statistics. In addition, the authors define 4 stages : early, intermediate, late, and mature for counting but provide only 3 panels for representative examples by mixing late/mature.

We were torn about which ribbon quantification graph to show. Ultimately, we decided to keep the summary data in Figure 1E. This is primarily because the supplementary Figure will be adjacent to the main Figure in the Elife format, and the statistics will be easy to find and view.

Figure 1 now provides a representative image for both late and mature hair cells.

(4.) The ribbon that jumps from one microtubule to another one is eye-catching. Can the authors provide any statistics on this (e.g. percentage)?

Good point. In our revision, we have added quantification for these events. We observe 2.8 switching events per neuromast during our fast timelapses. This information is now in the text and is also shown in a graph in Figure 3-S1D.

“Third, we often observed that precursors switched association between neighboring microtubules (2.8 switching events per neuromast, n= 10 neuromasts; Figure 3-S1C-D, Movie S7).”

(5) With regard to acetyl-a-tub immunocytochemistry, I would suggest obtaining a profile of the fluorescence intensity on a horizontal plane (at the apical part and at the base).

(6) Same issue with microtubule destruction by nocodazole. Can the authors provide fluorescence intensity measurements to convince readers of microtubule disruption for long and short-term application.

Regarding quantification of microtubule disruption using nocodazole and taxol. We did attempt to create profiles of the acetylated tubulin or YFP-tubulin label along horizontal planes at the apex and base, but the amount variability among cells and the angle of the cell in the images made this type of display and quantification challenging. In our revision we as stated above in our response to Reviewer #1’s public comment, we have added representative side-view images to show the disruptions to microtubules more clearly after short and long-term drug experiments (Figure 4-S1A-C, F-H). In addition, we quantified the reduction in acetylated tubulin label after overnight treatment with nocodazole and found the signal was significantly reduced (Figure 3-S1D-E). Unfortunately, we were unable to do a similar quantification due to the variability in YFP-tubulin intensity due to variations in mounting. The following text has been added to the results:

“Quantification revealed significantly less mean acetylated-a-tubulin label in hair cells after nocodazole treatment (Figure 4-S1D).”

“Quantification revealed an overall increase in mean acetylated-a-tubulin label in hair cells after taxol treatment, but this increase did not reach significance (Figure 4-S1A,C,E).”

(7) It is a bit difficult to understand that the long-term (overnight) microtubule destabilization leads to a reduction in the number of synapses (Figure 4F) whereas short-term (30 min) microtubule destabilization leads to the opposite phenotype with an increased number of ribbons (Figure 6G). Are these ribbons still synaptic in short-term experiments? What is the size of the ribbons in the short-term experiments? Alternatively, could the reduction in synapse number upon long-term application of nocodazole be a side-effect of the toxicity within the hair cell?

Agreed-this is a bit confusing. In our revision, we have changed our analyses, so the comparisons are more similar between the short- and long-term experiments–we examined the number of ribbons and precursor per cells (apical and basal) in both experiments (Changed the panel in Figure 4G, Figure 4-S2G and Figure 5G). In our live experiments we cannot be sure that ribbons are synaptic as we do not have a postsynaptic co-label. Also, we are unable to reliably quantify ribbon and precursor size in our live images due to variability in mounting. We have changed the text to clarify as follows:

Results:

“In each developing cell, we quantified the total number of Riba-TagRFP puncta (apical and basal) before and after each treatment. In our control samples we observed on average no change in the number of Riba-TagRFP puncta per cell (Figure 6G). Interestingly, we observed that nocodazole treatment led to a significant increase in the total number of Riba-TagRFP puncta after 3-4 hrs (Figure 6G). This result is similar to our overnight nocodazole experiments in fixed samples, where we also observed an increase in the number of ribbons and precursors per hair cell. In contrast to our 3-4 hr nocodazole treatment, similar to controls, taxol treatment did not alter the total number of Riba-TagRFP puncta over 3-4 hrs (Figure 6G). Overall, our overnight and 3-4 hr pharmacology experiments demonstrate that microtubule destabilization has a more significant impact on ribbon numbers compared to microtubule stabilization.”

Discussion:

“Ribbons and microtubules may interact during development to promote fusion, to form larger ribbons. Disrupting microtubules could interfere with this process, preventing ribbon maturation. Consistent with this, short-term (3-4 hr) and long-term (overnight) nocodazole increased ribbon and precursor numbers (Figure 6AG; Figure 4G), suggesting reduced fusion. Long-term treatment (overnight) resulted in a shift toward smaller ribbons (Figure 4H-I), and ultimately fewer complete synapses (Figure 4F).”

Nocodazole toxicity: in response to Reviewer # 2’s public comment we have added the following text in our discussion:

Discussion:

“Another important consideration is the potential off-target effects of nocodazole. Even at non-cytotoxic doses, nocodazole toxicity may impact ribbons and synapses independently of its effects on microtubules. While this is less of a concern in the short- and medium-term experiments (30 min to 4 hr), long-term treatments (16 hrs) could introduce confounding effects. Additionally, nocodazole treatment is not hair cell-specific and could disrupt microtubule organization within afferent terminals as well. Thus, the reduction in ribbon-synapse formation following prolonged nocodazole treatment may result from microtubule disruption in hair cells, afferent terminals, or a combination of the two.”

(8) Does ribbon motion depend on size or location?

It is challenging to reliability quantify the actual area of precursors in our live samples, as there is variability in mounting and precursors are quite small. But we did examine the location of ribbon precursors (using tracks > 1 µm as these tracks can easily be linked to cell location in Imaris) with motion in the cell. We found evidence of ribbons with tracks > 1 µm throughout the cell, both above and below the nucleus. This is now plotted in Figure 3M. We have also added the following test to the results:

“In addition, we examined the location of precursors within the cell that exhibited displacements > 1 µm. We found that 38.9 % of these tracks were located above the nucleus, while 61.1 % were located below the nucleus (Figure 3M).”

Although this is not an area or size measurement, this result suggests that both smaller precursors that are more apical, and larger precursors/ribbons that are more basal all show motion.

(9) The fusion event needs to be analyzed in further detail: when one ribbon precursor fuses with another one, is there an increase in size or intensity (this should follow the law of mass conservation)? This is important to support the abstract sentence "ribbon precursors can fuse together on microtubules to form larger ribbons".

As mentioned above it is challenging accurately estimate the absolute size or intensity of ribbon precursors in our live preparation. But we did examine whether there is a relative increase in area after ribbon fuse. We have plotted the change in area (within the same samples) for the two fusion events in shown in Figure 8-S1A-B. In these examples, the area of the puncta after fusion is larger than either of the two precursors that fuse. Although the areas are not additive, these plots do provide some evidence that fusion does act to form larger ribbons. To accompany these plots, we have added the following text to the results:

“Although we could not accurately measure the areas of precursors before and after fusion, we observed that the relative area resulting from the fusion of two smaller precursors was greater than that of either precursor alone. This increase in area suggests that precursor fusion may serve as a mechanism for generating larger ribbons (see examples: Figure 8-S1A-B).”

Because we were unable to provide more accurate evidence of precursor fusion resulting in larger ribbons, we have removed this statement from our abstract and lessened our claims elsewhere in the manuscript.

(10) The title in Figure 8 is a bit confusing. If fusion events reflect ribbon precursors fusion, it is obvious it depends on ribbon precursors. I'd like to replace this title with something like "microtubules and kif1aa are required for fusion events"

We have changed the figure title as suggested, good idea.

Reviewer #2 (Recommendations For The Authors):

(1) Figure 1C. The purple/magenta colors are hard to distinguish.

We have made the magenta color much lighter in the Figure 1C to make it easier to distinguish purple and magenta.

(2) There are places where some words are unnecessarily hyphenated. Examples: live-imaging and hair-cell in the abstract, time-course in the results.

In our revision, we have done our best to remove unnecessary hyphens, including the ones pointed out here.

(3) Figure 4H and elsewhere - what is "area of Ribeye puncta?" Related, I think, in the Discussion the authors refer to "ribbon volume" on line 484. But they never measured ribbon volume so this needs to be clarified.

We have done best to clarify what is meant by area of Ribeye puncta in the results and the methods:

Results:

“We also observed that the average of individual Ribeyeb puncta (from 2D max-projected images) was significantly reduced compared to controls (Figure 4H). Further, the relative frequency of individual Ribeyeb puncta with smaller areas was higher in nocodazole treated hair cells compared to controls (Figure 4I).”

Methods:

“To quantify the area of each ribbon and precursor, images were processed in a FIJI ‘IJMacro_AIRYSCAN_simple3dSeg_ribbons only.ijm’ as previously described (Wong et al., 2019). Here each Airyscan z-stack was max-projected. A threshold was applied to each image, followed by segmentation to delineate individual Ribeyeb/CTBP puncta. The watershed function was used to separate adjacent puncta. A list of 2D objects of individual ROIs (minimum size filter of 0.002 μm2) was created to measure the 2D areas of each Ribeyeb/CTBP puncta.”

We did refer to ribbon volume once in the discussion, but volume is not reflected in our analyses, so we have removed this mention of volume.

(4) More validation data showing gene/protein removal for the crispants would be helpful.

Great suggestion. As this is a relatively new method, we have created a figure that outlines how we genotype each individual crispant animal analyzed in our study Figure 6-S1. In the methods we have also added the following information:

“fPCR fragments were run on a genetic analyzer (Applied Biosystems, 3500XL) using LIZ500 (Applied Biosystems, 4322682) as a dye standard. Analysis of this fPCR revealed an average peak height of 4740 a.u. in wild type, and an average peak height of 126 a.u. in kif1aa F0 crispants (Figure 6-S1). Any kif1aa F0 crispant without robust genomic cutting or a peak height > 500 a.u. was not included in our analyses.”

Reviewer #3 (Recommendations For The Authors):

Lines 208-209--should refer to the movie in the text.

Movie S1 is now referenced here.

It would be helpful if the authors could analyze and quantify the effect of nocodozole and taxol on microtubules (movie 7).

See responses above to Reviewer #1’s similar request.

Figure 7 caption says "500 mM" nocodozole.

Thank you, we have changed the caption to 500 nM.

One problem with the MSD analysis is that it is dependent upon fits of individual tracks that lead to inaccuracies in assigning diffusive, restricted, and directed motion. The authors might be able to get around these problems by looking at the ensemble averages of all the tracks and seeing how they change with the various treatments. Even if the effect is on a subset of ribeye spots, it would be reassuring to see significant effects that did not rely upon fitting.

We are hesitant to average the MSD tracks as not all tracks have the same number of time steps (ribbon moving in and out of the z-stack during the timelapse). This makes it challenging for us to look at the ensembles of all averages accurately, especially for the duration of the timelapse. This is the main reason why added another analysis, displacements > 1µm as another readout of directional motion, a measure that does not rely upon fitting.

The abstract states that directed movement is toward the synapse. The only real evidence for this is a statement in the results: "Of the tracks that showed directional motion, while the majority move to the cell base, we found that 21.2 % of ribbon tracks moved apically." A clearer demonstration of this would be to do the analysis of Figure 2G for the ribeye aggregates.

If was not possible to do the same analysis to ribbon tracks that we did for the EB3-GFP analysis in Figure 2. In Figure 2 we did a 2D tracking analysis and measured the relative angles in 2D. In contrast, the ribbon tracking was done in 3D in Imaris not possible to get angles in the same way. Further the MSD analysis was outside of Imaris, making it extremely difficult to link ribbon trajectories to the 3D cellular landscape in Imaris. Instead, we examined the direction of the 3D vectors in Imaris with tracks > 1µm and determined the direction of the motion (apical, basal or undetermined). For clarity, this data is now included as a bar graph in Figure 3L. In our results, we have clarified the results of this analysis:

“To provide a more comprehensive analysis of precursor movement, we also examined displacement distance (Figure 3J). Here, as an additional measure of directed motion, we calculated the percent of tracks with a cumulative displacement > 1 µm. We found 35.6 % of tracks had a displacement > 1 µm (Figure 3K; n = 10 neuromasts, 40 hair cells and 203 tracks). Of the tracks with displacement > 1 µm, the majority of ribbon tracks (45.8 %) moved to the cell base, but we also found a subset of ribbon tracks (20.8 %) that moved apically (33.4 % moved in an undetermined direction) (Figure 3L).”

Some more detail about the F0 crispants should be provided. In particular, what degree of cutting was observed and what was the criteria for robust cutting?

See our response to Reviewer 2 and the newly created Figure 6-S1.

eLife Assessment

This useful manuscript describes cryo-EM structures of archaeal proteasomes that reveal insights into how occupancy of binding pockets on the 20S protease regulates proteasome gating. The evidence supporting these claims is convincing, although inclusion of more quantitative comparisons would help strengthen the conclusions. This work will be of special interest to researchers interested in proteasome structure and regulation.

Reviewer #1 (Public review):

Summary:

In this manuscript, Chua, Daugherty, and Smith analyze a new set of archaeal 20S proteasomes obtained by cryo-EM that illustrate how the occupancy of the HbYX binding pocket induces gate opening. They do so primarily through a V24Y mutation in the α-subunit. These results are supported by a limited set of mutations in K66 in the α subunit, bringing new emphasis to this unit.

Strengths:

The new structure's analysis is comprehensive, occupying the entire manuscript. As such, the scope of this manuscript is very narrow, but the strength of the data is solid, and they offer an interesting and important new piece to the gate-opening literature.

Weaknesses:

Major Concerns

(1) This manuscript rests on one new cryo-EM structure, leading to a single (albeit convincing) experiment demonstrating the importance of occupying the pocket and moving K66. Could a corresponding bulky mutation at K66 not activate the 20S proteasome?

(2) To emphasize the importance of this work, the authors highlight the importance of gate-opening to human 20S proteasomes. However, the key distinctions between these proteasomes are not given sufficient weight.<br /> (a) As the authors note, the six distinct Rpt C-termini can occupy seven different pickets. However, how these differences would impact activation is not thoroughly discussed.<br /> (b) With those other sites, the relative importance of various pockets, such as the one controlling the α3 N-terminus, should be discussed more thoroughly as a potential critical difference.<br /> (c) These differences can lead to eukaryote 20S gates shifting between closed and open and having a partially opened state. This becomes relevant if the goal is to lead to an activated 20S. It would have been interesting to have archaea 20S with a mix of WT and V24Y α-subunits. However, one might imagine the subclassification problem would be challenging and require an extraordinary number of particles.<br /> (d) Furthermore, the conservation of the amino acids around the binding pocket was not addressed. This seems particularly important in the relative contribution of a residue analogous to K66 or V24.

Reviewer #2 (Public review):

Summary:

The manuscript by Chuah et al. reports the experimental results that suggest the occupancy of the HbYX pockets suffices for proteasome gate opening. The authors conducted cryo-EM reconstructions of two mutant archaeal proteasomes. The work is technically sound and may be of special interest in the field of structural biology of the proteasomes.

Strengths:

Overall, the work incrementally deepens our understanding of the proteasome activation and expands the structural foundation for therapeutic intervention of proteasome function. The evidence presented appears to be well aligned with the existing literature, which adds confidence in the presentation.

Weaknesses:

The paper may benefit from some minor revision by making improvements on the figures and necessary quantitative comparative studies.

Author Response

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

Reviewer #1 (Public Review):

Several concerns are raised from the current study.

1) Previous studies showed that iTregs generated in vitro from culturing naïve T cells with TGF-b are intrinsically unstable and prone to losing Foxp3 expression due to lack of DNA demethylation in the enhancer region of the Foxp3 locus (Polansky JK et al, Eur J Immunol., 2008, PMID: 18493985). It is known that removing TGF-b from the culture media leads to rapid loss of Foxp3 expression. In the current study, TGF-b was not added to the media during iTreg restimulation, therefore, the primary cause for iTreg instability should be the lack of the positive signal provided by TGF-b. NFAT signal is secondary at best in this culturing condition.

In restimulation, void of TGFb is necessary to cause iTreg instability. Otherwise, the setup is similar to the iTreg-inducing environment (Author response image 1). On the other hand, the ultimate goal of this study is to provide a scenario that bears some resemblance of clinical treatment, where TGFb may not be available. The reviewer is correct in stating that TGFb is essential for iTreg stability, we are studying the role played by NFAT in iTreg instability in vitro, and possibly in potential clinical use of iTreg .

Author response image 1.

Restimulation with TGFb will persist iTreg inducing environment, resulting in less pronounced instability. Sorted Foxp3-GFP+ iTregs were rested for 1d, and then rested or restimulated in the presence of TGF-β for 2 d. Percentages of Foxp3+ cells were analyzed by intracellular staining of Foxp3 after 2 d.

2) It is not clear whether the NFAT pathway is unique in accelerating the loss of Foxp3 expression upon iTreg restimulation. It is also possible that enhancing T cell activation in general could promote iTreg instability. The authors could explore blocking T cell activation by inhibiting other critical pathways, such as NF-kb and c-Jun/c-Fos, to see if a similar effect could be achieved compared to CsA treatment.

We thank the reviewer for this suggestion. We performed this experiment according to see extent of the role that NFAT plays, or whether other major pathways are involved. As Author response image 2 shows, solely inhibiting NFAT effectively rescued the instability of iTreg. The inhibition of NFkB (BAY 11-7082), c-Jun (SP600125), or a c-Jun/c-Fos complex (T5224) had no discernable effect, or in one case, possibly further reduction in stability. These results may indicate that NFAT plays a crucial and special role in TCR activation, which leads to iTreg instability. Other pathways, as far as how this experiment is designed, do not appear to be significantly involved.

Author response image 2.

Comparing effects of NFAT, NF-kB and c-Jun/c-Fos inhibitors on iTreg instability. Sorted Foxp3-GFP+ iTregs were rested for 1d, then restimulated by anti-CD3 and CD28 in the presence of listed inhibitors. Percentages of Foxp3+ cells were analyzed by intracellular staining after 2d restimulation.

3) The authors linked chromatin accessibility and increased expression of T helper cell genes to the loss of Foxp3 expression and iTreg instability. However, it is not clear how the former can lead to the latter. It is also not clear whether NFAT binds directly to the Foxp3 locus in the restimulated iTregs and inhibits Foxp3 expression.

T helper gene activation is likely to cause instability in iTregs by secreting more inflammatory cytokines, as shown in Figure Q9, for example, IL-21 secretion. Further investigation is needed to understand how these genes contribute to Foxp3 gene instability exactly. With our limited insight, there may be two possibilities. 1. IL-21 directly affects Foxp3 through its impact on certain inflammation-related transcription factors (TFs). 2. There could be an indirect relationship where NFAT has a greater tendency to bind to those inflammatory TFs when iTreg instability appears, promoting the upregulation of these Th genes like in activated T cells, while being less likely to bind to SMAD and Foxp3, representing a competitive behavior. We at the moment cannot comprehend the intricacies that lead to the differential effects on T helper genes and Treg related genes.

With that said, we have previously attempted to explore the direct effect of NFAT on Foxp3 gene locus. Foxp3 transcription in iTregs primarily relies on histone modifications such as H3K4me3 (Tone et al., 2008; Lu et al., 2011) rather than DNA demethylation (Ohkura et al., 2012; Hilbrands et al., 2016). Previous studies have reported that NFAT and SMAD3 can together promote the histone acetylation of Foxp3 genes (Tone et al., 2008). In our previous set of experiments, we simultaneously obtained information of NFAT binding sites and H3K4me3. In Foxp3 locus, we observed a decreasing trend in NFAT binding to the CNS3 region of Foxp3 in restimulated iTregs compared to resting iTregs (Author response image 3). Additionally, the H3K4me3 modification in the CNS3 region of Foxp3 decreased upon iTreg restimulation, but inhibiting NFAT nuclear translocation with CsA could maintain this modification at its original level (Author response image 3).

Author response image 3.

The NFAT binding and histone modification on Foxp3 gene locus. Genome track visualization of NFAT binding profiles and H3K4me3 profiles in Foxp3 CNS3 locus in two batches of dataset.

Based on these preliminary explorations, it is concluded that NFAT can directly bind to the Foxp3 locus, and it appears that NFAT decreases upon restimulation, resulting in a decrease in H3K4me3, ultimately leading to the close association of NFAT and Foxp3 instability. However, due to limited sample replicates, these data need to be verified for more solid conclusions. We speculate that during the induction of iTregs, NFAT may recruit histone-modifying enzymes to open the Foxp3 CNS3 region, and this effect is synergistic with SMAD. When instability occurs upon restimulation, NFAT binding to Foxp3 weakens due to the absence of SMAD's assistance, subsequently reducing the recruitment of histone modifications enzyme and ultimately inhibiting Foxp3 transcription.

Reviewer #2 (Public Review):

(1) Some concerns about data processing and statistic analysis.

The authors did not provide sufficient information on statistical data analysis; e.g. lack of detailed descriptions about

-the precise numbers of technical/biological replicates of each experiment

-the method of how the authors analyze data of multiple comparisons... Student t-test alone is generally insufficient to compare multiple groups; e.g. figure 1.

These inappropriate data handlings are ruining the evidence level of the precious findings.

We thank the reviewer for pointing out this important aspect. In the figure legend, numbers of independently-performed experiment repeats are shown as N, biological replicates of each experiment as n. Student’s t test was used for comparing statistical significance between two groups. In this manuscript, all calculations of significant differences were based on comparisons between two groups. There were no multiple conditions compared simultaneously within a single group, and thus, no other calculation methods were used.

(2) Untransparent data production; e.g. the method of Motif enrichment analysis was not provided. Thus, we should wait for the author's correction to fully evaluate the significance and reliability of the present study.

Per this reviewer’s request, we have provided detailed descriptions of the data analysis for Fig 5, including both the method section and the Figure legend, as presented below:

“The peaks annotations were performed with the “annotatePeak” function in the R package ChIPseeker (Yu et al, 2015).

The plot of Cut&Tag signals over a set of genomic regions were calculated by using “computeMatrix” function in deepTools and plotted by using “plotHeatmap” and “plotProfile” functions in deepTools. The motif enrichment analysis was performed by using the "findMotifsGenome.pl" command in HOMER with default parameters.

The motif occurrences in each peak were identified by using FIMO (MEME suite v5.0.4) with the following settings: a first-order Markov background model, a P value cutoff of 10-4, and PWMs from the mouse HOCOMOCO motif database (v11).”

Additionally, we have also supplemented the method section with further details on the analysis of RNA-seq and ATAC-seq data.

(3) Lack of evidence in human cells. I wonder whether human PBMC-derived iTreg cells are similarly regulated.

This is a rather complicated issue, human T cells express FoxP3 upon TCR stimulation (PNAS, 103(17): 6659–6664), whose function is likely to protect T cells from activation induced cell death, and does not offer Treg like properties. In contrast in mice, FoxP3 can be used as an indicator of Treg. Currently, this is not a definitive marker for Treg in human, our FoxP3 based readouts do not apply. Nevertheless, we have now investigated whether inhibiting calcium signaling or NFAT could enhance the stability of human iTreg. As shown in Author response image 4, we found that the proportion of Foxp3-expressing cells did not show significant changes across the different conditions, while the MFI analysis revealed that CsA-treated iTreg exhibited higher Foxp3 expression levels compared to both restimulated iTreg and rest iTreg. However, CM4620 had no significant effect on Foxp3 stability, consistent with the observation of its limited efficacy in suppressing human iTreg long term activation. In summary, our results suggest that inhibiting NFAT signaling through CsA treatment can help maintain higher levels of Foxp3 expression in human iTreg.

Author response image 4.

Effect of inhibiting NFAT and calcium on human iTreg stability. Human naïve CD4 cells from PBMC were subjected to a two-week induction process to generate human iTreg. Subsequently, human iTreg were restimulated for 2 days with dynabeads followed by 2 days of rest in the prescence of CsA and CM-4620. Four days later, percentages of Foxp3+ cells and Foxp3 mean fluorescence intensity (MFI) were analyzed by intracellular staining.

(4) NFAT regulation did not explain all of the differences between iTregs and nTregs, as the authors mentioned as a limitation. Also, it is still an open question whether NFAT can directly modulate the chromatin configuration on the effector-type gene loci, or whether NFAT exploits pre-existing open chromatin due to the incomplete conversion of Treg-type chromatin landscape in iTreg cells. The authors did not fully demonstrate that the distinct pattern of chromatin regional accessibility found in iTreg cells is the direct cause of an effector-type gene expression.

To our surprise, the inhibition of NFkB (BAY 11-7082), c-Jun (SP600125), and the c-Jun/c-Fos complex (T5224) resulted in minimal alterations, as shown in Fig Q1. This seems to argue that NFAT may play a more special role in events leading iTreg instability.

We hypothesize that NFAT takes advantage of pre-existing open chromatin state due to the incomplete conversion of chromatin landscape in iTreg cells. Because iTreg cells, after induction, already exhibit inherent chromatin instability, with highly-open inflammatory genes. Furthermore, when iTreg cells were restimulated, the subsequent change in chromatin accessibility was relatively limited and not rescued by NFAT inhibitor treatment (Author response image 5). Therefore, in the case of iTreg cells, we propose that NFAT exploits the easy access of those inflammatory genes, leading to rapid destabilization of iTreg cells in the short term.

In contrast, tTreg cells possess a relatively stable chromatin structure in the beginning, it would be interesting to investigate whether NFAT or calcium signaling could disrupt chromatin accessibility during the activation or expansion of tTreg cells. It is possible that NFAT might cause the loss of the originally established demethylation map and open up inflammatory loci, thereby inducing a shift in gene transcriptional profiles, equally leading to instability.

Author response image 5.

Chromatin accessibility of Rest, Retimulated, CsA/ORAIinh treated restimulated iTreg. PCA visualization of chromatin accessibility profiles of different cell types. Color indicates cell type.

To establish a direct relationship between gene locus accessibility and its overexpression, a controlled experimental approach can be employed. One such method involves precise manipulation of the accessibility of a specific genomic locus using CRISPR-mediated epigenetic modifications at targeted loci. Subsequently, the impact of this manipulation on the expression level of the target gene can be precisely examined. By conducting these experiments, it will be possible to determine whether the augmented gene accessibility directly causes the observed gene overexpression.

Reviewer #1 (Recommendations For The Authors):

1) It might be helpful to add TGF-b to the iTreg restimulation culture to remove the influence of the lack of TGF-b from the equation, and measure the influence of SOCE/NFAT on iTreg instability.

Please refer to Author response image 1.

2) Alternatively, authors can also culture iTreg cells with TGF-b for 2 weeks when they undergo epigenetic changes and become more stabilized (Polansky JK et al, Eur J Immunol., 2008, PMID: 18493985). At this point, the stabilized iTregs can be used to measure the influence of SOCE/NFAT on iTreg instability.

In the study conducted by Polansky, it was observed in Figure 1 that prolonged exposure to TGF-β fails to induce stable Foxp3 expression and demethylation of the Treg-specific demethylated region (TSDR). Based on this finding, we could consider exploring alternative approaches to obtain a more stabilized iTreg population. One such approach could be isolating Foxp3+helios-Nrp1- iTreg cells directly from the peripheral in vivo, which are also known as pTregs. Generally, pTreg cells generated in vivo tend to be more stable compared to iTreg cells induced in vitro, and they already exhibit partial demethylation of the Treg signature, as shown in Fig 6C (Polansky JK et al, Eur J Immunol., 2008, PMID: 18493985). Investigating the role of NFAT and calcium signaling in pTreg cells would provide further insights into the additional roles of NFAT in Treg phenotypical transitions, particularly its role in chromatin accessibility.

3) In Figure 3, NFAT binding to the inflammatory genes in iTreg cells was even stronger than in activated T conventional cells. This is possibly due to Tconv cells being stimulated only once while iTregs were restimulated. A fair comparison should be conducted with restimulated activated conventional T cells.

Figure 3 demonstrates the accessibility of inflammatory gene loci, rather than NFAT binding. Comparing restimulated Tconvs with restimulated iTreg cells is indeed a valuable suggestion, as their activation state and polarization in iTreg directions could lead to distinct chromatin accessibility. Although one is activated long term regularly and the other is activated long term under iTreg polarization, it is highly likely that the chromatin state of both activated Tconvs and iTreg cells is highly open, especially in terms of the accessibility of inflammatory genes. This may provide us with a new perspective to understand iTreg cells, but will unlikely affect our central conclusion.

4) In the in vivo experiment in Figure 6, a control condition without OVA immunization should be included as a baseline.

We have performed this experiment in the absence of OVA, as depicted in Author response image 6. In the absence of OVA immunization, both WT-ORAI and DN-ORAI iTreg exhibited substantial stability, although DN-ORAI demonstrated a slightly less stable trend. Upon activation with 40ug and 100ug of OVA, DN-ORAI iTreg demonstrated enhanced stability than WT-ORAI iTreg, maintaining a higher proportion of Foxp3 expression.

Author response image 6.

Stability of DN-ORAI iTreg in vivo with or without OVA immunization. WT-ORAI/DN-ORAI-GFP+-transfected CD45.2+ Foxp3-RFP+ OT-II iTregs were transferred i.v. into CD45.1 mice. Recipients were left or immunized with OVA323-339 in Alum adjuvant. On day 5, mLN were harvested and analyzed for Foxp3 expression by intracellular staining.

Reviewer #2 (Recommendations For The Authors):

Major

Some concerns about the data processing and statistic analysis, as mentioned in the public review. In the figure legend, what does it mean e.g. n=3, N=3? Technical triplicate experiments? Three mice? Independently-performed three experiments? The authors should define it at least in the "Statistical analysis" in the method section otherwise the readers cannot determine the reason why they mainly use SEM for the data description.

Moreover, in some cases, the number of experiments was not sure; e.g., Fig.1B, Fig. 5.

How did the authors analyze data including multiple comparisons? Student t-test alone is generally insufficient to compare multiple groups; e.g. figure 1.

We thank the reviewer for pointing out this omission. Now, in the figure legend, numbers of independently-performed experiment repeats are shown as N, biological replicates of each experiment as n. For Fig. 1B, N=2, and for Fig 5, we have acquired NFAT Cut&Tag data for 2 times, N=2. Student’s t test was used for comparing statistical significance between two groups. In this manuscript, all calculations of significant differences were based on comparisons between two groups. There were no multiple conditions compared simultaneously within a single group, and thus, no other calculation methods were involved apart from the Student's t-test.

In Figure 1A, the difference in suppressiveness seemed subtle. Data collection of multiple doses of Tconv:Treg ratio will enhance the reliability of such kind of analysis.

We have now attempted the suppression assay with varying Treg:Tconv ratios and observed that the suppressive effect of iTreg was more obvious than that of tTreg when co-cultured at a 1:1 ratio with Tconv cells. However, as the cell number of tTreg and iTreg decreased, the inhibitory effects converged.

Author response image 7.

Compare multiple dose of Tconv:Treg ratio in suppression function CFSE-labelled OT-II T cells were stimulated with OVA-pulsed DC, then different number of Foxp3-GFP+ iTregs and tTregs were added to the culture to suppress the OT-II proliferation. After 4 days, CFSE dilution were analyzed. Left, Representative histograms of CFSE in divided Tconvs. Right, graph for the percentage of divided Tconvs.

In Figure 3F, to which group did the shaded peaks belong? In this context, the authors should focus on "Activation Region" peaks (open chromatin signature in both TcAct & iTreg defined in Fig. 4D) but I did not find the peak in the focusing DNA regions in TcAct (e.g. the shaded regions in IL-4 loci). The clear attribution of the peaks to the heatmap will enhance the visibility and understanding of readers.

We have selected some typical peaks that belong to Fig 3D. These genes encompass some T-cell activation-associated transcription factors, such as Irf4, Atf3, as well as multiple members of the Tnf family including Lta, Tnfsf4, Tnfsf8, and Tnfsf14. Additionally, genes related to inflammation such as Il12rb2, Il9, and Gzmc are included. These genes show elevated accessibility upon T-cell activation, partially open in activated nTreg cells, referred to as the "Activation Region." They collectively exhibit high accessibility in iTreg cells, which may contribute to their instability.

Author response image 8.

Chromatin accessibility of some “Activation Region”. Genomic track showing chromatin accessibility of Irf4, Atf3, Lta, Tnfsf8, Tnfsf4, Tnsfsf14, Il12rb2, Il9, Gzmc in activated Tconv and iTreg.

In Figure 4A/S4A, the information on cell death will help the understanding of readers because the sustained SOCE is associated with cell survival as shown in Fig. S2. The authors can discuss the relationships between cell death and Foxp3 retention, which potentially leads to a further interesting question; e.g. the selective/resistance to activation-induced cell death as the identity of Treg cells.

As shown in Author response image 9, activated iTreg cells indeed exhibit a certain degree of cell death compared to resting iTreg cells. The inhibition of NFAT by CsA enhances the survival rate of iTreg cells, but the inhibition of ORAI by CM-4620 leads to more severe cell death. The cell death induced by CsA and CM-4620 is not consistent, indicating that there may not be a direct proportional relationship between cell death and the expression of Foxp3 and Treg identity.

Author response image 9.

Relationship of cell death and Foxp3 stability in restimulated iTregs. Sorted Foxp3-GFP+ iTregs were rested for 1d, then restimulated by anti-CD3 and CD28 in the presence of CsA or CM-4620. After 2d restimulation, live cell percentage were analyzed by staining of Live/Dead fixable Aqua, and percentages of Foxp3+ cells were analyzed by intracellular staining of Foxp3. Upper, live cell percentage of iTregs. Lower, percentages of Foxp3 in iTregs.

In Figure 5, the information for the data interpretation was insufficient.

We have provided detailed descriptions of the data analysis for Fig 5, including both the method section and the Figure legend, as presented below:

“The peaks annotations were performed with the “annotatePeak” function in the R package ChIPseeker (Yu et al, 2015). The plot of Cut&Tag signals over a set of genomic regions were calculated by using “computeMatrix” function in deepTools and plotted by using “plotHeatmap” and “plotProfile” functions in deepTools. The motif enrichment analysis was performed by using the "findMotifsGenome.pl" command in HOMER with default parameters. The motif occurrences in each peak were identified by using FIMO (MEME suite v5.0.4) with the following settings: a first-order Markov background model, a P value cutoff of 10-4, and PWMs from the mouse HOCOMOCO motif database (v11).”

Additionally, we have also supplemented the method section with further details on the analysis of RNA-seq and ATAC-seq data.

The correlation between the open chromatin status of the gene loci described in Fig.5E and the expression at mRNA level? e.g.; Do iTreg-Act cells produce a higher level of IL-21 than nTreg-act? The analysis in Fig.5F-G should be performed in parallel with nTreg cells to emphasize the distinct NFAT-chromatin regulation in iTreg cells.

We have now compared the secretion levels of IL-21 in tTreg and iTreg upon activation and treated with CsA by ELISA. As shown in Author response image 10, tTreg did not secrete IL-21 regardless of activation status (undetectable), while iTreg did not secrete IL-21 at resting state but exhibited IL-21 secretion after 48 h of activation. Moreover, the secretion of IL-21 was inhibited by CsA and CM-4620 treatment. This observation aligns with our earlier findings where we observed nuclear binding of NFAT to gene loci of these cytokines, enhancing their expression and pushing iTreg unstable under inflammatory conditions. These findings further underscore the likelihood that the inhibition of calcium and NFAT signaling might contribute to the stabilization of iTreg by suppressing the secretion of inflammatory cytokines.

Author response image 10.

IL-21 secretion in tTreg and iTreg upon activation. iTregs and tTregs were sorted and restimulated with anti-CD3 and anti-CD28 antibodies, in the presence of CsA and CM-4620. Cell culture supernatant were harvested after 2 d restimulation and IL-21 secretion was analyzed by ELISA.

Performing a parallel comparison of NFAT activity between tTreg and iTreg cells was initially part of our experimental plan. However, it proved challenging in practice, as we encountered difficulties in efficiently infecting tTreg cells with NFAT-flag. Consequently, we could not obtain a sufficient number of tTreg cells for conducting Cut&Tag experiments.

Based on our observations, we speculate that there might be substantial differences in the accessibility of genes in tTreg cells, leading to considerable variations in the repertoire of genes available for NFAT to regulate. As a result, we expect significant differences in the nuclear localization and activity of NFAT between iTreg and tTreg cells.

In Figure 6C, what does the FCM plot between Foxp3-CFSE look like?

The authors can discuss the mechanism of ORAI-DN-mediated through such analysis; e.g. the possibility that selective proliferation defect by ORAI-DN in Foxp3- cells led to an increased percentage of Foxp3, not only just unstable transcription of Foxp3.

This is an in vitro experiment to assess the suppressive effect of iTreg on Tconv proliferation. Therefore, CFSE is used to stain Tconv cells, but not iTreg cells, so we did not detect proliferation feature of iTreg.

Minor

Confusing terminology of "tTreg" at line 47, etc. "natural Treg" contains both thymic-derived Treg and periphery-derived Treg cells. (A Abbas et al. Nat Immunol. 2013)

We have now changed the designation to tTreg at line 47. tTreg refers to thymus-derived regulatory T cells, while nTreg includes both tTreg and pTreg. However, it is important to note that the Treg cells used in our study were isolated from the spleen of 2-4-month-old Foxp3-GFP or Foxp3-RFP mice. The CD4+ T cells were first enriched using the CD4 Isolation kit, and the FACSAriaII was utilized to collect CD4+ Foxp3-GFP/RFP+ Treg cells. Subsequently, Helios and Nrp-1 staining revealed that the majority of these cells were nTreg, with only approximately 6% being pTreg. Overall, we consider the cells we used as tTreg.

In all FCM analyses, the authors should clarify how to detect Foxp3 expression; Foxp3-GFP/Foxp3-RFP/Intracellular staining like Figure S5A (but not specified in the other FCM plots)

All Foxp3 expressions in the article were assessed using intracellular staining, as described in the methods section, and we have added specific descriptions to each figure legend. The reason for employing intracellular staining is that we used Foxp3-IRES-GFP mice, where GFP and Foxp3 are not fused into a single protein, existing as separate proteins after expression. Therefore, during induction, the appearance of GFP protein might potentially represent the presence of Foxp3. However, in cases of Foxp3 instability, the degradation of GFP protein may not be entirely synchronized with that of Foxp3 protein, making GFP an unreliable indicator of Foxp3 expression levels. As a result, for the purification of pure iTreg cells, we used Foxp3-GFP/RFP fluorescence, while for observing instability, we employed intranuclear staining of Foxp3.

In Figure 6B, the captions were lacking in the two graphs on the right side

The two restimulation conditions, 0.125+0.25 and 0.25+0.5, have been added into Fig 6B right side.

In Figure S2, the annotation of the x-y axis was missing.

Added.

Lack of reference at line 292.

Reference 42-46 were added.

In the method section, the authors should note the further product information of antibodies and reagents to enhance reproducibility and transparency. Making a list that clarifies the suppliers, Ab clone, product IDs, etc. is encouraged. The authors did not specify the supplier of recombinant proteins and which type of TGF-beta (TGF-beta 1, 2, or 3?).

A detailed description of the mice, antibodies, Peptide recombinant protein, commercial kit, and software has been provided and incorporated into the methods section.

In the method section, the authors should clarify which Foxp3-reporter strain. There are many strains of Foxp3-reporter mice in the world. In line 373, is the "FoxP3-IRES-GFP transgenic mice" true? Knock-in strain or BAC-transgene?

This mouse is a gift from Hai Qi Lab in Tsinghua University. They acquired this mouse strain from Jackson Laboratory, and the strain name is B6.Cg-Foxp3tm2Tch/J, Strain #:006772. An IRES-EGFP-SV40 poly A sequence was inserted immediately downstream of the endogenous Foxp3 translational stop codon, but upstream of the endogenous polyA signal, generating a bicistronic locus encoding both Foxp3 and EGFP.

The age of mice used in the experiments should be specified, and confusing words such as "young" should not be used in any method descriptions; e.g. line 405.

The detailed mouse age has been added in the methods section. “To prepare Tconv, tTreg and iTreg for experiments, spleen was isolated from 2-4-month-old Foxp3-GFP mice for Tconv and tTreg sorting, and 6-week-old mice for iTreg induction.”

The method of how the original ATAC-seq/Cut & Tag data were generated was not described in the method section.

Added in method section.

The reference section was incomplete, and the style was not unified. e.g.; ref 7, 24, 25, 26 ... I gave up checking all.

The style of ref 7, 22, 24, 26, 28, 31, 33, 35 were modified.

Changes in manuscript:

Author Name: “Huiyun Lv” to “Huiyun Lyu”.

Fig 1A was updated according to Reviwer 2’s suggestion.

Fig S3E and associated description was added according to Reviwer 2’s suggestion.

Fig S4C and associated description was added according to Reviwer 1’s suggestion.

Fig 5H and associated description was added according to Reviwer 2’s suggestion.

Fig 6D were updated according to Reviwer 1’s suggestion.

Fig 2D was corrected, the labels for gapdh and actin in the iTreg panel were inadvertently switched. The mistake has been rectified, and the original gel image will be provided.

Fig 2A and Fig 4A was updated.

The style of Fig 6B and Fig S2A was modified.

Method:

Mice: FoxP3-IRES-GFP with more description.

Flow Cytometry sorting and FACS: the detailed mouse age has been added. RNA-seq analysis, ATAC-sequencing, ATAC-seq analysis, Cut&Tag assay, Cut&Tag data analysis: more description was added.

Statistical analysis: “Numbers of independently-performed experiment repeats are shown as N, biological replicates of each experiment as n.” were added.

Reference: Ref 42-46 and 49-52 were added. The style of ref 7, 22, 24, 26, 28, 31, 33, 35 were corrected.

A detailed description of the mice, antibodies, Peptide recombinant protein, commercial kit, and software has been provided.

Author Response

Reviewer #1 (Public Review):

[...] Genes expressed in the same direction in lowland individuals facing hypoxia (the plastic state) as what is found in the colonised state are defined as adaptative, while genes with the opposite expression pattern were labelled as maladaptive, using the assumption that the colonised state must represent the result of natural selection. Furthermore, genes could be classified as representing reversion plasticity when the expression pattern differed between the plasticity and colonised states and as reinforcement when they were in the same direction (for example more expressed in the plastic state and the colonised state than in the ancestral state). They found that more genes had a plastic expression pattern that was labelled as maladaptive than adaptive. Therefore, some of the genes have an expression pattern in accordance with what would be predicted based on the plasticity-first hypothesis, while others do not.

Thank you for a precise summary of our work. We appreciate the very encouraging comments recognizing the value of our work. We have addressed concerns from the reviewer in greater detail below.

Q1. As pointed out by the authors themselves, the fact that temperature was not included as a variable, which would make the experimental design much more complex, misses the opportunity to more accurately reflect the environmental conditions that the colonizer individuals face at high altitude. Also pointed out by the authors, the acclimation experiment in hypoxia lasted 4 weeks. It is possible that longer term effects would be identifiable in gene expression in the lowland individuals facing hypoxia on a longer time scale. Furthermore, a sample size of 3 or 4 individuals per group depending on the tissue for wild individuals may miss some of the natural variation present in these populations. Stating that they have a n=7 for the plastic stage and n= 14 for the ancestral and colonized stages refers to the total number of tissue samples and not the number of individuals, according to supplementary table 1.

We shared the same concerns as the reviewer. This is partly because it is quite challenging to bring wild birds into captivity to conduct the hypoxia acclimation experiments. We had to work hard to perform acclimation experiments by taking lowland sparrows in a hypoxic condition for a month. We indeed have recognized the similar set of limitations as the review pointed out and have discussed the limitations in the study, i.e., considering hypoxic condition alone, short time acclimation period, etc. Regarding sample sizes, we have collected cardiac muscle from nine individuals (three individuals for each stage) and flight muscle from 12 individuals (four individuals for each stage). We have clarified this in Supplementary Table 1.

Q2. Finally, I could not find a statement indicating that the lowland individuals placed in hypoxia (plastic stage) were from the same population as the lowland individuals for which transcriptomic data was already available, used as the "ancestral state" group (which themselves seem to come from 3 populations Qinghuangdao, Beijing, and Tianjin, according to supplementary table 2) nor if they were sampled in the same time of year (pre reproduction, during breeding, after, or if they were juveniles, proportion of males or females, etc). These two aspects could affect both gene expression (through neutral or adaptive genetic variation among lowland populations that can affect gene expression, or environmental effects other than hypoxia that differ in these populations' environments or because of their sexes or age). This could potentially also affect the FST analysis done by the authors, which they use to claim that strong selective pressure acted on the expression level of some of the genes in the colonised group.

The reviewer asked how individual tree sparrows used in the transcriptomic analyses were collected. The individuals used for the hypoxia acclimation experiment and represented the ancestral lowland population were collected from the same locality (Beijing) and at the same season (i.e., pre-breeding) of the year. They are all adults and weight approximately 18g. We have clarified this in the Supplementary Table S1 and Methods. We did not distinguish males from females (both sexes look similar) under the assumption that both sexes respond similarly to hypoxia acclimation in their cardiac and flight muscle gene expression.

The Supplementary Table 2 lists the individuals that were used for sequence analyses. These individuals were only used for sequence comparisons but not for the transcriptomic analyses. The population genetic structure analyzed in a previously published study showed that there is no clear genetic divergence within the lowland population (i.e., individuals collected from Beijing, Tianjing and Qinhuangdao) or the highland population (i.e., Gangcha and Qinghai Lake). In addition, there was no clear genetic divergence between the highland and lowland populations (Qu et al. 2020).

Author response image 1.

Population genetic structure of the Eurasian Tree Sparrow (Passer montanus). The genetic structure generated using FRAPPE. The colors in each column represent the contribution from each subcluster (Qu et al. 2020). Yellow, highland population; blue, lowland population.

Q4. Impact of the work There has been work showing that populations adapted to high altitude environments show changes in their hypoxia response that differs from the short-term acclimation response of lowland population of the same species. For example, in humans, see Erzurum et al. 2007 and Peng et al. 2017, where they show that the hypoxia response cascade, which starts with the gene HIF (Hypoxia-Inducible Factor) and includes the EPO gene, which codes for erythropoietin, which in turns activates the production of red blood cell, is LESS activated in high altitude individuals compared to the activation level in lowland individuals (which gives it its name). The present work adds to this body of knowledge showing that the short-term response to hypoxia and the long term one can affect different pathways and that acclimation/plasticity does not always predict what physiological traits will evolve in populations that colonize these environments over many generations and additional selection pressure (UV exposure, temperature, nutrient availability). Altogether, this work provides new information on the evolution of reaction norms of genes associated with the physiological response to one of the main environmental variables that affects almost all animals, oxygen availability. It also provides an interesting model system to study this type of question further in a natural population of homeotherms.

Erzurum, S. C., S. Ghosh, A. J. Janocha, W. Xu, S. Bauer, N. S. Bryan, J. Tejero et al. "Higher blood flow and circulating NO products offset high-altitude hypoxia among Tibetans." Proceedings of the National Academy of Sciences 104, no. 45 (2007): 17593-17598. Peng, Y., C. Cui, Y. He, Ouzhuluobu, H. Zhang, D. Yang, Q. Zhang, Bianbazhuoma, L. Yang, Y. He, et al. 2017. Down-regulation of EPAS1 transcription and genetic adaptation of Tibetans to high-altitude hypoxia. Molecular biology and evolution 34:818-830.

Thank you for highlighting the potential novelty of our work in light of the big field. We found it very interesting to discuss our results (from a bird species) together with similar findings from humans. In the revised version of manuscript, we have discussed short-term acclimation response and long-term adaptive evolution to a high-elevation environment, as well as how our work provides understanding of the relative roles of short-term plasticity and long-term adaptation. We appreciate the two important work pointed out by the reviewer and we have also cited them in the revised version of manuscript.

Reviewer #2 (Public Review):

This is a well-written paper using gene expression in tree sparrow as model traits to distinguish between genetic effects that either reinforce or reverse initial plastic response to environmental changes. Tree sparrow tissues (cardiac and flight muscle) collected in lowland populations subject to hypoxia treatment were profiled for gene expression and compared with previously collected data in 1) highland birds; 2) lowland birds under normal condition to test for differences in directions of changes between initial plastic response and subsequent colonized response. The question is an important and interesting one but I have several major concerns on experimental design and interpretations.

Thank you for a precise summary of our work and constructive comments to improve this study. We have addressed your concerns in greater detail below.

Q1. The datasets consist of two sources of data. The hypoxia treated birds collected from the current study and highland and lowland birds in their respective native environment from a previous study. This creates a complete confounding between the hypoxia treatment and experimental batches that it is impossible to draw any conclusions. The sample size is relatively small. Basically correlation among tens of thousands of genes was computed based on merely 12 or 9 samples.

We appreciate the critical comments from the reviewer. The reviewer raised the concerns about the batch effect from birds collected from the previous study and this study. There is an important detail we didn’t describe in the previous version. All tissues from hypoxia acclimated birds and highland and lowland birds have been collected at the same time (i.e., Qu et al. 2020). RNA library construction and sequencing of these samples were also conducted at the same time, although only the transcriptomic data of lowland and highland tree sparrows were included in Qu et al. (2020). The data from acclimated birds have not been published before.

In the revised version of manuscript, we also compared log-transformed transcript per million (TPM) across all genes and determined the most conserved genes (i.e., coefficient of variance ≤  0.3 and average TPM ≥ 1 for each sample) for the flight and cardiac muscles, respectively (Hao et al. 2023). We compared the median expression levels of these conserved genes and found no difference among the lowland, hypoxia-exposed lowland, and highland tree sparrows (Wilcoxon signed-rank test, P<0.05). As these results suggested little batch effect on the transcriptomic data, we used TPM values to calculate gene expression level and intensity. This methodological detail has been further clarified in the Methods and we also provided a new supplementary Figure (Figure S5) to show the comparative results.

Author response image 2.

The median expression levels of the conserved genes (i.e., coefficient of variance ≤ 0.3 and average TPM ≥ 1 for each sample) did not differ among the lowland, hypoxia-exposed lowland, and highland tree sparrows (Wilcoxon signed-rank test, P<0.05).

