Zz đã áp dụng những câu mình học được tù các người lớn và để ý đến những vận dụng xung quanh mình. Như cây cam ngọt Pinkie, nó đã phát triển nhanh hơn cây cam ngọt bình thường

Et saddelpunkt antages at være kritisk til at begynde med. Eksemplet

$$ f(x, y, z) = x^2 - y^2 + z $$

har \( (0,0,0)\) som saddelpunkt i henhold til definitionen. Dette punkt er ikke kritisk!

Author response:

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

Reviewer #1 (Public Review):

Summary:

Wilson's Disease (WD) is an inherited rare pathological condition due to a mutation in ATP7B that alters mitochondrial structure and dysfunction. Additionally, WD results in dysregulated copper metabolism in patients. These metabolic abnormalities affect the functions of the liver and can result in cholecystitis. Understanding the immune component and its contribution to WD and cholecystitis has been challenging. In this work, the authors have performed single-cell RNA sequencing of mesenchymal tissue from three WD patients and three liver hemangioma patients.

Strengths:

The authors describe the transcriptomic alterations in myeloid and lymphoid compartments.

Weaknesses:

In brief, this manuscript lacks a clear focus, and the writing needs vast improvement. Figures lack details (or are misrepresented), the results section only catalogs observations, and the discussion needs to focus on their findings' mechanistic and functional relevance. The major weakness of this manuscript is that the authors do not provide a mechanistic link between the absence of ATP7B and NK cells' impaired/altered functions. While the work is of high clinical relevance, there are various areas that could be improved.

In this study, we reported for the first time that ATP7B mutation and the resulting metabolic abnormalities in hepatocytes cause functional alteration of immune cells in WD patients. We dissected the transcriptional profiles of liver mesenchymal cells and delineated the functional differences of main immune cells in WD patients through scRNA-seq. The NK cell exhaustion and its clinical significance were further demonstrated.

The mechanism study is of our concern. Given that the ATP7B mutation is hepatocyte-specific, its effect on immune cells is most probably through intercellular communication rather than through the direct action of ATP7B protein. How ATP7B mutation disturbs the metabolic homeostasis in hepatocyte, how metabolic pathways regulate the release of signal substances, and how signal substances act on the NK cells need to be explained. These contents, together with this manuscript, are beyond the scope of a single article, so we put the novelty in this manuscript.

We sincerely appreciate the comments. We have improved the manuscript based on your valuable suggestions. The mechanism study is our subsequent research topic. We are actively promoting it and have found that ATP7B mutation rewires a certain metabolism pathway in hepatocyte, and that a critical metabolite functions as the mediator causing NK cell exhaustion.

Reviewer #2 (Public Review):

Summary:

Wilson's disease is a rare genetic disorder caused by mutations in the ATP7B gene. Previous studies have documented that ATP7B mutations can disrupt copper metabolism, affecting brain and liver function. In this paper, the authors performed a retrospective clinical study and found that Wilson's disease has a high incidence of cholecystitis. Single-cell RNA-seq analysis revealed changes in the immune microenvironment, including the activation of immune responses and the exhaustion of natural killer cells.

Strengths:

A key finding of this study is that the predominant ATP7B gene mutation in the Chinese population is the 2333G>T (p. R778L) mutation. The authors reported associations between Wilson's disease and cholecystitis, as well as the exhaustion of natural killer cells.

Weaknesses:

The underlying mechanisms linking ATP7B mutations to cholecystitis and natural killer cell exhaustion remain unclear. Specifically, it is not yet determined whether copper metabolism alterations directly cause cholecystitis and natural killer cell exhaustion, or if these effects are secondary to liver dysfunction.

In this study, we reported for the first time that ATP7B mutation and the resulting metabolic abnormalities in hepatocytes cause functional alteration of immune cells in WD patients. We dissected the transcriptional profiles of liver mesenchymal cells and delineated the functional differences of main immune cells in WD patients through scRNA-seq, focusing on the NK cell exhaustion and its clinical significance.

The mechanism study is of our concern. Given that the ATP7B mutation is hepatocyte-specific, its effect on immune cells is most probably through intercellular communication, so we prioritize the studying of this aspect. How ATP7B mutation disturbs the metabolic homeostasis in hepatocyte, how metabolic pathways regulate the release of signal substances, and how signal substances act on the NK cells need to be explained. These contents, together with this manuscript, are beyond the scope of a single article, so we put the novelty in this manuscript.

We sincerely appreciate the comments. The mechanism study is the topic of our follow-up study. We are actively promoting the research and we have found that ATP7B mutation rewires a certain metabolism pathway in hepatocyte, and that a critical metabolite functions as the mediator causing NK cell exhaustion.

Reviewer #1 (Recommendations For The Authors):

Major:

(1) Abstract. A major portion of this manuscript focuses on non-NK cells. Data that describes NK cell exhaustion is only minimal. Therefore, the authors should modify the abstract.

Thank you for your valuable suggestion. We have supplemented the description of functional changes in other immune cells, and have modified the abstract (line 31-35).

(2) Introduction. There are three paragraphs. The first paragraph discusses cholecystitis. However, there are too many repetitions, and the information is unclear. In the second part, the authors discuss NK cells and their exhaustion. The authors do not establish a clear rationale or logic linking NK cells to WD or cholecystitis. In the last paragraph, the authors describe their findings. Their correlation between NK cell exhaustion and the poor healing process of cholecystitis has no direct experimental proof.

Thank you for your comments. We have deleted the repetitions and rephrased some sentences (line 72-74). Briefly, in the first paragraph, we proposed the significant prognostic value of immune cell dysfunction for cholecystitis. In the second paragraph, we introduced NK cell exhaustion and its potential to predict prognosis of certain diseases. In the third paragraph, we introduced that the liver is a central organ involved in metabolism and immunity, holding a large number of NK cells. Liver pathologies commonly impact the development and outcome of inflammation-associated diseases such as cholecystitis. WD was selected as a research model. In the last paragraph, we introduced our findings from clinical study, scRNA-seq, clinical samples, and bioinformatics analysis, and concluded at the end.

(3) Results. Overall, the results section lacks clarity and a clear focus. Figure legends need to be significantly detailed. The authors make too many broad statements without any support. The authors also make too many overstatements.

Thank you for your valuable suggestion. We have improved the inaccurate statements and made detailed refinement of figure legends. All the changes are marked in the manuscript, and related responses are described below.

Figure 1: No information is provided about the functional impairment of ATP7B protein due to the mutation found in the cohort of Chinese patients. What does 'immune abnormalities' (line 127) mean? What is the relevance of showing liver fibrosis and copper accumulation in the eye in Figure 1c and d, respectively? Total cholesterol concentrations are still within the range in the plasma of WD patients, but the authors call it higher. ECAR has not changed in WD patients, but the authors claim it has (line 117).

(1) All these gene mutations in WD disable the protein function and cause the same outcome. (2) We have deleted the inappropriate statement. (3) In clinical observation, we found that WD not only causes copper accumulation in hepatocytes, but also leads to a variety of diseases, including liver fibrosis, Kayser-Fleischer Ring, and lower risk of hyperglycemia. We showed these together with the data of cholecystitis incidence. We think these might suggest the significance of intercellular communication between hepatocytes and other cells in microenvironment. (4) We have deleted the inappropriate statement (line 108-110, 112-113).

Figure 2: Did the authors use the liver mesenchymal tissue or mesenchymal cells? Figure 2 states that they used mesenchymal cells, different from liver mesenchymal tissue. Numbers within Figure 2b UMAP are not visible. Were the initial T and NK cells annotated as indicated in Figure S2 (CD3D, CD#E, CD3G)? If so, that does not include NK cells.

(1) The liver mesenchymal cells were used for scRNA-seq. (2) It is possible that the image resolution was reduced due to the compression of files by the submission system during merging process. We confirm that the image resolution of all figures meets publishing requirements, and that all characters on the figures are visible. You can download figure files to view details. (3) It was our negligence that the incomplete cell markers were shown in Figure S2. We have updated the markers (CD3D, CD3E, NKG7), references (Ref #53, #55, and #56), and related figures (Figure 2e, and Figure S2c).

Figure 3: The authors should change 'Case' to 'WD patients' both in the text and figures. DEGs in Figure 3C indicate a transcriptomic alteration in the B cell compartment, which the authors do not delineate. Also, the rationale and explanation for the CellChat analyses are minimal. Concluding that a change occurred within the TME with minimal data and explanations is unfair.

Thank you for your comments. (1) We apologize for the confusion caused by the use of nomenclatures and abbreviations in the text and figures. In all scRNA-seq data analysis, presentation, and description, we used specific terms (CASE and CON) to refer to the group of WD patients and controls, as well as their cell population. We have now unified the use of nomenclature in full text and defined them when first appeared (line 126-127), avoiding using lowercase form to prevent confusion. (2) We have now compared the expression of key genes of B cell between the two group in the next section “The dysfunction of main immune cells in WD patients” (line 230-235, Figure 4e, Figure S4e). (3) We have described the results of cellular communication in more detail (line 188-194). (4) We have modified the conclusion and all the related statement in full text (line 29-31, 82-84, 149, 194-195).

Figure 4: This section deals with multiple cell types with minimal explanations. This section discusses various cell types, but it lacks focus. In particular, the T cell section should be separated and elaborated more in detail.

(1) In this section, we intended to show the comparison in function of main immune cells that account for a considerable proportion, instead of just showing differently expressed genes that provide minimal information. The evaluation of functional signature, based on the integration of multiple gene expression, allows a direct understanding of the final outcome owing to transcriptional changes. (2) Given that the main functions of T cells did not change significantly and there were more significant changes in innate immunity, the T cell section is relatively short and unsuitable as a separated part.

Figure 5: What are the distinct subsets of NK cells authors have found in the WD patients and controls? How do these subsets differ between the two groups in numbers and their transcriptomes? The presentation and labeling of Figure 5 and Supplementary Figure 5 need to be vastly improved. The pseudotime presentation in Figure 5b should be presented separately for the patients and the controls. Are the changes in gene expression presented in Figure 5a due to the change in the subset compositions? Figure 5c immuno-staining is not at all visible. A clear explanation should be given for the differences between Figure 5c and Figure 5e, where NKG2A expressions are shown. A better explanation for Figure 5d is required. Did the authors use all the antibodies with the same fluorochrome? If so, what color is that? Can the authors include the individual samples in the bar diagram in Figure 5e? Again, the data in Figure 5 is insufficient to conclude that NK cells are exhausted in WD patients. While the role of changes in the expression of T-BET and EOMES can be related to dysfunction and cellular exhaustion of NK cells, the statement made by the authors needs to be toned down as they do not test with independent experiments.

(1) The subsets of NK cell were clustered by gene expression profile and labeled by the characteristically expressed gene, using certain algorithm in the routine procedure. They cannot be distinguished in clinical samples by one or several genes or other sorting methods. Thus, we were not able to analyze these subsets in clinical samples. (2) We have supplemented the comparison of numbers and transcriptomes of three NK subtypes between the two groups (line 268-273). (3) We have checked the figures and confirmed that all characters on the figures are visible. (4) We have separately presented the plot in Figure S5d. (5) We compared the expression level of genes presented in Figure 5a between the two groups in three NK subtypes and supplemented this part (line 264-268). The results were very consistent across the three subtypes, suggesting that the results in total NK population were contributed by all three subtypes and not affected by a single composition. (6) KLRC1 is also known as NKG2A. We are sorry for not making a clear explanation, and now we use KLRC1 only in all text to avoid confusion. We have made a more clear and detailed description for Figure 5c, 5d, and 5e (now labeled as Figure 5b, 5c, and 5d), and have included the fluorochrome in Figure 5d (now labeled as Figure 5c) and the individual value in Figure 5e (now labeled as Figure 5d) (line 293-299). (7) In this section, we found the upregulated expression of inhibitory receptors, downregulated expression of effector molecules, and the impaired NK cell-mediated cytotoxicity in NK cell of WD patients from scRNA-seq. Then we validated the findings in clinical liver section samples and clinical blood samples by mIHC and flow cytometry, respectively. According to the recent articles, exhausted NK cells are characterized by decreased production of effector cytokines (e.g., IFNγ), as well as by impaired cytolytic activity, and downregulate expression of certain activating receptors and upregulate expression of inhibitory receptors (e.g., 10.3389/fimmu.2017.00760, 10.1038/s41590-018-0132-0, 10.1038/s41467-019-09212-y, 10.1080/2162402X.2016.1264562). Therefore, we concluded NK cell exhaustion in WD patients. (8) In the part about transcription factors, we kept the description of objective data and deleted the statement of the contribution of transcription factors to NK exhaustion.

Figure 6: Data presented in Figure 6 and the conclusion made in this manuscript are predictive. There is no direct testing of ATP7B in NK cells to show the functions of this gene. Extension of this to patient survival is purely speculative. As long as authors state these facts clearly in their text, it can be acceptable. However, they do not extend their conclusions to similar liver diseases.

ATP7B mutation is hepatocyte-specific, and it does not occur in any immune cells. The function of ATP7B in NK cell was not studied. We found the NK exhaustion and poor prognosis of cholecystitis in WD patients. Given that there were researches demonstrating that NK exhaustion is correlated with poor liver cancer prognosis, we hypothesized that NK exhaustion contributes to the poor prognosis of cholecystitis. Bioinformatics studies confirmed our hypothesis and supported the extension of this result to other inflammatory diseases. We had no experimental data, but this result was reliable in bioinformatics method.

(4) Discussion: While the authors analyzed multiple cell types, the discussion is primarily focused on NK cells. There is no clear link between copper utilization, NK cell function, and exhaustion that the authors articulate.

Thank you for your comments. The focus of our study is NK cell exhaustion, which is experimentally proven, so we discussed this aspect. We prioritize the effect of intercellular communication and metabolic alteration on the NK cell exhaustion in our follow-up study. Excess copper is released into the circulation in some circumstances in WD patients, but generally they receive long-term de-coppering therapy to maintain intracellular copper at a non-lethal level. Thus, we do not tend to consider copper as a critical factor in this study. In original manuscript, we mentioned the cuproptosis and its potential as a novel target. It is likely to lead to ambiguity and misunderstanding, so we deleted this part to put our point of view clearly.

(5) Supplementary Figures: The presentation and labeling of these figures need to be changed.

Thank you for your suggestions. We have modified the figures and confirmed that all characters on the figures are visible.

Reviewer #2 (Recommendations For The Authors):

It is better to test whether ATP7B mutation can directly affect immune functions.

Thank you for your suggestions. Given that the ATP7B mutation is hepatocyte-specific, its effect on immune cells is most probably through intercellular communication. Thus, we prioritize the effect of intercellular communication on the NK cell exhaustion and we are actively promoting the research.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

This manuscript from Schwintek and coworkers describes a system in which gas flow across a small channel (10^-4-10^-3 m scale) enables the accumulation of reactants and convective flow. The authors go on to show that this can be used to perform PCR as a model of prebiotic replication.

Strengths:

The manuscript nicely extends the authors' prior work in thermophoresis and convection to gas flows. The demonstration of nucleic acid replication is an exciting one, and an enzyme-catalyzed proof-of-concept is a great first step towards a novel geochemical scenario for prebiotic replication reactions and other prebiotic chemistry.

The manuscript nicely combines theory and experiment, which generally agree well with one another, and it convincingly shows that accumulation can be achieved with gas flows and that it can also be utilized in the same system for what one hopes is a precursor to a model prebiotic reaction. This continues efforts from Braun and Mast over the last 10-15 years extending a phenomenon that was appreciated by physicists and perhaps underappreciated in prebiotic chemistry to increasingly chemically relevant systems and, here, a pilot experiment with a simple biochemical system as a prebiotic model.

I think this is exciting work and will be of broad interest to the prebiotic chemistry community.

Weaknesses:

The manuscript states: "The micro scale gas-water evaporation interface consisted of a 1.5 mm wide and 250 µm thick channel that carried an upward pure water flow of 4 nl/s ≈ 10 µm/s perpendicular to an air flow of about 250 ml/min ≈ 10 m/s." This was a bit confusing on first read because Figure 2 appears to show a larger channel - based on the scale bar, it appears to be about 2 mm across on the short axis and 5 mm across on the long axis. From reading the methods, one understands the thickness is associated with the Teflon, but the 1.5 mm dimension is still a bit confusing (and what is the dimension in the long axis?) It is a little hard to tell which portion (perhaps all?) of the image is the channel. This is because discontinuities are present on the left and right sides of the experimental panels (consistent with the image showing material beyond the channel), but not the simulated panels. Based on the authors' description of the apparatus (sapphire/CNC machined Teflon/sapphire) it sounds like the geometry is well-known to them. Clarifying what is going on here (and perhaps supplying the source images for the machined Teflon) would be helpful.

We understand. We will update the figures to better show dimensions of the experimental chamber. We will also add a more complete Figure in the supplementary information. Part of the complexity of the chamber however stems from the fact that the same chamber design has also been used to create defined temperature gradients which are not necessary and thus the chamber is much more complex than necessary.

We added the scheme of the whole PTFE Chip to Figure 2 in the top left corner, indicating the ROI shown in the fluorescence micrographs. Additionally, the channel walls are now clearly indicated by white dotted lines. The dimensions of the setup are now shown clearer, by showing the total width of the channel as well as its height until the gas flux channel, as well as its depth. Changed caption of the figure accordingly and it now reads: “[…] The PTFE chip cutout in the top left corner shows the ROI used for the micrographs. The color scale is equal for both simulation and experiment and Channel dimensions are 4 x 1.5 x 0.25 mm as indicated. Dotted lines visualize the location of the channel walls. […]“

The data shown in Figure 2d nicely shows nonrandom residuals (for experimental values vs. simulated) that are most pronounced at t~12 m and t~40-60m. It seems like this is (1) because some symmetry-breaking occurs that isn't accounted for by the model, and perhaps (2) because of the fact that these data are n=1. I think discussing what's going on with (1) would greatly improve the paper, and performing additional replicates to address (2) would be very informative and enhance the paper. Perhaps the negative and positive residuals would change sign in some, but not all, additional replicates?

To address this, we will show two more replicates of the experiment and include them in Figure 2.

We are seeing two effects when we compare fluorescence measurements of the experiments.

Firstly, degassing of water causes the formation of air-bubbles, which are then transported upwards to the interface, disrupting fluorescence measurements. This, however, mostly occurs in experiments with elevated temperatures for PCR reactions, such as displayed in Figure 4.

Secondly, due to the high surface tension of water, the interface is quite flexible. As the inflow and evaporation work to balance each other, the shape of the interface adjusts, leading to alterations in the circular flow fields below.

Thus the conditions, while overall being in steady state, show some fluctuations. The strong dependence on interface shape is also seen in the simulation. However, modeling a dynamic interface shape is not so easy to accomplish, so we had to stick to one geometry setting. Again here, the added movies of two more experiments should clarify this issue.

We performed three more replicates of the experiment and included the averaged data points together with their respective standard deviation as error bars in Figure 2d. Additionally, the videos of each individual repeat are now added to the supplementary files for the reader to better understand where the strong fluctuations around half an hour come from. The Figure caption was adjusted to “ […] The maximum relative concentration of DNA increased within an hour to ~30 X the initial concentration, with the trend following the simulation. Error bars are the standard deviation from four independent measurements. […].

The main text was also changed to better explain how the fluctuations impact the measurements: […] Water continuously evaporated at the interface, but nucleic acids remained in the aqueous phase accumulating near the interface. They could only escape downward either by diffusion or by the vortex induced by the gas flowing across the interface, pushing the molecules back deeper into the bulk (See the flow lines in Fig2(b) taken from the simulation).  As the gas flow continuously removed excess vapor, the evaporation rate remained constant. Thus, except for fluctuations, a stable interface shape should be expected. However, due to the high surface tension of water, the interface is very flexible. As the inflow and evaporation work to balance each other, the shape of the interface adjusts, likely in response to small fluctuations in gas pressure and spatial variations in water surface tension. This is leading to alterations in the circular flow fields below (Supplementary Movie 2).

As these fluctuations are difficult to simulate, we decided to stick with one interface shape, matching evaporation and inflow speeds. The evaporation rate at the interface was therefore set to be proportional to the vapor concentration gradient and varied spatially along the interface between 5 and 10.5 µm/s (See Suppl. Fig. VI.1(d)). Using the known diffusion coefficient of 95 µm²/s for the 63mer[9]}, the simulation closely matched the experimental results. In both cases, DNA accumulated in regions with circular flow patterns driven by the gas flux (Fig.2(b), right panel).

5 minutes after starting the experiment, the maximum DNA accumulation was 3-fold, while after one hour of evaporation, around 30-fold accumulation was observed. Due to molecules residing in very shallow volumes when directly at the interface, the fluorescence signal can vary drastically compared to measurements deeper in the bulk. This can be seen in the fluctuations between independent measurements (See Supplementary Movies 2b,2b,2c), especially around 0.5~h shown in Figure 2(d). The simulated maximum accumulation followed the experimental results and starts saturating after about one hour (Fig.2(d)). […]”

The authors will most likely be familiar with the work of Victor Ugaz and colleagues, in which they demonstrated Rayleigh-Bénard-driven PCR in convection cells (10.1126/science.298.5594.793, 10.1002/anie.200700306). Not including some discussion of this work is an unfortunate oversight, and addressing it would significantly improve the manuscript and provide some valuable context to readers. Something of particular interest would be their observation that wide circular cells gave chaotic temperature profiles relative to narrow ones and that these improved PCR amplification (10.1002/anie.201004217). I think contextualizing the results shown here in light of this paper would be helpful.

Thanks for pointing this out and reminding us. We apologize. We agree that the chaotic trajectories within Rayleigh-Bénard convection cells lead to temperature oscillations similar to the salt variations in our gas-flux system. Although the convection-driven PCR in Rayleigh-Bénard is not isothermal like our system, it provides a useful point of comparison and context for understanding environments that can support full replication cycles. We will add a section comparing approaches and giving some comparison into the history of convective PCR and how these relate to the new isothermal implementation.

We added a main text paragraph after the last paragraph in section “Strand Separation Dynamics”: “[…]Rayleigh-Bénard convection cells generate similar patterns to those seen in Fig. 3(c) The oscillations in salt concentration resemble the temperature fluctuations observed in convection-based PCR reactions from earlier studies [32,33], which showed that chaotic temperature variations, compared to periodic ones, enhanced the efficiency of the PCR reaction.[…]

Again, it appears n=1 is shown for Figure 4a-c - the source of the title claim of the paper - and showing some replicates and perhaps discussing them in the context of prior work would enhance the manuscript.

We appreciate the reviewer for bringing this to our attention. We will now include the two additional repeats for the data shown in Figure 4c, while the repeats of the PAGE measurements are already displayed in Supplementary Fig. IX.2. Initially, we chose not to show the repeats in Figure 4c due to the dynamic and variable nature of the system. These variations are primarily caused by differences at the water-air interface, attributed to the high surface tension of water. Additionally, the stochastic formation of air bubbles in the inflow—despite our best efforts to avoid them—led to fluctuations in the fluorescence measurements across experiments. These bubbles cause a significant drop in fluorescence in a region of interest (ROI) until the area is refilled with the sample.

Unlike our RNA-focused experiments, PCR requires high temperatures and degassing a PCR master mix effectively is challenging in this context. While we believe our chamber design is sufficiently gas-tight to prevent air from diffusing in, the high surface-to-volume ratio in microfluidics makes degassing highly effective, particularly at elevated temperatures. We anticipate that switching to RNA experiments at lower temperatures will mitigate this issue, which is also relevant in a prebiotic context.

The reviewer’s comments are valid and prompt us to fully display these aspects of the system. We will now include these repeats in Figure 4c to give readers a deeper understanding of the experiment's dynamics. Additionally, we will provide videos of all three repeats, allowing readers to better grasp the nature of the fluctuations in SYBR Green fluorescence depicted in Figure 4c.

The data from the triplicates are now added to Figure 4c, showing how air bubbles, forming through degassing at the high temperatures required for Taq polymerase, disrupt the measurement, as they momentarily dry off the channel and stop the reaction until the channel fills again. Figure caption has been adapted and now reads: “[…] Dotted lines show the data from independent repeats. Air bubbles formed through degassing can momentarily disrupt the reaction. […]”

We additionally changed the main text to explain the reader the experimental difficulties: “[…] In other repetitions of the reaction, this increase was sometimes even observed earlier, around the one-hour mark (dotted lines). However, air bubbles nucleated by degassing events rise and temporarily dry out the channel, interrupting the reaction until the liquid refills the channel (Supplementary Movies 4,4b,4c\&5). Despite our best efforts, we were unable to fully prevent this, especially given the high temperatures required for Taq polymerase activity. In an identical setting when the gas- and water flux were switched off, no fluorescence increase was found (See Fig. 4(c) red lines). Fluorescence variations are additionally caused by fluctuations in the position of the gas-water interface, as discussed earlier. […]”

I think some caution is warranted in interpreting the PCR results because a primer-dimer would be of essentially the same length as the product. It appears as though the experiment has worked as described, but it's very difficult to be certain of this given this limitation. Doing the PCR with a significantly longer amplicon would be ideal, or alternately discussing this possible limitation would be helpful to the readers in managing expectations.

This is a good point and should be discussed more in the manuscript. Our gel electrophoresis is capable of distinguishing between replicate and primer dimers. We know this since we were optimizing the primers and template sequences to minimize primer dimers, making it distinguishable from the desired 61mer product. That said, all of the experiments performed without a template strand added did not show any band in the vicinity of the product band after 4h of reaction, in contrast to the experiments with template, presenting a strong argument against the presence of primer dimers.

We added a main text section explaining this to the reader: “[…]Suppl. Fig. IX.2 shows all independent repeats of the corresponding experiments. No product was detected in any of these cases, ruling out reaction limitations such as primer dimer formation. Primer dimers would form even in the absence of a template strand and would be identifiable through gel electrophoresis. As Taq polymerase requires a significant overlap between the two dimers to bind, this would result in a shorter product compared to the 61mer used here.  […]”

Reviewer #2 (Public review):

Schwintek et al. investigated whether a geological setting of a rock pore with water inflow on one end and gas passing over the opening of the pore on the other end could create a non-equilibrium system that sustains nucleic acid reactions under mild conditions. The evaporation of water as the gas passes over it concentrates the solutes at the boundary of evaporation, while the gas flux induces momentum transfer that creates currents in the water that push the concentrated molecules back into the bulk solution. This leads to the creation of steady-state regions of differential salt and macromolecule concentrations that can be used to manipulate nucleic acids. First, the authors showed that fluorescent bead behavior in this system closely matched their fluid dynamic simulations. With that validation in hand, the authors next showed that fluorescently labeled DNA behaved according to their theory as well. Using these insights, the authors performed a FRET experiment that clearly demonstrated the hybridization of two DNA strands as they passed through the high Mg++ concentration zone, and, conversely, the dissociation of the strands as they passed through the low Mg++ concentration zone. This isothermal hybridization and dissociation of DNA strands allowed the authors to perform an isothermal DNA amplification using a DNA polymerase enzyme. Crucially, the isothermal DNA amplification required the presence of the gas flux and could not be recapitulated using a system that was at equilibrium. These experiments advance our understanding of the geological settings that could support nucleic acid reactions that were key to the origin of life.

The presented data compellingly supports the conclusions made by the authors. To increase the relevance of the work for the origin of life field, the following experiments are suggested:

(1) While the central premise of this work is that RNA degradation presents a risk for strand separation strategies relying on elevated temperatures, all of the work is performed using DNA as the nucleic acid model. I understand the convenience of using DNA, especially in the latter replication experiment, but I think that at least the FRET experiments could be performed using RNA instead of DNA.

We understand the request only partially. The modification brought about by the two dye molecules in the FRET probe to be able to probe salt concentrations by melting is of course much larger than the change of the backbone from RNA to DNA. This was the reason why we rather used the much more stable DNA construct which is also manufactured at a lower cost and in much higher purity also with the modifications. But we think the melting temperature characteristics of RNA and DNA in this range is enough known that we can use DNA instead of RNA for probing the salt concentration in our flow cycling.

Only at extreme conditions of pH and salt, RNA degradation through transesterification, especially under alkaline conditions is at least several orders of magnitude faster than spontaneous degradative mechanisms acting upon DNA [Li, Y., & Breaker, R. R. (1999). Kinetics of RNA degradation by specific base catalysis of transesterification involving the 2 ‘-hydroxyl group. Journal of the American Chemical Society, 121(23), 5364-5372.]. The work presented in this article is however focussed on hybridization dynamics of nucleic acids. Here, RNA and DNA share similar properties regarding the formation of double strands and their respective melting temperatures. While RNA has been shown to form more stable duplex structures exhibiting higher melting temperatures compared to DNA [Dimitrov, R. A., & Zuker, M. (2004). Prediction of hybridization and melting for double-stranded nucleic acids. Biophysical Journal, 87(1), 215-226.], the general impact of changes in salt, temperature and pH [Mariani, A., Bonfio, C., Johnson, C. M., & Sutherland, J. D. (2018). pH-Driven RNA strand separation under prebiotically plausible conditions. Biochemistry, 57(45), 6382-6386.] on respective melting temperatures follows the same trend for both nucleic acid types. Also the diffusive properties of RNA and DNA are very similar [Baaske, P., Weinert, F. M., Duhr, S., Lemke, K. H., Russell, M. J., & Braun, D. (2007). Extreme accumulation of nucleotides in simulated hydrothermal pore systems. Proceedings of the National Academy of Sciences, 104(22), 9346-9351.].

Since this work is a proof of principle for the discussed environment being able to host nucleic acid replication, we aimed to avoid second order effects such as degradation by hydrolysis by using DNA as a proxy polymer. This enabled us to focus on the physical effects of the environment on local salt and nucleic acid concentration. The experiments performed with FRET are used to visualize local salt concentration changes and their impact on the melting temperature of dissolved nucleic acids.  While performing these experiments with RNA would without doubt cover a broader application within the field of origin of life, we aimed at a step-by-step / proof of principle approach, especially since the environmental phenomena studied here have not been previously investigated in the OOL context. Incorporating RNA-related complexity into this system should however be addressed in future studies. This will likely require modifications to the experimental boundary conditions, such as adjusting pH, temperature, and salt concentration, to account for the greater duplex stability of RNA. For instance, lowering the pH would reduce the RNA melting temperature [Ianeselli, A., Atienza, M., Kudella, P. W., Gerland, U., Mast, C. B., & Braun, D. (2022). Water cycles in a Hadean CO2 atmosphere drive the evolution of long DNA. Nature Physics, 18(5), 579-585.].

(2) Additionally, showing that RNA does not degrade under the conditions employed by the authors (I am particularly worried about the high Mg++ zones created by the flux) would further strengthen the already very strong and compelling work.

Based on literature values for hydrolysis rates of RNA [Li, Y., & Breaker, R. R. (1999). Kinetics of RNA degradation by specific base catalysis of transesterification involving the 2 ‘-hydroxyl group. Journal of the American Chemical Society, 121(23), 5364-5372.], we estimate RNA to have a half-life of multiple months under the deployed conditions in the FRET experiment (High concentration zones contain <1mM of Mg2+). Additionally, dsRNA is multiple orders of magnitude more stable than ssRNA with regards to degradation through hydrolysis [Zhang, K., Hodge, J., Chatterjee, A., Moon, T. S., & Parker, K. M. (2021). Duplex structure of double-stranded RNA provides stability against hydrolysis relative to single-stranded RNA. Environmental Science & Technology, 55(12), 8045-8053.], improving RNA stability especially in zones of high FRET signal. Furthermore, at the neutral pH deployed in this work, RNA does not readily degrade. In previous work from our lab [Salditt, A., Karr, L., Salibi, E., Le Vay, K., Braun, D., & Mutschler, H. (2023). Ribozyme-mediated RNA synthesis and replication in a model Hadean microenvironment. Nature Communications, 14(1), 1495.], we showed that the lifetime of RNA under conditions reaching 40mM Mg2+ at the air-water interface at 45°C was sufficient to support ribozymatically mediated ligation reactions in experiments lasting multiple hours.

With that in mind, gaining insight into the median Mg2+ concentration across multiple averaged nucleic acid trajectories in our system (see Fig. 3c&d) and numerically convoluting this with hydrolysis dynamics from literature would be highly valuable. We anticipate that longer residence times in trajectories distant from the interface will improve RNA stability compared to a system with uniformly high Mg2+ concentrations.

Added a new Supplementary section for this. We used the trace from Figure 3(c) and calculated the hydrolysis rate for each timestep by using literature values from RNA [Li, Y., & Breaker, R. R. (1999). Kinetics of RNA degradation by specific base catalysis of transesterification involving the 2 ‘-hydroxyl group. Journal of the American Chemical Society, 121(23), 5364-5372.]. We conclude that the conditions deployed for the experiment are not harsh on RNA, with hydrolysis rates in the E-6 1/min regime. The figure below (also now in the supplementary information) shows the hydrolysis of RNA deployed under the conditions of the experiment in Figure 3. RNA is not expected to hydrolyze under these conditions and timescales, in which a replication reaction would occur. With a half life of around 83 days, even a prebiotically plausible – very slow – replication reaction would not be constrained by hydrolysis boundary conditions in this scenario.

Referenced to this section in the supplementary information in the maintext: […] In the experimental conditions used here, RNA would also not readily degrade, even if the strand enters the high salt regimes (See Suppl. Sec. IX). Using literature values for hydrolysis rates under the deployed conditions, we estimate dissolved RNA to have a half life of around 83 days. […]

(3) Finally, I am curious whether the authors have considered designing a simulation or experiment that uses the imidazole- or 2′,3′-cyclic phosphate-activated ribonucleotides. For instance, a fully paired RNA duplex and a fluorescently-labeled primer could be incubated in the presence of activated ribonucleotides +/- flux and subsequently analyzed by gel electrophoresis to determine how much primer extension has occurred. The reason for this suggestion is that, due to the slow kinetics of chemical primer extension, the reannealing of the fully complementary strands as they pass through the high Mg++ zone, which is required for primer extension, may outcompete the primer extension reaction. In the case of the DNA polymerase, the enzymatic catalysis likely outcompetes the reannealing, but this may not recapitulate the uncatalyzed chemical reaction.

This is certainly on our to-do list for future experiments in this setting. Our current focus is on templated ligation rather than templated polymerization and we are working hard to implement RNA-only enzyme-free ligation chain reaction, based on more optimized parameters for the templated ligation from 2’3’-cyclic phosphate activation that was just published [High-Fidelity RNA Copying via 2′,3′-Cyclic Phosphate Ligation, Adriana C. Serrão, Sreekar Wunnava, Avinash V. Dass, Lennard Ufer, Philipp Schwintek, Christof B. Mast, and Dieter Braun, JACS doi.org/10.1021/jacs.3c10813 (2024)]. But we first would try this at an air-water interface which was shown to work with RNA in a temperature gradient [Ribozyme-mediated RNA synthesis and replication in a model Hadean microenvironment, Annalena Salditt, Leonie Karr, Elia Salibi, Kristian Le Vay, Dieter Braun & Hannes Mutschler, Nature Communications doi.org/10.1038/s41467-023-37206-4 (2023)] before making the jump to the isothermal setting we describe here. So we can understand the question, but it was good practice also in the past to first get to know the setting with PCR, then jump to RNA.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Could the authors comment on the likelihood of the geological environments where the water inflow velocity equals the evaporation velocity?

This is an important point to mention in the manuscript, thank you for pointing that out. To produce a defined experiment, we were pushing the water out with a syringe pump, but regulated in a way that the evaporation was matching our flow rate. We imagine that a real system will self-regulate the inflow of the water column on the one hand side by a more complex geometry of the gas flow, matching the evaporation with the reflow of water automatically. The interface would either recede or move closer to the gas flux, depending on whether the inflow exceeds or falls short of the evaporation rate. As the interface moves closer, evaporation speeds up, while moving away slows it down. This dynamic process stabilizes the system, with surface tension ultimately fixing the interface in place.

We have seen a bit of this dynamic already in the experiments, could however so far not yet find a good geometry within our 2-dimensional constant thickness geometry to make it work for a longer time. Very likely having a 3-dimensional reservoir of water with less frictional forces would be able to do this, but this would require a full redesign of a multi-thickness microfluidics. The more we think about it, the more we envisage to make the next implementation of the experiment with a real porous volcanic rock inside a humidity chamber that simulates a full 6h prebiotic day. But then we would lose the whole reproducibility of the experiment, but likely gain a way that recondensation of water by dew in a cold morning is refilling the water reservoirs in the rocks again. Sorry that I am regressing towards experiments in the future.

We added a paragraph after the second paragraph in Results and Discussion.

It now reads: […] For a real early Earth environment we envision a system that self-regulates the water column's inflow by automatically balancing evaporation with capillary flows. The interface adjusts its position relative to the gas flux, moving closer if the inflow is less than the evaporation rate, or receding if it exceeds it. When the interface nears the gas flux, evaporation accelerates, while moving it away slows evaporation. This dynamic process stabilizes the system, with surface tension ultimately fixing the interface's position. […]

(2) Could the authors speculate on using gases other than ambient air to provide the flux and possibly even chemical energy? For example, using carbonyl sulfide or vaporized methyl isocyanide could drive amino acid and nucleotide activation, respectively, at the gas-water interface.

This is an interesting prospect for future work with this system. We thought also about introducing ammonia for pH control and possible reactions. We were amazed in the past that having CO2 instead of air had a profound impact on the replication and the strand separation [Water cycles in a Hadean CO2 atmosphere drive the evolution of long DNA, Alan Ianeselli, Miguel Atienza, Patrick Kudella, Ulrich Gerland, Christof Mast & Dieter Braun, Nature Physics doi.org/10.1038/s41567-022-01516-z (2022)]. So going more in this direction absolutely makes sense and as it acts mostly on the length-selectively accumulated molecules at the interface, only the selected molecules will be affected, which adds to the selection pressure of early evolutionary scenarios.

Of course, in the manuscript, we use ambient air as a proxy for any gas, focusing primarily on the energy introduced through momentum transfer and evaporation. We speculate that soluble gasses could establish chemical gradients, such as pH or redox potential, from the bulk solution to the interface, similar to the Mg2+ accumulation shown in Figure 3c. The nature of these gradients would depend on each gas's solubility and diffusivity. We have already observed such effects in thermal gradients [Keil, L. M., Möller, F. M., Kieß, M., Kudella, P. W., & Mast, C. B. (2017). Proton gradients and pH oscillations emerge from heat flow at the microscale. Nature communications, 8(1), 1897.] and finding similar behavior in an isothermal environment would be a significant discovery.

Added a paragraph in the Conclusion to showcase this: [… ] Furthermore we expect that other gases, such as CO2, could establish chemical gradients in this environment. Such gradients have been observed in thermal gradients before [23] and finding similar behaviour in an isothermal environment would be a significant discovery.[…]

(3) Line 162: Instead of "risk," I suggest using "rate".

Thanks for pointing this out! Will be changed.

Fixed.

(4) Using FRET of a DNA duplex as an indicator of salt concentration is a decent proxy, but a more direct measurement of salt concentration would provide further merit to the explicit statement that it is the salt concentration that is changing in the system and not another hidden parameter.

Directly observing salt concentration using microscopy is a difficult task. While there are dyes that change their fluorescence depending on the local Na+ or Mg2+ concentration, they are not operating differentially, i.e. by making a ratio between two color channels. Only then we are not running into artifacts from the dye molecules being accumulated by the non-equilibrium settings. We were able to do this for pH in the past, but did not find comparable optical salt sensors. This is the reason we ended up with a FRET pair, with the advantage that we actually probe the strand separation that we are interested in anyhow. Using such a dye in future work would however without a doubt enhance the understanding of not only this system, but also our thermal gradient environments.

(5) Figure 3a: Could the authors add information on "Dried DNA" to the caption? I am assuming this is the DNA that dried off on the sides of the vessel but cannot be sure.

Thanks to the reviewer for pointing this out. This is correct and we will describe this better in the revised manuscript.

Added a sentence in the caption to address this: […] Fluctuations in interface position can dry and redissolve DNA repeatedly (see “Dried DNA” in right panel). […]

(6) Figure 4b and c: How reproducible is this data? Have the authors performed this reaction multiple independent times? If so, this data should be added to the manuscript.

The data from the gel electrophoresis was performed in triplicates and is shown in full in supplementary information. The data in c is hard to reproduce, as the interface is not static and thus ROI measurements are difficult to perform as an average of repeats. Including the data from the independent repeats will however give the reader insight into some of the experimental difficulties, such as air bubbles, which form from degassing as the liquid heats up, that travel upwards to the interface, disrupting the ongoing fluorescence measurements.

This was also pointed out by reviewer 1 and addressed there.

(7) Line 256: "shielding from harmful UV" statement only applies to RNA oligomers as UV light may actually be beneficial for earlier steps during ribonucleoside synthesis. I suggest rephrasing to "shielding nucleic acid oligomers from UV damage.".

Will be adjusted as mentioned.

Fixed.

(8) The final paragraph in the Results and Discussion section would flow better if placed in the Conclusion section.

This is a good point and we will merge results and discussion closer together.

Fixed.

(9) Line 262, "...of early Life" is slightly overstating the conclusions of the study. I suggest rephrasing to "...of nucleic acids that could have supported early life."

This is a fair comment. We thank the reviewer for his detailed analysis of the manuscript!

Changed the phrase to: […]In this work we investigated a prebiotically plausible and abundant geological environment to support the replication of nucleic acids. […]

(10) In references, some of the journal names are in sentence case while others are in title case (see references 23 and 26 for example).

Thanks - this will be fixed.

Fixed.

Author response:

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

Reviewer #1 (Public review):

Summary:

The manuscript by Yao S. and colleagues aims to monitor the potential autosomal regulatory role of the master regulator of X chromosome inactivation, the Xist long non-coding RNA. It has recently become apparent that in the human system, Xist RNA can not only spread in cis on the future inactive X chromosome but also reach some autosomal regions where it recruits transcriptional repression and Polycomb marking. Previous work has also reported that Xist RNA can show a diffused signal in some biological contexts in FISH experiments.

In this study, the authors investigate whether Xist represses autosomal loci in differentiating female mouse embryonic stem cells (ESCs) and somatic mouse embryonic fibroblasts (MEFs). They perform a time course of ESC differentiation followed by Capture Hybridization of Associated RNA Targets (CHART) on both female and male ESCs, as well as pulldowns with sense oligos for Xist. The authors also examine transcriptional activity through RNA-seq and integrate this data with prior ChIP-seq experiments. Additional experiments were conducted in MEFs and Xist-ΔB repeat mutants, the latter fails to recruit Polycomb repressors.

Based on this experimental design, the authors make several bold claims:

(1) Xist binds to about a hundred specific autosomal regions.

(2) This binding is specific to promoter regions rather than broad spreading.

(3) Xist autosomal signal is inversely correlated with PRC1/2 marks but positively correlated with transcription.

(4) Xist targeting results in the attenuation of transcription at autosomal regions.

(5) The B-repeat region is important for autosomal Xist binding and gene repression.

(6) Xist binding to autosomal regions also occurs in somatic cells but does not lead to gene repression.

Together, these claims suggest that Xist might play a role in modulating the expression of autosomal genes in specific developmental and cellular contexts in mice.

Strengths:

This paper deals with an interesting hypothesis that Xist ncRNA can also function at autosomal loci.

Weaknesses: The claims reported in this paper are largely unsubstantiated by the data, with multiple misinterpretations, lacking controls, and inadequate statistics. Fundamental flaws in the experimental design/analysis preclude the validity of the findings. Major concerns are listed below: (1) The entire paper is based on the CHART observation that Xist is specifically targeted to autosomal promoters. Overall, the data analysis is flawed and does not support such conclusions. Importantly the sense WT and the 0h controls are not used, nor are the biological replicates. 

We respectfully disagree with Rev1 but nevertheless thank the reviewer for making some suggestions that helped to strengthen our manuscript.  We have provided new experiments and analyses in the revised manuscript. Please see responses below.

Rev1 seems to have missed or misunderstood some key experiments. In fact, the sense WT and 0h controls were shown. Furthermore, we included at least two biological replicates for each experiment.

We used both male ES cells (which do not express Xist) and sense probes as key negative controls, as outlined in Figure S1. Crucially, we only analyzed peaks that were reproducible between biological replicates. The Xist CHART peaks in differentiating female ES cells were significantly enriched above the “background” defined by the sense probe and male controls. Specifically, in comparison to undifferentiated female ES cells (day 0) where both X chromosomes are active and Xist is not induced, Xist CHART robustly pulled down the X chromosome during cell differentiation (day 4, day 7, and day 14). In contrast, male ES cells showed no significant pull-down of the X chromosome, and the sense group also exhibited markedly reduced binding (new Figure S1B). Furthermore, Principal Component Analysis (PCA) of CHART-seq reads (day 4 as an example) include Xist, sense, and input in WT and ΔRepB female, further confirmed that the sense probe CHART was clearly distinguishable from Xist CHART signals. Please see revised Figure S1C. Together, these findings underscore the specificity and robustness of our CHART results.

Data is typically visualized without quantification, and when quantified, control loci/gene sets are erroneously selected. Firstly, CHART validation on the X in FigS1 is misleading and not based on any quantifications (e.g., see the scale on Kdm6a (0-190) compared to Cdkl5 (0-40)). If scaled appropriately, there is Xist signal on the escapee. 

Rev1 may have misread the presented data. In the example raised by Rev1, Fig. S1 is inherently quantitative: e.g., a ratio is a number in Fig. S1A (now Fig. S1B) and all gene tracks in Fig. 1B-E are shown with scales. We showed X-linked genes in Fig. S1 (now Fig. S2) as a control to demonstrate that the CHART worked and that Xist accumulated over time from day 0 to day 14. Our new Figure 1B demonstrates the Xist accumulation in graph format. 

