Antienshittification

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

CCK is the most abundant neuropeptide in the brain, and many studies have investigated the role of CCK and inhibitory CCK interneurons in modulating neural circuits, especially in the hippocampus. The manuscript presents interesting questions regarding the role of excitatory CCK+ neurons in the hippocampus, which has been much less studied compared to the well-known roles of inhibitory CCK neurons in regulating network function. The authors adopt several methods, including transgenic mice and viruses, optogenetics, chemogenetics, RNAi, and behavioral tasks to explore these less-studied roles of excitatory CCK neurons in CA3. They find that the excitatory CCK neurons are involved in hippocampal-dependent tasks such as spatial learning and memory formation, and that CCK-knockdown impairs these tasks.

However, these questions are very dependent on ensuring that the study is properly targeting excitatory CCK neurons (and thus their specific contributions to behavior). There needs to be much more characterization of the CCK transgenic mice and viruses to confirm the targeting. Without this, it is unclear whether the study is looking at excitatory CCK neurons or a more general heterogeneous CCK neuron population.

Strengths:

This field has focused mainly on inhibitory CCK+ interneurons and their role in network function and activity, and thus, this manuscript raises interesting questions regarding the role of excitatory CCK+ neurons, which have been much less studied.

Weaknesses:

(1a) This manuscript is dependent on ensuring that the study is indeed investigating the role of excitatory CCK-expressing neurons themselves and their specific contribution to behavior. There needs to be much more characterization of the CCK-expressing mice (crossed with Ai14 or transduced with various viruses) to confirm the excitatory-cell targeting. Without this, it is unclear whether the study is looking at excitatory CCK neurons or a more general heterogeneous CCK neuron population.

Thank you for this constructive comment. Indeed, the current study lacks comprehensive strategies to unequivocally distinguish excitatory CCK neurons from heterogeneous CCK neuronal populations. Nevertheless, we provide multiple lines of evidence supporting the distribution of CaMKIIα/Vglut1-expressing CCK⁺ neurons in the hippocampus (Figure 1F), using complementary approaches including transgenic mouse models as well as viral and antibody-based labeling (Figure 1A, Figure 1H-I). In addition, we demonstrate that 635 nm light reliably evokes field excitatory postsynaptic potentials (fEPSPs) at CA3-Schaffer collateral synapses expressing DIO-CaMKIIα-ChrimsonR in vitro (Figure 2A-F). Importantly, these light-evoked excitatory synaptic responses are abolished by AMPA and NMDA receptor antagonists (CNQX and APV), confirming the excitatory nature of the DIO-CaMKIIα-ChrimsonR-expressing synapses. To demonstrate the future works that can further support our findings and conclusions, we have added the strategies that can be conducted in the Discussion section in the revision:

“Due to technical limitations at the current stage, we were unable to perform whole-cell recordings or pharmacological manipulations using CCK receptor antagonists. In future studies, the application of these approaches to directly record and selectively block EPSPs from excitatory CCK neurons in the hippocampus will further strengthen and validate our conclusions.” (Line 265 - line 269 in the revision).

(1b) For the experiments that use a virus with the CCK-IRES-Cre mouse, there is no information or characterization on how well the virus targets excitatory CCK-expressing neurons. (Additionally, it has been reported that with CaMKIIa-driven protein expression, using viruses, can be seen in both pyramidal and inhibitory cells.

We thank the reviewer for this insightful comment regarding the specificity of viral targeting in CCK-IRES-Cre mice.

To address this concern, we performed additional characterization of viral expression in CA3. We found that DIO-CaMKIIα-mCherry expression showed a high degree of colocalization with CaMKIIα immunoreactivity, indicating preferential targeting of excitatory neurons (sFigure 1A-B; sFigure 2A-B; sFigure 3A-B). We showed an example to confirmed the high specificity of the AAV for infecting the excitatory CCK neurons in CA3 area.

Besides, we acknowledge prior reports showing that CaMKIIα-driven viral expression can, in some cases, be detected in a small subset of inhibitory neurons. However, because CA3-Schaffer collateral projections to CA1 arise exclusively from excitatory CA3 pyramidal neurons, any potential expression in inhibitory CCK⁺ interneurons are unlikely to directly contribute to the recorded CA1 synaptic responses in our electrophysiological experiments. That said, we cannot fully exclude the possibility that a minor population of inhibitory CCK⁺ neurons could indirectly modulate CA3 pyramidal neuron activity via local circuit mechanisms, particularly in experiments involving optogenetic manipulation or shRNA expression. We now explicitly acknowledge this limitation in the revised manuscript:

“Importantly, to further improve cell-type specificity, we propose an intersectional genetic strategy using CCK-IRES-Cre × VGlut1-Flp mice combined with a Cre-On/Flp-On (Con/Fon) AAV, which would restrict expression exclusively to excitatory CCK-expressing neurons and eliminate potential contributions from inhibitory CCK⁺ cells. This approach will be implemented in future studies to refine circuit specificity.” (Line 269 - line 273 in the revision).

(2) The methods and figure legends are extremely sparse, leading to many questions regarding methodology and accuracy. More details would be useful in evaluating the tools and data. More details would be useful in evaluating the tools and data. Additionally, further quantification would be useful-e.g. in some places, only % values are noted, or only images are presented.

Thank you for these constructive comments. We have expanded the methodological descriptions in both the Methods section and the figure legends to provide sufficient detail for evaluating the experimental tools and data accuracy. In addition, we have added quantitative analyses where previously only representative images or percentage values were shown. Specifically, quantification has now been included for each AAV condition in the corresponding figures in the revised manuscript.

(3) It is unclear whether the reduced CCK expression is correlated, or directly causing the impairments in hippocampal function. Does the CCK-shRNA have any additional detrimental effects besides affecting CCK-expression (e.g., is the CCK-shRNA also affecting some other essential (but not CCK-related) aspect of the neuron itself?)? Is there any histology comparison between the shRNA and the scrambled shRNA?

Recent studies from our lab demonstrated that knockout the CCK gene expression significantly attenuates the hippocampal-dependent spatial learning and CA3-CA1 LTP, indicating CCK plays a critical role in modulating the hippocampal functions[1,2]. Additionally, CCK-shRNA or CCK-scramble did not significantly affect the excitatory synaptic transmission in the CA3-CA1 projections, hinting that CCK-shRNA may exhibits no obvious adverse effect on other neural components.

Finally, we have provided the histology comparison between the shRNA and the scrambled shRNA regrading the expression level of the CCK protein (Pro-CCK) in the revision. Our result shows that CCK-shRNA (left panel) significantly reduced CCK expression in CA3^CCK-positive neurons compared with the CCK-Scramble group (right panel).

Citation:

(1) Wang, J. L., Sha, X. Y., Shao, Y., Zhang, Z. H., Huang, S. M., Lin, H., ... & Sun, J. P. (2025). Elucidating pathway-selective biased CCKBR agonism for Alzheimer’s disease treatment. Cell.

(2) Zhang, N., Sui, Y., Jendrichovsky, P., Feng, H., Shi, H., Zhang, X., ... & He, J. (2024). Cholecystokinin B receptor agonists alleviates anterograde amnesia in cholecystokinin-deficient and aged Alzheimer's disease mice. Alzheimer's research & therapy, 16(1), 109.

https://doi.org/10.7554/eLife.109001.1.sa2

Reviewer #2 (Public review):

Summary:

In this study, the authors have demonstrated, through a comprehensive approach combining electrophysiology, chemogenetics, fiber photometry, RNA interference, and multiple behavioral tasks, the necessity of projections from CCK+ CAMKIIergic neurons in the hippocampal CA3 region to the CA1 region for regulating spatial memory in mice. Specifically, authors have shown that CA3-CCK CAMKIIergic neurons are selectively activated by novel locations during a spatial memory task. Furthermore, authors have identified the CA3-CA1 pathway as crucial for this spatial working memory function, thereby suggesting a pivotal role for CA3 excitatory CCK neurons in influencing CA1 LTP. The data presented appear to be well-organized and comprehensive.

Strengths:

(1) This work combined various methods to validate the excitatory CCK neurons in the CA3 area; these data are convincing and solid.

(2) This study demonstrated that the CA3-CCK CAMKIIergic neurons are involved in the spatial memory tasks; these are interesting findings, which suggest that these neurons are important targets for manipulating the memory-related diseases.

(3) This manuscript also measured the endogenous CCK from the CA3-CCK CAMKIIergic neurons; this means that CCK can be released under certain conditions.

Weaknesses:

(1) The authors do not mention which receptors of the CCK modulate these processes.

We appreciate the reviewer for raising this important question. Based on our recent work, CCK-B receptors are the primary neural components mediating CCK functions in the hippocampus at both the synaptic plasticity and behavioral levels (Su et al., 2023; Zhang et al., 2024; Wang et al., 2025). To clarify this mechanism, we have added the following content to the revised manuscript:

“Based on our recent work, CCK signaling in the hippocampus is predominantly mediated by CCK-B receptors, which play a critical role in regulating synaptic plasticity and spatial memory-related behaviors.” (Line 105 - line 106 in the revision).

(2) This author does not test the CCK gene knockout mice or the CCK receptor knockout mice in these neural processes.

Thank you for this insightful comment. We previously tested these experiments in an earlier study. Our results showed that high-frequency electrical stimulation failed to induce significant LTP in the CA3-CA1 pathway in both CCK gene knockout (CCK-KO) mice and CCK-B receptor knockout (CCK-BR-KO) mice in vitro (Su et al., 2023; Zhang et al., 2024; Wang et al., 2025). These findings indicate that CCK mediates its synaptic effects predominantly through CCK-B receptors in the CA3-CA1 pathway. Accordingly, we have added this description to the revised manuscript.

“Additionally, high-frequency electrical stimulation fails to induce LTP in the CA3-CA1 pathway in both CCK-KO and CCK-BR-KO mice, indicating that CCK-dependent synaptic plasticity in this circuit is primarily mediated by CCK-B receptors.” (Line 170 - line 173 in the revision).

(3) The author does not test the source of CCK release during the behavioral tasks.

We thank the reviewer for raising this important point. In our previous work, we directly monitored CCK release in the hippocampus during an object-exploration task using a GPCR-based CCK-BR sensor combined with fiber photometry (Su et al., 2023). During object exploration, we observed a rapid and robust increase in CCK-BR sensor fluorescence, indicating activity-dependent CCK release in the hippocampus. Based on these findings, we deduced that hippocampal CCK release plays a critical role in hippocampus-dependent behavioral tasks.

We acknowledge that hippocampal neurons receive CCK-positive projections from multiple brain regions, making it technically challenging to isolate and monitor the precise source of CCK release in the CA1 area during behavioral tasks in vivo. One potential strategy to address this limitation is selective overexpression of CCK in CA3 neurons (e.g., AAV-CCK delivery), followed by assessment of CCK-BR sensor responses during hippocampal-dependent behaviors. We have added this discussion to the revised manuscript to clarify the source and functional relevance of CCK release during behavioral tasks.

“Besides, using a GPCR-based CCK-BR sensor combined with fiber photometry, our previous work demonstrated rapid, activity-dependent CCK release in the hippocampus during object-exploratory behavior, supporting a functional role for hippocampal CCK signaling in cognitive tasks (Su et al., 2023). Given that hippocampal neurons receive CCK-positive projections from multiple brain regions, it remains technically challenging to precisely identify the cellular source of CCK release in CA1 during behavior. Future studies employing selective CCK overexpression in CA3 neurons, together with CCK-BR sensor recordings, may help further delineate the contribution of CA3-derived CCK to hippocampal-dependent behaviors.” (Line 313 - line 321 in the revision).

Citation:

(1) Wang, J. L., Sha, X. Y., Shao, Y., Zhang, Z. H., Huang, S. M., Lin, H., ... & Sun, J. P. (2025). Elucidating pathway-selective biased CCKBR agonism for Alzheimer’s disease treatment. Cell.

(2) Zhang, N., Sui, Y., Jendrichovsky, P., Feng, H., Shi, H., Zhang, X., ... & He, J. (2024). Cholecystokinin B receptor agonists alleviates anterograde amnesia in cholecystokinin-deficient and aged Alzheimer's disease mice. Alzheimer's research & therapy, 16(1), 109.

(3) Su, J., Huang, F., Tian, Y., Tian, R., Qianqian, G., Bello, S. T., ... & He, J. (2023). Entorhinohippocampal cholecystokinin modulates spatial learning by facilitating neuroplasticity of hippocampal CA3-CA1 synapses. Cell Reports, 42(12).

https://doi.org/10.7554/eLife.109001.1.sa1

Reviewer #3 (Public review):

Summary:

Fengwen Huang et al. used multiple neuroscience techniques (transgenetic mouse, immunochemistry, bulk calcium recording, neural sensor, hippocampal-dependent task, optogenetics, chemogenetics, and interfer RNA technique) to elucidate the role of the excitatory cholecystokinin-positive pyramidal neurons in the hippocampus in regulating the hippocampal functions, including navigation and neuroplasticity.

Strengths:

(1) The authors provided the distribution profiles of excitatory cholecystokinin in the dorsal hippocampus via the transgenetic mice (Ai14::CCK Cre mice), immunochemistry, and retrograde AAV.

(2) The authors used the neural sensor and light stimulation to monitor the CCK release from the CA3 area, indicating that CCK can be secreted by activation of the excitatory CCK neurons.

(3) The authors showed that the activity of the excitatory CCK neurons in CA3 is necessary for navigation learning.

(4) The authors demonstrated that inhibition of the excitatory CCK neurons and knockdown of the CCK gene expression in CA3 impaired the navigation learning and the neuroplasticity of CA3-CA1 projections.

Weaknesses:

(1) The causal relationship between navigation learning and CCK secretion?

Thank you for pointing out this important issue. Previous studies have shown that CCK can be rapidly secreted during exploratory behaviors, as detected by the CCK-BR sensor. In parallel, CCK-positive neurons have been demonstrated to play a critical role in the precise execution of hippocampus-dependent spatial learning. Together, these findings suggest that exploratory behavior induces CCK secretion, which in turn contributes to the accuracy of hippocampal-dependent learning and memory processes. Based on this evidence, we propose that CCK secretion serves as a functional link between behavioral exploration and spatial learning. We have added these explanations in the revised manuscript to better clarify the causal relationship between behavioral exploration and CCK secretion:

“Besides, using a GPCR-based CCK-BR sensor combined with fiber photometry, our previous work demonstrated rapid, activity-dependent CCK release in the hippocampus during object-exploratory behavior, supporting a functional role for hippocampal CCK signaling in cognitive tasks (Su et al., 2023). Given that hippocampal neurons receive CCK-positive projections from multiple brain regions, it remains technically challenging to precisely identify the cellular source of CCK release in CA1 during behavior. Future studies employing selective CCK overexpression in CA3 neurons, together with CCK-BR sensor recordings, may help further delineate the contribution of CA3-derived CCK to hippocampal-dependent behaviors.” (Line 313 - line 321 in the revision)

(2) The effect of overexpression of the CCK gene on hippocampal functions?

We thank the reviewer for this comment. In fact, an earlier study from our laboratory demonstrated that intraperitoneal injection of exogenous CCK-4 significantly improved performance in hippocampus-dependent spatial learning tasks in both CCK gene knockout (CCK-KO) mice and Alzheimer’s disease (AD) mouse models. These findings suggest that enhancing CCK signaling can ameliorate hippocampal dysfunction at both the behavioral and synaptic plasticity levels (Zhang et al., 2024; Wang et al., 2025). Accordingly, although direct genetic overexpression of CCK in the hippocampus has not yet been extensively characterized, the observed benefits of exogenous CCK delivery support the notion that increased CCK availability positively modulates hippocampal function and spatial learning. We have cited this study in the revised manuscript to support this interpretation.

“Interestingly, an earlier study demonstrated that intraperitoneal injection of exogenous CCK-4 significantly improved performance in hippocampus-dependent spatial learning tasks in both CCK gene knockout (CCK-KO) mice and Alzheimer’s disease (AD) mouse models (Zhang et al., 2024). These findings suggest that enhancing CCK signaling can ameliorate hippocampal dysfunction at both the behavioral and synaptic plasticity levels.” (Line 291 - line 297 in the revision)

(3) What are the functional differences between the excitatory and inhibitory CCK neurons in the hippocampus?

In the hippocampus, CCK-expressing neurons consist of two major populations with distinct functions: excitatory (glutamatergic) and inhibitory (GABAergic) neurons. Excitatory CCK neurons are relatively sparse and intermingled with pyramidal cells. By releasing glutamate, they directly contribute to excitatory transmission and are thought to participate in synaptic plasticity and information processing related to learning and memory. In contrast, inhibitory CCK neurons are more abundant and include well-characterized interneuron subtypes such as CCK-positive basket cells. These neurons release GABA and primarily target the perisomatic region of pyramidal neurons, providing strong control over neuronal firing. Notably, inhibitory CCK interneurons are highly sensitive to neuromodulatory signals, particularly endocannabinoids via CB1 receptors, enabling dynamic regulation of inhibitory tone and network activity. Together, excitatory CCK neurons mainly support hippocampal excitation and plasticity, whereas inhibitory CCK neurons regulate network dynamics and spike timing. As the focus of the present study is on excitatory CCK neurons, a detailed comparison between these two populations was not included in the original manuscript.

(4) Do CCK sources come from the local CA3 or entorhinal cortex (EC) during the high-frequency electrical stimulation?

Thank you for this insightful comment. Our data indicate that the CCK detected during high-frequency stimulation originates from CA3 neurons rather than the entorhinal cortex (EC). As shown in Figure 2, we used an optogenetic approach combined with a GPCR-based CCK sensor to selectively examine CCK release from the CA3-CA1 pathway. ChrimsonR was specifically expressed in CA3 neurons projecting to CA1, restricting light stimulation to CA3 axon terminals. In parallel, the CCK sensor was locally expressed in CA1, allowing real-time detection of CCK release at CA3 presynaptic sites. High-frequency light stimulation robustly evoked CCK signals in CA1, demonstrating activity-dependent CCK release from CA3 terminals. Importantly, EC inputs were neither genetically targeted nor optically stimulated in this experiment, excluding the EC as a source of the detected CCK. Together, these results support the conclusion that CCK released during high-frequency stimulation is derived from local CA3 projections to CA1. Similarly, as the focus of the present study is on excitatory CCK neurons in the CA3 area, a detailed comparison between these two CCK sources was not included in the original manuscript.

Citation:

(4) Wang, J. L., Sha, X. Y., Shao, Y., Zhang, Z. H., Huang, S. M., Lin, H., ... & Sun, J. P. (2025). Elucidating pathway-selective biased CCKBR agonism for Alzheimer’s disease treatment. Cell.

(5) Zhang, N., Sui, Y., Jendrichovsky, P., Feng, H., Shi, H., Zhang, X., ... & He, J. (2024). Cholecystokinin B receptor agonists alleviates anterograde amnesia in cholecystokinin-deficient and aged Alzheimer's disease mice. Alzheimer's research & therapy, 16(1), 109.

(6) Su, J., Huang, F., Tian, Y., Tian, R., Qianqian, G., Bello, S. T., ... & He, J. (2023). Entorhinohippocampal cholecystokinin modulates spatial learning by facilitating neuroplasticity of hippocampal CA3-CA1 synapses. Cell Reports, 42(12).

Author Response:

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

We thank the reviewers for their constructive comments. A central concern raised is the comparison of performance with existing motion-correction methods. In response, we performed motion correction using several widely used approaches and compared results using the number of particles detected by 2DTM and their associated SNR. To minimize potential bias, we selected parameters to give each method a comparable level of model flexibility so that the results are as directly comparable as possible. Overall, Unbend performs the best. We note that extensive, method-specific parameter optimization could further affect absolute performance, and a comprehensive benchmarking study is therefore beyond the scope of this work

Public Reviews:

Reviewer #1 (Public review):

Kong et al.'s work describes a new approach that does exactly what the title states: "Correction of local beam-induced sample motion in cryo-EM images using a 3D spline model." I find the method appropriate, logical, and well-explained. Additionally, the work suggests using 2DTM-related measurements to quantify the improvement of the new method compared to the old one in cisTEM, Unblur. I find this part engaging; it is straightforward, accurate, and, of course, the group has a strong command of 2DTM, presenting a thorough study.

However, everything in the paper (except some correct general references) refers to comparisons with the full-frame approach, Unblur. Still, we have known for more than a decade that local correction approaches perform better than global ones, so I do not find anything truly novel in their proposal of using local methods (the method itself- Unbend- is new, but many others have been described previously). In fact, the use of 2DTM is perhaps a more interesting novelty of the work, and here, a more systematic study comparing different methods with these proposed well-defined metrics would be very valuable. As currently presented, there is no doubt that it is better than an older, well-established approach, and the way to measure "better" is very interesting, but there is no indication of how the situation stands regarding newer methods.

Regarding practical aspects, it seems that the current implementation of the method is significantly slower than other patch-based approaches. If its results are shown to exceed those of existing local methods, then exploring the use of Unbend, possibly optimizing its code first, could be a valuable task. However, without more recent comparisons, the impact of Unbend remains unclear.

We thank the reviewer for this important point. We agree that comparing against modern local motion-correction approaches is a valuable task. To address this, we added a new benchmarking section (pp. 17–18, lines 444–492, Fig. 8, Fig. 8—figure supplement 1) that compares Unbend against widely used patch-based local correction methods, including MotionCor2, MotionCor3, Warp, and CryoSPARC. Using the same 2DTM-based metrics described in the manuscript (detections per micrograph and SNR distributions for commonly detected particles), we find that Unbend provides the most stable performance across the tested datasets and, in most cases, yields higher detection counts and higher SNR than the alternative methods.

Regarding runtime, the current implementation is CPU-based and is therefore slower than some optimized GPU-accelerated packages. We now clarify this limitation in the manuscript (line 498–499). Our primary goal in this study is to improve motion-correction accuracy and quantify its impact using 2DTM-based measures. Importantly, higher-quality motion-corrected micrographs can reduce downstream processing cost (e.g., by increasing particle detection efficiency and reducing ambiguous candidates), so modest additional compute times at the motion-correction stage can be offset later in the workflow. We also note that GPU acceleration and additional code-level optimizations are planned for future releases (line 501-503); however, they are not required to evaluate the methodological contribution and the benchmarking results presented here.

Reviewer #2 (Public review):

Summary:

The authors present a new method, Unbend, for measuring motion in cryo-EM images, with a particular emphasis on more challenging in situ samples such as lamella and whole cells (that can be more prone to overall motion and/or variability in motion across a field of view). Building on their previous approach of full-frame alignment (Unblur), they now perform full-frame alignment followed by patch alignment, and then use these outputs to generate a 3D cubic spline model of the motion. This model allows them to estimate a continuous, per-pixel shift field for each movie frame that aims to better describe complex motions and so ultimately generate improved motion-corrected micrographs. Performance of Unbend is evaluated using the 2D template matching (2DTM) method developed previously by the lab, and results are compared to using full-frame correction alone. Several different in situ samples are used for evaluation, covering a broad range that will be of interest to the rapidly growing in situ cryo-EM community.

Strengths:

The method appears to be an elegant way of describing complex motions in cryo-EM samples, and the authors present convincing data that Unbend generally improves SNR of aligned micrographs as well as increases detection of particles matching the 60S ribosome template when compared to using full-frame correction alone. The authors also give interesting insights into how different areas of a lamella behave with respect to motion by using Unbend on a montage dataset collected previously by the group. There is growing interest in imaging larger areas of in situ samples at high resolution, and these insights contribute valuable knowledge. Additionally, the availability of data collected in this study through the EMPIAR repository will be much appreciated by the field.

Thank you for this positive assessment.

Weaknesses:

While the improvements with Unbend vs. Unblur appear clear, it is less obvious whether Unbend provides substantial gains over patch motion correction alone (the current norm in the field). It might be helpful for readers if this comparison were investigated for the in situ datasets. Additionally, the authors are open that in cases where full motion correction already does a good job, the extra degrees of freedom in Unbend can perhaps overfit the motions, making the corrections ultimately worse. I wonder if an adaptive approach could be explored, for example, using the readout from full-frame or patch correction to decide whether a movie should proceed to the full Unbend pipeline, or whether correction should stop at the patch estimation stage.

We thank the reviewer for suggesting an adaptive criterion to decide whether to proceed patch alignment or not. We agree that such an approach could be valuable for efficiency and for avoiding unnecessary model flexibility. However, our results indicate that a simple criterion based on the magnitude of estimated local patch motion is unlikely to be sufficient. For example, in the BS-C-1 cell lysate dataset, (see line 412-417 on page 16), we observe minimal local motion (Figure 4b) with mean patch shifts of only 0.7Å and full-frame alignment already yields comparable detection counts, yet local correction still produces a measurable SNR gain (13.84 ± 0.04 to 14.25 ± 0.04, 3%) and improves SNR for ~70% of the commonly detected targets (Figure 6c). This suggests that residual local distortion can remain even when overall local motion appears small. Establishing a robust, dataset-agnostic stopping rule would therefore require a dedicated, systematic benchmarking study across many samples and acquisition conditions.

Reviewer #3 (Public review):

Summary

Kong and coauthors describe and implement a method to correct local deformations due to beam-induced motion in cryo-EM movie frames. This is done by fitting a 3D spline model to a stack of micrograph frames using cross-correlation-based local patch alignment to describe the deformations across the micrograph in each frame, and then computing the value of the deformed micrograph at each pixel by interpolating the undeformed micrograph at the displacement positions given by the spline model. A graphical interface in cisTEM allows the user to visualise the deformations in the sample, and the method has been proven to be successful by showing improvements in 2D template matching (2DTM) results on the corrected micrographs using five in situ samples.

Impact

This method has great potential to further streamline the cryo-EM single particle analysis pipeline by shortening the required processing time as a result of obtaining higher quality particles early in the pipeline, and is applicable to both old and new datasets, therefore being relevant to all cryo-EM users.

Strengths

(1) One key idea of the paper is that local beam induced motion affects frames continuously in space (in the image plane) as well as in time (along the frame stack), so one can obtain improvements in the image quality by correcting such deformations in a continuous way (deformations vary continuously from pixel to pixel and from frame to frame) rather than based on local discrete patches only. 3D splines are used to model the deformations: they are initialised using local patch alignments and further refined using cross-correlation between individual patch frames and the average of the other frames in the same patch stack.

(2) Another strength of the paper is using 2DTM to show that correcting such deformations continuously using the proposed method does indeed lead to improvements. This is shown using five in situ datasets, where local motion is quantified using statistics based on the estimated motions of ribosomes.

Thank you for this positive assessment.

Weaknesses

(1) While very interesting, it is not clear how the proposed method using 3D splines for estimating local deformations compares with other existing methods that also aim to correct local beam-induced motion by approximating the deformations throughout the frames using other types of approximation, such as polynomials, as done, for example MotionCor2.

We thank the reviewer for this suggestion. We agree that positioning Unbend relative to existing local motion-correction methods is important. In the revised manuscript, we added a dedicated benchmarking section comparing Unbend with widely used local correction approaches, including MotionCor2, MotionCor3, Warp, and CryoSPARC, using the same 2DTM-based metrics (Fig. 8, Fig. 8—figure supplement 1). This section is included on pp. 17–18, lines 444–492. To make the comparison as fair as possible, we matched nominal model flexibility across methods and otherwise used default parameters to reduce method-specific tuning. This expanded comparison provides a direct baseline against current patch-/spline-based approaches and shows that Unbend performs consistently across the in situ datasets evaluated here, with improvements in detection counts and/or SNR in multiple cases.

(2) The use of 2DTM is appropriate, and the results of the analysis are enlightening, but one shortcoming is that some relevant technical details are missing. For example, the 2DTM SNR is not defined in the article, and it is not clear how the authors ensured that no false positives were included in the particles counted before and after deformation correction. The Jupyter notebooks where this analysis was performed have not been made publicly available.

We agree that these technical details improve clarity and reproducibility. We have therefore made three changes.

(1) Definition of 2DTM SNR. We added an explicit definition of the 2DTM SNR in Section “2DTM provides a one-step verification for motion correction”, pp. 11, lines 277–287). Briefly, at each image location we compute cross-correlation values over the searched orientation space and define the 2DTM SNR as the maximum per location z-score across orientations.

(2) False-positive control / detection threshold. We clarified how detection thresholds were set to control false positives (pp. 11, lines 285–287). Specifically, we used the standard 2DTM statistical framework in which the threshold  is chosen using the one-false-positive (1-FP) criterion (or equivalently, a specified expected false-positive rate). We applied the same thresholding procedure consistently across all motion-corrected micrographs. This ensures that particle counts before/after correction reflect changes in signal recovery.

(3) Reproducibility of the analysis. We have made the script used for the benchmarking and figure generation publicly available (pp. 24 line 622-623), and we provide a link in the Data Availability statement (pp. 25 line 650). The repository includes sample .star files and a python package that computes detections per micrograph, commonly detected particles, and SNR comparisons.

(3) It is also not clear how the proposed deformation correction method is affected by CTF defocus in the different samples (are the defocus values used in the different datasets similar or significantly different?) or if there is any effect at all.

We thank the reviewer for raising this point. In the revised manuscript, we now report the defocus ranges used for each dataset (Table 1) and clarify that all motion-correction comparisons were performed within each dataset using the same CTF estimation and 2DTM settings (pp. 23 line 615-618). Across the five datasets, four were collected at similar defocus ranges (1.0 µm to 1.5µm), whereas one dataset includes near-focus (0.4 µm) micrographs (Table 1). Because Unbend operates on frame alignment/warping rather than CTF modeling, we do not expect a defocus specific effect beyond indirect influences through image SNR and reliability of cross-correlation-based alignment.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The obvious recommendation would be to use their 2DTM approach for a comparison of their new method with other currently used ones

We agree and added a new comparison section (pp. 17–18, lines 444–492). Addressed above in Response to Reviewer #1 Public Review.

Reviewer #2 (Recommendations for the authors):

(1) Line 29, typo. 3 ~ 8% > 3 - 8%.

Corrected.

(2) Lines 220 and 226. Should this be e-/Angstrom squared for the exposure?

Corrected to e^-/Ų (Now pp. 9 lines 230, 236).

(3) Figure 2 c-d. These are good for instinctively seeing the movement, but I found the legend confusing, as a 10 x 10 pixel array is mentioned, yet the schematics show a higher sampling (30 x 30 pixels? in c-e).

Thank you for pointing this out. The “10×10” annotation refers to the physical scale, whereas the grid represents pixel sampling. We removed the “10×10” label and now show only the pixel grid to avoid confusion. The caption has been updated to state that the grid corresponds to a 30×30 pixel sampling. (Fig. 2c, d; pp. 31, line 766)

(4) Figure 4. It would be good if the n of movies analyzed was given in the figure legend.

Thank you for noticing this. We report the number of movies per dataset in the corresponding summary table (Table 1).

(5) Figure 5. X/Y axes labels missing (assume pixels). Also, suggest changing the strain scale to % to match the main text description of this figure.

We added X/Y axis labels, changed the strain scale to % (Figure 5), and specified that the strains are per pixel on pp. 14 line 367. Correspondingly, the X/Y labels and strain scale in strain plots in Figure 4—figure supplementary 1 to 5 are also changed.

(6) Unify labelling of Figure 4 and 6 (i.e., Bacteria vs. M. pneumoniae, etc.).

Corrected. Sample labels are now consistent across figures. (Figures 4 and 6)

Reviewer #3 (Recommendations for the authors):

Some recommendations related to the points mentioned in the 'Weaknesses' section in the public review:

(1) If feasible, it would be useful to see a comparison with other existing methods that estimate local deformations (e.g., MotionCor2), at least on some of the datasets. For example, does the proposed method lead to better 2DTM SNR in the detected particles compared to other methods, or higher detection numbers? Alternatively, if such a comparison would require too much additional work and the authors have good reasons to believe that the results are evident, it would be helpful to include a discussion about why the proposed method is expected to perform better, both in terms of the general approach and specific implementation details.

We agree that this comparison is important. (pp. 17–18, lines 444–492). Addressed above in Response to Reviewer #3 Public Review (1).

(2) It would be useful to define the 2DTM SNR in the main text of the paper, as well as to address the point about false positives in the picked particles.

We added an explicit definition of 2DTM SNR and clarified the detection thresholding/false-positive control used in our analysis (pp. 11, lines 277–287). Addressed above in Response to Reviewer #3 Public Review (2.1 and 2.2).

(3) Regarding the results shown in Figures 4 and 6: do the authors have any insight about how the CTF defocus affects the deformation estimation and correction across the different sample types?

We now report the defocus ranges used for each dataset (Table 1). We have addressed this problem in Response to Reviewer #3 Public Review (3).

(4) Will the Jupyter notebooks used for the 2DTM analysis be made publicly available?

Yes. We have deposited a python script used for the 2DTM benchmarking and figure generation in a public repository and added the link in Data Availability statement. (pp. 23 line 622, pp. 25 line 650). Addressed above in Response to Reviewer #3 Public Review (2.3).

(5) I would also appreciate a few words about the implementation details of the 3D spline model (e.g., what libraries have been used, if any, or if the authors have implemented their own code for this).

The 3D spline model and warping code were implemented by us (no external spline library was used) and the relevant implementation details are described in the “Sample distortion modeling and correction” section (pp. 7–10, lines 174–246). For optimization, we used the L-BFGS implementation provided by the dlib library, which is now explicitly cited (pp. 10, line 264).

Some comments regarding the presentation of the work:

(1) I found the mathematical background on splines on pages 7-9 a little distracting from the main ideas of the paper, and I believe it could be moved to the methods section. A short description of this in the main text of the paper would suffice, and it would be useful to state clearly when this is background material and when it is the authors' contribution.

We appreciate the suggestion. Because Unbend includes an in-house spline implementation (no external spline library) and it is the central part of this work, we retained the spline description to support reproducibility. (pp. 7–10, lines 174–246).

(2) More generally, I found the whole method very interesting, but understanding exactly what all the steps involved were was a bit cumbersome, as they are spread across different sections of the main text. I think it would be useful to have a dedicated section giving the exact steps taken in the algorithm, possibly pointing to the relevant section in the text for more details about each step. This could be, for example, in the form of an 'Algorithm' box or a flowchart.

We added an Algorithm box as Figure 2 supplement summarizing the end-to-end workflow and pointing to the relevant sections for details (Figure 2—figure supplement 1 Algorithm, pp. 4, line 96–103, pp. 32 line 799). This is intended to make the sequence of steps easier to follow.

(3) In Figure 3, panels (b) and (c), the difference between the two micrographs, before and after correction, is not very noticeable, particularly the Thon rings in the spectra. I don't know if this is due to the image quality in the paper or if a better example could be shown. For example, the differences are clear in some of the supplementary figures.

Thank you for the suggestion. We revised the figure by adding annotations to show the recovered Thon rings. This figure shows a vertex motion and is intended not only to show improvement but also to illustrate complex, spatially varying deformation patterns that motivate the 3D spline model (pp. 12, lines 304–308). The supplementary figures display those with highest motions in each sample type, thus the Thon rings for the motion corrected micrograph in higher frequency space look more obvious. We also refer readers to the supplementary examples where the differences are more pronounced (pp. 12, lines 310–312).

Author Response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Overall, this is an interesting and well-written manuscript on a fascinating question in a"charismatic" model system.

Strengths:

(1) The Introduction is concise, though it might be helpful to the non-specialist reader to learn a bit more about what is known about the social control of somatic growth across diverse species (including humans), which would help to make this work more generally interesting.

(2) The experiment is well-designed.

(3) The data collected are comprehensive.

(4) The complementary analysis of both feeding and aggression/submission data with and without known social roles is a neat idea and compelling!

Thank you for the positive feedback!

Here, we investigate phenotypic plasticity associated with the adoption of social roles in the clown anemonefish, with strategic growth being just one aspect of that plasticity. Strategic growth, also known as social control of growth, is a fascinating form of adaptive phenotypic plasticity, whereby individuals modify their growth and size in response to fine-scale changes in social conditions (Buston & Clutton-Brock, 2022). In cooperative breeding systems with high reproductive skew, particularly fishes and mammals (possibly including humans), individuals have been shown to i) increase growth/size on the acquisition of dominant status (Dengler-Crish & Catania, 2007; Johnston et al., 2021; Thorley et al., 2018; Van Schaik & Van Hooff, 1996; Walker & McCormick, 2009), ii) increase growth/size when paired with size matched reproductive rivals (Huchard et al., 2016; Reed et al., 2019; this study), and iii) decrease growth/size to avoid conflict (Buston, 2003; Heg et al., 2004; Wong et al., 2007). While strategic growth is fascinating and clearly occurring in this study, we show coordinated changes of multiple aspects of the phenotype as fish adopt social roles. Therefore, we deliberately framed the Introduction broadly to avoid biasing the reader toward viewing growth as the sole or main driver.

Weaknesses:

(1) I was surprised that the HPA/stress axis was not considered here at all. Wouldn't we expect that subordinates have increased stress axis activation, which in turn could inhibit their growth and aggressive behavior?

We also expected to see the HPA/stress axis activated in subordinates, which is why we carried out a targeted exploration of genes known to play a role in this axis. We did not find any genes that were significantly differentially expressed. We believe that there could be two explanations for this. First, from a methodological perspective, it could be due to our use of a whole-body RNA-seq, which may have masked this signal. Alternatively, the stress axis might play a more complex role than just acting as a simple on/off switch for reduced growth. Its activation may peak when competition over size is at its highest (during week one) or, conversely, it may peak later and help maintain reduced growth once hierarchies are firmly established (particularly after the dominant individual reaches its maximum size). To understand the role of the stress axis, future studies should observe how its activation varies over time. We acknowledge that the absence of a stress‑axis signal and its potential explanations were not clearly discussed in the original manuscript, in the revised version, we will address this issue.

(2) To what extent are growth, food intake, agonistic behavior, and/or gene expression patterns coordinated across P1 vs P2 pairs? The lack of such an analysis seems like a missed opportunity.

We had a similar thought. Specifically, we were interested in testing the hypothesis that the final size ratio of pairs, which is indicative of the amount of conflict remaining, would predict gene expression. We examined gene expression within pairs to test for coordinated changes and repeated the analysis, accounting for the pair size ratio. In both cases, we found no clear or consistent pattern within pairs. We will consider including these figures in the Supplementary Materials document.

(3) What was the rationale for using whole bodies for the transcriptome analysis? Given the hypotheses, the forebrain or hypothalamus and certain other organ systems (e.g.,liver, gonads, skin, etc.) would have been obvious candidate tissues here. I realize that cost is always a consideration, but maybe a focus on the fore-/midbrain could have been prioritized.

We decided to use whole-body samples for this initial transcriptomic analysis to capture a broad view of gene-expression differences while keeping sequencing costs and sample requirements manageable. We agree with the reviewer that future work should explore specific tissues sampled from individuals at multiple time points to disentangle transcriptomic differences across tissue types.

(4) Given the preceding point, why was a fold-change threshold used for assessing DEGs (supplementary Figure 3)? There is no biological justification to ever use a fold-change threshold, especially in bulk RNA-seq analysis. This is particularly true here, where wholebodies were used for RNA-seq analysis, which is a bit unusual. Relatively small cell populations (such as hypothalamic neurons that regulate growth or food intake) may show substantial gene expression variation across social types, yet will be masked by the masses of other cells in the whole body sample. However, gene expression may still vary significantly, albeit the fold-difference may be small. I therefore suggest a reanalysis that omits any fold-change threshold.

We thank the reviewer for this important point, and agree that an arbitrary fold‑change cutoff is inappropriate/unnecessary. It should be noted that this fold-change cut-off was only used in this single figure, and all other analyses used p-values from the entire dataset. We will remove the fold‑change threshold cutoff and correct Supplementary Figure 3, and any corresponding text.

(5) Why is the analysis of color (hue, saturation) buried in the supplementary materials?Based on the hypotheses that motivated the study, color seems just as relevant as food intake, growth, and agonistic behavior, so even if the results are negative, they should be presented in the main paper.

We agree that color can be an important social signal, so we included color measurements in our experimental design. However, after careful consideration of the color results, we decided that our experimental timing and husbandry changes introduced multiple confounding factors, preventing us from drawing confident conclusions. Specifically, our fish were ≈1 month old at the transfer from larval to experimental tanks and had already begun to deepen their orange hue, before our experiment. (In the wild, they would settle at two weeks of age, prior to the deepening of the orange hue). Once individuals attain a certain hue, it seems that color development can be halted, but not reversed. The transfer also involved changes in lighting, tank background, and diet, factors known to strongly affect coloration. Our results show a uniform shift in orange hue and saturation across social groups, suggesting that these confounding factors might have dominated changes in hue.

For transparency, we report the color data in the Supplementary Materials, but we caution against drawing any strong conclusions. In the revised manuscript, we will recommend that future work include a targeted experiment to robustly test for the effect of the adoption of social roles on coloration or the effect of coloration on the adoption of social roles.

(6) The Discussion is sometimes difficult to follow. The authors may want to consider including a conceptual graphic that integrates the different aspects of growth and satiety regulation, etc., into a work-in-progress model of sorts, which would also facilitate clearer hypotheses for future research.

Thank you for flagging that parts of the Discussion are a bit difficult to follow. In the revised manuscript, we will work to improve readability of the Discussion. We also appreciate the suggestion of including a conceptual schematic. We will consider whether adding such a graphic will add value to this manuscript or future manuscripts.

Reviewer #2 (Public review):

In this manuscript, the authors test growth, behavior, and gene expression in pairs of clownfish as they establish social dominance hierarchies, examining patterns of gene expression in these pairs after dominance has been established. The authors show solid evidence that emerging dominant clownfish show increased growth, aggression, and food consumption compared to their submissive or solitary counterparts, eventually adopting distinct gene expression profiles.

Major Comments:

(1) The Introduction is comprehensive, but it could be condensed. Likewise, the discussion could be condensed. There is considerable redundancy between the methods, the results,and the legend in Figure 1. The authors should consolidate and remove the redundancy.

Thank you for flagging that parts of the manuscript could be condensed, we will work on this as we revise the manuscript.

(2) For Figure 3, the authors are showing PC2 and PC3; why is PC1 not shown? There is so much overlap between the three groups in PC2 vs PC3; it seems unlikely that researchers could conclusively identify any individual as belonging to a group based on the expression profile. The ovals shown do not capture all the points within each of the groups, and particularly the grey S oval seems misaligned with the datapoints shown.

We understand the concern raised by the reviewer about the overlap among points in the PCA. We have explored PC1-PC3 and found that PC2 and PC3 showed the clearest, statistically significant clustering by social position, while PC1 did not capture any variation due to social position. We have explored whether other factors might be masking differences, such as genetic relatedness, tank effects, total read count per sample, and found that none of these factors explained sample clustering. Regarding the ellipses shown around the points, they were not intended to capture all points, but rather they show the estimated 95% multivariate t-distribution for that given social group. We will make sure this is clearly explained in the figure legend, and Methods section. In addition, in the revised version, we will show PC1 and PC2, and PC1 and PC3, in the Supplements for transparency.

(3) The authors indicate that the 15 replicates exhibiting the greatest size difference between P1 and P2 were selected for gene profiling. Does this mean that each of the P1and P2 were pairs with each other? Have the authors tried examining the gene expression patterns in a paired manner? E.g., for the pairs that showed the greatest size differences,do they also show the greatest differences in gene expression? Do the P1s show the most extreme differences from P2s that also show the most extreme P2 differences? Perhaps lines on Figure 3A connecting datapoints from the P1 and P2 pairs would be informative.

Yes, “15 replicates exhibiting the greatest size difference between P1 and P2 were selected for gene profiling” refers to pairs of P1 and P2, we will make sure this is clearly stated in the revised Methods. Yes, we have explored gene expression data considering the size difference between pairs, and found that it showed no clear differences in gene expression patterns (see earlier response to Reviewer #1). We will consider including these figures in the Supplementary Materials document, as well as adding a version of Figure 3A that clearly shows information on pairs, as suggested by the reviewer.

(4) For the specific target pathways that are up- and downregulated in the different backgrounds, I recommend that the authors include boxplots (or heatmaps) showing the actual expression values for these targets. Figure 6 shows a heatmap for appetite-related genes, and it would be great to see a similar graph for the metabolism and glycolytic genes; it would also be informative to see similar graphs for hormonal and sexual maturation pathways as well.

We have explored genes across a broad set of metabolic pathways (glycolysis, TCA cycle, lactic fermentation, PDH complex, cholesterol biosynthesis, fatty-acid synthesis, and beta-oxidation) and show all metabolic genes that showed significant differential expression between P1, P2, and S in Figure 6. Overall, very few metabolism-associated genes were significantly differentially expressed, which is why we decided to combine appetite-regulation and metabolism-associated genes into a single figure (Figure 6). In the revised version, we will ensure that Figure 6 clearly shows the gene sets associated with appetite and metabolism.

We also examined hormonal pathways (glucocorticoid and thyroid signaling), but did not find genes in these pathways that were significantly differentially expressed. Finally, we would like to clarify that our samples consist of two-month-old juvenile individuals that are sexually immature —under ideal conditions, clown anemonefish can mature in one to two years, but they can also remain sexually immature for a decade or more (Buston & García, 2007) — which is why we did not observe distinct molecular signatures of sexual maturation. We recognize that the sentence at line 520 may be misleading, as we did not identify any gene expression signature that we could confidently associate with signs of sexual maturation. We will make sure that these are clearly stated in the revised version of the manuscript.

(5) Particularly given that there is a relatively small number of genes enriched in the different rank conditions, I did not understand the need to do the WGCNA module analysis. I thought that an analysis of GO terms across the dataset would have been more meaningful than the GO term analysis shown in Figure 4, which considers only genes assigned to the "brown WGCNA module". This should be simplified or clarified.

To clarify, GO enrichment analysis does not establish correlations with traits, it only describes which functions or pathways are over-represented in a given gene set. That is why we began by using WGCNA to define gene sets (modules) that are correlated to phenotypes. Our primary rationale for WGCNA was to identify modules of co-expressed genes that show significant statistical correlation with the phenotypes of interest (social role: P1, P2, S; growth; and food intake). Pairwise differential expression analysis (Figure 3B) identified a few hundred significantly differentially expressed genes, but those tests treat genes independently and are not able to help us link coordinated changes of co-expressed genes to phenotypes of interest. Because WGCNA is blind to traits, it first identifies groups of co-expressed genes, which can help resolve gene expression patterns.

We therefore ran WGCNA on the rlog-transformed dataset to identify modules of co-expressed genes that show significant correlation with phenotypes of interests. For every module that showed such a correlation, we performed GO enrichment and carefully evaluated the resulting GO enrichment trees (see Supplementary Figs. 4–5). The brown module was highlighted in the main text because it was one of the modules with a significant correlation to growth, and its associated GO enrichment showed clear growth-related signals that were not identified in the pairwise differential expression analysis results.

(6) The authors say that they have identified coordinated changes in behaviors and the"underlying gene expression, leading to the emergence" of social roles. This is a little bit misleading, since the gene expression analysis occurred well after the behavioral and phenotypic differences emerged. Presumably, the hormonal and genetic shifts that actually caused the behavioral and phenotypic difference occurred during the weeks during which the experiment was underway, and earlier capture of the transcriptome would presumably reveal different patterns, and ones that would be considered more causative.The authors acknowledge this in 434-435, but it could be emphasized further.

We appreciate the reviewer raising this point. In the updated version of the manuscript, we will revise wording to convey that food intake, agonistic behavior, size and growth, and gene expression are all changing continuously, in response to each other and in response to social feedback. An underappreciated aspect of this system (and likely many other systems) is that phenotype (including transcriptome) influences the outcome of social interactions, and the outcome of social interactions influences the phenotype (including the transcriptome). Earlier capture of the transcriptome would reveal different levels of gene expression, reflecting the state of the system at that moment in time.

(7) The authors have measured a number of differences between the different dominance classes of fish. All these differences were measured relative to the other classes, but in my view, the Solitary group was the closest to a baseline control. So I'm not sure that it is fair to say that "P2 and S individuals showed consistent downregulation of these genes and pathways" (line 401). I encourage the authors to emphasize the differences in gene expression from the "perspective" of the P1 individuals compared to the baseline of P2and S individuals. Line 474 says that "P2 fish showed significant upregulation" of a number of pathways. It should be very clear what that is compared to (compared to P1, presumably?)

We agree with the reviewer that solitary individuals are the most intuitive baseline. Indeed, the experimental design included solitary fish because we expected they would serve as a useful control. Without social restraint, we anticipated they would show unrestricted growth, feeding, behavior, and associated gene‑expression patterns, similar to dominants.

We initially ran analyses using solitaries as the baseline, but after examining the results, which showed subordinate‑like characteristics for the solitary individuals, we concluded that solitary individuals are not an ecologically appropriate control for this context. Removing juveniles from a social context and housing them in isolation may be stressful and can affect physiology and behavior in ways that do not reflect a natural baseline. From a life‑history standpoint, solitary living is not the typical state for A. percula.

For these reasons, we reanalysed the dataset using the dominant (P1) as the reference to enable more ecologically meaningful comparisons (this choice was somewhat arbitrary, subordinates could also have been used as the reference). Given that gene expression is relative, we interpret results from both the dominant (P1) and subordinate (P2) perspectives in the Discussion to provide a complete view. We will clarify wording throughout the manuscript to make it clear that everything is relative (e.g., revising Line 474).

(8) Along the same lines, the authors say in line 514 that subordinates and solitaries strategically downregulate their growth. I'm not convinced that this is the case: I would consider this growth trajectory to be the default and the baseline. I would interpret that under certain social conditions, a P1 dominant pattern of growth, behavior, and gene expression is allowed to emerge.

We respectfully disagree with the idea that a single baseline/reference growth trajectory exists for any individual of this species. Growth of individuals is entirely social context-dependent: neither fast nor slow growth represents an inherent baseline. When two size‑matched juveniles meet and compete to establish dominance, accelerated growth is the expected trajectory. By contrast, juveniles joining an existing hierarchy are expected to exhibit reduced growth, which minimizes conflict and facilitates their social integration. Unlike species that show non socially mediated growth trajectories, clown anemonefish do not have a context‑independent growth rate, rather, individuals constantly readjust their growth according to their immediate social environment.

Therefore, growth trajectories must be considered from the perspective of all group members, because they emerge from interactions among individuals rather than reflecting an intrinsic baseline. In this study, we were interested in the establishment of dominance hierarchy and how individuals adjust their phenotypes during this process. By experimentally pairing size‑matched rivals, both individuals are initially expected to pursue the dominant trajectory, and thus neither individual represents a default state. Instead, the outcome reflects a social decision, after which both individuals reinforce their emerging social roles through coordinated changes.

Reviewer #3 (Public review):

Summary:

The authors tested the hypothesis that interactions among size- and age-matched rivals will lead to the emergence of social roles, accompanied by divergence in four aspects of individual phenotypes: growth, feeding behavior, fighting behaviors, and gene expression in clownfish.

Strengths:

The data on growth, feeding rate, and fighting behaviors support the authors' claims.

Thank you for the positive feedback!

Weaknesses:

Gene analysis conducted in this study is not sufficient to clarify how the relevant genes actually regulate growth and behavior.

The information obtained from whole-body gene expression analysis is very limited.Various gene expression is associated with the regulation of fighting behaviors, food intake, growth, and metabolism, and these genes are regulated differently across tissues,even within a single individual. Gene expression analysis should be performed separately for each tissue.

We understand the reviewer’s concern about whole‑body transcriptomes and agree that tissue‑specific sampling would provide greater resolution of the mechanisms linking gene expression to growth, agonistic behaviors, and food intake. For this initial study, however, we deliberately chose whole‑body samples to capture a broad, unbiased view of gene expression differences while keeping sequencing costs and sample requirements manageable. We explicitly acknowledge the resulting interpretational limits in the Discussion (lines 464; 529–533), and suggest in the last paragraph that the patterns reported here should be used to build on in future studies exploring targeted, tissue‑specific hypotheses.

Clownfish undergo sex change depending on social status and body size, as the authors mention in the manuscript. Numerous gene expressions are affected by sex change. It is unclear how this issue was addressed.

We thank the reviewer for raising this point. Sex change and sexual maturation can indeed drive major transcriptional shifts in clown anemonefish, but our experiment did not encompass such a life‑history transition. All individuals in this experiment were juveniles (≈1 month old at the start, ≈2 months old at the end) and were sexually immature at these ages. Clown anemonefish reach sexual maturation around one to two years under ideal conditions, can delay sexual maturation for years under normal conditions (Buston & García, 2007), and sex change in the genus Amphiprion is known to take over ~5 months (Moyer & Nakazono, 1978). Accordingly, individuals in this study were not sexually mature, and sex change was not biologically plausible over the five-week experimental period of our study. We recognize that the sentence at line 520 may be misleading, as we did not identify any gene expression signature that we could confidently associate with signs of sexual maturation. We will make sure that it is clearly stated that the fish in this study were sexually immature in the revised version.

References:

Buston, P. (2003). Forcible eviction and prevention of recruitment in the clown anemonefish. Behavioral Ecology, 14(4), 576–582. https://doi.org/10.1093/beheco/arg036

Buston, P. M., & García, M. B. (2007). An extraordinary life span estimate for the clown anemonefish Amphiprion percula. Journal of Fish Biology, 70(6), 1710–1719. https://doi.org/10.1111/j.1095-8649.2007.01445.x

Buston, P., & Clutton-Brock, Tim. (2022). Strategic growth in social vertebrates (WITH REVIEWER COMMENTS). Trends in Ecology & Evolution, 37(8), 694–705. https://doi.org/10.1016/j.tree.2022.03.010

Dengler-Crish, C. M., & Catania, K. C. (2007). Phenotypic plasticity in female naked mole-rats after removal from reproductive suppression. THE JOURNAL OF EXPERIMENTAL BIOLOGY.

Heg, D, Bender, N, & Hamilton, I. (2004). Strategic growth decisions in helper cichlids. Proceedings of the Royal Society of London. Series B: Biological Sciences, 271(suppl_6). https://doi.org/10.1098/rsbl.2004.0232

Huchard, E, English, S, Bell, M B. V., Thavarajah, N, & Clutton-Brock, T. (2016). Competitive growth in a cooperative mammal. Nature, 533(7604), 532–534. https://doi.org/10.1038/nature17986

Johnston, R A., Vullioud, P, Thorley, J, Kirveslahti, H., Shen, L., Mukherjee, S., Karner, C. M., Clutton-Brock, T, & Tung, J (2021). Morphological and genomic shifts in mole-rat ‘queens’ increase fecundity but reduce skeletal integrity. eLife, 10, e65760. https://doi.org/10.7554/eLife.65760

Moyer, J. T., & Nakazono, A. (1978). Protandrous Hermaphroditism in Six Species of the Anemonefish Genus Amphiprion in Japan (No. 2). The Ichthyological Society of Japan. https://doi.org/10.11369/jji1950.25.101

Reed, C., Branconi, R., Majoris, J., Johnson, C., & Buston, P. (2019). Competitive growth in a social fish. Biology Letters, 15(2), 20180737. https://doi.org/10.1098/rsbl.2018.0737

Thorley, J, Katlein, N, Goddard, K, Zöttl, M, & Clutton-Brock, T. (2018). Reproduction triggers adaptive increases in body size in female mole-rats. Proceedings of the Royal Society B: Biological Sciences, 285(1880), 20180897. https://doi.org/10.1098/rspb.2018.0897

Van Schaik, C P., & Van Hooff, J A. R. A. M. (1996). Toward an understanding of the orangutan’s social system. In Linda F. Marchant, Toshisada Nishida, & William C. McGrew (Eds.), Great Ape Societies (pp. 3–15). Cambridge University Press. https://doi.org/10.1017/CBO9780511752414.003

Walker, S P. W., & McCormick, M I. (2009). Sexual selection explains sex-specific growth plasticity and positive allometry for sexual size dimorphism in a reef fish. Proceedings of the Royal Society B: Biological Sciences, 276(1671), 3335–3343. https://doi.org/10.1098/rspb.2009.0767

Wong, M. Y. L., Buston, P. M., Munday, Philip L., & Jones, Geoffrey P. (2007). The threat of punishment enforces peaceful cooperation and stabilizes queues in a coral-reef fish. Proceedings of the Royal Society B: Biological Sciences, 274(1613), 1093–1099. https://doi.org/10.1098/rspb.2006.0284

On 2025-09-07 20:13:12, user S S Young wrote:

Milojevic et al. 2014 had access to all emergency room visits for all of England and Wales for the years 2003 to 2008, over 400,000 myocardial infarction (MI) events, and over 2 million CVD emergency hospital admissions. They found no effect of CO, NO2, Ozone, PM10, PM2.5, or SO2 on heart attacks, hospital admissions, or mortality, their Figures 1 and 2.

Milojevic, A., Wilkinson, P., Armstrong, B., Bhaskaran, K., Smeeth, L., Hajat, S. 2014. Short-term effects of air pollution on a range of cardiovascular events in England and Wales: Case-crossover analysis of the MINAP database, hospital admissions and mortality. Heart (British Cardiac Society) 100, 14: 1093-98. https://doi.org/10.1136/heartjnl-2013-304963 .

On 2020-05-06 08:05:15, user Prof Pranab Kumar Bhattacharya wrote:

Dear Editor<br /> In the world, Corona virus cases jumped up till 3rd May 2020 from December 2019 is 3,51,743 with death 2,45,617 (18%) and 31.5 death per one million people of infected.Almost 212 countries worldwide and most affected countries are USA,( death rate 304, followed by Spain (540),Itali 475, UK 414, France 379 per million population when in India total cases of positive by RT PCR is 40,266 death 1300 per one million people and in West Bengal province of India total infected is 963 with death 48 cases as per ministry of health government of India records on covid 19. The question is why such a huge percentage of death from this dangerous virus ( no more should be considered simple like influenza virus) inspite of lockdown, social distancing ventilation guided treatment protocol for mild moderate and severe pneumonia from covid 19?<br /> Mortality from covid 19 is higher in groups at higher risks of thromboembolism including hypertension, types 2DM, obesity, coronary artery disease ,cardiomyopathy, pre existing renal pathology as co morbid condition known to all. It has been also seen world wide that the risk of thromboembolism ( both venous and arterial) are more likely to occur when patients are admitted at ICU or in PEP ventilation, ànd in aged over 60 yrs( approximately 63% of death in India from covid 19).<br /> What did the autopsy studies revealed of these death, though very limited autopsy were performed with covid 19 death as the virus is HG 3 category virus. Brane Hanely (1) eral published in journal of clinical pathology of BMJ group showed histopathology of lungs on HE stain oedema, Type Ii pneumocytes hyperplasia,large pneumocytes with ground glass viral inclusions bodies focal inflammation, multinucleated giant cells,when no hyaline membrane ( a histopathological features of ARDS) diffuse alveolar damage. The pulmonary vessels showed hyaline necrosis with thrombus formation and capillary congestion.inflamatory infiltrate composed of alveolar macrophages in alveolar lumen and lymphocytes in interstitium. Zhe Xu et Al (2) in journal Lancet reported also one 50 year old man died on day 14 of covid 19 after being treated with lopinovir+retinovir+moxiflixain and high nasal cannula oxygen therapy and niddle autopsy of lungs liver and heart tissue showed diffuse alveolar damage with cellular fibrimyxiod exudate,dissquamation of pneumocytes and hyaline membrane formation (sign of ARDS) , interstitial mononuclear inflammatory infiltrate dominated by lymphocytes ( CD8) multi nucleated syncitial giant cells, atypical pneumocytes and microvascular thrombosis in pulmonary vessels (2).Sufang Tian et Al (3) did post mortem needle core autopsy of four patients who died of severe covid 19 pneumonia and patients age range were 59-81 years and time of death 15-52 days were in ventilation. Histology of their finding in lungs were again diffuse injury to alveolar epithelial cells, hyaline membrane formation, hyperplasia of type II pneumocytes , diffuse alveolar damage and consolidation by fibroblasts proliferation with extra cellular fibrin forming clusters.All these tour cases had vascular congestion with intravascular thrombus suggesting an acute phase components reaction and fibrinoid necrosis of blood vessels.The autopsy finding of heart was that endocardia and myocardia didn't contain inflammatory cellular infiltrate, although focally myocardium appeared irregular in shape with darkened cytoplasm and fibrinoid necrosis of blood vessels in myocardia.There were various degrees of focal oedema interstitial fibrosis and myocardial hypertrophy which suggests patients had underlying hypertension associated with hypertrophy or past ischemic injury. A large series of 38 cases of autopsy of lung by Luca carsana etall (5) showed from death cases of covid 19 in northern Itali on H&E stain showed also diffuse alveolar damage, capillary congratulations, necrosis, necrosis of pneumocytes, hyaline membrane, interstitial oedema,type II pneumocytes hyperplasia, platelet fibrin rich thrombus(5) .Electron microscopy showed viral particles within cytoplasmic vaccoule of pneumocytes.<br /> So from above post mortem studies, besides ARDS like pictures in terminal event , platelet fibrin rich thrombosis of pulmonary vessels, myocardial vessels, hyaline necrosis of blood vessels of both lungs and of myocardium are prominent picture along with endothelial dysfunction according to this author.The severe cases of pneumonia from covid 19 also shows increased D Dimer value (4) prognostically bad , increased c reactive protein, increased pro calcitonin and increased FDP value<br /> All these suggest to me that pathogenesis behind so many death in ventilation or at ICU of covid 19 patients are not ARDS itself but some kinds of coagulopathy or DIC occurred before death in severe pneumonia cases<br /> Though lymphopenia, inflammatory cytokine stroms ( raised IL6,raised TNF are for cytokine stroms)are typical abnormalities described in almost all literature in highly pathogenic Covid 19 infection with disease severity ,only one rapid response in BMJ (4) suggest , based on post mortem finding use of low molecular weight heparin (LMWH) to be included in the treatment modules of covid 19, particularly those who have high D Dimer high FDP value in serum though TT,APTT,PT,INR may not show any significant difference.use of heparin therapy with constant monitoring for bleeding manifestation should be instituted in patients showing clinical signs of turning towards severe pneumonia,along with antiviral therapy with remdesvir (within 7 days onset of symptoms at scheduled disease)<br /> If the pathology behind the death of severe pneumonia in covid 19 patients is DIC ( according to Autopsy finding the pneumocytes are not killed or destroyed by the virus nor by cytotoxic T cells, rather proliferation occur with much viral replication and virus load) there will be DIC , vascular congestion, thrombosis there will be AMI stroke ) then treatment at ICU with ventilation become useless unless if thromboembolism is not resolved first with LMWH infusion <br /> Referencs <br /> 1) Brain Hanley, Sebastian B Lucas,Esther youd,Benjamin swift,Michael Asbron "Autopsy in suspected covid 19 cases " JCP 73,(5):2020 http://dx.doi.org.10.1136/jclinpath-2020-20652<br /> 2) Xu Z,Shi L,Wang y eral "Pathological finding of covid 19 associated with acute respiratory distress syndrome " The Lancet respiratory medicine 8 (4):420-22 :2020<br /> 3) Sudan Tian, young xiong,Shu yuan xiao,Liu H et all "Study of 2019 novel Corona virus disease ( covid 19) through post mortem core biopsy" Modern pathology (Nature.com ) 14 th April 2020 http://doi.org/10.1038/s 41379-020-0536-<br /> 4) William Atenio ,Nadu Okonkwo "should prognostic models for covid 19 not also incorporate markers of thrombosis" Rapid Response published BMJ on 14th April 2020 to article"Prediction model for diagnosis and prognosis of covid 19 infection: systematic Review and clinical analysis" The BMJ 2020:369:m1328 published on 7th April 2020 https://doi.org/10.1136/bmj...<br /> 5)Luca carsana, Aurelo sanzogoni ,Ahmed Nast, Roberta Rossi etall"pulmonary post mortem finding in a large series of covid 19 cases from northern Itali" MedRxiv https://doi.org/1101/2020.0...

On 2021-07-26 04:09:55, user Matthew Robertson wrote:

“Our models estimate that nearly a third of COVID-19 cases would have been prevented if one of two exposures (diet and deprivation) were not present.”

The above sentence from the discussion section implies a causative relationship, but this study can not demonstrate causality, as has been correctly identified in the limitations section. In fact, it’s likely that socioeconomic deprivation (especially as it is measured in this study – postcode) is at least partially a surrogate indicator for other factors. Socioeconomic status is correlated with many things which could conceivably be more direct causes, for example: Vitamin D status[1], mental health[2], self-regulation[3] (and downstream effects there of), delayed gratification (even in people merely provided with environmental cues of poverty[4] ).

Also, only relative metrics are reported. Are you able to give any indication of where the sample/population diet scores sit in absolute terms, the HR of each additional serving of each food type (and plateau/high point), and/or describe the FFQ data (intra-quartile medians/distributions of each food)? I see the data that could inform the above is available, but given that there is an accessibility barrier to the data, it would be helpful to provide such granular information in an annex.

It is not only the use of a FFQ that reduces the resolution of the data, but also the use of an index to report and reduce the dataset to a single number. A plateau effect is not uncommon (for example the plateau in all-cause mortality observed at >5 servings of fruit/veg per day in one meta-analysis[5] ), but the point of plateau could also be the point at which the metric (index) ceases to have utility, and a refined, non-reductive or conditional-reasoning metric(s) continues to be useful. This point is highly significant in making any conclusions at all about the relative contribution of diet vs. socioeconomic status to Covid risk.

References

[1] Léger-Guist'hau J, Domingues-Faria C, Miolanne M, et al. Low socio-economic status is a newly identified independent risk factor for poor vitamin D status in severely obese adults. J Hum Nutr Diet. 2017;30(2):203-215. doi:10.1111/jhn.12405

[2] Isaacs AN, Enticott J, Meadows G, Inder B. Lower Income Levels in Australia Are Strongly Associated With Elevated Psychological Distress: Implications for Healthcare and Other Policy Areas. Front Psychiatry. 2018;9:536. Published 2018 Oct 26. doi:10.3389/fpsyt.2018.00536

[3] Palacios-Barrios, E. E., & Hanson, J. L. (2019). Poverty and self-regulation: Connecting psychosocial processes, neurobiology, and the risk for psychopathology. Comprehensive Psychiatry, 90, 52–64. https://doi.org/10.1016/j.comppsych.2018.12.012

[4] Liu L, Feng T, Suo T, Lee K, Li H. Adapting to the destitute situations: poverty cues lead to short-term choice. PLoS One. 2012;7(4):e33950. doi:10.1371/journal.pone.0033950

[5] Wang, X., Ouyang, Y., Liu, J., Zhu, M., Zhao, G., Bao, W., & Hu, F. B. (2014). Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ : British Medical Journal, 349(jul29 3), g4490–g4490. https://doi.org/10.1136/bmj.g4490

On 2020-03-21 20:58:27, user Sinai Immunol Review Project wrote:

This study was a single-arm, open label clinical trial with 600 mg hydroxychloroquine (HCQ) in the treatment arm (n = 20). Patients who refused participation or patients from another center not treated with HCQ were included as negative controls (n = 16). Among the patients in the treatment arm, 6 received concomitant azithromycin to prevent superimposed bacterial infection. The primary endpoint was respiratory viral loads on day 6 post enrollment, measured by nasopharyngeal swab followed by real-time reverse transcription-PCR.

HCQ alone was able to significantly reduce viral loads by day 6 (n = 8/14, 57.1% complete clearance, p < 0.001); azithromycin appears to be synergistic with HCQ, as 6/6 patients receiving combined treatment had complete viral clearance (p < 0.001).

Chloroquine is thought to inhibit viral infection, including SARS-Cov-2, by increasing pH within endosomes and lysosomes, altering the biochemical conditions required for viral fusion1,2. However, chloroquine also has immuno-modulatory effects that I think may play a role. Chloroquine has been shown to increase CTLA-4 expression at the cell surface by decreasing its degradation in the endo-lysosome pathway; AP-1 traffics the cytoplasmic tail of CTLA-4 to lysosomes, but in conditions of increased pH, the protein machinery required for degradation is less functional3. As such, more CTLA-4 remains in endosomes and is trafficked back to the cell surface. It is possible that this may also contribute to patient recovery via reduction of cytokine storm, in addition to the direct anti-viral effects of HCQ.

Despite what is outlined above, this study has a number of limitations that must be considered. First, there were originally n = 26 patients in the treatment arm, with 6 lost to follow up for the following reasons: 3 transferred to ICU, 1 discharge, 1 self-discontinued treatment d/t side effects, and 1 patient expired. Total length of clinical follow up was 14 days, but the data beyond day 6 post-inclusion are not shown.

Strikingly, in supplementary table 1, results of the real-time RT-PCR are listed for the control and treatment arms from D0 – D6. However, the data are not reported in a standard way, with a mix of broadly positive or negative result delineation with Ct (cycle threshold) values, the standard output of real time PCR. It is impossible to compare what is defined as a positive value between the patients in the control and treatment arms without a standardized threshold for a positive test. Further, the starting viral loads reported at D0 in the groups receiving HCQ or HCQ + azithromycin were significantly different (ct of 25.3 vs 26.8 respectively), which could explain in part the differences observed in the response to treatment between 2 groups. Finally, patients in the control arm from outside the primary medical center in this study (Marseille) did not actually have samples tested by PCR daily. Instead, positive test results from every other day were extrapolated to mean positive results on the day before and after testing as well (Table 2, footnote a).

Taken together, the results of this study suggest that HCQ represents a promising treatment avenue for COVID-19 patients. However, the limited size of the trial, and the way in which the results were reported does not allow for other medical centers to extrapolate a positive or negative result in the treatment of their own patients with HCQ +/- azithromycin. Further larger randomized clinical trials will be required to ascertain the efficacy of HCQ +/- azithromycin in the treatment of COVID-19.

References

1. Wang, M. et al. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Research vol. 30 269–271 (2020).
2. Thomé, R., Lopes, S. C. P., Costa, F. T. M. & Verinaud, L. Chloroquine: Modes of action of an undervalued drug. Immunol. Lett. 153, 50–57 (2013).
3. Lo, B. et al. Patients with LRBA deficiency show CTLA4 loss and immune dysregulation responsive to abatacept therapy. Science (80-. ). 349, 436–440 (2015).

On 2021-10-14 14:17:58, user Julian von Mendel wrote:

This paper has now been peer-reviewed and published, with no substantial revisions: https://www.mdpi.com/1311862

Citation:<br /> Borsche, L.; Glauner, B.; Mendel, J.v. COVID-19 Mortality Risk <br /> Correlates Inversely with Vitamin D3 Status, and a Mortality Rate Close <br /> to Zero Could Theoretically Be Achieved at 50 ng/mL 25(OH)D3: Results of a Systematic Review and Meta-Analysis. Nutrients 2021, 13, 3596. https://doi.org/10.3390/nu1...

On 2021-05-13 15:56:02, user Tatiana Araujo Pereira wrote:

It has been more than one year since the Coronavirus Disease 2019 (COVID-19) outbreak started. We already have effective vaccines around the world, but the imbalance between supply and demand allows Sars-CoV-2 to spread and mutate faster than mass immunization, especially in less developed countries. The arise of more transmissible variants is very worrying and motivates the search for biomarkers that enable early assessment of possible critical cases as well as therapeutic targets for the disease. In this sense Flora et al [1] performed laboratory and proteomic analysis of the plasma sample from a cohort of 163 COVID-19 patients admitted to Bauru State Hospital (São Paulo, Brasil) divided in three groups: “a) patients with mild symptoms that were discharged without admission to an ICU; b) patients with severe symptoms that were discharged after admission to an ICU; c) critical patients, who were admitted to an ICU and died”. The results point to a high concentration of ferritin (FTN) and absence of the IREB2 protein in volunteers exhibiting severe and critical symptoms, indicating that iron homeostasis would be a possible therapeutic target. These results are in line with previous researches, which also identified FTN levels directly related to the severity of the disease [2-5]. Ferritin is an iron reservoir protein, keeping it in its core shell to protect cells against oxidative stress. There are other proteins inhibiting iron redox reactivity in the body, helping with metal ions transport (Transferrin), import to (Divalent Metal Transport) and export from (Ferroportin) the cell [6, 7]. Due to its role in iron homeostasis, FTN is used to indirectly assess iron status in the body. Ordinarily, high levels of FTN mean iron overload [8]. However, circulating ferritin can be elevated independently of iron overload in inflammatory processes, in which it acts as immunosuppressant and proinflamatory modulator [4, 9, 10]. IREB2 is an Iron Regulatory Protein (IRP). When iron levels are low these proteins are able to attach to an untranslated region of mRNA known as Iron Responsive Elements (IRE). Through this mechanism it regulates expression of transferrin receptor and ferritin. In iron overload conditions the affinity of IRP for IRE is not enough to keep the attachment and the protein degrades or takes another role. IREB2 represses ferritin translation when bounded to IRE in FTN-mRNA and degrades in iron overload conditions [6, 11-13].<br /> Because of observed data, Flora et al [1] concluded that “increasing the expression of IREB2 might be a therapeutic possibility to reduce ferritin levels and, in turn, the severity of COVID-19”. Nonetheless, there is no data about iron status in the plasma of the subjects. So it is impossible to be sure whether the high levels of FTN and absence of IREB2 are associated with iron overload. In this case, suppressing ferritin production could culminate in greater oxidative damage, and even increase the risk of opportunistic infections, since intracellular segregation of iron is one of the main strategies to defend host against parasites [14]. In macrophages, this mechanism induces production of nitrogen and oxygen reactive species helping immune defenses [15, 16], but in chronic inflammation it affects iron recycling [17]. Another way to limit iron availability involves its main regulatory hormone hepcidin, which inhibits iron exit from the cell [18]. Hepcidin expression is induced by interleukine-6 (IL-6), which is produced in Sars-CoV-2 infection [19]. Also, Sepehr Ehsani identified a hepcidin mimetic in protein S region that plays a fundamental role in membrane fusion [20]. In this context it is important to verify the possibility that high levels of FTN are not associated with iron overload and only then consider increasing in IREB2 expression as a therapeutic strategy against COVID-19.

AUTHORS<br /> Pereira, T A and Espósito, B P.<br /> Institute of Chemistry – Univesity of São Paulo.

REFERENCES<br /> 1. Flora DC, Valle AD, Pereira HABS. et al. Quantitative plasma proteomics of survivor and non-survivor COVID19 patients admitted to hospital unravels potential prognostic biomarkers and therapeutic targets. MedRxiv; doi: https://doi.org/10.1101/202....<br /> 2. Cavezzi A, Troiani E, Corrao S. COVID-19: hemoglobin, iron, and hypoxia beyond inflammation. A narrative review. Clin Pract. 2020 May 28;10(2):1271.<br /> 3. Bellmann-Weiler R, Lanser L, Barket R, et al. Prevalence and Predictive Value of Anemia and Dysregulated Iron Homeostasis in Patients with COVID-19 Infection. J Clin Med. 2020;9(8):2429.<br /> 4. Colafrancesco S, Alessandri C, Conti F, Priori R. COVID-19 gone bad: A new character in the spectrum of the hyperferritinemic syndrome?. Autoimmun Rev. 2020;19(7):102573.<br /> 5. Perricone C, Bartoloni E, Bursi R et al. COVID-19 as Part of the Hyperferritinemic Syndromes: the Role of Iron Depletion Therapy. Immunologic Research, vol. 68, no. 4, 2020, pp. 213-224.<br /> 6. Halliwell B and Gutteridge JMC. Free Radicals in Biology and Medicine. 4th ed., Oxford: University Press, 2007.<br /> 7. Grotto HZW. Metabolismo do ferro: uma revisão sobre os principais mecanismos envolvidos em sua homeostase. Rev. Bras. Hematol. Hemoter., vol. 30, no 5, 2008, pp. 390-397.<br /> 8. World Health Organization, Centers for Disease Control and Prevention. Assessing the iron status of populations. 2nd ed., World Health Organization, 2007. ISBN: 978 92 4 1596107 (electronic version).<br /> 9. Ruddell RG, Hoang-Le D, Barwood JM et al. Ferritin functions as a proinflammatory cytokine via iron-independent protein kinase C zeta/nuclear factor kappaB-regulated signaling in rat hepatic stellate cells. Hepatology. 2009 Mar;49(3):887-900.<br /> 10. Chen TT, Li L, Chung DH et al. TIM-2 is expressed on B cells and in liver and kidney and is a receptor for H-ferritin endocytosis. J Exp Med. 2005;202(7):955-965.<br /> 11. Kuhn LC and Hentze MW. Coordination of Cellular Iron Metabolism by Post-transcriptional Gene Regulation. J Inorg Biochem, vol. 47, no 3-4, 1992, pp. 183-195.<br /> 12. Schalinske KL, Chen OS, Eisenstein RS. Iron differentially stimulates translation of mitochondrial aconitase and ferritin mRNAs in mammalian cells. Implications for iron regulatory proteins as regulators of mitochondrial citrate utilization. J Biol Chem, vol. 273, no 6, 1998, pp. 3740-3746.<br /> 13. Tong W.-H and Rouault TA. Metabolic Regulation Of Citrate And Iron By Aconitases: Role Of Iron-sulfur Clusters Biogenesis. Biometals, vol. 20, no 3-4, 2007, pp. 549-564.<br /> 14. Gan Z, Tang X, Wan Z et al. Regulation of macrophage iron homeostasis is associated with the localization of bacteria. Metallomics, vol. 11, no 3, 2019, pp. 454-461.<br /> 15. Ratledge C and Dover LG. Iron metabolism in pathogenic bacteria. Annu Rev Microbiol, vol. 54, 2000, pp. 881-941.<br /> 16. Schaible UE and Kaufmann SHE. Iron and microbial infection. Nature Reviews Microbiology, vol. 2, 2004, pp. 946–953.<br /> 17. Castro L, Tórtora V, Mansilla S, Radi R. Aconitases: Non-redox Iron-Sulfur Proteins Sensitive to Reactive Species. Acc Chem Res. 2019 Sep 17;52(9):2609-2619.<br /> 18. Martínez-Pastor M and Puig S. Adaptation to iron deficiency in human pathogenic fungi. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research, vol. 1867, no 10, 2020.<br /> 19. Liu W, Zhang S, Nekhai S, Liu S. Depriving Iron Supply to the Virus Represents a Promising Adjuvant Therapeutic Against Viral Survival [published online ahead of print, 2020 Apr 20]. Curr Clin Microbiol Rep. 2020;1-7.<br /> 20. Ehsani S. Distant sequence similarity between hepcidin and the novel coronavirus spike glycoprotein: a potential hint at the possibility of local iron dysregulation in COVID-19. Biol Direct, vol. 15, 2020, p. 19.

On 2025-11-30 17:00:32, user Cyril Burke wrote:

RESPONSE TO REVIEWER #2<br /> June 27, 2022<br /> Reviewer #2: Thank-you for the opportunity to review this work which highlights the importance of monitoring serum creatinine over time and how this can be a useful tool in detecting possible CKD. This is an important topic as the use of sCr on its own is certainly under-utilized and changes are often missed because they don’t fall into a predefined category.<br /> Thank you for considering our manuscript and for your detailed comments.

MAJOR CONCERNS

A. “Choi- rates of ESRD in Black and White Veterans” doesn’t fit with the rest of the paper including the title; the introduction and conclusion also don’t adequately address this portion of the paper. It feels disjointed from the main point of discussion which is the use of sCr in screening “pre-CKD”. This section and discussion should be removed and possibly considered for another type of publication.<br /> We have attempted to clarify this inclusion. This manuscript could be divided into three or four short papers, increasing the likelihood that any one of them would be read. However, different groups tend to read papers about screening for kidney impairment, racial disparities, cofactors in modeling physiologic parameters, or policy proposals to encourage best practices. Despite the appeal of perhaps three or four publications, we decided to tell a complete story in a single paper, but we are open to suggestions.

Black Americans suffer three times the kidney failure of White Americans. Other minority groups also have excessive rates of kidney disease. However, analysis of Veterans Administration interventions can bring that ratio close to one, similar interventions might also reduce to parity the risk for Hispanic, Asian, Native Americans, and others. Within-individual referencing should allow better monitoring of all patients and help to reveal the circumstances and novel kidney toxins that lead to progressive kidney decline. The ability to identify a healthy elderly cohort with essentially normal kidneys would help to calibrate expectations for all. Better modeling of GFR should help everyone, too.

Over eight decades, anthropologists have had little scholarly success in diminishing the inappropriate use of ‘race’. Keeping these parts together may be no more successful, but we feel compelled to try.

B. Cases 1 - 3, (lines 93 – 122): where are these cases from? There is no mention of ethics to publish these patient results, which appears to be a clear ethics violation. If so, these cases should be removed and patient consent and ethical approval obtained to publish them.<br /> The authors describe the reasons for not obtaining an ethics waiver for this secondary data analysis. Despite this, the relative ease of obtaining an ethics waiver for secondary data analysis usually means that this is done regardless.<br /> We take patient privacy seriously and have completely de-identified the Case data, as required by Privacy Act regulations. We understand that no authorization or waiver was necessary. We discussed the issues with an IRB representative, reviewed the relevant regulations, and confirmed no need for formal review of a secondary analysis of already publicly available IRB-approved data or of completely de-identified clinical data collected in the course of a treating relationship.

IRBs have a critical role to play, but many (including ours) are overworked. We understand the impulse authors feel to gain IRB approval even when the regulations clearly do not required it. As we discuss in the revision, there is a more significant matter that IRBs could help to resolve if they have the resources to do so. For all of these reasons, and even though we, too, felt the urge to obtain IRB approval, we resisted adding “just a little more” to their work.

C. The message of the article and data representation is unclear: do the authors wish to show that sCr is superior to eGFR in this “pre-CKD” stage, should both be used together? Do the authors wish to convey that a “creatinine blind range” does not exist? Or is the aim to demonstrate that continuous variables should not be interpreted in a categorical manner?<br /> Our interest is detection and prevention of progression of early kidney injury at GFRs above 60 mL/min – a range in which eGFR is especially unreliable. We have advanced the best argument we can to detect changes in sCr while kidney injury is still limited and perhaps reversible. If experience reveals that some avoidable exposure(s) begins the decline, then clinicians might alert patients and thereby reduce kidney disease. How best to use longitudinal sCr remains to be determined from experience. However, our message is that early changes in sCr can provide early warning of a decline in glomerular filtration. We are confident that clinicians can learn to separate other factors that may alter sCr, as we do for many other tests.

MINOR CONCERNS<br /> ABSTRACT<br /> A. Vague. Doesn’t give a clear picture of the study<br /> We have tried to clarify the title and abstract and are open to further suggestions.

INTRODUCTION<br /> B. 51 – 57: needs to state that these stats are from e.g. the US. The authors should consider adding international statistics to complement those from the US.<br /> We have updated the statistics on death rates from kidney disease to include US and global data.

C. 68: reference KDIGO guidelines, state year<br /> We now reference the KDIGO 2012 guidelines.

D. 75 – 77: is this reference of the New York Times the most appropriate?<br /> We have expanded this section with peer-reviewed, scholarly references. However, we found Hodge’s summary of the issue succinct and hence potentially more persuasive for some than decades of scholarly references that have had limited or no effect in the clinic.

E. 82: within-individual variation not changes (this is repetition of the point made in lines 425 – 427, but should match the language)<br /> We have matched the language.

F. 82 – 84: reference? If this is a question it should be presented as such<br /> We have attempted to clarify this statement.

G. 84: “normal GFR above 60” = guidelines (including KDIGO) do not refer to 60 as normal GFR, 60 – 89 is mildly decreased. (see line 126)<br /> We agree and have corrected the language.

H. 93: avoid the use of emotive words such as apparently (also in line 428)<br /> We wanted to emphasize appearance without proof and have made these changes.

I. 94: “Not meeting KDIGO guidelines”: KDIGO 2.1.3 includes a drop in category (including those with GFR >90). This would appear to include some of the cases listed. Additionally, albuminuria should have been measured for case 2 and 3.<br /> We have clarified that cases may or may not fit KDIGO categories, though that question will frequently arise in evaluating sCr changes. Where available, we have added urine protein and/or albumin results to the Cases.

J. 97: “progressive loss of nephrons equivalent to one kidney”: this is based on a single creatinine measurement.<br /> Since the original submission, we discovered for this Case (now Patient 3) early serum creatinine results and notes indicating a six-month period off thiazide diuretic. This data clarified the baseline and showed a remarkable effect of thiazide diuretic on sCr. We have added follow-up sCr results and details of thiazide use to the ASC chart.

K. 93 – 122: Could any of these shifts be explained by changes in creatinine methodology or standardization of assays, especially over 15 – 20 years (major differences between assays existed before standardization and arguably still exist with certain methods).<br /> It would be useful to see a comparison between serial sCr and eGFR measurements on the same figure. There appears to be significant (possibly more pronounced) changes when eGFR is used. As line 87 mentions changes in eGFR may be as useful (and in some situations more useful) than changes in sCr alone.

It would be helpful to have a chronology from each local laboratory with the date of every change in creatinine assay or standardization. However, any single shift draws attention but does not necessarily indicate significant change in glomerular filtration. After one or several incremental increases, over at least three months, the sCr pattern may meet the reference change value (RCV) that signals significant change. In the future, from age 20 or so, a patient’s medical record should retain the full range of the longitudinal sCr for true baseline comparison.

As noted in the revised manuscript, Rule et al showed that there is measurable nephrosclerosis even in the youngest kidney donors, suggesting that some injuries (perhaps exposure to dietary toxins) may begin in childhood and that early preventive counseling may be worthwhile. Experience will show whether this can slow progression to CKD. As we note, quoting Delanaye, sCr accounts for virtually 100% of the variability in eGFR equations based on sCr (eGFRcr), and these equations add their own uncertainties, so no, we do not believe that eGFR is more useful than sCr when GFR is above 60 mL/min and possibly much lower as well.

We have added eGFR results to the ASC charts (in blue), though availability was somewhat limited.

L. 127 – 142: should there be separate charts for males and females, the differences in creatinine between males and females needs to be discussed somewhere in the paper.

We do not think there should be separate charts for men and women based on size. The role of sex in eGFR equations is mainly based on the presumption that the average woman has less muscle mass than the average man. Clinicians care for individuals, not averages, and this sweeping generalization that increases agreement of the average of a population introduces unacceptable inaccuracy to individual care. Within-individual comparison eliminates the need for assumptions on relative size or muscle mass. Major changes in an individual’s muscle mass will usually be evident to the clinician who can adjust for them.

However, reports suggest significant influence of sex hormones on renal function, including effects of estrogen and estrogen receptors, such as reducing kidney fibrosis, increasing lupus nephritis, and increasing CKD after bilateral oophorectomy. The mechanism of these effects and how they might be incorporated into eGFR estimating equations is unclear, but the effort may benefit from a more individualized approach with focus on a measurand rather than matching population-based averages of a quantity value (calculated from measurands).

M. Similarly, is this suitable for all ages?<br /> We think so. Another sweeping generalization based on age merely introduces another inaccuracy which complicates the task of clinicians caring for individuals. Older persons have varying health, athleticism, muscle mass, dietary preferences, etc. Rule et al reported that biopsies of about 10% of older kidney donors had no nephrosclerosis. Within-individual comparison eliminates the need for assumptions on relative muscle mass or inevitable senescent decline in nephron number. We substitute the assumption that any change in an individual’s muscle mass will be evident and can be accounted for. A seemingly ubiquitous risk factor, or factors, starts injuring kidneys at a young age, which we may yet identify.

N. 162 – 163: rephrase<br /> Done.

METHODS<br /> O. 185 – 193: aim belongs in the introduction, can be adjusted to complement paragraph 178 – 182.<br /> Reorganized and rewritten.

P. 196 – 205: reference sources

References provided.

Q. 224 – 247: not in keeping with the rest of the article or title and conclusion

We have revised and restructured this section.

RESULTS<br /> R. If eGFR is treated as a continuous variable does inverted sCr still have higher accuracy?<br /> We believe so. Serum creatinine is a measurand and reflects the total sum of physiologic processes, known and unknown. In contrast, eGFR equations yield a quantity value, calculated from a measurand and dependent on the assumptions and approximations incorporated by their authors. The eGFR equations are thus necessarily less accurate than the measurands they are derived from, in this case, sCr. In a hyperbolic relationship, as the independent variable drops below one and approaches zero, the effect is to amplify the inaccuracy of the independent variable in the dependent variable. By avoiding the mathematical inverting, the data suggest that direct use of sCr is far more practical for pre-CKD.

S. As mentioned, the section on ESRD in black and white veterans doesn’t fit in with the rest of the article.<br /> We have revised, reorganized, and rewritten. We also outlined our rationale above.

DISCUSSION<br /> T. As mentioned, section 4.1 doesn’t fit in with the rest of the article. As the authors note the correlation between illiteracy and CKD is likely not causal.<br /> See above.

U. 387: erroneous creatinine blind range. The data presented does not show this is erroneous there is still a relative blind range. A distinction must be made between a population level “blind range” and an individual patient’s serial results. The data and figure 4 in particular demonstrate the lack of predictive ability of sCr above 40ml/min compared to below 40ml/min at a population level. For an individual patient this “blind range” is more relative, and a change in sCr even within the normal range may be predictive. (Note: the terminology “blind range” is problematic).<br /> We agree. On reading closer, Shemesh et al call attention to “subtle changes” in serum creatinine even though they had access only to the uncompensated Jaffe assay, so their recommendation to monitor sCr is even more forceful, today, due to more accurate and standardized creatinine assays. We have attempted to clarify this in the manuscript.

V. 399 – 400: “rose slowly at first and then more rapidly as mGFR decreased below 60” this refers to a relative blind range. Whether these slow initial changes can be distinguished from analytical and intra-individual variation is the question that needs to be answered before we can say a “blind-range” doesn’t exist for an individual patient.

We appreciate this observation. We believe longitudinal sCr is worth adopting to gain insights into individual sCr patterns, which may reveal early changes in GFR, among other influences on sCr. This is a low-cost, potentially high-impact population health measure, and there seems little risk in trying it because many clinicians already use components of the process.

W. 425 - 432: sCr is indeed very useful when baseline measurements are available. eGFR remains useful when baseline sCr is not available or when large intervals between measurements are found.<br /> As Delanaye et al noted, virtually 100% of the variability in longitudinal eGFR is due to sCr, so we understand that the errors in eGFR can be (and usually are) greater than but cannot be less than those in sCr.

X. 425: low analytical variation- if enzymatic methods are used<br /> Lee et al suggest that even the compensated Jaffe method provides some accuracy and reproducibility, which may allow longitudinal tracking of sCr even where more modern assays are as yet unavailable.

Y. 428: avoid the use of “apparently”<br /> Done.

Z. 430: reference 56 compares sCr and sCysC with creatinine clearance NOT with mGFR, this does not prove that mGFR has greater physiologic variability. Creatinine clearance is known to be highly variable (partially due to two sources of variability in the measurements of creatinine: serum and urine).<br /> The creatinine clearance is another form of mGFR, and our understanding of it begins with the units: if the clearance or removal of creatinine were being measured, the units should be umoles/minute, but they are mL/min. “Clearance” is an old concept coined by physiologists to describe many substances, such as urea, glucose, amino acids, and other metabolites. Since creatinine is mostly not reabsorbed and is only slightly secreted in the tubules, the “creatinine clearance” became a measure of GFR. The ratio of urine Creatinine to serum Creatinine is simply a factor for how much the original glomerular filtrate then gets concentrated (typically about 100-fold) by the kidney. Since the assumption is that the timed urine was once the rate of glomerular filtrate production, the creatinine clearance is a measure of the GFR.

Creatinine clearance has some inaccuracies based on tubular secretion, but also has some advantages: blood concentrations are essentially constant during urine collection, no need for exogenous administration, and reliable measurements in serum and urine. The methods that we often call mGFR also have problems, including unverifiable assumptions about distributions, dilutional effects, and others we cite in the text. None of these are direct measures of GFR. Due to changes in remaining nephrons, even true GFR itself is not strictly proportional to the lost number of functional nephrons, which seems the ultimate measure of CKD that Rule et al estimated from biopsy material.

AA. The limitations of sCr for screening should also be discussed: differences in performance and acceptability between enzymatic and Jaffe methods (still widely used in certain parts of the world), the effect of standardizing creatinine assays (an important initiative but one that could also produce shifts in results around the time of standardization- see cases), low InIx means that once-off values are exceedingly difficult to interpret, is a single raised creatinine value predictive (or should there be evidence of chronicity): similarly are there effects from protein rich meals, etc (The influence of a cooked-meat meal on estimated glomerular filtration rate. Annals of Clinical Biochemistry. 2007;44(1):35-42. doi:10.1258/000456307779595995)<br /> We have added discussion of additional references on reproducibility of sCr assays and discuss dietary meat and, in Part Three, possible dietary kidney toxins.

CONCLUSION<br /> BB. The discussion recommends using SCr above eGFR while the conclusion recommends the NKF-ASN eGFR for use in pre-CKD and ASC charts. While the use of both together in a complementary fashion is understandable- this needs to be congruent with the discussion, aims and results.<br /> We have rewritten this section. We would welcome any further recommendations.

Cyril O. Burke III, MD, FACP

On 2020-11-08 03:03:45, user perrottk wrote:

Comments on “A Benchmark Dose Analysis for Maternal Pregnancy Urine-Fluoride and IQ in Children”<br /> I question the validity of attempting to determine a BMC for the effect of fluoride intake on IQ without first ascertaining if there is a real effect. The problem of this document is that it assumes an effect without making a proper critical assessment of the evidence for a causal effect.<br /> The draft paper relies completely on two studies which reported very weak relationships from exploratory analyses. Nothing wrong with doing exploratory analyses – providing their limitations are accepted. Such analyses can indicate possibilities for future studies testing possibly causes – but, in themselves, they are not evidence of causation. These studies provide no evidence of causal effect<br /> The studies this draft relies as evidence that fluoride causes a lowering of child IQ illustrates have the following problems.<br /> 1: Correlation is not evidence of causation – no matter how good the statistical relationship. And reliance on p-values is not a reliable indicator of the strength of a relationship anyway The two studies relied on here do not report the full results of statical analyses which would have revealed the weaknesses of the relationships.<br /> 2: These two studies were exploratory – using existing data. They were not experiments specifically designed to establish a cause.<br /> 3: Many other factors besides those investigated can obviously be important in exploratory studies where there is no control of population selection. While authors may claim confounders are considered it is impossible to do this completely – there are so many possible factors to consider. Most are not included in the datasets used and the researchers may make their own selection, anyway.<br /> The study of Malin & Till (2015), referred to in this draft, illustrates the problems. Malin & Till (2015) reported what they considered reasonably strong relationships (p-values below 0.05 and R squared values of 0.21 to 0.34 indicating their relationships explained 21% to 34% of the variance in ADHD prevalence). However, their consideration of possible other risk-modifying factors was limited. They did not include state elevation which Huber et al (2015) showed was correlated with fluoridation. The strength of Huber’s relationship (R squared 0.31 indicating elevation explained 31% of the variance in ADHD prevalence) was similar to that reported by Malin & Till for fluoridation.<br /> Perrott (2018) showed that when elevation is included in the statistical analysis the relationship of ADHD prevalence with fluoridation was non-significant (p>0.05). This show the danger of relying on the results of statistical relationships from exploratory studies where consideration of other possible risk-modifying factors is limited.<br /> 4: This draft paper relies on the reported links between cognitive factors and F intake without testing for a causal effect. But it also does not critically assess those correlations. The problems of confounders have already been mentioned but these two studies report very weak relationships or, in most cases, no statistically significant relationships.<br /> For example, of the 10 relationships between measures of fluoride exposure and cognitive effects Green et al (2019) reported that only 4 were statistically significant (Perrott 2020). That is not evidence of a strong relationship and underlines the danger of assuming correlations (especially selected correlations) are evidence of causation. Incidentally, this draft paper mentions the study of Till et al (202) which also reported relationships between fluoride exposure with bottle-fed infants and later cognitive effects. In this case only three of the 12 relationships reported were statistically significant (Perrott 2020).<br /> Even those relationship reported as significant were still very weak. For example Green et al (2015) reported a relationship for boys which explained less than 5% of the variance of IQ measures.

The relationships reported by Bashash et al (2017) were also extremely weak – explaining only about 3.6% of the variance in IQ and 3.3% of the variance in GCI. This weakness is underlined by other reports of relationships found for the Mexican ELEMENT database. Thomas (2014) did not find a significant relationship of MDI with maternal urinary fluoride for children of ages 1 to 3 although in a conference poster paper Thomas et al (2018) reported a statistically significant relationship for urinary fluoride adjusted using creatinine concentrations.<br /> 5: As well as ignoring the incidence of non-significant relationships from these studies this draft paper also ignores the findings of positive relationships from other studies. For example, Santa-Marina et al (2019) reported a positive relationship between F intake indicated by maternal urinary F and child cognitive measures. Thomas (2014) also reported a positive relationship of child IQ (MDI for 6 – 15-year-old boys) with child urinary fluoride.<br /> 6: The draft paper describes the two studies it uses for its analysis as “robust” but ignores the fact that the findings in these and other relevant studies are contradictory. For example, the findings reported in the two papers differ in that Bashash et al (2017) did not report different effects for boys and girls whereas Green et al (2019) did. Santa-Marina et al (2019) reported opposite effect to those of Bashash et al (2017) and Green et al (2019). These contradictory findings, together with the lack of statistical significance for most of the relationships investigated, are perhaps what we should expect from relationships which are as weak as these are.<br /> Summary<br /> The paper relies on weak relationships from exploratory studies. Such relationships, even where strong, cannot be used as evidence for causation and to assume so can be misleading. BMCs and similar functions derived without any evidence of real effects are not justified. While the derived BMCs may be used by activists campaigning against community water fluoride, they will be misleading for policy makers. This sort of determination of BMC is a least premature and a worst meaningless.<br /> References:<br /> Bashash, M., Thomas, D., Hu, H., Martinez-mier, E. A., Sanchez, B. N., Basu, N., Peterson, K. E., Ettinger, A. S., Wright, R., Zhang, Z., Liu, Y., Schnaas, L., Mercado-garcía, A., Téllez-rojo, M. M., & Hernández-avila, M. (2017). Prenatal Fluoride Exposure and Cognitive Outcomes in Children at 4 and 6 – 12 Years of Age in Mexico. Enviromental Health Perspectives, 125(9).<br /> Green, R., Lanphear, B., Hornung, R., Flora, D., Martinez-Mier, E. A., Neufeld, R., Ayotte, P., Muckle, G., & Till, C. (2019). Association Between Maternal Fluoride Exposure During Pregnancy and IQ Scores in Offspring in Canada. JAMA Pediatrics, 1–9.<br /> Huber, R. S., Kim, T.-S., Kim, N., Kuykendall, M. D., Sherwood, S. N., Renshaw, P. F., & Kondo, D. G. (2015). Association Between Altitude and Regional Variation of ADHD in Youth. Journal of Attention Disorders.<br /> Malin, A. J., & Till, C. (2015). Exposure to fluoridated water and attention deficit hyperactivity disorder prevalence among children and adolescents in the United States: an ecological association. Environmental Health, 14(1), 17.<br /> Perrott, K. W. (2018). Fluoridation and attention deficit hyperactivity disorder a critique of Malin and Till (2015). British Dental Journal, 223(11), 819–822.<br /> Perrott, K. W. (2020). Health effects of fluoridation on IQ are unproven. New Zealand Medical Journal, 133(1522), 177–179.<br /> Santa-Marina, L., Jimenez-Zabala, A., Molinuevo, A., Lopez-Espinosa, M., Villanueva, C., Riano, I., Ballester, F., Sunyer, J., Tardon, A., & Ibarluzea, J. (2019). Fluorinated water consumption in pregnancy and neuropsychological development of children at 14 months and 4 years of age. Environmental Epidemiology, 3. <br /> Thomas, D. B. (2014). Fluoride exposure during pregnancy and its effects on childhood neurobehavior: a study among mother-child pairs from Mexico City, Mexico [University of Michigan].<br /> Thomas, D., Sanchez, B., Peterson, K., Basu, N., Angeles Martinez-Mier, E., Mercado-Garcia, A., Hernandez-Avila, M., Till, C., Bashash, M., Hu, H., & Tellez-Rojo, M. M. (2018). OP V – 2 Prenatal fluoride exposure and neurobehavior among children 1–3 years of age in mexico. Environmental Contaminants and Children’s Health, 75(Suppl 1), A10.1-A10.<br /> Till, C., Green, R., Flora, D., Hornung, R., Martinez-mier, E. A., Blazer, M., Farmus, L., Ayotte, P., Muckle, G., & Lanphear, B. (2020). Fluoride exposure from infant formula and child IQ in a Canadian birth cohort. Environment International, 134(September 2019), 105315.