The reviewer also raised the issue of sample size. We certainly would have liked to have more individuals in the study, but this was not possible due to the logistical problem of keeping wild bird in a common garden experiment for a long time. We have acknowledged this in the manuscript. In order to mitigate this we have tested the hypothesis of plasticity following by genetic change using two different tissues (cardiac and flight muscles) and two different datasets (co-expressed gene-set and muscle-associated gene-set). As all these analyses show similar results, they indicate that the main conclusion drawn from this study is robust.

Q2. Genes are classified into two classes (reversion and reinforcement) based on arbitrarily chosen thresholds. More "reversion" genes are found and this was taken as evidence reversal is more prominent. However, a trivial explanation is that genes must be expressed within a certain range and those plastic changes simply have more space to reverse direction rather than having any biological reason to do so.

Thank you for the critical comments. There are two questions raised we should like to address them separately. The first concern centered on the issue of arbitrarily chosen thresholds. In our manuscript, we used a range of thresholds, i.e., 50%, 100%, 150% and 200% of change in the gene expression levels of the ancestral lowland tree sparrow to detect genes with reinforcement and reversion plasticity. By this design we wanted to explore the magnitudes of gene expression plasticity (i.e., Ho & Zhang 2018), and whether strength of selection (i.e., genetic variation) changes with the magnitude of gene expression plasticity (i.e., Campbell-Staton et al. 2021).

As the reviewer pointed out, we have now realized that this threshold selection is arbitrarily. We have thus implemented two other categorization schemes to test the robustness of the observation of unequal proportions of genes with reinforcement and reversion plasticity. Specifically, we used a parametric bootstrap procedure as described in Ho & Zhang (2019), which aimed to identify genes resulting from genuine differences rather than random sampling errors. Bootstrap results suggested that genes exhibiting reversing plasticity significantly outnumber those exhibiting reinforcing plasticity, suggesting that our inference of an excess of genes with reversion plasticity is robust to random sampling errors. We have added these analyses to the revised version of manuscript, and provided results in the Figure 2d and Figure 3d.

Author response image 3.

Figure 2a (left) and Figure 2b (right). Frequencies of genes with reinforcement and reversion plasticity (>50%) and their subsets that acquire strong support in the parametric bootstrap analyses (≥ 950/1000).

In addition, we adapted a bin scheme (i.e., 20%, 40% and 60% bin settings along the spectrum of the reinforcement/reversion plasticity). These analyses based on different categorization schemes revealed similar results, and suggested that our inference of an excess of genes with reversion plasticity is robust. We have provided these results in the Supplementary Figure S2 and S4.

Author response image 4.

(A) and Figure S4 (B). Frequencies of genes with reinforcement and reversion plasticity in the flight and cardiac muscle. (A) For genes identified by WGCNA, all comparisons show that there are more genes showing reversion plasticity than those showing reinforcement plasticity for both the flight and cardiac msucles. (B) For genes that associated with muscle phentoypes, all comparisons show that there are more genes showing reversion plasticity than those showing reinforcement plasticity for the flight muscle, while more than 50% of comparisons support an excess of genes with reversion plasticity for the cardiac muscle. Two-tailed binomial test, NS, non-significant; , P < 0.05; , P < 0.01; **, P < 0.001.

The second issue that the reviewer raised is that the plastic changes simply have more space to reverse direction rather than having any biological reason to do so. While a causal reason why there are more genes with expression levels being reversed than those with expression levels being reinforced at the late stages is still contentious, increasingly many studies show that genes expression plasticity at the early stage may be functionally maladapted to novel environment that the species have recently colonized (i.e., lizard, Campbell-Staton et al. 2021; Escherichia coli, yeast, guppies, chickens and babblers, Ho and Zhang 2018; Ho et al. 2020; Kuo et al. 2023). Our comparisons based on the two genesets that are associated with muscle phenotypes corroborated with these previous studies and showed that initial gene expression plasticity may be nonadaptive to the novel environments (i.e., Ghalambor et al. 2015; Ho & Zhang 2018; Ho et al. 2020; Kuo et al. 2023; Campbell-Staton et al. 2021).

Q3. The correlation between plastic change and evolved divergence is an artifact due to the definitions of adaptive versus maladaptive changes. For example, the definition of adaptive changes requires that plastic change and evolved divergence are in the same direction (Figure 3a), so the positive correlation was a result of this selection (Figure 3d).

The reviewer raised an issue that the correlation between plastic change and evolved divergence is an artifact because of the definition of adaptive versus maladaptive changes, for example, Figure 3d. We agree with the reviewer that the correlation analysis is circular because the definition of adaptive and maladaptive plasticity depends on the direction of plastic change matched or opposed that of the colonized tree sparrows. We have thus removed previous Figure 3d-e and related texts from the revised version of manuscript. Meanwhile, we have changed Figure 3a to further clarify the schematic framework.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

In this study, Kim et al. investigated the mechanism by which uremic toxin indoxyl sulfate (IS) induces trained immunity, resulting in augmented pro-inflammatory cytokine production such as TNF and IL6. The authors claim that IS treatment induced epigenetic and metabolic reprogramming, and the aryl hydrocarbon receptor (AhR)-mediated arachidonic acid pathway is required for establishing trained immunity in human monocytes. They also demonstrated that uremic sera from end-stage renal disease (ESRD) patients can generate trained immunity in healthy control-derived monocytes.

These are interesting results that introduce the important new concept of trained immunity and its importance in showing endogenous inflammatory stimuli-induced innate immune memory. Additional evidence proposing that IS plays a critical role in the initiation of inflammatory immune responses in patients with CKD is also interesting and a potential advance of the field. This study is in large part well done, but some components of the study are still incomplete and additional efforts are required to nail down the main conclusions.

Thank you very much for your positive feedback.

Specific comments:

(1) Of greatest concern, there are concerns about the rigor of these experiments, whether the interpretation and conclusions are fully supported by the data. (1) Although many experiments have been sporadically conducted in many fields such as epigenetic, metabolic regulation, and AhR signaling, the causal relationship between each mechanism is not clear. (2) Throughout the manuscript, no distinction was made between the group treated with IS for 6 days and the group treated with the second LPS (addressed below). (3) Besides experiments using non-specific inhibitors, genetic experiments including siRNA or KO mice should be examined to strengthen and justify central suggestions.

We are grateful for the invaluable constructive feedback provided. 

(1) In response to the reviewer's feedback, we conducted additional experiments employing appropriate inhibitors to investigate the causal relationship among the AhR pathway, epigenetic modifications, and metabolic rewiring in IS-induced trained immunity. Notably, metabolic rewiring, particularly the upregulation of aerobic glycolysis via the mTORC1 signaling pathway, stands as a pivotal mechanism underlying the induction of trained immunity through the modulation of epigenetic modifications (Riksen NP et al. Figure 1). Initially, we assessed the enrichment of H3K4me3 at 6-day on promoters of TNFA and IL6 loci after treatment of zileuton, an inhibitor of ALOX5, and 2-DG, a glycolysis inhibitor. Additionally, we evaluated the alteration in the activity of S6K, a downstream molecule of mTORC1, following zileuton treatment. Our findings indicate that AhR-dependent arachidonic acid (AA) signaling induces epigenetic modifications, albeit without inducing metabolic rewiring, in IS-induced trained immunity (Author response image 1). However, IS stimulation promotes mTORC1-mediated glycolysis in an AhR-independent manner. Notably, inhibition of glycolysis with 2-DG impacts epigenetic modifications. We have updated Figure 7 of the revised manuscript to incorporate these additional experimental findings, elucidating the correlation between the diverse mechanisms implicated in IS-induced innate immune memory (Fig. 7 in the revised manuscript). These data have been integrated into the revised manuscript as Figure 3D and 5I, and supplementary Figure 5I.

(2) We apologize for any confusion arising from the unclear description regarding the distinction between the group treated with IS for 6 days and the group subjected to secondary lipopolysaccharide (LPS) stimulation. It is imperative to clarify that induction of trained immunity necessitates 1 day of IS stimulation followed by 5 days of rest, rendering the 6th day sample representative of a trained state. Subsequent to this, a 24-hour LPS stimulation is applied, designating the 7th day sample as a secondary LPS-stimulated cell. This clarification is now explicitly indicated throughout the entirety of Figure 1A and Figure 3A in the revised manuscript.

(3) In accordance with your feedback, we performed siRNA knockdown of AhR and ALOX5 in primary human monocytes. AhR knockdown markedly attenuated the mRNA expression of TNF-α and IL-6, which are augmented in IS-trained macrophages. Similarly, knockdown of ALOX5 using ALOX5 siRNA abrogated the increase in TNF-α and IL-6 levels upon LPS stimulation in IS-trained macrophages (Author response image 2). Our experiments utilizing AhR siRNA corroborate the involvement of AhR in the expression of AA pathway-related molecules, such as ALOX5, ALOX5AP, and LTB4R1, in IS-induced trained immunity. These data have been incorporated into the revised manuscript as Figure 4E and 5G, and supplementary Figure 5H.  

Author response image 1.

Epigenetic modification is regulated by arachidonic acid (AA) pathway and metabolic rewiring, but metabolic rewiring is not affected by the AA pathway. A-B. Monocytes were pre-treated with zileuton (ZLT), an inhibitor of ALOX5, or 2DG, a glycolysis inhibitor, followed by stimulation with IS for 24 hours. After a resting period of 5 days, the enrichment of H3K4me3 on the promoters of TNFA and IL6 loci was assessed. Normalization was performed using 2% input. C. Monocytes were pre-treated with zileuton (ZLT) and stimulated with IS for 24 hr. Cell lysates were immunoblotted for phosphorylated S6 Kinase, with β-actin serving as a normalization control. Band intensities in the immunoblots were quantified using densitometry. D, A schematic representation of the mechanistic framework underlying IS-trained immunity. Bar graphs show the mean ± SEM. * = p < 0.05, **= p < 0.01, and *** = p < 0.001 by two-tailed paired t-test.

Author response image 2.

Inhibition of IS-trained immunity by knockdown of AhR or ALOX5 in human monocytes. A-C. Human monocytes were transfected with siRNA targeting AhR (siAhR), ALOX5 (siALOX5), or negative control (siNC) for 1 day, followed by stimulation with IS for 24 hours. After a resting period of 5 days, cells were re-stimulated with LPS for 24 hours. mRNA expression levels of AhR and ALOX5 at 1 day after transfection, and TNF-α and IL-6 at 1 day after LPS treatment, were assessed using RT-qPCR. D. Human monocytes were transfected with AhR siRNA or negative control (NC) siRNA for 1 day, followed by stimulation with IS for 24 hours. After resting for 5 days, mRNA expression levels of ALOX5, ALOX5AP, and LTB4R1 were analyzed using RT-qPCR. Bar graphs show the mean ± SEM. * = p < 0.05, ** = p < 0.01, and *** = p < 0.001 by two-tailed paired t-test.  

(2) The authors showed that IS-trained monocytes showed no change in TNF or IL-6, but increased the expression levels of TNF and IL-6 in response to the second LPS (Fig. 1B). This suggests that the different LPS responsiveness in IS-trained monocytes caused altered gene expression of TNF and IL6. However, the authors also showed that IS-trained monocytes without LPS stimulation showed increased levels of H3K4me3 at the TNF and IL-6 loci, as well as highly elevated ECAR and OCR, leading to no changes in TNF and IL-6. Therefore, it is unclear why or how the epigenetic and metabolic states of IS-trained monocytes induce different LPS responses. For example, increased H3K4me3 in HK2 and PFKP is important for metabolic rewiring, but why increased H3K4me3 in TNF and IL6 does not affect gene expression needs to be explained.

We acknowledge the constructive critiques provided by the reviewer. While epigenetic modifications in the promoters of TNF-α, IL-6, HK2, and PFKP (Figure 3B and Supplementary Figure 3C in the revised manuscript), and metabolic rewiring (Figure 2A-D in the revised manuscript) were observed in IS-trained macrophages at 6 days prior to LPS stimulation, these macrophages do not exhibit an increase in TNF-α and IL-6 mRNA and protein levels before LPS stimulation. This lack of response is attributed to a 5-day resting period, allowing the macrophages to revert to a non-activated state, as depicted in Author response image 3 and 4. This phenomenon aligns with the concept of typical trained immunity.

Trained immunity is characterized by the long-term functional reprogramming of innate immune cells, which is evoked by various primary insults and which leads to an altered response towards a second challenge after the return to a non-activated state. Metabolic and epigenetic reprogramming events during the primary immune response persist partially even after the initial stimulus is removed. Upon a secondary challenge, trained innate immune cells exhibit a more robust and more prompt response than the initial response (Netea MG et al. Defining trained immunity and its role in health and disease. Nat Rev Immunol. 2020 Jun;20(6):375-388).

Numerous studies have demonstrated the observation of epigenetic modifications in the promoters of TNF-α and IL-6, and metabolic rewiring prior to LPS stimulation as a secondary challenge. However, cytokine production is contingent on LPS stimulation (Arts RJ et al. Glutaminolysis and Fumarate Accumulation Integrate Immunometabolic and Epigenetic Programs in Trained Immunity. Cell Metab. 2016 Dec 13;24(6):807-819; Arts RJW et al. Immunometabolic Pathways in BCG-Induced Trained Immunity. Cell Rep. 2016 Dec 6;17(10):2562-2571; Ochando J et al. Trained immunity - basic concepts and contributions to immunopathology. Nat Rev Nephrol. 2023 Jan;19(1):23-37). The prolonged presence of higher levels of H3K4me3 on immune gene promoters, even after returning to baseline, is associated with open chromatin and results in a more rapid and stronger response, such as cytokine production, upon a secondary insult (Netea MG et al. Defining trained immunity and its role in health and disease. Nat Rev Immunol. 2020 Jun;20(6):375-388).

The results in Figure 1B may be interpreted as indicating different LPS responsiveness in IStrained monocytes caused altered gene expression of TNF and IL-6. However, it is plausible that trained immune cells respond more robustly even to low concentrations of LPS. In fact, the aim of this experiment was to determine the appropriate LPS concentration.

Author response image 3.

The changes in mRNA and protein level of TNF-α and IL-6 during induction of IS-trained immunity. Human monocytes were treated with or without IS (1 mM) for 24 hrs, succeeded by 5-day resting period to induce trained immunity. Cells were stimulated with LPS for 24 hrs. Protein and mRNA levels were assessed by ELISA and RT-qPCR, respectively. Bar graphs show the mean ± SEM. * = p < 0.05 and **= p < 0.01, by two-tailed paired t-test.

Author response image 4.

The changes in mRNA of HK2 and PFKP induced by IS during induction of IS-trained immunity. Human monocytes were treated with or without IS (1 mM) for 24 hrs, succeeded by 5-day resting period to induce trained immunity. mRNA levels were assessed by RT-qPCR. Bar graphs show the mean ± SEM. * = p < 0.05 by two-tailed paired ttest.

(3) The authors used human monocytes cultured in human serum without growth factors such as MCSF for 5-6 days. When we consider the short lifespan of monocytes (1-3 days), the authors need to explain the validity of the experimental model.

We appreciate the reviewer’s constructive critiques. As pointed out by the reviewer, human circulating CD14+ monocytes exhibit a relatively short lifespan (1-3 days) when cultured in the absence of growth factors (Patel AA et al. The fate and lifespan of human monocyte subsets in steady state and systemic inflammation. J Exp Med. 2017 Jul 3;214(7):1913-1923). In this study, purified CD14+ monocytes were subjected to adherent culture for a duration of 7 days in RPMI1640 media supplemented with 10% human AB serum, a standard in vitro culture protocol widely employed in studies focusing on trained immunity (Domínguez-Andrés J et al. In vitro induction of trained immunity in adherent human monocytes. STAR Protoc. 2021 Feb 24;2(1):100365). In response to the reviewer's suggestions, we assessed cell viability on days 0, 1, 4, and 6, utilizing the WST assay. Despite a marginal reduction in cell viability observed at day 1, attributed to detachment from the culture plate, the cultured monocytes exhibited a notable enhancement in cell viability on days 4 and 6 when compared to days 0 or 1 (Author response image 5).

It has been demonstrated that the adhesion of human monocytes to a cell culture dish leads to their activation and induces the synthesis of substantial amounts of IL-1β mRNA as observed in monocytes adherent to extracellular matrix components such as fibronectin and collagen.

Morphologically, human adherent monocytes cultured with 10% human serum appear to undergo partial differentiation into macrophages by day 6, potentially explaining the observed lack of decrease in monocyte viability. Notably, Safi et al. have reported that adherent monocytes cultured with 10% human serum exhibit no significant difference in cell viability over a 7-day period when compared to cultures supplemented with growth factors such as M-CSF and IL-3 (Safi W et al. Differentiation of human CD14+ monocytes: an experimental investigation of the optimal culture medium and evidence of a lack of differentiation along the endothelial line. Exp Mol Med. 2016 Apr 15;48(4):e227).

Author response image 5.

Viability of human monocytes during the induction of trained immunity. Purified human monocytes were seeded on plates with RPIM1640 media supplemented with 10% human AB serum. Cell viability was assessed on days 0, 1, 4, and 6 utilizing the WST assay (Left panel). Cell morphology was examined under a light-inverted microscope at the indicated times (Right panel).

(4) The authors' ELISA results clearly showed increased levels of TNF and IL-6 proteins, but it is well established that LPS-induced gene expression of TNF and IL-6 in monocytes peaked within 1-4 hours and returned to baseline by 24 hours. Therefore, authors need to investigate gene expression at appropriate time points.

We appreciate the valuable constructive feedback provided by the reviewer. As indicated by the reviewer, the LPS-induced gene expression of TNF-α and IL-6 in IS-trained monocytes exhibited a peak within the initial 1 to 4 hours, followed by a decrease by the 24-hour time point, as illustrated in Author response image 6. Nevertheless, the mRNA expression levels of TNFα and IL-6 were still elevated at the 24-hour mark. Furthermore, the protein levels of both TNFα and IL-6 apparently increased 24 hours after LPS stimulation. Due to technical constraints, sample collection had to be conducted at a single time point, and the 24-hour post-stimulation interval was deemed optimal for this purpose.

Author response image 6.

Kinetics of protein and mRNA expression of TNF-α and IL-6 after treatment of LPS as secondary insult in IS-trained monocytes. IS-trained cells were re-stimulated by LPS (10 ng/ml) for the indicated time. The supernatant and lysates were collected for ELISA assay and RT-qPCR analysis, respectively. Bar graphs show the mean ± SEM. * = p <0.05 and **= p < 0.01, by two-tailed paired t-test.

(5) It is a highly interesting finding that IS induces trained immunity via the AhR pathway. The authors also showed that the pretreatment of FICZ, an AhR agonist, was good enough to induce trained immunity in terms of the expression of TNF and IL-6. However, from this point of view, the authors need to discuss why trained immunity was not affected by kynurenic acid (KA), which is a well-known AhR ligand accumulated in CKD and has been reported to be involved in innate immune memory mechanisms (Fig. S1A).

We appreciate the constructive criticism provided by the reviewer, and we comprehend the raised points. In our initial experiments, we hypothesized that kynurenic acid (KA), an aryl hydrocarbon receptor (AhR) ligand, might instigate trained immunity in monocytes, despite KA not being our primary target uremic toxin. However, our findings, as depicted in Fig. S1A, demonstrated that KA did not induce trained immunity. Notably, KA-treated monocytes exhibited induction of CYP1B1, an AhR-responsive gene, and elevated levels of TNF-α and IL-6 mRNA at 24 hours post-treatment, comparable to FICZ-treated monocytes. This observation underscores KA's role as an AhR ligand in human monocytes, as emphasized by the reviewer. 

Of particular interest, proteins associated with the arachidonic acid pathway, such as ALOX5 and ALOX5AP - integral to the mechanisms underlying IS-induced trained immunity - did not exhibit an increase at day 6 following KA treatment, in contrast to the significant elevation observed with IS and FICZ treatments (Author response image 7). The rationale behind this disparity remains unknown, necessitating further investigation to elucidate the underlying factors. These data have been incorporated into the revised manuscript as Supplementary Figure 5C.

Author response image 7.

Divergent impact of AhR agonists, especially IS, FICZ, and KA on the AhR-ALOX5 pathway. Purified ytes underwent treatment with IS (1 mM), FICZ (100 nM), or KA (0.5 mM) for 1 day, followed by 5-day resting period to trained immunity. Activation of AhR through ligand binding was assessed by examining the induction of CYP1B1, an AhR ene, and cytokines one day post-treatment. The expression of genes related to the arachidonic acid pathway, such as ALOX5, 5AP, and LTB4R1, was analyzed via RT-qPCR six days after inducing trained immunity. Bar graphs show the mean ± SEM. * .05, **= p < 0.01, and ***= p < 0.001 by two-tailed paired t-test.

Indeed, it has been demonstrated that FICZ and TCDD, two high-affinity AhR ligands, exert opposite effects on T-cell differentiation, with TCDD inducing regulatory T cells and FICZ inducing Th17 cells. This dichotomy has been attributed to ligand-intrinsic differences in AhR activation (Ho PP et al. The aryl hydrocarbon receptor: a regulator of Th17 and Treg cell development in disease. Cell Res. 2008 Jun;18(6):605-8; Ehrlich AK et al. TCDD, FICZ, and Other High Affinity AhR Ligands Dose-Dependently Determine the Fate of CD4+ T Cell Differentiation. Toxicol Sci. 2018 Feb 1;161(2):310-320). These outcomes imply the involvement of an intricate interplay involving metabolic rewiring, epigenetic reprogramming, and the AhR-ALOX5 pathway in IS-induced trained immunity within monocytes.

(6) The authors need to clarify the role of IL-10 in IS-trained monocytes. IL-10, an anti-inflammatory cytokine that can be modulated by AhR, whose expression (Fig. 1E, Fig. 4D) may explain the inflammatory cytokine expression of IS-trained monocytes.

We appreciate the reviewer’s valuable comment, recognizing its significant importance. IL-10, characterized by potent anti-inflammatory attributes, assumes a pivotal role in constraining the host immune response against pathogens. This function serves to mitigate potential harm to the host and uphold normal tissue homeostasis. In the context of atherosclerosis (Mallat Z et al. Protective role of interleukin-10 in atherosclerosis. Circ Res. 1999 Oct 15;85(8):e17-24.) and kidney disease (Wei W et al. The role of IL-10 in kidney disease. Int Immunopharmacol. 2022 Jul;108:108917), IL-10 exerts potent deactivating effects on macrophages and T cells, influencing various cellular processes that could impact the development and stability of atherosclerotic plaques. Additionally, it is noteworthy that IL-10-deficient macrophages exhibit an augmentation in the proinflammatory cytokine TNF-α (Smallie T et al. IL-10 inhibits transcription elongation of the human TNF gene in primary macrophages. J Exp Med. 2010 Sep 27;207(10):2081-8; Couper KN et al. IL-10: the master regulator of immunity to infection. J Immunol. 2008 May 1;180(9):5771-7). As emphasized by the reviewer, the reduced gene expression of IL-10 by IS-trained monocytes may contribute to the heightened expression of proinflammatory cytokines. We have thoroughly addressed and discussed this specific point in response to the reviewer's comment (Line 394-399 of page 18 in the revised manuscript).

(7) The authors need to show H3K4me3 levels in TNF and IL6 genes in all conditions in one figure. (Fig. 2B). Comparing Fig. 2B and Fig. S2B, H3K4me3 does not appear to be increased at all by LPS in the IL6 region. 

We are grateful for the constructive criticism provided by the reviewer. In response to the reviewer's comment, we endeavored to conduct an experiment demonstrating H3K4me3 enrichment on the promoters of TNF-α and IL-6 across all experimental conditions. However, due to limitations in the availability of purified human monocytes, we conducted an additional three independent experiments for ChIP-qPCR across all conditions. Despite encountering a notable variability among individuals, even within the healthy donor cohort, our results demonstrated an increase in H3K4me3 enrichment on the TNF-α and IL-6 promoters in IS-trained groups, irrespective of subsequent LPS treatment (Author response image 8).

Author response image 8.

Analysis of H3K4me3 enrichment on the promoters of TNFA and IL6 Loci in IS-trained macrophages. ChIP-qPCR was employed to assess the enrichment of H3K4me3 on the promoters of TNFA and IL6 loci before (day 6) and after LPS stimulation (day 7) in IS-trained macrophages. The normalization control utilized 2% input. Bar graphs show the mean ± SEM. The data presented are derived from three independent experiments utilizing samples from different donors.

(8) The authors need to address the changes of H3K4me3 in the presence of MTA.

We appreciate the constructive criticism provided by the reviewer. In response to the reviewer's feedback, we conducted an analysis of the changes in H3K4me3 in the presence of MTA, a general methyltransferase inhibitor, using identical conditions as depicted in Figure 2C of the original manuscript. Our findings revealed that MTA exerted inhibitory effects on the levels of H3K4me3, as isolated through the acid histone extraction method, which were otherwise increased by IS-training, as illustrated in Author response image 9. 

Author response image 9.

The reduction of H3K4me3 by MTA treatment in IS-trained macrophages. IS-trained cells were restimulated by LPS (10 ng/ml) as a secondary challenge for 24 hrs, followed by isolation of histone and WB analysis for H3K4me3, Histone 3 (H3), and β-actin. The blot data from two independent experiments with different donors were shown.

(9) Interpretation of ChIP-seq results is not entirely convincing due to doubts about the quality of sequencing results. First, authors need to provide information on the quality of ChIP-seq data in reliable criteria such as Encode Pipeline. It should also provide representative tracks of H3K4me3 in the TNF and IL-6 genes (Fig. 2F). And in Fig. 2F, the author showed the H3K4me3 track of replicates, but the results between replicates were very different, so there are concerns about reproducibility. Finally, the authors need to show the correlation between ChIP-seq (Fig. 2) and RNA-seq (Fig. 5).

We appreciate the constructive criticism provided by the reviewer. 

As indicated by the reviewer, for evaluation of sample read quality, analysis was performed using the histone ChIP-seq standard from the ENCODE project, focusing on metrics such as read depth, PCR bottleneck coefficient (PBC)1, PBC2, and non-redundant fraction (NRF). Five of the total samples were displayed moderate bottleneck levels (0.5 ≤ PBC1 < 0.8, 1 ≤ PBC2 < 3) with acceptable (0.5 ≤ NRF < 0.8) complexity. One sample showed mild bottlenecks (0.8 ≤ PBC1 < 0.9, 3 ≤ PBC2 < 10) with compliance (0.8 ≤ NRF < 0.9) complexity. This quality metrics indicated ChIP-seq data quality meets at least the standards required for downstream analysis according to ENCODE project criteria (Author response image 10A).

To examine the differences in H3K4me3 enrichment patterns between two groups, we normalized the read counts around the TSS ±2 kb of human genes to CPM. Sequentially, we compared the average values of IS-treated macrophage compare to control and displayed in waterfall plots. In addition, we marked genes of interest in red including the phenotypes of IStrained macrophages (TNF and IL6), the activation of the innate immune responses (XRCC5, IFI16, PQBP1), and the regulation of ornithine decarboxylase (OAZ3, PSMA3, PSMA1) (Author response image 10B and C). Also, H3K4me3 peak tracks of TNF and IL6 loci and H3K4me3 enrichment pattern were added in supplementary Figure 3D and 3F in the revised manuscript.

Next, to evaluate the consistency among replicates within a group, we analyzed enrichment values, expressed as Counts per Million (CPM) using edgeR R-package, by applying Spearman's correlation coefficients. we analyzed two sets included total 7,136 H3K4me3 peak sets, as described in Figure 3E in the revised manuscript and 2 kbp around transcription start sites (TSS) from hg19 human genomes. The resulting Spearman's correlation coefficients and associated P-values demonstrated a concordance between replicates, confirming reproducibility and consistent performance (Author response image 10D). 

Finally, the correlation between gene expression and H3K4me3 enrichment around transcription start sites (TSS) has been reported in previous research (Reshetnikov VV et al. Data of correlation analysis between the density of H3K4me3 in promoters of genes and gene expression: Data from RNA-seq and ChIP-seq analyses of the murine prefrontal cortex. Data Brief. 2020 Oct 2;33:106365). To verify this association in our study, we applied Spearman's correlation for comparative analysis and conducted linear regression to determine if a consistent global trend in RNA expression existed. In our analysis, count values from regions extending 2 kbp around the TSSs in H3K4me3 ChIP-seq data were converted to Counts per Million (CPM) using edgeR R-package. These were then contrasted with the Transcripts Per Million (TPM) values of genes. Our results revealed a significant positive correlation, reinforcing the consistent relationship between H3K4me3 enrichment and gene expression (Author response image 10E and Supplementary Fig. 6D in revised manuscripts).

Author response image 10.

The information on quality of ChIP-seq data and correlation between ChIP-seq and RNA-seq. A, information on quality of ChIP-seq data. B, H3K4me3 peak of promoter region on TNFA and IL6. C, The differences in H3K4me3 enrichment patterns between control group and IS-training group. D, The consistency among replicates within a group. E, Correlation between ChIP-seq and RNA-seq in IS-induced trained immunity.

(10) AhR changes in the cell nucleus should be provided (Fig. 4A).

We appreciate the constructive feedback from the reviewer. In response to the reviewer's suggestions, we investigated the nuclear translocation of AhR on 6 days after the induction of ISmediated trained immunity, as illustrated in Author response image 11. For this purpose, the lysate from IS-trained monocytes was fractionated into the nucleus and cytosol, and AhR protein was subsequently immunoblotted. The results depicted in Figure X demonstrate that IS-trained monocytes exhibited a higher level of AhR protein in the nucleus compared to non-trained monocytes. Notably, the nuclear translocation of AhR was significantly attenuated in IS-trained monocytes treated with GNF351. These findings imply that the activation of AhR, facilitated by the binding of IS, persisted partially up to 6 days, indicating that IS-mediated degradation of AhR was not fully recovered even on day 6 after the induction of IS training. Consequently, we have replaced Figure 4A in the revised manuscript.

Author response image 11.

The activation of AhR, facilitated by IS binding, is persisted partially up to 6 days during induction of trained immunity. The lysate of IS-trained cells treated with or without GNF351, were separated into nuclear and cytosol fraction, followed by WB analysis for AhR protein (Left panel). Band intensity in immunoblots was quantified by densitometry (Right panel). β-actin was used as a normalization control. Bar graphs show the mean ± SEM. * = p < 0.05, by two-tailed paired t-test.

(11) Do other protein-bound uremic toxins (PBUTs), such as PCS, HA, IAA, and KA, change the mRNA expression of ALOX5, ALOX5AP, and LTB4R1? In the absence of genetic studies, it is difficult to be certain of the ALOX5-related mechanism claimed by the authors.

We are grateful for the constructive criticism provided by the reviewer. In response to the reviewer's comment, we investigated whether uremic toxins, specifically PBUTs such as PCS, HA, IAA, and KA, induce changes in the mRNA expression of ALOX5, ALOX5AP, and LTB4R1 in trained monocytes. Intriguingly, the examination revealed no discernible induction in the mRNA expression of these genes by PBUTs, with the exception of IS, as depicted in Author response image 12 of the letter. These findings once again underscore the implication of the AhR-ALOX5 pathway in the induction of trained immunity in monocytes by IS.

Author response image 12.

No obvious impact of PBUTs except IS on the expression of arachidonic acid pathway-related genes on 6 days after treatment with PBUTs. Purified monocytes were treated with several PBUTs including IS, PCS, HA, IAA, and KA for 24 hrs., following by 5-day resting period to induce trained immunity. The mRNA expression of ALOX5, ALOX5AP, and LTB4R1 were quantified using RT-qPCR. Bar graphs show the mean ± SEM. * = p < 0.05, by two-tailed paired t-test.

(12) Fig.6 is based on the correlated expression of inflammatory genes or AA pathway genes. It does not clarify any mechanisms the authors claimed in the previous figures. 

We express our sincere appreciation for the constructive criticism provided by the reviewer, and we have taken careful note of the points raised. In response to the reviewer's feedback, we adopted two distinct approaches utilizing samples obtained from ESRD patients and IS-trained mice. Initially, we investigated the correlation between ALOX5 protein expression in monocytes and IS concentration in the plasma of ESRD patients presented in Figure 6E of the original manuscript. Despite the limited number of samples, our analysis revealed a nonsignificant correlation between IS concentration and ALOX5 expression; however, it demonstrated a positive trend (Author response image 13A). Subsequently, we examined the potential inhibitory effects of zileuton, an ALOX5 inhibitor, on the production of TNF-α and IL-6 in LPSstimulated splenic myeloid cells derived from IS-trained mice. Our findings indicate that zileuton significantly inhibits the production of TNF-α and IL-6 induced by LPS in splenic myeloid cells from IS-trained mice (Author response image 13B). These data were added in Figure 6N of the revised manuscript (Line 350-354 of page 16 in the revised manuscript).

Author response image 13.

Assessment of the correlation between ALOX5 and the concentration of IS in ESRD patients, and investigation of ALOX5 effects in mouse splenic myeloid cells in IS-trained mice. A. Examination of the correlation between ALOX5 protein expression in monocytes and IS concentration in the plasma of ESRD patients. B. C57BL/6 mice were administered daily injections of 200 mg/kg IS for 5 days, followed by a resting period of another 5 days. Subsequently, IS-trained mice were sacrificed, and spleens were mechanically dissociated. Isolated splenic myeloid cells were subjected to ex vivo treatment with LPS (10 ng/ml), along with zileuton (100 µM). The levels of TNF-α and IL-6 in the supernatants were quantified using ELISA. The graphs show the mean ± SEM. * = p < 0.05, by two-tailed paired t-test between zileuton treatment group and no-treatment group.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor corrections to the figures

(1) No indicators for the control group in Fig. 1B.

We thank you for the reviewer’s comment. According to the reviewer’s comment, the control group was indicated with (-).

(2) The same paper is listed twice in the references section. (No. 19 and 28)

We thank you for the reviewer’s comment. We deleted the reference No. 28.

Reviewer #2 (Public Review):

Manuscript entitled "Uremic toxin indoxyl sulfate (IS) induces trained immunity via the AhR-dependent arachidonic acid pathway in ESRD" presented some interesting findings. The manuscript strengths included use of H3K4me3-CHIP-Seq, AhR antagonist, IS treated cell RNA-Seq, ALOX5 inhibitor, MTA inhibitor to determine the roles of IS-AhR in trained immunity related to ESRD inflammation and trained immunity.

Thank you very much for your positive feedback.

Reviewer #2 (Recommendations For The Authors):

However, the manuscript needs to be improved by fixing the following concerns.

There are concerns:

(1) The experiments in Figs. 1G, 1H and 1I need to have AhR siRNA, and siRNA control to demonstrate that the results in uremic toxins-containing serum-treated experiments were related to IS;

We extend our gratitude to the reviewer for their invaluable comment, acknowledging its significant relevance to our study. In accordance with the reviewer's suggestion, we endeavored to conduct additional experiments utilizing AhR siRNA to elucidate the direct impact of IS present in the serum of end-stage renal disease (ESRD) patients on the induction of IS-mediated trained immunity. 

Regrettably, owing to limitations in the availability of monocytes post-siRNA transfection, we were unable to establish a direct relationship between the observed outcomes in experiments utilizing uremic toxins-containing serum and IS in AhR siRNA knockdown monocytes. However, treatment with GNF351, an AhR antagonist, resulted in the inhibition of TNF-α production in trained monocytes exposed to uremic toxins-containing serum (Author response image 14).

In our previous studies, we have already reported that uremic serum-induced TNF-α production in human monocytes is dependent on the AhR pathway, using GNF351 (Kim HY et al. Indoxyl sulfate (IS)-mediated immune dysfunction provokes endothelial damage in patients with end-stage renal disease (ESRD). Sci Rep. 2017 Jun 8;7(1):3057). Additionally, we have provided evidence demonstrating an augmentation in the activity of the AhR pathway within monocytes derived from ESRD patients, indicative of a significant reduction in AhR protein levels (Kim HY et al. Indoxyl sulfate-induced TNF-α is regulated by crosstalk between the aryl hydrocarbon receptor, NF-κB, and SOCS2 in human macrophages. FASEB J. 2019 Oct;33(10):10844-10858). It is noteworthy that other major protein-bound uremic toxins (PBUTs), such as PCS, HA, IAA, and KA, failed to induce trained immunity in human monocytes (Supplementary Figure 1A in the revised manuscript). Nevertheless, knockdown of AhR via siRNA effectively impeded the induction of IS-mediated trained immunity in human monocytes (Figure 4E in the revised manuscript). 

Taken collectively, our findings suggest a critical role for IS present in the serum of ESRD patients in the induction of trained immunity in human monocytes. 

Author response image 14.

Inhibition of uremic serum (US)-induced trained immunity by AhR antagonist, GNF351. Monocytes were pre-treated with or without GNF351 (AhR antagonist; 10 µM) for 1 hour, followed by treatment with pooled normal serum (NS) or uremic serum (US) at a concentration of 30% (v/v) for 24 hours. After a resting period of 5 days, cells were stimulated with LPS for 24 hours. The production of TNF-α and IL-6 in the supernatants was quantified using ELISA. The data presented are derived from three independent experiments utilizing samples from different donors.

(2) Fig. 3 needs to be moved as Fig. 2

We express appreciation for the constructive suggestion provided by the reviewer. In response to the reviewer's comment, the sequence of Figure 3 and Figure 2 was adjusted in the revised manuscript.

(3, 4) The connection between bioenergetic metabolism pathways and H3K4me3 was missing; The connection between bioenergetic metabolism pathways and ALOX5 was missing;

We appreciate the reviewer’s constructive criticism and fully understood the reviewer's points. In response to the reviewer's feedback, we conducted additional experiments employing appropriate inhibitors to elucidate the interrelation between bioenergetic metabolism and H3K4me3 and between bioenergetic metabolism and ALOX5. Initially, we assessed the enrichment of H3K4me3 at 6-day on promoters of TNFA and IL6 loci after treatment of 2-DG, a glycolysis inhibitor. Additionally, we evaluated the alteration in the activity of S6K, a downstream molecule of mTORC1, following treatment with zileuton, an inhibitor of ALOX5. Our findings indicate that AhR-dependent arachidonic acid (AA) signaling induces epigenetic modifications, albeit without inducing metabolic rewiring, in IS-induced trained immunity (Author response image 15). However, IS stimulation promotes mTORC1-mediated glycolysis in an AhR-independent manner. Notably, inhibition of glycolysis with 2-DG impacts epigenetic modifications. We have updated Figure 7 of the revised manuscript to incorporate these additional experimental findings, elucidating the correlation between the diverse mechanisms implicated in IS-induced innate immune memory (Fig. 7 in the revised manuscript).

Author response image 15.

Epigenetic modification is regulated by arachidonic acid (AA) pathway and metabolic rewiring, but metabolic rewiring is not affected by the AA pathway. A-B. Monocytes were pre-treated with zileuton (ZLT), an inhibitor of ALOX5, or 2DG, a glycolysis inhibitor, followed by stimulation with IS for 24 hours. After a resting period of 5 days, the enrichment of H3K4me3 on the promoters of TNFA and IL6 loci was assessed. Normalization was performed using 2% input. C. Monocytes were pre-treated with ziluton (ZLT) and stimulated with IS for 24 hr. Cell lysates were immunoblotted for phosphorylated S6 Kinase, with β-actin serving as a normalization control. Band intensities in the immunoblots were quantified using densitometry. D, A schematic representation of the mechanistic framework underlying IS-trained immunity. Bar graphs show the mean ± SEM. * = p < 0.05, **= p < 0.01, and *** = p < 0.001 by two-tailed paired t-test.

(5) It was unclear whether histone acetylations such as H3K27acetylation and H3K14 acetylation are involved in IS-induced epigenetic reprogramming or IS-induced trained immunity is highly histone methylation-specific.

We appreciate the constructive comment provided by the reviewer. As highlighted by the reviewer, alterations in epigenetic histone markers, specifically H3K4me3 or H3K27ac, have been recognized as the underlying molecular mechanism in trained immunity. Due to limitations in the availability of trained cells, this study primarily focused on histone methylation. In response to the reviewer's inquiry, we briefly investigated the impact of histone acetylation using C646, a histone acetyltransferase inhibitor, on IS-induced trained immunity (Author response image 16). Our experiments revealed that C646 treatment effectively hinders the production of TNF-α and IL-6 by IS-trained monocytes in response to LPS stimulation, comparable to the effects observed with MTA (5’methylthioadenosine), a non-selective methyltransferase inhibitor. This suggests that histone acetylation also contributes to the epigenetic modifications associated with IS-induced trained immunity. We sincerely appreciate the valuable input from the reviewer.

Author response image 16.

The role of histone acetylation in epigenetic modifications in IS-induced trained immunity. Monocytes were pretreated with MTA (methylthioadenosine, methyltransferase inhibitor) or C646 (histone acetyltransferase p300 inhibitor), followed treatment with IS 1 mM for 24 hrs. After resting for 5 days, trained cells were re-stimulated by LPS 10 ng/ml as secondary insult. TNF-α and IL-6 in supernatants were quantified by ELISA. Bar graphs show the mean ± SEM. * = p < 0.05 and **= p < 0.01 by two-tailed paired t-test.

Reviewer #3 (Public Review):

The manuscript entitled, "Uremic toxin indoxyl sulfate induces trained immunity via the AhRdependent arachidonic acid pathway in ESRD" demonstrates that indoxyl sulfate (IS) induces trained immunity in monocytes via epigenetic and metabolic reprogramming, resulting in augmented cytokine production. The authors conducted well-designed experiments to show that the aryl hydrocarbon receptor (AhR) contributes to IS-trained immunity by enhancing the expression of arachidonic acid (AA) metabolism-related genes such as arachidonate 5-lipoxygenase (ALOX5) and ALOX5 activating protein (ALOX5AP). Overall, this is a very interesting study that highlights that IS mediated trained immunity may have deleterious outcomes in augmented immune responses to the secondary insult in ESRD. Key findings would help to understand accelerated inflammation in CKD or RSRD.

We greatly appreciate your positive feedback.

Reviewer #3 (Recommendations for The Authors):

This reviewer, however, has the following concerns.

Major comments:

(1) Figure 1B: IS is known to induce the expression of TNF-a and IL-6. This reviewer wonders why these molecules were not detected in the IS (+) LPS (-) condition.

We appreciate the constructive comment provided by the reviewer. In our prior investigation, it was observed that the expression of TNF-α and IL-6 was induced 24 hours after IS treatment in human monocytes and macrophages (Couper KN et al. IL-10: the master regulator of immunity to infection. J Immunol. 2008 May 1;180(9):5771-7). In adherence to the trained immunity protocol, the medium was replaced at the 24 hours post-IS treatment to eliminate IS, with a subsequent change after a 5-day resting period. Probably, TNF-α and IL-6 are accumulated and detected in the IS (+) LPS (-) culture supernatant if the media was not changed at these specific time points. Our primary objective, however, was to ascertain the role of IS in the induction of trained immunity, prompting an investigation into whether IS contributes to an increase in the production of TNF-α and IL-6 in response to LPS stimulation as a secondary insult. 

(2) 1' stimulus is IS followed by 2' stimulus LPS/Pam3. It would be interesting to know what the immune profile is when other uremic toxin is used for secondary insult, this would be more relevant in clinical context of ESRD.

The reviewer's insightful comment is greatly appreciated. To address their feedback, IStrained macrophages were subjected to additional stimulation using protein-bound uremic toxins (PBUTs) as a secondary challenge. As illustrated in Letter figure 17, the examined uremic toxins, namely p-cresyl sulfate (PCS), Hippuric acid (HA), Indole 3-acetic acid (IAA), and kynurenic acid (KA), failed to elicit the production of proinflammatory cytokines, specifically TNF-α and IL-6, by IS-trained monocytes.

Author response image 17.

No obvious effect of protein-bound uremic toxin (PBUTs) as secondary insults on the production of proinflammatory cytokines in IS-trained monocytes. IS-trained monocytes were re-stimulated with several PBUTs, such as IS (1 mM), PCS (1 mM), HA (2 mM), IAA. (0.5 mM), and KA (0.5 mM) as a secondary challenge for 24 hrs. TNF-α and IL-6 in supernatants were quantified by ELISA. The data from two independent experiments with different donors were shown. ND indicates ‘not detected’.

(3) The authors need to explain a rationale why RNA and protein data used different markers.

We appreciate the constructive input provided by the reviewer. Given that TNF-α and IL6 represent prototypical cytokines synthesized by trained monocytes in humans, we conducted a comprehensive analysis of their mRNA and protein levels. In human macrophages, the release of active IL-1β necessitates a second priming event, such as the presence of ATP. Consequently, we posited that assessing the mRNA levels of IL-1β would suffice to demonstrate the induction of trained immunity in our experimental protocol. Nevertheless, in response to the reviewer's comment, we proceeded to assess the protein levels of IL-1β, IL-10, and MCP-1 as illustrated in Author response image 189. These data have been incorporated into the revised manuscript as supplementary Figure 1E. 

Author response image 18.

Modulation of cytokine levels in IS-trained macrophages in response to secondary stimulation with LPS. Human monocytes were stimulated with the IS for 24 hr, followed by resting period for 5 days. On day 6, the cells were re-stimulated with LPS for 24 hr. The levels of each cytokine in the supernatants were quantified using ELISA. Bar graphs show the mean ± SEM. ** = p < 0.01 and ***= p < 0.001 by two-tailed paired t-test.

(4) Epigenetic modification primarily involves histone modification and DNA methylation. The authors presented convincing data on histone modification (Figure 2), but did not provide any insights in the promoter DNA methylation status.

We express our gratitude to the reviewer for providing valuable comments, which highlight a crucial aspect of our study. Despite the well-established primary role of DNA methylation in epigenetic modifications, recent suggestions propose that histone modifications, particularly H3K4me3 or H3K27ac, play a predominant role in the induction of trained immunity. In this context, our primary inquiry was focused on determining whether IS, as an endogenous insult, induces trained immunity in monocytes, and if so, whether IS-trained immunity is mediated through metabolic and epigenetic modifications - recognized as the major mechanisms underlying the generation of trained immunity. It is imperative to note that our study's primary objective did not encompass the identification of various epigenetic changes. In response to the reviewer's inquiry, we conducted a brief examination of the impact of DNA methylation using ZdCyd (5-aza-2’-deoxycytidine), a DNA methylation inhibitor, on IS-induced trained immunity. Our experimental findings indicate that ZdCyd treatment exerts no discernible effect on the production of TNF-α and IL-6 by IS-trained monocytes upon stimulation with LPS, as illustrated in Author response image 19. However, a recent study has shed light on the role of DNA methylation in BCG vaccine-induced trained immunity in human monocytes (Bannister S et al. Neonatal BCG vaccination is associated with a long-term DNA methylation signature in circulating monocytes. Sci Adv. 2022 Aug 5;8(31):eabn4002). Consequently, further investigations utilizing DNA methylation sequencing are warranted to elucidate whether DNA methylation is implicated in the induction of IS-trained immunity.

Author response image 19.

The effect of DNA methylation on IS-induced trained immunity. Monocytes were pretreated with ZdCyd (5-aza-2’-deoxycytidine, DNA methylation inhibitor), followed by treatment with IS 1 mM for 24 hrs. After resting for 5 days, cells were re-stimulated by LPS 10 ng/ml as secondary insult. TNF-α and IL-6 in supernatants were quantified by ELISA. Bar graphs show the mean ± SEM. * = p < 0.05 and **= p < 0.01 by two-tailed paired t-test.

(5) Metabolic rewiring in trained immunity cells undergo metabolic changes which involved intertwined pathways of glucose and cholesterol metabolism. The authors presented nice data on glucose pathway (Figure 3) but failed to show any changes related to cholesterol metabolism.

We express our gratitude to the reviewer for providing valuable comments, which underscore a noteworthy observation. In the current investigation, our primary emphasis has been on glycolytic reprogramming, recognized as a principal mechanism for inducing trained immunity in monocytes. This focus stems from preliminary experiments wherein Fluvastatin, a cholesterol synthesis inhibitor, demonstrated no discernible impact on TNF-α production by IS-trained monocytes, as illustrated in Author response image 20. Intriguingly, Fluvastatin treatment exhibited a partial inhibitory effect on the production of IL-6 by IS-trained monocytes. Subsequent investigations are imperative to elucidate the role of cholesterol metabolism in the induction of IS-trained immunity.

Author response image 20.

The effect of cholesterol metabolism on IS-induced trained immunity. Monocytes were pretreated with Fluvastatin (cholesterol synthesis inhibitor, HMG-CoA reductase inhibitor), followed treatment with IS 1 mM for 24 hrs. After resting for 5 days, cells were re-stimulated by LPS 10 ng/ml as secondary insult. TNF-α and IL-6 in supernatants were quantified by ELISA. Bar graphs show the mean ± SEM. * = p < 0.05 and **= p < 0.01 by two-tailed paired t-test.

(6) Trained immunity involves neutrophils in addition to monocyte/macrophages. It is evident from the RNAseq data that neutrophil degranulation (Figure 5B) is the top enriched pathway. This reviewer wonders why the authors did not perform any assays on neutrophils.

We appreciate the reviewer for valuable comment. IS represents a major uremic toxin that accumulates in the serum of patients with chronic kidney disease (CKD), correlating with CKD progression and the onset of CKD-related complications, including cardiovascular diseases (CVD). Our prior investigations have demonstrated that IS promotes the production of TNF-α and IL-1β by human monocytes and macrophages. Additionally, macrophages pre-treated with IS exhibit a significant augmentation in TNF-α production when exposed to a low dose of lipopolysaccharide (LPS). Considering the pivotal role of proinflammatory macrophages and TNF-α, a principal cardiotoxic cytokine, in CVD pathogenesis, our focus in this study has primarily focused on elucidating the trained immunity of monocytes/macrophages. Consequently, all experiments were meticulously conducted using highly purified monocytes and monocytederived macrophages derived from both healthy controls and end-stage renal disease (ESRD) patients. The reviewer's observation regarding the potential involvement of neutrophils in trained immunity has been duly noted. Subsequent investigations will be imperative to explore the conceivable role of IS-trained neutrophils in the pathogenesis of CVD. Once again, we appreciate the reviewer for their valuable comment.