Our paper focuses on Xist autosomal binding sites. Thus, the X-linked examples were placed in the supplement. Escapee genes do in fact accumulate Xist at their promoter regions and this finding is consistent with data published by Simon et al. (2013, Nature). It was therefore not desirable in this paper to reanalyze X-linked genes, including escapees. Nevertheless, to address the reviewer’s concerns, we present new data in new Figure S3A. Here we analyzed the density of Xist binding across X-linked genes, including both active and inactive genes, as well as escapee genes. From this quantitative analysis, it should be clear that escapees do bind Xist. However, from the metagene plots in Figure S3B, we confirm the previous conclusion that escapees bind Xist at high levels just upstream of the promoter and that there is a depletion of Xist in the escapee gene body, consistent with a barrier preventing Xist from moving into the active gene. 

All X-linked loci should have been quantified and classified based on escape status; sense control should also be quantified, and biological replicates should be shown separately. 

Please see above response.

Additionally, in the revised manuscript, we have examined the Irreproducible Discovery Rate (IDR) to validate the reproducibility of peaks between the two replicates in the revised version, and we included a representative example from female WT ES cells at day 4 (revised Figure S4A). The results showed a strong correlation between the replicates, with an IDR threshold of 0.05 (red point > 0.05). As described in the Methods section, to ensure reliable and robust peak identification, we performed peak calling (MACS2) separately on each replicate, and then used bedtools intersect to identify peaks that overlapped between the two replicates. This stringent process, including strict q-value settings in MACS2, ensures the reliability and reproducibility of the peaks presented in this study.

Secondly, and most importantly, Figure 1 does not convincingly show specific Xist autosomal binding. Panel A quantification is on extremely variable y-scales and actually shows that Xist is recruited globally to nearly all autosomal genes, likely indicating an unspecific signal. Again, the sense and 0h controls should have been quantified along with biological replicates. 

Figure 1 shows heatmaps and corresponding metagenes for d0, d4, d7, and d14 female ES cells. Two biological replicates are analyzed. In our revised manuscript, we have used Pearson and Spearman correlation coefficients to measure the strength and direction of a relationship between two biological replicates and shown that the two replicates have high reproducibility (new Figure S1A). On d0, the Xist coverage on autosomes and X chromosome is low, but there is a clear increase on d4, d7, and d14, particularly at the TSS of autosomal genes, as shown by the metagene plots on in Figure 1A-B and the CHART density maps in new Figure 1E-F. We also show relative depletion of Xist signals in the male and sense negative controls.

Upon inspecting genome browser tracks of all regions reported in the manuscript (Rbm14, Srp9, Brf1, Cand2, Thra, Kmt2c, Kmt2e, Stau2, and Bcl7b), the signal is unspecific on all sites with the possible exception of Kmt2e. On all other loci, there is either a strong signal in the 0h ESC controls or more signal in some of the sense controls. This implies that peak calling is picking up false positive regions. How many peaks would have been picked up if the sense or the 0h controls were used for peak calling? It is likely that there would be a lot since there are also possible "peaks" (e.g., Fzd9) in control tracks. 

The analysis cannot be performed by visual inspection. A statistical analysis must be performed to call signal above noise. This is why we performed peak-calling on two biological replicates and identified overlapping peaks using bedtools intersect to improve reliability. Significant peaks are noted as black bars under each track. As mentioned above, for our analysis, we focused on the top 100 peaks based on peak scores to ensure robustness. Xist has significantly higher signal compared to the sense probe in the Xist-autosomal peak regions (revised Figure 1E-F). Additionally, we conducted peak calling on undifferentiated ES cells (d0) and detected a significantly higher number of peaks (~600) compared to the differentiated states (d4 or d7) (~100).

Single-cell sequencing studies have shown that about 2% of undifferentiated mESCs express detectable Xist (Pacini et al., Nat Commun, 2021). The Xist peaks in “day 0” cells may be due to the differentiating population.

Further inspection of the data was not possible as the authors did not provide access to the raw fastq files. When inspecting results from past published experiments {Engreitz, 2013 #1839} reported regions were not bound by Xist. 

On the contrary, we deposited the raw data files to GEO prior to the submission of the paper and included the reviewer link to access them. As of August 24, 2024, GEO publicly released these files, allowing for full inspection of the data. 

Regarding the Engreitz publication, it is not recommended to compare our current study to their analysis for the crucial reason that the Engreitz study was not conducted under physiological conditions. The authors overexpressed the Xist gene in male ES cells. Because Xist RNA can silence genes in male cells as well, this ectopic overexpression normally leads to cell death — thus forcing examination of effects in a narrow time window before Xist can fully spread and act across the genome. Comparing our experiments (endogenous Xist expression in female ES cells) to the ectopic overexpression in male ES cells of Engreitz et al. should therefore not be undertaken.

Thirdly, contrary to the authors' claim, deleting the B repeat does not lead to a loss of autosomal signal. Indeed, comparing Fig1A and Fig2B side by side clearly shows no difference in the autosomal signal, likely because the autosomal signal is CHART background. Properly quantifying the signal with separate replicates as well as the sense and 0h controls is vital. Overall current data together with published results indicate that CHART peak calling on autosomes is due to technical noise or artefacts.

In our revised manuscript, we have included the quantitative results as mentioned above in the main and supplementary figure (new Figure 1E-F, Figure 2E-F, and S3A). The data clearly show an enrichment in the Xist CHART samples in differentiating female ES cells.

We believe the reviewer may be comparing the original Figure 1A and Figure 2A (not Figure 2B). As mentioned above, the analysis cannot be performed by visual inspection. Please see new Figure 2E and 2F. From these data, it should be clear that deleting RepB causes a decrease in Xist targeting to autosomal loci.

(2) The RNA-seq analysis is also flawed and precludes strong statements. Firstly, the analysis frequently lacks statistical analysis (Fig3B, FigS2B-C) and is often based on visualizations (Fig 3D-G) without quantifications. Day 4 B-repeat deletion does not lead to a significant change in the expression of genes close to Xist signal (Fig3H, d14 does not fully show). 

Please see new revised Figure 3B and Figures S2B-C (now revised as Figures S6A and S6B). 

Secondly, for all transcriptional analysis, it is important to show autosomal non-target genes, which is not always done. 

In the revised manuscript, we included non-target genes for each analysis (new Figure 4E-F, 5D and 5F, 7C and 7E, S7F, S8).

Indeed, both males and B repeat deletion will lead to transcriptional changes on autosomes as a secondary effect from different X inactivation status. The control set, if used, is inappropriate as it compares one randomly selected set of ~100 genes. This introduces sampling error and compares different classes of genes. Since Xist signal targets more active genes, it is important to always compare autosomal target genes to all other autosomal genes with similar basal expression patterns.

Please see new Figure S8. We included 100 randomly selected non-target sites on autosomes for this comparative analysis. For consistency, we applied the same flanking regions (10 kb) in the analysis of both target and non-target genes. We believe that this selection method for nontargets is appropriate for two reasons: first, it allows us to control for Xist binding and non-binding; second, it ensures a similar number of genes in both groups, providing a robust foundation for statistical analysis. 

(3) The ChIP-seq analysis also has some problems. The authors claim that there is no positive correlation between genes close to Xist autosomal binding (10kb) compared to those 50kb away (Fig 3C, S2D); however, this analysis is based entirely on metagene visualization. Signal within the Xist binding sites should be quantified (not genes close by) and compared to other types of genomic loci and promoters. Focusing on the 50kb group only as controls is misleading.

We believe the reviewer may have misunderstood our conclusions. As stated in the paper, we observed lower coverage of the histone marks H3K27me3 and H2AK119ub, associated with PRC2 and PRC1, respectively. Our conclusions regarding PRC1/2 support the RNA-seq results, indicating that Xist tends to bind to actively expressed genes. In other words, these genes exhibit lower levels of PRC-mediated silencing signals. This observation underscores the relationship between Xist binding and gene activity, highlighting that Xist preferentially associates with regions that are less subject to silencing by polycomb repressive complexes.

Secondly, the authors only look at PRC mark signal upon differentiation; what about the 0h timepoint, i.e., is there pre-marking? 

Day 0 is not an appropriate timepoint for this analysis because Xist is not yet induced. There is also a small fraction of cells (<5%) that spontaneously differentiate and start to undergo XCI. Because of these reasons, the day 0 timepoint is considered somewhat heterogeneous and it would be difficult to make conclusions regarding Xist peaks in these samples.

Most worryingly, the data analysis is not consistent between figures (see Fig3C vs 5H-I). In Fig5, the group of Xist targets was chosen as those within 100kb of Xist binding, which would encompass all the control regions from Fig3C. In this analysis, the authors report that there is Xist-dependent H3K27me3 deposition, and in fact, here the Xist autosomal targets have more of it than the controls. Overall, all of this analysis is misleading, and clear conclusions cannot be made.

We believe that the reviewer may have also misunderstood the analysis in Figure 5. Figure 5 shows the effect of the Xist inhibitor, X1, on H3K27me3 and gene expression. X1 blocks reduces PRC2 targeting and gene silencing — consistent with X1’s effect on RepA as published in Aguilar et al. 2022. 

All in all, because the fundamental observation is not robust (see point 1), all subsequent analyses are also affected. There are also multiple other inconsistencies within the analysis; however, they have not been included here for brevity.

We again respectfully disagree with Rev1 but thank the reviewer for making suggestions that helped to strengthen our manuscript.  We believe that the revised manuscript with new analyses is improved in part because of the reviewer’s critical comments.

Reviewer #2 (Public review):

Summary:

To follow-up on recent reports of Xist-autosome interaction the authors examine female (and male transgenic) mESCs and MEFs by CHARTseq. Upon finding that only 10% of reads map to X, they sought to identify reproducible alternative sites of Xist-binding, and identify ~100 autosomal Xistbinding sites and show a transient impact on expression.

Strengths:

The authors address a topical and interesting question with a series of models including developmental timepoints and utilize unbiased approaches (CHARTseq, RNAseq). For the CHARTseq they have controls of both sense probes and male cells; and indeed do detect considerable background with their controls. The use of deletions emphasizes that intact functional Xist is involved. The use of 'metagene' plots provides a visual summation of genic impact.

Reviewer 2 has made some excellent suggestions. We have revised the manuscript accordingly and are grateful to the reviewer for the recommendations.

Weaknesses:

Overall, the result presentation has many 'sample' gene presentations (in contrast to the stronger 'metagene' summation of all genes). The manuscript often relies on discussion of prior X chromosomal studies, while the data generated would allow assessment of the X within this study to confirm concordance with prior results using the current methodology/cell lines. 

Many of the 'follow-up' analyses are in fact reprocessing and comparison of published datasets. The figure legends are limited, and sample size and/or source of control is not always clear. While similar numbers of autosomal Xist-binding sites were often observed, the presented data did not clarify how many were consistent across time-points/cell types. While there were multiple time points/lines assessed, only 2 replicates were generally done.

We apologize for the deficiencies in the legend.  The revised manuscript has corrected them.

We generated many new datasets with deep sequencing, with at least two biological replicates for each. Such experiments are extremely expensive by nature. Thus, two biological replicates are typically considered acceptable.

Additionally, we performed reanalysis of published datasets to test whether — in the hands of other investigators — cell lines expressing Xist also supported autosomal targeting. Figure 4 is a case in point. Here we examined Tg1 and Tg2, which respond to doxycycline to overexpress Xist from an ectopic site. Transcriptomic analysis showed significant downregulation of autosomal Xist targets, as exemplified by Rbm14 and Bcl7b (new Figure 4C, S9B). In contrast, non-targets of Xist such as Stau1 did not demonstrate significant changes in gene expression (new Figure 4E and 4G). Looking across all autosomal target genes, we observed a significant decrease in mean expression in the Xist overexpressing cell lines (new Figure 4D). The fact that the autosomal changes were also observed in datasets generated by other investigators greatly strengthen our conclusions. 

Aim achievement:

The authors do identify autosomal sites with enrichment of chromatin marks and evidence of silencing. More details regarding sample size and controls (both treatment, and most importantly choice of 'non-targets' - discussed in comments to authors) are required to determine if the results support the conclusions.

Specific scenarios for which I am concerned about the strength of evidence underlying the conclusion:

I found the conclusion "Thus, RepB is required not only for Xist to localize to the X- chromosome but also for its localization to the ~100 autosomal genes " (p5) in constrast to the statement 2 lines prior: "A similar number of Xist peaks across autosomes in ΔRepB cells was observed and the autosomal targets remained similar". Some quantitative statistics would assist in determining impact, both on autosomes and also X; perhaps similar to the quintile analysis done for expression.

We have added the Xist coverage panel for day 4 and 7 in the identified Xist-autosomal peak regions (new Figure 1E-F, Figure 2E-F), as mentioned above. The results clearly demonstrate that the deletion of RepB decreases Xist binding to autosomes. Also, we showed that ΔRepB increased X-linked genes expression in our revised Figure 3D. 

It is stated that there is a significant suppression of X-linked genes with the autosomal transgenes; however, only an example is shown in Figure 4B. To support this statement, a full X chromosomal geneset should be shown in panels F and G, which should also list the number of replicates. 

Please see new Figure 4B.

As these are hybrid cells, perhaps allelic suppression could be monitored? Is Med14 usually subject to X inactivation in the Ctrl cells, and is the expression reduced from both X chromosomes or preferentially the active (or inactive) X chromosome?

If Rev2 is referring to Figure 4, the dataset used in Figure 4 comes from another research group and was previously published (Loda, A. et al. Nat Commun, 2017).

If Rev2 is referring to our ES cells, they are N2 cell lines.  The X chromosomes are fully hybridized (Cas/Mus), but the autosomes are not fully hybridized (Ogawa et al., Science, 2008). Med14 is subject to XCI and is expressed from the Xa, silenced on the Xi. 

The expression change for autosomes after transgene induction is barely significant; and it was not clear what was used as the Ctrl? This is a critical comparator as doxycycline alone can change expression patterns.

We agree that there was a modest change in expression after transgene induction, but it is a significant change. Again, the dataset is from a published study where the authors generated doxycycline-responsive Xist transgenes (see above). The control in this case is Dox-treated wildtype cells. We now clarify these points.

In the discussion there is the statement. "Genetic analysis coupled to transcriptomic analysis showed that Xist down-regulates the target autosomal genes without silencing them. This effect leads to clear sex difference - where female cells express the ~100 or so autosomal genes at a lower level than male cells (Figure 7H)." This sweeping statement fails to include that in MEFs there is no significant expression difference, in transgenics only borderline significance, and at d14 no significant expression difference. The down-regulation overall seems to be transient during development while targeting is ongoing?

Indeed, the Xist effects on autosomes seem to occur during cell differentiation in ES cells. While there is no apparent effect in MEFs, we cannot exclude effects on other somatic cells. Regardless of whether the effects are in early development or throughout life, the sex differences may have life-long effects in mammals. The study conducted in human cells by the Plath lab also concluded that the differences primarily affect stem cells.

Finally, I would have liked to see discussion of the consistency of the identified genes to support the conclusion that the autosomal sites are not merely the results of Xist diffusion.

We address this in the third paragraph of the Discussion. Our main argument is that if autosomal binding were caused by diffusion, then RepB deletion or X1 treatment would have led to increased binding at autosomal sites, as Xist would bind less to the X chromosome. However, as demonstrated in our study, both treatments resulted in reduced Xist binding on both the X chromosome and autosomes. This finding suggests that the binding is specific and reliant on Xist's RepA and RepB domains, rather than being a passive diffusion process.

To examine overlap between the conditions (days of differentiation and WT/RepB cells), we generated Venn Diagrams as now shown in Figure S4E.

The impact of Xist on autosomes is important for consideration of impact of changes in Xist expression with disease (notably cancers). Knowing the targets (if consistent) would enable assessment of such impact.

We thank Rev2 for the very helpful review and for the forward-looking experiments. Indeed, the physiological changes brought on by autosomal targeting will be of future interest.

Reviewer #3 (Public review):

Summary:

Yao et al use CHART to identify chromatin associated with Xist in female mouse ESCs, and, as control, male ESCs at various timepoints of differentiation. Besides binding of Xist to X chromosome regions they found significant binding to autosomes, concentrating mostly on promoter regions of around 100 autosomal genes, as elucidated by MACS. The authors went on to show that the RepB repeat is mostly responsible for these autosomal interactions using a female ESC line in which RepB is deleted. Evidence is provided that Xist interacts with active autosomal genes containing lower coverage of repressive marks H3K27me3 and H2AK119ub and that RepB dependent Xist binding leads to dampening of expression, but not silencing of autosomal genes. These results were confirmed by overexpression studies using transgenic ESCs with doxycycline-inducible Xist as well as via a small molecule inhibitor of Xist (X1), inducing/inhibiting the dampening of autosomal genes, respectively. Finally, using MEFs and Xist mutants RepB or RepE the authors provide evidence that Xist is bound to autosomal genes in cells after the XCI process but appears not to affect gene expression. The data presented appear generally clear and consistent and indicate some differences between human and mouse autosomal regulation by Xist. Thus, these results are timely and should be published.

We thank Rev3 for the positive remarks and great suggestions.  We have amended the manuscript per below. 

Strengths:

Regulation of autosomal gene expression by Xist is a "big deal" as misregulation of this lncRNA causes developmental defects and human disease. Moreover, this finding may explain sexspecific developmental differences between the sexes. The results in this manuscript identify specific mouse autosomal genes bound by Xist and decipher critical Xist regions that mediate this binding and gene dampening. The methods used in this study are appropriate, and the overall data presented appear convincing and are consistent, indicating some differences between human and mouse autosomal regulation by Xist.

Weaknesses:

(1) The figure legends and/or descriptions of data are often very short lacking detail, and this unnecessarily impedes the reading of the manuscript, in particular the figures would benefit not only from more detailed descriptions/explanations of what has been done but also what is shown. 

We have included more detailed descriptions in the figure legends and throughout the manuscript.

This will facilitate the reading and overall comprehension by the reader. One out of many examples: In Fig S1B in the CHART data at d4 and d7 there is not only signal in female WT Xist antisense but also in female sense control. For a reader that is not an expert in XCI it would be helpful to point out in the legend that this signal corresponds to the lncRNA Tsix (I suppose), that is transcribed on the other strand.

We thank the reviewer for this excellent point.  We have amended the Results section accordingly.

(2) Different scales are used in the lower panels of Figures 1A and 2A, which makes it difficult to directly compare signals between the different differentiation stages.

We have included a figure combining all timepoints — d0, d4, d7, and d14 WT female Xist CHART signals  — on the X chromosome and autosomes to support our thesis. Please see new Figure 1B.

(3) In this study some of the findings on mouse cells contrast previously published results in human ESCs: 1) Xist binding occurs preferentially to promoters in mice, not in human. 2) Binding of Xist is mostly detected in polycomb-depleted regions in mice but there is a positive correlation between Xist RNA and PRC2 marks in human ESCs. These differences are surprising but may be very interesting and relevant. While I am aware that this might be a difficult task, it would be helpful to experimentally address this issue in order to distinguish whether species specific and/or methodological differences between the studies are responsible for these differences.

Indeed, our findings in mouse cells contrast with those observed in humans. As discussed in the manuscript, this discrepancy may be attributed to factors such as cell type, differentiation methods, and the Xist pull-down technique employed (our CHART method utilizes a 20 nt oligo library, whereas RAP uses long oligos). We agree that future work should investigate the underlying causes of these differences between mouse and human systems.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

For Figure 2: labelling ∆B on the panel A timeline (e.g. d0-∆B) would make the results clearer for the audience. Panel B makes most sense beside panel E of Figure 1, so combine here and skip in Figure 1?

We have modified Figure 2A and thank Rev2 for this suggestion. As for the embedded tables: since we performed peak calling for WT and ∆B separately, we believe that showing both the peak numbers and their corresponding peak patterns provides a clearer representation of the data.

I agree that at day 7 there appears to be a difference in X; but by day 14 this looks much more minimal - is it just time-shifted rather than altered? Perhaps this could be discussed. Autosomal binding sites show no change in number.

Day 7 exhibits the strongest Xist binding on the X chromosome, consistent with the de novo establishment phase of XCI when Xist is expressed at the highest levels (300 copies/cell during de novo XCI versus ~100 copies/cell during maintenance [Sunwoo et al., 2015 as cited]. Per our RNA-seq analysis here, we also observed highest Xist expression on day 7 and reduced levels on day 14 (Fig. S5A). This expression difference explains the reduced Xist CHART levels on day 14 compared to day 7. 

While the X has previously been examined, it would seem beneficial to conduct the same expression analyses (Figure 3) for the X (perhaps supplemental), as the authors have the data 'in hand'. I feel comparison to X in the main figure for panels A and B would fit, while a similar analysis for the X for panel C could be supplemental, presumably supporting the published data to which this data is currently compared. 

This is a good suggestion. Please find the new data in Figures 2E-F and 3D, which demonstrate that the RepB deletion inhibits Xist binding on the X chromosome, resulting in increased X-linked gene expression, as previously mentioned. Since Xist binds across the X chromosome, we did not perform peak calling as we did for the autosomes. Therefore, applying a similar analysis as in Figures 3A-B may not be appropriate in this case.

Such a direct comparison to X-data from the same study would be important. For panel H: How many replicates (2)? This should be in the legend. What is the change in median expression? Again, a supplemental figure showing impact on X-linked targets would be useful. Do male and female ESCs show an expression difference prior to differentiation (ie d0)? The data underlying this Figure should be in one of the supplementary tables, showing the full statistical tests and average change. The supplementary tables 8-12 list the WT target genes, not expression differences with the deletion. Again, given that the difference appears transient, might the ∆B cells be altered in rate of differentiation?

Panel H (revised Figure 3G) includes two replicates, and this has been added to the legends. We have provided a supplementary figure demonstrating that RepB increases the expression levels of X-linked genes on days 4, 7, and 14 (revised Figure 3D). Male and female ESCs show differences in the expression of X-linked genes, as both X chromosomes are active in females at this stage prior to differentiation (revised Figure S5C). 

A supplementary table with statistical tests and average change information has been included in our revised version (Table S11).

On the other hand, these Xist-autosomal target genes displayed no significant differences between WT male, female, or ∆B female cells on day 0 — prior to onset of XCI and Xist expression. Please see new Figure 3H. 

As for whether ∆B cells are altered in their rate of differentiation, the analysis by Colognori et al. 2019 indicates that ∆B cells differentiate similarly to WT cells. (In Figure 6 of Colognori et al. 2019, autosomal genes expressed similarly in WT and ∆B cells, whereas XCI is affected only in ∆B cells)

We have also modified the legends for our supplementary tables.

Why were the transgene lines examined upon neuronal differentiation rather than the same approach as in Figures 1-3? I would have thought neuronal differentiation might be more similar to d14, where limited changes remain? Could the authors clarify and discuss?

We apologize for the confusion. The Tg lines in Figure 4 came from a previously published study. We performed reanalysis of published datasets because we wanted to test whether — in the hands of other investigators — cell lines expressing Xist also supported autosomal targeting. Here we examined Tg1 and Tg2, which respond to doxycycline to overexpress Xist from an ectopic site. Transcriptomic analysis showed significant downregulation of autosomal Xist targets, as exemplified by Bcl7b and Rbm14 (Figure 4C and S9B). In contrast, non-targets of Xist such as Stau1 did not demonstrate significant changes in gene expression (Figure 4E and 4F). Looking across all autosomal target genes, we observed a significant decrease in mean expression in the Xist overexpressing cell lines (Figure 4D). The fact that the autosomal changes were also observed in datasets generated by other investigators greatly strengthen our conclusions. We have clarified this in the Results section.

Figure 5 - the legend should specify the number of replicates and clarify the blue/green (intuitive, but not specified). Are the 'target' / 'non-target' genes from d4 Chart (but the RNA from d5)? How are 'non-targets' defined - do they match the 'targets' in certain criteria (expression level, chromatin features, GC content)? Do they change per differentiation protocol?

We have modified the legends to clarify that the 'target' and 'non-target' genes are derived from the day 4 CHART-seq data, while the RNA data is from day 5, as that study sequenced day 5 and not day 4. Non-targets were randomly chosen based on (i) the absence of Xist binding and (ii) similar expression levels. Please see revised Figure S8.

It would be helpful to compare Xist expression levels across the various models, and the MEF model could be better described - are they polyploid as often happens?

We have included the Xist expression levels of ES cells and MEF cells in the revised version (revised Figure S5A, 6D). The transformed MEFs are indeed tetraploid, as is typical.

For 6A to be informative, one needs to know % mapping to X in ES timeline, which is in supplemental, so perhaps 6A should also be supplemental?

We have moved 6A to the supplemental figure.

It is odd that ∆B seems to have had more impact in MEFs, and I would like more discussion - but I also think I am missing something: "We observed that Xist signals were more substantially reduced on both the Xi and autosomal regions in ΔRepE MEFs compared to ΔRepB cells", yet in lower panel 6 G it looks like ∆B is LOWER than ∆E? Am I misinterpreting?

We apologize for the confusing writing.  The revised text now reads:  “To investigate, we utilized a deletion of Xist’s Repeat E (∆RepE), which was previously demonstrated to severely abrogate localization of Xist to the Xi 41,42. We reasoned that the severe loss of Xist binding might unmask a transcriptomic difference. As expected, we observed that Xist signals were somewhat more reduced on the Xi in ΔRepE MEFs compared to ΔRepB cells (Figure 6E-6F). Despite this reduction, peak coverages in autosomal target genes did not increase in ΔRepE MEFs (Figure 6E-6F). However, there was an overall decrease in the number of significant autosomal peaks in ∆RepE MEFs relative to WT cells (Figure 6A). Regardless, we observed no significant transcriptomic differences in ∆RepE MEFs relative to WT MEFs (Figure 7A-7E). Additionally, further examination of RNA sequencing data from male and female MEF cells in two published studies 43,44 corroborated that the expression levels of these autosomal Xist targets did not exhibit significant changes (Figure 7F and 7G). Altogether, the analysis in MEFs demonstrates that Xist continues to bind autosomal genes in post-XCI somatic cells. However, autosomal binding of Xist in post-XCI cells does not overtly impact expression of the associated autosomal genes. Nonetheless, we cannot exclude more subtle changes that do not meet the significance cut-off.”

Overall, I would like to see how consistent these autosomal peaks are - I shudder to suggest Venn diagrams, but something to show whether there are day/lineage specific peaks and/or ∆repeat B/E resistant peaks. 

We now present Venn diagrams comparing MEF, ES_d4, and ES_d7, showing approximately 50% overlap between MEF and ES cells (revised Figure S10B). This may be expected, as each timepoint is a different developmental stage of XCI, with expected gene expression differences.

Very minor comments:

It would be easier if the supplemental tables were tabs in 1 file!

We will defer to the editor on how best to format the supplemental tables.

Similar to the text, could gene names be included in the supplemental?

We have provided gene names in the supplemental files.

Figure 3 legend: should 'representing' be representative?

We have modified it.

"Xist patterns identified in human cells" p 5; it is challenging to follow human versus mouse, so specify or ensure correct use of XIST/Xist Indeed, we edited the manuscript accordingly.

Gene names should be italicized.

We have italicized gene names in our manuscript.

Ref. 38 lacks details (...).

We have updated the reference.

Peak-like characters - perhaps characteristics? P8

We have modified this.

Reviewer #3 (Recommendations for the authors):

On page 6, the 6th sentence in the first paragraph needs correction. "Consistent with Xist's behavior on the X chromosome."

We have modified the sentence. Thank you.

Author response:

We are submitting a revised manuscript with major additions that address the main concerns in the initial reviews. At the highest level, this revision provides i) orthogonal biochemical measurements that yield concrete evidence of lysosomal protein aggregates, and ii) a plausible mechanism linking lysosomal lipid handling and protein aggregation through disruption of ESCRT function. We believe these additions significantly improve the completeness of this study and the conclusions that can be drawn from the data.

Below are more specific highlights on the addition in this revision:

-       We included orthogonal techniques (thioflavin-T staining and Lyso-IP followed by differential extraction) and confirmed the accumulation of RIPA-insoluble protein aggregates at the lysosomes in cells under lipid perturbation (Figure 3).

-       We performed TMT-Proteomics and identified accumulation of insoluble ESCRT components at the lysosomes under lipid perturbation (Figure 4). Two new authors involved in this effort are added onto the manuscript.

-       The ESCRT result prompted us to revisit lysosomal membrane integrity. With improved imaging conditions and analysis we were able to see increased membrane permeabilization under lipid perturbation. VPS4A overexpression partially rescued this phenotype, suggesting that lipid accumulation impairs ESCRT disassembly (Figure 5).

-       Together, the results suggest that lipid perturbation impairs ESCRT function, compromising both lysosomal membrane repair and microautophagy, resulting in the accumulation of endogenous protein aggregates at the lysosomes (Graphical Abstract).

Reviewer #1 (Recommendations For The Authors):

(1) Perhaps the most prominent limitation of this work is the unilateral focus on native cells (i.e. cells under no endogenous or exogenous stress) as the model for protein aggregate formation. Furthermore, although the ProteoStat stain has been utilized by many investigators before, the sole reliance on this stain as the read-out for their assays is concerning. To compound the concern, the ProteoStat-positive puncta co-localize with lysosmal markers which was surprising even to the authors. All in all, it behooves the authors to test proteostasis in multiple parallel ways to actually define what they are studying. How is it possible that protein aggregates under native conditions are only co-localized with lysosomes? Are we really studying protein aggregates which should predominantly be cytoplasmic insoluble aggregates?

(a) They need to get away from a simple stain like ProteoStat and conduct co-stainings with other markers such as poly-ubiquitin antibodies and other chaperones to define what and where else exactly are these aggregates.

Co-staining with poly-ubiquitin was included in the original manuscript. We added orthogonal staining with another widely used amyloid dye, Thioflavin-T, and provided fine-grained quantification of lysosomal vs cytosolic localization of various signals (Figures S4A-C & 3A-B).

(b) They need to do Immunoblots with and without triton insolubility to see if these aggregates are insoluble as most would predict. They can do lysosomal isolation vs cytoplasmic to see if the insoluble aggregates are really lysosomal.

We performed Lyso-IP followed by differential detergent extraction to confirm the accumulation of insoluble proteins at the lysosomes (Figure 3C). Proteomic analysis identified some of these insoluble proteins as ESCRT subunits (Figure 4).

(c) They should compare aggregate formation in the native state versus cells with lysosomal inhibition via Bafilomycin or chloroquine versus cells with proteosomal inhibition. The lysosomal inhibition experiments are particularly informative given the lysosomal relevance they have uncovered.

We included other small molecule inhibitors and at different time points to compare the effect of different modes of proteostasis challenge (Figure S4A-D). Together with the ESCRT finding, our results suggest the role of microautophagy in our system, and provide a model of how ProteoStat- and/or ubiquitin- positive substrates become partitioned between the cytoplasm and lysosomes under different perturbations.

(d) Many protein aggregates which are too bulky for proteosome degradation will traditionally be dealt with by aggrephagy. Why is this not observed?

Knockdown of core macroautophagy components did not impact Proteostat intensity in our CRISPRi screen, suggesting that basal macroautophagy plays a negligible role in clearing endogenous amyloid-like structures in our experimental system. We provide an alternative model that these aggregates instead arrive at the lysosomes via microautophagy.

(2) After addressing #1, they can validate if the genes they identified by CRISPR screens are also important in modulation of protein aggregate burden in other systems. For example, if they inhibit lysosomes by Bafilo or Chloroquine to obtain protein aggregates and then Knockdown the identified genes in the CRISPR screens, will they get the same results?

We addressed the effect of different modes of proteostasis challenge as recommended above. Deacidifying the lysosomes alone causes intense protein aggregation (Figure S4A-D) and eventually cell death, and was thus not combined with other perturbations.

(3) They identify lysosomal lipid metabolism genes/pathways as the culprit for inducing proteostasis. In particular sphingolipid and cholesteryl ester species appear to be operational here. However, there are no specific lipids species or specific lipid metabolism gene that is causative. Rather, you have to knockdown entire processes to have an effect. This suggests that the focus on lysosome health (i.e. permeability, proteolysis, etc) is rudimentary. When you have to knockdown entire classes of lipids, this would indicate more broad effects on cellular lipids (including membrane lipids beyond the lysosome) and related cellular health?

We included data on the effect of knocking down MYLIP, PSAP, and as a comparison PSMD2 on the growth rate of K562 cells (Figure S5A). MYLIP and PSAP KDs, which cause predominantly an accumulation of lipids, do not impede cell growth. Increasing lipid uptake by MYLIP KD increases cell proliferation under our culture conditions, suggesting a general negative impact on cell health was not required for the association between lipid levels and protein aggregates.

(a) They conduct a superficial methyl-beta-cyclodextrin experiment with equivocal results. The use of MBCD for different time-courses to deplete various membrane cholesterol pools including the plasma membrane pool is important to ascertain what aspect of the cellular cholesterol is affecting proteostasis. MBCD +/- cholesterol reintroduction time-courses for rescue will also be key to determine the culprit cellular cholesterol pool.

The MBCD / Filipin experiment helped us determine that ProteoStat doesn’t directly stain cholesterol, nor any major plasma membrane components. Free cholesterol was implicated in neither the screen nor the lipidomics and was not the subject of targeted experiments.

(b) The same concept can be applied to sphingolipids. There are sphingolipids in abundance in multiple membrane compartments. Which ones are causal here? More nuanced evaluation of this with sphingolipid staining/tracking can be conducted.

We attempted experiments where sphingolipids were added back to cells grown in FBS-depleted media. Nevertheless, we were not able to consistently deliver these lipid species and doing so while ensuring the correct subcellular localization at physiologically relevant level would require substantial methods development.

(c) As part of this, are lipid rafts and/or caveolae being affected by the perturbations in cholesterol and sphingolipids? Lipid rafts are highly enriched in these 2 lipids which could link to their preteostasis observation.

Indeed, ceramides released from SM hydrolysis are proposed to self-assembled into microdomains with negative curvature that can promote the formation of intralumenal vesicles (Alonso and Goni, 2018; Niekamp et al 2022). We propose that SM accumulation may hinder this process by counteracting the negative membrane curvature and impede microautophagy.

(d) How about ER membrane lipids? The UPR and subsequent effects on proteostasis are intricately involved with ER lipid bilayer composition.

We did not perform lipidomics on ER membranes in this study, though we note that at steady state, sphingolipids and cholesterol esters are not expected to be enriched at the ER (Ikonen and Zhou, 2021). We checked whether lipid-related genetic perturbations induced the UPR in published perturb-seq data in K562 cells. Neither MYLIP nor PSAP knockdown induced a UPR.

In conclusion, the manuscript is interesting but the excitement over a link between lysosome-related lipid metabolism and proteostasis needs to be tamped until a more robust experimental approach is employed to generate supportive and corroborating results.

Reviewer #2 (Recommendations For The Authors):

- The paper has a number of grammatically awkward sentences. Editing these would enhance clarity.

- It is important to show the co-localization of aggregates with the lysosome. This is shown in supplements but should be in a main figure. Here the authors cite previous work indicating that ProteoStat puncta co-localize with ubiquitinated proteins and state that they do not see this, then essentially just move on. Is there an explanation for this discrepancy and can it be resolved? What do they think is really going on? What happens to levels of ubiquitinated proteins when lipid metabolism is perturbed as in these experiments?

We have included the lipid-induced lysosomal protein aggregation data in the main text (Figure 3A-B), and provided fine-grained quantification of the cytosolic-vs-lysosomal ProteoStat / Ub / ThT signals under different aggregate-inducing conditions (Figure S4A-D). We discuss these results in the main text and propose a model involving ESCRT-mediated microautophagy in the main text. This is supported further by the LysoIP-proteomics and LMP analysis.

- Please add an indicator of amino acid numbers to Fig. 3C.

These annotations are now included (now Figure S3C).

- The legend for 3D is mislabelled.

We have corrected the legend (now Figure S3D).

Reviewer #3 (Recommendations For The Authors):

Protein homeostasis and lipid homeostasis are both are important for maintaining cellular functions. However, the crosstalk remains largely unknown. The manuscript entitled as "Impairment of lipid homoeostasis causes accumulation of protein aggregates in the lysosome" deals with this interesting topic. An important link between lysosomal protein aggregation and sphingolipids/cholesterol esters metabolism were discovered. The topic belonging to the Cell Biology domain also falls into the aims and scope of eLife. Here are the revisions I recommend:

(1) From lipidomics analysis, a remarkable correlation between levels of sphingomyelin and cholesterol ester and ProteoStat staining was found. Could the authors explain how sphingomyelin and cholesterol ester are quantified? The two lipids are not included as internal standards from the lipidomics experiment.

Sphingomyelin and cholesterol ester internal standards are included in the Avanti 330707 SPLASH® LIPIDOMIX® Mass Spec Standard, which was supplied at 3% v/v to the MeOH/H2O cell lysis buffer. We have amended the Methods section to clarify this.

(2) Could the authors perhaps delete Figure 1B and show it on Figure 2A only? There is no need to show the same figure two times. The threshold of both False Discovery Rate and Median Enrichment needs to be added. From Figure 2A, the Lysosomal hydrolases (GBA, LIPA, GALC) seems located in statistically insignificant region. Based on previous studies, the GBA could have an effect on sphingolipid levels, then how to explain that sphingomyelin was highly correlated with ProteoSate staining?

We have combined the two volcano plots into a single figure (now Figure 1D), and added a line to help visualize the gene effects while considering the combined contribution of FDR and enrichment. Individual lysosomal hydrolases indeed have insignificant effects on ProteoStat and this is discussed in the main text as having relatively constrained impacts on the general lipidome. For example, while GBA and GALC KDs can lead to accumulation of their immediate substrates (glucosylceramide and galactosylceramide, respectively), they do not directly impinge on sphingomyelin.

(3) The authors show the corelation between ProteoState staining and different lipids/lipid classes in Figure 3B and Figure S3A. It is not necessary to show the corelation with individual lipids (such as sphingomyelin(d18:1/24:0) and cholesterol ester(18:2). The corelation with full collection of lipid classes would be more representative, which is only list in Figure 3B and Figure S3A. It is suggested to add the information of how many individual lipids in each chass are used for the correlation analysis. Replace Figure 3A to Figure S3A, and put Figure 3A as supplementary figure are suggested.

We decided to retain the correlation of two individual lipids (a sphingomyelin and a cholesterol ester species) with ProteoStat as examples to illustrate with clarity how we obtained the class-wide comparison. The number of individual lipids included in each class for correlation analysis is now included in Figures 2F and S3A.

(4) The authors state that lipid uptake and metabolism modulate proteostasis. However, only cholesterol and LDL were tested. It would be more precise to state as cholesterol uptake and metabolism modulate proteostasis. In addition, sphingolipids and cholesterol esters accumulate with increased lysosomal protein aggregation. It would be interesting to see the effects of sphingolipids uptake, since sphingolipids are correlated with proteostasis better than cholesterol.

We attempted to add back specific sphingolipids to assess sufficiency. However, we found it challenging to ensure that these lipids were distributed to the correct subcellular locations at physiologically relevant levels. Without this crucial information, it was difficult to draw any conclusions about the sufficiency of the sphingolipids we tested to impair proteostasis.

Alonso A, Goñi FM. 2018. The Physical Properties of Ceramides in Membranes. Annu Rev Biophys 47:633–654. doi:10.1146/annurev-biophys-070317-033309

Ikonen E, Zhou X. 2021. Cholesterol transport between cellular membranes: A balancing act between interconnected lipid fluxes. Dev Cell 56:1430–1436. doi:10.1016/j.devcel.2021.04.025

Niekamp P, Scharte F, Sokoya T, Vittadello L, Kim Y, Deng Y, Südhoff E, Hilderink A, Imlau M, Clarke CJ, Hensel M, Burd CG, Holthuis JCM. 2022. Ca2+-activated sphingomyelin scrambling and turnover mediate ESCRT-independent lysosomal repair. Nat Commun 13:1875. doi:10.1038/s41467-022-29481-4

DOI: 10.1371/journal.pntd.0008059

Resource: University of Minnesota Genomics Center Core Facility (RRID:SCR_012413)

Curator: @evieth

SciCrunch record: RRID:SCR_012413

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Reply to the reviewers

1. Point-by-point description of the revisions

• *Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The authors present the use of previously identified biosensors in a single-molecule concentration regime to address lipid effector recruitment. Using controlled and careful single-cell based analysis, the study investigates how expression of the commonly used PIP3 sensor based on Akt-PH domain interferes with the native detection of PIP3. Predominantly live-cell fluorescence microscopy coupled to image analysis drives their studies.

Conceptually, this manuscript carefully and quantitatively describes the influence of lipid biosensor overexpression and presents a means to overcome the inherent and long-recognized problems therein. This solution, namely employing low expression of the lipid biosensor, should be generally applicable. The work is of general interest to cell biologists focused on answering questions at membranes and organelles, including especially those interested in lipid-mediated signaling transductions.

Reviewer 1 Major:

#1.1 The terminology "single molecule biosensor" is not really appropriate. A protein is not "single-molecule". An enzyme does not "single molecule". Better is biosensors at single-molecule expression levels. In most cases, this should be changed. Single-molecule vs single-cell vs. bulk measurements are often poorly defined in quantifications and conflating these does not help the case, which is already supported by generally clear data.

We appreciate the reviewer’s thoughtful critique of our grammatically incorrect use of jargon; we saw this as soon as they mentioned it! We have amended the manuscript where appropriate as detailed:

• Title is now changed to “Lipid Biosensors Expressed at Single Molecule Levels Mitigates Inhibition of Endogenous Effector Proteins”
• Last paragraph of the introduction on __ 2__ now reads “As well as alleviating inhibition of PI3K signaling, biosensors expressed at these low levels show improved dynamic range and report more accurate kinetics than their over-expressed counterparts."
• The title of the results section on __ 6__ is now: Mitigating PIP3 competition using biosensors expressed at single molecule levels
• Last paragraph of the results section on 6 now reads: “this showed that when expressed at single molecule levels, the biosensor has substantially better dynamic range”. #1.2 Figure 1D-F, images not as clearly describing quantitation as one would hope. Untransfected cells in 1E should demonstrate more translocated Akt-pS473 than transfected, but it is difficult for this reviewer to find. Consider inset images in addition to the wider field. Consider also moving the "negative" data of Fig 1B-C to Supplement.

We regret not making this figure easier to interpret; we have substantially updated the figure, as comprehensively detailed in our point-by-point response to reviewer 2’s point 2.3. To specifically address this reviewer’s concerns:

The older figure used non-confocal, low-resolution images that were used for quantification. Such an approach was employed to enable fluorescence from the entire cellular volume to be captured, which produces more robust quantification. However, to the reviewer’s point, it is not possible to see the translocation of PH-AKT1 nor translocated AKT-pS473 in these images. Fortunately, we had in parallel captured high resolution confocal images for some experiments. These are now shown in Fig 1D-E, which clearly shows translocated AKT-pS473 and PH-AKT-EGFP

#1.3 The cell line being used is not clearly specified after the initial development of the NG1 followed by CRISPRed NG2 onto Akt. For example, for the Figure 3C experiments, the text states "complete ablation of endogenous AKT1-NG2" but this information is not apparent from the figure legend or figure. Throughout the cell line used and the aspects transfected need to be made explicitly clear.

We are grateful to the reviewer for highlighting this ambiguity. We have now defined the gene-edited cells used throughout as “AKT1-NG2 cells” and expressly used this term when referring to experiments in figures 2-5.

#1.4 Fig. 5 shows single cells. It is therefore unclear if broken promoters have resulted in decreased expression. This point is important because the expression plasmids should be made publicly available, and for their use to be understood properly, this must be clarified. The details of the plasmids are unclear. Perhaps listed in the table? - unclear. This aspect would be important for the field to effectively use the reagents.

Thank you for drawing our attention to the lack of adequate detail here. We have now updated the results text to expressly reference Morita et al., 2022 where the origins of the truncated CMV promoters are detailed. We have also updated the plasmids table 1 to add pertinent details for these constructs: *pCMVd3 plasmids are based on the pEGFP-C1 backbone, with the CMV promoter truncated to remove 18 of the 26 putative transcription factor binding sites in the human Cytomegalovirus Major Intermediate Enhancer/Promoter (pCMV∆3 as described in Morita et al., 2012). The full sequences will be deposited with the plasmids on Addgene.

We did not perform a formal comparison of full vs truncated promoters. Our only observation is that the truncated promoters greatly help in increasing the number of expressing cells presenting single-molecule resolvable expression levels (though the approach can still work with full promoters).

#1.5 This manuscript speculates several times that with more abundant PIs like PI45P2, the observed saturation effect is probably not happening. This should be removed. While the back of envelope calculations may reflect an ideal scenario, the heterogeneity of distribution and multiple key cellular structures involved would seem to corral increased PI45P2 levels in certain regions. These factors amid multivalency and electrostatic mechanisms of lipid effector recruitment (e.g. MARCKS) suggest that speculation may be too strong. Moreover, Maib et al JCB 2024 demonstrated PI4P probe overexpression could directly mask the ability to detect PI4P post-fixation - not fully, but partially. Repeating the titration experiments of this manuscript for multiple PIs is entirely beyond the scope of reasonable, and hence, such experiments are not requested, in favor of adopting more conscientious speculation.

The reviewer’s point is well taken. Whilst we still believe the overall argument for lipids is sounds (for example, PS or cholesterol are far too abundant for any expressed, stoichiometric binding protein to bind the majority of the population) even abundant phosphoinositides like PI4P and PI(4,5)P2 are an edge case. We have therefore undated the first paragraph of the introduction on __p. 1 __to be less explicit: One of the most prominent is the fact that lipid engagement by a biosensor occludes the lipid’s headgroup, blocking its interaction with proteins that mediate biological function. It follows that large fractions of lipid may be effectively outcompeted by the biosensor, inhibiting the associated physiology. We have argued that, in most cases, this is unlikely because the total number of lipid molecules outnumbers expressed biosensors by one to two orders of magnitude (Wills et al., 2018). However, for less abundant lipids, total molecule copy numbers may be in the order of tens to hundreds of thousands, making competition by biosensors a real possibility.

We also removed the explicit discussion of PI(4,5)P2 from the introduction, and focus now solely on the PI3K lipids.