On 2020-04-30 16:31:03, user Dr SK Gupta wrote:

Authors have reported the high prevalence of Mycobacterium Tuberculosis infection in Covid19. Authors have tried to portray not only the Higher prevalence of MTBI but also more severe and rapid progression of disease. However, since their findings are not in tune with observational data on the subject all across the world. Rather corona infection has been found to be low in South East Asia, Africa and other places where the tuberculosis is rampant. Also burden of Covid-19 has been highest in United States and Europe where the prevalence of Tuberculosis is low.

These Observations have prompted the scientist to look for the role of BCG vaccination/ past TB infection in prophylaxis and treatment of Covid 19.

Authors have erroneously relied upon use of Interferon gamma release assay (IGRA) to diagnose MTB Infection using a kit X.DOT-TB kits (TB Healthcare, Foshan, China). Not much has been described in article about the methodology used in these kits, but as the name suggests probably it is T-spot test which measures the number of IFN-?-secreting T cells via an enzyme-linked immunospot (ELISPOT) assay.<br /> Two types of IGRAs available, the QuantiFERON-TB Gold In-Tube test and the T-SPOT.TB blood test. Though both these tests are approved by the Food and Drug Administration as indirect tests for TB infection (including active disease) when used in combination with other medical and diagnostic evaluations. Since aging leads to a decline in the strength of immune responses, it is also argued that these tests loose their sensitivity with advancing age.

Overall Interferon gamma release assay (IGRA) has a poor sensitivity and specificity for the diagnosis of Tuberculosis.<br /> In patients with Non Tuberculous Mycobacterial Disease specificity of only 74% for infection and a relatively high indeterminate rate was found for QuantiFERON®-TB Gold(QTF) test assay with a sensitivity of 81.7 %. Hence the test is not able to discriminate between tuberculosis(TB) and non-tuberculous mycobacterial (NTM) disease with high degree of specificity.

The problem gets compounded even more in countries like China and India where the prevalence of TB is high and use of Tuberculin Testing and BCG vaccination is a routine and such cases have all the likelihood of being labelled as positive despite no active disease.<br /> Contrary to current practice Authors have also not used the available gold standards to define active TB based on either a positive Mycobacterium culture or a positive TB polymerase chain reaction/Gene expert/CBNAAT.

Not only that present investigators have also not describe any base line x-ray lung findings like cavitation, fibro-infiltration, lymph node enlargement, Spirometry based poor Lung function suggestive of tuberculosis in patients with positive MTBI or active tubercular disease which may have contributed to the rapid progression of superimposed pneumonia of Covid 19 in these patients.

In Covid-19 disease pathogenesis initially it is the role of Innate immunity mediated by Neutrophils Macrophages which mount a protective response. In tuberculosis Cell mediated immunity or the adaptive immunity involving T cells and B cells is at work. This has prompted world scientists to look for the role of BCG in treatment and prophylaxis of Covid-19. BCG Vaccine for Health Care Workers as Defense Against COVID 19 (BADAS) (NCT04348370) in USA and Brace trial by an Australian University are such attempts.

The current study needs the support of larger data which doesnot seem to be coming from other countries like India where TB is rampant. Till now the observations don’t support the hypothesis of increased susceptibility of TB patients for Covid-19 nor are there any indicators of more severe/ rapid progression of disease in patients with TB infection.

Dr S K Gupta <br /> Senior Consultant Physician <br /> Secretary Community Health Care Foundation<br /> Dr Prabhat Prakash Gupta <br /> Dr Mrs Praveen Gupta

References:<br /> 1.Comparison of the Sensitivity of QuantiFERON-TB Gold In-Tube and T-SPOT.TB According to Patient Age Won Bae,Kyoung Un Park,Eun Young Song,Se Joong Kim,Yeon Joo Lee,Jong Sun Park,Young-ae Cho,Ho Il Yoon,Jae-Joon Yim,Choon-Taek Lee,Jae Ho Lee <br /> Published: June 3,216 https://doi.org/10.1371/jou...<br /> 2. Sensitivity of the QuantiFERON-TB Gold test in culture-verified NTM disease and TB in a Danish setting Thomas Stig Hermansen, Vibeke Østergaard Thomsen, Pernille Ravn <br /> European Respiratory Journal 2012 40: P426; DOI:<br /> 3. https://clinicaltrials.gov/...<br /> 4. COVID-19: a recommendation to examine the effect of hydroxychloroquine in preventing infection and progression Dan Zhou , Sheng-Ming Dai and Qiang Tong J Antimicrob Chemother<br /> doi:10.1093/jac/dkaa114<br /> 5. Covid-19 coronavirus pandemic. https://www.worldometers.in...<br /> 6. 1. Mehta P. Mc Auley DF, brown M et al. Covid-19, consider cytokine storm syndromes and immunosuppression. Lancet. 2020. doi.org/10.1016/S0140-6736(...

1. Roitt I, Brostoff J, Male D. Immunology (Fifth Edition). Philadelphia: Mosby; 1998. ?

2. Wang L, Cai Y, Cheng Q, Hu Y, Xiao H. Imbalance of Th1/ Th2 cytokines in patients with pulmonary ?tuberculosis. Zhonghua Jie He He Hu Xi Za Zhi. 2002; 25 (9) : 535-537. ?

3. Collins FM. Cellular antimicrobial immunity. Crit Rev Microbiol. 1979;7:27–91. ?

4. Bretscher PA. An hypothesis to explain why cell-mediated immunity alone can contain infections by ?certain intracellular parasites and how immune class regulation of the response can be subverted. ?Immunol Cell Biol. 1992;70:343–351.

On 2022-09-23 15:15:43, user Yu Li wrote:

It is important to regularly check the primers and probe sequences of a PCR or qPCR assay against GenBank because newly generated sequences may cause erosions or failures of a published assay. The article Wide mismatches in the sequences of primers and probes for Monkeypox virus diagnostic assays | medRxiv attempted the in silico analysis of published monkeypox virus (MPXV) specific qPCR assays. However, the article contains numerous errors in its results, lacks experimental data to support its conclusions, and can impair the 2022 monkeypox outbreak response.

The genome sequences of monkeypox virus (MPXV) are highly similar (~95% identical) to that of other species of orthopoxviruses (OPXV). The similarities between MPXV clade I and clade II are over 99%. Therefore, identifying a qPCR targeting site for primer and probe design that perfectly matches MPXV and contains enough sequence differences to differentiate other OPXV can be very challenging. The probe sequence of a qPCR assay is often given priority for target selection in assay development. Multiple studies have reported that PCR primer mismatches do not necessarily affect performance of a PCR assay. For example, Kwok S et al (1) and Christopherson C et al (2) showed that up to 4 mismatches in the primer-template duplexes (28 and 30 base primers) did not have a significant effect on RT-PCR (the sequence similarity is as low as 80%). The mismatch positions and type of nucleotides involved in the mismatch play important roles. The buffer and annealing temperature used in a PCR assay can also be critical in determining the assay’s performance. A single base mismatch in the reverse primer of the Orthopoxvirus generic OPX3 assay led to a 100-fold decrease of the sensitivity of this assay in detecting the 2022 monkeypox outbreak predominate strain (clade IIb, lineage B.1) in one buffer (3) but switching to a different PCR buffer nearly reversed this lost sensitivity. This example highlights the critical nature of performing laboratory validation testing to ensure specificity and sensitivity. The published MPXV qPCR assays have largely been validated by inclusivity and exclusivity panels (4), and the MPXV_G2R generic assay has been used extensively without sensitivity issues in detecting different clades of MPXV. This article made claims that “Our results show that the current MPV real-time generic assay may be unsuitable to accurately detect MPV” without any supporting experimental data. In addition, the title of the article is misleading without supporting data and can lead to uncertainty surrounding MPXV diagnostics.

The authors performed sequence similarity analysis of 8 published MPXV qPCR assays, including three CDC qPCR assays specifically designed to detect all MPXV isolates (generic assay), only clade I isolates (MPXV clade I assay) and only clade II isolates (MPXV clade II assay). In Figure 1, the detailed sequences alignment of MPXV generic assay MPXV_G2R were presented relative to the sequence of MPXV clade I. The authors showed two sets of primers; one set of primers, MPV-F-mu/MPV-R-mu, perfectly matches with MPXV clade IIb, lineage B.1 and contain a single mismatch for both the forward and reverse primers compared to originally published primer sequences. The MPXV_G2R generic assay was designed to detect both monkeypox clade I and clade II (4), and the primer sequences were designed using the MPXV clade I sequence. The publication of the MPXV G2R generic assay showed that this assay detects both clade I and clade II of MPXV (4). The MPXV G2R generic assay has been used for MPXV diagnostics since its publication in our laboratory and demonstrates no differences in the sensitivity of detecting MPXV clade I and clade II. Clinical diagnostic data confirmed that the limited primer mismatches have little effect on the performance of the MPXV_G2R generic assay under current protocols.

In Figure 2 panel A, the authors claimed that the MPV_G2R_WA-P, the probe sequence of MPXV clade II specific assay, contains the Mutation1 sequences, which are in 4.2% of 683 MPXV genome sequences the authors have included in their analysis. However, there are no genome sequences from MPXV clade II containing the Mutation1 sequences by the BLAST analysis of GenBank database. It is likely that the authors mistakenly used the sequences from MPXV clade I (MPXV Congo basin) as the Mutation1 sequences of clade II (West Africa clades). MPV_G2R_WA-P was designed to specifically detect MPXV clade II; the probe targeting sequences contain a 3 base deletion compared to clade I. <br /> If the authors have sequence data supporting their claims of genome sequences of MPXV clade II containing the Mutation1 sequences, they should make these available for others to analyze.

We are deeply concerned about the errors in this article and the lack of experimental data to support the authors’ conclusions. The authors should promptly address the issues raised here and consider the potential negative impact of this article on the MPXV diagnostics in 2022 monkeypox outbreak responses.

References<br /> 1. Kwok S, Kellogg DE, McKinney N, Spasic D, Goda L, Levenson C, Sninsky JJ. Effects of primer-template mismatches on the polymerase chain reaction: human immunodeficiency virus type 1 model studies. Nucleic Acids Res. 1990 Feb 25;18(4):999-1005. doi: 10.1093/nar/18.4.999. PMID: 2179874; PMCID: PMC330356.<br /> 2. Cindy Christopherson, John Sninsky, Shirley Kwok, The Effects of Internal Primer-Template Mismatches on RT-PCR: HIV-1 Model Studies, Nucleic Acids Research, Volume 25, Issue 3, 1 February 1997, Pages 654–658, https://doi.org/10.1093/nar...<br /> 3. Crystal M. Gigante, Bette Korber, MatthewH. Seabolt, Kimberly Wilkins, Whitni Davidson, Agam K. Rao, Hui Zhao, Christine M. Hughes, Faisal Minhaj, Michelle A. Waltenburg, James Theiler, Sandra Smole, GlenR. Gallagher, David Blythe, Robert Myers, Joann Schulte, Joey Stringer, Philip Lee, Rafael M. Mendoza, LaToya A. Griffin-Thomas, Jenny Crain, Jade Murray, Annette Atkinson, AnthonyH. Gonzalez, June Nash, Dhwani Batra, Inger Damon, Jennifer McQuiston, Christina L. Hutson, Andrea M. McCollum, Yu Li. Multiple lineages of Monkeypox virus detected in the United States, 2021- 2022 bioRxiv 2022.06.10.495526; doi: https://doi.org/10.1101/202...<br /> 4. Li Y, Zhao H, Wilkins K, Hughes C, Damon IK. Real-time PCR assays for the specific detection of monkeypox virus West African and Congo Basin strain DNA. J Virol Methods. 2010 Oct;169(1):223-7. doi: 10.1016/j.jviromet.2010.07.012. Epub 2010 Jul 17. PMID: 20643162

On 2021-04-14 14:48:37, user David de Jong wrote:

The article has been published. <br /> Silveira, M., De Jong, D., Berretta, A. A., Galvão, E., Ribeiro, J. C., Cerqueira-Silva, T., Amorim, T. C., Conceição, L., Gomes, M., Teixeira, M. B., Souza, S., Santos, M., Martin, R., Silva, M., Lírio, M., Moreno, L., Sampaio, J., Mendonça, R., Ultchak, S. S., Amorim, F. S., … for the BeeCovid Team (2021). Efficacy of Brazilian Green Propolis (EPP-AF®) as an adjunct treatment for hospitalized COVID-19 patients: a randomized, controlled clinical trial. Biomedicine & Pharmacotherapy, 138:111526. https://doi.org/10.1016/j.b...

On 2025-11-23 17:32:31, user Charlotte Strøm wrote:

In the following “text in italics – inside quotations marks are copy-pasted from the reference in question." Underlining and/or bolded text are done by me.

1. SPIN AND FRAMING<br /> The title of the preprint is: “Randomised trial of not providing booster diphtheria-tetanus-pertussis (DTP) vaccination after measles vaccination and child survival: A failed trial”<br /> 1.1 Framing neutral findings as abnormal or disappointing<br /> The authors consistently imply the results are “unexpected” or “contradictory”, rather than acknowledging that the RCT failed to support earlier observational findings.

Examples<br /> “A failed trial” says the title ->The trial did not “fail”: it ran, randomised 6500+ participants, and produced valid estimates showing no harm of the DTP vaccine. Calling it “failed” is a framing tactic that positions the result as an error rather than what the data showed.

Page 8, lines 11-12: “The was no difference in non-accidental mortality … the HR being 0.84 (0.52–1.37).” ->This is an appropriate stating of results, but the subsequent framing undercuts it.

Page 8, lines 22-24: “Since no beneficial effect of not giving DTP4 was found, contradicting many observational studies… possible interactions were explored…” -> This subtly frames the RCT as problematic because it contradicts earlier observational research, rather than recognizing that RCTs supersede observational evidence.

Page 10, line 2:“The present RCT is therefore an outlier which needs an explanation.” -> This is spin: the RCT is not an "outlier" needing explanation; observational studies - upon which the research hypothesis are based - are know to have confounding and are biased. CONSORT encourages presenting results without exaggeration or defensive justification.

1.2 Causal interpretations of non-significant results<br /> The authors imply meaningful patterns where no statistically reliable findings exist.

Examples Page 8–9 (exploring interactions despite explicitly stating low power): “There was one significant interaction … DTP strain … observed only for females.” (p=0.05)

No correction for multiple testing; >20 interactions tested -> This is classic exploratory-analysis spin.

1.3. Hypothesis-confirming language<br /> The manuscript repeatedly positions NSE hypotheses as foundational truths rather than unproven claims.

Example Page 3, lines 5-7: “Several studies inidcated… beneficial non-specific effects… more pronounced in females.” -> These were observational or post-hoc analyses, being framed as established background biases the narrative.

1.4 Framing underpowering as the main explanation<br /> Repeated emphasis that the trial was “strongly underpowered” serves to discount the main finding.

Examples Page 8, lines 13-15: “...the trial was planned with 3% annual expected mortality rate… observed rate was 81% lower… we had 65% fewer deaths…” -> This is accurate but placed repeatedly tthroughout the text to frame the null result as flawed.

Page 9, lines 18-21: “The RCT was strongly underpowered… mortality declined …” -> The authors do not consider that a null finding study is plausible.

2. CONSORT NON-COMPLIANCE <br /> 2.1. Missing or unclear prespecified primary outcome<br /> CONSORT requires explicitly stating primary and secondary outcomes and linking to a prespecified Statistical Analysis Plan (SAP).

Issues: The manuscript says: Page 5, lines 25-27: “The outcomes were all-cause non-accidental mortality and hospitalisation, as well as sex-difference…”<br /> -> It is unclear which of these is the primary outcome. Mortality? Hospitalisation? Sex-differential mortality? AND - there is ...

-> No link to protocol-defined hierarchy.

2.2. Discrepancies between protocolled numbers and intervention<br /> There are discrepancies between numbers stated in the publicly available protocol and study record at http://clinicaltrials.gov and the numbers appearing in the preprint. The intervention described in the preprint is not aligned with the protocol.

These discrepancies are unexplained in the preprint. The preprint states that DTP3 has been reported elsewhere, but the reference that is included in the preprint (2) does not report on mortality data, moreover it includes both DTP3 and DTP4. And these protocol deviations are inadequately accounted for in the preprint.

It remains therefore unexplained what the actual flow of study subjects were, and it remains unclear what the results are from the DTP3+OPV+MV versus OPV + MV only – as stated in the protocol.

2.3. Randomisation procedure is not sufficiently described<br /> CONSORT requires allocation concealment method and sequence generation details

Example Page 5, lines 12-14: “...randomisation lots were prepared by the trial supervisor… kept in envelopes… mother asked to draw envelope…” -> No description of safeguards (opaque, sealed, sequentially numbered). -> Allocation was not blinded, but CONSORT requires explicit reporting of potential bias. DTP3 is not mentioned in the trial flowchart - figure 1.<br /> 2.4. Lack of intention-to-treat analysis <br /> CONSORT requires ITT or explanation for deviations.

Example Page 7, lines 1–3: “All children with follow-up and who received the per-protocol intervention were included in the analyses.”<br /> -> This is per-protocol only, inappropriate for a superiority RCT intended to detect harm.

-> No ITT analysis is presented.

2.5. No reporting of missing data handling<br /> CONSORT requires transparent handling of missing outcome data.

Example Page 7, line 14: “No imputation for missing data was done.” -> But the extent of missing data is not reported for mortality or hospitalization outcomes.

2.6. Discussion includes non-evidence-based explanations, violates CONSORT as Discussion should reflect results, not speculation<br /> The discussion drifts into immunological theory and historical interpretations unsupported by trial data.

Examples: Page 10, lines 4-6:“...likely that immune mediated NSEs are more pronounced when mortality is high…”<br /> Page 9-10 (multiple paragraphs): Repeatedly argues unexpected null results require explanation. -> This is speculative; not based on data reported from this RCT.

2.7. Lack of balanced discussion<br /> CONSORT item 22: discuss both limitations and strengths. -> The manuscript heavily emphasises limitations (underpowering, interventions, etc.), but does not discuss the strength of randomisation or lack of harmful signal which is odd considering the research hypothesis of the trial.

3. OVERALL REFLECTIONS ON THE IMPACT OF SPIN, FRAMING, AND CONSORT DEVIATIONS<br /> Altogether, it seems to be rather unusual for researchers to put the trial down already in the headline, downright devaluating the trial. The authors are known to advocate detrimental effects of the DTP vaccine, a hypothesis that is based on purely observational studies (3, 4), and very small numbers that have not managed to replicate even by the same group of researchers (5).

This preprint reports results from a large-scale randomized trial, outranking observational studies in the hierarchy of evidence. Hence – making use of “A failed trial” appears to be an attempt to frame the results as invalid, which is ethically disturbing and highly inappropriate towards trial participants and readers.

p. 10:“The present RCT is therefore an outlier which needs an explanation. The drop in power due to the declining mortality rate may not only have lowered the possibility of finding significant tendencies; it is also likely that the immune mediated NSEs are more pronounced when mortality is high, so when mortality declines by >80%, the residual deaths may be less likely to be affected by immunological changes.”<br /> There seems to be a deliberate misinterpretation, unsubstantiated, and highly speculative. It is difficult not to read this in any other way than as a deliberate attempt to spin the results, frame them to be perceived according to the authors’ hypothesis about DTP having detrimental effects and increasing child mortality. Spinning results is defined as questionable research practice (6). The study was a null finding study, not an outlier.

There were no signs of more pronounced negative NSE, i.e., higher mortality in the child participants, who got DTP with, or after the measles vaccine. However, the primary outcome analysis demonstrated that this trial is a null finding study and thus the hypothesis was rejected.

3.1.Spinning the facts around other interventions.<br /> Several times in the preprint, the authors argue that other health interventions affected the trial conduct and the results.

Examples<br /> Page 1; “During the trial period many new interventions, including many national health campaigns, were carried out.”<br /> and <br /> “due to the large number of health interventions, not envisioned at the initiation of the trial, a limited part of the follow-up was a comparison between DTP4+OPV4 vs OPV4 as the most recent vaccinations”<br /> Page 6:“Other interventions and interactions. As the number of routine vaccinations and national health campaigns vaccinations increased through the 1990s and the 2000s, it has become increasingly clear that there are numerous interactions between different health interventions, such as vaccines and micronutrient supplementation, which are usually not taken into consideration in planning a vaccination programme. For example, the sequence of vaccinations, the time difference between non-live and live vaccines, and booster exposure to the same vaccines all had impact on the mortality levels. In addition, most vaccines have sex-differential NSEs (16). Since children were enrolled at 18 months of age, there were numerous possibilities for interactions with (a) national health intervention campaigns before enrolment; (b) participation in previous RCTs; and (c) national health campaigns after enrolment in the trial.”

Page 10: “trials of NSEs were planned more or less as vaccine efficacy studies. However, it has become increasingly clear that there are interactions with other routine vaccinations, vaccination campaigns, and other interventions affecting the immune system like vitamin A (16,19,20). Hence, in the present RCT we examined possible interactions with campaigns before enrolment, previous RCTs, and campaigns given after enrolment.”

-> The reader is left with the impression that a series of other factors influenced the trial and possibly invalidated the results. However, this was a randomized trial set-up which to a great extent compensates for any potential confounding effects, ie. other interventions that may have affected the outcome; but they will do so in both the intervention and comparator group.

Moreover, from table 1 of the preprint – Baseline characteristics – it would seem that the authors tend to put too much weight on multiple other factors as the trial appears to be well randomized.

Finally, if it in fact was true that this trial was influenced by other RCTs, health interventions, or campaigns, then this argument applies to all trial data originated from this research group in Guinea Bissau and consequently invalidates all of them.<br /> Again it is remarkable that the authors put down their own trial, spin the data and frame them into letting the reader believe that the trial is worth nothing at all. This is not in accordance with appropriate reporting standards as per CONSORT (7).

3.2. Spinning the facts around the succession of vaccines<br /> p.3 “high-titre-measles-vaccine (HTMV) was protective against measles infection, but surprisingly, it was associated with higher female mortality, when tested against STMV (5,6). Hence, NSEs could be beneficial or deleterious and they were often sex-differential.

References 5 and 6 are self-citations and based on post hoc re-analyses. The hypothesis that the DTP – following HTMV induced higher mortality remain highly speculative and never replicated. A more likely explanation would be that the HTMV was dosed too high resulting in measles infections, attenuated but still, which unfortunately in some cases increased the subsequent risk of mortality. This is notably a specific effect of the vaccine. However, as the authors advocate that the live (attenuated) vaccines are inferring beneficial effects and the non-live vaccines infer detrimental effects, a post-hoc narrative was constructed on the succession of vaccines having relevance. Importantly, this current preprint where the DTP vaccine is given alongside or not a live attenuated vaccine does not support this highly speculative hypothesis. On the contrary: if anything the results pointed towards DTP increasing child survival.

1. OVERALL REFLECTIONS ON ETHICS

4.1. Troubling lack of ethical standards and compliance

p. 7 it is stated that the study was explained to mothers in the following way:

“...though DTP is highly protective against whooping cough, it can occasionally give adverse reactions or limit the effect of measles vaccine….”

This speculative hypothesis seems to be introduced in the study participant / guardian information material, although this was never defined as a research question in the protocol.

Moreover, the protocol states:

“Hypothesis: Not providing DTP together with or after MV is associated with a 35 % reduction in overall mortality and 23% reduction in hospitalizations.

Taking one step back – and reflecting just for a minute – it appears to be the wildest research question ever. How did the Ethics Committee and the relevant authorities allow for this largescale trial to be conducted in the first place? What could possibly justify a RCT of this magnitude based on an outrageous research question like the one that was raised in the protocol: A 35% reduction in mortality is expected from omission of a single shot of vaccine?

4.2. Underpowered or not<br /> The preprint states that the trial was “highly underpowered,” although 109% of the planned study population was enrolled. There seems to be a large contrast between how this trial and a recently reported trial (8) are interpreted based on whether there was a significant finding or not. These discrepancies indeed appear as tendentious framing.

A direct comparison of these two large RCTs conducted by the same research group – with vast discrepancies in the results (enrolment and conduct) as well as interpretation is available at this link: https://www.linkedin.com/pulse/review-preprint-reports-dtp-trial-nct00244673-charlotte-str%25C3%25B8m-awgtf/ <br /> 4.3. Self-citation rate of 95%<br /> Nineteen of 20 references include members of the same author group – and are thus self-citations. This may reflect a general lack outside this group of scientific support to the NSE hypothesis and / or selective citation which is considered to be questionable research practice (6). A rule of thumb is that a self-citation rate above 15% raises suspicion of selective citation.

4.4. Reflections on the “Postscript” of the preprint<br /> It is truly a good thing that these results have finally come to light. The study subjects, their families, and the scientific community have been waiting for these data to be published.

The preprint is concluded by a lengthy postscript explaining the unusual long delay (14 years) in publishing the results from this trial.

"Postscript. We apologise for the late reporting. The implementation of the trial went quite different from the scheduled plans. In this older age group, more children than expected were registered by an ID and address that could not be followed. Funding was lacking for the PhD student to complete the data cleaning and analysis. Before funding could be obtained, the Guinean field supervisor had died which made it difficult to resolve some inconsistencies in data. The senior authors had too many other commitments. Finally, from 2020, the COVID-19 pandemic changed all priorities"<br /> These explanations may very well be seen as a result of hypocrisy, as members of this group of authors have published numerous papers – including reporting of several clinical trials during the past 14 years. Moreover during this delay it has been argued by members of the author group that an RCT with the exact same research hypothesis should be conducted (10):

“Almost 4 years after WHO reviewed the evidence for NSEs and recommended further research, IVIR-AC has now submitted for public comments two protocols of RCTs to measure the NSE impact of BCG and MV on child mortality:<br /> a. A BCG trial will compare mortality between 0 and 14 weeks of age for children randomized to BCG-at birth plus routine vaccines at 6–14 weeks of age vs. placebo at birth and routine vaccines at 6–14 weeks, with BCG at 14 weeks of age.<br /> b. An MV trial will compare mortality between 14 weeks and 2 years of age for children randomized to an additional dose of MV co-administered with DTP3 vs. placebo co-administered with DTP3.”<br /> According to http://clinicaltrial.gov the study hypothesis of NCT00244673.<br /> “DTP3/4+OPV+MV versus OPV+MV or DTP4+OPV4 versus OPV4”<br /> And even worse – it was claimed in the same publication Expert Review of Vaccines, Vol 17, 2018 – Issue 5 (10) that: "Science is also about accounting for all data. ... it has not been possible to conduct RCTs of DTP in high-mortality areas."<br /> There has evidently been a complete lack of willingness from the research group behind this trial to report on this null finding study that rejected the research hypothesis and rejected the hypothesis that the DTP vaccine has detrimental NSE. Such selection bias in reporting trial results on mortality is scientifically troubling and ethically both irresponsible and unacceptable.

References:

1. Agergaard JN, S.; Benn, C.S.; Aaby, P. Randomised trial of not providing booster diphtheria-tetanus-pertussis (DTP) vaccination after measles vaccination and child survival: A failed trial. In: Bandim Health Project IN, Apartado 861, Bissau, Guinea-Bissau; Department of Infectious Diseases, Aarhus University Hospital, Denmark; Bandim Health Project, OPEN, Department of Clinical Research, University of Southern Denmark/Odense University Hospital, Denmark; Danish Institute for Advanced Study (DIAS), University of Southern Denmark, Denmark, editor. 2025.

2. Agergaard J, Nante E, Poulstrup G, Nielsen J, Flanagan KL, Ostergaard L, et al. Diphtheria-tetanus-pertussis vaccine administered simultaneously with measles vaccine is associated with increased morbidity and poor growth in girls. A randomised trial from Guinea-Bissau. Vaccine. 2011;29(3):487-500.

3. Mogensen SW, Andersen A, Rodrigues A, Benn CS, Aaby P. The Introduction of Diphtheria-Tetanus-Pertussis and Oral Polio Vaccine Among Young Infants in an Urban African Community: A Natural Experiment. EBioMedicine. 2017;17:192-8.

4. Aaby P, Mogensen SW, Rodrigues A, Benn CS. Evidence of Increase in Mortality After the Introduction of Diphtheria-Tetanus-Pertussis Vaccine to Children Aged 6-35 Months in Guinea-Bissau: A Time for Reflection? Front Public Health. 2018;6:79.

5. Sørensen MK, Schaltz-Buchholzer F, Jensen AM, Nielsen S, Monteiro I, Aaby P, et al. Retesting the hypothesis that early Diphtheria-Tetanus-Pertussis vaccination increases female mortality: An observational study within a randomised trial. Vaccine. 2022;40(11):1606-16.

6. Bouter LM, Tijdink J, Axelsen N, Martinson BC, Ter Riet G. Ranking major and minor research misbehaviors: results from a survey among participants of four World Conferences on Research Integrity. Res Integr Peer Rev. 2016;1:17.

7. Hopewell S, Chan AW, Collins GS, Hrobjartsson A, Moher D, Schulz KF, et al. CONSORT 2025 explanation and elaboration: updated guideline for reporting randomised trials. BMJ. 2025;389:e081124.

8. Thysen SM, da Silva Borges I, Martins J, Stjernholm AD, Hansen JS, da Silva LMV, et al. Can earlier BCG-Japan and OPV vaccination reduce early infant mortality? A cluster-randomised trial in Guinea-Bissau. BMJ Glob Health. 2024;9(2).

9. Benn CS. Non-specific effects of vaccines: The status and the future. Vaccine. 2025;51:126884.

10. Benn CS, Fisker AB, Rieckmann A, Jensen AKG, Aaby P. How to evaluate potential non-specific effects of vaccines: the quest for randomized trials or time for triangulation? Expert Rev Vaccines. 2018;17(5):411-20.

On 2024-11-08 16:21:12, user Kristin Ressel wrote:

Changes to this manuscript were made during the article submission process to the journal Archives of Physical Medicine & Rehabilitation. It is now published and can be found using the citation provided below.

Freburger, J. K., Mormer, E. R., Ressel, K., Zhang, S., Johnson, A. M., Pastva, A. M., Turner, R. L., Coyle, P. C., Bushnell, C. D., Duncan, P. W., & Berkeley, S. B. J. Disparities in Access to, Use of, and Quality of Rehabilitation Following Stroke in the United States: A Scoping Review. Archives of Physical Medicine and Rehabilitation. https://doi.org/10.1016/j.apmr.2024.10.010

On 2020-04-10 17:40:13, user Sinai Immunol Review Project wrote:

Key findings<br /> The authors investigated the use of a commercially available form of heparin, low molecular weight heparin (LMWH), as a therapeutic drug for patients with COVID-19. Previous studies showed that in addition to its anticoagulant properties, LMWH exerts anti-inflammatory effects by reducing the release of IL-6 and counteracting IL-6.

This was a retrospective single-center study conducted in Wuhan, China. Forty-two (42) hospitalized patients with coronavirus pneumonia were included, of which 21 underwent LMWH treatment (heparin group) and 21 did not (control). The general characteristics of the two groups of patients were statistically comparable. Both control and LMWH had the same hospitalization time and there were no critical cases in either group.

This study found that treatment with LMWH significantly reduced IL-6 levels in patients in the heparin group compared to the control group. However, LMWH treatment did not have an effect on the levels of other inflammatory factors: CRP, IL-2, IL-4, IL-10, TNF-?, and IFN-?. Compared with the control group, patients in the heparin group had a significantly increased percentage of lymphocytes after treatment, further suggesting that LMWH treatment has anti-inflammatory effects and can reduce the lymphopenia associated with COVID-19.

Consistent with other studies in COVID-19 patients, they found that LMWH treatment can improve hypercoagulability. D-dimer and FDP levels (biomarkers of coagulation) in the heparin group significantly decreased from baseline after treatment, whereas there was no significant change in levels for the control group. Of note however, patients in the heparin group had a significantly higher level of D-dimer and FDP at baseline compared to the control group.

Importance<br /> Many studies have shown that severely ill COVID-19 patients have significantly higher levels of IL-6 compared to patients with mild cases and it has been proposed that progression to severe disease may be caused by lymphopenia and cytokine storms. The anti-inflammatory effects of heparin may help prevent or reverse a cytokine storm caused by the virus and thus delay COVID-19 progression and improve overall condition in patients. The pleiotropic effects of heparin may have a greater therapeutic effect than compounds that are directed against a single target, such as an anti-IL-6 therapy. This is because COVID-19 patients commonly have complications such as coagulopathy and endothelial dysfunction leading to cardiac pathology that may be mitigated by heparin treatment (Li J, et.al; Wojnicz et.al).

Limitations<br /> This study is limited by a small sample size (n=44) and a single-center design. Double-blinded, randomized, placebo controlled clinical trials of LMWH treatment are needed to understand the possible benefit of the treatment. Additionally, this study was unable to control for the dose and days of treatment of LMWH. Identifying the correct dose and timing of LMWH is a matter of immediate interest. Of note, patients in the heparin group received two types of LMWH, enoxaparin sodium or nadroparin calcium, which have been reported to have differing anticoagulant activity. The use of different LMWHs in the heparin group warrants further explanation.

Another caveat of this study is that the levels of D-dimer and fibrinogen degradation products were significantly higher at baseline for patients in the heparin group compared to those in the control group. Therefore, it is difficult to decipher whether some of the positive effects of heparin treatment were due to its anti-coagulation effects or direct anti-inflammatory effects. Future studies are that delineate the anti-inflammatory functions of heparin independently of its anticoagulant properties in cases of COVID-19 would be useful.

Lastly, this study did not discuss any side-effects of heparin, such as the risk of bleeding. Moreover, coagulation can help to compartmentalize pathogens and reduce their invasion, therefore anticoagulant treatments like heparin may have risks and it remains to be determined which patients would benefit from this therapy.

References:<br /> Li J, Li Y, Yang B, Wang H, Li L. Low-molecular-weight heparin treatment for acute lung injury/acute respiratory distress syndrome: a meta-analysis of randomized controlled trials. Int J Clin Exp Med 2018;11(2):414-422

Wojnicz R, Nowak J, Szygula-Jurkiewicz B, Wilczek K, Lekston A, Trzeciak P, et al. Adjunctive therapy with low-molecular-weight heparin in patients with chronic heart failure secondary to dilated cardiomyopathy: oneyear follow-up results of the randomized trial. Am Heart J. 2006;152(4):713.e1-7

Review by Jamie Redes as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2021-10-11 18:46:13, user Andrew T Levin wrote:

Comment #2: Methodological Issues

1. Given the stated purpose of this study, it is remarkable that the manuscript never specifically defines the term “community-dwelling population.” In practice, the study analyzes the incidence of COVID-19 fatalities that have occurred outside of nursing homes, but even that distinction is not very precise. For example, the spectrum of U.S. nursing homes encompasses board & care homes, assisted care facilities, and skilled nursing facilities. About two-thirds of U.S. nursing home residents rely on Medicaid to cover that cost. By contrast, higher-income individuals can afford to receive home health care or choose to live in “retirement communities” with on-site medical staff. In effect, the distinction of whether someone is “community-dwelling” or a “nursing home resident” is linked to a complex set of socioeconomic characteristics as well as to various aspects of their individual health. Making international comparisons along these lines is even more fraught with difficulty, because the size and composition of the nursing home population inevitably reflects differences in social norms as well as socioeconomic factors, access to healthcare, and the extent of public assistance. Indeed, such comparisons may be practically meaningless when considering developing countries such as the Dominican Republic and India, where nursing home care may only be an option for a very small fraction of the population.

2. Search Procedure. This manuscript uses an arbitrary search cutoff date of 31 March 2021, which excludes some landmark seroprevalence studies that have been disseminated since then. For example, Sullivan et al. (2021) analyzed seroprevalence of the U.S. population over the second half of 2020 using a large representative sample that included 1154 adults ages 65+, and hence that study would clearly satisfy the stated eligibilitry criteria for this meta-analysis.[11] Moreover, the study carefully adjusts for assay characteristics and seroreversion and estimates that as of 31 October 2020, the IFR for U.S. adults ages 65+ was 7·1% (CI: 5·0¬-10·4%). Those results can be also be used in conjunction with data on nursing home deaths to obtain the corresponding IFR estimate of 4·7% for community-dwelling adults ages 65+.

3. Minimum Size Threshold. This analysis excludes seroprevalence results from any studies involving fewer than 1000 adults ages 70+, and hence it is remarkable that the manuscript neither provides any rationale for imposing such a constraint nor provides citations to any existing works that might motivate it. Indeed, this approach is inconsistent with basic principles of statistical analysis, e.g., making inferences based on all available information and avoiding arbitrary selection criteria that could induce bias in the results. Consequently, meta-analysis should downweight studies with relatively lower precision rather than simply discarding those studies. Moreover, it is incoherent to specify an eligibility criterion based solely on sample size, because the precision of seroprevalence estimates also hinges on the level of prevalence. A small sample may be adequate in a context of relatively high prevalence, whereas a much larger sample may be needed to obtain precise inferences in a context of very low prevalence. The national study of Hungary was included in this meta-analysis because that study included 1454 adults ages 70+. However, only nine of those individuals were seropositive. Consequently, the test-adjusted seroprevalence for this cohort of older adults is not statistically distinguishable from zero, and hence the confidence interval of the age-specific IFR is not even well-defined.[12] By contrast, the regional study of Geneva was excluded from this meta-analysis because it only included 369 individuals ages 65+. But that sample was large enough to facilitate inferences about seroprevalence (6·8%; CI: 3·8¬¬ 10·5%) and corresponding inferences regarding IFR for that age cohort (5·6%: CI: 4·3 7·4%).[13, 14] Finally, setting the sample size threshold at 1000 is clearly an arbitrary choice. Since seroprevalence studies can be readily identified using the SeroTracker tool, this meta-analysis should be extended using a lower threshold of 250 adults ages 65+ that would encompass the national studies of Netherlands and Sweden as well as a substantial number of regional studies.

4. Sample Selection. In characterizing which seroprevalence studies have been included in <br /> the meta-analysis, this manuscript specifies the key criterion as “aimed to generate samples reflecting the general population.” However, this criterion is extraordinarily vague and judgmental (as evident from subjective words like “aimed” and “reflecting”). <br /> (a) United Kingdom. The inadequacy of this approach to sample selection is evident from the fact that the meta-analysis places equal weight on four U.K. seroprevalence studies, even though only two of those studies (UK BioBank and REACT-2) utilized samples designed to be representative of the general population.[15, 16] By contrast, the other two studies used convenience samples that were not designed or even re-weighted to be broadly representative, and hence those two studies should have been excluded from this meta-analysis. First, Hughes et al. (2020) studied a panel of primary and secondary patients at a large Scottish health board, with the stated objective of assessing viral transmission patterns.[17] The paper never suggested that this panel could be interpreted as representative of the wider population; indeed, some of these patients may have been receiving care related to COVID-19. Second, in one of its weekly surveillance reports, Public Health England (2020) reported seroprevalence results for a panel of patients ages 65+ who had a routine blood test at the Royal College of General Practioners Research and Surveillance Centre.[18] Evidently, this panel was not aimed to reflect the general population and may well have included patients recovering from COVID or experiencing COVID-like symptoms. <br /> (b) United States. One of the two U.S. seroprevalence studies used a sampling design that is intended to be broadly representative, whereas the other U.S. study used a convenience sample of patients at kidney dialysis centers. Unfortunately, as a consequence of gross disparities in healthcare access, higher-income individuals typically utilize in-home dialysis machines, whereas low-income individuals must travel multiple times per week to a dialysis center, often using public transit. Consequently, the prevalence of COVID-19 infections among such patients has crucial public health implications but should not be interpreted as representative of the general population.<br /> (c) Canada. Among the three Canadian seroprevalence studies, two use representative sampling designs (Ontario and Canada-ABC), whereas the third study conducted by Canadian Blood Services (CBS) uses a convenience sample of blood donors. In its public announcement of those results, CBS specifically warned that “caution should be exercised in extrapolating findings to all healthy adult Canadians, because blood donors self-select to be blood donors, in some areas access to a donation clinic may be limited, and there are fewer elderly donors who donate blood compared to the general population.” [19] That caution was specifically cited as the reason for excluding this study from a previous meta-analysis.[5] Indeed, given the scarcity of elderly blood donors, there is an even stronger rationale for excluding that study from the analysis here. Indeed, this meta-analysis should have specifically excluded all convenience samples, whether from blood donors, commercial lab tests, or medical patients. Dodd et al. (2020) analyzed a large panel of U.S. blood donors and found that the proportion of first-time donors jumped in June 2020 following the introduction of COVID-19 antibody testing, consistent with the hypothesis of “donors with higher rates of prior exposure donating to obtain antibody test results,” and concluded that “blood donors are not representative of the general population.”[20] Bajema et al. (2021) found seroprevalence of 4·94% using commercial lab residual sera from residents of Atlanta (USA), compared to seroprevalence of 3·2% using a representative sample of the same location.[21, 22] These findings highlight the extent to which convenience samples may be associated with upward bias in seroprevalence and hence downward bias in IFR. It should also be noted that the incidence of COVID-19 infections has a strong association with race and ethnicity, reflecting disparities in employment, residential arrrangements, and various other factors. Such patterns have been evident in numerous countries (not just the USA), and hence the manuscript should follow a consistent approach in addressing this issue.

5. Open-Ended Age Brackets. This manuscript proceeds on the assumption that open-ended age brackets for older adults are essentially equivalent regardless of whether the bracket is 60+, 65+, or 70+. But this assumption is inconsistent with the consistent findings of preceding studies, namely, the IFR for COVID-19 increases continuously with age rather than jumping discretely at any specific age threshold. Indeed, the measured IFR for any particular age bracket is a convolution of the age distribution of the population, the age-specific pattern of prevalence, and the fact that IFR increases exponentially with age. The complexity of this convolution underscores the pitfalls of comparing IFRs for open-ended age brackets of older adults. Ontario serves as a useful case study for illustrating these issues. The Ontario Public Health seroprevalence study reported results for three broad age brackets: 0-19, 20-59, and 60+ years. However, this manuscript assesses IFR for ages 70+ using results obtained via private correspondence. However, that assessment may be very imprecise, because COVID-19 prevalence was very low in the general population and may well have been even lower among the oldest community-dwelling adults. By contrast, the Ontario study is very informative for characterizing the cohort of individuals ages 60-69 years. In particular, there were 9 positives among 804 specimens for that cohort; the test-adjusted prevalence of about 1% indicates that about 17000 Ontario residents ages 60-69 had been infected by mid-June 2020. As of 30 June 2020, that age group had 240 COVID-19 deaths—none of which occurred in nursing homes. Consequently, the IFR for community-dwelling Ontario adults ages 60-69 was 1·4% -- identical to the predicted IFR t the midpoint of this age interval from the metaregression of Levin et al. (2020).[5]

6. Adjusting for Assay Characteristics. Seroprevalence studies have generally been conducted using antibody assays with imperfect specificity and sensitivity, and these characteristics exhibit substantial variation across assays. Moreover, the implications of these characteristics depend on the actual level of prevalence, e.g., adjusting for specificity is crucial in a context of relatively low prevalence.[23] Consequently, all three of the preceding meta-analyses consistently used seroprevalence estimates and confidence intervals that had been adjusted for test sensitivity and specificity using the Gladen-Rogan formula and/or Bayesian methods.[5, 8, 9] By contrast, this meta-analysis simply uses raw seropositive data from those studies that did not report test-adjusted seroprevalence.

7. Low Prevalence. The shortcomings of this manuscript’s approach are particularly evident in assessing IFRs for locations with relatively low prevalence. For example, as shown in manuscript Table 1, the seroprevalence study of Hungary used the Abbott Architect IgG assay to analyze 1454 specimens and obtained 9 positive results, i.e., raw seropositivity of 0·6%. According to the manufacturer’s data submitted to the U.S. Food and Drug Administration, this assay has sensitivity of 100% and specificity of 99·6%.[24] Consequently, the Gladen-Rogan formula indicates that the test-adjusted prevalence is only 0·2%, i.e., only one-third of the observed seropositive results were likely to be true positives. Moreover, this test-adjusted estimate has a 95% confidence interval of 0 to 0·4%, i.e., the prevalence is not statistically distinguishable from zero, and hence its IFR does not have a well-defined confidence interval. Indeed, that was precisely the reason why this cohort was not included in the meta-analysis of Levin et al. (2020).

8. Unmeasured Antibodies. This manuscript follows a completely unorthodox approach in adjusting seroprevalence for unmeasured antibodies: “When only one or two types of antibodies (among IgG, IgM, IgA) were used in the seroprevalence study, seroprevalence was corrected upwards (and inferred IFR downwards) by 10% for each non-measured antibody.” (p.8) This approach is particularly objectionable when applied to test-adjusted seroprevalence results, since those estimates have already been adjusted to reflect sensitivity and specificity. Moreover, such an approach has never been used by any other epidemiologist or statistician, in the context of the COVID-19 pandemic or for any other purpose, and hence should not be applied in a meta-analysis without providing any compelling rationale for doing so.

9. Seroreversion. The manucript “explores” the issue of seroreversion using proportionality factors based on the timing of each seroprevalence study relative to the preceding peak of COVID-19 deaths. However, the manuscript provides no rationale for following this approach instead of the rigorous Bayesian methodology utilized in a preceding meta-analysis.[9] Moreover, the manuscript makes no reference to the findings of longitudinal studies of the evolution of antibodies in confirmed positive individuals, which have concluded that the degree of seroreversion is substantial for some assays and negligible for others.[25, 26]

10. Measurement of Fatalities. Data on COVID-19 fatalities should be obtained directly from official government sources, not from media reports, web aggregators, or Wikipedia. For example, the European Center for Disease Control has an online COVID-19 database with daily data on reported cases and fatalities for nearly every country in the world. Moreover, whenever possible, fatalities should be measured using official tabulations of case data (based on actual date of death) rather than real-time reports that may be relatively incomplete and subject to substantial revision over time. These issues are particularly relevant for assessing fatalities in nursing homes: If a patient tested positive for COVID-19 and died soon thereafter, investigation would be needed to determine whether the death resulted from COVID-19 or unrelated causes. To illustrate these issues, consider the manuscript’s estimate of IFR based on the U.S. national seroprevalence study of Kalish et al. (2021). As shown in table 1 and appendix table 2 of this manuscript, the U.S. CDC case database (accessed in February 2021) indicates a total of 103862 deaths for adults ages 70+ as of 04 July 2020. To determine the corresponding fatalities in U.S. nursing homes, however, the manuscript relies on a news summary dated 26 June 2020 that reported a total of 52428 nursing home deaths in 41 U.S. states.[27] Using that real-time report, manuscript infers a somewhat higher national total of 57291 nursing home deaths and hence 46571 deaths outside of nursing homes. By contrast, the U.S. CMS case database (accessed in August 2021) indicates 38239 deaths in U.S. nursing homes as of 05 July 2020.[28] Evidently, there were 65623 fatalities outside of nursing homes, implying a correspondingly higher IFR of 3·6% for U.S. community-dwelling adults ages 70+.

11. Developing Countries. The use of confirmed COVID-19 fatalities can be highly misleading in assessing IFRs of developing countries, where testing capacity has been much more limited than in Europe or North America. Consequently, in developing country locations, the measure of fatalities should include both confirmed and suspected COVID-19 cases, or alternatively, a measure of excess deaths relative to preceding years. Indeed, several recent studies of India have concluded that confirmed COVID-19 fatalities understate the true death toll by an order of magnitude.[29-31]

12. Younger Age Groups. The manuscript states that “the studies considered here offered a <br /> prime opportunity to assess IFR also in younger age strata” (p.9) even though such analysis <br /> had not been proposed in the protocol. Nevertheless, this secondary analysis is at odds with the key eligibility criterion of this meta-analysis, namely, seroprevalence studies with at least 1000 participants ages 70+. Indeed, imposing that eligibility criterion leads to the exclusion of numerous other seroprevalence studies that would be highly informative for analyzing IFRs of younger adults, with an unknown degree of bias associated with that exclusion.

13. Self-Citations. A meta-analysis is intended to serve as an objective synthesis of information extracted from existing studies. Consequently, methodological decisions and substantive claims should not be based solely on citations of the authors’ own prior work. For example, in discussing the preceding meta-analysis of Levin et al. (2020), the manuscript asserts that “almost all included studies came from hard-hit locations, where IFR may be substantially higher”, with a sole citation to Ioannidis (2021a). However, that assertion is clearly false: The meta-analysis of Levin et al. (2020) included locations such as Australia, New Zealand, Ontario, and Salt Lake City that experienced very few infections during the first wave of the pandemic. Similarly, the manuscript asserts that “selection bias for studies with higher seroprevalence and/or higher death counts may explain why their estimates for middle-aged and elderly are substantially higher than ours” (p.14), with a sole citation to Ioannidis (2021b).

References Cited Here:<br /> 1. Ferguson N, Laydon D, Nedjati-Gilani G, et al. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand2020.<br /> 2. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. eurosurveillance. 2020;25(10). doi:10.2807/1560-7917.ES.2020.25.10.2000180<br /> 3. Salje H, Tran Kiem C, Lefrancq N, et al. Estimating the burden of SARS-CoV-2 in France. Science. 2020;369(6500):208-11. doi:10.1126/science.abc3517<br /> 4. Verity R, Okell LC, Dorigatti I, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. lancet infectious diseases. 2020;20(6):669-77. doi:10.1016/S1473-3099(20)30243-7<br /> 5. Levin AT, Hanage WP, Owusu-Boaitey N, Cochran KB, Walsh SP, Meyerowitz-Katz G. Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications. European Journal of Epidemiology. 2020;35(12):1123-38. doi:10.1007/s10654-020-00698-1<br /> 6. Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020. doi:10.1038/s41586-020-2521-4<br /> 7. Mak JKL, Kuja-Halkola R, Wang Y, Hägg S, Jylhävä J. Frailty and comorbidity in predicting community COVID-19 mortality in the U.K. Biobank: The effect of sampling. Journal of the American Geriatrics Society. 2021;69(5):1128-39. doi:https://doi.org/10.1111/jgs...<br /> 8. O’Driscoll M, Ribeiro Dos Santos G, Wang L, et al. Age-specific mortality and immunity patterns of SARS-CoV-2. Nature. 2021;590(7844):140-5. doi:10.1038/s41586-020-2918-0<br /> 9. Brazeau N, Verity R, Jenks S, al. e. COVID-19 Infection Fatality Ratio: Estimates from Seroprevalence. 2020. doi:https://doi.org/10.25561/83545.<br /> 10. Arora RK, Joseph A, Van Wyk J, et al. SeroTracker: a global SARS-CoV-2 seroprevalence dashboard. The Lancet Infectious Diseases. 2020. doi:10.1016/s1473-3099(20)30631-9<br /> 11. Sullivan PS, Siegler AJ, Shioda K, et al. Severe Acute Respiratory Syndrome Coronavirus 2 Cumulative Incidence, United States, August 2020–December 2020. Clinical Infectious Diseases. 2021. doi:10.1093/cid/ciab626<br /> 12. Merkely B, Szabo AJ, Kosztin A, et al. Novel coronavirus epidemic in the Hungarian population, a cross-sectional nationwide survey to support the exit policy in Hungary. Geroscience. 2020;42(4):1063-74. doi:10.1007/s11357-020-00226-9<br /> 13. Perez-Saez J, Lauer SA, Kaiser L, et al. Serology-informed estimates of SARS-CoV-2 infection fatality risk in Geneva, Switzerland. The Lancet Infectious Diseases. doi:10.1016/S1473-3099(20)30584-3<br /> 14. Stringhini S, Wisniak A, Piumatti G, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. The Lancet. 2020;396(10247):313-9. doi:10.1016/s0140-6736(20)31304-0<br /> 15. United Kingdom BioBank. UK Biobank SARS-CoV-2 Serology Study Weekly Report - 21 July 2020. 2020.<br /> 16. Ward H, Atchison CJ, Whitaker M, et al. Antibody prevalence for SARS-CoV-2 in England following first peak of the pandemic: REACT2 study in 100,000 adults. medRxiv. 2020:2020.08.12.20173690. doi:10.1101/2020.08.12.20173690<br /> 17. Hughes EC, Amat JAR, Haney J, et al. Severe Acute Respiratory Syndrome Coronavirus 2 Serosurveillance in a Patient Population Reveals Differences in Virus Exposure and Antibody-Mediated Immunity According to Host Demography and Healthcare Setting. The Journal of Infectious Diseases. 2020;223(6):971-80. doi:10.1093/infdis/jiaa788<br /> 18. U.K. Public Health England. Weekly Coronavirus Disease 2019 (COVID-19) Surveillance Report, Week 32. 2020. <br /> 19. Canadian Blood Services and COVID-19 Immunity Task Force. Final Results of Initial Canadian SARS-Cov-2 Seroprevalence Study Announced. 2020. <br /> 20. Dodd RY, Xu M, Stramer SL. Change in Donor Characteristics and Antibodies to SARS-CoV-2 in Donated Blood in the US, June-August 2020. JAMA. 2020;324(16):1677-9. doi:10.1001/jama.2020.18598<br /> 21. Bajema KL, Dahlgren FS, Lim TW, et al. Comparison of Estimated Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence Through Commercial Laboratory Residual Sera Testing and a Community Survey. Clinical Infectious Diseases. 2020. doi:10.1093/cid/ciaa1804<br /> 22. Boyce RM, Shook-Sa BE, Aiello AE. A Tale of 2 Studies: Study Design and Our Understanding of Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence. Clinical Infectious Diseases. 2020. doi:10.1093/cid/ciaa1868<br /> 23. Gelman A, Carpenter B. Bayesian analysis of tests with unknown specificity and sensitivity. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2020;n/a(n/a). doi:10.1111/rssc.12435<br /> 24. U.S. Food and Drug Administration. EUA authorized serology test performance. 2020.<br /> 25. Dan JM, Mateus J, Kato Y, et al. Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science. 2021;371(6529):eabf4063. doi:10.1126/science.abf4063<br /> 26. Muecksch F, Wise H, Batchelor B, et al. Longitudinal Serological Analysis and Neutralizing Antibody Levels in Coronavirus Disease 2019 Convalescent Patients. The Journal of Infectious Diseases. 2020;223(3):389-98. doi:10.1093/infdis/jiaa659<br /> 27. Kaiser Family Foundation. This Week in Coronavirus: June 18 to June 25. 2020. <br /> 28. U.S. Center for Medicare & Medicaid Services (CMS). COVID-19 Nursing Home Data. 2021. <br /> 29. Anand A, Sandefur J, Subramanian A. Three New Estimates of India’s All-Cause Excess Mortality during the COVID-19 Pandemic. Center for Global Development. 2021. <br /> 30. Deshmukh Y, Suraweera W, Tumbe C, et al. Excess mortality in India from June 2020 to June 2021 during the COVID pandemic: death registration, health facility deaths, and survey data. medRxiv. 2021:2021.07.20.21260872. doi:10.1101/2021.07.20.21260872<br /> 31. Shewade HD, Parameswaran GG, Mazumder A, Gupta M. Adjusting Reported COVID-19 Deaths for the Prevailing Routine Death Surveillance in India. Frontiers in Public Health. 2021;9(1045). doi:10.3389/fpubh.2021.641991

On 2020-11-27 21:02:26, user Robert Brown wrote:

Vitamin D, Magnesium, Steroids, PPI and COVID-19; Interactions and Outcomes - Response to ‘Effect of Vitamin D3 Supplementation vs Placebo on Hospital Length of Stay in Patients with Severe COVID-19: A Multicenter, Double-blind, Randomized Controlled Trial’ [Preprint] [1]

Thank you and congratulations on your important and significant paper. This is only the fourth[2] [3] [4] reported RCT examining vitamin D supplementation as a therapeutic intervention for COVID-19. Biology provides multiple pathways by which vitamin D hydroxylated-derivatives[5] may impact Covid-19 risks [including via; ACE2 receptors; airway-epithelial-cell tight-junction-function, immune responses [affecting lymphocytes, macrophages T cells, T helper cells, Th1, -17; Tregs; cytokine secretion IL-1, -2, -4, -5, -6 -10,-12; IFN-beta, TNF-alpha; defensins and cathelicidin, and receptors HLA-DR, CD4, CD8, CD14, CD38. Vitamin D also regulates; mitochondrial respiratory, inflammatory, oxidative and other functions; RXR and other receptor links between steroids, retinoids, hormonal vitamin D, thyroid hormone, oxidised lipids and peroxisomal pathway immune responses.][6]

Significant evidence [40+ patient-papers[7]] suggests higher Vitamin D status [serum/plasma 25(OH)D concentration] is associated with diminished COVID-19 infection rates,and reduced severity [including ICU admission and mortality].[2 3 4]

Thus, it is crucial, to consider if the preprint’s broad-based conclusion “Vitamin D3 supplementation does not confer therapeutic benefits among hospitalized patients with severe COVID-19”, [time to discharge as well as lack of observed ICU and mortality rate benefits], stands scrutiny when any one, or combination of, the following factors are considered: -

Delay in vitamin D administration after severe symptoms onset

Patients presented “10.2 days after symptoms”, thus were already verging on serious outcomes at admission; “89.6% required supplemental oxygen at baseline [183 oxygen therapy; 32 non-invasive ventilation] and 59.6% had computed tomography<br /> scan findings suggestive of COVID-19.” [Days to dyspnoea from overt infection average 7-8, and acute-respiratory-distress-syndrome [ARDS] develops after median 2.5 days.[8]]

Further, the timing of vitamin D supplementation, at or after <br /> hospitalisation, was not specified, despite timing clearly being an important factor, given the advanced stage of illness at admission.

Baseline vitamin D status [serum 25(OH)D concentrations] were relatively ‘good’

Baseline 25(OH)D values averaged 21.0ng/ml and 20.6ng/ml in the treatment and control groups respectively, i.e. they were relatively ‘good’, and above levels reported as being associated with the greatest COVID-19 risks.[9] [10]Sub-analysis of patients < 10ngml +/-Dexamethasone would be instructive. Further, deficiencies such as magnesium (an essential ‘D’ enzyme co-factor) might factor more in the lack of observed benefits for Covid-19 severity, than vitamin D status itself.

Corticosteroids

COVID-19 related corticosteroid vitamin ‘D’ interactions require<br /> investigation. 64.2%(Treatment) and 60.8%(Control) group patients respectively, were treated with Corticosteroids (Dexamethasone?), and mortality was somewhat higher in the Treatment than Control arm. Interactions between vitamin D and steroids including dexamethasone are observed[11], including “decreased synthesis of active vitamin D, and impairment of biological action at tissue level.”[12] However these potential effects have not been investigated in COVID-19 patients treated with both vitamin D and dexamethasone.

It would be most useful to know therefore, at what stage corticosteroid treatment began, and at what dosages, what other treatments were given [and at what dosage], and when such treatments were stopped, so that potential interactions between vitamin D, corticosteroids and other treatments for COVID-19<br /> patients could be elucidated.

In particular, any negative or neutralising effect of corticosteroids on<br /> ‘D’-derivatives and pathways, could account for the lack of reduction in risks of ICU and mortality outcomes, including slightly higher mortality, in those given vitamin D, a matter of importance, since dexamethasone, given before onset of serious ARDS, was reported in Oxford[13] to increase, not reduce, mortality.

Proton pump inhibitors.

PPI are known to lower serum magnesium,[14] an essential ‘D’ hydroxylase-enzyme co-factor. 47/120-(39%)[Treatment] and 49/120-40%[Control] used PPI, compared to 9.2% population usage in USA.[15] PPI-induced related serum magnesium reduction, +/- dietary insufficiency, is a reported COVID-19 risk factor,[16] thus possibly helping account, for D3 treatment, failing to reduce Brazilian Covid-19 mortality. Thought-provokingly a Brazilian paper reported “There is chronic latent magnesium deficiency in apparently healthy university students”, which deficiency is potentially more widespread.[17]

Conversely, RCT administration of magnesium with vitamin D reduced COVID-19 in-patient mortality.2

Rate of increase of Serum 25(OH)D

It is unclear when blood was sampled for determination of serum 25(OH)D concentrations, or if this was standardised for all patients.

A large bolus will increase 25(OH)D values in the healthy, “Oral D2 and D3 (100,000 to 600,000 IU) significantly increased serum 25(OH)D from baseline in all reviewed studies” . . . “peak levels were measured at 3 days (34) and 7 days following dosing,”[18]

However, timing matters, because hepatic hydroxylation5 to form 25(OH)D (Calcifediol) is likely reduced by; severe illness, as well as by obesity diabetes, and possibly hypertension,[19] conditions already recognised as risk factors for covid-19 severity.[20]

The Cordoba study[3] suggests that 25(OH)D [Calcifediol, that could be given together with vitamin D3, cholecalciferol], may be key to effective treatment of severe COVID-19 illness. There is no suggestion Cordoba patients were treated with corticosteroids. Cordoba patients were administered calcifediol on admission-day, but the period between overt infection and hospital admission <br /> was not reported.

Risk-factor Differentials in Patient Groups

A skew in risk factors favouring the control?

Control-Placebo to Treatment-D3:

Increased risk factors - Overweight (31/37, 0,84); Obesity (58/63, 0,92); Hypertension (58/68, 0,74); Diabetes II 35/49, 0,71); COPD (5/7,0,71); Asthma (7/8, 0,88); Chronic Kidney Disease (0/2, 0,0); Rheumatic Disease (10/13, 0,77)[21]; Black (14/20) Male 965/70).

Decreased factors - White (79/62) Female (55-50)

Improved oxygen parameters are not reflected in conclusion

“Despite the D3 group being at a greater risk, including due to hypertension, COPD and diabetes, known risk factors, significant differences in oxygen supplementation favour the D3 treatment group“.21

Oxygen supplementation (%) Placebo No. (%) D3 <br /> No oxygen therapy 9 (7.5) 16 (13.3)<br /> Oxygen therapy 97 (80.8) 86 (71.7)<br /> Non-invasive ventilation 14 (11.7) 18 (15.0)

Conclusion requires Caveats?

Thus, the un-caveated conclusion “Vitamin D3 supplementation does not confer therapeutic benefits among hospitalized patients with severe COVID-19”, likely requires caveats about possible effects of the several factors discussed above.

Further, the reported finding cannot be extrapolated to care of all Covid-19 patients, since the above- mentioned-potential interactions require further investigation, including; as to effects of; magnesium

status; treatment with PPI inhibitors, impact of corticosteroids in severe Covid-19 illness on vitamin D biology and outcomes, and consideration of pre-existing vitamin D status.

Further public health policy directed at reducing vitamin D, and other nutrient deficiencies for mitigation of COVID-19 risks at population levels, should not be conflated with clinical optimisation of vitamin D and metabolites for treatment of severe COVID-19 illness.

[1] Murai,I., Fernandes, A., Sales, L., Pinto, A., Goessler, K., et. al. 17th November 2020). Effect of Vitamin D3 Supplementation vs placebo on Hospital Length of Stay in Patients with Severe COVID-19 A Multicenter, Double-blind, Randomized Controlled Trial. medRxiv 2020.11.16.20232397; doi: https://doi.org/10.1101 /2020.11.16.20232397 Available at: https://www.medrxiv.org/content/10.1101/2020.11.16.20232397v1<br /> [2] Tan, C., Ho, L., Kalimuddin, S., Cherng, B., Teh, Y., et.al. (10th June 2020). A cohort study to evaluate the effect of combination Vitamin D, Magnesium and Vitamin B12 (DMB) on progression to severe outcome in older COVID-19 patients. doi: https://doi.org/10.1101/202... Available at: https://www.medrxiv.org/content/10.1101/2020.06.01.20112334v2<br /> Now published in Nutrition doi:10.1016/j.nut.2020.111017 <br /> [3] Entrenas Castillo, M., Entrenas Costa, L., Vaquero Barrios, J., Alcalá Díaz, J., López Miranda, J., Bouillon, R., & Quesada Gomez, J. (29th August 2020). Effect of calcifediol treatment and best available therapy versus best available therapy on intensive care unit admission and mortality among patients hospitalized for COVID-19: A pilot randomized clinical study. The Journal of steroid biochemistry and molecular biology, 203, 105751. https://doi.org/10.1016/j.j... Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456194/<br /> [4] Rastogi, A., Bhansali, A., Khare, N., et. Al. (12th November 2020).<br /> Short term, high-dose vitamin D supplementation for COVID-19 disease: a randomised, placebo-controlled, study (SHADE study). Postgraduate Medical Journal Published Online First:. doi: 10.1136/postgradmedj-2020-139065 Available at: https://pmj.bmj.com/content/early/2020/11/12/postgradmedj-2020-139065<br /> [5] Bouillon, R., & Bikle, D. (2019). Vitamin D Metabolism Revised: Fall of Dogmas. J Bone Miner Res. 2019 Nov;34(11):1985-1992. doi:<br /> 10.1002/jbmr.3884. Epub 2019 Oct 29. PMID: 31589774. Available at: https://asbmr.onlinelibrary.wiley.com/doi/full/10.1002/jbmr.3884<br /> [6] Brown, R., Rhein, H., Alipio, M., Annweiler, C., Gnaiger, E., Holick M., Boucher, B., Duque, G., Feron, F., Kenny, R., Montero-Odasso, M., Minisola, M., Rhodes, J.,Haq., A, Bejerot, S., Reiss, L., Zgaga, L., Crawford, M., Fricker, R., Cobbold, P., Lahore, H., Humble, M., Sarkar, A., Karras, S., Iglesias-Gonzalez, J.,Gezen-Ak, D., Dursun E., Cooper, I., Grimes, D. & de Voil C. (April 20, 2020). COVID-19 ’ICU’ risk – 20-fold greater in the Vitamin D Deficient. BAME, African Americans, the Older, Institutionalised and Obese, are at greatest<br /> risk. Sun and ‘D’-supplementation – Game-changers? Research urgently required’: ‘Rapid response re: Is ethnicity linked to incidence or outcomes of COVID-19?’: BMJ, 369(m1548). DOI: 10.1136/bmj.m1548. Available at: https://www.bmj.com/content... (Accessed: 24 November2020. - Alipio study<br /> now in question – rest stands)<br /> [7] Brown R. (15 Oct 2020). Vitamin D Mitigates COVID-19, Say 40+ Patient Studies (listed below) – Yet BAME, Elderly, Care-homers, and Obese are still ‘D’ deficient, thus at greater COVID-19 risk - WHY? BMJ 2020;371:m3872 Available at https://www.bmj.com/content/371/bmj.m3872/rr-5 (Retrieved 24 Nov 2020) <br /> [8] Cohen, P., Blau, J., Eds: Elmore, J., Kunins, L., & Bloom, A. (2020). MD disease 2019 (COVID-19): Outpatient evaluation and management in adults. Literature review. Wolters Kluwner. Available at: https://www.uptodate.com/contents/coronavirus-disease-2019-covid-19-outpatient-evaluation-and-management-in-adults/print<br /> (retrieved 25th November 2020)<br /> [9] Jain, A., Chaurasia, R., Sengar, N., Singh, M., Mahor, S., & Narain, S. (19th Nov 2020). Analysis of vitamin D level among asymptomatic and critically ill COVID-19 patients and its correlation with inflammatory markers. Sci Rep. 2020 Nov 19;10(1):20191. doi: 10.1038/s41598-020-77093-z. PMID: 33214648; PMCID: PMC7677378. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677378/<br /> [10] Radujkovic, A., Hippchen, T., Tiwari-Heckler, S., Dreher, S., Boxberger, M., & Merle, U. Vitamin D Deficiency and Outcome of COVID-19 Patients. Nutrients 2020, 12, 2757. Available at https://www.mdpi.com/2072-6643/12/9/2757 <br /> [11] Hidalgo, A. A., Trump, D. L., & Johnson, C. S. (2010). Glucocorticoid regulation of the vitamin D receptor. The Journal of steroid biochemistry and molecular biology, 121(1-2), 372–375. https://doi.org/10.1016/j.j... Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2907065/<br /> [12] Giustina, A., Bilezikian, J. (eds) (2018). Vitamin D and Glucocorticoid-Induced Osteoporosis. Vitamin D in Clinical Medicine. Front Horm Res. Basel, Karger, 2018, vol 50, pp 149-160 (DOI:10.1159/000486078) Available at https://www.karger.com/Article/Pdf/486078<br /> [13] The RECOVERY Collaborative Group. (17th July 2020). Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report. J New England Journal of Medicine R10.1056/NEJMoa2021436 https://www.nejm.org/doi/fu... Available at https://www.nejm.org/doi/full/10.1056/NEJMoa2021436<br /> [13] FDA. (8th Apr 2017). FDA Drug Safety Communication: Low magnesium levels can be associated with long-term use of Proton Pump Inhibitor drugs (PPIs) https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-communication-low-magnesium-levels-can-be-associated-long-term-use-proton-pump (Accessed 25th November 2020)<br /> [14] Hughes, J., Chiu, D., Kalra, P., & Green, D. (2018). Prevalence and outcomes of proton pump inhibitor associated hypomagnesemia in chronic kidney disease. PLoS ONE 13(5): e0197400. https://doi.org/10.1371/jou... Available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197400<br /> [15] Lee, S., Ha, E., Yeniova, A., et. al. (30th July 2020). Severe clinical outcomes of COVID-19 associated with proton pump inhibitors: a nationwide cohort study with propensity score matching. Gut Published Online First: 30 July 2020. doi: <br /> 10.1136/gutjnl-2020-322248 Available at: <br /> https://gut.bmj.com/content/early/2020/07/30/gutjnl-2020-322248<br /> [17] Hermes Sales, C., Azevedo Nascimento, D., Queiroz Medeiros, A., Costa Lima, K., Campos Pedrosa, L., & Colli, C. (2014). There is chronic latent magnesium deficiency in apparently healthy university students. Nutr Hosp. 2014 Jul 1;30(1):200-4. doi: 10.3305/nh.2014.30.1.7510. PMID: 25137281. Available at: http://www.aulamedica.es/nh/pdf/7510.pdf<br /> [18] Kearns, M., Alvarez, J., & Tangpricha, V. (2014). Large, single-dose, oral vitamin D supplementation in adult populations: a systematic review. Endocrine practice: official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists, 20(4), 341–351. https://doi.org/10.4158/EP1... Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4128480/<br /> [19] Kheiri,B., Abdalla, A., Osman, M. et al. (2018) Vitamin D deficiency and risk of cardiovascular diseases: a narrative review. Clin Hypertens 24, 9 (2018). https://doi.org/10.1186 /s40885-018-0094-4 Available at https://clinicalhypertension.biomedcentral.com/articles/10.1186/s40885-018-0094-4 <br /> [20] Kruglikov, L,. Shah, M., Scherer, E. (Sept 2020). Obesity and diabetes as comorbidities for COVID-19: Underlying mechanisms and the role of viral-bacterial interactions. Elife. 2020 Sep 5;9:e61330. doi: 10.7554/eLife.61330. PMID: 32930095; PMCID: PMC7492082.<br /> [21] Borsche L. Private email 19.11.20

On 2022-08-15 10:05:00, user james hurley wrote:

I congratulate the authors on their protocol for a ‘Systematic Review and Meta-Analysis of Selective Decontamination of the Digestive Tract in Invasively Ventilated Patients’ [1]. That there are already over 40 published articles with “Systematic Review”, “Meta-Analysis” and “Selective Digestive Decontamination” in the title indicate that this is a vexed topic and the definitive publication is yet to appear. <br /> A simple reading of recent Cochrane reviews appears to indicate that SDD lowers both infection incidence and mortality in this patient group, whereas four other interventions do not [2-7]. However, what are the substantial areas of doubt and how can these be best addressed [8]?<br /> May I make some suggestions that might increase the chance that their proposed Systematic Review might be definitive?<br /> Firstly, is the mechanism of action of how Selective Decontamination of the Digestive Tract decrease infection and mortality in invasively ventilated patients understood? Are the animal studies undertaken in mice in the early 1980’s, from which the term ‘Selective Decontamination’ originated, still regarded as valid? Is the term “Selective Digestive Decontamination” a triple misnomer? Several have proposed that the term ‘Control of Gut overgrowth’ as a more accurate term to describe the presumed mechanism [9, 10].<br /> Second, is it true to state that the “Uncertainty about the effectiveness of SDD is due to concerns about the generalisability of RCTs with limited internal and external validity.”? Why did the use of SDD fall out of favour among neutropenic patients in the 1990’s? Is there a potential for rebound infections? Will this proposed systematic review address the question of rebound? Is there a possibility that SDD is ineffective among ICU patients? Is there a possibility that SDD and the rebound effect on its withdrawal is harmful? <br /> Thirdly, the authors will need to confront data inconsistencies between various versions of the published SDD trials that appear in the two Cochrane reviews of this topic [2, 3]. The earlier review obtained ‘Intention to treat’ data from several of the authors of the primary SDD studies which differs from the ‘on treatment’ data as published. The latter often excluded patients who died before completing the four days regarded as necessary to achieve ‘Selective Decontamination’. As a consequence, there is both survivorship bias and an underestimation of infection and mortality incidences in the ‘on treatment’ data. In addition, will the authors use the original data for the study groups as randomly allocated or will they use the adjusted data as published?<br /> Fourth, the authors propose a subgroup analysis comparing the results for “Individual patient vs unit level randomisation (i.e. cluster and cluster/cluster-cross-over).” However, their hypothesis is that the effect is unidirectional, i.e. they expect a benefit to be “,…greater in individual patient randomised trials compared to unit level randomised trials.” This expectation is a restatement of the ‘Stoutenbeek’ postulate, stated in the first SDD study undertaken in the ICU setting, that there would be a contextual effect of using SDD in the ICU context and that this effect would be beneficial to any concurrent control groups patients and, as a consequence, bias downwards the estimates of the SDD intervention within individual patient randomised trials [11, 12]. Stated otherwise, this postulate implies a herd effect similar to that of herd protection from vaccination within a population. <br /> This postulate creates several difficulties for this proposed systematic review. By raising this postulate, does this invalidate the Stable Unit Treatment Value Assumption (SUTVA) that is fundamental to valid estimates of effect size from concurrent controlled trials? If the SUTVA is questioned here, will this invalidate the estimates from the proposed systematic review? Moreover, given this postulate and proposed subgroup test, will the test be one-sided, with the expectation that the effect is uni-directional [only beneficial effect possible], or two sided?<br /> There is evidence that the results of individual [i.e. concurrent control] patient randomised trials of SDD differ to those of unit level [or historical control; i.e. non-concurrent controls] randomised trials and that the SUTVA is questionable for these studies. This has only been addressed in first and second meta-analyses on this topic both published 25 years ago [13, 14]. These indicate that the effect is greater in the former, i.e. contrary to the direction postulated by Stoutenbeek. There is further and more recent evidence for this discrepancy. On the one hand, the three largest subsequently published studies of SDD versus either standard care or SOD, which were all undertaken using unit level randomization [i.e. and non-concurrent controls], showed absolute differences in bacteremia and mortality [before any statistical adjustments] of less than 5 percentage points [15-17]. On the other hand, the most recent Cochrane review of the studies of SDD in this population, which included mostly trials using individual patient randomization [i.e. and concurrent controls], showed absolute differences in pneumonia and mortality of five percentage points or greater [3]. <br /> Will the proposed protocol use the unadjusted data or the adjusted data from these trials? Does the data adjustment account for the Stoutenbeek effect?<br /> Finally, to provide a definitive review, the authors will need to explain why event rates [pneumonia, bacteremia, candidemia and mortality] are generally higher among control groups within trials using individual patient randomization [i.e. with concurrent controls] versus control groups within trials using unit level randomization [i.e. with non-concurrent controls], versus control groups from studies of interventions other that SDD, and versus groups of studies without an intervention. Moreover, why is it that the event rates in the SDD intervention groups are similar to intervention groups from studies of interventions other that SDD in this patient group? The higher event rates are apparent in closer scrutiny of the summary results of the five Cochrane reviews [3-7]. On the one hand, the median control group event rates for pneumonia and mortality [18] are highest within the control groups of studies of SDD versus control groups of studies of other interventions and yet, on the other hand, the event rates for the intervention groups are paradoxically similar to intervention groups of studies of other interventions.<br /> I wish the authors well and hope that they succeed in providing the definitive systematic review of this topic over the arc of time [19].<br /> References<br /> 1. Hammond NE, Myburgh J, Di Tanna GL, Garside T, Vlok R, Mahendran S, Adigbli D, Finfer S, Goodman F, Guyatt G, Venkatesh B. Selective Decontamination of the Digestive Tract in Invasively Ventilated Patients in an Intensive Care Unit: A protocol for a Systematic Review and Meta-Analysis. medRxiv. 2022 Jan 1.<br /> 2. Liberati A, D'Amico R, Pifferi, et al: Antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving intensive care. Cochrane Database Syst Rev 2009; 4: CD000022.<br /> 3. Minozzi S, Pieri S, Brazzi L, Pecoraro V, Montrucchio G, D'Amico R. Topical antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving mechanical ventilation. Cochrane Database of Systematic Reviews 2021, Issue 1. Art. No.: CD000022.<br /> 4. Wang L, Li X, Yang Z, Tang X, Yuan Q, Deng L, Sun X. Semi-recumbent position versus supine position for the prevention of ventilator-associated pneumonia in adults requiring mechanical ventilation. Cochrane Database Syst Rev 2016(1). DOI: 10.1002/14651858.CD009946.pub2.<br /> 5. Gillies D, Todd DA, Foster JP, Batuwitage BT. Heat and moisture exchangers versus heated humidifiers for mechanically ventilated adults and children. Cochrane Database Syst Rev. 2017(9). DOI: 10.1002/14651858.CD004711.pub3.<br /> 6. Bo L, Li J, Tao T, Bai Y, Ye X, Hotchkiss RS, Kollef MH, Crooks NH, Deng X. Probiotics for preventing ventilator-associated pneumonia. Cochrane Database Syst Rev. 2014(10). DOI: 10.1002/14651858.CD009066.pub2.<br /> 7. Zhao T, Wu X, Zhang Q, Li C, Worthington HV, Hua F. Oral hygiene care for critically ill patients to prevent ventilator-associated pneumonia. Cochrane Database Syst Rev. 2020(12).<br /> 8. Hurley JC Selective digestive decontamination, a seemingly effective regimen with individual benefit or a flawed concept with population harm? Crit Care. 2021;25(1).<br /> 9. Silvestri L, Miguel A, van Saene HK. Selective decontamination of the digestive tract: the mechanism of action is control of gut overgrowth. Intensive Care Med. 2012;38(11):1738-50.<br /> 10. Hurley JC (2020) Structural equation modeling the “control of gut overgrowth” in the prevention of ICU-acquired Gram-negative infection. Crit Care 24(1):1-2.<br /> 11. Stoutenbeek CP, Van Saene HK, Miranda DR, et al: The effect of selective decontamination of the digestive tract on colonisation and infection rate in multiple trauma patients. Intensive Care Med 1984; 10(4):185-192.<br /> 12. Hurley JC. Incidences of Pseudomonas aeruginosa-associated ventilator-associated pneumonia within studies of respiratory tract applications of polymyxin: testing the Stoutenbeek concurrency postulates. Antimicrob Agents Chemother. 2018;62(8):e00291-18.<br /> 13. Vandenbroucke-Grauls CM, Vandenbroucke JP. Effect of selective decontamination of the digestive tract on respiratory tract infections and mortality in the intensive care unit. The Lancet. 1991;338:859-62.<br /> 14. Hurley JC. Prophylaxis with enteral antibiotics in ventilated patients: selective decontamination or selective cross-infection?. Antimicrobial agents and chemotherapy. 1995;39(4):941-7.<br /> 15. de Smet AMGA, Kluytmans JAJW, Cooper BS, et al: Decontamination of the digestive tract and oropharynx in ICU patients. N Engl J Med 2009, 360:20–31.<br /> 16. Oostdijk EA, Kesecioglu J, Schultz MJ, Visser CE, De Jonge E, van Essen EH, Bernards AT, Purmer I, Brimicombe R, Bergmans D, van Tiel F. Notice of retraction and replacement: Oostdijk et al. effects of decontamination of the oropharynx and intestinal tract on antibiotic resistance in ICUs: a randomized clinical trial. JAMA 2014; 312 (14): 1429-1437. JAMA 2017; 317(15):1583-4.<br /> 17. Wittekamp BH, Plantinga NL, Cooper BS, et al: Decontamination strategies and bloodstream infections with antibiotic-resistant microorganisms in ventilated patients: a randomized clinical trial. JAMA 2018;320(20):2087-2098. <br /> 18. Hurley JC Discrepancies in Control Group Mortality Rates Within Studies Assessing Topical Antibiotic Strategies to Prevent Ventilator-Associated Pneumonia: An Umbrella Review. Critical care explorations. 2020;2(1).<br /> 19. Pizzo PA. Management of patients with fever and neutropenia through the arc of time: a narrative review. Ann Intern Med. 2019;170(6):389–97.

On 2020-03-30 13:57:15, user Sinai Immunol Review Project wrote:

Summary: Based on a retrospective study of 85 hospitalized COVID patients in a Beijing hospital, authors showed that patients with elevated ALT levels (n = 33) were characterized by significantly higher levels of lactic acid and CRP as well as lymphopenia and hypoalbuminemia compared to their counterparts with normal ALT levels. Proportion of severe and critical patients in the ALT elevation group was significantly higher than that of normal ALT group. Multivariate logistic regression performed on clinical factors related to ALT elevation showed that CRP >= 20mg/L and low lymphocyte count (<1.1*10^9 cells/L) were independently related to ALT elevation—a finding that led the authors to suggest cytokine storm as a major mechanism of liver damage.

Limitations: The article’s most attractive claim that liver damage seen in COVID patients is caused by cytokine storm (rather than direct infection of the liver) hinges solely on their multivariate regression analysis. Without further mechanistic studies a) demonstrating how high levels of inflammatory cytokines can induce liver damage and b) contrasting types of liver damage incurred by direct infection of the liver vs. system-wide elevation of inflammatory cytokines, their claim remains thin. It is also worth noting that six of their elevated ALT group (n=33) had a history of liver disease (i.e. HBV infection, alcoholic liver disease, fatty liver) which can confound their effort to pin down the cause of hepatic injury to COVID.

Significance of the finding: Limited. This article confirms a rich body of literature describing liver damage and lymphopenia in COVID patients.

Review by Chang Moon as part of a project by students, postdocs and faculty at the<br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

The authors describe the results of a single study designed to investigate the extent to which horizontal orientation energy plays a key role in supporting view-invariant face recognition. The authors collected behavioral data from adult observers who were asked to complete an old/new face matching task by learning broad-spectrum faces (not orientation filtered) during a familiarization phase and subsequently trying to label filtered faces as previously seen or novel at test. This data revealed a clear bias favoring the use of horizontal orientation energy across viewpoint changes in the target images. The authors then compared different ideal observer models (cross-correlations between target and probe stimuli) to examine how this profile might be reflected in the image-level appearance of their filtered images. This revealed that a model looking for the best matching face within a viewpoint differed substantially from human data, exhibiting a vertical orientation bias for extreme profiles. However, a model forced to match targets to probes at different viewing angles exhibited a consistent horizontal bias in much the same manner as human observers.

Strengths:

I think the question is an important one: The horizontal orientation bias is a great example of a low-level image property being linked to high-level recognition outcomes, and understanding the nature of that connection is important. I found the old/new task to be a straightforward task that was implemented ably and that has the benefit of being simple for participants to carry out and simple to analyze. I particularly appreciated that the authors chose to describe human data via a lower-dimensional model (their Gaussian fits to individual data) for further analysis. This was a nice way to express the nature of the tuning function, favoring horizontal orientation bias in a way that makes key parameters explicit. Broadly speaking, I also thought that the model comparison they include between the view-selective and view-tolerant models was a great next step. This analysis has the potential to reveal some good insights into how this bias emerges and ask fine-grained questions about the parameters in their model fits to the behavioral data.

Weaknesses:

I will start with what I think is the biggest difficulty I had with the paper. Much as I liked the model comparison analysis, I also don't quite know what to make of the view-tolerant model. As I understand the authors' description, the key feature of this model is that it does not get to compare the target and probe at the same yaw angle, but must instead pick a best match from candidates that are at different yaws. While it is interesting to see that this leads to a very different orientation profile, it also isn't obvious to me why such a comparison would be reflective of what the visual system is probably doing. I can see that the view-specific model is more or less assuming something like an exemplar representation of each face: You have the opportunity to compare a new image to a whole library of viewpoints, and presumably it isn't hard to start with some kind of first pass that identifies the best matching view first before trying to identify/match the individual in question. What I don't get about the view-tolerant model is that it seems almost like an anti-exemplar model: You specifically lack the best viewpoint in the library but have to make do with the other options. Again, this is sort of interesting and the very different behavior of the model is neat to discuss, but it doesn't seem easy to align with any theoretical perspective on face recognition. My thinking here is that it might be useful to consider an additional alternate model that doesn't specifically exclude the best-matching viewpoint, but perhaps condenses appearance across views into something like a prototype. I could even see an argument for something like the yaw-averages presented earlier in the manuscript as the basis for such a model, but this might be too much of a stretch. Overall, what I'd like to see is some kind of alternate model that incorporates the existence of the best-match viewpoint somehow, but without the explicit exemplar structure of the view-specific model.

The design of the view-tolerant model aligned with the requirements of tolerant recognition and revealed the stimulus information enabling to abstract identity away from variations in face appearance. However, it did not involve the notion that such ability may depend on a prototype or summary representation of face identity built up through varied encounters (Burton, Jenkins and Schweinberger 2011, Jenkins, White et al. 2011, Mike Burton 2013, Burton, Kramer et al. 2016, Menon, Kemp and White 2018).

We agree with the Reviewer that the average of the different views of a face is a good proxy of its central tendency (i.e., stable identity properties; Figure 1). We thus followed their suggestion and included an additional model observer that compared specific views to full-spectrum view-averaged identities. The examination of the orientation tuning profile of this so-called view-average model observer confirmed the crucial contribution of horizontal identity cues to view-invariant recognition as the horizontal range best predicted the average summary of full-spectrum face appearances across views. This additional model observer is now presented in the Discussion and Supplementary files 2 and 3.

Besides this larger issue, I would also like to see some more details about the nature of the cross-correlation that is the basis for this model comparison. I mostly think I get what is happening, but I think the authors could expand more on the nature of their noise model to make more explicit what is happening before these cross-correlations are taken. I infer that there is a noise-addition step to get them off the ceiling, but I felt that I had to read between the lines a bit to determine this.

In the Methods section, we now provide detailed information about the addition of noise to model observer cross-correlations: ‘In a pilot phase, we measured the overall identification performance of each model. Initially, the view-selective model performed at ceiling, yielding a correlation of 1 since there was an exact target-probe match across all trials. To avoid ceiling effects and to keep model performance close to human levels (Supplementary File 2), we thus decreased the signal-to-noise ratio (SNR) of the target and probe images to .125 by combining each with distinct noise patterns (face RMS contrast: .01; noise RMS contrast: .08). Each trial (i.e. target-probe pairing) was iterated ten times with different random noise patterns.’

We also added a supplemental with the graphic illustration of the d’ distributions of each model and human observers: ‘Sensitivity d’ of the view-tolerant model was much lower than view-selective model and human sensitivity (Supplementary File 2), even without noise. The view-tolerant model therefore processed fully visible stimuli (SNR of 1). This decreased sensitivity in the view-tolerant compared to the view-selective model is expected, as none of the probes exactly matched the target at the pixel level due to viewpoint differences. In contrast to humans who rely on internally stored representations to match identity across views, the model observer lacks such internal representations and entirely relies on (less efficient) pixelwise comparisons.’

Another thing that I think is worth considering and commenting on is the stimuli themselves and the extent to which this may limit the outcomes of their behavioral task. The use of the 3D laser-scanned faces has some obvious advantages, but also (I think) removes the possibility for pigmentation to contribute to recognition, removes the contribution of varying illumination and expression to appearance variability, and perhaps presents observers with more homogeneous faces than one typically has to worry about. I don't think these negate the current results, but I'd like the authors to expand on their discussion of these factors, particularly pigmentation. Naively, surface color and texture seem like they could offer diagnostic cues to identity that don't rely so critically on horizontal orientations, so removing these may mean that horizontal bias is particularly evident when face shape is the critical cue for recognition.

Our stimuli were originally designed by Troje and Bulthoff (1996). These are 3D laser scans of white individuals aged between 20 and 40 years, posing with a neutral expression. Different views of the faces were shot under a fixed illumination. Ears and a small portion of the neck were visible while the hair region was removed. All face images had a normalized skin color and we further converted them to grayscales

While we agree that this stimulus set offers a restricted range of within- and between-identity variations compared to what is experienced in natural settings, we believe that the present findings generalize to more ecological viewing conditions. Indeed, past evidence showed that the recognition of face pictures shot under largely variable pose, age, expression, illumination, hair style is tuned to the horizontal range of the face stimulus (Dakin and Watt 2009, Dumont, Roux-Sibilon and Goffaux 2024). In other words, our finding that view-tolerant identity recognition is mainly driven by horizontal face information would likely replicate with the use of a more ecological stimulus set.

Moreover, the skin color normalization and grayscale conversion, while limiting the range of face variability, did not eliminate the contribution of surface pigmentation in our study. It is thus unlikely that our findings exclusively reflect the orientation dependence of face shape processing. Pigmentation refers to all surface reflectance properties (Russell, Sinha et al. 2006) and hue (color) is only one among others. The grayscaled 3D laser scanned faces used here contained natural variations in crucial surface cues such as skin albedo (i.e., how light or dark the surface appears) and texture (i.e., spatial variation in how light is reflected); they have actually been used to disentangle the role of shape and surface cues to identity recognition (e.g., Troje and Bulthoff 1996, Vuong, Peissig et al. 2005, Russell, Sinha et al. 2006, Russell, Biederman et al. 2007, Jiang, Dricot et al. 2009). Moreover, a past study of ours demonstrated that the diagnosticity of the horizontal range of face information is not restricted to face shape cues; the specialized processing of face shape and surface both selectively rely on horizontal information (Dumont, Roux-Sibilon and Goffaux 2024).

For these reasons, the present findings are unlikely to be fully determined by shape processing, and we expect them to generalize to more ecological stimulus sets. We discuss these aspects in the revised manuscript.

Reviewer #2 (Public review):

This study investigates the visual information that is used for the recognition of faces. This is an important question in vision research and is critical for social interactions more generally. The authors ask whether our ability to recognise faces, across different viewpoints, varies as a function of the orientation information available in the image. Consistent with previous findings from this group and others, they find that horizontally filtered faces were recognised better than vertically filtered faces. Next, they probe the mechanism underlying this pattern of data by designing two model observers. The first was optimised for faces at a specific viewpoint (view-selective). The second was generalised across viewpoints (view-tolerant). In contrast to the human data, the view-specific model shows that the information that is useful for identity judgements varies according to viewpoint. For example, frontal face identities are again optimally discriminated with horizontal orientation information, but profiles are optimally discriminated with more vertical orientation information. These findings show human face recognition is biased toward horizontal orientation information, even though this may be suboptimal for the recognition of profile views of the face.

One issue in the design of this study was the lowering of the signal-to-noise ratio in the view-selective observer. This decision was taken to avoid ceiling effects. However, it is not clear how this affects the similarity with the human observers.

In the Methods section, we now provide detailed information about the addition of noise to model observer cross-correlations: ‘In a pilot phase, we measured the overall identification performance of each model. Initially, the view-selective model performed at ceiling, yielding a correlation of 1 since there was an exact target-probe match across all trials. To avoid ceiling effects and to keep model performance close to human levels (Supplementary File 2), we thus decreased the signal-to-noise ratio (SNR) of the target and probe images to .125 by combining each with distinct noise patterns (face RMS contrast: .01; noise RMS contrast: .08). Each trial (i.e. target-probe pairing) was iterated ten times with different random noise patterns.’

We also added a supplemental with the graphic illustration of the d’ distributions of each model and human observers.

Another issue is the decision to normalise image energy across orientations and viewpoints. I can see the logic in wanting to control for these effects, but this does reflect natural variation in image properties. So, again, I wonder what the results would look like without this step.

All stimuli were matched for luminance and contrast. It is crucial to normalize image energy across orientations as natural image energy is disproportionately distributed across orientations (e.g., Hansen, Essock et al. 2003). Images of faces cropped from their background as used here contain most of their energy in the horizontal range (Keil 2008, Keil 2009, Goffaux and Greenwood 2016). If not normalized after orientation filtering, such uneven distribution of energy would boost recognition performance in the horizontal range across views. Normalization was performed across our experimental conditions merely to avoid energy from explaining the influence of viewpoint on the orientation tuning profile.

We were not aware of any systematic natural variations of energy across face views. To address this, we measured face average energy (i.e., RMS contrast) in the original stimulus set, i.e., before the application of any image processing or manipulation. Background pixels were excluded from these image analyses. Across yaws, we found energy to range between .11 and .14 on a 0 to 1 grayscale. This is moderate compared to the range of energy variations we measured across identities (from .08 to .18). This suggests that variations in energy across viewpoints are moderate compared to variations related to identity. It is unclear whether these observations are specific to our stimulus set or whether they are generalizable to faces we encounter in everyday life. They, however, indicate that RMS contrast did not substantially vary across views in the present study and suggest that RMS normalization is unlikely to have affected the influence of viewpoint on recognition performance.

In the revised methods section, we explicitly motivate energy normalization: ‘Images of faces cropped from their background as used here contain most of their energy in the horizontal range (Goffaux, 2019; Goffaux & Greenwood, 2016; Keil, 2009). Across yaws, we found face energy to range between .11 and .14 on a 0 to 1 grayscale, which is moderate compared to the range of face energy variations we measured across identities (from .08 to .18). To prevent energy from explaining our results, in all images, the luminance and RMS contrast of the face pixels were fixed to 0.55 and 0.15, respectively, and background pixels were uniformly set to 0.55. The percentage of clipped pixel values (below 0 or above 1) per image did not exceed 3%.’.

Despite the bias toward horizontal orientations in human observers, there were some differences in the orientation preference at each viewpoint. For example, frontal faces were biased to horizontal (90 degrees), but other viewpoints had biases that were slightly off horizontal (e.g., right profile: 80 degrees, left profile: 100 degrees). This does seem to show that differences in statistical information at different viewpoints (more horizontal information for frontal and more vertical information for profile) do influence human perception. It would be good to reflect on this nuance in the data.

Indeed, human performance data indicates that while identity recognition remains tuned to horizontal information, horizontal tuning peak shows some variation across viewpoints. We primarily focused on the first aspect because of its direct relevance to our research objective, but also discussed the second aspect: with yaw rotation, certain non-horizontal morphological features such as the jaw line or nose bridge, etc. may increasingly contribute to identity recognition, whereas at frontal or near frontal views, features are mostly horizontally-oriented (e.g., Keil 2008, Keil 2009). In the revised Discussion, we directly relate the modest fluctuations of peak location to yaw differences in face feature appearance.

Recommendations for the authors:

Reviewing Editor Comments:

Based on a discussion with the reviewers, we integrated the recommendations and reached a consensus on the eLife assessment. To move from a "solid" to a "compelling/convincing" strength-of-evidence rating, please address the reviewers' comments. Key points are to clarify and test the plausibility of the models (e.g., effects of different noise-addition steps, inclusion/exclusion of specific orientation channels in the view-dependent comparison, and alternative decision criteria), and to address or discuss the limitations of the stimulus set in capturing recognition under more naturalistic scenarios, for example, including texture cues.

Reviewer #1 (Recommendations for the authors):

I generally found the paper to be very well-written, so I have only a few minor comments here.

(1) I didn't really follow why the estimation of the Gaussian functions described in the text was preferred over a simpler ML framework. Do these approaches differ that much? I see references to prior studies in which these were applied, so I can certainly go check these out, but I could see value in adding just a bit of text to briefly make the case that this is important.

Employing a simpler linear framework, i.e. a linear model predicting d’ from the interaction between orientation and viewpoint, would result in an 8 (orientation) * 7 (viewpoint) design that is difficult to analyze. The interaction term would almost certainly reach significance but its interpretation would be limited. We would either have to rely on numerous local comparisons, which are not particularly informative for our research objectives (e.g., knowing whether d’ differs significantly between two adjacent orientations at a given viewpoint is of little relevance), or to use a polynomial contrast approach (testing the linear, quadratic, … up to the 7th order trends), which would also be difficult to interpret. For such complex, approximately Gaussian-shaped data, the highest-order polynomial trend would likely provide the best fit, but without offering meaningful insight.

In contrast, a nonlinear approach appears more appropriate. The Gaussian model we used allows us to characterize the parameters of the tuning profile, namely, peak location, peak amplitude, standard deviation (or bandwidth) and base amplitude. These parameters are not merely statistical parameters. Rather, they are directly interpretable in cognitive/functional terms. The peak location corresponds to the orientation at which the Gaussian curve is centred, i.e. the preferred orientation band for identity recognition. The standard deviation represents the width of the curve, reflecting the strength or selectivity of the tuning. The base amplitude is the height of the Gaussian curve base, indicating the minimum level of sensitivity, typically found near vertical orientation. Finally, the peak amplitude refers to the height of the Gaussian curve relative to its baseline, that is, it captures the advantage of horizontal over vertical orientations.

Moreover, the use of a nonlinear, Gaussian model is motivated by past work that showed that the Gaussian function fits the evolution of recognition performance as a function of orientation (Dakin and Watt 2009, Goffaux and Greenwood 2016). Orientation selectivity at primary stages of visual processing has also been modelled using Gaussian (or Difference of Gaussians; Ringach, Hawken and Shapley 2003).

We revised the data analysis section to include a justification for our use of a Gaussian model: ‘Therefore, fitting the human sensitivity data could be fitted using a simple Gaussian model. seemed most appropriate as it allows characterizing the parameters of the tuning profile, namely, peak location, peak amplitude, standard deviation and base amplitude, which are directly interpretable in cognitive/functional terms. Moreover, the use of a nonlinear, Gaussian model is motivated by past work that showed that the Gaussian function fits the evolution of recognition performance as a function of orientation (Dakin & Watt, 2009; Goffaux & Greenwood, 2016). Simpler frameworks, i.e. a linear model predicting d’ from the interaction between orientation and viewpoint, would result in an 8 (orientation) * 7 (viewpoint) design that is difficult to analyze and interpret.’

(2) When reporting the luminance and contrast of your stimuli, please make clear what these units and measures are. This was a case where I had to take a second to assure myself that I knew what the values meant.

We clarified that the luminance and contrast values reported in the manuscript are on a grey scale ranging from 0 to 1.