(7) Figure 5C (GSEA plots): This reviewer is not sure if one can present the plots assigned with groups (eg. IS(T) vs Control). More details are required in the Methods related to this.

We apologize for any ambiguity resulting from the previously unclear description of methods concerning Gene Set Enrichment Analysis (GSEA) plots. To provide clarification, additional details pertaining to this aspect have been explained upon in the revised manuscript's Methods section. 

(8) In vivo data (Figure 6 I-M): Instead of serum profile and whole set of spleen myeloid cells, it would be interesting to see changes of markers on peritoneal macrophages or bone marrow-derived macrophages since the in vitro findings are on monocyte-derived macrophages.

We appreciate comment and the insightful suggestion provided by the reviewer. In response to the reviewer's feedback, we conducted additional in vivo experiments to examine the production of TNF-α and IL-6 in bone marrow-derived macrophages (BMDMs) derived from IStrained mice. Upon LPS stimulation, we observed an increase in the production of TNF-α and IL-6 in spleen myeloid cells from IS-trained mice. However, no such increase in these cytokines was noted in BMDMs derived from the same mice (Author response image 22, A and B). In fact, we already observed that that the expression of ALOX5 was not elevated in BM cells derived from IS-trained mice presented in Figure 6L and M of the original manuscript (Author response image 22C). 

Recent studies have indicated that trained immunity can be induced in circulating immune cells, such as monocytes or resident macrophages (peripheral trained immunity), as well as in hematopoietic stem and progenitor cells (HSPCs) within the bone marrow (central trained immunity) (Kaufmann E et al. BCG Educates Hematopoietic Stem Cells to Generate Protective Innate Immunity against Tuberculosis. Cell. 2018 Jan 11;172(1-2):176-190.e19; Riksen NP et al. Trained immunity in atherosclerotic cardiovascular disease. Nat Rev Cardiol. 2023 Dec;20(12):799-811). It is plausible that central trained immunity in BM progenitor cells may not be elicited in our mouse model, which is relatively acute in nature. Further investigations are warranted to explore the role of IS in inducing central trained immunity, utilizing appropriate chronic disease models.

We have included this additional data as supplementary figures in the revised manuscript (Suppl. Fig. 7, D and E, and line 355-362 of page 16 in the revised manuscript).

Author response image 21.

Absence of trained immunity in bone marrow derived macrophages (BMDMs) derived from IStrained mice. A-B, IS was intraperitoneally injected daily for 5 days, followed by training for another 5 days. Isolated BM progenitor cells and spleen myeloid cells were differentiated or treated with LPS for 24 hr. The supernatants were collected for ELISA. C, The level of ALOX5 protein in BM cells isolated from IS-trained or control mice was analyzed by western blot. The graph illustrates the band intensity quantified by densitometry. Bar graphs show the mean ± SEM. * = p < 0.05 and **= p < 0.01, by unpaired t-test.

(9) Figure 7: There are no data on signaling pathway(s) that links IS and epigenetic changes, the authors therefore may want to add "?" to the proposed mechanism.

We extend our sincere appreciation to the reviewer for providing valuable feedback. In light of the constructive comments provided by three reviewers, we have undertaken a series of additional experiments. These efforts have enabled us to propose a more elucidating schematic representation of the proposed mechanism, free of any ambiguous elements (Figure 7 in the revised manuscript). We are grateful for your insightful input.

(10) Demographic data (Table S2): ESRD patients have co-morbidities including diabetes (33% of subjects), CAD (28%). How did the authors factor out the co-morbidities in the overall context of their findings?

We express gratitude to the reviewer for providing valuable comments, particularly on a noteworthy and significant aspect. The investigation employed an End-Stage Renal Disease (ESRD) Cohort involving approximately 60 subjects undergoing maintenance hemodialysis at Severance Hospital in Seoul, Korea. The subset of participants subjected to analysis consisted of stable individuals who provided informed consent and had not undergone hospitalization for reasons related to infection or acute events within the preceding three months.

(11) There are no data on the purity of IS.

According to the reviewer's suggestion, we have included information regarding the purity (99%) of IS in the Methods section.

(12) Figure 6L: Immunoblot on b-actin were merged. This reviewer wonders how the authors analyzed these blots. 

We express gratitude for the constructive criticism provided by the reviewer, and we acknowledge and comprehend the concerns raised. In response to the reviewer's comments, a reanalysis of the ALOX5 expression level in Figure 6M was conducted, employing immunoblot analysis on β-actin, as depicted in Figure 6L, with a short exposure time (Author response image 22).

Author response image 22.

ALOX5 protein exhibited an elevation in splenic myeloid cells obtained from IS-trained mice.

(13) qPCR data throughout the manuscript have control group with no error bar. The authors may not set all controls arbitrarily equal to 1 (Example Figure 1H and I). Data should be normalized in a test standard way. The average of a single datapoint may be scaled to 1, but variation must remain within the control groups.

We express gratitude to the reviewer for their valuable feedback, acknowledging a comprehensive understanding of their perspectives. Our qPCR assays predominantly investigated the impact of various treatments on the expression of specific target genes (e.g., TNF-α, IL-6, Alox5) within monocytes/macrophages obtained from the same donors.

Subsequently, normalization of gene expression levels occurred relative to ACTINB expression, followed by relative fold-increase determination using the comparative CT method (ΔΔCT).

Statistical significance was assessed through a two-tailed paired analysis in these instances. Additionally, a substantial portion of the qPCR data was validated at the protein level through ELISA and immunoblotting techniques.

Minor Comments:

(1) Molecular weight markers are missing in immunoblots throughout the manuscript.

According to the reviewer's comment, molecular weight markers are added into immunoblots

(2)  ESRD should be spelled out in the title.

According to the reviewer's comment, we spelled out ESRD in the title.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In their manuscript, Yu et al. describe the chemotactic gradient formation for CCL5 bound to - i.e. released from - glycosaminoglycans. The authors provide evidence for phase separation as the driving mechanism behind chemotactic gradient formation. A conclusion towards a general principle behind the finding cannot be drawn since the work focuses on one chemokine only, which is particularly prone to glycan-induced oligomerisation.

Strengths:

The principle of phase separation as a driving force behind and thus as an analytical tool for investigating protein interactions with strongly charged biomolecules was originally introduced for protein-nucleic acid interactions. Yu et al. have applied this in their work for the first time for chemokine-heparan sulfate interactions. This opens a novel way to investigate chemokine-glycosaminoglycan interactions in general.

Response: Thanks for the encouragement of the reviewer.

Weaknesses:

As mentioned above, one of the weaknesses of the current work is the exemplification of the phase separation principle by applying it only to CCL5-heparan sulfate interactions. CCL5 is known to form higher oligomers/aggregates in the presence of glycosaminoglycans, much more than other chemokines. It would therefore have been very interesting to see, if similar results in vitro, in situ, and in vivo could have been obtained by other chemokines of the same class (e.g. CCL2) or another class (like CXCL8).

Response: We share the reviewer’s opinion that to investigate more molecules/cytokines that interact with heparan sulfate in the system should be of interesting. We expect that researchers in the field will adapt the concept to continue the studies on additional molecules. Nevertheless, our earlier study has demonstrated that bFGF was enriched to its receptor and triggered signaling transduction through phase separation with heparan sulfate (PMID: 35236856; doi: 10.1038/s41467-022-28765-z), which supports the concept that phase separation with heparan sulfate on the cell surface may be a common mechanism for heparan sulfate binding proteins. The comment of the reviewer that phase separation is related to oligomerization is demonstrated in (Figure 1—figure supplement 2C and D), showing that the more easily aggregated mutant, A22K-CCL5, does not undergo phase separation.

In addition, the authors have used variously labelled CCL5 (like with the organic dye Cy3 or with EGFP) for various reasons (detection and immobilisation). In the view of this reviewer, it would have been necessary to show that all the labelled chemokines yield identical/similar molecular characteristics as the unlabelled wildtype chemokine (such as heparan sulfate binding and chemotaxis). It is well known that labelling proteins either by chemical tags or by fusion to GFPs can lead to manifestly different molecular and functional characteristics.

Response: We agree with the reviewer that labeling may lead to altered property of a protein, thus, we have compared chemotactic activity of CCL5 and CCL5-EGFP (Figure 2—figure supplement 1). To further verify this, we performed additional experiment to compare chemotactic activity between CCL5 and Cy3-CCL5 (see Author response image 1). For the convenience of readers, we have combined the original Figure 2—figure supplement 1 with the new data (Figure R1), which replaced original Figure 2—figure supplement 1.

Author response image 1.

Chemotactic function of CCL5-EGFP and CCL5-Cy3. Cy3-Labeled CCL5 has similar activity as CCL5, 50 nM CCL5 or CCL5-Cy3 were added to the lower chamber of the Transwell. THP-1 cells were added to upper chambers. Data are mean ± s.d. n=3. P values were determined by unpaired two-tailed t-tests. NS, Not Significant.

Reviewer #2 (Public Review):

Although the study by Xiaolin Yu et al is largely limited to in vitro data, the results of this study convincingly improve our current understanding of leukocyte migration.

(1) The conclusions of the paper are mostly supported by the data although some clarification is warranted concerning the exact CCL5 forms (without or with a fluorescent label or His-tag) and amounts/concentrations that were used in the individual experiments. This is important since it is known that modification of CCL5 at the N-terminus affects the interactions of CCL5 with the GPCRs CCR1, CCR3, and CCR5 and random labeling using monosuccinimidyl esters (as done by the authors with Cy-3) is targeting lysines. Since lysines are important for the GAG-binding properties of CCL5, knowledge of the number and location of the Cy-3 labels on CCL5 is important information for the interpretation of the experimental results with the fluorescently labeled CCL5. Was the His-tag attached to the N- or C-terminus of CCL5? Indicate this for each individual experiment and consider/discuss also potential effects of the modifications on CCL5 in the results and discussion sections.

Response: We agree with the reviewer that labeling may lead to altered property of a protein, thus, we have compared chemotactic activity of CCL5 and CCL5-EGFP (Figure 2—figure supplement 1). To further verify this, we performed additional experiment to compare chemotactic activity between CCL5 and Cy3-CCL5 (see Author response image 1). For the convenience of readers, we have combined the original Figure 2—figure supplement 1 with the new data (Author response image 1), which replaced original Figure 2—figure supplement 1.

The His-tag is attached to the C-terminus of CCL5, in consideration of the potential impact on the N-terminus.

(2) In general, the authors appear to use high concentrations of CCL5 in their experiments. The reason for this is not clear. Is it because of the effects of the labels on the activity of the protein? In most biological tests (e.g. chemotaxis assays), unmodified CCL5 is active already at low nM concentrations.

Response: We agree with the reviewer that the CCL5 concentrations used in our experiments were higher than reported chemotaxis assays and also higher than physiological levels in normal human plasma. In fact, we have performed experiments with lower concentration of CCL5, where the effect of LLPS was not seen though the chemotactic activity of the cytokine was detected. Thus, LLPS-associated chemotactic activity may represent a scenario of acute inflammatory condition when the inflammatory cytokines can increase significantly.

(3) For the statistical analyses of the results, the authors use t-tests. Was it confirmed that data follow a normal distribution prior to using the t-test? If not a non-parametric test should be used and it may affect the conclusions of some experiments.

Response: We thank the reviewer for pointing out this issue. As shown in Author response table 1, The Shapiro-Wilk normality test showed that only two control groups (CCL5 and 44AANA47-CCL5+CHO K1) in Figure 3 did not conform to the normal distribution. The error was caused by using microculture to count and calculate when there were very few cells in the microculture. For these two groups, we re-counted 100 μL culture medium to calculate the number of cells. The results were consistent with the positive distribution and significantly different from the experimental group (Author response image 3). The original data for the number of cells chemoattractant by 500 nM CCL5 was revised from 0, 247, 247 to 247, 123, 370 and 500 nM 44AANA47 +CHO-K1 was revised from 1111, 1111, 98 to 740, 494, 617. The revised data does not affect the conclusion.

Author response table 1.

Table R1 Shapiro-Wilk test results of statistical data in the manuscript

Author response image 3.

Quantification of THP-1collected from the lower chamber. Data are mean ± s.d. n=3. P values were determined by unpaired two-tailed t-tests.

Recommendations for the authors:

Reviewer #1:

See the weaknesses section of the Public Review. In addition, the authors should discuss the X-ray structure of CCL5 in complex with a heparin disaccharide in comparison with their docked structure of CCL5 and a heparin tetrasaccharide.

Response: Our study, in fact, is strongly influenced by the report (Shaw, Johnson et al., 2004) that heparin disaccharide interaction with CCL5, which is highlighted in the text (page5, line100-102).

Reviewer #2:

(1) Clearly indicate in the results section and figure legends (also for the supplementary figures) which form and concentration of CCL5 is used.

Response: The relevant missing information is indicated across the manuscript.

(2) Clearly indicate which GAG was used. Was it heparin or heparan sulfate and what was the length (e.g. average molecular mass if known) or source (company?)?

Response: Relevant information is added in the section “Materials and Methods.

(3) Line 181: What do you mean exactly with "tiny amounts"?

Response: “tiny amounts” means 400 transfected cells. This is described in the section of Materials and Methods. It is now also indicated in the text and legend to the figure.

(4) Lines 216-217: This is a very general statement without a link to the presented data. No combination of chemokines is used, in vivo testing is limited (and I agree very difficult). You may consider deleting this sentence (certainly as an opening sentence for the Discussion).

Response: We appreciate very much for the thoughtful suggestion of the reviewer. This sentence is deleted in the revised manuscript.

(5) Why was 5h used for the in vitro chemotaxis assay? This is extremely long for an assay with THP-1 cells.

Response: We apologize for the unclear description. The 5 hr includes 1 hr pre- incubation of CCL5 with the cells enable to form phase separation. After transferring the cells into the upper chamber, the actual chemotactic assay was 4 hr. This is clarified in the Materials and Methods section and the legend to each figure.

(6) Define "Sec" in Sec-CCL5-EGFP and "Dil" in the legend of Figure 4.

Response: The Sec-CCL5-EGFP should be “CCL5-EGFP’’, which has now been corrected. Dil is a cell membrane red fluorescent probe, which is now defined.

(7) Why are different cell concentrations used in the experiment described in Figure 5?

Response: The samples were from three volunteers who exhibited substantially different concentrations of cells in the blood. The experiment was designed using same amount of blood, so we did not normalize the number of the cell used for the experiment. Regardless of the difference in cell numbers, all three samples showed the same trend.

(8) Check the text for some typos: examples are on line 83 "ratio of CCL5"; line 142 "established cell lines"; line 196 "peripheral blood mononuclear cells"; line 224 "to mediate"; line 226 "bind"; line 247 "to form a gradient"; line 248 "of the glycocalyx"; line 343 and 346 "tetrasaccharide"; line 409-410 "wild-type"; line 543 "on the surface of CHO-K1 and CHO-677"; line 568 "white".

Response: Thanks for the careful reading. The typo errors are corrected and Manuscript was carefully read by colleagues.

Author response:

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

Reviewer #1 (Public Review):

Weaknesses:

(1) Figure 1: Histomorphological analysis using immunostaining for type I, IIA, IIX, and IIB should be performed and quantified across different muscle groups and also in the soleus. Fiber type switch measured based on qPCR and Westerns does not sufficiently indicate the extent of fiber type switch. Better images for Fig. 1c should be provided.

Thanks for your suggestion. In fact, we attempted immunofluorescent staining for Slow MyHC and Fast MyHC in GAS muscle. However, for the majority of our results, we only observed positive expression of Slow MyHC in a small portion of the muscle sections (as shown in the figure below), so we did not present this result.

In addition, due to the size limitations on uploading image files to Biorxiv, we had to compress the images, resulting in lower resolution pictures. We have attempted to submit clearer images in Fig. 1C

Author response image 1.

Green: Slow MyHC; Red: Fast MyHC

(2) Figure 2: Histomorphological analysis for SDH and NADH-TR should be performed and quantified in different muscle groups. Seahorse or oroborous respirometry experiments should be performed to determine the actually increase in mitochondrial respiratory capacity either in isolated mitochondria or single fibers from vehicle and Eugenol-treated mice. Em for mitochondrial should be added to determine the extent of mitochondrial remodeling. The current data is insufficient to indicate the extent of mitochondrial or oxidative remodeling.

That's a good suggestion. However, we regret to inform you that we are unable to present these results due to a lack of relevant experimental equipment and samples.

(3) Figure 2: Gene expression analysis is limited to a few transcriptional factors. A thorough analysis of gene expression through RNA-seq should be performed to get an unbiased effect of Eugenol on muscle transcriptome. This is especially important because eugenol is proposed to work through CaN/NFAT signaling, major transcriptional regulators of muscle phenotype.

Thanks for your suggestion. Indeed, we believe that in terms of reliability and accuracy, RNA-seq is not as good as RT-qPCR. The advantage of RNA-seq lies in its high throughput, making it suitable for screening unknown transcription factor regulatory mechanisms. In this study, the signaling pathways regulating myokines and muscle fiber type transformation are known and limited, with only the CaN/NFATc1 and the AMPK pathway. Since eugenol mainly acts through the Ca2+ pathway, we primarily focus on the CaN/NFATc1 signaling pathway.

(4) I suggest the inclusion of additional exercise or performance testing including treadmill running, wheel running, and tensiometry. Quantification with a swimming test and measurement of the exact intensity of exercise, etc. is limited.

That's a good suggestion. We apologize for being unable to detect this indicator due to a lack of relevant experimental equipment.

(5) In addition to muscle performance, whole-body metabolic/energy homeostatic effects should also be measured to determine a potential increase in aerobic metabolism over anaerobic metabolism.

That's a good suggestion. We apologize for being unable to detect this indicator due to a lack of relevant experimental equipment.

(6) For the swimming test and other measurements, only 4 weeks of vehicle vs. Eugenol treatment was used. For this type of pharmacological study, a time course should be performed to determine the saturation point of the effect. Does exercise tolerance progressively increase with time?

Thanks for your suggestion. Due to the potential damage that exhaustive swimming tests inflict on mice, the tested mice are subsequently eliminated to avoid potential interference with the experiment. Therefore, this experiment is only suitable for conducting tests at individual time points.

(7) The authors should also consider measuring adaptation to exercise training with or without Eugenol.

Thanks for your suggestion. The purpose of this study is to investigate whether eugenol mimics exercise under standard dietary conditions. In our future research, we will consider exploring the effects of eugenol under HFD and exercise conditions.

(8) Histomorphological analysis of Wat is also lacking. EchoMRI would give a better picture of lean and fat mass.

That's a good suggestion. However, we did not collect the slices of WAT tissue, so we are unable to supplement this result, we feel sorry for it. In addition, we apologize for being unable to detect lean and fat mass due to a lack of EchoMRI equipment.

(9) The experiments performed to demonstrate that Eugenol functions through trpv1 are mostly correlational. Some experiments are needed with trpv1 KO or KD instead of inhibitor. Similarly, KD for other trpv channels should be tested (at least 1-4 that seem to be expressed in the muscle). Triple KO or trpv null cells should be considered to demonstrate that eugenol does not have another biological target.

Thanks for your professional suggestion. AMG-517 is a specific inhibitor of TRPV1, with a much greater inhibitory effect on TRPV1 compared to other TRP channels. AMG-517 inhibits capsaicin (500 nM), acid (pH 5.0), or heat (45°C) induced Ca2+ influx in cells expressing human TRPV1, with IC50 values of 0.76 nM, 0.62 nM, and 1.3 nM, respectively. However, the IC50 values of AMG-517 for recombinant TRPV2, TRPV3, TRPV4, TRPA1, and TRPM8 cells are >20 μM (Gavva, 2008). Therefore, we believe that using AMG-517 instead of TRPV1 KO cells is sufficient to demonstrate the involvement of TRPV1 in the function of eugenol.

While this study did not exclude the possibility of other TRP channels' involvement, it was based on the fact that eugenol does not promote mRNA expression of other TRP channels, as shown in Fig4A-C. Indeed, as far as we know, besides TRPV1, the effects of other TRP channels on myofiber type transformation remain unknown. This is an aspect that we plan to investigate in the future.

Reference

Gavva NR, Treanor JJ, Garami A, et al. Pharmacological blockade of the vanilloid receptor TRPV1 elicits marked hyperthermia in humans. Pain. 2008;136(1-2):202-210.

(10) Eugenol + trpv1 inhibition studies are performed in c2c12 cells and only looks at myofiber genes expression. This is incomplete. Some studies in mitochondrial and oxsphos genes should be done.

Thanks for your suggestion. In the inhibition experiment, we additionally examined the expression of mitochondrial complex proteins as shown in Figure 5C. And the relevant description has been added in lines 178-183 and 764-765.

(11) The experiments linking Eugenol to ca handling, and calcineurin/nfat activation are all performed in c2c12 cells. There seems to be a link between Eugenol activation and CaN/NFAT activation and fiber type regulation in cells, however, this needs to be tested in mouse studies at the functional level using some of the parameters measured in aims 1 and 2.

Thank you for your professional suggestion. We will attempt to continue these experiments in future studies.

(12) The myokine studies are incomplete. The authors show a link between Eugenol treatment and myokines/IL-15 induction. However, this is purely co-relational, without any experiments performed to show whether IL-15 mediates any of the effects of eugenol in mice.

Indeed, previous studies have adequately demonstrated the regulation of skeletal muscle oxidative metabolism by IL-15. The initial aim of this experiment was to investigate the mechanism by which eugenol promotes IL-15 expression. Through inhibition assays, EMSA, and dual luciferase reporter gene experiments, we have thoroughly demonstrated that eugenol promotes IL-15 expression via the CaN/NFATc1 signaling pathway, thus establishing a novel link between CaN/NFATc1 signaling and the myokine IL-15 expression. In the subsequent experiments, we plan to knock out IL-15 in eugenol-treated C2C12 cells to explore whether IL-15 mediates the effects of eugenol. This will be another aspect of our investigation.

(13) An additional major concern is that it cannot be ruled out that Engenol is uniquely mediating its effects through trpv1. Ideally, muscle-specific trpv1 mice should be used to perform some experiments with Eugenol to confirm that this ion channel is involved in the physiological effects of eugenol.

As you suggested, we agree that muscle-specific TRPV1 mice should be used to conduct some experiments with eugenol. In our mice experiments, due to the lack of validation of skeletal muscle-specific TRPV1 knockout, we indeed cannot rule out that eugenol is uniquely mediating its effects through TRPV1. We acknowledge this as a limitation of our study. However, due to limitations in research funding and time, we are currently unable to supplement these experiments. Nevertheless, we believe that our results from in vitro experiments using a TRPV1 inhibitor (which selectively inhibits TRPV1) provide evidence of eugenol's action through TRPV1.

Reviewer #2 (Public Review):

Weaknesses:

(1) Apart from Fig.2A and 2B, they mostly utilised protein expression changes as an index of tissue functional changes. Most of the data supporting the conclusions are thus rather indirect. More direct functional evidence would be more compelling. For example, a lipolysis assay could be used to measure the metabolic function of adipocytes after eugenol treatment in Fig.3. Functional activation of NFAT can be demonstrated by examining the nuclear translocation of NFAT.

Thank you for your professional suggestion. Indeed, as shown in Figure 4G-I, we detected the expression of NFATc1 in the nucleus to illustrate its nuclear translocation.

(2) To further demonstrate the role of TRPV1 channels in the effects of eugenol, TRPV1-deficient mice and tissues could also be used. Will the improved swimming test in Fig. 2B and increased CaN, NFAT, and IL-15 triggered by eugenol be all prevented in TRPV1-lacking mice and tissues?

Thank you for your professional suggestion. We agree that muscle-specific TRPV1 mice should be used to conduct some experiments with eugenol. However, due to limitations in research funding and time, we are currently unable to supplement these experiments.

(3) Direct evidence of eugenol activation of TRPV1 channels in skeletal muscles is also lacking. The flow cytometry assay was used to measure Ca2+ changes in the C2C12 cell line in Fig. 5A. But this assay is rather indirect. It would be more convincing to monitor real-time activation of TRPV1 channels in skeletal muscles not in cell lines using Ca2+ imaging or electrophysiology.

Thank you for your professional suggestion. As you suggested, we initially planned to use patch-clamp technique to detect membrane potential changes in skeletal muscle cells under eugenol treatment. However, due to experimental technical limitations, this experiment was not successfully conducted. Therefore, we were compelled to rely solely on flow cytometry to detect Ca2+ levels.

Reviewer #2 (Recommendations For The Authors):

(1) Most of the mRNA and protein data are consistent with each other. However, some of them are not obvious. For example, PGC1a mRNA was increased by eugenol in Fig. 2C but not seen in protein in Fig. 2D. Similarly, Complex I and V mRNA was increased in Fig. 2C but not obvious at protein levels in Fig. 2D, even though they claimed that Complex I and V were both upregulated by eugenol (see: line 123). Another example: IL-15 mRNA was increased by EUG100 but not by EUG50 in the GAS muscle in Fig. 8A. However, EUG50 increased IL-15 protein expression in Fig. 8B. Similar conflict was also seen in IL-15 expression in the TA muscle in Fig. 8A and 8C.

Thanks for your question. As shown in the table below, by standardizing with β-Actin, our statistical data indeed indicate that eugenol promotes the expression of Complex I and V proteins (although the upregulation is minimal). Additionally, protein and mRNA expression do not always correlate, which may be due to potential post-transcriptional and post-translational regulation.

Author response table 1.

(2) Line 115: Figure 2A should be Figure 2B; Line 119: Figure 2B should be Figure 2A. Alternatively, swap Fig2A with Fig. 2B.

Thanks for your correction, we have revised the relevant content in lines 111-113 and 724-725.

(3) Abbreviations of ADF and ADG in Fig. 3A should be defined.

Thank you for your suggestion. We have defined these abbreviations in lines 123-125.

(4) Line 154: TRPV1 mRNA expression was promoted by 25 and 50uM eugenol, not by 12.5uM.

Thank you for your correction. We have revised it in line 150.

(5) Line 173: Increased expression of NFAT suggests that NFAT is activated. This is a rather weak statement. It is more convincing to show the nuclear translocation of NFAT by eugenol treatment.

Thank you for your correction. We have revised the describtion in line 166.

(6) Line 185: The data showing EUG increased slow MyHC fluorescence intensity in Fig. 5D are not clear at all. Quantification is required.

Thank you for your suggestion. We have attempted to submit clearer images in Figure 5E, and the quantification have been provided.

(7) Line 235: IL-15 expression is positively correlated with MyHC IIa, suggesting IL-15 is a slow muscle myokine (See line 2398). However, MyHC IIa is a marker of fast muscle fibres (see line 50).

Thank you for your correction. As you pointed, MyHC IIa is fast-twitch oxidative muscle fiber. We have replaced ‘slow’ with ‘oxidative’ in line 235.

(8) Fig.9C and 9D show that inhibition of TRPV1 and CaN attenuated the upregulation of IL-15 mRNA and protein by eugenol in C2C12 cell line. This result is important in demonstrating the link of TRPV1 and CaN to IL-15. It will be more interesting and physiologically relevant to perform this experiment in primary skeletal muscle cells isolated from mice.

Thank you for your suggestion. This is indeed an interesting idea. We will attempt to continue our experiments in mice and primary porcine muscle cells in future studies.

(9) It is concerning that 4-week-old male mice were used for the study. The 4-week-old mice are immature. Adult mice over 8 weeks should be used. It is thus unknown whether the findings are broadly applicable to adult age.

Thanks for your professional question. Age indeed has an impact on the muscle fiber type in mammals. Based on previously observed patterns of muscle fiber changes with age in various mammals (Katsumata et al., 2021; Pandorf et al., 2012; Hill et al., 2020), we believe that changes in muscle fiber types occur more frequently in juvenile mammals, mainly manifesting as a sharp increase in fast muscle fibers. Therefore, interventions during the juvenile stage might be more effective in promoting the transformation of fast to slow muscle fibers. As a result, in most of our group's research using nutritional interventions to regulate muscle fiber types, we tend to start interventions from the age of 4 weeks in mice. If we began intervention at 8 weeks, we speculate that the effectiveness would not be as potent as starting at 4 weeks. Below are the patterns of muscle fiber changes with age in various mammalian models, provided for reference:

(1) Changes in muscle fiber types with age in pigs:

As shown in the following figure, there is a dramatic change in the muscle fiber types 12 days post birth in pigs, especially with a sharp increase in fast muscle fibers, which continues until day 45. After 45 days of age, the changes in muscle fiber types become relatively gradual.

Author response table 2.

Developmental change Of proportions Of muscle fiber types in Longissimus dorsi muscle determined by histochemical analysis for myosin adenosine triphosphatase activity (%)

Least squares means and pooled standard errors (n = 3). MHC, myosin heavy chain; ND, not detected. *P<0.10, **P<0.01 Least square means followed by different letters on the same row are significantly different (P < 0.05).

Reference:

Katsumata, M., Yamaguchi, T., Ishida, A., & Ashihara, A. (2017). Changes in muscle fiber type and expression of mRNA of myosin heavy chain isoforms in porcine muscle during pre- and postnatal development. Animal science journal, 88(2), 364–371.

(2) Changes in muscle fiber types with age in rats:

As illustrated in the subsequent figure, the muscle fiber types in rats undergo significant changes before 20 days of age (3-week-old), notably with a pronounced increase in type IIb fast-twitch fibers. After reaching 20 days of age, the changes in type IIb muscle fibers tend to stabilize and become more gradual.

Author response image 2.

Reference:

Pandorf, C. E., Jiang, W., Qin, A. X., Bodell, P. W., Baldwin, K. M., & Haddad, F. (2012). Regulation of an antisense RNA with the transition of neonatal to IIb myosin heavy chain during postnatal development and hypothyroidism in rat skeletal muscle. American journal of physiology. 302(7), R854–R867.

(3) Changes in muscle fiber types with age in mice:

As depicted in the following figure, when comparing 10-week-old mice to 78-week-old aged mice, there are no significant changes in muscle fiber types.

Author response image 3.

Reference:

Hill, C., James, R. S., Cox, V. M., Seebacher, F., & Tallis, J. (2020). Age-related changes in isolated mouse skeletal muscle function are dependent on sex, muscle, and contractility mode. American journal of physiology. Regulatory, integrative and comparative physiology, 319(3), R296–R314.

eLife Assessment

The findings in this manuscript are fundamental because they identify an entry receptor MYL3 that belongs to the myosin family as a possible target that could inhibit a virus that has a high impact on aquaculture. The evidence is convincing as it contains strong in vitro and in vivo data that support their conclusions; however, studies on the presence of MYL3 in NNV target tissues will further strengthen their claims

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors discovered MYL3 of marine medaka (Oryzias melastigma) as a novel NNV entry receptor, elucidating its facilitation of RGNNV entry into host cells through macropinocytosis, mediated by the IGF1R-Rac1/Cdc42 pathway.

Strengths:

In this manuscript, the authors have performed in vitro and in vivo experiments to prove that MnMYL3 may serve as a receptor for NNV via macropinocytosis pathway. These experiments with different methods include Co-IP, RNAi, pulldown, SPR, flow cytometry, immunofluorescence assays and so on. In general, the results are clearly presented in the manuscript.

Comments on revisions:

The authors have addressed all my comments.

Reviewer #2 (Public review):

Summary:

The manuscript offers an important contribution to the field of virology, especially concerning NNV entry mechanisms. The major strength of the study lies in the identification of MmMYL3 as a functional receptor for RGNNV and its role in macropinocytosis, mediated by the IGF1R-Rac1/Cdc42 signaling axis. This represents a significant advance in understanding NNV entry mechanisms beyond previously known receptors such as HSP90ab1 and HSC70. The data, supported by comprehensive in vitro and in vivo experiments, strongly justify the authors' claims about MYL3's role in NNV infection in marine medaka.

Strengths:

(1) The identification of MmMYL3 as a functional receptor for RGNNV is a significant contribution to the field. The study fills a crucial gap in understanding the molecular mechanisms governing NNV entry into host cells.

(2) The work highlights the involvement of IGF1R in macropinocytosis-mediated NNV entry and downstream Rac1/Cdc42 activation, thus providing a thorough mechanistic understanding of NNV internalization process. This could pave the way for further exploration of antiviral targets.

Comments on revisions:

The authors have addressed the concerns from reviewers. This manuscript can be published in the current form.

Reviewer #3 (Public review):

Summary:

The manuscript presents a detailed study on the role of MmMYL3 in the viral entry of NNV, focusing on its function as a receptor that mediates viral internalization through the macropinocytosis pathway. The use of both in vitro assays (e.g., Co-IP, SPR, and GST pull-down) and in vivo experiments (such as infection assays in marine medaka) adds robustness to the evidence for MmMYL3 as a novel receptor for RGNNV. The findings have important implications for understanding NNV infection mechanisms, which could pave the way for new antiviral strategies in aquaculture.

Strengths:

The authors show that MmMYL3 directly binds the viral capsid protein, facilitates NNV entry via the IGF1R-Rac1/Cdc42 pathway, and can render otherwise resistant cells susceptible to infection. This multifaceted approach effectively demonstrates the central role of MmMYL3 in NNV entry.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1:

Specificity of MYL3 Selection:

My previous question focused on why MYL3 was prioritized over other myosin family members. While the response broadly implicates myosins in viral entry, it does not justify why MYL3 was specifically chosen. For clarity, the "Introduction sections" should explicitly state the unique features of MYL3 (e.g., domain structure, binding affinity, or prior evidence linking it to NNV) that distinguish it from other myosins.

Thank you for your valuable comment regarding the specificity of MYL3 selection. In response, we have revised the "Introduction" section to explicitly clarify the rationale for prioritizing MYL3 over other myosin family members. Specifically, we have now included prior evidence linking MYL3 to NNV infection, citing our studies that identified MYL3 as a potential host factor interacting with NNV CP protein. In our previous study, sixteen CP-interacting proteins were identified by Co-IP assays followed by MS, including HSP90ab1, Centrosomal protein 170B, MYL3 and so on. In addition to our findings, previous study by other researchers has also reported that Epinephelus coioides MYL3 can bind to NNV (page 3, lines 79–81). These revisions provide a clearer justification for the selection of MYL3 and distinguish it from other myosin proteins. The added content can be found in the revised manuscript on page 3, lines 81–84.

eLife Assessment

This valuable study provides in-vivo evidence that CCR4 regulates the early inflammatory response during atherosclerotic plaque formation. The authors propose that altered T-cell response plays a role in this process, shedding light on mechanisms that may be of interest to medical biologists, biochemists, cell biologists, and immunologists. The work is currently considered incomplete pending textual changes and the inclusion of proper controls.

Reviewer #2 (Public review):

Summary:

Tanaka et al. investigated the role of CCR4 in early atherosclerosis, focusing on the immune modulation elicited by this chemokine receptor under hypercholesterolemia. The study found that Ccr4 deficiency led to qualitative changes in atherosclerotic plaques, characterized by an increased inflammatory phenotype. The authors further analyzed the CD4 T cell immune response in para-aortic lymph nodes and atherosclerotic aorta, showing an increase mainly in Th1 cells and the Th1/Treg ratio in Ccr4-/-Apoe-/- mice compared to Apoe-/- mice. They then focused on Tregs, demonstrating that Ccr4 deficiency impaired their immunosuppressive function in in vitro assays. Authors also states that Ccr4-deficient Tregs had, as expected, impaired migration to the atherosclerotic aorta. Adoptive cell transfer of Ccr4-/- Tregs to Apoe-/- mice mimicked early atherosclerosis development in Ccr4-/-Apoe-/- mice. Therefore, this work shows that CCR4 plays an important role in early atherosclerosis but not in advanced stages.

Strengths:

Several in vivo and in vitro approaches were used to address the role of CCR4 in early atherosclerosis. Particularly, through the adoptive cell transfer of CCR4+ or CCR4- Tregs, the authors aimed to demonstrate the role of CCR4 in Tregs' protection against early atherosclerosis.

Weaknesses:

Flow cytometry experiments are not well controlled. Dead cells and doublets were not excluded from analysis.

Clinical relevance is unclear.

Comments on revisions:

I thank the authors for addressing my suggestions.<br /> I understand that excluding dead cells would require repeating the entire experiment. However, the authors can at least exclude doublets from the existing flow cytometry data.<br /> I also agree with the more cautious claim regarding the role of CCR4 in Treg migration.

Reviewer #3 (Public review):

Summary

Tanaka and colleagues addressed the role of the C-C chemokine receptor 4 (CCR4) in early atherosclerotic plaque development using ApoE-deficient mice on a standard chow diet as a model. Because several CD4+ T cell subsets express CCR4, they examined whether CCR4-deficiency alters the immune response mediated by CD4+ T cells. By histological analysis of aortic lesions, they demonstrated that the absence of CCR4 promoted the development of early atherosclerosis, with heightened inflammation linked to increased macrophages and pro-inflammatory CD4+ T cells, along with reduced collagen content. Flow cytometry and mRNA expression analysis for identifying CD4+ T cell subsets showed that CCR4 deficiency promoted higher proliferation of pro-inflammatory effector CD4+ T cells in peripheral lymphoid tissues and accumulation of Th1 cells in the atherosclerotic lesions. Interestingly, the increased pro-inflammatory CD4+ T cell response occurred despite the expansion of T CD4+ Foxp3+ regulatory cells (Tregs), found in higher numbers in lymphoid tissues of CCR4-deficient mice, suggesting that CCR4 deficiency interfered with Treg's regulatory actions. The findings contrast with earlier studies in a murine model of advanced atherosclerosis, where CCR4 deficiency did not alter the development of the aortic lesions. The authors included a thoughtful discussion about hypothetical mechanisms explaining these contrasting results, including putative differences in the role played by the CCL17/CCL22-CCR4 axis along the stages of atherosclerosis development in this murine model.

Major strengths

• Demonstration of CCR4 deficiency's impact on early atherosclerosis. CCR4 deficiency effects on the early atherosclerosis development in the Apoe-/-mice model were demonstrated by a quantitative analysis of the lesion area, inflammatory cell content and the expression profile of several pro- and anti-inflammatory markers.<br /> • Analysis of the T CD4+ response in various lymphoid tissues (peripheral and para-aortic lymph nodes and spleen) and the atherosclerotic aorta during the early phase of atherosclerosis in the Apoe-/-mice model. This analysis, combining flow cytometry and mRNA expression, showed that CCR4 deficiency enhanced T CD4+ cell activation, favouring the amplification of the typical biased Th1-mediated inflammatory response observed in the lymphoid tissues of hypercholesterolemic mice.<br /> • Treg transference experiments. Transference of Treg from Apoe-/- or Ccr4-/- Apoe-/- mice to Apoe-/- mice under a standard chow diet was useful for addressing the relevance of CCR4 expression on Tregs for the atheroprotective effect of this regulatory T cell subset during early atherosclerosis.

Major weaknesses

• Methodological Limitations: The controls used in the flow cytometry analysis were suboptimal, as neither cell viability nor doublets were assessed. This may have introduced artifacts, particularly when measuring less-represented cell populations within complex samples, such as in assays evaluating Treg migration to the aorta in atherosclerotic mice.<br /> • Incomplete understanding of CCR4-Mediated Mechanisms: The mechanisms by which CCR4 regulates early inflammation and the development of atherosclerosis were not fully clarified.

I have previously addressed the study limitations and their global impact in my earlier reviews.

Author response:

The following is the authors’ response to the previous reviews

Response to the reviewer #2 (Public review):

We greatly appreciate the reviewer’s high evaluation of our paper and helpful comments and suggestions.

Regarding in vivo Treg homing assay, we did not exclude doublets and dead cells from the analysis of Kaede-expressing Tregs migrated to the aorta, which may affect the results. We described this issue as the limitation of this study in the revised manuscript. Nonetheless, we believe the reliability of our findings because we repeated this experiment three times and obtained similar results.

There is no evidence to support the clinical relevance of our findings. Future clinical research on this topic is highly desired.

Response to the reviewer #3 (Public review):

We greatly appreciate the reviewer’s high evaluation of our paper and helpful comments and suggestions.

Despite the controversial role of Th17 cells in atherosclerosis, we understand the possible involvement of Th17 cells and the Th1 cell/Th17 cell balance in lymphoid tissues and aortic lesions in accelerated inflammation and atherosclerosis in Ccr4^-/-Apoe^-/- mice. Although we could not completely evaluate the changes in these immune responses in detail, future study may elucidate interesting mechanisms mediated by Th17 cell responses.

As the reviewer suggested, we understand that it is necessary to provide in vivo evidence for the Treg suppressive effects on DC activation. Based on the results of in vitro experiments, we described the discussion on the in vivo evidence in the revised manuscript.

We understand methodological limitations for flow cytometric analysis of immune cells in the aorta and in vivo Treg homing assay. We described this issue as the limitation of this study in the revised manuscript. Regarding in vivo Treg homing assay, we statistically re-analyzed the combined data from multiple experiments and observed a tendency toward reduction in the proportion of CCR4-deficient Kaede-expressing Tregs in the aorta of recipient Apoe^-/- mice, though there was no statistically significant difference in the migratory capacity of CCR4-intact or CCR4-deficient Kaede-expressing Tregs. Accordingly, we toned down our claim that CCR4 expression on Tregs plays a critical role in mediating Treg migration to the atherosclerotic aorta under hypercholesterolemia.

The reviewer requested us to evaluate aortic inflammation in Ccr4^-/-Apoe^-/- mice injected with CCR4-intact or CCR4-deficient Tregs. However, we think that this experiment will provide marginal information because Treg transfer experiments in Apoe^-/- mice have already shown the protective role of CCR4 in Tregs against aortic inflammation and early atherosclerosis.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) #1 and #2: CD103 and CD86 expression should be discussed on the text and not only in the response to reviewer.

In accordance with the reviewer’s suggestion, we added a discussion on the downregulated CD103 expression in peripheral LN Tregs and upregulated CD86 expression on DCs in Ccr4^-/-Apoe^-/- mice in the discussion section in the revised manuscript.

(2) #5: Authors response is not satisfactory. No gate percentage is shown. As it currently is, the difference in the number of cells shown in the figure could be due to differences in events recorded. Furthermore, the gate strategy is not thorough. Considering the very low frequency of Kaede + cells detected, it is crucial to properly exclude doublets and dead cells.

Authors reported a dramatic difference in Kaede + Tregs cells in the aorta across experiments. This could be addressed by normalization followed by appropriate statistical analysis (One sample t-test).

The data shown is not strong enough to conclude that there is a reduced migration to the aorta.

We understand the importance of reviewer’s suggestion. We described the percentage of Kaede+ Tregs in the aorta of Apoe^-/- mice receiving transfer of Kaede-expressing CCR4-intact or CCR4-deficient Tregs in Figure 5I.

As the reviewer pointed out, we understand that it would be important to properly exclude doublets and dead cells in in vivo Treg homing assay. However, it is difficult for us to resolve this issue because we need to perform the same experiments again which will require a great number of additional mice and substantial amount of time. We deeply regret that these important experimental procedures were not performed. We described this issue as the limitation of this study.

In accordance with the reviewer’s suggestion, we re-analyzed the combined data from multiple experiments using one-sample t-test. We observed a tendency toward reduction in the proportion of CCR4-deficient Kaede-expressing Tregs in the aorta of recipient Apoe^-/- mice, though there was no statistically significant difference in the migratory capacity of CCR4-intact or CCR4-deficient Kaede-expressing Tregs. By modifying the corresponding descriptions in the manuscript, we toned down our claim that CCR4 expression on Tregs plays a critical role in mediating Treg migration to the atherosclerotic aorta under hypercholesterolemia.

(3) #8: There are still several not shown data

In accordance with the reviewer’s suggestion, we showed the data on the responses of Tregs and effector memory T cells in 8-week-old wild-type or Ccr4^-/- mice and Ccr4 mRNA expression in Tregs and non-Tregs from Apoe^-/- or Ccr4^-/-Apoe^-/- mice in Supplementary Figures 4 and 7.

Reviewer #3 (Recommendations for the authors):

(1) Issue 1. For future studies, I recommend not omitting viability controls during cell staining. Removal of dead cells and doublets should always be included during the gating strategy to avoid undesirable artefacts, especially when analysing less-represented cell populations. According to your previous report (ref #40), I agree that isotype controls were unnecessary using the same staining protocol. FMO controls should always be included in flow cytometry analysis (not mentioned in the methodology description and ref#40).

As the reviewer suggested, we understand that it would be important to properly exclude dead cells and doublets and to prepare FMO controls in flow cytometric analysis. We deeply regret that these important experimental procedures were not performed. We described this issue as the limitation of this study.

(2) Issue 3. Although Th17's role in atherosclerosis remains controversial, the data obtained in this work could provide valuable insights if discussed appropriately. As noted in my public review, I found it noteworthy that ROR γ t+ cells represented around 13% of effector TCD45+CD3+CD4+ lymphocytes in the aorta of Apoe^-/- mice while Th1 less than 5% (Fig 4H and F, respectively). I recognise that differences in cell staining sensibility and robustness for different transcription factors may influence these percentages. However, analysing how CCR4 deficiency influences the Th1/TI h17 balance would yield interesting data, similar to what was done for the Th1/Treg ratio.

Considering the higher proportion of Th17 cells than Th1 or Th2 cells in atherosclerotic aorta, we understand the importance of reviewer’s suggestion. However, we could not evaluate the effect of CCR4 deficiency on the Th1/Th17 balance in aorta because we did not perform flow cytometric analysis of aortic Th1 and Th17 cells in the same mice. Meanwhile, we could examine the Th1/Th17 balance in peripheral lymphoid tissues by flow cytometry. We found a significant increase in the Th1/Th17 ratio in the peripheral LNs of Ccr4^-/-Apoe^-/- mice, while there were no changes in its ratio in the spleen or para-aortic LNs of these mice, which limits the contribution of the Th1/Th17 balance to exacerbated atherosclerosis. We showed these data below.

Author response image 1.

(3) Issue 4. I appreciate the authors for sharing data on the flow cytometry analysis of Tregs in para-aortic LNs of Apoe^-/- and Ccr4^-/- Apoe^-/- mice, which would have been included as a Supplementary figure. These results reinforce the notion that Treg dysfunction in CCR4-deficient mice may not be due to the downregulation of regulatory cell surface receptors.

We showed the data on the expression of CTLA-4, CD103, and PD1 in Tregs in the para-aortic LNs of Apoe^-/- and Ccr4^-/-Apoe^-/- mice in Supplementary Figure 8.

(4) Issue 5. I agree that CD4+ T cell responses are substantially regulated by DCs. While CD80 and CD86 on DC primarily serve as costimulatory signals for T-cell activation, cytokines secreted by DCs are primordial signals for determining the differentiation phenotype of effector Th cells. Since the analysis of DC phenotype in lymphoid tissues of Apoe^-/- and Ccr4^-/- Apoe^-/- mice could not be addressed in this study, it is not possible to differentiate which processes may be mainly affected by CCR4-deficiency during CD4+ T cell activation. In this scenario, and considering in vitro studies, the results suggest a possible role of CCR4 in controlling the extent of activation of CD4+T cells rather than shifting the CD4+T cell differentiation profile in peripheral lymphoid tissues, where a predominant Th1 profile was already established in Apoe^-/- mice. Therefore, I advise caution when concluding about shifts in CD4+ T cell responses.

We thank the reviewer for providing us thoughtful comments. As the reviewer pointed out, we understand that we should carefully interpret the mechanisms for the shift of CD4+ T cell responses by CCR4 deficiency.

(5) Regarding migration studies in the revised manuscript. I fully understand that Treg transference assays are challenging. The results do not suggest that CCR4 was critical for Treg migration to lymphoid tissues in the conditions assayed. Concerning migration to the aorta, I found the results inconclusive since the authors mention that: i) there was a dramatic difference in the absolute numbers of Kaede-expressing Tregs that migrated to the aorta impairing statistical analysis; ii) the number of Kaede-expressing Tregs that migrated to the aorta was extremely low; iii) dead cells and doublets were not removed in the flow cytometry analysis. In this context, I do not agree with the following statements and recommend revising them:

- "CCR4 deficiency in Tregs impaired their migration to the atherosclerotic aorta" (lines 36-7),

- "…we found a significant reduction in the proportion of CCR4 deficient Kaede-expressing Tregs in the aorta of recipient Apoe^-/- mice" (lines 356-7),

- "CCR4 expression on Tregs regulates the development of early atherosclerosis by....... mediating Treg migration to the atherosclerotic aorta" (lines 409-411),

- "…we found that CCR4 expression on Tregs is critical for regulating atherosclerosis by mediating their migration to the atherosclerotic aorta" (lines 437-438),

- "CCR4 protects against early atherosclerosis by mediating Treg migration to the aorta.... (lines 464-465),

- "We showed that CCR4 expression on Tregs is critical for ...... mediating Treg migration to the atherosclerotic aorta" (503-505).

We understand the importance of the reviewer’s suggestion. We described this issue as the limitation of this study. In accordance with the reviewer’s suggestion, we modified the above descriptions and toned down our claim that CCR4 expression on Tregs plays a critical role in mediating Treg migration to the atherosclerotic aorta under hypercholesterolemia.

(6) Line 206: Mention the increased expression of CD86 by DCs

We mentioned this result in the revised manuscript. We also added a discussion on the upregulated CD86 expression on DCs in Ccr4^-/-Apoe^-/- mice in the discussion section in the revised manuscript.

(7) Lines 304-305. According to Fig 4F-H, a selective accumulation of Th1 cells seems to have occurred only in the aorta, coinciding with a higher Th1/Treg ratio. No selective accumulation of Th1 cells was observed in para-aortic lymph nodes. These results could be clarified.