Reviewer 1 Minor:

1.6 Schematics throughout need simplification, enabling their enlargement.

We have now enlarged the size of all schematics

#1.7 Numerous spelling (Fig. 4 schemas) and capitalizations need fixing.

Thank you for drawing our attention to these. We have thoroughly proof-read the figure panels and corrected errors.

#1.8 Pg 1 Famous is not appropriate wording

We respectfully beg to differ with the reviewer here. We believe it is perfectly accurate to state that PIP3 is a second messenger molecule that is known about by many people; we see this as the dictionary definition of the word “famous”.

#1.9 Fig. 1A statistical testing of microscopy quantifications absent (generally, throughout) and should be included.

This was indeed an oversight on our part. We have now added appropriate multiple comparisons tests to the data presented in figures 1F, 3F, 4C, 4F and 5C.

#1.10 Fig.1. In a transient transfection, the protein expression is not uniform. Please explain how you normalized the quantification.

We hope this is now clarified by the expanded “Image Analysis” part of the methods section on pp. 10-11 (relevant sentence is underlined): For immunofluorescence, we identified individual cells by auto thresholding the DAPI channel using the “Huang” method, followed by the Watershed function to segment bunched cells that appeared to touch. We then used the Voronoi function to generate boundary lines for the segmentation of the cells. To identify cytoplasm, auto thresholding of the CellMask channel using the “Huang” function was employed, with the cells segmented by adding the nuclear Voronoi boundaries. The “analyze particles” function was then used to identify individual cellular ROIs that were greater than 10 µm2 and were not touching the image periphery. These ROIs were used to measure the raw 12-bit intensity of the EGFP and AKT-pS473 channels. A cutoff of EGFP > 100 was used to define EGFP-positive cells, since this value was greater than the mean ± 3 standard deviations of the non-transfected cells’ EGFP intensity. Background intensity of AKT-pS473 was estimated from control cells subject to immunofluorescence in the absence of AKT-pS473 antibody; this value was subtracted from the measured values of all other conditions.

#1.11 Fig. 1D. EGFP expression levels increased with EGF stimulation. How is this possible?

There appeared to be a difference due to the presence of 5 strongly expressing cells in the chosen field in the original field for the EGF stimulated, EGFP cells. However, this arose just by chance. The new set of high-resolution images in the new figure 1 were selected to be more representative.

#1.12 Fig. 1D. The images have pS473 whereas the y-axis label on box plots has p473. Can these box plots be labelled separately for consistency?

Thank you. This has now been corrected in the revised Figure 1.

#1.13 Fig.1. T308 phosphorylation is mentioned in Figure 1, but only pS473 data is shown.

Both T308 and S473 phosphorylation are indicative of AKT activation. However, antibodies suitable for immunofluorescence are only available for pS473, hence why our experiments are restricted to this moiety.

#1.14 Fig.1 legend. 'Over-expression of PH-AKT is hypothesised to outcompete the endogenous AKT's PH domain'. Why do you need to state a hypothesis in the legend?

We included this statement for the benefit of the casual reader – i.e. one who looks at the pictures, but doesn’t read the main text!

#1.15 Fig.1E You stated that the PH-AKT R25C-EGFP is stimulated by EGF addition. However, the GFP signal looks the same in both unstimulated and stimulated. Could you please clarify? Are you sure that the stimulation worked?

We have clarified the second paragraph of the results section “Inhibition of AKT activation by PIP3 biosensor”__on __p. 4 as follows: In the non PIP3 binding PH-AKT1R25C-EGFP positive cells, we still observed an increase in pS473 intensity.

The revised figure 1 images also show that PH-AKT1R25C does not translocate to the membrane with EGF stimulation.

#1.16 You mention...that the AKT enzyme is activated by PDK1 and TORC2, which phosphorylate at residues T308 and S473, respectively. Phosphorylation is also known to occur on T450 at c-tail. Does this phosphorylation also contribute to its activation?

Yes and no. Threonine 450 phosphorylation is thought to occur co-translationally and is important for AKT stability (see Truebestein et al as cited in the manuscript). It is not really relevant in the context for T308 and S473, which are phosphorylated acutely to activate the protein.

#1.17 Fig. 1 scale bar in all images equivalent?

We have now added scale bars to panels in both figure 1D and E to clarify.

__#1.18 __Pg. 1 paragraph 1 "we have argued..." vs. paragraph 3"...consider that an..." feels like arguing with themselves.

We believe the re-write we have done in response to major point #1.5 clarifies this point also.

#1.19 Pg. 1 para 3 what is RFC score - must explain

We have now defined this more clearly in third __paragraph of the __introduction on p. 1: PH domain containing PIP3effector proteins can be predicted based on sequence comparison to known PIP3 effectors vs non effectors using a recursive functional classification matrix for each amino acid (Park et al., 2008).

#1.20 Discussion of numbers of PIP3 vs. effectors etc may not be appropriate for the introduction, as the points made by these calculations are already made in the previous paragraphs. May fit better in pg 6 Mitigating PIP3 titration... with an accompanying schematic.

Respectfully, we prefer to keep this discussion of molecular concentrations, as this adds details and specifics to the pathway that is core to the paper.

#1.21 Pg 2 "a neonGreen" not well defined, needs accurate description.

We have clarified this in the sentence in the first paragraph of the results section “Genomic tagging of AKT1…” __on __p. 4, which includes the citation to the full description of the tag: To that end, we used gene editing to incorporate a bright, photostable neonGreen fluorescent protein to the C-terminus of AKT1 via gene editing using a split fluorescent protein approach (Kamiyama et al., 2016).

#1.22 Fig 2C should give a unstimulated trajectory of puncta/100 um2 to compare with the stimulated

Unfortunately, we did not record a full 5.5-minute video-rate time-lapse with unstimulated cells. However, we do not believe this control is essential for this experiment, since this example data is included to illustrate (1) the problem of photobleaching, which is clear in the 30-s pre-stimulus and (2) the variability in the raw molecule counts.

#1.23 Fig 2C and F and G should be systematized for easier comparison. E.g. min vs seconds, 0 timepoint of EGF/rapa addition

We have made the adjustment to figure 2C to be consistent with 2F and G:

#1.24 Pg 5 "...and calibrated them..." unclear what is being calibrated, as the text later states that the histograms are fit to monomer/dimer/multimer model resulting in 98.1% in monomer. Minor point.

We have clarified this point in the second paragraph of the results section “__Genomic tagging of AKT1…” __on __p. 4 __as follows: We analyzed the intensity of these spots and compared them to intensity distributions from a known monomeric protein localized to the plasma membrane (PM) and expressed at single molecule levels

#1.25 Explain why baselines in Fig2CFG are different

We did not comment on figure 2C; it is a single cell measurement, as opposed to the mean of 20 cells reported in F. However, we do now clarify the difference between figure 2F and G as the very end of the “Genomic tagging of AKT1…” results section on p 4: Notably, baseline AKT-NG2 localization increased from ~5 to ~15 per 100 µm2 in iSH2 cells, perhaps because the iSH2 construct does not contain the inhibitory SH2 domains of p85 regulatory subunits, producing higher basal PI3K activity.

#1.26 Fig. 2 has quantification with images; Fig. 3 has it separate. Make consistent.

We sometimes combine images with quantification, and other times separate the panel containing graphs. This is done deliberately, depending on whether the reader is directed to both together, or whether we consider the data separately in the results section.

#1.27 Fig. 3B comes before images? Where are the images? Also, y-axis = Intensity (a.u.). Is intensity just full image field? Or per cell? All very unclear.

We have modified both the graph y-axis label and the figure legend to clarify: (C) TIRF imaging of AKT1-NG2 cells from (B) stimulated with 10 ng/ml EGF

#1.28 Fig. 3C missing images

We believe the reviewer is referring to the mCherry channel for the “0 ng cDNA” condition. These images are missing because they do not exist. Since these cells were transfected with pUC19, there was no mCherry fluorescence to image.

#1.29 Fig 3 C needs brightness/contrast adjusted as images are nearly entirely black (zero values).

We believe the addition of insets addresses this concern. To the reviewer’s specific suggestion, we found that further increases in the brightness and contrast will bring up the camera noise, but this then occludes the signal from single molecules, such as those found after EGF stimulation of the 0 ng condition.

#1.30 Fig 3C needs scale bar systemization

We believe that the incorporation of scaled 6 µm insets addresses this point.

#1.31 Fig 4 needs 4 panels A-D

We have now added these individual panel labels to figure 4.

#1.32 Pg 6 5-OH phosphatases needs reference

We have added a citation to Trésaugues at the very end of the “Sequestration of PIP3 by lipid biosensors” results section on p. 6, which describes the activity of the whole 5-OH phosphatase activity against PIP3, not just the SHIP phosphatases.

#1.33 Fig 5B, make images bigger

Again, we trust that the addition of insets to all single molecule images has addressed this point.

Reviewer 1 Referees cross-commenting**

I have read the other reviews and find them entirely reasonable. My impression is we landed on similar general content that needs work, none of which is out of line. The importance and care taken in the author's work is uniformly lauded.

We agree. At the risk of restoring to alliteration, we have been delighted to receive a trio of clear, concise and consistent comments on the manuscript! We believe it is now much improved.

Reviewer #1 (Significance (Required)):

This manuscript clearly and reasonably demonstrates that the commonly used PIP3 sensor can be titrated to low concentrations, at which it does not interfere with Akt translocation and activation. This work is a good technical reference for the field. Signal transduction and membrane biologists should be especially interested in the data. The reviewer/s have core expertise in phosphoinositides, protein biochemistry, cell biology, and membrane biophysics.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The authors characterize the inhibition of lipid second messenger mediated cell signaling through lipid biosensors that outcompete endogenous effector proteins. This is a very important study that as it quantitatively assesses an issue that many people suspected to exit, yet never properly characterized. This paper is therefore as much a service to the community as a research study in its own right and should be published without undue delay. I am glad that the authors decided to carry out this study & really appreciate their work.

I do however, have a number of suggestions that I think will make the manuscript stronger and can be readily implemented, mostly by reformulating and/or re-analysis of exiting datasets. I've structured my comments by the datasets in the respective figures to follow the logic of the paper.

Reviewer 2 Major:

#2.1 Throughout the manuscript, statistical tests are missing, e.g. in figures 1C-F. This must be amended in the revised version. The authors are making a very quantitative point about buffering, data should be treated accordingly.

We have now added appropriate multiple comparisons tests to figures 1F, 3F, 4C, 4F and 5C.

#2.2 I do not think that "PIP3 titration" is the best term to describe the observed effect. "Titration" usually implies the controlled modulation of a concentration, e. g. in analytical chemistry. I think either "competitive binding of PIP3" or "buffering of free PIP3" are more adequate.

This point is well taken. We have now replaced the word “titration” throughout, replacing it with either “competitive binding” or “sequestration”.

#2.3 Specific comments: Figure 1

#2.3a Why are data in 1D-Ff shown as median, with interquartile ranges and 10-90 percentile distance when everything else in the paper is mean +/- se? There might be a good reason for it, but I did not find it mentioned everywhere

For consistency’s sake, we have changed figure 1F to show a bar graph, though as noted in the figure legend: Graphs show medians ± 95% confidence interval of the median from 82-160 cells pooled from three experiments (medians are reported since the data are not normally distributed).

#2.3b The authors should test, whether the difference between the +EGF conditions in 1D (EGFP) and 1F (PH-AktR25C-EGFP) is indeed statistically significant. If this observation holds up, what does it mean? Is the mutant still competing with endogenous Akt despite the much-reduced binding affinity? The authors should discuss.

We have re-analyzed the data in figure 1, with the quantitative data presented in figure 1F combined with statistical analysis. The new data shows no significant effect of the PH-AKT1R25C mutant in either resting or EGF stimulated condition

There results are also described in the__ second paragraph__ of the first results section on pp. 3-4: This analysis showed that the R25C mutant had no substantial effect on pS473 levels, whereas wild-type PH-AKT greatly inhibited pS473 staining in EGF-stimulated cells as well as reducing basal levels in serum starved cells (Fig. 1F).

#2.3c How were biosensor/GFP positive cells chosen? Did the authors choose a defined fluorescence intensity cut-off? I think that a pure manual selection is problematic from a methodological point of view as this may introduce biases. Since the authors use Fiji, they can also simply use the "Analyze particles" function, which allows to automatically segment cells from a thresholded image. By choosing the same threshold for all images, it would be ensured that all images are treated exactly the same way.

We had initially opted for manual outlining of cells since automatic segmentation of irregularly-shaped HEK293a cells is imperfect. However, we agree with André that this opens the possibility of bias. We have therefore re-run the analysis with an automated segmentation and thresholding approach, as suggested. This is detailed in the__ second paragraph__ of the first results section on pp. 3-4: In parallel, we imaged cells with a low resolution 0.75 NA air objective to capture fluorescence from the cells’ entire volume, then quantified these images using an automatically determined threshold for GFP-positive cells (see Materials and Methods). This analysis showed that the R25C mutant had no substantial effect on pS473 levels, whereas wild-type PH-AKT greatly inhibited pS473 staining in EGF-stimulated cells as well as reducing basal levels in serum starved cells (Fig. 1F).

Further detail is provided in the first paragraph of the “Image analysis” subsection of the methods on pp. 10-11: For immunofluorescence, we identified individual cells by auto thresholding the DAPI channel using the “Huang” method, followed by the Watershed function to segment bunched cells that appeared to touch. We then used the Voronoi function to generate boundary lines for the segmentation of the cells’ cytoplasm. To identify cytoplasm, auto thresholding of the CellMask channel using the “Huang” function was employed, with the images segmented by adding the nuclear Voronoi boundaries. The “analyze particles” function was then used to identify individual cellular ROIs that were greater than 10 µm2 and were not touching the image periphery. These ROIs were used to measure the raw 12-bit intensity of the EGFP and AKT-pS473 channels. A cutoff of EGFP > 100 was used to define EGFP-positive cells, since this value was greater than the mean ± 3 standard deviations of the untransfected cells’ EGFP intensity. Background intensity of AKT-pS473 was estimated from control cells subject to immunofluorescence with the AKT-pS473 antibody omitted; this value was subtracted from the measured values of all other conditions.

#2.3d I am missing a statement in the methods section that all images were acquired using the same settings.

This was indeed an important oversight on our part – thanks for spotting the omission of this crucial detail. This is now included at the end of the “Immunofluorescence” section of the Methods on pp. 9-10: Identical laser excitation power, scan speeds and photomultiplier gains were used across experiments to enable direct comparison.

#2.3e I recommend that the authors include a single cell correlation plot of EGFP fluorescence intensity vs AktpS473 intensity in Figure 1 D-F. This should be rather informative & make the concentration dependence clear.

We did not observe a strong correlation between PH-AKT1-EGFP intensity and pS473 staining, likely driven by both the imprecision of the cell segmentation and the fact that very low concentrations of PH domain effectively inhibit endogenous AKT1 (as we show in the later figures with the more precise, live cell AKT-NG2 recruitment experiments: see response to #2.5).

#2.3f I further recommend that the authors look at alterations of baseline Akt activity in the presence of the biosensor. In the images it looks like there might be an effect, but this is then lost in the analysis due to the normalization.

As covered in our response to #2.3b, there is indeed an inhibition of baseline pS473 in PH-AKT1-EGFP expressing cells, now explicitly quantified and documented in results.

#2.3g Please include zoomed image insets in Fig. 1D-F, in the current magnification one needs to zoom in quite a bit to see the effect in the raw data. It is a clear effect, but having a zoomed version would make for much easier reading.

We now include high-resolution confocal images instead of low power, low NA volumes as shown in the last version of the manuscript, which we believe addresses this point and also reviewer #1.2.

2.3h Up to the authors: I wonder whether it is possible to extract an IC50 value for the competitive inhibition of Akt by the respective biosensors. The transient expression gives the authors access to a wide range of expression levels at the single cell level, which could be quantified by counterstaining with a EGFP-nanobody at a different color (since the EGFP fluorophore went through the fixation process, it is likely unsuitable for quantification) and microscope calibration. Activity could be quantified as the ratio of observed and expected Akt-pS473 fluorescence (derived from the mean FI per cell from the EGFP control). This is not strictly necessary, but would be a beautiful quantitative experiment, give an easy-to-understand number & make the paper much stronger.

This is a great suggestion, but does not produce precise enough data to work out, as we detail in response to #2.3e. From our data in new figure 3F and figure 5, it seems we have not explored the appropriate expression range to see intermediate levels of inhibition necessary to estimate IC50. This would be a cool experiment though!

__#2.4 __Specific comments: Figure 2. Overall, compelling data. However, 25 molecules/100 um^2 at maximal recruitment feels low. Assuming a total cell surface area of appr. 2000 um^2 per cell and taking a baseline of 5 molecules/100 um^2 into account, this would mean that only about 400 copies of Akt are recruited in response to a pretty robust stimulus. Is it possible that the association reaction of the split GFP is not complete under these conditions? I think that a direct measurement of intracellular endogenous Akt concentration is required to put these numbers into context.

This is an excellent point that we had missed. We now specifically address this point in the third paragraph of the “Genomic tagging of AKT…” section on p. 4: __Accumulation of AKT-NG2 was ~25 molecules per 100 µm2, which assuming a surface area of ~1,500 µm2 per cell corresponds to ~375 molecules total. It should be noted that tagging likely only occurred at a single allele in each cell, and the population still exhibited expression of non-edited AKT1 (__Fig. 2B). Given that HEK293 are known to be pseudotriploid (Bylund et al., 2004), the true number of AKT1 molecules would be at least 1,125. However, given an estimated total copy number of 23,000 AKT1 in these cells (Cho et al., 2022), this is still only about 5%. However, we do not interpret these raw numbers due to uncertainties in the efficiency of NG2 complementation under these conditions, as well as potential for reduced expression from the edited allele.

We also removed the specific comment on molecule density from the abstract.

#2.5 Specific comments: Figure 3 I think that the classification by plasmid dose does not make a lot of sense, as the resulting expression levels are rather similar. I suggest to pool all traces and calculate mean curves by actual expression levels using a binning approach (e.g. 0-50 au, 50-100 au and so on in raw intensity from Figure 3b). If there is an effect in the realized concentration regime, this should pick it up.

This is an excellent suggestion, and we have done just that: thank you! The data is now included as a new panel Fig. 3F. The result is described in the results section, “Sequestration of PIP3 by lipid biosensors”, end of the first paragraph on pp. 4-6: To observe the concentration-dependence of AKT1-PH-mCherry inhibition, we pooled the single cell data from these experiments and split transfected cells into cohorts based on raw expression level (excitation and gain were consistent between experiments, allowing direct comparison). This analysis showed profound inhibition of AKT1-NG2 recruitment at all expression levels, with a slightly reduced effect only visible in the lowest expressing cohort (Fig. 2F).

#2.6 Specific comments: Figure 5 These are very interesting data, in particular with regard to the underlying PIP3 dynamics. I agree with the conclusion of the authors that shielding of PIP3 from degradation is the likely culprit. What I would like to see here is actual kinetic fits - and different terms. On- and off-rate imply biosensor binding, but these are likely rather fast and not on the minute-timescale. The detected processes are much more likely to reflect production and degradation of PIP3 and that should be reflected in the terminology. For the fit: I think that a simple rate law for subsequent reactions ([PIP3]=C(e^-k1t-e^k2t)) will give good results and yield effective rate constants for PIP3 generation and degradation. This implies the quasi-steady state assumption for biosensor binding and implies that [PIP3] is proportional to the biosensor bound [PIP3], but these are reasonable assumptions to make.

The is an excellent suggestion, which we have added. Specifically, fits are now present on Figs. 5G and 5I; we describe these in the last paragraph of results on p. 8: Normalizing data from both expression modes to their maximum response (Fig. 5G) and fitting kinetic profiles for cooperative synthesis and degradation reactionsrevealed the rate of synthesis is remarkably similar: 1.09 min–1 (95% C.I. 1.02-1.17) for single molecule expression vs 1.02 min-1 (95% C.I. 0.98-1.06) for over-expression. On the other hand, degradation slowed with over expression from 0.34 min–1 (95% C.I. 0.24-0.58) to 0.13 min–1 (95% C.I. 0.12-0.15). This is expected, since synthesis of PIP3molecules would not be prevented by biosensor. On the other hand, PIP3 degradation could be slowed by the over-expressed biosensor competing with PTEN and 5-OH phosphatases that degrade PIP3. An even more exaggerated result is achieved with the cPHx1 PI(3,4)P2 biosensor; this shows an increase in fold-change over baseline of 600% for single molecule expression levels, compared to only 100% in over-expressed cells (Fig. 5H). Again, the degradation rate of the signal is substantially slowed by the over-expressed sensor, reducing from 0.27 min–1 (95% C.I. 0.22-0.39) to 0.16 min–1 (95% C.I. 0.14-0.19), whereas synthesis remains only minorly impacted, changing from 0.61 min–1 (95% C.I. 0.57-0.64) to 0.54 min–1 (95% C.I. 0.52-0.56) with over-expression (Fig. 5I). Collectively, these data show that single molecule based PI3K biosensors show improved dynamic range and kinetic fidelity compared to the same sensors over-expressed.

Details of the fits are given in a new methods section on p. 11:

Fitting of reaction kinetics

Curve fitting was performed in Graphpad Prism 9 or later. For the data presented in Figs. 5G and 5I, both synthesis and degradation phases displayed clear “s” shaped profiles not well fit by simple first order kinetics. Since activation of the PI3K pathway involves many multiplicative interactions between adapters and allosteric activation of the enzymes themselves, we assumed cooperativity and fit reactions with the two phase reaction as follows:

Where Ft denotes ∆Ft/∆FMAX, nsyn and ndeg are the Hill coefficients of the respective synthesis and degradation reactions, and the rate constants for the reactions are derived from ksyn = 1/τsyn and kdeg = 1/τdeg.

André Nadler

Reviewer #2 (Significance (Required)):

This is an important paper, analyses the effects of over-expressed lipid biosensors on cell signalling in some detail and will be of significant interest to a broad readership.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

This is essentially a methods paper in which the authors provide a detailed and highly quantitative analysis of the potentially deleterious effects of expressing phosphoinositide-binding domains as biosensors. Specifically, they study the effects on PIP3 signalling, using biosensors that are widely used in the field.

They show that the most-commonly used method of expressing PIP3 biosensors using transient transfection with viral promotors has clear deleterious effects on downstream signalling due to out-competing the endogenous effectors. Importantly, they also describe a new approach to overcome this by developing new plasmids and methodology to express these reporters at low levels.

Reviewer 3 Major comments:

The work in this paper is thorough and very nicely done. I particularly appreciate the efforts to quantitate or estimate actual numbers and densities of molecules, which significantly strengthen their arguments. The data are excellent and strongly support all their conclusions. I would therefore be happy to see this work published in its current form.

Reviewer 3 Minor comments:

I only have some minor and optional suggestions for improvement.

#3.1 In figure 1D-F they show that PH-Atk-EGFP expression can suppress downstream Akt activation by quantifying P-Akt signal my microscopy. In these panels they say tgey selectively measure this in GFP-expressing cells, but it is not clear how they define which cells are expressing GFP - was a threshold used? Also, it would be nice to also measure both PH-Akt-GFP and P-Akt staining by flow cytometry to look for a correlation. Is there a threshold of biosensor expression that blocks downstream signalling, or is there a linear relationship? This might help specifically measure how much biosensor is too much.

This is an important comment, also raised by reviewer 2. We provide a detailed explanation and outline revisions that address this in our response to reviewer #2.3c; essentially, we replaced the analysis with an automated segmentation and quantification, estimating GFP-positive cells from a fraction of non transfected cells. We have not performed a FACS analysis, but as we note in our response to #2.3e __and #2.3h, the correlation between EGFP and pAKT staining is imprecise in these experiments. The new __Fig. 3C does address this point for AKT1-NG2 recruitment, as described in our response to #2.5.

#3.2 Some of their microscopy images (e.g. Fig 1D-F, Fig 5) are very small and would benefit from a zoom box - especially when they are trying to demonstrate single molecule detection.

This is a fair point raised by all of the reviewers in one form or another. We have added zoomed insets to all of the single molecule images in Figs 2-5, and added higher magnification, confocal section images to Fig. 1.

Reviewer #3 (Significance (Required)):

This is both a methods paper and cautionary tale for cell biologists working in this field. Whilst everyone who uses these probes should be aware of the potential risk of biosensors titrating our effectors, this is often not sufficiently acknowledged. This paper is a very nice and clear demonstration of these risks, exemplified with probably the most highly-used biosensor and key downstream signalling pathway.

Whilst the concepts presented are not especially novel, this paper nonetheless makes an important contribution to the community and hopefully will make others more cautious in how they use these biosensors.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #1

Evidence, reproducibility and clarity

The authors present the use of previously identified biosensors in a single-molecule concentration regime to address lipid effector recruitment. Using controlled and careful single-cell based analysis, the study investigates how expression of the commonly used PIP3 sensor based on Akt-PH domain interferes with the native detection of PIP3. Predominantly live-cell fluorescence microscopy coupled to image analysis drives their studies.

Conceptually, this manuscript carefully and quantitatively describes the influence of lipid biosensor overexpression and presents a means to overcome the inherent and long-recognized problems therein. This solution, namely employing low expression of the lipid biosensor, should be generally applicable. The work is of general interest to cell biologists focused on answering questions at membranes and organelles, including especially those interested in lipid-mediated signaling transductions.

Major:

1. The terminology "single molecule biosensor" is not really appropriate. A protein is not "single-molecule". An enzyme does not "single molecule". Better is biosensors at single-molecule expression levels. In most cases, this should be changed. Single-molecule vs single-cell vs. bulk measurements are often poorly defined in quantifications and conflating these does not help the case, which is already supported by generally clear data.
2. Figure 1D-F, images not as clearly describing quantitation as one would hope. Untransfected cells in 1E should demonstrate more translocated Akt-pS473 than transfected, but it is difficult for this reviewer to find. Consider inset images in addition to the wider field. Consider also moving the "negative" data of Fig 1B-C to Supplement.
3. The cell line being used is not clearly specified after the initial development of the NG1 followed by CRISPRed NG2 onto Akt. For example, for the Figure 3C experiments, the text states "complete ablation of endogenous AKT1-NG2" but this information is not apparent from the figure legend or figure. Throughout the cell line used and the aspects transfected need to be made explicitly clear.
4. Fig. 5 shows single cells. It is therefore unclear if broken promoters have resulted in decreased expression. This point is important because the expression plasmids should be made publicly available, and for their use to be understood properly, this must be clarified. The details of the plasmids are unclear. Perhaps listed in the table? - unclear. This aspect would be important for the field to effectively use the reagents.
5. This manuscript speculates several times that with more abundant PIs like PI45P2, the observed saturation effect is probably not happening. This should be removed. While the back of envelope calculations may reflect an ideal scenario, the heterogeneity of distribution and multiple key cellular structures involved would seem to corral increased PI45P2 levels in certain regions. These factors amid multivalency and electrostatic mechanisms of lipid effector recruitment (e.g. MARCKS) suggest that speculation may be too strong. Moreover, Maib et al JCB 2024 demonstrated PI4P probe overexpression could directly mask the ability to detect PI4P post-fixation - not fully, but partially. Repeating the titration experiments of this manuscript for multiple PIs is entirely beyond the scope of reasonable, and hence, such experiments are not requested, in favor of adopting more conscientious speculation.

Minor:

1. Schematics throughout need simplification, enabling their enlargement.
2. Numerous spelling (Fig. 4 schemas) and capitalizations need fixing.
3. Pg 1 Famous is not appropriate wording
4. Fig. 1A statistical testing of microscopy quantifications absent (generally, throughout) and should be included.
5. Fig.1. In a transient transfection, the protein expression is not uniform. Please explain how you normalized the quantification.
6. Fig. 1D. EGFP expression levels increased with EGF stimulation. How is this possible?
7. Fig. 1D. The images have pS473 whereas the y-axis label on box plots has p473. Can these box plots be labelled separately for consistency?
8. Fig.1. T308 phosphorylation is mentioned in Figure 1, but only pS473 data is shown.
9. Fig.1 legend. 'Over-expression of PH-AKT is hypothesised to outcompete the endogenous AKT's PH domain'. Why do you need to state a hypothesis in the legend?
10. Fig.1E You stated that the PH-AKT R25C-EGFP is stimulated by EGF addition. However, the GFP signal looks the same in both unstimulated and stimulated. Could you please clarify? Are you sure that the stimulation worked?
11. You mention...that the AKT enzyme is activated by PDK1 and TORC2, which phosphorylate at residues T308 and S473, respectively. Phosphorylation is also known to occur on T450 at c-tail. Does this phosphorylation also contribute to its activation?
12. Fig. 1 scale bar in all images equivalent?
13. Pg. 1 paragraph 1 "we have argued..." vs. paragraph 3"...consider that an..." feels like arguing with themselves.
14. Pg. 1 para 3 what is RFC score - must explain
15. Discussion of numbers of PIP3 vs. effectors etc may not be appropriate for the introduction, as the points made by these calculations are already made in the previous paragraphs. May fit better in pg 6 Mitigating PIP3 titration... with an accompanying schematic.
16. Pg 2 "a neonGreen" not well defined, needs accurate description.
17. Fig 2C should give a unstimulated trajectory of puncta/100 um2 to compare with the stimulated
18. Fig 2C and F and G should be systematized for easier comparison. E.g. min vs seconds, 0 timepoint of EGF/rapa addition
19. Pg 5 "...and calibrated them..." unclear what is being calibrated, as the text later states that the histograms are fit to monomer/dimer/multimer model resulting in 98.1% in monomer. Minor point.
20. Explain why baselines in Fig2CFG are different
21. Fig. 2 has quantification with images; Fig. 3 has it separate. Make consistent.
22. Fig. 3B comes before images? Where are the images? Also, y-axis = Intensity (a.u.). Is intensity just full image field? Or per cell? All very unclear.
23. Fig. 3C missing images
24. Fig 3 C needs brightness/contrast adjusted as images are nearly entirely black (zero values).
25. Fig 3C needs scale bar systemization
26. Fig 4 needs 4 panels A-D
27. Pg 6 5-OH phosphatases needs reference
28. Fig 5B, make images bigger

Referees cross-commenting

I have read the other reviews and find them entirely reasonable. My impression is we landed on similar general content that needs work, none of which is out of line. The importance and care taken in the author's work is uniformly lauded.

Significance

This manuscript clearly and reasonably demonstrates that the commonly used PIP3 sensor can be titrated to low concentrations, at which it does not interfere with Akt translocation and activation. This work is a good technical reference for the field. Signal transduction and membrane biologists should be especially interested in the data. The reviewer/s have core expertise in phosphoinositides, protein biochemistry, cell biology, and membrane biophysics.

Author Response:

Thank you for your interest in our paper. We would also like to thank the anonymous reviewers for their critical and constructive comments. Although the reviewers found our work interesting, they raised several important concerns about our study. To address these concerns, mostly we will perform new experiments as following.

1. Examine whether antioxidant-NAC can block SFN-induced TFEB-nuclear translocation in NPC cells;

2. Examine whether calcineurin inhibitor (FK506+CsA) or Ca 2+ inhibitor (Bapta-AM) can block SFN-induced TFEB-nuclear translocation in NPC cells.

3. Investigate whether cholesterol was cleared by activation of TFEB by SFN in vivo tissues.

4. Investigate whether SFN-evoked the lysosomal exocytosis is TFEB-dependent by using TFEB-KO cells.

5. Examine the effect of NPC1 deficiency on dextran trafficking by studying the localization of CF- dex and Lamp1.

6. Perform cytotoxicity experiments to examine whether SFN used in this study is cytotoxic in various cell lines

In addition, according to the reviewers’ suggestions, we will make clarifications and corrections wherever appropriate in the manuscript. Below please find our point-by-point responses and plans to the reviewers’ comments.

Reviewer #1 (Public review):

Summary:

The authors are trying to determine if SFN treatment results in dephosphorylation of TFEB, subsequent activation of autophagy-related genes, exocytosis of lysosomes, and reduction in lysosomal cholesterol levels in models of NPC disease.

Strengths:

(1) Clear evidence that SFN results in translocation of TFEB to the nucleus.

(2) In vivo data demonstrating that SFN can rescue Purkinje neuron number and weight in NPC1-/- animals.

Thank you for the support!

Weaknesses:

(1) Lack of molecular details regarding how SFN results in dephosphorylation of TFEB leading to activation of the aforementioned pathways. Currently, datasets represent correlations.

Thank you for this constructive comment. The reviewer is right that in this manuscript the molecular mechanism of SFN-activated TFEB has not been discussed in details. Because previously we have shown that SFN induces TFEB nuclear translocation via a Ca 2+ - dependent but MTOR (mechanistic target of rapamycin kinase)-independent mechanism through a moderate increase in reactive oxygen species (ROS). And calcineurin-mediated TFEB dephosphorylation underlies SFN-induced TFEB activation. These data have been published in 2021 autophagy (Li, Shao et al. 2021) . Therefore, in this study we did not mention this part. We will add the molecular mechanism of TFEB activation by SFN in the discussion part. And to further confirm this mechanism in NPC cells, we will also perform experiments including: 1) examine whether antioxidant-NAC can block SFN-induced TFEB-nuclear translocation in NPC cells; 2) examine whether calcineurin inhibitor (FK506+CsA) can block SFN-induced TFEB-nuclear translocation in NPC cells.

(2) Based on the manuscript narrative, discussion, and data it is unclear exactly how steady-state cholesterol would change in models of NPC disease following SFN treatment. Yes, there is good evidence that lysosomal flux to (and presumably across) the plasma membrane increases with SFN. However, lysosomal biogenesis genes also seem to be increasing. Given that NPC inhibition, NPC1 knockout, or NPC1 disease mutations are constitutively present and the cell models of NPC disease contain lysosomes (even with SFN) how could a simple increase in lysosomal flux decrease cholesterol levels? It would seem important to quantify the number of lysosomes per cell in each condition to begin to disentangle differences in steady state number of lysosomes, number of new lysosomes, and number of lysosomes being exocytosed.

Thank you for the suggestion. It is important to define the three states 1) original number of lysosomes, 2) number of new lysosomes, and 3) number of lysosomes being exocytosis. However, we have checked literature, so far it seems that there is no good method that could clearly differentiate the three states of lysosomes.

(3) Lack of evidence supporting the authors' premise that "SFN could be a good therapeutic candidate for neuropathology in NPC disease".

Suggestion was taken! We will investigate whether cholesterol was reduced by activation of TFEB by SFN in vivo to strength the point that SFN could be a potential therapeutic compound for NPC treatment. And to avoid confusion, we have removed this sentence.

Reviewer #2 (Public review):

Summary:

This study presents a valuable finding that the activation of TFEB by sulforaphane (SFN) could promote lysosomal exocytosis and biogenesis in NPC, suggesting a potential mechanism by SFN for the removal of cholesterol accumulation, which may contribute to the development of new therapeutic approaches for NPC treatment.

Strengths:

The cell-based assays are convincing, utilizing appropriate and validated methodologies to support the conclusion that SFN facilitates the removal of lysosomal cholesterol via TFEB activation.

Weaknesses:

(1) The in vivo experiments demonstrate the therapeutic potential of SFN for NPC. A clear dose-response analysis would further strengthen the proposed therapeutic mechanism of SFN. Additional data supporting the activation of TFEB by SFN for cholesterol clearance in vivo would strengthen the overall impact of the study

We understand the reviewer’s point. We examined two doses of SFN-30 and 50mg/kg. As shown in Fig.6, SFN (50mg/kg), but not 30mg/kg prevents a degree of Purkinje cell loss in the lobule IV/V of cerebellum, suggesting a dose-correlated preventive effect of SFN. In vivo experiments with higher concentrations of SFN and optimized dosage form of SFN were planned in the future study, but will not be included in this study.

We will investigate whether cholesterol was cleared by activation of TFEB by SFN in vivo.

(2) In Figure 4, the authors demonstrate increased lysosomal exocytosis and biogenesis by SFN in NPC cells. Including a TFEB-KO/KD in this assay would provide additional validation of whether these effects are TFEB-dependent.

Thank you for this valuable suggestion. We will investigate whether SFN-evoked the lysosomal exocytosis is TFEB-dependent by using TFEB-KO cells.

(3) For lysosomal pH measurement, the combination of pHrodo-dex and CF-dex enables ratiometric pH measurement. However, the pKa of pHrodo red-dex (according to Invitrogen) is ~6.8, while lysosomal pH is typically around 4.7. This discrepancy may account for the lack of observed lysosomal pH changes between WT and U18666A-treated cells. Notably, previous studies (PMID: 28742019) have reported an increase in lysosomal pH in U18666A-treated cells.

We understand the reviewer’s point. But we used pHrodo™ Green-Dextran (P35368, Invitrogen), but not pHrodo red-dex to measure the lysosomal luminal acidity. According to the product information from Invitrogen, pHrodo Green-dex conjugates are non-fluorescent at neural pH, but fluorescence bright green at acidic pH ranges 4-9, such as those in endosomes and lysosomes. Therefore, pHrodo Green-dex can be used to monitor the acidity of lysosome (Hu, Li et al. 2022) . We also used LysoTracker Red DND-99 (Thermo Scientific, L7528) to measure lysosomal pH (Fig. 4G, H), which is consistent with results of pHrodo Green/CF measurement. Overall, in our hands, we have not detected pH change of lysosomes in U18666A-treated NPC1 cell models.

(4) The authors are also encouraged to perform colocalization studies between CF-dex and a lysosomal marker, as some researchers may be concerned that NPC1 deficiency could reduce or block the trafficking of dextran along endocytosis.

Suggestion was taken! We will examine the effect of NPC1 deficiency on dextran trafficking by studying the localization of CF-dex and Lamp1.

(5) In vivo data supporting the activation of TFEB by SFN for cholesterol clearance would significantly enhance the impact of the study. For example, measuring whole-animal or brain cholesterol levels would provide stronger evidence of SFN's therapeutic potential.

We really appreciate the reviewer’s suggestions. We will investigate whether cholesterol was cleared by activation of TFEB by SFN in vivo.

Reviewer #3 (Public review):

Summary:

The authors demonstrate that activation of TFEB facilitates cholesterol clearance in cell models of Niemann-Pick type C (NPC). This is done through a variety of approaches including activation of TFEB by sulforaphane (SFN), a naturally occurring small-molecule TFEB agonist. SFN induces TFEB nuclear translocation and promotes lysosomal exocytosis. In an NPC mouse model, SFN dephosphorylates/activates TFEB in the brain and rescues the loss of Purkinje cells.

Strengths:

NPC is a severe disease and there is little in the way of treatment. The manuscript points towards some treatment options. However, the title, the title "Small-molecule activation of TFEB Alleviates Niemann-Pick Disease..." is far too strong and should be changed.

Weaknesses:

(1) The manuscript is extremely hard to read due to the writing; it needs careful editing for grammar and English.

We will thoroughly check grammar to improve the manuscript.

(2) There are a number of important technical issues that need to be addressed.

We will address the technical issues mentioned in the following.

(3) The TFEB influence on filipin staining in Figure 1A is somewhat subtle. In the mCherry alone panels there is a transfected cell with no filipin staining and the mCherry-TFEBS211A cells still show some filipin staining.

We understand the reviewer’s point. We will investigate whether cholesterol is cleared by activation of TFEB by SFN in vivo.

(4) Figure 1C is impressive for the upregulation of filipin with U18666A treatment. However, SFN is used at 15 microM. This must be hitting multiple pathways. Vauzour et al (PMID: 20166144) use SFN at 10 nM to 1microM. Other manuscripts use it in the low microM range. The authors should repeat at least some key experiments using SFN at a range of concentrations from perhaps 100 nM to 5 microM. The use of 15 microM throughout is an overall concern.

We understand the reviewer’s point. See RESPONSE #1, previously we have shown that SFN (10–15 μM, 2–9 h) induces robust TFEB nuclear translocation in a dose- and time-dependent manner in HeLa GFP-TFEB stable cells as well as in other human cell lines without cytotoxicity (Li, Shao et al. 2021) . According to previous results, in this study, we chose SFN (15 μM) to examine its effect on cholesterol clearance. We will add the information in the discussion part. In this study, we will perform dose-response TFEB nuclear translocation in NPC model cells as well as cytotoxicity experiments to examine whether the concentrations of SFN used in various cell lines are toxic.

References:

Hu, M. Q., P. Li, C. Wang, X. H. Feng, Q. Geng, W. Chen, M. Marthi, W. L. Zhang, C. L. Gao, W. Reid, J. Swanson, W. L. Du, R. Hume and H. X. Xu (2022). "Parkinson's disease-risk protein TMEM175 is a proton-activated proton channel in lysosomes.” Cell 185(13): 2292-+.

Li, D., R. Shao, N. Wang, N. Zhou, K. Du, J. Shi, Y. Wang, Z. Zhao, X. Ye, X. Zhang and H. Xu (2021). “Sulforaphane Activates a lysosome-dependent transcriptional program to mitigate oxidative stress.” Autophagy 17(4): 872-887.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2024-02546

Corresponding author: Woo Jae, Kim

1. General Statements

This is the second version of revision.

After thoroughly reviewing the comments provided by the EMBO Journal reviewers, we found their feedback to be highly constructive and valuable for enhancing our manuscript without the need for additional experiments. For example, Reviewer 1 acknowledged that our "data are intriguing and some of the experiments are quite convincing," but suggested that the manuscript contained excessive data that required simplification. This sentiment was echoed by Reviewer 2. In response, we have completely reformatted our manuscript to eliminate unnecessary imaging quantification data and CrzR-related screening data. The reviewers noted the density of our experimental data, which has led us to focus on the SIFa to Crz-CrzR circuit mechanisms related to heart function and interval timing in future projects.

Reviewer 2's comments were generally more moderate, and we successfully addressed all five of their points with detailed explanations and modifications to our manuscript. They positively remarked that "Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide and contributes to an understanding of how motivated behavior such as mating is orchestrated by modulatory peptides." Additionally, Reviewer 3 accepted our manuscript without any further comments.

In summary, we believe we have effectively addressed all concerns raised by Reviewers 1 and 2, resulting in a clearer manuscript that is more accessible to a broader audience.

2. Point-by-point description of the revisions

Reviewer #1

General Comments: In this revision of their manuscript, Zhang et al have attempted to address most of the points raised by the reviewers, however, they have not assuaged my most important concerns. The manuscript contains a ton of information, but at times this is to the detriment of the narrative flow. I had a lot of trouble following the rationale of each experiment, and the throughline from one experiment to the next is not always obvious. The data are intriguing, and some of the experiments are quite convincing, but other experiments are either superfluous or have methodological issues. I will summarize the most acute issues below.

• *Answer: Thank you for your thoughtful feedback and for acknowledging our efforts to address your previous comments. We appreciate your recognition of the intriguing nature of our data and the convincing aspects of our experiments. In this second revision, we have taken your concerns regarding the narrative flow and data overload to heart. We have completely reshaped our manuscript, significantly reducing unnecessary data, including the NP5270 data and overlapping quantification results that did not contribute meaningfully to the storytelling. Our goal was to streamline the presentation of our findings to enhance clarity and coherence, ensuring that each experiment clearly supports the overarching narrative. We believe these revisions will not only improve the readability of our manuscript but also allow readers to follow the rationale behind each experiment more easily. We are confident that this refined approach will make our contributions clearer and more impactful. Thank you once again for your constructive insights, which have been invaluable in guiding us toward a more focused and compelling presentation of our work.

Comment 1. *The authors argue that genetic controls are unnecessary because they have been conducted in previously published papers. I am concerned with this argument, as it is good practice to repeat controls with each experiment. However, I am overall convinced by the basic phenotype indicating that panneuronal SIFaR knockdown eliminates the changes in mating duration associated with previous experience. As for the more restricted 24F06-GAL4, the phenotype is odd-the flies do actually change their mating duration, just in the opposite direction of controls. Doesn't this imply that these flies are still capable of "interval timing", and of changing their mating strategy following exposure to rivals or following sexual experience? *

• *

__ Answer:__ We appreciate the reviewer's critical comments regarding genetic control and the intriguing phenotypes we observed in specific genetic combinations. We fully agree with the reviewer and have repeated all genetic control experiments for this revision, confirming that our genetic controls consistently demonstrate intact LMD and SMD behaviors, as previously reported. These genetic control experiments have been included in Supplementary Information 1-2. We are grateful to the reviewer for the opportunity to reaffirm that LMD and SMD represent stable behavioral phenotypes suitable for genetically studying interval timing, supported by reproducible data.

• *

We acknowledge the reviewer's insightful comments about the exciting phenotype observed when SIFaR is knockdown which shows both singly reared and sexually experienced male show lengthened mating duration in contrast to normal LMD and SMD behaviors. Actually, we have observed such phenotype when specific neural circuits are disrupted such as when sNPF peptidergic signaling is disrupted in restricted neuronal population [4]. We are now investigating such phenotype as hypothesis as disinhibition. We explained this phenotype and about disinhibition in main text as below.

In the spatial, the targeted reduction of SIFaR expression in the GAL424F06 neuronal subset resulted in a notable alteration of mating behavior. Both singly reared and sexually experienced flies exhibited an extended mating duration relative to naïve flies, contrary to the expected reduction. This observation indicates a deficit in the neural mechanism responsible for modulating mating duration, suggesting a disinhibition-like effect within the neural circuitry governing mating behavior. We have also previously observed a similar phenotype when sNPF peptidergic signaling is inhibited in specific neuronal circuits [62]. Disinhibition, characterized by the alleviation of inhibitory constraints, permits the activation of neural circuits that are ordinarily repressed. This process is instrumental in sculpting behavioral patterns and facilitating the sequential progression of behaviors. Through the orchestrated promotion of select neuronal activation and concurrent inhibition of competing neural routes, disinhibition empowers the brain with the ability to dynamically ascertain and preserve the requisite behavioral state, concurrently smoothing the transition to ensuing behavioral phases [63]. It is known that Drosophila neural circuits also exhibit disinhibition phenotypes in light preference and ethanol sensitization [64,65]. Further investigation is needed to uncover the underlying mechanisms of this disinhibition-like phenotype observed in LMD and SMD behaviors.