(3) In your Procedure section, I think describing the familiarization task right away would help the text flow more clearly. At present, you began talking about the old/new task, and I was immediately wondering how familiarization worked!

The procedure section now starts with the description of the familiarization task.

(4) p. 3 - "Culminates" doesn't seem like the right word here.

We agree and rephrased this way: ‘The tolerance of face identity recognition is stronger for familiar than unfamiliar faces’.

(5) p. 5 - I think "with the multiple" shouldn't have "the".

Indeed, we removed the “the”.

Reviewer #2 (Recommendations for the authors):

I enjoyed reading the manuscript, but thought the Introduction was a bit long. I wasn't sure about the relevance of the section on temporal contiguity. I think this might have been more relevant if this had been a manipulation in the design. So, I wonder if this might be shortened or removed to focus on the key questions. On the other hand, I found the overview of the view-selective and view-tolerant to be a bit brief. There is plenty of detail here, but I found it difficult to break down what was done when I first read it. It might be good to provide an overview in the Discussion too.

While past research on the contribution of temporal contiguity to face identity recognition brings interesting insights into the nature of the visual experience leading to view-tolerant performance, we agree with the Reviewer that this aspect is not directly at stake here. We reduced the review of this literature in the Introduction. We clarified the description of the model observers as suggested by the reviewer and made sure to provide an overview of the model observers in the Discussion as well.

References.

Burton, A. M., R. Jenkins and S. R. Schweinberger (2011). "Mental representations of familiar faces." Br J Psychol 102(4): 943-958.

Burton, A. M., R. S. Kramer, K. L. Ritchie and R. Jenkins (2016). "Identity From Variation: Representations of Faces Derived From Multiple Instances." Cogn Sci 40(1): 202-223.

Dakin, S. C. and R. J. Watt (2009). "Biological "bar codes" in human faces." J Vis 9(4): 2 1-10.

Dumont, H., A. Roux-Sibilon and V. Goffaux (2024). "Horizontal face information is the main gateway to the shape and surface cues to familiar face identity." PLoS One 19(10): e0311225.

Goffaux, V. and J. A. Greenwood (2016). "The orientation selectivity of face identification." Scientific Reports 6(34204): 34204.

Hansen, B. C., E. A. Essock, Y. Zheng and J. K. DeFord (2003). "Perceptual anisotropies in visual processing and their relation to natural image statistics." Network 14(3): 501-526.

Jenkins, R., D. White, X. Van Montfort and A. Mike Burton (2011). "Variability in photos of the same face." Cognition 121(3): 313-323.

Jiang, F., L. Dricot, V. Blanz, R. Goebel and B. Rossion (2009). "Neural correlates of shape and surface reflectance information in individual faces." Neuroscience 163(4): 1078-1091.

Keil, M. S. (2008). "Does face image statistics predict a preferred spatial frequency for human face processing?" Proc Biol Sci 275(1647): 2095-2100.

Keil, M. S. (2009). ""I look in your eyes, honey": internal face features induce spatial frequency preference for human face processing." PLoS Comput Biol 5(3): e1000329.

Menon, N., R. I. Kemp and D. White (2018). "More than a sum of parts: robust face recognition by integrating variation." R Soc Open Sci 5(5): 172381.

Mike Burton, A. (2013). "Why has research in face recognition progressed so slowly? The importance of variability." Q J Exp Psychol (Hove) 66(8): 1467-1485.

Ringach, D. L., M. J. Hawken and R. Shapley (2003). "Dynamics of orientation tuning in macaque V1: the role of global and tuned suppression." Journal of neurophysiology 90(1): 342-352.

Russell, R., I. Biederman, M. Nederhouser and P. Sinha (2007). "The utility of surface reflectance for the recognition of upright and inverted faces." Vision Res 47(2): 157-165.

Russell, R., P. Sinha, I. Biederman and M. Nederhouser (2006). "Is pigmentation important for face recognition? Evidence from contrast negation." Perception 35(6): 749-759.

Troje, N. F. and H. H. Bulthoff (1996). "Face recognition under varying poses: the role of texture and shape." Vision Res 36(12): 1761-1771.

Vuong, Q. C., J. J. Peissig, M. C. Harrison and M. J. Tarr (2005). "The role of surface pigmentation for recognition revealed by contrast reversal in faces and Greebles." Vision Res 45(10): 1213-1223.

Author response:

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

Public Reviews:

Reviewer #2 (Public review):

In the manuscript Ruhling et al propose a rapid uptake pathway that is dependent on lysosomal exocytosis, lysosomal Ca2+ and acid sphingomyelinase, and further suggest that the intracellular trafficking and fate of the pathogen is dictated by the mode of entry. Overall, this is manuscript argues for an important mechanism of a 'rapid' cellular entry pathway of S.aureus that is dependent on lysosomal exocytosis and acid sphingomyelinase and links the intracellular fate of bacterium including phagosomal dynamics, cytosolic replication and host cell death to different modes of uptake. 

Key strength is the nature of the idea proposed, while continued reliance on inhibitor treatment combined with lack of phenotype / conditional phenotype for genetic knock out is a major weakness. 

In the revised version, the authors perform experiments with ASM KO cells to provide genetic evidence of the role for ASM in S. aureus entry through lysosomal modulation. The key additional experiment is the phenotype of reduced bacterial uptake in low serum, but not in high serum conditions. The authors suggest this could be due to the SM from serum itself affecting the entry. While this explanation is plausible, prolonged exposure of cells to low serum is well documented to alter several cellular functions, particularly in the context of this manuscript, lysosomal positioning, exocytosis and Ca2+ signaling. A better control here could be WT cells grown in low serum.

As the reviewer suggested, we did culture both, WT control cells as well as ASM knock-outs, under low serum conditions before conducting the invasion assays. Hence, the detected effects on S. aureus invasion must be caused by lack of functional ASM in the mutant.

We apologize that this did not become evident from the manuscript’s text. We thus included a change in line 259 which now reads:

”To test whether FBS confounded our invasion experiments, we cultivated WT as well as ASM K.O. cells in medium with reduced FBS concentration (1%) and determined the S. aureus invasion efficiency (Figure 2I).”

If SM in serum can interfere, why do they see such pronounced phenotype on bacterial entry in WT cells upon chemical inhibition?

We explain the differences between inhibitor-treated WT cells and ASM K.O.s by the severe accumulation of SM upon genetic ablation of ASM. We demonstrated this by HPLC-MS/MS measurements in Figure 2L. If cells were cultured in 10% FBS, an ASM K.O. resulted in approx. 4-times higher levels of cellular SM C18:0 when compared to WT cells, while amitriptyline treatment of WT cells had no effect, and ARC39 treatment increased SM C18:0 levels only by 2-fold. This likely results from different durations of SM accumulation in the cell pools which is caused either by complete absence of ASM (in case of the ASM K.O.) or only in the hour-range upon treatment with the inhibitors.

Under low serum conditions, the severe SM C18:0 accumulation in the ASM K.O. was found decreased (from 4-fold to 2-fold when compared to WT cells; Figure 2M). Here, the WT cells used as reference also were cultured in the same manner as the ASM K.O. A similar pattern was observed for other SM species (Supp. Figure 3). This correlates with the S. aureus invasion phenotype in ASM K.O.: under high serum conditions (and resulting in severe SM accumulation) we did not detect an invasion defect, while under low serum conditions (resulting in only moderate SM accumulation) S. aureus invasion was reduced in the knock-outs when compared to WT cells cultured in the same conditions, respectively.

While the authors argue a role for undetectable nano-scale Cer platforms on the cell surface caused by ASM activity, results do not rule out a SM independent role in the cellular uptake phenotype of ASM inhibitors.

Since the comments starting with the line above are identical to the previous comments by the reviewer, we assume that these points of criticism still resound with the Reviewer, although we had agreed previously with the reviewer that we do not show formation of ceramide-enriched platforms, we had changed the manuscript accordingly in the previous revision round already (see also our comment below).

The authors have attempted to address many of the points raised in the previous revision. While the new data presented provide partial evidence, the reliance on chemical inhibitors and lack of clear results directly documenting release of lysosomal Ca2+, or single bacterial tracking, or clear distinction between ASM dependent and independent processes dampen the enthusiasm.

We continue to share the reviewer’s desire to discriminate between ASM-dependent and ASMindependent processes, but the simultaneous occurrence of multiple pathways of bacterial uptake is currently the limiting factor and technological challenge in our laboratory, since these events happen rapidly. We do hope that we or others will be able to address these limitations in the future, for instance with the technologies suggested by the reviewer.

I acknowledge the author's argument of different ASM inhibitors showing similar phenotypes across different assays as pointing to a role for ASM, but the lack of phenotype in ASM KO cells is concerning. The author's argument that altered lipid composition in ASM KO cells could be overcoming the ASMmediated infection effects by other ASM-independent mechanisms is speculative, as they acknowledge, and moderates the importance of ASM-dependent pathway. The SM accumulation in ASM KO cells does not distinguish between localized alterations within the cells. If this pathway can be compensated, how central is it likely to be ? 

We here want to elaborate again, since our revision experiments demonstrate the ASM-dependency of the rapid uptake under low serum conditions – see also above. We were convinced that the genetic evidence of an S. aureus invasion phenotype in ASM K.O.s under these conditions would eliminate the reviewer’s concern about the role of ASM during the bacterial invasion (see also above). Our lipidomics data of ASM K.O.s cultured in 1% and 10% FBS (Figure 2, M, Supp. Figure 3) and inhibitor-treated WT cells (Figure 2L, Supp. Figure 3) show a correlation between SM accumulation and the invasion phenotype observed by us.

We agree with the reviewer, however, that it remains elusive why changes in the sphingolipidome increase ASM-independent S. aureus internalization by host cells. One explanation is a dysfunction of the lipid raft-associated protein caveolin-1 upon strong SM accumulation, which was previously shown to appear in ASM-deficient cells (1, 2). A lack of caveolin-1 results in strongly increased host cell entry of S. aureus in certain cell types (3, 4). In other cell types, such as A549 cells, S. aureus invades in an αtoxin and caveolin-1 dependent fashion (5). It will be interesting to study, to what extent such processes as described by Goldmann and colleagues will depend on ASM. However, a characterization of the mechanism behind these observations requires further experimentation and is beyond the scope of the current manuscript. 

As to the centrality of the pathway: we cannot and do not make any assumptions on the centrality of the pathway and its importance in vivo. As scientists we were intrigued by our finding of an ASM dependent uptake pathway for S. aureus – especially its speed. In different as of yet still unidentified host cell types or cell lines such a pathway may pose a major entry point for pathogens. Alternatively, we may have identified an ASM-dependent mode of receptor uptake, with which the bacteria “piggyback” into the cells.

The authors allude to lower phagosomal escape rate in ASM KO cells compared to inhibitor treatment, which appears to contradict the notion of uptake and intracellular trafficking phenotype being tightly linked. As they point out, these results might be hard to interpret.

We again want to add that we measured phagosomal escape of S. aureus in WT and ASM K.O. cells cultured in 1% FBS (low serum conditions) and compared it to escape rates obtained with host cells cultured in 10% FBS. Again, we infected cells for 10 or 30 min and determined the escape rates 3h p.i. However, the results are similar to escape rates determined with 10% FBS (see Author response image 1). This was addressed already during the manuscript’s first revision. We found that escape rates of S. aureus were significantly decreased in absence of ASM regardless of the FBS concentration in the medium.

Author response image 1.

We therefore think that prolonged absence of ASM has additional side effects. For instance, certain endocytic pathways could be up- or down-regulated to adapt for the absence of ASM or could be affected by other changes in the lipidome (that can be minimized but not completely prevented by culturing cells in 1% FBS). This could, for instance, affect maturation of S. aureus-containing phagosomes and hence phagosomal escape.

As it is currently unclear in how far the prolonged absence of ASM activity affects cellular processes, we think other experiments investigating the role of ASM-dependent invasion for phagosomal escape are more reliable. Most importantly, bacteria that enter host cell early during infection (and thus, predominantly via the “rapid” ASM-dependent pathway) possess lower phagosomal escape rates than bacteria that entered host cells later during infection (Figure 5, D and E). This is confirmed by higher escapes rates upon blocking ASM-dependent invasion with Vacuolin-1 (Figure 4E) and three different ASM inhibitors (Figure 4C and D). We further demonstrate that sphingomyelin on the plasma membrane during invasion influences phagosomal escape, while sphingomyelin levels in the phagosomal membrane did not change phagosomal escape (Figure5 a and b). This is summarized in Figure 5F.

Could an inducible KD system recapitulate (some of) the phenotype of inhibitor treatment? If S. aureus does not escape phagosome in macrophages, could it provide a system to potentially decouple the uptake and intracellular trafficking effects by ASM (or its inhibitor treatment) ?

Knock-downs in our laboratory are based on the vector pLVTHM(6). Inducible knock-downs in the cells would require the introduction of an inducible Tet^on system, which the cells currently do not harbor.

However, it needs to be stated that for optimal gene knock-downs, the induction of this system has to be performed by doxycycline supplementation in the medium for 7 days thus leading to several days of growth of the cells, which will allow the cells to adapt their lipid metabolism thus reflecting a situation that we encounter for the K.O.s.

ASM-dependent uptake of S. aureus in macrophages has been demonstrated before (7). However, the course of infection in macrophages differs from non-professional phagocytes (8). E.g. in macrophages, S. aureus replicates within phagosomes, whereas in non-professional phagocytes replicates in the host cytosol. Absence of ASM therefore may influence the intracellular infection of macrophages with S. aureus in a distinct manner.

The role of ASM on cell surface remains unclear. The hypothesis proposed by the authors that the localized generation of Cer on the surface by released ASM leads to generation of Cer-enriched platforms could be plausible, but is not backed by data, technical challenges to visualize these platforms notwithstanding. These results do not rule out possible SM independent effects of ASM on the cell surface, if indeed the role of ASM is confirmed by controlled genetic depletion studies.

We agree with the reviewer that we do not show generation of ceramide-enriched platforms (see also above). We thus already had changed Figure 6F in the revised manuscript to make clear that it remains elusive whether ceramide-enriched platforms are formed. We also had added a sentence to the discussion (line 615) to emphasize that the existence of these microdomains is still debated in lipid research.

We think that the following observations support SM-dependent effects of ASM during S. aureus invasion:

(i) Reduced invasion upon removing SM from the plasma membrane (Figure 2N, Supp. Figure 2M)

(ii) Increased invasion in TPC1 and Syt7 K.O. (Figure 2, P) in presence of exogenously added SMase.

However, we agree with the reviewer that we do not directly demonstrate ASM-mediated SM cleavage during S. aureus invasion. Hence, we had added a sentence to the discussion that mentions a possible SM-independent role of ASM for invasion (line 556) that reads:

“Since it remains elusive to which extent ASM processes SM on the plasma membrane during S. aureus invasion, one may speculate that ASM could also have functions other than SM metabolization during host cell entry of the pathogen. However, we did not detect a direct interaction between S. aureus and ASM in an S. aureus-host interactome screen (9).”

The reviewer acknowledges technical challenges in directly visualizing lysosomal Ca2+ using the methods outlined. Genetically encoded lysosomal Ca2+ sensor such as Gcamp3-ML1 might provide better ways to directly visualize this during inhibitor treatment, or S. aureus infection. 

We again thank the reviewer for this suggestion. We already had included the following section in our discussion (then: line 593): “Since fluorescent calcium reporters allow to monitor this process microscopically, future experiments may visualize this process in more detail and contribute to our understanding of the underlying signaling. mechanisms.”

References for the purpose of this response letter:

(1) Rappaport, J., C. Garnacho, and S. Muro, Clathrin-mediated endocytosis is impaired in type AB Niemann-Pick disease model cells and can be restored by ICAM-1-mediated enzyme replacement. Mol Pharm, 2014. 11(8): p. 2887-95.

(2) Rappaport, J., et al., Altered Clathrin-Independent Endocytosis in Type A Niemann-Pick Disease Cells and Rescue by ICAM-1-Targeted Enzyme Delivery. Mol Pharm, 2015. 12(5): p. 1366-76.

(3) Hoffmann, C., et al., Caveolin limits membrane microdomain mobility and integrin-mediated uptake of fibronectin-binding pathogens. J Cell Sci, 2010. 123(Pt 24): p. 4280-91.

(4) Tricou, L.-P., et al., Staphylococcus aureus can use an alternative pathway to be internalized by osteoblasts in absence of β1 integrins. Scientific Reports, 2024. 14(1): p. 28643.

(5) Goldmann, O., et al., Alpha-hemolysin promotes internalization of Staphylococcus aureus into human lung epithelial cells via caveolin-1- and cholesterol-rich lipid rafts. Cell Mol Life Sci, 2024. 81(1): p. 435.

(6) Wiznerowicz, M. and D. Trono, Conditional suppression of cellular genes: lentivirus vectormediated drug-inducible RNA interference. J Virol, 2003. 77(16): p. 8957-61.

(7) Li, C., et al., Regulation of Staphylococcus aureus Infection of Macrophages by CD44, Reactive Oxygen Species, and Acid Sphingomyelinase. Antioxid Redox Signal, 2018. 28(10): p. 916-934.

(8) Moldovan, A. and M.J. Fraunholz, In or out: Phagosomal escape of Staphylococcus aureus. Cell Microbiol, 2019. 21(3): p. e12997.

(9) Rühling, M., et al., Identification of the Staphylococcus aureus endothelial cell surface interactome by proximity labeling. mBio, 2025. 0(0): p. e03654-24.

Author Response:

(1) Clarification of the distinction between resting-state trait measures and ongoing neural dynamics

All the Reviewers commented that this study provides a useful characterization of the relationship between trait-based resting-state neural dynamics and behavioral measures. At the same time, we agree that including ongoing EEG dynamics during task performance would have added important complementary information. In particular, task-related EEG would allow a more direct characterization of the relationship between ongoing neural activity and behavioral indices at the single trial level, thereby helping to clarify the role of ongoing neural dynamics in evidence accumulation and perceptual decision-making. It would also enable testing how pre-stimulus alpha oscillations and aperiodic activity dynamically influence temporal integration, serial dependence, and confidence on a trial-by-trial basis.

However, we would like to emphasize that the primary aim of the present study was to investigate trait-level resting-state neural dynamics, which are known to be relatively stable and consistent within individuals, such as individual alpha frequency (e.g., Grandy et al., 2013; Wiesman & Wilson, 2019; Gray & Emmanouil, 2020) and aperiodic neural dynamics (Demuru and Fraschini, 2020; Pathania et al., 2021; Euler et al., 2024), and to examine whether these stable neural characteristics predict behavioral measures indexing temporal perception. Accordingly, the present study was designed to address how stable individual differences in resting-state neural dynamics shape temporal performance, rather than within-task neural fluctuations during the temporal task. We agree that combining resting-state and task-related EEG would be a valuable direction for future work, but this lies beyond the scope of the current dataset, as EEG was not recorded during task performance. Furthermore, we agree with the Reviewers that some of the wording in the Discussion can be clarified to emphasize the trait-level, rather than trial-level, nature of the task and potential interpretations.

Additionally, we agree that the relationship between eyes-open (EO) and eyes-closed (EC) resting-state EEG, and their differential associations with behavior, warrants further discussion. In our data, EO resting-state activity emerged as a stronger predictor of behavioral performance than EC. Conceptually, resting-state EO and EC should not be considered interchangeable measures of the same underlying neural activity, but rather as related yet distinct brain states, with overlapping neural generators expressed under different state constraints. EC is typically associated with stronger posterior alpha activity and a more internally oriented mode, whereas EO reflects a more visually engaged and vigilant state, closer to the conditions under which perceptual judgments are formed. This may explain why, in our findings, brain–behavior associations are more evident in EO, consistent with the greater similarity between the EO condition and the task context. In this sense, EO may emphasize exteroceptive processing and visual readiness, whereas EC reflects a more internally oriented configuration. This difference in functional weighting could account for the stronger behavioral correlations observed in EO in the present study. The distinction between these resting states has been emphasized in previous EEG and neuroimaging work showing differences in power, topography, and large-scale network organization (e.g., Marx et al., 2004). Additionally, these state-related differences may reflect physiological changes related to sensory processing (El Boustani et al., 2009) and arousal (Lendner et al., 2020). Accordingly, the present dissociation may arise because EO provides a resting-state measure that is more proximal to the sensory and excitability conditions engaged during task performance (for similar findings, see also Deodato and Melcher, 2024). However, we agree with the reviewers that further clarification of these state-related differences is warranted. In the revised manuscript, we will (i) expand the Discussion to more clearly articulate the conceptual distinction between EO and EC and their expected links to perceptual and confidence measures, (ii) systematically describe EO–EC differences across all EEG measures analyzed, and (iii) quantify the relationship between EO and EC indices to directly assess the extent to which they share trait-like variance across individuals.

In the revised manuscript, we will clarify these points by adjusting the text, strengthening the conceptual framing, and expanding the Discussion, including a more detailed outline of future research directions.

(2) Functional interpretation of psychometric measures

The Reviewers raised an important point regarding the interpretation of the psychometric parameters investigated in our study. In particular, we agree that the slope of a binary psychometric function does not provide a direct measure of sensory temporal resolution or perceptual sensitivity, and that our original wording may have overstated this interpretation. Rather, the slope reflects the steepness of the transition between response categories and indexes overall behavioural variability, which can arise from multiple sources, including variability in sensory encoding, decision criteria, and occasional response errors (e.g., Wichmann and Hill 2001; Prins 2012).

We therefore agree that interpreting steeper slopes as necessarily reflecting “temporal precision” may be overly specific, and that there are other possible interpretations. In the revised manuscript, we will adopt more cautious terminology and describe the slope more generally as indexing behavioral variability in the transition between perceptual reports, which may reflect a combination of sensory and decisional factors. Importantly, our results demonstrate robust relationships between neural measures and the consistency or sharpness of perceptual categorization, rather than uniquely isolating sensory temporal resolution. While, in standard psychophysical frameworks, the slope is related to internal variability in the sensory representation, this relationship depends on model assumptions and does not uniquely isolate sensory precision (e.g., Prins, 2016). Following the reviewers’ suggestion, we will also refine our psychometric modeling by incorporating a lapse parameter. We agree with the Reviewer that accounting for occasional stimulus-independent errors (e.g., lapses) can improve parameter estimation and prevent biases in slope and threshold estimates when lapse rates are implicitly fixed to zero (Wichmann & Hill, 2001). In the revised manuscript, we will therefore (i) clarify the terminology used to describe psychometric parameters and (ii) report additional analyses including lapse rates.

In addition, we agree that complementary modeling approaches could help disentangle perceptual and decisional contributions to the observed effects by providing access to latent parameters of perceptual decision-making. For example, within a signal detection framework, one could test whether EEG measures relate to perceptual sensitivity versus decision criterion, while sequential sampling models such as the diffusion model (e.g., Ratcliff and McKoon, 2008) could assess whether neural measures are associated with parameters such as drift rate, decision boundary, starting bias, or trial-to-trial variability. However, several characteristics of the present paradigm limit the direct applicability of these approaches. First, the task relies on a continuous manipulation of sensory evidence across stimulus durations (ISIs), and behavioral responses are summarized through psychometric functions rather than modeled at the single-trial level. As a result, the current framework does not provide direct access to trial-by-trial latent decision variables required by these models. Second, reaction times were not collected, which constrains the application of sequential sampling models that rely on joint modeling of accuracy and response times. Finally, while the task involves categorical judgments (integration vs. segregation), it does not include explicit signal-absent or catch trials, which can help constrain sensitivity and criterion estimates within classical signal detection formulations. Despite these limitations, we agree that these approaches could still provide useful insights. In the revised manuscript, we will explore whether alternative modeling approaches (e.g., signal detection-based metrics or Bayesian psychometric modeling) can help further characterize the contributions of perceptual sensitivity, decision criterion, and response variability to our behavioral measures. While these analyses will necessarily remain exploratory given the structure of the current dataset, they may provide initial insights into whether the observed effects reflect perceptual or decisional dynamics. A more definitive dissociation, however, is beyond the scope of the present study and will be an important direction for future work.

(3) Control analyses and robustness of EEG–behavior relationships

The Reviewers raised interesting points regarding the interpretation of our control analyses and the potential influence of stimulus structure on the observed EEG–behavior relationships. We agree that these aspects require clarification and additional analyses to strengthen the robustness of our findings.

First, regarding the control analyses across frequency bands, we acknowledge that while our main analyses appropriately dissociate oscillatory and aperiodic components using spectral parameterization, the control analyses were based on conventional band-power measures. As correctly noted by the reviewers, band-limited power estimates can be influenced by the aperiodic background, which complicates the interpretation of null effects in the other frequency bands. In the revised manuscript, we will address this issue by extending our spectral parameterization approach to these control analyses. Specifically, we will recompute band-specific measures after removing the aperiodic component, allowing a clearer comparison across frequency bands and a more robust assessment of the specificity of alpha-related effects. Preliminary analyses suggest that these updated results are likely to be consistent with our initial findings, thereby reinforcing the robustness of the reported effects.

Another important point raised by the reviewers concerns the temporal structure of the stimulus stream. We agree that the continuous alternation of Gabor stimuli at varying durations introduces quasi-periodic stimulation rates that may induce entrainment of neural oscillations. Notably, some inter-stimulus intervals correspond to frequencies within the alpha range, which raises the possibility that the observed relationship between resting alpha frequency and integration thresholds may not solely reflect intrinsic sampling speed, but could also be influenced by the degree of alignment between an individual’s alpha rhythm and the temporal structure of the stimulus. As highlighted in prior work (e.g., Gulbinaite et al., 2017; Keitel et al., 2019; Gallina et al., 2023; Duecker et al., 2024), rhythmic stimulation in the alpha range can interact with intrinsic alpha oscillations and modulate both neural and perceptual processing. Although our study does not include EEG recordings during task performance and therefore cannot directly assess stimulus-locked responses or neural entrainment, we agree that this factor should be explicitly considered in the interpretation of our findings. To address this point, in the revised manuscript we will perform additional control analyses to assess the robustness of the observed relationships while accounting for potential rhythmic stimulation confounds. Specifically, we will explore whether the strength of behavioral effects and their relationship with EEG measures depends on the alignment between each participant’s individual alpha frequency and the effective stimulation rate induced by the stimulus presentation. In addition, we will test whether the association between resting-state alpha frequency and behavioral measures is disproportionately driven by stimulus durations corresponding to alpha-range temporal frequencies. These analyses will help determine whether the observed effects primarily reflect intrinsic sampling properties or are modulated by resonance-like interactions between endogenous rhythms and stimulus timing. We will also address all additional recommendations raised by the reviewers in the revised manuscript.

References

Demuru, M., & Fraschini, M. (2020). EEG fingerprinting: Subject-specific signature based on the aperiodic component of power spectrum. Computers in Biology and Medicine, 120, 103748.

Deodato, M., & Melcher, D. (2024). Correlations between visual temporal resolution and individual alpha peak frequency: Evidence that internal and measurement noise drive null findings. Journal of Cognitive Neuroscience, 36(4), 590-601.

Duecker, K., Doelling, K. B., Breska, A., Coffey, E. B., Sivarao, D. V., & Zoefel, B. (2024). Challenges and Approaches in the Study of Neural Entrainment. Journal of Neuroscience, 44(40).

El Boustani, S., Marre, O., Béhuret, S., Baudot, P., Yger, P., Bal, T., ... & Frégnac, Y. (2009). Network-state modulation of power-law frequency-scaling in visual cortical neurons. PLoS computational biology, 5(9), e1000519.

Euler, M. J., Vehar, J. V., Guevara, J. E., Geiger, A. R., Deboeck, P. R., & Lohse, K. R. (2024). Associations between the resting EEG aperiodic slope and broad domains of cognitive ability. Psychophysiology, 61(6), e14543.

Gallina, J., Marsicano, G., Romei, V., & Bertini, C. (2023). Electrophysiological and Behavioral Effects of Alpha-Band Sensory Entrainment: Neural Mechanisms and Clinical Applications. Biomedicines, 11(5), 1399.

Grandy, T. H., Werkle‐Bergner, M., Chicherio, C., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2013). Peak individual alpha frequency qualifies as a stable neurophysiological trait marker in healthy younger and older adults. Psychophysiology, 50(6), 570-582.

Gray, M. J., & Emmanouil, T. A. (2020). Individual alpha frequency increases during a task but is unchanged by alpha‐band flicker. Psychophysiology, 57(2), e13480.

Gulbinaite, R., Van Viegen, T., Wieling, M., Cohen, M. X., & VanRullen, R. (2017). Individual alpha peak frequency predicts 10 Hz flicker effects on selective attention. Journal of Neuroscience, 37(42), 10173-10184.

Keitel, C., Keitel, A., Benwell, C. S., Daube, C., Thut, G., & Gross, J. (2019). Stimulus-driven brain rhythms within the alpha band: The attentional-modulation conundrum. Journal of Neuroscience, 39(16), 3119-3129.

Lendner, J. D., Helfrich, R. F., Mander, B. A., Romundstad, L., Lin, J. J., Walker, M. P., ... & Knight, R. T. (2020). An electrophysiological marker of arousal level in humans. elife, 9, e55092.

Marx, E., Deutschländer, A., Stephan, T., Dieterich, M., Wiesmann, M., & Brandt, T. (2004). Eyes open and eyes closed as rest conditions: impact on brain activation patterns. Neuroimage, 21(4), 1818-1824.

Pathania, A., Euler, M. J., Clark, M., Cowan, R. L., Duff, K., & Lohse, K. R. (2022). Resting EEG spectral slopes are associated with age-related differences in information processing speed. Biological Psychology, 168, 108261.

Prins, N. (2012). The psychometric function: The lapse rate revisited. Journal of Vision, 12(6), 25-25.

Prins, N. (2016). Psychophysics: a practical introduction. Academic Press.

Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922.

Wichmann, F. A., & Hill, N. J. (2001). The psychometric function: I. Fitting, sampling, and goodness of fit. Perception & psychophysics, 63(8), 1293-1313.

Wiesman, A. I., & Wilson, T. W. (2019). Alpha frequency entrainment reduces the effect of visual distractors. Journal of cognitive neuroscience, 31(9), 1392-1403.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Using electron microscopy, the authors report discontinuities in the plasma membrane of C. elegans embryos. They associate these discontinuities with cell division and speculate that membrane rupture and subsequent resealing contribute to cytokinesis. They further discuss the proximity of these sites to vesicles and propose a role for vesicle-mediated membrane extension. 

Weaknesses:

(1) The possibility that the membrane discontinuity is an artifact

Although the authors focus on discontinuities in the plasma membrane, similar discontinuities are also observed in mitochondria, the nuclear envelope, and yolk granules. This raises concerns about whether the electron micrographs presented are suitable for assessing membrane continuity.

Electron micrographs result from a lengthy sample preparation process, including high-pressure freezing, freeze substitution in acetone containing OsO4, gradual warming, uranyl acetate staining, resin embedding, and ultrathin sectioning. In general, lipids are soluble in acetone at temperatures above −30 {degree sign}C, and preservation of membrane structures relies heavily on efficient OsO4 fixation.

Insufficient OsO4 treatment would be expected to reduce membrane contrast.

C. elegans embryos are encapsulated by an eggshell that forms at fertilization and gradually develops during the first few cell divisions. It is unclear how efficiently OsO4 in acetone penetrates the eggshell during freeze substitution, raising further concern about plasma membrane preservation under the conditions used.

We thank the reviewer for raising this important technical concern. We have taken this question seriously since first observing membrane discontinuities six years ago, and we have since conducted extensive controls to rule out fixation artifacts. Below, we present multiple lines of evidence—ranging from technical reproducibility to orthogonal imaging approaches—that collectively demonstrate the biological reality of these structures.

(1) Technical expertise and standard protocols

Our laboratory has extensive experience with electron microscopy across diverse biological systems, including neurons, muscle cells, and hypodermis in C. elegans, as well as tissues from Drosophila, mouse, bacteria, and cultured cells (Chen et al., 2013; Ding et al., 2018; Guan et al., 2022; Y. Li et al., 2018; Miao et al., 2024; Qin et al., 2014; Wang et al., 2026; J. Xu et al., 2022; M. Xu et al., 2021; L. Yang et al., 2020; X. Yang et al., 2019; Zhu et al., 2022). Importantly, we did not introduce any novel or unconventional steps in our EM preparation; all protocols were standard and well-established. Thus, the observed membrane discontinuities are unlikely to stem from technical inexperience or idiosyncratic methods.

In addition to membrane discontinuities, we would like to emphasize that a large number of single plasma membranes separating adjacent cytoplasmic domains were also detected under EM (Figure 1, 3 and 4, for instance). This observation is particularly significant because the invagination model cannot generate single plasma membrane barriers between adjacent cytoplasmic domains. Instead, independent extension of detached sister membranes could explain the formation of cytoplasm-enclosed membranes. Furthermore, as the morphology and continuity of these single cytoplasm-immersed membrane structures are well preserved, this indicates successful EM processing and argues against inefficient fixation or other technical issues.

(2) Reproducibility across independent preparations and techniques

To test whether the discontinuities were preparation-specific, we examined four independent sample batches collected in the lab over the years. Membrane discontinuities, as well as cytoplasm-immersed membranes, on embryonic cells were consistently observed across all batches, indicating that the phenomenon is not dependent on a single preparation method. Furthermore, we validated our findings using two EM techniques: transmission electron microscopy (HPF-TEM) and dualbeam scanning electron microscopy (SEM). Membrane discontinuities were clearly identifiable with both techniques, further supporting their robustness.

(3) Validation using an independent public dataset

We examined the publicly available C. elegans embryo EM collection (WormAtlas). In several instances, particularly at the embryonic periphery where plasma membrane discontinuities are more readily visualized (https://www.wormimage.org/image.php?id=140265&page=1), we identified similar structures. The presence of these features in an independent dataset generated by different researchers confirms that they are not artifacts unique to our sample preparation.

(4) Developmental regulation of membrane discontinuities

We analyzed embryos across multiple developmental stages. Membrane discontinuities were observed in both intrauterine and laid embryos at early stages. However, as embryos reached the comma stage—a period marked by the onset of elongation and reduced cell proliferation—the incidence of discontinuities dropped dramatically (0/13, 0/17, and 0/30 cells examined). This developmental specificity argues strongly against a general fixation artifact, which would be expected to occur randomly across stages. Additionally, the eggshell is present throughout the embryonic stage of C. elegans; therefore, the dramatic reduction of membrane discontinuities in comma-stage of embryo argues against the possibility that the eggshell poses a fixation problem.

(5) Rigorous criteria for identifying membrane discontinuities

To ensure unbiased analysis, we systematically collected images from early embryonic cells using the following criteria:

(1) Random section selection: For each sample, we randomly selected one section containing the largest number of embryos or cells (Sup Figure 2) for initial analysis. We found membrane discontinuities in 159 cells distributed across 57 embryos, representing 95% of the total sampled embryos This portion of the data is summarized in Figure 1.

(2) Whole-membrane examination: Each putative membrane discontinuity was identified only after examining the entire plasma membrane of the cell on a given section. Importantly, aside from the discontinuity, the remainder of the plasma membrane remained intact. Moreover, in most cells, only a single discontinuity was present per section, arguing against random, widespread membrane tearing during preparation.

(3) Neighboring section verification: Because EM preparation yields serial sections, we verified nearly all membrane discontinuities by examining adjacent sections. Again, the same membrane discontinuity was confirmed only after inspecting the entire plasma membrane on those neighboring sections as well. We will include this verification protocol in the revised Methods and additional imaging of consecutive sections would be provided if needed.

(4) Serial section reconstruction: To further determine whether a dividing cell indeed contains one membrane rupture, we performed two serial reconstruction experiments.

First, we used HPF-TEM to analyze 105 consecutive sections of a metaphase cell, reconstructing the entire plasma membrane and chromosome configuration. We found that one membrane rupture largely encircled the chromosomal disc (Figure 2 and Video S1), spatially aligning with the future segregation zone. Second, we used AutoCUTS-SEM to collect approximately 600 sections covering ~95% of a telophase cell containing three nuclei sharing a common cytoplasm. This tri-nucleated cell was enclosed by three distinct plasma membranes, each harboring a single rupture site. These three ruptures converged to form a Y-shaped exposed cytoplasmic region spanning >351 sections (Figure 5). Collectively, these reconstructions demonstrate that each cell contains only one discontinuity from a 3D point of view, further supporting that the phenomenon is not due to random sample preparation damage.

(6) Orthogonal validation by live imaging: In addition to EM, we performed live imaging of plasma membrane dynamics. While live imaging provides important temporal context, we recognize its limitations in resolving membrane ultrastructure. The rapid kinetics of membrane extension (approximately 20–30 seconds for metaphase and less than 3 minutes for cytokinesis), combined with embryo motility, introduces spatiotemporal ambiguities. To capture dynamic membrane events, our live imaging using the GFP::PH membrane marker was performed at 4-second intervals, approaching the practical limit for single-section scanning of the embryo. With single-plane live imaging, nevertheless, both membrane ruptures and free-ended sister membrane structures could be detected (Figures 6), providing additional evidence that membrane rupture and independent extension of detached sister membranes underlie cytokinesis in C. elegans embryos. Notably, 3D membrane dynamics analysis using light-sheet microscopy (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016. DOI:10.1038/ncomms11088) revealed membrane ruptures in dividing early C. elegans embryonic cells, including during telophase or metaphase. Therefore, live imaging further validates the membrane rupture phenomena in dividing embryonic cells in C. elegans. 

While future advances in imaging technology may enable real-time visualization at near-EM resolution, our extensive, multi-year effort to test the artifact hypothesis has convinced us that these membrane discontinuities are genuine biological features of dividing C. elegans embryonic cells.

We are confident that the cumulative evidence presented here addresses the reviewer's concerns and demonstrates that the observed membrane discontinuities, as well as cytoplasm-immersed membranes, are not procedural artifacts but rather reflect a previously underappreciated aspect of plasma membrane dynamics during embryonic cell division.

(2) Lack of evidence linking membrane discontinuity to cell division 

The reported plasma membrane discontinuities are not specific to mitotic cells. If this were a physiological process playing an important role in cytokinesis, it should occur in a temporally and spatially coordinated manner with nuclear division. However, it remains unclear at what stage of the cell cycle the membrane rupture occurs and where it is located relative to chromosomes and the mitotic spindle.

Thank you for this insightful comment. We agree that establishing a direct link between plasma membrane discontinuities and mitotic progression is critical, and we appreciate the opportunity to clarify this point.

In C. elegans embryos, the early stages of development are characterized by rapid and extensive cell division. Within approximately 100 minutes, a two-cell embryo develops into an embryo containing nearly 30 cells. The majority of the electron microscopy analyses in our study were performed on embryos at stages with fewer than 30 cells, where most cells are actively dividing. Thus, it is reasonable to infer that the cells exhibiting membrane discontinuities are predominantly mitotic cells.

Supporting this notion, as embryos reached the comma stage—a period marked by the onset of elongation and reduced cell proliferation—the incidence of membrane discontinuities dropped dramatically (0/13, 0/17, and 0/30 cells examined). This developmental specificity strongly suggests that membrane discontinuities are tightly linked to cell division.

Importantly, mitotic features such as metaphase chromosomes aligned at the equatorial plane or two (or more) nuclei sharing common cytoplasm can be identified in EM images. In our single random EM section analysis, we captured membrane discontinuities in cells at metaphase, anaphase (characterized by fewer than 10 chromosomal clumps), and telophase (defined by two nuclei sharing cytoplasm). Hence, membrane discontinuities are indeed present on mitotic cells. In addition, a published work by Fu et al (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016. DOI:10.1038/ncomms11088) using light-sheet microscopy captured similar membrane discontinuities in cells displaying classical mitotic features, including anaphase or telophase.

To further investigate the spatial relationship between membrane ruptures and chromosome organization, we performed three-dimensional reconstructions on a metaphase cell. As shown in Figure 2 and Video S1, the membrane discontinuities largely encircled the condensed chromosome disc and were spatially aligned with the future segregation zone, further revealing the relative location of membrane discontinuities to chromosomes, at least at metaphase.

We further collected 3D information for a telophase cell containing three nuclei. This tri-nucleated cell was enclosed by three distinct plasma membranes, each harboring a single rupture site that merged to form a single rupture. The observation that membrane ruptures are present in a tri-nucleated cell is particularly informative. The tri-nucleated feature indicates that this cell underwent two rounds of cell division and that both divisions were at telophase. The presence of a single membrane rupture suggests that membrane discontinuities may persist throughout the cell cycle, as the second cell cycle began from a mother cell that still shared cytoplasm with its sister cell and already had one membrane rupture. Therefore, in addition to the mitotic phase, membrane discontinuities—at least in this context—also exist during the DNA synthesis stage.

(3) Lack of evidence for extension of the separated membrane 

Although the authors speculate that resealing of the ruptured membrane occurs via extension of the separated membrane, no direct evidence supporting this mechanism is presented. Proximity to vesicles alone does not demonstrate that membrane extension occurs through vesicle fusion. More direct evidence is required to support this claim.

Thank you for raising this important point. We appreciate the opportunity to clarify our conclusion.

In our study, EM analysis revealed the presence of cellular vesicles in close proximity to both free membrane edges and the already separated sister plasma membranes (Figure 4). However, we acknowledge that without advanced live-cell imaging, it is not possible to conclusively determine whether the extension of these separated sister membranes occurs through vesicle fusion.

We realize that a statement in the Discussion section—“The expansion of the plasma membrane is generally driven by vesicle fusion”(page 16)—may have inadvertently led the reviewer to interpret this as our own conclusion regarding the mechanism of membrane extension in this context. In fact, that statement was intended to reflect the current general understanding of membrane expansion, not to imply that we had demonstrated such a mechanism for the free-ended sister membranes. As we subsequently noted, “However, this remains speculative and requires further experimental validation.”

To avoid any misunderstanding, we will revise this section to clearly state that the mechanism by which the separated sister membranes extend remains unknown and that further investigation is needed to determine how existing models of membrane expansion may apply to or be adapted for this novel context.

We thank the reviewer again for their thoughtful comment, which has helped us improve the clarity of our manuscript

(4) Inconsistency with published work

Numerous studies have examined cell division in developing C. elegans embryos using the GFP::PH(PLC1δ1) marker expressed from the ltIs38 transgene [pAA1; pie-1::GFP::PH(PLC1δ1) + unc-119(+)], generated by the Oegema lab (https://wormbase.org/species/c_elegans/transgene/WBTransgene00000911#01--10 ). To date, no study has reported membrane ruptures of the magnitude described here. The complexity of cell surface morphology from the 8- to 12-cell stages onward has been well documented, for example, by Fu et al. (2016) using light-sheet microscopy and 3D reconstruction (doi:10.1038/ncomms11088).

Supplementary Movies 5, 6, and 10 of this paper illustrate how single-plane images can easily produce apparent membrane discontinuities, for example, due to membrane orientations nearly parallel to the imaging plane.

The three single-plane images from only three embryos presented in Figure 6 are insufficient to support the authors' strong conclusions. Raw 3D data should be provided.

Thank you for this important comment. We fully agree that the GFP::PH(PLC1δ1) marker, generated by the Oegema lab, has been widely and effectively used to study various aspects of C. elegans embryonic development. In fact, we also employed this same marker in our study to assess membrane integrity.

However, while live imaging provides invaluable temporal resolution, its limitations in resolving membrane ultrastructure are substantial. In C. elegans embryos, early development is marked by rapid and extensive cell divisions. Within approximately 100 minutes, a two-cell embryo develops into one containing nearly 30 cells. During this fast-dividing stage, the rapid kinetics of membrane extension—approximately 20–30 seconds during metaphase and less than 3 minutes during cytokinesis— combined with embryo motility, introduce considerable spatiotemporal ambiguities. Furthermore, the longstanding invagination model of cytokinesis has shaped interpretations in the field, which may have led to ambiguous structures such as free-ended extensions being dismissed as potential artifacts rather than recognized as alternative morphological features. Theoretical and computational models have largely been built upon invagination-centric assumptions, which may have further constrained conceptual frameworks. Therefore, fluorescence protein-based live imaging analysis alone could not serve as a convincing approach to challenge the current dogma of cell division, nor did we intend it to.

However, when reexamined in light of our findings, previous studies using this same GFP marker have in fact revealed membrane discontinuities that went unnoticed. For example, Fu et al (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016. DOI:10.1038/ncomms11088) using light-sheet microscopy and 3D reconstruction, captured membrane discontinuities in cells undergoing mitotic phases such as anaphase or telophase. Similarly, an earlier study by Harrell and Goldstein (Harrell and Goldstein. 2011. Internalization of multiple cells during C. elegans gastrulation depends on common cytoskeletal mechanisms but different cell polarity and cell fate regulators. Developmental Biology. DOI:10.1016/j.ydbio.2010.09.012) showed regions where the GFP::PH signal appeared fuzzy and discontinuous.

Nevertheless, given the inherent limitations of fluorescence microscopy in resolving membrane ultrastructure, high-resolution electron microscopy—supported by rigorous controls and serial section analysis—remains the gold standard for definitively identifying such membrane discontinuities.

We acknowledge that our findings are surprising. We did not set out to challenge the long-held view of membrane integrity during cell division. In fact, this study began when our dedicated EM technician, Jingjing Liang, first observed membrane discontinuity phenomena in control samples—wild-type embryos. Had she not come across this observation, we likely would never have pursued this line of inquiry.

We appreciate the opportunity to clarify these points and thank the reviewer for thoughtful engagement with our work.

Reviewer #2 (Public review):

Summary:

Liang et al. explore an unusual observation of membrane discontinuities in dividing C. elegans embryonic cells. This report is the first to demonstrate that, instead of the classical invagination of membranes during cytokinesis, cells in the early embryos of C. elegans exhibit separation of sister membranes that extend independently. TEM images of high-pressure-frozen samples provide strong evidence for the presence of Membrane Openings (MOs) in cells at various stages of the cell cycle, predominantly during mitosis. High-resolution images (x 30,000) clearly show the wrinkled plasma membrane and smooth MOs.

The electron microscopy data are supported by the live cell imaging of strains with fluorescently tagged membrane markers. This study opens up the possibility of tracking MOs at other stages of C. elegans development, and also asks if it might be a common phenomenon in other species that exhibit rapid embryonic growth and divisions. 

Strengths:

(1) Thorough verification of Membrane Openings (MO) by several methods: 

(a) 4 independent sample batches.

(b) Examined historical collections.

(c) Analysed embryos at different stages of development. The absence of MOs in later stages (comma) serves as a negative control and gives confidence that MOs are genuine and not technical artifacts. 

(2) Live cell imaging of strain with fluorescently labelled membranes provides realtime dynamics of membrane rupture.

(3) After observing the membrane rupture, the next obvious question is - what prevents the cytosol from leaking out? The EM images showing PBL and PEL - extracellular matrix serving as barriers for the cytosol are convincing.

Thanks to the reviewer for the encouragement. Highly appreciated.

Weakness:

(1) The association of membrane discontinuities with cell division is not convincing, as there are 159 cells out of 425 showing MOs, but it is not mentioned clearly how many of these are undergoing cell division. Also, it's not clear whether the 20 dividing cells analysed for MOs are a part of the 159 cells or a separate dataset. A graphical representation of the number of samples and observed frequencies would be helpful to understand the data collection workflow.

We sincerely thank the reviewer for raising this important question and appreciate the opportunity to clarify these points.

(1) Relationship between membrane discontinuities and cell division

In C. elegans embryos, early development is characterized by rapid and extensive cell division: within approximately 100 minutes, a two-cell embryo develops into one containing nearly 30 cells. Most of our electron microscopy (EM) analyses were performed on embryos at stages with fewer than 30 cells, in which the majority of cells are actively dividing. Therefore, it is reasonable to infer that the cells exhibiting membrane discontinuities (MOs) are predominantly mitotic. Supporting this, as embryos reached the comma stage—when cell proliferation declines and elongation begins—the incidence of MOs dropped sharply (0/13, 0/17, and 0/30 cells examined. This developmental specificity strongly links MOs to cell division.

Moreover, in single random EM sections, we observed MOs in cells displaying clear mitotic features, such as metaphase chromosomes aligned at the equatorial plate, or anaphase/telophase configurations (fewer than 10 chromosomal clumps or two nuclei sharing common cytoplasm). Thus, MOs are indeed present in mitotic cells.

From our 3D reconstruction (Figure 5), we identified a telophase cell containing three nuclei, each enclosed by its own plasma membrane, with each membrane harboring a single rupture that converged into a single opening. This tri-nucleated configuration indicates that the cell had undergone two rounds of division and was at telophase in both. The presence of a single membrane rupture in this context suggests that MOs can persist beyond mitosis, as the second cell cycle initiated from a mother cell that already shared cytoplasm with its sister and already contained a rupture. Thus, in this case, MOs were also present during DNA synthesis stage.

(2) Clarification of sample numbers and datasets

In Figure 1, we present results from a single EM section per embryonic cell, with sections randomly selected per embryo as detailed in Sup Figure 2. This initial dataset (425 cells) forms the basis of Figure 1.

From the same pool of 425 cells, we used additional EM sections—distinct from those shown in Sup Figure 2—to locate 20 dividing cells for analysis of membrane discontinuities. Thus, while these 20 cells originated from the same set of embryos, they were not derived from the sections used in Figure 1 or Sup Figure 2.

A graphical summary of sample numbers from the single-section analysis is already provided in Figure 1. Notably, cells with two clearly visible nuclei are more likely to be sectioned through or near their maximal diameter. In contrast, the randomly selected sections used for Figure 1 captured cells at variable planes, reducing the likelihood of observing MOs. Consistent with this, in the three embryos where no MOs were detected (one example is Sup Figure 2N), the sections likely passed through peripheral regions of the cells. Consequently, the frequency of MOs in randomly sectioned cells (Figure 1) is not directly comparable to that observed in the 20 dividing cells, which were analyzed using sections more likely to capture cells near their maximal diameter. These 20 dividing cells should therefore be considered a separate analysis. We will add detailed explanations in the Methods section to ensure this distinction is clearly understood.

We are grateful for the reviewer’s thoughtful feedback and believe these clarifications will improve the clarity and rigor of the manuscript.

(2) In Figures 3A and 3B, the resolution of the images is not enough to verify 3A as classical membrane invagination and 3B as detached sister membranes.

Thank you for your valuable comment. In the revised manuscript, we will provide additional images at higher magnification to better illustrate the classical membrane invagination in Figure 3A and the detached sister membranes in Figure 3B.

(3) Figure 6 lacks controls. How does the classical invagination look in this strain? Also, adding nuclear dye would be informative, in order to correlate the nuclear division with membrane rupture, as claimed. 

Thank you for this important comment. As we addressed how we correlated nuclear division with membrane rupture in response to weakness (1), below we will focus on how we may distinguish classical invagination from membrane rupture.

While live imaging provides invaluable temporal resolution, its limitations in resolving membrane ultrastructure are substantial. In C. elegans embryos, early development is marked by rapid and extensive cell divisions. Within approximately 100 minutes, a two-cell embryo develops into one containing nearly 30 cells. During this fast-dividing stage, the rapid kinetics of membrane extension—approximately 20–30 seconds during metaphase and less than 3 minutes during cytokinesis— combined with embryo motility, introduce considerable spatiotemporal ambiguities. Furthermore, the longstanding invagination model of cytokinesis has shaped interpretations in the field, which may have led to ambiguous structures such as free-ended extensions being dismissed as potential artifacts rather than recognized as alternative morphological features. Theoretical and computational models have largely been built upon invagination-centric assumptions, which may have further constrained conceptual frameworks. Therefore, fluorescence protein-based live imaging analysis alone could not serve as a convincing approach to challenge the current dogma of cell division, nor did we intend it to.

However, when reexamined in light of our findings, previous studies using GFP::PH or similar markers have in fact revealed membrane discontinuities that went unnoticed. For example, using light-sheet microscopy and 3D reconstruction, Fu et al captured membrane discontinuities in cells undergoing division such as anaphase or telophase (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016.DOI:10.1038/ncomms11088)

Similarly, an earlier study by Goldstein et al. (Harrell and Goldstein. 2011. Internalization of multiple cells during C. elegans gastrulation depends on common cytoskeletal mechanisms but different cell polarity and cell fate regulators. Developmental Biology. DOI:10.1016/j.ydbio.2010.09.012) showed regions where the GFP::PH signal appeared fuzzy and discontinuous.

Here, to capture dynamic membrane events, our live imaging using the GFP::PH membrane marker was performed at 4-second intervals, approaching the practical limit for single-section scanning of the embryo. With single-plane live imaging, both membrane ruptures and free-ended sister membrane structures (Figures 6) could be detected, providing additional evidence that membrane rupture and independent extension of detached sister membranes underlie cytokinesis in C. elegans embryos.

However, given the inherent limitations of fluorescence microscopy in resolving membrane ultrastructure, high-resolution electron microscopy—supported by rigorous controls and serial section analysis—remains the gold standard for definitively distinguishing invagination from membrane discontinuities.

While future advances in imaging technology may enable real-time visualization at near-EM resolution, our extensive, multi-year effort to test the artifact hypothesis has convinced us that these membrane discontinuities are genuine biological features of dividing C. elegans embryonic cells.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors challenge a dogma in cell biology, namely that cells are at any time point engulfed by a continuous plasma membrane. Liang et al. find that during C elegans embryogenesis, a high number of cells are not entirely surrounded by a plasma membrane but show membrane openings (MOs). These openings are enriched at the embryo's periphery, towards the eggshell. The authors propose that plasma membrane discontinuities emerge during metaphase of mitosis and that independent extension of "sister membranes" engulfs the daughter cells.

Strengths:

On the positive side, the authors find plasma membrane discontinuities not only by electron microscopy but also by fluorescence microscopy and provide information about the dynamics of membrane openings and their emergence. While this is assuring, the authors conclude that MOs emerge during metaphase. From what the authors show, this particular information cannot be deduced, as there is no dynamic capture of a membrane scission event together with a chromatin marker that would indicate mitosis. The authors could, however, attempt to find such events in live movies, given the high incidence of MOs reported from their EM data.

Thanks to the reviewer for the encouragement. Highly appreciated.

Weaknesses:

In order to convincingly demonstrate the absence of any plasma membrane in the respective regions of the embryonic periphery or between cells of the embryo, the authors would have to show consecutive serial TEM sections where MOs are detected over more z-planes, beyond the mere 3D reconstructions. Although the authors state in the methods section that continuous ultrathin sections were cut for the metaphase sample (page 21, line 472), consecutive sections are never shown in TEM. While we do see the 3D reconstructions, better documentation of the underlying TEM data is missing. It would be necessary to show a membrane opening in consecutive z sections. Alternatively, the authors could seek the possibility to convincingly back up their claims with volume imaging by focused ion beam scanning EM (FIBSEM), where cellular volumes can be sectioned in almost isotropic resolution

We Thank the reviewer for raising these important technical concerns. We have taken this question seriously since first observing membrane discontinuities six years ago, and we have since conducted extensive controls to rule out fixation artifacts.

First of all, in addition to membrane discontinuities, we would like to highlight that a large number of single plasma membranes separating adjacent cytoplasmic domains were detected by EM (Figure 1, 3 and 4). This observation is particularly significant because the invagination model cannot account for the formation of single plasma membrane barriers between adjacent cytoplasmic domains. Instead, independent extension of detached sister membranes offers a plausible explanation for the generation of cytoplasm-immersed membranes. Furthermore, the morphology and continuity of these single cytoplasm-immersed membrane structures are well preserved, indicating successful EM processing and arguing against potential issues such as inadequate fixation or other technical limitations.

Second, we applied rigorous criteria for identifying membrane discontinuities:

(1) To test whether the discontinuities were preparation specific, we examined four independent sample batches and validated our findings using two EM techniques: transmission electron microscopy (HPF-TEM) and dual-beam scanning electron microscopy (SEM).

(2) We analyzed embryos across multiple developmental stages. Membrane discontinuities were observed in both intrauterine and laid embryos at early stages. However, as embryos reached the comma stage—a period marked by the onset of elongation and reduced cell proliferation—the incidence of discontinuities dropped dramatically (0/13, 0/17, and 0/30 cells examined). This developmental specificity argues strongly against a general fixation artifact, which would be expected to occur randomly across stages. Additionally, the eggshell is present throughout the embryonic stage of C. elegans; therefore, the dramatic reduction of membrane discontinuities in comma-stage of embryo argues against the possibility that the eggshell poses a fixation problem.

(3) Each putative membrane discontinuity was identified only after examining the entire plasma membrane of the cell on a given section. Importantly, aside from the discontinuity, the remainder of the plasma membrane remained intact. Moreover, in most cells, only a single discontinuity was present per section, arguing against random, widespread membrane tearing during preparation. Because EM preparation yields serial sections, we verified nearly all membrane discontinuities by examining adjacent sections. Again, the same membrane discontinuity was confirmed only after inspecting the entire plasma membrane on those neighboring sections as well. We will include this verification protocol in the revised Methods and additional imaging of consecutive sections would be provided if needed.

To further determine whether a dividing cell indeed contains one membrane rupture, we performed two serial reconstruction experiments using consecutive sections, as the reviewer suggested. First, we used HPF-TEM to analyze 105 consecutive sections of a metaphase cell, reconstructing the entire plasma membrane and chromosome configuration. We found that one membrane rupture largely encircled the chromosomal disc (Figure 2 and Video S1), spatially aligning with the future segregation zone. Second, we used AutoCUTS-SEM to collect approximately 600 sections covering ~95% of a telophase cell containing three nuclei sharing a common cytoplasm. This tri-nucleated cell was enclosed by three distinct plasma membranes, each harboring a single rupture site. These three ruptures converged to form a Yshaped exposed cytoplasmic region spanning >351 sections (Figure 5). Collectively, these reconstructions demonstrate that each cell contains only one discontinuity from a 3D point of view, further supporting that the phenomenon is not due to random sample preparation damage.

(4) In addition to EM, we performed live imaging of plasma membrane dynamics. While live imaging provides important temporal context, we recognize its limitations in resolving membrane ultrastructure. The rapid kinetics of membrane extension (approximately 20–30 seconds for metaphase and less than 3 minutes for cytokinesis), combined with embryo motility, introduces spatiotemporal ambiguities. To capture dynamic membrane events, our live imaging using the GFP::PH membrane marker was performed at 4-second intervals, approaching the practical limit for single-section scanning of the embryo. With single-plane live imaging, nevertheless, both putative membrane ruptures (Figure 6A) and free-ended sister membrane structures could be detected (Figures 6B and 6C), providing additional evidence that membrane rupture and independent extension of detached sister membranes underlie cytokinesis in C. elegans embryos. Notably, 3D membrane dynamics analysis using light-sheet microscopy (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016. DOI:10.1038/ncomms11088). revealed membrane ruptures in dividing early C. elegans embryonic cells, including during telophase and metaphase. Therefore, live imaging further validates the membrane rupture phenomena in dividing embryonic cells in C. elegans. 

We are confident that the cumulative evidence presented here addresses the reviewer's concerns and demonstrates that the observed membrane discontinuities, as well as cytoplasm-immersed membranes, are not procedural artifacts but rather reflect a previously underappreciated aspect of plasma membrane dynamics during embryonic cell division.

Another critical issue concerns the detection of the membrane discontinuities in electron micrographs, which, in my opinion, is ambiguous. How do the authors reliably discriminate in their TEM images whether there is a plasma membrane or not? The absence - or weak appearance - of the stain of the electron dense material at membranes, which seems to be their criterion for MOs, is also apparent at other, intracellular membranes, like at the NE or at the ER (for example, see Figure 1C). Also, the plasma membrane itself appears unevenly stained in regions that the authors delineate as intact (for example, Figure 1C, 2B/1).

We thank the reviewer for raising this important concern.

First, our laboratory has extensive experience with electron microscopy across diverse biological systems, including neurons, muscle cells, and hypodermis in C. elegans, as well as tissues from Drosophila, mouse, bacteria, and cultured cells (Chen et al., 2013; Ding et al., 2018; Guan et al., 2022; Y. Li et al., 2018; Miao et al., 2024; Qin et al., 2014; Wang et al., 2026; J. Xu et al., 2022; M. Xu et al., 2021; L. Yang et al., 2020; X. Yang et al., 2019; Zhu et al., 2022). Importantly, we did not introduce any novel or unconventional steps in our EM preparation; all protocols were standard and well established. Thus, the observed membrane discontinuities are unlikely to result from technical inexperience or idiosyncratic methods.

Second, because EM preparation yields serial sections, we verified nearly all membrane discontinuities by examining adjacent sections. Specifically, a membrane discontinuity was confirmed only after inspecting the entirety of the plasma membrane in neighboring sections. We will include this verification protocol in the revised Methods section, and additional images of consecutive sections can be provided if needed.

Third, in addition to membrane discontinuities, a large number of single plasma membranes separating adjacent cytoplasmic domains were detected by EM (Figure 1, 3 and 4). This observation is particularly significant because the invagination model cannot account for the formation of single plasma membrane barriers between adjacent cytoplasmic domains. Instead, independent extension of detached sister membranes offers a plausible explanation for the generation of cytoplasm-immersed membranes. Furthermore, the morphology and continuity of these single cytoplasm-immersed membrane structures are well preserved, indicating successful EM processing and arguing against potential issues such as inadequate fixation or other technical limitations.

EM-related publications by Jingjing Liang:

Chen D, Jian Y, Liu X, Zhang Y, Liang J, Qi X, Du H, Zou W, Chen L, Chai Y, Ou G, Miao L, Wang Y, Yang C. 2013. Clathrin and AP2 Are Required for Phagocytic Receptor-Mediated Apoptotic Cell Clearance in Caenorhabditis elegans. PLoS Genetics 9:e1003517. DOI: https://doi.org/10.1371/journal.pgen.1003517

Ding L, Yang X, Tian H, Liang J, Zhang F, Wang G, Wang Y, Ding M, Shui G, Huang X. 2018. Seipin regulates lipid homeostasis by ensuring calcium‐dependent mitochondrial metabolism. The EMBO Journal 37:e97572. DOI: https://doi.org/10.15252/embj.201797572

Guan L, Yang Y, Liang J, Miao Y, Shang A, Wang B, Wang Y, Ding M. 2022. ERGIC2 and ERGIC3 regulate the ER‐to‐Golgi transport of gap junction proteins in metazoans. Traffic 23:140–157. DOI: https://doi.org/10.1111/tra.12830

Li Y, Zhang Y, Gan Q, Xu M, Ding X, Tang G, Liang J, Liu K, Liu X, Wang X, Guo L, Gao Z, Hao X, Yang C. 2018. C . elegans -based screen identifies lysosome-damaging alkaloids that induce STAT3-dependent lysosomal cell death. Protein & Cell 9:1013–1026. DOI: https://doi.org/10.1007/s13238-018-0520-0

Miao Y, Du Y, Wang B, Liang J, Liang Y, Dang S, Liu J, Li D, He K, Ding M. 2024. Spatiotemporal recruitment of the ubiquitin-specific protease USP8 directs endosome maturation. eLife 13:RP96353. DOI: https://doi.org/10.7554/eLife.96353

Qin J, Liang J, Ding M. 2014. Perlecan Antagonizes Collagen IV and ADAMTS9/GON-1 in Restricting the Growth of Presynaptic Boutons. Journal of Neuroscience 34:10311–10324. DOI: https://doi.org/10.1523/JNEUROSCI.5128-13.2014

Wang Z, Zhang L, Zhou B, Liang J, Tian Y, Jiang Z, Tao J, Yin C, Chen S, Zhang W, Zhang J, Wei W. 2026. A single MYB transcription factor GmMYB331 regulates seed oil accumulation and seed size/weight in soybean. Journal of Integrative Plant Biology 68:470– 485. DOI: https://doi.org/10.1111/jipb.70101

Xu J, Chen S, Wang W, Man Lam S, Xu Y, Zhang S, Pan H, Liang J, Huang Xiahe, Wang Yu, Li T, Jiang Y, Wang Yingchun, Ding M, Shui G, Yang H, Huang Xun. 2022. Hepatic CDP-diacylglycerol synthase 2 deficiency causes mitochondrial dysfunction and promotes rapid progression of NASH and fibrosis. Science Bulletin 67:299–314. DOI: https://doi.org/10.1016/j.scib.2021.10.014

Xu M, Ding L, Liang J, Yang X, Liu Y, Wang Y, Ding M, Huang X. 2021. NAD kinase sustains lipogenesis and mitochondrial metabolism through fatty acid synthesis. Cell Reports 37:110157. DOI: https://doi.org/10.1016/j.celrep.2021.110157

Yang L, Liang J, Lam SM, Yavuz A, Shui G, Ding M, Huang X. 2020. Neuronal lipolysis participates in PUFA‐mediated neural function and neurodegeneration. EMBO reports 21:e50214. DOI: https://doi.org/10.15252/embr.202050214

Yang X, Liang J, Ding L, Li X, Lam S-M, Shui G, Ding M, Huang X. 2019. Phosphatidylserine synthase regulates cellular homeostasis through distinct metabolic mechanisms. PLOS Genetics 15:e1008548. DOI: https://doi.org/10.1371/journal.pgen.1008548

Zhu J, Lam SM, Yang L, Liang J, Ding M, Shui G, Huang X. 2022. Reduced phosphatidylcholine synthesis suppresses the embryonic lethality of seipin deficiency. Life Metabolism 1:175–189. DOI: https://doi.org/10.1093/lifemeta/loac02

This is mega yikes lowkey

Author response:

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

It is important to make a few key points about our work. First, our paper is largely a computational biophysics paper, augmented by experimental results. Generally speaking, computational biophysics work intends to achieve one of two things (or both). One is to provide more molecular level insight into various behaviors of biomolecular systems that have not been (or cannot be) provided by qualitative experimental results alone. The second general goal of computational biophysics it to formulate new hypotheses to be tested subsequently by experiment. In our paper, we have achieved both of these goals and then confirmed the key computational results by experiment.

eLife Assessment

This study investigates how the HIV inhibitor lenacapavir influences capsid mechanics and interactions with the nuclear pore complex. It provides important insights into how drug-induced hyperstabilization of the viral shell can compromise its structural integrity during nuclear entry. While the modeling is technically sophisticated and the results are promising, some mechanistic interpretations rely on assumptions embedded in the simulations, leaving parts of the evidence incomplete.

Given our response below, regarding the rigor and “completeness” of our work, we do not feel that an editorial judgement of “leaving parts of the evidence incomplete” is justified.

We also note that another recent experimental paper has validated essentially every prediction made in our eLife paper: https://www.biorxiv.org/content/10.64898/2026.01.05.697065v1

We thus disagree that the evidence we have presented in our paper is incomplete.

Public Reviews:

Reviewer #1 (Public review):

The paper from Hudait and Voth details a number of coarse-grained simulations as well as some experiments focused on the stability of HIV capsids in the presence of the drug lenacapavir. The authors find that LEN hyperstabilizes the capsid, making it fragile and prone to breaking inside the nuclear pore complex.

I found the paper interesting. I have a few suggestions for clarification and/or improvement. 

(1) How directly comparable are the NPC-capsid and capsid-only simulations? A major result rests on the conclusion that the kinetics of rupture are faster inside the NPC, but are the numbers of LENs bound identical? Is the time really comparable, given that the simulations have different starting points? I'm not really doubting the result, but I think it could be made more rigorous/quantitative.

We note (also in the manuscript) that it is difficult to compare the timescales obtained from coarse-grained MD simulations and experiments (“real time”) given that, by design, the CG simulations are accelerated to greatly enhance sampling. However, we can qualitatively compare the timescales of different CG simulations (without directly comparing the corresponding experimental timescales).

We agree with the reviewer that the starting point of NPC-capsid and capsid-only simulations is different, as is the biological environment in which the rupture occurs. When analyzing the NPC-only and capsid-only simulations, what was striking to us was that at the NPC the capsid-LEN complex ruptures in a multicomponent environment, where several FG-NUPs compete to displace the LENs. It is well established in experiments that LEN has a detrimental effect on capsid integrity.

In Figure 2, we plot the number of LEN molecules as a function of CG simulation time. The initial capsid-LEN complex was equilibrated without NPC and then placed at the cytoplasmic end of the NPC for docking. The number of LEN molecules for the capsid-only simulations and the NPC-docked simulations is nearly identical, and an insignificant number of LEN molecules unbind at the NPC. Hence, we added the following clarification:

Page 10, paragraph 11

“Note that the number of LEN molecules bound to the capsid for the free capsid and NPCdocked capsids are nearly identical. Hence, the disparity in timescale of lattice rupture is not only because of the effect of LEN on capsid lattice properties.”

Is the time really comparable, given that the simulations have different starting points?

Yes, the CG timescales of both the NPC and freely diffusing capsid unbiased simulations are comparable, since they were done using identical simulation settings.

(2) Related to the above, it is stated on page 12 that, based on the estimated free-energy barrier, pentamer dissociation should occur in ~10 us of CG time. But certainly, the simulations cover at least this length of time?

Our implicit solvent CG MD simulations are designed to access timescales far beyond the capabilities of the fully atomistic simulations. We reiterate here that it is difficult to directly compare the timescales obtained from CG MD simulations and experiments.

As described in the text, there are 12 pentamers in the capsid (7 in the wide end and 5 in the narrow end). For the narrow end to rupture, all 5 pentamers should progressively dissociate. In our unbiased simulations (Fig. S5), in 25 us of CG time, we observe (partial) dissociation of one or two pentamers. Hence, our unbiased CG simulation timescales were not long enough to observe rupturing of the narrow end.

(3) At first, I was surprised that even in a CG simulation, LEN would spontaneously bind to the correct site. But if I read the SI correctly, LEN was parameterized specifically to bind to hexamers and not pentamers. This is fine, but I think it's worth describing in the main text.

We modified (see below) the main text to include the details.

Page 4, paragraph 1

“We model LEN and CA interactions such that LEN molecules can only bind to CA hexamers, and all interactions to CA pentamers are turned off, as in experiments, CA selectively associates with hexamers (25, 36).”

Reviewer #2 (Public review):

Here, Hudait et al. use CG modeling to investigate the mechanism by which Lenacapavir (LEN) treats HIV capsids that dock to the nuclear pore complex (NPC). However, the manuscript fails to present meaningful findings that were previously unreported in the literature and is thus of low impact. Many claims made in the manuscript are not substantiated by the presented data. Key mechanistic details that the work purports to reveal are artifacts of the parameterization choices or simulation/analysis design, with the simulations said to reveal details that they were specifically biased to reproduce. This makes the manuscript highly problematic, as its contributions to the literature would represent misconceptions based on oversights in modeling and thus mislead future readers. 

We strongly disagree with these statements, and they do not reflect the facts. We provide a rebuttal to these statements in the “Author Response” statements below.

(1) Considering the literature, it is unclear that the manuscript presents new scientific discoveries. The following are results from this paper that have been previously reported:

(a) LEN-bound capsid can dock to the nuclear pore (Figure 2; see e.g. 10.1016/j.cell.2024.12.008 or 10.1128/mbio.03613-24). 

(b) NUP98 interacts with the docked capsid (Figure 2; see e.g. 10.1016/j.virol.2013.02.008 or 10.1038/s41586-023-06969-7 or 10.1016/j.cell.2024.12.008). 

(c) LEN and NUP98 compete for a binding interface (Figure 2; see e.g. 10.1126/science.abb4808 or 10.1371/journal.ppat.1004459). 

(d) LEN creates capsid defects (Figure 3 and 5, see e.g. 10.1073/pnas.2420497122). 

(e) RNP can emerge from a damaged capsid (Figure 3 and 5; see e.g. 10.1073/pnas.2117781119 or 10.7554/eLife.64776). 

(f) LEN hyperstabilizes/reduces the elasticity of the capsid lattice (Figure 6; see e.g. 10.1371/journal.ppat.1012537). 

The goal of our simulations (in combination with experiments from the Pathak group) is to provide molecular-level insight into the sequence of events of NPC docking of capsid and the effect of LEN binding leading to sequential dissociation of pentamers and leading to rupturing of the narrow end of the cone-shaped capsid. We also compare the events leading to capsid rupture at the NPC with the same for a freely diffusing capsid, akin to that in cytoplasm. The reviewer should carefully read the abstract of our paper. In fact, the above are all papers that present qualitative experimental results that help validate our model, but they do not provide details on the molecule-scale events. For example, the paper (10.1073/pnas.2420497122 written by our coauthors in the Pathak group) is extensively used to compare the behavior of LEN-bound capsid in the cytoplasm.

(2) The mechanistic findings related to how these processes occur are problematic, either based on circular reasoning or unsubstantiated, based on the presented data. In some cases, features of parameterization and simulation/analysis design are erroneously interpreted as predictions by the CG models. 

We strongly disagree with this assessment. Our CG NPC model is largely a “bottomup” model derived from molecular scale interactions sampled in atomistic simulations (see our previous paper in PNAS https://doi.org/10.1073/pnas.2313737121). The reviewer appears to be ignorant of the “bottom-up” approach based on rigorous statistical mechanics to derive moleculescale model (please refer to a detailed review on bottom-up coarse-graining: J. Chem. Theory. Comput., 2022, 18. 5759-5791).

Using the “bottom-up” CG model of the NPC, we predicted several molecular-level details of capsid import and docking to the NPC. Our key predictions were that there is an intrinsic capsid lattice elasticity and also the pleomorphic nature of the NPC channel is key for successful capsid docking https://doi.org/10.1073/pnas.2313737121). Our computational predictions have benn, for example, validated in a recently published paper by an experimental group: Hou, Z., Shen, Y., Fronik, S. et al. HIV-1 nuclear import is selective and depends on both capsid elasticity and nuclear pore adaptability. Nat Microbiol 10, 1868–1885 (2025). https://doi.org/10.1038/s41564025-02054-z). Our work is an excellent example of how systematically derived “bottom-up” CG models can accurately predict molecular details of complex biological processes.

We have now added the following statement:

Page 3, Paragraph 1

“Importantly, the computational predictions of capsid docking to the NPC central channel have been recently validated in a HIV-1 core import at the NPC using cryo-ET (33), demonstrating how systematically derived “bottom-up” CG models can accurately predict molecular details of complex biomolecular processes.”

(a) Claim: LEN-bound capsids remain associated with the NPC after rupture. CG simulations did not reach the timescale needed to demonstrate continued association or failure to translocate, leaving the claim unsubstantiated.

The reviewer fails to recognize that the statement is based on the experimental results of LEN-bound capsid that remains bound to the NPC after rupture and fails to translocate to the nuclear side (from the Pathak group in the section “Ruptured LEN-viral complexes remain bound to the NPC”). The Reviewers’ comment is incorrect. 

(b) Claim: LEN contributes to loss of capsid elasticity. The authors do not measure elasticity here, only force constants of fluctuations between capsomers in freely diffusing capsids. Elasticity is defined as the ability of a material to undergo reversible deformation when subjected to stress. Other computational works that actually measure elasticity (e.g., 0.1371/journal.ppat.1012537) could represent a point of comparison but are not cited. The changes in force constants in the presence of LEN are shown in Figure 6C, but the text of the scale bar legend and units of k are not legible, so one cannot discern the magnitude or significance of the change.

The concept of elasticity can extend down to the mesoscopic scale. Many examples can be found in the large number of elastic network models (ENMs) of proteins published by many authors. The reviewer also fails to comprehend the meaning of the effective spring constants in the HeteroENM model and how they relate to the response of the capsid to stress (e.g., in the NPC). Note, in the NPC central channel, the capsid encounters several nucleoporins (including disordered FG Nucleoporins that not have specific interactions to rest of the proteins), and also a confined environment. This environment can exert inward stress to the capsid, which is also reflected in stress on the capsid lattice. Furthermore, the cited computational AFM studies are very far from a realistic in vivo or even in vitro set of conditions. In contrast, our study presents a realistic environment which the capsid will encounter in NPC, and then these predictions are validated by experimental results.

(c) Claim: Capsid defects are formed along striated patterns of capsid disorder. Data is not presented that correlates defects/cracks with striations. 

We presented the data of formation of striated patterns of lattice stress in the capsid that runs from capsid narrow end to the wide end in coarse-grained model (https://doi.org/10.1073/pnas.2313737121), and atomistic model (https://doi.org/10.1073/pnas.2117781119). Both of our papers are extensively cited in the current manuscript. Also, when the capsid is ruptured, one cannot visualize the striated patterns.

(d) Claim: Typically 1-2 LEN, but rarely 3 bind per capsid hexamer. The authors state: "The magnitude of the attractive interactions was adjusted to capture the substoichiometric binding of LEN to CA hexamers (Faysal et al., 2024). ... We simulated LEN binding to the capsid cone (in the absence of NPC), which resulted in a substoichiometric binding (~1.5 LEN per CA hexamer), consistent with experimental data (Singh et al., 2024)." This means LEN was specifically parameterized to reproduce the 1-2 binding ratio per hexamer apparent from experiments, so this was a parameterization choice, not a prediction by CG simulations as the authors erroneously claim: "This indicates that the probability of binding a third LEN molecule to a CA hexamer is impeded, likely due to steric effects that prevent the approach of an incoming molecule to a CA hexamer where 2 LEN molecules are already associated. ... Approximately 20% of CA hexamers remain unoccupied despite the availability of a large excess of unbound LEN molecules. This suggests a heterogeneity in the molecular environment of the capsid lattice for LEN binding." These statements represent gross over-interpretation of a bias deliberately introduced during parameterization, and the "finding" represents circular reasoning. Also, if "steric effects" play any role, the authors could analyze the model to characterize and report them rather than simply speculate.

Reviewer comment: “This means LEN was specifically parameterized to reproduce the 1-2 binding ratio per hexamer apparent from experiments, so this was a parameterization choice, not a prediction by CG simulations as the authors erroneously claim.” – This comment by reviewer is deeply flawed and we strongly disagree. In our CG model there is no restriction on the number of LEN molecules that can bind to a CA hexamer. We again restate that, the experimental results on LEN binding to CA hexamers and inability of LEN to bind to pentamers were used as no allatom (AA) forcefield yet exists.

The steric effect of the lack of third LEN binding to a hexamer is a likely hypothesis (which one is allowed to make). More importantly, an investigation of the steric effect of LEN binding to the CA hexamer is not the main goal of the manuscript.

(e) Claim: Competition between NUP98 and LEN regulates capsid docking. The authors state: "A fraction of LEN molecules bound at the narrow end dissociate to allow NUP98 binding to the capsid ... Therefore, LEN can inhibit the efficient binding of the viral cores to the NPC, resulting in an increased number of cores in the cytoplasm." Capsid docking occurs regardless of the presence of LEN, and appears to occur at the same rate as the LEN-free capsid presented in the authors' previous work (Hudait &Voth, 2024). The presented data simply show that there is a fluctuation of bound LEN, with about 10 fewer (<5%) bound at the end of the simulation than at the beginning, and the curve (Figure 2A) does not clearly correlate with increased NUP98 contact. In that case, no data is shown that connects LEN binding with the regulation of the docking process. Further, the two quoted statements contradict each other. The presented data appear to show that NUP outcompetes LEN binding, rather than LEN inhibiting NUP binding. The "Therefore" statement is an attempt to reconcile with experimental studies, but is not substantiated by the presented data.

We disagree with this spurious statement, and we see no real contradiction. We have now added a minor clarification that LEN can inhibit efficient capsid binding at significantly high concentration.

Page 6, Paragraph 1

“Therefore, at significantly high concentration LEN can inhibit the efficient binding of the viral cores to the NPC, resulting in an increased number of cores in the cytoplasm.”

(f) Claim: LEN binding leads to spontaneous dissociation of pentamers. The CG simulation trajectories show pentamer dissociation. However, it is quite difficult to believe that a pentamer in the wide end of the capsid would dissociate and diffuse 100 nm away before a hexamer in the narrow end (previously between two pentamers and now only partially coordinated, also in a highly curved environment, and further under the force of the extruding RNA) would dissociate, as in Figure 2B. A more plausible explanation could be force balance between pent-hex versus hex-hex contacts, an aspect of CG parameterization. No further modeling is presented to explain the release of pentamers, and changes in pent-hex stiffness are not apparent in the force constant fluctuation analysis in Figure 6C.

This is both a misrepresentation of the simulations and a failure to understand them (as well as the supporting experiments) on the part of the reviewer. In the presence of LEN, the hexameric lattice is hyperstabilized. In contrast, the pentamers are not. As a consequence, the pentamers are dissociated. The pentamers at the narrow end are dissociated first, due to high curvature. The reviewer, from a point of being uninformed, simply speculates on what they think should happen. Moreover, as emphasized earlier and which the reviewer fails to comprehend is that ours is a “bottom-up CG model” so it predicts, not builds in, these effects.

(g) Claim: WTMetaD simulations predict capsid rupture. The authors state: "In WTMetaD simulations, we used the mean coordination number (Figure S6) between CA proteins in pentamers and in hexamers as the reaction coordinate." This means that the coordination number, the number of pent-hex contacts, is the bias used to accelerate simulation sampling. Yet the authors then interpret a change in coordination number leading to capsid rupture as a discovery, representing a fundamental misuse of the WTMetaD method. Changes in coordination number cannot be claimed as an emergent property when they are in fact the applied bias, when the simulation forced them to sample such states. The bias must be orthogonal to the feature of interest for that feature to be discoverable. While the reported free energies are orthogonal to the reaction coordinate, the structural and stepwise-mechanism "findings" here represent circular reasoning.

Unfortunately, the reviewer appears to be quite uninformed on the WTMetaD method and what it does. The chosen collective variable (CV) in our case is the coordination variable and the MetaD samples along that variable (the conditional free energy) as it is designed to do. The reviewer may wish to educate themself by reading Dama et al (https://doi.org/10.1103/PhysRevLett.112.240602). We also note that “emergent properties” are not along some other, uncoupled coordinate.

(3) Another major concern with this work is the excessive self-citation, and the conspicuous lack of engagement with similar computational modeling studies that investigate the HIV capsid and its interactions with LEN, capsid mechanical properties relevant to nuclear entry, and other capsidNPC simulations (e.g., 10.1016/j.cell.2024.12.008 and 10.1371/journal.ppat.1012537). Other such studies available in the literature include examination of varying aspects of the system at both CG and all-atom levels of resolution, which could be highly complementary to the present work and, in many cases, lend support to the authors' claims rather than detract from them. The choice to omit relevant literature implies either a lack of perspective or a lack of collegiality, which the presentation of the work suffers from. Overall, it is essential to discuss findings in the context of competing studies to give readers an accurate view of the state of the field and how the present work fits into it. It is appropriate in a CG modeling study to discuss the potential weaknesses of the methodology, points of disagreement with alternative modeling studies, and any lack of correlation with a broader range of experimental work. Qualitative agreement with select experiments does not constitute model validation. 

We disagree with this statement and point out where we have cited other work, including the ones mentioned above. However, our CG model is a largely bottom-up CG model which differs from other more ad hoc CG approaches (and some well-known CG models). We do not wish to emphasize the obvious flaws in those other CG approaches and models, since that is not the focus of our manuscript.

(4) Other critiques, questions, concerns:

(a) The first Results sub-heading presents "results", complete with several supplementary figures and a movie that are from a previous publication about the development of the HIV capsid-NPC model in the absence of LEN (Hudait &Voth, 2024). This information should be included as part of the introduction or an abbreviated main-text methods section rather than being included within Results as if it represents a newly reported advancement, as this could be misleading. 

The movie in question (capsid docking to NPC without LEN) is essential for comparison of LEN-binding dynamics. Different from our previous paper, we simulated significantly longer timescales of capsid docking and performed several additional analyses that is relevant to this paper. Moreover, the first section of the result is titled “Coarse-grained modeling and simulation”, hence we only present a summary of the CG models and key validation steps in this section.

(b) The authors say the unbiased simulations of capsid-NPC docking were run as two independent replicates, but results from only one trajectory are ever shown plotted over time. It is not mentioned if the time series data are averaged or smoothed, so what is the shadow in these plots (e.g., Figures 1,2, and Supplementary Figure 5)?

These simulations are the average from two replicas. “For all the plots, the solid lines are the mean values calculated from the time series of two independent replicas, and the shaded region is the standard deviation at each timestep.” This was mentioned in the original figure caption.

(c) Why do the insets showing LEN binding in Figure 2A look so different from the models they are apparently zoomed in on? Both instances really look like they are taken from different simulation frames, rather than being a zoomed-in view.

It is difficult to discern a high curvature region of the capsid due to object overlap of different regions of the capsid. This is likely a case of “perspective distortion” in image processing.

(d) What are the sudden jerks apparent in the SI movies? Perhaps this is related to the rate at which trajectory frames are saved, but occasionally, during the relatively smooth motion of the capsidNPC complex, something dramatic happens all of a sudden in a frame. For example, significant and apparently instantaneous reorientation of the cone far beyond what preceding motions suggest is possible (SI movie 2, at timestamp 0.22), RNP extrusion suddenly in a single frame (SI movie 2, at timestamp 0.27), and simultaneous opening of all pentamers all at once starting in a single frame (SI movie 2, at timestamp 0.33). This almost makes the movie look generated from separate trajectories or discontinuous portions of the same trajectory. If movies have been edited for visual clarity (e.g., to skip over time when "nothing" is happening and focus on the exciting aspects), then the authors should state so in the captions. 

This is due to the rate at which trajectory frames are saved for movie generation for faster processing of the movies. We added the following in movie caption: 

“The movie frames correspond to snapshots every 250000 𝜏_CG.” 

(e) Figure 3c presents a time series of the degree of defects at pent-hex and hex-hex interfaces, but I do not understand the normalization. The authors state, "we represented the defects as the number of under-coordinated CA monomers of the hexamers at the pentamer-hexamer-pentamer and hexamer-hexamer interface as N_Pen-Hex and N_Hex-Hex ... Note that in N_Pen-Hex and N_Hex-Hex are calculated by normalizing by the total number of CA pentamer (12) and hexamer rings (209) respectively." Shouldn't the number of uncoordinated monomers be normalized by the number of that type of monomer, rather than the number of capsomers/rings? E.g., 12*5 and 209*6, rather than 12 and 209?

We prefer to continue with the current normalization, since typically in the HIV-1 literature capsids are represented as a collection of hexamers and pentamers (rather than total number of CA monomers).

(f) The authors state that "Although high computational cost precluded us from continuing these CG MD simulations, we expect these defects at the hexamer-hexamer interface to propagate the high curvature ends of the capsid." The defects being reported are apparently propagating from (not towards) the high curvature ends of the capsid. 

We corrected the statement as follows:

“Although high computational cost precluded us from continuing these CG MD simulations, we expect these defects at the hexamer-hexamer interface to propagate from the high curvature to low curvature end of the capsid.”

(g) The first half of the paper uses the color orange in figures to indicate LEN, but the second half uses orange to indicate defects, and this could be confusing for some readers. Both LEN and "defects" are simply a cluster of spheres, so highlighted defects appear to represent LEN without careful reading of captions.

We only show LEN in Figure 1, and in rest of the figures the bound LEN molecules are not shown for clarity. The defects are shown in a darker shade of orange (amber). 

(h) SI Figure S3 captions says "The CA monomers to which at least one LEN molecule is bound are shown in orange spheres. The CA monomers to which no LEN molecule is bound are shown in white spheres. " While in contradiction, the main-text Fig 2 says "The CA monomers to which at least one LEN molecule is bound are shown in white spheres. The CA monomers to which no LEN molecule is bound are shown in orange spheres. " One of these must be a typo.

We have corrected the erroneous caption in Fig. S3. The color scheme in Fig. 2 and Fig. S3 are now consistent.

(i) The authors state that: "CG MD simulations and live-cell imaging demonstrate that LEN-treated capsids dock at the NPC and rupture at the narrow end when bound to the central channel and then remain associated to the NPC after rupture." However, the live cell imaging data do not show where rupture occurs, such that this statement is at least partially false. It is also unclear that CG simulations show that cores remain bound following rupture, given that simulations were not extended to the timescale needed to observe this, again rendering the statement partially false.

We modified the statement as follows:

“CG MD simulations complemented by the outcome of live-cell imaging demonstrate that LENtreated capsids dock at the NPC and rupture at the narrow end when bound to the central channel and then remain associated with the NPC after rupture.”

(j) The authors state: "We previously demonstrated that the RNP complex inside the capsid contributes to internal mechanical strain on the lattice driven by CACTD-RNP interactions and condensation state of RNP complex (Hudait &Voth, 2024). " In that case, why do the present CG models detect no difference in results for condensed versus uncondensed RNP?

In our previous paper, the difference from condensation state of RNP complex appear only in the pill-shaped capsid, and not in the cone-shaped capsid. In this manuscript, we only investigated the cone-shaped capsid.

(k) The authors state: "The distribution demonstrates that the binding of LEN to the distorted lattice sites is energetically favorable. Since LEN localizes at the hydrophobic pocket between two adjoining CA monomers, it is sterically favorable to accommodate the incoming molecule at a distorted lattice site. This can be attributed to the higher available void volume at the distorted lattice relative to an ordered lattice, the latter being tightly packed. This also allows the drug molecule to avoid the multitude of unfavorable CA-LEN interactions and establish the energetically favorable interactions leading to a successful binding event. " What multitude of unfavorable interactions are the authors referring to? Data is not presented to substantiate the claim of increased void volume between hexamers in the distorted lattice. Capsomer distortion is shown as a schematic in Figure 6A rather than in the context of the actual model.

“What multitude of unfavorable interactions are the authors referring to?” We have now added the following sentence to clarify

“Here we denote unfavorable CA-LEN interactions as all interactions other than the electrostatic and van der Waal interactions that lead to CA-LEN binding (17).”

“In the distorted lattice, there is an increase of void volume is based on standard solid-state physics understanding. We added the word “likely” in the statement. “. This can likely be attributed to the higher available void volume at the distorted lattice relative to an ordered lattice, the latter being tightly packed (41).”

Moreover, in one of our previous manuscripts, we established that compressive or expansive strain induces more closely packed or expanded lattice (A. Yu et al., Strain and rupture of HIV-1 capsids during uncoating. Proceedings of the National Academy of Sciences 119, e2117781119 (2022)).

(l) The authors state that "These striated patterns also demonstrate deviations from ideal lattice packing. " What does ideal lattice packing mean in this context, where hexamers are in numerous unique environments in terms of curvature? What is the structural reference point?

The ideal lattice packing definition is provided in our previous manuscripts: 1. A. Yu et al., Strain and rupture of HIV-1 capsids during uncoating. Proceedings of the National Academy of Sciences 119, e2117781119 (2022), 2. A. Hudait, G. A. Voth, HIV-1 capsid shape, orientation, and entropic elasticity regulate translocation into the nuclear pore complex. Proceedings of the National Academy of Sciences 121, e2313737121 (2024).

These manuscripts are cited in the previous statement. The ideal lattice packing is defined based on lattice separations in each core (in cryo-ET and atomistic simulations) using a local order parameter, which measures the near-neighbor contacts of a particle. Moreover, the ideal packing reference is calculated from all available capsid shapes (cone, ellipsoid, and tubular), and takes into account different curvatures.

(m) If pentamer-hexamer interactions are weakened in the presence of LEN, why are differences at these interfaces not apparent in the Figure 6C data that shows stiffening of the interactions between capsomer subunits?

We have added a statement as follows:

“Based on our analysis, we hypothesize that LEN binding hyperstabilzes the CA hexamerhexamer interactions relative to CA hexamer-pentamer interaction.”

(n) The authors state: "Lattice defects arising from the loss of pentamers and cracks along the weak points of the hexameric lattice drive the uncoating of the capsid." The word rupture or failure should be used here rather than uncoating; it is unclear that the authors are studying the true process of uncoating and whether the defects induced by LEN binding relate in any way to uncoating. 

We have now changed “uncoating” to “rupture” throughout the manuscript.

(o) The authors state: " LEN-treated broken cores are stabilized by the interaction with the disordered FG-NUP98 mesh at the NPC." But no data is presented to demonstrate that capsid stability is increased by NUP98 interaction. In fact, the presented data could suggest the opposite since capsids in contact with NUP98 in the NPC appeared to rupture faster than freely diffusing capsids.

We have modified the statement as follows

“We hypothesize that LEN-treated broken cores are stabilized by the interaction with the disordered FG-NUP98 mesh at the NPC.”

(p) The authors state: "LEN binding stimulates similar changes in free capsids, but they occur with lower frequency on similar time scales, suggesting that the cores docked at the NPC are under increased stress, resulting in more frequent weakening of the hexamer-pentamer and hexamerhexamer interactions, as well as more nucleation of defects at the hexamer-hexamer Interface. ... Our results suggest that in the presence of the LEN, capsid docking into the NPC central channel will increase stress, resulting in more frequent breaks in the capsid lattice compared to free capsids." The first is a run-on sentence. The results shown support that LEN stimulates changes in free capsids to happen faster, but not more frequently. The frequency with which an event occurs is separate from the speed with which the event occurs.

We have fixed the run-on sentence.

The results shown support that LEN stimulates changes in free capsids to happen faster, but not more frequently. The frequency with which an event occurs is separate from the speed with which the event occurs.

We disagree with the reviewer. The statement was intended to provide a comparison between free capsid and NPC-bound capsid.

(q) The authors state: "A possible mechanistic pathway of capsid disassembly can be that multiple pentamers are dissociated from the capsid sequentially, and the remaining hexameric lattice remains stabilized by bound LEN molecules for a time, before the structural integrity of the remaining lattice is compromised." This statement is inconsistent with experimental studies that say LEN does not lead to capsid disassembly, and may even prevent disassembly as part of its disruption of proper uncoating (e.g., 10.1073/pnas.2420497122 previously published by the authors).

We disagree with the interpretation of the reviewer. Our interpretation based on our results is LEN binding accelerates capsid rupture (from pentamer-rich high curvature ends), and the rest of the broken hexameric lattice is hyperstabilized. Ultimately, lattice rupture will lead to release the RNP, and hence the intended goal of the drug is achieved.

(r) Finally, it remains a concern with the authors' work that the bottom-up solvent-free CG modeling software used in this and supporting works is not open source or even available to other researchers like other commonly used molecular dynamics software packages, raising significant questions about transparency and reproducibility.

The simulations were performed in LAMMPS, which is open source. This software is already stated in the Methods. Input data is provided upon request.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 1: In part B, it appears the middle panel was screenshotted from a ppt, given the red line underneath Lenacapavir. You can export it to an image instead.

The figure is fixed.

(2) Figure 6: In part A, the LEN_d in the graph is illegible. Also, in the panel next to it, it also appears to have been screenshotted from a ppt.

The figure is fixed.

(3) Page 6: There's an errant quotation mark at the end of a paragraph.

Removed the errant quotation

Reviewer #2 (Recommendations for the authors):

The code used to perform bottom-up solvent-free CG modeling simulations is not made available.

This is not true. LAMMPS was used as stated in Methods.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, Besson et al. investigate how environmental nutrient signals regulate chromosome biology through the TORC1 signaling pathway in Schizosaccharomyces pombe. Specifically, the authors explore the impact of TORC1 on cohesin function - a protein complex essential for chromosome segregation and transcriptional regulation. Through a combination of genetic screens, biochemical analysis, phospho-proteomics, and transcriptional profiling, they uncover a functional and physical interaction between TORC1 and cohesin. The data suggest that reduced TORC1 activity enhances cohesin binding to chromosomes and improves chromosome segregation, with implications for stress-responsive gene expression, especially in subtelomeric regions.

Strengths:

This work presents a compelling link between nutrient sensing and chromosome regulation. The major strength of the study lies in its comprehensive and multi-disciplinary approach. The authors integrate genetic suppression screens, live-cell imaging, chromatin immunoprecipitation, co-immunoprecipitation, and mass spectrometry to uncover the functional connection between TORC1 signaling and cohesin. The use of phospho-mutant alleles of cohesin subunits and their loader provides mechanistic insight into the regulatory role of phosphorylation. The addition of transcriptomic analysis further strengthens the biological relevance of the findings and places them in a broader physiological context. Altogether, the dataset convincingly supports the authors' main conclusions and opens up new avenues of investigation.

Weaknesses:

While the study is strong overall, a few limitations are worth noting. The consistency of cohesin phosphorylation changes under different TORC1-inhibiting conditions (e.g., genetic mutants vs. rapamycin treatment) is unclear and could benefit from further clarification. The phosphorylation sites identified on cohesin subunits do not match known AGC kinase consensus motifs, raising the possibility that the modifications are indirect. The study relies heavily on one TORC1 mutant allele (mip1-R401G), and additional alleles could strengthen the generality of the findings. Furthermore, while the results suggest that nutrient availability influences cohesin function, this is not directly tested by comparing growth or cohesin dynamics under defined nutrient conditions.

We thank the reviewer for his overall positive assessment and constructive criticism. We broadly agree with the few limitations he pointed out, which we will comment on below.

(1) The consistency of cohesin phosphorylation changes under different TORC1-inhibiting conditions (e.g., genetic mutants vs. rapamycin treatment) is unclear and could benefit from further clarification.

The basis of our study was to search for suppressor mutants, a situation in which an unviable strain becomes viable. It turns out that the suppressor mutants affect TORC1, necessarily in a partial manner given that TORC1 kinase activity is essential for proliferation. Likewise rapamycin partially inhibits TORC1 and does not prevent proliferation of wild-type S. pombe cells. TORC1 mutants cause a constitutive decrease in activity with possible adaptive effects, whereas rapamycin is applied for a single cell cycle. In addition, it is known that bona fide TORC1 substrates respond differently to rapamycin. Some phosphosites show acute sensitivity, while others are less sensitive or even insensitive (Kang et al., 2013, PMID: 23888043). Therefore, both hypomorphic TORC1 genetic mutants and rapamycin treatment result in partial inhibition of TORC1 kinase activity. While the lists of affected TORC1 substrates may overlap, they are unlikely to be identical. Furthermore, the phosphorylation level of the relevant substrates is not necessarily altered to the same extent. Nevertheless, both conditions suppress the heatsensitive phenotype of the mis4 mutant, although the suppressor effect of rapamycin is weaker. Consequently, some phosphorylation sites involved in mis4-ts suppression may behave similarly in rapamycin and TORC1 mutants (i.e. Psm1-S1022), while others (i.e. Mis4-183) may behave differently.

It is clear that there are phenotypic differences between the suppression of mis4-ts by rapamycin treatment or by genetic alteration of TORC1. This can be seen also in our ChIP analysis of Rad21 distribution at CARs. The trend is upward, but the pattern is not identical. We have added the following text to summarize the above considerations:

“It is important to note at this stage that, although rapamycin and TORC1 mutants both decrease TORC1 kinase activity, the two are not equivalent. The mechanisms by which TORC1 kinase activity is reduced are different, and TORC1 mutants suppress the mis4G1487D phenotype more effectively than rapamycin. It is known that bona fide TORC1 substrates respond differently to rapamycin. Some phosphosites show acute sensitivity, while others are less sensitive or even insensitive (Kang et al, 2013). TORC1 mutants cause a constitutive decrease in activity with possible adaptive effects, whereas rapamycin is applied for a single cell cycle. While the lists of affected TORC1 substrates may overlap, they are unlikely to be identical. Furthermore, the phosphorylation level of the relevant substrates is not necessarily altered to the same extent. It is therefore remarkable that negative regulation of TORC1 by rapamycin or a genetic mutation both alleviate mis4G14878D phenotypes and have a fairly similar effect on cohesin dynamics.”

(2) The phosphorylation sites identified on cohesin subunits do not match known AGC kinase consensus motifs, raising the possibility that the modifications are indirect.