We modified the above description in the revised manuscript.

eLife Assessment

This study presents a new, fundamental finding to the field interested in recurrent processing and its neuromodulatory underpinnings, finding unexpectedly that memantine (blocking NMDA-receptors) enhances the decoding of features thought to rely on NMDA-receptors. This interesting, compelling result identifies new directions for researchers studying consciousness, sensory processing, attention, and neurotransmitters.

Reviewer #1 (Public review):

The authors investigate the function and neural circuitry of reentrant signals in visual cortex. Recurrent signaling is thought to be necessary to common types of perceptual experience that are defined by long-range relationships or prior expectation. Contour illusions - where perceptual objects are implied by stimuli characteristics - are a good example of this. The perception of these illusions is thought to emerge as recurrent signals from higher cortical areas feedback onto early visual cortex, to tell early visual cortex that it should be seeing object contours where none are actually present.

The authors test the involvement of reentrant cortical activity in this kind of perception using a drug challenge. Reentrance in visual cortex is thought to rely on NMDAR-mediated glutamate signalling. The authors accordingly employ an NMDA antagonist to stop this mechanism, looking for the effect of this manipulation on visually evoked activity recorded in EEG.

The motivating hypothesis for the paper is that NMDA antagonism should stop recurrent activity, and that this should degrade perceptual activity supporting perception of a contour illusion, but not other types of visual experience. Results in fact show the opposite. Rather than degrading cortical activity evoked by the illusion, memantine makes it more likely that machine learning classification of EEG will correctly infer the presence of the illusion.

On the face of it, this is confusing. But the paper does a good job of providing possible accounts based on specific details of neurochemical signalling and receptor populations.

I broadly find the paper interesting, graceful, and creative. The hypotheses are clear and compelling, the techniques for both manipulation of brain state and observation of that impact are cutting edge and well suited, and the paper draws clear and convincing conclusions that are made necessary by the results. The work sits at the very interesting crux of systems neuroscience, neuroimaging, and pharmacology.

Reviewer #2 (Public review):

This study presents an important finding to the field interested in recurrent processing and the role of NMDA-receptors herein. The evidence for improved decoding under memantine is convincing, while some open questions remain to be followed up in future studies (the lack of a behavioural effect, why is decoding improved rather than decreased?). It is an excellent example of how an unexpected finding can generate novel research ideas to the mechanisms underlying recurrent processing, suggesting that the answer lies in the differences in the effects of ketamine and memantine, rather than their commonalities.

I would like to thank the authors for the great care they have taken in addressing my concerns. I think the revised manuscript is significantly easier to follow now that specific hypothesis have been formulated in the introduction, and the direction of the results is explicitly stated throughout the manuscript. I further appreciate the dampening of some of the claims that are not completely supported by the appropriate interactions.

I think the resulting manuscript is an incredibly exciting contribution to our understanding of NMDA-receptor function, and a great example of how an unexpected finding can raise questions that could potentially drive the field forward. It shows how NMDA's role in recurrent processing is much more complicate than previously assumed, and reveals that it is not the commonalities between memantine and ketamine that are important in understanding recurrent processing, but rather the differences. I look forward to future studies that will target these differences.

Overall great job.

Reviewer #3 (Public review):

Summary:

In this study, Stein and colleagues use a clever masking/attentional blink paradigm using Kanisza stimuli, coupled with EEG decoding and the NMDA antagonist memantine, to isolate putative neural markers of feedforward, lateral, and feedback processing.

In two elegant experiments, they show that memantine selective influences EEG decoding of only illusory Kanisza surfaces (but not contour continuation or raw contrast), only when unmasked, only when attention is available (not when "blinked"), and only when task-relevant.

This neatly implicates NMDA receptors in the feedback mechanisms that are believed to be involved in inferring illusory Kanisza surfaces, and builds a difficult bridge between the large body of human perceptual experiments and pharmacological and neurophysiological work in animals.

Strengths:

Three key strengths of the paper are 1) its elegant and thorough experimental design, which includes internal replication of some key findings, and 2) the clear pattern of results across the full set of experiments, and 3) its clear writing and presentation of results.

The paper effectively reports a 4-way interaction, with memantine only influencing decoding of surfaces (1) that are unmasked (2), with attention available (3) and task-relevant (4). Nevertheless, the results are very clear, with a clear separation between null effects on other conditions and quite a strong (and thus highly selective) effect on this one intersection of conditions. This makes the pattern of findings very convincing.

Weaknesses:

Overall this is an impressive and important paper. However, to my mind there are two minor weaknesses.

First, despite its clear pattern of neural effects, there is no corresponding perceptual effect. Although the manipulation fits neatly within the conceptual framework, and there are many reasons for not finding such an effect (floor and ceiling effects, narrow perceptual tasks etc), this does leave open the possibility that the observation is entirely epiphenomenal, and that the mechanisms being recorded here are not actually causally involved in perception per se.

Second, although it is clear that there is an effect on decoding in this particular condition, what that means is not entirely clear - particularly since performance improves, rather than decreases. It should be noted here that improvements in decoding performance do not necessarily need to map onto functional improvements, and we should all be careful to remain agnostic about what is driving classifier performance. Here too, the effect of memantine on decoding might be epiphenomenal - unrelated to the information carried in the neural population, but somehow changing the balance of how that is electrically aggregated on the surface of the skull. *Something* is changing, but that might be a neurochemical or electrical side-effect unrelated to actual processing (particularly since no corresponding behavioural impact is observed.)

Comments on revisions:

I think the authors responsed fairly to my comments. Even if they weren't really able to add new insight into why behaviour didn't show the same effects as decoding, they discuss this in the revised text.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

(1) I miss some treatment of the lack of behavioural correlate. What does it mean that metamine benefits EEG classification accuracy without improving performance? One possibility here is that there is an improvement in response latency, rather than perceptual sensitivity. Is there any hint of that in the RT results? In some sort of combined measure of RT and accuracy? 

First, we would like to thank the reviewer for their positive assessment of our work and for their extremely helpful and constructive comments that helped to significantly improve the quality of our manuscript.  

The reviewer rightly points out that, to our surprise, we did not obtain a correlate of the effect of memantine in our behavioral data, neither in the reported accuracy data nor in the RT data. We do not report RT results as participants were instructed to respond as accurately as possible, without speed pressure. We added a paragraph in the discussion section to point to possible reasons for this surprising finding:

“There are several possible reasons for this lack of behavioral correlate.  For example, EEG decoding may be a more sensitive measure of the neural effects of memantine, in particular given that perceptual sensitivity may have been at floor (masked condition, experiment 1) or ceiling (unmasked condition, experiment 1, and experiment 2). It is also possible that the present decoding results are merely epiphenomenal, not mapping onto functional improvements (e.g., Williams et al., 2007). However, given that we found a tight link between these EEG decoding markers and behavioral performance in our previous work (Fahrenfort et al., 2017; Noorman et al., 2023), it is possible that the effect of memantine was just too subtle to show up in changes in overt behavior.”

(2) An explanation is missing, about why memantine impacts the decoding of illusion but not collinearity. At a systems level, how would this work? How would NMDAR antagonism selectively impact long-range connectivity, but not lateral connectivity? Is this supported by our understanding of laminar connectivity and neurochemistry in the visual cortex?

We have no straightforward or mechanistic explanation for this finding. In the revised discussion, we are highlighting this finding more clearly, and included some speculative explanations:

“The present effect of memantine was largely specific to illusion decoding, our marker of feedback processing, while collinearity decoding, our marker of lateral processing, was not (experiment 1) or only weakly (experiment 2) affected by memantine. We have no straightforward explanation for why NMDA receptor blockade would impact inter-areal feedback connections more strongly than intra-areal lateral connections, considering their strong functional interdependency and interaction in grouping and segmentation processes (Liang et al., 2017). One possibility is that this finding reflects properties of our EEG decoding markers for feedback vs. lateral processing: for example, decoding of the Kanizsa illusion may have been more sensitive to the relatively subtle effect of our pharmacological manipulation, either because overall decoding was better than for collinearity or because NMDA receptor dependent recurrent processes more strongly contribute to illusion decoding than to collinearity decoding.”

(3) The motivating idea for the paper is that the NMDAR antagonist might disrupt the modulation of the AMPA-mediated glu signal. This is in line with the motivating logic for Self et al., 2012, where NMDAR and AMPAR efficacy in macacque V1 was manipulated via microinfusion. But this logic seems to conflict with a broader understanding of NMDA antagonism. NMDA antagonism appears to generally have the net effect of increasing glu (and ACh) in the cortex through a selective effect on inhibitory GABAergic cells (eg. Olney, Newcomer, & Farber, 1999). Memantine, in particular, has a specific impact on extrasynaptic NMDARs (that is in contrast to ketamine; Milnerwood et al, 2010, Neuron), and this type of receptor is prominent in GABA cells (eg. Yao et al., 2022, JoN). The effect of NMDA antagonists on GABAergic cells generally appears to be much stronger than the effect on glutamergic cells (at least in the hippocampus; eg. Grunze et al., 1996).

This all means that it's reasonable to expect that memantine might have a benefit to visually evoked activity. This idea is raised in the GD of the paper, based on a separate literature from that I mentioned above. But all of this could be better spelled out earlier in the paper, so that the result observed in the paper can be interpreted by the reader in this broader context.

To my mind, the challenging task is for the authors to explain why memantine causes an increase in EEG decoding, where microinfusion of an NMDA antagonist into V1 reduced the neural signal Self et al., 2012. This might be as simple as the change in drug... memantine's specific efficacy on extrasynaptic NMDA receptors might not be shared with whatever NMDA antagonist was used in Self et al. 2012. Ketamine and memantine are already known to differ in this way. 

We addressed the reviewer’s comments in the following way. First, we bring up our (to us, surprising) result already at the end of the Introduction, pointing the reader to the explanation mentioned by the reviewer:

“We hypothesized that disrupting the reentrant glutamate signal via blocking NMDA receptors by memantine would impair illusion and possibly collinearity decoding, as putative markers of feedback and lateral processing, but would spare the decoding of local contrast differences, our marker of feedforward processing. To foreshadow our results, memantine indeed specifically affected illusion decoding, but enhancing rather than impairing it. In the Discussion, we offer explanations for this surprising finding, including the effect of memantine on extrasynaptic NMDA receptors in GABAergic cells, which may have resulted in boosted visual activity.”

Second, as outlined in the response to the first point by Reviewer #2, we are now clear throughout the title, abstract, and paper that memantine “improved” rather than “modulated” illusion decoding.

Third, and most importantly, we restructured and expanded the Discussion section to include the reviewer’s proposed mechanisms and explanations for the effect. We would like to thank the reviewer for pointing us to this literature. We also discuss the results of Self et al. (2012), specifically the distinct effects of the two NMDAR antagonists used in this study, more extensively, and speculate that their effects may have been similar to ketamine and thus possibly opposite of memantine (for the feedback signal):

“Although both drugs are known to inhibit NMDA receptors by occupying the receptor’s ion channel and are thereby blocking current flow (Glasgow et al., 2017; Molina et al., 2020), the drugs have different actions at receptors other than NMDA, with ketamine acting on dopamine D2 and serotonin 5-HT2 receptors, and memantine inhibiting several subtypes of the acetylcholine (ACh) receptor as well as serotonin 5HT3 receptors. Memantine and ketamine are also known to target different NMDA receptor subpopulations, with their inhibitory action displaying different time courses and intensity (Glasgow et al., 2017; Johnson et al., 2015). Blockade of different NMDA receptor subpopulations can result in markedly different and even opposite results. For example, Self and colleagues (2012) found overall reduced or elevated visual activity after microinfusion of two different selective NMDA receptor antagonists (2-amino-5phosphonovalerate and ifendprodil) in macaque primary visual cortex. Although both drugs impaired the feedback-related response to figure vs. ground, similar to the effects of ketamine (Meuwese et al., 2013; van Loon et al., 2016) such opposite effects on overall activity demonstrate that the effects of NMDA antagonism strongly depend on the targeted receptor subpopulation, each with distinct functional properties.”

Finally, we link these differences to the potential mechanism via GABAergic neurons:

“As mentioned in the Introduction, this may be related to memantine modulating processing at other pre- or post-synaptic receptors present at NMDA-rich synapses, specifically affecting extrasynaptic NMDA receptors in GABAergic cells (Milnerwood et al, 2010; Yao et al., 2022). Memantine’s strong effect on extrasynaptic NMDA receptors in GABAergic cells leads to increases in ACh levels, which have been shown to increase firing rates and reduce firing rate variability in macaques (Herrero et al., 2013, 2008). This may represent a mechanism through which memantine (but not ketamine or the NMDA receptor antagonists used by Self and colleagues) could boost visually evoked activity.”

(4) The paper's proposal is that the effect of memantine is mediated by an impact on the efficacy of reentrant signaling in visual cortex. But perhaps the best-known impact of NMDAR manipulation is on LTP, in the hippocampus particularly but also broadly.

Perception and identification of the kanisza illusion may be sensitive to learning (eg. Maertens & Pollmann, 2005; Gellatly, 1982; Rubin, Nakayama, Shapley, 1997); what argues against an account of the results from an effect on perceptual learning? Generally, the paper proposes a very specific mechanism through which the drug influences perception. This is motivated by results from Self et al 2012 where an NMDA antagonist was infused into V1. But oral memantine will, of course, have a whole-brain effect, and some of these effects are well characterized and - on the surface - appear as potential sources of change in illusion perception. The paper needs some treatment of the known ancillary effects of diffuse NMDAR antagonism to convince the reader that the account provided is better than the other possibilities. 

We cannot fully exclude an effect based on perceptual learning but consider this possibility highly unlikely for several reasons. First, subjects have performed more than a thousand trials in a localizer session before starting the main task (in experiment 2 even more than two thousand) containing the drug manipulation. Therefore, a large part of putative perceptual learning would have already occurred before starting the main experiment. Second, the main experiment was counterbalanced across drug sessions, so half of the participants first performed the memantine session and then the placebo session, and the other half of the subjects the other way around. If memantine would have improved perceptual learning in our experiments, one may actually expect to observe improved decoding in the placebo session and not in the memantine session. If memantine would have facilitated perceptual learning during the memantine session, the effect of that facilitated perceptual learning would have been most visible in the placebo session following the memantine session. Because we observed improved decoding in the memantine session itself, perceptual learning is likely not the main explanation for these findings. Third, perceptual learning is known to occur for several stimulus dimensions (e.g., orientation, spatial frequency or contrast). If these findings would have been driven by perceptual learning one would have expected to see perceptual learning for all three features, whereas the memantine effects were specific to illusion decoding. Especially in experiment 2, all features were equally often task relevant and in such a situation one would’ve expected to observe perceptual learning effects on those other features as well.  

To further investigate any potential role of perceptual learning, we analyzed participants’ performance in detecting the Kanizsa illusion over the course of the experiments. To investigate this, we divided the experiments’ trials into four time bins, from the beginning until the end of the experiment. For the first experiment’s first target (T1), there was no interaction between the factors bin and drug (memantine/placebo; F_3,84=0.89, P\=0.437; Figure S6A). For the second target (T2), we performed a repeatedmeasures ANOVA with the factors bin, drug, T1-T2 lag (short/long), and masks (present/absent). There was only a trend towards a bin by drug interaction (F_3,84=2.57, P\=0.064; Figure S6B), reflecting worse performance under memantine in the first three bins and slightly better performance in the fourth bin. The other interactions that include the factors bin and drug factors were not significant (all P>0.117). For the second experiment, we performed a repeated-measures ANOVA with the factors bin, drug, masks, and task-relevant feature (local contrast/collinearity/illusion). None of the interactions that included the bin and drug factors were significant (all P>0.219; Figure S6C). Taken together, memantine does not appear to affect Kanizsa illusion detection performance through perceptual learning. Finally, there was no interaction between the factors bin and task-relevant feature (F_6,150=0.76, P\=0.547; Figure S6D), implying there is no perceptual learning effect specific to Kanizsa illusion detection. We included these analyses in our revised Supplement as Fig. S6.

(5) The cross-decoding approach to data analysis concerns me a little. The approach adopted here is to train models on a localizer task, in this case, a task where participants matched a kanisza figure to a target template (E1) or discriminated one of the three relevant stimuli features (E2). The resulting model was subsequently employed to classify the stimuli seen during separate tasks - an AB task in E1, and a feature discrimination task in E2. This scheme makes the localizer task very important. If models built from this task have any bias, this will taint classifier accuracy in the analysis of experimental data. My concern is that the emergence of the kanisza illusion in the localizer task was probably quite salient, respective to changes in stimuli rotation or collinearity. If the model was better at detecting the illusion to begin with, the data pattern - where drug manipulation impacts classification in this condition but not other conditions - may simply reflect model insensitivity to non-illusion features.

I am also vaguely worried by manipulations implemented in the main task that do not emerge in the localizer - the use of RSVP in E1 and manipulation of the base rate and staircasing in E2. This all starts to introduce the possibility that localizer and experimental data just don't correspond, that this generates low classification accuracy in the experimental results and ineffective classification in some conditions (ie. when stimuli are masked; would collinearity decoding in the unmasked condition potentially differ if classification accuracy were not at a floor? See Figure 3c upper, Figure 5c lower).

What is the motivation for the use of localizer validation at all? The same hypotheses can be tested using within-experiment cross-validation, rather than validation from a model built on localizer data. The argument may be that this kind of modelling will necessarily employ a smaller dataset, but, while true, this effect can be minimized at the expense of computational cost - many-fold cross-validation will mean that the vast majority of data contributes to model building in each instance. 

It would be compelling if results were to reproduce when classification was validated in this kind of way. This kind of analysis would fit very well into the supplementary material.

We thank the reviewer for this excellent question. We used separate localizers for several reasons, exactly to circumvent the kind of biases in decoding that the reviewer alludes to. Below we have detailed our rationale, first focusing on our general rationale and then focusing on the decisions we made in designing the specific experiments.  

Using a localizer task in the design of decoding analysis offers several key advantages over relying solely on k-fold cross-validation within the main task:

(1) Feature selection independence and better generalization: A separate localizer task allows for independent feature selection, ensuring that the features used for decoding are chosen without bias from the main task data. Specifically, the use of a localizer task allows us to determine the time-windows of interest independently based on the peaks of the decoding in the localizer. This allows for a better direct comparison between the memantine and placebo conditions because we can isolate the relevant time windows outside a drug manipulation. Further, training a classifier on a localizer task and testing it on a separate experimental task assesses whether neural representations generalize across contexts, rather than simply distinguishing conditions within a single dataset. This supports claims about the robustness of the decoded information.

(2) Increased sensitivity and interpretability: The localizer task can be designed specifically to elicit strong, reliable responses in the relevant neural patterns. This can improve signal-to-noise ratio and make it easier to interpret the features being used for decoding in the test set. We facilitate this by having many more trials in the localizer tasks (1280 in E1 and 5184 in E2) than in the separate conditions of the main task, in which we would have to do k-folding (e.g., 2, mask, x 2 (lag) design in E1 leaves fewer than 256 trials, due to preprocessing, for specific comparisons) on very low trial numbers. The same holds for experiment 2 which has a 2x3 design, but also included the base-rate manipulation. Finally, we further facilitate sensitivity of the model by having the stimuli presented at full contrast without any manipulations of attention or masking during the localizer, which allows us to extract the feature specific EEG signals in the most optimal way.

(3) Decoupling task-specific confounds: If decoding is performed within the main task using k-folding, there is a risk that task-related confounds (e.g., motor responses, attention shifts, drug) influence decoding performance. A localizer task allows us to separate the neural representation of interest from these taskrelated confounds.

Experiment 1 

In experiment 1, the Kanizsa was always task relevant in the main experiment in which we employed the pharmacological manipulation. To make sure that the classifiers were not biased towards Kanizsa figures from the start (which would be the case if we would have done k-folding in the main task), we used a training set in which all features were equally relevant for task performance. As can be seen in figure 1E, which plots the decoding accuracies of the localizer task, illusion decoding as well as rotation decoding were equally strong, whereas collinearity decoding was weaker. It may be that the Kanizsa illusion was quite salient in the localizer task, which we can’t know at present, but it was at least less salient and relevant than in the main task (where it was the only task-relevant feature). Based on the localizer decoding results one could argue that the rotation dimension and illusion dimension were most salient, because the decoding was highest for these dimensions. Clearly the model was not insensitive to nonillusory features. The localizer task of experiment 2 reveals that collinearity decoding tends to be generally lower, even when that feature is task relevant.  

Experiment 2 

In experiment 2, the localizer task and main task were also similar, with three exceptions: during the localizer task no drug was active, and no masking and no base rate manipulation were employed. To make sure that the classifier was not biased towards a certain stimulus category (due to the bias manipulation), e.g. the stimulus that is presented most often, we used a localizer task without this manipulation. As can be seen in figure 4D decoding of all the features was highly robust, also for example for the collinearity condition. Therefore the low decoding that we observe in the main experiment cannot be due to poor classifier training or feature extraction in the localizer. We believe this is actually an advantage instead of a disadvantage of the current decoding protocol.

Based on the rationale presented above we are uncomfortable performing the suggested analyses using a k-folding approach in the main task, because according to our standards the trial numbers are too low and the risk that these results are somehow influenced by task specific confounds cannot be ruled out.  

Line 301 - 'Interestingly, in both experiments the effect of memantine... was specific to... stimuli presented without a backward mask.' This rubs a bit, given that the mask broadly disrupted classification. The absence of memantine results in masked results may simply be a product of the floor ... some care is needed in the interpretation of this pattern. 

In the results section of experiment 1, we added:

“While the interaction between masking and memantine only approached significance (P\=0.068), the absence of an effect of memantine in the masked condition could reflect a floor effect, given that illusion decoding in the masked condition was not significantly better than chance.”

While floor is less likely to account for the absence of an effect in the masked condition in experiment 2, where illusion decoding in the masked condition was significantly above chance, it is still possible that to obtain an effect of memantine, decoding accuracy needed to be higher. We therefore also added here:

“For our time window-based analyses of illusion decoding, the specificity of the memantine effect to the unmasked condition was supported by a significant interaction between drug and masking (note, however, given overall much lower decoding accuracy in the masked condition, the lack of a memantine effect could reflect a floor effect).”

In the discussion, we changed the sentence to read “…the effect of memantine on illusion decoding tended to be specific to attended, task-relevant stimuli presented without a backward mask.”

Line 441 - What were the contraindications/exclusion parameters for the administration of memantine? 

Thanks for spotting this. We have added the relevant exclusion criteria in the revised version of the supplement. See also below.

– Allergy for memantine or one of the inactive ingredients of these products;

– (History of) psychiatric treatment;

– First-degree relative with (history of) schizophrenia or major depression;

– (History of) clinically significant hepatic, cardiac, obstructive respiratory, renal, cerebrovascular, metabolic or pulmonary disease, including, but not limited to fibrotic disorders;

– Claustrophobia;

–  Regular usage of medicines (antihistamines or occasional use of paracetamol);

– (History of) neurological disease;

–  (History of) epilepsy;

–  Abnormal hearing or (uncorrected) vision;

–  Average use of more than 15 alcoholic beverages weekly;

– Smoking

– History of drug (opiate, LSD, (meth)amphetamine, cocaine, solvents, cannabis, or barbiturate) or alcohol dependence;

– Any known other serious health problem or mental/physical stress;

– Used psychotropic medication, or recreational drugs over a period of 72 hours prior to each test session,  

– Used alcohol within the last 24 hours prior to each test session;

– (History of) pheochromocytoma.

– Narrow-angle glaucoma;

– (History of) ulcer disease;

– Galactose intolerance, Lapp lactase deficiency or glucose­galactose malabsorption.

– (History of) convulsion;

Line 587 - The localizer task used to train the classifier in E2 was collected in different sessions. Was the number of trials from separate sessions ultimately equal? The issue here is that the localizer might pick up on subtle differences in electrode placement. If the test session happens to have electrode placement that is similar to the electrode placement that existed for a majority of one condition of the localizer... this will create bias. This is likely to be minor, but machine classifiers really love this kind of minor confound.

Indeed, the trial counts in the separate sessions for the localizer in E2 were equal. We have added that information to the methods section.  

Experiment 1: 1280 trials collected during the intake session.

In experiment 2: 1728 trials were collected per session (intake, and 2 drug sessions), so there were 5184 trials across three sessions.

Reviewer #2:

To start off, I think the reader is being a bit tricked when reading the paper. Perhaps my priors are too strong, but I assumed, just like the authors, that NMDA-receptors would disrupt recurrent processing, in line with previous work. However, due to the continuous use of the ambiguous word 'affected' rather than the more clear increased or perturbed recurrent processing, the reader is left guessing what is actually found. That's until they read the results and discussion finding that decoding is actually improved. This seems like a really big deal, and I strongly urge the authors to reword their title, abstract, and introduction to make clear they hypothesized a disruption in decoding in the illusion condition, but found the opposite, namely an increase in decoding. I want to encourage the authors that this is still a fascinating finding.

We thank the reviewer for the positive assessment of our manuscript, and for many helpful comments and suggestions.  

We changed the title, abstract, and introduction in accordance with the reviewer’s comment, highlighting that “memantine […] improves decoding” and “enhances recurrent processing” in all three sections. We also changed the heading of the corresponding results section to “Memantine selectively improves decoding of the Kanizsa illusion”.

Apologies if I have missed it, but it is not clear to me whether participants were given the drug or placebo during the localiser task. If they are given the drug this makes me question the logic of their analysis approach. How can one study the presence of a process, if their very means of detecting that process (the localiser) was disrupted in the first place? If participants were not given a drug during the localiser task, please make that clear. I'll proceed with the rest of my comments assuming the latter is the case. But if the former, please note that I am not sure how to interpret their findings in this paper.

Thanks for asking this, this was indeed unclear. In experiment 1 the localizer was performed in the intake session in which no drugs were administered. In the second experiment the localizer was performed in all three sessions with equal trial numbers. In the intake session no drugs were administrated. In the other two sessions the localizer was performed directly after pill intake and therefore the memantine was not (or barely) active yet. We started the main task four hours after pill intake because that is the approximate peak time of memantine. Note that all three localizer tasks were averaged before using them as training set. We have clarified this in the revised manuscript.

The main purpose of the paper is to study recurrent processing. The extent to which this study achieves this aim is completely dependent to what extent we can interpret decoding of illusory contours as uniquely capturing recurrent processing. While I am sure illusory contours rely on recurrent processing, it does not follow that decoding of illusory contours capture recurrent processing alone. Indeed, if the drug selectively manipulates recurrent processing, it's not obvious to me why the authors find the interaction with masking in experiment 2. Recurrent processing seems to still be happening in the masked condition, but is not affected by the NMDA-receptor here, so where does that leave us in interpreting the role of NMDA-receptors in recurrent processing? If the authors can not strengthen the claim that the effects are completely driven by affecting recurrent processing, I suggest that the paper will shift its focus to making claims about the encoding of illusory contours, rather than making primary claims about recurrent processing.

We indeed used illusion decoding as a marker of recurrent processing. Clearly, such a marker based on a non-invasive and indirect method to record neural activity is not perfect. To directly and selectively manipulate recurrent processing, invasive methods and direct neural recordings would be required. However, as explained in the revised Introduction,

“In recent work we have validated that the decoding profiles of these features of different complexities at different points in time, in combination with the associated topography, can indeed serve as EEG markers of feedforward, lateral and recurrent processes (Fahrenfort et al., 2017; Noorman et al., 2023).”  

The timing and topography of the decoding results of the present study were consistent with our previous EEG decoding studies (Fahrenfort et al., 2017; Noorman et al., 2023). This validates the use of these EEG decoding signatures as (imperfect) markers of distinct neural processes, and we continue to use them as such. However, we expanded the discussion section to alert the reader to the indirect and imperfect nature of these EEG decoding signatures as markers of distinct neural processes: “Our approach relied on using EEG decoding of different stimulus features at different points in time, together with their topography, as markers of distinct neural processes. Although such non-invasive, indirect measures of neural activity cannot provide direct evidence for feedforward vs. recurrent processes, the timing, topography, and susceptibility to masking of the decoding signatures obtained in the present study are consistent with neurophysiology (e.g., Bosking et al., 1997; Kandel et al., 2000; Lamme & Roelfsema, 2000; Lee & Nguyen, 2001; Liang et al., 2017; Pak et al., 2020), as well as with our previous work (Fahrenfort et al., 2017; Noorman et al., 2023).” 

The reviewer is also concerned about the lack of effect of memantine on illusion decoding in the masked condition in experiment 2. In our view, the strong effect of masking on illusion decoding (both in absolute terms, as well as when compared to its effect on local contrast decoding), provides strong support for our assumption that illusion decoding represents a marker of recurrent processing. Nevertheless, as the reviewer points out, weak but statistically significant illusion decoding was still possible in the masked condition, at least when the illusion was task-relevant. As the reviewer notes, this may reflect residual recurrent processing during masking, a conclusion consistent with the relatively high behavioral performance despite masking (d’ > 1). However, rather than invalidating the use of our EEG markers or challenging the role of NMDA-receptors in recurrent processing, this may simply reflect a floor effect. As outlined in our response to reviewer #1 (who was concerned about floor effects), in the results section of experiment 1, we added:

“While the interaction between masking and memantine only approached significance (P\=0.068), the absence of an effect of memantine in the masked condition could reflect a floor effect, given that illusion decoding in the masked condition was not significantly better than chance.”

And for experiment 1:

“For our time window-based analyses of illusion decoding, the specificity of the memantine effect to the unmasked condition was supported by a significant interaction between drug and masking (note, however, given overall much lower decoding accuracy in the masked condition, the lack of a memantine effect could reflect a floor effect).”

An additional claim is being made with regards to the effects of the drug manipulation. The authors state that this effect is only present when the stimulus is 1) consciously accessed, and 2) attended. The evidence for claim 1 is not supported by experiment 1, as the masking manipulation did not interact in the cluster-analyses, and the analyses focussing on the peak of the timing window do not show a significant effect either. There is evidence for this claim coming from experiment 2 as masking interacts with the drug condition. Evidence for the second claim (about task relevance) is not presented, as there is no interaction with the task condition. A classical error seems to be made here, where interactions are not properly tested. Instead, the presence of a significant effect in one condition but not the other is taken as sufficient evidence for an interaction, which is not appropriate. I therefore urge the authors to dampen the claim about the importance of attending to the decoded features. Alternatively, I suggest the authors run their interactions of interest on the time-courses and conduct the appropriate clusterbased analyses.

We thank the reviewer for pointing out the importance of key interaction effects. Following the reviewer’s suggestion, we dampened our claims about the role of attention. For experiment 1, we changed the heading of the relevant results section from “Memantine’s effect on illusion decoding requires attention” to “The role of consciousness and attention in memantine’s effect on illusion decoding”, and we added the following in the results section:

“Also our time window-based analyses showed a significant effect of memantine only when the illusion was both unmasked and presented outside the AB (t_28\=-2.76, _P\=0.010, BF₁₀=4.53; Fig. 3F). Note, however, that although these post-hoc tests of the effect of memantine on illusion decoding were significant, for our time window-based analyses we did not obtain a statistically significant interaction between the AB and memantine, and the interaction between masking and memantine only approached significance (P\= 0.068). Thus, although these memantine effects were slightly less robust than for T1, probably due to reduced trial counts, these results point to (but do not conclusively demonstrate) a selective effect of memantine on illusion-related feedback processing that depends on the availability of attention. In addition to the lack of the interaction effect, another potential concern…”

For experiment 2, we added the following in the results section:

“Note that, for our time window-based analyses of illusion decoding, although the specificity of the memantine effect to the unmasked condition was supported by a significant interaction between drug and masking, we did not obtain a statistically significant interaction between memantine and task-relevance. Thus, although the memantine effect was significant only when the illusion was unmasked and taskrelevant, just like for the effect of temporal attention in experiment 1, these results do not conclusively demonstrate a selective effect of memantine that depends attention (task-relevance).”

In the discussion, we toned down claims about memantine’s effects being specific to attended conditions, we are highlighting the “preliminary” nature of these findings, and we are now alerting the reader explicitly to be careful with interpreting these effects, e.g.:

“Although these results have to be interpreted with caution because the key interaction effects were not statistically significant, …”

How were the length of the peak-timing windows established in Figure 1E? My understanding is that this forms the training-time window for the further decoding analyses, so it is important to justify why they have different lengths, and how they are determined. The same goes for the peak AUC time windows for the interaction analyses. A number of claims in the paper rely on the interactions found in these posthoc analyses, so the 223- to 323 time window needs justification.

Thanks for this question. The length of these peak-timing windows is different because the decoding of rotation is temporarily very precise and short-lived, whereas the decoding of the other features last much longer and is more temporally variable. In fact, we have followed the same procedure as in a previously published study (Noorman et al., elife 2025) for defining the peak-timing and length of the windows. We followed the same procedure for both experiments reported in this paper, replicating the crucial findings and therefore excluding the possibility that these findings are in any way dependent on the time windows that are selected. We have added that information to the revised version of the manuscript.

Reviewer #3:

First, despite its clear pattern of neural effects, there is no corresponding perceptual effect. Although the manipulation fits neatly within the conceptual framework, and there are many reasons for not finding such an effect (floor and ceiling effects, narrow perceptual tasks, etc), this does leave open the possibility that the observation is entirely epiphenomenal, and that the mechanisms being recorded here are not actually causally involved in perception per se.

We thank the reviewer for the positive assessment of our work. The reviewer rightly points out that, to our surprise, we did not obtain a correlate of the effect of memantine in our behavioral data. We agree with the possible reasons for the absence of such an effect highlighted by the reviewer, and expanded our discussion section accordingly:

“There are several possible reasons for this lack of behavioral correlate.  For example, EEG decoding may be a more sensitive measure of the neural effects of memantine, in particular given that perceptual sensitivity may have been at floor (masked condition, experiment 1) or ceiling (unmasked condition, experiment 1, and experiment 2). It is also possible that the present decoding results are merely epiphenomenal, not mapping onto functional improvements (e.g., Williams et al., 2007). However, given that in our previous work we found a tight link between these EEG decoding markers and behavioral performance (Fahrenfort et al., 2017; Noorman et al., 2023), it is possible that the effect of memantine in the present study was just too subtle to show up in changes in overt behavior.”

Second, although it is clear that there is an effect on decoding in this particular condition, what that means is not entirely clear - particularly since performance improves, rather than decreases. It should be noted here that improvements in decoding performance do not necessarily need to map onto functional improvements, and we should all be careful to remain agnostic about what is driving classifier performance. Here too, the effect of memantine on decoding might be epiphenomenal - unrelated to the information carried in the neural population, but somehow changing the balance of how that is electrically aggregated on the surface of the skull. *Something* is changing, but that might be a neurochemical or electrical side-effect unrelated to actual processing (particularly since no corresponding behavioural impact is observed.)

We would like to refer to our reply to the previous point, and we would like to add that in our previous work (Fahrenfort et al., 2017; Noorman et al., 2023) similar EEG decoding markers were often tightly linked to changes in behavioral performance. This indicates that these particular EEG decoding markers do not simply reflect some sideeffect not related to neural processing. However, as stated in the revised discussion section, “it is possible that the effect of memantine in the present study was just too subtle to show up in changes in overt behavior.”

eLife Assessment

This is a fundamental cell biological study of host responses during symbiotic microbial infection of plants. Compelling imaging-based approaches using genetically encoded cell cycle markers show that in Medicago truncatula root cortex cells, early rhizobial infection events are associated with cell-cycle re-entry, but once the infection is established, host cells exit the cell cycle. The work will be of interest to a wide range of readers working in fields from development and cell biology to plant-microbe interactions.

Reviewer #1 (Public Review):

Many studies reported findings implying that rhizobial infection is associated with cell cycle re-entry and progression, however, our understanding has been fragmented. This study provides exciting new insights as it represents a comprehensive description of the cell cycle progression during early stages of nodulation using fluorescence markers.

To briefly summarize, the authors first monitor H3.1 / H3.3 replacement to distinguish between replicating (S phase) and non-replicating cells to show that M. truncatula cortex cells along the bacterial infection thread are non-replicating (while neighbors enter the S phase). Nuclear size measurements revealed that these non-replicative cells are in the post-replicative stage (G2) rather than in the pre-replicative G1 phase, which the authors confirm with the Plant Cell Cycle Indicator (PlaCCI) fluorescent marker to track cell cycle progression in more detail. Cortex cells in the trajectory of the infection thread did not accumulate the late G2 marker of the PlaCCI nor the G2/M marker KNOLLE, indicating that these cells indeed remain in G2. Because nuclear size measurements indicated that infected cells are polyploid, the authors used the centromere histone marker CENH3 to determine chromosome number. They find that cortex cells giving rise to the nodule primordium are endomitotic and tetraploid, probably because their cell cycle is halted at centromere separation. Although not a focus of this manuscript, the authors also use their fluorescent tools to track cell cycle progression during arbuscular mycorrhiza symbiosis. They confirm that infected cells transition from a replicating to a non-replicating state (H3.1 to H3.3) with progressing development of the arbuscules. In addition, the CENH3 marker confirms previous findings that cortex cells infected by fungi are endocycling (i.e., DNA synthesis without segregation of replicated parts). This represents an important confirmation of previous findings and contrasts with the situation during nodulation symbiosis, where chromosomes separate after replication.

In general, all microscopy images are of very high quality and support the authors' conclusions. While individually each set of fluorescent markers has its limitations, combined they constitute a powerful tool to track various stages of cell cycle progression in individual root cells during symbiosis. Overall, this is a very strong manuscript that comprehensively elucidates root cell cycle changes during microbial infection.

Reviewer #2 (Public Review):

Cell cycle control during nitrogen-fixing symbiosis is an important topic, but our understanding of the process is poor and lacks resolution, as the nodule is a complex organ with many cell types that undergo profound changes. The authors aim to define the cell cycle state of individual plant cells in the emerging nodule primordium, as a transcellular infection thread passes through the meristem to reach cells deep in the incipient nodule and releases bacteria to form symbiosomes. The authors used a number of cell cycle reporters, such as different Histone 3 variants and cyclins, to follow cell cycle progress in exquisite detail. They showed that the host cells in the path of an infection thread exhibit a cell fate distinct from their immediate neighbors: after entering the S phase similar to their neighbors, these cells exit the cell cycle and enter a special differentiated state. This is likely an important shift that allows the proper passage of the infection thread. Although definitive proof needs more investigation, they showed that a pioneering transcription factor, NF-YA1, likely represses these endoreduplicated cells from completing the cell cycle.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

(…) In my view, the part about NF-YA1 is less strong - although I realize this is a compelling candidate to be a regulator of cell cycle progression, the experimental approaches used to address this question falls a bit short, in particular, compared to the very detailed approaches shown in the rest of the manuscript. The authors show that the transcription factor NF-YA1 regulates cell division in tobacco leaves; however, there is no experimental validation in the experimental system (nodules). All conclusions are based on a heterologous cell division system in tobacco leaves. The authors state that NF-YA1 has a nodule-specific role as a regulator of cell differentiation. I am concerned the tobacco system may not allow for adequate testing of this hypothesis.

Reviewer #1 makes a valid point by asking to focus the manuscript more explicitly on the role of NF-YA1 as a differentiation factor in a symbiotic context. We have now addressed this formally and experimentally.

The involvement of A-type NF-Y subunits in the transition to the early differentiation of nodule cells has been documented in model legumes through several publications that we refer to in the revised version of the discussion (lines 617/623). We fully agree that the CDEL system, because it is heterologous, does not allow us more than to propose a parallel explanation for these observations - i.e_., that the Medicago NF-YA1 subunit presumably acts in post-replicative cell-cycle regulation at the G2/M transition. Considering your recommendations and those of reviewer #2, we sought to support this conclusion by testing the impact of localized over-expression of _NF-YA1 on cortical cell division and infection competence at an early stage of root colonization. The results of these experiments are now presented in the new Figure 9 and Figure 9-figure supplement 1-5 and described from line 435 to 495.

With the fluorescent tools the authors have at hand (in particular tools to detect G2/M transition, which the authors suggest is regulated by NF-YA1), it would be interesting to test what happens to cell division if NF-YA1 is over-expressed in Medicago roots?

To limit pleiotropic effects of an ectopic over-expression, we used the symbiosis-induced, ENOD11 promoter to increase NF-YA1 expression levels more specifically along the trajectory of infected cells. We chose to remain in continuity with the experiments performed in the CDEL system by opting for a destabilized version of the KNOLLE transcriptional reporter to detect the G2/M transition. The results obtained are presented in Figure 9B (quantification of split infected cells), in Figure 9-figure supplement 1B (ENOD11 expression profile), in Figure 9-figure supplement 3B (representative confocal images) and Figure 9-figure supplement 4D (quantification of pKNOLLE reporter signal). There, we show that mitosis remains inhibited in cells accommodating infection threads, but is completed in a higher proportion of outer cortical cells positioned on the infection trajectory, where ENOD11 gene transcription is active before their physical colonization.

Based on NF-YA1 expression data published previously and their results in tobacco epidermal cells, the authors hypothesize that NF-YA regulates the mitotic entry of nodule primordial cells. Given that much of the manuscript deals with earlier stages of the infection, I wonder if NF-YA1 could also have a role in regulating mitotic entry in cells adjacent to the infection thread?

The expression profile of NF-YA1 at early stages of cortical infection (Laporte et al., 2014) is indeed similar to the one of ENOD11 (as shown in Figure 9-figure supplement 1C) in wild-type Medicago roots, with corresponding transcriptional reporters being both activated in cells adjacent to the infection thread. Under our experimental conditions, additional expression of NF-YA1 (driven by the ENOD11 promoter) in these neighbouring cells did not impact their propensity to enter mitosis and to complete cell division. These results are presented in Figure 9-figure supplement 4D (quantification of pKNOLLE reporter signal) and Figure 9-figure supplement 5 (quantification of split neighbouring cells).

Reviewer #1 (Recommendations For The Authors):

- In the first part, images show the qualitative presence/absence of H3.1 or H3.3 histones.

Upon closer inspection, many cells seem to have both histones. In Fig1-S1 for example (root meristem), it is evident that there are many cells with low but clearly present H3.1 content in the green channel; however, in the overlay, the green is lost and H3.3 (pink) is mainly visible. What does this mean in terms of the cell cycle? 

We fully agree with reviewer #1 on these points. Independent of whether they have low or high proliferation potential, most cells retain histone H3.1 particularly in silent regions of the genome, while H3.3 is constitutively produced and enriched at transcriptionally active regions. When channels are overlaid, cells in an active proliferation or endoreduplication state (in G1, S or G2, depending on the size of their nuclei) will appear mainly "green" (H3.1-eGFP positive). Cells with a low proliferation potential (e.g., in the QC), G2-arrested (e.g., IT-traversed) or terminally differentiating (e.g., containing symbiosomes or arbuscules) will appear mainly "magenta" (H3.1-low, medium to high H3.3-mCherry content).

Furthermore, all nodule images only display the overlay image, and individual fluorescence channels are not shown. Does the same masking effect happen here? It may be helpful to quantify fluoresce intensity not only in green but also in red channels as done for other experiments.

Quantifying fluorescence intensity in the mCherry channel may indeed help to highlight the likely replacement of H3.1-eGFP by H3.3-mCherry in infected cells, as described by Otero and colleagues (2016) at the onset of cellular differentiation. However, the quantification method as established (i.e., measuring the corrected total nuclear fluorescence at the equatorial plane) cannot be applied, most of the time, to infected cells' nuclei due to the overlapping presence of mCherry-producing S. meliloti in the same channel (e.g., in Figure 2B). Nevertheless, and to avoid this masking effect when the eGFP and mCherry channels are overlaid, we now present them as isolated channels in revised Figures 1-3 and associated figure supplements. As the cell-wall staining is regularly included and displayed in grayscale, we assigned to both of them the Green Fire Blue lookup table, which maps intensity values to a multiple-colour sequential scheme (with blue or yellow indicating low or high fluorescence levels, respectively). We hope that this will allow a better appreciation of the respective levels of H3.1- and H3.3-fusions in our confocal images.

- Fig 1 B - it is hard to differentiate between S. meliloti-mCherry and H3.3-mCherry. Is there a way to label the different structures?

In the revised version of Figure 1B, we used filled or empty arrowheads to point to histone H3-containing nuclei. To label rhizobia-associated structures, we used dashed lines to delineate nodule cells hosting symbiosomes and included the annotation “IT” for infection threads. We also indicated proliferating, endoreduplicating and differentiating tissues and cells using the following annotations: “CD” for cell division, “En” for endoreduplication and “TD” for terminal differentiation. All annotations are explained in the figure legend.

- Fig 1 - supplement E and F - no statistics are shown.

We performed non-parametric tests using the latest version of the GraphPad Prism software (version 10.4.1). Stars (Figure 1-figure supplement 1F) or different letters (Figure 1-figure supplement 1G) now indicate statistically significant differences. Results of the normality and non-parametric tests were included in the corresponding Source Data Files (Figure 1 – figure supplement 1 – source data 1 and 2). We have also updated the compact display of letters in other figures as indicated by the new software version. The raw data and the results of the statistical analyses remain unchanged and can be viewed in the corresponding source files.

- Fig 2 A - overview and close-up image do not seem to be in the same focal plane. This is confusing because the nuclei position is different (so is the infection thread position).

We fully agree that our former Figure may have confused reviewers #1 and #2 as well as readers. Figure 2A was designed to highlight, from the same nodule primordium, actively dividing cells of the inner cortex (optical section z 6-14) and cells of the outer cortex traversed, penetrated by or neighbouring an infection thread (optical section z 11-19). We initially wanted to show different magnification views of the same confocal image (i.e_._, a full-view of the inner cortex and a zoomed-view of the outer layers) to ensure that audiences can identify these details. In the revised version of Figure 2A, we displayed these full- and zoomed-views in upper and lower panels, respectively and we removed the solid-line inset to avoid confusion. 

- Fig 1A and Fig 2E could be combined and shown at the beginning of the manuscript. Also, consider making the cell size increase more extreme, as it is important to differentiate G2 cells after H3.1 eviction and cells in G1. You have to look very closely at the graph to see the size differences.

We have taken each of your suggestions into account. A combined version of our schematic representation with more pronounced nuclei size differences is now presented in Figure 1A.

- Fig. 3 C is difficult to interpret. Can this be split into different panels?

We realized that our previous choice of representation may have been confusing. Each value corresponds only to the H3.1-eGFP content, measured in an infected cell and reported to that of the neighbouring cell (IC / NC) within individual root samples. Therefore, we removed the green-magenta colour code and changed the legend accordingly. We hope that these slight modifications will facilitate the interpretation of the results - namely, that the relative level of H3.1 increases significantly in infected cells in the selected mutants compared to the wild-type. This mode of representation also highlights that in the mutants, there are more individual cases where the H3.1 content in an infected cell exceeds that of the neighbouring cell by more than two times. These cases would be masked if the couples of infected cells and associated neighbours would be split into different panels as in Figure 3B.

- Line 357/359. I assume you mean ...'through the G2 phase can commit to nuclear division'.

We have edited this sentence according to your suggestion, which now appears in line 370. 

Reviewer #2 (Recommendations For The Authors):

Cell cycle control during the nitrogen-fixing symbiosis is an important question but only poorly understood. This manuscript uses largely cell biological methods, which are always of the highest quality - to investigate host cell cycle progression during the early stages of nodule formation, where cortical infection threads penetrate the nodule primordium. The experiments were carefully conducted, the observations were detail oriented, and the results were thought-provoking. The study should be supported by mechanistic insights. 

(1) One thought provoked by the authors' work is that while the study was carried out at an unprecedented resolution, the relationship between control of the cell cycle and infection thread penetration remains correlative. Is this reduced replicative potential among cells in the infection thread trajectory a consequence of hosting an infection thread, or a prerequisite to do so?

We understand and share the point of view of reviewer #2. At this stage, we believe that our data won’t enable us to fully answer the question, thus this relationship remains rather correlative. The reasons are that 1) the access to the status of cortical cells below C2 is restricted to fixed material and therefore only represents a snapshot of the situation, and 2) we are currently unable to significantly interfere with mechanisms as intertwined as cell cycle control and infection control. What we can reasonably suggest from our images is that the most favorable window of the cell cycle for cells about to be crossed by an infection thread is post-replicative, i.e., the G2 phase. Typical markers of the G2 phase were recurrently observed at the onset of physical colonization – enlarged nucleus, containing less histone H3.1 than neighbouring cells in S phase (e.g., in Figure 2A). Reaching the G2 phase could therefore be a prerequisite for infection (and associated cellular rearrangements), while prolonged arrest in this same phase is likely a consequence of transcellular passage towards a forming nodule primordium.

More importantly, in either scenario, what is the functional significance of exiting the cell cycle or endocycle? By stating that "local control of mitotic activity could be especially important for rhizobia to timely cross the middle cortex, where sustained cellular proliferation gives rise to the nodule meristem" (Line 239), the authors seem to believe that cortical cells need to stop the cell cycle to prepare for rhizobia infection. This is certainly reasonable, but the current study provides no proof, yet. To test the functional importance of cell cycle exit, one would interfere with G2/M transition in nodule cells,  and examine the effect on infection.

We fully agree with reviewer #2 that the functional importance of a cell-cycle arrest on the infection thread trajectory remains to be demonstrated. Interfering with cell-cycle progression in a system as complex and fine-tuned as infected legume roots certainly requires the right timing – at the level of the tissue and of individual cells; the right dose; and the right molecular player(s) (i.e., bona fide activators or repressors of the G2/M transition). Using the symbiosis-specific NPL promoter, activated in the direct vicinity of cortical infection threads (Figure 9-figure supplement 1B), we tried to force infectable cells to recruit the cell division program by ectopically over-expressing the Arabidopsis CYCD3.1, “mimicking” the CDEL system. So far, this strategy has not resulted in a significant increase in the number of uninfected nodules in transgenic hairy roots - though the effect on symbiosome release remains to be investigated. Provided that a suitable promoter-cell cycle regulator combination is identified, we hope to be able to answer this question in the future.