This reversed phenotype strongly suggests a disruption in interval timing, as one would expect that if interval timing were normal and intact, male flies would decrease their mating duration in response to appropriate environmental changes. For instance, research has shown that patients with Parkinson's disease exhibit heterogeneity in temporal processing, leading to disrupted interval timing phenotypes [5]. Therefore, if male flies subjected to social isolation or sexual experience do not show a reduction in mating duration compared to control conditions, it indicates a potential disruption in their interval timing mechanisms. We appreciate the reviewer's encouragement to further explore this intriguing disinhibition-like phenotype, and we plan to investigate this aspect in our future projects.

Comment 2. *I am glad the see the addition of data assessing the extent of SIFaR and CrzR RNAi knockdown; however, this has not completely addressed my concerns about interpretation of behavioral phenotypes. In both cases, the knockdown was assessed by qPCR using the very strong tub-GAL4 driver. mRNA levels are decreased but not nearly eliminated. Thus, when in line 177-178 the authors assert: "Consequently, we infer that the knockdown of SIFaR using the HMS00299 line nearly completely diminishes the levels of the SIFaR protein," the statement is not supported by the data. The qPCR results showed a knockdown at the mRNA level of ~50%. No assays were conducted to measure protein levels. The conclusions should be tempered to align with the data. Furthermore, it is not clear that knockdown is as successful with other drivers, which means that negative behavioral data must be interpreted with caution. For example, the lack of phenotype with repo-GAL4 driving SIFaR RNAi or elav-GAL4 driving CrzR RNAi could be due to a lack of efficient knockdown. This should be acknowledged. *

   __Answer:__ We appreciate the reviewer's critical observation regarding the efficiency of SIFaR knockdown. We fully agree that it is essential to confirm both for ourselves and our readers that the SIFaR knockdown phenotype is robust and convincing. At the outset of this project, we tested all available SIFaR-RNAi strains following established protocols within the fly community to ensure consistency in our findings. When we employed strong drivers such as tub-GAL4 and nSyb-GAL4 for SIFaR-RNAi knockdown, we observed that the flies failed to eclose and exhibited a lethal phenotype during the larval stage, which closely resembles the homozygous lethal phenotype seen in SIFaR mutants. This suggests that, in most cases, the effects of SIFaR knockdown can effectively mimic those of SIFaR mutations. To share our methodology and reinforce our findings, we have added clarifying statements in the main text as follows:

"Employment of broad drivers, including the tub-GAL4 and the strong neuronal driver nSyb-GAL4, with HMS00299 line consistently results in 100% embryonic lethality (data not shown). This phenotype mirrors the homozygous lethality observed in the SIFaRB322 mutant."

• *

Due to the significant lethality phenotype observed, we conducted PCR analyses using a combination of tub-GAL80ts and SIFaR-RNAi. As detailed in Fig. 1E, we reared the flies at 22{degree sign}C to suppress RNAi expression and then shifted the temperature to 29{degree sign}C for just three days prior to performing PCR. While our PCR results indicate a 50% reduction in SIFaR levels, we believe that experiments conducted without the tub-GAL80ts system would likely demonstrate an even greater reduction in SIFaR expression. To clarify this point and provide additional context, we have included the following description in the main text:

"The silencing of SIFaR mRNA was achieved at approximately 50% using the HMS00299 knockdown line in combination with tub-GAL80ts, with RNAi induction lasting for three days (bottom diagram in Fig. 1E). Notably, the same tub-GAL4 driver, when used without the tub-GAL80ts combination, resulted in embryonic lethality while still reducing SIFaR mRNA levels by 50% after three days of RNAi induction. This finding suggests that SIFaR knockdown using the HMS00299 line with GAL4 drivers is likely sufficient to elicit the observed LMD and SMD behaviors. This rationale underscores the effectiveness of our experimental approach and its potential implications for understanding the role of SIFaR in mating behaviors."

We also concur with the reviewer that the absence of a behavioral phenotype associated with CrzR-RNAi may be due to inefficient RNAi knockdown. Consequently, we have included a description of this issue in the main text as follows:

• *

"It is important to consider that the 50% knockdown of SIFaR and CrzR may be sufficient to disrupt LMD and/or SMD behavior. However, the lack of phenotype with repo-GAL4 or elav-GAL4 could be due to a less efficient knockdown. This possibility highlights the need for cautious interpretation of negative behavioral data."

Comment 3. *Regarding the issue of outcrossing, I am confused by the authors' statement: "To reduce the variation from genetic background, all flies were backcrossed for at least 3 generations to CS strain. For the generation of outcrosses, all GAL4, UAS, and RNAi lines employed as the virgin female stock were backcrossed to the CS genetic background for a minimum of ten generations. Notably, the majority of these lines, which were utilized for LMD assays, have been maintained in a CS backcrossed state for long-term generations subsequent to the initial outcrossing process, exceeding ten backcrosses." It's not clear what this means. Perhaps the authors could definitively state how many times each line was outcrossed. The genetic background is important because of 1) the lack of all controls, and 2) the variability of the behavioral phenotype. Often, the presence or absence of LMD or SMD appears to depend on the behavior of the control flies. When these flies show low mating duration, there is typically not a reduction following sexual experience or group raising. Could these differences derive from genetic background or transgenic insertion effects? *

Answer: We appreciate the reviewer's concern regarding the potential for confusion stemming from our descriptions of the genetic background. As the reviewer noted, we have published multiple papers on LMD and SMD behaviors, and we have conducted our experiments with careful attention to controlling the genetic background [1-3,6-8]. In response to the reviewer's comments about the importance of genetic control and background, we have completed all necessary genetic control experiments and confirmed that all our flies have been backcrossed for more than ten generations to the Canton-S (CS) strain. We believe that we have adequately addressed the reviewer's concerns regarding potential differences arising from genetic background or transgenic insertion effects. To provide readers with more detailed information about our genetic background, we have added a paragraph in the MATERIALS AND METHODS section as follows:

"The CS background was selected as the experimental background due to its well-characterized and consistent LMD and SMD behaviors. To ensure that genetic variation did not confound our results, all GAL4, UAS, and RNAi lines employed in our assays were rigorously backcrossed into the CS strain, often exceeding ten generations of backcrossing. This approach was undertaken to isolate the effects of our genetic manipulations from those of genetic background. We assert that the extensive backcrossing to the CS background, in concert with the internal control in LMD and SMD, provides a stable platform for the accurate interpretation of the LMD and SMD phenotypes observed in our experiments."

Comment 4. *I continue to have substantial concerns about the thresholding method used across many experiments to quantify overlap, and then to claim that this indicates that synaptic connections are being made between different neuronal populations. The degree of overlap will depend on factors including the settings during imaging (was care taken to prevent pixel saturation?). It is also not clear to me from the methods whether analysis was done on single confocal images or on projections. The images shown in the figures look like maximum projections of a confocal stack. Overlap would have to be assessed on individual confocal sections-it is possible that this is what was done for analysis but not clear from the description in the methods. Furthermore, a lot of figure space is dedicated to superfluous information. For example, in Figure 1F-J, there is a massive amount of space dedicated to assessing the agree of overlap between red stinger and CD4GFP, each driven from the same SIFaR2A driver, and further assessing what percentage of the CD4GFP signal overlaps with nc82, with the apparent goal of showing that a lot of the SIFaR signal is at active zones. This information does little to drive the narrative forward, and is quite confusing to read. Finally, the confocal images are generally too small to actually assess. *

   __Answer:__ We appreciate the reviewer's concerns regarding our imaging quantification methods. We recognize the importance of providing a clear and transparent methodology for both readers and the broader scientific community. Instead of using maximum projection of confocal images, we employed a projection method that incorporates the standard deviation function available in ImageJ. Based on our experience, this approach yields more reliable quantification results, allowing for a more accurate assessment of our data. To ensure clarity and reproducibility, we have detailed our methods in the MATERIALS AND METHODS section as follows:

• *

"The quantification of the overlap was performed using confocal images with projection by standard deviation function provided by ImageJ to ensure precise measurements and avoid pixel saturation artifacts."

We appreciate the reviewer's suggestion regarding the inclusion of image quantification data for overlapping regions, which may not be essential to the logical flow of our narrative and could lead to confusion for readers. In response, we have removed nearly all of the quantification data related to overlapping regions, retaining only those that we consider critical for the paper. Currently, only Fig. S3B-E remains, as it is important for illustrating how SIFa neuronal arborization interacts with SIFaR neurons in the central nervous system.

Additionally, we fully agree with the reviewer that the overall size of the confocal images was too small for effective assessment. To address this concern, we have enlarged all confocal images and increased the spacing in the figures. We believe these improvements will enhance the clarity of our manuscript and facilitate a better understanding of our findings.

• *

Comment 5. *In general, the figures are still very cluttered, with panels too close together, and the labels are hard to read. *

Answer: We thank the reviewer for their valuable feedback regarding the clarity of our figures. In response to their concern, we have enlarged the figures to enhance readability and ensure that the panels are more distinct. We believe these adjustments will significantly improve the viewer's ability to interpret the data. We appreciate the reviewer's attention to detail, which has helped us to refine the presentation of our findings.

Comment 6. *There are no methodological details on how the VFB was used. The authors have not addressed my concern that they are showing only the neuronal skeleton (rather than the actual site of synapses). They are simply identifying all locations where the neuronal skeleton overlaps an entire brain region, and suggesting that these represent synapses. Many papers use the VFB to denote the actual location of synapses, which should be done in Figures 3B and S4A. *

Answer: We appreciate the reviewer's constructive comments regarding the methodological details of using VFB data. We fully agree that we cannot draw definitive conclusions about SIFa projections to specific regions based solely on neuronal skeleton data, which do not indicate the actual locations of synapses. To address this concern, we have made it clear to readers that the VFB skeleton data serves only as a preliminary indication of potential SIFa projections to GA, FB, and AL.

To confirm the presence of actual synapses from SIFa neurons, we conducted a thorough analysis using FlyWire data, which validated our findings from VFB. By integrating insights from VFB with the detailed synaptic mapping provided by FlyWire, we can confidently assert the functional relevance of these connections within the context of SIFa neuronal activity. This comprehensive approach not only bolsters our conclusions but also enhances our understanding of how SIFa neurons interact within the broader neural circuitry. We believe this rationale highlights the significance of our work in elucidating the complex relationships among these neuronal populations. We have detailed our findings in the main text as follows:

"We utilized the "Virtual Fly Brain (VFB)" platform, an interactive tool designed for exploring neuronal connectivity, to gain insights into the connectivity of SIFa neurons with four other neurons, specifically GA, FB, and AL (Fig. 3B and Fig. S4B) [74]. While VFB provides valuable information, it does not offer precise locations of synapses originating from SIFa neurons. To address this limitation, we incorporated data from the FlyWire connectome, which allowed us to confirm that SIFa projections indeed form actual synapses with GA, AL, FB, and SMP (Fig. S3F and S3G) [75]. This multi-faceted approach enhances the robustness of our findings by integrating different data sources to validate neuronal connections."

• *

Comment 7. *The changes in GRASP and CaLexA with experience are very interesting, and suggest a substantial rearrangement of synaptic connectivity associated with changes in mating duration following group rearing or female exposure. I am still concerned, however, that the nsyb and tGRASP images look so different. I wouldn't expect them to be identical, but it is puzzling that the nsyb-GRASP data show connections in a few discrete brain areas, while the tGRASP data show connections in a much larger overall brain area, but curiously not in the major regions seen with nsyb-GRASP (ie PI, FB and GA). Shouldn't the tGRASP signal appear in all the places that the nsyb-GRASP does? For CaLexA and GRASP data, the methods should indicate the timing of the dissections and staining relative to the group/sexual experience. *

Answer: We appreciate the reviewer's constructive comments regarding our GRASP data, which indeed reveal an intriguing neural plasticity phenotype, as the reviewer noted. In our previous response, we suggested that the observed differences may be attributed to the distinct SIFa-GAL4 strains utilized, as described in another manuscript focused on SIFa inputs [9]. In that manuscript, we classified the four SIFa neurons into two groups: SIFaDA (dorsal-lateral) and SIFaVP (ventral-posterior). The SIFa2A-GAL4 specifically labels only the SIFaVP neurons, while the SIFa-PT driver labels all four neurons. We acknowledge that we did not clearly communicate this distinction to the reviewer or our readers, and we apologize for any confusion this may have caused. To rectify this oversight, we have added a detailed explanation of these differences in the main text as follows:

"The subtle differences in GRASP signals observed in Fig. 3A may stem from the distinct expression patterns of the SIFa2A-lexA and GAL4SIFa.PT drivers. We would like to emphasize that the SIFa2A driver labels only a subset of SIFa neurons in other regions (Kim 2024)."

We recognize that a clear and transparent methodology is essential for generating reproducible data. In response to the reviewer's suggestion, we have revised our MATERIALS AND METHODS section to include more detailed descriptions of the dissection conditions. This enhancement aims to provide readers with the necessary information to replicate our experiments effectively.

"To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. For group reared (naïve) flies, the flies were reared in group condition and dissect right after 5 days of rearing without any further action. For single reared flies, the flies were reared in single condition and dissect at the same time as group reared flies right after 5 days of rearing without any further action. For sexual experienced flies, the flies were reared in group condition after 4 days of rearing and will be given virgins to give them sexual experience for one day, those flies will also be dissected at the same time as group and single reared flies after one day."

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Comment 8. *The calcium imaging data are odd. In most cases, the experimental flies don't actually show an increase in calcium levels but rather a lack of a decrease that is present in the ATR- controls. Also, in the cases where they argue for an excitatory affect of SIF neuron stimulation, the baseline signal intensity appears higher in ATR- controls compared to ATR+ experimental flies (eg Fig 5L, 6O), while it is significantly higher in ATR+ flies compared to ATR- controls when the activation results in decreased calcium signals. Perhaps more details on how these experiments were conducted and whether data were normalized in some way would help to clarify this. *

Answer: Thank you for your valuable feedback. We appreciate your careful analysis of our calcium imaging data and have addressed your concerns below:

In our experiments, we observed that ATR+ flies maintained relatively stable calcium levels, whereas ATR- controls exhibited a gradual decrease. Under confocal imaging, GFP signals typically decrease over time, which we observed in ATR- controls. However, ATR+ flies did not exhibit this decline. To better convey this observation, we have refined the language in the manuscript. Specifically, we now describe this as a tendency to sustain the activity of Crz neurons in the OL and AG regions (Fig. 6K-M, Fig. S6G-I). This is supported by the sustained intracellular calcium activity in ATR+ flies compared to the gradual decline to baseline levels observed in ATR- controls (Fig. 6K-M).

Baseline signal intensity differences: You correctly noted that in some cases, the baseline signal intensity appears higher in ATR- controls compared to ATR+ flies. These differences are likely due to technical factors, such as variations in the distance between the imaged brain and the objective lens. Even minor positional shifts in the brain (forward or backward) can affect the observed signal intensity.

Our analyses focus on relative changes in fluorescence intensity within the same sample, which we present as line graphs to highlight trends rather than absolute values. However, we acknowledge that showing the magnitude of relative values instead of absolute values may have caused some confusion. We have revised the images to better align with our conclusions, ensuring that the adjustments do not affect the observed relative changes.

Normalization and experimental details: The calcium imaging data were normalized to ΔF/F to account for differences in baseline fluorescence intensity. However, we recognize that further clarification of the normalization process and experimental setup is essential. We have expanded the methods section to include detailed descriptions of data acquisition, normalization steps, and statistical analyses.

As the reviewer correctly noted, calcium signals in ATR+ flies are generally higher than those in ATR- flies. However, it appears that the calcium levels exhibit a maintained response rather than a dramatic increase compared to the control ATR- condition, particularly in the case shown in Fig. 6K, which illustrates SIFa-to-Crz signaling. We believe this observation may reflect the actual physiological conditions under which SIFa influences SIFaR neurons to sustain activity during activation. We have included our interpretation of these findings in the main text as follows:

"Upon optogenetic stimulation of SIFa neurons, we observed a tendency to maintain the activity of Crz neurons in OL and AG regions (Fig. 6K-M, Fig. S6H-J), evidenced by a sustained activity in intracellular Ca2+ levels that persisted in a high level compared to control ATR- condition which shows gradual declining to baseline levels (Fig. 6K-M). In contrast to the OL and AG regions, the cells in the upper region of the SIP consistently show a decrease in Ca2+ levels following stimulation of the SIFa neurons (Fig. 6N-P)."

To enhance readers' understanding of our calcium imaging results, we have reformatted our GCaMP data for improved clarity and included additional details in the MATERIALS AND METHODS section regarding the quantification of GCaMP imaging methods. Furthermore, as the reviewer correctly noted, discrepancies in baseline activity were due to our error in presenting the baseline data. We have now corrected this oversight accordingly.

• *

Comment 9. *The models in Fig 4 J and T show data from Song et al, though I could not find a citation for this. I would omit this part of the model since these data are not discussed at all in the manuscript. *

Answer: We appreciate the reviewer for correctly identifying our oversight in failing to properly cite Song et al.'s paper. This error occurred partly because the preprint was not available at the time we submitted our manuscript. We now have a preprint for Song et al.'s paper, which discusses the contributions of SIFa neurons to various energy balance behaviors, and we plan to submit this paper back-to-back with our current submission to PLOS Biology. We have briefly cited Song et al.'s work in the manuscript; however, we have removed references to it from Fig. 4J and T to avoid any potential confusion for readers.

Comment 10. *The graphs for the SCOPE data (eg Figure 8I-L) are still too small to make sense of. *

Answer: We enlarged the tSNE plot generated from the SCOPE data.

• *

Comment 11. The rationale behind including the data in Figure 9 is not well explained. I would omit this data to help streamline and focus the manuscript.

Answer: We fully understand and agree with the reviewer's concerns, and we have removed all previous versions of Figure 9 from the manuscript to prevent any confusion regarding the storyline.

• *

Comment 12. *The single control group is still being duplicated in two different graphs but with different names in each graph. The authors updated figure caption hints at this but does not make it explicit. At the very least, these should be given the same name across all graphs, as is done, for example, in the CaLexA experiments in Figure 4B-C. *

Answer: We concur with the reviewer and have changed the label for all "group" conditions to "naïve" in all figures.

• *

Comment 13. *Lines 640-641: Moreover, the pacemaker function is essential for the generation of interval timing capabilities (Meck et al, 2012; Matell, 2014; Buhusi & Meck, 2005), with the heart being recognized as the primary pacemaker organ within the animal body". This is an intriguing idea, however, I attempted to look at the cited references and don't see any claim about the heart being involved in interval timing. I could not find a paper matching the citation of Matell 2014. Meck et al 2012 is an introduction to a Frontiers in Integrative Neuroscience Research Topic and does not mention the heart, nor does the Buhusi and Meck 2005 paper. Perhaps there is a more suitable reference to make the assertion that the fly's interval timer would be affected by changes in heart rate. My suggestion would be to simplify the manuscript, focusing on the most robust findings-the behavioral effect of SIFaR knockdown, the GRASP and CaLexA data showing differences following group rearing or female exposure, and the effect of Crz knockdown in SIFaR neurons. Other details could be included but would have to be verified with more rigorous experiments. *

__ Answer:__ We appreciate the reviewer's interest in our exploration of the role of heart function in interval timing. While we found that knocking down CrzR in the heart specifically disrupts LMD behavior, we agree that our manuscript needs to be streamlined for clarity. As a result, we have eliminated all CrzR-RNAi knockdown data except for the oenocyte, neuronal and glial knockdown data presented in Fig. S8C-H. This decision was made to ensure a more focused comparison with the SIFaR knockdown experiments shown in Fig. 1. We are dedicated to further investigating the role of Crz-CrzR in heart function and its influence on interval timing in a future project. This approach allows us to maintain clarity in our current manuscript while laying the groundwork for more comprehensive studies ahead.

In line with the reviewer's suggestions, we have simplified our manuscript by eliminating unnecessary data, such as overlapping image quantification and CrzR-RNAi screening, allowing us to focus on SIFaR knockdown and GRASP, as well as CaLexA with GCaMP imaging. We are grateful to the reviewer for providing us with the opportunity to delineate the role of CrzR in heart function related to LMD as a significant future project. We believe that our manuscript has been greatly improved by the reviewer's constructive feedback.

• *

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Reviewer #2

General Comments:* The authors investigate mating behavior in male fruit flies, Drosophila melanogaster, and test for a role of the SIFamide receptor (SIFaR) in this type of behavior, in particular mating duration in dependence of social isolation and prior mating experience. The anatomy of SIFamide-releasing neurons in comparison with SIFamide receptor-expressing neurons is characterized in a detail-rich manner. Isolating males or exposing them to mating experience modifies the anatomical organization of SIFamidergic axon termini projecting onto SIFamide receptor-expressing neurons. This structural synaptic plasticity is accompanied by changes in calcium influx. Lastly, it is reported that corazonin-releasing neurons are modulated by SIFamide releasing neurons and impact the duration of mating behavior.

Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide, and contributes to an understanding how motivated behavior such as mating is orchestrated by modulatory peptides. The manuscript has some points that are less convincing.*

__ Answer:__ We appreciate the reviewer's positive feedback regarding our investigation into the role of the SIFamide receptor (SIFaR) in mating behavior in male Drosophila melanogaster. We are pleased that the detailed characterization of SIFamide-releasing neurons and their anatomical changes in response to social isolation and mating experience has been recognized as a valuable contribution to the understanding of synaptic plasticity and its impact on behavior. We are also grateful that the reviewer described our manuscript as a "highly interesting study" that advances knowledge about the behavioral roles of SIFamide and contributes to the understanding of how motivated behaviors, such as mating, are orchestrated by modulatory peptides. We sincerely thank the reviewer for these encouraging comments about our work.

We acknowledge the reviewer's concerns about certain aspects of our manuscript that may be less convincing. We are committed to addressing these points thoroughly to strengthen our arguments and enhance the clarity of our findings. In response to the feedback, we have made several revisions throughout the manuscript, including clarifying our methodology, enhancing the presentation of our data, and providing additional context where needed. We believe these changes will improve the overall quality of the manuscript and make our conclusions more compelling. Thank you for your thoughtful review, and we look forward to your further insights.

Comment 1. *It remains unclear why the authors link the differentially motivated duration of mating behavior with the psychological concept of interval timing. This distracts from the actually interesting neurobiology and is not necessary to make the study interesting. The study deals with the modulation of mating behavior by SIFamide. The abstraction that SIFamide plays a role in the neuronal calculation of time intervals for the perception of time sequenc es is not convincing in itself. *

• Answer: We appreciate the reviewer's thoughtful comments regarding our conclusion that links SIFamide to interval timing in mating behavior. We recognize that our data primarily indicate that SIFamide is essential for normal mating duration and influences the motivation-dependent aspects of this behavior. We also acknowledge the need for more robust evidence to establish a clearer connection between these findings and interval timing. Recent research by Crickmore et al. has provided valuable insights into how mating duration in Drosophila *serves as an effective model for examining changes in motivation over time as behavioral goals are achieved. For example, around six minutes into mating, sperm transfer occurs, resulting in a significant shift in the male's nervous system, where he no longer prioritizes continuing the mating at the expense of his own survival. This pivotal change is mediated by four male-specific neurons that release the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not take place, and as a result, the male fails to reduce his motivation, leading to matings that can extend for hours instead of the typical duration of approximately 23 minutes [10].

Recent research conducted by Crickmore et al. has secured NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to investigate mating duration and sperm transfer timing in Drosophila as a genetic model for understanding interval timing. Their study emphasizes how fluctuations in motivation over time can affect mating behavior, particularly noting that significant behavioral changes occur during mating. For instance, around six minutes into the mating process, sperm transfer takes place, which corresponds with a notable decrease in the male's motivation to continue mating [10]. These findings indicate that mating duration serves not only as an endpoint for behavior but may also reflect fundamental mechanisms associated with interval timing.

   We believe that by leveraging the robustness and experimental tractability of these findings, along with our own work on SIFamide's role in mating behavior, we can gain deeper insights into the molecular and circuit mechanisms underlying interval timing. We will revise our manuscript to clarify this relationship and emphasize how SIFamide may interact with other neuropeptides and neuronal circuits involved in motivation and timing.

   In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper [3], as detailed below:

"We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the 'interval timing' that allows an animal to subjectively experience the passage of physical time [11-16]."

   Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. *PLoS Genetics*, *19*(5), e1010753.

   We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently [6-8].

   Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. *G3: Genes, Genomes, Genetics*, jkae255.

   Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body's Influence on Interval Timing Behaviors of Drosophila melanogaster. *Behavior Genetics*, *54*(5), 416-425.

   Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. *Gene Reports*, *36*, 101999.

   Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in *Drosophila*.

"The dimension of time is the fundamental basis for an animal's survival. Being able to estimate and control the time between events is crucial for all everyday activities [25]. The perception of time in the seconds-to-hours range, referred to as 'interval timing', is involved in foraging, decision making, and learning via activation of cortico-striatal circuits in mammals [26]. Interval timing requires entirely different neural mechanisms from millisecond or circadian timing [27-29]. There is abundant psychological research on time perception because it is a universal cognitive dimension of experience and behavioral plasticity. Despite decades of research, the genetic and neural substrates of temporal information processing have not been well established except for the molecular bases of circadian timing [30,31]. Thus, a simple genetic model system to study interval timing is required. Considering that the mating duration in fruit flies, which averages approximately 20 minutes, is well within the range addressed by interval timing mechanisms, this behavioral parameter provides a relevant context for examining the neural circuits that modulate the Drosophila's perception of time intervals. Such an investigation necessitates an understanding of the extensive neural and behavioral plasticity underlying interval timing [32-37]."

We would like to highlight that many researchers are currently working to bridge the gap between interval timing as a purely psychological concept and its neurobiological underpinnings, as illustrated in the following articles [15,17-20]. We appreciate the reviewer's concerns regarding the relationship between mating duration and interval timing. However, we believe that our LMD and SMD model can effectively bridge the gap between psychological concepts and neurobiological mechanisms using a straightforward genetic model organism. By employing Drosophila as our model, we aim to elucidate the underlying neural circuits that govern these behaviors, thereby contributing to a deeper understanding of how interval timing is represented in both psychological and biological contexts.

Matell, M. S. Neurobiology of Interval Timing. Adv. Exp. Med. Biol. 209-234 (2014) doi:10.1007/978-1-4939-1782-2_12.

Matell, M. S. & Meck, W. H. Cortico-striatal circuits and interval timing: coincidence detection of oscillatory processes. Cogn. Brain Res. 21, 139-170 (2004).

Merchant, H. & Lafuente, V. de. Introduction to the neurobiology of interval timing. Adv Exp Med Biol 829, 1-13 (2014).

Golombek, D. A., Bussi, I. L. & Agostino, P. V. Minutes, days and years: molecular interactions among different scales of biological timing. Philosophical Transactions Royal Soc B Biological Sci 369, 20120465 (2014).

Balcı, F. & Toda, K. Editorial: Psychological and neurobiological mechanisms of time perception and temporal information processing: insight from novel technical approaches. Front. Behav. Neurosci. 17, 1208794 (2023).

Comment 2. *For all behavioral experiments, genetic controls should always be conducted. That is, both the heterozygous Gal4-line as well as the heterozygous UAS-line should be used as controls. This is laborious, but important and common standard. The authors often report data only for offspring from genetc crosses in which UAS-lines and Gal4-lines are combined (e.g. figure S1). This is not sufficient. *

• *Answer: We are grateful for the reviewer's constructive suggestions regarding the genetic control experiments. In response to similar concerns raised by another reviewer, we have conducted all necessary genetic control experiments and included the results in Supplementary Information 1-2. We hope that this thorough effort will demonstrate to both the reviewer and readers that the LMD and SMD behaviors represent stable and reproducible phenotypes for investigating the genetic components of interval timing.

Comment 3. *There are quite a lot of citations of preprints, including preprints from the authors's own lab. It seems inappropriate to cite non-peer reviewed preprints in order to present the basic principles of the study (interval timing in flies) as recognized knowledge. In general, it is unclear whether the information presented in these multiple preprints will turn out to be credible and acceptable. *

• *Answer: We concur with the reviewer and have removed most of the preprint material, retaining only one preprint that discusses SIFa function, which has been co-submitted with this manuscript.

Comment 4. *Anatomical images are often very small and not informative. For example, figure S1 O, R, S and U shows small images of fly brains and ventral nerve chords that do not convincingly describe the expression of fluorescent proteins. The choice of a threshold to quantify fluorescence seems arbitrary. It is also not clear what the quantification "83% of brain and 71% of VNC SIFaR+ neurons" actually tells us. This quantification does not rely on counting neurons (such as 83% of neurons), but only shows how fluorescence in these neurons overlaps with an immunostaining of an ubiquitous active zone protein. The same is true for figure S2 or S3: overlapping brain areas do not inform you about numbers of cells, as stated in the text. *

Answer: We appreciate the reviewer's concerns regarding our imaging quantification methods. In response to similar questions raised by another reviewer, we have thoroughly reformatted our methods section and eliminated much of the overlapping data that appeared unnecessary for this paper. We recognize the importance of providing a clear and transparent methodology for both readers and the broader scientific community. Instead of using maximum projection of confocal images, we employed a projection method that incorporates the standard deviation function available in ImageJ. Based on our experience, this approach yields more reliable quantification results, allowing for a more accurate assessment of our data. To ensure clarity and reproducibility, we have detailed our methods in the MATERIALS AND METHODS section as follows:

• *

"The quantification of the overlap was performed using confocal images with projection by standard deviation function provided by ImageJ to ensure precise measurements and avoid pixel saturation artifacts."

We appreciate the reviewer's suggestion regarding the inclusion of image quantification data for overlapping regions, which may not be essential to the logical flow of our narrative and could lead to confusion for readers. In response, we have removed nearly all of the quantification data related to overlapping regions, retaining only those that we consider critical for the paper. Currently, only Fig. S3B-E remains, as it is important for illustrating how SIFa neuronal arborization interacts with SIFaR neurons in the central nervous system.

Additionally, we fully agree with the reviewer that the overall size of the confocal images was too small for effective assessment. To address this concern, we have enlarged all confocal images and increased the spacing in the figures. We believe these improvements will enhance the clarity of our manuscript and facilitate a better understanding of our findings.

Comment 5. *The authors have consistently confused the extensive overlap of neuronal processes (dendrites and presynaptic regions) across large brain areas with synaptic connections. One cannot infer functional synaptic connectivity from the overlap of these fluorescent signals. *

Answer: We appreciate the reviewer's feedback and, in light of similar comments from another reviewer, we have removed most of the DenMark and syt.eGFP data, retaining only Fig. 3A. We are grateful for the constructive suggestions, which have significantly enhanced our manuscript. We believe that these revisions have clarified the narrative for readers, allowing for a more focused exploration of SIFaR's role in synaptic plasticity and neuronal orchestration.

Reviewer #3

General Comments: In this revised manuscript, the authors have fully and satisfactorily addressed my comments on the previous version. I recommend publication of this manuscript.

__ Answer:__ We would like to extend our heartfelt thanks for the careful consideration and positive assessment of our revised manuscript. Your insightful feedback has been instrumental in shaping the final version of our work, and we are delighted to hear that our revisions have met your expectations.

Your dedication to ensuring the quality and rigor of the scientific literature is truly commendable, and we are immensely grateful for the time and effort you have devoted to reviewing our paper. Your support for publication is a significant encouragement to us and validates the hard work we have put into addressing the issues you raised.

Please accept our sincere appreciation for your professional and constructive approach throughout the review process. We look forward to the possibility of contributing to the scientific community through the dissemination of our research.

REFERENCES

1. Kim WJ, Jan LY, Jan YN. Contribution of visual and circadian neural circuits to memory for prolonged mating induced by rivals. Nat Neurosci. 2012;15: 876-883. doi:10.1038/nn.3104
2. Kim WJ, Jan LY, Jan YN. A PDF/NPF Neuropeptide Signaling Circuitry of Male Drosophila melanogaster Controls Rival-Induced Prolonged Mating. Neuron. 2013;80: 1190-1205. doi:10.1016/j.neuron.2013.09.034
3. Lee SG, Sun D, Miao H, Wu Z, Kang C, Saad B, et al. Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. PLOS Genet. 2023;19: e1010753. doi:10.1371/journal.pgen.1010753
4. Zhang X, Miao H, Kang D, Sun D, Kim WJ. Male-specific sNPF peptidergic circuits control energy balance for mating duration through neuron-glia interactions. bioRxiv. 2024; 2024.10.17.618859. doi:10.1101/2024.10.17.618859
5. Merchant H, Luciana M, Hooper C, Majestic S, Tuite P. Interval timing and Parkinson's disease: heterogeneity in temporal performance. Exp Brain Res. 2008;184: 233-248. doi:10.1007/s00221-007-1097-7
6. Sun Y, Zhang X, Wu Z, Li W, Kim WJ. Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. G3: Genes, Genomes, Genet. 2024; jkae255. doi:10.1093/g3journal/jkae255
7. Zhang T, Zhang X, Sun D, Kim WJ. Exploring the Asymmetric Body's Influence on Interval Timing Behaviors of Drosophila melanogaster. Behav Genet. 2024; 1-10. doi:10.1007/s10519-024-10193-y
8. Huang Y, Kwan A, Kim WJ. Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. Gene Rep. 2024; 101999. doi:10.1016/j.genrep.2024.101999
9. Kim WJ, Song Y, Zhang T, Zhang X, Ryu TH, Wong KC, et al. Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster. bioRxiv. 2024; 2024.06.04.597277. doi:10.1101/2024.06.04.597277
10. Thornquist SC, Langer K, Zhang SX, Rogulja D, Crickmore MA. CaMKII Measures the Passage of Time to Coordinate Behavior and Motivational State. Neuron. 2020;105: 334-345.e9. doi:10.1016/j.neuron.2019.10.018
11. Buhusi CV, Meck WH. What makes us tick? Functional and neural mechanisms of interval timing. Nat Rev Neurosci. 2005;6: 755-765. doi:10.1038/nrn1764
12. Merchant H, Harrington DL, Meck WH. Neural Basis of the Perception and Estimation of Time. Annu Rev Neurosci. 2012;36: 313-336. doi:10.1146/annurev-neuro-062012-170349
13. Allman MJ, Teki S, Griffiths TD, Meck WH. Properties of the Internal Clock: First- and Second-Order Principles of Subjective Time. Annu Rev Psychol. 2013;65: 743-771. doi:10.1146/annurev-psych-010213-115117
14. Rammsayer TH, Troche SJ. Neurobiology of Interval Timing. Adv Exp Med Biol. 2014; 33-47. doi:10.1007/978-1-4939-1782-2_3
15. Golombek DA, Bussi IL, Agostino PV. Minutes, days and years: molecular interactions among different scales of biological timing. Philosophical Transactions Royal Soc B Biological Sci. 2014;369: 20120465. doi:10.1098/rstb.2012.0465
16. Jazayeri M, Shadlen MN. A Neural Mechanism for Sensing and Reproducing a Time Interval. Curr Biol. 2015;25: 2599-2609. doi:10.1016/j.cub.2015.08.038
17. Balcı F, Toda K. Editorial: Psychological and neurobiological mechanisms of time perception and temporal information processing: insight from novel technical approaches. Front Behav Neurosci. 2023;17: 1208794. doi:10.3389/fnbeh.2023.1208794
18. Gür E, Duyan YA, Arkan S, Karson A, Balcı F. Interval timing deficits and their neurobiological correlates in aging mice. Neurobiol Aging. 2020;90: 33-42. doi:10.1016/j.neurobiolaging.2020.02.021
19. Merchant H, Lafuente V de. Introduction to the neurobiology of interval timing. Adv Exp Med Biol. 2014;829: 1-13. doi:10.1007/978-1-4939-1782-2_1
20. Matell MS. Neurobiology of Interval Timing. Adv Exp Med Biol. 2014; 209-234. doi:10.1007/978-1-4939-1782-2_12

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Referee #3

Evidence, reproducibility and clarity

Summary

The article investigates the role of the neuropeptide SIFa and its receptor SIFaR in regulating two distinct mating duration behaviors in male Drosophila melanogaster, Longer-Mating-Duration (LMD) and Shorter-Mating-Duration (SMD). The study reveals that SIFaR expression in specific neurons is required for both behaviors. It shows that social context and sexual experience lead to synaptic reorganization between SIFa and SIFaR neurons, altering internal brain states. The SIFa-SIFaR/Crz-CrzR neuropeptide relay pathway is essential for generating these behaviors, with Crz neurons responding to SIFa neuron activity. Furthermore, CrzR expression in non-neuronal cells is critical for regulating LMD and SMD behaviors. The study utilizes neuropeptide RNAi screening, chemoconnectome (CCT) knock-in, and genetic intersectional methods to elucidate these findings.

Major Comments

• Are the key conclusions convincing? The key conclusions are intriguing but require more robust data to be fully convincing. While the study presents compelling evidence for the involvement of SIFa and SIFaR in mating behaviors, additional experiments are needed to firmly establish the proposed mechanisms.
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify certain claims as preliminary or speculative. Specifically, the proposed SIFa-SIFaR/Crz-CrzR neuropeptide relay pathway is only investigated via imaging approach. More experiments using behavioral tests are needed to confirm that Crz relays the SIFa signaling pathway. For example, Crz-Gal4>UAS-SIFaR RNAi should be done to show that SIFaR+ Crz+ cells are necessary for LMD and SMD.
• Would additional experiments be essential to support the claims of the paper? Yes, additional experiments are essential. Detailed molecular and imaging studies are needed to support claims about synaptic reorganization. For example:
  • More controls are needed for RNAi and Gal80ts experiments, such as Gal4-only control, RNAi-only control, etc.
  • Using synaptic markers and high-resolution imaging to observe synaptic changes directly.
  • Electrophysiological recordings from neurons expressing SIFa and SIFaR to analyze their functional connectivity and activity patterns.
• Are the suggested experiments realistic in terms of time and resources? The suggested experiments are realistic but will require considerable time and resources. Detailed molecular interaction studies, imaging synaptic plasticity, and electrophysiological recordings could take several months to over a year, depending on the complexity and availability of necessary equipment and expertise. The cost would be moderate to high, involving expenses for reagents, imaging equipment, and animal husbandry for maintaining Drosophila stocks.
• Are the data and the methods presented in such a way that they can be reproduced? The methods are generally described in detail, allowing for potential reproducibility. However, more precise documentation of certain experimental conditions, such as the timing and conditions of RNAi induction and temperature controls, is necessary. The methods about imaging analysis are too detailed. The exact steps about how to use ImageJ should be removed.
• Are the experiments adequately replicated and statistical analysis adequate? Most figures in the manuscript need to be re-plotted. The right y-axis "Difference between means" is not necessary, if not confusing. The image panels are too small to see, while the quantification of overlapping cells are unnecessarily large. The figures are too crowded with labels and texts, which makes it extremely difficult to comprehend the data.

Minor Comments

• Specific experimental issues that are easily addressable. Clarify the timing of RNAi induction and provide more detailed figure legends for better understanding and reproducibility.
• Are prior studies referenced appropriately? Yes.
• Are the text and figures clear and accurate? The text is generally clear, but the figures need re-work. See comment above.
• Suggestions to improve the presentation of data and conclusions. Use smaller fonts in the bar plots and make the plots smaller. Enlarge the imaging panels and let the pictures tell the story.

Significance

Nature and Significance of the Advance

This study aims to advance understanding of how neuropeptides modulate context-dependent behaviors in Drosophila. It provides novel insights into the role of SIFa and SIFaR in interval timing behaviors, contributing to the broader field of neuropeptide research and behavioral neuroscience. However, the significance of the findings is limited by the preliminary nature of some claims and the need for additional supporting data.

Context in Existing Literature

The work builds on previous studies that identified various roles of neuropeptides in behavior modulation but lacked detailed mechanistic insights. By elucidating the SIFa-SIFaR/Crz-CrzR pathway, this study attempts to fill a gap in the literature, but more robust evidence is required to solidify its contributions.

Interested Audience

The findings will interest neuroscientists, behavioral biologists, and researchers studying neuropeptides and their roles in behavior and neural circuitry. Additionally, this research may have implications for understanding neuropeptidergic systems in other organisms, making it relevant to a broader audience in the fields of neurobiology and physiology.

Field of Expertise

Keywords: Neuropeptides, Drosophila melanogaster, Behavioral Neuroscience. Areas without sufficient expertise: courtship behavior.

Recommendation

I recommend a major revision of this manuscript. The study presents intriguing findings, but several key claims are preliminary and require additional experiments for support. The data is poorly presented and the figures can be significantly improved. Detailed molecular and imaging studies, as well as more rigorous statistical analyses, are necessary to strengthen the conclusions. Addressing these concerns will significantly improve the robustness and impact of the paper.

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Reply to the reviewers

We want to thank both reviewers for their thorough and constructive review of our manuscript. Below, we have re-iterated their comments followed by an explanation of how we have revised the manuscript to address this.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This manuscript presented by Segeren et al. applied an interesting HRASG12V inducible cell model to study the mechanism of cellular resistance to replication stress inducing agents. They also employed a novel reversible fixation technique which allows them to FAC sort cells according to their replication stress levels before applying single cell sequencing analysis to the same cell populations. By comparing cells with low levels of replication stress to cells with high levels of replication stress, they found that reduction in gene expression of FOXM1 target genes potentially protects cells against replication stress induced by CHK1i plus gemcitabine combination. Overall, this is a very interesting study. However, the following points should be addressed prior to publication:

Major: 1. Figure 3E and 3F showed two lists of differentially expressed genes in γH2Ax low cells. However, instead of arbitrarily extracting the FOXM1 target genes and TP53 targeted genes, it would be appreciated if the author could perform an unbiased and unsupervised gene set enrichment analysis such as Enrichr.

As recommended, we performed an enrichment analysis using Enrichr to identify transcriptional programs associated with the we used the genes that were downregulated in the γH2AX-low cells. FOXM1 appeared as a prominent hit in different databases (both experimental and computational). We have included the lists of differentially expressed genes as an additional supplemental table (Table S1) and have included the Enrichr results as Table S3 (i.e. CHEA and ENCODE). We have described our results in lines 198-200 of the revised manuscript.

1. At the experiment design stage, the authors also included HRASG12V status as a test condition because they previously found that HRASG12V mutation induces basal level replication stress and they would like to include this condition to study the adaptation to replication stress (line 110). However, the difference in HRASG12V negative and HRASG12V positive cells was not followed up in the later part of the paper. Can they show lists of differentially expressed genes identified under HRASG12V negative conditions as well (in the same format of Figure 3E and 3F) and comment on the differences as well?

In the original manuscript, we included heatmaps of differentially expressed genes in the control cells in Figure S2. For improved clarity, we have modified this figure so that the heatmaps are labeled "Control cells". In the revised manuscript, we have also included Table S2, which lists the differentially expressed genes between yH2AX low and yH2AX high control cells, and Table S3, which lists the Enrichr results obtained based on these gene lists.

We observed FOXM1 target genes in both the control and HRASG12V cells. Thus, the mechanism we identify does not appear to be specific to oncogenic Ras expression. We discuss this in lines 221-225. Because there were no other notable differences between the gene sets, we do not focus on this in the manuscript.

1. In line 194 and in Figure S2B, the authors claimed that ANLN, HMGB2, CENPE, MKI67, and UBE2C demonstrated co-expression, but other genes displaying similar correlation scores were not commented (such as F3, CYR61, CTGF, etc). To avoid being biased at the analysis stage, the authors should define clearly what the cut-off of correlation score is and why only co-expression of ANLN, HMGB2, CENPE, MKI67, and UBE2C were mentioned.

As suggested, we explain now in the revised manuscript that we focused on gene clusters consisting of at least 3 genes, that had a correlation coefficient greater than or equal to 0.4 with at least one other gene within the clusters. This cutoff is typically defined as representing a "moderate to good" correlation in biological data (Overholser, Sowinski, 2008). To make clear which clusters correlating gene sets passed these criteria, we have also highlighted these genes in Figure S3B. This returned the cluster we had already identified as FOXM1 targets, and as well spotted by the reviewer, a larger cluster which included F3, CYR61, CTGF, SERPINE1, ANKRD1, KRTAP2-3, UGCG, and AMOTL. Our Enrichr analysis did not identify any putative transcription factors linking the genes in this larger cluster. We are still interested to identify the putative transcription regulation mechanism linking these genes in future studies, but this is beyond the scope of the current manuscript. We have described these observations in lines 211-218.

1. In line 215, instead of validating CENPE, UBE2C, HMGB2, ANLN, and MKI67 individually, the authors decided to validate FOXM1 instead, because they believe all the aforementioned genes are targets of FOXM1, therefore, validating FOXM1 alone would suffice. Again, this makes the validation process also biased. CENPE, UBE2C, HMGB2, ANLN, and MKI67 should be validated individually because they might sensitize cells to replication stress via different mechanisms. Besides, if all these genes were identified together because they are FOXM1 target genes, why did the authors not identify FOXM1 itself as a differentially expressed gene from the single cell sequencing? The sequencing only analyzed the S/G2/M cells, expression of FOXM1 should be detected easily.

We agree with the reviewer that the omission of individual FOXM1 target genes in the validation process makes a biased impression. Therefore we ordered siRNAs against CENPE, UBE2C, HMGB2, ANLN, and MKI67. Similar to the other DE genes in the original mini-screen we first knocked down these genes using the siRNA Smartpools (pools of 4 individual siRNAs against each genes). Here, we observed a decrease in γH2AX signal compared to drug-treated cells transfected with all 5 Smartpools compared to drug-treated cells transfected with control siRNA. We next moved on to the deconvolution step of the screen, where we transfected cells with 4 individual siRNA against each gene. Here, we observed inconsistent effects of ANLN, CENPE, and HMGB2 when comparing the individual siRNAs, which all produce efficient knockdown of their target genes. But interestingly, for both MKI67 and UBE2C, each of the 4 individual siRNAs similar decreased yH2AX signal, though it was not as strong as the decrease observed when FOXM1 is knocked out. Understanding the exact mechanism of how MKI67 and UBE2C reduce replication stress is beyond the scope of this paper, but we hypothesize that, as with FOXM1, it is likely linked to their role in promoting progression through the cell cycle. These results are shown in Figures S5, and we mention these remarkable findings in the revised abstract and discuss these in the light of the recent literature in the Discussion section (lines 275-286).