The genetic and biochemical analyses provided in this study show that the AGC kinases Sck1 and Sck2 influence cohesin phosphorylation and function. Whether Sck1, Sck2 or TORC1 directly phosphorylates cohesin components are the next questions to address. The fact that the phosphorylation of Psm1-S1022 and Mis4-S183 were never abolished in the sck1-2 mutants may suggest they are indirectly involved. This should be taken with caution because we have been using deletion mutants. In this situation, cells adapt and other kinases may substitute, at least partially (Plank et al, 2020, PMID: 32102971). Asking whether cohesin components display consensus sites for AGC kinases is a complementary approach. The consensus site for Sck1 and Sck2 is unknown. If we assume some conservation with budding yeast SCH9, the consensus sequence would be RRxS/T. Psm1S1022 (DQMSP) and Mis4-S183 (QLCSP) do not fit the consensus. However, this kind of information should be taken with care as many SCH9-dependent phosphorylation sites did not fall within the consensus in a study using analogue-sensitive AGC kinases and phosphoproteomics (Plank et al, 2020, PMID: 32102971). Alternatively, Sck1-2 may regulate other kinases. Indeed Psm1-S1022 and Mis4-183 lie within CDK consensus sites and Psm1-S1022 phosphorylation is Pef1-dependent. In summary, yes, the changes may be indirect, that remains to be seen, but in any case they are influenced by TORC1 signalling. The following paragraph was added:

“The consensus site for Sck1 and Sck2 is unknown. If we assume some conservation with budding yeast SCH9, the consensus sequence would be RRxS/T. Psm1-S1022 (DQMSP) and Mis4-S183 (QLCSP) do not fit the consensus. However, this should be taken with care as many SCH9-dependent phosphorylation sites did not fall within the consensus in a study using analogue-sensitive AGC kinases and phosphoproteomics (Plank et al, 2020). Alternatively, Sck1-2 may regulate other kinases. Indeed Psm1-S1022 and Mis4-183 lie within CDK consensus sites and Psm1-S1022 phosphorylation is Pef1-dependent.”

(3) The study relies heavily on one TORC1 mutant allele (mip1-R401G), and additional alleles could strengthen the generality of the findings.

It is true that we focused our attention on mip1-R401G, which is present in all the experiments presented. That said, other alleles were used in one or more figures. Five mip1 alleles and one tor2 allele were identified as mis4-ts suppressors (Fig. 1). We have also shown that another mip1 allele, mip1-Y533A, created by another group (Morozumi et al, 2021), is also a suppressor of mis4-ts and affects the phosphorylation of Mis4-S183 and Psm1-S1022 (Fig. 1, Figure 5—figure supplement 1). To this we can add the effect of mutants that render TORC1 hyperactive (Fig. 1E, Fig. 2H) as well as AGC kinase mutants (Figure 5—figure supplement 3.). And finally, the effect of rapamycin. So yes, mip1-R401G has been used extensively, but we have still broadly covered the TORC1 signalling pathway.

(4) Furthermore, while the results suggest that nutrient availability influences cohesin function, this is not directly tested by comparing growth or cohesin dynamics under defined nutrient conditions

We agree that studying the dynamics of cohesin, genome folding and gene expression in relation to nutrient availability is a very exciting topic, and we hope to address these issues in detail in the future.

Reviewer #2 (Public review):

Summary:

In this study, the authors follow up on a previous suppressor screen of a temperaturesensitive allele of mis4 (mis4-G1487D), the cohesin loading factor in S. pombe, and identify additional suppressor alleles tied to the S. pombe TORC1 complex. Their analysis suggests that these suppressor mutations attenuate TORC1 activity, while enhanced TORC1 activity is deleterious in this context. Suppression of TORC1 activity also ameliorates chromosome segregation and spindle defects observed in the mis4-G1487D strain, although some more subtle effects are not reconstituted. The authors provide evidence that this genetic suppression is also tied to the reconstitution of cohesin loading. Moreover, disrupting TORC1 also enhances Mis4/cohesin association with chromatin (likely reflecting enhanced loading) in WT cells, while rapamycin treatment can enhance the robustness of chromosome transmission. These effects likely arise directly through TORC1 or its downstream effector kinases, as TORC1 co-purifies with Mis4 and Rad21; these factors are also phosphorylated in a TORC1-dependent fashion. Disrupting Sck2, a kinase downstream of TORC1, also suppresses the mis4-G1487D allele while simultaneous disruption of Sck1 and Sck2 enhances cohesin association with chromatin, albeit with differing effects on phosphorylation of Mis4 and Psm1/Scm1. Phosphomutants of Mis4 and Psm1 that mimic observed phosphorylation states identified by mass spectrometry that are TORC1-dependent also suppressed phenotypes observed in the mis4-G1487D background. Last, the authors provide evidence that the mis4-G1487D background and TORC1 mutant backgrounds display an overlap in the dysregulation of genes that respond to environmental conditions, particularly in genes tied to meiosis or other "stress".

Overall, the authors provide compelling evidence from genetics, biochemistry, and cell biology to support a previously unknown mechanism by which nutrient sensing regulates cohesin loading with implications for the stress response. The technical approaches are generally sound, well-controlled, and comprehensive.

Specific Points:

(1) While the authors favor the model that the enhanced cohesin loading upon diminished TORC1 activity helps cells to survive harsh environmental conditions, as starvation of S. pombe also drives commitment to meiosis, it seems as plausible that enhanced cohesin loading is related to preparing the chromosomes to mate.

(2) Related to Point 1, the lab of Sophie Martin previously published that phosphorylation of Mis4 characterizes a cluster of phosphotargets during starvation/meiotic induction (PMID: 39705284). This work should be cited, and the authors should interrogate how their observations do or do not relate to these prior observations (are these the same phosphosites?).

We agree this is a possibility and the following paragraph was added in the discussion section:

“TORC1-based regulation of cohesin may be relevant to preparing cells for meiosis. Since nitrogen deprivation stimulates meiosis initiation, subsequent TORC1 down-regulation may regulate the cohesin complex, preparing the chromosomes for fusion and meiosis. A recent phosphoproteomic study conducted by Sophie Martin's laboratory showed that Mis4-S107 phosphorylation increases during cellular fusion (Bérard et al, 2024). It is unknown whether the phosphorylation of S107 is controlled by TORC1 signalling. As the phosphorylation of Mis4-S183 and Psm1-S1022 was not detected in these experiments, the potential involvement of the TORC1-cohesin axis in the sexual programme remains to be investigated.”

(3) It would be useful for the authors to combine their experimental data sets to interrogate whether there is a relationship between the regions where gene expression is altered in the mis4-G1487D strain and changes in the loading of cohesin in their ChIP experiments.

(4) Given that the genes that are affected are predominantly sub-telomeric while most genes are not affected in the mis4-G1487D strain, one possibility that the authors may wish to consider is that the regions that become dysregulated are tied to heterochromatic regions where Swi6/HP1 has been implicated in cohesin loading

We agree that it would be interesting to see if there are correlations between cohesin positioning, heterochromatin and gene expression. That said, this would need to be done at the whole-genome level and include many other parameters (genome folding, histone modifications, Pol2 occupancy). These issues require substantial investment and may be addressed in a follow-up project.

(5) It would be helpful to show individual data points from replicates in the bar graphs - it is not always clear what comprises the data sets, and superplots would be of great help.

We verified that the figure captions clearly indicate the data sets considered, their mean, standard deviation, and statistical analysis method. As for the type of plot, we used the tools at our disposal.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Besson et al. investigate how the nutrient-responsive TORC1 signaling pathway modulates cohesin function in S. pombe. Using a genetic screen, the authors identify TORC1 mutants that suppress the thermosensitive growth defects of a cohesin loader mutant (mis4-G1487D). They show that reducing TORC1 activity-either genetically or pharmacologically-enhances cohesin binding to chromosomal sites (CARs), improves chromosome segregation, and alters the phosphorylation state of cohesin and its loader. They also show, through coimmunoprecipitation, that TORC1 and cohesin physically associate, and that this functional interaction extends to the transcriptional regulation of stress-responsive, subtelomeric genes. Together, the data suggest that environmental cues influence chromosome stability and gene expression via a TORC1-cohesin axis.

Overall, the study is well-supported by thoughtful genetic epistasis analyses and a combination of genetic, biochemical, cell biological, and transcriptomic approaches. While not all data are equally strong, the cumulative evidence convincingly supports the authors' conclusions.

Specific Concerns and Suggestions

(1) Figure 2A - Division rates of wild-type and mip1-R401G cells are missing and should be provided for proper comparison.

This is now done in revised Figure 2A. We also made a change in the manuscript, replacing “The mip1-R401G mutation efficiently suppressed the proliferation and viability defects (Figure 2A)” by “The mip1-R401G mutation efficiently attenuated the proliferation and viability defects (Figure 2A)”, to acknowledge the fact that the proliferation rate did not return to wild-type levels.

(2) Figure 3 - Figure Supplement 1 - The authors claim that "Rapamycin treatment during a single cell cycle provoked a similar effect although less pronounced." However, for most CARs, the effect appears insignificant. This should be acknowledged in the text.

The text has been changed accordingly:

“Rapamycin treatment during a single cell cycle provoked a similar stimulation of Rad21 binding at CARs (Figure 3—figure supplement 1), albeit with noticeable differences. In mis4+ cells, both mip1-R401G and rapamycin induced a significant increase in Rad21 binding at several CARs (tRNA-left, cc2, 3323, NTS, Tel1-R). However, some CARs that exhibited increased Rad21 binding in the mip1 mutant did not respond significantly to rapamycin (dg2-R, tRNA-R). Conversely, rapamycin (but not mip1-R401G) induced a significant increase in Rad21 binding at imr2-L and CAR1806 (Figure 3D and Figure 3— figure supplement 1). In the mis4-G1487D mutant background, mip1-R401G induced a significant increase in Rad21 binding at all examined sites (Figure 3B). Similarly, rapamycin did increase Rad21 binding at all sites but only at the Tel1-R site did this reach statistical significance (Figure 3—figure supplement 1).”

(3) Figure 4 - The analysis of interactions between TORC1 and the cohesin complex is somewhat limited. The authors may wish to test interactions between Mip1 and cohesin subunits (e.g., Rad21). More interestingly, it would be valuable to explore whether MIP1 mutations that suppress cohesin mutants affect the interaction between Tor2 and Rad21.

We have added some additional data that answer this question (Figure 4—figure supplement 1) and a paragraph in the manuscript:

“Tor2, the kinase subunit of TORC1, is particularly well detected in Rad21 and Mis4 coimmunoprecipitation experiments (Figure 4 and Figure 4—figure supplement 1). To determine whether the R401G mutation in Mip1 affects these interactions, coimmunoprecipitation experiments were repeated in both the mip1-R401G and mip1+ contexts. The data obtained indicate that Tor2 co-immunoprecipitation with Mis4 and Rad21 is largely unaffected by the mip1-R401G mutation (Figure 4—figure supplement 1). If mip1-R401G affects the regulation of cohesin by TORC1, this does not appear to stem from a gross defect in their interaction, at least at this level of resolution.”

(4) Figure 5 - There appears to be a lack of correlation between cohesin subunit phosphorylation in TORC1-reducing mutants and in response to rapamycin. The reason for this discrepancy is unclear.

This point was addressed in the previous section (Public review, reviewer 1, point 1). The response is pasted below:

The basis of our study was to search for suppressor mutants, a situation in which an unviable strain becomes viable. It turns out that the suppressor mutants affect TORC1, necessarily in a partial manner given that TORC1 kinase activity is essential for proliferation. Likewise rapamycin partially inhibits TORC1 and does not prevent proliferation of wild-type S. pombe cells. TORC1 mutants cause a constitutive decrease in activity with possible adaptive effects, whereas rapamycin is applied for a single cell cycle. In addition, it is known that bona fide TORC1 substrates respond differently to rapamycin. Some phosphosites show acute sensitivity, while others are less sensitive or even insensitive (Kang et al., 2013, PMID: 23888043). Therefore, both hypomorphic TORC1 genetic mutants and rapamycin treatment result in partial inhibition of TORC1 kinase activity. While the lists of affected TORC1 substrates may overlap, they are unlikely to be identical. Furthermore, the phosphorylation level of the relevant substrates is not necessarily altered to the same extent. Nevertheless, both conditions suppress the heatsensitive phenotype of the mis4 mutant, although the suppressor effect of rapamycin is weaker. Consequently, some phosphorylation sites involved in mis4-ts suppression may behave similarly in rapamycin and TORC1 mutants (i.e. Psm1-S1022), while others (i.e. Mis4-183) may behave differently.

It is clear that there are phenotypic differences between the suppression of mis4-ts by rapamycin treatment or by genetic alteration of TORC1. This can be seen also in our ChIP analysis of Rad21 distribution at CARs. The trend is upward, but the pattern is not identical. We have added the following text to summarize the above considerations:

“It is important to note at this stage that, although rapamycin and TORC1 mutants both decrease TORC1 kinase activity, the two are not equivalent. The mechanisms by which TORC1 kinase activity is reduced are different, and TORC1 mutants suppress the mis4G1487D phenotype more effectively than rapamycin. It is known that bona fide TORC1 substrates respond differently to rapamycin. Some phosphosites show acute sensitivity, while others are less sensitive or even insensitive (Kang et al, 2013). TORC1 mutants cause a constitutive decrease in activity with possible adaptive effects, whereas rapamycin is applied for a single cell cycle. While the lists of affected TORC1 substrates may overlap, they are unlikely to be identical. Furthermore, the phosphorylation level of the relevant substrates is not necessarily altered to the same extent. It is therefore remarkable that negative regulation of TORC1 by rapamycin or a genetic mutation both alleviate mis4G14878D phenotypes and have a fairly similar effect on cohesin dynamics.”

(5) The phosphorylation sites examined on cohesin subunits are not canonical AGC kinase consensus motifs, suggesting they are unlikely to be direct targets of Sck1 or Sck2. I suggest that this point should be mentioned in the manuscript.

This is now done:

“The consensus site for Sck1 and Sck2 is unknown. If we assume some conservation with budding yeast SCH9, the consensus sequence would be RRxS/T. Psm1-S1022 (DQMSP) and Mis4-S183 (QLCSP) do not fit the consensus. However, this should be taken with care as many SCH9-dependent phosphorylation sites did not fall within the consensus in a study using analogue-sensitive AGC kinases and phosphoproteomics (Plank et al, 2020). Alternatively, Sck1-2 may regulate other kinases. Indeed Psm1-S1022 and Mis4-183 lie within CDK consensus sites and Psm1-S1022 phosphorylation is Pef1-dependent.”

(6) Figure 5 - Figure Supplement 3 - The reduction in Psm1 phosphorylation in the sck1Δ sck2Δ double mutant is not convincing without replicates and statistical analysis.

This is now done and the data are presented in Figure 5—figure supplement 3. Panel D shows the data for Psm1-S1022p and Panel E for Mis4-S183p. Each graph shows the mean ratios +/- SD from 3 experiments.

(7) Figure 5C - It would be helpful if the authors validated the effect of pef1 deletion on Mis4 phosphorylation by Western blotting, rather than relying solely on mass spectrometry data.

This is now done. The data appears in Figure 5—figure supplement 2, panel B.

(8) The statement: "The frequency of chromosome segregation defects of mis4‐G1487D was markedly reduced in a sck2‐deleted background and further decreased by the additional deletion of sck1 (Figure 5-figure supplement 3)" is not supported by the data. According to the figure, the difference between sck2Δ and sck1Δ sck2Δ is not statistically significant.

The sentence was changed to:

“The frequency of chromosome segregation defects in the mis4-G1487D strain remained unchanged in a sck1-deleted background, but was significantly reduced when either the sck2 or both the sck1 and sck2 genes were deleted (Figure 5—figure supplement 3).”

(9) Figure 6A - The data shown are not convincing. The double mutants carrying the phosphomimetic and phospho-null psm1 alleles should be shown on the same plate for direct comparison.

This is now done. The new data are shown Figure 6A.

(10) Figure 6E - The wild-type control is missing. Including it would provide an essential reference point to assess whether the mutants rescue cohesin binding to wild-type levels.

This is true that the effects were small when compared to wild-type but still significant when compared to mis4-G1487D. The comparison with wild-type is now available in Figure 6—figure supplement 1 and the paragraph was modified accordingly:

“Cohesin binding to CARs as assayed by ChIP tend to increase for the mutants mimicking the non-phosphorylated state and to decrease with the phospho-mimicking forms (Figure 6E). The rescue of mis4-G1487D by the non-phosphorylatable form was modest but significant, notably within centromeric regions (imr2-L, dg2-R) and at the telomere (Tel1-R) site (Figure 6E and see Figure 6—figure supplement 1 for comparison with wild-type levels). Conversely, the mutant mimicking the phosphorylated state displayed a significant reduction of Rad21 binding at those sites as well as to several other sites at the centromere (cc2, tRNA-R), CAR2898, and at the ribosomal non-transcribed spacer site NTS).”

Limitations of the Study (not requiring additional experiments for publication, but worth noting).

(11) The authors suggest that nutrient status affects cohesin, but this is not directly demonstrated-e.g., by comparing growth or cohesin dynamics or phosphorylation under defined nutrient conditions. That said, the paper is sufficiently detailed to allow this question to be addressed in follow-up work.

We agree that studying the dynamics of cohesin, genome folding and gene expression in relation to nutrient availability is a very exciting topic, and we hope to address these issues in detail in the future.

(12) The upstream signaling cascade remains unresolved. The identity of kinases downstream of TORC1 (e.g., whether Sck1/Sck2 or other factors are responsible) and whether TORC1 directly phosphorylates Mis4 or Psm1 are not established.

This is something we can all agree on, and it might be something we look at in a future project.

(13) The conclusions rely heavily on one TORC1 mutant allele (mip1-R401G). While this allele is informative, additional alleles or orthogonal methods could further support the generality of the findings.

It is true that we focused our attention on mip1-R401G, which is present in all the experiments presented. That said, other alleles were used in one or more figures. Five mip1 alleles and one tor2 allele were identified as mis4-ts suppressors (Fig. 1). We have also shown that another mip1 allele, mip1-Y533A, created by another group (Morozumi et al, 2021), is also a suppressor of mis4-ts and affects the phosphorylation of Mis4-S183 and Psm1-S1022 (Fig. 1, Figure 5—figure supplement 1). To this we can add the effect of mutants that render TORC1 hyperactive (Fig. 1E, Fig. 2H) as well as AGC kinase mutants (Figure 5—figure supplement 3.) and finally, the effect of a transient treatment with rapamycin. So yes, mip1-R401G has been used extensively, but we have still broadly covered the TORC1 signalling pathway.

Reviewer #2 (Recommendations for the authors):

(1) Given the lack of CTCF in fission yeast, it is worth noting that cohesin ChIP data nonetheless can predict topological domains, which reinforces its important role in dictating chromatin folding (PMID: 39543681).

We thank the reviewer for this suggestion. We now refer to this study in the discussion section.

(2) Providing context for the S. pombe nomenclature for the conserved cohesin subunits would help the reader navigate the manuscript, possibly using a cartoon as for the TORC complexes. For example, Psm1 (aka Smc1) is not introduced and therefore its phosphorylation comes into the manuscript without explanation.

Cohesin subunits and their names are given in the introduction section.

双组分调节 M. A. Artsiusheuski , N. P. M. Casati , A. H. Clark , M. Nachtegaal , R. Verel , J. A. van Bokhoven and V. L. Sushkevich , Controlling the Mechanism of Nucleation and Growth Enables Synthesis of UiO-66 Metal–Organic Framework with Desired Macroscopic Properties, Angew. Chem., Int. Ed., 2024, e202415919 40.X. Chen , Y. Lyu , Z. Wang , X. Qiao , B. C. Gates and D. Yang , Tuning Zr12O22 Node Defects as Catalytic Sites in the Metal–Organic Framework hcp UiO-66, ACS Catal., 2020, 10 , 2906 —2914

26.L. Zhou , N. Li , G. Owens and Z. Chen , Simultaneous removal of mixed contaminants, copper and norfloxacin, from aqueous solution by ZIF-8, Chem. Eng. J., 2019, 362 , 628 —637 27.G. Chaturvedi , A. Kaur , A. Umar , M. A. Khan , H. Algarni and S. K. Kansal , Removal of fluoroquinolone drug, levofloxacin, from aqueous phase over iron based MOFs, MIL-100(Fe), J. Solid State Chem., 2020, 281 , 121029 28.R. Yu and Z. Wu , High adsorption for ofloxacin and reusability by the use of ZIF-8 for wastewater treatment, Microporous Mesoporous Mater., 2020, 308 , 110494 29.X. Fang , S. B. Wu , Y. H. Wu , W. Yang , Y. L. Li , J. Y. He , P. D. Hong , M. X. Nie , C. Xie , Z. J. Wu , K. S. Zhang , L. T. Kong and J. H. Liu , High-efficiency adsorption of norfloxacin using octahedral UIO-66-NH2 nanomaterials: Dynamics, thermodynamics, and mechanisms, Appl. Surf. Sci., 2020, 518 , 146226 30.H. Wang , X. Pei , M. J. Kalmutzki , J. Yang and O. M. Yaghi , Large Cages of Zeolitic Imidazolate Frameworks, Acc. Chem. Res., 2022, 55 , 707 —721

机理以及苯Π电子导致C-F的离子性

SAs的吸附动力学：CUH 和 CUF 对磺胺甲噁唑的接触时间。CUH 和 CUF 的准一级动力学模型(b)、准二级动力学模型(c)和颗粒内扩散模型(d)。条件：SMX = 50 mg/L，T = 25 °C。

Author response:

eLife Assessment

This study uses a Bayesian framework to characterize latent brain state dynamics associated with memory encoding and performance in children, as measured with functional magnetic resonance imaging. The novelty of the approach offers valuable insights into memory-related brain activity, but the consideration of developmental changes in memory and brain dynamics, and the evidence to support the proposed mapping between specific states and distinct aspects of memory, are incomplete. This work will be of interest to researchers interested in cognitive neuroscience and the development of memory.

We are grateful to the editor and reviewers for their positive feedback and constructive evaluation. Their comments have identified important areas where the manuscript can be strengthened. Below, we outline our planned revisions.

Reviewer #1 (Public review):

Zeng et al. characterized the dynamic brain states that emerged during episodic encoding and the reactivation of these states during the offline rest period in children aged 8-13. In the study, participants encoded scene images during fMRI and later performed a memory recognition test. The authors adopted the BSDS approach and identified four states during encoding, including an "active-encoding" state. The occupancy rate of, and the state transition rates towards, this active-encoding state positively predicted memory accuracy across participants. The authors then decoded the brain states during pre- and post-encoding rests with the model trained on the encoding data to examine state reactivation. They found that the state temporal profile and transition structure shifted from encoding to post-encoding rest. They also showed that the mean lifetime and stability (measured with self-transition probability) of the "default-mode" state during post-encoding rest predict memory performance. How brain dynamics during encoding and offline rest support long-term memory remains understudied, particularly in children. Thus, this study addresses an important question in the field. The authors implemented an advanced computational framework to identify latent brain states during encoding and carefully characterized their spatiotemporal features. The study also showed evidence for the behavioral relevance of these states, providing valuable insights into the link between state dynamics and successful encoding and consolidation.

We thank Reviewer #1 for the positive feedback on our study. And we would like to thank you for the reviewer's constructive feedback. We plan to incorporate detailed methodological justifications and a thorough limitation analysis. We also plan to enhance the overall logical coherence of the manuscript, ensuring a more robust and scientifically sound presentation.

Weaknesses:

(1) If applicable, please provide information on the decoding performance of states during pre- and post-encoding rests. The Methods noted that the authors applied a threshold of 0.1 z-scored likelihood, and based on Figure S2, it seems like most TRs were assigned a reinstated state during post-encoding rest. It would be useful to know, for the decodable TRs, how strong the evidence was in favor of one state over others. Further, was decoding performance better during post- vs. pre- encoding rest? This is critical for establishing that these states were indeed "reinstated" during rest. The authors showed individual-specific correlations between encoding and post-encoding state distribution, which is an important validation of the method, but this result alone is not sufficient to suggest that the states during encoding were the ones that occurred during rest. The authors found that the state dynamics vary substantially between encoding and rest, and it would be helpful to clarify whether these differences might be related to decoding performance. I am also curious whether, if the authors apply the BSDS approach to independently identify brain states during rest periods (instead of using the trained model from encoding), they find similar states during rest as those that emerged during encoding?

We plan three additional analyses to strengthen the evidence for state reinstatement during rest: First, we will report quantitative decoding confidence metrics for each decoded time point, including the log-likelihood between the winning state and the next-best state. We will compare these distributions between pre- and post-encoding rest to test whether decoding quality differs between conditions, as the reviewer suggests. Second, we will provide a more detailed characterization of the decoding process, including the proportion of TRs that survive the log-likelihood threshold of 0.1 during pre- vs. post-encoding rest and whether this proportion relates to memory performance. Third, we will train an independent BSDS model directly on the rest data (rather than using the encoding-trained model) and assess the degree of correspondence between the independently discovered rest states and the encoding states in terms of amplitude profiles and covariance structures. Convergence between the two approaches would provide strong validation that the encoding-defined states genuinely re-emerge at rest. Together with our evidence from our previous analyses, these additional analyses will strengthen our claims.

(2) During post-encoding rest, the intermediate activation state (S1) became the dominant state. Overall, the paper did not focus too much on this state. For example, when examining the relationship between state transitions and memory performance, the authors also did not include this state as a part of the analyses presented in the paper (lines 203-211). Could the author report more information about this state and/or discuss how this state might be relevant to memory formation and consolidation?

We thank the reviewer for this suggestion. During encoding, S1 had the lowest occupancy (~10%) and showed no significant relationship with memory performance, which led us to interpret it as a non-essential transient configuration. In the revision, we will provide a more thorough characterization of S1, and conduct correlation analyses to probe whether its dynamic properties during post-encoding rest correlate with individual memory performance.

(3) Two outcome measures from the BSDS model were the occupancy rate and the mean lifetime. The authors found a significant association with behavior and occupancy rate in some analyses, and mean lifetime in others. The paper would benefit from a stronger theoretical framing explaining how and why these two different measures provide distinct information about the brain dynamics, which will help clarify the interpretation of results when association with behavior was specific to one measure.

We thank the reviewer for this suggestion. Occupancy rate and mean lifetime, while related, capture fundamentally different aspects of brain state dynamics. Occupancy rate reflects the total proportion of time the brain spends in a given state, capturing the overall prevalence of that configuration across the scanning session. Mean lifetime, by contrast, measures the average uninterrupted duration of each state visit, indexing the temporal stability or persistence of a given network configuration once it is entered. Critically, two states could have identical occupancy rates but very different mean lifetimes, a state visited frequently but briefly versus one visited rarely but sustained, implying distinct underlying neural dynamics. In the context of memory, high occupancy of the active-encoding state may reflect repeated engagement of encoding-optimal circuits, while long mean lifetime of the default-mode state during rest may reflect sustained consolidation-related processing. We will expand the theoretical framework in the revised manuscript to articulate these distinctions and connect them to extant findings suggesting that temporal stability versus frequency of state visits may have dissociable behavioral correlates in working memory and episodic memory (He et al., 2023; Stevner et al., 2019).

(4) For performance on a memory recognition test, d' is a more common metric in the literature as it isolates the memory signal for the old items from response bias. According to Methods (line 451), the authors have computed a different metric as their primary behavioral measure (hits + correction rejections - misses - false alarms). Please provide a rationale for choosing this measure instead. Have the authors considered computing d' as well and examining brain-behavior relationships using d'?

Our primary memory recognition metric computed as (hits + correct rejections − misses − false alarms) / total trials, provides an unbiased linear estimate of discrimination ability that is mathematically consistent with d' in directional effects. We selected this measure because it is particularly robust with limited trial counts per condition (Verde et al., 2006; Wickens, 2001). Nonetheless, we agree that reporting d' is important for comparability with the broader literature. In the revision, we will compute d' for each participant and conduct parallel brain–behavior correlation analyses to demonstrate that our findings are robust across both metrics.

(5) While this study examined brain state dynamics in children, there was no adult sample to compare with. Therefore, it is hard to conclude whether the findings are specific to children (or developing brains). It would be helpful to discuss this point in the paper.

We thank the reviewer for raising this point. While several studies have documented memory-related replay and reinstatement in adults at both the regional and systems levels(Tambini et al., 2017; Wimmer et al., 2020), few have examined whether analogous state-level reinstatement occurs in children. Our study was motivated by this gap: we sought to test whether children show dynamic brain state reinstatement mechanisms similar to those described in adults. However, we acknowledge that without a direct adult comparison, we cannot determine whether the observed patterns are unique to children or reflect general principles of episodic memory organization. In the revised manuscript, we will: (a) frame the study more carefully as examining whether established state-level consolidation mechanisms also operate during childhood, (b) discuss findings in relation to adult studies, and (c) include exploratory analyses of age-related variability in both memory performance and BSDS dynamics within our sample, while acknowledging that the narrow age range (8–13) and small sample size limit the power of such developmental analyses. We will clearly identify the absence of an adult comparison as a limitation.

Reviewer #2 (Public review):

This paper investigates the latent dynamic brain states that emerge during memory encoding and predict later memory performance in children (N = 24, ages: 8 -13 years). A novel computational approach (Bayesian Switching Dynamic Systems, BSDS) discovers latent brain states from fMRI data in an unsupervised and parameter-free manner that is agnostic to external stimuli, resulting in 4 states: an active-encoding state, a default-mode state, an inactive state, and an intermediate state. The key finding is that the percentage of time occupied in the active-encoding state (characterized by greater activity in hippocampal, visual, and frontoparietal regions), as well as greater transitions to this state, predicts memory accuracy. Memory accuracy was also predicted by the mean lifetime and transitions to the default-mode state (characterized by greater activity in medial prefrontal cortex and posterior cingulate cortex) during post-encoding rest. Together, the results provide insights into dynamic interactions between brain regions that may be optimal for encoding novel information and consolidating memories for long-term retention.

We thank Reviewer #2 for recognizing the novelty and broader utility of our methodology and for noting that the manuscript is well-written and concise.

Weaknesses:

(1) The study focuses on middle childhood, but there is a lack of engagement in the Introduction or Discussion about what is known about memory development and the brain during this period. Many of the brain regions examined in this study, particularly frontoparietal regions, undergo developmental changes that could influence their involvement in memory encoding and consolidation. The paper would be strengthened by more directly linking the findings to what is already known about episodic memory development and the brain.

We thank the reviewer for this suggestion. In response, we will substantially expand the Introduction and Discussion to situate our findings within the developmental cognitive neuroscience literature on episodic memory. In particular, we will address the protracted developmental trajectory of frontoparietal regions, the well-documented maturation of hippocampal–cortical connectivity during middle childhood, and how these developmental changes may influence the brain state configurations we observed (He et al., 2023; Ryali et al., 2016). This will provide the necessary developmental context for interpreting our state dynamics results.

(2) A more thorough overview of the BSDS algorithm is needed, since this is likely a novel method for most readers. Although many of the nitty-gritty details can be referenced in prior work, it was unclear from the main text if the BSDS algorithm discovered latent states based on activation patterns, functional connectivity, or both. Figure 1F is not very informative (and is missing labels).

We thank the reviewer for this suggestion. We agree that a more accessible overview of the BSDS algorithm (Lee et al., 2025; Taghia et al., 2018) is needed. In the revision, we will expand the Methods and provide a concise algorithmic overview in the main text that clarifies the following key points: (a) BSDS operates on multivariate time series from the ROIs and infers latent brain states defined jointly by their mean activation patterns (amplitude vectors) and inter-regional covariance matrices (functional connectivity); (b) it employs a hidden Markov model framework with Bayesian inference and automatic relevance determination to identify the number of states without manual specification; and (c) state assignments are made at each TR, yielding a temporal sequence that enables computation of occupancy rates, mean lifetimes, and transition probabilities. We will also revise Figure 1F to include appropriate labels and a clearer schematic of the model's inputs, latent structure, and outputs.

(3) A further confusion about the BSDS algorithm was whether it necessarily had to work on the rest data. Figure 4A suggests that each TR was assigned one of the four states based on the maximum win from the log-likelihood estimation. Without more details about how this algorithm was applied to the rest data, it is difficult to evaluate the claim on page 14 about the spontaneous emergence of the states at rest.

The key methodological point is that the BSDS model, once trained on encoding data, can be applied to new (rest) time series via log-likelihood estimation: for each TR during rest, the model computes the log-likelihood of each state given the observed multivariate signal, and the state with the maximum log-likelihood is assigned to that TR. This "decoding" approach tests whether the spatial configurations learned during encoding are present during rest, rather than fitting new states de novo. We applied a threshold to the log-likelihood values to exclude TRs where the evidence for any single state was weak, thus controlling for potential misassignment. We will substantially clarify this process in the revised Methods and main text, and as described in our response to Reviewer #1 point 1, we will also conduct additional analyses to address the concerns raised.

(4) Although the BSDS algorithm was validated in prior simulations and task-based fMRI using sustained block designs in adults, it is unclear whether it is appropriate for the kind of event-related design used in the current study. Figure 1G shows very rapid state changes, which is quantified in the low mean lifetime of the states (between 1-3 TRs on average) in Figure 4C. On the one hand, it is a strength of the algorithm that it is not necessarily tied to external stimuli. On the other hand, it would be helpful to see simulations validating that rapid transitions between states in fMRI data are meaningful and not due to noise.

This is an important methodological question. The rapid state changes observed in our event-related design (mean lifetimes of 1–3 TRs) differ from the longer state durations typically observed with block designs(He et al., 2023; Zeng et al., 2024), where sustained cognitive demands stabilize brain configurations. We believe these rapid transitions are consistent with the inherent dynamics of event-related encoding, where each trial involves rapid shifts between sensory processing, memory binding, and attentional engagement. Several considerations support the meaningfulness of these transitions: (a) the identified states have interpretable amplitude profiles consistent with well-established memory-related brain systems; (b) state dynamics show statistically significant, directionally consistent correlations with subsequent memory performance; and (c) the transition structure during encoding is distinct from that observed during rest, indicating sensitivity to task demands. Nonetheless, we acknowledge the concern about noise and will conduct additional analyses in the revision to address the concerns raised.

(5) The Methods section mentions that participants actively imagined themselves within the encoded scenes and were instructed to memorize the images for a later test during the post-encoding rest scan. This detail needs to be included in the main text and incorporated into the interpretation of the findings, as there are likely mechanistic differences between spontaneous memory replay/reinstatement vs. active rehearsal.

We thank the reviewer for this suggestion. We will include these experimental details in the main text and incorporate it into the interpretation of our findings in the context of spontaneous memory replay/reinstatement vs. active rehearsal (Liu et al., 2019; Wimmer et al., 2020).

(6) Information about the general linear model used to discover the 16 ROIs that showed a subsequent memory effect are missing, such as: covariates in the model (motion, etc.), group analysis approach (parametric or nonparametric), whether and how multiple-comparisons correction was performed, if clusters were overlapping at all or distinct, if the total number of clusters was 16 or if this was only a subset of regions that showed the effect.

We apologize for the missing methodological details. In the revised manuscript, we will provide complete information on the general linear model used to identify the 16 ROIs, including: the event regressors and parametric modulators included in the model, nuisance covariates (motion parameters, white matter and CSF regressors), the group-level analysis approach and statistical thresholding, the method for multiple-comparisons correction, whether the 16 ROIs represent all significant clusters or a subset, and whether any clusters were spatially overlapping. We will also clarify how peak voxels were selected for ROI definition.

Reviewer #3 (Public review):

This paper uses a novel method to look at how stable brain states and the transitions between them promote memory formation during encoding and post-encoding rest in children. I think the paper has some weaknesses (detailed below) that mean that the authors fall short of achieving their aims. Although the paper has an interesting methodological approach, the authors need better logic, and are potentially "double dipping" in their results - meaning their logic is circular. I think the method that they are using could be useful to the broader neuroimaging community, although they need to make this argument clearer in the paper.

We thank Reviewer #3 for recognizing the novelty of our approach and its potential utility for the broader neuroimaging community.

(1) The authors use children as their study subjects but fail to reconcile why children are used, if the same phenomena are expected to be seen in adults (or only children), and if and how their findings change with age across an age range that ranges from middle childhood into early adolescence. They need to include more consideration for the development of their subject population. The authors should make it clear why and how memory was tested in children and not adults. Are adults and children expected to encode and consolidate in a similar manner to children? Do the findings here also apply to adults? How was the age range of 8-13-year-old children selected? Why didn't the authors look at change with age? Does memory performance change with age? Do the BSDS dynamics change with age in the authors' sample?

Our study was motivated by the observation that while adult studies have documented memory replay and reinstatement, very little is known about whether these dynamic state-level mechanisms operate during middle childhood, a period characterized by substantial improvements in episodic memory ability and ongoing maturation of frontoparietal and hippocampal–cortical circuits. The age range of 8–13 was defined a priori based on typical developmental classifications of middle childhood through early adolescence, representing a period when episodic memory abilities are developing rapidly.

In response to the reviewer's specific questions: (a) we will conduct exploratory analyses testing whether memory accuracy, BSDS state dynamics (occupancy, mean lifetime, transitions), and brain–behavior correlations vary as a function of age within our sample; (b) we will clearly discuss whether adults are expected to show similar patterns, drawing on the extant adult literature; and (c) we will acknowledge as a limitation that our sample size (N = 24) and narrow age range provide limited statistical power for detecting continuous age-related changes, and that a dedicated cross-sectional or longitudinal developmental design would be needed to draw firm conclusions about developmental trajectories. Please also see responses to Reviewer #1 point 5 and Reviewer #2 point 1.

(2) The authors look for brain state dynamics within a preselected set of ROIs that are selected because they display a subsequent memory effect. This is problematic because the state that is most associated with subsequent memory (S3, or State 3) is also the one that shows most activity in these regions (that have already been a priori selected due to displaying a subsequent memory effect). This logic is circular. It would be helpful if they could look at brain state dynamics in a more ROI agnostic whole brain approach so that we can learn something beyond what a subsequent memory analysis tells us. I think the authors are "double dipping" in that they selected regions for further analysis based on a subsequent memory association (remembered > forgotten contrast) and then found states within those regions showing a subsequent memory effect to further analyze for being associated with subsequent memory. Would it be possible instead to do a whole-brain analysis (something a bit more agnostic to findings) using the BSDS framework, and then, from a whole-brain perspective, look for particular brain states associated with subsequent memory? As it stands, it looks like S3 (state 3) has greater overall activation in all brain regions associated with subsequent memory, so it makes sense that this brain state is also most associated with subsequent memory. The BSDS analysis is therefore not adding anything new beyond what the authors find with the simple subsequent memory contrast that they show in Figure 1C. This particularly effects the following findings: (a) active-encoding state occupancy rate correlated positively with memory accuracy, (b) transitions to the active-encoding state were beneficial / Conversely, transitions toward the inactive state (S4) were detrimental, with incoming transitions showing negative correlations with memory accuracy / The active-encoding state serves as a "hub" configuration that facilitates memory formation, while pathways leading to this state enhance performance and transitions away from it impair encoding.

We appreciate this critique, which raises an important concern about analytical circularity.

a) Why BSDS adds information beyond the static subsequent memory contrast. The reviewer notes that S3 (the active-encoding state) shows high activation in the same regions selected by the subsequent memory contrast, and therefore questions whether BSDS provides new information. We respectfully argue that BSDS captures dimensions of neural organization that a static contrast cannot. Specifically: (a) the subsequent memory contrast identifies which regions are differentially active for remembered vs. forgotten items, averaged across the entire encoding session, it provides no temporal information about when or for how long these regions are co-active; (b) BSDS reveals the moment-to-moment temporal evolution of brain states, including the duration and stability of each configuration (mean lifetime), which independently predicts behavior; (c) BSDS uniquely captures transition dynamics, the rates and patterns of switching between states, which we show are predictive of memory in ways not derivable from the contrast map (e.g., transitions from S2→S3 positively predict memory, transitions toward S4 negatively predict memory); and (d) BSDS characterizes the full covariance structure among regions within each state, revealing distinct connectivity patterns (e.g., the high clustering coefficient and global efficiency of S3), which are not captured by univariate activation contrasts. Thus, while the ROI selection is informed by the subsequent memory effect, the information BSDS extracts from those regions, temporal dynamics, transition patterns, and multivariate covariance, is orthogonal to the information used for selection.

b) Additional validation. To directly address the circularity concern empirically, we will conduct additional analysis using ROIs from previous studies (e.g. network templates) / meta-analyses/Neurosynth ROIs (He et al., 2023; Meer et al., 2020; Taghia et al., 2018), without resorting to selection based on the subsequent memory contrast.

(3) The task used to test memory in children seems strange. Why should children remember arbitrary scenes? How this was chosen for encoding needs to be made clear. There needs to be more description of the memory task and why it was chosen. Why was scene encoding chosen? What does scene encoding have to do with the stated goal of (a) "Understanding how children's brains form lasting memories", (b) "optimizing education" and (c) "identifying learning disabilities"? What was the design of the recognition memory test? How many novel scenes were included in the test, and how were they chosen? How close were the "new" images to previously seen "old" images? Was this varied parametrically (i.e., was the similarity between new and old images assessed and quantified?)

Scene encoding was chosen for several reasons: (a) scenes are rich, complex stimuli that engage the hippocampal–parahippocampal memory system, eliciting robust subsequent memory effects suitable for BSDS modeling; (b) scene encoding recruits distributed networks spanning visual cortex, MTL, and frontoparietal regions, enabling detection of multi-region brain states; and (c) scene encoding paradigms have been widely used in both adult and developmental studies of episodic memory and replay(Tambini et al., 2017; Tompary et al., 2017), facilitating comparison with prior work.

Regarding the recognition test: participants viewed 200 images (100 old, 100 new), with novel scenes drawn from the same categories (buildings and natural scenes) but chosen to be perceptually distinct from studied images. Similarity between old and new images was not parametrically manipulated or quantified: we will note this limitation. We will also expand the main text to include full task details and have deleted claims about implications for educational optimization and learning disability identification (see also Reviewer #3 point 7).

(4) They ultimately found four brain states during encoding. It would be helpful if they could make the logic and foundation for arriving at this number clear.

The number of brain states is not predetermined by the user but is automatically determined by the BSDS algorithm through Bayesian automatic relevance determination (ARD). The model is initialized with a maximum number of possible states, and during inference, states that contribute minimally to explaining the data are effectively pruned, their associated parameters are driven to near-zero by the ARD prior. In our data, the model converged on four states. This is a key advantage of BSDS over conventional HMM approaches, which require the user to specify the state number a priori. We will clarify this process in the revised Methods and Results, referencing the original BSDS methodology paper (Taghia et al., 2018) for full mathematical details.

(5) There is already extant work on whether brain states during post-encoding rest predict memory outcomes. This work needs to be cited and referred to. The present manuscript needs to be better situated within prior work. The authors should look at the work by Alexa Tompary and Lila Davachi. They have already addressed many of the questions that the authors seek to answer. The authors should read their papers (and the papers they cite and that cite them) and then situate their work within the prior literature.

We agree that the manuscript must be better situated within the existing literature on post-encoding rest and memory consolidation. We will revise the Introduction and Discussion to further discuss with the foundational work in adults by Tompary & Davachi (2017, Neuron; 2024, eLife) on consolidation-related hippocampal–mPFC representational overlap, as well as Tambini & Davachi (2013, PNAS; 2019, Trends in Cognitive Sciences) on hippocampal persistence during post-encoding rest and awake reactivation(Tambini et al., 2019; Tambini et al., 2017; Tompary et al., 2017). We will explicitly discuss how our BSDS-based approach to state-level reinstatement complements and extends these earlier findings, which largely focused on region-specific pattern similarity or hippocampal–cortical connectivity, by characterizing reinstatement at the level of dynamic, whole-network configurations.

(6) The authors should back up the claim that "successful episodic memory formation critically depends on the temporal coordination between these systems. Brain regions must coordinate their activity through dynamic functional interactions, rapidly reconfiguring their activity and connectivity patterns in response to changing cognitive demands and stimulus characteristics." Do they have any specific evidence supporting this claim?

The claim that episodic memory depends on temporal coordination and dynamic functional interactions is supported by several lines of evidence: (a) within our study, the significant correlations between state transition rates and memory performance directly demonstrate that dynamic inter-state communication predicts memory outcomes; (b) studies showing that hippocampal–prefrontal theta coherence during encoding predicts subsequent memory (e.g., Zielinski et al., 2020)(Zielinski et al., 2020); and (c) recent work demonstrating that rapid reconfiguration of large-scale brain networks supports cognitive functions including working memory (Shine et al., 2018; Braun et al., 2015)(Braun et al., 2015; Shine et al., 2018) and episodic encoding (Phan et al., 2024)(Phan et al., 2024) We will revise this passage to include specific citations and to make clear that our own transition–behavior correlations constitute direct evidence for this claim.

(7) These claims seem overstated: "this work has broad implications for understanding memory function in children, for developing educational interventions that enhance memory formation, and enabling early identification of children at risk for learning disabilities." Can the authors add citations that would support these claims, or if not, remove them?

We thank the reviewer for raising this point. We agree that the current framing overstates the practical implications. We have now removed these claims and remark on future studies that are needed here.

References

(1) Braun, U., Schafer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., . . . Bassett, D. S. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci U S A, 112(37), 11678-11683.

(2) He, Y., Liang, X., Chen, M., Tian, T., Zeng, Y., Liu, J., . . . Qin, S. (2023). Development of brain-state dynamics involved in working memory. Cerebral Cortex.

(3) Lee, B., Young, C. B., Cai, W., Yuan, R., Ryman, S., Kim, J., . . . Menon, V. (2025). Dopaminergic modulation and dosage effects on brain state dynamics and working memory component processes in Parkinson’s disease. Nature Communications, 16(1), 2433.

(4) Liu, Y., Dolan, R. J., Kurth-Nelson, Z., & Behrens, T. E. J. (2019). Human Replay Spontaneously Reorganizes Experience. Cell, 178(3), 640-652.e614.

(5) Meer, J. N. v. d., Breakspear, M., Chang, L. J., Sonkusare, S., & Cocchi, L. (2020). Movie viewing elicits rich and reliable brain state dynamics. Nature Communications, 11(1), 5004.

(6) Phan, A. T., Xie, W., Chapeton, J. I., Inati, S. K., & Zaghloul, K. A. (2024). Dynamic patterns of functional connectivity in the human brain underlie individual memory formation. Nature Communications, 15(1), 8969.

(7) Ryali, S., Supekar, K., Chen, T., Kochalka, J., Cai, W., Nicholas, J., . . . Menon, V. (2016). Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling. PLoS Comput Biol, 12(12), e1005138.

(8) Shine, J. M., & Poldrack, R. A. (2018). Principles of dynamic network reconfiguration across diverse brain states. Neuroimage, 180, 396-405.

(9) Stevner, A. B. A., Vidaurre, D., Cabral, J., Rapuano, K., Nielsen, S. F. V., Tagliazucchi, E., . . . Kringelbach, M. L. (2019). Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nature Communications, 10(1), 1035.

(10) Taghia, J., Cai, W., Ryali, S., Kochalka, J., Nicholas, J., Chen, T., & Menon, V. (2018). Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition. Nature Communications, 9(1), 2505.

(11) Tambini, A., & Davachi, L. (2019). Awake Reactivation of Prior Experiences Consolidates Memories and Biases Cognition. Trends in Cognitive Sciences, 23(10), 876-890.

(12) Tambini, A., Rimmele, U., Phelps, E. A., & Davachi, L. (2017). Emotional brain states carry over and enhance future memory formation. Nature Neuroscience, 20(2), 271-278.

(13) Tompary, A., & Davachi, L. (2017). Consolidation Promotes the Emergence of Representational Overlap in the Hippocampus and Medial Prefrontal Cortex. Neuron, 96(1), 228-241.e225.

(14) Verde, M. F., Macmillan, N. A., & Rotello, C. M. (2006). Measures of sensitivity based on a single hit rate and false alarm rate: The accuracy, precision, and robustness of′, A z, and A’. Perception & psychophysics, 68(4), 643-654.

(15) Wickens, T. D. (2001). Elementary signal detection theory: Oxford university press.

(16) Wimmer, G. E., Liu, Y., Vehar, N., Behrens, T. E. J., & Dolan, R. J. (2020). Episodic memory retrieval success is associated with rapid replay of episode content. Nature Neuroscience, 23(8), 1025-1033.

(17) Zeng, Y., Xiong, B., Gao, H., Liu, C., Chen, C., Wu, J., & Qin, S. (2024). Cortisol awakening response prompts dynamic reconfiguration of brain networks in emotional and executive functioning. Proceedings of the National Academy of Sciences, 121(52), e2405850121.

(18) Zielinski, M. C., Tang, W., & Jadhav, S. P. (2020). The role of replay and theta sequences in mediating hippocampal-prefrontal interactions for memory and cognition. Hippocampus, 30(1), 60-72.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Lipid transfer proteins (LTPs) play a crucial role in the intramembrane lipid exchange within cells. However, the molecular mechanisms that govern this activity remain largely unclear. Specifically, the way in which LTPs surmount the energy barrier to extract a single lipid molecule from a lipid bilayer is not yet fully understood. This manuscript investigates the influence of membrane properties on the binding of Ups1 to the membrane and the transfer of phosphatidic acid (PA) by the LTP. The findings reveal that Ups1 shows a preference for binding to membranes with positive curvature. Moreover, coarse-grained molecular dynamics simulations indicate that positive curvature decreases the energy barrier associated with PA extraction from the membrane. Additionally, lipid transfer assays conducted with purified proteins and liposomes in vitro demonstrate that the size of the donor membrane significantly impacts lipid transfer efficiency by Ups1-Mdm35 complexes, with smaller liposomes (characterized by high positive curvature) promoting rapid lipid transfer.

This study offers significant new insights into the reaction cycle of phosphatidic acid (PA) transfer by Ups1 in mitochondria. Notably, the authors present compelling evidence that, alongside negatively charged phospholipids, positive membrane curvature enhances lipid transfer - an effect that is particularly relevant at the mitochondrial outer membrane. The experiments are technically robust, and my primary feedback pertains to the interpretation of specific results.

(1) The authors conclude from the lipid transfer assays (Figure 5) that lipid extraction is the rate-limiting step in the transfer cycle. While this conclusion seems plausible, it should be noted that the authors employed high concentrations of Ups1-Mdm35 along with less negatively charged phospholipids in these reactions. This combination may lead to binding becoming the rate-limiting factor. The authors should take this point into consideration. In this type of assay, it is challenging to clearly distinguish between binding, lipid extraction, and membrane dissociation as separate processes.

We have included a detailed consideration of this issue on page 11 of the revised manuscript.

(2) The authors should discuss that variations in the size of liposomes will also affect the distance between them at a constant concentration, which may affect the rate of lipid transfer. Therefore, the authors should determine the average size and size distribution of liposomes after sonication (by DLS or nanoparticle analyzer, etc.)

We have included DLS measurements for all lipid sizes (page 6) (SupFig. 2A). Due to the sensitivity of the intensity distribution in DLS measurements by larger particles, we also conducted cryo-EM analysis of vesicles with different sizes (page 6) (SupFig. 2B).

We also now discuss the challenges posed by a fixed membrane-binding surface, which can lead to variations in vesicle spacing when using liposomes of different sizes and its possible influence on the interpretation of results (page 10-11).

(3) The authors use NBD-PA in the lipid transfer assays. Does the size of the donor liposomes affect the transfer of NBD-PA and DOPA similarly? Since NBD-labeled lipids are somewhat unstable within lipid bilayers (as shown by spontaneous desorption in Figure 5B), monitoring the transfer of unlabeled PA in at least one setting would strengthen the conclusion of the swap experiments.

To experimentally address this comment, we explored several different approaches. We first performed transfer experiments using unlabelled lipids, following the general procedures described in the manuscript. After the transfer reaction, we attempted to separate donor and acceptor vesicles by centrifugation and subsequently analyzed the samples by high-resolution mass spectrometry and thin-layer chromatography. Despite considerable effort, we were not able to reliably separate the differently sized liposomes. In particular, small liposomes proved difficult to handle during centrifugation, which is a well-known challenge (Kučerka et al. 1994, BBA; Boucrot et al. 2012, Cell). In addition, liposomes exhibited a tendency to cross-link in the presence of protein, further complicating the separation. Even if this separation step were straightforward, an important limitation of such an approach is that it is very difficult to monitor lipid transfer with sufficient time resolution. Much of the relevant activity occurs within the first 20–30 seconds, and precise interruption at defined time points would be essential.

We therefore set out to establish a fluorescence-based assay that would allow us to follow lipid transfer in real time. For this, we adapted a dequenching-type assay based on a PE coupled fluorescein dye, whose fluorescence is quenched in the proximity of negative charges (e.g., negatively charged lipid headgroups). In principle, this assay should allow us to monitor the movement of negatively charged PA lipids away from donor membranes. Although a fluorescein-based passive lipid-transfer assay has been described previously (Richens et al., 2017), it is used only rarely in the lipid-transfer field. While establishing this assay, we encountered several technical challenges. For example, immediately after protein addition, fluorescence intensity changed in unexpected ways that could not be attributed to lipid transfer. Such effects have been reported in the literature (Wall et al., 1995) and are most likely caused by changes in membrane charge density upon protein binding. After extensive fine -tuning of the experimental conditions and careful evaluation of the data, we were ultimately able to demonstrate that lipid-transfer rates are significantly higher with smaller than with larger liposomes. These results confirm our initial observations, and importantly, they were obtained using unlabelled PA.

The revised manuscript now includes this independent lipid-transfer assay demonstrating the transfer of non-labelled PA (page 11) (SupFig. 4).

(4) The present study suggests that membrane domains with positive curvature at the outer membrane may serve as starting points for lipid transport by Ups1-Mdm35. Is anything known about the mechanisms that form such structures? This should be discussed in the text.

We included a detailed consideration of this interesting point in the discussion section on page 13-14.

Reviewer #2 (Public review):

Summary:

Lipid transfer between membranes is essential for lipid biosynthesis across different organelle membranes. Ups1-Mdm35 is one of the best-characterized lipid transfer proteins, responsible for transferring phosphatidic acid (PA) between the mitochondrial outer membrane (OM) and inner membrane (IM), a process critical for cardiolipin (CL) synthesis in the IM. Upon dissociation from Mdm35, Ups1 binds to the intermembrane space (IMS) surface of the OM, extracts a PA molecule, re-associates with Mdm35, and moves through the aqueous IMS to deliver PA to the IM. Here, the authors analyzed the early steps of this PA transfer - membrane binding and PA extraction - using a combination of in vitro biochemical assays with lipid liposomes and purified Ups1-Mdm35 to measure liposome binding, lipid transfer between liposomes, and lipid extraction from liposomes. The authors found that membrane curvature, a previously overlooked property of the membrane, significantly affects PA extraction but not PA insertion into liposomes. These findings were further supported by MD simulations.

Strengths:

The experiments are well-designed, and the data are logically interpreted. The present study provides an important basis for understanding the mechanism of lipid transfer between membranes.

Weaknesses:

The physiological relevance of membrane curvature in lipid extraction and transfer still remains open.

We thank the reviewer for the constructive feedback on our work. We agree that the physiological relevance of membrane curvature in lipid extraction and transfer remains an open question. Our data show that Ups1 binding to native-like OM membranes under physiological pH conditions is curvature-dependent, supporting the idea that this mechanism may optimize lipid transfer in vivo. While the intricate biophysical basis of this behaviour can only be dissected in vitro, these findings offer valuable insight into how curvature may functionally regulate Ups1 activity in the cellular context. To directly test this, it will be important in future studies to identify Ups1 mutants that lack curvature sensitivity and assess their performance in vivo, which will help clarify the physiological importance of this mechanism.

Reviewer #3 (Public review):

The manuscript by Sadeqi et al. studies the interactions between the mitochondrial protein Ups1 and reconstituted membranes. The authors apply synthetic liposomal vesicles to investigate the role of pH, curvature, and charge on the binding of Ups1 to membranes and its ability to extract PA from them. The manuscript is well written and structured. With minor exceptions, the authors provide all relevant information (see minor points below) and reference the appropriate literature in their introduction. The underlying question of how the energy barrier for lipid extraction from membranes is overcome by Ups1 is interesting, and the data presented by the authors could offer a valuable new perspective on this process. It is also certainly a challenging in vitro reconstitution experiment, as the authors aim to disentangle individual membrane properties (e.g., curvature, charge, and packing density) to study protein adsorption and lipid transfer. I have one major suggestion and a few minor ones that the authors might want to consider to improve their manuscript and data interpretation:

Major Comments:

The experiments are performed with reconstituted vesicles, which are incubated with recombinant protein variants and quantitatively assessed in flotation and pelleting assays. According to the Materials and Methods section, the lipid concentration in these assays is kept constant at 5 µM. However, the authors change the size of the vesicles to tune their curvature. Using the same lipid concentration but varying vesicle sizes results in different total vesicle concentrations. Moreover, larger vesicles (produced by freeze-thawing and extrusion) tend to form a higher proportion of multilamellar vesicles, thus also altering the total membrane area available for binding. Could these differences in the experimental system account for the variation in binding? To address this, the authors would need to perform the experiments either under saturated (excess protein) conditions or find an experimental approach to normalize for these differences.

To experimentally address this comment, we have conducted a detailed structural analysis of liposomes of different sizes using cryo-EM to determine the degrees of multi-lamellarity and to estimate how much membrane surface is available for protein binding. We found that while indeed as expected liposomes extruded through a 400 nm sized filter showed about 75 % of the initially calculated membrane surface is still available (SupFig. 3A). For 50 nm extruded liposomes, this number went up to about 93 % and for sonicated liposomes the number was about 94 %. Given the fact that we found about 70 % binding of Ups1 to sonicated liposomes, while this number went down to about 40 % with 50 nm liposomes and to about 30 % for 400 nm extruded liposomes, we can rule out that the effects we observe are due to an increased or decreased available membrane binding area.

Additionally, we performed experiments with increasing amounts of lipids to analyse the impact of lipid concentration on Ups1 membrane binding, when comparing 400 nm extruded liposomes with sonicated liposomes. Interestingly, while we do observe an increased binding of Ups1 to sonicated liposomes with concentrations varying between 2.5 mM to 10 mM no major increase in binding was observed with 400 nm extruded liposomes. Ups1 membrane binding to sonicated liposomes highly exceeded binding to 400 nm extruded liposomes under all tested conditions (page 7) (SupFig. 3B).

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors:):

(1) Figures 1, 2, and 3 - In the flotation assays, the Ups1-containing fractions differ between experiments. The presence of liposomes in these fractions should be confirmed, for example, by fluorescence measurements. In relation to this, the broad low MW bands in Supplementary Figure 3 may reflect liposomes (mixed micelles of lipids and SDS?), as their fractionation patterns coincide with those of Ups1 at pH 5.5 -6.7 but deviate at pH 7.0 and 7.5. Could the authors clarify this discrepancy?

Flotation profiles vary with changing conditions of the experiment. We have included a picture of a gel showing the Coomassie staining and the fluorescence of the used lipids side by side to show that the protein bands co-migrate together with liposomes (SupFig. 5). 

(2) Figures 2, 3, and 5 - The sizes of the liposomes (400 nm and 50 nm) should be experimentally confirmed, e.g., by dynamic light scattering (DLS).

We have included DLS measurements confirming the differences of liposome sizes. Please see answer to point 2 of Reviewer 1.

(3) Figure 4C - The free energy landscape for different phospholipids is interesting. What about other acidic phospholipids, such as PS?

This is indeed an interesting point. Our molecular dynamics simulations show that PE has a similar free energy landscape to PA while PC is significantly different. This might point into the direction that the headgroup size plays a major role. For intra-mitochondrial PS transport a specific protein complex consisting of Ups2/Mdm35 has been identified, and it will be an interesting question for future studies if PS transfer is regulated by similar factors.

(4) Supplementary Figure 2 - The deformation of liposomes by Ups1 is interesting. Does this depend on the presence of PA or other acidic phospholipids?

We asked ourself the same question throughout the project. As pointed out in the manuscript, the membrane-deforming activity of Ups1 is relatively mild when compared to proteins found for example in endocytosis. This made a proper static analysis challenging. We weren’t able to unambiguously show whether other acidic phospholipids showed comparable effects to PA.

(5) It may not be easy to assess experimentally, but the OM in mitochondria should have scramblase activity. Then, such scramblase activity could influence the observed effects of membrane curvature on Ups1-mediated PA transfer.

(6) It would be helpful to discuss this possibility in the manuscript.

In the revised version of the manuscript, we now discuss the existence of scramblases, such as Sam50 and VDAC, in the outer mitochondrial membrane with regard to their likely effect on membrane packing (page 13 - 14). As for a co-reconstitution experiment we considered the in vitro analysis of the impact that a scramblase in liposomes might have on lipid transfer outside the scope of this study. 

(7) Figure 6 is not referenced in the main text.

Thank you, this oversight was corrected.

(8) The non-abbreviated forms of LUV and SUV should be defined in the text upon first use.

We now include a definition in the manuscript.

(9) The term "transfer velocity" would be better expressed as "transfer rate".

We agree, and we changed the wording accordingly.

Reviewer #3 (Recommendations for the authors):

(1) As flotation assays are a central technique of the study, readers who are not familiar with this method could benefit from a few explanatory sentences and appropriate references in the introduction section.

Figure 1B now contains an updated version of a cartoon outlining the flotation assay and a description in the manuscript (page 4) that should make it easier to understand the assay. We have also included a direct reference within the methods section to a paper describing this assay in more detail.

(2) Related to the major point, but also to improve the manuscript overall, the authors could add DLS (for size distribution and zeta potential) and cryo-EM (for multilamellarity analysis) data. This would aid future efforts to reproduce their observations.

In the revised version of the manuscript we include DLS and zeta potential measurements as well as a detailed analysis of liposome multilamellarity by cryo-EM (also see answer to point 2 by Reviewer 1) (SupFig. 2A & B; SupFig. 3E).

(3) Could the authors state the specific zeta potentials of the negatively charged (under varying pH) and neutral liposomes and relate these to natural membranes?

We have included zeta potential measurements of differently charged liposomes in and changed the text accordingly (page 8) (SupFig. 3E).

(4) Changes in pH affect several characteristics of membranes (including lipid dipoles, charge, packing density, fluidity, and phase separation), particularly charge density. This experimental system does not allow all of these factors to be disentangled and studied separately. Some of the observations presented in Figures 2 and 5 could also be explained by these effects.

The effects of pH on various membrane properties, such as lipid headgroup dipoles, lipid packing, interfacial tension, and others, are well described in the literature. For example, it was implied that increasing pH leads to phosphatidic acid (PA) becoming more negatively charged when in proximity to phosphatidylethanolamine (PE). We already discuss this effect in the manuscript, as our observation that Ups1 binding to membranes depends on negatively charged lipids but nevertheless increases with decreasing pH is unexpected.

As pointed out, many of the parameters mentioned above are beyond control in our assays, and a systematic analysis of each of these factors with respect to Ups1 membrane binding and lipid transfer would be well beyond the scope of this manuscript. We have therefore included a passage discussing this issue in more detail (page 4-5).

(5) Is the curvature simulated in the theoretical models comparable to the curvature of the liposome systems (e.g., a sphere of 100 nm diameter)?

The simulated curvature spans a defined range, with the highest curvature corresponding to vesicles with diameters of approximately 15 nm. This corresponds reasonably well to the vesicle size distribution as analyzed by cryo-EM.

Reference

Connerth, M., Tatsuta, T., Haag, M., Klecker, T., Westermann, B., & Langer, T. (2012). Intramitochondrial transport of phosphatidic acid in yeast by a lipid transfer protein. Science, 338(6108), 815-818. https://doi.org/10.1126/science.1225625

Lu, J., Chan, C., Yu, L., Fan, J., Sun, F., & Zhai, Y. (2020). Molecular mechanism of mitochondrial phosphatidate transfer by Ups1. Commun Biol, 3(1), 468. https://doi.org/10.1038/s42003-020-01121-x

Miliara, X., Garnett, J. A., Tatsuta, T., Abid Ali, F., Baldie, H., Perez-Dorado, I., Simpson, P., Yague, E., Langer, T., & Matthews, S. (2015). Structural insight into the TRIAP1/PRELI-like domain family of mitochondrial phospholipid transfer complexes. EMBO Rep, 16(7), 824-835. https://doi.org/10.15252/embr.201540229

Miliara, X., Tatsuta, T., Berry, J. L., Rouse, S. L., Solak, K., Chorev, D. S., Wu, D., Robinson, C. V., Matthews, S., & Langer, T. (2019). Structural determinants of lipid specificity within Ups/PRELI lipid transfer proteins. Nat Commun, 10(1), 1130. https://doi.org/10.1038/s41467-019-09089-x

Miliara, X., Tatsuta, T., Eiyama, A., Langer, T., Rouse, S. L., & Matthews, S. (2023). An intermolecular hydrogen-bonded network in the PRELID-TRIAP protein family plays a role in lipid sensing. Biochim Biophys Acta Proteins Proteom, 1871(1), 140867. https://doi.org/10.1016/j.bbapap.2022.140867

Potting, C., Tatsuta, T., Konig, T., Haag, M., Wai, T., Aaltonen, M. J., & Langer, T. (2013). TRIAP1/PRELI complexes prevent apoptosis by mediating intramitochondrial transport of phosphatidic acid. Cell Metab, 18(2), 287-295. https://doi.org/10.1016/j.cmet.2013.07.008

Richens, J. L., Tyler, A. I. I., Barriga, H. M. G., Bramble, J. P., Law, R. V., Brooks, N. J., Seddon, J. M., Ces, O., & O'Shea, P. (2017). Spontaneous charged lipid transfer between lipid vesicles. Sci Rep, 7(1), 12606. https://doi.org/10.1038/s41598-017-12611-0

Wall, J., Golding, C. A., Van Veen, M., & O'Shea, P. (1995). The use of fluoresceinphosphaCdylethanolamine (FPE) as a real-time probe for peptide-membrane interactions. Mol Membr Biol, 12(2), 183-192. https://doi.org/10.3109/09687689509027506

Watanabe, Y., Tamura, Y., Kawano, S., & Endo, T. (2015). Structural and mechanistic insights into phospholipid transfer by Ups1-Mdm35 in mitochondria. Nat Commun, 6, 7922. https://doi.org/10.1038/ncomms8922

Author response:

Reviewer 1 (Public review):

(1) Figure 1B shows the PREDICTED force-extension curve for DNA based on a worm-like chain model. Where is the experimental evidence for this curve? This issue is crucial because the F-E curve will decide how and when a catch-bond is induced (if at all it is) as the motor moves against the tensiometer. Unless this is actually measured by some other means, I find it hard to accept all the results based on Figure 1B.

The Worm-Like-Chain model for the elasticity of DNA was established by early work from the Bustamante lab (Smith et al., 1992)  and Marko and Siggia (Marko and Siggia, 1995), and was further validated and refined by the Block lab (Bouchiat et al., 1999; Wang et al., 1997). The 50 nm persistence length is the consensus value, and was shown to be independent of force and extension in Figure 3 of Bouchiat et al (Bouchiat et al., 1999). However, we would like to stress that for our conclusions, the precise details of the Force-Extension relationship of our dsDNA are immaterial. The key point is that the motor stretches the DNA and stalls when it reaches its stall force. Our claim of the catch-bond character of kinesin is based on the longer duration at stall compared to the run duration in the absence of load. Provided that the motor is indeed stalling because it has stretched out the DNA (which is strongly supported by the repeated stalling around the predicted extension corresponding to ~6 pN of force), then the stall duration depends on neither the precise value for the extension nor the precise value of the force at stall.

(2) The authors can correct me on this, but I believe that all the catch-bond studies using optical traps have exerted a load force that exceeds the actual force generated by the motor. For example, see Figure 2 in reference 42 (Kunwar et al). It is in this regime (load force > force from motor) that the dissociation rate is reduced (catch-bond is activated). Such a regime is never reached in the DNA tensiometer study because of the very construction of the experiment. I am very surprised that this point is overlooked in this manuscript. I am therefore not even sure that the present experiments even induce a catch-bond (in the sense reported for earlier papers).

It is true that Kunwar et al measured binding durations at super-stall loads and used that to conclude that dynein does act as a catch-bond (but kinesin does not) (Kunwar et al., 2011). However, we would like to correct the reviewer on this one. This approach of exerting super-stall forces and measuring binding durations is in fact less common than the approach of allowing the motor to walk up to stall and measuring the binding duration. This ‘fixed trap’ approach has been used to show catch-bond behavior of dynein (Leidel et al., 2012; Rai et al., 2013) and kinesin (Kuo et al., 2022; Pyrpassopoulos et al., 2020). For the non-processive motor Myosin I, a dynamic force clamp was used to keep the actin filament in place while the myosin generated a single step (Laakso et al., 2008). Because the motor generates the force, these are not superstall forces either.

(3) I appreciate the concerns about the Vertical force from the optical trap. But that leads to the following questions that have not at all been addressed in this paper:

(i) Why is the Vertical force only a problem for Kinesins, and not a problem for the dynein studies?

Actually, we do not claim that vertical force is not a problem for dynein; our data do not speak to this question. There is debate in the literature as to whether dynein has catch bond behavior in the traditional single-bead optical trap geometry - while some studies have measured dynein catch bond behavior (Kunwar et al., 2011; Leidel et al., 2012; Rai et al., 2013), others have found that dynein has slip-bond or ideal-bond behavior (Ezber et al., 2020; Nicholas et al., 2015; Rao et al., 2019). This discrepancy may relate to vertical forces, but not in an obvious way.

(ii) The authors state that "With this geometry, a kinesin motor pulls against the elastic force of a stretched DNA solely in a direction parallel to the microtubule". Is this really true? What matters is not just how the kinesin pulls the DNA, but also how the DNA pulls on the kinesin. In Figure 1A, what is the guarantee that the DNA is oriented only in the plane of the paper? In fact, the DNA could even be bending transiently in a manner that it pulls the kinesin motor UPWARDS (Vertical force). How are the authors sure that the reaction force between DNA and kinesin is oriented SOLELY along the microtubule?

We acknowledge that “solely” is an absolute term that is too strong to describe our geometry. We will soften this term in our revision to “nearly parallel to the microtubule”. In the Geometry Calculations section of Supplementary Methods, we calculate that if the motor and streptavidin are on the same protofilament, the vertical force will be <1% of the horizontal force. We also note that if the motor is on a different protofilament, there will be lateral forces and forces perpendicular to the microtubule surface, except they are oriented toward rather than away from the microtubule. The DNA can surely bend due to thermal forces, but because inertia plays a negligible role at the nanoscale (Howard, 2001; Purcell, 1977), any resulting upward forces will only be thermal forces, which the motor is already subjected to at all times.

(4) For this study to be really impactful and for some of the above concerns to be addressed, the data should also have included DNA tensiometer experiments with Dynein. I wonder why this was not done?

As much as we would love to fully characterize dynein here, this paper is about kinesin and it took a substantial effort. The dynein work merits a stand-alone paper.

While I do like several aspects of the paper, I do not believe that the conclusions are supported by the data presented in this paper for the reasons stated above.

The three key points the reviewer makes are the validity of the worm-like-chain model, the question of superstall loads, and the role of DNA bending in generating vertical forces. We hope that we have fully addressed these concerns in our responses above.

Reviewer #2 (Public review):

Major comments:

(1) The use of the term "catch bond" is misleading, as the authors do not really mean consistently a catch bond in the classical sense (i.e., a protein-protein interaction having a dissociation rate that decreases with load). Instead, what they mean is that after motor detachment (i.e., after a motor protein dissociating from a tubulin protein), there is a slip state during which the reattachment rate is higher as compared to a motor diffusing in solution. While this may indeed influence the dynamics of bidirectional cargo transport (e.g., during tug-of-war events), the used terms (detachment (with or without slip?), dissociation, rescue, ...) need to be better defined and the results discussed in the context of these definitions. It is very unsatisfactory at the moment, for example, that kinesin-3 is at first not classified as a catch bond, but later on (after tweaking the definitions) it is. In essence, the typical slip/catch bond nomenclature used for protein-protein interaction is not readily applicable for motors with slippage.

We appreciate the reviewer’s point and we will work to streamline and define terms in our revision.

(2) The authors define the stall duration as the time at full load, terminated by >60 nm slips/detachments. Isn't that a problem? Smaller slips are not detected/considered... but are also indicative of a motor dissociation event, i.e., the end of a stall. What is the distribution of the slip distances? If the slip distances follow an exponential decay, a large number of short slips are expected, and the presented data (neglecting those short slips) would be highly distorted.

The reviewer brings up a good point that there may be undetected slips. To address this question, we plotted the distribution of slip distances for kinesin-3, which by far had the most slip events. As the reviewer suggested, it is indeed an exponential distribution. Our preliminary analysis suggests that roughly 20% of events are missed due to this 60 nm cutoff. This will change our unloaded duration numbers slightly, but this will not alter our conclusions.\

(3) Along the same line: Why do the authors compare the stall duration (without including the time it took the motor to reach stall) to the unloaded single motor run durations? Shouldn't the times of the runs be included?

The elastic force of the DNA spring is variable as the motor steps up to stall, and so if we included the entire run duration then it would be difficult to specify what force we were comparing to unloaded. More importantly, if we assume that any stepping and detachment behavior is history independent, then it is mathematically proper to take any arbitrary starting point (such as when the motor reaches stall), start the clock there, and measure the distribution of detachments durations relative to that starting point.

More importantly, what we do in Fig. 3 is to separate out the ramps from the stalls and, using a statistical model, we compute a separate duration parameter (which is the inverse of the off-rate) for the ramp and the stall. What we find is that the relationship between ramp, stall, and unloaded durations is different for the three motors, which is interesting in itself.

(4) At many places, it appears too simple that for the biologically relevant processes, mainly/only the load-dependent off-rates of the motors matter. The stall forces and the kind of motor-cargo linkage (e.g., rigid vs. diffusive) do likely also matter. For example: "In the context of pulling a large cargo through the viscous cytoplasm or competing against dynein in a tug-of-war, these slip events enable the motor to maintain force generation and, hence, are distinct from true detachment events." I disagree. The kinesin force at reattachment (after slippage) is much smaller than at stall. What helps, however, is that due to the geometry of being held close to the microtubule (either by the DNA in the present case or by the cargo in vivo) the attachment rate is much higher. Note also that upon DNA relaxation, the motor is likely kept close to the microtubule surface, while, for example, when bound to a vesicle, the motor may diffuse away from the microtubule quickly (e.g., reference 20).

We appreciate the reviewer’s detailed thinking here, and we offer our perspective. As to the first point, we agree that the stall force is relevant and that the rigidity of the motor-cargo linkage will play a role. The goal of the sentence on pulling cargo that the reviewer highlights is to set up our analysis of slips, which we define as rearward displacements that don’t return to the baseline before force generation resumes. We agree that force after slippage is much smaller than at stall, and we plan to clarify that section of text. However, as shown in the model diagram in Fig. 5, we differentiate between the slip state (and recovery from this slip state) and the detached state (and reattachment from this detached state). This delineation is important because, as the reviewer points out, if we are measuring detachment and reattachment with our DNA tensiometer, then the geometry of a vesicle in a cell will be different and diffusion away from the microtubule or elastic recoil perpendicular to the microtubule will suppress this reattachment.

Our evidence for a slip state in which the motor maintains association with the microtubule comes from optical trapping work by Tokelis et al (Toleikis et al., 2020) and Sudhakar et al (Sudhakar et al., 2021). In particular, Sudhakar used small, high index Germanium microspheres that had a low drag coefficient. They showed that during ‘slip’ events, the relaxation time constant of the bead back to the center of the trap was nearly 10-fold slower than the trap response time, consistent with the motor exerting drag on the microtubule. (With larger beads, the drag of the bead swamps the motor-microtubule friction.) Another piece of support for the motor maintaining association during a slip is work by Ramaiya et al. who used birefringent microspheres to exert and measure rotational torque during kinesin stepping (Ramaiya et al., 2017). In most traces, when the motor returned to baseline following a stall, the torque was dissipated as well, consistent with a ‘detached’ state. However, a slip event is shown in S18a where the motor slips backward while maintaining torque. This is best explained by the motor slipping backward in a state where the heads are associated with the microtubule (at least sufficiently to resist rotational forces). Thus, we term the resumption after slip to be a rescue from the slip state rather than a reattachment from the detached state.

To finish the point, with the complex geometry of a vesicle, during slip events the motor remains associated with the microtubule and hence primed for recovery. This recovery rate is expected to be the same as for the DNA tensiometer. Following a detachment, however, we agree that there will likely be a higher probability of reattachment in the DNA tensiometer due to proximity effects, whereas with a vesicle any elastic recoil or ‘rolling’ will pull the detached motor away from the microtubule, suppressing reattachment. We plan to clarify these points in the text of the revision.

(5) Why were all motors linked to the neck-coil domain of kinesin-1? Couldn't it be that for normal function, the different coils matter? Autoinhibition can also be circumvented by consistently shortening the constructs.

We chose this dimerization approach to focus on how the mechoanochemical properties of kinesins vary between the three dominant transport families. We agree that in cells, autoinhibition of both kinesins and dynein likely play roles in regulating bidirectional transport, as will the activity of other regulatory proteins. The native coiled-coils may act as as ‘shock absorbers’ due to their compliance, or they might slow the motor reattachment rate due to the relatively large search volumes created by their long lengths (10s of nm). These are topics for future work. By using the neck-coil domain of kinesin-1 for all three motors, we eliminate any differences in autoinhibition or other regulation between the three kinesin families and focus solely on differences in the mechanochemistry of their motor domains.

(6) I am worried about the neutravidin on the microtubules, which may act as roadblocks (e.g. DOI: 10.1039/b803585g), slip termination sites (maybe without the neutravidin, the rescue rate would be much lower?), and potentially also DNA-interaction sites? At 8 nM neutravidin and the given level of biotinylation, what density of neutravidin do the authors expect on their microtubules? Can the authors rule out that the observed stall events are predominantly the result of a kinesin motor being stopped after a short slippage event at a neutravidin molecule?

We will address these points in our revision.

(7) Also, the unloaded runs should be performed on the same microtubules as in the DNA experiments, i.e., with neutravidin. Otherwise, I do not see how the values can be compared.

We will address this point in our revision.

(8) If, as stated, "a portion of kinesin-3 unloaded run durations were limited by the length of the microtubules, meaning the unloaded duration is a lower limit." corrections (such as Kaplan-Meier) should be applied, DOI: 10.1016/j.bpj.2017.09.024.

(9) Shouldn't Kaplan-Meier also be applied to the ramp durations ... as a ramp may also artificially end upon stall? Also, doesn't the comparison between ramp and stall duration have a problem, as each stall is preceded by a ramp ...and the (maximum) ramp times will depend on the speed of the motor? Kinesin-3 is the fastest motor and will reach stall much faster than kinesin-1. Isn't it obvious that the stall durations are longer than the ramp duration (as seen for all three motors in Figure 3)?

The reviewer rightly notes the many challenges in estimating the motor off-rates during ramps. To estimate ramp off-rates and as an independent approach to calculating the unloaded and stall durations, we developed a Markov model coupled with Bayesian inference methods to estimate a duration parameter (equivalent to the inverse of the off-rate) for the unloaded, ramp, and stall duration distributions. With the ramps, we have left censoring due to the difficulty in detecting the start of the ramps in the fluctuating baseline, and we have right censoring due to reaching stall (with different censoring of the ramp duration for the three motors due to their different speeds). The Markov model assumes a constant detachment probability and history independence, and thus is robust even in the face of left and right censoring (details in the Supplementary section). This approach is preferred over Kaplan-Meier because, although these non-parametric methods make no assumptions for the distribution, they require the user to know exactly where the start time is.

Regarding the potential underestimate of the kinesin-3 unloaded run duration due to finite microtubule lengths. The first point is that the unloaded duration data in Fig. 2C are quite linear up to 6 s and are well fit by the single-exponential fit (the points above 6s don’t affect the fit very much). The second point is that when we used our Markov model (which is robust against right censoring) to estimate the unloaded and stall durations, the results agreed with the single-exponential fits very well (Table S2). For instance, the single-exponential fit for the kinesin-3 unloaded duration was 2.74 s (2.33 – 3.17 s 95% CI) and the estimate from the Markov model was 2.76 (2.28 – 3.34 s 95% CI). Thus, we chose not to make any corrections due to finite microtubule lengths.

(10) It is not clear what is seen in Figure S6A: It looks like only single motors (green, w/o a DNA molecule) are walking ... Note: the influence of the attached DNA onto the stepping duration of a motor may depend on the DNA conformation (stretched and near to the microtubule (with neutravidin!) in the tethered case and spherically coiled in the untethered case).

In Figure S6A kymograph, the green traces are GFP-labeled kinesin-1 without DNA attached (which are in excess) and the red diagonal trace is a motor with DNA attached. There are also two faint horizontal red traces, which are labeled DNA diffusing by (smearing over a large area during a single frame). Panel S6B shows run durations of motors with DNA attached. We agree that the DNA conformation will differ if it is attached and stretched (more linear) versus simply being transported (random coil), but by its nature this control experiment is only addressing random coil DNA.

(11) Along this line: While the run time of kinesin-1 with DNA (1.4 s) is significantly shorter than the stall time (3.0 s), it is still larger than the unloaded run time (1.0 s). What do the authors think is the origin of this increase?

Our interpretation of the unloaded kinesin-DNA result is that the much slower diffusion constant of the DNA relative to the motor alone enables motors to transiently detach and rebind before the DNA cargo has diffused away, thus extending the run duration. In contrast, such detachment events for motors alone normally result in the motor diffusing away from the microtubule, terminating the run. This argument has been used to reconcile the longer single-motor run lengths in the gliding assay versus the bead assay (Block et al., 1990). Notably, this slower diffusion constant should not play a role in the DNA tensiometer geometry because if the motor transiently detaches, then it will be pulled backward by the elastic forces of the DNA and detected as a slip or detachment event. We will address this point in the revision.

(12) "The simplest prediction is that against the low loads experienced during ramps, the detachment rate should match the unloaded detachment rate." I disagree. I would already expect a slight increase.

Agreed. We will change this text to: “The prediction for a slip bond is that against the low loads experienced during ramps, the detachment rate should be equal to or faster than the unloaded detachment rate.”

(13) Isn't the model over-defined by fitting the values for the load-dependence of the strong-to-weak transition and fitting the load dependence into the transition to the slip state?

Essentially, yes, it is overdefined, but that is essentially by design and it is still very useful. Our goal here was to make as simple a model as possible that could account for the data and use it to compare model parameters for the different motor families. Ignoring the complexity of the slip and detached states, a model with a strong and weak state in the stepping cycle and a single transition out of the stepping cycle is the simplest formulation possible. And having rate constants (k_S-W and k_slip in our case) that vary exponentially with load makes thermodynamic sense for modeling mechanochemistry (Howard, 2001). Thus, we were pleasantly surprised that this bare-bones model could recapitulate the unloaded and stall durations for all three motors (Fig. 5C-E).

(14) "When kinesin-1 was tethered to a glass coverslip via a DNA linker and hydrodynamic forces were imposed on an associated microtubule, kinesin-1 dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (37)." This statement appears not to be true. In reference 37, very similar to the geometry reported here, the microtubules were fixed on the surface, and the stepping of single kinesin motors attached to large beads (to which defined forces were applied by hydrodynamics) via long DNA linkers was studied. In fact, quite a number of statements made in the present manuscript have been made already in ref. 37 (see in particular sections 2.6 and 2.7), and the authors may consider putting their results better into this context in the Introduction and Discussion. It is also noteworthy to discuss that the (admittedly limited) data in ref. 37 does not indicate a "catch-bond" behavior but rather an insensitivity to force over a defined range of forces.

The reviewer misquoted our sentence. The actual wording of the sentence was: “When kinesin-1 was connected to micron-scale beads through a DNA linker and hydrodynamic forces parallel to the microtubule imposed, dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (Urbanska et al., 2021).” The sentence the reviewer quoted was in a previous version that is available on BioRxiv and perhaps they were reading that version. Nonetheless, in the revision we will note in the Discussion that this behavior was indicative of an ideal bond (not a catch-bond), and we will also add a sentence in the Introduction highlighting this work.

Reviewer #3 (Public review):

The authors attribute the differences in the behaviour of kinesins when pulling against a DNA tether compared to an optical trap to the differences in the perpendicular forces. However, the compliance is also much different in these two experiments. The optical trap acts like a ~ linear spring with stiffness ~ 0.05 pN/nm. The dsDNA tether is an entropic spring, with negligible stiffness at low extensions and very high compliance once the tether is extended to its contour length (Fig. 1B). The effect of the compliance on the results should be addressed in the manuscript.

This is an interesting point. To address it, we calculated the predicted stiffness of the dsDNA by taking the slope of theoretical force-extension curve in Fig. 1B. Below 650 nm extension, the stiffness is <0.001 pN/nM; it reaches 0.01 pN/nM at 855 nm, and at 960 nm where the force is 6 pN the stiffness is roughly 0.2 pN/nm. That value is higher than the quoted 0.05 pN/nm trap stiffness, but for reference, at this stiffness, an 8 nm step leads to a 1.6 pN jump in force, which is reasonable. Importantly, the stiffness of kinesin motors has been estimated to be in the range of 0.3 pN (Coppin et al., 1996; Coppin et al., 1997). Granted, this stiffness is also nonlinear, but what this means is that even at stall, our dsDNA tether has a similar predicted compliance to the motor that is pulling on it. We will address this point in our revision.  

Compared to an optical trapping assay, the motors are also tethered closer to the microtubule in this geometry. In an optical trap assay, the bead could rotate when the kinesin is not bound. The authors should discuss how this tethering is expected to affect the kinesin reattachment and slipping. While likely outside the scope of this study, it would be interesting to compare the static tether used here with a dynamic tether like MAP7 or the CAP-GLY domain of p150glued.

Please see our response to Reviewer #2 Major Comment #4 above, which asks this same question in the context of intracellular cargo. We plan to address this in our revision. Regarding a dynamic tether, we agree that’s interesting – there are kinesins that have a second, non-canonical binding site that achieves this tethering (ncd and Cin8); p150glued likely does this naturally for dynein-dynactin-activator complexes; and we speculated in a review some years ago (Hancock, 2014) that during bidirectional transport kinesin and dynein may act as dynamic tethers for one another when not engaged, enhancing the activity of the opposing motor.

In the single-molecule extension traces (Figure 1F-H; S3), the kinesin-2 traces often show jumps in position at the beginning of runs (e.g., the four runs from ~4-13 s in Fig. 1G). These jumps are not apparent in the kinesin-1 and -3 traces. What is the explanation? Is kinesin-2 binding accelerated by resisting loads more strongly than kinesin-1 and -3?

Due to the compliance of the dsDNA, the 95% limits for the initial attachment position are +/- 290 nm (Fig. S2). Thus, some apparent ‘jumps’ from the detached state are expected. We will take a closer look at why there are jumps for kinesin-2 that aren’t apparent for kinesin-1 or -3.

When comparing the durations of unloaded and stall events (Fig. 2), there is a potential for bias in the measurement, where very long unloaded runs cannot be observed due to the limited length of the microtubule (Thompson, Hoeprich, and Berger, 2013), while the duration of tethered runs is only limited by photobleaching. Was the possible censoring of the results addressed in the analysis?

Yes. Please see response to Reviewer #2 points (8) and (9) above.

The mathematical model is helpful in interpreting the data. To assess how the "slip" state contributes to the association kinetics, it would be helpful to compare the proposed model with a similar model with no slip state. Could the slips be explained by fast reattachments from the detached state?

In the model, the slip state and the detached states are conceptually similar; they only differ in the sequence (slip to detached) and the transition rates into and out of them. The simple answer is: yes, the slips could be explained by fast reattachments from the detached state. In that case, the slip state and recovery could be called a “detached state with fast reattachment kinetics”. However, the key data for defining the kinetics of the slip and detached states is the distribution of Recovery times shown in Fig. 4D-F, which required a triple exponential to account for all of the data. If we simplified the model by eliminating the slip state and incorporating fast reattachment from a single detached state, then the distribution of Recovery times would be a single-exponential with a time constant equivalent to t₁, which would be a poor fit to the experimental distributions in Fig. 4D-F.

We appreciate the efforts and helpful suggestions of all three reviewers and the Editor.

References:

Block, S.M., L.S. Goldstein, and B.J. Schnapp. 1990. Bead movement by single kinesin molecules studied with optical tweezers. Nature. 348:348-352.

Bouchiat, C., M.D. Wang, J. Allemand, T. Strick, S.M. Block, and V. Croquette. 1999. Estimating the persistence length of a worm-like chain molecule from force-extension measurements. Biophys J. 76:409-413.

Coppin, C.M., J.T. Finer, J.A. Spudich, and R.D. Vale. 1996. Detection of sub-8-nm movements of kinesin by high-resolution optical-trap microscopy. Proc Natl Acad Sci U S A. 93:1913-1917.

Coppin, C.M., D.W. Pierce, L. Hsu, and R.D. Vale. 1997. The load dependence of kinesin's mechanical cycle. Proc Natl Acad Sci U S A. 94:8539-8544.

Ezber, Y., V. Belyy, S. Can, and A. Yildiz. 2020. Dynein Harnesses Active Fluctuations of Microtubules for Faster Movement. Nat Phys. 16:312-316.

Hancock, W.O. 2014. Bidirectional cargo transport: moving beyond tug of war. Nat Rev Mol Cell Biol. 15:615-628.

Howard, J. 2001. Mechanics of Motor Proteins and the Cytoskeleton. Sinauer Associates, Inc., Sunderland, MA. 367 pp.

Kunwar, A., S.K. Tripathy, J. Xu, M.K. Mattson, P. Anand, R. Sigua, M. Vershinin, R.J. McKenney, C.C. Yu, A. Mogilner, and S.P. Gross. 2011. Mechanical stochastic tug-of-war models cannot explain bidirectional lipid-droplet transport. Proc Natl Acad Sci U S A. 108:18960-18965.

Kuo, Y.W., M. Mahamdeh, Y. Tuna, and J. Howard. 2022. The force required to remove tubulin from the microtubule lattice by pulling on its alpha-tubulin C-terminal tail. Nature communications. 13:3651.

Laakso, J.M., J.H. Lewis, H. Shuman, and E.M. Ostap. 2008. Myosin I can act as a molecular force sensor. Science. 321:133-136.

Leidel, C., R.A. Longoria, F.M. Gutierrez, and G.T. Shubeita. 2012. Measuring molecular motor forces in vivo: implications for tug-of-war models of bidirectional transport. Biophys J. 103:492-500.

Marko, J.F., and E.D. Siggia. 1995. Stretching DNA. Macromolecules. 28:8759-8770.

Nicholas, M.P., F. Berger, L. Rao, S. Brenner, C. Cho, and A. Gennerich. 2015. Cytoplasmic dynein regulates its attachment to microtubules via nucleotide state-switched mechanosensing at multiple AAA domains. Proc Natl Acad Sci U S A. 112:6371-6376.

Purcell, E.M. 1977. Life at low Reynolds Number. Amer J. Phys. 45:3-11.

Pyrpassopoulos, S., H. Shuman, and E.M. Ostap. 2020. Modulation of Kinesin's Load-Bearing Capacity by Force Geometry and the Microtubule Track. Biophys J. 118:243-253.

Rai, A.K., A. Rai, A.J. Ramaiya, R. Jha, and R. Mallik. 2013. Molecular adaptations allow dynein to generate large collective forces inside cells. Cell. 152:172-182.

Ramaiya, A., B. Roy, M. Bugiel, and E. Schaffer. 2017. Kinesin rotates unidirectionally and generates torque while walking on microtubules. Proc Natl Acad Sci U S A. 114:10894-10899.

Rao, L., F. Berger, M.P. Nicholas, and A. Gennerich. 2019. Molecular mechanism of cytoplasmic dynein tension sensing. Nature communications. 10:3332.

Smith, S.B., L. Finzi, and C. Bustamante. 1992. Direct mechanical measurements of the elasticity of single DNA molecules by using magnetic beads. Science. 258:1122-1126.

Sudhakar, S., M.K. Abdosamadi, T.J. Jachowski, M. Bugiel, A. Jannasch, and E. Schaffer. 2021. Germanium nanospheres for ultraresolution picotensiometry of kinesin motors. Science. 371.

Toleikis, A., N.J. Carter, and R.A. Cross. 2020. Backstepping Mechanism of Kinesin-1. Biophys J. 119:1984-1994.

Urbanska, M., A. Ludecke, W.J. Walter, A.M. van Oijen, K.E. Duderstadt, and S. Diez. 2021. Highly-Parallel Microfluidics-Based Force Spectroscopy on Single Cytoskeletal Motors. Small. 17:e2007388.

Wang, M.D., H. Yin, R. Landick, J. Gelles, and S.M. Block. 1997. Stretching DNA with optical tweezers. Biophys J. 72:1335-1346.

Guide de décodage et d'optimisation du bulletin scolaire : Note de synthèse

Ce document propose une analyse approfondie des mécanismes du bulletin scolaire en Ontario, basée sur l'expertise pédagogique partagée lors du webinaire « Mieux comprendre le bulletin scolaire de votre enfant ».

Il vise à fournir aux parents et tuteurs les outils nécessaires pour interpréter les évaluations et soutenir efficacement le parcours académique de l'élève.

Résumé analytique

Le bulletin scolaire ne doit pas être perçu comme un simple classement, mais comme un outil de communication dynamique entre l'école et la famille.

Les points essentiels à retenir sont :

• Finalité pédagogique : Le bulletin mesure le progrès par rapport au curriculum provincial et non le potentiel intrinsèque ou la valeur de l'enfant.

• Indicateurs de réussite : Les habiletés d'apprentissage et les habitudes de travail (HH) sont souvent les prédicteurs les plus fiables des résultats académiques futurs.

• Approche constructive : La compréhension des verbes d'action dans les commentaires et l'identification des « prochaines étapes » sont cruciales pour la progression.

• Soutien ciblé : L'accompagnement à domicile doit privilégier la valorisation de l'effort et la régularité (10 minutes par année d'études) plutôt que la surcorrection.

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1. Structure et cycle des rapports scolaires

L'année scolaire est jalonnée de trois rapports distincts, chacun ayant une fonction spécifique dans le suivi de l'élève :

| Type de bulletin | Période | Contenu principal | | --- | --- | --- | | Bulletin de progrès | Automne (quelques mois après la rentrée) | Commentaires qualitatifs en français et mathématiques ; évaluation des habiletés d'apprentissage. | | Étape 1 | Hiver (en cours) | Notes détaillées (lettres ou pourcentages) et commentaires pour chaque sujet du curriculum. | | Étape 2 (Final) | Fin juin | Bilan de l'année complète avec notes finales et prochaines étapes pour l'année suivante. |

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2. Système de notation et interprétation des résultats

La notation varie selon le niveau scolaire, mais suit une logique de progression par rapport aux attentes du curriculum :

Niveaux de performance

• A (80 % - 100 %) : L'élève dépasse les attentes fixées par le curriculum.

• B (70 % - 79 %) : L'élève répond pleinement aux attentes. C'est un niveau de réussite solide et positif.

• C (60 % - 69 %) : L'élève approche des attentes ; des ajustements sont nécessaires pour consolider les acquis.

• D (50 % - 59 %) : L'élève est bien en dessous des attentes.

Cela constitue souvent un « drapeau rouge » nécessitant une intervention.

Les limites de la note

Il est impératif de comprendre que les lettres ou pourcentages ne mesurent pas :

• L'intelligence globale ou le quotient intellectuel.

• Le potentiel de réussite future.

• L'effort fourni spécifiquement à la maison.

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3. Habiletés d'apprentissage et habitudes de travail (HH)

Situées généralement en première page, les habiletés telles que l'organisation, l'autorégulation, l'esprit de collaboration et l'utilisation du français sont fondamentales.

• Corrélation avec les notes : Il existe un lien direct entre les HH et les résultats académiques.

Une baisse des habiletés (ex: désorganisation) précède souvent une baisse des notes.

• Indicateurs de comportement : Contrairement aux matières académiques, ces notes reflètent la manière dont l'élève apprend et interagit.

Par exemple, l'« utilisation du français » évalue la volonté de s'exprimer dans la langue, tandis que la « communication orale » évalue la compétence linguistique technique.

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4. Analyse des commentaires de l'enseignant

Les commentaires servent à expliciter la note et à tracer une feuille de route pour l'élève.

• Le choix des verbes : Un élève qui « maîtrise » une compétence est à un stade différent de celui qui « commence à » ou « continue de ».

Les parents doivent porter une attention particulière à ces nuances.

• Les prochaines étapes : C'est l'élément le plus critique du commentaire.

Il indique précisément ce que l'élève doit travailler pour passer au niveau supérieur (ex: passer d'un C+ à un B-).

• Communication orale : Ce domaine ne doit pas être confondu avec la personnalité (ex: timidité).

Il évalue la capacité à structurer des messages et à utiliser un vocabulaire approprié.

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5. Stratégies de soutien à domicile

Le rôle du parent est de soutenir, non d'enseigner à nouveau la matière.

Recommandations de gestion du temps

Le temps de travail à la maison devrait suivre la règle des 10 minutes par niveau scolaire :

• 1ère année : 10 minutes (lecture ou devoirs).

• 6ème année : 60 minutes.

• Note : Si aucun devoir n'est assigné, ce temps doit être consacré à la lecture ou à l'écoute de contenus en français (ex: Netflix en français pour les plus âgés).

Pratiques favorisantes

• Poser des questions ouvertes : Plutôt que de vérifier uniquement les réponses, interrogez l'enfant sur le contenu (« Pourquoi le personnage a-t-il fait cela ? »).

• Valoriser l'effort : Encourager le progrès quotidien plutôt que la perfection.

• Éviter la comparaison : Ne pas comparer les résultats avec la fratrie ou les pairs pour préserver l'estime de soi.

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6. Interventions spécialisées : Le PEI

Si un élève présente des difficultés persistantes (notes de niveau D), l'équipe école peut proposer un Plan d'enseignement individualisé (PEI).

• Adaptations : Changements dans les stratégies d'enseignement (temps supplémentaire, outils technologiques comme Google Reading & Write, diminution du nombre de questions) sans modifier le niveau du curriculum.

• Modifications : Changement du niveau de difficulté des attentes (ex: un élève en 4ème année travaillant sur le curriculum de mathématiques de 3ème année).

Cela nécessite le consentement formel des parents et s'appuie souvent sur des évaluations psychologiques ou éducationnelles.

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7. Communication efficace avec l'école

La collaboration avec l'enseignant titulaire et l'enseignant ressource est la clé du succès.

Voici des questions pertinentes pour une rencontre :

• Quelles sont les forces principales observées en classe ?

• Quelle est la priorité académique actuelle (ex: quel temps de verbe ou concept mathématique est étudié) ?

• Quelles sont les une ou deux stratégies prioritaires à appliquer à la maison ? (Il est déconseillé de tenter de suivre plus de deux objectifs simultanément).