Given that the authors have already identified a candidate, and showed it represses cell division in the CDEL system, not testing the same gene in a more relevant context seems a lost opportunity. If one ectopically expressed NY-YA1 in hairy roots, thus repressing mitosis in general, would more cells become competent to host infection threads? This seems a straightforward experiment and readily feasible with the constructs that the authors already have. If this view is too naive, the authors should explain why such a functional investigation does not belong in this manuscript.

Reviewer #2's point is entirely valid, and we decided to address it through additional experiments. To avoid possible side effects on development by affecting cell division in general, we placed NF-YA1 under control of the symbiosis-induced ENOD11 promoter. Based on the results obtained in the CDEL system, the pENOD11::FLAG-NF-YA1 cassette was coupled to a destabilized version of the KNOLLE transcriptional reporter to detect the G2/M transition. Competence for transcellular infection was maintained upon local NFYA1 overexpression, the latter leading to a slight (non-significant) increase in the number of infected cells per cortical layer. These results are presented in Figure 9-figure supplement 3A-B (representative confocal images) and in Figure 9-figure supplement 4A-

G.

(1b) A related comment: on Line 183, it was stated that "The H3.1-eGFP fusion protein was also visible in cells penetrated but not fully passed by an infection thread". Presumably, the authors were talking about the cell marked by the arrowhead. But its H3.1-GFP signal looks no different from the cell immediately to its left. It is hard to say which cells are ones "preparing for intracellular infection pass through S-phase", and which ones are just "regularly dividing cortical cells forming the nodule primordium". What can be concluded is that once a cell has been fully transversed by an infection thread, its H3.1 level is low. Whether this is the cause or consequence of infection cannot be resolved simply by timing the appearance or disappearance of H3.1-GFP.

We basically agree with comment 1b. In an unsynchronized system such as infected hairy roots, it is challenging to detect the event where a cell is penetrated, but not yet completely crossed by an infection thread. What we wanted to emphasize in Figure 2A, is that host cells in the path of an infection thread re-enter the cell cycle and pass through S-phase just as their neighbours do (as pointed out by reviewer #2 in his summary). The larger nucleus with slightly lower H3.1-eGFP signal than the neighbouring cell (as indicated by the use of the Green Fire Blue lookup table) suggests that the infected cell marked by the arrowhead in Figure 2A is actually in the G2 phase. The main difference is indeed that cells allowing complete infection thread passage exit the cell cycle and largely evict H3.1 while their neighbours proceed to cell division (as exemplified by PlaCCI reporters in Figure 4CD and the new Figure 5-figure supplement 2). Whether cell-cycle exit in G2 is a cause, or a consequence of cortical infection is a question that cannot be easily answered from fixed samples, which is a limitation of our study.

(2) The authors have convincingly demonstrated that cortical cells accommodating infection threads exit the cell cycle, inhibit cell division, and down-regulate KNOLLE expression. How do these observations reconcile with the feature called the pre-infection thread? The authors devoted one paragraph to this question in the Discussion, but this does seem sufficient given that the pre-infection thread is a prominent concept. Is the resemblance to the cell division plane superficial, or does it reflect a co-option of the normal cytokinesis machinery for accommodating rhizobia?

From our point of view, cortical cells forming pre-infection threads are likely in an intermediate state. PIT structures undoubtedly share many similarities with cells establishing a cell division plane. The recruitment of at least some of the players normally associated with cytokinesis has been demonstrated and is consistent with the maintenance of infectable cells in a pre-mitotic phase in Medicago, as discussed in lines 558 to 568. We nevertheless think that the arrest of the cell cycle in the G2 phase, presumably occurring in crossed cortical cells, constitutes an event of cellular differentiation and specialization in transcellular infection. 

The following are mainly points of presentation and description: 

(3) Line 158: I can't see "subnuclear foci" in Figure 1-figure supplement 1C-E. However, they are visible in Fig. 1C.

We hope that presenting the eGFP and mCherry channels in separate panels and assigning them the Green Fire Blue colour scheme provides better visibility and contrast of these detailed structures. We now refer to Figure 1C in addition to Figure 1–figure supplement 1E in the main text (line 161). 

(4) Line 160: The authors should outline a larger region containing multiple QC cells, rather than pointing to a single cell, as there are other areas in the image containing cells with the same pattern.

We updated Figure 1-figure supplement 1E accordingly.

(5) Fig. 1B should include single channels, since within a single plant cell, the nucleus, the infection thread, and sometimes symbiosomes all have the same color. This makes it hard to see whether the nuclei in these cells are less green, or are simply overwhelmed by the magenta color.

To improve the readability of Figure 1B and to address suggestions from individual reviewers, we now include separate channels and have annotated the different structures labeled by mCherry.

(6) Fig. 2A: the close-up does not match the boxed area in the left panel. Based on the labeling, it seems that the two panels are different optical sections. But why choose a different optical depth for the left panel? This can be disorienting to the author, because one expects the close-up to be the same image, just under higher magnification.

We fully agree that our previous choice of representation may have been confusing. As we also specified to reviewer #1, we wanted to show a full-view of proliferating cells in the inner cortex and a zoomed-view of infected cells in the outer layers of the same nodule primordium. In the revised version of Figure 2A, we displayed these full- and zoomedviews in separate panels and removed the boxed area to avoid confusion. 

(7) Figure 2-figure supplement 1B: the cell indicated by the empty arrowhead has a striking pattern of H3.1 and H3.3 distribution on condensed chromosomes. Can you comment on that?

Reviewer #2 may be referring to the apparent enrichment of H3.3 at telomeres, previously described in Arabidopsis, while pericentromeric regions are enriched in H3.1. This distribution is indeed visible on most of the condensed chromosomes shown in Figure 2-figure supplement 1B. We included this comment in the corresponding caption.

(8) Fig. 4: It is not very easy to distinguish M phase. Can the authors describe how each phase is supposed to look like with the reporters?

We agree with reviewer #2 and attempted to improve Figure 4, which is now dedicated to the Arabidopsis PlaCCI reporter. ECFP, mCherry, and YFP channels were presented separately and the corresponding cell-cycle phases (in interphase and mitosis) were annotated. The Green Fire Blue lookup table was assigned to each reporter to provide the best visibility of, for example, chromosomes in early prophase. We included a schematic representation corresponding to the distribution of each reporter, using the colors of the overlaid image to facilitate its interpretation.

(9) Line 298: what is endopolyploid? This term is used at least three times throughout the manuscript. How is it different from polyploid?

In the manuscript, we aimed to differentiate the (poly)ploidy of an organism (reflecting the number of copies of the basic genome and inherited through the germline) from endopolyploidy produced by individual somatic cells. As reviewed by Scholes and Paige, polyploidy and endopolyploidy differ in important ways, including allelic diversity and chromosome structural differences. In the Medicago truncatula root cortex for example, a tetraploid cell generated via endoreduplication from the diploid state would contain at most two alleles at any locus. The effects of endopolyploidy on cell size, gene expression, cell metabolism and the duration of the mitotic cell cycle are not shared among individual cells or organs, contrasting to a polyploid individual (Scholes and Paige, 2015).

See Scholes, D. R., & Paige, K. N. (2015). Plasticity in ploidy : A generalized response to stress. Trends in Plant Science, 20(3), 165‑175. https://doi.org/10.1016/j.tplants.2014.11.007

(10) Line 332: "chromosomes on mitotic figures" - what does this mean?

Reviewer #2 is right to point out this redundant wording. Mitotic “figures” are recognized, by definition, based on chromosome condensation. We now use the term "mitotic chromosomes" (line 344).

(11) Fig. 6A: could the authors consider labeling the doublets, at least some of them? I understand that this nucleus contains many doublets. However, this is the first image where one is supposed to recognize these doublets, and pointing out these features can facilitate understanding. Otherwise, a reader might think the image is comparable to nuclei with no doublets in the rest of the figure.

Following this suggestion, five of these doublets are now labeled in Figure 7A (formerly Figure 6A).

eLife Assessment

In this convincing work by Yamaguchi et al. the cryo-EM structure of the heterohexameric 3:3 LGI1-ADAM22 complex is presented. The findings suggest that LGI1 can cluster ADAM22 in a trimeric fashion. The clustering of cell surface proteins is important in controlling signaling in the nervous system. This new version of the manuscript has been improved substantially and the figures have been enhanced and clarified.

Reviewer #1 (Public review):

The structure of a heterohexameric 3:3 LGI1-ADAM22 complex is resolved by Yamaguchi et al. It reveals the intermolecular LGI1 interactions and its role in bringing three ADAM22 molecules together. This may be relevant for the clustering of axonal Kv1 channels and control over their density. While it is currently not clear if the heterohexameric 3:3 LGI1-ADAM22 complex has a physiological role, the detailed structural information presented here allows to pinpoint mutations or other strategies to probe the relevance of the 3:3 complex in future work.

The experimental work is done to a high standard, and all my comments have been addressed. This new version of the manuscript has been improved substantially, and the figures have been enhanced and clarified.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

(1) A previously determined 2:2 heterodimeric complex of LGI1-ADAM22 was suggested to play a role in trans interactions. Could the authors discuss if the heterohexameric 3:3 LGI1-ADAM22 is more likely to represent a cis complex or a trans complex, or if both are possible?

We noticed that there was no obvious structural feature strongly suggesting that the heterohexameric 3:3 LGI1-ADAM22 is more likely to represent a cis complex or a trans complex. Both are possible at the synapse (and similarly, for LGI3-ADAM23 at the jaxtaparanode of myelinated axons). Therefore, we revised the Introduction and Discussion sections as follows:

Introduction: (about potential structural mechanisms of the 3:3 complex)

“Similarly to the 2:2 complex, the 3:3 complex might serve as an extracellular scaffold to stabilize Kv1 channels or AMPA receptors in a trans-synaptic fashion. In addition, the 3:3 assembly in a cis fashion on the same membrane might regulate the accumulation of Kv1 channel complexes at axon initial segment. However, no clear evidence to prove these potential mechanistic roles of the 3:3 assembly has been provided, and the three-dimensional structure of the 3:3 complex has not yet been determined.”

Discussion: (about a role of the LGI3–ADAM23 complex at the jaxtaparanode of myelinated axons)

“In this context, as discussed in (30), either or both of the 2:2 and 3:3 complexes might be formed in a trans fashion at the juxtaparanode of myelinated axons and bridge the axon and the innermost myelin membrane. Alternatively, the 3:3 complex formed in a cis fashion might positively regulate the clustering of the axonal Kv channels at the juxtaparanode, possibly in a similar manner at the axon initial segment.”

*Ref. 30: Y. Miyazaki et al., Oligodendrocyte-derived LGI3 and its receptor ADAM23 organize juxtaparanodal Kv1 channel clustering for short-term synaptic plasticity. Cell Rep 43, 113634 (2024).

(2) It is not entirely clear to me if the LGI1-ADAM22 complex is also crosslinked in the HS-AFM experiments. Could this be more clearly indicated? In addition, if this is the case, could an explanation be given about how the complex can still dissociate?

Thank you for the constructive suggestions. A non-crosslinked 3:3 LGI-ADAM22 complex was used for HS-AFM observations. To clarify the sample used for HS-AFM, we have modified the text as follows.

P.8 “Dynamics of the LGI1‒ADAM22 higher-order complex observed by HS-AFM

HS-AFM images of gel filtration chromatography fractions containing the 3:3 LGI1-ADAM22_ECD complex (not chemically crosslinked with glutaraldehyde) predominantly…”

P.10 Materials and methods

“HS-AFM observations of the LGI1–ADAM22_ECD complex (not chemically crosslinked with glutaraldehyde) were conducted on AP-mica,…”

(3) The LGI1 and ADAM22 are of similar size. To me, this complicates the interpretation of dissociation of the complex in the HS-AFM data. How is the overinterpretation of this data prevented? In other words, what confidence do the authors have in the dissociation steps in the HS-AFM data?

Our criteria for assigning HS-AFM images to the 3:3 LGI1–ADAM22_ECD complex were based on a comparison of the simulated AFM image of the 3:3 complex obtained by cryo-EM. The automatized fitting process (42) identifies the optimal orientation of cryo-EM images that closely matches the HS-AFM image. In the present study, the concordance coefficient (CC) reached 0.8, indicating that the protein orientation in HS-AFM images of the 3:3 complex was objectively satisfactory.

Regarding the dissociation step of ADAM22 from the 3:3 complex, we carefully analyzed the HS-AFM videos frame by frame and observed that the protrusion corresponding to ADAM22 in the 3:3 complex disappeared at a specific frame (4.5 s in the third molecule in Movie S1). The dissociation steps of ADAM22 were further confirmed by integrating multiple independent HS-AFM experiments and observations. Thus, although HS-AFM images alone cannot determine the orientation of LGI1 and ADAM22 in the 3:3 complex, the comparison of cryo-EM images with simulated AFM images enables objective assignment and orientation of proteins in the 3:3 complex through automated fitting.

*Ref. 42: R. Amyot et al., Flechsig, Simulation atomic force microscopy for atomic reconstruction of biomolecular structures from resolution-limited experimental images. PLoS Comput Biol 18, e1009970 (2022).

(4) What is the "LGI1 collapse" mentioned in Figure 4c?

Thank you for the constructive suggestions. The term “LGI1 collapse” was intended the dissociation of LGI1 from the 3:3 complex. To avoid confusion, we have revised it to “LGI1 release”.

(5) Am I correct that the structure indicates that the trimerization is entirely organized by LGI1? This would suggest LGI1 trimerizes on its own. Can this be discussed? Has this been observed?

Yes. The present cryo-EM structure of the 3:3 complex indicates that the trimerization can be entirely organized by LGI1. In addition, during the HS-AFM imaging, the triangle shape seems to be maintained even if one ADAM22_ECD molecule is released. These findings suggest the possibility that LGI1 could trimerize on its own although this possibility could not be tested due to the difficulty in the expression of the full-length LGI1 alone for biophysical analysis in our hands. On the other hand, considering the dynamic property of the 3:3 complex and spatial alignment of LGI1LRR and ADAM22, we cannot exclude the possibility that ADAM22 could act as a platform to facilitate the intermolecular interaction between LGI1_LRR and LGI1*_EPTP for the trimerization of LGI1. This discussion was added in the first paragraph of the subsection "Dynamics of the LGI1–ADAM22 higher-order complex by HS-AFM".

(6) C3 symmetry was not applied in the cryo-EM reconstruction of the heterohexameric 3:3 LGI1-ADAM22 complex. How much is the complex deviating from C3 symmetry? What interactions stabilize the specific trimeric conformation reconstructed here, compared to other trimeric conformations?

According to this comment, we compared the non-symmetric, present cryo-EM structure to the previously calculated _C_3 symmetry-restrained structure based on small-angle X-ray scattering analysis and the _C_3 symmetric structure generated by AlphaFold3. Their differences in the domain or protomer configuration are illustrated in Fig. S9.

We did not find interactions that could obviously stabilize the specific trimeric conformation but the closure motion of LGI1_LRR (relative to LGI1_EPTP) in chain F appears to locate it in close proximity to LGI1LRR in chain D to make the triangular assembly slightly more compact. This (partly) compact configuration might stabilize the non-symmetric trimeric configuration observed in the cryo-EM structure. This was described in the last sentence in the subsection "Cryo-EM structure of the 3:3 LGI1– ADAM22_ECD complex".

Reviewer #2 (public review):

The functional significance of these two complexes in the context of synapse remains speculative.

To assess the functional significance of the 3:3 complex, we spent time and effort designing mutations that solely inhibit the 3:3 assembly but failed to find such mutations. In this paper, we just focused on structural characterization of the 3:3 complex.

Additionally, the structural presentations in Figures 1-3 (especially Figures 2-3) lack the clarity needed for general readers to fully understand the authors' key points. Enhancing the quality of these visual representations would greatly improve accessibility and comprehension.

We made an effort to improve Figures 1-3 accordingly. Specifically, we revised them based on the strategy suggested in the Editorial comment regarding this reviewer's comment.

Editorial comments:

We noticed that in the reconstruction of the 3:3 complex, which is claimed to be at 3.8A resolution, beta-strands are not separated in the map and local resolution estimates vary from 6-10A. Please clarify.

We revised Fig. S8 to show the local resolution and volume quality, which correspond to nominal resolution of 3.8 Å, estimated from gold-standard FSC.

Reviewer #1 (Recommendations for the authors):

(1) PDB validation reports should be presented to allow further validation

The PDB validation reports were attached to the revised manuscript (uploaded as "related manuscript file").

(2) In Figure 4, models below the AFM figures are difficult to see because of the light coloring. In addition, in panel c, the orientation of some of the parts of the models below the 19.2 and 34.5 s. panels do not seem to correlate with the AFM figures. Could the models be adjusted so that they represent the data better?

Thank you for the constructive suggestions. According to the Reviewer’s comments, we have revised the AFM figures (Fig. 4).

(3) References are sometimes missing for important statements. Please check throughout.

Some examples:

P3, "it has been suggested that the 3:3 complex regulates the density of synaptic molecules such as scaffolding proteins and synaptic vesicles".

P3. "Furthermore, LGI1 forms a complex with the voltage-gated potassium channel (VGKC) through ADAM22/23".

According to this comment, we rewrote the description about potential physiological roles of the 3:3 complex and added references as follows:

"Similarly to the 2:2 complex, the 3:3 complex might serve as an extracellular scaffold to stabilize Kv1 channels or AMPA receptors in a trans-synaptic fashion (9, 17, 19). In addition, the 3:3 assembly in a cis fashion on the same membrane might regulate the accumulation of Kv1 channel complexes at axon initial segment (18, 20). However, no clear evidence to prove these potential mechanistic roles of the 3:3 assembly has been provided, and the three-dimensional structure of the 3:3 complex has not yet been determined."

We also added references to the following sentences:

p.2, (the last sentence in the first paragraph of the Introduction) “Additionally, some epilepsy-related mutations have been identified in genes encoding non-ion channel proteins such as LGI1 (4-7).”

p.3, ln 4-5, “The metalloprotease-like domain interacts with the EPTP domain of LGI1 in the extracellular space (11, 14).”

p.3, ln 9-10, “Furthermore, LGI1 forms a complex with the voltage-gated potassium channel (VGKC) through ADAM22/23 (9, 17, 18)”

p.3, ln 20-22, “The results revealed the structural basis of the interaction between the EPTP domain of one LGI1 and the LRR domain of the other LGI1, as well as the interaction between the EPTP domain of LGI1 and the metalloproteinase-like domain of ADAM22 (14)”

(4) S5 for clarity please add an overview of the complex highlighting where the different parts shown in the panels are located.

Fig. S5 was modified accordingly. Every panel showing a zoom-up view was indicated by a box in an overview of the complex.

(5) S7 a+b, also here add models for the structures to indicate which parts are shown.

Could labels be added to highlight important parts?

We added an overview of the complex with boxes that indicate the parts shown as the panels, according to this comment. We also added labels to highlight residues that are important for the LGI1_EPTP–ADAM22_ECD interaction in the panel showing the LGI1_EPTP–ADAM22_ECD interface.

(6) S7c also shows the cartoon of the structure. How is it possible that the local resolution is not much higher than 6 Å? The overall resolution was 3.8 Å? This looks like a figure of the density plotted at a low level, and not as stated a "surface representation". Could an extra panel be shown of the density plotted at a higher level? Also, please add Å to the legend in this figure.

Local resolution maps of the 3:3 LGI1-ADAM22_ECD complex were shown as Fig. S8 in the revised manuscript. According to this comment, the distribution of the resolution was plotted onto the density at high (0.06) and low (0.03) levels. "Å" was added to the legend in the figure.

Reviewer #2 (Recommendations for the authors):

(1) The study was conducted using the ectodomain (ECD) of ADAM22. It remains unclear whether the 3:3 complex could form if the transmembrane domain (TMD) of ADAM22 were included. In other words, it is difficult to assess whether the observed 3:3 complex represents plausible cis interactions.

As mentioned in our reply to the first comment from Reviewer #1, we noticed that there was no obvious structural feature strongly suggesting that the heterohexameric 3:3 LGI1–ADAM22 is more likely to represent a cis complex or a trans complex. Both are possible at the synapse (and similarly, for LGI3–ADAM23 at the jaxtaparanode of myelinated axons). Therefore, we revised the Introduction and Discussion sections as follows:

Introduction: (about potential structural mechanisms of the 3:3 complex)

“Similarly to the 2:2 complex, the 3:3 complex might serve as an extracellular scaffold to stabilize Kv1 channels or AMPA receptors in a trans-synaptic fashion. In addition, the 3:3 assembly in a cis fashion on the same membrane might regulate the accumulation of Kv1 channel complexes at axon initial segment. However, no clear evidence to prove these potential mechanistic roles of the 3:3 assembly has been provided, and the three-dimensional structure of the 3:3 complex has not yet been determined.”

Discussion: (about a role of the LGI3–ADAM23 complex at the jaxtaparanode of myelinated axons)

“In this context, as discussed in (30), either or both of the 2:2 and 3:3 complexes might be formed in a trans fashion at the juxtaparanode of myelinated axons and bridge the axon and the innermost myelin membrane. Alternatively, the 3:3 complex formed in a cis fashion might positively regulate the clustering of the axonal Kv channels at the juxtaparanode, possibly in a similar manner at the axon initial segment.”

*Ref. 30: Y. Miyazaki et al., Oligodendrocyte-derived LGI3 and its receptor ADAM23 organize juxtaparanodal Kv1 channel clustering for short-term synaptic plasticity. Cell Rep 43, 113634 (2024).

(2) Page 2, line 1: "...caused by genetic mutations." - Specify the mutations involved. Which genes are mutated? Providing this information would enhance clarity and context.

According to this comment, we rephrased the sentence as follows:

"LGI1 is linked to epilepsy, a neurological disorder that can be caused by genetic mutations of genes regulating neuronal excitability (e.g., voltage- or ligand-gated ion channels)."

(3) The experimental strategy and data for both cryo-EM and HS-AFM are of high quality. However, improvements are needed in the cryo-EM/structural figures to enhance clarity. Structural components should be labeled, and the protein interfaces should be identified within the overall complex figures in Figures 2 and 3, as the current presentation is challenging for general readers to follow. For example, in Figure 2, panel a would benefit from clear labeling to indicate the locations of ADAM22 and LGI1. Panels b and c lack context unless the authors specify which interface corresponds to panel a. Additionally, panels e and f are unlabelled, making it difficult to interpret the figures. Improved annotations and descriptions would significantly enhance figure accessibility and comprehension.

Thank you for the constructive suggestion for enhancing accessibility and comprehension of cryo-EM/structural figures. According to this comment, we labeled structural components and indicated the protein interfaces as boxes in the overall complex figures in Figures 2 and 3. Further, in Figure 2, the locations that panels b and c show were indicated as two boxes in the close-up view in panel a.

eLife Assessment

This solid study assesses a mitochondrial polymerase inhibitor in combination with the BCL-2 inhibitor venetoclax, with the aim to increase the elimination of acute myeloid leukemia. It provides valuable findings of combinatorial efficacy using preclinical models in vitro and in vivo, confirming the overall importance of targeting oxidative phosphorylation to overcome venetoclax resistance in acute myeloid leukemia, and could be strengthened through mechanistic studies demonstrating on target effects and pharmacodynamic efficacy in vivo. The study is of interest to hematologists because it addresses a key biomedical issue in acute myeloid leukemia (venetoclax resistance) and provides data regarding the safety and activity of a novel inhibitor of the mitochondrial polymerase in combination with venetoclax.

Reviewer #1 (Public review):

This study exploits novel agent (IMT) that inhibits mitochondrial activity in combination with venetoclax. While the concept is not novel, the agent is novel (inhibitor of the mitochondrial RNA polymerase, described in Nature in other tumor models), and quest for safe mitochondrial inhibitors is highly warranted. The strength is in vivo activity data shown in CLDX and in one of the two AML PDX models tested, and apparent safety of the combination. However, the impact on survival is impressive in CLDX but not in PDX, and unclear why Ven-sensitive PDX is resistant to combination (opposite what cell line data show). There is no real evidence that this agent overcome Ven resistance, which could be done for example in primary AML cells. Finally, no on-target pharmacodynamic endpoints are measured in vivo to support the activity of the compound on mitochondrial activity at the doses used (which are safe).

Both Reviewers requested to demonstrate that IMT1 inhibits the target at doses used in vitro or in vivo; while the prior paper showed this for original compound, it is imperative to demonstrate this for this modified agent in a different tumor type such as AML.

These points have not been addressed in the Revision.

Reviewer #2 (Public review):

Summary:

The manuscript by Arabanian and colleagues presents studies showing how inhibition of mitochondrial transcription and replication with a novel inhibitor of the mitochondrial polymerase, IMT, can promote AML cell death in combination with the Bcl2 inhibitor venetoclax. They further show that this combinatorial efficacy is evident in vivo in both the AML cell line MV411 and in a PDX model. Given the multiple studies showing the importance of Oxphos in maintaining AML cell survival, the current studies provide an additional strategy to inhibit Oxphos and thus improve the therapeutic management of AML.

Strengths:

A novel aspect of this work is that IMT is a new class of mitochondrial inhibitor that acts through inhibiting the mitochondrial polymerase. In addition, the demonstration of therapeutic efficacy both in vitro and in vivo (including with PDX), together with some data showing minimal toxicity, adds to the impact of this work. Their overall conclusion that IMT increases the potency of Vex in treating AMLs is supported.

Comments on revisions:

In all, the authors responded to most of the critiques, while two of the major critiques were not experimentally addressed. The work will still have potential impact, but will depend on further studies under more clinically relevant conditions and with a better understanding of drug effects.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

(1) The data are generated using ATP read-out (CTG assay). For any inhibitor of mitochondrial function, ATP assays are highly sensitive reflecting metabolic stress, yet these do not necessarily translate into cell growth inhibition using standard Trypan blue assays and tend to overestimate the effects. Please show orthogonal more robust assays of cell growth or proliferation.

We acknowledge the sensitivity of the ATP read-out assay in reflecting metabolic stress. While additional cell growth assays such as Trypan blue exclusion could provide further insights, we believe that the current ATP assay data robustly demonstrate the effect of the IMT and venetoclax combination on cellular metabolism, which is a critical aspect of our study. The scope of our current work focused on metabolic inhibition, and we suggest that future studies could further explore cell proliferation assays to complement these findings.

(2) It is concluded that AML cells do not utilize glucose for ATP production. Please provide formal measurements of glycolysis/lactate upon combinatorial treatment.

We appreciate the reviewer’s suggestion to include glycolysis and lactate measurements, which could indeed add further granularity to our metabolic analysis. However, the primary focus of our study is on mitochondrial function and oxidative phosphorylation (OXPHOS) in AML cells treated with IMT and venetoclax. We believe the data presented in Figure 3 provide strong support for the conclusion that glycolysis is not a major energy source in these cells.

Specifically, in Figure 3C, we demonstrate that AML cells maintain ATP levels and viability when cultured in galactose, a condition that restricts ATP production through glycolysis and forces cells to rely on OXPHOS. This result strongly suggests that these AML cells are not dependent on glycolysis for ATP production. Furthermore, in Supplementary Figure S3B, we show that oxygen consumption rate (OCR) measurements remain stable in the presence of excess glucose, further supporting our conclusion that the cells do not switch to glycolysis when OXPHOS is inhibited.

These findings collectively indicate a primary reliance on OXPHOS for energy generation in AML cells, consistent with our study’s objectives to explore mitochondrial dependency and the therapeutic potential of targeting mitochondrial transcription in AML. Future studies could certainly expand on these insights by incorporating a more detailed analysis of glycolytic flux and lactate production under combinatorial treatment, but we believe the current data are sufficient to support our main conclusions.

(3) The transcriptome data are shown without any analysis of pathways. The conclusion from this data beyond the higher number of genes impacted in the combination arm is unclear. Please provide analysis for example GO pathways and interpret in the context of the drugs' mechanism of action.

In response to the reviewer’s question, we have added gene ontology (GO) pathway analysis to clarify the transcriptomic impact of our combination treatment with IMT and venetoclax. Functional annotation identified significant enrichment in pathways relevant to innate immune response, mitochondrial function, and cellular signaling processes. Specifically, pathways associated with immune defense, mitochondrial signaling, and intracellular signaling were notably affected. These findings suggest that the combination treatment not only disrupts cellular energy metabolism but also potentially primes immune signaling mechanisms. This aligns with the proposed mechanism, where IMT targets mitochondrial transcription and venetoclax induces apoptosis, together enhancing sensitivity in AML cells. The enriched pathways, therefore, support the mechanism of action of both drugs, showing how the combined inhibition of BCL-2 and mitochondrial transcription creates a compounded cellular disruption that enhances the therapeutic effect.

(4) Please demonstrate (could be in supplement) matrix of combination to support the statement that the combination is synergistic using Bliss index. The actual Bliss values are missing.

For the revision, we have now included a matrix of combination treatment effects with the corresponding Bliss synergy index values to substantiate our claim of synergy between IMT and venetoclax. This analysis, provided in the supplement, demonstrates that the observed effects exceed the expected additive impact of each drug alone, as calculated by the Bliss independence model. Specifically, the Bliss values confirm a synergistic interaction in venetoclax-sensitive AML cell lines, highlighting that the combined treatment significantly enhances inhibition of cell viability and apoptosis induction compared to single treatments. This data supports our interpretation of synergy and strengthens the mechanistic conclusions drawn from our findings on the combination therapy’s efficacy.

(5) Please show KG1 data (OCR), here or in Supplement.

In response to the reviewer’s request to include OCR data for the KG-1 cell line, we would like to clarify that OCR measurements were attempted; however, they did not yield conclusive results. This is noted in the revised manuscript (Results section), where we explain that the KG-1 cell line did not provide usable OCR data, likely due to limitations in detecting reliable mitochondrial respiration in this particular line under our experimental conditions. Therefore, we were unable to include KG-1 OCR data in the main figures or the supplement.

Reviewer #2:

(1) It's important that the authors show that the drug's effects in AML are due to on-target inhibition. It's critical that they show that IMT actually inhibits the mito polymerase in the AML cells in the dose range employed.

We appreciate the importance of demonstrating on-target inhibition of mitochondrial RNA polymerase by IMT1, especially in light of the detailed characterization of IMT1b, a closely related compound, as presented in Bonekamp et al., Nature 2020. The work by Bonekamp et al. established the specificity and efficacy of IMT1b in targeting mitochondrial RNA polymerase across various tumor models. Building on these findings, we designed our study to primarily evaluate the combinatorial efficacy of IMT1 with venetoclax in AML models, assuming a similar mechanism of action as described for IMT1b. While direct confirmation of on-target inhibition in AML cells by IMT1 would undoubtedly provide additional mechanistic insight, we focused on translational aspects in this study. We believe that the foundational work provided by Bonekamp et al. supports the assumption of on-target effects by IMT1, and we suggest that future studies could explicitly verify this in the context of AML.

(2) For Fig 1, the stated synergism between Venetoclax (Vex) and IMT in p53 mutant THP1 cells is really not evident, despite what the statistical analysis says. In some ways, the more interesting conclusion is that inhibiting mitochondrial transcription does NOT potentiate the efficacy of Bcl2 inhibition in TP53 mutant AML.

We appreciate the reviewer’s observation regarding the lack of evident synergy between IMT and venetoclax in TP53 mutant THP-1 cells. In line with this comment, we have now expanded the discussion to emphasize that, while statistical analysis suggested a potential interaction, the biological response in TP53 mutant cells was minimal. This contrasts with the strong synergy observed in TP53 wild-type cell lines, such as MV4-11 and MOLM-13. We have now highlighted that TP53 mutation status may limit the effectiveness of mitochondrial transcription inhibition in potentiating BCL-2 inhibition. This addition underscores the importance of mutation profiles, such as TP53 status, in predicting response to combination therapies in AML and is now clearly addressed in the revised discussion.

(3) They combine IMT with Vex, but Vex plus azacytidine or decitabine is the approved therapy for AML. Any clinical trial would likely start with this backbone (like Vex+Aza). They should test combinations of IMT with Vex/Aza or Vex/Dec.

While we recognize the importance of testing IMT in combination with clinically approved therapies like Vex+Aza, our current study was designed to explore the potential of IMT in combination with venetoclax alone. Expanding to other combinations would be an excellent direction for future research but is beyond the scope of our current investigation.

(4) It's interesting that AML cell lines do not show any reliance on ATP generation from glycolysis, but would this still be the case when OxPhos is inhibited with IMT? Such a simple experiment would be much more interesting and could help them better understand the mechanism of IMT efficacy.

We thank the reviewer for highlighting this point regarding the reliance of AML cell lines on glycolysis under OxPhos inhibition. In our study, we observed that AML cells predominantly rely on OxPhos, and we did test for ATP production in conditions that favored glycolysis by growing AML cells with galactose instead of glucose in the medium. As described in the manuscript, we did not observe significant ATP production or cell viability from glycolysis, even under these conditions. This finding suggests that AML cells have a low capacity to adapt to glycolytic ATP generation when OxPhos is disrupted by IMT, reinforcing the view that they are highly dependent on mitochondrial function for energy production. We agree that this adaptation—or lack thereof—is an intriguing aspect of IMT efficacy in targeting energy metabolism in AML cells, and we have clarified this point in the discussion.

(5) OxPhos measurements need statistical analyses.

We appreciate the reviewer’s suggestion to include statistical analyses for the OXPHOS measurements. We would like to clarify that statistical analyses were included in the initial submission. These are detailed in Figure 3 and its legend, as well as in the Statistical Analysis section, where we specify methods such as the calculation of standard error across replicates. This approach was implemented to ensure the rigor of our OCR data and its conclusions on OXPHOS inhibition in AML cells.

(6) Given that the combo-treated mice do not exhibit much leukemia in the blood through ~180 days, and yet start dying after 100 days, the authors should comment on this, given that the bone marrow has been shown to be a refuge that protects leukemia cells from various therapies.

We thank the reviewer for highlighting the observed discrepancy between peripheral blood leukemia levels and survival in combo-treated mice. While leukemic cells were minimally detected in the blood up to approximately 180 days, treated mice began to show signs of disease progression and reduced survival around 100 days. This may suggest that residual leukemic cells persist within the bone marrow, which has been established as a sanctuary site for leukemic cells, providing protection from various therapies. The bone marrow environment likely supports a survival niche, enabling these residual cells to evade treatment effects and potentially initiate disease relapse. We have added this interpretation to the discussion to acknowledge the possibility of bone marrow as a protective refuge, which may limit the full eradication of leukemia in these models despite apparent peripheral blood clearance.

(7) For Fig 5C, the authors should statistically compare the Combo with Vex alone.

We have now included statistical comparisons between the combination treatment and venetoclax alone in Fig 5C to provide a clearer interpretation of the data.

(8) The analyses of gene expression using RNAseq of harvested leukemia cells from the PDX model (Table S2), some more discussion of these results would be helpful, particularly given that neither drug is directly targeting nuclear gene expression.

We thank the reviewer for their suggestion to discuss the RNAseq findings in more detail. In the revised manuscript, we have expanded on the functional annotation of the gene expression changes observed in leukemia cells from the PDX model following combination treatment (Table S2). The enriched pathways include innate immune involvement, mitochondrial function and immune signaling, and intracellular signaling. This suggests that while neither IMT nor venetoclax directly targets nuclear gene expression, the combined treatment induces secondary effects that alter these pathways, potentially contributing to the treatment’s efficacy in AML. This expanded discussion provides greater insight into how the drug combination impacts gene expression and cellular pathways.

(9) We need more information on the PDX models, in terms of the classification (M1 to M6) of the patient AMLs and genetics (specific mutations, not just the genes mutated, and chromosomal alterations).

Additional details regarding the classification and genetic background of the PDX models have been included in the manuscript to better contextualize our findings.

(10) The authors should discuss whether or not IMT represents an improvement over other therapies intended to target Oxphos in AML (clearly, the low toxicity of IMT is a plus, at least in mice).

We appreciate the reviewer’s suggestion to discuss IMT in comparison with other OXPHOS-targeting therapies for AML. In the revised discussion, we highlight IMT’s unique properties, particularly its low toxicity profile, which may offer advantages over other OXPHOS inhibitors. This low toxicity, demonstrated in preclinical studies, suggests that IMT might improve patient tolerability compared to existing therapies that target mitochondrial function.

(11) The authors examined toxicity by weighing the mice and performing CBCs. Measurements of liver and kidney toxicity will be necessary for further clinical development.

We thank the reviewer for the suggestion to further investigate liver and kidney toxicity. In our study, we assessed toxicity through regular weight monitoring and complete blood counts (CBCs) to evaluate overall health status. While additional liver and kidney toxicity measurements will indeed be important in future studies, resource limitations currently prevent us from performing these additional analyses in this model. We agree that these assessments will be essential as we progress towards clinical development, and we plan to address them in upcoming preclinical studies.

eLife Assessment

The study presents extensive gene expression profiling and bioinformatic analyses, offering insights into the roles of fibroblasts in cardiac development. The large volume of scRNA-seq data is both compelling and important to the scientific community. All three reviewers agree that the revised manuscript represents a significant improvement and addresses most, if not all, of their previous concerns. The reviewers also acknowledge that detailed mechanistic studies on how fibroblast-derived collagen regulates myocardial and coronary vasculature development are beyond the scope of the current study.

Reviewer #1 (Public review):

Summary:

The study by Deng et al reports single cell expression analysis of developing mouse hearts and examines the requirements for cardiac fibroblasts in heart maturation. The work includes extensive gene expression profiling and bioinformatic analysis. The prenatal fibroblast ablation studies show new information on the requirement of these cells on heart maturation before birth.

The strengths of the manuscript are the new single cell datasets and comprehensive approach to ablating cardiac fibroblasts in pre and postnatal development in mice. Extensive data are presented on mouse embryo fibroblast diversity and morphology in response to fibroblast ablation. Histological data support localization of major cardiac cell types and effects of fibroblast ablation on cardiac gene expression at different times of development.

A weakness of the study is that the major conclusions regarding collagen signaling and heart maturation are based on gene expression patterns and are not functionally validated.

Reviewer #2 (Public review):

This study aims to elucidate the role of fibroblasts in regulating myocardium and vascular development through signaling to cardiomyocytes and endothelial cells. This focus is significant, given that fibroblasts, cardiomyocytes, and vascular endothelial cells are the three primary cell types in the heart. The authors employed a Pdgfra-CreER-controlled diphtheria toxin A (DTA) system to ablate fibroblasts at various embryonic and postnatal stages, characterizing the resulting cardiac defects, particularly in myocardium and vasculature development. Single-cell RNA sequencing (scRNA-seq) analysis of the ablated hearts identified collagen as a crucial signaling molecule from fibroblasts that influences the development of cardiomyocytes and vascular endothelial cells.

This is an interesting manuscript; however, there are several major issues, including an over-reliance on the scRNA-seq data, which shows inconsistencies between replicates.

Some of the major issues are described below.

(1) The CD31 immunostaining data (Figure 3B-G) indicate a reduction in endothelial cell numbers following fibroblast deletion using PdgfraCreER+/-; RosaDTA+/- mice. However, the scRNA-seq data show no percentage change in the endothelial cell population (Figure 4D). Furthermore, while the percentage of Vas_ECs decreased in ablated samples at E16.5, the results at E18.5 were inconsistent, showing an increase in one replicate and a decrease in another, raising concerns about the reliability of the RNA-seq findings.

(2) Similarly, while the percentage of Ven_CMs increased at E18.5, it exhibited differing trends at E16.5 (Fig. 4E), further highlighting the inconsistency of the scRNA-seq analysis with the other data.

(3) Furthermore, the authors noted that the ablated samples had slightly higher percentages of cardiomyocytes in the G1 phase compared to controls (Fig. 4H, S11D), which aligns with the enrichment of pathways related to heart development, sarcomere organization, heart tube morphogenesis, and cell proliferation. However, it is unclear how this correlates with heart development, given that the hearts of ablated mice are significantly smaller than those of controls (Figure 3E). Additionally, the heart sections from ablated samples used for CD31/DAPI staining in Figure 3F appear much larger than those of the controls, raising further inconsistencies in the manuscript.

(4) The manuscript relies heavily on the scRNA-seq dataset, which shows inconsistencies between the two replicates. Furthermore, the morphological and histological analyses do not align with the scRNA-seq findings.

(5) There is a lack of mechanistic insight into how collagen, as a key signaling molecule from fibroblasts, affects the development of cardiomyocytes and vascular endothelial cells.

(6) In Figure 1B, Col1a1 expression is observed in the epicardial cells (Figure 1A, E11.5), but this is not represented in the accompanying cartoon.

(7) Do the PdgfraCreER+/-; RosaDTA+/- mice survive after birth when induced at E15.5, and do they exhibit any cardiac defects?

Reviewer #3 (Public review):

Summary:

The authors investigated fibroblasts' communication with key cell types in developing and neonatal hearts, with focus on critical roles of fibroblast-cardiomyocyte and fibroblast-endothelial cells network in cardiac morphogenesis. They tried to map the spatial distribution of these cell types and reported the major pathways and signaling molecules driving the communication. They also used Cre-DTA system to ablate Pdgfra labeled cells and observed myocardial and endothelial cell defects at development. They screened the pathways and genes using sequencing data of ablated heart. Lastly they reported a compensatory collagen expression in long term ablated neonate heart. Overall, this study provides us with important insight on fibroblasts' roles in cardiac development and will be a powerful resource for collagens and ECM focused research.

Strengths:

The authors utilized good analyzing tools to investigate on multiple database of single cell sequencing and Multi-seq. They identified significant pathways, cellular and molecular interactions of fibroblasts. Additionally, they compared some of their analytic findings with human database, and identified several groups of ECM genes with varying roles in mice.

Weaknesses:

This study is majorly based on sequencing data analysis. At the bench, they used very strident technique to study fibroblast functions by ablating one of the major cell population of heart. Also, experimental validation of their analyzed downstream pathways will be required eventually.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study by Deng et al reports single-cell expression analysis of developing mouse hearts and examines the requirements for cardiac fibroblasts in heart maturation. Much of this work is overlapping with previous studies, but the single-cell gene expression data may be useful to investigators in the field. The significance and scope of new findings are limited and major conclusions are largely based on correlative data.

Strengths:

The strengths of the manuscript are the new single-cell datasets and comprehensive approach to ablating cardiac fibroblasts in pre and postnatal development in mice.

Weaknesses:

There are several major weaknesses in the analysis and interpretation of the results.

(1) The major conclusions regarding collagen signaling and heart maturation are based on gene expression patterns and are not functionally validated. The potential downstream signaling pathways were not examined and known structural contributions of fibrillar collagen to heart maturation are not discussed.

We thank the reviewer for the comment. In this study, we mainly focused on the functional analysis of fibroblasts in heart development at embryonic and neonatal stages by using cell ablation system and single cell mRNA sequencing analysis. The further functional analysis of collagen pathway is interesting but out of the scope of this study. We will continue this line of research and share the results in the future. Moreover, through the analysis of single cell mRNA-sequencing data, we have predicted the downstream genes that are regulated by the collagen pathway in Fig 5C. We have also added sentences to highlight the structural role of collagen in affecting the related heart developmental processes.

(2) The heterogeneity of fibroblast populations and contributions to multiple structures in the developing heart are not well-considered in the analysis. The developmental targeting of fibroblasts will likely affect multiple structures in the embryonic heart and other organs. Lethality is described in some of these studies, but additional analysis is needed to determine the effects on heart morphogenesis or other organs beyond the focus on cardiomyocyte maturation being reported. In particular, the endocardial cushions and developing valves are likely to be affected in the prenatal ablations, but these structures are not included in the analyses.

We thank the reviewer for the comment. We have included a new figure presenting the fibroblast heterogeneity in developing hearts (Fig S3). We have also compared the valve structural differences at E18.5 (Fig S11).

(3) ECM complexity and extensive previous work on specific ECM proteins in heart development and maturation are not incorporated into the current study. Different types of collagen (basement membrane Col4, filamentous Col6, and fibrillar Col1) are known to be expressed in fibroblast populations in the developing heart and have been studied extensively. Much also has been reported for other ECM components mentioned in the current work.

We thank the reviewer for the comment. We agree that the ECM is complex, and the functions of many of its components have been previously reported, as mentioned in the introduction. In this study, our focus is to analyze the spatial and temporal expression patterns of various ECM genes in fibroblasts throughout developmental progression (Fig. S5–7). To further acknowledge previous work, we have added additional sentences and cited relevant literature on the role of collagen genes in developing hearts (page 4).

Reviewer #2 (Public review):

This study aims to elucidate the role of fibroblasts in regulating myocardium and vascular development through signaling to cardiomyocytes and endothelial cells. This focus is significant, given that fibroblasts, cardiomyocytes, and vascular endothelial cells are the three primary cell types in the heart. The authors employed a Pdgfra-CreER-controlled diphtheria toxin A (DTA) system to ablate fibroblasts at various embryonic and postnatal stages, characterizing the resulting cardiac defects, particularly in myocardium and vasculature development. scRNA-seq analysis of the ablated hearts identified collagen as a crucial signaling molecule from fibroblasts that influences the development of cardiomyocytes and vascular endothelial cells. This is an interesting manuscript; however, there are several major issues, including an over-reliance on the scRNA-seq data, which shows inconsistencies between replicates. Some of the major issues are described below.

The comments are the same as the comments for “Recommendations for the authors”. Please see the responses below.

Reviewer #3 (Public review):

The authors investigated fibroblasts' communication with key cell types in developing and neonatal hearts, with a focus on the critical roles of fibroblast-cardiomyocyte and fibroblast-endothelial cell networks in cardiac morphogenesis. They tried to map the spatial distribution of these cell types and reported the major pathways and signaling molecules driving the communication. They also used Cre-DTA system to ablate Pdgfra labeled cells and observed myocardial and endothelial cell defects at development. They screened the pathways and genes using sequencing data of ablated hearts. Lastly, they reported compensatory collagen expression in long-term ablated neonate hearts. Overall, this study provides us with important insight into fibroblasts' roles in cardiac development and will be a powerful resource for collagens and ECM-focused research.

Strengths:

The authors utilized good analyzing tools to investigate multiple databases of single-cell sequencing and Multiseq. They identified significant pathways and cellular and molecular interactions of fibroblasts. Additionally, they compared some of their analytic findings with a human database, and identified several groups of ECM genes with varying roles in mice.

Weaknesses:

This study is majorly based on sequencing data analysis. At the bench, they used a very strident technique to study fibroblast functions by ablating one of the major cell populations of the heart. Considering the importance of the fibroblast population, intriguing in vivo findings were expected. Also, they analyzed the downstream genes in ablated hearts, but did not execute any experimental validation for any of the targets.

Recommendations for the authors:

Reviewing Editor Comments:

All three reviewers found the large amount of scRNA-Seq data compelling and valuable, and they noted that the study's conclusions based on the scRNA Seq and fibroblast ablating align closely with previously published studies. Therefore, a more thorough discussion and integration of the current findings with prior studies are recommended. Each reviewer provided specific feedback to improve the manuscript, correct errors, and strengthen the overall presentation, and please edit the manuscript accordingly. Additionally, further validation of the scRNA-Seq data through more data analysis, reference comparisons, or additional experiments is encouraged.

Reviewer #1 (Recommendations for the authors):

(1) The heterogeneity of fibroblasts and ECM components in the developing heart needs to be considered in the analysis and description of results. There are extensive reports in both of these areas that would inform the gene expression and ablation studies being reported.

We thank the reviewer for the comment. We have added a supplemental figure (Fig. S3) analyzing the heterogeneity of fibroblasts during development and described the results on page 3 and 4. Through the analysis of single-cell mRNA sequencing data, we identified four distinct populations of fibroblasts and further performed RNA scope to examine their spatial locations. Additionally, we agree with the reviewer that there are many types of ECM components, which we have addressed in the introduction (page 2). Furthermore, we have conducted a detailed analysis of the spatial and temporal expression patterns of ECM genes throughout developmental progression (Figs. S5–7).

(2) One of the novel aspects of the work is the prenatal ablation of cardiac fibroblasts. Embryonic lethality was observed in some cases, but the specific cardiac structural anomalies or potential vascular effects were not described. The contributing role of cardiac fibroblasts to valvuloseptal development, which was likely affected in these studies, was not described.

We thank the reviewer for the comment. Since the heart sections were not initially prepared to compare valve differences between control and ablation conditions, most sections do not include valve structures. However, in the small subset of sections that do contain valves, we have compared valve structures in control and ablated hearts at E18.5 following three doses of tamoxifen treatment from E15.5 to E17.5. In mutants, the valves appear shorter compared to controls. Specifically, we observed that in control hearts, the mitral valve was already connected to the papillary muscle, whereas in ablated hearts, the valve leaflet at similar position was not. We have included these images as a new supplemental figure (Fig. S11). Regarding vascular defects, we have described them in Fig. 3C and 3F.

(3) The major conclusions regarding collagen signaling and heart development are based on correlations in gene expression and are not validated by functional data. What are the downstream signaling pathways affected and are they affected during development or with ablation? The main conclusions of the study do not take into account well-known structural functions of collagen in the developing heart.

We thank the reviewer for the comment. Through regulatory prediction analysis, we identified the collagen ligands Col1a1, Col5a1, and Col4a1 from the collagen family (Fig. 5C), which regulate multiple genes in cardiomyocytes, including Masp1. Masp1 is a member of the lectin complement pathway and potentially regulates cardiomyocyte migration during development. These collagen ligands also regulate multiple mitochondria-related genes, such as Etfa, Ndufb10, Ndufs6, and Slc25a4, which are potentially important for cardiomyocyte development and maturation. Moreover, we agree with the reviewer that collagen is an important structural ECM protein, and its deletion or reduction could cause heart developmental defects due to its structural role. We have added a discussion on this possibility (page 8).

(4) The postnatal ablation studies are very similar to studies with the same mouse lines reported by Kurabara et al 2022 in JMCC (PMID 35569524) which came to similar conclusions and was not cited in the current work.