Then, we also addressed the comment about FOXM1 not being changed in the single cell RNA-seq analysis. We could indeed readily detect FOXM1 expression our single-cell RNA sequencing data. The difference in expression did not change significantly in cells sorted according to γH2AX level (Figure 4C). Because FOXM1 is highly regulated post-translationally, we hypothesized that an increase in the (active) protein is correlated to increased replication stress rather than transcript levels. This was indeed the case and we further explain our experiment to test this hypothesis in response to Point #6 (results are displayed in Figure 4D and described in lines 201-209).

1. As pointed out by the author in the Discussion, single cell sequencing is not good at differentiating the causes from the consequences. The author tried to validate many of the differentially expressed genes in γH2Ax low cells. However, the fact that only FOXM1 knockdown passed the validation and deconvolution pointed out that the great majority of the identified genes are not the cause of the sensitivity change to replication stress inducing agents but likely the consequences. Therefore, in Figure S2C and S2D, it would be better that the authors could just name the genes as 'downregulated genes' in Figure S2C and 'upregulated genes' in Figure S2D. Taking into consideration that the expression change in the great majority of these genes are just consequences of sensitivity change to replication stress, defining them as 'potentially sensitizing' genes and 'potentially conferring resistance' genes is rather misleading.

We agree that the way we originally labeled these plots may have been misleading. We have renamed then to "Downregulated in yH2AXlow" and "Upregulated in yH2AXlow", as recommended by the reviewer.

1. To better prove that FOXM1 is the leading cause of the sensitivity to CHK1i+Gemcitabine induced replication stress, can the authors show the FOXM1 expression status in the tolerant cell population identified in Figure 1B (lowest panel)? Alternatively, can they plot FOXM1 expression level in the same tSNE plots shown in Figure 3B to 3D to see whether some of the γH2Ax low populations also show reduced FOXM1 expression?

FOXM1 expression levels were not increased with gH2AXhigh versus gH2AXlow HRASG12V cells in the single cell RNA-sequencing data (Figure 4C in revised manuscript). However, as mentioned in our answer to point #4 we performed an additional experiment, which showed a strong positive correlation between phospho-FOXM1 and γH2AX (as measured by flow cytometry) in S-phase cells (Figure 4D). This indicates that the active form of the FOXM1 indeed increases as yH2AX levels increase, consistent with the observed increase in FOXM1 target genes. These results are described in lines 201-209.

1. Clonogenic survival assay in Figure 4D was not quantified properly in Figure 4E. To rule out the siFOXM1 mediated growth/survival defects and to only focus on the siFOXM1 mediated resistance to CHK1i+Gemcitabine, the survival rate (intensity percent in this case) of CHK1i+Gemcitabine treated condition should be normalized against the survival rate of the Vehicle condition. E.g., the intensity percent of the siSCRAMBLE after treatment should be divided by the intensity percent of the untreated siSCRAMBLE; the intensity percent of the si#1 after treatment should be divided by the intensity percent of the untreated si#1, and so on. If the authors would like to show siFOXM1 induced growth/survival defects, they can still present the left part of the Figure 4E (the Vehicle group).

Originally, we chose to show the absolute IntensityPercent for all groups, without normalizing to the untreated group, because we wanted to also highlight the FOXM1-mediated changes in growth. We agree that normalizing the IntensityPercent of the drug-treated group to the vehicle group better highlights the siFOXM1-mediated resistance. We have therefore re-analyzed the data and presented it this way in Figure 5E (described in lines 293-295). We moved our original Figure 4E to a new supplemental figure (Figure S4B) to still point out the effects of siFOXM1 on cell growth in untreated cells.

Minor:

1. In line 176, the author claimed that 'Interestingly, rare cells treated with CHK1i + gemcitabine are located within the untreated cell cluster (Fig. 3C)'. However, it is not as obvious where these cells are in the plot, especially to people who are new to tSNE plots. It would be appreciated if the authors could label these cells by circling them with red lines and make the point stronger.

Rather than circling these points (we thought this would make the plot too "busy"), we have created an inset that zooms in on the region where we see the untreated cells within the untreated cell cluster. Within the inset, we use arrows to point out the cells we are referring to. This can be seen in our updated Figure 3C.

1. In Figure S2B, it will be ideal to label clearly which genes are upregulated genes and which are downregulate.

On the x-axis of the heatmap, we have drawn lines to separate the downregulated and upregulated genes.

1. In line 50, the word 'multifaced' needs to be corrected to 'multifaceted'.

Thank you for catching this, we have fixed it.

1. It is unclear what 'underly drug resistance' means in line 150.

We have reworded this sentence so that is more clear. It is now written as follows: "we aimed to identify gene-expression programs that mediate the low level of RS in a subset of cells, which could potentially mediate drug resistance". This change is in lines 155.

1. It is advised that the phrase 'cell cycle position' could be changed to 'cell cycle phase' or 'cell cycle stage'.

We purposefully used the phrase "cell cycle position" because we wanted to emphasis gradient-like progress through the cell cycle rather than a discrete distinction from one-phase to the next. We have reworded the text slightly to now say "position within S-phase" (lines 163, 187, 191, 208), since all the cells we are interested in are in S phase, but some are further through S phase than others.

1. In line 185, the word 'in' after 'within' can be removed.

Thank you for catching this, we have fixed it.

1. In line 194, 'Among genes downregulated in γH2AXlow cells, the expression of ANLN, HMGB2, CENPE, MKI67 and UBE2C correlated' is missing an 'are' in front of the word 'correlated'.

Thank you for catching this, we have fixed it.

1. In line 239, Fig.SC3 should be Fig. S3C.

Thank you for catching this, we have fixed it.

1. FOXM1 is known as a crucial gene for G2/M transition. Therefore, FOXM1 knockdown cells are expected to be mostly arrested at the G2/M interface. Therefore, in line 244, it is incorrect to say stronger FOXM1 knockdown induced a 'lower proportion of cells in G2 phase'. In fact, as shown in Figure 4C, cells are accumulating in G2 phase (peaking around 11M on the DAPI axis) and depleted from G1 phase (peaking around 7M).

We have reworded this to say that there is "a higher proportion of cells in S-phase and a less distinct G2 peak" (lines 270-271). The DAPI profiles of the scrambled, siFOXM1 #1, and siFOXM1 #2 conditions all show an S-phase "valley" between a G1 and G2 peak (the valley sits at about 8M-9M). In the siFOXM1 #3 and siFOXM1 #4 conditions, we no longer see this valley, therefore we interpret this as cells still in S-phase. If they had progressed from S-phase into G2 phase, we expect that we would again see this "valley" to the left of a clear G2 peak. In the figure below, we overlayed DNA content histograms of the different FOXM1 targeting siRNAs with the scrambled siRNA to demonstrate this point more clearly.

Reviewer #1 (Significance (Required)):

Advance: The study reported a novel reversible fixation technique which can lead to potentially good citations. However, the findings from the single cell sequencing alone fell short in novelty to reach high impact because FOXM1 has been reported to impact on cellular sensitivity to CHK1 inhibition mediated replication stress (PMC7970065). Moreover, the study did not provide mechanistic explanation to the observed phenotype but only validated the finding from the sequencing, and the gene of focus (FOXM1) was not originally identified from the sequencing, slightly undermining the paper's foundation. To make it a better paper. the authors need to be less biased when it comes to data analysis and interpretation.

Audience: People who are interested in basic research in cell cycle, DNA damage, cancer, chemotherapy would be interested.

My expertise: Cancer, DNA damage, cell cycle

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary:

Replication stress activates ATR and CHEK1 kinases as part of the inter S phase DNA damage response. CHEK1 kinase inhibitors (CHK1i) have been shown to induce an accumulation of unresolved replication stress and widespread DNA damage and cell death caused by replication catastrophe, and are therefore under clinical evaluation. At the same time, CHEK1 inhibition results in the activation of CDK1 and FOXM1 and premature expression of G2/M genes (Saldivar et al., 2018 Science). FOXM1-drivent premature mitosis has been shown to be required for the replication catastrophe and CHK1i sensitivity (Branigan et al., 2021 Cell Rep.). In this study, Segeren and colleagues set out to investigate the mechanisms of replication stress tolerance. They used CHK1i inhibitors in combination with the DNA-damaging chemotherapeutic agent Gemcitabine and oncogenic HRASG12V expression to increase replication stress. The authors utilized an intriguing setup of combined immunofluorescence staining followed by single cell RNA-seq analysis to overcome limitations of bulk cell analyses. In particular, the authors sought to identify genes that are differentially regulated in replication stress-tolerant cells compared to sensitive cells. However, even single cell analyses can be confounded by differences in cell cycle distribution. To mitigate this, the authors selected mid S-phase cells for their analysis. While this may not have completely eliminated minor differences in cell cycle progression, the authors identified FOXM1-regulated G2/M cell cycle genes, among others, that were down-regulated in the tolerant cells. When the authors followed up on the effect of these genes on replication stress tolerance, they identified FOXM1 knockdown as the only robust mediator of replication stress tolerance.

Major comments:

The authors observed that cell cycle distribution could be a major confounding factor in their single cell analysis and attempted to reduce this variation by selecting mid S-phase cells based on the DAPI signal. The authors then chose to compare gH2AXlow and gH2AXhigh subpopulations of RPE-HRASG12V cells because their "DAPI signal was comparable" (line 181-184). However, their data show that these subpopulations also show differences in their DAPI signal distribution, with gH2AXlow cells tending to have lower DAPI signals than gH2AXhigh cells (Supplementary Figure 2A). Thus, the major confounding factor that the authors sought to remove seems to have prevailed and it remains possible that the difference in cell cycle gene expression is merely due to differences in cell cycle progression of the individual cells. Given that DAPI information seem to be readily available for the individual cells, the authors should normalize their analysis to the DAPI signal to remove this potential confounding effect or clearly state this potential limitation.

We agree that indeed it is very challenging to fully disentangle the influence of cell cycle distribution on our analysis. And indeed, the γH2AXlow HRASG12V cells have slightly reduced median DNA content compared to γH2AXmid and γH2AXhigh. However, this was not the case in the RPE control cells, and we still found that FOXM1 target genes were strongly enriched in the γH2AXhigh cells (Fig S2C and Table S4). Therefore, it is highly unlikely that bias in S-phase position distributions does not explain our results. Nevertheless, to be transparent about this write in the Results on lines 192-193 the following: "The other groups all showed similar DAPI intensities, although gH2AXlow RPE-HRASG12V cells showed a slight but statistically significant reduction compared to their gH2AXhigh counterparts (Fig. S2A)".

In our subsequent experiments to assess the relationship between phospho-FOXM1 (representing the transcriptionally active protein) and γH2AX, we observed that though there was a strong correlation between pFOXM1 and γH2AX, there was no correlation between phospho-FOXM1 and DAPI (Figure 4D-E). We therefore would like to point out that although our readout for replication stress inevitably increases as cells progress through DNA replication, heterogeneity in phospho-FOXM1 levels cannot be explained by position in S-phase. These results are described in lines 203-209.

Finally, we do not think it would be statistically appropriate to use the DAPI signal (generated by fluorescence intensity as measured by the flow cytometer) as a normalization factor for our gene expression data.

Minor comments:

The findings of Saldivar et al., 2018 Science and Branigan et al., 2021 Cell Rep. should be mentioned in the introduction.

As recommended, we mentioned both these papers in the introduction. In line 62, we cite the Branigan paper as showing that modulation of cell cycle regulators is a strategy used by cancer cells to resist replication stress. In lines 63-65, we reference them as follows: "The RS response is tightly linked with cell cycle progression, as multiple intra S-phase checkpoint kinases play a role in curtailing proteins involved in the S-G2 transition (Branigan et al., 2021, Saldivar et al., 2018)."

The authors conclude that "cell cycle position can be a major confounding factor when evaluating the transcriptomic response to RS." It should be noted that stochastic differences in the cell cycle distribution of bulk cells are perhaps the best-known confounder in single cell analyses (see, for example, Buettner et al., 2015 Nat. Biotechnol.).

We chose to reference the Buettner paper to justify our decision to select only cycling cells in our scRNA seq approach. Our reference to the paper, and to the fact that cell cycle distribution is a major confounder in single cell analysis, is in lines 138-140.

Supplementary Figure 2A: The median should be added to the violin plots.

As suggested, we have added medians to the violin plots. In addition, we added details on statistical analysis.

The statement "Differential expression analysis revealed 19 genes that were significantly downregulated in gH2AXlow RPE-HRASG12V cells, suggesting that elevated levels of these genes are correlated with sensitivity to RS-inducing drugs" refers to Figure 3E and Table S1. However, Table S1 lists the "key resources" and does not seem to be related to this statement. A table showing log2fold-changes and FDR values should be added and referenced here.

We have generated tables with the fold change values of differentially expressed genes between the yH2AX low and yH2AX high cells. These are found in Table S1 (for HRAS G12V cells) and Table S2 (for Control cells) in the supplementary file of the revised manuscript. The "key resources" has been moved to Table S5.

The statement "Remarkably, Braningan and co-workers observed no effect of full FOXM1 deletion on cell cycle progression" seems somewhat inconsistent with what has been stated and assessed in that study. The authors may want to replace "progression" with "distribution". A reduction in proliferation is commonly observed when FOXM1 levels are reduced.

In addition, the authors may want to consider that their addition of HRASG12V and Gemcitabine may contribute to a more substantial S phase checkpoint response.

We agree with the reviewer that a reduction in proliferation is commonly observed when FOXM1 levels are reduced (Barger et al., 2021, Cheng et al., 2022, Yang et al., 2015, Wu et al., 2010), but in Branigan et al., they see no decrease in proliferation with knockout of FOXM1. They state "There were no apparent differences in the growth rate of the LIN54 and FOXM1 KO versus EV cells over 10 days (Figure 1G)". Though they do not elaborate on why they see this unexpected response, we suspect a permanent full knockout of FOXM1 could cause compensatory adaptation in their cell lines. In our experiments, we perform transient knockdowns, so cells may not have the time to adapt to the loss of FOXM1 and obtain compensatory mechanisms that would allow them to continue cycling as rapidly as control cells treated with non-targeting siRNA.

However, we decided to remove this from the Discussion section, as it seemed to interrupt the discussion about the potential mechanisms underlying protection against DNA damage by FOXM1 depletion.

The statement that "the mechanism by which high FOXM1 activity is a prerequisite to accumulate DNA damage in S-phase during CHK1 inhibition remains to be uncovered" seems to neglect that premature mitosis has been suggested as a mechanistic cause (Branigan et al., 2021 Cell Rep.). It would be helpful if the authors could elaborate on this.

In our discussion, we do already emphasize the described role of FOXM1 in promoting premature mitosis (lines 330-337), but we argue that in our experimental conditions we are observing another - previously undescribed- role for FOXM1 in promoting replication stress during S phase. We previously observed with live cell imaging that CHK1i + gemcitabine does not cause premature mitosis in RPE-HRASG12V cells (published in Segeren et al. Oncogene 2022, Figure 5). Instead, these cells typically showed a cell cycle exit from G2. This makes it highly unlikely that premature mitosis is the reason why these cells would accumulate excessive DNA damage. We realize now that it was an important omission not to elaborate on this and have added this clarification to the Discussion (lines 341-345 in revised manuscript). In addition, we have removed a few lines of less important text (about the lack of direct effect of FOXM1 KO in the Branigan paper; see answer to previous point) to improve clarity and readability.

Reviewer #2 (Significance (Required)):

General assessment: The strength of the study is the intriguing methodology of combined immunofluorescence followed by single cell RNA-seq. The limitations are that this methodology does not seem to fully solve the stated problems. In addition, the study is essentially limited to confirming previous findings.

Advance: The study strengthens current knowledge but provides essentially no advance. The authors confirm existing knowledge with an additional approach. While this is not an advance in itself, it is important to the community.

Audience: I felt that the study would appeal to a basic science audience. In particular, the CHK1i and intra S-phase checkpoint areas, with limited interest beyond that.

My relevant expertise lies in transcriptomics, gene regulation and the cell cycle.

Reference list

Barger, C.J., Chee, L., Albahrani, M., Munoz-Trujillo, C., Boghean, L., Branick, C., Odunsi, K., Drapkin, R., Zou, L. & Karpf, A.R. 2021, "Co-regulation and function of FOXM1/RHNO1 bidirectional genes in cancer", eLife, vol. 10, pp. 10.7554/eLife.55070.

Branigan, T.B., Kozono, D., Schade, A.E., Deraska, P., Rivas, H.G., Sambel, L., Reavis, H.D., Shapiro, G.I., D'Andrea, A.D. & DeCaprio, J.A. 2021, "MMB-FOXM1-driven premature mitosis is required for CHK1 inhibitor sensitivity", Cell reports, vol. 34, no. 9, pp. 108808.

Cheng, Y., Sun, F., Thornton, K., Jing, X., Dong, J., Yun, G., Pisano, M., Zhan, F., Kim, S.H., Katzenellenbogen, J.A., Katzenellenbogen, B.S., Hari, P. & Janz, S. 2022, "FOXM1 regulates glycolysis and energy production in multiple myeloma", Oncogene, vol. 41, no. 32, pp. 3899-3911.

Overholser, B.R. & Sowinski, K.M. 2008, "Biostatistics primer: part 2", Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition, vol. 23, no. 1, pp. 76-84.

Saldivar, J.C., Hamperl, S., Bocek, M.J., Chung, M., Bass, T.E., Cisneros-Soberanis, F., Samejima, K., Xie, L., Paulson, J.R., Earnshaw, W.C., Cortez, D., Meyer, T. & Cimprich, K.A. 2018, "An intrinsic S/G(2) checkpoint enforced by ATR", Science (New York, N.Y.), vol. 361, no. 6404, pp. 806-810.

Segeren, H.A., van Liere, E.A., Riemers, F.M., de Bruin, A. & Westendorp, B. 2022, "Oncogenic RAS sensitizes cells to drug-induced replication stress via transcriptional silencing of P53", Oncogene, vol. 41, no. 19, pp. 2719-2733.

Wu, Q., Liu, C., Tai, M., Liu, D., Lei, L., Wang, R., Tian, M. & Lu, Y. 2010, "Knockdown of FoxM1 by siRNA interference decreases cell proliferation, induces cell cycle arrest and inhibits cell invasion in MHCC-97H cells in vitro", Acta Pharmacologica Sinica, vol. 31, no. 3, pp. 361-366.

Yang, K., Jiang, L., Hu, Y., Yu, J., Chen, H., Yao, Y. & Zhu, X. 2015, "Short hairpin RNA- mediated gene knockdown of FOXM1 inhibits the proliferation and metastasis of human colon cancer cells through reversal of epithelial-to-mesenchymal transformation", Journal of experimental & clinical cancer research : CR, vol. 34, no. 1, pp. 40-1.

We want to thank both reviewers for their thorough and constructive review of our manuscript. Below, we have re-iterated their comments followed by an explanation of how we have revised the manuscript to address this.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2024-02545

Corresponding author(s): Woo Jae, Kim

1. General Statements

We sincerely appreciate the positive and constructive feedback provided by all three reviewers. Their insightful comments have been invaluable in guiding our revisions. In response, we have made every effort to address their suggestions through additional experiments and by restructuring our manuscript to improve clarity and coherence.

In this revision, we have streamlined the presentation of our data to enhance the narrative flow, ensuring that it is more accessible to a general readership. We believe that these changes not only strengthen our manuscript but also align with the reviewers' recommendations for improvement.

We are hopeful that the revisions we have implemented meet the expectations of the reviewers and contribute to a clearer understanding of our findings. Thank you once again for your thoughtful critiques, which have greatly aided us in refining our work.

2. Point-by-point description of the revisions

Reviewer #1

General comment: This manuscript by Song et al. investigates the molecular mechanisms underlying changes in mating duration in Drosophila induced by previous experience. As they have shown previously, they find that male flies reared in isolation have shorter mating duration than those reared in groups, and also that male flies with previous mating experience have shorter mating duration than sexually naïve males. They have conducted a myriad of experiments to demonstrate that the neuropeptide SIFa is required for these changes in mating duration. They have further provided evidence that SIFa-expressing neurons undergo changes in synaptic connectivity and neuronal firing as a result of previous mating experience. Finally, they argue that SIFa neurons form reciprocal connections with sNPF-expressing neurons, and that communication within the SIFa-sNPF circuit is required for experience-dependent changes in mating duration. These results are used to assert that SIFa neurons track the internal state of the flies to modulate behavioral choice.

   __Answer:__ We appreciate the reviewer's thoughtful comments and commendations regarding our manuscript. The recognition of our investigation into the molecular mechanisms influencing mating duration in *Drosophila* is greatly valued. In particular, we are grateful for the reviewer's positive remarks about our comprehensive experimental approach to demonstrate the role of the neuropeptide SIFa in these changes. The evidence we provided indicating that SIFa-expressing neurons undergo alterations in synaptic connectivity and neuronal firing due to previous social experiences is crucial for elucidating the underlying neural circuitry involved in experience-dependent behaviors. Finally, we are thankful for the recognition of our assertion that SIFa neurons form reciprocal connections with sNPF-expressing neurons, emphasizing the importance of this circuit in modulating behavioral choices based on internal states. To provide stronger evidence for the interactions between SIFa and sNPF, we conducted detailed GCaMP experiments, which revealed intriguing neural connections between these two neuropeptides. We have included this new data in our main figure. We believe these insights contribute significantly to the existing literature on neuropeptidergic signaling and its implications for understanding complex behaviors in *Drosophila*. We look forward to addressing any further comments and enhancing our manuscript based on your invaluable feedback. Thank you once again for your constructive critique and support.

Major concerns:

Comment 1. The authors are to be commended for the sheer quantity of data they have generated, but I was often overwhelmed by the figures, which try to pack too much into the space provided. As a result, it is often unclear what components belong to each panel. Providing more space between each panel would really help.

   __Answer:__ We sincerely appreciate the reviewer’s commendation regarding the extensive data we have generated in our study. It is gratifying to know that our efforts to provide a comprehensive analysis of the molecular mechanisms underlying changes in mating duration have been recognized. We understand the concern regarding the density of information presented in our figures. We aimed to convey a wealth of data to support our findings, but we acknowledge that this may have led to some confusion regarding the organization and clarity of the panels. We are grateful for your constructive feedback on this matter. In response, we have significantly reduced the density of the main figures and decreased the size of the graphs to improve clarity. We have also increased the spacing between panels to ensure that each component is more easily distinguishable. Further details will be provided in our responses to each comment below.

• *

Comment 2. This is a rare instance where I would recommend paring down the paper to focus on the more novel, clear and relevant results. For example, all of Figure 2 shows the projection pattern of SIFa+ neuron dendrites and axons, which have been reported by multiple previous papers. Figure 7G and J show trans-tango data and SIFaR-GAL4 expression patterns, which were previously reported by Dreyer et al., 2019. These parts could be removed to supplemental figures. Figure 5 details experiments that knock down expression of different neurotransmitter receptors within the SIFa-expressing cells. The results here are less definitive than the SIFa knockdown results, and the SCope data supporting the idea that these receptors are expressed in SIFa-expressing neurons is equivocal. I would recommend removing these data (perhaps they could serve as the basis for another manuscript) or focusing solely on the CCHa1R results, which is the only manipulation that affects both LMD and SMD.

   __Answer:__ We sincerely appreciate the reviewer’s positive feedback regarding the extensive data generated in our study. We also fully agree with the reviewer that the sheer volume of our data made it challenging to support our hypothesis that SIFa neurons serve as a hub for integrating multiple neuropeptide inputs and orchestrating various behaviors related to energy balance, as highlighted in our new Figure 5N.

   In response to the reviewer's suggestions, we have streamlined our manuscript by removing excessive and redundant data to enhance clarity and simplicity. First, we have moved Figure 2 to the supplementary materials as the reviewer noted that the branching patterns of SIFa neurons are well-documented in previous literature. Second, we relocated the trans-tango data from Figure 7G to Figure S7, since this information is also well-established. We retained this data in the supplementary section to illustrate the connection of SIFa to our recent findings regarding SIFaR24F06 neuron connections. Additionally, we have completely removed the neuropeptide receptor input screening data previously included in Figure 5, as well as Figure S8, which presented fly SCope tSNE data. As suggested by the reviewer, we plan to utilize these data for a future paper focused on investigating the underlying mechanisms of SIFa inputs that modulate SIFa activity. Thanks to the reviewer’s constructive suggestions, we believe our manuscript is now more convincing and clearer for readers.

Comment 3. Finally, I would like the authors to spend more time explaining how they think the results tie together. For example, how do the authors think the changes in branching and activity in SIFa-expressing neurons tie to the change in mating duration provoked by previous experience? It would benefit the manuscript to simplify and clarify the message about what the authors think is happening at the mechanistic level. The various schematics (eg. Fig 7N) describe the results but the different parts feel like separate findings rather than a single narrative. (MECHANISMS diagram and explanation)

   __Answer:__ We appreciate the reviewer’s constructive comments, which have significantly improved our manuscript and conclusions for our readers. As the reviewer will see, we have made substantial revisions in line with the suggestions provided. We dedicated additional time to clarify the electrical activities and synaptic plasticity of SIFa neurons in relation to internal states that orchestrate various behaviors. We have summarized our hypothesis regarding the mechanistic role of SIFa neurons in Figure 5N. In brief, we propose that SIFa neurons function as a hub that receives diverse neuropeptidergic signals, which subsequently alters their electrical activity and synaptic branching. This, in turn, leads to different internal states. The internal states of SIFa neurons can then be interpreted by SIFaR-expressing cells, which help orchestrate various behaviors and physiological responses. We aim to address these aspects further in another manuscript that has been co-submitted alongside this one [1].

Comment 4. Most of the experiments lack traditional controls. For example, in experiments in Fig 1C-K, one would typically include genetic controls that contain either the GAL4 or UAS elements alone. The authors should explain their decision to omit these control experiments and provide an argument for why they are not necessary to correctly interpret the data. In this vein, the authors have stated in the methods that stocks were outcrossed at least 3x to Canton-S background, but 3 outcrosses is insufficient to fully control for genetic background.

   __Answer:__ We sincerely thank the reviewer for insightful comments regarding the absence of traditional genetic controls in our study of LMD and SMD behaviors. We acknowledge the importance of such controls and wish to clarify our rationale for not including them in the current investigation. The primary reason for not incorporating all genetic control lines is that we have previously assessed the LMD and SMD behaviors of GAL4/+ and UAS/+ strains in our earlier studies. Our past experiences have consistently shown that 100% of the genetic control flies for both GAL4 and UAS exhibit normal LMD and SMD behaviors. Given these findings, we deemed the inclusion of additional genetic controls to be non-essential for the present study, particularly in the context of extensive screening efforts. We understand the value of providing a clear rationale for our methodology choices. To this end, we have added a detailed explanation in the "MATERIALS AND METHODS" section and the figure legends of Figure 1. This clarification aims to assist readers in understanding our decision to omit traditional controls, as outlined below.

"Mating Duration Assays for Successful Copulation

The mating duration assay in this study has been reported[33,73,93]. To enhance the efficiency of the mating duration assay, we utilized the Df (1)Exel6234 (DF here after) genetic modified fly line in this study, which harbors a deletion of a specific genomic region that includes the sex peptide receptor (SPR)[94,95]. Previous studies have demonstrated that virgin females of this line exhibit increased receptivity to males[95]. We conducted a comparative analysis between the virgin females of this line and the CS virgin females and found that both groups induced SMD. Consequently, we have elected to employ virgin females from this modified line in all subsequent studies. For naïve males, 40 males from the same strain were placed into a vial with food for 5 days. For single reared males, males of the same strain were collected individually and placed into vials with food for 5 days. For experienced males, 40 males from the same strain were placed into a vial with food for 4 days then 80 DF virgin females were introduced into vials for last 1 day before assay. 40 DF virgin females were collected from bottles and placed into a vial for 5 days. These females provide both sexually experienced partners and mating partners for mating duration assays. At the fifth day after eclosion, males of the appropriate strain and DF virgin females were mildly anaesthetized by CO2. After placing a single female in to the mating chamber, we inserted a transparent film then placed a single male to the other side of the film in each chamber. After allowing for 1 h of recovery in the mating chamber in 25℃ incubators, we removed the transparent film and recorded the mating activities. Only those males that succeeded to mate within 1 h were included for analyses. Initiation and completion of copulation were recorded with an accuracy of 10 sec, and total mating duration was calculated for each couple. All assays were performed from noon to 4pm. Genetic controls with GAL4/+ or UAS/+ lines were omitted from supplementary figures, as prior data confirm their consistent exhibition of normal LMD and SMD behaviors [33,73,93,96,97]. Hence, genetic controls for LMD and SMD behaviors were incorporated exclusively when assessing novel fly strains that had not previously been examined. In essence, internal controls were predominantly employed in the experiments, as LMD and SMD behaviors exhibit enhanced statistical significance when internally controlled. Within the LMD assay, both group and single conditions function reciprocally as internal controls. A significant distinction between the naïve and single conditions implies that the experimental manipulation does not affect LMD. Conversely, the lack of a significant discrepancy suggests that the manipulation does influence LMD. In the context of SMD experiments, the naïve condition (equivalent to the group condition in the LMD assay) and sexually experienced males act as mutual internal controls for one another. A statistically significant divergence between naïve and experienced males indicates that the experimental procedure does not alter SMD. Conversely, the absence of a statistically significant difference suggests that the manipulation does impact SMD. Hence, we incorporated supplementary genetic control experiments solely if they deemed indispensable for testing. All assays were performed from noon to 4 PM. We conducted blinded studies for every test[98,99] .

   While we have previously addressed this type of reviewer feedback in our published manuscript [2–7], we appreciate the reviewer’s suggestion to include traditional genetic control experiments. In response, we conducted all feasible combinations of genetic control experiments for LMD/SMD during the revision period. The results are presented in the supplementary figures and are described in the main text.

   We appreciate the reviewer's inquiry regarding the genetic background of our experimental lines. In response to the comments, we would like to clarify the following. All of our GAL4, UAS, or RNAi lines, which were utilized as the virgin female stock for outcrosses, have been backcrossed to the Canton-S (CS) genetic background for over ten generations. The majority of these lines, particularly those employed in LMD assays, have been maintained in a CS backcrossed status for several years, ensuring a consistent genetic background across multiple generations. Our experience has indicated that the genetic background, particularly that of the X chromosome inherited from the female parent, plays a pivotal role in the expression of certain behavioral traits. Therefore, we have consistently employed these fully outcrossed females as virgins for conducting experiments related to LMD and SMD behaviors. It is noteworthy that, in contrast to the significance of genetic background for LMD behaviors, we have previously established in our work [6] that the genetic background does not significantly influence SMD behaviors. This distinction is important for the interpretation of our findings. To provide a comprehensive understanding of our experimental design, we have detailed the genetic background considerations in the __"Materials and Methods"__ section, specifically in the subsection __"Fly Stocks and Husbandry"__ as outlined below.

"To reduce the variation from genetic background, all flies were backcrossed for at least 3 generations to CS strain. For the generation of outcrosses, all GAL4, UAS, and RNAi lines employed as the virgin female stock were backcrossed to the CS genetic background for a minimum of ten generations. Notably, the majority of these lines, which were utilized for LMD assays, have been maintained in a CS backcrossed state for long-term generations subsequent to the initial outcrossing process, exceeding ten backcrosses. Based on our experimental observations, the genetic background of primary significance is that of the X chromosome inherited from the female parent. Consequently, we consistently utilized these fully outcrossed females as virgins for the execution of experiments pertaining to LMD and SMD behaviors. Contrary to the influence on LMD behaviors, we have previously demonstrated that the genetic background exerts negligible influence on SMD behaviors, as reported in our prior publication [6]. All mutants and transgenic lines used here have been described previously."

Comment 5. Throughout the manuscript, the authors appear to use a single control condition (sexually naïve flies raised in groups) to compare to both males raised singly and males with previous sexual experience. These control conditions are duplicated in two separate graphs, one for long mating duration and one for short mating duration, but they are given different names (group vs naïve) depending on the graph. If these are actually the same flies, then this should be made clear, and they should be given a consistent name across the different "experiments".

   __Answer:__ We are grateful to the reviewer for highlighting the potential for confusion among readers regarding the visualization methods used in our figures. In response to this valuable feedback, we have now included a more detailed explanation of the graph visualization techniques in the legends of Figure 1, as detailed below. This additional information should enhance the clarity and understanding of the figure for all readers.

In the mating duration (MD) assays, light grey data points denote males that were group-reared (or sexually naïve), whereas blue (or pink) data points signify males that were singly reared (or sexually experienced). The dot plots represent the MD of each male fly. The mean value and standard error are labeled within the dot plot (black lines). Asterisks represent significant differences, as revealed by the unpaired Student’s t test, and ns represents non-significant differences M.D represent mating duration. DBMs represent the 'difference between means' for the evaluation of estimation statistics (See MATERIALS AND METHODS). Asterisks represent significant differences, as revealed by the Student’s t test (* p

Comment 6. The authors use SCope data to provide evidence for co-expression of SIFa and other neurotransmitters or neuropeptide receptors. The graphs they show are hard to read and it is not clear to what extent the gene expression is actually overlapping. It would be more definitive to show graphs that indicate which percentage of SIFa-expressing cells co-express other neurotransmitter components, and what the actual level of expression of the genes is. The authors should also provide more information on how they identified the SIFa+ cells in the fly atlas dataset. These are important pieces of information to be able to interpret the effects of manipulation of these other neurotransmitter systems within SIFa-expressing cells on mating duration.

__ Answer: We appreciate the reviewer for pointing out the potential for confusion among readers regarding the visualization methods used in our figures, particularly concerning the tSNE plots of scRNA-seq data. As mentioned in our previous response, we have removed most of the tSNE plots related to co-expression data with SIFa and NPRs, which we believe will reduce any confusion for readers interpreting these plots. However, we have retained a few tSNE plots, specifically Figures 2N-O, to confirm the potential co-expression of the ple and Vglut genes in SIFa cells. We understand the reviewer’s concerns about the clarity of the presented data and the necessity for more detailed information regarding the extent of co-expression and the identification of SIFa-expressing cells. To address these concerns, we have included a comprehensive description of our methods in the __MATERIALS AND METHODS section below.

"Single-nucleus RNA-sequencing analyses

The snRNAseq dataset analyzed in this paper is published in [112] and available at the Nextflow pipelines (VSN, https://github.com/vib-singlecell-nf), the availability of raw and processed datasets for users to explore, and the development of a crowd-annotation platform with voting, comments, and references through SCope (https://flycellatlas.org/scope), linked to an online analysis platform in ASAP (https://asap.epfl.ch/fca). For the generation of the tSNE plots, we utilized the Fly SCope website (https://scope.aertslab.org/#/FlyCellAtlas/*/welcome). Within the session interface, we selected the appropriate tissues and configured the parameters as follows: 'Log transform' enabled, 'CPM normalize' enabled, 'Expression-based plotting' enabled, 'Show labels' enabled, 'Dissociate viewers' enabled, and both 'Point size' and 'Point alpha level' set to maximum. For all tissues, we referred to the individual tissue sessions within the '10X Cross-tissue' RNAseq dataset. Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Dashed lines denote the significant overlap of cell populations annotated by the respective genes. Coexpression between genes or annotated tissues is visually represented by differentially colored cell populations. For instance, yellow cells indicate the coexpression of a gene (or annotated tissue) with red color and another gene (or annotated tissue) with green color. Cyan cells signify coexpression between green and blue, purple cells for red and blue, and white cells for the coexpression of all three colors (red, green, and blue). Consistency in the tSNE plot visualization is preserved across all figures.

Single-cell RNA sequencing (scRNA-seq) data from the Drosophila melanogaster were obtained from the Fly Cell Atlas website (https://doi.org/10.1126/science.abk2432). Oenocytes gene expression analysis employed UMI (Unique Molecular Identifier) data extracted from the 10x VSN oenocyte (Stringent) loom and h5ad file, encompassing a total of 506,660 cells. The Seurat (v4.2.2) package (https://doi.org/10.1016/j.cell.2021.04.048) was utilized for data analysis. Violin plots were generated using the “Vlnplot” function, the cell types are split by FCA.

   We have also included detailed descriptions in the figure legends for the initial tSNE plot presented below to help readers clearly understand the significance of this visualization.

"Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and/or bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Consistency in the tSNE plot visualization is preserved across all figures."

Comment 7. I would like to see more information on how the thresholding and normalization was done for immunohistochemistry experiments. Was thresholding applied equally across all datasets? Furthermore, "overlap" of Denmark and Syt-eGFP is taken as evidence for synaptic connectivity, but the latter requires more than just overlap in the location of the axon terminal and dendrite regions of the neuron.

__ Answer: Thank you for your continued engagement with our manuscript and for highlighting the need for further clarification on our methods. Your attention to the details of our immunohistochemistry experiments is commendable, and we agree that providing a clear explanation of our thresholding and normalization procedures is essential for the transparency and reproducibility of our results. We concur that the intensity of these signals is indeed correlated with the area measurements, which is a critical factor to consider. In response to the reviewer's valuable suggestion, we have revised our approach and now present our data based on intensity measurements. Additionally, we have updated the labeling of our Y-axis to "Norm. GFP Int.", which stands for "normalized GFP intensity". This change ensures clarity and consistency in the presentation of our data. We primarily adhered to the established methods outlined by Kayser et al. [8]. To address your first point, we have now included a more detailed description of our thresholding and normalization procedures in the __MATERIALS AND METHODS section as below.

"Quantitative analysis of fluorescence intensity

To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. The GFP signal in the brains and VNCs was amplified through immunostaining with chicken anti-GFP primary antibody. Image analysis was conducted using ImageJ software. For the quantification of fluorescence intensities, an investigator, blinded to the fly's genotype, thresholded the sum of all pixel intensities within a sub-stack to optimize the signal-to-noise ratio, following established methods [93]. The total fluorescent area or region of interest (ROI) was then quantified using ImageJ, as previously reported. For CaLexA or TRIC signal quantification, we adhered to protocols detailed by Kayser et al. [94], which involve measuring the ROI's GFP-labeled area by summing pixel values across the image stack. This method assumes that changes in the GFP-labeled area and intensity are indicative of alterations in the CaLexA and TRIC signal, reflecting synaptic activity. ROI intensities were background-corrected by measuring and subtracting the fluorescent intensity from a non-specific adjacent area, as per Kayser et al. [94]. For normalization, nc82 fluorescence is utilized for CaLexA, while RFP signal is employed for TRIC experiments, as the RFP signal from the TRIC reporter is independent of calcium signaling [76]. For the analysis of GRASP or tGRASP signals, a sub-stack encompassing all synaptic puncta was thresholded by a genotype-blinded investigator to achieve the optimal signal-to-noise ratio. The fluorescence area or ROI for each region was quantified using ImageJ, employing a similar approach to that used for CaLexA or TRIC quantification [93]. 'Norm. GFP Int.' refers to the normalized GFP intensity relative to the RFP signal."

Comment 8. None of the RNAi experiments have been validated to demonstrate effective knockdown. In many cases, this would be difficult to do because of a lack of an antibody to quantify in a cell-specific manner; however, this fact should be acknowledged, especially in cases where there was found to be a lack of phenotype, which could result from lack of knockdown. The authors could also look for evidence in the literature of cases where RNAi lines they have used have been previously validated. For SIFa, knockdown can be easily confirmed with the SIFa antibody the authors have used elsewhere in the manuscript.

__ Answer:__ We appreciate the reviewer’s constructive and critical comments regarding the validation of our RNAi experiments through effective knockdown. We understand the reviewer’s concerns about achieving effective knockdown with RNAi; however, we have demonstrated in our unpublished preprint that the neuronal knockdown using independent SIFa-RNAi lines aligns with the SIFa mutant phenotype, which is consistent with our current findings on SIFa knockdown (Wong 2019). In most cases involving RNAi experiments, we have utilized independent RNAi strains to confirm consistent phenotypes and have compared these results with those from mutant phenotypes [1,9]. Therefore, we are confident that our observed SIFa phenotype results from effective RNAi knockdown. Nevertheless, we respect the reviewer’s comments and have conducted additional SIFa knockdown experiments using various GAL4 drivers, followed by immunostaining with SIFa antibodies. As shown in Figure S1B, both neuronal GAL4 drivers and SIFa-GAL4 effectively reduced SIFa immunoreactivity. We believe this indicates that our SIFa knockdown efficiently phenocopies the SIFa mutant phenotype. We also described this result in manuscript as below:

"Using the GAL4SIFa.PT driver and the elavc155 driver, we observed a significant decrease in SIFa immunoreactivity following SIFa-RNAi treatment, thereby confirming the effective knockdown of SIFa in these cells. In contrast, when SIFa-RNAi was expressed under the control of the repo-GAL4 driver, no significant change in SIFa immunoreactivity was detected (Fig. S1B). This control experiment highlights the specificity of the SIFa-RNAi effect and supports the conclusion that the behavioral changes observed in SMD and LMD are likely attributable to the targeted reduction of SIFa in the intended neuronal populations."

Minor comments:

Comment 1. There are quite a lot of citations to preprints, including preprints of the manuscripts under review. It seems inappropriate to cite a preprint of the manuscript you are submitting because it gives a false sense of strengthening the assertions being made in the manuscript.

   __Answer:__ We agree with the reviewer and have omitted all preprints that are currently under review, except for those that are deemed necessary, such as the Zhang et al. 2024 preprint, which is being submitted alongside this manuscript.

Comment 2. It seems that labels are incorrect on a number of the immunohistochemistry figures. For example, in Fig 2N, it labels dendrites as green, but this is sytEGFP, which is the presynaptic terminal.

__ Answer:__ We thoroughly reviewed and corrected the errors in the labels.

Comment ____3. Fig 4N shows grasp between SIFa-LexA and sNPF-R-GAL4, but the authors have argued that these two components should both be expressed in SIFa-expressing cells. This would make grasp signal misleading, because it would appear in the SIFa-expressing cells even without synaptic contacts due to both split GFP molecules being expressed in these cells.

   __Answer:__ We appreciate the reviewer’s critical comments regarding the interpretation of our GRASP experiments. As the reviewer noted, we acknowledge that the GRASP results also indicate synaptic contacts between SIFa cells. We have elaborated on these results in the following sections.

"This indicates that the synapses between SIFa cells expressing sNPF-R become stronger (S5K to S5M Fig)."

   However, we understand that readers may find the interpretation of this GRASP data confusing, so we have included additional explanations below to clarify.

This indicates that the synapses between SIFa cells expressing sNPF-R become stronger (S5K to S5M Fig) since we have found that SIFa cells express sNPF-R (Fig 3M, S5E and S5G)

Comment 4. For quantifying TRIC and CaLexA experiments (eg. Figure 6A-E), intensity of signal should be measured in addition to the area covered by the signal.

__ Answer:__ We concur with the reviewer. Since all of our analyses indicated that the area covered by the signal correlates with the signal intensity, we opted to use normalized intensity rather than area coverage.

Conclusive Comments: This study will be most relevant to researchers interested in understanding neuronal control of behavior. It has provided novel information about the mechanisms underlying mating duration in flies, which is used to delineate how internal state influences behavioral outcomes. This represents a conceptual advance, particularly in identifying a cell type and molecule that influences mating duration decisions. The strength of the manuscript is the number of different assays used to investigate the central question from a number of angles. The limitation is that there is a lack of a big picture tying the different components of the manuscript together. Too much data is presented without providing a framework to understand how the data points fit together.

• Answer: We sincerely appreciate the reviewer’s positive feedback regarding our study and the recognition of its relevance to researchers interested in understanding the neuronal control of behavior. We are grateful for the acknowledgment of our novel insights into the mechanisms underlying mating duration in Drosophila*, particularly in how internal states influence behavioral outcomes. The identification of specific cell types and molecules that affect mating duration decisions indeed represents a significant conceptual advance. We also appreciate the reviewer’s commendation of the diverse array of assays employed in our investigation, which allowed us to approach our central question from multiple perspectives.

  In response to the reviewer’s constructive criticism regarding the lack of a cohesive framework tying the various components of our manuscript together, we have completely restructured our manuscript. We removed redundant data and incorporated additional convincing experiments, such as GCaMP analyses, to enhance clarity and coherence. Furthermore, we have provided a simplified yet comprehensive overview that describes the role of SIFa as a hub for neuropeptidergic signaling. This framework illustrates how SIFa orchestrates multiple behaviors related to energy balance through calcium signaling and synaptic plasticity via SIFaR-expressing cells.

  We believe these revisions address the reviewer’s concerns and provide a clearer understanding of how the different elements of our study fit together, ultimately strengthening the overall impact of our manuscript. Thank you for your valuable feedback, which has guided us in improving our work.

Reviewer #2

General Comments:* In the present study, the authors employ mating behavior in male fruit flies, Drosophila melanogaster, to investigate the behavioral roles of the neuropeptide SIFamide. The duration of mating behavior in these animals varies depending on context, previous experience, and internal metabolic state. The authors use this variability to explore the neuronal mechanisms that control these influences. In an abstraction step, they compare the different mating durations to concepts of neuronal interval timing.

The behavioral functions of the neuropeptide SIFamide have been thoroughly characterized in several studies, particularly in the contexts of circadian rhythm and sleep, courtship behavior, and food uptake. This study adds new data, demonstrating that SIFamide is essential for the proper control of mating behavior, highlighting the interconnection of various state- and motivation-dependent behaviors at the neuronal level. However, the hypothesis that mating behavior is related to interval timing is not convincingly supported.