70B-107 acts as rotation 1 in the development. m. 70 lands a half step below P0 in Eb; and the succeeding bars restate P0 on different tonal levels. Pianissimo minor-major tonal fluctuations follow staging the musical process to slip free of the dysphoric minors grip, P1.1 reappears on B minor with a deceptive move onto an A flat minor chord. m.91-96 serve to correctively lift the brief dominatn pedal of B flat minor onto B minor, 89-96 lift us into C major in M.107 and a contropuntal variant of P0.

m. 107 has another ambiguous cadence.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

This study presents an interesting investigation into the role of trained immunity in inflammatory bowel disease, demonstrating that β-glucan-induced reprogramming of innate immune cells can ameliorate experimental colitis. The findings are novel and clinically relevant, with potential implications for therapeutic strategies in IBD. The combination of functional assays, adoptive transfer experiments, and single-cell RNA sequencing provides comprehensive mechanistic insights. However, some aspects of the study could benefit from further clarification to strengthen the conclusions.

We are grateful for the reviewer’s positive assessment of our study and constructive suggestions to improve the manuscript.

Strengths:

(1) This study elegantly connects trained immunity with IBD, demonstrating how βglucan-induced innate immune reprogramming can mitigate chronic inflammation.

(2) Adoptive transfer experiments robustly confirm the protective role of monocytes/macrophages in colitis resolution.

(3) Single-cell RNA sequencing provides mechanistic depth, revealing the expansion of reparative Cx3cr1⁺ macrophages and their contribution to epithelial repair.

(4) The work highlights the therapeutic potential of trained immunity in restoring gut homeostasis, offering new directions for IBD treatment.

Weaknesses:

While β-glucan may exert its training effect on hematopoietic stem cells, performing ATAC-seq on HSCs or monocytes to profile chromatin accessibility at antibacterial defense and mucosal repair-related genes would further validate the trained immunity mechanism. Alternatively, the authors could acknowledge this as a study limitation and future research direction.

We appreciate your comments on assessing the chormoatain accessibility of HSCs induced by b-glucan training, as epigenetic reprogramming is known to be one of the underlying mechanisms for trained immunity suggest by many groups including our group. To delineate the genome-wide epigenetic reprogramming induced by β-glucan (BG), we reanalyzed publicly available chromatin profiling datasets where ATACseq of HSC from control and β-glucan trained mice was performed (accession number: CRA014389). Comparative analysis revealed HSC from BG-trained mice demonstrated pronounced enrichment at promoters and distal intergenic regions—key regulatory loci governing transcriptional activity (Fig. S7A). This divergent genomic targeting was further corroborated by distinct signal distribution profiles (Fig. S7B), supporting pronounced upregulation-driven remodeling of the epigenomic landscape induced by BG treatment. Functional annotation of these epigenetically primed promoters via GO term analysis revealed significant enrichment of immune-relevant processes, including leukocyte migration, cell-cell adhesion, and chemotaxis (Fig. S7C). Consistently, KEGG pathway analysis highlighted the enrichment of signaling cascades such as chemokine signaling and cell adhesion molecules (Fig. S7D), reinforcing the involvement of BG-induced trained immunity in inflammatory and mucosal homing pathways.

Furthermore, promoter-centric enrichment of terms related to “defense response to bacterium” (Fig. S7E) underscored the role of BG in priming antibacterial transcriptional programs, which is a crucial axis for maintaining intestinal homeostasis. Locus-specific examination of chromatin states further validated BG-induced epigenetic modifications in the upstream regions of selected target genes, including Gbp5, Gbp2 and S100a8 and Nos2 (Fig. S7F). Collectively, our integrative reanalysis demonstrates that BG reshapes the epigenomic architecture at regulatory elements, thereby orchestrating immune gene expression programs directly relevant to IBD pathophysiology and mucosal immunity. (Line 201-211)

Reviewer 1 (Recommendations for the authors):

(1) It’s better to include a schematic summarizing the proposed mechanism for reader clarity.

We appreciate your comments and proposed a graphical abstract as in Author response image 1.

Author response image 1.

(2) Discuss potential off-target effects of β-glucan-induced trained immunity (e.g., risk of exacerbated inflammation in other contexts).

We appreciate this important comment regarding the potential off-target or side-effects of β-glucan induced trained immunity. As trained immunity is known to augment inflammatory responses upon heterologous stimulation and has been implicated in chronic inflammation–prone conditions such as atherosclerosis, this is an important consideration. Previous in vivo studies have shown that β-glucan pretreatment can enhance antibacterial or antitumor responses without inducing basal inflammation after one week of administration (PMID: 22901542, PMID: 30380404, PMID: 36604547, PMID: 33125892). Nevertheless, it remains possible that β-glucan–induced trained immunity could have unintended effects in certain contexts, which warrants further investigation and caution. We have discussed this potential caveat in the discussion (Lines 299-302)

Reviewer #2 (Public review):

Summary:

The study investigates whether β-glucan (BG) can reprogram the innate immune system to protect against intestinal inflammation. The authors show that mice pretreated with BG prior to DSS-induced colitis experience reduced colitis severity, including less weight loss, colon damage, improved gut repair, and lowered inflammation. These effects were independent of adaptive immunity and were linked to changes in monocyte function.

The authors show that the BG-trained monocytes not only help control inflammation but confer non-specific protection against experimental infections (Salmonella), suggesting the involvement of trained immunity (TI) mechanisms. Using single-cell RNA sequencing, they map the transcriptional changes in these cells and show enhanced differentiation of monocytes into reparative CX3CR1⁺ macrophages. Importantly, these protective effects were transferable to other mice via adoptive cell transfer and bone marrow transplantation, suggesting that the innate immune system had been reprogrammed at the level of stem/progenitor cells.

Overall, this study provides evidence that TI, often associated with heightened inflammatory programs, can also promote tissue repair and resolution of inflammation. Moreover, this BG-induced functional reprogramming can be further harnessed to treat chronic inflammatory disorders like IBD.

Strengths:

(1) The authors use advanced experimental approaches to explore the potential therapeutic use of myeloid reprogramming by β-glucan in IBD.

(2) The authors follow a data-to-function approach, integrating bulk and single-cell RNA sequencing with in vivo functional validation to support their conclusions.

(3) The study adds to the growing evidence that TI is not a singular pro-inflammatory program, but can adopt distinct functional states, including anti-inflammatory and reparative phenotypes, depending on the context.

We are grateful for your positive assessment of our study and recognition of its translational implications. We particularly appreciate the acknowledgment that our work expands the therapeutic potential of β-glucan–mediated trained immunity in ameliorating colitis.

Weaknesses:

(1) The epigenetic and metabolic basis of TI is not explored, which weakens the mechanistic claim of TI. This is especially relevant given that a novel reparative, antiinflammatory TI program is proposed.

We appreciate your valuable comment highlighting the importance of the epigenetic and metabolic basis of TI in providing mechanistic insight. While previous studies, including work from our group (S.-C. Cheng), have extensively characterized the epigenetic and metabolic signatures of monocytes from BG-trained mice—primarily in the context of inflammatory genes—we acknowledge that these aspects are not directly addressed in our current manuscript as the current manuscript was aimed to build on the foundation of β-glucan-induced trained immunity established by many other groups including us and address its potential as a therapeutic approaches in the colitis setup.

That being said, we fully agree with your comments to analyze the epigenetic profile on key pathways similar to the question raised by reviewer 1, we reanalyze the relevant public datasets and presenting summarize the finding in Supplementary Figure S7. ATAC-seq analysis further validated and provide the epigenetic basis of the enhanced inflammatory and antibacterial capacity of monocytes which are seeded back in the HSC compartment.

(2) The absence of a BG-only group limits interpretation of the results. Since the authors report tissue-level effects such as enhanced mucosal repair and transcriptional shifts in intestinal macrophages (colonic RNA-Seq), it is important to rule out whether BG alone could influence the gut independently of DSS-induced inflammation. Without a BG-only control, it is hard to distinguish a true trained response from a potential modulation caused directly by BG.

We thank the reviewer for this important suggestion. Although we did not perform qPCR for mucosal repair genes in Figure S1C and Figure S1D, our colon RNA-seq analysis in Figure 5G included a BG-only control group (Colitis_d0). These results indicate that BG preconditioning alone does not alter baseline expression of colon mucosal repair genes, supporting the conclusion that the observed effects occur in the context of DSS-induced inflammation.

(3) Although monocyte transfer experiments show protection in colitis, the fate of the transferred cells is not described (e.g., homing or differentiation into Cx3cr1⁺ macrophage subsets). This weakens the link between specific monocyte subsets and the observed phenotype.

We thank the reviewer for this important point. We acknowledge that direct in vivo tracking of the adoptively transferred monocytes to confirm their homing to the colon and differentiation into specific macrophage subsets would strengthen the mechanistic link. However, due to technical limitations in reliably tracing the fate of transferred cells in our experimental setting, we were unable to provide this direct evidence. Instead, we present a strong correlative and functional evidence chain that supports the proposed model:

(a) Following BG pretreatment, we observed a significant decrease in circulating Ly6Chi monocytes specifically at the peak of colitis (day 7, Fig. 5D), concurrent with a marked increase in monocytes/macrophages within the colonic lamina propria (Fig. 2D). This inverse relationship strongly suggests enhanced recruitment of monocytes from the blood into the inflamed colon upon BG training.

(b) Using CX3CR1-GFP reporter mice, we found that BG pretreatment led to an increased proportion of colonic myeloid cells in an intermediate state (P5: Ly6C⁺MHCII⁺CX3CR1⁺, Fig. 5F). This population represents monocytes actively undergoing differentiation into intestinal macrophages, supporting the idea that BG accelerates the monocyte-to-macrophage transition in situ.

(c) Our scRNA-seq analysis independently revealed an expansion of monocyte-derived macrophage clusters (e.g., Macro1, Macro2) in BG-treated mice, which express canonical tissue macrophage markers (including Cx3cr1) and genes associated with tissue repair (e.g., Vegfa, Fig. 4A, 5H, 5I).

These data collectively indicate that BG-trained monocytes exhibit enhanced capacity for colonic recruitment and preferential differentiation toward reparative macrophage subsets, which aligns with the protective phenotype observed after adoptive transfer. We have explicitly noted the absence of direct fate-mapping data as a limitation in the revised Discussion and agree that future studies employing advanced tracing techniques would be valuable to definitively establish this cellular trajectory. (Line 378-380)

(4) While scRNA-seq reveals distinct monocyte/macrophage subclusters (Mono1-3.), their specific functional roles remain speculative. The authors assign reparative or antimicrobial functions based on transcriptional signatures, but do not perform causal experiments (depletion or in vitro assays). The biological roles of these cells remain correlative.

We agree that the functional role of CX3CR1⁺ macrophages is not comprehensively validated and is currently inferred from scRNA-seq clustering. While our flow cytometry data show increased CX3CR1⁺ macrophages in the BG-TI group, and our CCR2 KO and monocyte adoptive transfer experiments indicate these macrophages are monocyte-derived, suggesting at least that β-glucan pretreatment alters the monocyte capacity which directly contribute to the enhanced colitis alleviation phenotype as observed. However, due to the fact that we fail to find a cluster dependent marker, which is also the current biggest caveats of the scRNAseq defined cell subclusters, we were not able to show direct casual evidence via specifically depleting subcluster cells. However, the result from the monocyte adoptive transfer experiment with Ccr2 KO mice experimental strongly suggest the presence of monocytes is crucial for this protective effect. We fully acknowledge this as a limitation of current study and clarify in the discussion that our conclusions regarding CX3CR1⁺ macrophage function are mainly based on transcriptional profiling and association with protective phenotypes, rather than direct causal evidence (Lines 400-404).

(5) While Rag1^-/- mice were used to rule out adaptive immunity, the potential role of innate lymphoid cells (ILCs), particularly ILC2s and ILC3s, which are known to promote mucosal repair (PMID: 27484190 IF: 7.6 Q1 IF: 7.6 Q1 IF: 7.6 Q1), was not explored. Given the reparative phenotype observed, the contribution of ILCs remains a confounding factor.

We appreciate your valuable comment regarding the potential role of ILCs in the observed mucosal repair. Indeed, in our current manuscript examining the BG-trained immunity effect, the contribution of ILCs was not evaluated. Due to the fact that adoptive transfer of trained monocytes into CCR2 KO mice could recapitulate the colitis alleviation phenotype, we think at least the β-glucan enhanced protection are dependent on trained monocytes. While acknowledge that the limitation and we could not rule out the possible role of ILCs in this process and discuss this limitation in the discussion in the revised manuscript.

The literature (PMID: 21502992; PMID: 32187516) supports a role for ILC3-mediated IL-22 production in tissue repair, which could overlap with our observed effects. However, our monocyte adoptive transfer experiments show that monocytes alone can alleviate DSS-induced colitis, suggesting a dominant role for monocytes in this context. Nonetheless, we will make it clear that ILC contributions cannot be excluded. (Line 322-326).

Reviewer 2 (Recommendations for the authors):

(1) The authors do not provide direct mechanistic evidence of TI (e.g., epigenetic and metabolic reprogramming). The absence of such data weakens the mechanistic strength of the TI claim. The authors should soften the terminology to BGinduced myeloid reprogramming suggestive of trained immunity, acknowledge, and discuss this limitation.

We appreciate your comment highlighting the lack of direct epigenetic and metabolic assessment in our current study. Previous work from our group (S.-C. Cheng) and others has extensively documented the epigenetic and metabolic profiles of monocytes from β-glucan–trained mice, focusing primarily on inflammatory-related genes. Based on this established foundation, our current manuscript focuses on exploring the translational potential of BG-induced trained immunity.

That said, as mentioned in our response to the identified weakness, we performed reanalysis from the public epigenetic datasets with a focus on pathways related to reparative and antibacterial functions and integrated this part in the revised manuscript (Fig S7, Lines 201-211).

(2) CX3CR1⁺ macrophages' role is not functionally validated. The data relies solely on scRNA-seq and cluster annotations, which are insufficient to confirm functional roles in vivo. Depletion or in vitro studies would provide stronger causal evidence. The authors should acknowledge this limitation in the Discussion.

We agree that the functional role of CX3CR1⁺ macrophages is not comprehensively validated and is currently inferred from scRNA-seq clustering. While our flow cytometry data show increased CX3CR1⁺ macrophages in the BG-TI group, and our CCR2 KO and monocyte adoptive transfer experiments indicate these macrophages are monocyte-derived, suggesting at least that β-glucan pretreatment alters the monocyte capacity which directly contribute to the enhanced colitis alleviation phenotype as observed. However, due to the fact that we fail to find a cluster dependent marker, which is also the current biggest caveats of the scRNAseq defined cell subclusters, we were not able to show a direct casual evidence. We fully acknowledge this as a limitation of current study and clarify in the discussion that our conclusions regarding CX3CR1⁺ macrophage function are mainly based on transcriptional profiling and association with protective phenotypes, rather than direct causal evidence (Lines 395-404).

(3) Rag1^-/- mice retain innate lymphoid cells (ILCs), particularly ILC3, which are mucosal and produce IL-22, contributing to tissue repair (PMID: 21502992; PMID: 32187516). The potential for BG to activate ILCs remains unexplored in this study. This limits the interpretation of whether the observed protection arises from monocyte/macrophage reprogramming or is partially mediated by residual ILC activity. The authors should explicitly acknowledge this limitation and discuss the possible contribution of ILCs to the observed phenotype.

We appreciate your valuable comment regarding the potential role of ILCs in the observed mucosal repair. Indeed, in our current manuscript examining the BG-trained immunity effect, the contribution of ILCs was not evaluated. Due to the fact that adoptive transfer of trained monocytes into CCR2 KO mice could recapitulate the colitis alleviation phenotype, we think at least the β-glucan enhanced protection are dependent on trained monocytes. While acknowledge that the limitation and we could not rule out the possible role of ILCs in this process and discuss this limitation in the discussion in the revised manuscript

The literature (PMID: 21502992; PMID: 32187516) supports a role for ILC3-mediated IL-22 production in tissue repair, which could overlap with our observed effects. However, our monocyte adoptive transfer experiments show that monocytes alone can alleviate DSS-induced colitis, suggesting a dominant role for monocytes in this context. Nonetheless, we will make it clear that ILC contributions cannot be excluded. (Line 322-327).

(4) Figure 1-It would help to clarify whether a BG-only control group (without DSS) was included in the design. This would be critical to determine if BG alone alters the colon. If omitted, the authors should clearly state this and consider adding such a group in future experiments. This would help define the baseline effects of BG and support the claim that its benefits are dependent on TI (upon second challenge - DSS).

We appreciate this valuable suggestion. While we did not perform qPCR to assess mucosal repair genes in Figure S1C and Figure S1D, our colon RNA-seq analysis in Figure 5G included a dedicated BG-only control group at based line before DSStreatment (Colitis_d0). These data indicate that BG preconditioning alone does not alter the baseline expression of colon mucosal repair genes.

(5) Figure 3 - It would strengthen the conclusions to include a vehicle-treated PBS BMT donor control group, or to state its absence. It is unclear whether the protective effect observed in recipients of BG-treated BM is due to trained immunity or to non-specific effects of transplantation, irradiation, or batch variation.

We fully agree with your comments that it is critical to including the vehicle-treated PBS BMT control to rule out any non-specific effects induced by transplantation, irradiation or batch variation. We actually did the blank PBS transfer control everytime after mice received irradiation treatment as a control to assess the successful induction of irradiation to get rid of bone marrow from irradiated mice. Mice that receive PBS only will die after 8 days while only mice receiving either bone marrow from PBScontrol or BG-treatment group will survive. We also perform flowcytometry to examine the successful BMT transplantation (Fig S5C). We have added part regarding the vehicle-treated control for BMT in the material method section for clarification (Lines 456-466).

(6) No gene expression or phenotypic data is provided for monocytes/macrophages in BMT recipients; therefore, it cannot be confidently stated that these cells were reprogrammed. Expression/phenotypic data should be added or discussed.

We thank the reviewer for raising this important point. We acknowledge that a detailed transcriptomic or phenotypic analysis of donor-derived tissue-resident myeloid cells in the BMT recipients would provide the most direct evidence for their reprogrammed state.

While our BMT study focused primarily on assessing the transferability of the protective phenotype via endpoint disease parameters and circulating immune cell composition, we present a coherent and compelling line of evidence supporting the conclusion that BG's training effect is maintained within the hematopoietic system of recipients and mediated by reprogrammed myeloid cells:

(a) A key finding is the significant increase in the proportion of donor-derived Ly6Chi monocytes in the peripheral blood of recipients receiving BG-trained bone marrow (Fig. 3J). This is not a bystander effect but direct evidence that the BG-induced on donor hematopoietic stem/progenitor cells instructs a biased differentiation program towards a specific effector precursor population within the new host, demonstrating the functional persistence of the trained state post-transplantation.

(b) The core of reprogramming in trained immunity lies in persistent epigenetic and functional changes. Our new analysis of public datasets (Fig. S7) confirms that BG directly reshapes the chromatin accessibility landscape in hematopoietic stem cells (HSCs), particularly at loci regulating immune and antibacterial responses. This provides the fundamental mechanism explaining how the trained phenotype is both long-lasting and transplantable: the reprogramming occurs at the progenitor level.

(c) The most causally compelling data in our study comes from the independent adoptive transfer experiment, where transfer of purified BG-trained monocytes alone was sufficient to ameliorate colitis in recipient mice (Fig. 3K, L). This definitively proves that the trained monocytes themselves carry the protective functional program. It strongly suggests that these reprogrammed monocytes/macrophages are the likely effectors mediating protection in the BMT model.

(d) Our interpretation aligns with well-established paradigms in the field. Precedent studies confirm that the BG-trained phenotype (e.g., enhanced cytokine potential) can be transferred via BMT or monocyte adoption. For instance, Haacke et al. (PMID: 40020679) demonstrated that splenic monocytes from BG-trained donors, when transferred into arthritic recipient mice, led to elevated inflammatory cytokine (e.g., Tnf, Il6) expression in recipient joints, directly proving the maintained functional reprogramming of trained cells in a heterologous host environment. This provides a strong precedent supporting the functional activity of transferred trained cells in our model.

(7) The study is consistent with emerging evidence that distinct TI programs may exist depending on the stimulus and context, including immunoregulatory and tissue-reparative responses (PMID: 35133977; PMID: 31732931; PMID: 32716363; PMID: 30555483). The authors should integrate this perspective into the Discussion to acknowledge that their findings may represent one example of such context-dependent, potentially reparative TI programs. This would place the study within the growing literature describing functional heterogeneity in innate immune training.

We appreciate this suggestion and have incorporated it into the discussion. In the revised manuscript, we discussed how our findings of BG-induced protective myeloid reprogramming align with the concept of tissue-reparative or immunoregulatory TI, which is distinct from the pro-inflammatory TI phenotypes described in other contexts. By highlighting the functional heterogeneity of innate immune training, we position our work as an example of a stimulus-specific, reparative TI program. (Lines 356-379)

Reviewer #3 (Public review):

Summary:

In the present work, Yinyin Lv et al offer evidence for the therapeutic potential of trained immunity in the context of inflammatory bowel disease (IBD). Prior research has demonstrated that innate cells pre-treated (trained) with β-glucan show an enhanced pro-inflammatory response upon a second challenge.

While an increased immune response can be beneficial and protect against bacterial infections, there is also the risk that it will worsen symptoms in various inflammatory disorders. In the present study, the authors show that mice preconditioned with β-glucan have enhanced resistance to Staphylococcus aureus infection, indicating heightened immune responses.

The authors demonstrate that β-glucan training of bone marrow hematopoietic progenitors and peripheral monocytes mitigates the pro-inflammatory effects of colitis, with protection extending to naïve recipients of the trained cells.

Using a dextran sulfate sodium (DSS)-induced model of colitis, β-glucan pre-treatment significantly dampens disease severity. Importantly, the use of Rag1^-/- mice, which lack adaptive immune cells, confirms that the protective effects of β-glucan are mediated by innate immune mechanisms. Further, experiments using Ccr2^-/- mice underline the necessity of monocyte recruitment in mediating this protection, highlighting CCR2 as a key factor in the mobilization of β-glucan-trained monocytes to inflamed tissues. Transcriptomic profiling reveals that β-glucan training upregulates genes associated with pattern recognition, antimicrobial defense, immunomodulation, and interferon signaling pathways, suggesting broad functional reprogramming of the innate immune compartment. In addition, β-glucan training induces a distinct monocyte subpopulation with enhanced activation and phagocytic capacity. These monocytes exhibit an increased ability to infiltrate inflamed colonic tissue and differentiate into macrophages, marked by increased expression of Cx3cr1. Moreover, among these trained monocyte and macrophage subsets, other gene expression signatures are associated with tissue and mucosal repair, suggesting a role in promoting resolution and regeneration following inflammatory insult.

Strengths:

(1) Overall, the authors present a mechanistically insightful investigation that advances our understanding of trained immunity in IBD.

(2) By employing a range of well-characterized murine models, the authors investigate specific mechanisms involved in the effects of β-glucan training.

(3) Furthermore, the study provides functional evidence that the protection conferred by the trained cells persists within the hematopoietic progenitors and can be transferred to naïve recipients. The integration of transcriptomic profiling allows the identification of changes in key genes and molecular pathways underlying the trained immune phenotype.

(4) This is an important study that demonstrates that β-glucan-trained innate cells confer protection against colitis and promote mucosal repair, and these findings underscore the potential of harnessing innate immune memory as a therapeutic approach for chronic inflammatory diseases.

Thank you for the positive evaluation and constructive feedback on our manuscript.

Weaknesses:

However, FPKM is not ideal for between-sample comparisons due to its within-sample normalization approach. Best practices recommend using raw counts (with DESeq2) for more robust statistical inference.

We appreciate the reminder about best practices for RNA-seq analysis. We apologize for the inaccurate description in the Materials and Methods section. For all differential expression analyses, we have in fact used raw count data as input for DESeq2. FPKM values were only used for visualization purposes, such as in heatmaps and clustering analyses. We correct this description in the revised manuscript to accurately reflect our analysis workflow. (Lines 488-499)

Reviewer 3 (Recommendations for the authors):

(1) Current best practices recommend working with raw count data when using DESeq2 to ensure statistically robust differential expression analysis between samples. However, for visualization and clustering, like heatmaps, FPKMs can be used. Could the authors explain why they have used FPKM for differential gene expression analysis?

We appreciate the reminder about best practices for RNA-seq analysis. We apologize for the inaccurate description in the Materials and Methods section. For all differential expression analyses, we have in fact used raw count data as input for DESeq2. FPKM values were only used for visualization purposes, such as in heatmaps and clustering analyses. We correct this description in the revised manuscript to accurately reflect our analysis workflow. (Lines 488-499)

Minor Comment

(1) Line 92: remove extra word "that".

We remove the extra word “that” from Line 92 in the revised manuscript.

(2) Line 201: please state here what "GBP" stands for, as it appears first.

We define “GBP” as “Guanylate-Binding Protein” at its first appearance in Line 201. (Lines 213)

(3) Line 235: consider rewriting "we analyzed the day 7 RNA-seq data, which revealed significant enrichment of the myeloid"; added spacing for "day 7", "which", and "the".

We revise the sentence in Line 235 to read: “We analyzed the day 7 RNA-seq data, which revealed significant enrichment of the myeloid…” to improve readability. (Lines

246-247)

(4) Line 290: consider rewriting " as seen in conditions such as rheumatoid arthritis and ...".

We revise Line 290 to: “as observed in conditions such as rheumatoid arthritis and…” for clarity. (Lines 301-302)

(5) Line 375-376: please check sentence starting lower case "with minor modifications, by assessing ".

We correct the sentence to start with a capital letter: “With minor modifications, by assessing…” (Lines 422-423)

(6) Line 399: kindly consider adding "was" after "cDNA".

We revise Line 399 to include “was” as suggested: “cDNA was synthesized…” (Lines 446)

(7) Line 346-347: consider adding "which" after "monocytes": "We transferred BGpreconditioned monocytes which significantly alleviated clinical symptoms".

We revise Line 346-347 to include “which” as suggested for grammatical clarity. (Lines 385-386)

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

(1) Figure 1B shows the PREDICTED force-extension curve for DNA based on a worm-like chain model. Where is the experimental evidence for this curve? This issue is crucial because the F-E curve will decide how and when a catch-bond is induced (if at all it is) as the motor moves against the tensiometer. Unless this is actually measured by some other means, I find it hard to accept all the results based on Figure 1B.

The Worm-Like-Chain model for the elasticity of DNA was established by early work from the Bustamante lab (Smith et al., 1992) and Marko and Siggia (Marko and Siggia, 1995), and was further validated and refined by the Block lab (Bouchiat et al., 1999; Wang et al., 1997). The 50 nm persistence length is the consensus value, and was shown to be independent of force and extension in Figure 3 of Bouchiat et al (Bouchiat et al., 1999). However, we would like to stress that for our conclusions, the precise details of the Force-Extension relationship of our dsDNA are immaterial. The key point is that the motor stretches the DNA and stalls when it reaches its stall force. Our claim of the catch-bond character of kinesin is based on the longer duration at stall compared to the run duration in the absence of load. Provided that the motor is indeed stalling because it has stretched out the DNA (which is strongly supported by the repeated stalling around the predicted extension corresponding to ~6 pN of force), then the stall duration depends on neither the precise value for the extension nor the precise value of the force at stall.

(2) The authors can correct me on this, but I believe that all the catch-bond studies using optical traps have exerted a load force that exceeds the actual force generated by the motor. For example, see Figure 2 in reference 42 (Kunwar et al). It is in this regime (load force > force from motor) that the dissociation rate is reduced (catch-bond is activated). Such a regime is never reached in the DNA tensiometer study because of the very construction of the experiment. I am very surprised that this point is overlooked in this manuscript. I am therefore not even sure that the present experiments even induce a catch-bond (in the sense reported for earlier papers).

It is true that Kunwar et al measured binding durations at super-stall loads and used that to conclude that dynein does act as a catch-bond (but kinesin does not) (Kunwar et al., 2011). However, we would like to correct the reviewer on this one. This approach of exerting super-stall forces and measuring binding durations is in fact less common than the approach of allowing the motor to walk up to stall and measuring the binding duration. This ‘fixed trap’ approach has been used to show catch-bond behavior of dynein (Leidel et al., 2012; Rai et al., 2013) and kinesin (Kuo et al., 2022; Pyrpassopoulos et al., 2020). For the non-processive motor Myosin I, a dynamic force clamp was used to keep the actin filament in place while the myosin generated a single step (Laakso et al., 2008). Because the motor generates the force, these are not superstall forces either.

(3) I appreciate the concerns about the Vertical force from the optical trap. But that leads to the following questions that have not at all been addressed in this paper:

(i) Why is the Vertical force only a problem for Kinesins, and not a problem for the dynein studies?

Actually, we do not claim that vertical force is not a problem for dynein; our data do not speak to this question. There is debate in the literature as to whether dynein has catch bond behavior in the traditional single-bead optical trap geometry - while some studies have measured dynein catch bond behavior (Kunwar et al., 2011; Leidel et al., 2012; Rai et al., 2013), others have found that dynein has slip-bond or ideal-bond behavior (Ezber et al., 2020; Nicholas et al., 2015; Rao et al., 2019). This discrepancy may relate to vertical forces, but not in an obvious way.

(ii) The authors state that "With this geometry, a kinesin motor pulls against the elastic force of a stretched DNA solely in a direction parallel to the microtubule". Is this really true? What matters is not just how the kinesin pulls the DNA, but also how the DNA pulls on the kinesin. In Figure 1A, what is the guarantee that the DNA is oriented only in the plane of the paper? In fact, the DNA could even be bending transiently in a manner that it pulls the kinesin motor UPWARDS (Vertical force). How are the authors sure that the reaction force between DNA and kinesin is oriented SOLELY along the microtubule?

We acknowledge that “solely” is an absolute term that is too strong to describe our geometry. We softened this term in our revision to “nearly parallel to the microtubule” (Line 464). In the Geometry Calculations section of Supplementary Methods, we calculate that if the motor and streptavidin are on the same protofilament, the vertical force will be <1% of the horizontal force. We also note that if the motor is on a different protofilament, there will be lateral forces and forces perpendicular to the microtubule surface, except they are oriented toward rather than away from the microtubule. The DNA can surely bend due to thermal forces, but because inertia plays a negligible role at the nanoscale (Howard, 2001; Purcell, 1977), any resulting upward forces will only be thermal forces, which the motor is already subjected to at all times.

(4) For this study to be really impactful and for some of the above concerns to be addressed, the data should also have included DNA tensiometer experiments with Dynein. I wonder why this was not done?

As much as we would love to fully characterize dynein here, this paper is about kinesin and it took a substantial effort. The dynein work merits a stand-alone paper.

While I do like several aspects of the paper, I do not believe that the conclusions are supported by the data presented in this paper for the reasons stated above.

The three key points the reviewer makes are the validity of the worm-like-chain model, the question of superstall loads, and the role of DNA bending in generating vertical forces. We hope that we have fully addressed these concerns in our responses above.

Reviewer #2 (Public review):

Major comments:

(1) The use of the term "catch bond" is misleading, as the authors do not really mean consistently a catch bond in the classical sense (i.e., a protein-protein interaction having a dissociation rate that decreases with load). Instead, what they mean is that after motor detachment (i.e., after a motor protein dissociating from a tubulin protein), there is a slip state during which the reattachment rate is higher as compared to a motor diffusing in solution. While this may indeed influence the dynamics of bidirectional cargo transport (e.g., during tug-of-war events), the used terms (detachment (with or without slip?), dissociation, rescue, ...) need to be better defined and the results discussed in the context of these definitions. It is very unsatisfactory at the moment, for example, that kinesin-3 is at first not classified as a catch bond, but later on (after tweaking the definitions) it is. In essence, the typical slip/catch bond nomenclature used for protein-protein interaction is not readily applicable for motors with slippage.

We acknowledge that our treatment of kinesin-3 was confusing. In response, we deleted any reference to kinesin-3 catch-bond in the Results section, and restricted it to the Discussion where it is interpretation. In Line 635 in the Discussion, we softened the statement of catch-bond activity to “…all three dominant kinesin transport families display catch-bond like behavior at stall…”. We acknowledge that, classically, the catch/slip bond nomenclature refers to simple protein-protein interactions and is easier to interpret there. However, the term ‘catch-bond’ has been used in the literature for myosin, dynein and kinesin, and thus we feel that it is sufficiently established to use it here.

(2) The authors define the stall duration as the time at full load, terminated by >60 nm slips/detachments. Isn't that a problem? Smaller slips are not detected/considered... but are also indicative of a motor dissociation event, i.e., the end of a stall. What is the distribution of the slip distances? If the slip distances follow an exponential decay, a large number of short slips are expected, and the presented data (neglecting those short slips) would be highly distorted.

The reviewer brings up a good point that there may be undetected slips. To address this question, we plotted the distribution of slip distances for kinesin-3, which by far had the most slip events. As the reviewer suggested, it is indeed an exponential distribution, and we calculated a corrected kinesin-3 stall duration due to these undetected slips. This data and analysis are included as a new Supplementary Figure S8. In the main text on Lines 283-293 we included the following text:

“It was notable that the kinesin-3 stall durations at high load are longer than the ramp durations at low load, because this indicates that the kinesin-3 off-rate slows with increasing load. However, because kinesin-3 had the most slip events at stall, we were concerned that there may be undetected slip events below the 60 nm threshold of detection that led to an overestimation of the kinesin-3 stall duration. To test this hypothesis, we plotted the distribution of kinesin-3 slip distances at stall, fit an exponential, and calculated the fraction of missed slip events (Fig. S8). From this analysis, we calculated a correction factor of 1.42 that brought the kinesin-3 stall duration down 1.33 s. Notably, this stall duration value is still well above the kinesin-3 ramp duration value of 0.75 s in Fig. 3C and thus does not qualitatively change our conclusions.”

We thank the reviewer for this suggestion.

(3) Along the same line: Why do the authors compare the stall duration (without including the time it took the motor to reach stall) to the unloaded single motor run durations? Shouldn't the times of the runs be included?

The elastic force of the DNA spring is variable as the motor steps up to stall, and so if we included the entire run duration then it would be difficult to specify what force we were comparing to unloaded. More importantly, if we assume that any stepping and detachment behavior is history independent, then it is mathematically proper to take any arbitrary starting point (such as when the motor reaches stall), start the clock there, and measure the distribution of detachments durations relative to that starting point. More importantly, what we do in Fig. 3 is to separate out the ramps from the stalls and, using a statistical model, we compute a separate duration parameter (which is the inverse of the off-rate) for the ramp and the stall. What we find is that the relationship between ramp, stall, and unloaded durations is different for the three motors, which is interesting in itself.

(4) At many places, it appears too simple that for the biologically relevant processes, mainly/only the load-dependent off-rates of the motors matter. The stall forces and the kind of motor-cargo linkage (e.g., rigid vs. diffusive) do likely also matter. For example: "In the context of pulling a large cargo through the viscous cytoplasm or competing against dynein in a tug-of-war, these slip events enable the motor to maintain force generation and, hence, are distinct from true detachment events." I disagree. The kinesin force at reattachment (after slippage) is much smaller than at stall. What helps, however, is that due to the geometry of being held close to the microtubule (either by the DNA in the present case or by the cargo in vivo) the attachment rate is much higher. Note also that upon DNA relaxation, the motor is likely kept close to the microtubule surface, while, for example, when bound to a vesicle, the motor may diffuse away from the microtubule quickly (e.g., reference 20).

We appreciate the reviewer’s detailed thinking here, and we offer our perspective. As to the first point, we agree that the stall force is relevant and that the rigidity of the motor-cargo linkage will play a role. The goal of the sentence on pulling cargo that the reviewer highlights is to set up our analysis of slips, which we define as rearward displacements that don’t return to the baseline before force generation resumes. We revised this sentence to the following: “In the context of pulling a large cargo through the viscous cytoplasm or competing against dynein in a tug-of-war, these slip events enable the motor to continue generating force after a small rearward displacement, rather than fully detaching and ‘resetting’ to zero load.” (Line 339-342)

It should be noted that, as shown in the model diagram in Fig. 5, we differentiate between the slip state (and recovery from this slip state) and the detached state (and reattachment from this detached state). This delineation is important because, as the reviewer points out, if we are measuring detachment and reattachment with our DNA tensiometer, then the geometry of a vesicle in a cell will be different and diffusion away from the microtubule or elastic recoil perpendicular to the microtubule will suppress this reattachment.

Our evidence for a slip state in which the motor maintains association with the microtubule comes from optical trapping work by Tokelis et al (Toleikis et al., 2020) and Sudhakar et al (Sudhakar et al., 2021). In particular, Sudhakar used small, high index Germanium microspheres that had a low drag coefficient. They showed that during ‘slip’ events, the relaxation time constant of the bead back to the center of the trap was nearly 10-fold slower than the trap response time, consistent with the motor exerting drag on the microtubule. (With larger beads, the drag of the bead swamps the motor-microtubule friction.) Another piece of support for the motor maintaining association during a slip is work by Ramaiya et al. who used birefringent microspheres to exert and measure rotational torque during kinesin stepping (Ramaiya et al., 2017). In most traces, when the motor returned to baseline following a stall, the torque was dissipated as well, consistent with a ‘detached’ state. However, a slip event is shown in S18a where the motor slips backward while maintaining torque. This is best explained by the motor slipping backward in a state where the heads are associated with the microtubule (at least sufficiently to resist rotational forces). Thus, we term the resumption after slip to be a rescue from the slip state rather than a reattachment from the detached state.

To finish the point, with the complex geometry of a vesicle, during slip events the motor remains associated with the microtubule and hence primed for recovery. This recovery rate is expected to be the same as for the DNA tensiometer. Following a detachment, however, we agree that there will likely be a higher probability of reattachment in the DNA tensiometer due to proximity effects, whereas with a vesicle any elastic recoil or ‘rolling’ will pull the detached motor away from the microtubule, suppressing reattachment. To address this point, we added in the Discussion on lines 654-656:

“Additionally, any ‘rolling’ of a spherical cargo following motor detachment will tend to suppress the motor reattachment rate.”

(5) Why were all motors linked to the neck-coil domain of kinesin-1? Couldn't it be that for normal function, the different coils matter? Autoinhibition can also be circumvented by consistently shortening the constructs.

We chose this dimerization approach to focus on how the mechoanochemical properties of kinesins vary between the three dominant transport families. We agree that in cells, autoinhibition of both kinesins and dynein likely play roles in regulating bidirectional transport, as will the activity of other regulatory proteins. The native coiled-coils may act as ‘shock absorbers’ due to their compliance, or they might slow the motor reattachment rate due to the relatively large search volumes created by their long lengths (10s of nm). These are topics for future work. By using the neck-coil domain of kinesin-1 for all three motors, we eliminate any differences in autoinhibition or other regulation between the three kinesin families and focus solely on differences in the mechanochemistry of their motor domains.

(6) I am worried about the neutravidin on the microtubules, which may act as roadblocks (e.g. DOI: 10.1039/b803585g), slip termination sites (maybe without the neutravidin, the rescue rate would be much lower?), and potentially also DNA-interaction sites? At 8 nM neutravidin and the given level of biotinylation, what density of neutravidin do the authors expect on their microtubules? Can the authors rule out that the observed stall events are predominantly the result of a kinesin motor being stopped after a short slippage event at a neutravidin molecule?

(7) Also, the unloaded runs should be performed on the same microtubules as in the DNA experiments, i.e., with neutravidin. Otherwise, I do not see how the values can be compared.

To address the question of neutravidin acting as a roadblock, we did the following. Because of the sequence of injections used to assemble the tensiometer in the flow cell, there are often some residual GFP-kinesin motors that aren’t attached to DNA and thus serve as internal controls for unloaded motility on the neutravidin-functionalized Mt. We quantified the run durations of these free kinesin-GFP and found that their run duration was 0.92 s (95% CI: 0.79 to 1.04 by MEMLET). This is slightly lower but not statistically different from the 1.04 s [0.78, 1.31] on control microtubules in Fig 2A. This result is included in Figure S6 in the revised manuscript.

We don’t have a precise estimate for the amount of neutravidin on the microtubules. Based on Fig. 3C of Korten and Diez (Korten and Diez, 2008), the reduction in the unloaded run duration that we see corresponds to a ~2% biotinylation ratio. We polymerize Mt with 10% biotinylated tubulin and add 8 nM neutravidin to the flow cell, so in principle the microtubules could be 10% biotin-streptavidin coated. However, there are a number of uncertainties that push this estimate lower – a) the precise degree of biotinylation, b) whether the %biotinylated tubulin in polymerized microtubules is lower than the mixing ratio due to unequal incorporation, and 3) what fraction of the biotinylated tubulin are occupied by the neutravidin when using this neutravidin flow-in method. Thus, our best estimate is ~2% biotin-streptavidin functionalization.

The ramp durations in Fig. 3 provide another argument that biotinylated microtubules are not affecting the motors. Compared to unloaded durations for each motor, the kinesin-1 ramps were longer, the kinesin-2 ramps were the same, and the kinesin-3 ramps were shorter duration. That argues against any systematic effect of biotinylation on motor run durations, with the caveat that family-dependent differences could in principle be masking an effect. The fact that ramp durations aren’t systematically longer or shorter than the unloaded run durations also argues that the stalls we see, which are at the expected extension length of the dsDNA, are not caused by neutravidin roadblocks.

The final point the reviewer brings up is whether neutravidin may be contributing to the rescues from slips events that we observe. This is difficult to fully rule out. However, because the unloaded run durations aren’t significantly altered by the biotin-streptavidin on the microtubules, we don’t expect the rescue events following a slip to be significantly affected. In principle, we could systematically increase and decrease the biotinylation and see whether the slip rescues change, but we haven’t done this.

(8) If, as stated, "a portion of kinesin-3 unloaded run durations were limited by the length of the microtubules, meaning the unloaded duration is a lower limit." corrections (such as Kaplan-Meier) should be applied, DOI: 10.1016/j.bpj.2017.09.024.

(9) Shouldn't Kaplan-Meier also be applied to the ramp durations ... as a ramp may also artificially end upon stall? Also, doesn't the comparison between ramp and stall duration have a problem, as each stall is preceded by a ramp ...and the (maximum) ramp times will depend on the speed of the motor? Kinesin-3 is the fastest motor and will reach stall much faster than kinesin-1. Isn't it obvious that the stall durations are longer than the ramp duration (as seen for all three motors in Figure 3)?

The reviewer rightly notes the many challenges in estimating the motor off-rates during ramps. To estimate ramp off-rates and as an independent approach to calculating the unloaded and stall durations, we developed a Markov model coupled with Bayesian inference methods to estimate a duration parameter (equivalent to the inverse of the off-rate) for the unloaded, ramp, and stall duration distributions. With the ramps, we have left censoring due to the difficulty in detecting the start of the ramps in the fluctuating baseline, and we have right censoring due to reaching stall (with different censoring of the ramp duration for the three motors due to their different speeds). The Markov model assumes a constant detachment probability and history-independence, and thus is robust even in the face of left and right censoring (details in the Supplementary section). This approach is preferred over Kaplan-Meier because, although non-parametric methods such as K-M make no assumptions for the distribution, they require the user to know exactly where the start time is.

Regarding the potential underestimate of the kinesin-3 unloaded run duration due to finite microtubule lengths. The first point is that the unloaded duration data in Fig. 2C are quite linear up to 6 s and are well fit by the single-exponential fit (the points above 6 s don’t affect the fit very much). The second point is that when we used our Markov model (which is robust against right censoring) to estimate the unloaded and stall durations, the results agreed with the single-exponential fits very well (Table S2). Specifically, the single-exponential fit for the kinesin-3 unloaded duration was 2.74 s (2.33 – 3.17 s 95% CI) and the estimate from the Markov model was 2.76 (2.28 – 3.34 s 95% CI). Thus, we chose not to make any corrections to the kinesin-3 unloaded run durations due to finite microtubule lengths. To address this point in the revision, we added the following note in Table S2: “* Because the Markov-Bayesian model, which is unaffected by left and right censoring of data gave same unloaded run durations for kinesin-3 as the MEMLET fit, we did not the kinesin-3 unloaded run durations for any right censoring due to finite microtubule lengths.” We also added the following point in the legend of Fig. S1: “A fraction of kinesin-3 unloaded run durations were limited by the length of the microtubules, but fitting to a model that took into account missed events gave a similar mean duration as an exponential fit, and so no correction was made (Table S2).”

(10) It is not clear what is seen in Figure S6A: It looks like only single motors (green, w/o a DNA molecule) are walking ... Note: the influence of the attached DNA onto the stepping duration of a motor may depend on the DNA conformation (stretched and near to the microtubule (with neutravidin!) in the tethered case and spherically coiled in the untethered case).

In Figure S6 kymograph, the green traces are GFP-labeled kinesin-1 without DNA attached (which are in excess) and the red diagonal trace is a motor with DNA attached. We clarified this in the revised Figure S6 legend. We agree that the DNA conformation will differ if it is attached and stretched (more linear) versus simply being transported (random coil), but by its nature this control experiment is only addressing random coil DNA.

(11) Along this line: While the run time of kinesin-1 with DNA (1.4 s) is significantly shorter than the stall time (3.0 s), it is still larger than the unloaded run time (1.0 s). What do the authors think is the origin of this increase?

We addressed this point in lines 200-212 of the revised manuscript:

“We carried out two additional control experiments. First, to confirm that the neutravidin used to link the DNA to the microtubule wasn’t affecting kinesin motility, we analyzed the run durations of kinesin-1 motors on neutravidin-coated microtubules and found no change compared to unlabeled microtubules (Fig. S6). Second, we measured the run duration of kinesin-1 linked to a DNA tether that was not bound to the microtubule and thus was being transported (Fig. S6). The kinesin-DNA run duration was 1.40 s, longer than the 1.04 s of motors alone (Fig. 2A). We interpret this longer duration to reflect the slower diffusion constant of the dsDNA relative to the motor alone, which enables motors to transiently detach and rebind before the DNA cargo has diffused away, thus extending the run duration (Block et al., 1990). Notably, this slower diffusion constant should not play a role in the DNA tensiometer geometry because if the motor transiently detaches, it will be pulled backward by the elastic forces of the DNA and detected as a slip or detachment event.“

(12) "The simplest prediction is that against the low loads experienced during ramps, the detachment rate should match the unloaded detachment rate." I disagree. I would already expect a slight increase.

Agreed. We changed this text (Lines 265-267) to: “The prediction for a slip bond is that against the low loads experienced during ramps, the detachment rate should be equal to or faster than the unloaded detachment rate.”

(13) Isn't the model over-defined by fitting the values for the load-dependence of the strong-to-weak transition and fitting the load dependence into the transition to the slip state?

Essentially, yes, it is overdefined, but that is essentially by design and the model is still very useful. Our goal here was to make as simple a model as possible that could account for the data and use it to compare model parameters for the different motor families. Ignoring the complexity of the slip and detached states, a model with a strong and weak state in the stepping cycle and a single transition out of the stepping cycle is the simplest formulation possible. And having rate constants (k_S-W and k_slip in our case) that vary exponentially with load makes thermodynamic sense for modeling mechanochemistry (Howard, 2001). Thus, we were pleasantly surprised that this bare-bones model could recapitulate the unloaded and stall durations for all three motors (Fig. 5C-E).

(14) "When kinesin-1 was tethered to a glass coverslip via a DNA linker and hydrodynamic forces were imposed on an associated microtubule, kinesin-1 dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (37)." This statement appears not to be true. In reference 37, very similar to the geometry reported here, the microtubules were fixed on the surface, and the stepping of single kinesin motors attached to large beads (to which defined forces were applied by hydrodynamics) via long DNA linkers was studied. In fact, quite a number of statements made in the present manuscript have been made already in ref. 37 (see in particular sections 2.6 and 2.7), and the authors may consider putting their results better into this context in the Introduction and Discussion. It is also noteworthy to discuss that the (admittedly limited) data in ref. 37 does not indicate a "catch-bond" behavior but rather an insensitivity to force over a defined range of forces.

The reviewer misquoted our sentence. The actual wording of the sentence was: “When kinesin-1 was connected to micron-scale beads through a DNA linker and hydrodynamic forces parallel to the microtubule imposed, dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (Urbanska et al., 2021).” The sentence the reviewer quoted was in a previous version that is available on BioRxiv and perhaps they were reading that version. Nonetheless, in the Discussion of the revision, we added text to note that this behavior is indicative of an ideal bond (not a catch-bond) on Lines 480-483: “When kinesin-1 was connected to micron-scale beads through a DNA linker and hydrodynamic forces parallel to the microtubule imposed, dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics and instead characteristic of an ideal-bond.” We also added a sentence in the Introduction highlighting this work, Lines 84-87: “Fourth, when kinesin-1 was connected to a bead through a micron-long segment of DNA and hydrodynamic forces were imposed on the bead, motor interaction times were insensitive to hindering loads up to 3 pN, indicative of an ideal-bond.”

Reviewer #3 (Public review):

The authors attribute the differences in the behaviour of kinesins when pulling against a DNA tether compared to an optical trap to the differences in the perpendicular forces. However, the compliance is also much different in these two experiments. The optical trap acts like a ~ linear spring with stiffness ~ 0.05 pN/nm. The dsDNA tether is an entropic spring, with negligible stiffness at low extensions and very high compliance once the tether is extended to its contour length (Fig. 1B). The effect of the compliance on the results should be addressed in the manuscript.

This is an interesting point. We added the following paragraph in Lines 101-111 in the Geometry Consideration section of the Supplementary Methods.

“Another consideration when comparing the DNA tensiometer to optical trap measurements is the relative stiffness of the trap and dsDNA. Optical trap stiffnesses are generally in the range of 0.05 pN/nm [12,13]. To calculate the predicted stiffness of the dsDNA spring, we computed the slope of theoretical force-extension curve in Fig. 1B. The stiffness is highly nonlinear and is <0.001 pN/nM below 650 nm extension. At the predicted stall force of 6 pN (960 nm extension), the dsDNA stiffness ~0.2 pN/nm, which is stiffer than most optical traps, but it is similar to the estimated 0.3 pN/nm stiffness of kinesin motors themselves[12,13]. An 8 nm step at this stiffness leads to a 1.6 pN jump in force, so it is reasonable to expect that motors are dynamically stepping at stall. Therefore, there is no reason to expect that stiffness differences between optical traps and the dsDNA spring are affecting the motor detachment kinetics.”

Compared to an optical trapping assay, the motors are also tethered closer to the microtubule in this geometry. In an optical trap assay, the bead could rotate when the kinesin is not bound. The authors should discuss how this tethering is expected to affect the kinesin reattachment and slipping. While likely outside the scope of this study, it would be interesting to compare the static tether used here with a dynamic tether like MAP7 or the CAP-GLY domain of p150glued.

Please see our response to Reviewer #2 Major Comment #4 above, which asks this same question in the context of intracellular cargo. In response to the point from Reviewer #3, we added the following sentence on Lines 654-656: “Additionally, any ‘rolling’ of a spherical cargo following motor detachment will tend to suppress the motor reattachment rate.”

Regarding a dynamic tether, we agree that’s interesting – there are kinesins that have a second, non-canonical binding site that achieves this tethering (e.g. ncd and Cin8); p150glued likely does this naturally for dynein-dynactin-activator complexes; and we speculated in a review some years ago (Hancock, 2014) that during bidirectional transport kinesin and dynein may act as dynamic tethers for one another when not engaged, enhancing the activity of the opposing motor.

In the single-molecule extension traces (Figure 1F-H; S3), the kinesin-2 traces often show jumps in position at the beginning of runs (e.g., the four runs from ~4-13 s in Fig. 1G). These jumps are not apparent in the kinesin-1 and -3 traces. What is the explanation? Is kinesin-2 binding accelerated by resisting loads more strongly than kinesin-1 and -3?

We agree that at first glance those jumps are puzzling. To investigate this question the first thing we did was to go back to our tensiometer dataset and look systematically at jumps for all three motors. We found roughly 4-6 large jumps like these for all three motors (kinesin-1: 250 +/- 99 nm (mean +/- SD; N=5); kinesin-2: 249 +/- 165 nm (N=6); kinesin-3: 490 +/- 231 nm (N=4)). Thus, although the apparent jumps may be more pronounced due to the specific rebinding kinetics of kinesin-2, this behavior is not unique to this motor. (Note that the motor binding position distribution in Fig. S2 is taken from initial binding positions that follow a clear period of detachment; thus, not all jumps are captured there.)

Our interpretation is that these apparent jumps are simply a reflection of the long length and high compliance of the dsDNA tether. For instance, below 650 nm extension the stiffness, k <0.001 pN/nM (see Reviewer #3, point #1 above). Thus, we expect large fluctuations of the tethered motor when not bound to the microtubule. One reason that these events look like ‘jumps’ is that the sub-ms fluctuations during detached periods are not captured by the ~25 fps movies (40 ms frame acquisition time). Instead, the fitted Qdot position represents the average position during the acquisition window. Actually, due to these rapid fluctuations (and the limited depth of the TIRF illumination field) the position often can’t be determined during these periods of fluctuation (e.g. see gaps at ~2.5 s, 11 s and 24 s in Fig. 1F).

When comparing the durations of unloaded and stall events (Fig. 2), there is a potential for bias in the measurement, where very long unloaded runs cannot be observed due to the limited length of the microtubule (Thompson, Hoeprich, and Berger, 2013), while the duration of tethered runs is only limited by photobleaching. Was the possible censoring of the results addressed in the analysis?

Yes. Please see response to Reviewer #2 points (8) and (9) above.

The mathematical model is helpful in interpreting the data. To assess how the "slip" state contributes to the association kinetics, it would be helpful to compare the proposed model with a similar model with no slip state. Could the slips be explained by fast reattachments from the detached state?

In the model, the slip state and the detached states are conceptually similar; they only differ in the sequence (slip to detached) and the transition rates into and out of them. The simple answer is: yes, the slips could be explained by fast reattachments from the detached state. In that case, the slip state and recovery could be called a “detached state with fast reattachment kinetics”. However, the key data for defining the kinetics of the slip and detached states is the distribution of Recovery times shown in Fig. 4D-F, which required a triple exponential to account for all of the data. If we simplified the model by eliminating the slip state and incorporating fast reattachment from a single detached state, then the distribution of Recovery times would be a single-exponential with a time constant equivalent to t₁, which would be a poor fit to the experimental distributions in Fig. 4D-F.

Recommendations for the authors: 

Reviewing Editor Comments:

The reviewers are in agreement with the motivation and approach of this study. The use of DNA tethers is an important advance in tethering motor proteins to gain insight into how motors respond to load. However, all 3 reviewers express reservations on how well the results support the claims. In particular, the use of the term catch bond was problematic, with Reviewer #2 suggesting some alternative nomenclature. Reviewer #1 expressed concern with experimental evidence for the predicted force-extension curve shown in Figure 1. I agree with the reviewers that additional experimental evidence would be required to conclude the catch-bond detachment kinetics of kinesin.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) By eye, the run lengths, e.g., of kin-1 look very long in Figure S1 ... certainly above the expected 1 µm. Please check and comment.

We agree that the long runs do stick out by eye in this figure. To address this point, we analyzed the run lengths and run times from the kymograph shown in Fig. S1. Fitting the run duration distribution gave t = 1.31 s with a 95% CI of 0.96 to 1.67. This is slightly longer than the 1.04 s duration in Fig. 2A, but the 95% CI include this population mean, and so the S1 data are not statistically significantly different. The run time distribution from the S1 kymograph is given in Author response image 1.

Author response image 1.

(2) The upper right kymograph in Figure 4A does not show a motor return to the baseline. Also, the scale bars, etc., are unreadable. Please modify.

Our purpose for showing the kymographs in Fig. 4A was to show the specific features of slips and fast and slow reattachment. Because we blew up the kymographs to show those specific features, it precluded us from showing the entire return to baseline. As suggested, we magnified the scale bars and the labels on the kymograph labels to make them readable.

Reviewer #3 (Recommendations for the authors):

(1) The frequent references to 95% confidence intervals disrupt the flow of the text. Perhaps the confidence intervals could be listed in a table rather than in the body of the text.

We deleted those from the text; they are shown in Fig. 2D and listed in Table S2.

We appreciate the efforts and helpful suggestions of all three reviewers and the Editor.

References

Block, S.M., L.S. Goldstein, and B.J. Schnapp. 1990. Bead movement by single kinesin molecules studied with optical tweezers. Nature. 348:348-352.

Bouchiat, C., M.D. Wang, J. Allemand, T. Strick, S.M. Block, and V. Croquette. 1999. Estimating the persistence length of a worm-like chain molecule from force-extension measurements. Biophys J. 76:409-413.

Ezber, Y., V. Belyy, S. Can, and A. Yildiz. 2020. Dynein Harnesses Active Fluctuations of Microtubules for Faster Movement. Nat Phys. 16:312-316.

Hancock, W.O. 2014. Bidirectional cargo transport: moving beyond tug of war. Nat Rev Mol Cell Biol. 15:615-628.

Howard, J. 2001. Mechanics of Motor Proteins and the Cytoskeleton. Sinauer Associates, Inc., Sunderland, MA. 367 pp.

Korten, T., and S. Diez. 2008. Setting up roadblocks for kinesin-1: mechanism for the selective speed control of cargo-carrying microtubules. Lab Chip. 8:1441-1447.

Kunwar, A., S.K. Tripathy, J. Xu, M.K. Mattson, P. Anand, R. Sigua, M. Vershinin, R.J. McKenney, C.C. Yu, A. Mogilner, and S.P. Gross. 2011. Mechanical stochastic tug-ofwar models cannot explain bidirectional lipid-droplet transport. Proc Natl Acad Sci U S A. 108:18960-18965.

Kuo, Y.W., M. Mahamdeh, Y. Tuna y J. Howard. 2022. The force required to remove tubulin from the microtubule lattice by pulling on its alpha-tubulin C-terminal tail. Nature communications. 13:3651.

Laakso, J.M., J.H. Lewis, H. Shuman, and E.M. Ostap. 2008. Myosin I can act as a molecular force sensor. Science. 321:133-136.

Leidel, C., R.A. Longoria, F.M. Gutierrez, and G.T. Shubeita. 2012. Measuring molecular motor forces in vivo: implications for tug-of-war models of bidirectional transport. Biophys J. 103:492-500.

Marko, J.F., and E.D. Siggia. 1995. Stretching DNA. Macromolecules. 28:8759-8770.

Nicholas, M.P., F. Berger, L. Rao, S. Brenner, C. Cho, and A. Gennerich. 2015. Cytoplasmic dynein regulates its attachment to microtubules via nucleotide state-switched mechanosensing at multiple AAA domains. Proc Natl Acad Sci U S A. 112:63716376.

Purcell, E.M. 1977. Life at low Reynolds Number. Amer J. Phys. 45:3-11.

Pyrpassopoulos, S., H. Shuman, and E.M. Ostap. 2020. Modulation of Kinesin's Load-Bearing Capacity by Force Geometry and the Microtubule Track. Biophys J. 118:243253.

Rai, A.K., A. Rai, A.J. Ramaiya, R. Jha, and R. Mallik. 2013. Molecular adaptations allow dynein to generate large collective forces inside cells. Cell. 152:172-182.

Ramaiya, A., B. Roy, M. Bugiel, and E. Schaher. 2017. Kinesin rotates unidirectionally and generates torque while walking on microtubules. Proc Natl Acad Sci U S A. 114:10894-10899.

Rao, L., F. Berger, M.P. Nicholas, and A. Gennerich. 2019. Molecular mechanism of cytoplasmic dynein tension sensing. Nature communications. 10:3332.

Smith, S.B., L. Finzi, and C. Bustamante. 1992. Direct mechanical measurements of the elasticity of single DNA molecules by using magnetic beads. Science. 258:11221126.

Sudhakar, S., M.K. Abdosamadi, T.J. Jachowski, M. Bugiel, A. Jannasch, and E. Schaher. 2021. Germanium nanospheres for ultraresolution picotensiometry of kinesin motors. Science. 371.

Toleikis, A., N.J. Carter, and R.A. Cross. 2020. Backstepping Mechanism of Kinesin-1. Biophys J. 119:1984-1994.

Urbanska, M., A. Ludecke, W.J. Walter, A.M. van Oijen, K.E. Duderstadt, and S. Diez. 2021. Highly-Parallel Microfluidics-Based Force Spectroscopy on Single Cytoskeletal Motors. Small. 17: e2007388.

Wang, M.D., H. Yin, R. Landick, J. Gelles, and S.M. Block. 1997. Stretching DNA with optical tweezers. Biophys J. 72:1335-1346.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

The manuscript by Xu et al. reported base-resolution mapping of RNA pseudouridylation in five bacterial species, utilizing recently developed BID-seq. They detected pseudouridine (Ψ) in bacterial rRNA, tRNA, and mRNA, and found growth phase-dependent Ψ changes in tRNA and mRNA. They then focused on mRNA and conducted a comparative analysis of Ψ profiles across different bacterial species. Finally, they developed a deep learning model to predict Ψ sites based on RNA sequence and structure.

This is the first comprehensive Ψ map across multiple bacterial species, and systematically reveals Ψ profiles in rRNA, tRNA, and mRNA under exponential and stationary growth conditions. It provides a valuable resource for future functional studies of Ψ in bacteria.

We thank Reviewer 1 for the supportive and positive comments, particularly for highlighting the novelty and value of our comprehensive pseudouridine landscapes across multiple bacterial species as a valuable resource for the scientific community.

Ψ is highly abundant on non-coding RNA such as rRNA and tRNA, while its level on mRNA is very low. The manuscript focuses primarily on mRNA, which raises questions about the data quality and the rigor of the analysis. Many conclusions in the manuscript are speculative, based solely on the sequencing data but not supported by additional experiments.

We appreciate the insightful comments of Reviewer 1. We fully agree that Ψ is highly abundant on rRNA and tRNA, while its fractions on mRNA are generally lower. Ψ is highly conserved at specific positions in rRNA and tRNA, such as Ψ within tRNA T‑arm (position 55), where it plays essential roles in tRNA structural folding, tRNA stability, and mRNA translation, across plants, mammals, and bacteria[1–3]. However, most Ψ sites in mRNA exhibit lower fractions compared to rRNA and tRNA. This phenomenon is also widely observed in HeLa cell mRNA and plant mRNA, as evidenced by bisulfite-induced deletion sequencing and 2-bromoacrylamide-assisted cyclization sequencing[3–5]. In bacteria, the modifications on mRNA are harder to map and quantify, due to its low abundance in total RNA and difficulty in bacterial rRNA removal. This highlights the significance of our study.

To prove our data quality and analytical rigor, we first present the most convincing sites in bacteria, as benchmark sites. Specifically, we detected 9 out of 10 known conserved pseudouridine (Ψ) sites in E. coli across two biological replicates [6], displaying notable modification fraction. Ψ516 site in E. coli 16S rRNA, which serves as a benchmark site, consistently exhibited a high modification fraction (~100%) under multiple growth conditions, underscoring the robustness of our method. In other strains, we also observed conserved 16S rRNA Ψ sites.

To further demonstrate strong reproducibility and sensitivity. We selected three positive Ψ sites from two independent biological replicates for experimental validation, alongside one negative control site, using pseU‑TRACE method[6]. Ct values were first normalized to the corresponding Ct value of the negative control site, and the treated samples were then further normalized to their corresponding input controls (new Supplementary Fig. 2e).

Four Ψ sites were tested with pseU‑TRACE: Ψ site at position 944 on 23S rRNA, a negative control site located within guaA gene, a Ψ site within clpV1 gene, and an intergenic Ψ site located between guaA and guaB genes. We successfully validated these Ψ sites in P. aeruginosa. The detailed pseU‑TRACE experimental procedures and corresponding data figures have been added to the revised manuscript, in either Results or Methods sections (Line 171-175, 594–617).

Previous transcriptome-wide mapping of Ψ have primarily relied on CMC-based methods to induce RT truncation signatures at the modified sites, exhibiting a limited Ψ detection sensitivity caused by low labeling efficiency[5]. In contrast, BID-seq method used in this study provides substantially higher sensitivity of Ψ detection, particularly the low-stoichiometry Ψ sites within mRNA. The high reliability and quantitative performance of BID-seq have been extensively validated in prior work using mammalian cells and synthetic Ψ-containing oligonucleotides[4].

To further ensure robustness and minimize false positives—when identifying low-level mRNA Ψ sites through bioinformatic analysis—we have applied stringent and uniform filtration criteria to all candidate sites on mRNA (new Supplementary Table 1):

(1) Total sequencing coverage >20 reads in both ‘Treated’ (BID-seq; Σd_t > 20) and ‘Input’ libraries (Σd_i > 20);

(2) An average deletion count >5 in ‘Treated’ libraries;

(3) An average modification fraction >0.02 (2%) in ‘Treated’ libraries;

(4) A deletion ratio in ‘Treated’ libraries at least two-fold higher than that in ‘Input’ libraries.

Sites with a Ψ stoichiometry >0.5 (50%) were classified as highly modified. These filtration criteria have now been explicitly described in Methods section (Lines 739–745). We strictly adhered to these Ψ site identification standards, leading to all subsequent analysis and functional studies.

Finally, to address concerns regarding reproducibility, we calculated mRNA Ψ site overlap and correlation of Ψ fractions, between two biological replicates, which has been presented in (new Supplementary Fig. 2a,d).

Overall, we have revised the manuscript to clarify these methodological strengths, and validate mRNA Ψ detection. We also tone down all speculative conclusions, with more clear linkage to the actual sequencing data, which await future functional validation.

Reviewer #2 (Public review):

Summary:

In this study, Xu et al. present a transcriptome-wide, single-base resolution map of RNA pseudouridine modifications across evolutionarily diverse bacterial species using an adapted form of BID-Seq. By optimizing the method for bacterial RNA, the authors successfully mapped modifications in rRNA, tRNA, and, importantly, mRNA across both exponential and stationary growth phases. They uncover evolutionarily conserved Ψ motifs, dynamic Ψ regulation tied to bacterial growth state, and propose functional links between pseudouridylation and bacterial transcript stability, translation, and RNA-protein interactions. To extend these findings, they develop a deep learning model that predicts pseudouridine sites from local sequence and structural features.

Strengths:

The authors provide a valuable resource: a comprehensive Ψ atlas for bacterial systems, spanning hundreds of mRNAs and multiple species. The work addresses a gap in the field - our limited understanding of bacterial epitranscriptomics, by establishing both the method and datasets for exploring post-transcriptional modifications.

We thank Reviewer 2 for the supportive and positive comments. We appreciate the reviewer’s recognition of the novelty and value of our work in providing a comprehensive pseudouridine atlas across multiple bacterial species.

Weaknesses:

The main limitation of the study is that most functional claims (i.e., translation efficiency, mRNA stability, and RNA-binding protein interactions) are based on correlative evidence. While suggestive, these inferences would be significantly strengthened by targeted perturbation of specific Ψ synthases or direct biochemical validation of proposed RNA-protein interactions (e.g., with Hfq).

We thank Reviewer 2 for the constructive feedback. We fully agree that our functional claims regarding translation efficiency, mRNA stability, and RNA-binding protein interactions rely primarily on correlative evidence from existing datasets rather than a direct experimental validation. We agree that the perturbation of specific pseudouridine synthases and direct biochemical validation of proposed RNA-protein interactions (for instance, Hfq) would substantially strengthen the conclusions on bacterial Ψ function. In Discussion section, we have added a discussion on this limitation of our current study (Line 517–523). Considering the scope of our current work, we anticipate such validation experiments in future research.

Additionally, the GNN prediction model is a notable advance, but methodological details are insufficient to reproduce or assess its robustness.

In response to methodological concerns regarding our pseU_GNN prediction model, we have undertaken substantial improvements to address these issues comprehensively. We have updated the complete codebase on GitHub (https://github.com/Dylan-LT/pseU_NN.git) with comprehensive documentation and a user-friendly prediction tool specifically designed for Ψ site prediction across the four bacterial species examined in this study.

We further systematically evaluated multiple neural network architectures and implemented critical architectural refinements. Specifically, we incorporated bidirectional LSTM (bid-LSTM) layers upstream of the transformer block to more effectively capture sequential dependencies and contextual information in RNA sequences. This enhanced architecture demonstrates substantially improved predictive performance, achieving an AUC-ROC of 0.89 on independent test datasets using 41-nucleotide input sequences (new Figure 6).

We have revised Figure 6 and Supplementary Fig. 7, along with their corresponding content and figure legends (Lines 428-430, 434–436, 440-447, 1065-1073), to reflect these architectural improvements and performance enhancements. We have detailed the methods part (Lines 679–708), including model architecture, validation methods and evaluation score calculation. Additionally, we have provided detailed documentation of the evaluation score calculation methodology to ensure reproducibility and transparency.

Reviewer #3 (Public review):

Summary:

This study aimed to investigate pseudouridylation across various RNA species in multiple bacterial strains using an optimized BID-seq approach. It examined both conserved and divergent modification patterns, the potential functional roles of pseudouridylation, and its dynamic regulation across different growth conditions.

Strengths:

The authors optimized the BID-seq method and applied this important technique to bacterial systems, identifying multiple pseudouridylation sites across different species. They investigated the distribution of these modifications, associated sequence motifs, their dynamics across growth phases, and potential functional roles. These data are of great interest to researchers focused on understanding the significance of RNA modifications, particularly mRNA modifications, in bacteria.

We thank Reviewer 3 for the supportive and positive assessment. We are particularly grateful for the reviewer’s acknowledgment of the value of our analyses on modification distribution, sequence motifs, growth‑phase dynamics, and potential functional roles, which we hope will be of broad interest to researchers studying bacterial RNA modifications, particularly mRNA Ψ.

Weaknesses:

(1) The reliability of BID-seq data is questionable due to a lack of experimental validations.

We thank Reviewer 3 for the constructive feedback. We have undertaken comprehensive revisions to address the concerns regarding manuscript structure and information organization. We have incorporated pseU‑TRACE experiments and data quality results to provide orthogonal validation of Ψ detection, strengthening the robustness of our work.

Here we copied the response in Reviewer 1 section:

“To further demonstrate strong reproducibility and sensitivity. We selected three positive Ψ sites from two independent biological replicates for experimental validation, alongside one negative control site, using pseU‑TRACE method[6]. Ct values were first normalized to the corresponding Ct value of the negative control site, and the treated samples were then further normalized to their corresponding input controls (new Supplementary Fig. 2e ).

Four Ψ sites were tested with pseU‑TRACE: Ψ site at position 944 on 23S rRNA, a negative control site located within guaA gene, a Ψ site within clpV1 gene, and an intergenic Ψ site located between guaA and guaB genes. We successfully validated these Ψ sites in P. aeruginosa. The detailed pseU‑TRACE experimental procedures and corresponding data figures have been added to the revised manuscript, in either Results or Methods sections (Line 171-175, 594–617).”

(2) The manuscript is not well-written, and the presented work shows a major lack of scientific rigor, as several key pieces of information are missing.

We thank Reviewer 3 for the suggestion. We restructured the main text to present a clearer logical flow, with key objectives (Lines 83–96, 171–175, 428–447, 517-523) explicitly stated in Introduction section and Conclusions section, with data figures directly addressing these stated aims (Supplementary Fig. 1–7).

(3) The manuscript's organization requires significant improvement, and numerous instances of missing or inconsistent information make it difficult to understand the key objectives and conclusions of the study.

We thank Reviewer 3 for the constructive feedback. All supplementary figures have been updated with detailed figure legend, methodology description, and consistent formatting. We also systematically inspected and resolved instances of missing or inconsistent information throughout the main text and supplementary materials (Supplementary Fig. 1–7; Supplementary Table 1). To enhance computational reproducibility, we have updated our GitHub repository with well-documented code and developed user-friendly prediction tools for Ψ identification across the four bacterial species examined in this study.

(4) The rationale for selecting specific bacterial species is not clearly explained, and the manuscript lacks a systematic comparison of pseudouridylation among these species.

We thank Reviewer 3 for the constructive feedback. The bacterial species analyzed in this study were selected based on both diversity and significance. K. pneumoniae, B. cereus, and P. aeruginosa are top model human pathogens responsible for a wide range of clinically significant infections, yet transcriptome-wide pseudouridylation has not been systematically explored in these organisms[7–9]. P. syringae, the most important model plant pathogen, was included to extend our analysis beyond human pathogens and to examine Ψ modification in a distinct ecological and evolutionary context, where epitranscriptomic regulation also remains poorly characterized[10]. Importantly, the selected species represent both Gram-positive (B. cereus) and Gram-negative (K. pneumoniae, P. aeruginosa, and P. syringae) bacteria, spanning substantial differences in genome size, GC content, lifestyle, and pathogenic strategies. This diversity enables a comparative framework for examining conserved and species-specific pseudouridylation patterns across bacterial lineages.

To address the reviewer’s concern, we have revised the manuscript to more clearly articulate the rationale for species selection and have added a comparative analysis highlighting similarities and differences in Ψ site distribution and modification levels among these species (Lines 83–96). We systematically compared Ψ-carrying motif for analyzing sequence context of 10 bases flanking Ψ sites in bacterial mRNA, with Supplementary Fig. 4 added.

Reference

(1) Leppik, M., Liiv, A. & Remme, J. Random pseuoduridylation in vivo reveals critical region of Escherichia coli 23S rRNA for ribosome assembly. Nucleic Acids Res. 45, (2017).

(2) Rajan, K. S. et al. A single pseudouridine on rRNA regulates ribosome structure and function in the mammalian parasite Trypanosoma brucei. Nat. Commun. 14, (2023).

(3) Li, H. et al. Quantitative RNA pseudouridine maps reveal multilayered translation control through plant rRNA, tRNA and mRNA pseudouridylation. Nat. Plants 11, 234–247 (2025).

(4) Dai, Q. et al. Quantitative sequencing using BID-seq uncovers abundant pseudouridines in mammalian mRNA at base resolution. Nat. Biotechnol. 41, 344–354 (2023).

(5) Xu, H. et al. Absolute quantitative and base-resolution sequencing reveals comprehensive landscape of pseudouridine across the human transcriptome. Nat. Methods 21, 2024–2033 (2024).

(6) Fang, X. et al. A bisulfite-assisted and ligation-based qPCR amplification technology for locus-specific pseudouridine detection at base resolution. Nucleic Acids Res. 52, (2024).

(7) Wyres, K. L., Lam, M. M. C. & Holt, K. E. Population genomics of Klebsiella pneumoniae. Nature Reviews Microbiology vol. 18 Preprint at https://doi.org/10.1038/s41579-019-0315-1 (2020).

(8) Kerr, K. G. & Snelling, A. M. Pseudomonas aeruginosa: a formidable and ever-present adversary. Journal of Hospital Infection vol. 73 Preprint at https://doi.org/10.1016/j.jhin.2009.04.020 (2009).

(9) Ehling-Schulz, M., Lereclus, D. & Koehler, T. M. The Bacillus cereus Group: Bacillus Species with Pathogenic Potential . Microbiol. Spectr. 7, (2019).

(10) Xin, X. F., Kvitko, B. & He, S. Y. Pseudomonas syringae: What it takes to be a pathogen. Nature Reviews Microbiology vol. 16 Preprint at https://doi.org/10.1038/nrmicro.2018.17 (2018).

Author response:

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

eLife Assessment

This study presents results supporting a model that tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the stem cell niche and inhibit the differentiation of neighboring cells. The valuable findings show that GSC tumors often contain non-mutant cells whose differentiation is suppressed by the GSC tumorous cells. However, the evidence showing that the GSC tumors produce BMP ligands to suppress differentiation of non-mutant cells is incomplete. It could be strengthened by the use of sensitive RNA in situ hybridization approaches.

Thank you for your valuable assessment. RNA in situ hybridization evidence has been added to the revised manuscript (Figure 5A-D) to support that GSC tumors produce BMP ligands.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This preprint from Shaowei Zhao and colleagues presents results that suggest tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the ovarian stem cell niche and inhibit the differentiation of neighboring non-mutant GSC-like cells. The authors use FRT-mediated clonal analysis driven by a germline-specific gene (nos-Gal4, UASp-flp) to induce GSC-like cells mutant for bam or bam's co-factor bgcn. Bam-mutant or bgcn-mutant germ cells produce tumors in the stem cell compartment (the germarium) of the ovary (Figure 1). These tumors contain non-mutant cells - termed SGC for single-germ cells. 75% of SGCs do not exhibit signs of differentiation (as assessed by bamP-GFP) (Figure 2). The authors demonstrate that block in differentiation in SGC is a result of suppression of bam expression (Figure 2). They present data suggesting that in 73% of SGCs, BMP signaling is low (assessed by dad-lacZ) (Figure 3) and proliferation is less in SGCs vs GSCs. They present genetic evidence that mutations in BMP pathway receptors and transcription factors suppress some of the non-autonomous effects exhibited by SGCs within bam-mutant tumors (Figure 4). They show data that bam-mutant cells secrete Dpp, but this data is not compelling (see below) (Figure 5). They provide genetic data that loss of BMP ligands (dpp and gbb) suppresses the appearance of SGCs in bam-mutant tumors (Figure 6). Taken together, their data support a model in which bam-mutant GSC-like cells produce BMPs that act on non-mutant cells (i.e., SGCs) to prevent their differentiation, similar to what is seen in the ovarian stem cell niche.

Strengths:

(1) Use of an excellent and established model for tumorous cells in a stem cell microenvironment.

(2) Powerful genetics allow them to test various factors in the tumorous vs non-tumorous cells.

(3) Appropriate use of quantification and statistics.

We greatly appreciate your valuable comments.

Weaknesses:

(1) What is the frequency of SGCs in nos>flp; bam-mutant tumors? For example, are they seen in every germarium, or in some germaria, etc, or in a few germaria?

This is a good question. Because the SGC phenotype depends on the presence of both germline tumor clones and out-of-niche wild-type germ cells, our quantification was restricted to germaria containing both. In 14-day-old fly ovaries, 70% of germaria (432/618) met this criterion (Line 103). Each of them contained an average of 1.5 SGCs (Figure 1K).

(2) Does the breakdown in clonality vary when they induce hs-flp clones in adults as opposed to in larvae/pupae?

Our attempts to induce ovarian hs-FLP germline clones by heat-shocking adult flies were unsuccessful, with very few clones being observed. Therefore, we shifted our approach to an earlier developmental stage. Successful induction was achieved by subjecting late-L3/early-pupal animals to a twice-daily heatshock at 37°C for 6 consecutive days (2 hours per session with a 6-hour interval, see Lines 331-335) (Zhao et al., 2018).

(3) Approximately 20-25% of SGCs are bam+, dad-LacZ+. Firstly, how do the authors explain this? Secondly, of the 70-75% of SGCs that have no/low BMP signaling, the authors should perform additional character rization using markers that are expressed in GSCs (i.e., Sex lethal and nanos).

These 20-25% of SGCs are bamP-GFP⁺ dad-lacZ^-, not bam⁺ dad-lacZ⁺ (see Figure 2C and 3D). They would be cystoblast-like cells that may have initiated a differentiation program toward forming germline cysts (see Lines 122-130). The 70-75% of SGCs that have low BMP signaling exhibit GSC-like properties, including: 1) dot-like spectrosomes; 2) dad-lacZ positivity; 3) absence of bamP-GFP expression. While additional markers would be beneficial, we think that this combination of properties is sufficient to classify these cells as GSC-like.

(4) All experiments except Figure 1I (where a single germarium with no quantification) were performed with nos-Gal4, UASp-flp. Have the authors performed any of the phenotypic characterizations (i.e., figures other than Figure 1) with hs-flp?

Yes, we initially identified the SGC phenotype through hs-FLP-mediated mosaic analysis of bam or bgcn mutant in ovaries. However, as noted in our response to Weakness (2), this approach was very labor-intensive. Therefore, we switched to using the more convenient nos>FLP system for subsequent experiments. To our observation, there was no difference in inducing the SGC phenotype by these two approaches.

(5) Does the number of SGCs change with the age of the female? The experiments were all performed in 14-day-old adult females. What happens when they look at a young female (like 2-day-old). I assume that the nos>flp is working in larval and pupal stages, and so the phenotype should be present in young females. Why did the authors choose this later age? For example, is the phenotype more robust in older females? Or do you see more SGCs at later time points?

These are very good questions. The SGC phenotype was consistent over the 14-day analysis period (Figure 1J) and was specifically dependent on the presence of germline tumor clones. In 14-day-old fly ovaries, these clones were both larger and more frequent than in younger flies. This age-dependent enhancement in clone size and frequency significantly improved our quantification efficiency (see Lines 101-112).

(6) Can the authors distinguish one copy of GFP versus 2 copies of GFP in germ cells of the ovary? This is not possible in the Drosophila testis. I ask because this could impact the clonal analyses diagrammed in Figure 4A and 4G and in 6A and B. Additionally, in most of the figures, the GFP is saturated, so it is not possible to discern one vs two copies of GFP.

Thank you for this valuable comment. It was also difficult for us to distinguish 1 and 2 copies of GFP in the Drosophila ovary. In Figure 4A-F, to resolve this problem, we used a triple-color system, in which red germ cells (RFP^+/+ GFP^-/-) are bam mutant, yellow germ cells (RFP^+/- GFP^+/-) are wild-type, and green germ cells (RFP^-/- GFP^+/+) are punt or med mutant. In Figure 4G-J, we quantified the SGC phenotype only in black germ cells (GFP^-/-), which are wild-type (control) or mad mutant. In Figure 6, we quantified the SGC phenotype only in green germ cells (both GFP^+/+ and GFP^+/-), all of which are wild-type.

(7) More evidence is needed to support the claim of elevated Dpp levels in bam or bgcn mutant tumors. The current results with the dpp-lacZ enhancer trap in Figure 5A, B are not convincing. First, why is the dpp-lacZ so much brighter in the mosaic analysis (A) than in the no-clone analysis (B)? It is expected that the level of dpp-lacZ in cap cells should be invariant between ovaries, and yet LacZ is very faint in Figure 5B. I think that if the settings in A matched those in B, the apparent expression of dpp-lacZ in the tumor would be much lower and likely not statistically significant. Second, they should use RNA in situ hybridization with a sensitive technique like hybridization chain reactions (HCR) - an approach that has worked well in numerous Drosophila tissues, including the ovary.

Thank you for this critical comment. The settings of immunofluorescent staining and confocal parameters in the original Figure 5A were the same as those in 5B. To our observation, the levels of dpp-lacZ in terminal filament and cap cells were highly variable across germaria, even within the same ovary. We have omitted these results from the revised Figure 5. Instead, the HCR-FISH data have been added (Figure 5A-D) to support that bam mutant germline tumors secret BMP ligands.

(8) In Figure 6, the authors report results obtained with the bamBG allele. Do they obtain similar data with another bam allele (i.e., bamdelta86)?

No. Given that bam^BG was functionally indistinguishable from bam^Δ86 in inducing the SGC phenotype (Figure 1J), we believe that repeating these experiments with bam^Δ86 would be redundant and would not alter the key conclusion of our study. Thank you for your understanding!

Reviewer #2 (Public review):

While the study by Zhang et al. provides valuable insights into how germline tumors can non-autonomously suppress the differentiation of neighboring wild-type germline stem cells (GSCs), several conceptual and technical issues limit the strength of the conclusions.

Major points:

(1) Naming of SGCs is confusing. In line 68, the authors state that "many wild-type germ cells located outside the niche retained a GSC-like single-germ-cell (SGC) morphology." However, bam or bgcn mutant GSCs are also referred to as "SGCs," which creates confusion when reading the text and interpreting the figures. The authors should clarify the terminology used to distinguish between wild-type SGCs and tumor (bam/bgcn mutant) SGCs, and apply consistent naming throughout the manuscript and figure legends.

We apologize for any confusion. In our manuscript, the term "SGC" is reserved specifically for wild-type germ cells that maintain a GSC-like morphology outside the niche. bam or bgcn mutant germ cells are referred to as GSC-like tumor cells (Lines 89-90), not SGCs.

(a) The same confusion appears in Figure 2. It is unclear whether the analyzed SGCs are wild-type or bam mutant cells. If the SGCs analyzed are Bam mutants, then the lack of Bam expression and failure to differentiate would be expected and not informative. However, if the SGCs are wild-type GSCs located outside the niche, then the observation would suggest that Bam expression is silenced in these wild-type cells, which is a significant finding. The authors should clarify the genotype of the SGCs analyzed in Figure 2C, as this information is not currently provided.

The SGCs analyzed in Figure 2A-C are wild-type, GSC-like cells located outside the niche. They were generated using the same genetic strategy depicted in Figures 1C and 1E (with the schematic in Figure 1B). The complete genotypes for all experiments are available in Source data 1.

(b) In Figures 4B and 4E, the analysis of SGC composition is confusing. In the control germaria (bam mutant mosaic), the authors label GFP⁺ SGCs as "wild-type," which makes interpretation unclear. Note, this is completely different from their earlier definition shown in line 68.

The strategy to generate SGCs in Figure 4B-F (with the schematic in Figure 4A) is different from that in Figure 1C-F, H, and I (with the schematic in Figure 1B). In Figure 4B-F, we needed to distinguish punt^-/- (or med^-/-) with punt^+/- (or med^+/-) germ cells. As noted in our response to Reviewer #1’s Weakness (6), it was difficult for us to distinguish 1 and 2 copies of GFP in the Drosophila ovary. Therefore, we chose to use the triple-color system to distinguish these germ cells in Figure 4B-F (see genotypes in Source data 1).

(c) Additionally, bam^+/- GSCs (the first bar in Figure 4E) should appear GFP⁺ and Red>sup>+</sup> (i.e., yellow). It would be helpful if the authors could indicate these bam^+/- germ cells directly in the image and clarify the corresponding color representation in the main text. In Figure 2A, although a color code is shown, the legend does not explain it clearly, nor does it specify the identity of bam^+/- cells alone. Figure 4F has the same issue, and in this graph, the color does not match Figure 4A.

The color-to-genotype relationships for the schematics in Figures 2A and 4E are provided in Figures 1B and 4A, respectively. Due to the high density of germ cells, it is impractical to label each genotype directly in the images. In contrast to Figure 4E, the colors in Figure 4F do not represent genotypes; instead, blue denotes the percentage of SGCs, and red denotes the percentage of germline cysts, as indicated below the bar chart.

(2) The frequencies of bam or bgcn mutant mosaic germaria carrying [wild-type] SGCs or wild-type germ cell cysts with branched fusomes, as well as the average number of wild-type SGCs per germarium and the number of days after heat shock for the representative images, are not provided when Figure 1 is first introduced. Since this is the first time the authors describe these phenotypes, including these details is essential. Without this information, it is difficult for readers to follow and evaluate the presented observations.

Thank you for this constructive suggestion. These quantification data have been added to the revised Figure 1 (Figure 1J, K).

(3) Without the information mentioned in point 2, it causes problems when reading through the section regarding [wild-type] SGCs induced by impairment of differentiation or dedifferentiation. In lines 90-97, the authors use the presence of midbodies between cystocytes as a criterion to determine whether the wild-type GSCs surrounded by tumor GSCs arise through dedifferentiation. However, the cited study (Mathieu et al., 2022) reports that midbodies can be detected between two germ cells within a cyst carrying a branched fusome upon USP8 loss.

Unlike wild-type cystocytes, which undergo incomplete cytokinesis and lack midbodies, those with USP8 loss undergo complete cell division, with the presence of midbodies (white arrow, Figure 1F’ from Mathieu et al., 2022) as a marker of the late cytokinesis stage (Mathieu et al., 2022).

(a) Are wild-type germ cell cysts with branched fusomes present in the bam mutant mosaic germaria? What is the proportion of germaria containing wild-type SGCs versus those containing wild-type germ cell cysts with branched fusomes?

(b) If all bam mutant mosaic germaria carry only wild-type GSCs outside the niche and no germaria contain wild-type germ cell cysts with branched fusomes, then examining midbodies as an indicator of dedifferentiation may not be appropriate.

We appreciate your critical comment. bam mutant mosaic germaria indeed contained wild-type germline cysts, as evidenced by an SGC frequency of ~70%, rather than 100% (see Figures 2H, 4F, 4J, 6F, 6I, and Figure 6-figure supplement 3C). Since the SGC phenotype depends on the presence of bam or bgcn mutant germline tumors, we quantified it as “the percentage of SGCs relative to the total number of SGCs and germline cysts that are surrounded by germline tumors” (see Lines 103-108). Quantifying the SGC phenotype as "the percentage of germaria with SGCs" would be imprecise. This is because the presence and number of SGCs were variable among germaria with bam or bgcn mutant germline clones, and a small number of germaria entirely lacked these clones. The data of "SGCs per germarium with both germline clones and out-of-niche wild-type germ cells" have been added to the revised Figure 1 (Figure 1K).

(c) If, however, some germaria do contain wild-type germ cell cysts with branched fusomes, the authors should provide representative images and quantify their proportion.

Such germaria could be found in Figure 2G, 3B, 3C, 6D, 6E, and 6H. The percentage of germline cysts can be calculated by “100% - SGC%”.

(d) In line 95, although the authors state that 50 germ cell cysts were analyzed for the presence of midbodies, it would be more informative to specify how many germaria these cysts were derived from and how many biological replicates were examined.

As noted in our response to points a) and b) above, the germ cells surrounded by germline tumors, rather than germarial numbers, are more precise for analyzing the phenotype. For this experiment, we examined >50 such germline cysts via confocal microscopy. As the analysis was performed on a defined cellular population, this sample size should be sufficient to support our conclusion.

(4) Note that both bam mutant GSCs and wild-type SGCs can undergo division to generate midbodies (double cells), as shown in Figure 4H. Therefore, the current description of the midbody analysis is confusing. The authors should clarify which cell types were examined and explain how midbodies were interpreted in distinguishing between cell division and differentiation.

We assayed for the presence of midbodies or not specifically within the wild-type germline cysts surrounded by bam or bgcn mutant tumors, not within the tumors themselves (Lines 96-97). As detailed in Lines 90-100, the absence of midbodies was used as a key criterion to exclude the possibility of dedifferentiation.

(5) The data in Figure 5 showing Dpp expression in bam mutant tumorous GSCs are not convincing. The Dpp-lacZ signal appears broadly distributed throughout the germarium, including in escort cells. To support the claim more clearly, the authors should present corresponding images for Figures 5D and 5E, in which dpp expression was knocked down in the germ cells of bam or bgcn mutant mosaic germaria. Showing these images would help clarify the localization and specificity of Dpp-lacZ expression relative to the tumorous GSCs.

Thank you for your constructive comment. RNA in situ hybridization data have been added to support that bam or bgcn mutant germline tumors secret BMP ligands (Figure 5A-D).

(6) While Figure 6 provides genetic evidence that bam mutant tumorous GSCs produce Dpp to inhibit the differentiation of wild-type SGCs, it should be noted that these analyses were performed in a dpp⁺/⁻ background. To strengthen the conclusion, the authors should include appropriate controls showing [dpp^+/-; bam^+/-] SGCs and [dpp^+/-; bam^+/-] germ cell cysts without heat shock (as referenced in Figures 6F and 6I).

Schematic cartoons in Figure 6A and 6B demonstrate that these analyses were performed in a dpp^+/- background. Figure 6-figure supplement 1 indicates tha dpp^+/- or gbb^+/- does not affect GSC maintenance, germ cell differentiation, and female fly fertility. Figure 6C is the control for 6D and 6E, and 6G is the control for 6H, with quantification in 6F and 6I. We used nos>FLP, not the heat shock method, to induce germline clones in these experiments (see genotypes in Source data 1).

(7) Previous studies have reported that bam mutant germ cells cause blunted escort cell protrusions (e.g., Kirilly et al., Development, 2011), which are known to contribute to germ cell differentiation (e.g., Chen et al., Frontiers in Cell and Developmental Biology, 2022). The authors should include these findings in the Discussion to provide a broader context and to acknowledge how alterations in escort cell morphology may further influence differentiation defects in their model.

Thank you for teaching us! We have included the introduction of these two papers in the revised manuscript (Lines 197-199).

(8) Since fusome morphology is an important readout of SGCs vs differentiation. All the clonal analysis should have fusome staining.

SGC is readily distinguishable from multi-cellular germline cyst based on morphology. In some clonal-analysis experiments, fusome staining was not feasible due to technical limitations such as channel saturation or antibody incompatibility. Thank you for your understanding!

(9) Figure arrangement. It is somewhat difficult to identify the figure panels cited in the text due to the current panel arrangement.

The figure panels were arranged to optimize space while ensuring that related panels are grouped in close proximity for logical comparison. We would be happy to consider any specific suggestions for an alternative layout that could improve clarity.

(10) The number of biological replicates and germaria analyzed should be clearly stated somewhere in the manuscript-ideally in the Methods section or figure legends. Providing this information is essential for assessing data reliability and reproducibility.

The detailed quantification information is labeled directly in figures or described in figure legends, and all raw quantification data are provided in Source data 2.

Reviewer #3 (Public review):

Summary:

Zhang et al. investigated how germline tumors influence the development of neighboring wild-type (WT) germline stem cells (GSC) in the Drosophila ovary. They report that germline tumors inhibit the differentiation of neighboring WT GSCs by arresting them in an undifferentiated state, resulting from reduced expression of the differentiation-promoting factor Bam. They find that these tumor cells produce low levels of the niche-associated signaling molecules Dpp and Gbb, which suppress bam expression and consequently inhibit the differentiation of neighboring WT GSCs non-cell-autonomously. Based on these findings, the authors propose that germline tumors mimic the niche to suppress the differentiation of the neighboring stem cells.

Strengths:

This study addresses an important biological question concerning the interaction between germline tumor cells and WT germline stem cells in the Drosophila ovary. If the findings are substantiated, they could provide valuable insights applicable to other stem cell systems.

We greatly appreciate your valuable comments.

Weaknesses:

Previous work from Xie's lab demonstrated that bam and bgcn mutant GSCs can outcompete WT GSCs for niche occupancy. Furthermore, a large body of literature has established that the interactions between escort cells (ECs) and GSC daughters are essential for proper and timely germline differentiation (the differentiation niche). Disruption of these interactions leads to arrest of germline cell differentiation in a status with weak BMP signaling activation and low bam expression, a phenotype virtually identical to what is reported here. Thus, it remains unclear whether the observed phenotype reflects "direct inhibition by tumor cells" or "arrested differentiation due to the loss of the differentiation niche." Because most data were collected at a very late stage (more than 10 days after clonal induction), when tumor cells already dominate the germarium, this question cannot be solved. To distinguish between these two possibilities, the authors could conduct a time-course analysis to examine the onset of the WT GSC-like single-germ-cell (SGC) phenotype and determine whether early-stage tumor clones with a few tumor cells can suppress the differentiation of neighboring WT GSCs with only a few tumor cells present. If tumor cells indeed produce Dpp and Gbb (as proposed here) to inhibit the differentiation of neighboring germline cells, a small cluster or probably even a single tumor cell generated at an early stage might prevent the differentiation of their neighboring germ cells.

Thank you for your critical comment. The revised manuscript now includes a time-course analysis of the SGC phenotype (Figure 1J). Our data in Figure 6 demonstrate that BMP ligands from germline tumors are required to inhibit SGC differentiation. Furthermore, we have incorporated into the manuscript the possibility that disruption of the differentiation niche may also contribute to the SGC phenotype (Lines 197-199).

The key evidence supporting the claim that tumor cells produce Gpp and Gbb comes from Figures 5 and 6, which suggest that tumor-derived dpp and gbb are required for this inhibition. However, interpretation of these data requires caution. In Figure 5, the authors use dpp-lacZ to support the claim that dpp is upregulated in tumor cells (Figure 5A and 5B). However, the background expression in somatic cells (ECs and pre-follicular cells) differs noticeably between these panels. In Figure 5A, dpp-lacZ expression in somatic cells in 5A is clearly higher than in 5B, and the expression level in tumor cells appears comparable to that in somatic cells (dpp-lacZ single channel). Similarly, in Figure 5B, dpp-lacZ expression in germline cells is also comparable to that in somatic cells. Providing clear evidence of upregulated dpp and gbb expression in tumor cells (for example, through single-molecular RNA in situ) would be essential.

We greatly appreciate your critical comment. In our data, the expression levels of dpp-lacZ in terminal filament and cap cells were highly variable across germaria, even within the same ovary. We have omitted these results in the revised Figure 5. RNA in situ hybridization data have been added to visualize the expression of BMP ligands within bam mutant germline tumor cells (Figure 5A-D).

Most tumor data present in this study were collected from the bam[86] null allele, whereas the data in Figure 6 were derived from a weaker bam[BG] allele. This bam[BG] allele is not molecularly defined and shows some genetic interaction with dpp mutants. As shown in Figure 6E, removal of dpp from homozygous bam[BG] mutant leads to germline differentiation (evidenced by a branched fusome connecting several cystocytes, located at the right side of the white arrowhead). In Figure 6D, fusome is likely present in some GFP-negative bam[BG]/bam[BG] cells. To strengthen their claim that the tumor produces Dpp and Gbb to inhibit WT germline cell differentiation, the authors should repeat these experiments using the bam[86] null allele.

Although a structure resembling a "branched fusome" is visible in Figure 6E (right of the white arrowhead), it is an artifact resulting from the cytoplasm of GFP-positive follicle cells, which also stain for α-Spectrin, projecting between germ cells of different clones (see the merged image). In both our previous (Zhang et al., 2023) and current studies, bam^BG was functionally indistinguishable from bam^Δ86 in its ability to block GSC differentiation and induce the SGC phenotype (Figure 1J). Given this, we believe that repeating the extensive experiments in Figure 6 with the bam^Δ86 allele would be scientifically redundant and would not change the key conclusion of our study.

It is well established that the stem niche provides multiple functional supports for maintaining resident stem cells, including physical anchorage and signaling regulation. In Drosophila, several signaling molecules produced by the niche have been identified, each with a distinct function - some promoting stemness, while others regulate differentiation. Expression of Dpp and Gbb alone does not substantiate the claim that these tumor cells have acquired the niche-like property. To support their assertion that these tumors mimic the niche, the authors should provide additional evidence showing that these tumor cells also express other niche-associated markers. Alternatively, they could revise the manuscript title to more accurately reflect their findings.

Dpp and Gbb are the key niche signals from cap cells for maintaining GSC stemness. Our work demonstrates that germline tumors can specifically mimic this signaling function, not the full suite of cap cell properties, to create a non-cell-autonomous differentiation block. The current title “Tumors mimic the niche to inhibit neighboring stem cell differentiation” reflects this precise concept: a partial, functional mimicry of the niche's most relevant activity in this context. We feel it is an appropriate and compelling summary of our main conclusion.

In the Method section, the authors need to provide details on how dpp-lacZ expression levels were quantified and normalized.

Because of the highly variable expression levels in terminal filament and cap cells, we have omitted the dpp-lacZ results in the revised manuscript.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Minor points

(1) Not all readers may be familiar with the nos>FLP/FRT or hs-FLP/FRT systems. It would be helpful if the authors could briefly introduce these genetic mosaic systems and explain how they were used in this study before presenting the results.

Thank you for this constructive suggestion. Such brief introduction has been added to the revised manuscript (Lines 64-70).

(2) Line 68-70: "Surprisingly, ...outside the niche retained a GSC-like single-germ-cell (SGC) morphology, even when encapsulated within egg chambers (Figure 1C, D, Figure 1- figure supplement 1).

(3) The figure citation is not appropriate, as Figures 1C and 1D do not show "single germ cells (SGCs) encapsulated within egg chambers." To improve clarity, the authors could revise the sentence as follows: "Surprisingly, wild-type germ cells located outside the niche retained a GSC-like single-germ-cell (SGC) morphology (Figures 1C and D), even when encapsulated within egg chambers (Figure 1-figure supplement 1)." This modification would make the description consistent with the figure content and easier for readers to follow.

Thank you for teaching us! The manuscript has been revised following this suggestion (Lines 70-73).