We thank the reviewer for the comment and apologize for overlooking this study. We have now included the citation on page 8.

(5) The discussion of a regenerative response with DTA ablation of fibroblasts is confusing. Proliferation was examined in cardiomyocytes which lose their regenerative capacity after birth in mice. However, cardiac fibroblasts can proliferate in response to injury throughout life which is not really a regenerative process.

We appreciate the reviewer’s comment. To avoid confusion, we have replaced the term "regeneration" with "response to cell loss" and "compensation."

(6) Some of the descriptions of single-cell expression data are overstated (Page 7). Regulatory interactions, signaling pathway activation, or function cannot be determined from gene expression data alone.

We thank the reviewer for the comment. We agree that these conclusions rely on results from multiple assays. We have weakened the description of the analysis by emphasizing that the findings are predictive results from scRNA-seq analysis.

(7) In the last paragraph of the discussion "data not shown" should be shown or this information should be deleted. As written, the discussion does not present a clear description of what major new findings are being reported or why they are significant. The new insights into heart development are not specified.

We thank the reviewer for the comment. We have added the data as a supplemental figure (Fig. S19). Since this paragraph is part of the discussion, we believe the results are not conclusive at this stage and require further research to explore the potential protective role of fibroblast ablation in neonatal hearts.

Minor comments.

(1) Figure legends are missing information needed to understand what is being shown. For example, in Figure 2, collagen is visualized using CHP staining.

Thanks. We have gone through all figure legends to ensure that all necessary information has been provided.

(2) The hearts in Figure S15 are upside down.

Thanks. We have updated the figure.

(3) In Figure S16A, "brian" should be "brain".

Thanks. We have updated it.

Reviewer #2 (Recommendations for the authors):

This is an interesting manuscript; however, there are several major issues, including an overreliance on the scRNA-seq data, which shows inconsistencies between replicates. Some of the major issues are described below.

(1) The CD31 immunostaining data (Figures 3B-G) indicate a reduction in endothelial cell numbers following fibroblast deletion using PdgfraCreER+/-; RosaDTA+/- mice. However, the scRNA-seq data show no percentage change in the endothelial cell population (Figure 4D). Furthermore, while the percentage of Vas_ECs decreased in ablated samples at E16.5, the results at E18.5 were inconsistent, showing an increase in one replicate and a decrease in another, raising concerns about the reliability of the RNA-seq findings.

We thank the reviewer for the comment. We believe that measuring cell proportions in scRNA-seq results is sensitive and relies on a high number of total and target cells, similar to other cell counting assays such as FACS. As the reviewer pointed out, the proportions of Vas_EC in E18.5 replicates are inconsistent. Specifically, Col_4 at E18.5 showed a relatively low proportion of Vas_EC. Upon examining the cell numbers in each sample, we found that Col_4 had the lowest number of recovered cells, with approximately 760 in total, whereas the other samples had more than 920 cells each. Additionally, since immunofluorescence staining for CD31 marks both Vas_EC and Endo_EC, we combined these two cell types to increase the number of targeted cells. This analysis consistently showed that the ablated samples had lower proportions. However, given that the quantifications have also produced inconsistent results for other cell types, such as Ven_CM, as mentioned in the reviewer’s next question, we have decided to delete this plot to avoid confusion.

Author response image 1.

(2) Similarly, while the percentage of Ven_CMs increased at E18.5, it exhibited differing trends at E16.5 (Figure 4E), further highlighting the inconsistency of the scRNA-seq analysis with the other data.

We thank the reviewer for the comment. Please see the response above.

(3) Furthermore, the authors noted that the ablated samples had slightly higher percentages of cardiomyocytes in the G1 phase compared to controls (Figures 4H, S11D), which aligns with the enrichment of pathways related to heart development, sarcomere organization, heart tube morphogenesis, and cell proliferation. However, it is unclear how this correlates with heart development, given that the hearts of ablated mice are significantly smaller than those of controls (Figure 3E). Additionally, the heart sections from ablated samples used for CD31/DAPI staining in Figure 3F appear much larger than those of the controls, raising further inconsistencies in the manuscript.

We thank the reviewer for the comment. We observed changes in G1-phase cardiomyocytes at both E16.5 and E18.5, with pathway enrichment primarily identified in E16.5 cardiomyocytes. At E16.5, the ablated hearts exhibited myocardial defects, including an increased trabecular-to-compact myocardium ratio and reduced vascular density. By E18.5, the ablated embryos had smaller hearts with reduced vascular density, although the trabecular-to-compact myocardium ratio showed no obvious changes. Regarding the larger section size in the ablated hearts compared to the control hearts, there are two reasons contributing to this discrepancy. First, the control and ablated heart sections have different scale bars. The ablated hearts were enlarged compared to control section. Secondly, the heart sections vary in size depending on their position. Sections taken from the middle of the heart are larger than those from the edges. In our initial comparison, we used an edge-positioned section from the control hearts and a middle-positioned section from the ablated hearts. To avoid confusion, we have now updated the control section to match the position of the ablated embryos more closely and used the same size of scale bars in the two images (Fig 3F).

(4) The manuscript relies heavily on the scRNA-seq dataset, which shows inconsistencies between the two replicates. Furthermore, the morphological and histological analyses do not align with the scRNA-seq findings.

We respectfully disagree with this comment from the reviewer. As shown in Figure 4B, the scRNAseq data from the two replicates are highly consistent. For inconsistencies in cell proportions and tissue section sizes, please refer to our responses above.

(5) There is a lack of mechanistic insight into how collagen, as a key signaling molecule from fibroblasts, affects the development of cardiomyocytes and vascular endothelial cells.

We thank the reviewer for the comment. In this study, we primarily focused on analyzing fibroblast function in heart development using cell ablation and single-cell mRNA sequencing. While further mechanistic analysis of the collagen pathway is intriguing, it falls outside the scope of this study. Additionally, our scRNAseq analysis identified multiple collagen ligands derived from fibroblasts that may regulate gene expression in Ven_CM and influence their development, as shown in Figure 5C. Although validating these predictions would be valuable, it is beyond the scope of this study. We will continue this line of research and share our findings in the future.

(6) In Figure 1B, Col1a1 expression is observed in the epicardial cells (Figure 1A, E11.5), but this is not represented in the accompanying cartoon.

We thank the reviewer for the comment. As stated in the main text (page 3), based on scRNA-seq and IF staining results, we observed that Col1a1 is also expressed in epicardial cells. In the cartoon, we depicted the pattern of fibroblasts rather than Col1a1-positive cells, which is why we did not include epicardial cells.

(7) What is the genotype of the control animals used in the study?

We thank the reviewer for the comment. We have added the genotype information for the control embryos in the legends of the relevant figures.

(8) Do the PdgfraCreER+/-; RosaDTA+/- mice survive after birth when induced at E15.5, and do they exhibit any cardiac defects?

We thank the reviewer for the comment. This is an interesting question; however, we did not perform the experiment because administering tamoxifen to pregnant mice from E15.5 to E18.5 causes delivery complications, as reported in the literature (PMID: 23139287). Unfortunately, this prevents us from exploring this question further.

Reviewer #3 (Recommendations for the authors):

Overall, this is a comprehensive study substantiated by the evidence the authors provided in their findings. However, I have a few concerns to be addressed.

(1) The claim by the authors that "at E17.5 and P3, each FB was in contact with approximately one Vas_EC and four CMs at both stages" is not fully convincing. RNA scope images for Actn2 are not clear enough to lead the quantification (RNA scope images for Cdh5 look better). I suggest performing imaging at higher magnification and the Z stack technique to provide a better understanding of their localization. Also, no changes in FBs adjacent cell numbers (CM&EC) with ages (P3) compared to E17.5? Any thoughts on the explanation?

We thank the reviewer for the comment. We imaged the staining results using a confocal microscope at 20X resolution. We also considered imaging them at 40X; however, due to the large areas that need to be imaged in these sections, it was challenging to do so. Additionally, we identified each CM based on Actn2 and DAPI staining information and are confident in the accuracy of our quantification results. Moreover, since each FB interacts with multiple CMs and Vas_ECs in 3D projections, but our calculations are based only on 2D imaging sections, there may be discrepancies compared to a true 3D environment. We have added a sentence to address this limitation (page 9). Regarding the similar number of interactions observed at E17.5 and P3, we think there are two possibilities. First, the three cell types may proliferate in a synchronized manner, maintaining a consistent number of interactions. Second, these cell types may exhibit minimal proliferation during late embryonic and early neonatal stages. Instead, heart growth primarily occurs through CM hypertrophy, which does not significantly alter the number of interactions.

(2) Fix the Capitalized font of RNA markers in Figure S2.

Thanks. We have updated them.

(3) I appreciate the visualization of ligand-receptor interactions in collagen network comparison between FB to CM and FB to EC, and predictive analysis on the FB ligands that regulate differentially expressed genes in ablated heart CM and ECs.

We appreciate the reviewer for the comment.

(4) The authors depleted Pdgfra-Cre cells at E10.5, and reported 100% DTA+ lethality after 3 days. Induction at E13.5 to ablate Pdgfra-Cre cells resulted in survival at least up to E16.5 age. What could be the possible reasons authors think that lead to embryo lethality when induced at E10.5? Did the authors analyze the expression of Pdgfra at E10.5 to E13.5 using Pdgfra antibody or Pdgfra-Cre labeling, or using the ScRNA seq data?

We thank the reviewer for the comment. The expression pattern of Pdgfra at E10.5 has been previously reported (PMID: 18297729) and shown to be highly expressed in the atrioventricular region, consistent with the Col1a1 expression pattern we profiled in this study. Therefore, we believe the embryonic lethality observed in the ablated embryos at E10.5 was likely due to the disruption of the atrioventricular structure. However, since Pdgfra is also expressed in other tissues at this stage, we cannot rule out the possibility that the ablation of non-cardiac tissues also contributed to the lethality.

(5) In terms of the findings on the trabeculation and compaction defects, please provide the images of the ventricles with markers to indicate the compact and trabecular zones and their defects.

Thanks! We have included images that illustrate the quantification of compact and trabecular myocardium thickness in control and ablated hearts (FigS10C).

(6) Did the author check the expression of any other marker for the vascular system in addition to CD31 to see the effects of ablated FB on coronary vasculature development?

We thank the reviewer for the comment. We analyzed only Cd31 to assess the effects of fibroblast ablation on the overall endothelial cell population. We did not separately examine the subpopulations, but this would be an interesting direction for future studies.

(7) Can the authors interpret how findings from PHH3 proliferation explain thinner compact and thicker trabeculae in ablated hearts?

We thank the reviewer for the comment and apologize for the misinterpretation of the results. We observed that the ablated hearts have a thinner compact myocardium, while the thickness of the trabecular myocardium remains unchanged, leading to an increased trabecular-to-compact myocardium ratio (Fig 3D). We have corrected the description in the manuscript accordingly. Moreover, since the compact myocardium has a higher proliferation rate than the trabecular myocardium, a reduction in overall cell proliferation is expected to have a more pronounced impact on the compact myocardium. Inhibition of compact myocardium proliferation has been reported to lead thinner compact myocardium and non-compaction defects (PMID: 31342111).

(8) The authors did not execute experiments to find the downstream target that causes compaction defects and endothelial cell density defects upon ablation of FBs. Can you project from your sequencing analysis what could be the potential downstream if you could execute bench-side experiments on this?

We appreciate the reviewer for the comment. We believe that the regulatory predictive results in Figures 5C and D from the scRNA-seq data analysis have provided a set of downstream candidates for validation. We could select some of the ligands, such as the collagen ligands Col1a1, Col4a1, and Col5a1, to treat the ablated embryos in vivo to assess whether they could partially rescue the myocardium defects. Additionally, we could conduct ex vivo experiments by co-culturing CM and FB, comparing them with CM alone and CM treated with the identified ligands. This would allow us to evaluate CM proliferation and the expression of downstream genes identified in the prediction results. However, as the reviewer suggested, these experiments are planned for future studies.

(9) Please provide the echocardiographic M mode images with a comparable number of cardiac cycles in control and ablated (Fig. 6H). Also, the heart rate of the ablated heart is too low to compare other parameters with the control. If you could stabilize the heart rate at comparable values to control the heart, it is possible that EF and FS values will be largely changed.

We thank the reviewer for the comment. As the echocardiographic analysis was performed on conscious mice, the lower heart rates in the ablated mice are a phenotype associated with the ablation. Unfortunately, we are unable to adjust them to the same as the control mice.

(10) Can you provide a numerical dataset for any one of the cell chat figures? Like in figure 2A, supporting the claim "However, in terms of interaction strength, FB exhibited the highest values compared to those of other cell types (Fig. 2A)".

Yes, we have added a supplemental table (Table S2) containing the numerical interaction weights. As shown in the table, the interactions between FB and other cell types have the highest values.

eLife Assessment

This work demonstrates the therapeutic potential of recombinant human PDGF-AB/BB proteins in alleviating the senescent signatures of primary human intervertebral disc cells. The study represents a fundamental, significant advance in the treatment of intervertebral disc degeneration through the suppression of senescence. The strength of evidence supporting these conclusions is compelling, as it is primarily based on transcriptomic analysis and direct protein measurements from relatively homogeneous cell populations. This work will be of interest to spine basic scientists and clinicians, as well as to musculoskeletal scientists more broadly. The revised manuscript adds greater clarity, and the impact of the study is greatly enhanced.

Reviewer #1 (Public review):

The authors, Zhang et al., demonstrate the beneficial effects of treating degenerate human primary intervertebral disc (IVD) cells with recombinant human PDGF-AB/BB on the senescence transcriptomic signatures. Utilizing a combination of degenerate cells from elderly humans and experimentally induced senescence in young, healthy IVD cells, the authors show the therapeutic effects on mRNA transcription as well as cellular processes through informatics approaches.

One notable strength of this study is the use of human primary cells and recombinant forms of human PDGF-AB/BB proteins, which increases the translational potential of these in vitro studies. The manuscript is well-written, and the informatics analyses are thorough and clearly presented.

Comments on revisions:

The revised manuscript adds greater clarity, and the impact of the study is greatly enhanced.

Reviewer #2 (Public review):

Summary:

This work highlights a novel role for platelet-derived growth factor (PDGF) in mitigating cellular senescence associated with age-related and painful intervertebral disc degeneration. Prior literature has demonstrated the importance of accumulation of senescent cells in mediating many of the pathological effects associated with the degenerate disc joint, such as inflammation and tissue breakdown. In this study the authors treat clinically relevant human nucleus pulposus and annulus fibrosus cells from patients undergoing discectomy with recombinant PDGF-AB/BB for 5 days and then deep phenotyped the outcomes using bulk RNA sequencing. In addition they irradiated healthy human disc cells which they subsequently treated with PDGF-AB/BB examining the expression of SASP-related markers and also PDGFRA receptor gene expression. Overall PDGF was able to down-regulate many senescent associated pathways and the degenerate phenotype in IVD cells. Altered pathways were associated with neurogenesis, mechanical stimuli, metabolism, cell cycle, reactive oxygen species and mitochondrial dysfunction. Overall the authors achieved their aims and the results by and large support their conclusions although improvements could be made to enhance the rigor of the study and findings

Strengths:

A major strength of this study is the use of human cells from patients undergoing discectomy for disc herniation as well as access to healthy human cells. Investigating the role of PDGF regarding cellular senescence in the degenerate disc joint is novel and an underexplored area of research which is a significant contribution to the field of spine. This study highlights a potential target for addressing cellular senescence where most of the prior focus has been on senolytic drugs. Such studies have broad implications to other age-related diseases where senescence plays a major role. The use of transcriptomics and therefore an unbiased approach to investigating the role of PDGF is also considered a strength as is the follow-up studies involving irradiating healthy human disc cells and treating these cells with PDGF. The combined assessment of both nucleus pulposus and annulus fibrosus cells in the context of these studies adds to the impact.

Weaknesses:

A weakness of these studies relates to qualitative data presented for the B-galactosidase assay. Quantification of such data sets would greatly strengthen the studies and lend further support to the hypotheses. The study in its current form could be strengthened by the inclusion of mechanistic studies probing the downstream PDGF receptor associated pathways for example specifically targeting or modulating the activity of the PDGF receptor PDGFRA.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

The Reviewer asks that we provide the source of PDGF-AB/BB proteins.

We apologize for omitting such information. We now provide the source of PDGF-AB/BB in the Methods as PeproTech. In our revised manuscript we clearly state in Page 7, line 142: “Cells were then treated with recombinant human PDGF-AB (40ng/ml; PeproTech, 10770584) or -BB (20ng/ml; PeproTech, 10771918) for 5 days. “

The Reviewer asks that we adequately report our chosen irradiation parameters suggesting that we consider (PMCID: PMC5495460) for appropriate parameter reporting.

We thank the Reviewer for this excellent suggestion. We now provide a more detailed irradiation reporting based on the shared manuscript in Page 9, line 10, line 204.

The Reviewer requests more details about the age range to distinguish young from old donors.

In the Methods section of our revised manuscript, we now provide the age range for our old donors being between 53 and 67 while our younger donor population ranged between 19 and 27 years of age. These changes are reflected in Page 6, line 128: “Human degenerated NP and AF tissues (Grade IV or V on Pfirrman grade; 64.6 ±8.5 years old)) were obtained as the surgical waste from donors with discogenic pain, with each donor providing written informed consent. Healthy NP and AF cells (23.0 ±3.7 years old) were gifted by Professor Lisbet Haglund from McGill University (Tissue Biobank #2019-4896).”

The Reviewer wonders about the rationale for using different concentrations of PDGF-AB/BB in the degenerate cell and irradiation experiments.

We apologize for our lack of clarity. We initially treated cells with different concentrations (20 and 40 ng/ml) of PDGF-AB/BB to first establish a dose-response. From our MTT and gene expression analyses we determined that 20ng/ml was sufficient to elicit significant changes in cell proliferation markers, including MKI67, CCNB1 and CCND1. Increasing the concentration to 40 ng/ml of either growth factor did not significantly influence these parameters. However, we felt that for our bulk RNA seq experiments, we may see better changes in signaling molecules under 40ng/ml of PDGF-AB since its effects on cell growth at this concentration were maximal while PDGF-BB was maintained at 20ng/ml based on its efficacy in our mitogenic response.

The Reviewer asks that we consider describing the effects of PDGF-AB/BB as mitigating or therapeutic rather than protective both in the title and throughout the manuscript.

We agree with the Reviewer’s recommendation, and we have now changed the title to “Therapeutic effects of PDGF-AB/BB against cellular senescence in human intervertebral disc”. Moreover, we implemented this change in the revised manuscript as requested.

The Reviewer believes that changes in the NP are more clinically evident (by imaging methods), despite degeneration often initiating from the AF (annulus fibrosus), e,g. through tears/microtears and would like for us to reflect this in our revised manuscript.

We agree with the Reviewer’s comment, and we thank them for this added accuracy. On this basis, we now corrected our language in the introduction by stating in Page 4, line 68 that: “To date, the main focus of IVD cell studies has been on the NP, as changes in the NP are easily detected through imaging techniques like MRI, making it the most visible indicator of disc degeneration in clinical practice. In addition, NP plays a crucial role in the progression of IVD degeneration due to its susceptibility to significant structural and functional changes during aging and degeneration.”

The Reviewer points out a prior study which examined the effects of X-ray irradiation on NF-kB signaling in young and aged IVDs (PMCID: PMC5495460) suggesting that we include this reference in our revised manuscript.

We thank the Reviewer for this suggestion, and we are now referencing this elegant study in the discussion section of our revised manuscript. Thus, in page 20, line 440 we state: “ In fact, it has been shown that NF-kB signaling was elevated in mouse IVDs exposed to a single 20 Gy dose of irradiation in an ex vivo culture model.”

The Reviewer asks that our experimental methods are described in the order of the experimental workflow. For example, section 2.2 describes RNA sequencing, which is a terminal assay. Section 2.2 may be more appropriate for detailing the methods of PDGF-AB/BB treatment, along with the rationale.

We thank the Reviewer for pointing this out and have reorganized the Methods section accordingly.

Reviewer #2:

The Reviewer requests more experimental details in the methodology including the rationale for such methods/conditions as well as specific culture models utilized, substrates, cell density, and media components.

We apologize for our lack of clarity. We now revised the methods section based on the comments.

The Reviewer asks about the quantitative data for b-galactosidase assay and immunofluorescence of senescence-associated proteins such as P21 and P16.

We apologize for omitting this information. We now included the quantification of P21 and P16 positive cells, which is presented in the revised Figures 4. For b-galactosidase assay, we were unable to quantify the percentage of positive cells because we did not perform nuclei staining, making it difficult to accurately determine the total cell number. Instead, we provided representative images showing the full field of view at 10X magnification using Echo microscope.

The Reviewer requests the protein level data of PDGFRA to determine if the transcripts are being translated to protein.

We thank the Reviewer for this suggestion. The protein expression of PDGFRA has been included in the Supplementary Figure 2. We found that PDGFRA protein levels were decreased in both NP and AF cells in response to PDGF treatments. It is known that upon binding with PDGF ligands, PDGFRA undergoes rapid internalization and degradation, a mechanism that prevents overstimulation of the signaling pathway (doi: 10.1042/BST20200004). The upregulated gene expression probably attempting to compensate for this degradation and supports continued activation of PDGFRA signaling activation, emphasizing its crucial role in response to the PDGF treatment. Thus, we implemented it in the discussion section in page 22, line486:” Interestingly, while mRNA level was increased in PDGF treated NP cells, its protein level was decreased, highlighting the complexity in PDGF receptor dynamics. Upon binding with PDGF ligands, PDGFRA is known to undergo rapid internalization and degradation, a mechanism that prevents overstimulation of the signaling pathway (Rogers and Fantauzzo 2020). The upregulated gene expression probably attempting to compensate for this degradation and supports continued activation of PDGFRA signaling activation, emphasizing its crucial role in response to the PDGF treatment.”

The Reviewer points out that our conclusion that “PDGF do not mediate their effects via the PDGFRA” is not supported by the current data asking that further discussion, interpretation, and direct comparison of the nucleus pulposus and annulus fibrosus data sets be presented to the readers.

We thank the Reviewer for the insightful comment. In page 20, line 432, we have corrected our language to now state: “In contrast, while PDGF treatment alleviated the senescent phenotype in AF cells, it also induced changes in pathways such as response to mechanical stimuli and neurogenesis, which were distinct from those in NP cells. This indicates that the treatment enhanced IVD functionality through different mechanisms within the two compartments.”

The Reviewer cannot appreciate the changes in S-phase between control and treated groups.

We apologize for the poor quality of the figure in our initial submission. We analyzed the data in S phase and included them in our revised Figures 5C and 5F.

The Reviewer believes that discectomies are typically not performed on patients with discogenic back pain but on patients who are undergoing surgery for a herniated disc.

We agree with the Reviewer, and we corrected our language in the revised manuscript. In Page 6, line 128, we now stated: “Human degenerated NP and AF tissues (Grade IV or V on Pfirrman grade; 64.6 ±8.5 years old)) were obtained as the surgical waste from donors with disc herniation, with each donor providing written informed consent.”

The Reviewer asks about the protein-protein interactions in AF cells.

We thank the Reviewer for this suggestion, and we now included it in Figure 3.

The Reviewer requests more details about the protocol and doses for the irradiation studies.

In the revised manuscript, we added this information in page 10, line 204.

The Reviewer asks whether the gene expression of PDGFRA was increased or decreased in irradiated cells compared to non-irradiated cells.

The gene expression of PDGFRA was decreased in NP cells exposed to irradiation compared to non-irradiated cells. The data are shown in Figure 4 and their description in the text is in page 17, line 411.

eLife Assessment

This study presents important new insights linking obesity to kidney disease using a Drosophila model. A series of compelling experiments demonstrate that a high-fat diet induces excretion of a leptin-like JAK-STAT ligand from fat body, driving the adipose-nephrocyte axis through activated JAK-STAT signaling and subsequently causing a functional defect in nephrocytes. The approach using combination of genetic tools and pharmacological intervention is solid and confirms the mechanistic link, together with phenotypic analysis that further supports the authors conclusions.

Reviewer #1 (Public review):

Summary:

Zhao and colleagues employ Drosophila nephrocytes as a model to investigate the effects of a high-fat diet on these podocyte-like cells. Through a highly focused analysis, they initially confirm previous research in their hands demonstrating impaired nephrocyte function and move on to observe the mislocalization of a slit diaphragm-associated protein (pyd) and a knock-in into the locus of the Drosophila nephrin (sns). Employing another reporter construct, they identify activation of the JAK/STAT signaling pathway in nephrocytes. Subsequently, the authors demonstrate the involvement of this pathway in nephrocyte function from multiple angles, using a gain-of-function construct, silencing of an inhibitor, and ectopic overexpression of a ligand. Silencing the effector Stat92E via RNAi or inhibiting JAK/STAT with Methotrexate effectively restored impaired nephrocyte function and slit diaphragm architecture induced by a high-fat diet, while showing no impact under normal dietary conditions.

Strengths:

The findings establish a link between JAK/STAT activity and the impact of a high-fat diet on nephrocytes. This nicely underscores the importance of organ crosstalk for nephrocytes and supports a potential role for JAK/STAT in diabetic nephropathy, as previously suggested by other models.

Weaknesses:

While the analysis provides valuable insights, it appears somewhat over-reliant on tracer uptake in certain instances. Clinical inferences based on a Drosophila model should be interpreted with caution.

Reviewer #2 (Public review):

Summary:

In their manuscript, Zhao et al. describe a link between JAK-STAT pathway activation in nephrocytes upon a high-fat diet. Nephrocytes are the homologs to mammalian podocytes, and it has been previously shown that metabolic syndrome and obesity is associated with worse outcomes for chronic kidney disease. A study from 2021 (Lubojemska et al.) could already confirm a severe nephrocyte phenotype upon feeding Drosophila a high fat diet and also linking lipid overflow by expressing adipose triglyceride lipase in the fat body to nephrocyte dysfunction. In this study, the authors identified a second pathway and mechanism, how lipid dysregulation impact on nephrocyte function. In detail, they show an activation of JAK-STAT signaling in nephrocytes upon feeding a high-fat diet, which was induced by Upd2 expression (a leptin-like hormone) in the fat body, the adipose tissue in Drosophila. Further, they could show genetic and pharmacological interventions can reduce JAK-STAT activation and thereby prevent the nephrocyte phenotype in the high-fat diet model.

Strengths:

The strength of this study is the combination of genetic tools and pharmacological intervention to confirm a mechanistic link between the fat body/adipose tissue and nephrocytes. Inter-organ communication is crucial in the development of several diseases, but the underlying mechanisms are only poorly understood. Using Drosophila, it is possible to investigate several players of one pathway, here JAK-STAT. This was done, by investigating the functional role of Hop, Socs36E and Stat92E in nephrocytes and has also been combined with feeding a high-fat diet, to assess restoration of nephrocyte morphology and function by inhibiting JAK-STAT signaling. Adding a translational approach was done by inhibiting JAK-STAT signaling with methotrexate, which also resulted in attenuated nephrocyte dysfunction. Expression of the leptin-like hormone upd2 in the fat body is a good approach to study inter-organ communication and the impact of other organs/tissue on nephrocyte function and expands their findings from nephrocyte function towards whole animal physiology.

Weaknesses:

Although the general findings of this study are of great interest, the number of flies investigated for the majority of the experiments is very low (6 flies). Also it is not clear whether the 6 flies used are from independent experiments to exclude differences in food/diet.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zhao and colleagues employ Drosophila nephrocytes as a model to investigate the effects of a high-fat diet on these podocyte-like cells. Through a highly focused analysis, they initially confirm previous research in their hands demonstrating impaired nephrocyte function and move on to observe the mislocalization of a slit diaphragmassociated protein (pyd). Employing a reporter construct, they identify the activation of the JAK/STAT signaling pathway in nephrocytes. Subsequently, the authors demonstrate the involvement of this pathway in nephrocyte function from multiple angles, using a gain-of-function construct, silencing of an inhibitor, and ectopic overexpression of a ligand. Silencing the effector Stat92E via RNAi or inhibiting JAK/ STAT with Methotrexate effectively restored impaired nephrocyte function induced by a high-fat diet, while showing no impact under normal dietary conditions.

Strengths:

The findings establish a link between JAK/STAT activity and the impact of a high-fat diet on nephrocytes. This nicely underscores the importance of organ crosstalk for nephrocytes and supports a potential role for JAK/STAT in diabetic nephropathy, as previously suggested by other models.

Weaknesses:

The analysis is overly reliant on tracer endocytosis and single lines. Immunofluorescence of slit diaphragm proteins would provide a more specific assessment of the phenotypes.

We thank the reviewer for the positive comments and pointing out that slit diaphragm markers would provide a more specific assessment of the phenotypes. In our revised manuscript, we used Sns-mRuby3, in which mRuby3 was tagged endogenously at the C-terminal of Sns (PMID: 39195240 and PMID: 39431457), to show the slit diaphragm pattern.

Reviewer #2 (Public Review):

Summary:

In their manuscript, Zhao et al. describe a link between JAK-STAT pathway activation in nephrocytes on a high-fat diet. Nephrocytes are the homologs to mammalian podocytes and it has been previously shown, that metabolic syndrome and obesity are associated with worse outcomes for chronic kidney disease. A study from 2021 (Lubojemska et al.) could already confirm a severe nephrocyte phenotype upon feeding Drosophila a high-fat diet and also linking lipid overflow by expressing adipose triglyceride lipase in the fat body to nephrocyte dysfunction. In this study, the authors identified a second pathway and mechanism, how lipid dysregulation impact on nephrocyte function. In detail, they show activation of JAK-STAT signaling in nephrocytes upon feeding them a high-fat diet, which was induced by Upd2 expression (a leptin-like hormone) in the fat body, and the adipose tissue in Drosophila. Further, they could show genetic and pharmacological interventions can reduce JAK-STAT activation and thereby prevent the nephrocyte phenotype in the high-fat diet model.

Strengths:

The strength of this study is the combination of genetic tools and pharmacological intervention to confirm a mechanistic link between the fat body/adipose tissue and nephrocytes. Inter-organ communication is crucial in the development of several diseases, but the underlying mechanisms are only poorly understood. Using Drosophila, it is possible to investigate several players of one pathway, here JAK-STAT. This was done, by investigating the functional role of Hop, Socs36E, and Stat92E in nephrocytes and has also been combined with feeding a high-fat diet, to assess restoration of nephrocyte function by inhibiting JAK-STAT signaling. Adding a translational approach was done by inhibiting JAK-STAT signaling with methotrexate, which also resulted in attenuated nephrocyte dysfunction. Expression of the leptin-like hormone upd2 in the fat body is a good approach to studying inter-organ communication and the impact of other organs/tissue on nephrocyte function and expands their findings from nephrocyte function towards whole animal physiology.

Weaknesses:

Although the general findings of this study are of great interest, there are some weaknesses in the study, which should be addressed. Overall, the number of flies investigated for the majority of the experiments is very low (6 flies) and it is not clear whether the flies used, are from independent experiments to exclude problems with food/diet. For the analysis, the mean values of flies should be calculated, as one fly can be considered a biological replicate, but not all individual cells. By increasing the number of flies investigated, statistical analysis will become more solid. In addition, the morphological assessment is rather preliminary, by only using a Pyd antibody. Duf or Sns should be visualized as well, also the investigation of the different transgenic fly strains studying the importance of JAK-STAT signaling in nephrocytes needs to include a morphological assessment. Moreover, the expected effect of feeding a high-fat diet on nephrocytes needs to be shown (e.g. by lipid droplet formation) and whether upd2 is actually increased here should also be assessed. The time points of assessment vary between 1, 3, and 7 days and should be consistent throughout the study or the authors should describe why they use different time points.

We thank the reviewer for the comments and suggestions. HFD causes enlarged crop (Liao et al, 2021, PMID: 33171202) and accumulation of lipid droplets in the intestine. To exclude the problems with different batches of food/diet, we checked crop and the intestine during the sample preparation as indications of food consistency.

We followed the suggestion to take the mean values of flies in the data analysis, one was considered a biological replicate in the revised version. We added in another slit diaphragm protein reporter Sns-mRuby3, in which mRuby3 fluorescent protein was tagged at the C-terminal of endogenous Sns. This reporter was used to show the effect of HFD on slit diaphragm protein, manipulation of Jak/Stat pathway (ppl-Gal4>upd2 and dot-Gal4>UAS-Stat92E-RNAi), and drug treatment.

Lubojemska et al 2021 (PMID: 33945525) showed that HFD leads to lipid droplet accumulation in larval nephrocytes. Following the reviewer’s suggestion, we stained the adult nephrocytes with Nile red and found lipid droplet formation caused by HFD, verifying the HFD effects on lipid droplet accumulation.

Regarding the timepoints, the newly eclosed flies (1-day old) were treated for 7 days (transferred to fresh diet or shifted from 18 to 29 °C for 7 days to induce target gene expression). Thus, the flies were 7 days old. In the revised manuscript, we changed “1-day-old females” to “7-day-old females” in the figure legend. The exception was Figure 4 panel G and H, we used Day 3 for the UAS-hop.Tum overexpression in the flp-out clones, which is different from the HFD approach (Day 7). This is because Hop.Tum is a strong gain of function mutation. UAS-hop.Tum overexpression in the eye imaginal disc leads to apoptosis via up-regulating a proapoptotic gene hid (Bhawana Maurya et al, 2021, PMID: 33824299). Thus, we used Day 3 for this experiment.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

There are relevant issues, that should be addressed:

Major:

- The analysis of JAK/STAT signaling in nephrocytes is limited to nephrocyte function, despite the nice slit diaphragm phenotype shown in Figure 2A. What happens to the slit diaphragm in the other genotypes, the rescue settings in particular? Immunofluorescence of Pyd should be explored for all conditions to evaluate proper phenocopy. Tracer endocytosis is much less specific.

We thank the reviewer for the suggestion. We made a transgenic line Sns-mRuby3, in which mRuby3 was tagged to the endogenous Sns C-terminal. It has been used as a slit diaphragm reporter (PMID: 39195240 and PMID: 39431457). Apart from the tracer assays, we used Sns-mRuby3 reporter and/or Pyd staining to visualize the changes in slit-diaphragm structures.

- The interventions are restricted to single RNAi lines and reporters, raising concerns about specificity/potential off-targets. Additional lines should be tested for verification.

Different versions of RNAi lines are available for targeting fly genes. For UAS-Socs36E-RNAi, we chose the one that was generated with a short hairpin, which is known to restrict the off-target effects (Ni et al, 2011, PMID: 21460824). For UAS-Stat92E-RNAi, we added in an independent RNAi line (Figure 6 - figure supplement 1 and 2).   

Minor:

- In Figure 2C, the image of HFD shows a section that cuts through the surface at a shallower angle, making everything appear blurry. This image should be replaced.

We replaced Figure 2C (the image of HFD) with another one.

- What is the relevance (if any) of reduced electrodense vacuoles with a high-fat diet? An effect on endocytic trafficking/endosome architecture remains unexplored.

Lubojemska et al (PMID: 33945525) studied the endocytic trafficking/endosome architecture of the larval nephrocytes and found that HFD impaired the endocytosis. We studied the adult pericardial nephrocytes. It is very likely that the endocytic trafficking/endosome architecture is affected by HFD in the adult nephrocytes.  

- How do the findings presented in this manuscript correlate with a similar study by Lubojemska et al.? At least the discussion should provide more evaluation of this aspect.

Lubojemska et al (PMID: 33945525) assayed the larval nephrocytes and found that a HFD leads to the ectopic accumulation of lipid droplets in the nephrocytes and decreased endocytosis. They further demonstrated that lipid droplet lipolysis and PGC1α counteracts the harmful effects of a HFD. We performed Nile red staining and verified the accumulation of lipid droplets in the adult pericardial nephrocytes upon HFD feeding, which agrees with Lubojemska discovery. We found that a HFD activates Jak/Stat pathway, which mediates the nephrocyte functional defects. A previous study showed that Stat1 has an inhibitory effect on PGC1α transcription (PMID: 26689548). Further study is needed to investigate the interaction between Jak/Stat pathway and PGC1α transcription. We added the information to the discussion.

- Please check spelling and grammar.

Reviewer #2 (Recommendations For The Authors):

(1) Which cells are investigated? Please state.

Pericardial nephrocytes were used in this study. The information was added to the result parts.

(2) Rephrase 'chronic kidney disease model'. Feeding for 7 days and assessment after 7 days cannot be considered chronic as flies can live more than 60 days.

Lubojemska et al (PMID: 33945525) fed the newly hatched larvae with a HFD and used the third instar larvae for the experiments. The term “chronic kidney disease” has been used in the reference PMID: 33945525. It takes about 4 days for fly larvae to develop from the first instar to the third instar. Thus, the animals were fed on the HFD for only 4 days. In this regard, feeding for seven days might be considered as chronic.

(3) Line 89: Curran et al., 2014). with risk increasing risk as BMI increases (Hsu et al., 2006). Please correct this sentence.

We thank the reviewer for finding the error. In the revised version, the sentence was changed as “with increasing risk as BMI increases (Hsu et al., 2006)”.

(4) Figure 1: The authors should explain why they use FITC-Albumin and 10kDA dextran, what are the differences, and why are both used?

The tracers are different in size (70kD FITC-Albumin and 10kDA dextran). Both FITC-Albumin and 10kDA dextran have been used in previous publications (Zhao et al 2024, PMID: 39431457 and Weavers et al 2009, PMID: 18971929) to show that the nephrocytes can efficiently take up the tracers of different sizes.

(5) Figure 3: The JAK-STAT sensor was used on Day 1 to confirm activation of JAKSTAT signaling, which means a very fast response towards the HFD after 24hrs. How is the activation after 7 days? The nephrocyte assessment in Figures 1 and 2 is done at the later time point, how about earlier time points in HFD? One would expect an earlier phenotype as well if JAK-STAT signaling is causative.

In Figure 3C, newly eclosed flies (1-day old) were fed on a control diet or a HFD for 7 days. Thus, in the legend it shall be “7-day-old females”. Sorry for misleading. The caption was updated as “7-day-old females”.

(6) Figure 4H: I don't understand how many cells or flies are depicted and analysed? Are the dots one nephrocyte from 4 flies? If yes, the numbers need to be increased.

In figure 4H, we quantified 5 UAS-hop.Tum clones and 5 neighbor cells. We only found 5 clones from 4 flies. We didn’t quantify all the nephrocytes, since we compared the clone with its neighbor cell. To make it easier to follow, we changed the description as “n= 5 clones and 5 neighbor cells”.

(7) Figure 4: Why are flies investigated at different ages? Day 1 vs Day 3? This should be consistent with the HFD approach and day 7. Or investigate the HFD at earlier time points as well.

In Figure 4, the newly eclosed flies (1-day old) were shifted from 18 to 29 °C for 7 days to induce target gene expression. Thus, the flies were 7-day old. In the revised manuscript, we changed “1-day-old females” to “7-day-old females” in the figure legend. We used Day 3 for the UAS-hop.Tum overexpression in the flp-out clones, which is different from the HFD approach (Day 7). This is because Hop.Tum is a strong gain of function mutation. UAS-hop.Tum overexpression in the eye imaginal disc leads to apoptosis via up-regulating a proapoptotic gene hid (Bhawana Maurya et al, 2021, PMID: 33824299). Thus, we used Day 3 for this experiment.

(8) Figure 5: Do the authors see upd2-GFP in the nephrocyte or at the nephrocyte? Is upd2 filtered to bind the JAK-STAT-receptor? They should show this, which is easy to do due to the GFP label.

We thank the reviewer for the suggestion. We looked into the nephrocyte from ppl-Gal4>upd2-GFP flies and found Upd2-GFP in the nephrocytes. We further showed that ppl-Gal4 was not expressed in the nephrocytes, suggesting that Upd2-GFP is secreted from the fat body and transported to the nephrocytes. We stained the nephrocytes for Pyd and found compromised fingerprint pattern caused by Upd2-GFP expression in the fat body. The data was added to Figure 5 - figure supplement 1.

(9) Figure 5: What are the upd2 levels after day 1 and compared to HFD at day 7? In the Rajan et al manuscript, upd2 levels have been assessed by qPCR, this can be done here as well. Although there is a mechanistic link shown here, I think it would be interesting to test the upd2 levels at the different time points assessed.

In the Rajan et al manuscript, they showed that the expression of upd2 was up regulated by HFD. My previous work showed that HFD changes taste perception. We performed qPCR to determine the expression of upd2 and verified that upd2 was upregulated in HFD fed flies (Yunpo Zhao et al. 2023. PMID: 37934669). We included the reference in the revised version.

(10) Figure 6: Does a Socs36E overexpression e.g. with the Bloomington strain 91352 also rescue the HFD phenotype, by blocking JAK-STAT signaling?

We thank the reviewer for the suggestion. We tested the effect of Socs36E overexpression and observed that UAS-Socs36E can partially rescue HFD caused nephrocyte functional decline. The data was not included in the revised manuscript. Notably, apart from having an inhibitory effect on the Jak/Stat, Socs36E represses MAPK pathway (Amoyel et al, 2016, PMID: 26807580).    

(11) Figure 7: What is the control for the methotrexate treatment? What is the solvent?

We used DMSO as the solvent for methotrexate and used it as the control for the methotrexate treatment. We added the following sentences to the method parts, “Methotrexate (06563, Sigma-Aldrich, MO) was dissolved in DMSO to make a 10mM stock solution”, and “The samples incubated in Schneider’s Medium supplemented with DMSO vehicle were used a control”.

(12) Why did the authors use Dot-Gal4 for the Socs36E knockdown and Dot-Gal4ts for the Stat92E knockdown?

We used Dot-Gal4ts and temperature shifting to restrict the Stat92E knockdown at adult stages.

(13) Supplementary Figure 1: Please add the individual data to the figure as done for all other figures.

We thank the reviewer for this comment. The figure individual data was added according to the suggestion.

eLife Assessment

Using microscopy experiments and theoretical modelling, the authors present convincing evidence of cellular coordination in the gliding filamentous cyanobacterium Fluctiforma draycotensis. The results are fundamental for the understanding of cyanobacterial motility and the underlying molecular and mechanical pathways of cellular coordination.

Reviewer #1 (Public review):

Summary:

The authors use microscopy experiments to track the gliding motion of filaments of the cyanobacteria Fluctiforma draycotensis. They find that filament motion consists of back and forth trajectories along a "track", interspersed with reversals of movement direction, with no clear dependence between filament speed and length. It is also observed that longer filaments can buckle and form plectonemes. A computational model is used to rationalize these findings.

Strengths:

Much work in this field focuses on molecular mechanisms of motility; by tracking filament dynamics this work helps to connect molecular mechanisms to environmentally and industrially relevant ecological behavior such as aggregate formation.

The observation that filaments move on tracks is interesting and potentially ecologically signifiant.

The observation of rotating membrane-bound protein complexes and tubular arrangement of slime around the filament provide important clues to the mechanism of motion.

The observation that long filaments buckle has potential to shed light on the nature of mechanical forces in the filaments, e.g. through study of the length dependence of buckling.

The comparison between motility on agar and on glass is interesting since it shows that filaments have both intrinsic propensity to reverse (that is seen on glass) and mechanically triggered reversal (that is seen on agar when the filament reaches the end of a track).

Weaknesses:

The manuscript makes the interesting statement that the distribution of speed vs filament length is uniform, which would constrain the possibilities for mechanical coupling between the filaments. However Fig 2C does not show a uniform distribution but rather an apparent lack of correlation between speed and filament length, although the statistical degree of correlation is not given. In my view, Fig 2C should not be described as a uniform distribution since mathematically that means something very different than what is shown here. Instead the figure should be described quantitatively with the use of a measured correlation coefficient. This also applies to Fig. S3A.

The statement "since filament speed results from a balance between propulsive forces and drag, these observations of no or positive correlation between filament speed and length show that all (or a fixed proportion of) cells in a filament contribute to propulsive force generation" helps to clarify the important link between Fig 2C and the concept that all cells contribute, but I think this statement is not obvious for many readers, and could be made clearer, e.g. by the use of a simple mathematical model for a chain of bacterial that accounts for drag forces and propulsion forces for each bacterium.

The authors have now clarified that the computational model is 1D and cannot explain the coupling between rotation, slime generation and motion. I find it encouraging and important that model predictions for the dwell time distributions (Fig S12 and S13) are similar to experimental measurements, but I think it would be better to put these results in the main text, together also with Fig S4. If these important results are in the supplement it is harder for the reader to assess the match between model and experiments.

Filament buckling is not analysed in quantitative detail, but the authors have now clarified that this will be the topic of future work with a 2D or 3D computational model.

Reviewer #2 (Public review):

Summary:

The authors combined time-lapse microscopy with biophysical modeling to study the mechanisms and timescales of gliding and reversals in filamentous cyanobacterium Fluctiforma draycotensis. They observed the highly coordinated behavior of protein complexes moving in a helical fashion on cells' surfaces and along individual filaments as well as their de-coordination, which induces buckling in long filaments.

Strengths:

The authors provided concrete experimental evidence of cellular coordination and de-coordination of motility between cells along individual filaments. The evidence is comprised of individual trajectories of filaments that glide and reverse on surfaces as well as the helical trajectories of membrane-bound protein complexes that move on individual filaments and are implicated in generating propulsive forces.

Limitations:

The biophysical model is one-dimensional and thus does not capture the buckling observed in long filaments. I expect that the buckling contains useful information since it reflects the competition between bending rigidity, the speed at which cell synchronization occurs, and the strength of the propulsion forces.

Future directions:

The study highlights the need to identify molecular and mechanical signaling pathways of cellular coordination. In analogy to the many works on the mechanisms and functions of multi-ciliary coordination, elucidating coordination in cyanobacteria may reveal a variety of dynamic strategies in different filamentous cyanobacteria.

Reviewer #3 (Public review):

Summary:

The authors present new observations related to the gliding motility of the multicellular filamentous cyanobacteria Fluctiforma draycotensis. The bacteria move forward by rotating their about their long axis, which causes points on the cell surface to move along helical paths. As filaments glide forward they form visible tracks. Filaments preferentially move within the tracks. The authors device a simple model in which each cell in a filament exerts a force that either pushes forwards or backwards. Mechanical interactions between cells cause neighboring cells to align the forces they exert. The model qualitatively reproduces the tendency of filaments to move in a concerted direction and reverse at the end of tracks.

The authors seek to understand how cells in a filament synchronize their motion to move in a concerted direction. This question connects to the evolution of multicellular life and so is important well beyond the specific field of cyanobacterial locomotion.

Strengths:

The biophysical model used to describe cell-cell coordination of locomotion is clear and reasonable. This model provides a useful phenomenological framework in which to consider the roles of individual cells in the coordinated motion of the group. The qualitative consistency between theory and observation suggests that this model captures some essential qualities of the true system.

The observation that filaments reverse at the ends of tracks is compelling, but difficult to clearly connect to any one microscopic model.

The observations of helical motion of the filament are compelling.

Weaknesses:

The comparison of theory and observation is mainly qualitative. While the authors have done a good job fitting the observations to the theory, it is not possible to systematically vary parameters, independently estimate parameter values, or apply external forces. Consequently, more experiments are needed before the proposed model can the accepted or rejected. This manuscript provides a promising hypothesis but not a compelling justification for it.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary

The authors use microscopy experiments to track the gliding motion of filaments of the cyanobacteria Fluctiforma draycotensis. They find that filament motion consists of back-and-forth trajectories along a "track", interspersed with reversals of movement direction, with no clear dependence between filament speed and length. It is also observed that longer filaments can buckle and form plectonemes. A computational model is used to rationalise these findings.

We thank the reviewer for this accurate summary of the presented work.

Strengths:

Much work in this field focuses on molecular mechanisms of motility; by tracking filament dynamics this work helps to connect molecular mechanisms to environmentally and industrially relevant ecological behavior such as aggregate formation.

The observation that filaments move on tracks is interesting and potentially ecologically significant.

The observation of rotating membrane-bound protein complexes and tubular arrangement of slime around the filament provides important clues to the mechanism of motion.

The observation that long filaments buckle has the potential to shed light on the nature of mechanical forces in the filaments, e.g. through the study of the length dependence of buckling.

We thank the reviewer for listing these positive aspects of the presented work.

Weaknesses:

The manuscript makes the interesting statement that the distribution of speed vs filament length is uniform, which would constrain the possibilities for mechanical coupling between the filaments. However, Figure 1C does not show a uniform distribution but rather an apparent lack of correlation between speed and filament length, while Figure S3 shows a dependence that is clearly increasing with filament length. Also, although it is claimed that the computational model reproduces the key features of the experiments, no data is shown for the dependence of speed on filament length in the computational model. The statement that is made about the model "all or most cells contribute to propulsive force generation, as seen from a uniform distribution of mean speed across different filament lengths", seems to be contradictory, since if each cell contributes to the force one might expect that speed would increase with filament length.

We agree that the data shows in general a lack of correlation, rather than strictly being uniform. In the revised manuscript, we intend to collect more data from observations on glass to better understand the relation between filament length and speed.

In considering longer filaments, one also needs to consider the increased drag created by each additional cell - in other words, overall friction will either increase or be constant as filament length increases. Therefore, if only one cell (or few cells) are generating motility forces, then adding more cells in longer filaments would decrease speed.

Since the current data does not show any decrease in speed with increasing filament length, we stand by the argument that the data supports that all (or most) cells in a filament are involved in force generation for motility. We would revise the manuscript to make this point - and our arguments about assuming multiple / most cells in a filament contributing to motility - clear.

The computational model misses perhaps the most interesting aspect of the experimental results which is the coupling between rotation, slime generation, and motion. While the dependence of synchronization and reversal efficiency on internal model parameters are explored (Figure 2D), these model parameters cannot be connected with biological reality. The model predictions seem somewhat simplistic: that less coupling leads to more erratic reversal and that the number of reversals matches the expected number (which appears to be simply consistent with a filament moving backwards and forwards on a track at constant speed).