Experimentally, the authors show that RNAi-mediated downregulation of SIFamide affects mating duration in male flies. They use combinations of RNAi lines under the control of various Gal4 lines to identify additional neurotransmitters, neuropeptides, and receptors involved in this process. This approach is complemented by neuroanatomical staining and single-cell RNA sequencing.*

* Overall, the study advances our knowledge about the behavioral roles of SIFamide, which is certainly important, interesting, and worthy of being reported. However, the manuscript also raises several serious caveats and includes points that remain speculative, are less convincing, or are simply incorrect.*

• Answer: We would like to thank the reviewer for their thoughtful and constructive comments regarding our study. We appreciate the recognition of our investigation into the behavioral roles of the neuropeptide SIFamide in male Drosophila melanogaster*, particularly how we explored the variability in mating duration influenced by context, previous experience, and internal metabolic state. We are grateful for the acknowledgment that our study adds valuable data demonstrating the essential role of SIFamide in regulating mating behavior, highlighting the interconnectedness of various state- and motivation-dependent behaviors at the neuronal level.

  We also appreciate the reviewer's recognition of our experimental approach, which includes RNAi-mediated downregulation of SIFamide, the use of various Gal4 lines to identify additional neurotransmitters, neuropeptides, and receptors involved in this process, as well as our incorporation of neuroanatomical staining and single-cell RNA sequencing.

  In response to the reviewer’s concerns regarding the hypothesis that mating behavior is related to interval timing, we acknowledge that this aspect requires further clarification and support. We have revisited this hypothesis in our manuscript to strengthen its foundation and address any speculative elements. We aim to provide more robust evidence and clearer connections between mating behavior and neuronal interval timing.

  Furthermore, we have taken care to address any points that may have been perceived as less convincing or incorrect. We are committed to refining our manuscript to ensure that all claims are well-supported by our data. Thank you once again for your valuable feedback. We believe that these revisions will enhance the clarity and impact of our study while addressing the concerns raised.

Major concerns:

Comment 1. The authors conclude from their mating experiments that SIFamide controls interval timing. This conclusion is not supported by the data, which only indicate that SIFamide is required for normal mating duration and modulates the motivation-dependent component of this behavior. There is no clear evidence linking this to interval timing.

__ Answer: __We appreciate the reviewer’s insightful comments regarding our conclusion linking SIFamide to interval timing in mating behavior. We acknowledge that our data primarily demonstrate that SIFamide is required for normal mating duration and modulates the motivation-dependent aspects of this behavior, and we recognize the need for clearer evidence connecting these observations to interval timing. Current research by Crickmore et al. has shed light on how mating duration in Drosophila serves as a powerful model for exploring changes in motivation over time as behavioral goals are achieved. For instance, at approximately six minutes into mating, sperm transfer occurs, leading to a significant shift in the male's nervous system: he no longer prioritizes sustaining the mating at the expense of his own survival. This change is driven by the output of four male-specific neurons that produce the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not occur, and the male fails to downregulate his motivation, resulting in matings that can last for hours instead of the typical ~23 minutes [10].

   Recent research by Crickmore et al. has received NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to explore mating duration in *Drosophila* as a genetic model for interval timing. Their work highlights how changes in motivation over time can influence mating behavior, particularly noting that significant behavioral shifts occur during mating, such as the transfer of sperm at approximately six minutes, which correlates with a decrease in the male's motivation to continue mating [10]. These findings suggest that mating duration is not only a behavioral endpoint but may also reflect underlying mechanisms related to interval timing.

   We believe that by leveraging the robustness and experimental tractability of these findings, along with our own work on SIFamide's role in mating behavior, we can gain deeper insights into the molecular and circuit mechanisms underlying interval timing. We will revise our manuscript to clarify this relationship and emphasize how SIFamide may interact with other neuropeptides and neuronal circuits involved in motivation and timing.

   In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper [6], as detailed below:

"We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the ‘interval timing’ that allows an animal to subjectively experience the passage of physical time [11–16]."

   Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. *PLoS Genetics*, *19*(5), e1010753.

   We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently [4,5,7].

   Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. *G3: Genes, Genomes, Genetics*, jkae255.

   Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body’s Influence on Interval Timing Behaviors of Drosophila melanogaster. *Behavior Genetics*, *54*(5), 416-425.

   Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. *Gene Reports*, *36*, 101999.

   Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in *Drosophila*.

"The mating duration of male fruit flies is a suitable model for studying interval timing and it could change based on internal states and environmental context. Previous studies by our group[27–30] and others[31,32] have established several frameworks for investigating the mating duration using sophisticated genetic techniques that can analyze and uncover the neural circuits’ principles governing interval timing. In particular, males exhibit LMD behavior when they are exposed to an environment with rivals, which means they prolong their mating duration. Conversely, they display SMD behavior when they are in a sexually saturated condition, meaning they reduce their mating duration[33,34]."

Comment 2. On line 160, the authors state, "The connection between the dendrites and axons of the SIFamide neuronal processes is unknown." This is not entirely correct. State-of-the-art connectome analyses can determine synaptic connectivities between SIFamidergic neurons and pre-/postsynaptic neurons. The authors also overlook the thorough connectivity analysis by Martelli et al. (2017), which includes functional analyses and detailed anatomical descriptions that the current study confirms.

__ Answer:__ We appreciate the reviewer for acknowledging the efforts of Martelli et al. in elucidating the neuronal architecture of SIFa neurons. We recognize that it was an oversight on our part to state that "the connection between the dendrites and axons of SIFa neurons is unknown." This error arose because our manuscript has been in preparation for over ten years, predating the publication of Martelli et al.'s work. That statement likely reflects an outdated section of the manuscript.

We fully acknowledge the findings from previous publications and have removed that sentence entirely from our manuscript. In its place, we have added the following statement:

"The established connections and architecture of SIFa neurons has been described by Martelli et al., which enhances our understanding of their functional roles within the neuronal circuitry [51]. To identify the dendritic and axonal components of SIFa-neuronal processes, we employed a similar approach to that reported by Martelli [51]."

Thank you for your valuable feedback, which has helped us improve the clarity and accuracy of our manuscript.

Comment 3. The mating experiments are overall okay, with sufficiently high sample sizes and appropriate statistical tests. However, many experiments lack genetic controls for the heterozygous parental strains, such as Gal4-ines AND UAS-lines. This is of course of importance and common standard.

__ Answer: __While we have previously addressed this type of reviewer feedback in our published manuscript [2–7] as well as this manuscript by Reviewer #1, we appreciate the reviewer’s suggestion to include traditional genetic control experiments. In response, we conducted all feasible combinations of genetic control experiments for LMD/SMD during the revision period. The results are presented in the supplementary figures and are described in the main text.

Comment 4. *Using a battery of RNAi lines, the authors aim to uncover which neurotransmitters might be co-released from SIFamide neurons to influence mating behavior. However, a behavioral effect of an RNAi construct expressed in SIFamidergic neurons does not demonstrate that the respective transmitter is actually released from these neurons. Alternative methods are needed to show whether glutamate, dopamine, serotonin, octopamine, etc., are present and released from SIFamide neurons. It is particularly challenging to prove that a certain substance acts as a transmitter released by a specific neuron. For example, anti-Tdc2 staining does not actually cover SIFamide neurons, and dopamine has not been described as present in SIFamide neurons. *

__ Answer:__ We appreciate the reviewer’s constructive comments regarding the need to demonstrate the presence of the responsible neurotransmitters in SIFa neurons. While many studies utilize neurotransmitter-synthesizing enzymes such as TH, VGlut, Gad1, and Trhn to assess neurotransmitter effects, we recognize the importance of conclusively establishing that glutamate and dopamine play significant roles in modulating energy balance within SIFa neurons.

   First, the enrichment of tyramine (TA), octopamine (OA), and dopamine (DA) in SIFa neurons was suggested in the study by Croset et al. (2018) [17]. Although we tested Tdc2-RNAi and observed interesting phenotypes, we chose not to publish these findings, as our data on glutamate and dopamine provide a more compelling explanation for how SIFa cotransmission with these neurotransmitters can independently influence various behaviors, including sleep and mating duration.

   To confirm the expression of DA in SIFa neurons, we employed a well-established genetic toolkit for dissecting dopamine circuit function in *Drosophila* [18]. Our findings indicate that TH-C-GAL4 specifically labels SIFa neurons, which have been confirmed as dopaminergic (S4M Fig). Our genetic intersection data, along with Xie et al.'s findings from 2018, confirm that a subset of SIFa neurons is indeed dopaminergic. We have described these new results in the main text as follows:

To further verify the presence of DA neurons within the SIFa neuron population, we utilized a well-established genetic toolkit for dissecting DA circuits and confirmed part of SIFa neurons are dopaminergic (S4M Fig) [58].

    To confirm the glutamatergic characteristics of SIFa neurons, we conducted several experiments that established glutamate as the most critical neurotransmitter for generating interval timing in both SIFa and SIFaR neurons. First, to demonstrate the presence of glutamatergic synaptic vesicles in SIFa neurons, we utilized a conditional glutamatergic synaptic vesicle marker for *Drosophila*, developed by Certel et al. [19]. Our results confirmed that SIFa neurons exhibit strong expression of glutamatergic synaptic vesicles (Fig. 2P and Fig. S4N as a genetic control). We have described these new results in the main text as follows:

“To further verify the presence of DA neurons within the SIFa neuron population, we utilized a well-established genetic toolkit for dissecting DA circuits and confirmed part of SIFa neurons are dopaminergic (S4M Fig) [58]. We also employed a conditional glutamatergic synaptic vesicle marker to confirm the presence of glutamatergic SIFa neurons (Fig 2P and Fig S4N) [59].”

   To further confirm that glutamate release from SIFa neurons influences the function of SIFaR neurons, we tested several RNAi strains targeting glutamate receptors. Our results showed that the knockdown of glutamate receptors in SIFaR-expressing neurons produced phenotypes similar to those observed with VGlut-RNAi knockdown in SIFa neurons (Fig. G-L). We believe that this series of experiments demonstrates that glutamate and dopamine work in conjunction with SIFa to modulate interval timing and other behaviors related to energy balance. We have described these new results in the main text as follows:

"To further substantiate the role of glutamate in SIFa-mediated behaviors. we targeted knockdown of VGlut receptors in SIFaR-expressing neurons. Strikingly, the knockdown of VGlut receptors in these neurons also disrupted SMD behavior, mirroring the phenotype observed upon direct suppression of glutamatergic signaling in SIFa neurons (S4G to S4L Fig). This suggests that glutamate is an essential neurotransmitter for modulating interval timing in SIFa neurons.”

Comment 5. Single-cell RNA sequencing data alone is insufficient to claim multiple transmitter co-release from SIFamide neurons. Figures illustrating single-cell RNA sequencing, such as Figure 3P-R, are not intuitively understandable, and the figure legends lack sufficient information to clarify these panels. As a side note, Tdc2 is not only present in octopaminergic neurons, but also in tyraminergic neurons.

__ Answer:__ We agree with the reviewer that scRNA-seq data alone is insufficient to support claims of multiple transmitter co-release in SIFa neurons. We also appreciate the reviewer for highlighting the potential for confusion among readers regarding the visualization methods used in our figures, particularly the tSNE plots of the scRNA-seq data. As noted in our previous response to Reviewer #1, we have removed most of the tSNE plots related to co-expression data involving SIFa and NPRs, which we believe will help clarify the interpretation for readers. However, we have retained a few tSNE plots, specifically Figures 2N-O, to illustrate the potential co-expression of the ple and Vglut genes in SIFa cells.

   We understand the reviewer’s concerns regarding the clarity of the presented data and the need for more detailed information about the extent of co-expression and the identification of SIFa-expressing cells. To address these concerns, we have provided a comprehensive description of our methods in the __MATERIALS AND METHODS__ section below.

"Single-nucleus RNA-sequencing analyses

The snRNAseq dataset analyzed in this paper is published in [20]and available at the Nextflow pipelines (VSN, https://github.com/vib-singlecell-nf), the availability of raw and processed datasets for users to explore, and the development of a crowd-annotation platform with voting, comments, and references through SCope (https://flycellatlas.org/scope), linked to an online analysis platform in ASAP (https://asap.epfl.ch/fca). For the generation of the tSNE plots, we utilized the Fly SCope website (https://scope.aertslab.org/#/FlyCellAtlas/*/welcome). Within the session interface, we selected the appropriate tissues and configured the parameters as follows: 'Log transform' enabled, 'CPM normalize' enabled, 'Expression-based plotting' enabled, 'Show labels' enabled, 'Dissociate viewers' enabled, and both 'Point size' and 'Point alpha level' set to maximum. For all tissues, we referred to the individual tissue sessions within the '10X Cross-tissue' RNAseq dataset. Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Dashed lines denote the significant overlap of cell populations annotated by the respective genes. Coexpression between genes or annotated tissues is visually represented by differentially colored cell populations. For instance, yellow cells indicate the coexpression of a gene (or annotated tissue) with red color and another gene (or annotated tissue) with green color. Cyan cells signify coexpression between green and blue, purple cells for red and blue, and white cells for the coexpression of all three colors (red, green, and blue). Consistency in the tSNE plot visualization is preserved across all figures.

Single-cell RNA sequencing (scRNA-seq) data from the Drosophila melanogaster were obtained from the Fly Cell Atlas website (https://doi.org/10.1126/science.abk2432). Oenocytes gene expression analysis employed UMI (Unique Molecular Identifier) data extracted from the 10x VSN oenocyte (Stringent) loom and h5ad file, encompassing a total of 506,660 cells. The Seurat (v4.2.2) package (https://doi.org/10.1016/j.cell.2021.04.048) was utilized for data analysis. Violin plots were generated using the “Vlnplot” function, the cell types are split by FCA."

   We have also included detailed descriptions in the figure legends for the initial tSNE plot presented below to help readers clearly understand the significance of this visualization.

"Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and/or bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Consistency in the tSNE plot visualization is preserved across all figures."

   We appreciate the reviewer for acknowledging that Tdc2 is present in both TA and OA neurons. As we mentioned earlier, we have completely removed the Tdc2-related results from this manuscript, as we believe that more detailed experiments are necessary to confirm the roles of TA and OA in SIFa neurons.

Comment 6. The same argument applies to the expression of sNPF receptors in SIFamide neurons. The rather small anatomical stainings shown in figure 4M do not convincingly and unambiguously show that actually sNPF receptors are located on SIFamide neurons.

__ Answer:__ We appreciate the reviewer for pointing out that the co-expression of sNPF-R and SIFa needs further verification, and we agree with this assessment. To confirm the co-expression of SIFa with sNPF-R, we conducted a mini-screen of various sNPF-R driver lines and found that the chemoconnectome (CCT) sNPF-R2A driver which represent the physiological expression patterns of sNPF-R, consistently labels SIFa neurons [21].

   To further establish the functional connection between the SIFa and sNPF systems, we performed GCaMP experiments using SIFa-driven GCaMP in conjunction with sNPF-R neurons expressing P2X2, which can be activated by ATP treatment. As shown in Figures 3N-P, we demonstrated that activation of sNPF-R neurons by ATP significantly increases calcium levels in SIFa neurons. Our results strongly suggest that the sNPF-sNPF-R/SIFa system is functionally present and plays a role in modulating interval timing behaviors.

Comment 7. The authors use the GRASP technique (figure 4N) to determine whether synaptic connections are subject to modulation as a result from the animals' individual experience. The overall extremely bright fluorescence at the dorsal areas of both brain hemispheres (figure 4 N, middle panel) raises doubts whether this signal is actually a specific GRASP fluorescence between two small populations of neurons.

Answer: We appreciate the reviewer for critically highlighting the inadequacies in our presentation of the GRASP data. We agree that one of our previous panels contained excessive background noise, making it difficult for reviewers and readers to discern the different neuronal connections. To address this issue, we have replaced it with a more representative image that clearly illustrates the strengthening of synaptic connections from SIF to sNPF-R in several neurons, including SIFa cells (Fig. S5J). We hope that this updated image will help convince both the reviewer and readers of the validity of our GRASP data.

Comment 8. The authors cite Martelli et al. (2017) with the hypothesis that sNPF-releasing neurons provide input signals to SIFamide neurons to modulate feeding behavior. However, the cited manuscript does not contain such a hypothesis. The authors should review the reference in more detail.

__ Answer:__ We appreciate reviewer to correctly point our misunderstanding of references. We agree with reviewer that Martelli et al.'s paper didn't mention about sNPF signaling transmits hunger and satiety information to SIFa neurons. We removed this sentence and replaced it as below correctly mentioning that sNPF signaling is related to feeding behavior however it's connection to SIFa neurons are not known. We greatly appreciate the reviewer for acknowledging our efforts to accurately cite previous articles that support our rationale and ideas.

" Short neuropeptide F (sNPF) signaling plays a crucial role in regulating feeding behavior in Drosophila melanogaster, influencing food intake and body size [60,66,67]. However, there is currently no direct evidence reported linking sNPF signaling to SIFa neurons."

Comment ____9. In lines 281 ff., the authors state that SIFamide neurons receive inputs from peptidergic neurons but simultaneously claim that "this speculation is based on morphological observations." This is incorrect. The functional co-activation/imaging analyses provided in Martelli et al. (2017) should not be ignored.

* Answer: We fully agree with the reviewer that we misinterpreted Martelli et al.'s analysis. We have removed "this speculation is based on morphological observations." from* the following sentence and finalize as below:

"The SIFa neurons receive inputs from many peptidergic pathways including Crz, dilp2, Dsk, sNPF, MIP, and hugin"

Comment 10. Figure 6: A transcriptional calcium sensor (TRIC) was used to quantify the accumulation GFP induced by calcium influx in SIFamide neurons. However, I could not find any description of the method in the materials and methods section, nor any explanation how the data were acquired or analyzed. What is the RFP expression good for? How exactly are thresholds determined, and why are areas rather than fluorescence intensities quantified? Overall, this part of the manuscript is rather confusing and needs more explanation.

__ Answer: Thank you for your continued engagement with our manuscript and for highlighting the need for further clarification on our methods. Your attention to the details of our immunohistochemistry experiments is commendable, and we agree that providing a clear explanation of our thresholding and normalization procedures is essential for the transparency and reproducibility of our results. We primarily adhered to the established methods outlined by Kayser et al. [8]. To address your first point, we have now included a more detailed description of our thresholding and normalization procedures in the __MATERIALS AND METHODS section as below.

"Quantitative analysis of fluorescence intensity

To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. The GFP signal in the brains and VNCs was amplified through immunostaining with chicken anti-GFP, rabbit anti-DsRed, and mouse anti-nc82 primary antibodies. Image analysis was conducted using ImageJ software. For the quantification of fluorescence intensities, an investigator, blinded to the fly's genotype, thresholded the sum of all pixel intensities within a sub-stack to optimize the signal-to-noise ratio, following established methods [100]. The total fluorescent area or region of interest (ROI) was then quantified using ImageJ, as previously reported. For CaLexA or TRIC signal quantification, we adhered to protocols detailed by Kayser et al. [101], which involve measuring the ROI's GFP-labeled area by summing pixel values across the image stack. This method assumes that changes in the GFP-labeled area and intensity are indicative of alterations in the CaLexA and TRIC signal, reflecting synaptic activity. ROI intensities were background-corrected by measuring and subtracting the fluorescent intensity from a non-specific adjacent area, as per Kayser et al. [101]. For normalization, nc82 fluorescence is utilized for CaLexA, while RFP signal is employed for TRIC experiments, as the RFP signal from the TRIC reporter is independent of calcium signaling [72] . For the analysis of GRASP or tGRASP signals, a sub-stack encompassing all synaptic puncta was thresholded by a genotype-blinded investigator to achieve the optimal signal-to-noise ratio. The fluorescence area or ROI for each region was quantified using ImageJ, employing a similar approach to that used for CaLexA or TRIC quantification [100]. 'Norm. GFP Int.' refers to the normalized GFP intensity relative to the RFP signal.

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__Comment 11. __Similarly, it remains unclear how exactly syteGFP fluorescence and DenMark fluorescence were quantified. Why are areas indicated and not fluorescence intensity values? In fact, it appears worrisome that isolation of males should lead to a drastic decline in synaptic terminals (as measure through a vesicle-associated protein) by ~ 30%, or, conversely, keeping animals in groups lead to an respective increase (figure 7D). The technical information how exactly this was quantified is not sufficient.

__ Answer: __Thank you for your ongoing engagement with our manuscript and for emphasizing the need for clarification on our methods. We appreciate your attention to the details of our immunohistochemistry experiments and agree that a clear explanation of our thresholding and normalization procedures is vital for transparency and reproducibility. We acknowledge that signal intensity correlates with area measurements, which is an important consideration. In response to your valuable suggestion, we have revised our approach to present data based on intensity measurements and updated the Y-axis labeling to "Norm. GFP Int." (normalized GFP intensity) for clarity. We primarily followed the established methods from Kayser et al. (2014) [8]. Additionally, we have included a more detailed description of our thresholding and normalization procedures in the "Quantitative analysis of fluorescence intensity" in __MATERIALS AND METHODS __section as we quoted above.

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Minor concerns:

Comment 1. Reference 29 and reference 33 are the same.

   __Answer:__ We removed reference 29.

Comment 2. In figure legends, abbreviations should be explained when used first (e.g., figure 1 A "MD", is explained below for panel C-F), or "CS males". __ __

__Answer: __We have ensured that abbreviations are explained only when they are first used in the figure legends.

Comment 3. Indications for statistical significance must be shown in all figure legends at the end of each figure legend, not only in figure 1. __ __

__ Answer:__ We appreciate the reviewer’s advice. However, we have published all our other manuscripts using the same format for mating duration, stating, "The same notations for statistical significance are used in other figures," in the first figure where we describe our statistical significances. We intend to continue with this approach initially and will then adhere to the journal's policy.

Comment 4. The figures appear overloaded. For example why do you need two different axis designations (mating duration and differences between means)? __ __

__ Answer: __We appreciate the reviewer's suggestion to refine our figures, and we have indeed reformatted them to provide clearer presentation and improved readability. Our decision is based on the fact that our analysis encompasses not only traditional t-tests but also incorporates estimation statistics, which have been demonstrated to be effective for biological data analysis [22]. The inclusion of DBMs is essential for the accurate interpretation of these estimation statistics, ensuring a comprehensive representation of our findings. This is the primary area where we present two different axis designations.

Comment 5. Line: 1154: Typo: gluttaminergic should be glutamatergic.

   __Answer:__ We fixed all.

Comment 6. The authors frequently write "system" when referring to transmitter types, e.g., "glutaminergic system", "octopaminergic system", etc. It I not clear what the term "system" actually refers to. If the authors claim that SIFamide neurons release these transmitters in addition to SIFamide, they should state that precisely and then add experiments to show that this is the case.

   __Answer:__ We agree with reviewer and removed the word 'system' after the name of neurotransmitter's name.

Comment 7. Figure S6: It is not explained in the figure legend what fly strain "UAS-ctrl" actually is. Does "ctrl" mean control? And what genotype is hat control? __ __

__Answer: __It was wild-type strain. We fixed it as "+".

Comment 8. Figure legend S6, line 1371: The authors indicate experiments using UAS-OrkDeltaC. I could not find these data in the figure. __ __

__Answer: __It's now in Fig.S6U-W.

Comment 9. Line 470: "...reduced branching of SIFa axons at the postsynaptic level" should perhaps be "presynaptic level"?

Answer: Reviewer is correct. We fixed it.

Conclusive Comments:* Overall, the study advances our knowledge about the behavioral roles of SIFamide, which is certainly important, interesting, and worthy of being reported. However, the manuscript also raises several serious caveats and includes points that remain speculative and are less convincing.

Overall, the neuronal basis of action selection based on motivational factors (metabolic state, mating experience, sleep/wake status, etc.) is not well understood. The analysis of SIFamide function in insects might provide a way to address the question how different motivational signals are integrated to orchestrate behavior.*

• *Answer: Thank you for your thoughtful review and for recognizing the significance of our study in advancing knowledge about the behavioral roles of SIFamide. We appreciate your acknowledgment that our work is important, interesting, and worthy of publication.

We understand your concerns regarding the caveats and speculative points raised in the manuscript. We agree that the neuronal basis of action selection influenced by motivational factors—such as metabolic state, mating experience, and sleep/wake status—remains poorly understood. We believe that our analysis of SIFamide function in insects offers valuable insights into how various motivational signals are integrated to orchestrate behavior.

In response to your comments, we have made revisions to clarify our findings and address the concerns raised. We aim to strengthen the arguments presented in the manuscript and provide a more robust discussion of the implications of our results. Thank you once again for your constructive feedback, which has been instrumental in improving the clarity and impact of our work.

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* *

Reviewer #3

General Comments:* The Manuscript Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster by Yuton Song and colleagues addresses the question how SIFamidergic neurons coordinate behavioral responses in a context-dependent manner. In this context the authors investigate how SIFa neurons receive information about the physiological state of the animal and integrate this information into the processing of external stimuli. The authors show that SIFamidergic neurons and sNPPF expressing neurons form a feedback loop in the ventral nerve cord that modulate long mating (LMD) and shorter mating duration (SMD).

The manuscript is well written and very detailed and provides an enormous amount of data corroborating the claims of the authors. However, before publication the authors may want to address some points of concern that warrant some deeper explanation.*

• *__Answer: __Thank you for your positive feedback on our manuscript. We appreciate your recognition of the importance of our study in investigating how SIFa neurons integrate information about the physiological state of the animal with external stimuli, as well as your acknowledgment of the substantial data we provide to support our claims. We understand your concerns regarding certain points that require deeper explanation, and we are committed to addressing these issues to enhance the clarity and robustness of our findings. Your insights into the neuronal basis of action selection influenced by motivational factors are invaluable, and we believe that our exploration of SIFamide function in insects contributes significantly to understanding how various motivational signals orchestrate behavior. Thank you once again for your constructive comments, which will help us improve our manuscript before publication.

Major concerns:

Comment 1. On page 6 line 110 the authors describe that knocking-down SIFamide in glia cell does not change LMD or SMD and say that SIFa expression in glia does not contribute to interval timing behavior. However, the authors do not provide any information why they investigate the role of SIFa expression in glia. Is there any SIFa-expression in glia? The authors should somehow demonstrate using antibody labelling against SIFamide whether any glia specific expression of this peptide is to be expected. If they cannot provide this data - the take home message of the experiment cannot be that glia knockdown of SIFamide does not affect the behavior because you cannot knockdown anything that is not there.

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• In the latter case the experiment could be considered as a nice negative control for the elav-Gal4 pan-neuronal knockdown of SIFamide. The authors provide some Figure supplement where they use repo-Gal80 to partially answer this question. However, the authors should keep in mind that Gal4-drivers are not always complete in the expression pattern. Accordingly, the result should be corroborated with immune-labelling against SIFamide directly.*

__ Answer: __We appreciate the reviewer's constructive and critical comments regarding the use of our glial cell drivers. As the reviewer rightly pointed out, we believe that glial control is not essential for our manuscript, given that the expression of SIFa is well established in only four neurons. Therefore, we have removed the data related to glial drivers from this manuscript.

Comment 2. At this point I would like to directly comment on the figure quality. The figures are so crowded that the described anatomical details are hardly visible. In my opinion the manuscript would profit from less data in the main part and more stringent description of the core of the biological problem the authors want to address. The authors may want to reduce data from the main text and provide additional data that are not directly related to the main story as supplementary information.

__ Answer: __We agree with the reviewer. As another reviewer also suggested that we streamline our figures and data, we have completely restructured our figures and their presentation. In response, we have significantly reduced the density of the main figures and decreased the size of the graphs to enhance clarity. Additionally, we have increased the spacing between panels to ensure that each component is more easily distinguishable. Further details will be provided in our responses to each comment below.

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Comment 3. On page 8 starting with line 140 the authors describe the architecture of SIFamidergic neurons using several anatomical markers e.g., Denmark and further state that they have discovered that the dendrites of SIFa neurons span just the central brain area. Seeing that these data have been published in Martelli et al., 2017 the authors should tune down the claim that this was discovered in their work but rather corroborated earlier results.

__ Answer: __We acknowledge this error, as another reviewer also raised this issue. We have corrected our manuscript as follows:

"The established connections and architecture of SIFa neurons has been described by Martelli et al., which enhances our understanding of their functional roles within the neuronal circuitry [51]. To identify the dendritic and axonal components of SIFa-neuronal processes, we employed a similar approach to that reported by Martelli [51]."

Comment 4. In the next chapter, the authors aim at identifying the presynaptic inputs from SIFa positive neurons that may influence interval timing behavior and make a broad RNAi knock-down screen targeting a majority of neuromodulators. The authors claim that glutaminergic and dopaminergic signaling is necessary for interval timing behavior. I guess the authors mean "glutamatergic" instead of "glutaminergic" as glutamine is the precursor but not the neurotransmitter.

__ Answer: __The reviewer is correct. We have corrected this error and changed all instances to "glutamatergic."

Comment 5____. Furthermore, the authors show that the knock down of Tdc2 with RNAi has comparable effects on SMD than Glutamate and dopamine but appear to not further discuss this in the main text. To me it is not clear why the authors exclude Tdc2 from their resume. The authors should explain this in detail.

   __Answer:__ We appreciate the reviewer’s constructive comments regarding the need for a more detailed demonstration of the role of Tdc2 data. While we did test Tdc2-RNAi and observed interesting phenotypes, we decided not to include these findings in our publication, as our data on glutamate and dopamine offer a more compelling explanation for how SIFa cotransmission with these neurotransmitters can independently influence various behaviors, such as sleep and mating duration. Consequently, we have removed all data related to Tdc2. We believe that further evaluation is necessary to better understand the roles of the tyramine and octopamine systems in SIFa neurons.

Comment 6. The authors base their assumptions that the tested neurotransmitters are expressed in SIFamidergic neurons on Scope database analysis. But a transcript does not necessarily mean that it will be translated too. To my knowledge there is no available data in the literature showing that tyrosine hydroxylase is expressed in SIFamidergic neurons (see e.g., Mao and Davis, 2010). To show that ple or Tdc2 are indeed expressed and translated into functional enzymes in SIFamidergic neurons the authors should provide the according antibody labelling corroborating the result from the transcriptome analysis.

__ Answer:__ We appreciate the reviewer’s constructive comments regarding the role of neurotransmitters in conjunction with SIFa in modulating interval timing behaviors. To confirm the expression of dopamine (DA) in SIFa neurons, we utilized a well-established genetic toolkit for dissecting dopamine circuit function in Drosophila [18]. Our findings demonstrate that TH-C-GAL4 specifically labels SIFa neurons, which have been confirmed to be dopaminergic (Fig. S4M). This aligns with the genetic intersection data and the findings from Xie et al. (2018), confirming that a subset of SIFa neurons is indeed dopaminergic. We have included these new results in the main text as follows:

" To further verify the presence of DA neurons within the SIFa neuron population, we utilized a well-established genetic toolkit for dissecting DA circuits and confirmed part of SIFa neurons are dopaminergic (S4M Fig) [58]."

   To confirm the glutamatergic characteristics of SIFa neurons, we conducted several experiments that established glutamate as the most critical neurotransmitter for generating interval timing in both SIFa and SIFaR neurons. First, to demonstrate the presence of glutamatergic synaptic vesicles in SIFa neurons, we utilized a conditional glutamatergic synaptic vesicle marker for *Drosophila*, developed by Certel et al. [19]. Our results confirmed that SIFa neurons exhibit strong expression of glutamatergic synaptic vesicles (Fig. 2P and Fig. S4N as a genetic control). We have described these new results in the main text as follows:

"To further substantiate the role of glutamate in SIFa-mediated behaviors. we targeted the expression of VGlut receptor in neurons that carry the SIFaR. Strikingly, the knockdown of VGlut receptor in these neurons also disrupted SMD behavior, mirroring the phenotype observed upon direct suppression of glutamatergic signaling in SIFa neurons (S4O-L Fig)."

   To further confirm that glutamate release from SIFa neurons influences the function of SIFaR neurons, we tested several RNAi strains targeting glutamate receptors. Our results showed that the knockdown of glutamate receptors in SIFaR-expressing neurons produced phenotypes similar to those observed with VGlut-RNAi knockdown in SIFa neurons (Fig. S4I-N). We believe that this series of experiments demonstrates that glutamate and dopamine work in conjunction with SIFa to modulate interval timing and other behaviors related to energy balance. We have described these new results in the main text as follows:

"We also further verified that the knockdown of glutamate receptors in SIFaR-expressing neurons produces phenotypes similar to those resulting from VGlut knockdown in SIFa neurons (S4G to S4L Fig). This suggests that glutamate is an essential neurotransmitter for modulating interval timing in SIFa neurons."

Comment 7. The authors compare the LMD and SMD behavior of the animals with reduced expression with "heterozygous control animals" the authors should describe in detail what these are - are these controls the driver lines or the effector lines or a mix of both? The authors should provide the data for heterozygous driver line controls as well as heterozygous effector line controls to exclude any genetic background influence on the measured behavior. Accordingly, the authors should provide the data for the same controls for the sleep experiment in figure 3O and all the other behavioral experiments in the following parts of the manuscript.

__ Answer: __We sincerely thank the reviewer for insightful comments regarding the absence of traditional genetic controls in our study of LMD and SMD behaviors. We acknowledge the importance of such controls and wish to clarify our rationale for not including them in the current investigation. The primary reason for not incorporating all genetic control lines is that we have previously assessed the LMD and SMD behaviors of GAL4/+ and UAS/+ strains in our earlier studies. Our past experiences have consistently shown that 100% of the genetic control flies for both GAL4 and UAS exhibit normal LMD and SMD behaviors. Given these findings, we deemed the inclusion of additional genetic controls to be non-essential for the present study, particularly in the context of extensive screening efforts. We understand the value of providing a clear rationale for our methodology choices. To this end, we have added a detailed explanation in the "MATERIALS AND METHODS" section and the figure legends of Figure 1. This clarification aims to assist readers in understanding our decision to omit traditional controls, as outlined below.

"Mating Duration Assays for Successful Copulation

The mating duration assay in this study has been reported [33,73,93]. To enhance the efficiency of the mating duration assay, we utilized the Df (1) Exel6234 (DF here after) genetic modified fly line in this study, which harbors a deletion of a specific genomic region that includes the sex peptide receptor (SPR)[94,95]. Previous studies have demonstrated that virgin females of this line exhibit increased receptivity to males [95]. We conducted a comparative analysis between the virgin females of this line and the CS virgin females and found that both groups induced SMD. Consequently, we have elected to employ virgin females from this modified line in all subsequent studies. For naïve males, 40 males from the same strain were placed into a vial with food for 5 days. For single reared males, males of the same strain were collected individually and placed into vials with food for 5 days. For experienced males, 40 males from the same strain were placed into a vial with food for 4 days then 80 DF virgin females were introduced into vials for last 1 day before assay. 40 DF virgin females were collected from bottles and placed into a vial for 5 days. These females provide both sexually experienced partners and mating partners for mating duration assays. At the fifth day after eclosion, males of the appropriate strain and DF virgin females were mildly anaesthetized by CO2. After placing a single female in to the mating chamber, we inserted a transparent film then placed a single male to the other side of the film in each chamber. After allowing for 1 h of recovery in the mating chamber in 25℃ incubators, we removed the transparent film and recorded the mating activities. Only those males that succeeded to mate within 1 h were included for analyses. Initiation and completion of copulation were recorded with an accuracy of 10 sec, and total mating duration was calculated for each couple. All assays were performed from noon to 4pm. Genetic controls with GAL4/+ or UAS/+ lines were omitted from supplementary figures, as prior data confirm their consistent exhibition of normal LMD and SMD behaviors [33,73,93,96,97]. Hence, genetic controls for LMD and SMD behaviors were incorporated exclusively when assessing novel fly strains that had not previously been examined. In essence, internal controls were predominantly employed in the experiments, as LMD and SMD behaviors exhibit enhanced statistical significance when internally controlled. Within the LMD assay, both group and single conditions function reciprocally as internal controls. A significant distinction between the naïve and single conditions implies that the experimental manipulation does not affect LMD. Conversely, the lack of a significant discrepancy suggests that the manipulation does influence LMD. In the context of SMD experiments, the naïve condition (equivalent to the group condition in the LMD assay) and sexually experienced males act as mutual internal controls for one another. A statistically significant divergence between naïve and experienced males indicates that the experimental procedure does not alter SMD. Conversely, the absence of a statistically significant difference suggests that the manipulation does impact SMD. Hence, we incorporated supplementary genetic control experiments solely if they deemed indispensable for testing. All assays were performed from noon to 4 PM. We conducted blinded studies for every test[98,99] .

   While we have previously addressed this type of reviewer feedback in our published manuscript [2–7], we appreciate the reviewer’s suggestion to include traditional genetic control experiments. In response, we conducted all feasible combinations of genetic control experiments for LMD/SMD during the revision period. The results are presented in the supplementary figures and are described in the main text.

__Comment 8. __On page 11 line 231 to page 12 line 233 the authors claim that "sNPF signaling transmits hunger and satiety information to SIFa neurons in order to control food search and feeding" and cite Martelli et al., 2017. Could the authors explain more in detail how the Martelli paper somehow proposes this idea? I do not find the link between sNPF signaling hunger and SIFamide in this precise paper.

__ Answer:__ We appreciate the reviewer for accurately pointing out our misunderstanding of the references. We agree that Martelli et al.'s paper does not mention that sNPF signaling transmits hunger and satiety information to SIFa neurons. Consequently, we have removed the relevant sentence and replaced it with a statement correctly indicating that while sNPF signaling is related to feeding behavior, its connection to SIFa neurons remains unknown. We are grateful to the reviewer for acknowledging our efforts to accurately cite previous articles that support our rationale and ideas.

" Short neuropeptide F (sNPF) signaling plays a crucial role in regulating feeding behavior in Drosophila melanogaster, influencing food intake and body size [60,66,67] . However, there is currently no direct evidence reported linking sNPF signaling to SIFa neurons."

Comment 9. On page 15 line 302 - 303 the authors write that "except for PK2-R2, all other genes coexpress with SIFa in SCope data, indicating that hugin inputs to SIFa may not be transmitted through peptidergic signaling" - if SIFamidergic neurons do not express hugin-receptors how do the authors explain the inverted effect of PK2-R2-RNAi on single housed male courtship index when compared to heterozygous SIFaPT Gal4 control that show a reduction under comparable conditions.

__ Answer:__ We appreciate the reviewer’s constructive comments. In line with another reviewer’s suggestion, we have completely removed results of other neuropeptidergic inputs, focusing instead on how sNPF inputs modulate SIFa-mediated behavioral modulation using more advanced techniques such as GCaMP (Fig 3N). Consequently, the phenotypes resulting from various knockdowns of neuropeptide receptors are currently under investigation for a separate manuscript that we are preparing. We hope to successfully address how different neuropeptidergic inputs regulate SIFa neuron activity through various strategies.

Comment 10. On page 17 line 350 - 351 the authors write that "Stimulation of SIFa neurons resulted in an elevation in food consumption. Further, the authors write that "deactivation of SIFa neurons leads to a decrease in food consumption in male flies". From the way this is formulated it is not visible that the role of SIFamide in feeding control was published by Martelli and colleagues before. As the authors do not discuss the finding further in their discussion but cite the concerned paper in other aspects it appears as the authors intentionally want to omit this information to the reader. The authors may add a note that this has been shown before for female flies by Martelli and colleagues.

__ Answer:__ We appreciate reviewer's concern for properly mention previous Martelli et al.'s results about female feeding behavior modulated by SIFa neurons' activity. We agree with reviewer and added sentence as below in main text.

"Nevertheless, the temporary deactivation of SIFa neurons leads to a decrease in food consumption in male flies (Fig 4N and S6F to S6H) as previously described by Martelli et al.'s report in female flies [43]."

Comment 11. SIFamide receptor and GnIHR are discussed as descendants from a common ancestor and the authors nicely demonstrate that SIFamide does not only control homeostatic behavior as shown by Martelli and colleagues but also controls reproductive behavior. The evolution of such behavior control mechanisms may be integrated in the discussion too.

Answer: We appreciate the reviewer’s constructive comments, which enhance the evolutionary significance of our study. We agree with the reviewer and have added the following paragraph to the DISCUSSION section:

"The relationship between SIFamide receptors (SIFaR) and gonadotropin inhibitory hormone receptors (GnIHR) [89] highlights an intriguing evolutionary connection, as both are believed to have descended from a common ancestor [90,91]. This study expands on previous findings by Martelli et al., demonstrating that SIFamide not only regulates homeostatic behaviors but also plays a significant role in reproductive behavior [43]. GnIHR regulates food intake and reproductive behavior in opposing directions, thereby prioritizing feeding behavior over other behavioral tasks during times of metabolic need [92]. The evolution of these behavioral control mechanisms suggests a complex interplay between neuropeptides that modulate both physiological states and reproductive strategies. As SIFamide influences various behaviors, including feeding and sexual activity, it may be integral to understanding how organisms adapt their reproductive strategies in response to environmental and internal cues. This integration of behavioral modulation underscores the evolutionary significance of SIFamide signaling in coordinating essential life functions in Drosophila melanogaster and potentially other species, revealing pathways through which neuropeptides can shape behavior across different contexts."

Conclusive Comments: The manuscript by Song and colleagues is very interesting and may attract a broad readership. However, the authors miss to make clear what was already known and published on the role of SIFamide in homeostatic behavior control before their own study. Seen that the receptors for SIFamide and GnRHI derive from a common ancestor and apparently both GnRHI and SIFamide share similar roles in behavioral control this might indeed suggests that the basic function of this SIFaR/GnIHR-signaling pathway is conserved. This more broad evolutionary aspect is missing in the discussion of the manuscript.

• *Answer: We wholeheartedly agree with the reviewer regarding the evolutionary significance of SIFaR's function in relation to GnIHR, and we have expanded the DISCUSSION section to emphasize this important aspect.

"The relationship between SIFamide receptors (SIFaR) and gonadotropin inhibitory hormone receptors (GnIHR) [89] highlights an intriguing evolutionary connection, as both are believed to have descended from a common ancestor [90,91]. This study expands on previous findings by Martelli et al., demonstrating that SIFamide not only regulates homeostatic behaviors but also plays a significant role in reproductive behavior [43]. GnIHR regulates food intake and reproductive behavior in opposing directions, thereby prioritizing feeding behavior over other behavioral tasks during times of metabolic need [92]. The evolution of these behavioral control mechanisms suggests a complex interplay between neuropeptides that modulate both physiological states and reproductive strategies. As SIFamide influences various behaviors, including feeding and sexual activity, it may be integral to understanding how organisms adapt their reproductive strategies in response to environmental and internal cues. This integration of behavioral modulation underscores the evolutionary significance of SIFamide signaling in coordinating essential life functions in Drosophila melanogaster and potentially other species, revealing pathways through which neuropeptides can shape behavior across different contexts."

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Author response:

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

Reviewer 1:

- The manuscript needs comprehensive proofreading for language and formatting. In many instances, spaces are missing or not required.

Thank you for your comments. The manuscript has been thoroughly proofread for errors in language and formatting.

- Could the authors explore correlation network analyses to get additional insights into the structure of different clusters? 

We have added a co-occurrence analysis (at species taxonomic level) based on SparCC to the manuscript (Figure 2).

This is described on Page 9 line 141-148

- The GitHub link is not correct. 

The github repository has now been made public.

- It is not possible to access the dataset on ENA. 

We have changed the ENA study PRJEB57401 status to open.

- Add the graphs obtained with decontam analysis as a supplementary figure. 

We have added the outputs of decontam (.csv files with feature lists of ASVs that were filtered based on the prevalence and frequency tests) to the github repository.

- There is nothing about the RPL group in the results section, while the authors discuss this issue in the introduction. What about the controls with proven fertility? 

Thank you. We have amended the manuscript to compare characteristics between the RPL, unexplained subfertility and controls groups.

Line 1279-130 page 8:  

“The study group represented 85% of samples with high sperm DNA fragmentation, 85% of samples with elevated ROS and 79% of samples with oligospermia. Rates of abnormal seminal parameters including low sperm concentration, reduced progressive motility and ROS concentrations were found to be highest in the MFI group (Supplementary Figure 1). Baseline characteristics between the RPL, unexplained subfertility and controls groups were similar.

Line 150-154 Page 9: 

“Bacterial richness, diversity and load were similar between all patient groups examined in the study (Supplementary Figure 4).

- While correctly stated in the title, the term microbiota should be used throughout the manuscript instead of "microbiome" 

Thank you. This misnomer has been amended throughout the manuscript.

Minor corrections:

Line 25: provoke is not a good term here. 

Thank you. The term ‘provoke’ has been removed

Line 26: why does semen culture have a limited scope? 

Thank you. Line 40-41 Page 3 has been amended:

“It is therefore plausible that asymptomatic seminal infections may be associated with impaired reproductive function in some men. Since semen culture has a limited scope for studying the seminal microbiota due to its inability to identify all present microbiota next generation sequencing (NGS) approaches have been reported recently by a growing number of investigators (13, 14, 15, 16, 17, 18, 19)”.

Line 68: write μl correctly

Thank you. This has been corrected

Line 131: several organisms at the genus level. 

Thank you. This has been corrected

Line 136: what are the relative abundances of these genera? Is this relevant? 

The mean relative abundances for the key taxa mention in each cluster are all above 20%. This information has been added to the manuscript text on page 9, line 153.

Line 173: Molina et al. 

Thank you. This has been corrected

Line 173: the contaminations are referred to the low biomass nature of testicular samples. If present, bacteria of accessory gland secretions are an integral part of the seminal microbiota itself. Please review these sentences. 

Thank you. This had been reworked to highlight the important of urethral contamination, which you later allude to as a limitation of our study is the failure to provide paired urine and semen samples.

Page 11 line 194-196

“Molina et al report that 50%-70% of detected bacterial reads may be environmental contaminants in a sample from extracted testicular spermatozoa (35); with the addition of passage along the urethra it is likely that contamination of ejaculated semen would be much higher.”

Table 1: remove results interpretation from table caption. 

Thank you this has been acted upon.

Table 1: why in some cases, like in DNA fragmentation index, the total is not equal to n=223? 