(4) Line 106-110. The description is confusing. The authors state, "Under normal conditions... Notably, 74% of SGCs (n = 132) were GFP-negative, while the remaining 26% were GFP-positive (Figure 2B, C). However, Figure 2B shows the bam mutant mosaic germaria, and Figure 2C does not specify the genotypes of the germaria used for the analysis of GSCs, CBs, and SGCs. The authors should clarify the experimental conditions and genotypes corresponding to each panel. In addition, it would be more informative to indicate how many germaria these quantified GSCs, CBs, and SGCs were derived from.

(5) Throughout the manuscript, the authors report the number of SGCs analyzed (e.g., Lines 149-151). However, it would be more informative to also indicate how many germaria these quantified SGCs were derived from. Providing this information would help readers assess the sampling size and variability across biological replicates.

Thank you for your suggestion. As shown in Figure 2B, these wild-type (RFP-positive) GSCs and CBs were also derived from bam mutant mosaic germaria. The phrase "under normal conditions" has been deleted from the revised manuscript to prevent any potential ambiguity. Given the specificity of the SGC phenotype, the germ cells surrounded by germline tumors, rather than germarial numbers, are more precise for its quantification (Lines 103-108). The data of “SGCs per germarium with both germline clones and out-of-niche wild-type germ cells” have been added to the revised Figure 1K.

Reviewer #3 (Recommendations for the authors):

(1) Additionally, the authors should clarify what the "red dot" signal in the GFP-positive cap cell in Figure 3 F (left panel) represents.

The “red dot” is an asterisk that is used to mark a cap cell (Line 620).

(2) Finally, on line 266, "bamP-GFP-positive" should be corrected to "bamP-GFP-negative."

It should be “bamP-GFP-positive”, not “bamP-GFP-negative” (see Figure 2B).

Reference:

Mathieu, J., Michel-Hissier, P., Boucherit, V., and Huynh, J.R. (2022). The deubiquitinase USP8 targets ESCRT-III to promote incomplete cell division. Science 376, 818-823.

Zhang, Q., Zhang, Y., Zhang, Q., Li, L., and Zhao, S. (2023). Division promotes adult stem cells to perform active niche competition. Genetics 224.

Zhao, S., Fortier, T.M., and Baehrecke, E.H. (2018). Autophagy Promotes Tumor-like Stem Cell Niche Occupancy. Curr Biol 28, 3056-3064.e3053.

Author Response:

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

Public Reviews:

Reviewer #1 (Public Review):

While the authors have proved their hypothesis by temporally increasing the activity of cholinergic neurons at different life stages through the auxin-inducible degron system, their work raises two major concerns. First, they might want to discuss the conflicting data from Zullo et al (Nature 2019, vol 574, pp 359-364). For example, the authors show that increasing the activity of acr-2-expressing neurons after the 7th day of adulthood increases lifespan. However, Zullo et al (2019) show that the reciprocal experiment, inhibiting cholinergic neuron activity on the 1st day or the 8th day of adulthood, also increases lifespan. Is this because the two studies are using different promoters, that of the acr-2 ACh receptor (this work) versus that of the unc-17 vesicular ACh transporter (Zullo et al., 2019)? The two genes are expressed in different subsets of cells that do not completely overlap. CeNGEN shows that acr-2 is expressed in motor and non-motor neurons, but some of these neurons are also different from those that express unc-17. Is it possible that different cholinergic neurons also have opposite lifespan effects during adulthood? Or is it because both lack of signaling and hypersignaling can lead to a long-life phenotype? Leinwand et al (eLife 2015, vol 4, e10181) previously suggested that disturbing the balance in neurotransmission alone can extend lifespan. A simple discussion of these possibilities in the Discussion section is likely sufficient. Or can the auxin treatment and removal be confounding factors? Loose and Ghazi (Biol Open 2021, vol 10, bio058703) show that auxin IAA alone can affect lifespan and that this effect can depend on the time the animal is exposed to the auxin.

We thank the reviewer for the thoughtful comments and valuable suggestions. In response, we have expanded the Discussion section to address the points raised, as detailed below.

We fully agree with the reviewer that the different results between our study (activating acr-2-expressing neurons) and Zullo et al. (inhibiting unc-17- expressing neurons) are most likely due to the distinct cholinergic neurons targeted. Our new preliminary data further support this neuron-specific model, as inhibition of acetylcholine synthesis at mid-late life stages produces opposing lifespan effects in different cholinergic neurons. At the same time, we cannot rule out the alternative possibility raised by the reviewer (eLife, 2015) that both activation and inhibition of neuronal activity may extend lifespan by similarly disrupting the balance of neurotransmission. This hypothesis requires further experimental validation in the context of cholinergic motor neurons. Regarding the potential technical concern related to auxin exposure (Biol Open, 2021), our control experiments using 0.5 mM auxin did not show non-specific lifespan effects.

Accordingly, in the revised manuscript, we have discussed the first two possibilities in the Discussion by stating (page 17-18): “Nevertheless, it is still unclear whether other neuronal populations share similar temporal regulatory mechanisms. A previous study reported that inhibiting cholinergic neurons activity (using unc-17 promoter) extends lifespan regardless of timing[2], which is different from the temporal lifespan regulation we observed in cholinergic motor neurons (using acr-2 promoter). This discrepancy is likely due to differences in subsets of neurons, as the unc-17 promoter labels a broad repertoire of cholinergic neurons, while the acr-2 promoter mainly marks cholinergic motor neurons[53]. Thus, the distinct lifespan-modulating effects of cholinergic motor neurons may be overshadowed by opposing contributions from other cholinergic subtypes when a mixed population is manipulated. Alternatively, both activation and inhibition of cholinergic activity may perturb neurotransmission balance, leading to similar effects on lifespan[54]. It will be interesting to test these hypotheses in future studies.”

Second, the daf-16-dependence of the early longevity-inhibiting effect of ACh signaling needs clarification and further experimentation. The authors present a model in Figure 6D, where DAF-16 inhibits longevity. This contradicts published literature. Libina et al (Cell 2003, vol 115, pp 489-502) have shown that intestinal DAF-16 increases lifespan. From the authors' data, it is possible that ACh signaling inhibits DAF-16, not promotes it as they have drawn in Figure 6D.

We thank the reviewer for this important point. We agree that intestinal DAF-16 promotes longevity. Our original model Figure 6D aimed to show that the larval pathway shortens lifespan by inhibiting DAF-16, not that DAF-16 itself shortens lifespan. The arrowhead style used in the original Fiugure 6D might have given an impression that DAF-16 shortens lifespan. Our apologies. We have now fixed this error in Figure 6D. In addition, as suggested, we have performed additional daf-16 experiments (see below).

In Figure 3F, the authors used Pacr-2::TeTx, which inhibits cholinergic neuron activity, to show an increase in the expression of DAF-16 targets. Why did the authors not use the worms that express the transgene Pacr-2::syntaxin(T254I), which increases cholinergic neuron activity? What happens to the expression of DAF-16 targets in these animals? Do their expression go down? What happens if intestinal daf-16 is knocked down in animals with increased cholinergic neuron activity, instead of reduced cholinergic neuron activity?”

Thanks for these insightful questions. In Figure 3F-H, we used TeTx instead of syntaxin(T254I) to investigate the function of DAF-16 in the early stage pathway based on the two main reasons. First, Pacr-2::TeTx transgene extends lifespan in early life by inhibiting cholinergic activity, which provides a genetic background complementary to that of syntaxin(T254I) for characterizing the role of DAF-16. Second, TeTx pathway is expected to activate DAF-16 and upregulate its target genes. This approach is more sensitive than measuring gene downregulation in Pacr-2::syntaxin(T254I) transgenic worms.

We fully agree with the reviewer that performing the corresponding experiments in the syntaxin(T254I) background would strengthen the overall evidence. As suggested, we have now examined the expression of DAF-16 target genes in Pacr-2::syntaxin(T254I) transgenic worms, and performed intestine-specific RNAi of daf-16 in the same background. We found that these worms exhibit downregulation of DAF-16 target genes. Furthermore, intestinal daf-16 knockdown did not further shorten the already reduced lifespan of these transgenic worms. Together, these results from both the TeTx and syntaxin(T254I) lines confirms that cholinergic motor neurons require DAF-16 in the intestine to regulate lifespan. These new data has now been described in Figure S5A-5D (page 11-12): “As expected, the expression level of sod-3 and mtl-1, two commonly characterized DAF-16 target genes, was upregulated in transgenic worms deficient in releasing ACh from cholinergic motor neurons (Figure 3F), and downregulated in transgenic worms with enhanced ACh release from cholinergic motor neurons (Figure S5A), consistent with the notion that DAF-16 acts downstream of cholinergic motor neurons.”, and “RNAi of daf-16 in the intestine abolished the ability of cholinergic motor neurons to regulate lifespan at early life stage (Figure 3G, 3H and Figure S5C-S5E).”

Recommendations for The Authors:

Reviewer #1 (Recommendations for The Authors):

(1) “The Methods section needs to be clarified/expanded.”

(a) “For example, are the authors using indole-3-acetic acid or a synthetic auxin? How long does it take for syntaxin to be made after the removal of the auxin?”

We have now included auxin information and recovery time in the Method for auxin treatment by stating (page 24): “natural auxin indole-3-acetic acid (G&K Scientific)”, and “Expression of syntaxin(T254I) can be suppressed by auxin treatment and restored in 24 hours following auxin removal.”

(b) “How much FUDR was used in some of the lifespan assays?”

2 μg/mL FUDR was used in some of the lifespan assays. We have now included the concentration in the Method for lifespan assay by stating (page 23 line 526): “2 μg/mL 5-Fluoro-2’-deoxyuridine (FUDR) was included in assays involving TeTx transgene worms, unc-31 and unc-17 mutant worms, which show a defect in egg laying.”

(c) “In line 494 of the Methods section, worms were anesthetized with 50 mM sodium azide. That concentration seems a bit high.”

It is an error indeed. We used 5 mM NaN3. This has now been fixed in the text and in line 548.

(d) “What are the concentrations of the transgenes used in the extrachromosomal arrays?”

We have now included the concentrations in the Method for strains and genetics by stating (line 507-509 on page 22): “Microinjections were performed using standard protocols. Each plasmid DNA listed above in the transgenic line was injected at a concentration of 50 ng/μL. Each marker for RNAi was co-injected at a concentration of 25 ng/μL.”

(2) “Gene expression can vary in different parts of the worm intestine. Do the measurements in Figure 6C represent the entire intestine or only certain parts of the intestine?”

We have now included the intestine area used for quantification in the Method for microscopy by stating (page 24): “and the entire intestine area was selected by ImageJ”, and in the legends of Figure 6C by stating (page 36): “The entire intestinal area was selected for measurement.”

(3) “In Figure S1C, does tph-1 have a slight effect? Might serotonin partly counteract the effects of ACh?”

We thank the reviewer for raising this interesting point regarding the potential role of serotonin. We have re-examined our data in Figure S2C (the original Figure S1C) and agree that loss of tph-1 partly counteracted the lifespan-shortening effect of Pacr-2::syntaxin(T254I) transgene in early life stage, thought the whole-life suppression effect is slight. To assess whether the acr-2 promoter-driven manipulation might directly affect serotonergic neurons, we checked the CeNGen. We found that the transcript expression of acr-2 can be detected in serotonergic neurons (ADF, HSN, and NSM), but the levels are extremely low. In this regard, it is unlikely that the Pacr-2::syntaxin(T254I) transgene exerts its primary effect by substantially altering serotonin release. While a potential indirect interaction between cholinergic and serotonergic signaling in lifespan regulation remains, it falls beyond the primary focus of the current study. We would like to follow up this in future studies. We have now pointed this out in the text by stating (page 9):“As a control, we also tested mutants deficient in other types of small neurotransmitters, including glutamate (eat-4), GABA (unc-25), serotonin (tph-1), dopamine (cat-2), tyramine (tdc-1), and octopamine (tbh-1), but detected no effect, with the exception of tph-1, which showed a modest, partial suppression of the phenotype (Figure S2A-S2F). This observation suggests that the lifespan effects of cholinergic signaling can be modulated by serotonin.”

(4) “Where else is GAR-2 expressed? Might there be redundancies between neuronal and intestinal GAR-2?”

We appreciate this insightful question. Based on available single-cell gene expression atlases of C. elegans at both embryonic and adult stages[1,2], gar-2 expression has been detected not only in neurons and the intestine, but also in additional tissues such as the muscle. Regarding the observed lack of effects upon neuronal or intestinal gar-2 RNAi on the ability of cholinergic motor neurons to extend lifespan in mid-late life, and also suggested by another reviewer, we performed muscle-specific RNAi experiments. Together with our previously presented data, the results show that intestinal (but not neuronal or muscle) RNAi of gar-3 abolished the ability of cholinergic motor neurons to extend lifespan at mid-late life stages, while muscle-specific (but not neuronal or intestinal) RNAi of gar-2 suppresses this effect. This finding indicates that GAR-3 and GAR-2 mediate cholinergic signaling in distinct peripheral tissues, with GAR-3 primarily in the intestine and GAR-2 primarily in muscle, to produce their effects on longevity. Given our focus on neuron-gut signaling, the role of GAR-2 in the muscle will be further investigated in future studies. The new data have now been described in Figure S8 by stating (page 13-14): “RNAi of gar-2 in the intestine (Figure 4D and 4E), but not in neurons or the muscle (Figure 4D-4F, and Figure S8A, S8D-S8E), abolished the ability of cholinergic motor neurons to extend lifespan at mid-late life stage. Thus, GAR-3 may function in the intestine to regulate lifespan. Surprisingly, RNAi of gar-2 in the muscle (Figure S8A-S8C), but not in neurons or the intestine (Figure S7F-S7H) had an effect on the ability of cholinergic motor neurons to extend lifespan in mid-late life, indicating that GAR-2 acts in the muscle to regulate lifespan.”

(1) Packer, J. S. et al. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science 365, doi:10.1126/science.aax1971 (2019).

(2) Roux, A. E. et al. Individual cell types in C. elegans age differently and activate distinct cell-protective responses. Cell Rep 42, 112902, doi:10.1016/j.celrep.2023.112902 (2023).

(3) Chun, L. et al. Metabotropic GABA signalling modulates longevity in C. elegans. Nat Commun 6, 8828, doi:10.1038/ncomms9828 (2015).

(4) Izquierdo, P. G. et al. Cholinergic signaling at the body wall neuromuscular junction distally inhibits feeding behavior in Caenorhabditis elegans. J Biol Chem 298, 101466, doi:10.1016/j.jbc.2021.101466 (2022).

(5) “In line 344, please correct "fwork" to "work".”

This has now been fixed.

(6) “In line 360, please correct "acts" to "act".”

This has now been fixed.

(7) “Please check citations within the main text. Some of the citations do not fit the cited material. For example, in line 112, reference 28 is not about GABAergic neurons.”

We thank the reviewer for pointing out these important details. We have now carefully checked and corrected the citations throughout the manuscript as suggested.

Reviewer #2 (Recommendations for The Authors):

(1) “How are the authors assessing the efficacy of the TeTx manipulations in their strains? Likely TeTx has a concentration-dependent effect. Are there any phenotypes associated with the loss of cholinergic signaling? Also, does TeTx expression in cholinergic neurons alter the neuronal activity of other associated neurons, or alter muscle integrity?”

Thanks for the question. Our observations show that overexpression of TeTx results in defects including small size, slow growth, egg-laying deficiencies, and severe locomotion impairment, which are all associated with the loss of cholinergic signaling. While we did not directly examine the activity of interconnected neurons in our strains, we tested the muscle integrity by recording muscle reaction to 1 mM levamisole and found that overexpression of TeTx does not affect muscle integrity. To circumvent these pleiotropic complications, we instead employed Syntaxin(T254I) transgenic worms, which exhibits only slight locomotion defects, to further characterize the temporal effect of cholinergic motor neurons on lifespan. This data has now been described in Figure S1A by stating (page 6): “Overexpression of TeTx induces characteristic phenotypes of cholinergic deficiency, such as developmental delay and severe locomotion impairment[32], yet does not compromise muscle function (Figure S1A).”

(2) “The authors are expressing TeTx throughout the lifespan of the animal, including during development. How does this contribute to the organismal phenotype?”

As described above, chronic TeTx expression from egg stage results in developmental delay, which is similar to the development phenotype of unc-17 mutant worms defective in acetylcholine transmission. However, unc-17 mutation has no effect on lifespan[3], which is different from TeTx overexpression, indicating that the developmental delay caused by TeTx overexpression may not affect the lifespan phenotype.

(3) Chun, L. et al. Metabotropic GABA signalling modulates longevity in C. elegans. Nat Commun 6, 8828, doi:10.1038/ncomms9828 (2015).

(3) “A previous study has shown that increasing cholinergic activity by altering ACR-2 expression can cause neurodegeneration (DOI: https://doi.org/10.1523/JNEUROSCI.1515-10.2010). Does overexpressing syntaxin, or AID-mediated degradation of syntaxin cause motor neuron degeneration, which could also contribute to the lifespan phenotype?”

We thank the reviewer for raising this important point regarding potential motor neuron degeneration. In response, we performed confocal microscopy to assess the motor neurons. We found that worms expressing the transgene Pacr-2::syntaxin::mCherry do not exhibit a defect in the number or morphology of labeled neuronal cell bodies compared to control worms expressing Pacr-2::mCherry. This observation indicates that chronic, increased cholinergic activity through syntaxin overexpression, under our experimental conditions, does not induce motor neuron degeneration. This data has now been described in Figure S1B by stating (page 7): “This transgene simply shortened lifespan without causing a pleotropic effect (Figure 1B), and critically, without inducing motor neuron degeneration (Figure S1B).”

(4) “Figures 1I-1L: The authors do not show how long it takes for the expression of syntaxin to be restored following the removal of auxin from plates. This would be important to assess the age-dependent effects of neuronal signaling.”

We thank the reviewer for pointing this out. In general, complete restoration of syntaxin expression occurred within 24 hours after auxin withdrawal. We have now pointed this out in the text by stating (the last sentence on page 24):“Expression of syntaxin(T254I) can be suppressed by auxin treatment and restored in 24 hours following auxin removal.”

(5) “In Figures S1A-E: Although the mutant backgrounds decrease the lifespan of animals expressing the Pacr2::syntaxin(T254I) transgene, the lifespan of these transgenic animals appears to be extended compared to what was shown in Figure 1B. Is this the case? (can these experiments be repeated alongside wild-type N2s to assess if their lifespan is indeed extended compared to the N2?). Also, if so, could it be that the lifespan effects are modified to different extents by other small neurotransmitters?”

We thank the reviewer for pointing this out. All the experiments presented in current Figure S2 (original Figure S1) were performed with wild-type N2 controls, which are now included in the updated Figure S2. This data shows that, in the Pacr-2::syntaxin(T254I) transgenic background, loss of unc-25 (GABA) or tph-1 (serotonin) leads to a further extension of lifespan, while loss of other genes had no effect. Importantly, while unc-25 mutation also extends lifespan in wild-type worms, tph-1 mutation does not. This observation indicates that the lifespan effects of cholinergic signaling can be modulated by serotonin. We have now pointed this out in the text by stating (page 9):“As a control, we also tested mutants deficient in other types of small neurotransmitters, including glutamate (eat-4),, GABA (unc-25), serotonin (tph-1), dopamine ,(cat-2), tyramine (tdc-1), and octopamine (tbh-1), but detected no effect, with the exception of tph-1, which showed a modest, partial suppression of the phenotype (Figure S2A-S2F). This observation suggests that the lifespan effects of cholinergic signaling can be modulated by serotonin.”

(6) “RNAi of several of the receptors appear to modulate wild-type lifespan. Although I understand that this is not the main focus of the manuscript, the fact that this occurs should be mentioned in the results and discussed later on.”

We thank the reviewer for pointing this out. As suggested by the reviewer, we have now pointed this out in the text by stating (page 9):“Notably, RNAi of several ACh receptors such as acr-11 appears to shorten wild-type lifespan, whereas RNAi of several other ACh receptors such as acr-9 extends wild-type lifespan, suggesting lifespan-modulating potential of ACh receptors (Figure S3).”

(7) “Cholinergic signaling and ACR-6 have been previously shown to regulate pharyngeal pumping/feeding behavior. (https://doi.org/10.1016/j.jbc.2021.10146”). Could the requirements for ACR-6/cholinergic signaling in longevity be related to caloric restriction/nutritional intake which in turn could be expected to alter DAF-16 and HSF-1 activity? These previous studies should be referenced and discussed.”

Thanks for the suggestion. As suggested by the reviewer, we have examined the pumping rate of acr-6 mutant worms. Our results showed that acr-6 mutation slightly reduced the pumping rate. As the decrease is relatively minor, we do not expect a major DR effect, though we cannot completely rule out such a possibility. Furthermore, as acr-6 acts in the pharynx to regulate pumping but in the intestine to regulate the role of cholinergic signaling in lifespan, we do not expect this would have a major contribution to our pathway. This new data has now been described in Figure S4I. As suggested by the reviewer, we have now pointed this out in the text by stating (page 10): Previous data has shown that cholinergic signaling and ACR-6 may control pharyngeal pumping[42]. As expected, we found that acr-6 mutation slightly reduced pumping rates (Figure S4G).”

(8) “The expectation for the studies in Figure 3/DAF-16, is that animals expressing Ex[Pacr-2::syntaxin(T254I)], should have downregulated DAF-16 in the intestine. This needs to be shown through some method (increased daf-16 activation upon loss of cholinergic signaling does not necessarily imply that the converse is also true).”

We thank the reviewer for the insightful suggestion. The reviewer has suggested us performing additional measurements to confirm that DAF-16 is the downstream transcription factor in the intestine. Specifically, the reviewer suggested testing if syntaxin(T254I) transgene signaling could inhibit DAF-16 activity. We have now followed the reviewer’s suggestion by performing two different assays. First, as also suggested by the first reviewer, we detected the expression of DAF-16 target genes in Pacr-2::syntaxin(T254I) transgenic worms, which exhibited downregulation of these genes, consistent with the notion that increasing cholinergic motor neuron activity inhibits DAF-16. This data has now been described in Figure S5A. Second, we performed an assay to detect DAF-16 subcellular localization pattern in the intestine. We found that acr-6 RNAi notably promotes nuclear translocation of DAF-16, suggesting that ACR-16 inhibits DAF-16, which is consistent with our model. This new data has now been described in Figure S5E. As suggested by the reviewers, we have now pointed this out in the text by stating (page 11): “As expected, the expression level of sod-3 and mtl-1, two commonly characterized DAF-16 target genes, was upregulated in transgenic worms deficient in releasing ACh from cholinergic motor neurons (Figure 3F), and downregulated in transgenic worms with enhanced ACh release from cholinergic motor neurons (Figure S5A), consistent with the notion that DAF-16 acts downstream of cholinergic motor neurons. To obtain further evidence, we assessed the subcellular localization pattern of DAF-16::GFP fusion and found that acr-6 RNAi notably promoted nuclear translocation of DAF-16, confirming that ACh signaling inhibits DAF-16 activity (Figure S5B).”

(9) “Similarly, it would be good to have additional lines of evidence that signaling through GAR-3 impinges on HSF1, and that the lifespan effects are not due to non-specific effects of hsf-1 knockdown, which could lead to several un-related deficiencies and compromise lifespan (Figure 5b).”

We thank the reviewer for the valuable suggestions. The reviewer correctly noted that the observed lifespan effect from hsf-1 RNAi could involve non-specific deficiencies. In response, we performed an assay to detect HSF-1 subcellular localization in the intestine upon gar-3 overexpression by using the strain EQ87 (iqIs28[pAH71(hsf-1p::hsf-1::gfp) + pRF4(rol-6)]). We found that the induced nuclear translocation of HSF-1 was weak. This result suggests that GAR-3 may modulate HSF-1 activity through a mechanism distinct from, or more subtle than, robust nuclear accumulation, or that its effect is highly dependent on the expression level and timing.

(10) “Figure 6: An N2 control should be provided to assess the specificity of the mCherry signal from the intestine (given autofluorescence in the animals' gut).”

Thanks for the suggestion. As suggested by the reviewer, we have now included the control in Figure S10.

Reviewer #3 (Recommendations for The Authors):

(1) “While the model is consistent with the data, there are alternatives that were not addressed. Additionally, there are some deficiencies in the interpretation of results that should be addressed, in my opinion. Possibly most importantly given the claims, the authors should address an alternative model: that it is the level of acetylcholine signaling that matters. Is it possible that the level auxin-inducible degradation of syntaxin(T254I) in acr-2 expressing cells is age dependent, such that one level increases lifespan and the other shortens it, and that the timing doesn't matter at all? A chronic dose response to auxin concentration would address if the level of syntaxin is a non-monotonic determinant of lifespan.”

We sincerely thank the reviewer for raising this important alternative model. The reviewer suggested that the apparent temporal effect we observed might instead be explained by an age-dependent change in the efficiency of AID system in degrading syntaxin(T254I) in acr-2 expressing cells. That is, different levels of acetylcholine signaling, rather than timing, produce opposite lifespan outcomes. We agree that this is a formal possibility that our current data cannot fully rule out. On the other hand, other data in the manuscript suggests otherwise. For example, the expression of ACR-6 and GAR-3 in the intestine exhibited a temporal switch in early and mid-late life, providing support for a time-dependent mechanism. In addition, the differential requirement of the downstream transcription factors DAF-16 and HSF-1 in the early and mid-late life, respectively, provides further evidence supporting a temporal mechanism. Thus, while we agree that the possibility raised by the reviewer cannot be formally ruled out, the temporal mechanism we proposed may play an important role.

The reviewer suggested performing a chronic dose-response experiment with varying auxin concentrations. Actually when we first employed the AID system to temporally manipulate motor neuron output at different life stages, we tested potential effects of auxin concentration. Using the soma-expressed TIR1 system, we found that, restoring syntaxin(T254I) activity from day 10 of adulthood extends lifespan, regardless of whether the prior suppression was maintained with 0.1 mM or 0.5 mM auxin. This suggests that the pro-longevity effect is likely not triggered by differences in the efficacy of prior suppression within this concentration range. We acknowledge that the tested dose range may not cover potential threshold concentrations. Furthermore, we cannot exclude the possibility of a non-linear relationship between auxin concentration and degradation efficiency. We agree that a comprehensive chronic dose-response analysis remains a valuable future direction, and we plan to employ more precise tools in the future to investigate the interplay between signal level and temporal context in lifespan regulation. The auxin concentration data have now been described in Figure S1C-1D by stating (page 7): “Comparable outcomes were obtained with both 0.1 mM and 0.5 mM auxin treatments (Figure S1C-1D).” As suggested by the reviewer, we have discussed the alternative model in the Discussion by stating (page 19): “An alternative mechanism based on differential levels of cholinergic signaling could also contribute to the observed lifespan effects.”

(2) “Several times, including in several section headings, it is claimed that daf-16 (eg line 205-206) and acr-6 (eg line 185-186) function "early in life". This was not tested, so the claim is not warranted. For instance, these genes could act later in life to respond to signals made or sent early in life, or they could act both early and late, or only early (as they claim).”

We thank the reviewer for this precise and important clarification. The reviewer is correct that our genetic interventions do not by themselves define the temporal window.

Our experimental rationale was based on the observation that the lifespan-shortening effect of Pacr-2::syntaxin(T254I) expression is similar whether it is induced throughout life or specifically during larval stages (early life), indicating the detrimental effect results from enhanced motor neuron output in early life. Therefore, we used the lifelong expression paradigm as a tool to genetically dissect the downstream pathway triggered by early-life neuronal activation. We acknowledge the reviewer's point that this design does not formally prove that daf-16 or acr-6 acts only in early life; they could be required continuously or again later. However, we would like to note that our expression data show that the gut expression of ACR-6 is restricted to early life, which is consistent with a primary early-life function in this context.

To reflect this more accurate interpretation, we have revised all relevant statements, including section headings. We now consistently state that daf-16 is required for the lifespan-shortening effect of cholinergic motor neuron, rather than claiming it functions "in early life". We have also toned down the discussion regarding their temporal function by stating (page 12): “Because this lifespan-shortening effect results from enhanced motor neuron output in early life and overwrites its beneficial effect at later stages, we propose this signaling circuit mediates the lifespan-shortening effect in early life.”

(3) “In line 118, they note that such intervention led to a complex effect on the lifespan curve "by initially promoting worm's survival followed by inhibiting it at later stages." I think that while findings from later experiments support a time-dependent lifespan effect stemming from syntaxin function in the cholinergic motor neurons, this experiment's TeTx expression in those neurons is not time-dependent. Lifespan is an endpoint measure, so there is no sense in which a non-timed perturbation has an early or late effect on an individual. Rather, the effect on survival they observed is at the population level, their intervention increases the average lifespan while decreasing the worm-to-worm variation in lifespan.”

We thank the reviewer for the critical and precise comment regarding our interpretation of the survival curves of TeTx transgenic worms. As suggested by the reviewers, we have revised the text by stating (page 6): “Surprisingly, such intervention led to a complex effect on the population survival curve by reducing both early mortality and the proportion of long-lived individuals (Figure 1A). Specifically, the 25% lifespan of these worms was prolonged, while their 75% and maximal lifespan were slightly shortened, leading to a mean lifespan slightly increased or unchanged compared to that of wild-type worms. This suggests that inhibiting cholinergic motor neurons may exert temporally distinct effects on survival, leading to decreased individual variation in lifespan.”

(4) “The layout of the plots separating the responses of wild type and mutants to different panels makes it often difficult to interpret the results. For instance, do acr-6, gar-3, and other receptor mutants or knockdowns affect lifespan on their own? If they do, it matters to the interpretation whether they live longer or shorter than the wild type: which of the mutants phenocopy the lack of a lifespan-extending signal that activates them? Which phenocopy lacks a lifespan-shortening signal that activates them? Could they phenocopy the effect of an inhibitory signal? And critically, are the effects of these mutants on lifespan consistent with their model?”

“The paper would be stronger if they determined when ACR-6 and GAR-3 functions are necessary and sufficient. Is it possible that the receptor doesn't matter, just that there be one of the two expressed in the intestine, and that other mechanisms determine the lifespan response to modulation of syntaxin(T254I)? What does time-dependent knockdown of these receptors do to daf-16 and hsf-1 localization and to the transcription of the targets of these transcription factors?”

We thank the reviewer for these insightful comments. We have addressed the points as follows:

As suggested, we have reorganized the lifespan data in Figure S4 to directly compare wild type and mutant/RNAi conditions within the same panels. This new presentation clarifies the autonomous effects of these genes. The data shows that loss of acr-6 or gar-2 (via RNAi or mutation) has minimal effect on lifespan. Notably, acr-8 RNAi shortens lifespan, whereas the acr-8 mutation does not, supporting our hypothesis of tissue-specific or compensatory roles for this receptor, as detailed in our following response to point (5). The reviewer's key question regarding when these receptors are necessary and sufficient is central to our model. We agree with the reviewer that complementary loss-of-function experiments with temporal precision, such as time-specific knockdown of the two receptors, would provide even stronger evidence. To this end, we attempted to generate endogenous degron-tagged alleles of acr-6 and gar-3 to apply the AID system for precise, stage-specific degradation. Unfortunately, despite multiple design attempts and screening efforts, we were unable to obtain homozeygous strains with the desired genomic edits using the same gRNA we used to knock in mCherry or other gRNAs. This is rather frustrating. Consequently, we are currently unable to perform the ideal temporally controlled loss-of-function experiments suggested by the reviewer.

(5) “Why does RNAi but not mutation of acr-8 and gar-2 suppress the lifespan shortening effect of Pacr-2::syntaxin(T254I)?”

Thanks for this important question regarding the differential effects of feeding RNAi versus mutation of acr-8 and gar-2. The discrepancy likely arises from the potential off-target effects of RNAi. RNAi is not strictly specific as it may target other related genes, generating a non-specific effect, whereas precise mutations in acr-8 and gar-2 alone may not produce the same effect.

(6) “sid-1(-); Ex[Pacr-2::tetx lives longer than sid-1(-); in daf-16(+) worms in Figure 3G; so it is very hard to interpret the lack of effect of Pacr-2::tetx in daf-16(-) worms, since this transgene behaves differently in sid-1 mutants than in wild type worms. This would be clear if the two plots were combined (appropriately, since it is the same experiment). It looks like daf-16 RNAi has a shortening effect in the sid-1 mutant, but not in in sid-1 mutants expressing Pacr-2::text.”

Thanks for this helpful suggestion. As suggested by the reviewer, we have now merged Figure 3G and 3H into one figure to present as Figure S5F. This combined presentation clarifies the comparison and shows that intestinal daf-16 RNAi shortens lifespan in both sid-1 mutants and sid-1 mutants expressing Pacr-2::TeTx.

Reviewer #4 (Recommendations for The Authors):

(1) “Lines 50-52: I would replace "leading to increased incidents in age-related diseases and probability of death" with "leading to the onset of age-related diseases and increased probability of death". Instead of "such an aging process" I would use "the aging process".”

This has now been fixed.

(2) “Figure 2E-F: By rescuing the expression of ACR-6 in neurons or intestinal cells alone, the authors show that the release of ACh from cholinergic neurons has effects on the intestine to shorten lifespan. Is ACR-6 expressed in other tissues (e.g. muscle?) It might be interesting to assess whether ACh also regulates lifespan through activating the ACR-6 receptor in other tissues or specifically targets the intestine. This question is partially answered with the tissue-specific RNAi experiments for DAF-16, but it is possible that ACR-6 also modulates other pathways beyond the tested transcription factors.”

Analyzing the role of other tissues could also be applied to understand how GAR-3 influences lifespan. Along these lines, it would be interesting to expand the tissue-specific knockdown experiments for GAR-3 to other tissues. More importantly, these experiments can address whether activation of ACR-6 and GAR-3 can also have different effects on lifespan by regulating distinct tissues in addition to the intestine, and not only due to temporal expression patterns. For instance, whereas DAF-16 regulates lifespan primarily through its effects in the intestine, HSF1 could have effects on additional tissues. Although it would interesting to perform these experiments, I understand that the authors main focus is the nervous system-gut axis.

We thank the reviewer for the insightful suggestions regarding the potential tissue-specific functions of ACR-6 and GAR-3. As noted in our response to point #6, endogenous expression imaging indicates that ACR-6 and GAR-3 are primarily expressed in neurons and the intestine with weak expression of GAR-3 in the muscle, so we tested the muscle. We found that muscle-specific RNAi of gar-2 abolished the ability of cholinergic motor neurons to extend lifespan at mid-late life stages, whereas muscle-specific RNAi of gar-3 does not. This result further supports that GAR-3 primarily exerts this effect in the intestine.

(3) “Can the authors specify in the corresponding figure legend at what age they tested sod-3 and mtl-1 expression in Pacr-2::TeTx worms (Figure 3F)? This is important to support the conclusions of the paper. Along these lines, can the authors also specify at what age they quantified the expression of HSF-1 targets (Figure 5F).”

Thanks for the suggestion. As recommended, we have now provided the worm age in Figure 3F (day 1 adult) and Figure 5F legends (day 10 adult).

(4) “To further strengthen the authors' conclusions, it might be interesting to examine the intracellular localization of DAF-16 in the intestine of Pacr-2::TeTx and syntaxin(T254I) worms compared to controls.”

We thank the reviewer for this valuable suggestion, which was also raised by another reviewer. In response, we examined the subcellular localization of DAF-16 in the intestine. Direct imaging in the Pacr-2::TeTx or Pacr-2::syntaxin(T254I) backgrounds was technically challenging because their fluorescent protein tags (YFP or mCherry) would interfere with the detection of DAF-16::GFP. Therefore, we adopted an alternative approach by modulating the activity of acr-6, the intestinal acetylcholine receptor that transmits cholinergic signals from motor neurons to DAF-16. We found that acr-6 RNAi promotes the nuclear translocation of DAF-16. These new data are presented in Figure S5E by stating (page 11): “To obtain further evidence, we assessed the subcellular localization pattern of DAF-16::GFP fusion and found that acr-6 RNAi notably promotes nuclear translocation of DAF-16, confirming that ACh signaling modulate DAF-16 activity (Figure S5B).”

(5) “The results with gar-2 RNAi are fascinating. I am very curious (and I assume potential readers too) about what tissues mediate the mid-late life effects of GAR-2 in longevity. Perhaps the authors could add experiments in a couple of other tissues known to regulate organismal lifespan (e.g. muscle). However, I totally understand why the authors focused on GAR-3, especially because both GAR-3 and ACR-6 have effects on the intestine and this is sufficient for the main conclusions of the paper.”

We sincerely thank the reviewer for the insightful suggestion and for highlighting the potential role of GAR-2. In response, we performed muscle-specific RNAi experiments. Together with our previously presented data, the results show that intestinal (but not neuronal or muscle) RNAi of gar-3 abolished the ability of cholinergic motor neurons to extend lifespan at mid-late life stages, while muscle-specific (but not neuronal or intestinal) RNAi of gar-2 suppresses this effect. This finding indicates that GAR-3 and GAR-2 mediate cholinergic signaling in distinct peripheral tissues, with GAR-3 primarily in the intestine and GAR-2 primarily in the muscle, to produce their effects on longevity. Given our focus on neuron-gut signaling, the role of GAR-2 will be investigated in future studies. The new data have now been described in Figure S8 by stating (page 13-14): “RNAi of gar-3 in the intestine (Figure 4D and 4E), but not in neurons or the muscle (Figure 4D-4F, and Figure S8A, S8D-S8E), abolished the ability of cholinergic motor neurons to extend lifespan at mid-late life stage. Thus, GAR-3 may function in the intestine to regulate lifespan. Surprisingly, RNAi of gar-2 in the muscle (Figure S8A-S8C), but not in neurons or the intestine (Figure S7F-S7H) had effect on the ability of cholinergic motor neurons to extend lifespan in mid-late life, indicating that GAR-2 acts in the muscle to regulate lifespan.”

(6) “Figure 6: It seems that the genes are also expressed in the muscle. Can the authors include images of other tissues in supplementary figures?”

Thanks for the suggestion. As suggested by the reviewer, we have now included images of whole worms expressing mCherry, which was knocked in the endogenous locus off gar-3 or acr-6 by CRISPR in Figure S10. However, we did not detect strong expression of gar-3 or acr-6 in the muscle under the conditions examined, which may be limited by the low endogenous protein expression level of the two genes in the muscle, though the CeNGEN website shows they are expressed in the muscle. Determining the precise spatiotemporal expression profiles of these receptors will likely require more sensitive methods. We plan to address this important question in future studies by using such refined approaches.

Author response:

General Statements

We thank all three reviewers for their time taken to provide valuable feedback on our manuscript, and for appreciating the quality and usefulness of our data and results presented in our study. We have improved the manuscript based on their suggestions and provide a detailed, point-by-point response below.

Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity):

The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

Thank you for your positive feedback.

There are several single-cell methodologies all claim to co-profile chromatin modifications and gene expression from the same individual cell, such as CoTECH, Paired-tag and others. Although T-ChIC employs pA-Mnase and IVT to obtain these modalities from single cells which are different, could the author provide some direct comparisons among all these technologies to see whether T-ChIC outperforms?

In a separate technical manuscript describing the application of T-ChIC in mouse cells (Zeller, Blotenburg et al 2024, (Zeller et al., 2024)), we have provided a direct comparison of data quality between T-ChIC and other single-cell methods for chromatin-RNA co-profiling (Please refer to Fig. 1C,D and Fig. S1D, E, of the preprint). We show that compared to other methods, T-ChIC is able to better preserve the expected biological relationship between the histone modifications and gene expression in single cells.

In current study, T-ChIC profiled H3K27me3 and H3K4me1 modifications, these data look great. How about other histone modifications (eg H3K9me3 and H3K36me3) and transcription factors?

While we haven’t profiled these other modifications using T-ChIC in Zebrafish, we have previously published high quality data on these histone modifications using the sortChIC method, on which T-ChIC is based (Zeller, Yeung et al 2023)(Zeller et al., 2022). In our comparison, we find that histone modification profiles between T-ChIC and sortChIC are very similar (Fig. S1C in Zeller, Blotenburg et al 2024). Therefore the method is expected to work as well for the other histone marks.

T-ChIC can detect full length transcription from the same single cells, but in FigS3, the authors still used other published single cell transcriptomics to annotate the cell types, this seems unnecessary?

We used the published scRNA-seq dataset with a larger number of cells to homogenize our cell type labels with these datasets, but we also cross-referenced our cluster-specific marker genes with ZFIN and homogenized the cell type labels with ZFIN ontology. This way our annotation is in line with previous datasets but not biased by it. Due the relatively smaller size of our data, we didn’t expect to identify unique, rare cell types, but our full-length total RNA assay helps us identify non-coding RNAs such as miRNA previously undetected in scRNA assays, which we have now highlighted in new figure S1c .

Throughout the manuscript, the authors found some interesting dynamics between chromatin state and gene expression during embryogenesis, independent approaches should be used to validate these findings, such as IHC staining or RNA ISH?

We appreciate that the ISH staining could be useful to validate the expression pattern of genes identified in this study. But to validate the relationships between the histone marks and gene expression, we need to combine these stainings with functional genomics experiments, such as PRC2-related knockouts. Due to their complexity, such experiments are beyond the scope of this manuscript (see also reply to reviewer #3, comment #4 for details).

In Fig2 and FigS4, the authors showed H3K27me3 cis spreading during development, this looks really interesting. Is this zebrafish specific? H3K27me3 ChIP-seq or CutTag data from mouse and/or human embryos should be reanalyzed and used to compare. The authors could speculate some possible mechanisms to explain this spreading pattern?

Thanks for the suggestion. In this revision, we have reanalysed a dataset of mouse ChIP-seq of H3K27me3 during mouse embryonic development by Xiang et al (Nature Genetics 2019) and find similar evidence of spreading of H3K27me3 signal from their pre-marked promoter regions at E5.5 epiblast upon differentiation (new Figure S4i). This observation, combined with the fact that the mechanism of pre-marking of promoters by PRC1-PRC2 interaction seems to be conserved between the two species (see (Hickey et al., 2022), (Mei et al., 2021) & (Chen et al., 2021)), suggests that the dynamics of H3K27me3 pattern establishment is conserved across vertebrates. But we think a high-resolution profiling via a method like T-ChIC would be more useful to demonstrate the dynamics of signal spreading during mouse embryonic development in the future. We have discussed this further in our revised manuscript.

Reviewer #1 (Significance):

The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

Thank you very much for your supportive remarks.

Reviewer #2 (Evidence, reproducibility and clarity):

Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and full-length transcriptome and applied the method on early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields. There are some concerns before the manuscript is considered for publication.

We thank the reviewer for appreciating the quality of our study.

Major concerns:

(1) A major point of this study is to understand embryo development, especially gastrulation, with the power of scMulti-Omics assay. However, the current analysis didn't focus on deciphering the biology of gastrulation, i.e., lineage-specific pioneer factors that help to reform the chromatin landscape. The majority of the data analysis is based on the temporal dimension, but not the cell-type-specific dimension, which reduces the value of the single-cell assay.

We focussed on the lineage-specific transcription factor activity during gastrulation in Figure 4 and S8 of the manuscript and discovered several interesting regulators active at this stage. During our analysis of the temporal dimension for the rest of the manuscript, we also classified the cells by their germ layer and “latent” developmental time by taking the full advantage of the single-cell nature of our data. Additionally, we have now added the cell-type-specific H3K27me3 demethylation results for 24hpf in response to your comment below. We hope that these results, together with our openly available dataset would demonstrate the advantage of the single-cell aspect of our dataset.

(2) The cis-spreading of H3K27me3 with developmental time is interesting. Considering H3k27me3 could mark bivalent regions, especially in pluripotent cells, there must be some regions that have lost H3k27me3 signals during development. Therefore, it's confusing that the authors didn't find these regions (30% spreading, 70% stable). The authors should explain and discuss this issue.

Indeed we see that ~30% of the bins enriched in the pluripotent stage spread, while 70% do not seem to spread. In line with earlier observations(Hickey et al., 2022; Vastenhouw et al., 2010), we find that H3K27me3 is almost absent in the zygote and is still being accumulated until 24hpf and beyond. Therefore the majority of the sites in the genome still seem to be in the process of gaining H3K27me3 until 24hpf, explaining why we see mostly “spreading” and “stable” states. Considering most of these sites are at promoters and show signs of bivalency, we think that these sites are marked for activation or silencing at later stages. We have discussed this in the manuscript (“discussion”). However, in response to this and earlier comment, we went back and searched for genes that show H3K27me3 demethylation in the most mature cell types (at 24 hpf) in our data, and found a subset of genes that show K27 demethylation after acquiring them earlier. Interestingly, most of the top genes in this list are well-known as developmentally important for their corresponding cell types. We have added this new result and discussed it further in the manuscript (Fig. 2d,e, , Supplementary table 3).

Minors:

(1) The authors cited two scMulti-omics studies in the introduction, but there have been lots of single-cell multi-omics studies published recently. The authors should cite and consider them.

We have cited more single-cell chromatin and multiome studies focussed on early embryogenesis in the introduction now.

(2) bT-ChIC seems to have been presented in a previous paper (ref 15). Therefore, Fig. 1a is unnecessary to show.

Figure 1a. shows a summary of our Zebrafish TChIC workflow, which contains the unique sample multiplexing and sorting strategy to reduce batch effects, which was not applied in the original TChIC workflow. We have now clarified this in “Results”.

(3) It's better to show the percentage of cell numbers (30% vs 70%) for each heatmap in Figure 2C.

We have added the numbers to the corresponding legends.

(4) Please double-check the citation of Fig. S4C, which may not relate to the conclusion of signal differences between lineages.

The citation seems to be correct (Fig. S4C supplements Fig. 2C, but shows mesodermal lineage cells) but the description of the legend was a bit misleading. We have clarified this now.

(5) Figure 4C has not been cited or mentioned in the main text. Please check.

Thanks for pointing it out. We have cited it in Results now.

Reviewer #2 (Significance):

Strengths:

This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish.

Limitations:

The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited.

Advance:

The zebrafish single-cell datasets are valuable. The T-ChIC method is new and interesting.

The audience will be specialized and from basic research fields, such as developmental biology, epigenomics, bioinformatics, etc.

I'm more specialized in the direction of single-cell epigenomics, gene regulation, 3D genomics, etc.

Thank you for your remarks.

Reviewer #3 (Evidence, reproducibility and clarity):

This manuscript introduces T‑ChIC, a single‑cell multi‑omics workflow that jointly profiles full‑length transcripts and histone modifications (H3K27me3 and H3K4me1) and applies it to early zebrafish embryos (4-24 hpf). The study convincingly demonstrates that chromatin-transcription coupling strengthens during gastrulation and somitogenesis, that promoter‑anchored H3K27me3 spreads in cis to enforce developmental gene silencing, and that integrating TF chromatin status with expression can predict lineage‑specific activators and repressors.

Major concerns

(1) Independent biological replicates are absent, so the authors should process at least one additional clutch of embryos for key stages (e.g., 6 hpf and 12 hpf) with T‑ChIC and demonstrate that the resulting data match the current dataset.

Thanks for pointing this out. We had, in fact, performed T-ChIC experiments in four rounds of biological replicates (independent clutch of embryos) and merged the data to create our resource. Although not all timepoints were profiled in each replicate, two timepoints (10 and 24hpf) are present in all four, and the celltype composition of these replicates from these 2 timepoints are very similar. We have added new plots in figure S2f and added (new) supplementary table (#1) to highlight the presence of biological replicates.

(2) The TF‑activity regression model uses an arbitrary R² {greater than or equal to} 0.6 threshold; cross‑validated R² distributions, permutation‑based FDR control, and effect‑size confidence intervals are needed to justify this cut‑off.

Thank you for this suggestion. We did use 10-fold cross validation during training and obtained the R²> values of TF motifs from the independent test set as an unbiased estimate. However, the cutoff of R² > 0.6 to select the TFs for classification was indeed arbitrary. In the revised version, we now report the FDR-adjusted p-values for these R² estimates based on permutation tests, and select TFs with a cutoff of padj < 0.01. We have updated our supplementary table #4 to include the p-values for all tested TFs. However, we see that our arbitrary cutoff of 0.6 was in fact, too stringent, and we can classify many more TFs based on the FDR cutoffs. We also updated our reported numbers in Fig. 4c to reflect this. Moreover, supplementary table #4 contains the complete list of TFs used in the analysis to allow others to choose their own cutoff.

(3) Predicted TF functions lack empirical support, making it essential to test representative activators (e.g., Tbx16) and repressors (e.g., Zbtb16a) via CRISPRi or morpholino knock‑down and to measure target‑gene expression and H3K4me1 changes.

We agree that independent validation of the functions of our predicted TFs on target gene activity would be important. During this revision, we analysed recently published scRNA-seq data of Saunders et al. (2023) (Saunders et al., 2023), which includes CRISPR-mediated F0 knockouts of a couple of our predicted TFs, but the scRNAseq was performed at later stages (24hpf onward) compared to our H3K4me1 analysis (which was 4-12 hpf). Therefore, we saw off-target genes being affected in lineages where these TFs are clearly not expressed (attached Fig 1). We therefore didn’t include these results in the manuscript. In future, we aim to systematically test the TFs predicted in our study with CRISPRi or similar experiments.

(4) The study does not prove that H3K27me3 spreading causes silencing; embryos treated with an Ezh2 inhibitor or prc2 mutants should be re‑profiled by T‑ChIC to show loss of spreading along with gene re‑expression.

We appreciate the suggestion that indeed PRC2-disruption followed by T-ChIC or other forms of validation would be needed to confirm whether the H3K27me3 spreading is indeed causally linked to the silencing of the identified target genes. But performing this validation is complicated because of multiple reasons: 1) due to the EZH2 contribution from maternal RNA and the contradicting effects of various EZH2 zygotic mutations (depending on where the mutation occurs), the only properly validated PRC2-related mutant seems to be the maternal-zygotic mutant MZezh2, which requires germ cell transplantation (see Rougeot et al. 2019 (Rougeot et al., 2019)) , and San et al. 2019 (San et al., 2019) for details). The use of inhibitors have been described in other studies (den Broeder et al., 2020; Huang et al., 2021), but they do not show a validation of the H3K27me3 loss or a similar phenotype as the MZezh2 mutants, and can present unwanted side effects and toxicity at a high dose, affecting gene expression results. Moreover, in an attempt to validate, we performed our own trials with the EZH2 inhibitor (GSK123) and saw that this time window might be too short to see the effect within 24hpf (attached Fig. 2). Therefore, this validation is a more complex endeavor beyond the scope of this study. Nevertheless, our further analysis of H3K27me3 de-methylation on developmentally important genes (new Fig. 2e-f, Sup. table 3) adds more confidence that the polycomb repression plays an important role, and provides enough ground for future follow up studies.

Minor concerns

(1) Repressive chromatin coverage is limited, so profiling an additional silencing mark such as H3K9me3 or DNA methylation would clarify cooperation with H3K27me3 during development.

We agree that H3K27me3 alone would not be sufficient to fully understand the repressive chromatin state. Extension to other chromatin marks and DNA methylation would be the focus of our follow up works.

(2) Computational transparency is incomplete; a supplementary table listing all trimming, mapping, and peak‑calling parameters (cutadapt, STAR/hisat2, MACS2, histoneHMM, etc.) should be provided.

As mentioned in the manuscript, we provide an open-source pre-processing pipeline “scChICflow” to perform all these steps (github.com/bhardwaj-lab/scChICflow). We have now also provided the configuration files on our zenodo repository (see below), which can simply be plugged into this pipeline together with the fastq files from GEO to obtain the processed dataset that we describe in the manuscript. Additionally, we have also clarified the peak calling and post-processing steps in the manuscript now.

(3) Data‑ and code‑availability statements lack detail; the exact GEO accession release date, loom‑file contents, and a DOI‑tagged Zenodo archive of analysis scripts should be added.

We have now publicly released the .h5ad files with raw counts, normalized counts, and complete gene and cell-level metadata, along with signal tracks (bigwigs) and peaks on GEO. Additionally, we now also released the source datasets and notebooks (Rmarkdown format) on Zenodo that can be used to replicate the figures in the manuscript, and updated our statements on “Data and code availability”.

(4) Minor editorial issues remain, such as replacing "critical" with "crucial" in the Abstract, adding software version numbers to figure legends, and correcting the SAMtools reference.

Thank you for spotting them. We have fixed these issues.

Reviewer #3 (Significance):

The method is technically innovative and the biological insights are valuable; however, several issues-mainly concerning experimental design, statistical rigor, and functional validation-must be addressed to solidify the conclusions.

Thank you for your comments. We hope to have addressed your concerns in this revised version of our manuscript.

Author response image 1.

(1) (top) expression of tbx16, which was one of the common TFs detected in our study and also targeted by Saunders et al by CRISPR. tbx16 expression is restricted to presomitic mesoderm lineage by 12hpf, and is mostly absent from 24hpf cell types. (bottom) shows DE genes detected in different cellular neighborhoods (circled) in tbx16 crispants from 24hpf subset of cells in Saunders et al. None of these DE genes were detected as “direct targets” in our analysis and therefore seem to be downstream effects. (2) Effect of 3 different concentrations of EZH2 inhibitor (GSK123) on global H3K27me3 quantified by flow cytometry using fluorescent coupled antibody (same as we used in T-ChIC) in two replicates. The cells were incubated between 3 and 10 hpf and collected afterwards for this analysis. We observed a small shift in H3K27me3 signal, but it was inconsistent between replicates.

References

Chen, Z., Djekidel, M. N., & Zhang, Y. (2021). Distinct dynamics and functions of H2AK119ub1 and H3K27me3 in mouse preimplantation embryos. Nature Genetics, 53(4), 551–563. den Broeder, M. J., Ballangby, J., Kamminga, L. M., Aleström, P., Legler, J., Lindeman, L. C., & Kamstra, J. H. (2020). Inhibition of methyltransferase activity of enhancer of zeste 2 leads to enhanced lipid accumulation and altered chromatin status in zebrafish. Epigenetics & Chromatin, 13(1), 5.

Hickey, G. J., Wike, C. L., Nie, X., Guo, Y., Tan, M., Murphy, P. J., & Cairns, B. R. (2022). Establishment of developmental gene silencing by ordered polycomb complex recruitment in early zebrafish embryos. eLife, 11, e67738.

Huang, Y., Yu, S.-H., Zhen, W.-X., Cheng, T., Wang, D., Lin, J.-B., Wu, Y.-H., Wang, Y.-F., Chen, Y., Shu, L.-P., Wang, Y., Sun, X.-J., Zhou, Y., Yang, F., Hsu, C.-H., & Xu, P.-F. (2021). Tanshinone I, a new EZH2 inhibitor restricts normal and malignant hematopoiesis through upregulation of MMP9 and ABCG2. Theranostics, 11(14), 6891–6904.

Mei, H., Kozuka, C., Hayashi, R., Kumon, M., Koseki, H., & Inoue, A. (2021). H2AK119ub1 guides maternal inheritance and zygotic deposition of H3K27me3 in mouse embryos. Nature Genetics, 53(4), 539–550.

Rougeot, J., Chrispijn, N. D., Aben, M., Elurbe, D. M., Andralojc, K. M., Murphy, P. J., Jansen, P. W. T. C., Vermeulen, M., Cairns, B. R., & Kamminga, L. M. (2019). Maintenance of spatial gene expression by Polycomb-mediated repression after formation of a vertebrate body plan. Development (Cambridge, England), 146(19), dev178590.

San, B., Rougeot, J., Voeltzke, K., van Vegchel, G., Aben, M., Andralojc, K. M., Flik, G., & Kamminga, L. M. (2019). The ezh2(sa1199) mutant zebrafish display no distinct phenotype. PloS One, 14(1), e0210217.

Saunders, L. M., Srivatsan, S. R., Duran, M., Dorrity, M. W., Ewing, B., Linbo, T. H., Shendure, J., Raible, D. W., Moens, C. B., Kimelman, D., & Trapnell, C. (2023). Embryo-scale reverse genetics at single-cell resolution. Nature, 623(7988), 782–791.

Vastenhouw, N. L., Zhang, Y., Woods, I. G., Imam, F., Regev, A., Liu, X. S., Rinn, J., & Schier, A. F. (2010). Chromatin signature of embryonic pluripotency is established during genome activation. Nature, 464(7290), 922–926.

Zeller, P., Blotenburg, M., Bhardwaj, V., de Barbanson, B. A., Salmén, F., & van Oudenaarden, A. (2024). T-ChIC: multi-omic detection of histone modifications and full-length transcriptomes in the same single cell. In bioRxiv (p. 2024.05.09.593364). https://doi.org/10.1101/2024.05.09.593364

Zeller, P., Yeung, J., Viñas Gaza, H., de Barbanson, B. A., Bhardwaj, V., Florescu, M., van der Linden, R., & van Oudenaarden, A. (2022). Single-cell sortChIC identifies hierarchical chromatin dynamics during hematopoiesis. Nature Genetics. https://doi.org/10.1038/s41588-022-01260-3

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors describe a method to probe both the proteins associated with genomic elements in cells, as well as 3D contacts between sites in chromatin. The approach is interesting and promising, and it is great to see a proximity labeling method like this that can make both proteins and 3D contacts. It utilizes DNA oligomers, which will likely make it a widely adopted method. However, the manuscript over-interprets its successes, which are likely due to the limited appropriate controls, and of any validation experiments. I think the study requires better proteomic controls, and some validation experiments of the "new" proteins and 3D contacts described. In addition, toning down the claims made in the paper would assist those looking to implement one of the various available proximity labeling methods and would make this manuscript more reliable to non-experts.

Strengths:

(1) The mapping of 3D contacts for 20 kb regions using proximity labeling is beautiful.

(2) The use of in situ hybridization will probably improve background and specificity.

(3) The use of fixed cells should prove enabling and is a strong alternative to similar, living cell methods.

Weaknesses:

(1) A major drawback to the experimental approach of this study is the "multiplexed comparisons". Using the mtDNA as a comparator is not a great comparison - there is no reason to think the telomeres/centrosomes would look like mtDNA as a whole. The mito proteome is much less complex. It is going to provide a large number of false positives. The centromere/telomere comparison is ok, if one is interested in what's different between those two repetitive elements.

We appreciate the reviewers' point here. In fact we selected the mitochondrial DNA as a target for just the reason that the reviewer notes. mtDNA should be spatially distinct from the nuclear targets and allow us to determine if we were in fact seeing spatially distinct proteins at the interorganelle (mtDNA vs. telomeres/centrosomes) and intraorganelle (telomeres vs centromeres) levels.

But the more realistic use case of this method would be "what is at a specific genomic element"? A purely nuclear-localized control would be needed for that. Or a genomic element that has nothing interesting at it (I do not know of one).

We have now added two studies in Figure 4 and Figure 5 detailing the use of OMAP to investigate specific genomic elements. In this case the Hox clusters (HOXA and HOXB) and haplotype-specific analysis of X-chromosome inactivation centers in female murine (EY.T4) cells. The controls in these cases are more specific, in line with those suggested by the reviewer as we (1) compare HOXA and HOXB with or without EZH2 inhibition using the same sets of probes and (2) specifically compare the region surrounding the XIC in female cells for the inactive and active X chromosomes.

You can see this in the label-free work: non-specific, nuclear GO terms are enriched likely due to the random plus non-random labeling in the nucleus. What would a Telo vs general nucleus GSEA look like? (GSEA should be used for quantitative data, no GO). That would provide some specificity. Figures 2G and S4A are encouraging, but a) these proteins are largely sequestered in their respective locations, and b) no validation by an orthogonal method like ChIP or Cut and Run/Tag is used.

We performed GSEA on the enrichment scores for the label-free proteomics data from the SAINT output in Figure 1D and that several of these proteins (e.g., those highlighted in Figure 2A: TERF1, CENPN, TOM70) have already been extensively validated to co-localize to these locations.

To the reviewers request for additional validation, we analyzed ChIP-seq data for several proteins to determine if they were enriched surrounding specific loci. In the case of the HoxA/B analysis, we found that HDAC3 and TCF12 were enriched at HOXB compared to HOXA, and SMARCB1 and ZC3H13 were enriched at HOXA compared to HOXB (Figure 4C). HDAC3 and TCF12 ChIP data confirmed increased peak calls at HOXB and SMARCB1 and ZC3H13 ChIP data confirmed increased peak calls at HOXA for these four selected proteins (Figure 4D).

You can also see this in the enormous number of "enriched" proteins in the supplemental volcano plots. The hypothesis-supporting ones are labeled, but do the authors really believe all of those proteins are specific to the loci being looked at? Maybe compared to mitochondria, but it's hard to believe there are not a lot of false positives in those blue clouds. I believe the authors are more seeing mito vs nucleus + Telo than the stated comparison. For example, if you have no labeling in the nucleus in the control (Figures 1C and 2C) you cannot separate background labeling from specific labeling. Same with mito vs. nuc+Telo. It is not the proper control to say what is specifically at the Telo.

We agree with the reviewer that compared to mitochondrial targeting, there could be non-specific nuclear comparisons. We note again though that we purposefully stayed away from using the word “specifically” when describing the proteomics work developed here. The reason being that we are not atlasing a large number of targets to define specificity. Instead, we highlight in Figure 2 that we did observe differences in proteins associating with telomeres and mitochondrial DNA. That may be non-specific, and in fact, this is also why we decided to include two nuclear targets to determine what might be specifically enriched. Thus, we compared centromeric and telomeric protein enrichment as determined by OMAP and observed consistent differential enrichment of shelterin proteins at telomeres (Figure 2I) and CENP-A complex members at centromeres (Figure 2J). We could have done the relative comparisons to no-oligo controls, analogous to how CASPEX compared targeted analyses to no-sgRNA controls (PMID: 29735997). However, we found that the mitochondrial targeted samples were generally better as a comparator because (1) we have clear means to validate differences and (2) the local environment around DNA is being labeled.

I would like to see a Telo vs nuclear control and a Centromere vs nuc control. One could then subtract the background from both experiments, then contrast Telo vs Cent for a proper, rigorous comparison. However, I realize that is a lot of work, so rewriting the manuscript to better and more accurately reflect what was accomplished here, and its limitations, would suffice.

Assuming the nuclear control was the same, It is unclear how this ratio-of-ratios ([Telo/Ctrl]/[Cent/ctrl]) experiment would be inherently different from the direct comparison between Telo and Centromere. Again, assuming the backgrounds are derived from the same cellular samples. More than likely adding the extra ratios could increase the artifactual variance in the estimates, reducing the power of the comparisons as has been seen in proteomics data using ratio-of-ratio comparisons in the past (Super-SILAC).

(2) A second major drawback is the lack of validation experiments. References to literature are helpful but do not make up for the lack of validation of a new method claiming new protein-DNA or DNA-DNA interactions. At least a handful of newly described proximal proteins need to be validated by an orthogonal method, like ChIP qPCR, other genomic methods, or gel shifts if they are likely to directly bind DNA. It is ok to have false positives in a challenging assay like this. But it needs to be well and clearly estimated and communicated.

We appreciate the reviewers' point here. To be clear, we have not made any claims about new proteins at specific loci. Instead we validated that known telomeric and centromeric associating proteins were consistently enriched by DNA OMAP (Figure 2). We also want to emphasize that while valuable, the current paper is not an atlasing paper to define the full and specific proteomes of two genomic loci. We instead show how this method can be used to observe quantitative differences in proteins enriched at certain loci (HOXA/B work, Figure 4) and even between haplotypes (Xi/Xa work, Figure 5).

(3) The mapping of 3D contacts for 20 kb regions is beautiful. Some added discussion on this method's benefits over HiC-variants would be welcomed.

We appreciate the reviewers' point here and have added the following text to the discussion: “Additionally, we show that this method is also able to detect DNA-DNA contacts through biotinylation of loop anchors. Our approach functions similarly to 4C[86]. However, our approach of biotin labeling of contacts does not rely on pairwise ligation events. Thus, detection of contacts through DNA O-MAP will vary in the sampling of DNA-DNA contacts in comparison.”

(4) The study claims this method circumvents the need for transfectable cells. However, the authors go on to describe how they needed tons of cells, now in solution, to get it to work. The intro should be more in line with what was actually accomplished.

We took the reviewers point and have worked to scale down the DNA OMAP experiments while revising this manuscript. As noted in Figure 5, we have been able to scale this work down to work on plates with ~10x fewer cells than with our initial experiments. This is on top of the initial DNA OMAP work in Figure 1 and 2, as well as our additional work in Figure 4, where we are using 30-60 million cells in solutions which is still 10x less material than previous work (PMID: 29735997). Thus, the newest DNA OMAP platform uses ~100x fewer cells than previous work.

(5) Comments like "Compared to other repetitive elements in the human genome...." appear to circumvent the fact that this method is still (apparently) largely limited to repetitive elements. Other than Glopro, which did analyze non-repetitive promoter elements, most comparable methods looked at telomeres. So, this isn't quite the advancement you are implying. Plus, the overlap with telomeric proteins and other studies should be addressed. However, that will be challenging due to the controls used here, discussed above.

As noted above, we have added Figures 4 and 5 to address the reviewer concerns by targeting multiple non-repetitive loci (HOXA and HOXB clusters and a 4.5Mb region straddling X-inactivation center on both the active and inactive X homolog). Targeting the regions around the X-inactivation center shows the potential to perform haplotype-resolved proteome analysis of chromatin interactors.

For the telomeric protein overlap, we tried to do this specifically in Figure 1F, we agree with the reviewer that the controls used dramatically change the proteins considered enriched. The goal of the network analysis was to show (1) that we identify proteins previously observed in telomere proteomic datasets and (2) that we gain a more complete view of proteins based on capturing more known interacting proteins than many previous methods as was noted for the RNA OMAP platform (PMID: 39468212). For example, we observed enrichment of PRPF40A in the telomeric DNA OMAP data. From the Bioplex interactome, PRPF40A was observed to interact with TERF2IP and TERF2, suggesting that through these interactions PRPF40A may colocalize at telomeres. Similarly, we observed enrichment of SF3A1, SF3B1, and SF3B2. The SF3 proteins are known regulators of telomere maintenance (PMID: 27818134), but have not previously been observed in telomeric proteomics datasets, except now in DNA OMAP.

We have added the following text to the Results to clarify these points:

“To benchmark DNA O-MAP, we compared the full set of telomeric proteins to proteins observed in five established telomeric datasets (PICh, C-BERST, CAPLOCUS, CAPTURE, BioID)12,14,16,35,36 (Figure 1F). DNA O-MAP captured both previously observed telomeric interacting proteins (shelterins) as well as telomere associated proteins (ribonucleoproteins). We identified multiple heterogeneous nuclear ribonucleoproteins (hnRNPs) previously annotated as telomere-associated, including HNRNPA1 and HNRNPU. HNRNPA1 has been demonstrated to displace replication protein A (RPA) and directly interact with single-stranded telomeric DNA to regulate telomerase activity37–39. HNRNPU belongs to the telomerase-associated proteome40 where it binds the telomeric G-quadruplex to prevent RPA from recognizing chromosome ends41. We mapped DNA O-MAP enriched telomeric proteins to the BioPlex protein interactome and observed that in addition to capturing proteins from previously observed telomeric datasets (Figure 1F), DNA O-MAP enriched for interactors of previously observed telomeric proteins. Previous data found RBM17 and SNRPA1 at telomeres, and in BioPlex these proteins interact with three SF3 proteins (SF3A1, SF3B1, SF3B2). Though they were not identified in previous telomeric proteome datasets, all three of these SF3 proteins were enriched in the DNA O-MAP telomeric data. Furthermore, through interactions with G-quadruplex binding factors, these SF3 proteins are regulators of telomere maintenance (PMID: 27818134). Taken together, this data supports the effectiveness of DNA O-MAP for sensitively and selectively isolating loci-specific proteomes.”

Reviewer #2 (Public review):

Summary

Liu and MacGann et al. introduce the method DNA O-MAP that uses oligo-based ISH probes to recruit horseradish peroxidase for targeted proximity biotinylation at specific DNA loci. The method's specificity was tested by profiling the proteomic composition at repetitive DNA loci such as telomeres and pericentromeric alpha satellite repeats. In addition, the authors provide proof-of-principle for the capture and mapping of contact frequencies between individual DNA loop anchors.

Strengths

Identifying locus-specific proteomes still represents a major technical challenge and remains an outstanding issue (1). Theoretically, this method could benefit from the specificity of ISH probes and be applied to identify proteomes at non-repetitive DNA loci. This method also requires significantly fewer cells than other ISH- or dCas9-based locus-enrichment methods. Another potential advantage to be tested is the lack of cell line engineering that allows its application to primary cell lines or tissue.

We thank the reviewers for their comments and note that we have followed up on the idea of targeting non-repetitive DNA loci (HOXA and HOXB clusters and a 4.5Mb section of the X chromosome on each homolog) in the revised manuscript (Figures 4 and 5).

Weaknesses

The authors indicate that DNA O-MAP is superior to other methods for identifying locus-specific proteomes. Still, no proof exists that this method could uncover proteomes at non-repetitive DNA loci. Also, there is very little validation of novel factors to confirm the superiority of the technique regarding specificity.

Our primary claim for DNA OMAP is that it requires orders of magnitude fewer cells than previous studies. Based on comments along these lines from both reviewers, we performed DNA OMAP targeting non-repetitive DNA loci (HOXA and HOXB clusters and a 4.5Mb section of the X chromosome on each homolog) in the revised manuscript (Figure 4 and 5). For the X chromosome targeting, we used ~3 million cells per condition with methods that we optimized during revision. When targeting HOXA and HOXA, we were able to identify HDAC3 and TCF12 enrichment at HOXB compared to HOXA as well as ZC3H13 and SMARB1 enrichment at HOXA compared to HOXB, which is consistent with ChIP-seq reads from ENCODE for these proteins (Figure 4C, D). Both the HOXand X chromosome work help to address limitations noted in the Gauchier et al. paper the reviewer notes as both show progress towards overcoming “the major signal-to-noise ratio problem will need to be addressed before they can fully describe the specific composition of single-copy loci”.

The authors first tested their method's specificity at repetitive telomeric regions, and like other approaches, expected low-abundant telomere-specific proteins were absent (for example, all subunits of the telomerase holoenzyme complex). Detecting known proteins while identifying noncanonical and unexpected protein factors with high confidence could indicate that DNA O-MAP does not fully capture biologically crucial proteins due to insufficient enrichment of locus-specific factors. The newly identified proteins in Figure 1E might still be relevant, but independent validation is missing entirely. In my opinion, the current data cannot be interpreted as successfully describing local protein composition.

We analyzed ChIP-seq reads for our HOXA and HOXB (Figure 4C,D) which recapitulate our findings for four of our differentially enriched proteins. We also note that with the addition of the nonrepetitive loci (Figures 4 and 5), we have performed DNA OMAP on seven different targets (telomeres, pericentromeres, mitoDNA, HOXA, HOXB, Xi, and Xa) and identified expected targets at each of these. The consistency of these data, which mirrors the consistency of the RNA implementation of OMAP (PMID: 39468212), reinforces that we can successfully enrich local proteomes at genomic loci.

Finally, the authors could have discussed the limitations of DNA O-MAP and made a fair comparison to other existing methods (2-5). Unlike targeted proximity biotinylation methods, DNA O-MAP requires paraformaldehyde crosslinking, which has several disadvantages. For instance, transient protein-protein interactions may not be efficiently retained on crosslinked chromatin. Similarly, some proteins may not be crosslinked by formaldehyde and thus will be lost during preparation (6).

Based on this critique we have gone back through the manuscript to improve the fairness of our comparisons and expanded the limitations in our discussion section.

To the point about fixation, Schmiedeberg et al., which the reviewer references, does describe crosslinking requiring longer interactions (~5 s). Yet, as featured in reviews, many additional studies have found that “it has been possible to perform ChIP on transcription factors whose interactions with chromatin are known from imaging studies to be highly transient” (Review PMID: 26354429). We note similar results in proteomics analysis in Subbotin and Chait that state that the linkage of lysine-based fixatives like formaldehyde and “glutaraldehyde to reactive amines within the cellular milieu were sufficient to preserve even labile and transient interactions (PMID: 25172955).

(1) Gauchier M, van Mierlo G, Vermeulen M, Dejardin J. Purification and enrichment of specific chromatin loci. Nat Methods. 2020;17(4):380-9.

(2) Dejardin J, Kingston RE. Purification of proteins associated with specific genomic Loci. Cell. 2009;136(1):175-86.

(3) Liu X, Zhang Y, Chen Y, Li M, Zhou F, Li K, et al. In Situ Capture of Chromatin Interactions by Biotinylated dCas9. Cell. 2017;170(5):1028-43 e19.

(4) Villasenor R, Pfaendler R, Ambrosi C, Butz S, Giuliani S, Bryan E, et al. ChromID identifies the protein interactome at chromatin marks. Nat Biotechnol. 2020;38(6):728-36.

(5) Santos-Barriopedro I, van Mierlo G, Vermeulen M. Off-the-shelf proximity biotinylation for interaction proteomics. Nat Commun. 2021;12(1):5015.

(6) Schmiedeberg L, Skene P, Deaton A, Bird A. A temporal threshold for formaldehyde crosslinking and fixation. PLoS One. 2009;4(2):e4636.

Reviewer #3 (Public review):

Significance of the Findings:

The study by Liu et al. presents a novel method, DNA-O-MAP, which combines locus-specific hybridisation with proximity biotinylation to isolate specific genomic regions and their associated proteins. The potential significance of this approach lies in its purported ability to target genomic loci with heightened specificity by enabling extensive washing prior to the biotinylation reaction, theoretically improving the signal-to-noise ratio when compared with other methods such as dCas9-based techniques. Should the method prove successful, it could represent a notable advancement in the field of chromatin biology, particularly in establishing the proteomes of individual chromatin regions - an extremely challenging objective that has not yet been comprehensively addressed by existing methodologies.

Strength of the Evidence:

The evidence presented by the authors is somewhat mixed, and the robustness of the findings appears to be preliminary at this stage. While certain data indicate that DNA-O-MAP may function effectively for repetitive DNA regions, a number of the claims made in the manuscript are either unsupported or require further substantiation. There are significant concerns about the resolution of the method, with substantial biotinylation signals extending well beyond the intended target regions (megabases around the target), suggesting a lack of specificity and poor resolution, particularly for smaller loci.

We thank the reviewers for their comments and note that we have followed up on the idea of targeting non-repetitive DNA loci (HOX clusters and part of the X chromosome) in the revised manuscript (Figures 4 and 5).

Furthermore, comparisons with previous techniques are unfounded since the authors have not provided direct comparisons with the same mass spectrometry (MS) equipment and protocols. Additionally, although the authors assert an advantage in multiplexing, this claim appears overstated, as previous methods could achieve similar outcomes through TMT multiplexing. Therefore, while the method has potential, the evidence requires more rigorous support, comprehensive benchmarking, and further experimental validation to demonstrate the claimed improvements in specificity and practical applicability.

We have made the comparisons as best as possible. In fact, we found it difficult to find examples of recent implementations of many of these methods. Purchasing the exact mass spectrometers or performing every version of chromatin proteomics would be well beyond the scope of this work. On the other hand, OMAP has already generated data for three manuscripts. We are making the claim that using the instrumentation and methods available to us, we were able to reduce the number of cells required to analyze a given genomic loci. We then applied TMT multiplexing to further improve the throughput and perform replicate analyses. To fully validate that one protein exists at one loci and no other would require exhaustive atlasing of protein-genomic interactions which would be well beyond the scope of this single paper. Similarly, ChIP for every target identified to assess an empirical FDR would be well beyond the scope of this work.

Recommendations for the authors:

Reviewing Editor Comments:

In summary, all three reviewers raised major concerns about the limitations of the method, many of which could be resolved by more precise and transparent language about these limitations. If you choose to resubmit a revised version, you should address questions like: What scale does "individual locus" refer to? At what scale can the method map protein-DNA interactions at individual targeted loci, rather than large repetitive domains? What is the estimated false discovery rate for a set of enriched proteins? The eLife assessment for this version of the manuscript is based on reviewer concerns. Note that this assessment can be updated after receiving a response to reviewer comments.

Reviewer #1 (Recommendations for the authors):

(1)The first couple of paragraphs make it sound like your method would exclusively benefit from sample multiplexing with MS-based proteomics. That is a bit misleading. The other stated methods use TMT. They don't use it to compare very different genomic (or compartmental) regions, but there is no reason cberst, glopro or CasID could not.

A good point and we have updated the manuscript to reflect this. While previous methods generally did not use TMT, they could be adapted to do so and, similar to OMAP, improved by the use of more replicates in their analyses.

(2) Please make the colors in 1F for the dataset overlap easier to read. 2 and 4+ are too similar.

We appreciate the comment on making the colors easier to discern. Along these lines we’ve changed the color of “2” to make it easier to distinguish from “4+”.

(3) Label as many dots as legible in your volcano plots.

We’ve labeled a number of proteins that are relevant to the discussion in this paper as well as some additional proteins. We feel that additional labeling would detract from the points that we are trying to make in individual figure panels about groups of proteins, rather than general remodeling of all proteins.

(4) Figure 2E needs a divergent color scheme since it crosses 0. And is it scaled, log-transformed, or both? And compared to what then?

Figure 2E (heatmap) is z-scaled relative protein abundance measurements based on TMTpro reporter ion signal to noise (“s/n”). We have added additional information to the legend to highlight the information that the reviewer points out here. For the color, we are unsure of what is being asked for, as above 0 is red and below 0 is blue.

(5) Unclear what you are implying with "...only 1-2 biological replicates." I would omit or clarify.

Fair point, we have updated the manuscript to omit this section to simplify the introduction.

(6) H2O2 and biotin phenols might be toxic to living organisms. But so is 4% PFA and ISH. I realize you are trying to justify your new approach but you don't need to do it with exaggerated contrasts. This O-MAP is a great approach and probably more likely for people to adopt it because it's DNA ISH based. Plus, with the clinking, you are likely not displacing proteins via Cas9 landing.

We appreciate the reviewer’s comments about adoption and lack of protein displacement. We’ve scaled back on the claims and added more about limitations owing to crosslinking and ISH.

(7) How much genome does the Cent regions take up? You state 500 kb for Telos.

In the text we delineate how large of a region the PanAlpha probes target “The genome-wide binding profile of the pan-alpha probe closely overlaps with centromeres (Figure S1) and covers approximately 35 Mb of the genome according to in silico predictions.” Additionally, we’ve added Table S4 to summarize target locus sizes for all of the included targets.

(8) You seem to be underestimating the lysine labeling. Is that after TMT labeling and analysis? If so, you're already ignoring what couldn't be seen. I don't think it's that important but you included it, so please describe clearly why it's an issue and how much of an issue it is. How does that relate to lit values? And it's not just TMTpro, it's any lysine labeler.

We appreciate the reviewers point about specifying the reasoning and the lack of clarity around overall lysine labeling. That 1.38% is the number of peptides with remainder modifications due to formaldehyde crosslinking. For overall acylation of lysines with TMT labels, we generally expect (and achieve) >97% labeling of lysines with TMT reagents as the Kuster and Carr labs nicely demonstrated across a range of labeling conditions (PMID: 30967486).

Decrosslinking is a critical step generally for proteomics workflows on fixed or FFPE tissues and thus we sought to explore whether we could achieve sufficiently low residual lysine alkylation to enable protein quantitation by TMTpro reagents (or any lysine labeler, as the reviewer notes). For TMTpro-based methods on peptides, this is less of a concern generally as protease cleavage frees new primary amines at the N-termini of peptides which can be labeled for quantitation. But in part since we are describing a proteomics method on fixed tissues we wanted to share these data and the potential inclusion of residual fixation modifications for readers to potentially take into consideration when performing this method.

Reviewer #3 (Recommendations for the authors):

Liu et al. describe an original locus labelling approach that enables the isolation of specific genomic regions and their associated proteins. I have mixed views on this work, which, in my opinion, remains preliminary at this stage. Establishing the proteome of a single chromatin region is one of the most complex challenges in chromatin biology, as extensively discussed in Gauchier et al. (2020). Any breakthrough towards this goal is of significant interest to the community, making this manuscript potentially compelling. Indeed, some data suggest that the method works for repetitive DNA to some extent. However, much of the data is not very convincing, and in the case of small DNA targets, it argues against the use of DNA-O-MAP.

In contrast to existing methods, DNA-O-MAP combines locus-specific hybridisation in situ (using affordable oligonucleotides) with proximity biotinylation. A major advantage of this strategy over other locus-specific biotinylation methods is the possibility of extensively washing excess or non-specifically hybridised probes before the biotinylation reaction, theoretically limiting biotinylation to the target region and thus significantly enhancing the signal-to-noise ratio. Other methods involving proximity biotinylation, such as targeted dCas9, do not have this capacity, meaning biotinylation occurs not only at the locus where a small fraction of dCas9 molecules is targeted but also around non-bound dCas9 molecules (representing the vast majority of dCas9 expressed in a given cell). This aspect potentially represents an interesting advance.

We thank the reviewer for their thoughts and critiques, which we hope have in part relieved concerns pertaining to limitation on repetitive elements. To the latter points, we confirmed this with new specificity analysis that showed labeling to be highly specific to a given probe locus (Figure S3).

Below, I outline the significant issues:

The manuscript implies that DNA-O-MAP has better sensitivity than earlier techniques like CAPTURE, GLOPRO, or PICh. The authors state that PICh uses one trillion cells (which I doubt is accurate), and other methods require 300 million cells, whereas DNA-O-MAP uses only 60 million cells, suggesting the latter is more feasible. However, these earlier experiments were conducted almost 15 and 6 years ago, when mass spectrometry (MS) sensitivity was considerably lower than that of current instruments. The authors cannot know whether the proteome obtained by previous methods using 60 million cells, but analysed with current MS technology, would yield results inferior to those of DNA-O-MAP. Unless the authors directly compare these methods using the same number of cells and identical MS setups, I find their argument unjustified and misleading.

Based on the instrumentation listed, we actually do have a good idea of how sensitivity changes may have affected identifications and overall sensitivity. For example, the CASPEX data was collected on an Orbitrap Fusion Lumos, while our data was collected on an Orbitrap Fusion Eclipse. From our work characterizing these two instruments during the Eclipse development (PMID: 32250601), we do actually know that the ion optics improvements boosted sensitivity of the Eclipse used in our work compared to the Lumos by ~50%, meaning if GLOPRO was run on an Eclipse it would still require >200 million cells per replicate for input.

It is suggested that DNA-O-MAP is capable of 'multiplexing', whereas previous methods are not. This statement is also misleading. As I understand it, the targeted regions do not originate from a common pool of cells. Instead, TMT multiplexing only occurs after each group of cells has been independently labelled (Telo, Centro, Mito, control). Therefore, previous methods could also perform multiplexing with TMT. Moreover, it is unclear how each proteome was compared: one would expect many more proteins from centromeres than from telomeres (I am unsure about the number of mitochondria in these cells) since these regions are significantly larger than telomeres (possibly 10 to 100 times larger?). Have the authors attempted to normalise their proteomics data to the size (concatenated) of each target? This is particularly relevant when comparing histone enrichment at chromatin regions of differing sizes.

We agree with the reviewers that this was overstated. In fact the GLOPRO paper notes that they performed a MYC analysis with a previous generation of TMT that could multiplex 10 samples. We have amended the manuscript to be more specific in those contexts. As stated in the methods section, “Samples were column normalized for total protein concentration”, to account for the amount of protein and size of the different targets.

Figure 1C shows streptavidin dots resembling telomeres. To substantiate this claim, simultaneous immunofluorescence with a telomere-specific protein (e.g., TRF1 or TRF2) is required. It is currently unknown whether all or only a subset of telomeres are targeted by DNA-O-MAP, and it is also unclear if some streptavidin foci are non-telomeric. Quantification is needed to indicate the reproducibility of the labelling (the same comment applies to the centromere probes later in the manuscript; an immunofluorescence assay with CENPB would be informative, alongside quantifications).

We understand the reviewer’s concern about specificity and reproducibility of DNA-O-MAP. To address this we have added analysis showing the efficiency and specificity of our FISH and biotin labeling for Telomere, PanAlpha, and Mitochondria targeting oligos (Figure S3). We found that biotin deposition was highly specific to the intended targets with an average across the three probes of 98% specificity.

Perhaps more importantly, the authors suggest that it may be possible to enrich proteins that are not necessarily present at the target locus but are instead in spatial proximity (e.g., RNA polymerase I subunits enriched upon centromere targeting). Does this not undermine the purpose of retrieving locus-specific proteomes?

The goal of DNA OMAP is to identify a local neighborhood of proteins around a specific genomic loci, similar to GLOPRO. As we note in the work presented in Figure 4 and 5 now, these neighborhoods are inherently interesting for comparison of quantitative changes that occur around a genomic locus.

Possibly related to the previous issue, when DNA-O-MAP is used to assess DNA-DNA interactions, probes covering regions of 20-25 kb are employed. Therefore, one would expect these regions to be significantly biotinylated compared to flanking regions. However, Genome Browser screenshots indicate extensive biotinylation signals spanning several megabases around the 20-25 kb targets. If the method were highly resolutive, the target region would be primarily enriched, with possibly discrete lower enrichment at distant interacting regions. The lack of discrete enrichment suggests poor resolution, likely due to the likely large scale of proximity biotinylation. This compromises the effectiveness of DNA-O-MAP, especially if it is intended to target small loci with complex sequences. Could the authors quantify the absolute number of reads from the target region compared to those from elsewhere in the genome (both megabases around the locus and other chromosomes, where many co-enriched regions seem to exist)? This would provide insights into both enrichment and specificity.

Thanks for this suggestion, we have included a new Figure S8 to look at normalized read depth as a function of distance from the genomic target. The resolution of DNA OMAP, like all peroxidase mediated proximity labeling methods, is not dependent on the sequence length of the DNA region, but the 30-40nm of physical space around the HRP molecule that is targeted to the genomic loci. 

Minor Issues:

(1) Page 3, second paragraph: It is unclear why probes producing a visible signal in situ necessarily translates to their ability to retrieve a specific proteome.

We have revised the manuscript to de-emphasize the visible signal aspect of probe targeting and re-emphasize our initial point that the number of probes needed to properly target unique regions makes the use of locked nucleic acid probes cost-prohibitive. The basic point though, we and others previously showed with RNA OMAP (PMID: 39468212) and Apex/proximity labeling strategies, the ability to deposit biotin and visualize generally directly translates to recovery of proximally labeled proteins (PMID: 26866790).

(2) Page 3, last paragraph: "to reach a higher degree of enrichment...": Has it been demonstrated that direct protein biotinylation provides higher enrichment of relevant proteins? Certainly, there is higher enrichment of proteins, but whether they are relevant is another matter.

Our point here was that the methods using direct protein biotinylation have higher levels of enrichment and thus require less cells than the previously mentioned PICh method, which is why we wrote the following: “In the case of GLoPro, APEX-based proximity labeling enhanced protein detection sensitivity, reducing the input required for each replicate analysis to ~300 million cells—a 10-fold reduction in cell input compared to PICh which used 3 billion cells.”

Regarding if these proteins are relevant or not, we show enrichment of known proteins that are critical to the function of their occupied genomic region at telomeres and centromeres. Additionally, we’ve made added quantitative comparisons to assess relevance in our analysis of Hox and our targeted region of the X chromosome through comparisons to ChIP data at these regions. The improved enrichment that we’ve established in our initial submission as well as in the updated version also means that we can further scale down the number of cells required.

(3) Figure 2B is misleading; it appears as though all three regions are targeted in the same cell, suggesting true multiplexing, which, I believe, is not the case.

To avoid any potential confusion about how the samples were derived we’ve updated this figure panel to show three separate cells, each with a different region being targeted.

(3) If I understand correctly, the 'no probe' control should primarily retrieve endogenously biotinylated proteins (carboxylases), which are mainly found in mitochondria. Why does the Pearson clustering in Supplementary Figure 2 not place this control proteome closer to the mitochondrial proteome?

Under the assumption that the ~10 carboxylases are biotinylated at the same levels in all cells, yet the proportion of these carboxylases compared to all enriched proteins for a given target is markedly reduced. Thus, as a proportion of the enriched proteome we note in Figure S4 that mitochondrial DNA OMAP enriches proteins besides the carboxylases. We believe this explains why the ‘no probe’ sample can be clearly separated along PC2 in Figure 2D.

(4) Was CENPA enriched in the centromere DNA-O-MAP? If not, have the authors scaled up (e.g., with ten times more cells) to see if the local proteome becomes deeper and detects relevant low-abundance proteins like CENPA or HJURP? This would be very informative.

We did not observe CENPA, and we had originally contemplated the experiment the reviewer suggested, but noted that CENPA has only two tryptic peptides (>7 AA, <35AA), and they are both in the commonly phosphorylated region of the protein. Rather than scale up these experiments, we decided to attempt DNA OMAP on the non-repetitive locus experiments.

(5) Using a few million cells, I do not see how the starting chromatin amount could range from 0.5 to 7 mg, as shown in Figures 2 and 3. How were these figures calculated? One diploid cell contains approximately 6 pg of DNA/chromatin, which means one billion cells represent about 6 mg of DNA/chromatin (a typical measurement for these methods).

Thanks to the reviewer for catching this, that should have been the total lysate amount, not chromatin mass. We have corrected Figures 2 and 3.

(6) Figure S1: There is no indication of the metrics used for the shades of red.

We have added a gradient legend to depict this.

(7) What is the purpose of HCl in the experiment?

HCl treatment was done to reduce autofluorescence for imaging (PMID: 39548245).

(8) I could not find the MS dataset on the server using the provided accession number (PDX054080).

Thank you for pointing this out, we have confirmed the dataset is public now and added the new datasets for the Xi/Xa and Hox studies. We also note that the accession should be “PXD054080”

(9) Why desthiobiotin instead of biotin?

We have tested both; desthiobiotin was helpful to reduce adsorption to surfaces. Either biotin or desthiobiotin can be used, though, for OMAP.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

The authors test the hypotheses, using an effort-exertion and an effort-based decision-making task, while recording brain dynamics with EEG, that the brain processes reward outcomes for effort differentially when they earned for themselves versus others.

The strengths of this experiment include what appears to be a novel finding of opposite signed effects of effort on the processing of reward outcomes when the recipient is self versus others. Also, the experiment is well-designed, the study seems sufficiently powered, and the data and code are publicly available.

We thank Reviewer #1 for the affirmative appraisal of our manuscript as well as the thoughtful and insightful comments, which have enabled us to significantly improve the manuscript.

(1) Inferences rely heavily on the results of mixed effects models which may or may not be properly specified and are not supported by complementary analyses.

We thank Reviewer #1 for raising this critical issue of model specification. We have re-fitted our mixed-effects models and performed complementary analyses to validate the robustness of our findings. Specifically, we adopted the maximal converging random-effects structure (including random slopes for Recipient, Effort, and Magnitude where feasible) while ensuring model stability (see Responses to Reviewer #1’s Recommendations point 2). Crucially, our primary findings, including the Recipient × Effort and Recipient × Effort × Magnitude interactions, remained robust. Furthermore, additional analyses confirmed that these results were not confounded by factors such as response speed and subjective effort rating (see Responses to Reviewer #1’s Recommendations point 5).

(2) Also, not all results hang together in a sensible way. For example, participants report feeling less subjective effort, but also more disliking of tasks when they were earning rewards for others versus self. Given that participants took longer to complete tasks when earning effort for others, it is conceivable that participants might have been working less hard for others versus themselves, and this may complicate the interpretation of results.

We thank Reviewer #1 for this insightful point (which also relates to Reviewer #3’s point 5). In our study, participants were asked to rate three specific dimensions: Effort (“How much effort did you exert to complete each effort condition when earning rewards for yourself [or the other person]?”), Difficulty (“How much difficulty did you perceive in each effort condition when earning rewards for yourself [or the other person]?”), and liking (“How much did you like each effort condition when earning rewards for yourself [or the other person]?”).

We acknowledge the Reviewer #1’s concern that the lower subjective effort ratings for others seems contradictory to the higher disliking and longer completion times. We propose that in this paradigm, subjective effort ratings are susceptible to demand characteristics and likely captured motivational engagement (e.g., “how hard I tried” or “how willing I was”) rather than perceived task demands. To disentangle these factors, we included a measure of perceived task difficulty, which is anchored in task properties and is less prone to social desirability biases (Harmon-Jones et al., 2020; Wright et al., 1990). We found no differences in perceived difficulty between self- and other-benefiting trials (Figure 2D), suggesting that the task demands were perceived as equivalent across conditions. To examine this interpretation more directly, we analyzed correlations among participants’ ratings of difficulty, effort, and liking. As illustrated in Figure S1, we found no correlation between difficulty and effort ratings. Crucially, liking ratings were negatively correlated with difficulty ratings.

More importantly, our performance data contradict the interpretation that participants “worked less hard” for others in terms of task completion. While participants took longer to complete tasks for others, they maintained comparable, near-ceiling success rates for self (97%) and other (96%) recipients (b = -0.46, p = 0.632; Supplementary Table S1). This dissociation suggests that although participants were less motivated (e.g., lower subjective ratings, longer completion times, and greater disliking) to work for others, they ultimately exerted the necessary physical effort to achieve successful outcomes. Thus, the results consistently point to a decrease in prosocial motivation (consistent with prosocial apathy) rather than a failure of effort exertion.

Wright, R. A., Shaw, L. L., & Jones, C. R. (1990). Task demand and cardiovascular response magnitude: Further evidence of the mediating role of success importance. Journal of Personality and Social Psychology, 59(6), 1250-1260. https://doi.org/10.1037/0022-3514.59.6.1250

Harmon-Jones, E., Willoughby, C., Paul, K., & Harmon-Jones, C. (2020). The effect of perceived effort and perceived control on reward valuation: Using the reward positivity to test a dissonance theory prediction. Biological Psychology, 107910. https://doi.org/10.1016/j.biopsycho.2020.107910

Reviewer #2 (Public review):

Measurements of the reward positivity, an electrophysiological component elicited during reward evaluation, have previously been used to understand how self-benefitting effort expenditure influences the processing of rewards. The present study is the first to complement those measurements with electrophysiological reward after-effects of effort expenditure during prosocial acts. The results provide solid evidence that effort adds reward value when the recipient of the reward is the self but discounts reward value when the beneficiary is another individual.

An important strength of the study is that the amount of effort, the prospective reward, the recipient of the reward, and whether the reward was actually gained or not were parametrically and orthogonally varied. In addition, the researchers examined whether the pattern of results generalized to decisions about future efforts. The sample size (N=40) and mixed-effects regression models are also appropriate for addressing the key research questions. Those conclusions are plausible and adequately supported by statistical analyses.

We appreciate Reviewer #2’s positive appraisal of our manuscript. We are fortunate to receive your thoughtful and insightful suggestions and have revised the manuscript accordingly.

(1) Although the obtained results are highly plausible, I am concerned whether the reward positivity (RewP) and P3 were adequately measured. The RewP and P3 were defined as the average voltage values in the time intervals 300-400 ms and 300-440 ms after feedback onset, respectively. So they largely overlapped in time. Although the RewP measure was based on frontocentral electrodes (FC3, FCz, and FC4) and the P3 on posterior electrodes (P3, Pz, and P4), the scalp topographies in Figure 3 show that the RewP effects were larger at the posterior electrodes used for the P3 than at frontocentral electrodes. So there is a concern that the RewP and P3 were not independently measured. This type of problem can often be resolved using a spatiotemporal principal component analysis. My faith in the conclusions drawn would be further strengthened if the researchers extracted separate principal components for the RewP and P3 and performed their statistical analyses on the corresponding factor scores.

We thank Reviewer #2 for raising this issue. We would like to clarify that these two components were time-locked to different types of feedback and therefore reflect neural responses to distinct stages of the prosocial effort task. Specifically, the P3 was time-locked to performance feedback (the effort-completion cue; e.g., the tick shown in Figure 1B), whereas the RewP was time-locked to reward feedback (e.g., the display of “+0.6”). Thus, despite the numerical similarity in the post-stimulus windows, the components capture neural activity evoked by independent events separated in time, corresponding to the performance monitoring versus reward evaluation stages of the task. To avoid misunderstanding, we have made this distinction more explicit in the revised manuscript, which now reads, “Single-trial RewP amplitude was measured as mean voltage from 300 to 400 ms relative to reward feedback onset (i.e., reward delivery) over frontocentral channels (FC3, FCz, FC4). We also measured the parietal P3 (300–440 ms; averaged across P3, Pz, and P4) in response to performance feedback (i.e., effort completion), given its relationship with motivational salience (Bowyer et al., 2021; Ma et al., 2014)” (page 27, para. 1, lines 2–6).

Reviewer #3 (Public review):

This study investigates how effort influences reward evaluation during prosocial behaviour using EEG and experimental tasks manipulating effort and rewards for self and others. Results reveal a dissociable effect: for self-benefitting effort, rewards are evaluated more positively as effort increases, while for other-benefitting effort, rewards are evaluated less positively with higher effort. This dissociation, driven by reward system activation and independent of performance, provides new insights into the neural mechanisms of effort and reward in prosocial contexts.

This work makes a valuable contribution to the prosocial behaviour literature by addressing areas that previous research has largely overlooked. It highlights the paradoxical effect of effort on reward evaluation and opens new avenues for investigating the mechanisms underlying this phenomenon. The study employs well-established tasks with robust replication in the literature and innovatively incorporates ERPs to examine effort-based prosocial decision-making - an area insufficiently explored in prior work. Moreover, the analyses are rigorous and grounded in established methodologies, further enhancing the study's credibility. These elements collectively underscore the study's significance in advancing our understanding of effort-based decision-making.

We thank Reviewer #3 for the positive assessment. We are particularly encouraged by the reviewer’s recognition of our novel integration of ERPs to uncover the distinct effects of effort on reward evaluation for self versus others. We have carefully addressed the specific recommendations raised in the subsequent comments to further strengthen the rigor and clarity of the manuscript.

(1) Incomplete EEG Reporting: The methods indicate that EEG activity was recorded for both tasks; however, the manuscript reports EEG results only for the first task, omitting the decision-making task. If the authors claim a paradoxical effect of effort on self versus other rewards, as revealed by the RewP component, this should also be confirmed with results from the decision-making task. Omitting these findings weakens the overall argument.

We thank Reviewer #3 for giving us the opportunity to verify the specific roles of our two tasks. The primary aim of our study is to elucidate the neural after-effects of effort exertion on subsequent reward evaluation during prosocial acts. The prosocial effort task was specifically designed for this purpose, as it involves actual effort expenditure followed by reward outcomes. Furthermore, this task uses preset effort-reward combinations, ensuring balanced trial counts and adequate signal-to-noise ratios across conditions, a critical requirement for robust ERP analysis. In contrast, the prosocial decision-making task was included specifically to quantify behavioral preference (i.e., prosocial effort discounting) rather than neural reward processing. Specifically, this task involves choices without immediate effort execution and reward feedback, making it impossible to examine the neural after-effects of effort exertion. However, the decision-making task remains indispensable for our study structure: it provides an independent behavioral phenomenon of prosocial apathy, which allowed us to link individual differences in behavioral motivation to the neural dissociations observed in the prosocial effort tasks (as detailed in our Responses to Reviewer #3’s 2). Thus, the two tasks provide complementary, rather than redundant, insights into the behavioral and neural mechanism of prosocial effort.

(2) Neural and Behavioural Integration: The neural results should be contrasted with behavioural data both within and between tasks. Specifically, the manuscript could examine whether neural responses predict performance within each task and whether neural and behavioural signals correlate across tasks. This integration would provide a more comprehensive understanding of the mechanisms at play.