We agree that the coupling between rotation, slime generation and motion is interesting and important when studying the specific mechanism leading to filament motion. However, we believe it is even more fundamental to consider the intercellular coordination that is needed to realise this motion. Individual filaments are a collection of independent cells. This raises the question of how they can coordinate their thrust generation in such a way that the whole filament can both move and reverse direction of motion as a single unit. With the presented model, we want to start addressing precisely this point.

The model allows us to qualitatively understand the relation between coupling strength and reversals (erratic vs. coordinated motion of the filament). It also provides a hint about the possibility of de-coordination, which we then look for and identify in longer filaments.

While the model’s results seem obvious in hindsight, the analysis of the model allows phrasing the question of cell-to-cell coordination, which so far has not been brought up when considering the inherently multi-cell process of filament motility.

Filament buckling is not analysed in quantitative detail, which seems to be a missed opportunity to connect with the computational model, eg by predicting the length dependence of buckling.

Please note that Figure S10 provides an analysis of filament length and number of buckling instances observed. This suggests that buckling happens only in filaments above a certain length.

We do agree that further analyses of buckling - both experimentally and through modelling would be interesting. This study, however, focussed on cell-to-cell coupling / coordination during filament motility. We have identified the possibility of de-coordination through the use of a simple 1D model of motion, and found evidence of such de-coordination in experiments. Notice that the buckling we report does not depend on the filament hitting an external object. It is a direct result of a filament activity which, in this context, serves as evidence of cellular de-coordination.

Now that we have observed buckling and plectoneme formation, these processes need to be analysed with additional experiments and modelling. The appropriate model for this process needs to be 3D, and should ideally include torques arising from filament rotation. Experimentally, we need to identify means of influencing filament length and motion and see if we can measure buckling frequency and position across different filament lengths. These works are ongoing and will have to be summarised in a separate, future publication.

Reviewer #2 (Public review):

Summary:

The authors combined time-lapse microscopy with biophysical modeling to study the mechanisms and timescales of gliding and reversals in filamentous cyanobacterium Fluctiforma draycotensis. They observed the highly coordinated behavior of protein complexes moving in a helical fashion on cells' surfaces and along individual filaments as well as their de-coordination, which induces buckling in long filaments.

We thank the reviewer for this accurate summary of the presented work.

Strengths:

The authors provided concrete experimental evidence of cellular coordination and de-coordination of motility between cells along individual filaments. The evidence is comprised of individual trajectories of filaments that glide and reverse on surfaces as well as the helical trajectories of membrane-bound protein complexes that move on individual filaments and are implicated in generating propulsive forces.

We thank the reviewer for listing these positive aspects of the presented work.

Limitations:

The biophysical model is one-dimensional and thus does not capture the buckling observed in long filaments. I expect that the buckling contains useful information since it reflects the competition between bending rigidity, the speed at which cell synchronization occurs, and the strength of the propulsion forces.

Cell-to-cell coordination is a more fundamental phenomenon than the buckling and twisting of longer filaments, in that the latter is a consequence of limits of the former. In this sense, we are focussing here on something that we think is the necessary first step to understand filament gliding. The 3D motion of filaments (bending, plectoneme formation) is fascinating and can have important consequences for collective behaviour and macroscopic structure formation. As a consequence of cellular coupling, however, it is beyond the scope of the present paper.

Please also see our response above. We believe that the detailed analysis of buckling and plectoneme formation requires (and merits) dedicated experiments and modelling which go beyond the focus of the current study (on cellular coordination) and will constitute a separate analysis that stands on its own. We are currently working in that direction.

Future directions:

The study highlights the need to identify molecular and mechanical signaling pathways of cellular coordination. In analogy to the many works on the mechanisms and functions of multi-ciliary coordination, elucidating coordination in cyanobacteria may reveal a variety of dynamic strategies in different filamentous cyanobacteria.

We thank the reviewer for highlighting this point again and seeing the value in combining molecular and dynamical approaches.

Reviewer #3 (Public review):

Summary:

The authors present new observations related to the gliding motility of the multicellular filamentous cyanobacteria Fluctiforma draycotensis. The bacteria move forward by rotating their about their long axis, which causes points on the cell surface to move along helical paths. As filaments glide forward they form visible tracks. Filaments preferentially move within the tracks. The authors devise a simple model in which each cell in a filament exerts a force that either pushes forward or backwards. Mechanical interactions between cells cause neighboring cells to align the forces they exert. The model qualitatively reproduces the tendency of filaments to move in a concerted direction and reverse at the end of tracks.

We thank the reviewer for this accurate summary of the presented work.

Strengths:

The observations of the helical motion of the filament are compelling. The biophysical model used to describe cell-cell coordination of locomotion is clear and reasonable. The qualitative consistency between theory and observation suggests that this model captures some essential qualities of the true system.

The authors suggest that molecular studies should be directly coupled to the analysis and modeling of motion. I agree.

We thank the reviewer for listing these positive aspects of the presented work and highlighting the need for combining molecular and biophysical approaches.

Weaknesses:

There is very little quantitative comparison between theory and experiment. It seems plausible that mechanisms other than mechano-sensing could lead to equations similar to those in the proposed model. As there is no comparison of model parameters to measurements or similar experiments, it is not certain that the mechanisms proposed here are an accurate description of reality. Rather the model appears to be a promising hypothesis.

We agree with the referee that the model we put forward is one of several possible. We note, however, that the assumption of mechanosensing by each cell - as done in this model - results in capturing both the alignment of cells within a filament (with some flexibility) and reversal dynamics. We have explored an even more minimal 1D model, where the cell’s direction of force generation is treated as an Ising-like spin and coupled between nearest neighbours (without assuming any specific physico-chemical basis). We found that this model was not fully able to capture both phenomena. In that model, we found that alignment required high levels of coupling (which is hard to justify except for mechanical coupling) and reversals were not readily explainable (and required additional assumptions). These points led us to the current, mechanically motivated model.

The parameterisation of the current model would require measuring cellular forces. To this end, a recent study has attempted to measure some of the physical parameters in a different filamentous cyanobacteria [1] and in our revision we will re-evaluate model parameters and dynamics in light of that study. We will also attempt to directly verify the presence of mechano-sensing by obstructing the movement of filaments.

Summary from the Reviewing Editor:

The authors present a simple one-dimensional biophysical model to describe the gliding motion and the observed statistics of trajectory reversals. However, the model does not capture some important experimental findings, such as the buckling occurring in long filaments, and the coupling between rotation, slime generation, and motion. More effort is recommended to integrate the information gathered on these different aspects to provide a more unified understanding of filament motility. In particular, the referees suggest performing a more quantitative analysis of the buckling in long filaments. Finally, it is also recommended to discuss the results in the context of previous literature, in order to better explain their relevance. Please find below the detailed individual recommendations of the three reviewers.

We thank the editor for this accurate summary of the presented work and for highlighting the key points raised by the reviewers. We have provided below point-by-point replies to these.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The relevance of the study organism Fluctiforma draycotensis is not clearly explained, and the results are not discussed in the context of previous literature. The motivation would be clearer if the manuscript explained why this model organism was chosen and how the results compare with those previously observed for this or other organisms.

We have extended the introduction and discussion sections to make it clearer why we have worked with this organism and how the findings from this work relate to previous ones. In brief, Flucitforma draycotensis is a useful organism to work with as it not only displays significant motility but it also displays intriguing collective behaviour at different scales. Previous works on gliding motility in filamentous cyanobacteria have mostly focussed on the model organism Nostoc punctiforme, which only displays motility after differentiation into hormogonia [1]. There have also been studies in a range of different filamentous species, including those of the non-monophyletic genus, Phormidium, but these studies mostly looked at effects of genetic deletions on motility [2] or utilised electron microscopy to identify proteins (or surface features) involved in motility [3-5]. It must be noted that motility is also described and studied in non-filamentous cyanobacteria, but the dynamics of motion and molecular mechanisms there are different to filamentous cyanobacteria [6,7]. These previous studies are now cited / summarised in the revised introduction and discussion sections.

The inferred tracks, probably associated with secreted slime, play a key role since it is supposed that the tracks provide the external force that keeps the filaments straight. Movie S3, in phase contrast, provides convincing evidence for the tracks, but they cannot be seen in the fluorescence images presented in the main text. Clearer evidence of them should be shown in the main text. An especially important aspect of the tracks is where they start and end since the computational model assumes that reversal happens due to forces generated by reaching the end of a track. Therefore it seems important to comment on what produces the tracks, to check whether reversals actually happen at the end of a track, etc. Perhaps tracks could be strained with Concanavalin-A?

To confirm that reversals happen on track ends, we have now performed an analysis on agar, where we can see tracks on phase microscopy. This analysis confirms that, on agar, reversals indeed happen on track ends. We added this analysis, along with images showing tracks clearly as a new Fig in the main text (see new Fig. 1).

Further confirming the reversal at track ends, we note that filaments on circular tracks do not not reverse over durations longer than the ‘expected reversal interval’ of a filament on a straight track (see details in response to Reviewer 2).

Regarding what produces the tracks on agar, we are still analysing this using different methods and these results will be part of a future study. Fluorescent staining can be used to visualise slime tubes using TIRF microscopy, as shown in Fig. S8, however, visualising tracks on agar using low magnification microscopy has been difficult due to background fluorescence from agar.

We would also like to clarify that the model does not incorporate any assumptions regarding the track-filament interaction, other than that the track ends behave akin to a physical boundary for the filament. The observed reversal at track ends and “what” produces the track are distinct aspects of filament motion. We do not think that the model’s assumption of filament reversal at the end of the track requires understanding of the mechanism of slime production.

Reviewer #3 (Recommendations for the authors):

The manuscript combines three distinct topics: (1) the difference in locomotion on glass vs agar, (2) the development of a biophysical model, and (3) the helical motion of filament. It is not clear what insight one can gain from any one of these topics about the two others. The manuscript would be strengthened by more clearly connecting these three aspects of the work. A stronger comparison of theory to observation would be very useful. Some suggestions:

(1) The observation that it is only the longest filaments that buckle is interesting. It should be possible to predict the critical length from the biophysical model. Doing so could allow fits of some model parameters.

(2) What model parameters change between glass and agar? Can you explain these qualitative differences in motility by changing one model parameter?

(3) Is it possible to exert a force on one end of a filament to see if it is really mechano-sensing that couples their motion?

We thank the reviewer for this comment and agree with them that a better connection between model and experiment should be sought. We believe that the new analyses, presented below in response to the 2nd suggestion of the reviewer, provide such a connection in the context of reversal frequency. As stated below, we think that the 1st suggestion falls outside of the scope of the current work, but should form the basis of a future study.

Regarding suggestion (1) - addressing buckling:

We agree with the reviewer that using a model to predict a critical buckling length would be useful. We note, however, that the presented study focussed on cell-to-cell coupling / coordination during filament motility using a 1D, beadchain model. The buckling observations served, in this context, as evidence of cellular de-coordination. Now that we have observed buckling (and plectoneme formation), these processes need to be analysed with further experiments and modelling. The appropriate model for studying buckling would have to be at least 2D (ideally 3D) and consider elastic forces and torques relating to filament bending, rotation, and twisting. Experimentally, we need to identify means of influencing filament length and motion and undertake further measurements of buckling frequency and position across different filament lengths. These investigations are ongoing and will be summarised in a separate, future publication.

Regarding suggestion (2) - addressing differences in motility on agar vs. glass:

We believe that the two key differences between agar and glass experiments are the occasional detachment of filaments from substrate on glass and the lack of confining tracks on glass. These differences might arise from the interactions between the filament, the slime, and the surface. As both slime and agar contain polysaccharides, the slime-agar interaction can be expected to be different from the slime-glass interaction. Additionally, in the agar experiments, the filaments are confined between the agar and a glass slide, while they are not confined on the glass, leaving them free to lift up from the glass surface. We expect these factors to alter reversal frequency between the two conditions. To explore this possibility, we have now extended the analysis of experimental data from glass and present that (see details below):

(i) dwell times are similar between agar and glass, and

(ii) reversal frequency distribution is different between glass and agar, and remains constant across filament length on glass.

We were able to explore these experimental findings with new model simulations, by removing the assumption of an “external bounding frame”. We then analysed reversal frequency within against model parameters, as detailed below.

“The movement of the filaments on glass. We have extended our analysis of motility on glass resulting in the following noted features. Firstly, the median speed shows a weak positive correlation with filament length on glass (see original Fig S3B vs. updated Fig. S3A). This is slightly different to agar, where we do not observe any strong correlation in either direction (see original, Fig. 1 vs. updated Fig 2). Both the cases of positive, and no correlation, support our original hypothesis that the propulsion force is generated by multiple cells within the filament.

Secondly, the filaments on glass display ‘stopping’ events that are not followed by a reversal, but are instead followed by a continuation in the original direction of motion, which we term ‘stop-go’ events, in contrast to the reversals. The dwell times associated with reversals and ‘stop-go’ events are similarly distributed (see original Fig S3A vs. updated Fig S3B). Furthermore, the dwell time distributions are similar between agar and glass (compare old Fig. 1C vs. new Fig 2C and new Fig. S3B). This suggests that the reversal process is the same on both agar and glass.

Thirdly, we find that the frequencies of both reversal and stop-go events on glass are uncorrelated with the filament length (see new Fig. S4A) and there are approximately twice as many reversals as stop-go events. In contrast, the filaments on agar reverse with a frequency that is inversely proportional to the filament length (which is in turn proportional to the track length) (see original Fig. S1). The distribution of reversal frequencies on agar is broader and flatter than the distribution on glass (see new Fig. S4B). These findings are inline with the idea that tracks on agar (which are defined by filament length) dictate reversal frequency, resulting in the strong correlations we observe between reversal frequency, track length, and filament length. On glass, filament movement is not constrained by tracks, and we have a specific reversal frequency independent of filament length.”

“Model can capture movement of filaments on glass and provides hypotheses regarding constancy of reversal frequency with length. We believe the model parameters controlling cellular memory (ω_max) and strength of cellular coupling (K_ω) describe the internal behaviour of a filament and therefore should not change depending on the substrate. Thus, we expect the model to be able to capture movement on glass just by removal of any ‘confining tracks’, i.e external forces, from the simulations. Indeed, we find that the model displays both stop-go and reversal events when simulated without any external force and can capture the dwell time distribution under this condition (compare new Figs. S12,S13 with S3).

In terms of reversal frequency, however, the model shows a reduction in reversal frequency with filament length (see new Fig. S15). This is in contrast to the experimental data. We find, however, that model results also show a reduction in reversal frequency with increasing (ω_max and K_ω (see new Fig. S14 and S15). This effect is stronger with (ω_max, while it quickly saturates with K_ω (see new Fig. S14). Therefore, one possibility of reconciling the model and experiment results in terms of constant reversal frequency with filament length would be to assume that (ω_max is decreasing with filament length (see new Fig. S16). Testing this hypothesis - or adding additional mechanisms into the model - will constitute the basis of future studies.”

Regarding suggestion (3) - role of mechanosensing:

We have tried several experiments to evaluate mechanosensing. First, we have used a micropipette or a thin wire placed on the agar, to create a physical barrier in the way of the filaments. The micropipette approach was not quite feasible in our current setup. The wire approach was possible to implement, but the wire caused a significant undulation / perturbation on agar. Possibly relating to this, filaments tended to continue moving alongside the wire barrier. Therefore, these experiments were inconclusive at this stage with regards to mechanosensing a physical barrier. As an alternative, we have attempted trapping gliding filaments using an optical trap with a far red laser that should not affect the physiology of the cells. This did not cause an immediate reversal in filament motion. However, this could be due to the optical trap strength being below the threshold value for mechanosensing. The force per unit length generated by filamentous cyanobacteria has been calculated via a model of self-buckling rods, giving a value of ≈1nN/μm [8]. In comparison, the optical trap generates forces on the scale of pN. Thus, the trap force is several orders of magnitude lower than the propulsive force generated by a filament, given filament lengths in the range of ten to several hundreds μm. We conclude that the lack of observed response may be due to the optical trap force being too weak.

Thus, the experiments we can perform using our current available methods and equipment are not able to prove either the presence or the absence of mechanosensing in the filament. We plan to perform further experiments in this direction, involving new and/or improved experimental setups, such as use of Atomic Force Microscopy.

We would like to note that there is an additional observation that supports the idea of reversals being mediated by mechanosensing at the end of a track, instead of the locations of the track ends being caused by the intrinsic reversal frequency of the filament. In a few instances (N = 4), filaments on agar ended up on a circular track (see Movie S4 for an example). These filaments did not reverse over durations a few times longer than the ‘expected reversal interval’ of a filament on a straight track.

Should $N$ following eq 7 and in eq 9 be $N_f$?

We have corrected this typo.

It would be useful to include references to what is known about mechanosensing in cyanobacteria.

We agree with the reviewer, and we have not updated the discussion section to include this information. Mechanosensing has not yet been shown directly in any cyanobacteria, but several species are shown to harbor genes that are implicated (by homology) to be involved in mechanosensing. In particular, analysis of cyanobacterial genomes predicts the presence of a significant number of homologues of the Escherichia coli mechanosensory ion channels MscS and MscL [9]. We have also identified similar MscS protein sequences in F. draycotensis. These channels open when the membrane tension increases, allowing the cell to protect itself from swelling and rupturing when subject to extreme osmotic shock. [10,11]

We also note that F. draycotensis, as with other filamentous cyanobacteria, have genes associated with the type IV pili, which may be involved in the surface-based motility [1]. Type IV pili have been shown to be mechanosensitive. For example, in cells of Pseudomonas aeruginosa that ‘twitch’ on a surface using type IV pili, application of mechanical shear stress results in increased production of an intracellular signalling molecule involved in promoting biofilm production. The pilus retraction motor has been shown to be involved in this shear-sensing response [12]. Additionally, twitching P. aeruginosa cells often reverse in response to collisions with other cells. Reversal is also caused by collisions with inert glass microfibres, which suggests that the pili-based motility can be affected by a mechanical stimulus [13].

References

(1) D. D. Risser, Hormogonium Development and Motility in Filamentous Cyanobacteria. Appl Environ Microbiol 89, e0039223 (2023).

(2) T. Lamparter et al., The involvement of type IV pili and the phytochrome CphA in gliding motility, lateral motility and photophobotaxis of the cyanobacterium Phormidium lacuna. PLoS One 17, e0249509 (2022)

(3) E. Hoiczyk, Gliding motility in cyanobacteria: observations and possible explanations. Arch Microbiol 174, 11-17 (2000).

(4) D. G. Adams, D. Ashworth, B. Nelmes, Fibrillar Array in the Cell Wall of a Gliding Filamentous Cyanobacterium. Journal of Bacteriology 181 (1999).

(5) L. N. Halfen, R. W. Castenholz, Gliding in a blue-green alga: a possible mechanism. Nature 225, 1163-1165 (1970).

(6) S. N. Menon, P. Varuni, F. Bunbury, D. Bhaya, G. I. Menon, Phototaxis in Cyanobacteria: From Mutants to Models of Collective Behavior. mBio 12, e0239821 (2021).

(7) F. D. Conradi, C. W. Mullineaux, A. Wilde, The Role of the Cyanobacterial Type IV Pilus Machinery in Finding and Maintaining a Favourable Environment. Life (Basel) 10 (2020).

(8) M. Kurjahn, A. Deka, A. Girot, L. Abbaspour, S. Klumpp, M. Lorenz, O. Bäumchen, S. Karpitschka Quantifying gliding forces of filamentous cyanobacteria by self-buckling. eLife 12:RP87450 (2024).

(9) S.C. Johnson, J. Veres, H. R. Malcolm, Exploring the diversity of mechanosensitive channels in bacterial genomes. Eur Biophys J 50, 25–36 (2021).

(10) S.I. Sukharev, W.J. Sigurdson, C. Kung, F. Sachs, Energetic and spatial parameters for gating of the bacterial large conductance mechanosensitive channel, MscL. Journal of General Physiology, 113(4), 525-540 (1999).

(11) N. Levina, S. Tötemeyer, N.R. Stoke, P. Louis, M.A. Jones, I.R. Boot. Protection of Escherichia coli cells against extreme turgor by activation of MscS and MscL mechanosensitive channels: identification of genes required for MscS activity. The EMBO journal (1999).

(12) V.D. Gordon, L. Wang, Bacterial mechanosensing: the force will be with you, always. Journal of cell science 132(7):jcs227694 (2019).

(13) M.J. Kühn, L. Talà, Y.F. Inclan, R. Patino, X. Pierrat, I. Vos, Z. Al-Mayyah, H. Macmillan, J. Negrete Jr, J.N. Engel, A. Persat, Mechanotaxis directs Pseudomonas aeruginosa twitching motility. Proceedings of the National Academy of Sciences. 118(30):e2101759118 (2021).

eLife Assessment

This fundamental work by Yamamoto and colleagues advances our understanding of how positional information is coordinated between axes during limb outgrowth and patterning. They provide solid evidence that the dorsal-ventral axis feeds into anterior-posterior signaling, and identify the responsible molecules by combining transplantations with molecular manipulations. This work will be of broad interest to regeneration, tissue engineering, and evolutionary biologists.

Reviewer #1 (Public review):

Summary:

The manuscript by Yamamoto et al. presents a model by which the four main axes of the limb are required for limb regeneration to occur in the axolotl. A longstanding question in regeneration biology is how existing positional information is used to regenerate the correct missing elements. The limb provides an accessible experimental system by which to study the involvement of the anteroposterior, dorsoventral, and proximodistal axes in the regenerating limb. Extensive experimentation has been performed in this area using grafting experiments. Yamamoto et al. use the accessory limb model and some molecular tools to address this question. There are some interesting observations in the study. In particular, one strength is the potent induction of accessory limbs in the dorsal axis with BMP2+Fgf2+Fgf8, which is very interesting.

Strengths:

The manuscript presents some novel phenotypes generated in axolotl limbs due to Wnt signaling. This is generally the first example in which Wnt signaling has provided a gain-of-function in the axolotl limb model. They also present a potent way of inducing limb patterning in the dorsal axis by the addition of just beads loaded with Bmp2+Fgf8+Fgf2.

Weaknesses:

Although interesting, the study makes bold claims about determining the molecular basis of DV positional cues, but the experimental evidence is not definitive and does not take into account the previous work on DV patterning in the amniote limb. Also, testing the hypothesis on blastemas after limb amputation would be needed to support the strong claims in the study. There are several examples of very strong claims, but the evidence lacks support for these claims.

Reviewer #2 (Public review):

Summary:

This study explores how signals from all sides of a developing limb, front/back and top/bottom, work together to guide the regrowth of a fully patterned limb in axolotls, a type of salamander known for its impressive ability to regenerate limbs. Using a model called the Accessory Limb Model (ALM), the researchers created early limb regenerates (called blastemas) with cells from different sides of the limb. They discovered that successful limb regrowth only happens when the blastema contains cells from both the top (dorsal) and bottom (ventral) of the limb. They also found that a key gene involved in front/back limb patterning, called Shh (Sonic hedgehog), is only turned on when cells from both the dorsal and ventral sides come into contact. The study identified two important molecules, Wnt10B and FGF2, that help activate Shh when dorsal and ventral cells interact. Finally, the authors propose a new model that explains how cells from all four sides of a limb, dorsal, ventral, anterior (front), and posterior (back), contribute at both the cellular and molecular level to rebuilding a properly structured limb during regeneration.

Strengths:

The techniques used in this study, like delicate surgeries, tissue grafting, and implanting tiny beads soaked with growth factors, are extremely difficult, and only a few research groups in the world can do them successfully. These methods are essential for answering important questions about how animals like axolotls regenerate limbs with the correct structure and orientation. To understand how cells from different sides of the limb communicate during regeneration, the researchers used a technique called in situ hybridization, which lets them see where specific genes are active in the developing limb. They clearly showed that the gene Shh, which helps pattern the front and back of the limb, only turns on when cells from both the top (dorsal) and bottom (ventral) sides are present and interacting. The team also took a broad, unbiased approach to figure out which signaling molecules are unique to dorsal and ventral limb cells. They tested these molecules individually and discovered which could substitute for actual dorsal and ventral cells, providing the same necessary signals for proper limb development. Overall, this study makes a major contribution to our understanding of how complex signals guide limb regeneration, showing how different regions of the limb work together at both the cellular and molecular levels to rebuild a fully patterned structure.

Weaknesses:

Because the expressional analyses are performed on thin sections of regenerating tissue, they provide only a limited view of the gene expression patterns in their experiments, opening the possibility that they could be missing some expression in other regions of the blastema. Additionally, the quantification method of the expressional phenotypes in most of the experiments does not appear to be based on a rigorous methodology. Therefore, performing alternate expressional analysis, using RNA-seq or qRT-PCR (for example) on the entire blastema would help validate that the authors are not missing something.

Overall, the number of replicates per sample group is quite low (sometimes as low as 3), which is especially risky with challenging techniques like the ones the authors employ. The authors don't appear to have performed a power analysis to calculate the number of animals used in each experiment that are sufficient to identify possible statistical differences between groups. Increasing the sample sizes would substantially increase the rigor of their experiments.

Likewise, the authors' use of an AI-generated algorithm to quantify symmetry on the dorsal/ventral axis, and this approach doesn't appear to account for possible biases due to tissue sectioning angles. They also appear to arbitrarily pick locations in each sample group to compare symmetry measurements. There are other methods, which include using specific muscle groups and nerve bundles as dorsal/ventral landmarks, that would more clearly show differences in symmetry.

Reviewer #3 (Public review):

Summary:

After salamander limb amputation, the cross-section of the stump has two major axes: anterior-posterior and dorsal-ventral. Cells from all axial positions (anterior, posterior, dorsal, ventral) are necessary for regeneration, yet the molecular basis for this requirement has remained unknown. To address this gap, Yamamoto et al. took advantage of the ALM assay, in which defined positional identities can be combined on demand and their effects assessed through the outgrowth of an ectopic limb. They propose a compelling model in which dorsal and ventral cells communicate by secreting Wnt10b and Fgf2 ligands, respectively, with this interaction inducing Shh expression in posterior cells. Shh was previously shown to induce limb outgrowth in collaboration with anterior Fgf8 (PMID: 27120163). Thus, this study completes a concept in which four secreted signals from four axial positions interact for limb patterning. Notably, this work firmly places dorsal-ventral interactions upstream of anterior-posterior, which is striking for a field that has been focussed on anterior-posterior communication. The ligands identified (Wnt10b, Fgf2) are different from those implicated in dorsal-ventral patterning in the non-regenerative mouse and chick models. The results in the context of ALM/ectopic limb engineering are impressive, but the authors do not extend their experiments to assay 'normal' regeneration after amputation.

Strengths:

(1) The ALM and use of GFP grafts for lineage tracing (Figures 1-3) take full advantage of the salamander model's unique ability to outgrow patterned limbs under defined conditions. As far as I am aware, the ALM has not been combined with precise grafts that assay 2 axial positions at once, as performed in Figure 3. The number of ALMs performed in this study deserves special mention, considering the challenging surgery involved.

(2) The authors identify that posterior Shh is not expressed unless both dorsal and ventral cells are present. This echoes previous work in mouse limb development models (AER/ectoderm-mesoderm interaction), but this link between axes was not known in salamanders. The authors elegantly reconstitute dorsal-ventral communication by grafting, finding that this is sufficient to trigger Shh expression (Figure 3 - although see also the Weaknesses section.)

(3) Impressively, the authors discovered two molecules sufficient to substitute dorsal or ventral cells through electroporation into dorsal- or ventral-depleted ALMs (Figure 5). These molecules did not change the positional identity of target cells. The same group previously identified the ventral factor (Fgf2) to be a nerve-derived factor essential for regeneration. In Figure 6, the authors demonstrate that nerve-derived factors, including Fgf2, are alone sufficient to grow out ectopic limbs from a dorsal wound. Limb induction with a 3-factor cocktail without supplementing with other cells is conceptually important for regenerative engineering.

(4) The writing style and presentation of results are very clear.

Weaknesses:

(1) The expression data are the weakest part of this study.

• Despite being a central message, I found the Shh in situs unconvincing (e.g. Figure 2I, 3C, 5C), especially without sense probe controls. An additional assay would be essential to make the Shh data convincing - perhaps like in Figure 5D (qPCR?), RNA-sequencing, or a downstream target gene.

• It is not clear what the n numbers mean for the in situ data (slides analysed / number of biological samples / other?). This is crucial to understanding the reliability of the results.

• The authors do not assay where and when Wnt10b and Fgf2 are expressed beyond the bulk RNA-sequencing (which presumably contains both epidermis and mesenchyme cells). This is a shame, as understanding which cell types express these molecules, and when, would be important for understanding the mechanism.

(2) It is important to consider that the ALM is not 'regeneration', even if the authors have previously argued that ALM bumps and regenerating blastemas are equivalent (PMID: 17959163). The start- and end- points of ALM are different from regeneration, even though there are undoubtedly common principles involved. Thus, I find the word 'regeneration' in the title and last sentence of the abstract unsubstantiated unless evidence is provided that the same mechanisms (Wnt10b/Fgf2/Shh) function during normal limb regeneration.

(3) Drawing the exact boundaries of the Ant/Pos/Dor/Ven BL and grafts in the cartoon in Figure 1 (with respect to anatomical landmarks) would help to better understand the experiments in Figures 3 and 4.

(4) I find the 'positional cue' and 'positional value' terminology confusing, despite the authors' efforts. It is not clear if they refer to cell autonomous or secreted signals, and, as the authors mention, the definitions partially overlap. Lmx1b is defined as a positional value, even though it is necessary and sufficient for dorsal identity (so, isn't it positional information?). Much simpler would be to describe Wnt10b and Fgf2 as what they are: dorsally or ventrally expressed signals that substitute for dorsal or ventral tissue without inducing changes in positional information.

Overall appraisal:

This is a logical and well-executed study that creatively uses the axolotl model to advance an important framework for understanding limb patterning. The reliability of the Shh expression data is a weak point in this otherwise impressive study. The relevance of the mechanisms to normal limb regeneration is not substantiated.

Author response:

We sincerely thank the editor and all three reviewers for their constructive comments. We deeply appreciate the reviewers’ efforts in highlighting both the strengths and the weaknesses of our study. To enhance the quality and clarity of our work, we plan to address the concerns raised in the public reviews through the following actions:

(1) Improving the tone and language of the manuscript

We will revise the manuscript thoroughly, incorporating additional explanations and clarifications where necessary, and improving the tone and language to enhance readability and precision. Especially, we will pay careful attention on the terms “positional information,” “positional value,” and “positional cue,” and we plan to explain them in a historical context.

(2) Extending analysis to regular blastemas

To validate the applicability of our proposed model beyond the accessory limb model (ALM), we will examine the gene expression patterns of key signaling molecules in regular blastemas generated by limb amputation. This will allow us to test whether the mechanisms we describe are also active during normal limb regeneration.

(3) Increasing sample sizes in critical experiments

In order to ensure reproducibility and statistical reliability, we will increase the number of biological replicates in key experiments within the limitations regulated by our animal ethics approval. Additionally, we will collect data that clearly defines the dorsal/ventral axis within the structures, as far as possible. We will also revise the manuscript to pay closer attention to the anterior/posterior/dorsal/ventral axis in the existing data, ensuring that it is clearly described.

(4) Adding quantitative gene expression data

To support and reinforce our in situ hybridization results, we will include additional quantitative gene expression analyses (e.g., qRT-PCR), thereby strengthening the conclusions drawn from our expression data.

We are grateful for the reviewers’ insights and are confident that these revisions will significantly strengthen our manuscript.

eLife Assessment

This valuable study investigates how stochastic and deterministic factors are integrated during cellular decision-making, particularly in situations where cells differentiate into distinct fates despite being exposed to the same environmental conditions. The authors present convincing evidence that gene expression variability contributes to the robustness of cell fate decisions in D. discoideum, which sheds light into the role of stochasticity during cell differentiation.

Joint Public Review:

Summary:

The authors investigate how stochastic and deterministic factors are integrated in cell fate decisions, using Dictyostelium discoideum as a model system. They show that cells in different cell cycle phases (a deterministic factor) are predisposed to different fates, albeit with deviations, when exposed to the same environmental stimulus. However, gene expression variability (a stochastic factor) enhances the robustness of cellular responses to environmental cues that disrupt the cell cycle.

Using a simple, tractable mathematical model, the authors demonstrate that cell fate decisions in D. discoideum depend on a combination of deterministic and stochastic factors, i.e., cell cycle phase and gene expression variability, respectively. They then identify Set1 - a key regulator of gene expression variability - indicate the mechanism through which it modulates this variability, and link it to a phenotype in D. discoideum development. Finally, they confirm that gene expression variability contributes to the robustness of the cell's response to environmental disruptions that interfere with the cell cycle.

Strengths:

The authors are careful in the choice of their experiments and in measuring gene expression variability, using methods that account for expected trends with average gene expression.

Weaknesses:

However, in terms of mathematical modelling, it would be important to rule out sources of stochasticity (other than gene expression variability), and also to consider cases where stochastic factors are not necessarily completely independent of the deterministic ones.

eLife Assessment

The manuscript explores how bacterial evolution in the presence of lytic phages modulates b-lactams resistance and virulence properties in methicillin-resistant Staphylococcus aureus (MRSA). This important work improves our knowledge of how mutation in genes required for phage infection confers sensitivity to b-lactams and alter virulence properties. Altogether, the findings are convincing.

Reviewer #1 (Public review):

Summary:

These authors have asked how lytic phage predation impacts antibiotic resistance and virulence phenotypes in methicillin-resistant Staphylococcus aureus (MRSA). They report that staphylococcal phages cause MRSA strains to become sensitized to b-lactams and to display reduced virulence. Moreover, they identify mutations in a set of genes required for phage infection that may impact antibiotic resistance and virulence phenotypes.

Strengths:

Phage-mediated re-sensitization to antibiotics has been reported previously but the underlying mutational analyses have not been described. These studies suggest that phages and antibiotics may target similar pathways in bacteria.

Weaknesses:

One limitation is the lack of mechanistic investigations linking particular mutations to the phenotypes reported here. This limits the impact of the work.

Another limitation of this work is the use of lab strains and a single pair of phages. However, while incorporation of clinical isolates would increase the translational relevance of this work it is unlikely to change the conclusions.

Comments on revisions:

The authors have addressed my concerns.

Reviewer #2 (Public review):

Summary:

The work presented in the manuscript by Tran et al deals with bacterial evolution in the presence of bacteriophage. Here, authors have taken three methicillin-resistant S. aureus strains that are also resistant to beta-lactams. Eventually, upon being exposed to phage, these strains develop beta-lactam sensitivity. Besides this, the strains also show other changes in their phenotype such as reduced binding to fibrinogen and hemolysis.

Strengths:

The experiments carried out are convincing to suggest such in vitro development of sensitivity to the antibiotics. Authors were also able to "evolve" phage in similar fashion thus showing enhanced virulence against the bacterium. In the end, authors carry out DNA sequencing of both evolved bacteria and phage and show mutations occurring in various genes. Overall, the experiments that have been carried out are convincing.

Weaknesses:

None. In the current version of the manuscript, I find the study complete.

Author response:

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

We sincerely thank the reviewers for their thoughtful review and feedback. We believe that our work will provide valuable insights into how MRSA evolves under bacteriophage predation and stimulate efforts to use genetic trade-offs to combat drug resistance. We have substantially revised the paper and performed several additional experiments to address the reviewers' questions and concerns.

Summary:

(1) Testing for genetic trade-offs in additional S. aureus strains

We obtained 30 clinical isolates of the S. aureus USA300 strain that were isolated between 2008 and 2011 (see Table S1). We first tested the FStaph1N, Evo2, and FNM1g6 phages against this expanded strain panel and found that Evo2 showed strong activity against all 30 strains (Table S4). We tested whether Evo2 infection could elicit trade-offs in b-lactam resistance for a subset of these strains. We found that Evo2 infection caused a ~10-100-fold reduction in their MIC against oxacillin. This data is now incorporated into a revised Figure 2 in panel C.

(2) Testing additional staphylococcal phages

We isolated from the environment a phage called SATA8505. Similar to FStaph1N and Evo2, SATA8505 belongs to the Kayvirus genus and infects the MRSA strains MRSA252, MW2, and LAC. Phage-resistant MRSA recovered following SATA8505 infection also showed a strong reduction in oxacillin resistance (Figure S5). Furthermore, we confirmed that resistance against FNM1g6, which belongs to the Dubowvirus genes, does not elicit tradeoffs in b-lactam resistance (Figure S4). Sequencing analysis of FNM1g6 - resistant LAC strains showed a different mutation fmhC, which was not observed with the FStaph1N and Evo2 phages (Table 1). We have added this new data into the main text and supplemental figures and tables. Future work will focus on obtaining comprehensive analysis of a wide range of phage families. 

(3) Testing additional antibiotics

We also expanded our trade-off analysis include wider range of antibiotic classes (Table S3). Overall, the loss of resistance appears to be confined to b-lactams.

(4) Genetic analysis of ORF141

In order determine the function of ORF141, which is mutated in Evo2, we attempted to clone wild-type ORF141 into a staphylococcal plasmid and perform complementation assays with Evo2. Unfortunately, obtaining the plasmid-borne wild-type ORF141 has proven to be tricky, as all clones developed frameshift or deletions in the open reading frame. We posit that the gene product of ORF141 is toxic to the bacteria. We are currently working on placing the gene under more stringent expression conditions but feel that these efforts fall outside of the scope of this paper.  

(5) Testing the effect of single mutants  

Our genomic analysis showed that phage-resistant MRSA evolved multiple mutations following phage infection, making it difficult to determine the mechanism of each mutation alone. For example, phage-resistant MW2 and LAC evolved nonsense mutations in transcriptional regulators mgrA, arlR, and sarA. To test whether these mutations alone were sufficient to confer resistance, we obtained MRSA strains with single-gene knockouts of mgrA, arlR, and sarA and tested their ability to resist phage. We observed that deletion of mgrA in the MW2 resulted in a modest reduction in phage sensitivity (Figure S7). However, we did not the observe any changes in the other mutant strains. These results suggest that phage resistance in these strains is likely caused by a combination of mutations. Determining the mechanisms of these mutations is the focus if our future work.

(6) Transcriptomics of phage-resistant MRSA strains

To further assess the effects of the phage resistance mutations, we performed bulk RNA-seq on phage-resistant MW2 and LAC strains and compared their differential expression levels to the respective wild-type strains. We picked these strains because our genomic data showed that they had evolved mutations in known transcriptional regulators (e.g. mgrA). Our analysis shows that both strains significantly modulate their gene expression (Figure 4). Notably, both strains upregulate the cell wall-associated protein ebh, while downregulating several genes involved in quorum sensing, virulence, and secretion. We have included this new data in Figure 4 and Table S5 and added an entire section in the manuscript discussing these results and their implications.  

(7) Co-treatment of MRSA with phage and b-lactam

We performed checkerboard experiments on MRSA strains with phage and b-lactam gradients (Figure 6). We found that under most conditions, MRSA cells were only able to recover under low phage and b-lactam concentrations. Notably, these recovered cells were still phage resistant and b-lactam sensitive. However, under one condition where MW2 was treated with FStaph1N and b-lactam, we found that some recovered cells still had high levels of b-lactam resistance, showing a distinct mutational profile. We discuss these results in detail in the main text.

Reviewer # 1:

Strengths:

Phage-mediated re-sensitization to antibiotics has been reported previously but the underlying mutational analyses have not been described. These studies suggest that phages and antibiotics may target similar pathways in bacteria.

We thank Reviewer 1 for this assessment. We hope that the data provided in this work will help stimulate further inquiries into this area and help in the development of better phage-based therapies to combat MRSA.

Weaknesses:

One limitation is the lack of mechanistic investigations linking particular mutations to the phenotypes reported here. This limits the impact of the work.

We acknowledge the limitations of our initial analysis. We note (and cite) that separate studies have already linked mutations in femA, mgrA, arlR, and sarA with reduced b-lactam resistance and virulence phenotypes in MRSA, but not to phage resistance. For the other mutations, we could not find literature linking them to our observed phenotypes. We analyzed the effects of single gene knockouts of mgrA, arlR, and sarA on MRSA’s phage resistance. However, as shown above, the results only showed modest effects on phage resistance in the MW2 strain (see Figure S7 and lines 309-317). We therefore believe that mutations in single genes are not sufficient to cause the trade-offs in phage/ b-lactam resistance. Because each MRSA strain evolved multiple mutations (e.g. MW2 evolved 6 or more mutations), we feel that determining the effects of all possible permutations of those mutations was beyond the scope of the paper.

However, to bridge the mutational data with our phenotypic observations, we performed RNAseq and compared the transcriptomes of un-treated and phage-treated MRSA strains (see Figure 4, Table S5, and lines 337-391). Our results show that phage-treated MRSA strains significantly modulate their transcript levels. Indeed, some of the changes in gene expression can explain for the phenotypic observations (e.g. overexpression of ebh can lead to reduced clumping). Further, the results shown some unexpected patterns, such as the downregulation of quorum sensing genes or genes involved in type VII secretion.

Another limitation of this work is the use of lab strains and a single pair of phages. However, while incorporation of clinical isolates would increase the translational relevance of this work it is unlikely to change the conclusions.

We thank the reviewer for this suggestion. We would like to clarify that MW2, MRSA252, and LAC are pathogenic clinical isolates that were isolated between 1997 and 2000’s. However, we acknowledge that, because these 3 strains have been propagated for many generations, they might have acquired laboratory adaptations. We therefore obtained 30 USA300 clinical strains that were isolated in more recent years (~2008-2011) and tested our phages against them. We note that these clinical isolates (generously provided by Dr. Petra Levin’s lab) were preserved with minimal passaging to reduce the effects of laboratory adaptation. We found that the Evo2 phage was able to elicit oxacillin trade-offs in those strains as well. (see Table S1, Table S7, Fig 2C, and lines 210 – 225)

For the phages, we had to work with phage(s) that could infect all three MRSA strains. That is why in our initial tests, we focused on FStaph1N and Evo2, both members of the Kayvirus genus. Now in our revised work, we extend our analysis to FNM1g6, a member of the Dubowvirus genus, that also infects the LAC strain, but not MW2 and MRSA252. We find that FNM1g6 is unable to drive trade-offs in b-lactam resistance (see lines 229 – 238). Next, we analyzed the effects of SATA8505, also a member of the Kayvirus genus. Here, we observed that SATA8505 can elicit trade-offs in b-lactam resistance (see Figure S5 and lines 238 – 246). These results suggest that not all staphylococcal phages can elicit these trade-offs and call for more comprehensive analyses of different types of phages.

Reviewer #1 (Recommendations for the authors):

Specific questions:

(1) The Evo2 isolate is an evolved version of phage Staph1N with more potent lytic activity. Is this reflected in more pronounced antibiotic sensitivity?

We did not observe that Evo2-treated MRSA cells showed more sensitivity towards b-lactams. However, we did observe that Evo2 was able to elicit these trade-offs at lower multiplicities of infection (MOI) (see lines 173 – 176 and Figure S2). Further, we did observe that Evo2 caused a greater trade-off in virulence phenotypes (hemolysis and cell agglutination) (see lines 416 - 419 lines 433 – 435, and Figure 5)

In our revisions, we also tested Evo2-treated MRSA against a wide range of antibiotics. We did not observe significant changes in MICs against those agents.   

(2) Are there mutations in the SCCmec cassette or the MecA gene after selection against ΦStaph1N?

We did not observe any mutations in known resistance genes SCCmec or blaZ. Furthermore, we did not see any differential expression of those genes in our transcriptomic data (see lines 344 and 346).  

(3) The authors report that phage ΦNM1γ6 does not induce antibiotic sensitivity changes despite being effective against bacterial strain LAC. Were mutational sequencing studies performed with the resistant isolates that emerged against this strain? Can the authors hypothesize why these did not impact the virulence or resistance of LAC despite effective killing? How does this align with their models for ΦStaph1N?

We thank the reviewer for that insightful question. In our revised manuscript, we found that ΦNM1γ6 elicits a point mutation in the fmhC gene, which is involved in cell wall maintenance (see lines 326 – 335). To our knowledge, this point mutation has not been linked to phage resistance or drug sensitivity MRSA. Notably this mutation was not observed with ΦStaph1N or Evo2. We therefore speculate that ΦNM1γ6 binds to a different receptor molecule on the MRSA cell wall.   

(4) If I understand correctly, the authors attribute these effects of phage predation on antibiotic sensitivity and virulence to orthogonal selection pressures. A good test of this model would be to examine the mutations that emerge in antibiotic/phage co-treatment. This should be done.

We thank the reviewer for this suggestion. As described in the summary section above, we performed checkerboard experiments on MRSA strains with phage and b-lactam gradients (see lines 440 – 494 and Figure 6). We found that under most conditions, MRSA cells were only able to recover under low phage and b-lactam concentrations. Notably, these recovered cells were still phage resistant and b-lactam sensitive. However, under one condition where MW2 was treated with FStaph1N and b-lactam, we found that some recovered cells still had high levels of b-lactam resistance and only limited phage resistance, showing a distinct mutational profile (Figure S6). Under these conditions, we think that the selective pressure exerted by FStaph1N is “overcome” by the selective pressure of the high oxacillin concentration, a point that we discuss in the main text.

Reviewer #2 (Public review):

Summary:

The work presented in the manuscript by Tran et al deals with bacterial evolution in the presence of bacteriophage. Here, the authors have taken three methicillin-resistant S. aureus strains that are also resistant to beta-lactams. Eventually, upon being exposed to phage, these strains develop beta-lactam sensitivity. Besides this, the strains also show other changes in their phenotype such as reduced binding to fibrinogen and hemolysis.

Strengths:

The experiments carried out are convincing to suggest such in vitro development of sensitivity to the antibiotics. Authors were also able to "evolve" phage in a similar fashion thus showing enhanced virulence against the bacterium. In the end, authors carry out DNA sequencing of both evolved bacteria and phage and show mutations occurring in various genes. Overall, the experiments that have been carried out are convincing.

We thank Reviewer 2 for their positive comments.

Weaknesses:

Although more experiments are not needed, additional experiments could add more information. For example, the phage gene showing the HTH motif could be reintroduced in the bacterial genome and such a strain can then be assayed with wildtype phage infection to see enhanced virulence as suggested. At least one such experiment proves the discoveries regarding the identification of mutations and their outcome.

We thank the reviewer for this suggestion. We attempted to clone ORF141 into an expression plasmid and perform complementation experiments with Evo2 phage; however, all transformants that were isolated had premature stop-codons and frameshifts in the wild-type ORF141 insert that would disrupt protein function. We therefore think that the gene product of ORF141 might be toxic to the cells. We are currently working on placing the gene under more stringent transcriptional control but feel that these efforts fall outside of the scope of this paper.  

Secondly, I also feel that authors looked for beta-lactam sensitivity and they found it. I am sure that if they look for rifampicin resistance in these strains, they will find that too. In this case, I cannot say that the evolution was directed to beta-lactam sensitivity; this is perhaps just one trait that was observed. This is the only weakness I find in the work. Nevertheless, I find the experiments convincing enough; more experiments only add value to the work.  

We thank the reviewer for their comments. Because both phages and β-lactams interface with the bacterial cell wall, we posited that phage resistance would reduce resistance in cell wall targeting antibiotics. In our revisions, we have expanded our analysis to include a much wider range of antibiotic classes, including rifampicin, mupirocin, erythromycin, and other cell wall disruptors, such as daptomycin and teicoplanin. We did not observe any significant changes to the MICs of these other antibiotics (see Table S3 and lines 191-199). It therefore appears that the effects of these trade-offs are confined to beta-lactams.

eLife Assessment

The manuscript explores how bacterial evolution in the presence of lytic phages modulates b-lactams resistance and virulence properties in methicillin-resistant Staphylococcus aureus (MRSA). The work is valuable as it identifies underlying mutations that may confer sensitivity to b-lactams and alter virulence properties. While the findings are generally convincing, additional experiments linking how particular mutations regulate phenotypic changes are required to improve the work mechanistically.

Reviewer #1 (Public review):

Summary:

These authors have asked how lytic phage predation impacts antibiotic resistance and virulence phenotypes in methicillin-resistant Staphylococcus aureus (MRSA). They report that staphylococcal phages cause MRSA strains to become sensitized to b-lactams and to display reduced virulence. Moreover, they identify mutations in a set of genes required for phage infection that may impact antibiotic resistance and virulence phenotypes.

Strengths:

Phage-mediated re-sensitization to antibiotics has been reported previously but the underlying mutational analyses have not been described. These studies suggest that phages and antibiotics may target similar pathways in bacteria.

Weaknesses:

One limitation is the lack of mechanistic investigations linking particular mutations to the phenotypes reported here. This limits the impact of the work.

Another limitation of this work is the use of lab strains and a single pair of phages. However, while incorporation of clinical isolates would increase the translational relevance of this work it is unlikely to change the conclusions.

Reviewer #2 (Public review):

Summary:

The work presented in the manuscript by Tran et al deals with bacterial evolution in the presence of bacteriophage. Here, the authors have taken three methicillin-resistant S. aureus strains that are also resistant to beta-lactams. Eventually, upon being exposed to phage, these strains develop beta-lactam sensitivity. Besides this, the strains also show other changes in their phenotype such as reduced binding to fibrinogen and hemolysis.

Strengths:

The experiments carried out are convincing to suggest such in vitro development of sensitivity to the antibiotics. Authors were also able to "evolve" phage in a similar fashion thus showing enhanced virulence against the bacterium. In the end, authors carry out DNA sequencing of both evolved bacteria and phage and show mutations occurring in various genes. Overall, the experiments that have been carried out are convincing.