This is due to missing data/ analysis not possible for some men due to the requirement of a minimum number of sperm in the ejaculate to perform DNA fragmentation testing.

Table 1: "frag" is not defined. 

Thank you, this has been amended

Tables 2, 3 & 4: bacterial genera in italics. 

Thank you, this has been amended

Figure 1A: add the fertility status information above the cluster colors. 

Thank you, this has been amended in Figure 1.

Figure 1C: the color code is confusing. Use different colors for each cluster. 

Figure 1 legend: bacterial genera in italics. 

Figures 1 & 2: the authors should use similar chart formatting in the two tables. 

Thank you, this has been amended

Reviewer 2:

(1) The patient groups have different diagnoses and should be handled as different groups, and not fused into one 'patient' group in analyses. <br /> Why are the data in tables presented as controls and cases? I would consider men from couples with recurrent pregnancy loss, unexplained infertility, and male factor infertility to have different seminal parameters (not to fuse them into one group). This means, that the statistical analyses should be performed considering each group separately, and not to fuse 3 different infertility diagnoses into one patient group. 

We have conducted detailed analyses, requested by the reviewer, comparing seminal DNA, ROS and microbiota characteristics between each individual patient groups (Supplimental figures 1 and 4). No specific taxa (at either genera or species-level) were found to differ in relative abundance between the diagnostic groups. However, we expect associations between parameters such as reactive oxygen species, or DNA fragmentation, and relative abundance of bacterial species, to be general and not restricted to or specific to each diagnostic group. Therefore, we also conducted further analyses aggregating data from all patient groups to investigate relationships common to these different forms of male reproductive dysfunction.

(2) Were any covariables included in the statistical analyses, e.g. age, BMI, smoking, time of sexual abstinence, etc? 

Covariates were not included in the statistical analyses. This has been added in the manuscript to the limitations.

Page 14 line 267-268

“Additionally, we did not have other covariables such as smoking status with which to include in further analyses”.

(3) Furthermore, it is known that 16S rRNA gene analysis does not provide sensitive enough detection of bacteria on the species level. How much do the authors trust their results on the species level? 

The limitations of taxonomic assignment using 16S rRNA gene metataxonomics are well documented. However, the capacity to assign sequence amplicons at species level depends on the sequence variability of the 16S rRNA gene for each of the taxa reported and the specific gene region chosen. In this study, amplification of the V1-V2 region was performed using a mixed 28f primer set (see methods for details) that enables resolution and assignment of several bacterial species highly relevant to the reproductive tract including Lactobacillus spp., such as L. crispatus and L. iners, (e.g. https://doi.org/10.3389/fcell.2021.641921, https://doi.org/10.1128/msystems.01039-23, https://doi.org/10.1186/s12915-023-01702-2). In this study, we report the presence of L. iners, but not L. crispatus in semen samples, and we have also identified a specific association/co-occurrence between Gardnerella vaginalis and Lactobacillus iners, similar to that observed in vaginal bacterial communities.

(4) Were the analyses of bacterial genera and species abundances with seminal quality parameters controlled for diagnosis and other confounders? 

As stated in point 2, no adjustment was made for co-variates. No differences in microbiome composition were observed among the three diagnostic groups, so no adjustments were made to our analysis.

(5) The authors stress that their study is the biggest on the microbiome in semen. However, when considering that the study consists of 4 groups (with n=46-63), it does not stand out from previous studies. 

Our study is overall the largest investigating interactions between the seminal microbiome and male reproductive dysfunction. Other studies have included greater numbers of men with infertility.

(6) Weaknesses: There is a lack of paired seminal/urinal samples. 

Thank you. This limitation has been added.

Page 14 line 266-267

“A further limitation of this study, and others, is the lack of reciprocal genital tract microbiota testing of the female partners, or paired seminal and urinary samples from male participants”.

Recommendation for authors to consider:

Including previous classical reviews in the introduction: DOI:10.1097/MOU.0000000000000742 <br /> DOI: 10.1038/s41585-019-0250-y 

Thank you. This has been added.

Mentioning in the M&M section that there is a supplementary text with a more detailed M&M part. 

Thank you. This has been added. Further methodological detail can be found in supplementary text.

Revising the use of 'microbiota' and 'microbiome', they are not synonyms. When talking of 16S rRNA gene analysis, we consider 'microbiome' analysis. 

Thank you. This misnomer has been amended throughout the manuscript.

Revising the text, there are several erratas (e.g. verb missing, etc). 

Thank you for your comments. The manuscript has been thoroughly proofread for errors in language and formatting.

Khác với các nghiên cứu gần đây sử dụng LLM cho việc sinh prompt tự động, mỗi bước tối ưu hóa sẽ tạo sinh các prompt mới có mục tiêu tăng điểm accuracy dựa trên lân cận của các prompt đã được tạo sinh trước đó, thay vì chỉnh sửa một prompt đầu vào dựa trên phản hồi ngôn ngữ tự nhiên hoặc yêu cầu prompt mới phải có ý nghĩa ngữ cảnh tương tự.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review):

Summary: 

In the manuscript entitled "Magnesium modulates phospholipid metabolism to promote bacterial phenotypic resistance to antibiotics", Li et al demonstrated the role of magnesium in promoting phenotypic resistance in V. alginolyticus. Using standard microbiological and metabolomic techniques, the authors have shown the significance of fatty acid biosynthesis pathway behind the resistance mechanism. This study is significant as it sheds light on the role of an exogenous factor in altering membrane composition, polarization, and fluidity which ultimately leads to antimicrobial resistance. 

Strengths: 

(1) The experiments were carried out methodically and logically. 

(2) An adequate number of replicates were used for the experiments. 

Weaknesses: 

(1) The introduction section needs to be more informative and to the point.  

Thank you so much for your suggestion. We have revised the introduction to make it more informative and to the point as following:

“Non-inheritable antibiotic or phenotypic resistance represents a serious challenge for treating bacterial infections. Phenotypic resistance does not involve genetic mutations Phenotypic resistance does not involve genetic mutations and is transient, allowing bacteria to resume normal growth. Biofilm and bacterial persisters are two phenotypic resistance types that have been extensively studied (Brandis et al., 2023; Corona & Martinez, 2013). Biofilms have complex structures, containing elements that impede antibiotic diffusion, sequestering and inhibiting their activity (Ciofu et al., 2022). Biofilm-forming bacteria and persisters also have distinct metabolic states that significantly reduce their antibiotic susceptibility (Yan & Bassler, 2019). These two types of phenotypic resistance share the common feature in their retarded or even cease of growth in the presence of antibiotics (Corona & Martinez, 2013). However, specific factors that promote phenotypic resistance and allow bacteria to proliferate in the presence of antibiotics remain poorly defined.

Metal ions have a diverse impact on the chemical, physical, and physiological processes of antibiotic resistance  (Booth et al, 2011; Lu et al, 2020; Poole, 2017). This includes genetic elements that confer resistance to metals and antibiotics (Poole, 2017) and metal cations that directly hinder (or enhance) the activity of specific antibiotic drugs (Zhang et al., 2014). The metabolic environment can also impact the sensitivity of bacteria to antibiotics (Jiang et al., 2023; Lee & Collins, 2012; Peng et al., 2015; Zhang et al., 2020; Zhao et al., 2021). Light metal ions, such as magnesium, sodium, and potassium, can behave as cofactors for different enzymes (Du et al., 2016) and influence drug efficacy. Heavy metal ions, including Cu2+ and Zn2+, confer resistance to antibiotics (Yazdankhah et al., 2014; Zhang et al., 2018). Recent reports suggest that sodium negatively regulates redox states to promote the antibiotic resistance of Vibrio alginolyticus (Yang et al., 2018), while actively growing Bacillus subtilis cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al, 2019). In Gram-negative bacteria, by contrast, zinc enhances antibiotic efficacy by potentiating carbapenem, fluoroquinolone, and β-lactam-mediated killing (Isaei et al., 2016; Zhang et al., 2014). Magnesium influences bacterial structure, cell motility, enzyme function, cell signaling, and pathogenesis (Wang et al., 2019). This mineral also modulates microbiota to harvest energy from the diet (Garcia-Legorreta et al., 2020), allowing Bacillus subtilis to cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al., 2019). However, the role of magnesium in promoting phenotypic resistance is less well understood.

Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesiumdependent phenotypic resistance mechanisms of pathogenic Vibrio species. 

The current study evaluated whether magnesium induces phenotypic resistance in Vibrio species and defined the molecular/genetic basis for this resistance. Genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility testing were used for the evaluations.”

(2) The weakest point of this paper is in the logistics through the results section. The way authors represented the figures and interpreted them in the results section (or the figure legends) does not match. The figures are difficult to interpret and are not at all self-explanatory. 

Thank you so much for your suggestion. We have followed your suggestion to check the match between result and figures. They are now revised. 

(3) There are too many mislabeling of the figure panels in the main text which makes it difficult to find out which figures the authors are explaining. There should be more explanation on why and how they did the experiments and how the results were interpreted. 

Thank you so much for your suggestion. We have checked the figures and main text to ensure that we make every figure clearly stated.  

Reviewer #2 (Public Review): 

Summary: 

In this study, the authors aimed to identify if and how magnesium affects the ability of two particular bacteria species to resist the action of antibiotics. In my view, the authors succeeded in their goals and presented a compelling study that will have important implications for the antibiotic resistance research community. Since metals like magnesium are present in all lab media compositions and are present in the host, the data presented in this study certainly will inspire additional research by the community. These could include research into whether other types of metals also induce multi-drug resistance, whether this phenomenon can be observed in other bacterial species, especially pathogenic species that cause clinical disease, and whether the underlying molecular determinants (i.e. enzymes) of metal-induced phenotypic resistance could be new antimicrobial drug targets themselves. 

Strengths: 

This study's strengths include that the authors used a variety of methodologies, all of which point to a clear effect of exogenous Mg2+ on drug resistance in the targeted species. I also commend the authors for carrying out a comprehensive study, spanning evaluation of whole cell phenotypes, metabolic pathways, genetic manipulation, to enzyme activity level evaluation. The fact that the authors uncovered a molecular mechanism underlying Mg2+-induced phenotypic resistance is particularly important as the key proteins should be studied further.

Weaknesses: 

I believe there are weaknesses in the manuscript, however. The authors take for granted that the reader is familiar with all the assays utilized, and do not properly explain some experiments, and thus I highly suggest that the authors add a brief statement in each situation describing the rationale for each selected methodology (more details are in the private review to the authors). The Results section is also quite long and bogs down at times, and I suggest that the authors reduce its length by 10 to 20%. In contrast, the Introduction is sparse and lacks key aspects, for example, there should be mention of the study's main purpose and approaches, plus an introduction to the authors' choice of species and their known drug resistance properties, as well as the drug of choice (balofloxacin). Another notable weakness is that the authors evaluated Mg2+-induced phenotypic resistance only against two closely related species, and thus the generalizability of this mechanism of drug resistance is not known. The paper would be strengthened if the authors could demonstrate this type of phenotypic resistance in at least one more Gram-negative species and at least one Gram-positive species (antimicrobial susceptibility evaluations would suffice), each of which should be pathogenic to humans. Demonstrating magnesium-induced phenotypic drug resistance in the WHO Priority Bacterial Pathogens would be particularly important. 

In general, the conclusions drawn by the authors are justified by the data, except for the interpretation of some experiments. Importantly, this paper has discovered new antimicrobial resistance mechanisms and has also pointed to potential new targets for antimicrobials. 

Thank you so much for your suggestion! We followed your idea the revise the manuscript as following:

(1) We added a brief statement in the situation to explain the result and methodology according to your suggestion in the private review.

(2) To make the streamline of the story more logic, we moved the whole second result to supplementary text and supplementary figure. 

(3) We revised the introduction part by adding additional information to make it informative and to the point as following:

“Non-inheritable antibiotic or phenotypic resistance represents a serious challenge for treating bacterial infections. Phenotypic resistance does not involve genetic mutations Phenotypic resistance does not involve genetic mutations and is transient, allowing bacteria to resume normal growth. Biofilm and bacterial persisters are two phenotypic resistance types that have been extensively studied (Brandis et al., 2023; Corona & Martinez, 2013). Biofilms have complex structures, containing elements that impede antibiotic diffusion, sequestering and inhibiting their activity (Ciofu et al., 2022). Biofilm-forming bacteria and persisters also have distinct metabolic states that significantly reduce their antibiotic susceptibility (Yan & Bassler, 2019). These two types of phenotypic resistance share the common feature in their retarded or even cease of growth in the presence of antibiotics (Corona & Martinez, 2013). However, specific factors that promote phenotypic resistance and allow bacteria to proliferate in the presence of antibiotics remain poorly defined.

Metal ions have a diverse impact on the chemical, physical, and physiological processes of antibiotic resistance  (Booth et al, 2011; Lu et al, 2020; Poole, 2017). This includes genetic elements that confer resistance to metals and antibiotics (Poole, 2017) and metal cations that directly hinder (or enhance) the activity of specific antibiotic drugs (Zhang et al., 2014). The metabolic environment can also impact the sensitivity of bacteria to antibiotics (Jiang et al., 2023; Lee & Collins, 2012; Peng et al., 2015; Zhang et al., 2020; Zhao et al., 2021). Light metal ions, such as magnesium, sodium, and potassium, can behave as cofactors for different enzymes (Du et al., 2016) and influence drug efficacy. Heavy metal ions, including Cu2+ and Zn2+, confer resistance to antibiotics (Yazdankhah et al., 2014; Zhang et al., 2018). Recent reports suggest that sodium negatively regulates redox states to promote the antibiotic resistance of Vibrio alginolyticus (Yang et al., 2018), while actively growing Bacillus subtilis cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al, 2019). In Gram-negative bacteria, by contrast, zinc enhances antibiotic efficacy by potentiating carbapenem, fluoroquinolone, and β-lactam-mediated killing (Isaei et al., 2016; Zhang et al., 2014). Magnesium influences bacterial structure, cell motility, enzyme function, cell signaling, and pathogenesis (Wang et al., 2019). This mineral also modulates microbiota to harvest energy from the diet (Garcia-Legorreta et al., 2020), allowing Bacillus subtilis to cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al., 2019). However, the role of magnesium in promoting phenotypic resistance is less well understood.

Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesiumdependent phenotypic resistance mechanisms of pathogenic Vibrio species. 

The current study evaluated whether magnesium induces phenotypic resistance in Vibrio species and defined the molecular/genetic basis for this resistance. Genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility testing were used for the evaluations.”

(4) We examined the effect of magnesium in WHO listed priority strains, which confirmed the results as following:

“Importantly, exogenous MgCl2 also increased MICs of clinic isolates, carbapenemresistant Escherichia coli, carbapenem-resistant Klebsiella pneumoniae, carbapenemresistant Pseudomonas aeruginosa and carbapenem-resistant Acinetobacter baumannii to balofloxacin (Fig 1G).”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

(1) There are many grammatical mistakes to point out. The manuscript needs proofreading and editing.

We appreciate this comment! The manuscript has been revised by a native speaker.

(2) The introduction could be more informative. A little more description of magnesium - such as what it does to antibiotics and how it's known to affect the microbiome - might be helpful for the general readers. The question remains why out of all the metal ions that might affect antibiotic resistance (many of them are less explored), authors particularly decided to work on the effect of magnesium. The introduction should cover the rationale of their hypothesis. Also, the authors might want to briefly talk about the model organisms (V. algonolyticus and V. parahemolyticus) describing how threatening they are and how they are becoming resistant to antibiotics. 

We appreciate this comment! We revise the introduction by providing additional information as following:

“In Gram-negative bacteria, by contrast, zinc enhances antibiotic efficacy by potentiating carbapenem, fluoroquinolone, and β-lactam-mediated killing (Isaei et al., 2016; Zhang et al., 2014). Magnesium influences bacterial structure, cell motility, enzyme function, cell signaling, and pathogenesis (Wang et al., 2019). This mineral also modulates microbiota to harvest energy from the diet (Garcia-Legorreta et al., 2020), allowing Bacillus subtilis to cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al., 2019). However, the role of magnesium in promoting phenotypic resistance is less well understood.

Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesiumdependent phenotypic resistance mechanisms of pathogenic Vibrio species. 

The current study evaluated whether magnesium induces phenotypic resistance in Vibrio species and defined the molecular/genetic basis for this resistance. Genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility testing were used for the evaluations. ”

(3) Figure 1C is mislabeled as 1B (line 100). Line 101: The sentence is not clear and very confusing. What is meant by 15.6mM - 62.4 mM? Are they talking about the concentration of BLFX (though in the figure the concentration was shown in µg)? Please rewrite the sentence in a simplified way. Also, the zone of inhibition was decreased with increasing MgCl2, not increased. 

We appreciate this comment! These have been revised, including that Fig 1B is now corrected as Fig. 1C. Line 101, which is now Line 122. The sentence was revised as following:

“At balofloxacin doses of 1.56, 3.125, 6.25, and 12.5 µg, the zone of inhibition decreased with increasing MgCl2 (Fig 1D)”

(4) In the western blot images, it would be nice to indicate the MW of the protein bands shown. The loading control used for the experiments should be clearly mentioned in the figure legends. 

We appreciate this comment! The MWs are indicated in the western-blot image throughout the manuscript. 

The loading control is clearly stated in the figure legend as following:

“Whole cell lysates resolved by SDS-PAGE gel was stained with Coomassie brilliant blue as loading control.”. 

(5) Figures 2 B and C: the figure legend does not explain what the authors wanted to show. It's not clear how they plotted the inhibitory curve, or the binding efficacy. These panels need an explanation of how the analysis was done.

We appreciate this comment! The figure 2 is now removed to Suppl. Fig 2, and the description of figure 2 is moved to Suppl. Text. We revise the description of the result as following, which is in Suppl. Text:

“Prior studies suggest that the chelation of antibiotics by magnesium ions inhibits antibiotic uptake (Deitchman et al., 2018; Lunestad and Goksøyr, 1990). To investigate whether magnesium binds to balofloxacin, balofloxacin was pre-incubated with magnesium, and zone of inhibition (ZOI) analysis was conducted. Six different concentrations of balofloxacin were separately incubated with six different concentrations of MgCl2, and then spotted on filter paper so that a defined amount of balofloxacin could be used for ZOI. While lower concentrations of MgCl2, (0.78, 3.125, or 12.5 mM) did not alter the ZOI, higher concentrations, including 50 and 200 mM MgCl2, decreased the ZOI (Suppl. Fig 2A), suggesting that even high doses of magnesium had only a partial effect on balofloxacin through direct binding. For example, at 200 mM MgCl2 and 5 or 10 μg/mL balofloxacin, the balofloxacin ZOI was 53.2 and 70.3% of the ZOI at 0 mM MgCl2, suggesting that  50% of the antibiotics were still functional. Intracellular BLFX also decreased with increasing MgCl2 (Suppl. Fig 2B), while exogenous Mg2+ increased intracellular Mg2+ levels in a dose-dependent manner. For example, exogenous 50 and 200 mM MgCl2 increased intracellular Mg2+ levels to 1.21 and 1.31 mM, respectively (Suppl. Fig 2C). The relationship between TolC, an efflux pump that transports quinolones from bacterial cells, and Mg2+ was also assessed (Kobylka et al., 2020; Song et al., 2020). The expression of TolC/tolC was unaffected by Mg2+ (Suppl. Fig 2D). Magnesium is critical for LPS stability. LPS levels increased at 200 mM Mg2+ (Suppl. Fig 2E), however, the loss of waaF, lpxA, and lpxC, three key genes involved in LPS biosynthesis, did not influence balofloxacin sensitivity/resistance in the presence of Mg2+ (Suppl. Fig 2F). These findings suggest that magnesium-induced LPS biosynthesis does not contribute directly to BLFX resistance and demonstrate that Mg2+ influx is involved in balofloxacin resistance.”

(6) For the metabolomics results, it will help immensely if the authors provide a volcano plot of the identified metabolites and plot the heat map according to the -log2 metabolite intensities. In Figure 3A, it's not clear what information is conveyed through Euclidean distance calculations of the heat map. In Figure 3 B, the authors mentioned that the OPLS-DA test was conducted, although the figure shows a PCA plot, so it's not clear how these two are connected. Figure 3 E: the figure legend says scattered plot, but the panel represents color-coded numerical values, not a scattered plot. Also, it's not clear how they got those values. 

We appreciate this comment! We quite agree with you that if the differential metabolites could be shown as volcano plot. However, we didn’t adopt volcano plot in this study because this is a magnesium concentration-dependent metabolomes that includes 6 groups in parallel. Volcano plots may give a complex view of the comparison among different groups. We also tried to plot the heat map according to the -log2 metabolite intensities. Although this analysis cluster 200 mM and 50 mM groups better, the data of low magnesium concentrations was not consistent, which may be due to the minor metabolic change of low concentrations magnesium. Thank you for your understanding. 

For Euclidean distance calculations, we explain in the figure legend as following:

“Euclidean distance calculations were used to generate a heatmap that shows clustering of the biological and technical replicates of each treatment.” 

In Figure 2B, which was Figure 3B in previous version, it has been replaced with OPLS-DA analysis in the revised version. 

In Figure 2E, which was Figure 3E in previous version, it is revised as following:

“E. Areas of the peaks of palmitic acid and stearic acid generated by GC-MS analysis.” 

(7) In Figure 4, the figure legends (as well as the in the text) are not properly referred to. Please make sure to refer to the correct panel. 

We appreciate this comment! The figure legends have been corrected to match the panel and text. 

Figure 4F: how was the synergy analysis done? In the methods section, the authors described the antibiotic bactericidal assay protocol, but there was no clear indication of how they generated the isobologram. 

We appreciate this comment! We provide additional information in the Figure 3F legend, which was Figure 4F in previous version,  as following: 

“Synergy analysis for BFLX with palmitic acid for V. alginolyticus. Synergy was performed by comparing the dose needed for 50% inhibition of the synergistic agents (white) and non-synergistic (i.e., additive) agents (purple).”

(8) Figure 5 A: the scatter plot is plotted according to the area along the Y axis: which "area" is represented here? There is absolutely no explanation, neither in the results nor in the figure legends. Using box plots might be a better option than using a scattered plot.

We appreciate this comment! “Area” has been noted in the revised manuscript as following:

“The area indicates the area of the peak of the metabolite in total ion chromatography of GC-MS.” 

(9) In Figure 6 A, the heat map is plotted according to the column Z scores. What is meant by "column Z score"? The corresponding figure legend says, "heat map showing differential abundance of lipid". Z scores do not represent an abundance of a variable, so the conclusion might not be appropriate here. 

We appreciate this comment! In Figure 5A, which was Figure 6A in previous version, column Z score shows the abundance of metabolites analyzed, which is automatically generated in the heat map analysis to give a sign of these metabolites tested. The legend has been revised as following: 

“Heatmap showing changes in differential lipid levels at the indicated concentration of MgCl2.”  

(10) Line 313-314: it should be Figure EV6C.  

We appreciate this comment! The citation has been corrected.

(11) The authors have shown that Mg+2 does not alter the LPS transport system, however, there was some significant increase in LPS expression at 200mM MgCl2. It would be interesting if the authors could also check if Mg+2 has any effect on the outer membrane protein (OMP) integrity (by checking OMP components BamA and LptD).  

We appreciate this comment!  We have carefully examined the membrane permeability in Figure 7. We thus didn’t perform additional experiment here to see the change of BamA and LptD. Thank you very much for your understanding.

(12) I wonder if the authors could check the effect of extracellular Mg+2 during the co-treatment of palmitic acid, linoleic acid, and balofloxacin. Will there still be the antagonistic effect or the presence of Mg+2 could change the phenotype? 

We appreciate this comment! Additional experiments is performed as following:

“Furthermore, magnesium had a minimal effect on the antagonistic effect of palmitic acid, linolenic acid, and balofloxacin (Fig 4G), suggesting that this mineral functions through lipid metabolism.” 

Reviewer #2 (Recommendations For The Authors): 

(1) As mentioned in the Public Review, I strongly believe that the impact of this study will be more significant if magnesium-induced phenotypic drug resistance could be demonstrated in at least one other Gram-negative and one other Grampositive species, both of which should be human pathogens. The full suite of experiments would not be necessary for this suggestion; evaluation of the effect of Mg concentration in growth media on the drug resistance of other species, testing the different antibiotic types used in this study, would be sufficient. 

We appreciate this comment! Additional experiments have performed to test this idea. Mg2+ has the similar effect on carbapenem-resistant Escherichia coli, carbapenem-resistant Klebsiella pneumoniae, carbapenem-resistant Pseudomonas aeruginosa and carbapenem-resistant Acinetobacter baumannii as the similar as on the Vibrio species in shown in Figure 1G. These have been described following as

“Importantly, exogenous MgCl2 also increased MICs of clinic isolates, carbapenemresistant Escherichia coli, carbapenem-resistant Klebsiella pneumoniae, carbapenemresistant Pseudomonas aeruginosa and carbapenem-resistant Acinetobacter baumannii to balofloxacin (Fig 1G).”

(2) I recommend that the Introduction section be expanded. I recommend one or two sentences introducing the two Vibrio species selected for study. I.e. why did the authors choose these two species? What is known about their phenotypic drug resistance in the literature? Why did the authors select balofloxacin for their studies, is it a common antimicrobial used vs Vibrios? As well, the end of the Introduction section ends abruptly with no transition to the present study itself. The end of the introduction should include one or two sentences introducing the main purpose of the study, its approach, and the techniques undertaken. For example, "In this study, we evaluated whether magnesium induces phenotypic resistance in Vibrio species and the molecular/genetic basis for such resistance. We used genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility evaluations." 

We appreciate this comment! We revise the introduction by providing additional information as following:

“In Gram-negative bacteria, by contrast, zinc enhances antibiotic efficacy by potentiating carbapenem, fluoroquinolone, and β-lactam-mediated killing (Isaei et al., 2016; Zhang et al., 2014). Magnesium influences bacterial structure, cell motility, enzyme function, cell signaling, and pathogenesis (Wang et al., 2019). This mineral also modulates microbiota to harvest energy from the diet (Garcia-Legorreta et al., 2020), allowing Bacillus subtilis to cope with ribosome-targeting antibiotics by modulating ion flux (Lee et al., 2019). However, the role of magnesium in promoting phenotypic resistance is less well understood.

Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesiumdependent phenotypic resistance mechanisms of pathogenic Vibrio species. 

The current study evaluated whether magnesium induces phenotypic resistance in Vibrio species and defined the molecular/genetic basis for this resistance. Genetic approaches, GC-MS analysis of metabolite and membrane remodeling upon antibiotic exposure, membrane physiology, and extensive antimicrobial susceptibility testing were used for the evaluations. ”

(3) The authors introduce the acronym AWST but never use it again in the paper, instead they use SWT. The authors should introduce SWT only for consistency. 

We appreciate this comment! We have corrected all the “SWT” to “ASWT”

(4) Line 76 is not clear: what is meant by "some of which could influence drug efficacy" - the enzymes that utilize light metal ions are co-factors? Or the metals directly?  

We appreciate this comment! The information we wanted to deliver is that light metal ions can serve as cofactors to catalyze biochemical reaction. Such chemical reaction would alter the drug efficacy, e.g. the Fe-S cluster are metallocofactor for proteins which regulates redox chemistry including antibioticinduced redox change. However, this information is not appropriate for this manuscript, so we delete this sentence. 

(5) Line 90: add a reference corroborating that this chemical composition is a mimic of marine water. The NaCl concentration used in particular looks quite low. 

We appreciate this comment! It was a typo error. The NaCl concentration was 210 mM as shown in Suppl. Table 1. We also provide details of the chemical composition of the marine water as following:

“Marine environments and agriculture, where antibiotics are commonly used, are rich in magnesium. To investigate whether this mineral impacts antibiotic activity, the minimal inhibitory concentration (MIC) of V. alginolyticus ATCC33787 and V. parahaemolyticus VP01, which we referred as ATCC33787 and VP01 afterwards, isolated from marine aquaculture, to balofloxacin (BLFX) in Luria-Bertani medium

(LB medium) plus 3% NaCl as LBS medium and “artificial seawater” (ASWT) medium that included the major ion species in marine water (Wilson, 1975) (LB medium plus 210 mM NaCl, 35 mM Mg2SO4, 7 mM KCl, and 7 mM CaCl2) were assessed”

(6) Line 98 and Figure 1B. M9 is indicated in the text but does not appear in the figure, the figure only shows SWT. This should be checked. Line 99: based on Figure 1C, the authors are adding MgCl2 to SWT, SWT should be mentioned in this line. Line 100: I believe this is referring to Figure 1C, which should be checked. 

We appreciate this comment! 

Line 98, which is now Line 118: We have corrected M9 to ASWT as following:

“However, the MIC for BLFX was higher in ASWT medium supplemented with Mg2SO4 or MgCl2 than in LB medium (Fig 1B).”

Line 99, which is now Line 133: the sentence is corrected as following:

“The MIC for BLFX increased at higher concentrations of MgCl2 in ASWT”

Line 100, which is now Line 135: we have corrected Fig 1B to Fig. 1C.

(7) Line 101: text and Figure 1D are not consistent, as Figure 1D does not show this level of precision in added MgCl2 as indicated in the text (15.6 - 62.4 mM).  

We appreciate this comment! The sentence has been corrected as following: “At balofloxacin doses of 1.56, 3.125, 6.25, and 12.5 µg, the zone of inhibition decreased with increasing MgCl2 (Fig 1D)””.  

(8) MgCl2 clearly induces increasing levels of BLFX resistance, and to high levels, but not for every antibiotic. For example, the level of increased resistance to blactams is low (ceftriaxone) and plateaus (ceftazidime). As well, resistance to gentamicin plateaus at a lower level than the other aminoglycosides. These observations do not take away from the conclusion that Mg induces multi-drug resistance, but since the behaviour of the MICs for these drugs is different than the other drugs, they should be mentioned. Also, Figure 1F - tetracyclines (plural) is used for vertical axis label - does this refer to the tetracycline itself or the class itself, and if the class, which one was tested? 

We appreciate this comment! We revise the description as following: “Notably, magnesium had a reduced effect on ceftriaxone and gentamicin than other antibiotics.”

The tetracyclines is labeled as “Oxytetracycline” in the revised manuscript. 

- The magnesium chelation experiments presented in Figure 2 are not clear. The authors should briefly mention how this was done around line 128, and what data underlies the values in Figure 2C. Figure 2B is also not clear to me at all. Similarly, how the authors measured intracellular balofloxacin and Mg2+ is not clear and should be mentioned briefly around lines 130-132. 

We appreciate this comment! These have been rewritten following as  “To investigate whether magnesium binds to balofloxacin, balofloxacin was preincubated with magnesium, and zone of inhibition (ZOI) analysis was conducted. Six different concentrations of balofloxacin were separately incubated with six different concentrations of MgCl2, and then spotted on filter paper so that a defined amount of balofloxacin could be used for ZOI. While lower concentrations of MgCl2, (0.78, 3.125, or 12.5 mM) did not alter the ZOI, higher concentrations, including 50 and 200 mM MgCl2, decreased the ZOI (Suppl. Fig 2A), suggesting that even high doses of magnesium had only a partial effect on balofloxacin through direct binding. For example, at 200 mM MgCl2 and 5 or 10 μg/mL balofloxacin, the balofloxacin ZOI was 53.2 and 70.3% of the ZOI at 0 mM MgCl2, suggesting that  50% of the antibiotics were still functional. Intracellular BLFX also decreased with increasing MgCl2 (Suppl. Fig 2B), while exogenous Mg2+ increased intracellular Mg2+ levels in a dose-dependent manner. For example, exogenous 50 and 200 mM MgCl2 increased intracellular Mg2+ levels to 1.21 and 1.31 mM, respectively (Suppl. Fig 2C). The relationship between TolC, an efflux pump that transports quinolones from bacterial cells, and Mg2+ was also assessed (Kobylka et al., 2020; Song et al., 2020). The expression of TolC/tolC was unaffected by Mg2+ (Suppl. Fig 2D). Magnesium is critical for LPS stability. LPS levels increased at 200 mM Mg2+ (Suppl. Fig 2E), however, the loss of waaF, lpxA, and lpxC, three key genes involved in LPS biosynthesis, did not influence balofloxacin sensitivity/resistance in the presence of Mg2+ (Suppl. Fig 2F). These findings suggest that magnesium-induced LPS biosynthesis does not contribute directly to BLFX resistance and demonstrate that Mg2+ influx is involved in balofloxacin resistance.”

- Line 135: LPS cannot be "expressed", as the authors word it here. This should be corrected. Also, the inspection of Figure 2G actually shows the levels of LPS increase with increased Mg2+. The authors should re-evaluate these results and change their description around this area of the Results. 

We appreciate this comment! We have removed the whole Figure 2 to Supplementary Text and Supplementary Figure 2. We rewrite this part as following: “The relationship between TolC, an efflux pump that transports quinolones from bacterial cells, and Mg2+ was also assessed (Kobylka et al., 2020; Song et al., 2020). The expression of TolC/tolC was unaffected by Mg2+ (Suppl. Fig 2D). Magnesium is critical for LPS stability. LPS levels increased at 200 mM Mg2+ (Suppl. Fig 2E), however, the loss of waaF, lpxA, and lpxC, three key genes involved in LPS biosynthesis, did not influence balofloxacin sensitivity/resistance in the presence of Mg2+ (Suppl. Fig 2F). These findings suggest that magnesium-induced LPS biosynthesis does not contribute directly to BLFX resistance and demonstrate that Mg2+ influx is involved in balofloxacin resistance.”

- Section: MgCl2 affects bacterial metabolism. Authors switched to M9 medium - why? This contrasts with other sections using SWT and should be explained. Also, I cannot evaluate whether the statistical analysis of the data here was performed correctly and was appropriate for this type of experiment. I advise the authors to move the details in lines 166-169 to the Materials and Methods and replace this section instead with a more accessible description of the statistical analysis that a non-expert would be able to appreciate. Furthermore, analysis of Figure 3A indicates that the levels of asparagine, 4-hydroxybutyric acid, uracil, cystathionine, fumaric acid, and aminoethanol have significantly changed at high MgCl2, but these are not mentioned in the text. I suggest the authors mention these if they are relevant to the 12 enriched pathways, especially the biosynthesis of fatty acids. 

We appreciate this comment! 

We indicate the reason we use M9 medium as following:

“To better understand how magnesium affects bacterial metabolism” for explaining why the M9 medium was used.”

The information lines 166-169 indicated has been removed to M &M. 

We have carefully examined the abundance of the metabolites and the enriched pathway. Among the listed metabolites, only fumarate is within the enriched pathways. We mention this point in our revised manuscript as following:

“The increase in fatty acid biosynthesis could be partially explained by an imbalanced pyruvate cycle/TCA cycle, in which fumarate levels increased at higher Mg2+ while succinate levels increased at lower Mg2+ (Suppl. Fig 5B). These findings indicated that glycolysis fluxes into fatty acid biosynthesis rather than the pyruvate cycle/TCA cycle. The relevance of fatty acids and BLFX was demonstrated by the observation that exogenous palmitic acid increased bacterial resistance to balofloxacin (Fig 2F). These results suggest that fatty acid metabolism may be critical to magnesium-based phenotypic resistance.”

- Line 211 appears to refer to Figure 4F and should be checked. Similarly in line 216 - appears this should be Figure 4H, and line 218 should be Figure 4H. Line 226: add a reference to Fig 4I (after arcA was decreased). Line 227: what are genes N646_1004 and N646_1885? Based on Fig 4J these are crp - authors should add to line 227. Line 228 appears to refer to Figure 4J, not Figure 4I. Line 229 - should be Figure 4K, not Figure 4I. Line 231 - should be 4L, not 4K. Line 239 - should be 4M.

We appreciate this comment! The text and figure is now matched. 

- Line 312: the descriptions of "11 lipids, 32 lipids, and 53", and then "26 lipids, 52 lipids, and 107 lipids" are not clear at all and should be corrected. 

We appreciate this comment! The sentence is revised as following:

“The abundance of 11, 32, and 53 lipids was increased in 3.125, 50, and 200 mM MgCl2-treated bacteria, respectively, while the abundance of 26, 52, and 107 lipids was decreased in 3.125, 50, and 200 mM MgCl2-treated bacteria, respectively (Suppl. Fig 7C)”

- Line 340. What is the assay the authors are using to measure the levels of the PGS and PSS enzymes? This is not mentioned or clear in this part of the Results.  

We appreciate this comment!  We provide the information in the manuscript as following:

“Levels of PGS and PSS were quantified by ELISA kits according to manufacture’s instruction (Shanghai Fusheng Industrial Co., Ltd., China)”

- Line 372: What is the assay for measuring membrane depolarization? This is not mentioned and I suggest it should be. Line 374: Figure 7B does not show time dependence, only dose dependence, this should be corrected, it is assumed the authors are referring to Fig 7C for the time dependence data. 

We appreciate this comment! We provide the information in the result as following:  

“The voltage-sensitive dye, DiBAC4(3) showed that 12.5–200 mM MgCl2 promoted membrane depolarization in a dose-dependent manner (Fig 6A)”

We also explain how DiBAC4(3) can be used to measure membrane depolarization in the Materials and Methods section as following:

“DiBAC4(3) is a s voltage-sensitive probe that penetrates depolarized cells, binding intracellular proteins or membranes exhibiting enhanced fluorescence and red spectral shift.”

To make it clear the specific figure, we revise the sentence as following:

“Meanwhile, MgCl2 had a dose-dependent (Fig 6B) and time-dependent (Fig 6C) effect on proton motive force (PMF).”

- Line 384: mention how FM5-95 measures membrane permeability. The authors should also clarify how this reagent is used to measure membrane fluidity, and it is not clear if the data for this is presented in Figure 7 - please clarify. Regarding SYTO9 dye experiment: the authors should briefly explain the experimental design - how SYTO9 dye operates and why FACS was chosen. What is labeled with FITC?  

We appreciate this comment! We clarify the reason we use FM5-95 in the Methods and Materials section as following:

“Measurement of fluidity by fluorescence microscopy

Measurement of membrane fluidity is performed as previously described (Wen et al., 2022). Briefly, ATCC33787 were cultured in medium with indicated concentrations of MgCl2, collected and then adjusted to OD 0.6. Aliquot of 100 μL bacteria cells of each sample were diluted to 1 mL and 10 μL (10 mg/mL) FM5-95 (Thermo Fisher

Scientific, USA) was added. FM5-95 is a lipophilic styryl dye that insert into the outer leaflet of bacterial membrane and become fluorescence. This dye preferentially bind to the microdomains with high membrane fluidity(Wen et al., 2022). After incubated for 20 min at 30 ℃ at vibration without light, the sample was centrifuged for 10 min at 12,000 rpm. The pellets were resuspended with 20 μL of 3% NaCI. Aliquot of 2 μL sample was dropped on the agarose slide, and take photos under the inverted fluorescence microscope.”

This data is presented as micrographs in Fig. 6D, which shows the decreased FM5-95 staining with increasing concentrations of MgCl2. We make this description clear with the following revision:

“FM5-95 staining decreased with increasing concentrations of Mg2+, and no staining was observed in the presence of 200 mM Mg2+ (Fig 6D).”

We explain the reason why we use SYTO9 as following:

“SYTO9, a green fluorescent dye that binds to nucleic acid, enters and stains bacteria cells when there is an increase in membrane permeability (Lehtinen et al., 2004; McGoverin et al., 2020). Staining decreased with increasing MgCl2, indicating that bacterial membrane permeability declined in an Mg2+ dose-dependent manner (Fig 6E).”

We didn’t use FACS in this study, while we only analyze the fluorescence distribution with the equipment. To make it clear, we revise the sentence as following:

“After incubated for 15 min at 30 ℃ at vibration without light, the mixtures were filtered and measured by flow cytometry (BD FACSCalibur, USA).”

- Lines 391-397. The statement that palmitic acid shifts the peaks in Figure 7F is not supported by the data. There is essential no change in the major peak position within each MgCl2 concentration set with increasing palmitic acid. For the linolenic acid data, it is clear that linolenic acid increases permeability only at 50 mM MgCl2-this should be mentioned in the text. 

We appreciate this comment! We revise the sentence as following:

“Exogenous palmitic acid also shifted the fluorescence signal peaks to the left in an MgCl2-dependent manner while palmitic acid only slightly shifted the peaks (Fig 6F). In contrast, exogenous linolenic acid shifted the peak to the right in a dose-dependent manner at 50 mM MgCl2 (Fig 6G).” 

- Line 404-405 - as mentioned earlier, the assay for the update of BLFX should be mentioned (if it is done so earlier in the text, then it does not need to be here).  

We appreciate this comment! It has been mentioned in the introduction.  

- Discussion: CpxA/R-OmprF pathway is mentioned here for the first time. Is this one of the pathways modified by MgCl2 as determined during the course of the study? If so, this should be reworded to mention that. If not, the relevance of this particular pathway as it relates to light metals and phenotypic resistance should be discussed.

We appreciate this comment! Since it is not relevant to the discussion of Mg2+ and fatty acid biosynthesis, we delete this sentence in the revised manuscript.  

-The following grammatical errors should be corrected:

-line 55 change to: "genetic mutations; instead, this type of resistance is transient, and bacteria resume normal growth"

-line 57: change to "resistance types are biofilm" 

-line 61: change to "states that significantly" 

-line 63: change to "resistance share the common feature in they retard or even cease in the presence" 

-line 65: change to "resistance that allow bacteria to proliferate" 

-line 81: change "But whether" to "Whether" 

-line 178: change to "may be critical to the Mg-based phenotypic resistance"

-line 86: change to "Marine environments and agriculture are rich in magnesium, where..." 

-line 93: change in to vs

-line 154: insert space after metabolism 

-line 158: change 'identified" to "focused on the levels of" 

-line 160: change "The levels of forty-one metabolites" 

-line 198: change shared to share 

-line 310: increased is duplicated, delete one 

-line 451: add "the" before ratio 

-line 453: gram should be capitalized 

-line 462: "the regulation" should be reworded to "More importantly, the effect of exogenous MgCl targets the..." 

-line 469: add dash between Mg2+ and limited

-line 478: change "the crucial" to "a crucial" 

-there are numerous locations in the manuscript where the word "magnetism" is used when clearly the word is supposed to be magnesium - this should be corrected

We appreciate this comment! These have been corrected or revised. 

Editors comments:

Page 2 line 27; Page 25 line number 426; page 27 line number 481: In the abstract and discussion, only Vibrio alginolyticus was mentioned, even though two Vibrio species were used in the study. It would be helpful to understand the rationale behind the focus on this particular species.

We appreciate this comment! We have revised the introduction to provide additional information as following:

“Vibrios inhabit seawater, estuaries, bays, and coastal waters, regions full of metal ions such as magnesium (Kumarage et al., 2022). Magnesium is the second most dissolved element in seawater after sodium. At a salinity of 3.5% seawater, the magnesium concentration is about 54 mM (Potis, 1968), and in deep seawater, can be as high as 2,500 mM (Wang et al., 2024). Vibrio parahaemolyticus and V. alginilyticus are two representative Vibrio pathogens that infect humans and aquatic animals, resulting in illness and economic loss, respectively (Grimes, 2020). (Fluoro)quinolones such as balofloxacin are used to treat Vibrio infection, however, resistance has emerged due to overuse (Suyamud et al., 2024). Indeed, (fluoro)quinolones are one of China's two primary residual chemicals associated with aquaculture (Liu et al., 2017). Vibrio can develop quinolone resistance through mutations in the DNA gyrase gene or through plasmid-mediated mechanisms (Dutta et al., 2021). Thus, the use of V. parahaemolyticus and V. alginilyticus as bacterial representatives, and balofloxacin as a quinolone-based antibacterial representative, can help to define novel magnesium-dependent phenotypic resistance mechanisms of pathogenic Vibrio species.”

On Page 2, line 34: The abstract contains some undefined abbreviations, such as 'PE' and 'PG', which should be explained. 

We appreciate this comment! We explain the PE and PG in the revised abstract as following:

“phosphatidylethanolamine (PE) biosynthesis is reduced and phosphatidylglycerol (PG)”

On Page 2, line 31-32: For the statement "Exogenous supplementation of fatty acids confirm the role of fatty acids in antibiotic resistance…" it would be beneficial to specify whether the fatty acids were saturated or unsaturated. 

Response, We appreciate this comment! We revise the sentence as following:

“Exogenous supplementation of unsaturated and saturated fatty acids increased and decreased bacterial susceptibility to antibiotics, respectively, confirming the role of fatty acids in antibiotic resistance.”

The potential effects of the specific ions (SO4 and Cl2) present in the Mg2SO4 and MgCl2 compounds used in the study were not discussed. It would be useful to understand if these ions had any influence on the observed outcomes.

We appreciate this comment! We revise the sentence as following:

“However, the MIC for BLFX was higher in ASWT medium supplemented with Mg2SO4 or MgCl2 than in LB medium (Fig 1B). And Mg2SO4 or MgCl2 had no

difference on MIC, suggesting it is Mg2+ not other ions contribute to the MIC change.”

On Page 8, line 141: The heading of Figure 2, "Mg2+ elevates intracellular Mg2+," seems redundant and could be revised for clarity or modified. 

We appreciate this comment! Figure 2 is now moved to supplementary figure as Suppl. Fig 2. The title is revised as following:

“Figure 2. Mg2+ decreases balofloxacin uptake.”

On Page 4, line 91: some terms/abbreviations, such as 'LB' and 'M9,' require expansion or definition to ensure the reader's understanding.

We appreciate this comment! We include the expansion for LB and M9 in the  revised manuscript as following:

“Luria-Bertani medium (LB medium)” and “M9 minimal medium (M9 medium)”

Page 4, line 92: The real seawater composition used in the experiments should be supported by a reference.

We appreciate this comment! We provide the reference in the revised manuscript as following:

““artificial seawater” (ASWT) medium that included the major ion species in marine water (Wilson, 1975) (LB medium plus 210 mM NaCl, 35 mM Mg2SO4, 7 mM KCl, and 7 mM CaCl2)”

Page 4 line, number 93: the he full names of the bacterial strains (e.g., ATCC33787 and VP01) should be provided instead of just the strain numbers.