We thank Reviewer #3 for this insightful and helpful suggestion. We agree that linking neural signatures with behavioral patterns is crucial for establishing the functional significance for our ERP findings. Regarding within-task association, it is important to note that the prosocial effort task was designed to require participants to exert fixed, preset levels of physical effort to earn uncertain rewards. This experimental control was necessary to standardize effort exertion across self-benefiting and other benefiting trials, thereby minimizing confounds such as differences in physical or perceived effort prior to the feedback phase. Indeed, the neural after-effects remained after controlling for these behavioral measures (i.e., response speed and self-reported effort; as detailed in responses to Reviewer #1’Recommendations point 5). Furthermore, unlike the prosocial effort task, the decision-making task inherently precludes the examination of the neural after-effects of effort; therefore, within-task association in this task was not possible.

Given these considerations, we focused on the cross-task association. We examined whether the neural after-effects of effort (indexed by the RewP) in the prosocial effort task were modulated by individual differences in effort discounting. We used the K value estimated from the prosocial decision-making task as the index of effort discounting. We entered the K value (log-transformed and z-scored) as a continuous predictor into the mixed-effects models of RewP amplitudes. The full regression estimates for the model are presented in Table S1 (left).

We observed a significant four-way interaction among recipient, effort, magnitude, and K value (b = 0.58, p = 0.013). To decompose this complex interaction, we performed simple slopes analyses separately for self- and other-benefiting trials at high and low levels of reward magnitude and discounting rate (±1 SD). As shown in Figure S2, for self-benefiting trials, the effort-enhancement effect on the RewP was significant only for participants with high discounting rates at low reward magnitude (b = 1.02, 95% CI = [0.22, 1.82], p = 0.012). In contrast, participants with low discounting rates exhibited no significant effort effect (b = -0.37, 95% CI = [-0.89, 0.15], p = 0.159). At high reward magnitude, simple slopes analyses detected no significant effort effects for either high (b = 0.35, 95% CI = [-0.44, 1.14], p = 0.383) or low (b = 0.45, 95% CI = [-0.07, 0.97], p = 0.093) discounting individuals. These findings strongly support the cognitive dissonance account (Aronson & Mills, 1959): those who find effort most aversive are most compelled to inflate the value of small rewards to justify their exertion. For these individuals, the completion of a costly action for a small reward may trigger a stronger internal justification effect, resulting in an amplified neural reward response.

For other-benefiting trials, participants with low discounting rates exhibited a significant effort-discounting effect at high reward magnitude (b = -0.97, 95% CI = [-1.74, -0.20], p = 0.014). In contrast, no significant effort effects were observed for participants with high discounting rates at either high (b = -0.45, 95% CI = [-0.97, 0.08], p = 0.098) or low (b = -0.16, 95% CI = [-0.69, 0.38], p = 0.564) reward magnitudes, nor for participants with low discounting rates at low reward magnitude (b = 0.14, 95% CI = [-0.64, 0.92], p = 0.729). These results suggest that the justification mechanism observed for self-benefiting effort appears absent for other-benefiting effort. Instead, we observed a persistent effort discounting before, during, and after effort expenditure, which was most pronounced in individuals with low effort sensitivity (low K) when reward magnitude was high. This seemingly paradoxical pattern might be interpreted through the lens of disadvantageous inequity aversion (Fehr & Schmidt, 1999). Specifically, the combination of high personal effort and high monetary reward for another person creates a salient disparity between the participant’s incurred cost and the recipient’s gain. Although low-K individuals are behaviorally willing to tolerate this cost, their neural valuation system may nonetheless track the “unfairness” of this asymmetry, thereby attenuating the neural reward signal (Tricomi et al., 2010). These insights suggest that facilitating prosocial behavior may require not just lowering costs, but potentially framing outcomes to trigger the effort justification mechanisms that drive the effort paradox observed in self-benefiting acts (Inzlicht & Campbell, 2022).

To confirm this four-way interaction, we also replaced the high-effort choice proportions in the decision-making task and observed a similar four-way interaction among recipient, effort, magnitude, and high-effort choice proportions (b = -0.58, p = 0.014; see Table S1 for detailed regression estimates). Together, this cross-task analysis not only provides a more comprehensive understanding of the mechanisms at play but also justifies the inclusion of the prosocial decision-making task. We sincerely thank Reviewer #3’ for this valuable suggestion, which has significantly strengthened our manuscript. We have included this analysis (page 16, para. 2; page 17, paras. 1–2) and discussed the results (page 20, para. 2, lines 10–15; page 20, para. 3; page 21, para. 1, lines 1–8) in the revised manuscript.

Aronson, E., & Mills, J. (1959). The effect of severity of initiation on liking for a group. The Journal of Abnormal and Social Psychology, 59(2), 177-181. https://doi.org/10.1037/h0047195

Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114(3), 817-868. http://www.jstor.org/stable/2586885

Tricomi, E., Rangel, A., Camerer, C. F., & O'Doherty, J. P. (2010). Neural evidence for inequality-averse social preferences. Nature, 463(7284), 1089-1091. https://doi.org/10.1038/nature08785

(3) Success Rate and Model Structure: The manuscript does not clearly report the success rate in the prosocial effort task. If success rates are low, risk aversion could confound the results. Additionally, it is unclear whether the models accounted for successful versus unsuccessful trials or whether success was included as a covariate. If this information is present, it needs to be explicitly clarified. The exclusion criteria for unsuccessful trials in both tasks should also be detailed. Moreover, the decision to exclude electrodes as independent variables in the models warrants an explanation.

We appreciate the opportunity to clarify these points. In the revised manuscript, we have now explicitly reported the descriptive statistics and the results of a mixed-effects logistic model on response success in the revised manuscript (page 8, para. 1, lines 2–4; Supplementary Table S1). Participants achieved similarly high success rates in both self (M = 97%) and other trials (M = 96%; Figure S3). As shown in Table S2, success rates decreased as effort increased (b = -4.77, p < 0.001). However, no other effects reached significance (ps > 0.245). These near-ceiling success rates indicate strong task engagement and effectively rule out risk aversion as a potential confound.

Regarding model structure, we excluded unsuccessful trials from statistical analyses because they were rare and distributed equally across conditions. Given the near-ceiling performance, we did not include success rate as a covariate, as it offers limited variance.

Finally, we did not include electrodes as an independent variable because our hypotheses focused on condition effects rather than topographic differences. Following established research (e.g., Krigolson, 2018; Proudfit, 2015), we averaged RewP amplitudes across a frontocentral cluster (FC3, FCz, and FC4) and P3 amplitudes across a parietal cluster (P3, Pz, and P4), where activity is typically maximal. Averaging across these theoretically grounded clusters improves the signal-to-noise ratio and provides more reliable estimates of the underlying components. We have explicitly included this rationale in the revised manuscript, which reads, “Data were averaged across the selected electrode clusters to improve signal-to-noise ratio and reliability” (page 27, para. 1, lines 9–10).

Proudfit, G. H. (2015). The reward positivity: From basic research on reward to a biomarker for depression. Psychophysiology, 52(4), 449-459. https://doi.org/10.1111/psyp.12370

Krigolson, O. E. (2018). Event-related brain potentials and the study of reward processing: Methodological considerations. Int J Psychophysiol, 132(Pt B), 175-183. https://doi.org/10.1016/j.ijpsycho.2017.11.007

(4) Prosocial Decision Computational Modelling: The prosocial decision task largely replicates prior behavioural findings but misses the opportunity to directly test the hypotheses derived from neural data in the prosocial effort task. If the authors propose a paradoxical effect of effort on self-rewards and an inverse effect for prosocial effort, this could be formalised in a computational model. A model comparison could evaluate the proposed mechanism against alternative theories, incorporating the complex interplay of effort and reward for self and others. Furthermore, these parameters should be correlated with neural signals, adding a critical layer of evidence to the claims. As it is, the inclusion of the prosocial decision task seems irrelevant.

We thank Reviewer #3 for this thoughtful suggestion regarding the value of computational modelling. We fully agree that formalizing mechanisms is crucial, but we would like to clarify why a computational model of decision-making cannot directly capture the paradoxical after-effects observed in our neural data. The paradoxical after-effect of effort exertion we report refers to experienced utility (i.e., how prior costs modulate the hedonic consumption of a reward), whereas the decision task measures decision utility (i.e., how prospective costs and benefits are integrated to guide choice). We included the prosocial decision task to establish a behavioral baseline and replicate the well-documented phenomenon of prosocial apathy. Consistent with prior work (e.g., Lockwood et al., 2017; Lockwood et al., 2022), our data show that at the decision stage (ex-ante), effort functions as a universal cost: participants discounted rewards for both self and others, differing only quantitatively (steeper discounting for others). It is only after effort is exerted (ex-post) that the pattern reverses: effort is valued for self but remains costly for others, representing a qualitative shift. Crucially, incorporating a "paradoxical valuation" parameter (i.e., effort as a reward) into our decision model would mathematically contradict the behavioral reality. Since participants actively avoided high-effort options, a model assuming effort adds value might fail to fit the choice data. The theoretical novelty of our study lies precisely in this temporal dissociation: whereas self-benefiting effort paradoxically enhances reward valuation, other-benefiting effort induces a persistent reward devaluation.

To address the reviewer’s interest in bridging these two domains, we examined whether these distinct stages are linked at the level of individual differences. We hypothesized that an individual’s sensitivity to prospective effort cost (discounting rate K) might modulate their susceptibility to the retrospective neural after-effect. As detailed in our Responses to Reviewer #3’s point 2, we found that for self-benefiting trials, high-discounting individuals showed an effort-enhancement effect on the RewP at low reward magnitude, while for other-benefiting trials, low-discounting individuals exhibited effort-discounting effects at high reward magnitude. We sincerely thank Reviewer #3’ for this valuable suggestion, which has successfully correlated the two tasks and facilitated our understanding of the mechanisms at play.

Lockwood, P. L., Hamonet, M., Zhang, S. H., Ratnavel, A., Salmony, F. U., Husain, M., & Apps, M. A. J. (2017). Prosocial apathy for helping others when effort is required. Nat Hum Behav, 1(7), 0131. https://doi.org/10.1038/s41562-017-0131.

Lockwood, P. L., Wittmann, M. K., Nili, H., Matsumoto-Ryan, M., Abdurahman, A., Cutler, J., Husain, M., & Apps, M. A. J. (2022). Distinct neural representations for prosocial and self-benefiting effort. Curr Biol, 32(19), 4172-4185 e4177. https://doi.org/10.1016/j.cub.2022.08.010.

(5) Contradiction Between Effort Perception and Neural Results: Participants reported effort as less effortful in the prosocial condition compared to the self condition, which seems contradictory to the neural findings and the authors' interpretation. If effort has a discounting effect on rewards for others, one might expect it to feel more effortful. How do the authors reconcile these results? Additionally, the relationship between behavioural data and neural responses should be examined to clarify these inconsistencies.

This point aligns with the issues raised in Reviewer #1’s point 2. We acknowledge the apparent discrepancy between lower reported effort in the prosocial condition and the neural discounting effect. As detailed in our Responses to Reviewer #1’s point 2, we reconcile this by proposing that subjective effort ratings in this paradigm likely reflect motivational engagement (e.g., “how hard I tried” or “how willing I was”) rather than perceived task demands. Under this interpretation, the lower effort ratings for others reflect a withdrawal of engagement (consistent with prosocial apathy), which conceptually aligns with, rather than contradicts, the neural discounting effect. To validate this, we contrasted effort ratings with difficulty ratings (a more reliable index of objective demand). Our correlational analysis revealed no significant relationship between difficulty and effort ratings (r = -0.21, p = 0.196), suggesting that they capture distinct constructs. Furthermore, liking ratings were negatively correlated with difficulty ratings (r = -0.43, p = 0.011) but not with effort ratings (r = 0.32, p = 0.061), further dissociating the two measures. Crucially, as detailed in our Responses to Reviewer #1’s Recommendations point 5, our RewP effects remained significant even after controlling for individual effort ratings. This demonstrates that the neural effort-discounting effect for others is a physiological signature that operates independently of the subjective report bias.

(6) Necessary Revisions to Manuscript: If the authors address the issues above, corresponding updates to the introduction and discussion sections could strengthen the narrative and align the manuscript with the additional analyses.

We thank Reviewer #3 for the above insightful and helpful comments. We have carefully addressed these issues raised above and have updated the manuscript accordingly, including abstract, introduction, result, and discussion sections.

Recommendations for the Authors:

Reviewer #1 (Recommendations for the authors):

Major comments:

(1) The two biggest concerns I have are

- Whether the mixed-effect models are properly specified, and

- Whether the main interaction between the Recipient and effort on the reward positivity (RewP) reflects different levels of effort exertion when working for self versus others.

We thank Reviewer #1 for identifying these two critical issues. We have carefully considered these points and conducted additional analyses to address them. Below, we provide a detailed response to each concern, explaining how we have improved the model specification and ruled out alternative interpretations regarding effort exertion.

(2) On the first point, I noticed that the authors selectively excluded random effects for Effort and Magnitude when regressing RewP on Effort, Magnitude, Recipient, and Valence. This is important because the key result in the paper is a fixed effect two-way interaction between Recipient and Effort and a three-way interaction between Recipient, Effort, and Magnitude. It is not clear that these results will remain significant when Effort and Magnitude are included as random effects in the model. Thus the authors should justify their exclusion as random effects, and/or show that the results don't depend on including those random effects in the model. The same logic applies to the specification of other mixed effects models (e.g. the effect of Magnitude in the model predicting RTs).

We thank Reviewer #1 for raising this important methodological point. We fully agree that including random slopes wherever possible reduces Type 1 error rates and yields more conservative tests of fixed effects. In our analyses, we determined the random effects structure for each model using singular value decomposition (SVD). Specifically, we began with a maximal model that included by-participant random slopes for all main effects and interactions as well as a participant-level random intercept. When the model failed to converge or yielded a singular fit, we applied SVD to identify redundant dimensions (i.e., components explaining zero variance) and iteratively removed these terms until convergence was achieved. This procedure allowed us to retain the maximal converging random-effects structure while ensuring model stability. We have clarified this procedure in the revised manuscript as follows, “For each model, we fitted the maximal random-effects structure and, when the model was overparameterized, used singular value decomposition to simplify the random-effects structure until the model converged” (page 28, para. 1, lines 5–8).

Regarding the RewP model, including all variables (i.e., Recipient, Effort, Magnitude, and Valence) in the random-effects structure resulted in a boundary (singular) fit. Examination of the variance-covariance structure of the random effects revealed that the random slopes for Valence and Magnitude were perfectly negatively correlated (r = -1.00), indicating severe overparameterization. In our original submission, we removed the random slopes for Effort and Magnitude because the SVD analysis indicated redundant dimensions in the model structure.

However, we agree with the Reviewer that retaining slopes for variables involved in key interactions is crucial. Therefore, we re-evaluated the model strategy: instead of removing Effort and Magnitude, we removed the random slope for Valence (which was the primary source of the perfect correlation). This modification successfully resolved the singularity while allowing us to retain the random slopes for the critical variables (i.e., Effort and Magnitude).

Critically, this updated model yielded the same pattern of results as our original submission: the two-way interaction between Recipient and Effort and the three-way interaction between Recipient, Effort, and Magnitude remained significant (see Table S3). As expected, including the random slopes for Effort and Magnitude yielded a more conservative test of the fixed effects. While the critical three-way interaction remained significant (p = 0.019), the simple slope for the Self condition at high reward magnitude shifted slightly from significant (p = 0.041) to marginally significant (p = 0.056). However, the effect size remained largely unchanged (b = 0.42 vs. original b = 0.43), and the dissociation pattern, where self-benefiting trials show a positive trend while other-benefiting trials show a significant negative slope, remains robust and is statistically supported by the significant interaction. We have adopted this updated model in the revised manuscript and updated the relevant sections accordingly. Finally, note that we have removed the RewP table from the Supplementary Materials because the RewP model results are now presented as a figure in the main text (as suggested by Reviewer #1’s Recommendations point 3).

We have also carefully verified the random effects structures for other mixed-effects models, including the RT and Performance-P3 models in the prosocial effort task, as well as the decision time and decision choice models in the prosocial decision-making task. The updated information is detailed as follows:

Regarding the RT model, we replaced it with a more reasonable model of response speed (button presses per second), as suggested by Reviewer #1 (see our responses to Reviewer #1’s Recommendations point 4 for details).

Regarding the performance-P3 model, the random-effects structure could only support Effort, as in our original submission; thus, the results remain unchanged.

Regarding the decision time model, we have updated our results to include the quadratic effort term, as suggested by Reviewer #1 (see our responses to Reviewer #1’s Recommendations point 6 for details).

Regarding the decision choice model, we included Recipient, Effort, and Magnitude in the random-effects structure. As shown in Table S4, the results remain largely consistent with the original model, except for a newly significant interaction between effort and magnitude. Follow-up simple slopes analyses revealed that the discounted effect of effort was more pronounced at low reward magnitude (M − 1SD: b = -2.69, 95% CI = [-3.09, -2.29], p < 0.001) than at high reward magnitude (M + 1SD: b = -2.38, 95% CI = [-2.82, -1.94],p < 0.001).

In summary, we have improved the model specification following Reviewer #1’s suggestion. Crucially, the results remain qualitatively consistent with our original findings. We have updated the Results section, figures (Figures 2, 4, and 5), and OSF documents (including a new R Markdown file and an HTML output file detailing the final results) to reflect these analyses. Additionally, we have explicitly stated the method used for calculating p-values in the mixed-effects models (page 28, para. 1, lines 8–10), which was omitted in the original submission.

(3) Regarding the mixed models, it would also be good to show a graphical depiction summarizing key effects (e.g. the Recipient by Effort interaction on RewP) rather than just showing the predictions of the fitted mixed effects models.

This point is well-taken. Please see Figure S4, which visualizes the key effects and has now been included in the revised manuscript as Figure 4A.

(4) Finally, regarding the mixed effect models of RTs - given the common finding that RTs are not normally distributed, the Authors might be better off regressing 1/RT (interpreted as speed rather than latency) since 1/RT will often make distributions less asymmetric and heavy-tailed.

We thank Reviewer #1 for this helpful suggestion regarding data distribution. In our original analysis, the dependent variable was “completion time” (i.e., the latency to complete the required button presses with the 6-s window). We agree that these raw latency data exhibited characteristic non-normality (see Figure S5, Left). Based on Reviewer #1’s suggestion, we adopted “response speed” (calculated as button presses per second) as the dependent variable. As expected, this transformation substantially improved the normality of the distribution (see Figure S5, Right). We have refitted the mixed-effects model using this speed metric. Critically, the results largely replicated the patterns observed in our original model, with the exception that the main effect of reward magnitude did not reach significance in the speed model (see Table 5). Given the superior distributional properties of the speed metric, we have replaced the original latency analysis with the response speed model in the revised manuscript. We have updated the Results section (page 8, para. 1, lines 4–9) and Figures 2B–C accordingly.

(5) Regarding the level of effort exerted, there are two reasons to suspect that participants exerted less for others versus themselves. The first is that they were slower to complete the button pressing for others versus themselves. The second is that they reported paradoxically less subjective effort for others versus self (paradoxical because they also reported liking the task less for others versus self). The explanation for both may be that they exerted less effort for others versus self and this has important implications for interpreting the main effects. If they exerted less effort for others, this may partly account for the key Recipient:Effort and Recipient:Effort:Magnitude interactions in the mixed effects regression of RewP. Do either median effort durations or self-reported effort predict the magnitude of the Recipient:Effort and Recipient:Effort:Magnitude interactions (if these were included as random effects)? If so, that would provide evidence supporting this story. Alternatively, if median durations or self-reported effort were included as covariates, do these interactions still obtain? In any case, the Authors should include caveats regarding this potential explanation of the self-versus-other interactions with effort and magnitude on the RewP" (or explain why this can not explain the interactions).

We thank Reviewer #1 for raising this important interpretational issue. We acknowledge the concern that differences in physical exertion or perceived effort could potentially confound the neural findings. However, we argue that the observed RewP effects are not driven by these factors for several reasons.

First, the prosocial effort task enforced fixed effort thresholds (10%–90% of their maximum effort level) across self-benefiting and other-benefiting trials. Importantly, participants achieved ceiling-level success rates that were highly comparable between self-benefiting (97%) and other-benefiting (96%) trials, indicating that they successfully exerted the required effort across conditions.

Second, regarding the slower response speed for others (we used response speed instead of completion time, as the former is more suitable for statistical analysis; see details in Responses to Reviewer #1’s Recommendations point 4), we interpret this as a reduction in motivation rather than a reduction in the amount of effort exerted. Similarly, as detailed in our Responses to Reviewer#1’s point 2, subjective effort ratings in this paradigm appear to be influenced by demand characteristics and do not reliably track physical exertion. For instance, liking ratings were associated with difficulty (r = -0.43, p = 0.011) instead of effort (r = 0.32, p = 0.061) ratings.

To empirically rule out the possibility that these behavioral differences account for the neural effect, we followed the reviewer’s suggestion and re-ran the mixed-effects model predicting RewP amplitudes with trial-by-trial response speed and subjective effort rating included as covariates. These control analyses revealed that neither response speed (b = -0.07, p = 0.614) nor self-reported effort (b = 0.10, p = 0.186) significantly predicted RewP amplitudes (see Table S6). Most importantly, the key interactions of interest (Recipient × Effort and Recipient × Effort × Magnitude) remained significant and virtually unchanged. These findings suggest that the observed neural after-effects of prosocial effort are not driven by variations in motor execution or perceived effort.

Minor comments:

(6) In Figure 5A a quadratic effect (not a linear effect) seems fairly obvious in decision times as a function of effort level. This makes sense given that participants are close to indifference, on average, around the 50-70% effort level. I recommend fitting a model that has a quadratic predictor and not just a linear predictor when regression decision times on effort levels.

We thank Reviewer #1 for this insightful suggestion. We agree that decision times likely track decision conflict, which typically peaks near indifference points (e.g., moderate effort levels). Accordingly, we reanalyzed the decision time data using a mixed-effects model that included both linear and quadratic terms for effort. As detailed in Table S7, this analysis revealed a significant quadratic main effect of effort, which was further qualified by a significant interaction between the quadratic effort term and reward magnitude. Decomposition of this interaction (Figure S6) revealed that the quadratic effort effect was more pronounced at low reward magnitude (M − 1SD: b = -160.10, 95% CI = [-218.30, -101.90], p < 0.001) than at high reward magnitude (M + 1SD: b = -99.50, 95% CI = [-157.60, -41.40], p = 0.001). However, we found no significant interactions involving the quadratic effort term and recipient. We have updated the Results section (page 13, para. 2; page 14, para. 1) and Figures 5A–B (right panel) to reflect these findings.

(7) The distinction between the effort and decision-making tasks wasn't super clear from the main text. A sentence early on in the results section could be useful for readers' understanding.

This point is well taken. In the revised manuscript, we have clarified this distinction at the beginning of the Results section (page 6, para. 2, lines 1–10). In addition, we have explicitly indicated the corresponding task within each subsection heading in the Results:

“2.1 Investing effort for others is less motivating than for self in the prosocial effort task” (page 7)

“2.2 Effort adds reward value for self but discounts reward value for others in the prosocial effort task” (page 9)

“2.3 Reward is devalued by effort to a higher degree for others than for self in the prosocial decision-making task” (page 13)

(8) To what does "three trials" refer to on lines 143-144?

Thank you for raising this point. Participants completed three trials in which they were asked to press a button as rapidly as possible with their non-dominant pinky finger for 6000 ms. The maximum effort level was operationalized as the average button-press count across the three trials. To improve clarity, we have also provided more detailed description in the Results section, which reads: “The mean maximum effort level (i.e., the average button-press count across three 6000-ms trials; see Procedure for details) ….” (page 7, para. 1, lines 1–2).

(9) It is unclear how the authors select their time windows for ERP analyses.

We thank Reviewer #1 for this comment. Measurement parameters (i.e., time windows and channel sites) were determined based on the grand-averaged ERP waveforms and topographic maps collapsed across all conditions. This procedure is orthogonal to the conditions of interest and prevents bias in the selection of measurement windows and channels, consistent with the “orthogonal selection approach” (Luck & Gaspelin, 2017). We have clarified this point in the revised manuscript, which now reads, “Measurement parameters (time windows and channel sites) were determined from the grand-averaged ERP waveforms and topographic maps collapsed across all conditions, which was thus orthogonal to the conditions of interest (Luck & Gaspelin, 2017)” (page 27, para. 1, lines 6–9).

Luck, S., & Gaspelin, N. (2017). How to get statistically significant effects in any ERP experiment (and why you shouldn't). Psychophysiology, 54(1), 146-157.

(10) There are a few typos throughout. For example, Line 124 should read "other half benefitted...", Line 127 should read "interest at each effort level...", "following" on Line 369, and Supplemental table titles incorrectly spell the word "Results".

We thank Reviewer #1 for catching these errors. We have corrected all the specific typos noted (page 6, para. 2, lines 11 and 15; page 22, para. 3, line 2; Supplementary Table S2). Furthermore, we have conducted a thorough proofreading of the entire text and supplementary materials to ensure linguistic accuracy and consistency throughout the manuscript.

Reviewer #2 (Recommendations for the authors):

Minor comments:

(1) Lines 84-86. "The RewP ... has its neural sources in the anterior cingulate cortex (Gehring & Willoughby, 2002) and ventral striatum (Foti et al., 2011)." This is a better reference for the ACC source: https://pubmed.ncbi.nlm.nih.gov/23973408/. And perhaps remove the reference to the ventral striatum; most people would agree that activity in the ventral striatum cannot be measured with scalp EEG.

We thank Reviewer #2 for providing the updated reference, which has been cited in the revised manuscript. We agree that activity in the VS cannot be reliably measured with scalp EEG and thus have removed the reference to the VS. The revised sentence now reads, “… has its neural sources in the anterior cingulate cortex (Gehring & Willoughby, 2002; Hauser et al., 2014)” (page 4, para. 2, lines 12–13).

(2) Lines 152-153. What exactly is shown in Figure 2A? How did the authors average across subjects?

We thank Reviewer #2 for raising this issue. Figure 2A depicts the distribution of the maximum effort level, defined as the average button-press count across three 6000-ms trials completed before the prosocial effort task. In these trials, participants were instructed to press the button as rapidly as possible with their non-dominant pinky fingers. To improve clarity, we have revised the figure caption as: “(A) Distribution of the maximum effort level (i.e., the average button-press count across three 6000-ms trials) across participants” (Figure 2).

(3) Lines 160-164. "As expected (Figure 2D), participants perceived increased effort as more difficult ... and more disliking (b = -0.62, p < 0.001) when the beneficiary was others than themselves." Does this sentence describe the main effect of the beneficiary or the interaction between beneficiary and effort level, as the start of the sentence ("increased effort") suggests?

We thank Reviewer #2 for pointing out this ambiguity. The sentence describes the main effect of beneficiary rather than the interaction between beneficiary and effort level. In the revised manuscript, we have rephrased the sentence as: “They felt less effort (b = -0.32, p = 0.019) and more disliking (b = -0.62, p = 0.001) for other-benefiting trials compared to self-benefiting trials” (page 9, para. 1, lines 4–6).

(4) Lines 195-196. "..., we conducted post-hoc simple slopes analyses at -1 SD ("Low") and + SD ("High") reward magnitude." I did not understand what the authors meant with these reward magnitudes, given that the actual potential rewards were ¥0.2, ¥0.4, ¥0.6, ¥0.8, and ¥1.0.

In our analyses, the actual reward magnitudes (¥0.2, ¥0.4, ¥0.6, ¥0.8, and ¥1.0) were z-scored and entered as a continuous regressor in the mixed-effects models. Post-hoc simple slopes analyses were then conducted at ±1 SD from the mean of the z-scored reward magnitude. To clarify, we have revised the sentence as “… we conducted post-hoc simple slopes analyses at 1 standard deviation (SD) below (“Low”) and above (“High”) the mean reward magnitude” (page 11, para. 2, lines 8–9). This standard method for testing simple effects for continuous predictors is recommended by Aiken and West (1991). Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage.

(5) Lines 253 and 275. I would not call this a computational model. The authors fit a curve to data, there is no model of the computations involved.

This point is well taken. We have replaced “computational model” with “discounting” (Figure 5) and “parabolic discounting model” (page 15, para. 1, line 15).

(6) Line 710. Figure S1 does not show topographic maps of the P3, as the figure caption suggests.

We thank Reviewer #2 for identifying this oversight. We have now included topographic maps of the P3 in Figure S1.

(7) Please check language in lines 33 (effect between), 38 (shape), 49 (highest cost form?), 74 (tunning), 90 (omit following), 127 (interest on at each effort level), 135 (press buttons >> rapidly press a button?), 142 (motivated), 219 (should low be high?), 265-266 (missing word), 275 (confirmed by following), 292 (an action can be effortful, a feeling cannot), 315 (when it comes into), 330-331 (data is plural; the aftereffect of prosocial effect), 387 (interest on at each effort level), 405 (should quickly be often?).

We thank Reviewer #2 for the careful review and feedback about these language issues. We have revised all the phrasing you identified. The corrections are as follows:

Line 33: “effect between” has been changed to “effects for” (page 2, para. 1, line 6).

Line 38: “shape” has been updated to “shapes” (page 2, para. 1, line 13).

Line 49: “highest cost form?” has been revised to “the most common cost type” (page 3, para. 1, lines 7–8).

Line 74: “tunning” has been corrected to “tuning” (page 4, para. 2, line 1).

Line 90: omit following. Done (page 5, para. 1, line 2).

Line 127: “interest on at each effort level” has been corrected to “liking for each effort level” (page 6, para. 2, line 15).

Line 135: “press buttons” has been updated to “rapidly press a button” (the caption of Figure 1).

Line 142: “motivated” has been revised to “motivating” (page 7).

Line 219: should low be high? Yes, we have corrected this (the caption of Figure 4).

Lines 265–266: The missing word “with” has been inserted (page 15, para. 1, line 2).

Line 275: “confirmed by following” has been revised as “corroborated by a parabolic …” (page 15, para. 1, line 15).

Line 292: an action can be effortful, a feeling cannot. We have changed the word “effortful” to “effort” (page 18, para. 2, line 3).

Line 315: “when it comes into” has been revised to “when it came to” (page 19, para. 1, line 10).

Lines 330–331: These two expressions have been revised to “our data establish …” and “the after-effect of prosocial effort” (page 20, para. 1, lines 2–3).

Line 387: “interest on at each effort level” has been corrected to “interest at each effort level” (page 23, para. 2, line 5).

Line 405: should quickly be often? We agree that “quickly” might imply latency or speed of a single press, whereas the task required maximizing the frequency of presses within the time window. To capture this meaning accurately, we have revised the phrase to “pressed a button as rapidly as possible” (implying repetition rate) in the revised manuscript (page 24, para. 2, lines 3–4).

Author Response:

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

Public Reviews:

Reviewer #1 (Public review):

Weaknesses

As presented, the manuscript has limitations that weaken support for the central conclusions drawn by the authors. Many of the findings align with prior work on this topic, but do not extend those findings substantially.

An overarching limitation is the lack of temporal resolution in the manipulations relative to the behavioral assays. This is particularly important for anxiety-like behaviors, as antecedent exposures can alter performance. In the open field and elevated zero maze assays, testing occurred 30 minutes after CNO injection. During much of this interval, the targeted neurons were likely active, making it difficult to determine whether observed behavioral changes were primary - resulting directly from SuM neuronal activity - or secondary, reflecting a stress-like state induced by prolonged activation of SuM and related circuits. This concern also applies to the chronic inhibition of ventral subiculum (vSub) neurons during 10 days of CSDS.

We appreciate the reviewer's concern regarding the timing of CNO administration relative to behavioral testing. The 30-minute interval was selected according to some previous studies[1, 2]. This window ensures stable and specific neuronal manipulation while minimizing off-target effects and was strictly performed through all experiments. We acknowledge that shorter interval (~15 mins) can be efficient to produce biological effect in vivo[3, 4]. We repeated chemogenetic tests 2-3 times to make sure to get reliable data for statistical analysis. However, we cannot exclude potential side-effects caused by chemogenetically prolonged activation of SuM because of its poor temporal resolution compared to optogenetic manipulation. We agree that employing techniques with higher temporal resolution, such as optogenetics, in future studies would provide an excellent complement to these findings.

The combination of stressors (foot shock and CSDS) and behavioral assays further complicates interpretation. The precise role of SuM neurons, including SANs, remains unclear. Both vSub and dSub neurons responded to foot shock, but only vSub neurons showed activity differences associated with open-arm transitions in the EZM.

We agree that the use of multiple stressors (foot shock and CSDS) adds complexity to the interpretation. Our rationale was to test the generality of the SuM response and the role of SANs across different stress modalities (acute vs. chronic). The key finding is that while both vSub and dSub projections to the SuM were activated by the acute stressor of foot shock (Figure 5N-R), only the vSub-SuM pathway showed a significant increase in calcium activity specifically during the anxiety-provoking transition from the closed to the open arms of the EZM (Figure 5I-M). This dissociation suggests a selective role for the vSub-SuM circuit in encoding anxiety-related information, beyond a general response to stress.

In light of prior studies linking SuM to locomotion (Farrell et al., Science 2021; Escobedo et al., eLife 2024), the absence of analyses connecting subpopulations to locomotor changes weakens the claim that vSub neurons selectively encode anxiety. Because open- and closed-arm transitions are inherently tied to locomotor activity, locomotion must be carefully controlled to avoid confounding interpretations.

We thank the reviewer for highlighting the important studies linking the SuM to locomotion. We acknowledge this known function and carefully considered it in our analyses. Non-selective activation of the entire SuM didn’t affect total distance traveled in open field and elevated zero maze (Supplemental Figure 2 B-C). Although the locomotion of mice in OF and EZM was affected while targeting SANs, we also compared the travel distance in the central area of OF, to some extent, to minimize the influence of locomotion on the estimation of anxiety produced avoidance to the central area (Figure 4 I). We agree that future work delineating the specific subpopulations within the SuM that regulate locomotion versus anxiety would be highly valuable.

Another limitation is the narrow behavioral scope. Beyond open field and EZM, no additional assays were used to assess how SAN reactivation affects other behaviors. Without richer behavioral analyses, interpretations about fear engrams, freezing, or broader stress-related functions of SuM remain incomplete.

In addition, small n values across several datasets reduce confidence in the strength of the conclusions.

We acknowledge that the primary focus on OF and EZM tests is a limitation in fully characterizing the behavioral profile of SAN manipulation. These tests were selected as they are well-validated, standard assays for anxiety-like behavior in rodents[5–10]. However, we also included the reward-seeking test, where activation of SANs significantly suppressed sucrose consumption (Figure 4L), suggesting a broader impact on motivational state that is often linked to anxiety. We fully agree with the reviewer that employing a richer behavioral battery—such as tests for social avoidance, conditioned place aversion, or Pavlovian fear conditioning—in future studies will be essential to comprehensively define the functional scope of SuM SANs and to conclusively dissect their role from fear memory engrams.

Figure level concerns:

(1) Figure 1: In Figure 1, the acute recruitment of SuM neurons by for shock is paired with changes in neural activity induced by social defeat stress. Although interesting, the connections of changes induced by a chronic stressor to Fos induction following acute foot shock are unclear and do not establish a baseline for the studies in Figure 3 on activation of SANs by social stressors.

Thank you for this important comment. We agree that directly linking acute foot shock-induced cFos expression with chronic social defeat stress (CSDS) electrophysiological changes may create an interpretive gap. In Figure 1, we aimed to demonstrate that both acute (foot shock) and chronic (CSDS) stressors can activate SuM neurons, using complementary methods (cFos for acute, in vivo recording for chronic). We did not intend to imply that the same neuronal population responds identically to both stressors.

To address this, we have clarified in the text that the purpose of Figure 1 is to show that SuM is responsive to diverse stressors, rather than to establish a direct mechanistic link between acute and chronic activation patterns. The baseline for SAN studies in Figure 3 is established through the TRAP2 tagging protocol following foot shock, independent of the CSDS model. We acknowledge that future studies should compare SAN recruitment across acute vs. chronic stressors to better define their functional overlap.

(2) Figure 2: The chemogenetic experiments using AAV-hSyn-Gq-DREADDs lack data or images, or hit maps showing viral spread across animals. This omission is critical given the small size of SuM, where viral spread directly determines which neurons are manipulated. Without this, it is difficult to interpret findings in the context of prior studies on SuM circuits involved in threats and rewards.

Please see Supplemental Figure 2 for the infection area of AAV.

(3) Figure 3: The TRAP experiments show that the number of labeled neurons following foot shock (Figure 3F) is approximately double that of baseline home-cage animals, though y-axis scaling complicates interpretation. It is unclear whether this reflects true Fos induction, low TRAP efficiency, or baseline recombination.

We thank the reviewer for pointing out the axis scaling issue. We have modified the y-axis to start from 0. The SuM nucleus has been reported to play role in the awake of rodents, it’s reasonable to have some basal neuronal activation after 4-OHT i.p. injection.

Overlap analyses are also limited. For example, it is not shown what proportion of foot shock SANs are reactivated by subsequent foot shock. Comparisons of Fos induction after sucrose reward are also weakened by the very low Fos signal observed. If sucrose reward does not robustly induce Fos in SuM, its utility in distinguishing reward- versus stress-activated neurons is questionable. Thus, conclusions about overlap between SANs and socially stressed neurons remain uncertain due to the missing quantification of Fos+ populations.

Thank you for the question. We have replaced the reactivation chance graph with a new reactivation percent analysis graph to show the proportion of SANs that reactivated by subsequent sucrose reward or stress. The rationale we use social stress other than foot shock is to show the potential generality of foot-shock tagged neurons. The lower expression of cFos after sucrose exposure suggest first, the SuM may not involve in reward regulation, which we agree with you; second, those SANs are more likely to modulate anxiety-like behavior but not reward.

(4) Supplemental Figure 3: The claim that "SANs in the SuM encode anxiety but not fear memory" is not well supported. Inhibition of SANs (Gi-DREADDs) did not alter freezing behavior, but the absence of change could reflect technical issues (e.g., insufficient TRAP efficiency, low expression of Gi-DREADDs). Moreover, the manuscript does not provide a positive control showing that SuM SANs inhibition alters anxiety-like behavior, making it difficult to interpret the negative result. Prior work (Escobedo et al., eLife 2024) suggests SuM neurons drive active responses, not freezing, raising further interpretive questions.

We agree that here we didn’t provide enough data to confirm there is no regulation effect of SuM-SANs on fear memory. Relevant statement has been removed to avoid any further misunderstanding.

(5) Figure 4: The statement that corticosterone concentration is "usually used to estimate whether an individual is anxious" (line 236) is an overstatement. Corticosterone fluctuates dynamically across the day and responds to a broad range of stimuli beyond anxiety.

Thank you for your kind reminder. Corticosterone/cortisol, the primary stress hormone, is a well-established biomarker whose levels are elevated in response to stress and in anxiety states.[11, 12]. Some studies also reported that supplying corticosterone can produce anxiety-like behaviors in rodents[13–16]. We collect the blood sample at the same timepoint in Figure 4 C-D. We agree that line 236 is a kind of overstatement and has modified.

(6) Figures 5-6: The conclusion that vSub neurons encode anxiety-like behavior is not firmly supported. Data from photo-activating terminals in SuM is shown for ex vivo recording, but not in vivo behavior, which would strengthen support for this conclusion. Both vSub and dSub neurons responded to foot shock. The key evidence comes from apparent differential recruitment during open-arm exploration. However, the timing appears to lag arm entry, no data are provided for closed-arm entry, and there is heterogeneity across animals. These limitations reduce confidence in the authors' central claim regarding vSub-specific encoding of anxiety.

We thank the reviewer for this important point. To address the concern regarding the in vivo behavioral encoding specificity of the vSub-SuM pathway, we further analyzed the in vivo fiber photometry data. The new analysis revealed that calcium activity in vSub-SuM projection neurons exhibited bidirectional, instantaneous, and specific changes during transitions between the open and closed arms of the elevated plus maze: their activity significantly and immediately decreased when mice moved from the open arm to the closed arm (new results shown in Supplemental Figure 5), and conversely, significantly and immediately increased upon transitioning from the closed to the open arm. However, under the same behavioral events, dSub-SuM projection neurons showed no significant change in activity. We hope this finding could strengthens the role of the vSub-SuM pathway in encoding anxiety-like behavior.

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions:

(1) From the data presented, the authors conclude that "the SuM is the critical brain region that regulates anxiety" (line 190). This interpretation appears overstated, as it downplays well-established contributions of other brain regions and does not place SuM's role within a broader network context. The data support that SuM neurons are recruited by foot shock and, to a lesser extent, by acute social stress. However, the alterations in activity of SuM subpopulations following chronic stress reported in Figure 1 remain largely unexplored, limiting insight into their functional relevance.

Thank you for the suggestion. We have modified the line 190 with cautious “In this study, we combined multiple methods to determine whether the SuM is a brain region that involve in modulating anxiety.”

(2) The limited temporal resolution of DREADD-based manipulations leaves alternative explanations untested. For example, if SANs encode signals of threat, generalized stress, or nociception, then prolonged activation could indirectly alter behavior in the open field and EZM assays, rather than reflecting direct anxiety regulation.

We discussed the DREADD method in the first part in our response.

(3) The conclusion that "SuM store information about stress but not memory" (line 240) is not fully supported, particularly with respect to possible roles in memory. The lack of a role in memory of events, as opposed to the output of threat or stress memory, may be true, but is functionally untested in presented experiments. The data do indicate activation of the SuM neuron by foot shock, which has been previously reported (Escobedo et al eLife 2024). The changes in SuM activity following chronic stress (Figure 1) are intriguing, but their relationship to "stress information storage" is not clearly established.

Thank you for your valuable comments. Foot-shock-activated neurons may play role in modulate any of the following anxiety-like behaviors and emotional memory (fear memory). We realized that we didn’t fully test all aspects of anxiety and memory, thus resulting in some overstatements in the manuscript. It is more proper to focus on “anxiety avoidance” according to the reduced open-arm exploration in EZM/EPM.

Reviewer #2 (Public review):

This manuscript investigates the neural mechanisms of anxiety and identifies the supramammillary nucleus (SuM) as a critical hub in mediating anxiety-related behaviors. The authors describe a population of neurons in the SuM that are activated by acute and chronic stress. While their activity is not required for fear memory recall, reactivation of these neurons after chronic stress robustly increases anxiety-like behaviors as well as physiological stress markers. Circuit analysis further shows that these stress-activated neurons are driven by inputs from the ventral, but not dorsal, subiculum, and inhibition of this pathway exerts an anxiolytic effect.

The study provides an elegant integration of techniques to link stress, neuronal ensembles, and circuit function, thereby advancing our understanding of the neural substrates of anxiety. A particularly notable point is the selective role of these stress-activated neurons in anxiety, but not in associative fear memory, which highlights functional distinctions between neural circuits underlying anxiety and fear.

Some aspects would benefit from clarification. For example, how selective is the recruitment of this population to stress compared with other aversive states, and how should one best interpret their definition as "stress-activated neurons" given the relatively modest overlap across stress exposures? In addition, the use of the term "engram" in this context raises conceptual questions. Is it appropriate to describe a neuronal ensemble encoding an emotional state as an engram, a term usually tied to specific memory recall?

Overall, this work makes a valuable contribution by identifying SuM stress-activated neurons and their ventral subiculum inputs as central elements of the circuitry underlying anxiety. These findings provide a valuable framework for future studies investigating anxiety circuitry and may inform the development of targeted interventions for stress-related disorders.

We thank the reviewer for raising these important points. We agree that further clarification is warranted. In our study, we compared SAN reactivation across different stimuli: foot shock (acute physical stress), social stress (chronic psychosocial stress), and sucrose reward (non-aversive positive stimulus). As shown in Figure 3, SANs in the supramammillary nucleus (SuM) were significantly reactivated by social stress but not by sucrose reward. Moreover, the c-Fos response in SuM was markedly higher after foot shock compared to home cage controls (Figure 1). While we did not test all possible aversive states (e.g., pain, sickness), our data support that SuM SANs are preferentially recruited by stressors rather than by reward or neutral conditions. We acknowledge that the overlap across stress modalities is not complete, which may reflect differences in stress intensity, duration, or circuit engagement. Future work will systematically compare SAN recruitment across diverse aversive and non-aversive states to further define their selectivity.

The term “stress-activated neurons” (SANs) here refers to neurons that are reliably activated by at least one type of stressor and can be reactivated by subsequent stress exposure. The partial overlap across stressors likely reflects the diversity of stress responses and the possibility that distinct subpopulations within SuM may encode different aspects of aversive experience. Importantly, chemogenetic activation of SANs was sufficient to induce anxiety-like behavior and elevate corticosterone (Figure 4), supporting their functional role in stress-related behavioral and physiological outputs. We have revised the manuscript to clarify that SANs represent a stress-responsive ensemble rather than a uniform population activated identically by all stressors.

We appreciate the reviewer’s conceptual caution. In the revised manuscript, we intentionally avoided using the term “engram” to describe SANs. Our focus is on a stress-activated neuronal ensemble that drives anxiety-like behavior, not on memory recall per se. We refer to SANs as an “ensemble” or “population” rather than an engram, consistent with the TRAP-based labeling approach used to capture neurons activated during a specific experience. We agree that “engram” is best reserved for memory-encoding cells and will ensure this distinction remains clear throughout the text.

Reviewer #3 (Public review):

Weaknesses:

The strength of some of the evidence is judged to be incomplete. The paper provides good evidence that SuM contains stress-responsive neurons, and the activity of these neurons increases some measure of anxiety-like behavior. However, the evidence that the vSub-SuM projection "encodes anxiety" and that the SuM is a key regulator of anxiety is judged to be incomplete. The claim that SuM generates an "anxiety engram" is also judged to be incompletely supported by the evidence. Namely, what is unclear is whether these cells/regions encode anxiety per se versus modulate behaviors (like exploration) that tend to correlate with anxiety. Since many brain regions respond to footshock and other stressors, the response of SuM to these stimuli is not strong evidence for a role in anxiety. I am not convinced that the identified SuM cells have a specific anxiety function. As the authors mention in the introduction, SuM regulates exploration and theta activity. Since theta potently regulates hippocampal function, there is the concern that SuM manipulations could have broad effects. As shown in Supplementary Figure 2, stimulating stress-responsive cells in SuM potently reduces general locomotor exploration. This raises concerns that the manipulation could have broader effects that go beyond just changes in anxiety-like behavior. Furthermore, the meaning of an "anxiety engram" is unclear. Would this engram encode stress, the sense of a potential threat, or the behavioral response? A more developed analysis of the behavioral correlates of SuM activity and the behavioral effects of SuM manipulations could give insight into these questions.

We appreciate the reviewer’s thoughtful critique regarding the specificity of SuM’s role in anxiety and the interpretation of our findings. We acknowledge that SuM has broad functions, including regulating exploration and hippocampal theta. However, our data show that general SuM activation increases anxiety-like measures (reduced open-arm time in EZM, decreased center exploration in OF) without altering total locomotion (Fig. 2, Suppl. Fig. 2). The locomotor reduction in SAN activation experiments (Suppl. Fig. 2F–G) was observed alongside clear anxiety-like behavioral changes (e.g. suppressed reward seeking), suggesting that the effects are not solely due to motor suppression. We agree that the methods we used to estimate anxiety-like behaviors base on mice movement when testing, and this could be a shortage of this research when trying to link the data to anxiety. Therefore it will be more proper to interpret the results as modulation of anxiety-like behavior (anxiety related avoidance) but not anxiety itself. We have modified the manuscript to describe more precise to avoid overstatement.

Our fiber photometry data (Fig. 5) show that vSub–SuM projection neurons increase activity specifically when mice enter open arms of the EZM—a behavioral transition associated with anxiety—whereas dSub–SuM projections do not. This activity correlates with anxiety-related behavior, not merely with movement or stress per se.

We also agree that the term “engram” may be misleading in this context. In the manuscript, we refer to SANs as a “stress-activated neuronal ensemble” rather than an anxiety engram. Our data indicate that these neurons are recruited by stress and their reactivation produces more anxiety related avoidance to open arms. We have revised the text to avoid conceptual overreach and to clarify that SuM SANs likely contribute to a state of sustained anxiety/avoidance.

Recommendations for the authors:

Reviewing Editor Comments:

Should you choose to revise your manuscript, if you have not already done so, please include full statistical reporting, including exact p-values wherever possible alongside the summary statistics (test statistic and df) and, where appropriate, 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript.

Readers would also benefit from noting that the subjects were male in the abstract and discussion of the limitations of the exclusion of females.

Thank you for the suggestion. We have included the full statistical detail in a separate sheet as Table 1. Also, we have modified the title of the manuscript to reflect the sex of the mice.

Reviewer #1 (Recommendations for the authors):

(1) In line 211, the authors state, "we recorded neuronal action potentials via multichannel extracellular recording while the mice were moving in the EPM, a traditional type of maze used to test anxiety in rodents,". However, it is unclear what data is presented in the paper, that is, extracellular recordings from SuM in mice on the elevated plus maze.

We have deleted the description of multichannel recording data in EPM as the data was removed earlier.

Minor corrections to the text and figures.

(2) For bar plots, perhaps clarify how the data is presented. For example, in Figure 4, "The data in B, D, E and I-L are presented as the means {plus minus} SEMs," but this does not appear to be plotted as a mean with SEM error bars because the error bars cover all the values.

Corrected.

(3) In Figure 5, the white text for EGFP in panel B is very difficult to see.

Corrected.

(4) For Figure 5D, it would be helpful to more clearly specify which neurons in SuM were recorded from. Was it SANs or all SuM neurons?

We did whole-cell recording on all SuM neurons.

(5) Fos2A-iCreERT2 is mislabeled as "Fos2A-iCreERT" in the methods.

Corrected.

(6) The sentence at line 139 "To make sure foot shock induced anxiety won't last until manipulation, we subjected139mice to an acute stress protocol involving foot shocks and then performed the elevated plus140maze (EPM) and elevated zero maze (EZM) tests to evaluate anxiety on days 2 and 7," is unclear as written.

Thank you for pointing this. We have modified the sentence to make it more clear. “To make sure mice are on similar basal condition while applying chemo-genetic manipulation, we subjected mice to an acute stress protocol involving foot shocks and then performed the elevated plus maze (EPM) and elevated zero maze (EZM) tests to evaluate anxiety on days 2 and 7 (Figure 4 A). The mice that experienced foot shocks showed decreases in the exploration time in the open arms on day 2. However, acute stress-induced anxiety was not detected on day 7 (Figure 4 B), which allow us to compare the reactivation of SANs produced anxiety-like behavior between groups at the same baseline.”

(7) The details of the viral injections used for ex vivo electrophysiology are not sufficient to understand the experiment and the implications of the data. Which neurons (SANs?) are recorded from, what percent of those had inputs, were the sub-neurons globally labeled or just SANs?

We performed whole-cell recording on global SuM neurons to show if the projection is innervated by glutamergic neurons in Sub as shown in Figure 5-B that the projection neurons in Sub are exclusively vglut1 expressed. Based on this aim of the experiment, we didn’t keep any neurons that were not response to the light stimulation, therefore can’t calculate the input percent in this case. We have added words to clearly show that we did global SuM neurons in Methods.

(8) The scale used in Figure 6C renders that data unreadable. 120 to 40% changes in body weight are well beyond the variability in the data.

We have modified the axis (90 to 110%) to show the body weight change clearer.

(9) The dose of CNO used, 5 mg/kg, is high, and using lower doses or other DREADD ligands is worth considering.

Thank you for your valuable comment. We have noticed that people are using relatively lower dose of CNO or other DREADD ligands that are reported much higher affinity and less side-effect. The dose of 5mg/kg was adapted from earlier papers that using DREADD and show no obvious side-effect in mice[17], e.g locomotion (S Figure 2B), in our experiments, so we keep using this dose in this project to make it consistent across different cohorts of experiments. We are switching to DCZ to avoid any potential side-effect of CNO in the following experiments based on this project.

Reviewer #2 (Recommendations for the authors):

This is a strong manuscript that provides important insights into the role of the supramammillary nucleus (SuM) and its inputs from the ventral subiculum in regulating anxiety. The combination of behavioral, imaging, electrophysiological, and circuit manipulation approaches is impressive, and the distinction the authors propose between anxiety-related and fear-related circuits is conceptually important.

There are, however, some points that I think need clarification. The authors emphasize that the hippocampus is essential for fear memory recall, yet they do not directly evaluate whether the SuM-hippocampal pathway might contribute differentially to anxiety versus fear memory. Addressing this would help to explain where the dissociation between the two processes arises.

Thank you for the suggestion. We realized that we didn’t collect enough data to exclude the role of those SANs on memory, especially fear memory, a memory formation bases on strong emotional training as aforementioned. The data and relevant discussion have been removed to avoid misunderstanding and overstatement.

I am also not fully convinced about the definition of the "stress-activated neurons" (SANs). The overlap across repeated stress exposures is quite modest (around 20%), which suggests that this population may not be strictly stress-specific but rather a dynamic subset that is preferentially, though not exclusively, engaged by stress. Related to this, the use of the term "engram" raises conceptual questions. Since the classic engram refers to an ensemble encoding and recalling a specific memory, it is not obvious whether it is appropriate to apply the term to a neuronal population that appears to represent a persistent emotional state. The authors should consider justifying this choice of terminology more carefully or adopting a different term.

Thank you for your important comments. Yes we agree that the SANs in this manuscript are more likely dynamic subset other than exclusive foot-stress engaged “engram”. That’s why we use “stress-activated neurons” but not “engram” to describe this neuronal ensemble. To avoid further misleading, we have made some modification to reduce the use of “engram” across the manuscript.

Some parts of the text also need more precision. For example, the statement in lines 63-65 that "few studies have explored emotion-related engram cells" is potentially misleading, as most engram studies focus on memories with a strong emotional component. The rationale for this claim should be clarified.

This sentence has been deleted since it is not necessary to link the text and misleading.

In Figure 1, the choice of methods is also puzzling: cFos immunostaining is used after shock delivery, while electrophysiology is used for the CSDS paradigm. It would be helpful to explain why different readouts were chosen for different stress models, and whether this may affect the comparability of the results.

Thank you for this important comment. In Figure 1, we aimed to demonstrate that both acute (foot shock) and chronic (CSDS) stressors can activate SuM neurons, using complementary methods (cFos for acute, in vivo recording for chronic). The reason we chose different method is that acute stress produces transit effect while chronic stress produces long-lasting effect. To our knowledge, cFos is a well-established marker for strong neuronal activation, but with short lifespan (~4-6 hours) and suits acute paradigm better. In vivo recording allows us to compare the neuronal activity before and after chronic experiments within subjects and has ability to reveal cumulative effect which cFos cannot. To address this, we have clarified in the text that the purpose of Figure 1 in Line 112-113: “To investigate if SuM would be responsive to diverse stressors, we next examined whether chronic stress, which different mechanism underlying…”

Finally, some additional details would strengthen the presentation. The discussion of corticosterone and other physiological markers could be expanded to indicate whether these effects were robust across stress paradigms. Similarly, the relatively modest overlap between SANs activated by different stressors could be framed more explicitly as part of a broader principle of flexible ensemble recruitment in anxiety-related circuits.

Thank you for your suggestion. We have added more discussion about the corticosterone and the flexibility of SANs in the manuscript. See Line 267-270: “The serum corticosterone concentration can be used as a marker of stress-induced change in the peripheral blood. Previous studies showed serum corticosterone can be increased by various stress stimulation [39–42]; meanwhile, intentionally supplementing the diet with corticosterone can induce anxiety-like behaviors in rodents[43].” and Line 275-281: “However, the reactivation rate of SANs caused by different stressor was relatively lower than the initial activation rate caused by foot shock (Figure 3). This suggests that stress-activated neuronal clusters may have more flexible recruitment principles, with only a small number of neurons potentially encoding emotional information, while most other neurons remain involved in encoding other neural activities. Studies in other field, particularly studies of memory engram, has shown that the sets of neurons activated during learning are dynamic and exhibit high flexibility [44, 45].”

Overall, the work is of high quality and provides a valuable contribution to the field, but addressing these points would help sharpen the mechanistic claims and ensure that the conceptual framework is as clear and precise as the experimental data.

Reviewer #3 (Recommendations for the authors):

(1) Since increased SuM activity is hypothesized to mediate the effects of stress on anxiety-like behavior, a logical step would be to test for necessity by silencing the stress-activated SuM cells.

We agree this is a logical and valuable experiment. While our current study focused primarily on the sufficiency of SuM/SAN activation to induce anxiety-like behavior, we acknowledge that inhibition experiments would provide critical complementary evidence for necessity. We have added a statement in the Discussion noting that “future studies should examine whether silencing SuM SANs, either during stress exposure or during anxiety testing, can prevent or reduce stress-induced anxiety”. This will help establish a more complete causal role.

(2) Discuss what is meant by "anxiety engram" and what features of anxiety the labeled cells might encode.

We concur that “stress-activated neuron (SAN)” is a more precise descriptor than “engram” in this context. We have revised the text to avoid the potentially misleading term “engram” and instead refer to a “stress-activated neuron”. The labeled cells are preferentially reactivated by stress (not reward), and their activation promotes both behavioral avoidance and physiological stress markers (corticosterone). They likely contribute to the maintenance of an anxious state under perceived threat, rather than encoding discrete threat cues or memories.

(3) A more nuanced analysis of behavioral correlates of SuM activity and/or the behavioral effects of SuM manipulations would strengthen this paper.

To provide a more nuanced understanding of the behavioral correlates, we have performed additional analyses on our fiber photometry data (now presented in Supplemental Figure 6). and have also planned additional experiments for the future study to deepen our understanding.

References:

(1) Jendryka M, Palchaudhuri M, Ursu D, van der Veen B, Liss B, Kätzel D, et al. Pharmacokinetic and pharmacodynamic actions of clozapine-N-oxide, clozapine, and compound 21 in DREADD-based chemogenetics in mice. Sci Rep. 2019;9.

(2) Koike H, Demars MP, Short JA, Nabel EM, Akbarian S, Baxter MG, et al. Chemogenetic Inactivation of Dorsal Anterior Cingulate Cortex Neurons Disrupts Attentional Behavior in Mouse. Neuropsychopharmacology. 2016;41:1014–1023.

(3) Guettier J-M, Gautam D, Scarselli M, Ruiz De Azua I, Li JH, Rosemond E, et al. A chemical-genetic approach to study G protein regulation of cell function in vivo. Proceedings of the National Academy of Sciences. 2009;106:19197–19202.

(4) Wess J, Nakajima K, Jain S. Novel designer receptors to probe GPCR signaling and physiology. Trends Pharmacol Sci. 2013;34:385–392.

(5) Kraeuter AK, Guest PC, Sarnyai Z. The Elevated Plus Maze Test for Measuring Anxiety-Like Behavior in Rodents. Methods in Molecular Biology, vol. 1916, Humana Press Inc.; 2019. p. 69–74.

(6) Kraeuter AK, Guest PC, Sarnyai Z. The Open Field Test for Measuring Locomotor Activity and Anxiety-Like Behavior. Methods in Molecular Biology, vol. 1916, Humana Press Inc.; 2019. p. 99–103.

(7) Wall PM, Messier C. Methodological and conceptual issues in the use of the elevated plus-maze as a psychological measurement instrument of animal anxiety-like behavior. Neurosci Biobehav Rev. 2001;25:275–286.

(8) Carobrez AP, Bertoglio LJ. Ethological and temporal analyses of anxiety-like behavior: The elevated plus-maze model 20 years on. Neurosci Biobehav Rev. 2005;29:1193–1205.

(9) Seibenhener ML, Wooten MC. Use of the open field maze to measure locomotor and anxiety-like behavior in mice. Journal of Visualized Experiments. 2015. 6 February 2015. https://doi.org/10.3791/52434.

(10) Prut L, Belzung C. The open field as a paradigm to measure the effects of drugs on anxiety-like behaviors: A review. Eur J Pharmacol. 2003;463:3–33.

(11) Chen Y, Zhou X, Chu B, Xie Q, Liu Z, Luo D, et al. Restraint Stress, Foot Shock and Corticosterone Differentially Alter Autophagy in the Rat Hippocampus, Basolateral Amygdala and Prefrontal Cortex. Neurochem Res. 2024;49:492–506.

(12) Hassell JE, Nguyen KT, Gates CA, Lowry CA. The Impact of Stressor Exposure and Glucocorticoids on Anxiety and Fear. Curr. Top. Behav. Neurosci., vol. 43, Springer; 2019. p. 271–321.

(13) Peng B, Xu Q, Liu J, Guo S, Borgland SL, Liu S. Corticosterone attenuates reward-seeking behavior and increases anxiety via D2 receptor signaling in ventral tegmental area dopamine neurons. Journal of Neuroscience. 2021;41:1566–1581.

(14) Myers B, Greenwood-Van Meerveld B. Elevated corticosterone in the amygdala leads to persistant increases in anxiety-like behavior and pain sensitivity. Behavioural Brain Research. 2010;214:465–469.

(15) Demuyser T, Deneyer L, Bentea E, Albertini G, Van Liefferinge J, Merckx E, et al. In-depth behavioral characterization of the corticosterone mouse model and the critical involvement of housing conditions. Physiol Behav. 2016;156:199–207.

(16) Shoji H, Maeda Y, Miyakawa T. Chronic corticosterone exposure causes anxiety- and depression-related behaviors with altered gut microbial and brain metabolomic profiles in adult male C57BL/6J mice. Molecular Brain . 2024;17.

(17) Manvich DF, Webster KA, Foster SL, Farrell MS, Ritchie JC, Porter JH, et al. The DREADD agonist clozapine N-oxide (CNO) is reverse-metabolized to clozapine and produces clozapine-like interoceptive stimulus effects in rats and mice. Sci Rep. 2018;8.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Henning et al. examine the impact of GABAergic feedback inhibition on the motion-sensitive pathway of flies. Based on a previous behavioral screen, the authors determined that C2 and C3, two GABAergic inhibitory feedback neurons in the optic lobes of the fly, are required for the optomotor response. Through a series of calcium imaging and disruption experiments, connectomics analysis, and follow-up behavioral assays, the authors concluded that C2 and C3 play a role in temporally sharpening visual motion responses. While this study employs a comprehensive array of experimental approaches, I have some reservations about the interpretation of the results in their current form. I strongly encourage the authors to provide additional data to solidify their conclusions. This is particularly relevant in determining whether this is a general phenomenon affecting vision or a specific effect on motion vision. Knowing this is also important for any speculation on the mechanisms of the observed temporal deficiencies.

Strengths:

This study uses a variety of experiments to provide a functional, anatomical, and behavioral description of the role of GABAergic inhibition in the visual system. This comprehensive data is relevant for anyone interested in understanding the intricacies of visual processing in the fly.

Weaknesses:

(1) The most fundamental criticism of this study is that the authors present a skewed view of the motion vision pathway in their results. While this issue is discussed, it is important to demonstrate that there are no temporal deficiencies in the lamina, which could be the case since C2 and C3, as noted in the connectomics analysis, project strongly to laminar interneurons. If the input dynamics are indeed disrupted, then the disruption seen in the motion vision pathway would reflect disruptions in temporal processing in general and suggest that these deficiencies are inherited downstream. A simple experiment could test this. Block C2, C3, and both together using Kir2.1 and Shibire independently, then record the ERG. Alternatively, one could image any other downstream neuron from the lamina that does not receive C2 or C3 input.

Given the prominent connectivity of C2 and C3 to lamina neurons, we actually expected that lamina processing is also affected. We did the experiment of silencing C2 and recording in the lamina neuron L2 and found no significant difference in their response profile (Author response image 1).

Author response image 1.

Calcium responses of L2 axon terminals to full field ON and PFF flashes for controls (grey, N=8 flies, 59 cells) or while genetically silencing C2 using shibire^ts (magenta, N=4 flies, 26 cells). Traces show mean +- SEM.

We could include these data in the main manuscript, but we do not really feel comfortable in claiming that C2 and C3 have a specific role in motion processing only, even if it was predominantly affecting medulla neurons. To our knowledge, how peripheral visual circuitry contributes to any other visual behaviors, such as object detection, including the pursuit of mating partners, or escape behaviors, is not well understood. Instead, we added a sentence to the discussion stating that our work does not exclude that, given their wide connectivity, C2 and C3 are also involved in other visual computations.

(2) Figure 6c. More analysis is required here, since the authors claim to have found a loss in inhibition (ND). However, the difference in excitation appears similar, at least in absolute magnitude (see panel 6c), for PD direction for the T4 C2 and C3 blocks. Also, I predict that C2 & C3 block statistically different from C3 only, why? In any case, it would be good to discuss the clear trend in the PD direction by showing the distribution of responses as violin plots to better understand the data. It would also be good to have some raw traces to be able to see the differences more clearly, not only polar plots and averages.

We apologize: The plots in the manuscript show the mean across all cells, but the statistics were done more conservatively, across flies. We corrected this mismatch and the figure now shows the mean ± ste across flies after first averaging across cells within each fly. Thank you for pointing this out. Since we recorded n=6-8 flies per genotype, we did not include violin plots, which would indeed make sense if we showed data for each cell.

(3) The behavioral experiments are done with a different disruptor than the physiological ones. One blocks chemical synapses, the other shunts the cells. While one would expect similar results in both, this is not a given. It would be great if the authors could test the behavioral experiments with Kir2.1, too.

We have tried this experiment, but unfortunately, flies were not walking well on the ball, and we were not able to obtain data of sufficient quality.

Reviewer #2 (Public review):

Summary:

The work by Henning et al. explores the role of feedback inhibition in motion vision circuits, providing the first identification of inhibitory inheritance in motion-selective T4 and T5 cells of Drosophila. This work advances our current knowledge in Drosophila motion vision and sets the way for further exploring the intricate details of direction-selective computations.

Strengths:

Among the strengths of this work is the verification of the GABAergic nature of C2 and C3 with genetic and immunohistochemical approaches. In addition, double-silencing C2&C3 experiments help to establish a functional role for these cells. The authors holistically use the Drosophila toolbox to identify neural morphologies, synaptic locations, network connectivity, neuronal functions, and the behavioral output.

Weaknesses:

The authors claim that C2 and C3 neurons are required for direction selectivity, as per the publication's title; however, even with their double silencing, the directional T4 & T5 responses are not completely abolished. Therefore, the contribution of this inherited feedback in direction-selective computations is not a prerequisite for its emergence, and the title could be re-adjusted.

We adjusted the title to “are involved in motion detection.”

Connectivity is assessed in one out of the two available connectome datasets; therefore, it would make the study stronger if the same connectivity patterns were identified in both datasets.

We did not assume large differences between the datasets because Nern et al. 2025 described no major sexual dimorphism. To verify this, we now plotted C2 and C3 connectivity from the three major EM datasets that include C2/C3 connectivity, the female FAFB dataset (Zheng et al. 2018, Dorkenwald et al. 2024, Schlegel et al. 2024) the male visual system (Nern et al. 2025), and the 7-column dataset (Takemura et al. 2015) and see no major differences (Author response image 2 and Author response image 3).

Author response image 2.

Relative pres- and post-synaptic counts for C3 from 3 different data sets. Shown are up to ten post- or pre-synaptic partner neurons.

Author response image 3.

Relative pres- and post-synaptic counts for C2 from 3 different data sets. Shown are up to ten post- or pre-synaptic partner neurons.

The mediating neural correlates from C2 & C3 to T4 & T5 are not clarified; rather, Mi1 is found to be one of them. The study could be improved if the same set of silencing experiments performed for C2-Mi1 were extended to C2 &C3-Tm1 or Tm4 to find the T5 neural mediators of this feedback inhibition loop. Stating more clearly from the connectomic analysis, the potential T5 mediators would be equally beneficial. Future experiments might also disentangle the parallel or separate functions of C2 and C3 neurons.

We fully agree that one could go down this route. Given the widespread connectivity of C2 and C3, and the fact that these are time-consuming experiments with often complex genetics, we had decided to instead study the “compound effect” of C2 and C3 silencing by analyzing T4/T5 physiological properties and motion-guided behavior. We now explicitly explain this logic by saying, “To understand the compound effect of C2 and C3 on motion processing, we focused on the direction-selective T4/T5 neurons, which are downstream of many of the neurons that C2 and C3 directly connect to.”

Finally, the authors' conclusions derive from the set of experiments they performed in a logical manner. Nonetheless, the Discussion could benefited from a more extensive explanation on the following matters: why do the ON-selective C2 and C3 neurons control OFF-generated behaviors, why the T4&T5 responses after C2&C3 silencing differ between stationary and moving stimuli and finally why C2 and not C3 had an effect in T5 DS responses, as the connectivity suggests C3 outputting to two out of the four major T5 cholinergic inputs.

Apart from the behavioral screen results, we only tested ON edges in our more detailed behavioral characterizations. And while we show phenotypes for the OFF-DS cell T5, it is well established that inhibitory cells that respond to one contrast polarity can function in the pathway with the opposite contrast polarity (e.g., the OFF-selective Mi9 in the ON pathway). We realized that our narrative in the results section was misleading in this regard (we had given the ON selectivity of C2/C3 as one argument why we first focused on the ON pathway) and eliminated this argument.

For the differential involvement of C2/C3 for T4/T5 responses to stationary and moving stimuli (C2 and C3 silencing affects both T4 and T5 DS responses, but mostly T4 flash responses): We mostly took the disinhibition of flash responses in T4 as a motivation to look more specifically at a potential role in motion-computation. We now added a sentence about the potential emergence of these flash responses to the already extensive discussion paragraph “How could inhibitory feedback neurons affect motion detection in the ON pathway?”

Last, we added a discussion point about the relationship between C2 and C3 connectivity and the functional consequences, and discussed the fact that C3 connectivity alone does not correlate with a functional role of C3 (alone) in DS computation.

Reviewer #3 (Public review):

Summary:

This article is about the neural circuitry underlying motion vision in the fruit fly. Specifically, it regards the roles of two identified neurons, called C2 and C3, that form columnar connections between neurons in the lamina and medulla, including neurons that are presynaptic to the elementary motion detectors T4 and T5. The approach takes advantage of specific fly lines in which one can disable the synaptic outputs of either or both of the C2/3 cell types. This is combined with optical recording from various neurons in the circuit, and with behavioral measurements of the turning reaction to moving stimuli.

The experiments are planned logically. The effects of silencing the C2/C3 neurons are substantial in size. The dominant effect is to make the responses of downstream neurons more sustained, consistent with a circuit role in feedback or feedforward inhibition. Silencing C2/C3 also makes the motion-sensitive neurons T4/T5 less direction-selective. However, the turning response of the fly is affected only in subtle ways. Detection of motion appears unaffected. But the response fails to discriminate between two motion pulses that happen in close succession. One can conclude that C2/C3 are involved in the motion vision circuit, by sharpening responses in time, though they are not essential for its basic function of motion detection.

Strengths:

The combination of cutting-edge methods available in fruit fly neuroscience. Well-planned experiments carried out to a high standard. Convincing effects documenting the role of these neurons in neural processing and behavior.

Weaknesses:

The report could benefit from a mechanistic argument linking the effects at the level of single neurons, the resulting neural computations in elementary motion detectors, and the altered behavioral response to visual motion.

We agree that we cannot fully draw this mechanistic argument, but we also do not think that this is a realistic goal of this study. Even in a scenario where one would measure the temporal and spatial properties of “all” neurons that are connected to C2 and C3, this would likely not reveal the full mechanisms linking the single neurons to DS computation, but would require silencing specific connections, or specific molecular components of the connection, or could be complemented by models. A beautiful example where such a mechanistic understanding was achieved, recently published in Nature, essentially focused on a single synaptic connection (between Mi9 and T4) (Groschner et al. 2024), and built on extensive work that had already highlighted the importance of these neurons. We would further argue that the field does not have a good understanding of how T4/T5 responses are translated into behavior. Although possible pathways emerge from connectomes, it is for example not understood why the temporal frequency tuning of T4/T5 substantially differs from the temporal frequency tuning of the optomotor response.

We therefore would like to highlight that the focus of our study was not to connect all those pieces, but rather to highlight the hitherto unknown overall importance of inhibitory feedback neurons for visual computations along the visual hierarchy, from individual neuron properties, via DS computation, to the temporal precision of the optomotor response.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Line 52: "The functional significance of feedback neurons, particularly inhibitory feedback mechanisms, in early visual processing is not understood."

This is incorrect not only because it is referred to as a general statement, but also because many studies have examined inhibition in flies. It may not be solely GABAergic inhibition, but that is just one type. While some discussions later address feedback from horizontal cells in the retina, etc., there is no mention of work on color vision, which requires feedback. Please rephrase.

We now say “visual motion processing” in this sentence, and added a sentence on color vision: “... color-opponent signalling requires reciprocal inhibition between photoreceptors as well as feedback inhibition from distal medulla (Dm) neurons. (Schnaitmann et al., 2018, Heath et al., 2020, Schnaitmann et al., 2024). “

(2) Line 197: "Because a previous studies" One or many?, but more important, please cite them.

We corrected to “a previous study” and cite Tuthill et al. 2013

(3) Line 172: I noticed a few minor grammatical errors and wording issues, such as the use of "we next" twice in one sentence. "To next identify potential GABAergic neurons that are important for motion computation in the ON pathway, we next intersected 12 InSITE-Gal4." I am bad at picking them out, but since I noticed them, I would strongly suggest looking at the text carefully again.

We deleted one occurrence of ‘next’, thank you for catching that.

(4) Question to the authors. Why did you use twice independent lines and not checkers for the white noise analysis in Figure 3e?

We used flickering bars because many visual system neurons tested in our lab respond with a better signal-to-noise ratio as compared to checkerboards. Flickering bars also appear to be more suited to isolate the spatial surround of neurons. This type of stimulus has been successfully used in previous studies to extract receptive fields of neurons in the fly visual system (Arenz et al. 2017; Leong et al., 2016, Salazar-Gatzimas et al. 2016; Fisher et al. 2015, …).

(5) Line 248: "Because C2 emerged as a prominent candidate from the behavioral screen, we focused on C2 and asked how silencing C2 affects..." Please state how here. I would need to go to the methods.

We added a sentence “C2 was silenced by expression of UAS-shibire^ts (UAS-shi^ts) for temporal control of the inhibition of synaptic activity.”

(6) Much of the work in the blowfly uses picrotoxinin to block GABAergic inhibition in the visual motion pathway. It would be useful to mention some of this early work and its results, particularly that of Single et al. (1997). It might be interesting to reinterpret their results.

Thank you for pointing this out. We added this paragraph to the discussion: ‘Work in blowflies has found a severe impact of GABAergic signaling for DS in LPTCs downstream of T4 and T5 cells, using application of picrotoxin to the whole brain (Single et al. 1997; Schmid and Bülthoff 1988). Although the loss of DS in LPTCs could originate from direct inhibitory synapses onto LPTCs (Mauss et al. 2015; Ammer et al. 2023), the disruption of GABAergic signaling in upstream circuitry, which reduces DS in T4 and T5, may also contribute to the phenotype seen in LPTCs.’

Reviewer #2 (Recommendations for the authors):

The following set of corrections aims to better the scientific and presentation aspects of this work.

(1) The title of the work implies that C2 and C3 neurons are required for motion processing, whereas the study shows their participation in motion computations, which persists post their silencing. Therefore, "Inhibitory columnar feedback neurons contribute to Drosophila motion processing" would be a more appropriate title.

We rephrased the title to say that inhibitory feedback neurons “are involved in” motion processing.

(2) The morphology of C2 and C3 neurons, i.e., ramifications in medulla & cell body in medulla and axonal targeting to lamina, implies their feedback role. It would be important to mention the specific feedback loop they participate in and the role of Mi1 more extensively in lines 36, 120.

We find it hard to speculate on the specific feedback loops that C2 and C3 are involved in from their widespread input and output connectivity. If we had, we would have wanted to support this by functional measurements of this specific loop, which was not the goal of this study.

(3) In lines 55-89, the authors explore the instances of feedback inhibition within and across species and modalities. For the Drosophila visual example (lines 76-89), given that it also addresses motion circuits, the following studies should be included:

Ammer, G., Serbe-Kamp, E., Mauss, A.S., et al. Multilevel visual motion opponency in Drosophila. Nat Neurosci 26, 1894-1905 (2023). https://doi.org/10.1038/s41593-023-01443-z. Mabuchi Y, Cui X, Xie L, Kim H, Jiang T, Yapici N. Visual feedback neurons fine-tune Drosophila male courtship via GABA-mediated inhibition. Curr Biol. 2023 Sep 25;33(18):3896-3910.e7. doi: 10.1016/j.cub.2023.08.034.

We added a sentence on the Ammer et al. finding to the introduction. Since the introduction paragraph focuses on known physiological effects within the visual system, we did not find a good fit for the Mabuchi et al. study, which focuses on serotonergic feedback neurons with a role far downstream in courtship behavior.

(4) In lines 102-103, the following work should be referenced: Groschner LN, Malis JG, Zuidinga B, Borst A. A biophysical account of multiplication by a single neuron. Nature. 2022 Mar;603(7899):119-123. doi: 10.1038/s41586-022-04428-3.

We cited a few of the many papers that used “modeling frameworks” and selected the ones focusing on the entire feedforward circuitry. To also give credit to the Borst lab, we instead added Serbe et al. 2016 here.

(5) In lines 107-108, the Braun et al. (2023) study has not performed Rdl knockdown experiments in T4 cells; hence, it needs to be better clarified in the text.

We corrected this in the text.

(6) Even though the dataset was previously published, a summary plot of the different phenotypes would be very helpful to the reader. Moreover, in line 131, as the study focuses on motion vision, it would be better to use "early motion visual processing" rather than "early visual processing.”

We added a summary plot of the behavioral screen data to Supplementary figure 1, and rephrased previous line 131.

(7) The first result section title excludes C3 neurons, even though in lines 172-179 they are addressed; therefore, the C3 inclusion is suggested as in "GABAergic C2 and C3 neurons control behavioral responses to motion cues". The term "required" should be excluded from the title as the other neuronal types encountered in the InSITE drivers were never quantified; thus, the "behavioral requirement" might come from these other neurons as well.

From the experiments shown in this paragraph alone we cannot make conclusive claims about C3, as it was also weakly visible in one of our genetic control in the intersectional strategy that we took (we had written: “This strategy also revealed other GABAergic cell types, including the columnar neuron C3 and the large amacrine cell CT1 which were however also weakly present in the gad1-p65AD control).

We changed the title of this paragraph to: A forward genetic behavioral screen identifies GABAergic C2 neurons to be involved in motion detection.

(8) In line 142, it should be clearly stated that the MultiColor FlpOut technique was used and should also be cited: Nern A, Pfeiffer BD, Rubin GM. Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system. Proc Natl Acad Sci U S A. 2015 Jun 2;112(22):E2967-76. doi: 10.1073/pnas.1506763112.

We did not use MCFO clones, but simple Flp-out clones, and the genotype and reference for this were given in the methods: UAS-FRT-CD2y+-RFT-mCD8::GFP; UAS-Flp , (Wong et al. 2002). To make this clearer, we now also cite (Wong et al. 2002) in the results section.

(9) In Figure 1c, a description of RFP should be written as it is already in Supplementary Figure 1c.

We added this to the Figure caption.

(10) In line 172, "next" is redundant as it was previously used at the beginning of the sentence.

Removed

(11) In line 175, based on both figures that the authors refer to, instead of C2, C3 should be written.

We do indeed see C3 labeled in the images, but also in a gad1-p65AD control. We thus cannot be sure if C3 indeed reflects the intersection pattern. However, the three lines shown in Figure 1d clearly also label C2, which is not seen in the control condition.

(12) In line 184, a split-C2 line is used (and a split C3 as in Supplementary Figure 2). It would enhance the credibility of the work and even be appropriate afterwards to use the word "requirement" if this split-C2 line was used for behavioral experiments, as in Gohl et al., 2011, and Sillies et al.,2013 studies.

We are indeed using the same split-C2 line for imaging and for behavioral experiments in Figure 7. We see Figure 1 (and with that, Silies et al. 2013) as a first pass screen, from which we obtained candidates, which we then more thoroughly tested throughout the remaining manuscript, with more specific lines. We are no longer using the word “requirement”

(13) In lines 186-188, is DenMark used as a postsynaptic marker? If yes, an additional control would be the use of Discs-large (DLG) as a postsynaptic marker, as DenMark would not be restricted to postsynaptic densities.

Yes, we used DenMark as written in the sentence “we expressed GFP-tagged Synaptotagmin (Syt::GFP) to label pre-synapses together with the dendritic marker DenMark (Nicolai et al., 2010)”. Since our claims about widespread C2 and C3 connectivity are further supported by connectomics, we did not use another postsynaptic marker.

(14) In line 191, L2 is mentioned as presynaptic, whereas in Figure 2b is clearly postsynaptic.

We write “This revealed that C2 forms several presynaptic contacts with the lamina neurons L5, L1, and L2” . L5, L1, and L2 are hence postsynaptic to C2, which is what is plotted in Figure 2b. 

(15) In line 197, the "a" in "because a previous studies" should be removed, and these studies should be cited as the authors do in line 514.

Done as suggested.

(16) In line 1191, the figure title uses the term "required", whereas the plotted data suggest that T4 and T5 responses remain DS after C2&C3 silencing. Rephrasing to "C2 and C3 affect direction-selective.." would be better suited.

We replaced “required” with “contribute to”

(17) In the legend of Figure 2b, the "Counts of synapses" is misleading. The number plotted refers to the percentage of synapse counts from the target neuron.

Corrected.

(18) A general question about the C2 and C3 ON selectivity: How would the authors explain the OFF deficits from the published behavioral screening in Supplementary Figure 1a? Do the other InSITE neurons contribute to it? This needs to be further elaborated in the discussion.

A neuron being ON selective does not imply that it is functionally required in the ON pathway only. In fact, Mi9, a major component of the ON pathway (even if not “required” under many stimulus conditions), is OFF selective.

Furthermore, both we (Ramos-Traslosheros and Silies, 2021) and others (Salazar-Gatzimas et al. 2019) have shown that both ON and OFF signals are combined in ON and OFF pathways, which is further supported by connectomics data. We clarified the transition from physiology to function in the results section, as already explained above.

(19) In line 216, the authors' image from layer M1, but the reasoning behind this choice is missing. The explanation gap intensifies after you proceed with further examining the layer-specific responses in Supplementary Figure 2. Is this because C2 and C3 receive their inputs in M1, as is insinuated in line 219?

As Supplementary Figure 2 shows, we initially imaged from all layers of the medulla, where C2 arborizes. Because the response properties, including kinetics, weren’t different, we had no reason to believe that C2 is highly compartmentalized. We thus subsequently focused on layer M1, where amplitudes were highest. We clarified this in the text.

(20) In line 229, it should be clear whether the STRFs come from M1 measurements. STRF analysis in M5, M8, and M9/10 also verifies that the C2, C3 multicolumnar span would further strengthen the results. Given the focus of the work in Mi1 and T4/T5, Mi1-C2 connections should be clarified in terms of which medulla layer they formulate. Additionally, the reasoning behind showing in Figure 3 STRFs from M1 measurements, even though Supplementary Figure 2b implies equal responses in M9/10, where also Tm1 and Tm4 output from C3, should be explained.

We never recorded STRFs in the silenced condition and make no claims about C2 changing spatial properties of Mi1. We added the information that STRFs were recorded in layer M1 to the figure caption. We checked the specific connectivity of C2 and Mi1 and they indeed connect in M1 (Author response image 4), but regardless of this result, there is no evidence for compartmentalization in these columnar neurons.

Author response image 4.

Image of a C2 (blue) and Mi1 (yellow) neuron from EM Data (FAFB). Circles depict synapses from C2 to Mi1 in layer M1 of the medulla.

(21) In Figure 3e, the statistical significance or lack thereof is not visible at the bar plot.

Consistently throughout the manuscript, we now just indicate if a comparison is significant. If nothing is shown, it means that it is not.

To clarify this, we added a sentence to the statistics section in the methods now saying: We show significant differences in figures using asterisks (p<0.05 *,p<0.01 **, p<0.001***). Non-significant differences are not further indicated.

Please note that based on another reviewer comment, we also adapted the analysis of the kernels. This changed the statistics to be significant for the timing of the on peak response (Figure 3e).’

(22) In line 249, it is mentioned that the strongest C2 connection is Mi1; this does not derive from the data shown in Figure 2b.

We intended to look at medulla neurons, and Mi1 is the most connected medulla neuron to C2. We clarified that in the text, which now reads: “Because C2 emerged as a prominent candidate from the behavioral screen, we focused on C2 and asked how silencing C2 affects temporal and spatial filter properties of the medulla neurons that provide direct input to T4 neurons. We chose to test Mi1 as it is the medulla neuron most strongly connected to C2.”

(23) The result section title "C2 & C3 neurons shape response properties of the ON pathway medulla neuron Mi1" does not include C3 results. This would be fundamental to have. As previously mentioned, the neural correlates of this inhibitory feedback loop should be clearly defined, and the current version of this work evades doing so.

We corrected the title. As discussed elsewhere, it was not the goal of this study to work the specific contributions of C2 (and C3) to all neurons they connect to, but rather focus on the compound effect for motion detection.

(24) In line 276, the following work should be cited: Maisak MS, Haag J, Ammer G, Serbe E, Meier M, Leonhardt A, Schilling T, Bahl A, Rubin GM, Nern A, Dickson BJ, Reiff DF, Hopp E, Borst A. A directional tuning map of Drosophila elementary motion detectors. Nature. 2013 Aug 8;500(7461):212-6. doi: 10.1038/nature12320.

We added the citation.

(25) In line 273, the title implies the investigation of the spatial filtering of T4 and T5 cells. This does not take place in the respective result section.

We changed the title to: “C2 and C3 shape temporal and spatial response properties of T4 and T5 neurons.”

(26) In line 280, Kir2.1 is used, whereas previously thermogenetic silencing with Shibirets was preferred; could the authors elaborate on this choice in the text, for example, genetic reasons?

We generally prefer shibire[ts] because of its inducible nature. However, our T4/T5 recordings too included more stimuli (motion stimuli) than the Mi1 recordings, and the effect of shi[ts] mediated silencing by pre-heating the flies (as established by Joesch et al. 2010) was not longlasting enough for these experiments, which is why we used Kir2.1. In a previous set of experiments, we had tried incubating flies while imaging, but this induced too large movements of the brain and T4/T5 recordings were not stable enough.

(27) In lines 290-291, T5 ON suppression is found to be affected by C2 silencing, but the bar plot in Figure 5b uses the OFF-step data. It would be best if the ON-step data for T5 cells were also plotted.

ON-step data for T5 are plotted in Supplementary Fig. 3e

(28) In line 288, "when C2 was also blocked", "also" should be included, as you are referring to double silencing.

Sorry for the confusion, we called the wrong figure in that sentence. Here, we wanted to point at the increased response of T4 to the ON-step upon C2 silencing, which was quantified in Supplementary Fig. 3e.

(29) In line 312, it is important to mention in the discussion why it is the case that C2 and not C3 had an effect on T5 DS responses. C2 outputs to Tm1, whereas C3 to Tm1 and Tm4, based on Figure 2b, with Tm1 and Tm4 being one of the four major cholinergic T5 inputs. Hence, it would be natural to think that C3 and not C2 would affect T5 responses.

We addressed this in the discussion.

(30) In lines 326-328, it is crucial to mention the neural correlates that connect C2 and C3 to T4 and T5. Additionally, the Shinomiya et al. (2019) study shows C3 to T4 connections, which are mentioned in the discussion and should be cited in line 429.

We do not think that mentioning neural correlates at this point is crucial, as these sentences were concluding a paragraph in which we link C2/C3 silencing to T4/T5 responses. We also do not know the neural correlates (but for Mi1) so this would not be accurate.

We have been mentioning C3 to T4 connection in both the results and discussion, and our analysis (Figure 2) stems from the FAFB dataset. We added citations to both results and discussion.

(31) In Figure 6a, compared to Figure 3b, the term compass plots is used instead of polar plots. It would be best to use one consistent term. Additionally, in Figure 6c, it is not mentioned if the responses across genotypes are the outcome of averaging across subtype responses.

These two plots are not the same; a compass plot is a sub-category of polar plots. Polar plots, as in Figure 3, show the response amplitude of the neurons to the different directions of motion. Instead, compass plots, as in Figure 6, show vectors that depict the tuning direction and the strength of tuning of individual neurons.

We added the following sentence to clarify the calculation in Figure 6c: ‘To average responses of all neurons, the PD of each neuron was determined by its maximal response to one of 8 directions shown.'

(32) In line 344, the title could be adjusted to "C2 is controlling the temporal dynamics of ON behavior", under the same reasoning of 'requirements' explained before.

We think that “is controlling” is a stronger claim than “being required”. For a geneticist, the word “required” simply means that there is a(ny) loss of function phenotype, i.e., a reduction in DS when C2 and C3 are silenced/blocked. Many neurons are sufficient but not required to induce a certain behavior (i.e., they can induce a behavior when ectopically activated, but show no significant loss of function phenotype). We therefore consider it remarkable that C2 and C3 silencing indeed shows a significant reduction in DS.

However, we do not want to overclaim anything, and the title now reads: “T4 tunes the temporal dynamics of ON behavior”

(33) In Figure 7c, the plot legend should be "deceleration".

Corrected

(34) In line 424, the Braun et al. (2023) experiments were performed in T5 cells as previously mentioned.

Corrected

(35) In line 435, the authors mention that both ON-selective C2 and C3 neurons act partially in parallel pathways. In Figure 2b, the upstream circuitry between C2 and C3 is identical. How would they explain the functional-connectivity contradiction?

In terms of acting in parallel pathways, downstream, not upstream, connectivity of C2 and C3 will matter, which is not identical. C2 for example connects to Mi1, L1, and L4, whereas C3 does not. On the other hand, C3 connects to Mi9 and Tm4, which C2 does not.

(36) In lines 445-447, the authors address C2 and C3 neurons as columnar, whereas they previously showed in Figure 3 that they are multicolumnar.

Here, we refer to the nomenclature of Nern et al, that use the term “columnar” whenever something is present in each column. We specifically define this by saying “only 15 cells are truly columnar in the sense that they are present once per column and present in each column”. In the results section, we instead talk about “functionally multicolumnar” and changed a sentence in the discussion to say “The spatial receptive fields of C2 and C3 are consistent with the multicolumnar branching of their projections in the medulla” to avoid any such confusion.

(37) In line 448, "thus" is repetitive, and the extracted view in line 449 does not contribute to the essence of the study.

Fixed.

(38) In line 459, the authors refer to inhibition inheritance; this term should be used frequently in the text in case the neural correlates between C2 & C3 and T4 & T5 are not deciphered.

We think this point is very clear throughout the manuscript now. As one prominent example, we added a sentence to the first paragraph of the discussion saying “Given the widespread connectivity of C2 and C3 to neurons upstream of T4/T5, this effect [on DS tuning] is likely inherited from upstream neurons of T4/T5.”

(39) In line 521, the transition between sentences is problematic.

Corrected

(40) For Supplementary Figure 1, why were the ON-motion deficits not addressed with the antibody approach used for Supplementary Figure 1a?

The approach using anti-GABA stainings turned out to be largely redundant with the intersectional strategy. Furthermore, the intersectional strategy provided the full morphology of the cell and, hence, led to easier identification of the cell types involved.

(41) In line 1169, C2 is mentioned, whereas C3 is annotated in the figure.

Corrected

(42) A general comment is that Tm1 inputs could be a good candidate for assessing T5 inputs, as performed for Mi1-T4 in Fig.4. Such experiments would enhance the understanding of inhibitory inheritance to T5 responses.

We fully agree.

(42) Do the authors have any indication or experiments done regarding the C2&C3 role in T4&T5 velocity tuning? This would be complementary to the direction of this study.

This is a good idea, that we had tried. However, we did not see a difference between control and C2 silencing for the temporal frequency tuning of T4/T5. As velocity is closely related to temporal frequency tuning, we would not expect to see a difference there either.

While it would have been nice to be able to draw such a link, we would also state that our behavioral data are a bit different: We did not look at temporal frequency tuning per se, and overall, it is not well understood how responses in T4/T5 relate to behavior, as they for example have different frequency tunings (T4/T5 physiology: Maisak et al., 2013, Arenz et al., 2017; optomotor behaviour: Strother et al.,2017, Clark et al., 2013). 

(43) As a suggestion, Figure 7 would be better positioned as Figure 4, right after the ON-selectivity finding of C2 neurons.

We preferred to keep the current order.

Reviewer #3 (Recommendations for the authors):

Main recommendation:

It would be useful to propose a neural circuit model that connects the various observations. One can draw here on the many circuit models for motion vision in the prior literature.

(1) How might the extended response in upstream neurons Mi1 lead to the inappropriate nulldirection responses in T4/T5?

This is a good question and we can only speculate. Mi1 responses are enhanced upon C2 silencing and T4 responses to full field flash responses are also enhanced. Likely, these motionindependent responses are also seen when the edge travels into the non-preferred direction, whereas this non-motion response would likely be masked by the motion response to the preferred direction. The phenotype seen in T5 is likely inherited from medulla neurons, e.g. Tm1, to which C2 connects. How the delay of the Mi1 response upon C2 silencing may specifically affect ND responses, we don’t know. 

(2) How is the loss of DS in T4/T5 compatible with the continued sensitivity to motion in the turning response? Perhaps the signal from 180-degree oppositely tuned T-cells gets subtracted, so as to remove the baseline activity?

This is a great question that we cannot answer. Overall, perturbations that affect T4/T5 physiology do not necessarily manifest in equivalent phenotypes when looking at behavioral turning responses. Prominent examples come from silencing core neurons of motion-detection circuits, such as Mi1 and Tm3 (see Figure 4, Strother et al. 2017).

(3) How do the altered dynamics in upstream neurons relate to the loss of high-frequency discrimination in the behavior? One would want to explain why the normal fly has a pronounced decay in the response even though the motion is still ongoing (Figure 7b left, starting at 0.4 s). That decay is missing in the mutant response.

That is an excellent question that we unfortunately do not have an answer for. Please note that our visual stimuli is a single edge which is sweeping across the eye, and which might not elicit equally strong responses at each position of the eye, or each time during the stimulus presentation.

In terms of linking the dynamics of upstream neurons to behavior, we already pointed out above that it is not well understood how responses in T4/T5 relate to behavior, as they for example have different frequency tuning, with T4/T5 neurons being tuned to lower temporal frequencies than the turning behavior of a fly walking on a ball (T4/T5 physiology: Maisak et al., 2013, Arenz et al., 2017; optomotor behaviour: Strother et al.,2017, Clark et al., 2013).

Other recommendations:

(1) Abstract line 37 "At the behavioral level, feedback inhibition temporally sharpens responses to ON stimuli, enhancing the fly's ability to discriminate visual stimuli that occur in quick succession." It may be worth specifying *moving* stimuli.

Done as suggested

(2) Line 52: "The functional significance of feedback neurons, particularly inhibitory feedback mechanisms, in early visual processing is not understood." This seems overly negative. Subsequent text mentions a number of such instances that are understood, and one could add more from the retina.

We agree. We rephrased to say ‘motion vision’ and added more examples of known roles of feedback inhibition

(3) Line 69: "inhibitory feedback signals from horizontal cells and amacrine cells to photoreceptors and bipolar cells, respectively, are involved in multiple mechanisms of retinal processing, including global light adaptation, spatial frequency tuning, or the center-surround organization (Diamond 2017)." Maybe add the proven role in temporal sharpening of responses, which is of relevance to the present report.

We added temporal sharpening to that introduction point.

(4) Figure 1: The text for this figure talks about behavioral motion detection deficits in various lines. Maybe add an example of the behavioral effects to this figure.

We added a summary plot of the behavioral screen data to Supplementary figure 1.

(5) Line 325: "the timing of the ON peak tended to be slower for C3 compared to C2 for both the vertical and the horizontal STRF": It's hard to see evidence for that in the data.

Based on your next comment we reanalysed the kernels of C2 and C3. This resulted in a significant difference in peak timing between C2 and C3. 

(6) When presenting kernels as in Figure 3d and Figure 4b, extend the time axis to positive times until the kernel goes to zero. This "prediction of future stimuli" allows the reader to see the degree of correlation within the stimulus, which affects how one interprets the shape of the kernel. Also, plotting the entire peak gives a better assessment of whether there are any shape differences between conditions. An alternative is to compute the kernel via deconvolution, which gets closer to the actual causal kernel, but that procedure tends to highlight high-frequency noise in the measurement.

We replotted the kernels in Figure 3d and 4b to show positive times. The kernels of C2 and C3 stayed at a positive level. Going back through the data we found a severe decrease in GCaMP signal in the first 2 seconds of the recording. We reanalyzed the kernels by ignoring the first seconds. All kernels now go back to zero. The shape of the kernels did not change but we now find a significant difference in peak timing between C2 and C3. Thank you for pointing this out.

(7) Line 280 "simultaneously blocked C2 and C3 using Kir2.1": First use of that acronym. Please explain what the method is.

We now explain “we simultaneously blocked C2 and C3 by overexpression of the inwardrectifying potassium channel Kir2.1”

(8) Line 350 "temporal dynamics for C2 silencing": suggests "dynamics of silencing"; maybe better "response dynamics during C2 silencing".

Edited as suggested

(9) Figure 7: Explain the details of the stimulus containing two subsequent on edges. What happens between one edge and the next? Does the screen switch back to black? Or does the second edge ride on top of the final level of the first edge? This matters for interpreting the response.

Yes, the screen turns dark between subsequent edge presentations. We added a sentence to the methods to clarify that. 

(10) Line 402 "novel, critical components of motion computation.": This seems exaggerated. At the behavioral level, motion computation is mostly unaffected, except for some details of time resolution. Whether those matter for the fly's life is unclear.

We deleted the word ‘critical.’

(11) Line 413 "GABAergic inhibition required for motion detection is mediated by C2 and C3": Again, this seems exaggerated. Motion *detection* appears to work fine, but the *discrimination* of two closely successive motion stimuli is affected. The rest of the text does properly distinguish "discrimination" from "detection".

We changed the title to say: ‘GABAergic inhibition in motion detection is mediated by C2 and C3.’

(12) Line 489 "Whereas the role of C2 and C3 for the OFF pathway may be more generally to suppress neuronal activity,": Unclear to what this refers. The present report emphasizes that there is no effect on OFF activity (Figure 5).

We did not see an effect of T5 responses to OFF flashes as shown in Figure 5 but we found a significant reduction of DS when silencing C2, as well as slightly overall increased responses to all directions for C2 and C3 silencing, which was significant for null directions when silencing C2. This is shown in Figure 6.

Typos:

(1) Line 521.

Fixed

(2) Line 1170: context of the citation unclear.

Fixed

Author Response:

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

Public Reviews:

Reviewer #1 (Public review):

Mutations in CDHR1, the human gene encoding an atypical cadherin-related protein expressed in photoreceptors, are thought to cause cone-rod dystrophy (CRD). However, the pathogenesis leading to this disease is unknown. Previous work has led to the hypothesis that CDHR1 is part of a cadherin-based junction that facilitates the development of new membranous discs at the base of the photoreceptor outer segments, without which photoreceptors malfunction and ultimately degenerate. CDHR1 is hypothesized to bind to a transmembrane partner to accomplish this function, but the putative partner protein has yet to be identified.