Weaknesses:

Although more experiments are not needed, additional experiments could add more information. For example, the phage gene showing the HTH motif could be reintroduced in the bacterial genome and such a strain can then be assayed with wildtype phage infection to see enhanced virulence as suggested. At least one such experiment proves the discoveries regarding the identification of mutations and their outcome. Secondly, I also feel that authors looked for beta-lactam sensitivity and they found it. I am sure that if they look for rifampicin resistance in these strains, they will find that too. In this case, I cannot say that the evolution was directed to beta-lactam sensitivity; this is perhaps just one trait that was observed. This is the only weakness I find in the work. Nevertheless, I find the experiments convincing enough; more experiments only add value to the work.

eLife Assessment

The study by Power and colleagues is important, as elucidating the dynamic immune responses to photoreceptor damage in vivo potentiates future work in the field to better understand the disease process. The evidence supporting the authors' claims is compelling.

Reviewer #2 (Public review):

Summary:

This study uses in vivo multimodal high-resolution imaging to track how microglia and neutrophils respond to light-induced retinal injury from soon after injury to 2 months post-injury. The in vivo imaging finding was subsequently verified by ex vivo study. The results suggest that despite the highly active microglia at the injury site, neutrophils were not recruited in response to acute light-induced retinal injury.

Strengths:

An extremely thorough examination of the cellular-level immune activity at the injury site. In vivo imaging observations being verified using ex vivo techniques is a strong plus.

Reviewer #3 (Public review):

Summary

This work investigated the immune response in the murine retina after focal laser lesions. These lesions are made with close to 2 orders of magnitude lower laser power than the more prevalent choroidal neovascularization model of laser ablation. Histology and OCT together show that the laser insult is localized to the photoreceptors and spares the inner retina, the vasculature and the pigment epithelium. As early as 1-day after injury, a loss of cell bodies in the outer nuclear layer is observed. This is accompanied by strong microglial proliferation to the site of injury in the outer retina where microglia do not typically reside. The injury did not seem to result in the extravasation of neutrophils from the capillary network, constituting one of the main findings of the paper. The demonstrated paradigm of studying the immune response and potentially retinal remodeling in the future in vivo is valuable and would appeal to a broad audience in visual neuroscience.

Strengths

Adaptive optics imaging of murine retina is cutting edge and enables non-destructive visualization of fluorescently labeled cells in the milieu of retinal injury. As may be obvious, this in vivo approach is a benefit for studying fast and dynamic immune processes on a local time scale - minutes and hours, and also for the longer days-to-months follow-up of retinal remodeling as demonstrated in the article. In certain cases, the in vivo findings are corroborated with histology.

The analysis is sound and accompanied by stunning video and static imagery. A few different sets of mouse models are used: a) two different mouse lines, each with a fluorescent tag for neutrophils and microglia, b) two different models of inflammation - endotoxin-induced uveitis (EAU) and laser ablation are used to study differences in the immune interaction.

One of the major advances in this article is the development of the laser ablation model for 'mild' retinal damage as an alternative to the more severe neovascularization models. This model would potentially allow for controlling the size, depth and severity of the laser injury opening interesting avenues for future study.

The time-course, 2D and 3D spatial activation pattern of microglial activation are striking and provide an unprecedented view of the retinal response to mild injury.

Editor's note: The authors have addressed all the previous concerns raised by the reviewers.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #2 (Public review):

Summary:

This study uses in vivo multimodal high-resolution imaging to track how microglia and neutrophils respond to light-induced retinal injury from soon after injury to 2 months post-injury. The in vivo imaging finding was subsequently verified by ex vivo study. The results suggest that despite the highly active microglia at the injury site, neutrophils were not recruited in response to acute light-induced retinal injury.

Strengths:

An extremely thorough examination of the cellular-level immune activity at the injury site. In vivo imaging observations being verified using ex vivo techniques is a strong plus.

Thank you!

Weaknesses:

This paper is extremely long, and in the perspective of this reviewer, needs to be better organized. Update: Modifications have been made throughout, which has made the manuscript easier to follow.

Thank you!

Study weakness: though the finding prompts more questions and future studies, the findings discussed in this paper is potentially important for us to understand how the immune cells respond differently to different severity level of injury. The study also demonstrated an imaging technology which may help us better understand cellular activity in living tissue during earlier time points.

We agree that AOSLO has much to offer and this represents some of the earliest reports of its kind.  

Comments on revisions:

I appreciate the thorough clarification and re-organization by the authors, and the messages in the manuscript are now more apparent. I recommend also briefly discussing limitations/future improvements in the discussion or conclusion.

We have added a section to the discussion entitled “Limitations and future improvements”, please see lines 665 – 677.

Reviewer #3 (Public review):

Summary

This work investigated the immune response in the murine retina after focal laser lesions. These lesions are made with close to 2 orders of magnitude lower laser power than the more prevalent choroidal neovascularization model of laser ablation. Histology and OCT together show that the laser insult is localized to the photoreceptors and spares the inner retina, the vasculature and the pigment epithelium. As early as 1-day after injury, a loss of cell bodies in the outer nuclear layer is observed. This is accompanied by strong microglial proliferation to the site of injury in the outer retina where microglia do not typically reside. The injury did not seem to result in the extravasation of neutrophils from the capillary network, constituting one of the main findings of the paper. The demonstrated paradigm of studying the immune response and potentially retinal remodeling in the future in vivo is valuable and would appeal to a broad audience in visual neuroscience.

Strengths

Adaptive optics imaging of murine retina is cutting edge and enables non-destructive visualization of fluorescently labeled cells in the milieu of retinal injury. As may be obvious, this in vivo approach is a benefit for studying fast and dynamic immune processes on a local time scale - minutes and hours, and also for the longer days-to-months follow-up of retinal remodeling as demonstrated in the article. In certain cases, the in vivo findings are corroborated with histology.

Thank you!

The analysis is sound and accompanied by stunning video and static imagery. A few different sets of mouse models are used, a) two different mouse lines, each with a fluorescent tag for neutrophils and microglia, b) two different models of inflammation - endotoxin-induced uveitis (EAU) and laser ablation are used to study differences in the immune interaction.

Thank you!

One of the major advances in this article is the development of the laser ablation model for 'mild' retinal damage as an alternative to the more severe neovascularization models. This model would potentially allow for controlling the size, depth and severity of the laser injury opening interesting avenues for future study.

Thank you!

The time-course, 2D and 3D spatial activation pattern of microglial activation are striking and provide an unprecedented view of the retinal response to mild injury.

We agree that this more complete spatial and temporal evaluation made possible by in vivo imaging is novel.

Weaknesses

Generalization of the (lack of) neutrophil response to photoreceptor loss - there is ample evidence in literature that neutrophils are heavily recruited in response to severe retinal damage that includes photoreceptor loss. Why the same was not observed here in this article remains an open question. One could hypothesize that neutrophil recruitment might indeed occur under conditions that are more in line with the more extreme damage models, for example, with a stronger and global ablation (substantially more photoreceptor loss over a larger area). This parameter space is unwieldy and sufficiently large to address the question conclusively in the current article, i.e. how much photoreceptor loss leads to neutrophil recruitment? By the same token, the strong and general conclusion in the title - Photoreceptor loss does not recruit neutrophils - cannot be made until an exhaustive exploration be made of the same parameter space. A scaling back may help here, to reflect the specific, mild form of laser damage explored here, for instance - Mild photoreceptor loss does not recruit neutrophils despite...

We are striving for clarity and accuracy in our title without adding too many qualifiers.  At present, we feel that the title as submitted is consistent and aligned with the central finding of our manuscript.  The nuance that the reviewer points to is elaborated in the body of the manuscript and we hope the general readership appreciates the same level of detail as appreciated by reviewer #3.

EIU model - The EIU model was used as a positive control for neutrophil extravasation. Prior work with flow cytometry has shown a substantial increase in neutrophil counts in the EIU model. Yet, in all, the entire article shows exactly 2 examples in vivo and 3 ex vivo (Figure 7) of extravasated neutrophils from the EIU model (n = 2 mice). The general conclusion made about neutrophil recruitment (or lack thereof) is built partly upon this positive control experiment. But these limited examples, especially in the case where literature reports a preponderance of extravasated neutrophils, raise a question on the paradigm(s) used to evaluate this effect in the mild laser damage model.

This is a helpful suggestion. We agree that readers should see more evidence of the positive control. Therefore we have now included two more supplementary files that show that there is a strong neutrophil response to EIU.  In Figure 7 – supplementary figure 1, we show many Ly-6G-positive neutrophils in the retina seen with histology at the 24 hour time point. In Figure 7 – video 3, we show massive Catchup-positive neutrophil presence in vivo at 24hrs as well.  This aligns with our positive control and also the literature.

Overall, the strengths outweigh the weaknesses, provided the conclusions/interpretations are reconsidered.

With the added clarification about the magnitude of the neutrophil response in EIU, we feel that the conclusions presented in the manuscript as-is are valid and appropriate.

Recommendations for the authors:

Reviewer #3 (Recommendations for the authors):

The authors are applauded for embracing the reviewers' feedback and making substantial revisions. Some minor comments below:

The weakness noted in the public review encourages the authors to reconsider the interpretations drawn based on the results. One would have expected to see far more examples of extravasated neutrophils from the EIU model. That this was not seen weakens the neutrophil recruitment claim substantially. Even without this claim, the methods, laser damage model, time-course and spatial activation pattern of microglial activation are all striking and unprecedented. So, as stated in the public review, the strengths do indeed outweigh the weaknesses once the neutrophil claim is softened.

We address this in the response above. A strong neutrophil response was observed to EIU. This was confirmed with both histology and in vivo imaging.

This was alluded to by Reviewer 1 in the prior review - at times, there is an overemphasis on imaging technology that distracts from the scientific questions. The imaging is undoubtedly cutting-edge but also documented in prior work by the authors. Any efforts to reduce or balance the emphasis would help with the general flow.

Given that these discoveries are made possible partly through new technology, we prefer to keep the details of the innovation in the current manuscript. Given the exceptionally large readership of eLife, we feel some description of the AOSLO imaging is warranted in the manuscript.

eLife Assessment

This study shows that a peptide called galanin can decrease or increase seizure activity in experimental models of seizures depending on the model. The authors use zebrafish and several methods to address the effects of galanin. The study will be useful to researchers who use zebrafish as experimental animals and who are interested in how peptides like galanin regulate seizures. However, the strength of evidence was considered incomplete at the present time due to several limitations of the results.

Reviewer #1 (Public review):

Summary:

In this study, authors explored how galanin affects whole-brain activity in larval zebrafish using wide-field Ca2+ imaging, genetic modifications, and drugs that increase brain activity. The authors conclude that galanin has a sedative effect on the brain under normal conditions and during seizures, mainly through the galanin receptor 1a (galr1a). However, acute "stressors(?)" like pentylenetetrazole (PTZ) reduce galanin's effects, leading to increased brain activity and more seizures. Authors claim that galanin can reduce seizure severity while increasing seizure occurrence, speculated to occur through different receptor subtypes. This study confirms galanin's complex role in brain activity, supporting its potential impact on epilepsy.

Strengths:

The overall strength of the study lies primarily in its methodological approach using whole-brain Calcium imaging facilitated by the transparency of zebrafish larvae. Additionally, the use of transgenic zebrafish models is an advantage, as it enables genetic manipulations to investigate specific aspects of galanin signaling. This combination of advanced imaging and genetic tools allows for addressing galanin's role in regulating brain activity.

Weaknesses:

The weaknesses of the study also stem from the methodological approach, particularly the use of whole-brain Calcium imaging as a measure of brain activity. While epilepsy and seizures involve network interactions, they typically do not originate across the entire brain simultaneously. Seizures often begin in specific regions or even within specific populations of neurons within those regions. Therefore, a whole-brain approach, especially with Calcium imaging with inherited limitations, may not fully capture the localized nature of seizure initiation and propagation, potentially limiting the understanding of Galanin's role in epilepsy.

Furthermore, Galanin's effects may vary across different brain areas, likely influenced by the predominant receptor types expressed in those regions. Additionally, the use of PTZ as a "stressor" is questionable since PTZ induces seizures rather than conventional stress. Referring to seizures induced by PTZ as "stress" might be a misinterpretation intended to fit the proposed model of stress regulation by receptors other than Galanin receptor 1 (GalR1).

The description of the EAAT2 mutants is missing crucial details. EAAT2 plays a significant role in the uptake of glutamate from the synaptic cleft, thereby regulating excitatory neurotransmission and preventing excitotoxicity. Authors suggest that in EAAT2 knockout (KO) mice galanin expression is upregulated 15-fold compared to wild-type (WT) mice, which could be interpreted as galanin playing a role in the hypoactivity observed in these animals.

However, the study does not explore the misregulation of other genes that could be contributing to the observed phenotype. For instance, if AMPA receptors are significantly downregulated, or if there are alterations in other genes critical for brain activity, these changes could be more important than the upregulation of galanin. The lack of wider gene expression analysis leaves open the possibility that the observed hypoactivity could be due to factors other than, or in addition to, galanin upregulation.

Moreover, the observation that in double KO mice for both EAAT2 and galanin there was little difference in seizure susceptibility compared to EAAT2 KO mice alone further supports the idea that galanin upregulation might not be the reason to the observed phenotype. This indicates that other regulatory mechanisms or gene expressions might be playing a more pivotal role in the manifestation of hypoactivity in EAAT2 mutants.

These methodological shortcomings and conceptual inconsistencies undermine the perceived strengths of the study, and hinders understanding of Galanin's role in epilepsy and stress regulation.

Comments on revisions:

The revised manuscript and the answers of the authors is appreciated. However, the criticisms were addressed only partially and main weaknesses of the manuscript are still remaining.

Reviewer #2 (Public review):

This revised study is an investigation of galanin and galanin receptor signaling on whole-brain activity in the context of recurrent seizure activity or under homeostatic basal conditions. The authors primarily use calcium imaging to observe whole-brain neuronal activity accompanied by galanin qPCR to determine how manipulations of galanin or the galr1a receptor affect the activity of the whole-brain under non-ictal conditions or when seizure activity occurs. The authors use their eaat2a-/- model (introduced in their Glia 2022 paper, PMID 34716961) that shows recurrent seizure activity as well as suppression of neuronal activity and locomotion interictally. It is compared to the well-known pentylenetetrazole (PTZ) pharmacological model of seizures in zebrafish. Given the literature cited in their Introduction, the authors hypothesize that galanin will exert a net inhibitory effect on brain activity in models of seizures/epilepsy. They were surprised to find that this hypothesis was only moderately supported in their eaat2a-/- model. In contrast, after PTZ, fish with galanin overexpression showed increased seizure number and reduced duration while fish with galanin KO showed reduced seizure number and increased duration.

Previous concerns about sex or developmental biological variables were addressed, as their model's seizure phenotype emerges rapidly and long prior to the establishment of zebrafish sexual maturity. However, in the course of re-review, some additional concerns (below) were detected that, if addressed, could further improve the manuscript. These concerns relate to how seizures were defined from the measurement of fluorescent calcium imaging data. Overall, this study is important and convincing, and carries clear value for understanding the multifaceted functions that neuronal galanin can perform under homeostatic and disease conditions.

Additional Concerns:

- The authors have validated their ability to measure behavioral seizures quantitatively in their 2022 Glia paper but the information provided on defining behavioral seizures was limited. The definition of behavioral seizure activity is not expanded upon in this paper, but could provide detail about how the behavioral seizures relate to a seizure detected via calcium imaging.

- Related to the previous point, for the calcium imaging, the difference between an increase in fluorescence that the authors think reflects increased neuronal activity and the fluorescence that corresponds to seizures is not very clear. This detail is necessary because exactly when the term "seizure" describes a degree of increased activity can be difficult to distinguish objectively.

- The supplementary movies that were added were very useful, but raised some questions. For example, what brain regions were pulsating? What areas seemed to constantly exhibit strong fluorescence and was this an artifact? It seemed that sometimes there was background fluorescence in the body. Perhaps an anatomical diagram could be provided for the readers. In addition, there were some movies with much greater fluorescence changes - are these the seizures? These are some reasons for our request for clarified definitions of the term "seizure".

Reviewer #3 (Public review):

Summary:

The neuropeptide galanin is primarily expressed in the hypothalamus and has been shown to play critical roles in homeostatic functions such as arousal, sleep, stress, and brain disorders such as epilepsy. Previous work in rodents using galanin analogs and receptor-specific knockout have provided convincing evidence for anti-convulsant effects of galanin.

In the present study, the authors sought to determine the relationship between galanin expression and whole-brain activity. The authors took advantage of the transparent nature of larval zebrafish to perform whole-brain neural activity measurements via widefield calcium imaging. Two models of seizures were used (eaat2a-/- and pentylenetetrazol; PTZ). In the eaat2a-/- model, spontaneous seizures occur and the authors found that galanin transcript levels were significantly increased and associated with reduced frequency of calcium events. Similarly, two hours after PTZ galanin transcript levels roughly doubled and the frequency and amplitude of calcium events were reduced.

The authors also used a heat shock protein line (hsp70I:gal) where galanin transcripts levels are induced by activation of heat shock protein, but this line also shows higher basal transcript levels of galanin. Due to problems with whole-brain activity in wild-type larvae, the authors used the line without heat shock. They found higher level of galanin in hsp70I:gal larval zebrafish resulted in a reduction of calcium events and a reduction in amplitude of events. In contrast, galanin knockout (gal-/-) increased calcium activity, indicated by an increased number of calcium events, but a reduction in amplitude and duration. New data in the supplementary figure 2 used antibody staining to confirm the absence of galanin expression in gal-/- knockouts. Knockout of the galanin receptor subtype galr1a via crispants also increased the frequency of calcium events. New data in the revised manuscript reports that galr1aKO did not cause an upregulation of galanin, thereby ruling out genetic compensation effects.

In subsequent experiments in eaat2a-/- mutants were crossed with hsp70I:gal or gal-/- to increase or decrease galanin expression, respectively. These experiments showed modest effects, with eaat2a-/- x gal-/- knockouts showing an increased normalized area under the curve and seizure amplitude.

Lastly, the authors attempted to study the relationship between galanin and brain activity during a PTZ challenge. The hsp70I:gal larva showed increased number of seizures and reduced seizure duration during PTZ. In contrast, gal-/- mutants showed increased normalized area under the curve and a stark reduction in number of detected seizures, a reduction in seizure amplitude, but an increase in seizure duration. The authors then ruled out the role of Galr1a in modulating this effect during PTZ, since the number of seizures was unaffected, whereas the amplitude and duration of seizures was increased.

Strengths:

(1) The gain- and loss-of function galanin manipulations provided convincing evidence that galanin influences brain activity (via calcium imaging) during interictal and/or seizure-free periods. In particular, the relationship between galanin transcript levels and brain activity in figures 1 & 2 was convincing. New antibody staining confirms the absence of galanin in gal-/- mutants. New data also shows galanin transcript levels were unchanged in galr1ako brains.

(2) The authors use two models of epilepsy (eaat2a-/- and PTZ).

(3) Focus on the galanin receptor subtype galr1a provided good evidence for an important role of this receptor in controlling brain activity during interictal and/or seizure-free periods.

(4) The authors have added supplementary video files for calcium imaging to support their observations.

Weaknesses:

(1) Although the relationship between galanin and brain activity during interictal or seizure-free periods was clear, the revised manuscript still lacks mechanistic insight in the role of galanin during seizure-like activity induced by PTZ.

(2) The revised manuscript continues to heavily rely on calcium imaging of different mutant lines. Confirmation of knockouts has been provided with immunostaining in a new supplementary figure. Additional methods could strengthen the data, translational relevance, and interpretation (e.g., acute pharmacology using galanin agonists or antagonists, brain or cell recordings, biochemistry, etc).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, the authors explored how galanin affects whole-brain activity in larval zebrafish using wide-field Ca2+ imaging, genetic modifications, and drugs that increase brain activity. The authors conclude that galanin has a sedative effect on the brain under normal conditions and during seizures, mainly through the galanin receptor 1a (galr1a). However, acute "stressors(?)" like pentylenetetrazole (PTZ) reduce galanin's effects, leading to increased brain activity and more seizures. The authors claim that galanin can reduce seizure severity while increasing seizure occurrence, speculated to occur through different receptor subtypes. This study confirms galanin's complex role in brain activity, supporting its potential impact on epilepsy.

Strengths:

The overall strength of the study lies primarily in its methodological approach using whole-brain Calcium imaging facilitated by the transparency of zebrafish larvae. Additionally, the use of transgenic zebrafish models is an advantage, as it enables genetic manipulations to investigate specific aspects of galanin signaling. This combination of advanced imaging and genetic tools allows for addressing galanin's role in regulating brain activity.

Weaknesses:

The weaknesses of the study also stem from the methodological approach, particularly the use of whole-brain Calcium imaging as a measure of brain activity. While epilepsy and seizures involve network interactions, they typically do not originate across the entire brain simultaneously. Seizures often begin in specific regions or even within specific populations of neurons within those regions. Therefore, a whole-brain approach, especially with Calcium imaging with inherited limitations, may not fully capture the localized nature of seizure initiation and propagation, potentially limiting the understanding of Galanin's role in epilepsy.

Furthermore, Galanin's effects may vary across different brain areas, likely influenced by the predominant receptor types expressed in those regions. Additionally, the use of PTZ as a "stressor" is questionable since PTZ induces seizures rather than conventional stress. Referring to seizures induced by PTZ as "stress" might be a misinterpretation intended to fit the proposed model of stress regulation by receptors other than Galanin receptor 1 (GalR1).

The description of the EAAT2 mutants is missing crucial details. EAAT2 plays a significant role in the uptake of glutamate from the synaptic cleft, thereby regulating excitatory neurotransmission and preventing excitotoxicity. Authors suggest that in EAAT2 knockout (KO) mice galanin expression is upregulated 15-fold compared to wild-type (WT) mice, which could be interpreted as galanin playing a role in the hypoactivity observed in these animals.

Indeed, our observation of the unexpected hypoactivity in EAAT2a mutants, described in our description of this mutant (Hotz et al., 2022), prompted us to initiate this study formulating the hypothesis that the observed upregulation of galanin is a neuroprotective response to epilepsy.

However, the study does not explore the misregulation of other genes that could be contributing to the observed phenotype. For instance, if AMPA receptors are significantly downregulated, or if there are alterations in other genes critical for brain activity, these changes could be more important than the upregulation of galanin. The lack of wider gene expression analysis leaves open the possibility that the observed hypoactivity could be due to factors other than, or in addition to, galanin upregulation.

We have performed a transcriptome analysis that we are still evaluation. We can already state that AMPA receptor genes are not significantly altered in the mutant.

Moreover, the observation that in double KO mice for both EAAT2 and galanin, there was little difference in seizure susceptibility compared to EAAT2 KO mice alone further supports the idea that galanin upregulation might not be the reason for the observed phenotype. This indicates that other regulatory mechanisms or gene expressions might be playing a more pivotal role in the manifestation of hypoactivity in EAAT2 mutants.

We agree that upregulation of galanin transcripts is at best one of a suite of regulatory mechanisms that lead to hypoactivity in EAAT2 zebrafish mutants.

These methodological shortcomings and conceptual inconsistencies undermine the perceived strengths of the study, and hinders understanding of Galanin's role in epilepsy and stress regulation.

Reviewer #2 (Public Review):

Summary:

This study is an investigation of galanin and galanin receptor signaling on whole-brain activity in the context of recurrent seizure activity or under homeostatic basal conditions. The authors primarily use calcium imaging to observe whole-brain neuronal activity accompanied by galanin qPCR to determine how manipulations of galanin or the galr1a receptor affect the activity of the whole-brain under non-ictal or seizure event conditions. The authors' Eaat2a-/- model (introduced in their Glia 2022 paper, PMID 34716961) that shows recurrent seizure activity alongside suppression of neuronal activity and locomotion in the time periods lacking seizures is used in this paper in comparison to the well-known pentylenetetrazole (PTZ) pharmacological model of epilepsy in zebrafish. Given the literature cited in their Introduction, the authors reasonably hypothesize that galanin will exert a net inhibitory effect on brain activity in models of epilepsy and at homeostatic baseline, but were surprised to find that this hypothesis was only moderately supported in their Eaat2a-/- model. In contrast, under PTZ challenge, fish with galanin overexpression showed increased seizure number and reduced duration while fish with galanin KO showed reduced seizure number and increased duration. These results would have been greatly enriched by the inclusion of behavioral analyses of seizure activity and locomotion (similar to the authors' 2022 Glia paper and/or PMIDs 15730879, 24002024). In addition, the authors have not accounted for sex as a biological variable, though they did note that sex sorting zebrafish larvae precludes sex selection at the younger ages used. It would be helpful to include smaller experiments taken from pilot experiments in older, sex-balanced groups of the relevant zebrafish to increase confidence in the findings' robustness across sexes. A possible major caveat is that all of the various genetic manipulations are non-conditional as performed, meaning that developmental impacts of galanin overexpression or galanin or galr1a knockout on the observed results have not been controlled for and may have had a confounding influence on the authors' findings. Overall, this study is important and solid (yet limited), and carries clear value for understanding the multifaceted functions that neuronal galanin can have under homeostatic and disease conditions.

Strengths:

- The authors convincingly show that galanin is upregulated across multiple contexts that feature seizure activity or hyperexcitability in zebrafish, and appears to reduce neuronal activity overall, with key identified exceptions (PTZ model).

- The authors use both genetic and pharmacological models to answer their question, and through this diverse approach, find serendipitous results that suggest novel underexplored functions of galanin and its receptors in basal and disease conditions. Their question is well-informed by the cited literature, though the authors should cite and consider their findings in the context of Mazarati et al., 1998 (PMID:982276). The authors' Discussion places their findings in context, allowing for multiple interpretations and suggesting some convincing explanations.

- Sample sizes are robust and the methods used are well-characterized, with a few exceptions (as the paper is currently written).

- Use of a glutamatergic signaling-based genetic model of epilepsy (Eaat2a-/-) is likely the most appropriate selection to test how galanin signaling can alter seizure activity, as galanin is known to reduce glutamatergic release as an inhibitory mechanism in rodent hippocampal neurons via GalR1a (alongside GIRK activation effects). Given that PTZ instead acts through GABAergic signaling pathways, it is reasonable and useful to note that their glutamate-based genetic model showed different effects than did their GABAergic-based model of seizure activity.

Weaknesses:

- The authors do not include behavioral assessments of seizure or locomotor activity that would be expected in this paper given their characterizations of their Eaat2a-/- model in the Glia 2022 paper that showed these behavioral data for this zebrafish model. These data would inform the reader of the behavioral phenotypes to expect under the various conditions and would likely further support the authors' findings if obtained and reported.<br />

We agree that a thorough behavioral assessment would have strengthened the study, but we deemed it outside of the scope of this study.

- No assessment of sex as a biological variable is included, though it is understood that these specific studied ages of the larvae may preclude sex sorting for experimental balancing as stated by the authors.

The study was done on larval zebrafish (5 days post fertilization). The first signs of sexual differentiation become apparent at about 17 days post fertilization (reviewed in Ye and Chen, 2020). Hence sex is no biological variable at the stage studied. 

- The reported results may have been influenced by the loss or overexpression of galanin or loss of galr1a during developmental stages. The authors did attempt to use the hsp70l system to overexpress galanin, but noted that the heat shock induction step led to reduced brain activity on its own (Supplementary Figure 1). Their hsp70l:gal model shows galanin overexpression anyways (8x fold) regardless of heat induction, so this model is still useful as a way to overexpress galanin, but it should be noted that this galanin overexpression is not restricted to post-developmental timepoints and is present during development.

The developmental perspective is an important point to consider. Due to the rapid development of the zebrafish it is not trivial to untangle this. In the zebrafish we first observe epileptic seizures as early as 3 days post fertilization (dpf), where the brain is clearly not well developed yet (e.g. behaviroal response to light are still minimal). Even the 5 dpf stage, where most of our experiments have been conducted, cannot by far not be considered post-development.  

Reviewer #3 (Public Review):

Summary:

The neuropeptide galanin is primarily expressed in the hypothalamus and has been shown to play critical roles in homeostatic functions such as arousal, sleep, stress, and brain disorders such as epilepsy. Previous work in rodents using galanin analogs and receptor-specific knockout has provided convincing evidence for the anti-convulsant effects of galanin.

In the present study, the authors sought to determine the relationship between galanin expression and whole-brain activity. The authors took advantage of the transparent nature of larval zebrafish to perform whole-brain neural activity measurements via widefield calcium imaging. Two models of seizures were used (eaat2a-/- and pentylenetetrazol; PTZ). In the eaat2a-/- model, spontaneous seizures occur and the authors found that galanin transcript levels were significantly increased and associated with a reduced frequency of calcium events. Similarly, two hours after PTZ galanin transcript levels roughly doubled and the frequency and amplitude of calcium events were reduced. The authors also used a heat shock protein line (hsp70I:gal) where galanin transcript levels are induced by activation of heat shock protein, but this line also shows higher basal transcript levels of galanin. Again, the higher level of galanin in hsp70I:gal larval zebrafish resulted in a reduction of calcium events and a reduction in the amplitude of events. In contrast, galanin knockout (gal-/-) increased calcium activity, indicated by an increased number of calcium events, but a reduction in amplitude and duration. Knockout of the galanin receptor subtype galr1a via crispants also increased the frequency of calcium events.

In subsequent experiments in eaat2a-/- mutants were crossed with hsp70I:gal or gal-/- to increase or decrease galanin expression, respectively. These experiments showed modest effects, with eaat2a-/- x gal-/- knockouts showing an increased normalized area under the curve and seizure amplitude.

Lastly, the authors attempted to study the relationship between galanin and brain activity during a PTZ challenge. The hsp70I:gal larva showed an increased number of seizures and reduced seizure duration during PTZ. In contrast, gal-/- mutants showed an increased normalized area under the curve and a stark reduction in the number of detected seizures, a reduction in seizure amplitude, but an increase in seizure duration. The authors then ruled out the role of Galr1a in modulating this effect during PTZ, since the number of seizures was unaffected, whereas the amplitude and duration of seizures were increased.

Strengths:

(1) The gain- and loss-of function galanin manipulations provided convincing evidence that galanin influences brain activity (via calcium imaging) during interictal and/or seizure-free periods. In particular, the relationship between galanin transcript levels and brain activity in Figures 1 & 2 was convincing.

(2) The authors use two models of epilepsy (eaat2a-/- and PTZ).

(3) Focus on the galanin receptor subtype galr1a provided good evidence for the important role of this receptor in controlling brain activity during interictal and/or seizure-free periods.

Weaknesses:

(1) Although the relationship between galanin and brain activity during interictal or seizure-free periods was clear, the manuscript currently lacks mechanistic insight in the role of galanin during seizure-like activity induced by PTZ.

We completely agree and concede that this study constitutes only a first attempt to understand the (at least for us) perplexing complexity of galanin function on the brain.

(2) Calcium imaging is the primary data for the paper, but there are no representative time-series images or movies of GCaMP signal in the various mutants used.

We have now added various movies in supplementary data.

(3) For Figure 3, the authors suggest that hsp70I:gal x eaat2a-/-mutants would further increase galanin transcript levels, which were hypothesized to further reduce brain activity. However, the authors failed to measure galanin transcript levels in this cross to show that galanin is actually increased more than the eaat2a-/- mutant or the hsp70I:gal mutant alone.

After a couple of unsuccessful mating attempts with our older mutants, we finally decided not to wait for a new generation to grow up, deeming the experiment not crucial (but still nice to have).

(4) Similarly, transcript levels of galanin are not provided in Figure 2 for Gal-/- mutants and galr1a KOs. Transcript levels would help validate the knockout and any potential compensatory effects of subtype-specific knockout.

To validate the gal-/- mutant line, we decided to show loss of protein expression (Suppl. Figure 2), which we deem to more relevant to argue for loss of function. Galanin transcript levels in galr1a KOs were also added into the same Figure. However, validation of the galr1a KO could not be performed due to transcript levels being close to the detection limit and lack of available antibodies.

(5) The authors very heavily rely on calcium imaging of different mutant lines. Additional methods could strengthen the data, translational relevance, and interpretation (e.g., acute pharmacology using galanin agonists or antagonists, brain or cell recordings, biochemistry, etc).

Again, we agree and concede that a number of additional approaches are needed to get more insight into the complex role of galanin in regulation overall brain activity. These include, among others, also behavioral, multiple single cell recordings and pharmacological interventions.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Minor issues:

(1) "Sedative" effect of galanin is somewhat vague and seems overapplied without the inclusion of behavioral data showing sedation effects. I would replace "sedative" with something clearer, like the phrase "net inhibitory effect" or similar.

We have modified the wording as deemed appropriate.

(2) Include new data that is sufficiently powered to detect or rule out the effects of sex as a biological variable within the various experiments.

At this stage sex is not a biological variable. Sex determination starts a late larval stage around 14dpf. Our analysis is based on 5pdf larvae.

(3) Attempt to perform some experiments with galanin/galr1a manipulations that have been induced after the majority of development without using heat shock induction if possible (unknown how feasible this is in current model systems).

In the current model this is not feasible, but an excellent suggestion for future studies that would then also address more longterm effects in the model.

(4) Figure 2 should include qPCR results for galanin or galr1a mRNA expression to match Figure 1C, F, and Figure 2C and to confirm reductions in the respective RNA transcript levels of gal or galr1a. It could be useful to perform qPCR for galanin in all galr1aKO mice to ascertain whether compensatory elevations in galanin occur in response to galr1aKO.

(5) Axes should be made with bolder lines and bolder/larger fonts for readability and consistency throughout.

Indeed, an excellent suggestion. We have adjusted the axes significantly improving the readability of the graphs.

(6) The bottom o,f the image for Figure 2 appears to have been cut off by mistake (page 5).

(7) The ending of the legend text for Figure 3 appears to have been cut off by mistake (page 6).

Both regrettable mistakes have been corrected (already in the initial posted version)

Reviewer #3 (Recommendations For The Authors):

(1) The introduction or first paragraph of the results should be revised to more directly state the hypotheses. Several critical details were only clear after reading the discussion.

We added some words to the introduction, hoping that the critical points are now more apparent to the reader.

(2) Galanin is known to be rapidly depleted by seizures (Mazarati et al., 1998; Journal of Neuroscience, PMID #9822761) but this paper did not appear to be cited or considered. Could the rapid depletion of galanin during seizures help explain the confusing effects of galanin manipulations during PTZ?

We have added a sentence and the reference to the discussion.

(3) Figure 1 panels are incorrect. For example, Panel 'F' is used twice and the figure legend is also incorrect due to the labeling errors. In-text references to the figure should also be updated accordingly.

(4) In Figure 2 N-P, the delta F/F threshold wording is partially cropped. The figure should be updated.

Thank you for pointing out this mistake. Both figures have now been updated (already in the initial posted version)

(5) The naming and labeling of groups in the manuscript and figures should be updated to more accurately reflect the fish used for each experiment. As it currently stands, I found the labeling confusing and sometimes misleading. For example, Figure 3 'controls' are actually eaat2a-/- mutants, whereas the other group is hsp70I:gal x eaat2a-/- crosses or gal-/- x eaat2a-/- crosses. In other Figures, 'controls' are eaat2a+/+larva, or wild-type siblings (sometimes unclear).

We have made appropriate changes to the manuscript to make this point clearer to the reader, especially when the controls are eaat2a mutants.

(6) Figure 4J and 4K only show 5 data points, when the authors clearly indicate that 6 fish had seizures. Continuation of this data in Figure 4L shows 6 data points.

Indeed the 6 data points in Figure 4J and K are hard to see due to their nearly complete overlap. On larger magnification all six data points become distinguishable. We will try some different plotting approaches for the revision.

eLife Assessment

This fundamental study identified a novel role of NOLC1 in regulating p53 nuclear transcriptional activity and p53-mediated ferroptosis in gastric cancer. After major revisions, the evidence supporting the conclusions is solid. However, some new experiments are needed to draw more robust conclusions regarding the ferroptosis-associated studies.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors addressed the previous comments from reviewers.

Strengths:

This study identified that NOLC1 could bind to p53 and decrease its nuclear transcriptional activity, then inhibit p53-mediated ferroptosis in gastric cancer.

Weaknesses:

There are a few Western blot images that were processed with excessive contrast adjustment, such as Figure 2I (Caspase-3 in MKN-45 group), Figure 4H (GPX4 in MKN-45 group), and Figure 5G/5I.

Reviewer #2 (Public review):

Summary:

Shengsheng Zhao et al. investigated the role of nucleolar and coiled-body phosphoprotein 1 (NOLC1) in relegating gastric cancer (GC) development and cisplatin-induced drug resistance in GC. They found a significant correlation between high NOLC1 expression and the poor prognosis of GC. Meanwhile, upregulation of NOLC1 was associated with cis-resistant GC. Experimentally, the authors demonstrate that knocking down NOLC1 increased GC sensitivity to Cis possibly by regulating ferroptosis. Mechanistically, they found NOLC1 suppressed ferroptosis by blocking the translocation of P53 from the cytoplasm to the nucleus and promoting its degradation. In addition, the authors also evaluated the effect of combinational treatment of anti-PD-1 and cisplatin in NOLC1 -knockdown tumor cells, revealing a potential role of NOLC1 in the targeted therapy for GC.

Strengths:

Chemoresistance is considered a major reason causing failure of tumor treatment and death of cancer patients. This paper explored the role of NOLC1 in the regulation of Cis-mediated resistance, which involves a regulated cell death named ferroptosis. These findings provide more evidence highlighting the study of regulated cell death to overcome drug resistance in cancer treatment, which could give us more potential strategies or targets for combating cancer.

Weaknesses:

More evidence supporting the regulation of ferroptosis induced by Cisplatin by NOLC1 should be added. Particularly, the role of ferroptosis in the cisplatin-resistance should be verified and whether NOLC1 regulates ferroptosis induced by additional FINs should be explored. Besides, the experiments to verify the regulation of ferroptosis sensitivity by NOLC1 are sort of superficial. The role of MDM2/p53 in ferroptosis or cisplatin resistance mediated by NOLC1 should be further studied by genetic manipulation of p53, which is the key evidence to confirm its contribution to NOLC1 regulation of GC and relative cell death.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

Summary:

In this manuscript (eLife-RP-RA-2024-103904), the authors identified that NOLC1 was upregulated in gastric cancer samples, which promoted cancer progression and cisplatin resistance. They further found that NOLC1 could bind to p53 and decrease its nuclear transcriptional activity, then inhibit p53-mediated ferroptosis. There are several major concerns regarding the conclusions.

Strengths:

This study identified that NOLC1 could bind to p53 and decrease its nuclear transcriptional activity, then inhibit p53-mediated ferroptosis in gastric cancer.

Weaknesses:

The major conclusions were not sufficiently supported by the results. The experiments were not conducted in a comprehensive manner.

Major concerns

(1) The authors investigated NOLC1 expression in gastric cancer (GC) using clinical samples, which is valuable; however, the sample array includes only 3 patients. This sample size is insufficient to support conclusions for human samples. Please increase the sample size and apply a more robust statistical analysis. Additionally, specify the statistical methods used in the figure legend.

Thanks very much for the kind comments and great suggestions. As suggested, we have increased the sample size of GC patients, and the new data (six pair samples) was shown in Fig. S1A, further reflecting that NOLC1 was upregulate in gastric cancer (GC). Moreover, the statistical methods have been added in each figure legend.

(2) These data are not sufficient to support the key conclusion of this study "NOLC1 is significantly upregulated in GC tissues and Cis-resistant GC cells". There is no convincing data showing that NOLC1 upregulation is specific to cancer cells or any other cell types. Based on the following results that NOLC1 expressed in cancer cells can support cancer cell survival and drug resistance, the authors switched to investigating the role of NOLC1 in cancer cells without demonstrating cancer cells indeed highly upregulate NOLC1.

Thanks for raising this good question. As shown in Fig. 1E-F, the TCGA database have shown that NOLC1 was upregulated in GC. Moreover, we further analyzed the NOLC1 expression level in other cancer type, according to the Human Protein Atlas (https://www.proteinatlas.org/). The results indicated that NOLC1 mRNA level was much higher in almost all cancers except acute myeloid leukemia (LAML). In addition, according to the gene expression profiling interactive analysis (GEPIA, http://gepia.cancer-pku.cn/index.html), NOLC1 mRNA level was above 100 nTPM in most gastric cancer cell lines, however in most non-cancerous cell lines was below 100 nTPM, indicating that NOLC1 was up-regulated in gastric cancer.

Author response image 1.

The mRNA level of NOLC1 in different GC cells and non-cancerous cells.

(3) The authors primarily use MGC-803 cells for experiments; however, MGC-803 is known to be a HeLa-contaminated cell line. Could the authors explain this choice of using this cell line only? Did they validate key findings with additional cell lines? This is particularly important for assays such as cisplatin resistance validation, in vivo experiments, TEM imaging, and MitoPeDPP fluorescence imaging.

Thanks for raising this good question. We are not only use MGC-803 cells, the key findings in vitro was also validated in MKN-45 cells (Fig. 2), and in vivo experiment also validated in Mouse Forestomach Carcinoma cells (MFC)-tumor bearing 615 mice model (Fig 7). Furthermore, we further added some experiments in MKN-45 cells. The TEM imaging showed that NOLC1 could significantly inhibit cisplatin (Cis) induced lipid membrane damage in MKN-45 cells (Fig. S6A). Moreover, MitoPeDPP fluorescence assay analyzed by FCAs also indicating that rapid ROS was enriched in mitochondria in MKN-45 cells (Fig. 4E, Fig. S6J).

(4) In Figure 2, did the authors perform assays with NOLC1 overexpression? If so, please include these results to strengthen the conclusions.

Thanks very much for the kind comments and great suggestions. As suggested, we added new data about NOLC1 overexpression assay Cell counting kit-8 assay shows that NOLC1-overexpression group is more resistance to Cis compared to vector group (Fig. S4E, S5A).

(5) The authors show in Figures 2A-B that shNOLC1 without cisplatin treatment does not affect cell viability. However, Figures 2D-E suggest increased apoptosis in shNOLC1 cells without cisplatin treatment. Additionally, in vivo studies in Figure 3 show no significant difference between the shNC+PBS and shNOLC1+PBS groups, which appears contradictory to the apoptosis assays. Similarly, Ki67 staining shows decreased scores in the shNOLC1 group compared to shNC. Could the authors clarify this inconsistency?

Thanks for raising this good question. In Fig 2D-E, the difference in proportion of death cells between shNOLC1 and shNC treated with PBS groups were only 3% (MGC-803) and 7% (MKN-45) which is much lower than that treated with cisplatin in vitro. Moreover, in vivo analysis indicated that the average tumor volume in NOLC1+PBS group was smaller than that in NC group, but there was no statistical significance (p value = 0.3962). Moreover, tumor proliferation is a complex process regulated by many factors [1,2], thus the level of Ki67 is by no means the same as the rate of tumor proliferation, might be positively correlated.

(6) In Figure 4, NOLC1 knockdown appears to enhance cisplatin-induced ferroptosis rather than apoptosis. Given p53's role in apoptosis, did the authors compare the effects of NOLC1 on cisplatin-induced apoptosis vs. ferroptosis? If so, please clarify whether NOLC1 predominantly regulates apoptosis or ferroptosis.

Thanks for raising this good question. We do have compared the effects of NOLC1 on cisplatin-induced apoptosis vs. ferroptosis. As shown in Fig. 5A, NOLC1 knockdown obviously increased the BCL-2 protein level which is an anti-apoptotic protein and mediated by p53 via protein interaction in cytoplasm[3,4], this phenomenon may cause by the increasing level of p53 in cytoplasm (Fig. 6I). Also, the TEM imaging showed the classic ferroptotic morphological changes rather than apoptosis (Fig. 5A, S6A). Taken together, NOLC1 mainly regulates p53 mediated ferroptosis rather than apoptosis.

(7) Did the authors perform co-IP assays with p53 or HA antibodies to immunocapture NOLC1? If not, please add this experiment to support protein interactions. The mechanistic correlation between p53 and NOLC1 can be supported by adding experiments using multiple GC cell lines with various p53 alterations (such as loss-of- function or gain-of-function mutations/deletions). This is critical because the authors specifically claimed that NOLC1 can inhibit p53-mediated ferroptosis, but not other tumor suppressors.

Thanks very much for the kind comments and great suggestions. As suggested, we had performed Co-IP assay with anti-HA antibodies to immunocapture NOLC1-FLAG. As shown in Fig. 5K, p53 DNA binding domain (DBD)-HA could immunocapture with NOLC1, further indicated that NOLC1 could binding to p53 DBD. Moreover, we concur with the reviewer that adding experiments using multiple p53 alterations, however considering that different p53 mutants have completely different functional changes. Therefore, we using siRNA to knockdown p53 level in MGC-803 cells, the results shown that NOLC1 mediated resistance was disappear and the GPX4 level was increased (Fig. S10). These data have shown that NOLC1 promotes GC resistance via mediated p53 functions.

(8) In Figure S5B, the LDH release can be blocked by Fer-1?

Thanks for raising this good question. As suggested, Fer-1 (20 μmol/mL) significantly blocked the LDH release in NOLC1 knockdown group (Fig S6E). This data further confirmed that NOLC1 suppressed Cis-induced ferroptosis.

(9) How about the ubiquitination assay in MGC-803 cells?

Thanks for raising this good question. As suggested, we also analyzed the ubiquitination assay in MGC-803 cells. As the result showed that NOLC1 also could increasing level of ubiquitination of p53 (Fig. 6H).

(10) In Figure 6H, the DBD domain of NOLC1 is required for inhibiting P53 ubiquitination.

Thanks for your opinion. However, in our paper, we only mentioned that p53 DBD domain, rather than NOLC1 DBD domain. Also, we did not find any DNA binding function of NOLC1 in the Pubmed database. Therefore, we would like to ask whether the revised opinion is correct.

(11) In Figure 8B, the CD3 antibody is not specific, please change it to a new one.

Thanks very much for the kind comments and great suggestions. As suggested, we have used new CD3 antibody and the new data was added in Fig. 8B.

(12) The authors report that NOLC1 influences peripheral blood lymphocytes with cisplatin treatment, with or without PD-1. Could the authors explain why NOLC1 would affect peripheral blood lymphocytes? Additionally, did they assess immune cell infiltration in the tumor microenvironment (TME) by flow cytometry?

Thanks for raising good question. The tumor size of the knockdown group treated with Cis + PD-1 was too small (less than 100 mg) to extract enough infiltrated immune cells (less than 10000 CD45⁺ cells), thus we chose to detect immune cells in the blood of the mice. Considering that the infiltrating immune cells including CTLs were originate from peripheral blood by circulation. Under the normal conditions, serval tumor biology behavior impact the TME to limit immune responses and present barriers to cancer therapy. For example, tumor could express or secret lots of negative regulator like PD-L1. Causing immune cells cannot recognize tumor cells and infiltrate into tumor tissue. Ferroptosis, as a new from of ICD, could damage tumor cell plasm and release amount of tumor associated antigen and tumor-specific antigens causing immune cells priming and activation. Eventually, the activated immune cells in peripheral blood travel towards the tumor site, infiltrating the tumor tissue under favorable co-stimulatory conditions and guided by chemokine gradients. Once within the tumor microenvironment, these activated T cells can control tumor growth through direct tumor cell destruction and cytokine-mediated processes [5–8]

To assess immune cell infiltration in the TME, we analyzed the tumor infiltrated CD3⁺ and CD8⁺ immune cells in tumor tissue by immunofluorescence (Fig. 8B). Thus, the peripheral blood lymphocytes could reflect the infiltration of immune cells in the tumor.

Minor concerns:

(1) Please clarify the statistical methods in each figure legend.

Thanks for your opinion. We have added statistical methods in each figure legend.

(2) In Figure 2D, please provide statistical data of cleaved-caspase3 expression.

Thanks for your opinion. As is shown in Fig. S5B-C, the relative cleaved-caspase3 were provided.

(3) Please ensure that the canonical expressions used in the research paper are adhered to.

Thanks for your opinion. We have carefully modified our expressions in our paper.

(4) Please pay more attention to the grammar and formatting of texts.

Thanks for your opinion. We revised our manuscript through the American Journal Experts (AJE) service.

Reviewer #2:

Summary:

Shengsheng Zhao et al. investigated the role of nucleolar and coiled-body phosphoprotein 1 (NOLC1) in relegating gastric cancer (GC) development and cisplatin-induced drug resistance in GC. They found a significant correlation between high NOLC1 expression and the poor prognosis of GC. Meanwhile, upregulation of NOLC1 was associated with cis-resistant GC. Experimentally, the authors demonstrate that knocking down NOLC1 increased GC sensitivity to Cis possibly by regulating ferroptosis. Mechanistically, they found NOLC1 suppressed ferroptosis by blocking the translocation of p53 from the cytoplasm to the nucleus and promoting its degradation. In addition, The authors also evaluated the effect of combinational treatment of anti- PD-1 and cisplatin in NOLC1-knockdown tumor cells, revealing a potential role of NOLC1 in the targeted therapy for GC.

Strengths:

Chemoresistance is considered a major reason causing failure of tumor treatment and death of cancer patients. This paper explored the role of NOLC1 in the regulation of Cis-mediated resistance, which involves a regulated cell death named ferroptosis. These findings provide more evidence highlighting the study of regulated cell death to overcome drug resistance in cancer treatment, which could give us more potential strategies or targets for combating cancer.

Weaknesses:

More evidence supporting the regulation of ferroptosis induced by Cisplatin by NOLC1 should be added. Particularly, the role of ferroptosis in the cisplatin-resistance should be verified and whether NOLC1 regulates ferroptosis induced by additional FINs should be explored. Besides, the experiments to verify the regulation of ferroptosis sensitivity by NOLC1 are sort of superficial. The role of MDM2/p53 in ferroptosis or cisplatin resistance mediated by NOLC1 should be further studied by genetic manipulation of p53, which is the key evidence to confirm its contribution to NOLC1 regulation of GC and relative cell death.

Major points:

(1) More evidence supporting the regulation of ferroptosis induced by Cisplatin by NOLC1 should be added. Particularly, the role of ferroptosis in the cisplatin-resistance should be verified and whether NOLC1 regulates ferroptosis induced by additional FINs should be explored.