The manuscript by Patel et al.makes an important contribution toward improving our understanding of the cellular and molecular basis of CDHR1-associated CRD. Using gene editing, they generate a loss of function mutation in the zebrafish cdhr1a gene, an ortholog of human CDHR1, and show that this novel mutant model has a retinal dystrophy phenotype, specifically related to defective growth and organization of photoreceptor outer segments (OS) and calyceal processes (CP). This phenotype seems to be progressive with age. Importantly, Patel et al, present intriguing evidence that pcdh15b, also known for causing retinal dystrophy in previous Xenopus and zebrafish loss of function studies, is the putative cdhr1a partner protein mediating the function of the junctional complex that regulates photoreceptor OS growth and stability.

This research is significant in that it:

(1) Provides evidence for a progressive, dystrophic photoreceptor phenotype in the cdhr1a mutant and, therefore, effectively models human CRD; and

(2) Identifies pcdh15b as the putative, and long sought after, binding partner for cdhr1a, further supporting the theory of a cadherin-based junction complex that facilitates OS disc biogenesis.

Nonetheless, the study has several shortcomings in methodology, analysis, and conceptual insight, which limits its overall impact.

Below I outline several issues that the authors should address to strengthen their findings.

Major comments:

(1) Co-localization of cdhr1a and pcdh15b proteins

The model proposed by the authors is that the interaction of cdhr1a and pcdh15b occurs in trans as a heterodimer. In cochlear hair cells, PCDH15 and CDHR23 are proposed to interact first as dimers in cis and then as heteromeric complexes in trans. This was not shown here for cdhr1a and pcdh15b, but it is a plausible configuration, as are single heteromeric dimers or homodimers. Regardless, this model depends on the differential compartmental expression of the cdhr1a and pcdh15b proteins. Data in Figure 1 show convincing evidence that these two proteins can, at least in some cases, be distributed along the length of photoreceptor membranes that are juxtaposed, as would be the case for OS and CP. If pcdh15b is predominantly expressed in CPs, whereas cdhr1a is predominantly expressed in OS, then this should be confirmed with actin double labeling with cdhr1a and pcdh15b since the apicobasal oriented (vertical) CPs would express actin in this same orientation but not in the OS. This would help to clarify whether cdhr1a and pcdh15b can be trafficked to both OS and CP compartments or whether they are mutually exclusive.

First let me thank the reviewer for taking the time to comprehensively evaluate our work and provide constructive criticism which will improve the quality of our final version.

To address this issue, we are completed imaging of actin/cdhr1a and actin/pcdh15b using SIM in both transverse and axial sections (Fig 1C-H). Additionally, we have recently established an immuno-gold-TEM protocol and showcase co-labeling of cdhr1a and pcdh15b at TEM resolution along the CP (Fig 1I).

Photoreceptor heterogeneity goes beyond the cone versus rod subtypes discussed here and it is known that in zebrafish, CP morphology is distinct in different cone subtypes as well as cone versus rod. It would be important to know which specific photoreceptor subtypes are shown in zebrafish (Figures 1A-C) and the non-fish species depicted in Figures 1E-L. Also, a larger field of view of the staining patterns for Figures 1E-L would be a helpful comparison (could be added as a supplementary figure).

The revised manuscript includes labels for the location of different cone subtypes in figure 1. All of the images showcasing CHDR1 localization across species concentrate on the PNA positive R/G cones. Larger fields of view were not collected as we prioritized the highest resolution possible and therefore collected small fields of view.

(2) Cdhr1a function in cell culture

The authors should explain the multiple bands in the anti-FLAG blots. Also, it would be interesting to confirm that the cdhr1a D173 mutant prevents the IP interaction with pcdh15b as well as the additive effects in aggregate assays of Figure 2.

The multiple bands on the WB is like our previous results (Piedade 2020), which we believe arise due to ubiquitination and proteolytic cleavage of cdhr1a. We expect the D173 mutation to result in a complete absence of cdhr1a polypeptide, based on the lack of in situ signal in our WISH studies.

Is it possible that the cultured cells undergo proliferation in the aggregation assays shown in Figure 2? Cells might differentially proliferate as clusters form in rotating cultures. A simple assay for cell proliferation under the different transfection conditions showing no differences would address this issue and lend further support to the proposed specific changes to cell adhesion as a readout of this assay.

This is a possibility; however we did not use rotating cultures, this was a monolayer culture. We did not observe any differences in total cell number between the differing transfections. As such, we do not feel proliferation explains the aggregation of K562 cells.

Also, the authors report that the number of clusters was normalized to the field of view, but this was not defined. Were the n values different fields of view from one transfection experiment, or were they different fields of view from separate transfection experiments? More details and clarification are needed.

This will be clarified in the revised manuscript, in short we replicated this experiment 3 times, quantifying 5 different fields of view in each replicate.

(3) Methodological issues in quantification and statistical analyses

Were all the OS and CP lengths counted in the observation region or just a sample within the region? If the latter, what were the sampling criteria? For CPs, it seems that the length was an average estimate based on all CPs observed surrounding one cone or one-rod cell. Is this correct? Again, if sampled, how was this implemented? In Fig 4M', the cdhr1a-/- ROS mostly looks curvilinear. Did the measurements account for this, or were they straight linear dimension measurements from base to tip of the OS as depicted in Fig 5A-E? A clearer explanation of the OS and CP length quantification methodology is required.

The revised manuscript will clearly outline measurement methods. In short, we measured every CP/OS in the imaged regions. We did not average CPs/cell, we simply included all CP measurements in our analysis. All our CP measurements (actin or cdhr1a or pcdh15), were measured in the presence of a counter stain, WGA, prph2, gnb1 or PNA to ensure proper measurements (landmark) and association with proper cell type. Our new figure 7 now includes cone OS counter staining to better highlight the OS.

All measurements were taken as best as possible to reflect a straight linear dimension for consistency.

How were cone and rod photoreceptor cell counts performed? The legend in Figure 4 states that they again counted cells in the observation region, but no details were provided. For example, were cones and rods counted as an absolute number of cells in the observation region (e.g., number of cones per defined area) or relative to total (DAPI+) cell nuclei in the region? Changes in cell density in the mutant (smaller eye or thinner ONL) might affect this quantification so it would be important to know how cell quantification was normalized.

The revised manuscript will clearly outline measurement methods. In short, rod and cone cell counts were based on the number of outer segments that were observed in the imaging region and previously measured for length. We did not observe any eye size differences in our mutant fish.

In Figure 6I, K, measuring the length of the signal seems problematic. The dimension of staining is not always in the apicobasal (vertical) orientation. It might be more accurate to measure the cdhr1a expression domain relative to the OS (since the length of the OS is already reduced in the mutants). Another possible approach could be to measure the intensity of cdhr1 staining relative to the intensity within a Prph2 expression domain in each group. The authors should provide complementary evidence to support their conclusion.

The revised manuscript will clearly outline measurement methods. In short, all of our CP measurements (actin or cdhr1a or pcdh15), were done in the presence of a counter stain, WGA, prph2, gnb1 or PNA to ensure proper measurements and association with proper cell type.

A better description of the statistical methodology is required. For example, the authors state that "each of the data points has an n of 5+ individuals." This is confusing and could indicate that in Figure 4F alone there were ~5000 individuals assayed (~100 data points per treatment group x n=5 individuals per data point x 10 treatment groups). I don't think that is what the authors intended. It would be clearer if the authors stated how many OS, CP, or cells were counted in their observation region averaged per individual and then provided the n value of individuals used per treatment group (controls and mutants), on which the statistical analyses should be based.

This has been addressed in the revised manuscript. In short, we had an n=5 (individual fish) analyzed for each genotype/time point.

There are hundreds of data points in the separate treatment groups shown in several of the graphs. It would not be correct to perform the ANOVA on the separate OS or CP length measurements alone as this will bias the estimates since they are not all independent samples. For example, in Figure 6H, 5dpf pcdh15b+/- have shorter CPs compared to WT but pcdh15b-/- have longer compared to WT. This could be an artifact of the analysis. Moreover, the authors should clarify in the Methods section which ANOVA post hoc tests were used to control for multiple pairwise comparisons.

We have re-analyzed the data using multiple pairwise comparison ANOVA with post hoc tests (Tukey test). This new analysis did not significantly alter the statistical significance outcome of the study.

(4) Cdhr1a function in photoreceptors

The Cdhr1a IHC staining in 5dpf WT larvae in Figure 3E appears different from the cdhr1a IHC staining in 5dpf WT larvae in Figure 1A or Figure 6I. Perhaps this is just the choice of image. Can the authors comment or provide a more representative image?

The image in figure 3E was captured using a previous non antigen retrieval protocol which limits the resolution of the cdhr1a signal along the CP. In the revised manuscript we have included an image that better represents cdhr1a staining in the WT and mutant.

The authors show that pcdh15b localization after 5dpf mirrored the disorganization of the CP observed with actin staining. They also show in Figure 5O that at 180dpf, very little pcdh15b signal remains. They suggest based on this data that total degradation of CPs has occurred in the cdhr1a-/- photoreceptors by this time. However, although reduced in length, COS and cone CPs are still present at 180dpf (Figure 5E, E'). Thus, contrary to the authors' general conclusion, it is possible that the localization, trafficking, and/or turnover of pcdh15b is maintained through a cdhr1a-dependent mechanism, irrespective of the degree to which CPs are maintained. The experiments presented here do not clearly distinguish between a requirement for maintenance of localization versus a secondary loss of localization due to defective CPs.

We agree, this point has been addressed in our revised manuscript. Additionally, we have also included data from 1 and 2 year old samples.

(5) Conceptual insights

The authors claim that cdhr1a and pcdh15b double mutants have synergistic OS and CP phenotypes. I think this interpretation should be revisited.

First, assuming the model of cdhr1a-pcdh15b interaction in trans is correct, the authors have not adequately explained the logic of why disrupting one side of this interaction in a single mutant would not give the same severity of phenotype as disrupting both sides of this interaction in a double mutant.

Second, and perhaps more critically, at 10dpf the OS and CP lengths in cdhr1a-/- mutants (Figure 7J, T) are significantly increased compared to WT. In contrast, there are no significant differences in these measurements in the pcdh15b-/- mutants. Yet in double homozygous mutants, there is a significant reduction of ~50% in these measurements compared to WT. A synergistic phenotype would imply that each mutant causes a change in the same direction and that the magnitude of this change is beyond additive in the double mutants (but still in the same direction). Instead, I would argue that the data presented in Figure 7 suggest that there might be a functionally antagonistic interaction between cdhr1a and pcdh15b with respect to OS and CP growth at 10dpf.

If these proteins physically interacted in vivo, it would appear that the interaction is complex and that this interaction underlies both OS growth-promoting and growth-restraining (stabilizing) mechanisms working in concert. Perhaps separate homodimers or heterodimers subserve distinct CP-OS functional interactions. This might explain the age-dependent differences in mutant CP and OS length phenotypes if these mechanisms are temporally dynamic or exhibit distinct OS growth versus maintenance phases. Regardless of my speculations, the model presented by the authors appears to be too simplistic to explain the data.

We agree with the reviewer, as such we have revised the discussion in our revised manuscript.

Reviewer #2 (Public review):

Summary:

The goal of this study was to develop a model for CDHR1-based Con-rod dystrophy and study the role of this cadherin in cone photoreceptors. Using genetic manipulation, a cell binding assay, and high-resolution microscopy the authors find that like rods, cones localize CDHR1 to the lateral edge of outer segment (OS) discs and closely oppose PCDH15b which is known to localize to calyceal processes (CPs). Ectopic expression of CDHR1 and PCDH15b in K652 cells indicates these cadherins promote cell aggregation as heterophilic interactants, but not through homophilic binding. This data suggests a model where CDHR1 and PCDH15b link OS and CPs and potentially stabilize cone photoreceptor structure. Mutation analysis of each cadherin results in cone structural defects at late larval stages. While pcdh15b homozygous mutants are lethal, cdhr1 mutants are viable and subsequently show photoreceptor degeneration by 3-6 months.

Strengths:

A major strength of this research is the development of an animal model to study the cone-specific phenotypes associated with CDHR1-based CRD. The data supporting CDHR1 (OS) and PCDH15 (CP) binding is also a strength, although this interaction could be better characterized in future studies. The quality of the high-resolution imaging (at the light and EM levels) is outstanding. In general, the results support the conclusions of the authors.

Weaknesses:

While the cellular phenotyping is strong, the functional consequences of CDHR1 disruption are not addressed. While this is not the focus of the investigation, such analysis would raise the impact of the study overall. This is particularly important given some of the small changes observed in OS and CP structure. While statistically significant, are the subtle changes biologically significant? Examples include cone OS length (Figures 4F, 6E) as well as other morphometric data (Figure 7I in particular). Related, for quantitative data and analysis throughout the manuscript, more information regarding the number of fish/eyes analyzed as well as cells per sample would provide confidence in the rigor. The authors should also note whether the analysis was done in an automated and/or masked manner.

First let me thank the reviewer for taking the time to comprehensively evaluate our work and provide constructive criticism which will improve the quality of our final version.

The revised manuscript outlines both methods and statistics used for quantitation of our data. (please see comments from reviewer 1). While we do not include direct evidence of the mechanism of CDHR1 function, we do propose that its role is important in anchoring the CP and the OS, particularly in the cones, while in rods it may serve to regulate the release of newly formed disks (as previously proposed in mice). We do plan to test both of these hypothesis directly, however, that will be the basis of our future studies.

Reviewer #3 (Public review):

Summary:

The manuscript by Patel et al investigates the hypothesis that CDHR1a on photoreceptor outer segments is the binding partner for PCDH15 on the calyceal processes, and the absence of either adhesion molecule results in separation between the two structures, eventually leading to degeneration. PCDH15 mutations cause Usher syndrome, a disease of combined hearing and vision loss. In the ear, PCDH15 binds CDH23 to form tip links between stereocilia. The vision loss is less understood. Previous work suggested PCDH15 is localized to the calyceal processes, but the expression of CDH23 is inconsistent between species. Patel et al suggest that CDHR1a (formerly PCDH21) fulfills the role of CDH23 in the retina.

The experiments are mainly performed using the zebrafish model system. Expression of Pcdh15b and Cdhr1a protein is shown in the photoreceptor layer through standard confocal and structured illumination microscopy. The two proteins co-IP and can induce aggregation in vitro. Loss of either Cdhr1a or Pcdh15, or both, results in degeneration of photoreceptor outer segments over time, with cones affected primarily.

The idea of the study is logical given the photoreceptor diseases caused by mutations in either gene, the comparisons to stereocilia tip links, and the protein localization near the outer segments. The work here demonstrates that the two proteins interact in vitro and are both required for ongoing outer segment maintenance. The major novelty of this paper would be the demonstration that Pcdh15 localized to calyceal processes interacts with Cdhr1a on the outer segment, thereby connecting the two structures. Unfortunately, the data presented are inadequate proof of this model.

Strengths:

The in vitro data to support the ability of Pcdh15b and Cdhr1a to bind is well done. The use of pcdh15b and cdhr1a single and double mutants is also a strength of the study, especially being that this would be the first characterization of a zebrafish cdhr1a mutant.

Weaknesses:

(1) The imaging data in Figure 1 is insufficient to show the specific localization of Pcdh15 to calyceal processes or Cdhr1a to the outer segment membrane. The addition of actin co-labelling with Pcdh15/Cdhr1a would be a good start, as would axial sections. The division into rod and cone-specific imaging panels is confusing because the two cell types are in close physical proximity at 5 dpf, but the cone Cdhr1a expression is somehow missing in the rod images. The SIM data appear to be disrupted by chromatic aberration but also have no context. In the zebrafish image, the lines of Pcdh15/Cdhr1a expression would be 40-50 um in length if the scale bar is correct, which is much longer than the outer segments at this stage and therefore hard to explain.

First let me thank the reviewer for taking the time to comprehensively evaluate our work and provide constructive criticism which will improve the quality of our final version.

To address this issue, we have added images of actin/cdhr1a and actin/pcdh15b using SIM in both transverse and axial sections. Additionally, we have established an immuno-gold-TEM protocol and provide data showcasing co-labeling of cdhr1a and pcdh15b at TEM resolution.

(2) Figure 3E staining of Cdhr1a looks very different from the staining in Figure 1. It is unclear what the authors are proposing as to the localization of Cdhr1a. In the lab's previous paper, they describe Cdhr1a as being associated with the connecting cilium and nascent OS discs, and fail to address how that reconciles with the new model of mediating CP-OS interaction. And whether Cdhr1a localizes to discrete domains on the disc edges, where it interacts with Pcdh15 on individual calyceal processes.

The image in figure 3E was captured using a previous non antigen retrieval protocol which limits the resolution of the cdhr1a signal along the CP. In the revised manuscript we include an image that better represents cdhr1a staining in the WT and mutant.

(3) The authors state "In PRCs, Pcdh15 has been unequivocally shown to be localized in the CPs". However, the immunostaining here does not match the pattern seen in the Miles et al 2021 paper, which used a different antibody. Both showed loss of staining in pcdh15b mutants so unclear how to reconcile the two patterns.

We agree that our staining appears different, but we attribute this to our antigen retrieval protocol which differed from the Miles et al paper. We also point to the fact that pcdh15b localization has been shown to be similar to our images in other species (monkey and frog). As such, we believe our protocol reveals the proper localization pattern which might be lost/hampered in the procedure used in Miles et al 2021.

(4) The explanation for the CRISPR targets for cdhr1a and the diagram in Figure 3 does not fit with crRNA sequences or the mutation as shown. The mutation spans from the latter part of exon 5 to the initial portion of exon 6, removing intron 5-6. It should nevertheless be a frameshift mutation but requires proper documentation.

This was an overlooked error in figure making, we have corrected this typo in the revised manuscript.

(5) There are complications with the quantification of data. First, the number of fish analyzed for each experiment is not provided, nor is the justification for performing statistics on individual cell measurements rather than using averages for individual fish. Second, all cone subtypes are lumped together for analysis despite their variable sizes. Third, t-tests are inappropriately used for post-hoc analysis of ANOVA calculations.

As we discussed for reviewer 1 and 2, all methods and quantification/statistics will be clearly described in the revised manuscript.

(6) Unclear how calyceal process length is being measured. The cone measurements are shown as starting at the external limiting membrane, which is not equivalent to the origin of calyceal processes, and it is uncertain what defines the apical limit given the multiple subtypes of cones. In Figure 5, the lines demonstrating the measurements seem inconsistently placed.

As we discussed for reviewer 1 and 2, all methods and quantification/statistics will be clearly described in the revised manuscript. We have also clarified that CP measurements were made based on a counterstain for the cone/rod OS so that the actin signal was only CP associated. We have included the counter stain in our revised Figure 7.

(7) The number of fish analyzed by TEM and the prevalence of the phenotype across cells are not provided. A lower magnification view would provide context. Also, the authors should explain whether or not overgrowth of basal discs was observed, as seen previously in cdhr1-null frogs (Carr et al., 2021).

The revised manuscript now includes the n number for our TEM samples. We have also added text comparing our results directly to Carr 2021.

(8) The statement describing the separation between calyceal processes and the outer segment in the mutants is not backed up by the data. TEM or co-labelling of the structures in SIM could be done to provide evidence.

We have completed both more SIM as well as immuno-gold TEM to support our conclusions, see new Figure 1.

(9) "Based on work in the murine model and our own observations of rod CPs, we hypothesize that zebrafish rod CPs only extend along the newly forming OS discs and do not provide structural support to the ROS." Unclear how murine work would support that conclusion given the lack of CPs in mice, or what data in the manuscript supports this conclusion.

In the revised manuscript we have adjusted our discussion to hypothesize that the small length of rod CPs is most likely to represent their interaction with newly forming discs rather than connect with mature discs which are enclosed in the OS.

(10) The authors state "from the fact that rod CPs are inherently much smaller than cone CPs" without providing a reference. In the manuscript, the measurements do show rod CPs to be shorter, but there are errors in the cone measurements, and it is possible that the RPE pigment is interfering with the rod measurements.

We have included references where rod CPs have been found to be shorter. We have no doubt that in zebrafish the rod CPs are significantly shorter. All our CP measurements are done with a counter stain for rods and cones to be sure that we are measuring the correct cell type.

(11) The discussion should include a better comparison of the results with ocular phenotypes in previously generated pcdh15 and cdhr1 mutant animals.

The revised manuscript has included these points.

(12) The images in panels B-F of the Supplemental Figure are uncannily similar, possibly even of the same fish at different focal planes.

We assure the reviewer that each of the images in supplemental figure 1 are distinct and represent different in situ experiments.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) In the second sentence of the Introduction section, the acronym 'PRC' should be defined.

This has been corrected

(2) In the Discussion section, it would be useful to comment on differences between the published Xenopus cdhr1-/- OS phenotypes and the published zebrafish pcdh15b-/- OS phenotypes compared to the present zebrafish cdhr1a-/- phenotypes. In the published studies, OS in these mutants demonstrated dysmorphic and overgrown disc membranes compared to the relatively minor disc layering defects shown for cdhr1a-/- in the present study.

This discussion has been added.

(3) CDHR1 mutations in patients cause cone-rod dystrophy, but mutations in PCDH15 (Usher 1F) cause rod-cone dystrophy. In the Discussion section, the authors should comment on what might lead to these different phenotypic trajectories in humans in the context of their proposed model.

We have added to our discussion highlighting that is not possible to assess rod-cone dystrophy in the pcdh15b model as the mutation is lethal by 15dpf, which is still before most rods mature.

Reviewer #2 (Recommendations for the authors):

In addition to defining the 'n' for animal and cell numbers (as well as methods of analysis - automated/masked), there are a few additional recommendations for the authors.

(1) Expression of USH1 genes in larval zebrafish (Figure S1) is not very convincing. SC RNAseq data exists and argues against this cell type restriction.

Based on extensive experience with WISH we are confident that our interpretation of the data are valid. Furthermore, analysis of the daniocell data base confirms that cdh23, ush1ga, ush1c (harmonin) and myo7aa all have either no expression in photoreceptors or very low levels especially compared to pcdh15b and cdhr1a.

(2) The model in Figure 1 is great. The coloring was a bit confusing. Cdhr1 and axoneme are both in green, while Pcdh15 and actin are both in red. Can each have its own color?

Changed pcdh15b color to blue

(3) Figure 2A: Please explain the multiple bands in some lanes. What do the full blots look like?

Full blots were uploaded to eLife and do not exhibit any additional bands. The multiple bands are likely due to ubiquitination or proteolytic cleavage of cdhr1a and have been documented in our previous publication (Piedade 2020).

(4) Is "data not shown" permissible? (lack of compensation of cdh1b in cdh1a mutants) (nonsense-mediated decay of the mutant transcript).

We have added a supplementary figure showcasing this data.

(5) Figure 4: Is there a TEM phenotype in discs before 15dpf? One would think there would be...?

Due to technical limitations, we have not been able to examine disc phenotypes prior to 15dpf.

(6) Figure 5: How are calyceal processes discriminated from cortical/PM-associated actin? A bonafide calyceal marker seems to be needed. Espin or Myo3, for example.

We discriminate to identify CPs as actin signal that originates at the base of the OS and travels along the OS. Pcdh15b is a bonafinde CP marker which we show overlaps with actin signal along CPs.

(7) Figures 5A-J: How is actin staining for CPs discriminating between rod and cones??? Apical - basal level imaging? This could be better clarified.

CP identification is based on co-stain for either rod or cone Oss

(8) Figure 6: Het phenotype for pcdh15b+/- (cone OS length and CP length at 5 and 10 dpf) is surprising ... worth discussing. (Figures 6E, H).

The discussion section has been updated to discuss this finding.

(9) Last, the authors state "Data not shown" throughout the manuscript. I do not believe this is allowed for the journal.

This data (cdhr1b expression in cdhr1a mutants as well as cdhr1a WISH in cdhr1a mutants) has been added as supplementary figures.

Reviewer #3 (Recommendations for the authors):

Major comments are addressed above and the most important is the need for a convincing demonstration of Cdhr1a localization on the outer segment and proximity to Pcdh15b. The SIM could be a powerful tool, but the images provided are impossible to assess without any basis for context. Could a membrane, Prph2, and/or actin label be added? And lower magnification views?

Minor comments.

(1) The mention of "short CPs" in rodents is not an accurate description. Particular rodents (e.g. mouse, rat) lack CPs altogether or have a single vestigial structure.

We have adjusted the text to reflect this point.

(2) Inconsistent spacing between numbers and units.

We have corrected these inconsistencies

(3) Missing references.

We have added missing references

(4) Indicate the mean or median for bar graphs.

The materials and methods section now specifies that all of our graphs depict a mean value

(5) Unclear how rods are distinguished from cones in the cone analysis if both are labeled with prph2 antibody.

Rods are physiological separate from cones in zebrafish retina and therefore easily identified by location as well as their distinct pattern of actin staining.

(6) Red and green should not be used together for microscopy images.

(7) The diagram in Figure 1D is confusing because of the repeated use of red and green for disparate structures. Also, the location and structure of actin are misrepresented, as is the transition of disc structure during maturation in rods.

We have adjusted the color of pcdh15b to blue.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Point-by-point response to Reviewer comments:

We copied the Reviewer comments below in italics. Revisions we propose in response to Reviewer comments are underlined.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The manuscript by Yin et al investigates how epidermal cells shape somatosensory neuron (SSN) morphology and function through selective ensheathment in Drosophila. This study builds on earlier work by another group showing that the phagocytic receptor Draper (Drpr) as a crucial epidermal factor that is important for dendrite pruning and clearance. In the present study, the authors how that Drpr also functions in the epidermis to establish the characteristic stretches of epidermal ensheathment of dendrite arborization neurons in the fruit fly Drosophila melanogaster. This is particularly true for highly branched types of dendrites but ont dendrites that show simpler branching patterns. Overexpression of Drpr increases ensheathment and nociceptor sensitivity, linking molecular recognition to sensory modulation. Further, Drpr is known to recognize phosphatidylserine (PS) on neurites to promote ensheathment and the authors show localization of a reporter for PS with epidermal membranes. Genetic manipulations that reduce PS results in a reduction in epidermal sheaths and the chemokine-like protein Orion promoting Drpr/PS interactions is required for these processes. Overall, the manuscript is well written, although at times maybe primarily for a fly audience. Reach could be improved by making it more accessible to a non-fly audience. The observation that Drpr is not only required for removing damaged or degenerating dendrites but also for their correct ensheathment of highly branched dendrites presents an important finding that could be of interest for a wider audience provided the following points are adequately addressed:

1. • The Introduction could be further elaborated to help readers understand the significance of epidermal dendrite ensheathment. Addressing the following points may achieve this: (i) The Introduction would benefit from including details on developmental disorders and neurological diseases associated with defects or abnormalities in dendrite ensheathment.*

We appreciate this suggestion. We allude to possible connections between ensheathment defects and human disease in the discussion but agree that it would be appropriate for the introduction; we will underscore this possible connection more clearly in our revised manuscript. We note studies of epidermal ensheathment are limited in mammalian systems, so links between dysregulation of epidermal ensheathment and disease have not been firmly established.

(ii) In lines 74-79, it is unclear whether the described findings are conserved across evolution or were demonstrated in a specific model organism.

The Reviewer refers to our statement about similarities in the cellular mechanism of epidermal ensheathment and phagocytosis. Indeed, these features are evolutionarily conserved in vertebrates, and we agree that it is worthwhile to emphasize this point. We added a statement underscoring the evolutionary conservation of the morphogenetic mechanism along with the relevant citation.

(iii) Including a description of the known literature on phagocytosis in this process would help readers better understand the novelty and significance of this study.

We agree with the Reviewer. In our revised introduction we will include a more detailed description of key features of phagocytic engulfment and highlight the salient differences between ensheathment and phagocytosis including the failure to complete the endocytic event in ensheathment and the persistence of PIP2 at the membrane.

(iv) Details of published Draper function in Han et al 2014 should be elaborated along with unanswered question that is addressed in this study.

The Han et al 2014 study established that epidermal cells, not Drosophila hemocytes (professional phagocytes), are primarily responsible for phagocytic clearance of damaged dendrites in the periphery. Similarly, the Rasmussen et al 2015 study we cite established that skin cells in vertebrates (zebrafish) act as primary phagocytes in removal of damaged peripheral neurites. These studies demonstrate the phagocytic capacity of epidermal cells, particularly in recognition of somatosensory neurites, and the Han study demonstrates that Draper is required for this epidermal phagocytosis. Neither of these studies addresses mechanisms of epidermal ensheathment; we will clarify this point in our revised introduction.

• It is unclear why the authors focus exclusively on Drpr and Crq, without addressing emp and CG4006, both of which show higher expression levels than the former. Moreover, the conclusion that 14 out of 16 engulfment receptor genes have no role based solely on RNAi knockdown experiments is a very strong statement that may requires additional validation. The authors should provide evidence that the RNAi knockdowns achieved complete loss of gene function to support their claim about 16 engulfment receptors. In addition, at most the authors can conclude that the tested genes are individually not required.*

The Reviewer makes several points that warrant discussion. First, the Reviewer asks “why the authors focus exclusively on Drpr and Crq, without addressing emp and CG40066.” The rationale for focusing on Drpr and Crq in our discussion of the expression data is that both Drpr and Crq function in phagocytic engulfment of damaged neurites. Our focus on Drpr for the remainder of the study is guided by the knockdown phenotypes; if either emp, CG40066, or any other receptor showed robust and reproducible effects on ensheathment we would have discussed them at length. Indeed, we identified a potentially novel ensheathment phenotype for NimB4 and devote a small portion of our discussion to its possible function. However, our primary focus in this study was to identify phagocytic receptors required for epidermal ensheathment of somatosensory neurites and drpr was the top hit from our RNAi screen.

Second, we acknowledge that RNAi knockdown is often incomplete and without additional validation a negative result using RNAi is difficult to interpret. In our original text we state: “epidermal RNAi of 14/16 engulfment receptor genes had no significant effect on the extent of dendrite ensheathment in third instar larvae (Figure 1, F and G), consistent with the notion that most epidermal engulfment receptors are dispensable for dendrite ensheathment.” We do not claim that other receptors have “no role”, simply that our results are consistent with the interpretation that most receptors are dispensable. Furthermore, we acknowledge that multiple other receptors likely contribute to other aspects of ensheathment (lines 131-145; NimB4 knockdown causes an “empty sheath” phenotype). However, the Reviewer’s comments convince us that we should more clearly word our interpretation of the negative RNAi results more to reflect the limitations of the approach; we will incorporate this into our revision.

Third, the Reviewer brings up the very important point that receptor redundancy could mask phenotypes. Indeed, our studies suggest that additional pathways likely function in parallel with Drpr. We agree that potential redundancy is an important consideration and absolutely warrants discussion in this section of the results; we will add this to our revised text and we have already updated the statement in the results to read “most epidermal phagocytic receptors are individually dispensable for dendrite ensheathment.”

The final point the Review makes is that analysis of the knockdown efficacy is warranted if we want to make strong claims about gene function for other receptors. We agree that this would be an important first step, but in many cases protein perdurance masks RNAi phenotypes as well. So, efficient knockdown alone is not enough to make concrete conclusions about gene function in this developmental context.

• What kind of genes are crq and ea?*

Crq is a Scavenger receptor and Eater is a Nimrod-family receptor (indicated in Figure 1A).

• Comparing Figures 1C and 1E, it appears that drpr knockdown has a differential effect on epidermal dendrite ensheathment between main and secondary branches. If this observation is correct, separate quantification for each branch type would be more appropriate, along with an explanation for the observed differences.*

We agree with the Reviewer’s assessment that ensheathment appears to be largely absent on terminal dendrites following drpr knockdown but some ensheathment persists on major dendrites. In prior published studies we demonstrated that terminal branches are less extensively ensheathed than primary dendrites in wild-type larvae (Jiang et al 2019 eLife). We will provide this important context in our revised submission. We hypothesize that Drpr uniformly affects ensheathment across the arbor but agree with the Reviewer that quantification is warranted to evaluate this hypothesis. We will add this analysis to our revised submission.

• For Figure 1K, it would be informative to examine how drpr knockdown affects dendrite length in these neurons.*

We agree with the Reviewer. We demonstrate that drpr null mutants have exuberant terminal branching, but we have not yet analyzed effects of epidermal drpr RNAi. We will add this analysis to our revised manuscript.

• For Drpr expression (Figure 3), it would be valuable to highlight any differences in expression between primary and secondary dendritic branches.*

The Reviewer’s question about Drpr distribution at sites of ensheathment will be particularly relevant if we observe differential impacts of Drpr knockdown on ensheathment at primary and higher order dendrites. In our initial submission we showed that >70% of PIP2+ (Fig. 3B) and cora+ (Fig. 3D) epidermal sheaths also exhibited Drpr accumulation; we likewise showed that Drpr accumulation adjacent to dendrites only occurred at sites labeled by the sheath marker cora (Fig. 3G). In our revised submission, we will examine whether Drpr accumulation is more prevalent at sites of PIP2 accumulation on main branches compared to terminal branches.

• Removing drpr leads to excessive branching of SSN dendrites. Does overexpression of drpr affect dendrite morphology in the opposite manner?*

The Reviewer asks an intriguing question about effects of drpr overexpression. We have not examined effects of epidermal drpr overexpression on dendrite morphogenesis, but we will add these experiments to our revised manuscript.

• Although drpr role in dendrite ensheathment is well explored, the interactions between drpr and PS seem underexplored. For example, do the changes in ensheathment as a result of manipulating PS levels require drpr? Does changing PS levels affect Drpr localization or levels?*

The Reviewer raises two questions about the relationship between PS exposure and Drpr.

First, they inquire whether changes in ensheathment resulting from manipulating PS levels require Drpr. We show that overexpressing the ATP8a flippase in C4da neurons, which limits PS exposure, limits the extent of ensheathment. Similarly, we show that sheath formation requires Drpr. In principle, we could assay effects of simultaneously overexpressing ATP8a in neurons and inactivating Drpr (using the Drpr null mutation), but such an experiment will likely be difficult to interpret because the individual treatments cause an almost complete loss of sheaths. We did not investigate whether increasing PS exposure increases ensheathment because prior studies demonstrated that ectopic PS exposure induces membrane shedding in C4da dendrites.

Second, they inquire whether PS levels affect Drpr localization or levels. We demonstrate that inactivation of the PS bridging molecule Orion prevents Drpr localization at sheaths, hence we predict that neuronal overexpression of the ATP8a flippase should have a similar effect. In the revised manuscript, we will examine this possibility (monitoring Drpr distribution at epidermal contact sites with neurons overexpressing ATP8a).

Minor Points:

1. • Why there is no gene in bold category for hemocytes in Figure 1A*

The bold type was used to indicate the receptors that were selected for screening, using a relaxed criteria for identifying receptors that were “expressed”: any receptor detected at a level of 0.1 TPM. To this point, the figure legend states: “Epidermal candidate genes in bold exhibited a TPM value > 0.1 in at least one biological sample and were selected for inclusion in RNAi screen for epidermal phagocytic receptors required for ensheathment.”

We acknowledge that this is a relaxed criteria for “expression” and likely includes receptors that are not appreciably expressed in epidermal cells. Within the text we compare the repertoire of hemocyte and epidermal phagocytic receptors using a more standard (albeit still relatively relaxed) threshold of 0.5 TPM. We added shading to the histograms in Fig. 1A to facilitate comparison of phagocytic receptor gene expression in hemocytes and epidermal cells.

• Line 67: "neurons BEING the most extensively..."*

• Line 126: should read "epidermal engulfment receptors are INDIVIDUALLY dispensable"*

• Line 216: "THE DrprD 5 mutation had no significant..."*

• Line 230: "overexpression" instead of "overexpressed"*

• Line 385: similar "TO"*

These grammatical errors have been corrected. We thank the Reviewer for their careful reading of the manuscript.

Reviewer #1 (Significance (Required)):

This is an interesting study that adds to our understanding of the role of phagocytic receptors in shaping dendrites. Specifically, the role of Drpr (Draper) is studied, a gene previously known as an important for removal degenerating dendrites. The limitations of the manuscript as is is that it seems to be written primarily for a fly audience. Contextualizing the results and in the significance of this like conserved pathway could increase the significance.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary:

Innervation of the skin by somatosensory neurons is a conserved process that enables perception and discrimination of mechanical stimuli. How do molecules exposed by neurons and skin cells collaborate to promote neurite-induced epidermal sheath formation? Here, the authors combine fruit fly molecular and genetic tools with high resolution imaging to address this fundamental question. Based on morphological similarity between phagocytosis and SSN ensheathment, the authors hypothesized that one or more phagocyte receptors might promote ensheathment through ligand-driven interactions with neurites. To test this hypothesis, the authors systematically screened phagocytic receptors expressed in the epidermis for functional roles in ensheathment. Through this screening approach, the authors found that the Draper (Drpr) receptor functions in epidermal cells as a significant factor required to promote ensheathment. They support this conclusion using a suite of cell- and tissue-specific RNAi tools and mutant fly lines in conjunction with elegant mechanistic work that establishes a role for the conserved "eat-me" signal phosphatidylserine (PS) in driving ensheathment.

Major comments:

Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

The seven key claims presented in the abstract are strongly supported by experimental data and analyses presented in the manuscript. At least one experimental result displayed in a main figure in support of the indicated key claim is summarized below. This summary does not present a comprehensive list of all data in support of a particular claim. Rather, it is an effort to confirm that each key result presented to the readership in the abstract is supported by at least one rigorously analyzed experimental result.

We concur with the Reviewer’s interpretations of our work and appreciate the clarity of their summaries below.

1. • Drpr functions in epidermal cells to promote ensheathment: Expressing a Draper RNAi under control of a larval epidermal driver (A58) led to significant reduction in total sheath length (Fig 1H), average sheath length (Fig 1I), and fraction ensheathed (Fig 1J). Similar results were obtained using two different Draper RNAi constructs.*

The argument presented through RNAi results in Fig 1 is bolstered by data using an existing validated Draper mutant line in Fig 2A-E. A question of interest to this reviewer upon receiving the paper was whether Draper functions at initial stages of sheath formation, maintenance of existing sheaths, or both. The timelapse data in Fig 2F suggests that Draper activity is dispensable for maintaining existing sheaths.

• ...that Draper accumulates at sites of epidermal ensheathment but not contact sites of unsheathed neurons:*

Immunostaining experiments demonstrate that Drpr immunoreactivity is enriched at PIP2-positive membrane domains in epidermal cells (Fig 3A-B). Is this accumulation selective for epidermal sheaths? Yes. In Fig. 3E-G, the authors show that Drpr enrichment overlaps with the sheath marker cora but not with dendrites of C1da neurons or from unsheathed portions of C4da dendrite arbors. The authors confirm specificity of Drpr immunoreactivity through control experiments using a Drpr mutant (Supplementary Fig 2).

• ...that Drpr overexpression increased ensheathment:*

Enforced overexpression of Draper in epidermal cells via Epidermal GAL4 driving UAS-Drpr (Fig 5A) shows significantly higher levels of ensheathment of C4da neurons as compared to controls. The authors demonstrate specificity by showing that epidermal Drpr overexpression did not induce ectopic sheath formation in C1da neurons (Fig 5E-G).

• ...that extracellular PS accumulates at sites of ensheathment:*

Using a previously developed secreted AnnV-mScarlet reporter (Ji et al. 2023 https://doi.org/10.1073/pnas.2303392120), the authors demonstrate that PLC-PH-GFP labeled stretches were also labeled by AnnV-mScarlet (Fig 6A-B), consistent with their model that ensheathment by Drpr is mediated by PS exposure on dendrites.

• ...that overexpression of the PS Flippase ATP8a blocks ensheathment:*

This claim is supported by demonstrating that overexpression of ATP8A, a protein that drives drives unidirectional PS translocation from the outer to the inner leaflet of the plasma membrane, impacts C4da neurite ensheathment. Selective overexpression of ATP8A in C4da neurons using a ppk-GAL4 induced a significant reduction in epidermal sheaths (Fig 6C).

• ...that Orion is required for sheath formation:*

Inactivation of the chemokine-like PS bridging molecule Orion significantly reduces fraction of ensheathment (Fig 6I-L).

• Overexpression of Draper enhanced nociceptor sensitivity to mechanical stimulus*

Consistent with a functional role for epidermal ensheathment in responses to mechanical stimuli, the authors report a significant reduction in nocifensive responses in a behavioral assay presented in Fig 6H.

In conclusion, the authors' claims are supported by the data as presented in this version of the manuscript.

• Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.

n/a

• If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".

n/a

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.

n/a

• Are the data and the methods presented in such a way that they can be reproduced?

Yes. The quality of the cell imaging data presented in the figures is high. The figure legends are sufficient to follow the investigators' conceptual approach and technical progress as they build their model. Transparent presentation of the screening data in Fig. 1 F-G was particularly appreciated by these reviewers.

Are the experiments adequately replicated and statistical analysis adequate?

Yes. We specifically commend the table outlining all statistical tests presented in the supplementary methods and linked to each figure.

Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Minor comments:

1. Could the authors further clarify Drpr's anticipated window of activity during sheath formation and/or speculate further on this point in the discussion? Live imaging in Fig. 2 suggest that Drpr is dispensable for maintenance of existing sheaths. Given that Drpr is proposed to be activated through transient phosphorylation that recruits the binding partner Shark (PMCID PMC2493287), it might be useful to clarify Drpr's window of activation (ie transient or constitutive) for an audience more familiar with Drpr's canonical functions in engulfment. The section prior to speculation about a possible role for negative regulators of phagocytosis (Line 360) might be a possible location for this addition.

We appreciate the insightful suggestion. As the Reviewer notes, our results are consistent with a model in which Drpr is required for formation but not maintenance of sheaths. Our original hypothesis was that Drpr would transiently localize to sheaths and be largely absent from mature sheaths. However, our antibody staining suggests that Drpr persists at mature sheaths (signal from endogenously labeled Drpr protein was too dim for live imaging in our hands). We therefore favor a model in which Drpr is transiently activated to promote sheath assembly.

In the context of engulfment, Src42A-dependent tyrosine phosphorylation of Draper promotes association of Shark and Draper pathway activation. Src42A activation is regulated by integrins and RTKs, providing a potential point of crosstalk with other pathway(s) likely involved in ensheathment. Intriguingly, membrane recruitment and activation of Talin depends in part on PIP2, and Talin promotes both Integrin activation and recruitment of PIP2-prodicing PIP5K Kinases, providing a potential feed-forward mechanism for increasing PIP2 accumulation, Talin recruitment, and Integrin activation, which can promote Src42A activation. In our revised discussion we will provide a more thorough treatment of mechanism(s) of Drpr activation.

• The authors might consider developing their conclusion a bit further for a broad audience. For example, the gesture to Piezo dependence in the current final sentence might provide an opening to discuss an exciting future avenue focused on integrating molecular mechansensors into a comprehensive model of selective SSN ensheathment important for the perception and discrimination of touch and pain.*

We appreciate the suggestion and agree that it is worthwhile to expand on the potential links between ensheathment and sensory neuron function in our revised discussion. Our studies thus far have largely explored mechanosensation, but it’s worth noting that the nociceptive neurons under study here are polymodal, and other functional classes of somatosensory neurons are ensheathed to differing degrees, so an intriguing open question is whether ensheathment selectively potentiates the function of mechanosensors or more generally enhances functional coupling of somatosensory neurons to the epidermis. Our finding that ensheathment levels can be bidirectionally regulated by drpr levels provides an entry point to more broadly characterizing functions for ensheathment.

• Word missing or extra "in" in Line 69 after ECM?*

Corrected.

• In Fig 1 and Fig 3, the PLC(delta)-PH-GFP reporter contains the delta symbol, in other throughout the paper it does not. In addition, Fig 5 is denoted "PIP2 (PLC-PH-GFP)". For consistency the authors might consider using PLC(delta)-PH-GFP across all figures.*

As suggested, we updated the figures and text to include the delta symbol in the reporter PLC(delta)-PH-GFP.

• Fig 6P - do the authors suggest Orion is distributed at high concentration throughout the entire upper portion of the figure? Perhaps the coloration could be changed if Orion binding is suggested to occur between Drpr and PS.*

We have not examined Orion distribution in the periphery, though prior studies demonstrate that it is secreted into the hemolymph from multiple sources. Our schematic focuses on sites of contact between epidermal cells and dendrites but omits the hemolymph, muscle, and other cell types in the periphery. In our initial schematic epidermal cells and Orion were shaded similarly; in our revision we chose a different color for epidermal cells to prevent confusion.

Optional suggestions for consideration to provide further context for a broad audience:

Optional 6. The authors might consider placing their work in the context of an emerging literature focused developmental roles for immune cell signaling molecules/other phagocyte receptors at steady state. While the present study focused on epidermal ensheathment of SSNs stands on its own as a notable contribution and does not require these citations to support its conclusions, context from an emerging literature bridging immunity and development might be of interest to a broad readership. Should the authors wish to strengthen the link between their work and findings from other systems indicating a shared role in immunity and development for key immunoreceptors and their binding partners, they might consider adding citations/phrasing indicating that Draper's molecular collaborator Shark kinase (PMCID PMC2493287) was initially discovered as a developmental gene required for dorsal closure (PMCID PMC316420). They might also consider highlighting the role of Draper's mammalian orthologs Megf10/Megf11 in regulating mosaic spacing of retinal neurons (PMCID PMC3310952).

We appreciate the Reviewer’s suggestions, in particular the value of further highlighting relevant links between immunity and development. Not including Megf10/Megf11 (Drpr vertebrate orthologue) in our discussion was an oversight as we predict that Megf10/Megf11 serves a similar role in ensheathment of vertebrate somatosensory neurons. In our revised manuscript we will incorporate a more thorough discussion of the emerging literature bridging immunity and development.

Optional 7. The authors might consider tying their extended discussion of integrins (~Line 320-Line 335) into their overall argument in a more cohesive manner. For example, how (if at all) do the authors see Drpr collaborating with other receptors to regulate initiation versus maintenance of sheaths? Is a model in which Drpr initiates ensheathment maintained by other molecules possible? Speculation on this point in the discussion might integrate other molecules into the authors' model in a cohesive manner and/or bolster the authors' discussion of Drpr's window of activation/deactivation during ensheathment.

Indeed, we envision a model in which Drpr cooperates with other receptors; we discussed one possible connection to integrins above and will incorporate a fuller treatment of the possible crosstalk between these pathways in our discussion. Regarding a model in which Drpr initiates ensheathment maintained by other molecules: yes, we agree that this is possible, but our results suggest that additional receptors likely participate in sheath initiation as well. Drpr inactivation substantially reduces but does not totally eliminate ensheathment, however the sheaths that form in drpr mutants are structurally distinct from mature sheaths (shorter, narrower, appear to recruit less Cora). Hence, we favor a model in which drpr signaling cooperates with a parallel, partially redundant pathway for initiating sheath formation in response to sheath-promoting signals. Integrin signaling is a plausible candidate for this parallel pathway for reasons we discuss in our original submission (and above); in our revised discussion we will more extensively discuss the potential cross-talk between Drpr signaling and Integrin signaling in initiation and maintenance of epidermal sheaths.

Reviewer #2 (Significance (Required)):

This study provides a new link between a conserved phagocyte receptor (Drpr) and epidermal ensheathment of somatosensory neurons, an important process at the heart of the regulated development and function of the nervous system. As such, the Yin et al. submission is a significant contribution to a rapidly moving research area of broad interest to an intellectually diverse readership interested in the molecular and cellular basis of neurodevelopment and interactions between the nervous system and the immune system.* *

An important strength of this study is the striking degree of the epidermal ensheathment phenotypes observed when normal Drpr expression is disrupted either through depletion, mutation, or targeted overexpression. For example, depletion of Drpr via RNAi induces a ~three fold reduction in total sheath length (Fig 1F - ~1.45 mm in controls as compared to ~0.5 mm with Drpr RNAi). Notably, epidermal enforced overexpression of Drpr induces a notable increase in the fraction of ensheathed neurons (Fig 5A-D). This strength of phenotype enables the investigators to deploy an elegant sequence of molecular and genetic tools to further probe mechanism and implicate extracellular PS in this process.* *

Reviewer area keywords as requested: phagocytes, immune cell signaling, signal transduction

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

The study by Yin and colleagues investigates how epidermal cells recognize and ensheath somatosensory neuron (SSN) dendrites in Drosophila larvae. The authors identify the phagocytic receptor Draper (Drpr) as a key mediator of selective epidermal ensheathment and demonstrate that this process relies on phosphatidylserine (PS) exposure on dendrites and the bridging molecule Orion. The work significantly advances our understanding of neuron/epidermis interactions and reveals a novel role for phagocytic recognition pathways in non-glial ensheathment.

The manuscript is clearly written, methodologically solid and supported by compelling data. The authors combine genetic, imaging and functional approaches to uncover a mechanism of structural and functional modulation of nociceptive neurons. The results will interest researchers studying neuronal morphogenesis, epithelial biology and non-glial phagocytic pathways.

Specific Critiques:

While the study is strong and timely, several issues should be addressed prior to acceptance:

Figure 1: The authors refer to the receptors as "engulfment receptors." I recommend calling them "phagocytic receptors" since not all are required for the engulfment step (e.g., Crq).

The Reviewer makes an important distinction. We have updated our manuscript to reflect this point, replacing “engulfment receptor” with “phagocytic receptor” in the text and in our title.

Figure 2: The title states "Drpr is required in epidermal cells..." yet the authors analyze a drpr null mutant, which lacks Drpr in all expressing cells (glia, macrophages and epidermal cells). The rationale for using the null mutant instead of epidermal-specific RNAi should be explained.

The increased dendrite number in drpr RNAi larvae should also be noted here.

We agree – the title is not appropriate for this version of the figure; we changed the title to better reflect the experiments being portrayed.

Our RNAi experiments in Figure 1 and 2 demonstrate that drpr is cell autonomously required in epidermal cells for dendrite ensheathment. Here, we include analysis of an amorphic drpr allele to (1) provide further genetic support underscoring the requirement for drpr in dendrite ensheathment and (2) to underscore the observation that a small number of immature sheaths form in the complete absence of drpr, arguing for the presence of an additional pathway that contributes to sheath formation.

Effects of epidermal drpr RNAi on dendrite number is not something we evaluated with our time-lapse studies in Figure 2. Instead, we monitored the effects of drpr knockdown on growth behavior of epidermal sheaths and found that epidermal drpr RNAi triggered an increase in the frequence of sheath retraction events and a decrease in sheath growth events.

Figure 3: Explain the numbers on the X-axis in panels B and D. Add a panel without blue dashed outlines to better visualize Drpr expression. Adjust the red boxes to precisely match the enlarged regions.

Each bar represents a single neuron; the numbers denote the number of sheaths sampled from each neuron. We added this to the figure and figure legend in our manuscript. We thank the Reviewer for identifying this oversight.

We appreciate the Reviewer’s perspective on the blue hatched lines; we removed the hatched lines from the ROI and adjusted the position of the red hatched box.

Figure 4: Why is the drpr mutant used here rather than RNAi? Please clarify the reasoning for choosing mutants in some experiments and knockdown in others.

In Figure 2, we show analysis of the amorphic allele to further corroborate our RNAi studies, as described above. We chose to use the drpr amorphic mutant for these studies because we have no GAL4-independent reporter to label C1da neurons for analysis of dendrite arborization patterns. Although we could use HRP staining in combination with epidermal drpr RNAi, live imaging of dendrite arbors labeled by a C1da neuron GAL4 driver provides a more sensitive and reliable readout for morphogenesis studies.

In our revised manuscript we will add analysis of C4da dendrite patterns in larvae expressing drpr RNAi in epidermal cells to evaluate whether the dendrite defects reflect epidermal requirements for drpr function.

Figure 5: Correct the placement of white boxes in panels E-F′.

We thank the Reviewer for identifying the mismatch. We corrected the placement to match the size of the ROIs.

*Figure 6: AnnV staining in B is difficult to detect. Please add a version of the panel showing AnnV alone. *

In our initial submission we include the overlay of PLC-PH-GFP and AnnV-mScarlet (B), an image showing the PLC-PH-GFP alone (B’) and an image showing the AnnV-mScarlet alone (B”).

AnnV labeling appears weak on sheaths. Since epidermal membranes are strongly labeled, confirm PS exposure on dendrites with a commercial fluorescent Annexin V reagent.

We appreciate the suggestion to use a commercial fluorescent Annexin V reagent and agree that it would strengthen our findings if such a reagent labeled sheaths. However, we intentionally prioritized analysis using the in vivo reporter because numerous studies indicate that epidermal sheaths are inaccessible to large molecules in solution (in the absence of detergent). One of the first assays used to monitor the in vivo distribution of sheaths was based on the inaccessibility of antibodies to ensheathed neurites (Kim et al, Neuron, 2012; also Tenenbaum et al, Current Biology, 2017; Jiang et al, eLife, 2019). More recently, we demonstrated that 10kDa dextran dyes are excluded from epidermal sheaths (Luedke et al, PLoS Genetics, 2024). Nevertheless, as part of our revision we will examine whether commercially available Annexin V reagents label sheaths.

In F and F" sheaths are labeled in areas without visible dendrites. Please clarify.

We note that although C4da dendrites are the most extensively ensheathed among da neurons, other neurons (most prominently C3da neurons) also exhibit significant ensheathment (Jiang et al, eLife, 2019). We use established markers of epidermal sheaths (Cora immunoreactivity in this panel; PIP2 reporters and/or Cora-GFP localization in other panels), hence Drpr accumulates at Cora+ sheaths on C4da neurons and Cora+ sheaths that form on other da neurons. We will clarify this point in the text of our revised manuscript.

In O and P, show Drpr staining without blue dashed sheath outlines.

We have removed the blue dashed outlines from the figure panels.

The legend contains numerous labeling errors: there is no B′ or B"; C-G should be E-G; G-I should be H-J; I-L should be K-N; M-O should be O-R. Please revise carefully.

The labeling errors have been corrected.

Sup Fig 1: Add a panel with only c4da labeling to visualize dendrites.

We have added a panel displaying only C4da dendrites to this figure.

Sup Fig 2: The anti-Drpr signal is unexpected in the null mutant. Validate with an additional antibody (e.g., mouse monoclonal anti-Drpr from the DSHB).

We appreciate the suggestion and have already tested the mouse monoclonal anti-Drpr antibody from DSHB and found that it is unsuitable for use in our preparations (ie, no Drpr-dependent immunoreactivity, even in specimens overexpressing Drpr).

With respect to the comment about the unexpected signal in the null mutant, we note that the antibody is a rabbit polyclonal and is not purified. In our experience it is not uncommon for rabbit serum (even pre-immune serum) to recognize multiple antigens in the larval skin. Nevertheless, our control studies demonstrate that Drpr immunoreactivity is eliminated at epidermal sheaths in Drpr null mutants.

Sup Fig 3: No panels A or B are shown; no PIP2 marker is present despite the legend. Please revise. Drpr overexpression appears to increase Cora levels in some cell. Could Drpr affect Cora expression or distribution? This should be addressed. Also dendrite number appears higher in Drpr-overexpressing larvae. Please state whether this is significant.

The labeling errors in the legend have been corrected; the corresponding studies with the PIP2 marker are presented in Figure 5.

All epidermal drivers we have characterized exhibit a low level of variegation in expression within a hemisegment that we have previously documented (Jiang et al 2014 Development; Jiang et al 2019 eLife), and we suspect that it may be related to epidermal endoreplication (epidermal cells do not synchronously endoreplicate). However, we have not observed any systematic difference in epidermal GAL4 driver or Cora-GFP expression in larvae overexpressing Drpr. We note that a single cell in the field of view in Supplemental Figure 3 exhibits a higher level of GFP fluorescence. We occasionally observe this, independent of background genotype.

All gene names must be italicized and lowercase (e.g., drpr), including in figure labels and legends.

All protein names must be capitalized and non-italic (e.g., Drpr, Cora).

We appreciate the Reviewer’s feedback. We used Drpr in keeping with many recent reports, but the Reviewer is correct in outlining the standard naming conventions. We have changed the gene names to reflect convention (lowercase, italics for genes that were initially identified according to phenotypic characterization; uppercase, italics for genes named according to homology to orthologues in other species such as NimB4 and ATP8A)

Define ROI on first use.

Done. We defined ROI in the methods section.

Ensure consistent phrasing: use "anti-Cora or anti-Drpr immunoreactivity" uniformly.

We have done so.

There a few typos which must be corrected:

• • Line 196: "containing" → "contain"*

• • Line 205: "antibodies Drpr" → "antibodies to Drpr" or "anti-Drpr antibodies"*

• • Line 331: "predominan" → "predominant"*

• • Line 353: "phagocyting" → "phagocytic"*

• • Line 385: "similar the effect" → "similar to the effect"*

• • Line 432: Title should be underlined*

• • Line 544: "drpr∆5" is missing the 5*

• • Line 569: "immunoreactivity a" → "immunoreactivity of"*

The typographical errors have been corrected. We thank the Reviewer for the close reading of the manuscript.

Reviewer #3 (Significance (Required)):

The manuscript makes a meaningful contribution to the field of neuron/epidermal cells interactions by demonstrating that recognized phagocytic machinery components can be co-opted for ensheathment of sensory neurites. This not only expands our understanding of skin innervation and mechanosensation but also raises intriguing implications for how similar mechanisms might operate in vertebrates (e.g., epidermal/nerve interactions, peripheral neuropathy). Given the functional link to nociceptive sensitivity, the work may have broader relevance for pain biology and sensory disorders.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

The study by Yin and colleagues investigates how epidermal cells recognize and ensheath somatosensory neuron (SSN) dendrites in Drosophila larvae. The authors identify the phagocytic receptor Draper (Drpr) as a key mediator of selective epidermal ensheathment and demonstrate that this process relies on phosphatidylserine (PS) exposure on dendrites and the bridging molecule Orion. The work significantly advances our understanding of neuron/epidermis interactions and reveals a novel role for phagocytic recognition pathways in non-glial ensheathment. The manuscript is clearly written, methodologically solid and supported by compelling data. The authors combine genetic, imaging and functional approaches to uncover a mechanism of structural and functional modulation of nociceptive neurons. The results will interest researchers studying neuronal morphogenesis, epithelial biology and non-glial phagocytic pathways.

While the study is strong and timely, several issues should be addressed prior to acceptance: Figure 1: The authors refer to the receptors as "engulfment receptors." I recommend calling them "phagocytic receptors" since not all are required for the engulfment step (e.g., Crq).

Figure 2: The title states "Drpr is required in epidermal cells..." yet the authors analyze a drpr null mutant, which lacks Drpr in all expressing cells (glia, macrophages and epidermal cells). The rationale for using the null mutant instead of epidermal-specific RNAi should be explained. The increased dendrite number in drpr RNAi larvae should also be noted here.

Figure 3: Explain the numbers on the X-axis in panels B and D. Add a panel without blue dashed outlines to better visualize Drpr expression. Adjust the red boxes to precisely match the enlarged regions.

Figure 4: Why is the drpr mutant used here rather than RNAi? Please clarify the reasoning for choosing mutants in some experiments and knockdown in others.

Figure 5: Correct the placement of white boxes in panels E-F′.

Figure 6: AnnV staining in B is difficult to detect. Please add a version of the panel showing AnnV alone. AnnV labeling appears weak on sheaths. Since epidermal membranes are strongly labeled, confirm PS exposure on dendrites with a commercial fluorescent Annexin V reagent. In F and F" sheaths are labeled in areas without visible dendrites. Please clarify. In O and P, show Drpr staining without blue dashed sheath outlines. The legend contains numerous labeling errors: there is no B′ or B"; C-G should be E-G; G-I should be H-J; I-L should be K-N; M-O should be O-R. Please revise carefully.

Sup Fig 1: Add a panel with only c4da labeling to visualize dendrites. Sup Fig 2: The anti-Drpr signal is unexpected in the null mutant. Validate with an additional antibody (e.g., mouse monoclonal anti-Drpr from the DSHB). Sup Fig 3: No panels A or B are shown; no PIP2 marker is present despite the legend. Please revise. Drpr overexpression appears to increase Cora levels in some cell. Could Drpr affect Cora expression or distribution? This should be addressed. Also dendrite number appears higher in Drpr-overexpressing larvae. Please state whether this is significant.

All gene names must be italicized and lowercase (e.g., drpr), including in figure labels and legends. All protein names must be capitalized and non-italic (e.g., Drpr, Cora). Define ROI on first use. Ensure consistent phrasing: use "anti-Cora or anti-Drpr immunoreactivity" uniformly. There a few typos which must be corrected:

• Line 196: "containing" → "contain"
• Line 205: "antibodies Drpr" → "antibodies to Drpr" or "anti-Drpr antibodies"
• Line 331: "predominan" → "predominant"
• Line 353: "phagocyting" → "phagocytic"
• Line 385: "similar the effect" → "similar to the effect"
• Line 432: Title should be underlined
• Line 544: "drpr∆5" is missing the 5
• Line 569: "immunoreactivity a" → "immunoreactivity of"

Significance

The manuscript makes a meaningful contribution to the field of neuron/epidermal cells interactions by demonstrating that recognized phagocytic machinery components can be co-opted for ensheathment of sensory neurites. This not only expands our understanding of skin innervation and mechanosensation but also raises intriguing implications for how similar mechanisms might operate in vertebrates (e.g., epidermal/nerve interactions, peripheral neuropathy). Given the functional link to nociceptive sensitivity, the work may have broader relevance for pain biology and sensory disorders.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Summary:

Innervation of the skin by somatosensory neurons is a conserved process that enables perception and discrimination of mechanical stimuli. How do molecules exposed by neurons and skin cells collaborate to promote neurite-induced epidermal sheath formation? Here, the authors combine fruit fly molecular and genetic tools with high resolution imaging to address this fundamental question. Based on morphological similarity between phagocytosis and SSN ensheathment, the authors hypothesized that one or more phagocyte receptors might promote ensheathment through ligand-driven interactions with neurites. To test this hypothesis, the authors systematically screened phagocytic receptors expressed in the epidermis for functional roles in ensheathment. Through this screening approach, the authors found that the Draper (Drpr) receptor functions in epidermal cells as a significant factor required to promote ensheathment. They support this conclusion using a suite of cell- and tissue-specific RNAi tools and mutant fly lines in conjunction with elegant mechanistic work that establishes a role for the conserved "eat-me" signal phosphatidylserine (PS) in driving ensheathment.

Major comments:

Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

The seven key claims presented in the abstract are strongly supported by experimental data and analyses presented in the manuscript. At least one experimental result displayed in a main figure in support of the indicated key claim is summarized below. This summary does not present a comprehensive list of all data in support of a particular claim. Rather, it is an effort to confirm that each key result presented to the readership in the abstract is supported by at least one rigorously analyzed experimental result.

1. Drpr functions in epidermal cells to promote ensheathment: Expressing a Draper RNAi under control of a larval epidermal driver (A58) led to significant reduction in total sheath length (Fig 1H), average sheath length (Fig 1I), and fraction ensheathed (Fig 1J). Similar results were obtained using two different Draper RNAi constructs. The argument presented through RNAi results in Fig 1 is bolstered by data using an existing validated Draper mutant line in Fig 2A-E. A question of interest to this reviewer upon receiving the paper was whether Draper functions at initial stages of sheath formation, maintenance of existing sheaths, or both. The timelapse data in Fig 2F suggests that Draper activity is dispensable for maintaining existing sheaths.
2. ...that Draper accumulates at sites of epidermal ensheathment but not contact sites of unsheathed neurons: Immunostaining experiments demonstrate that Drpr immunoreactivity is enriched at PIP2-positive membrane domains in epidermal cells (Fig 3A-B). Is this accumulation selective for epidermal sheaths? Yes. In Fig. 3E-G, the authors show that Drpr enrichment overlaps with the sheath marker cora but not with dendrites of C1da neurons or from unsheathed portions of C4da dendrite arbors. The authors confirm specificity of Drpr immunoreactivity through control experiments using a Drpr mutant (Supplementary Fig 2).
3. ...that Drpr overexpression increased ensheathment: Enforced overexpression of Draper in epidermal cells via Epidermal GAL4 driving UAS-Drpr (Fig 5A) shows significantly higher levels of ensheathment of C4da neurons as compared to controls. The authors demonstrate specificity by showing that epidermal Drpr overexpression did not induce ectopic sheath formation in C1da neurons (Fig 5E-G).
4. ...that extracellular PS accumulates at sites of ensheathment: Using a previously developed secreted AnnV-mScarlet reporter (Ji et al. 2023 https://doi.org/10.1073/pnas.2303392120), the authors demonstrate that PLC-PH-GFP labeled stretches were also labeled by AnnV-mScarlet (Fig 6A-B), consistent with their model that ensheathment by Drpr is mediated by PS exposure on dendrites.
5. ...that overexpression of the PS Flippase ATP8a blocks ensheathment: This claim is supported by demonstrating that overexpression of ATP8A, a protein that drives drives unidirectional PS translocation from the outer to the inner leaflet of the plasma membrane, impacts C4da neurite ensheathment. Selective overexpression of ATP8A in C4da neurons using a ppk-GAL4 induced a significant reduction in epidermal sheaths (Fig 6C).
6. ...that Orion is required for sheath formation: Inactivation of the chemokine-like PS bridging molecule Orion significantly reduces fraction of ensheathment (Fig 6I-L).
7. Overexpression of Draper enhanced nociceptor sensitivity to mechanical stimulus Consistent with a functional role for epidermal ensheathment in responses to mechanical stimuli, the authors report a significant reduction in nocifensive responses in a behavioral assay presented in Fig 6H.

In conclusion, the authors' claims are supported by the data as presented in this version of the manuscript.

• Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.

n/a - If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".

n/a - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments. n/a - Are the data and the methods presented in such a way that they can be reproduced?

Yes. The quality of the cell imaging data presented in the figures is high. The figure legends are sufficient to follow the investigators' conceptual approach and technical progress as they build their model. Transparent presentation of the screening data in Fig. 1 F-G was particularly appreciated by these reviewers.

Are the experiments adequately replicated and statistical analysis adequate?

Yes. We specifically commend the table outlining all statistical tests presented in the supplementary methods and linked to each figure.

Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Minor comments:

1. Could the authors further clarify Drpr's anticipated window of activity during sheath formation and/or speculate further on this point in the discussion? Live imaging in Fig. 2 suggest that Drpr is dispensable for maintenance of existing sheaths. Given that Drpr is proposed to be activated through transient phosphorylation that recruits the binding partner Shark (PMCID PMC2493287), it might be useful to clarify Drpr's window of activation (ie transient or constitutive) for an audience more familiar with Drpr's canonical functions in engulfment. The section prior to speculation about a possible role for negative regulators of phagocytosis (Line 360) might be a possible location for this addition.
2. The authors might consider developing their conclusion a bit further for a broad audience. For example, the gesture to Piezo dependence in the current final sentence might provide an opening to discuss an exciting future avenue focused on integrating molecular mechansensors into a comprehensive model of selective SSN ensheathment important for the perception and discrimination of touch and pain.
3. Word missing or extra "in" in Line 69 after ECM?
4. In Fig 1 and Fig 3, the PLC(delta)-PH-GFP reporter contains the delta symbol, in other throughout the paper it does not. In addition, Fig 5 is denoted "PIP2 (PLC-PH-GFP)". For consistency the authors might consider using PLC(delta)-PH-GFP across all figures.
5. Fig 6P - do the authors suggest Orion is distributed at high concentration throughout the entire upper portion of the figure? Perhaps the coloration could be changed if Orion binding is suggested to occur between Drpr and PS.

Optional suggestions for consideration to provide further context for a broad audience: Optional 6. The authors might consider placing their work in the context of an emerging literature focused developmental roles for immune cell signaling molecules/other phagocyte receptors at steady state. While the present study focused on epidermal ensheathment of SSNs stands on its own as a notable contribution and does not require these citations to support its conclusions, context from an emerging literature bridging immunity and development might be of interest to a broad readership. Should the authors wish to strengthen the link between their work and findings from other systems indicating a shared role in immunity and development for key immunoreceptors and their binding partners, they might consider adding citations/phrasing indicating that Draper's molecular collaborator Shark kinase (PMCID PMC2493287) was initially discovered as a developmental gene required for dorsal closure (PMCID PMC316420). They might also consider highlighting the role of Draper's mammalian orthologs Megf10/Megf11 in regulating mosaic spacing of retinal neurons (PMCID PMC3310952).

Optional 7. The authors might consider tying their extended discussion of integrins (~Line 320-Line 335) into their overall argument in a more cohesive manner. For example, how (if at all) do the authors see Drpr collaborating with other receptors to regulate initiation versus maintenance of sheaths? Is a model in which Drpr initiates ensheathment maintained by other molecules possible? Speculation on this point in the discussion might integrate other molecules into the authors' model in a cohesive manner and/or bolster the authors' discussion of Drpr's window of activation/deactivation during ensheathment.

Significance

This study provides a new link between a conserved phagocyte receptor (Drpr) and epidermal ensheathment of somatosensory neurons, an important process at the heart of the regulated development and function of the nervous system. As such, the Yin et al. submission is a significant contribution to a rapidly moving research area of broad interest to an intellectually diverse readership interested in the molecular and cellular basis of neurodevelopment and interactions between the nervous system and the immune system.

An important strength of this study is the striking degree of the epidermal ensheathment phenotypes observed when normal Drpr expression is disrupted either through depletion, mutation, or targeted overexpression. For example, depletion of Drpr via RNAi induces a ~three fold reduction in total sheath length (Fig 1F - ~1.45 mm in controls as compared to ~0.5 mm with Drpr RNAi). Notably, epidermal enforced overexpression of Drpr induces a notable increase in the fraction of ensheathed neurons (Fig 5A-D). This strength of phenotype enables the investigators to deploy an elegant sequence of molecular and genetic tools to further probe mechanism and implicate extracellular PS in this process.

Reviewer area keywords as requested: phagocytes, immune cell signaling, signal transduction

Author response:

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

We thank the reviewers for their constructive and precise comments, which have helped us improve the consistency and clarity of our manuscript. Below, we provide a point-by-point response to each comment. In summary, the main changes introduced in the revised version are as follows:

(1) We replaced all the statistical analyses to their non-parametric equivalents to ensure compliance with test assumptions and consistency of the results;

(2) We compare the participants’ reaction times before and during connected practice, revealing a significant reduction in reaction times of both partners when connected;

(3) We added, in the supplementary materials, a table reporting the vigor scores of each participant in each experimental condition, facilitating the assessment of individual and dyadic behaviors;

(4) We have reviewed and refined the terminology throughout the manuscript and reduced the number of abbreviations to improve clarity.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors present a novel investigation of the movement vigor of individuals completing a synchronous extension-flexion task. Participants were placed into groups of two (so-called "dyads") and asked to complete shared movements (connected via a virtual loaded spring) to targets placed at varying amplitudes. The authors attempted to quantify what, if any, adjustments in movement vigor individual participants made during the dyadic movements, given the combined or co-dependent nature of the task. This is a novel, timely question of interest within the broader field of human sensorimotor control.

Participants from each dyad were labeled as "slow" (low vigor) or "fast" (high vigor), and their respective contributions to the combined movement metrics were assessed. The authors presented four candidate models for dyad interactions: (a) independent motor plans (i.e., co-activity hypothesis), (b) individual-led motor plans (i.e., leader-follower hypothesis), (c) generalization to a weighted average motor plan (i.e., weighted adaptation hypothesis), and (d) an uncertainty-based model of dynamic partner-partner interaction (i.e., interactive adaptation hypothesis). The final model allowed for dynamic changes in individual motor plans (and therefore, movement vigor) based on partner-partner interactions and observations. After detailed observations of interaction torque and movement duration (or vigor), the authors concluded that the interactive adaptation model provided the best explanation of human-human interaction during self-paced dyadic movements.

Strengths:

The experimental setup (simultaneous wrist extension-flexion movements) has been thoroughly vetted. The task was designed particularly well, with adequate block pseudo-randomization to ensure general validity of the results. The analyses of torque interaction, movement kinematics, and vigor are sound, as are the statistical measures used to assess significance. The authors structured the work via a helpful comparison of several candidate models of human-human interaction dynamics, and how well said models explained variance in the vigor of solo and combined movements. The research question is timely and extends current neuroscientific understanding of sensorimotor control, particularly in social contexts.

We thank the reviewer for their in-depth analysis and constructive assessment of our manuscript.

Weaknesses:

(1) My chief concern about the study as it currently stands is the relatively low number of data points (n=10). The authors recruited 20 participants, but the primary conclusions are based on dyad-specific interactions (i.e., analyses of "fast" vs "slow" participants in each pair). Some of these analyses would benefit greatly, in terms of power, from the addition of more data points.

We understand and appreciate the reviewer’s concern regarding the effective sample size at the dyad level (n=10). While our primary analyses focus on dyad-specific interactions, we note that the reported effects are consistent across multiple dynamic conditions and are associated with large effect sizes. To provide a conservative assessment the Cohen’s D values reported correspond to the smallest effect size observed across the relevant statistical tests, thereby limiting the risk of false positives or overinterpretation. In addition, to ensure robustness given the sample size and distribution properties of the data, we have replaced all parametric tests with their non-parametric counterparts, as some analyses violated ANOVA assumptions. Friedman and Kruskal-Wallis tests are now used for paired and unpaired main effects respectively, and Wilcoxon and Mann-Whitney tests for paired and unpaired post-hoc comparisons respectively. Note that these changes did not alter the conclusions of the study.

(a) The distribution of delta-vigor (Fast group vs Slow group) is highly skewed (see Figures 3D, S6D), with over half of the dyads exhibiting delta-vigor less than 0.2 (i.e., less than 20% of unit vigor). Given the relatively low number of dyads, it would be helpful for the authors to provide explicit listings of VigorFast, VigorSlow, and VigorCombined for each of the 10 separate dyads or pairings.

We agree with this comment. However, we note that the distribution of vigor scores within a population is typically centered around 1, with large deviations observed only for the fastest and slowest participants [1]. As a result, the distri bution of ∆-vigor is inherently skewed. Correcting for this skewness would (i) require pairing participants based on their vigor, which is logistically difficult, and (ii) lead to an atypical sampling of dyads, with an over representation of pairs exhibiting very large vigor differences. The distributions of vigor scores for the fast and slow groups before and after the interaction are reported in Supplementary Fig. S21. In addition, as suggested by the reviewer, we have now included Table S.1 in the supplementary materials, listing the values VigorFast, VigorSlow, and VigorCombined for each of the 10 dyads. This table provides a complete view of the evolution of participant’s vigor throughout the experiment.

(b) The authors concluded that the interactive adaptation hypothesis provided the best summary of the combined movement dynamics in the study. If this is indeed the case, then the relative degree of difference in vigor between the fast and slow participants in a dyad should matter. How well did the interactive adaptation model explain variance in the dyads with relatively low delta-vigor (e.g., less than 0.2) vs relatively high delta-vigor?

We initially expected the magnitude of difference in individual vigor within a dyad to play a significant role. However, our analysis did not reveal any systematic effect of ∆-vigor on either the interaction force or the resulting dyadic vigor, as shown by the LMM analysis. Importantly, the interactive adaptation hypothesis does per se imply that the magnitude of vigor differences between the two partners should matter, only that their respective roles in selecting the adapted behavior is different. Although the model includes several free parameters, we did not attempt to fit it to individual dyads as would in principle be possible. Instead, we performed a sensitivity analysis to assess how variations in the difference in vigor between the partners influence model predictions. For this purpose, we simulated increasing values of µ and variations in the fast partner’s cost of time. In addition, we demonstrated that uncertainty in the estimated behavior of the slow partner, which is a priori specific to each individual, has a substantial impact on the optimal movement duration of the dyad. Overall, this analysis shows that the model captures the full range of qualitative trends observed in the experimental data. When applied to predict the behavior of the average dyad, the resulting movement time prediction error remain small, as detailed in the Results section.

(2) The authors shared the results of one analysis of reaction time, showing that the reaction times of the slow partners and the fast partners did not differ during the initial passive block. Did the authors observe any changes in RT of either the slow or fast partner during the combined (primary task) blocks (KL, KH, etc.)? If the pairs of participants did indeed employ a form of interactive adaptation, then it is certainly plausible that this interaction would manifest in the initial movement planning phase (i.e., RT) in addition to the vigor and smoothness of the movements themselves.

We thank the reviewer for this interesting question, that prompted us to extend our analysis of reaction times to the connected conditions. This additional analysis revealed a significant main effect of the condition on the reaction time for both the fast and slow groups (in both cases: W₂ > 0.39, p < 0.02). Post-hoc comparisons showed a significant reduction in reaction time between the initial null-field block (NF1) and the KH condition for the slow group (p = 0.03, D = 1.46), and a similar trend for the fast group (p = 0.06, D = 1.03). However, the reaction times remained comparable between the two groups, with no significant difference between them. We have incorporated these observations in the Results section (p.4, l.100–109) and expanded the Discussion (p.11, l.341–348) to address their implications for interactive adaptation in human-human and human-robot physical interactions.

Reviewer #2 (Public review):

Summary:

This study examines how individual movement vigor is integrated into a shared, dyadic vigor when two individuals are physically coupled. Participants performed wrist-reaching movements toward targets at different distances while mechanically linked via a virtual elastic band, and dyads were formed by pairing participants with different baseline vigor profiles. Under interaction conditions, movements converged to coordinated patterns that could not be explained by simple averaging, indicating that each dyad behaved as a single functional unit. Notably, under coupling, movement durations for both partners were shorter than in the solo condition, arguing against the view that each individual simply executed an independent movement plan. Furthermore, dyadic vigor was primarily predicted by the slower partner’s vigor rather than by the faster partner’s, suggesting that neither a leader-follower strategy nor a weighted averaging account fully explains the observed behavior. The authors propose a computational model in which both partners adapt to the emerging interaction dynamics ("interactive adaptation strategy"), providing a coherent explanation of the behavioral observations.

Strengths:

The study is carefully designed and addresses an important question about how individual movement vigor is integrated during joint action. The experimental paradigm allows systematic manipulation of interaction strength and partner asymmetry. The behavioral results show clear and robust patterns, particularly the shortening of movement durations under elastic coupling (KL and KH conditions) and the asymmetrical contribution of the slower partner’s vigor to dyadic vigor. The computational model captures the main behavioral patterns well and provides a principled framework for interpreting dyadic vigor not as a simple combination of two independent motor plans, but as an emergent property arising from mutual adaptation. Conceptually, the study is notable in extending the notion of vigor from an individual attribute to a dyad-level construct, opening a new perspective on coordinated movement and motor decision-making.

We thank the reviewer for their thorough analysis of our manuscript and their constructive feedback.

Weaknesses:

(1) A key conceptual issue concerns the apparent asymmetry between partners in the computational framework. While dyadic vigor is empirically better predicted by the slower partner’s vigor, the model formulation appears to emphasize the faster partner’s time-related cost and interaction forces. Although the cost function includes an uncertaintyrelated component associated with the slower partner, it remains unclear from the current formulation and description how dyadic vigor is formally derived from the slower partner’s control policy within the same modeling framework. This raises an important question regarding whether the model offers a symmetric account of dyadic vigor formation for both partners or whether it is effectively anchored to the faster partner’s control architecture.

We have modified our phrasing to clarify the principles according to which the computational framework was designed (p.7, l.226–231 and p.9, l.260–264). As stated in the Results section, the model is indeed asymmetric by design, which corresponds to the different roles of the fast and slow partner exhibited in the data. In that context, the uncertain term associated with the slow partners should be understood as an overarching constraint that conditions the strategy of the dyad, while the fast partner cost of time acts as a contributor to the expected dyad strategy. Conceptually and numerically as reported in the sensitivity analysis, this asymmetry corresponds to the role of the slow partners in setting the vigor ranking among the dyads and the role of the fast partner in setting the average dyadic behavior.

(2) A second conceptual issue concerns the interpretation of the term "motor plan." It remains unclear whether this term refers primarily to movement-related characteristics such as speed or duration, or more broadly to the underlying optimization structure that governs these variables. This distinction is theoretically important, as it determines whether the reported interaction effects should be understood as adjustments in movement characteristics or as changes in the structure of the control policy itself.

We agree with the reviewer that this terminology required clarification. In this paper, the term “motor plan” refers to the time series of control inputs planned by the CNS, rather than solely to kinematic descriptors such as speed or duration. These planned control signals are a direct consequence of the underlying optimization structure and cost functions that govern trajectory generation. We have clarified this definition in the Introduction (p.1, l.23–24).

Reviewer #3 (Public review):

Strengths:

This study provides novel insights into how individuals regulate the speed of their movements both alone and in pairs, highlighting consistent differences in movement vigor across people and showing that these differences can adapt in dyadic contexts. The findings are significant because they reveal stable individual patterns of action that are flexible when interacting with others, and they suggest that multiple factors, beyond reward sensitivity, may contribute to these idiosyncrasies. The evidence is generally strong, supported by careful behavioral measurements and appropriate modeling, though clarifying some statistical choices and including additional measures of accuracy and smoothness would further strengthen the support for the conclusions.

Thank you for this analysis and the insightful feedback.

Major Comments:

(1) Given the idiosyncrasies in individual vigor, would linear mixed models (LMMs) be more appropriate than ANOVAs in some analyses (e.g., in the section "Solo session"), as they can account for random intercepts and slopes on vigor measures? Some figures (e.g., Figure 2.B and 3.E) indeed seem to show that some aspects of behaviour may present variability in slopes and intercepts across participants. In fact, I now realize that LMMs are used in the "Emergence of dyadic vigor from the partners’ individual vigor" section, so could the authors clarify why different statistical approaches were applied depending on the sections?

We thank the reviewer for this thoughtful comment. We deliberately used different statistical approaches throughout the paper in order to address different types of questions. Note that the statistical tests were converted to their nonparametric equivalent for consistency (see answer to Reviewer 1).

- Friedman tests were used in a limited number of cases to assess population- or group-level effects, such as differences in movement time, smoothness, or accuracy across the solo, connected, and after-effects conditions. Such tests provide a straightforward framework for these descriptive, condition-level comparisons.

- The stability of individual and dyadic vigor scores across conditions was assessed using Pearson correlations across all condition pairs, which we consider the most direct and interpretable approach for evaluating consistency across sessions.

- LMMs were employed to examine how dyadic vigor relates to the partners’ individual vigor measured in the solo conditions, which revealed the critical contribution of the slow partner.

Rather than applying a single statistical framework throughout, we selected the method best suited to each question. While LMMs are well suited for modeling participant-specific variability when linking individual and dyadic measures, their systematic use in all analyses would be less intuitive and would not directly address several of the population-level comparisons central to this study.

(2) If I understand correctly, the introduction suggests that idiosyncrasies in movement vigor may be driven by interindividual differences in reward sensitivity. However, the current task does not involve any explicit rewards, yet the authors still observe idiosyncrasies in vigor, which is interesting. Could this indicate that other factors contribute to these consistent individual differences? For example, could sensitivity to temporal costs or physical effort explain the slow versus fast subgrouping? Specifically, might individuals more sensitive to temporal costs move faster to minimize opportunity costs, and might those less sensitive to effort costs also move faster? Along the same lines, could the two subgroups (slow vs. fast) be characterized in terms of underlying computational "phenotypes," such as their sensitivities to time and effort? If this is not feasible with the current dataset, it would still be valuable to discuss whether these factors could plausibly account for the observed patterns, based on existing literature.

We thank the reviewer for this interesting question. We first note that the notion of reward in motor control is quite broad. Although our task did not include explicit external (e.g. monetary) rewards, we assumed that participants attribute an implicit value to completing the task in accordance with the experimenter’s instructions. This assumption has been shown to be appropriate for characterising baseline behavior in previous studies [2–5].

As discussed in the Introduction, vigor is generally understood to emerge from a tradeoff between effort, accuracy, and time. The reviewer is correct in noting that inter-individual differences in vigor may reflect differences in reward sensitivity or in its discounting [3,6], given that time and reward are intrinsically coupled. Differences in vigor may also arise from inter-individual variability in sensitivity to effort or perceived task difficulty. Because these factors are intertwined—for example, increasing accuracy through co-contraction typically incurs greater effort [7])—it is challenging to disentangle their respective contributions based solely on behavioral data.

In the present study, our inverse optimal control procedure to identify the cost of time (and thus predict individuals’ vigor) relies on a predefined effort-accuracy tradeoff under fixed final time across multiple movement amplitudes [8]. As a result, the model does not allow us to independently estimate individual sensitivities to effort, accuracy, and time. Such characterization of computational "phenotypes" would likely require experimental paradigms in which each of these factors is systematically manipulated while the others are held constant, which is beyond the scope of the current dataset. In practice, the main value of behavioral modeling lies in revealing the relative weighting of these criteria by the CNS during motor planning [5]. We have expanded the Discussion to clarify these limitations and considerations (see Discussion p.12, l.396–401 & l.407–412).

Finally, we chose not to emphasize these broader issues in the present manuscript because (i) they are peripheral to our primary research question on how individual vigor influences human-human interaction, and (ii) although we do not yet have definitive and consensual answers, they have been addressed in multiple studies reviewed elsewhere [9,10].

(3) The observation that dyads did not lose accuracy or smoothness despite changes in vigor is interesting and suggests a shift in the speed-accuracy tradeoff. Could the authors include accuracy and smoothness measures in the main figures rather than only in supplementary materials? I think it would make the manuscript more complete.

We also find that the preservation of accuracy and smoothness despite changes in vigor is an interesting result, and we therefore chose to report these measures in the Supplementary Materials. However, we believe it is preferable not to include them in the main figures for the following reasons:

- We avoid framing our results in terms of a speed-accuracy trade-off, as Fitts’ work was initially designed to study fast movements [11], whereas our work focuses on self-paced movements. As outlined in the Introduction, vigor is more appropriately interpreted as reflecting a tradeoff between effort (related to movement speed), accuracy, and time. From this perspective, the reported changes of vigor already capture a shift in the underlying trade-off selected by the CNS, using a framework better suited to our experimental paradigm.

- The manuscript is technically dense and reports multiple analyses that are essential to establish (i) the existence and definition of dyadic vigor, and (ii) how it emerges from interaction between partners. Although the observed preservation of accuracy and improvements in smoothness are informative, they are not central to these two primary questions and would risk diverting attention from the core contributions of the paper. In addition, accuracy is not a feature predicted by our deterministic modeling and extensions would be needed to capture these aspect. Here we only attempted to replicate average behaviors.

(4) It is a bit unclear to me whether the variance assumptions for ANOVAs were checked, for instance, in Figure 3H.

We thank the reviewer for this comment, which prompted us to verify the assumptions underlying our ANOVAs. We found that a few distributions in the original analysis, as well as in some of the new tests, did not meet these assumptions. To ensure consistency, all statistical analyses have now been replaced with non-parametric tests: Friedman and Kruskal-Wallis tests for paired and unpaired main effects, Wilcoxon and Mann-Whitney tests for paired and unpaired post-hocs. The updated results do not change any of the conclusions. the only minor change is accuracy, that appeared slightly improved in a restricted number of connected conditions, and now appears mostly non-impacted.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Minor points:

(1) Lines 146-147. The authors state, "Whereas the fast partners maintained a similar duration". Figures S6H,I suggest that fast partners made slower movements during the paired task relative to the solo task, not movements with a similar duration.

We agree that Fig. S.6H,I suggest slightly slower movements for the fast partners, though not significant. We have modified the sentence to be less assertive than in the previous version (see p.6, l.155).

(2) In the Discussion (Lines 318-319), the authors state that their findings confirm and extend the "benefits of dyadic control in collaborative actions". What benefits are they referring to here, relative to individual control? It would be helpful if the authors would elaborate on this claim.

We have modified this sentence to clarify that the benefits of dyadic control refer to previously reported advantages over individual control, namely reduced movement time Reed and Peshkin (2008) [12] and improved tracking accuracy [13,14] (see p.11, l.336–337).

(3) On Lines 87-89, the authors reference a decomposition of variance of vigor scores across the NF1, VL, and VH conditions; however, I did not see an explanation of how this decomposition was performed. The method used to estimate variance explained by inter-individual vs intra-individual differences in vigor should be outlined for the reader.

Thank you for pointing out this missing information. We now explain in the statistical analysis section (see p.14, l.504–507), that the percentage of inter-individual variability in vigor is estimated using sum-square values as an estimation of inter- and intra-individual variability.

(4) How was the absolute interaction torque for a paired movement calculated? Was it an integral of the temporal profile of torque for some portion of the combined movement? The method for calculating the absolute interaction torque needs to be specified.

We have now clarified in the Methods (see p.14, l.490–491) that the reported average interaction effort was computed as the absolute value of the interaction torque as a function of time averaged over the entire movement.

(5) Lines 123-124: "... interaction torque showed no significant correlation with differences in individual vigor within dyads." This statement should be supported by appropriate statistical measures.

This result is now supported by reporting the corresponding Pearson correlation analyses. No significant correlations were found between interaction torque and differences in individual vigor within dyads (KL conditions: |r| < 0.43, p> 0.22; KH conditions: |r| < 0.18, p > 0.61, see p.5, l.132–133).

(6) For the analysis, presented in Figure 3C, and specified on lines 116-123, the text mentions the main effects of both condition and target. There doesn’t appear to be much of an effect of the target for the KH data. Should these results not be reported as an interaction effect between the two factors instead?

We agree with the reviewer and have corrected our presentation of these results (see p.4, l.126–128). Consistent with the reviewer’s observation, no significant effect of the target is found in the KH condition.

(7) Figures 3E and S6B. What is the purpose of including the averaged data for each pair in addition to both individuals’ data from each pair? It would be useful to distinguish the individual data from the average data for each pair. Frankly, the number of data points shown on this sub-figure is excessive.

There may have been a misunderstanding. Because the partners of a dyad are connected by a virtual elastic band (rather than a rigid bar), they do not execute identical movements. Therefore Figs. 3E,S6B display the movement time of all individual participants, together with the corresponding 20 individual regression lines, like in Fig. 2B. The solid black line represents the average across all individuals, and the averaged behaviors of dyads are not included. We have clarified this point by revising the caption of Fig. 3E (see p.5).

Noted mis-spellings:

Figure S.3A caption: "trials towards this target."

Page 10 Line 313: "Importantly, these findings show ...".

These mis-spellings have been corrected at supplementary p.2 and main text p.11, l.331. Thank you!

Reviewer #2 (Recommendations for the authors):

(1) To illustrate the contribution of the three components used to calibrate the overall cost function, it would be informative to include simulation analyses in which each component is selectively removed (i.e., ablation analyses).

We did not perform ablation analyses, as selectively removing components of the model can lead to instability or ill-suited control inputs, making the resulting simulations difficult to interpret. Instead, we conducted a sensitivity analysis of the key parameters shaping the overall cost function, including the estimated mean and deviation of the slow partner’s movement duration, the weight associated with uncertain torque minimization (Figs. S.18,S.19), and the fast partner’s cost of time (Fig. S20). This analysis reveals the predominant roles of the estimated slow partner movement patterns in determining the model predictions, in agreement with our experimental observations.

(2) Although the authors refer to the motor-off condition as "passive," participants actively generated the movements in the absence of external forces. Thus, this condition corresponds to active, unassisted movement. A different term may therefore reduce potential confusion for readers.

We agree that term “passive” was not well-chosen given the context of the paper, thus we have instead replaced this denomination as “null-field” condition. Consequently, the P1 and P2 blocks are now referred to as NF1 and NF2.

(3) Please clarify the instructions given to participants. Were they informed in advance that their movements would physically interact with those of their partner?

Thank you for pointing out this missing clarification. We have now specified in the Methods (p.14, l.465–469) that participants were not informed prior to any condition that they would interact with a human partner; they were only told that the robot would provide assistance. When debriefed at the end of the experiment, only one out of the 20 participants reported having realized that they were connected to another human. Most participants believed they were interacting either with a version of themselves or with a robot with some randomness.

(4) Line 475. Should "Fig. 2D" be "Fig. 2B"?

Thank you for catching this error. The reference has been corrected to Fig. 2B (see p.15, l.522).

Reviewer #3 (Recommendations for the authors):

(1) The analysis of reaction times shows no difference between groups in the passive block, which challenges the assumption that movement vigor covaries with decision speed or action initiation speed. It may be worth discussing this in the context of recent literature.

We agree that the initial analysis and discussion of reaction times were too superficial. In the revised manuscript, we now report that dyadic interaction leads to significantly shorter reaction times (p.4, l.100–109), concomitantly with improved movement velocity. We have also expanded the Discussion, on the relationship between decision and action speeds/durations (p.11, l.340–348).

(2) Many abbreviations are unusual for a non-expert. I would recommend using the full terms instead. At least initially, I found it difficult to follow the results because the abbreviations were not immediately clear (at least to me).

We agree that the paper had to many abbreviations. Therefore, we have removed the abbreviated names of the models and, when possible without impacting the readability, used the full names of the conditions.

(3) Relatedly, the notation in Figure 1 may be confusing. The labels "S" and "F" (slow and fast) correspond to different concepts than "F" and "L" (follower and leader), so the same participant could be labeled "F" as fast but not "F" as a leader.

Thank you for pointing out this potential source of confusion. We have therefore modified Fig. 1A (p.2) to avoid any potential confusion by using the full model names rather than abbreviations. In the remainder of the manuscript, "S" and "F" exclusively denote the slower and faster partners within a dyad, and we do not use abbreviations for "leader" or "follower" in the text.

(4) In figures like 2.C and 3.I, keeping the same scales on the x and y axes and adding a diagonal reference line would make it easier to see shifts across conditions.

As explained in the Methods, vigor scores in the low- and high-viscosity conditions were computed using the average movement durations from the NF1 condition as a reference. Consequently, because movements are slower in these conditions, the corresponding vigor values are lower than those in NF1. For this reason, using identical scales on the x- and y-axes and adding a 45◦ reference line could mislead the reader in thinking that the vigor scores are expected to be identical and reduce the readability of the figure.

(5) Multiple hypotheses about dyadic regulation of vigor are nicely explained; it could help to indicate if any of these were a priori favored based on prior literature.

Previous literature provides mixed evidence regarding how vigor might be regulated in dyadic interaction. For instance, Takagi et al. (2016) [15] reported that mechanically connected partners may rely on independent motor plans, which corresponds to the co-activity hypothesis considered here. However, in that study, movement duration was prescribed. We therefore expected that removing this constraint on movement duration could allow coordination strategies to emerge, particularly in view of findings on haptic communication during tracking of random targets while connected via an elastic band [13,14].

At the same time, a large body of work on human–human and human–robot interaction has interpreted coordination through a leader–follower framework. In our context, vigor is understood as the outcome of a tradeoff between effort and elapsed time, with time being associated with a decaying reward. Based on this framework, we hypothesized a priori that a leader–follower scheme would emerge, in which the fast partner—being more sensitive to time costs and/or less sensitive to effort—would tend to drive the interaction, even at the expense of increased effort. For these reasons, the leader–follower hypothesis was formulated as the expected outcome throughout the manuscript.

(6) In the introduction, statements such as "relative vigor of an individual is remarkably stable" appear true only in the solo condition. The same is true in the discussion where it is said that vigor is a stable trait. The whole study show that an individual can shift his/her vigor to the same vigor of another individual, so it doesn’t appear stable to me in such conditions but adaptable.

Let us first clarify that when we describe vigor as “remarkably stable”, we do not imply that individuals do not adjust their movement timing in response to changes in external dynamics. For example, movement durations increase in visco-resistive conditions even during solo performance; nevertheless, individuals who move faster in the absence of resistance will remain faster relative to others when resistance is introduced. In this sense, stability refers to the preservation of relative rankings across conditions, rather than invariance of absolute movement timing. Because interaction with another individual constitutes a substantial change in task dynamics, an effect on individual pace is therefore expected.

Told that (and as pointed to by the reviewer) (i) dyadic interactions lead to the emergence of a dyadic vigor characterized by average movement durations close to those of the fast partners, while the ranking across dyads is largely imposed by the slow partners; and (ii) these adaptations persist after the interaction phase. Importantly, the observed vigor adaptations appear to last longer in our physical interaction task than in previous attempts to manipulate vigor using visual feedback [16]. To account for this adaptability of vigor, we have (i) clarified claims in the Introduction regarding the stability of vigor (see p.1, l.18–20), and (ii) expanded the Discussion to more explicitly address vigor adaptability and the possible resulting consequences for the concept of vigor (see p.12, l.407–412).

References

(1) O. Labaune, T. Deroche, C. Teulier, and B. Berret, “Vigor of reaching, walking, and gazing movements: on the consistency of interindividual differences,” Journal of Neurophysiology, vol. 123, pp. 234–242, jan 2020.

(2) L. Rigoux and E. Guigon, “A model of reward-and effort-based optimal decision making and motor control,” PLoS Computational Biology, vol. 8, pp. 1–13, Jan. 2012.

(3) R. Shadmehr, J. J. O. de Xivry, M. Xu-Wilson, and T.-Y. Shih, “Temporal discounting of reward and the cost of time in motor control,” Journal of Neuroscience, vol. 30, pp. 10507–10516, aug 2010.

(4) B. Berret and G. Baud-Bovy, “Evidence for a cost of time in the invigoration of isometric reaching movements,” Journal of Neurophysiology, vol. 127, pp. 689–701, feb 2022.

(5) D. Verdel, O. Bruneau, G. Sahm, N. Vignais, and B. Berret, “The value of time in the invigoration of human movements when interacting with a robotic exoskeleton,” Science Advances, vol. 9, sep 2023.

(6) K. Jimura, J. Myerson, J. Hilgard, T. S. Braver, and L. Green, “Are people really more patient than other animals? evidence from human discounting of real liquid rewards,” Psychonomic Bulletin & Review, vol. 16, pp. 1071–1075, dec 2009.

(7) P. L. Gribble, L. I. Mullin, N. Cothros, and A. Mattar, “Role of cocontraction in arm movement accuracy,” Journal of Neurophysiology, vol. 89, pp. 2396–2405, may 2003.

(8) B. Berret and F. Jean, “Why Don’t We Move Slower? The Value of Time in the Neural Control of Action,” Journal of Neuroscience, vol. 36, pp. 1056–1070, Jan. 2016.

(9) R. Shadmehr and A. A. Ahmed, Vigor : neuroeconomics of movement control. The MIT Press, 2020.

(10) D. Thura, A. M. Haith, G. Derosiere, and J. Duque, “The integrated control of decision and movement vigor,” Trends in Cognitive Sciences, vol. 29, pp. 1146–1157, Dec. 2025.

(11) P. M. Fitts, “The information capacity of the human motor system in controlling the amplitude of movement,” Journal of Experimental Psychology, vol. 47, pp. 381–391, June 1954.

(12) K. B. Reed and M. A. Peshkin, “Physical collaboration of human-human and human-robot teams,” IEEE Transactions on Haptics, vol. 1, pp. 108–120, July 2008.

(13) G. Gowrishankar, A. Takagi, R. Osu, T. Yoshioka, M. Kawato, and E. Burdet, “Two is better than one: physical interactions improve motor performance in humans,” Scientific Reports, vol. 4, Jan. 2014.

(14) A. Takagi, G. Ganesh, T. Yoshioka, M. Kawato, and E. Burdet, “Physically interacting individuals estimate the partner’s goal to enhance their movements,” Nature Human Behaviour, vol. 1, pp. 1–6, Mar. 2017.

(15) A. Takagi, N. Beckers, and E. Burdet, “Motion plan changes predictably in dyadic reaching,” PLOS ONE, vol. 11, p. e0167314, Dec. 2016.

(16) P. Mazzoni, B. Shabbott, and J. C. Cortes, “Motor control abnormalities in Parkinson’s disease,” Cold Spring Harbor Perspectives in Medicine, vol. 2, pp. a009282–a009282, Mar. 2012.