We appreciate this comment! We revised the sentence as following:

“To investigate whether this mineral impacts antibiotic activity, the minimal inhibitory concentration (MIC) of V. alginolyticus ATCC33787 and V. parahaemolyticus VP01, which we referred as ATCC33787 and VP01 afterwards,”

Finally, there appears to be a potential contradiction between the statements on page 12, lines 211-212 and 214-216, regarding the effects of Mg2+ on the synthesis of unsaturated fatty acids. Further explanation may be needed to reconcile these seemingly contradictory points.

We appreciate this comment! For line 221-226, which was previously line 211-212, is about the gene expression for fatty acid biosynthesis. While, Line 228 and 233, which was previously line 214-216 is about the gene expression for fatty acid degradation. We agree that the previous description is a little bit confuse. We revise the sentence to emphasize that we focus on fatty acid degradation so that the readers can tell them apart. 

In the text, we revised it as following:

“In addition, we also quantified gene expression during fatty acid degradation to determine whether Mg2+ affects this process”  In the figure legend, we also indicate that 

“H. qRT-PCR for the expression of genes encoding fatty acid degradation in the absence or presence of the indicated concentrations of MgCl2”

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Reply to the reviewers

Manuscript number: RC-2024-02648

Corresponding author(s): Kevin Berthenet (kevin.berthenet@lyon.unicancer.fr) and Gabriel Ichim (gabriel.ichim@lyon.unicancer.fr)

1. General Statements

We thank all the reviewers for their time and their constructive criticism, based on which we propose the revision plan detailed bellow. All our responses are indicated in italics font. When is the case, the figures for the reviewers are included just below the answer. Only where indicated they have been included in the manuscript. The line numbers indicated here refer to those in original manuscript.

The two reviews are listed in full at the end of the document.

2. Description of the planned revisions

Reviewer #1

In this manuscript, the authors report a non-apoptotic role for caspase 3 in promoting cell migration. RNA sequencing revealed a "gene signature" associated with caspase 3 knockdown in a melanoma cell line, although there is no investigation of the connection between caspase 3 expression and the regulation of gene expression. Mass spectrometry-based experiments (AP-MS and BioID) identified numerous interacting proteins, with coronin 1B being the most extensively characterized. Data provided indicates that there is a direct interaction between caspase 3 and coronin 1B, and that caspase 3 influences coronin 1B phosphorylation basally and following ligand stimulation. Both proteins are required for efficient cell migration in scratch wound assays. Data is provided indicating that the actions of caspase 3 are independent of proteolytic activity, although the pharmacological inhibition of caspase activity is not complete, nor is the knockdown of BAX/BAK, making these conclusions poorly substantiated. Evaluation of pathways regulating caspase 3 expression implicates the SP1 transcription factor.

Response: We thank the reviewer for their supportive comment. Regarding specific pharmacological inhibition of caspase-3, work is under way to complement the results obtained with a pan-caspase inhibitor (qVD-OPh). We will use specific effector caspases inhibitors, complemented by several other approaches: complete KO of BAX and BAK proteins to prevent all eventual mitochondrial permeabilization and low-level effector caspase activation, overexpression (OE) of the anti-apoptotic protein BCL-xL to also prevent residual mitochondrial permeabilization, while also OE XIAP, a potent caspase inhibitor. The promising preliminary data using two effector caspases specific inhibitors (Ac-DEVD-CHO and Ac-DNLD-CHO) in two different melanoma cells, during wound healing migration, is shown below, with no effect on melanoma cell migration.

Line 129 - The data in Sup. Fig. 1H-L are technical, but where are the mass spectrometry results from the BioID2 experiments? These technical figures are really only relevant if the BioID2 system has been used for protein pull-downs, not for the IF analysis in Fig. 2B.

Response: We apologize for lack of precision in the article logical flow, we will now incorporate the MS data based on the BioID2 experiment earlier in the manuscript.

Line 143 - Figure 2C - it is not entirely convincing that caspase 7 is not associated with the cytoskeleton, there is a visible band in lysates from both cell lines, in contrast with GAPDH which is convincingly cytoplasmic. This is particularly true in the WM852 cell lines, in which the Caspase 3 band is almost the same as Caspase 7. These results would also be more convincing if there was IF of Caspase 7 and actin to show whether it is or is not enriched in regions of higher F-actin levels.

Response: Indeed, our data points towards an enrichment of caspase-3 at the cell cortex. Since generally caspase-7 protein levels are lower, we detected it less in the cytosolic fraction. As suggested, now we performed more sensitive IF colocalization confocal imaging between caspase-7 and F-actin and find it also partially localized to the cortical cytoskeleton (see below). However, this effector caspase is not involved in melanoma cell migration (see wound healing assay below, with two different siRNAs for CASP7 and the positive control of siRNA CASP3).

Figure 2D - knockdowns with only a single siRNA are insufficient, this should be replicated with additional siRNAs. In addition to the effect on actin anisotropy, it appears as though cells are smaller, is this and any other morphological changes reproducible?

Response: We plan to strengthen the data shown in Fig.2D with additional siRNAs, as shown below. In addition, high-content screening (HCS) microscopy will provide several other cell morphology descriptors.

Figure 2D-E. Is it cytochalasin B or D used in these experiments? The text and figures don't agree with each other. 5. Figure 2F-G, same comments above for 2D-E (i.e. comments 3 & 4).

Response: The experimental conditions will be better detailed in the revised manuscript.

Figure 2F-G, it appears as though the fewer focal adhesions in the Caspase 3 knockdown cells are bigger per focal adhesion, is this a consistent result? If so, what is the explanation?

Response: In addition to number, we also plan to quantify the size of focal adhesions.

Figure 2H - it's not clear how this RNAseq data is relevant to the manuscript. There are some genes in the heat map, but it's not clear which ones are changed in their expression in the caspase 3 knockdown cells, nor is it clear how this is relevant to the proposed mechanisms of Caspase 3 interacting with and influencing the phosphorylation of coronin 1B. If there is no connection, then these data can be removed.* *

Response: As suggested by the reviewer, the RNAseq data presented in Figure 2H will be removed from the revised manuscript since it is not very relevant.

Supp. Figure 3 - given that there is data from multiple siRNAs for the incucyte migration data, it should be in the primary figures. And since there are multiple siRNAs and CRISPR/Cas9 KO cells, there should be nothing limiting the replication of the other data presented from only a single siRNA.

Response: Several siRNA are now used for replicating key results as shown above.

Figure 3A - how was cell adhesion measured? The methods section says "cell adhesion was determined through cell shape analysis and scoring" But this is very vague.

Response: We thank the reviewer for spotting out this ambiguity, in the revised manuscript we will be more precise in Material and Methods section.

Figure 3L - was the Casp7 knockdown experiments done with multiple siRNAs? Both melanoma cell lines? Why is this figure only shown out to 24 hours, whereas the other Incucyte experiment run out to 48 hours? Where is the western blot confirming the caspase 7 knockdown? This is important to establish a clear lack of effect.

Response*: We apologize for lacking more details, we now provide several siRNA for caspase-7, all showing no or minimal effect of melanoma cell migration (see answer to point 2). *

Line 190 - it is not true to say that in the presence of QVD there is no longer any caspase activity induced by actinomycin D/ABT263 in supplemental Figures 3J-K. The way that the Y axis has been broken diminishes the difference between untreated and treated cells. In fact, there is apparently over 3-4 times more caspase activity in the actinomycin D/ABT263 treated cells in the presence of QVD relative to basal caspase activity. As a result, it cannot be concluded that there is no residual caspase activity.

Response: We were not precise enough in describing the data in S3J-K. In the revised manuscript we will clearly say that since treatment with a pan-caspase inhibitor does not have the effect of lowering any basal caspase activity (column 1 versus 2), we conclude that in melanoma cells (WM793 and WM852) there is no basal caspase activation that could drive cell motility. The ActD/ABT263 treatment was used as positive control for bona fide induction of effector caspase activation. These results will be complemented by BAX/BAK DKO and BCLxL OE.

Line 192 - Does the knockout of BAX/BAK (which apparently reduced but did not eliminate BAX/BAK protein levels in Supp. Fig. 3L) actually "completely block" caspase activity via the mitochondrial pathway? This has not been demonstrated.

Response: We now provide a fluorometric effector caspases assay showing abrogation of caspase activity in BAX/BAK DKO cells (see below, caspase activating treatment is ActinomycinD plus ABT263). In addition, we will improve the DKO efficacy.

Line 217 - coronin 1B was a hit from which assays? IP-MS and/or BioID2? I see that this is shown in Figure 5A but not referenced in this sentence.

Line 218 - the reference to Figure 5A should be in the previous sentence. Line 220 - Can it really be said that the interaction is specific since there is a coronin 1B band in the GFP "negative" control?__ __

Response*: The revised manuscript will address these inadequacies. *

Line 222 - it is a good control to show that siRNA-knockdown of Caspase 3 reduced the PLA signal in Figure 5C, but the reciprocal experiment of looking at what happens with Coronin 1B knockdown should be included. How does the PLA signal relate to phalloidin-stained F-actin?

Response: The proximity ligation assay (PLA) is now complemented by KD of Coronin 1B (see below) and we will try to also add the phalloidin staining for F-actin, if compatible with the PLA protocol.

Line 224 - looking at the line scans, is the lack of recruitment of coronin 1B to the F-actin at the edge of the protrusion in the Caspase 3 knockdown cells reproducible? Is the point that caspase 3 recruits Coronin 1B? There is an obvious difference in the F-actin at the cell edge, but if the F-actin were as dense in the Caspase 3 knockdown cells as they are for the control, would the same lack of coronin 1B be apparent?

Response: This aspect will be better addressed/discussed in the revised manuscript.

Line 227 - where is the western blot showing the effectiveness of the coronin 1B knockdown to accompany Figure 5F.

Response: The efficacy of coronin 1B KD will be added in the revised manuscript.

Figure 5G - the blots indicate that there is no change in phospho-PKCalpha in the caspase 3 knockdown cells, although phospho-coronin 1B does decrease. This has not been commented upon in the text. Is the implication that there is a non-PKCalpha mediated mechanism for coronin 1B phosphorylation that is dependent on caspase 3?

Figure 5H - following from the previous point, there is no phospho-PKCalpha blot that would be a positive control for the effect of PDGF stimulation on PKC activation, in control and caspase 3 knockdown cells, to evaluate whether the effect on coronin 1B phosphorylation was upstream or downstream of PKCalpha. This is also true for Supp. Fig. 4H.

Response*: Since there are several PKC isoforms that might be co-expressed in melanoma cells, it is possible that PKCalpha is not the one responsible for phosphorylating Coronin 1B. We will be more precise in our investigations by using a pan-phospho-PKC antibody. *

Does phosphorylation of coronin 1B affect its interaction with caspase 3?

Response: We will assess by Co-IP the interaction of caspase-3 with both non-phosphorylated and phosphorylated Coronin 1B.

Figure 6 - as before, only a single siRNA to knockdown SP1 is insufficient to robustly support the conclusions.

Response: As shown below, we addressed this helpful comment by using several siRNAs to assess the role of SP1 in melanoma cell motility, in two different melanoma cell lines.

• *

Reviewer #2

In this manuscript, the authors provide substantial amounts of experimental evidence that caspase-3, more precisely pro-caspase-3, might be involved in promoting melanoma cell migration and invasion. As such, this function, which might stem from scaffolding roles independent of proteolytic activity (yet not shown entirely convincingly), could possibly be similar to those attributed to other caspases, yet the latter omitted experiments testing for the necessity of enzyme activity. The data are novel and interesting and obviously deserve publication. Yet, a number of criticisms need to be listed.

Response*: We thank the reviewer for upholding the novelty of our study. As also rightfully pointed by R1, we will strive in a revised manuscript to definitely show that caspase-3 participate to melanoma cell motility independently of its pro-apoptotic protease role: we will use two effector caspases specific inhibitors (Ac-DEVD-CHO and Ac-DNLD-CHO, as shown above) complemented by several other approaches: complete KO of BAX and BAK protein to prevent all eventual mitochondrial permeabilization and low-level effector caspase activation, OE of the anti-apoptotic protein BCL-xL to also prevent residual mitochondrial permeabilization, while also OE XIAP, a potent caspase inhibitor. *

• *

• First and foremost, I don't seem to find ethical approval information on the animal experiments. While I do not work with zebrafish myself, I am also somewhat concerned by the size of tumours seen in some of the depicted fish. It is highly important that appropriate information in this direction, including possible endpoints, is provided. Response*: We completely agree with the reviewer, yet the ethical approval is already provided in the manuscript (line 588) and will be complemented by adding the endpoints. *

The second major issue lies in figure 1. The figure as a whole seems to be very much forced to support or motivate later experimental findings. The authors lack sufficient clarity on some of the approaches and seem to judge on the data to a good bit as they see fit. (…)

I´d suggest to largely take out Fig1 in its current form, spend time on properly describing any analysis of public data, carefully interpret these and move them probably to the end of the results. Currently, it just leaves the impression that the data were pushed as hard as possible to promote the good work that follows.

Response*: We will carefully consider the reviewer’s comments and rework the bioinformatics analysis presented in Figure 1 (and associated supplementary figure), making sure we will present certain data as correlation (and not causality) and go into more details on the physio-pathological features of melanoma patients with low/high caspase-3 expression. *

• *

The text on line 129ff seems to have omitted any outcomes from the Suppl. Fig1H-L. What was found and what are we supposed to learn from this?

Response: We apologize for lack of precision in the article logical flow, we will now incorporate the MS data based on the BioID2 experiment earlier in the manuscript.* *

Lines 146/147 state similar effects upon CASP3 depletion and cytochalasin D. I cannot make that out from Fig.2D. Can you be more specific or visualize this better?

Response: We will fix this by including zoomed and detailed images of individual cells.

• Is it possible to state whether effects such as in Fig.3B are general rather than showing just 1 cell?

Response: The defects in cell adhesion for caspase-3-depleted cells are quantified in Figure 3A. Moreover, we will add representative images.

• *

It is unclear how the genes in Fig.2H were defined and why would all of these differ (unless this was an inclusion criterion for the panel). Are these considered to be downstream of CASP3 somehow? I don't fully get the message here. Is this panel even required here?

Response: As it brings little information, panel 2H will be excluded from the revised manuscript.

To fully prove independence of caspase-3 activity, it would be appropriate to k/o caspase-3 to then reconstitute the cells with inactive caspase-3.

• *

Response: We will try our best of addressing this comment in the revised manuscript.

Fig.4C and associated text: Statements on changes in tumor size cannot be made from data on tumor free survival.

Response: We apologize for the misleading data interpretation; this will be tuned down in a revised manuscript.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, the authors report a non-apoptotic role for caspase 3 in promoting cell migration. RNA sequencing revealed a "gene signature" associated with caspase 3 knockdown in a melanoma cell line, although there is no investigation of the connection between caspase 3 expression and the regulation of gene expression. Mass spectrometry-based experiments (AP-MS and BioID) identified numerous interacting proteins, with coronin 1B being the most extensively characterized. Data provided indicates that there is a direct interaction between caspase 3 and coronin 1B, and that caspase 3 influences coronin 1B phosphorylation basally and following ligand stimulation. Both proteins are required for efficient cell migration in scratch wound assays. Data is provided indicating that the actions of caspase 3 are independent of proteolytic activity, although the pharmacological inhibition of caspase activity is not complete, nor is the knockdown of BAX/BAK, making these conclusions poorly substantiated. Evaluation of pathways regulating caspase 3 expression implicates the SP1 transcription factor.

Major comments:

1. Line 129 - The data in Sup. Fig. 1H-L are technical, but where are the mass spectrometry results from the BioID2 experiments? These technical figures are really only relevant if the BioID2 system has been used for protein pull-downs, not for the IF analysis in Fig. 2B.
2. Line 143 - Figure 2C - it is not entirely convincing that caspase 7 is not associated with the cytoskeleton, there is a visible band in lysates from both cell lines, in contrast with GAPDH which is convincingly cytoplasmic. This is particularly true in the WM852 cell lines, in which the Caspase 3 band is almost the same as Caspase 7. These results would also be more convincing if there was IF of Caspase 7 and actin to show whether it is or is not enriched in regions of higher F-actin levels.
3. Figure 2D - knockdowns with only a single siRNA are insufficient, this should be replicated with additional siRNAs. In addition to the effect on actin anisotropy, it appears as though cells are smaller, is this and any other morphological changes reproducible?
4. Figure 2D-E. Is it cytochalasin B or D used in these experiments? The text and figures don't agree with each other.
5. Figure 2F-G, same comments above for 2D-E (i.e. comments 3 & 4).
6. Figure 2F-G, it appears as though the fewer focal adhesions in the Caspase 3 knockdown cells are bigger per focal adhesion, is this a consistent result? If so, what is the explanation?
7. Figure 2H - it's not clear how this RNAseq data is relevant to the manuscript. There are some genes in the heat map, but it's not clear which ones are changed in their expression in the caspase 3 knockdown cells, nor is it clear how this is relevant to the proposed mechanisms of Caspase 3 interacting with and influencing the phosphorylation of coronin 1B. If there is no connection, then these data can be removed.
8. Supp. Figure 3 - given that there is data from multiple siRNAs for the incucyte migration data, it should be in the primary figures. And since there are multiple siRNAs and CRISPR/Cas9 KO cells, there should be nothing limiting the replication of the other data presented from only a single siRNA.
9. Figure 3A - how was cell adhesion measured? The methods section says "cell adhesion was determined through cell shape analysis and scoring" But this is very vague.
10. Figure 3L - was the Casp7 knockdown experiments done with multiple siRNAs? Both melanoma cell lines? Why is this figure only shown out to 24 hours, whereas the other Incucyte experiment run out to 48 hours? Where is the western blot confirming the caspase 7 knockdown? This is important to establish a clear lack of effect.
11. Line 190 - it is not true to say that in the presence of QVD there is no longer any caspase activity induced by actinomycin D/ABT263 in supplemental Figures 3J-K. The way that the Y axis has been broken diminishes the difference between untreated and treated cells. In fact, there is apparently over 3-4 times more caspase activity in the actinomycin D/ABT263 treated cells in the presence of QVD relative to basal caspase activity. As a result, it cannot be concluded that there is no residual caspase activity.
12. Line 192 - Does the knockout of BAX/BAK (which apparently reduced but did not eliminate BAX/BAK protein levels in Supp. Fig. 3L) actually "completely block" caspase activity via the mitochondrial pathway? This has not been demonstrated.
13. Line 217 - coronin 1B was a hit from which assays? IP-MS and/or BioID2? I see that this is shown in Figure 5A but not referenced in this sentence.
14. Line 218 - the reference to Figure 5A should be in the previous sentence.
15. Line 220 - Can it really be said that the interaction is specific since there is a coronin 1B band in the GFP "negative" control?
16. Line 222 - it is a good control to show that siRNA-knockdown of Caspase 3 reduced the PLA signal in Figure 5C, but the reciprocal experiment of looking at what happens with Coronin 1B knockdown should be included. How does the PLA signal relate to phalloidin-stained F-actin?
17. Line 224 - looking at the line scans, is the lack of recruitment of coronin 1B to the F-actin at the edge of the protrusion in the Caspase 3 knockdown cells reproducible? Is the point that caspase 3 recruits Coronin 1B? There is an obvious difference in the F-actin at the cell edge, but if the F-actin were as dense in the Caspase 3 knockdown cells as they are for the control, would the same lack of coronin 1B be apparent?
18. Line 227 - where is the western blot showing the effectiveness of the coronin 1B knockdown to accompany Figure 5F?
19. Figure 5G - the blots indicate that there is no change in phospho-PKCalpha in the caspase 3 knockdown cells, although phospho-coronin 1B does decrease. This has not been commented upon in the text. Is the implication that there is a non-PKCalpha mediated mechanism for coronin 1B phosphorylation that is dependent on caspase 3?
20. Figure 5H - following from the previous point, there is no phospho-PKCalpha blot that would be a positive control for the effect of PDGF stimulation on PKC activation, in control and caspase 3 knockdown cells, to evaluate whether the effect on coronin 1B phosphorylation was upstream or downstream of PKCalpha. This is also true for Supp. Fig. 4H.
21. Does phosphorylation of coronin 1B affect its interaction with caspase 3?
22. Figure 6 - as before, only a single siRNA to knockdown SP1 is insufficient to robustly support the conclusions.

Minor comments:

1. Figure 2C - all caps for CASP7
2. Figures 2D,F - Cytochalsin
3. Figure 2H, the labelling of gene names is too small to read.
4. Supplemental Fig 1A - why is A375 here? Why plot a graph and not just write a percentage protein remaining under the figure? There are no errors indicated, so presumably this is N = 1.
5. Line 127 - smal

Significance

The manuscript is interesting and novel, making it relevant for a broad basic research audience. The role of caspase 3 in non-apoptotic biological processes is not extensively characterized, making this study an advance in the field. The methods are appropriate and well-executed. The statistical methods are mostly appropriate, although some assays (e.g. wound healing assays) do not have associated statistical analysis. Most of the conclusions are adequately substantiated by the results, but as indicated above and in the points below, this is not entirely consistent. There is an issue with only a single siRNA being used in several experiments that should be addressed.

DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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DOI: 10.1038/s41419-024-07243-y

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What is this?

Faltan paréntesis (,)

We made more explicit the connection between individual herbivore nutrition and population- and landscape-level patterns and integrated more studies throughout the introduction.

Este párrafo me parece muy interesante y logrado. Creo que marca un ecuador en el texto que sube la intensidad. Además hay cierta musicalidad que lo hace genial.

Esto es Hypothesis. Una tecnología que permite anotar colaborativamente la web. La iniciativa surgió desde el mundo académico intentando responder a la pregunta: ¿cómo sería tomar notas colectivas en internet? De ahí surgió este proyecto. Puedes tomar notas públicas, privadas o en grupo. Lo bueno es que cualquier persona que llegue a la página en concreto verá tus comentarios y podrá seguirlos. En Filosofía PEC está activado por defecto pero si lo activas en tu navegador podrás tomar notas de todas las webs que desees. Tienes más info en https://hypothes.is

DOI: 10.1016/j.stem.2024.10.015

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What is this?

la parte dura de esto

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What is this?

No me cansaré de recomendar este libro. Si me lees, saca un rato para leerlo. Hoy más que nunca necesitamos ser un poco luditas.

Some useful tips

Have you actively engaged in this competency in the past few months since starting the program? If so, can you reflect on your efforts? If not--what is required for change?

.h2

add is a curried function so partial application returns another function. (add 5) 1 would be roughly equivalent to (func y -> 5 + y) 1 which evaluates to the integer 6

Author response:

Reviewer #1 (Public review):

Summary:

Urination requires precise coordination between the bladder and external urethral sphincter (EUS), while the neural substrates controlling this coordination remain poorly understood. In this study, Li et al. identify estrogen receptor 1-expressing neurons (ESR1+) in Barrington's nucleus as key regulators that faithfully initiate or suspend urination. Results from peripheral nerve lesions suggest that BarEsr1 neurons play independent roles in controlling bladder contraction and relaxation of the EUS. Finally, the authors performed region-specific retrograde tracing, claiming that distinct populations of BarEsr1 neurons target specific spinal nuclei involved in regulating the bladder and EUS, respectively.

Strength:

Overall, the work is of high quality. The authors integrate several cutting-edge technologies and sophisticated, thorough analyses, including opto-tagged single unit recordings, combined optogenetics, and urodynamics, particularly those following distinct peripheral nerve lesions.

Weakness:

(1) My major concern is the novelty of this study. Keller et al. 2018 have shown that BarEsr1 neurons are active during urination and play an essential role in relaxing the external urethral sphincter (EUS). Minimally, substantial content that merely confirms previous findings (e.g. Figures 1A-E; Figures 3A-E) should be move to the supplementary datasets.

Indeed, we are aware of and have carefully studied the literature of Keller et al. Our manuscript here presents novel experiments beyond the scopes of that paper. Thanks to this comment, we will substantially revise our manuscript to enhance the visibility of novel data while keeping the agreeing data in the supplementary.

(2) I also have concerns regarding the results showing that the inactivation of BarEsr1 neurons led to the cessation of EUS muscle firing (Figures 2G and S5C). As shown in the cartoon illustration of Figure 8, spinal projections of BarEsr1 neurons contact interneurons (presumably inhibitory) that innervate motor neurons, which in turn excite the EUS. I would therefore expect that the inactivation of BarEsr1 should shift the EUS firing pattern from phasic (as relaxation) to tonic (removal of relaxation), rather than stopping their firing entirely. Could the authors comment on this and provide potential reasons or mechanisms for this finding?

We agree with this point. We meant that the EUS’ phasic bursting pattern was rapidly stopped upon BarEsr1 photoinhibition, but not all the firing stopped instantaneously. According to the previous studies (Chang et al., 2007, de Groat, 2009, de Groat and Yoshimura, 2015, Kadekawa et al., 2016), the voiding physiology of rodents is probably different from that of humans, such that for rodents the urine is step-wise pumped out in the gap time between multiple consecutive EUS phasic bursting epochs, and for humans the urine is continuously pumped out once the EUS firing is almost fully inhibition during a period of time. Namely, for mice, the EUS display sustained tonic activity following phasic bursting, while, in contrast, for humans the EUS keeps tonic firing until the moment of voiding onset (complete inhibition, muscle relaxed). Despite the prominent differences in the basic physiological properties, our assumption is that the logic of circuits from the brainstem to the urethra in this pathway is evolutionally conserved for both species; thus the logic of brainstem coordination of voiding could also be the same for both species, which is the main interest of our study (of using an animal model to address concerns of human health). Thus, to interpret our data for a broader audience we made a simplified and inaccurate expression. We apologize for the inaccuracy and we will correct our previous inaccurate description in the revised manuscript.

(3) Current evidence is insufficient to support the claim that the majority of BarEsr1 neurons innervate the SPN but not DGC. The current spinal images are uninformative, as the fluorescence reflects the distribution of Esr1- or Crh-expressing neurons in the spinal cord, along with descending BarEsr1 or BarCrh axons. Given the close anatomical proximity of these two nuclei, a more thorough histological analysis is required to demonstrate that the spinal injections were accurately confined to either the SPN or the DGC.

We agree that current evidence is insufficient to support the current claim. To address this concern and strengthen our claim, we will repeat the retrograde viral tracing experiments, combined with CTB647 injections to label the injection site, to validate specific targeting of SPN or DGC populations. We will also add higher-magnification imaging to distinguish BarESR1 axonal projections targeting SPN versus DGC. Results from these ongoing experiments will be incorporated into the revised manuscript.

Reviewer #2 (Public review):

Summary:

The authors have performed a rigorous study to assess the role of ESR1+ neurons in the PMC to control the coordination of bladder and sphincter muscles during urination. This is an important extension of previous work defining the role of these brainstem neurons, and convincingly adds to the understanding of their role as master regulators of urination. This is a thorough, well-done study that clarifies how the Pontine micturition center coordinates different muscle groups for efficient urination, but there are some questions and considerations that remain.

Strengths:

These data are thorough and convincing in showing that ESR1+PMC neurons exert coordinated control over both the bladder and sphincter activity, which is essential for efficient urination. The anatomical distinctions in pelvic versus pudendal control are clear, and it's an advance to understand how this coordination occurs. This work offers a clearer picture of how micturition is driven.

Weaknesses:

The dynamics of how this population of ESR1+ neurons is engaged in natural urination events remains unclear. Not all ESR1+neurons are always engaged, and it is not measured whether this is simply variation in population activity, or if more neurons are engaged during more intense starting bladder pressures, for instance. In particular, the response dynamics of single and doubly-projecting neurons are not defined. Additionally, the model for how these neurons coordinate with CRH+ neuron activity in the PMC is not addressed, although these cell types seem to be engaged at the same time. Lastly, it would be interesting to know how sensory input can likely modulate the activity of these neurons, but this is perhaps a future direction.

In response to the reviewer’s comments, we will attempt perform the following revisions for this round:

(1) Engagement of ESR1+ neurons in natural urination events:

We agree that probably not all ESR1+ neurons are consistently engaged during urination. To address this, we will perform a detailed analysis of the opto-tagged single unit recordings data.

(2) Response dynamics of single- and doubly-projecting neurons:

(a) We will use retrograde labelling combined with Ca2+ photometry recordings to differentiate the response dynamics of SPN- and DGC-projecting neurons during urination.

(b) We will perform functional validations to assess the specific roles of single- and doubly-projecting neurons in coordinating bladder and EUS activity.

(3) Coordination with CRH+ neurons in the PMC:<br /> We appreciate the suggestion to include CRH+ neurons in our model. We will expand our model to incorporate CRH+ neurons and their potential interactions with ESR1+ neurons.

(4) Sensory modulation of ESR1+ neurons:<br /> The reviewer raises an excellent point regarding sensory input modulation of ESR1+ neuron activity. Although this is beyond the scope of our current study, we recognize its importance and propose to include this as a future direction.

Reviewer #3 (Public review):

Summary:

The paper by Li et al explored the role of Estrogen receptor 1 (Esr1) expressing neurons in the pontine micturition center (PMC), a brainstem region also known as Barrington's nucleus (Hou et al 2016, Keller et al 2018). First, the author conducted bulk Ca2+ imaging/unit recording from PMCESR1 to investigate the correlations of PMCESR1 neural activity to voiding behavior in conscious mice and bladder pressure/external urethral muscle activity in urethane anesthetized mice. Next, the authors conducted optogenetics inactivation/activation of PMCESR1 to confirm the contribution to the voiding behavior also conducted peripheral nerve transection together with optogenetics activation to confirm the independent control of bladder pressure and urethral sphincter muscle.

Weaknesses:

(1) The study demonstrates that pelvic nerve transection reduces urinary volume triggered by PMCESR1+ cell photoactivation in freely moving mice. Could the role of pudendal nerve transection also be examined in awake mice to provide a more comprehensive understanding of neural involvement?

Thank you for the suggestion, the pudendal nerve transection in awake mice is indeed a challenging experiment that has been missed. We will try it for the revision.

(2) While the paper primarily focuses on PMCESR1+ cells in bladder-sphincter coordination, the analysis of PMCESR1+-DGC/SPN neural circuits - given their distinct anatomical projections in the sacral spinal cord - feels underexplored. How do these circuits influence bladder and sphincter function when activated or inhibited? Also, do you have any tracing data to confirm whether bladder-sphincter innervation comes from distinct spinal nuclei?

Thank you for this great comment. The projection-specific neuronal function analysis is, as also suggested by Reviewer 2 in a similar comment (#8), missing in our first submission. These are so challenging experiments that we have missed in the first round of tests, but we decide to pursuit this goal again. Namely, we will perform photometry recordings of PMC neurons projecting to the DGC/SPN during measuring bladder pressure and urethral sphincter EMG activity. Additionally, while our study does not include direct tracing data to confirm distinct spinal nuclei for bladder and sphincter innervation, this has been well-documented in classic literature (Yao et al., 2018, Karnup and De Groat, 2020, Karnup, 2021). Specifically, anatomical studies have shown that SPN primarily innervates the bladder, while the DGC is associated with the innervation of the urethral sphincter. We will cite these references to provide context and support for our interpretations.

(3) Although the paper successfully identifies the physiological role of PMCESR1+ cells in bladder-sphincter coordination, the study falls short in examining the electrophysiological properties of PMCESR1+-DGC/SPN cells. A deeper investigation here would strengthen the findings.

While our study primarily focuses on the functional role of PMCESR1+ neurons in bladder-sphincter coordination, we acknowledge that understanding their intrinsic electrophysiological characteristics could further strengthen our findings. However, this aspect falls beyond the scope of the current study. Nevertheless, we recognize the significance of this direction and are excited to pursue it in future research. We appreciate the reviewer’s suggestion, as it highlights an important avenue for expanding upon our current findings.

(4) The parameters for photoactivation (blue light pulses delivered at 25 Hz for 15 ms, every 30 s) and photoinhibition (pulses at 50 Hz for 20 ms) vary. What drove the selection of these specific parameters? Moreover, for photoactivation experiments, the change in pressure (ΔP = P5 sec - P0 sec) is calculated differently from photoinhibition (Δpressure = Ppeak - Pmin). Can you clarify the reasoning behind these differing approaches?

We sincerely thank the reviewer for raising these important points and for the opportunity to clarify our experimental design and data analysis methods.

Photoactivation versus photoinhibition parameters: The differences in photoactivation (25 Hz, 15 ms pulses) and photoinhibition (50 Hz, 20 ms pulses) protocols are based on the distinct physiological and technical requirements for activating versus inhibiting PMCESR1+ neurons. For photoactivation, 25 Hz stimulation aligns with the natural firing patterns of central neurons, allowing for intermittent activation without exceeding the neuronal refractory period. The shorter pulse duration (15 ms) minimizes phototoxicity and avoids overstimulation, as performed in previous studies (Keller et al., 2018). In contrast, photoinhibition requires sustained suppression of neuronal activity, achieved through higher frequencies (50 Hz) and longer pulses (20 ms) to ensure continuous coverage of neuronal activity.

Calculation of pressure changes (ΔP) for photoactivation and photoinhibition: The differing methods for calculating pressure changes reflect the distinct physiological effects we aimed to capture. In photoactivation experiments (ΔP = P5 sec - P0 sec), the pressures before (P0 sec) and 5 seconds after (P5 sec) light delivery were compared to capture the immediate effect of light activation on bladder pressure, focusing on the onset and early dynamics of activation. In contrast, photoinhibition experiments assessed the immediate impact of light-induced suppression on bladder pressure during an ongoing voiding event. Here, Δpressure was calculated as Ppeak – Pmin to measure the rapid drop in pressure directly attributable to neuronal inhibition.

We will expand these details in the methods section of the revised manuscript to provide greater transparency.

(5) The discussion could further emphasize how PMCESR1+ cells coordinate bladder contraction and sphincter relaxation to control urination, highlighting their central role in the initiation and suspension of this process.

We fully agree with this point. Additionally, in response to your and other reviewers’ suggestions, we are preparing a new round of experiments with projection-specific recording, and thus our discussion and conclusion will also be updated according to the newly obtained data.

(6) In Figure 8, The authors analyze the temporal sequence of bladder pressure and EUS bursting during natural voiding and PMC activation-induced voiding. It would be acceptable to consider the existence of a lower spinal reflex circuit, however, the interpretation of the data contains speculation. Bladder pressure measurement is hard to say reflecting efferent pelvic nerve activity in real time. (As a biological system, bladder contraction is mediated by smooth muscle, and does not reflect real-time efferent pelvic nerve activity. As an experimental set-up, bladder pressure measurement has some delays to reflect bladder pressure because of tubing, but EUS bursting has no delay.) Especially for the inactivation experiment, these factors would contribute to the interpretation of data. This reviewer recommends a rewrite of the section considering these limitations. Most of the section is suitable for the results.

Thank you for mentioning the possibility of bladder pressure measurement delay. We would prefer to perform a physical control test to quantify how much delay this measurement is under our experimental conditions. We will use a small ballon to mimic the bladder and use two identical pressure sensors, one with a very short tube inserted into the ballon and one with an extended tube same as in our animal experiments. We will then mimic both contraction initiation and halting, and quantify the delay between the two sensors.

References

• Chang HY, Cheng CL, Chen JJJ, de Groat WC. 2007. Serotonergic drugs and spinal cord transections indicate that different spinal circuits are involved in external urethral sphincter activity in rats. American Journal of Physiology-Renal Physiology 292: F1044-F1053. DOI: 10.1152/ajprenal.00175.2006

• de Groat WC. 2009. Integrative control of the lower urinary tract: preclinical perspective. British Journal of Pharmacology 147. DOI: 10.1038/sj.bjp.0706604

• de Groat WC, Yoshimura N. 2015. Anatomy and physiology of the lower urinary tract. Handb Clin Neurol 130: 61-108. DOI: 10.1016/B978-0-444-63247-0.00005-5

• Kadekawa K, Yoshimura N, Majima T, Wada N, Shimizu T, Birder LA, Kanai AJ, de Groat WC, Sugaya K, Yoshiyama M. 2016. Characterization of bladder and external urethral activity in mice with or without spinal cord injury—a comparison study with rats. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 310: R752-R758. DOI: 10.1152/ajpregu.00450.2015

• Karnup S. 2021. Spinal interneurons of the lower urinary tract circuits. Autonomic Neuroscience 235. DOI: 10.1016/j.autneu.2021.102861

• Karnup SV, De Groat WC. 2020. Mapping of spinal interneurons involved in regulation of the lower urinary tract in juvenile male rats. IBRO Rep 9: 115-131. DOI: 10.1016/j.ibror.2020.07.002

• Keller JA, Chen J, Simpson S, Wang EH-J, Lilascharoen V, George O, Lim BK, Stowers L. 2018. Voluntary urination control by brainstem neurons that relax the urethral sphincter. Nature Neuroscience 21: 1229-1238. DOI: 10.1038/s41593-018-0204-3             

• Yao J, Zhang Q, Liao X, Li Q, Liang S, Li X, Zhang Y, Li X, Wang H, Qin H, Wang M, Li J, Zhang J, He W, Zhang W, Li T, Xu F, Gong H, Jia H, Xu X, Yan J, Chen X. 2018. A corticopontine circuit for initiation of urination. Nature Neuroscience 21: 1541-1550. DOI: 10.1038/s41593-018-0256-4

for - stats - 27% of the gap can be reduced by wind and solar deployment and 20% by action on forests, while efficiency, electrification, fuel switching in buildings, transport and industry sectors can also contribute - UN Emissions Gap Report 2024 - Key Messages

for - stats - Current National Declared Commitments (NDCs) only take us to a disastrous 2.6 Deg. C over the course of the century.- UN Emissions Gap Report 2024 - Key Messages

for - stats - GHG emissions grew 1.3 % year-on-year to 57.1 Gton CO2 eq in 2023 - UN Emissions Gap Report 2024 - Key Messages - stats - 7.5% decarbonization rate is now required every year to stay under 1.5 Deg C - UN Emissions Gap Report 2024 - Key Messages

for - stats - 2030 emissions - 42% lower than 2019 to stay within 1.5 Deg C - UN 2024 Emission's Gap Report

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Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

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Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

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¿Es realmente eso lo que fascina a los magos? ¿Que el público intente averiguar, sin éxito, cómo hacen lo que hacen? ¿O más bien disfrutan ver el asombro de la gente?

Parece demasiado cientificista. ¿Siempre hubo "ciencia"? ¿Cómo conocíamos el mundo antes, si no?

Author response:

Reviewer #1 (Public review):

Summary:

The manuscript by Rühling et al analyzes the mode of entry of S. aureus into mammalian cells in culture. The authors propose a novel mechanism of rapid entry that involves the release of calcium from lysosomes via NAADP-stimulated activation of TPC1, which in turn causes lysosomal exocytosis; exocytic release of lysosomal acid sphingomyelinase (ASM) is then envisaged to convert exofacial sphingomyelin to ceramide. These events not only induce the rapid entry of the bacteria into the host cells but are also described to alter the fate of the intracellular S. aureus, facilitating escape from the endocytic vacuole to the cytosol.

Strengths:

The proposed mechanism is novel and could have important biological consequences.

Weaknesses:

Unfortunately, the evidence provided is unconvincing and insufficient to document the multiple, complex steps suggested. In fact, there appear to be numerous internal inconsistencies that detract from the validity of the conclusions, which were reached mostly based on the use of pharmacological agents of imperfect specificity.

We thank the reviewer for the detailed evaluation of our manuscript. We will address the criticism below.

We agree with the reviewer that many of the experiments presented in our study rely on the usage of inhibitors. However, we want to emphasize that the main conclusion (invasion pathway affects the intracellular fate/phagosomal escape) was demonstrated without the use of inhibitors or genetic ablation in two key experiments (Figure4 G/H). These experiments were in line with the results we obtained with inhibitors (amitriptyline [Supp. Figure 4E], ARC39, PCK310, [Figure 4c] and Vacuolin-1 [Supp. Figure4f]). Importantly, the hypothesis was also supported by another key experiment, in which we showed the intracellular fate of bacteria is affected by removal of SM from the plasma membrane before invasion, but not by removal of SM from phagosomal membranes after bacteria internalization (Figure4d-f). Taken together, we thus believe that the main hypothesis is strongly supported by our data.

Moreover, we either used different inhibitors for the same molecule (ASM was inhibited by ARC39, amitriptyline and PCK310 with similar outcome) or supported our hypothesis with gene-ablated cell pools (TPC1, Syt7, SARM1), as we will point out in more detail below.

Firstly, the release of calcium from lysosomes is not demonstrated. Localized changes in the immediate vicinity of lysosomes need to be measured to ascertain that these organelles are the source of cytosolic calcium changes. In fact, 9-phenantrol, which the authors find to be the most potent inhibitor of invasion and hence of the putative calcium changes, is not a blocker of lysosomal calcium release but instead blocks plasmalemmal TRPM4 channels. On the other hand, invasion is seemingly independent of external calcium. These findings are inconsistent with each other and point to non-specific effects of 9-phenantrol. The fact that ionomycin decreases invasion efficiency is taken as additional evidence of the importance of lysosomal calcium release. It is not clear how these observations support involvement of lysosomal calcium release and exocytosis; in fact treatment with the ionophore should itself have induced lysosomal exocytosis and stimulated, rather than inhibited invasion. Yet, manipulations that increase and others that decrease cytosolic calcium both inhibited invasion.

With respect to lysosomal Ca2+ release, we agree with the reviewer that direct visual demonstration of lysosomal Ca2+ release upon infection will improve the manuscript. We therefore will perform additional experimentation to show alterations of Ca2+ at the lysosomes during infection.

As to the TRPM4 involvement in S. aureus host cell internalization, it has been reported that TRPM4 is activated by cytosolic Ca2+. However, the channel conducts monovalent cations such as K+ or Na+ but is impermeable for Ca2+ 1, 2. The following of our observations are supporting this:

i) S. aureus invasion is dependent on intracellular Ca2+, but is independent from extracellular Ca2+  (Figure 1c).

ii) 9-phenantrol treatment reduces S. aureus internalization by host cells, illustrating the dependence of this process on TRPM4 (Figure 1b). We therefore hypothesize that TRPM4 is activated by Ca2+ released from lysosomes (see above).

TRPM4 is localized to focal adhesions and is connected to actin cytoskeleton3, 4 – a requisite of host cell entry of S. aureus.5, 6 This speaks for an important function of TRPM4 in uptake of S. aureus in general, but does not necessarily have to be involved exclusively in the rapid uptake pathway.

TRPM4 itself is not permeable for Ca2+ but is activated by the cation.  Thus, it is unlikely to cause lysosomal exocytosis. The stronger bacterial uptake reduction by treatment with 9-phenantrol when compared to Ned19 thus may be caused by the involvement of TRPM4 in additional pathways of S. aureus host cell entry involving that association of TRPM4 with focal adhesions or, as pointed out by the reviewer, unspecific side effects of 9-phenantrol that we currently cannot exclude. We will include this information in the revised manuscript.

Regarding the reduced S. aureus invasion after ionomycin treatment, we agree with the reviewer that ionomycin is known to lead to lysosomal exocytosis as was previously shown by others7 as well as our laboratory8.

We hypothesized that pretreatment with ionomycin would trigger lysosomal exocytosis and thus would reduce the pool of lysosomes that can undergo exocytosis before host cells are contacted by S. aureus. As a result, we should observe a marked reduction of S. aureus internalization in such “lysosome-depleted cells”, if the lysosomal exocytosis is coupled to bacterial uptake. Our observation of reduced bacterial internalization after ionomycin treatment supports this hypothesis.

However, ionomycin treatment and S. aureus infection of host cells are distinct processes.

While ionomycin results in strong global and non-directional lysosomal exocytosis of all “releasable” lysosomes (~5-10 % of all lysosomes according to previous observations)7, we hypothesize that lysosomal exocytosis upon contact with S. aureus only involves a very small proportion of lysosomes at host-bacteria contact sites.

Since ionomycin disturbs the overall cellular Ca2+ homeostasis, we agree with the reviewer that this does not directly show lysosomal Ca2+ liberation. We will discuss this in more detail in the revised manuscript.

The proposed role of NAADP is based on the effects of "knocking out" TPC1 and on the pharmacological effects of Ned-19. It is noteworthy that TPC2, rather than TPC1, is generally believed to be the primary TPC isoform of lysosomes. Moreover, the gene ablation accomplished in the TPC1 "knockouts" is only partial and rather unsatisfactory. Definitive conclusions about the role of TPC1 can only be reached with proper, full knockouts. Even the pharmacological approach is unconvincing because the high doses of Ned-19 used should have blocked both TPC isoforms and presumably precluded invasion. Instead, invasion is reduced by only ≈50%. A much greater inhibition was reported using 9-phenantrol, the blocker of plasmalemmal calcium channels. How is the selective involvement of lysosomal TPC1 channels justified?

As to partial gene ablation of TPC1: To avoid clonal variances, we usually perform pool sorting to obtain a cell population that predominantly contains cells -here- deficient in TPC1, but also a small proportion of wildtype cells as seen by the residual TPC1 protein on the Western blot. We observe a significant reduction of bacterial uptake in this cell pool suggesting that the uptake reduction in a pure K.O. population may be even larger.

As to the inhibition by Ned19: We agree with the reviewer that Ned19 inhibits TPC1 and TPC2. Since ablation of TPC1 reduced invasion of S. aureus, we concluded that TPC1 is important for S. aureus host cell invasion. We thus agree with the reviewer that a role for TPC2 cannot be excluded. We will clarify this in the reviewed manuscript. It needs to be noted, however, that deficiency in either TPC1 or TPC2 alone was sufficient to prevent Ebola virus infection9, which is in line with our observations.

The 50% reduction of invasion upon Ned19 treatment (Figure 1d) is comparable with the reduction caused by other compounds that influence the ASM-dependent pathway (such as amitriptyline, ARC39 [Figure 2c], BAPTA-AM [Figure 1c], Vacuolin-1 [Figure 2a], β-toxin [Figure 2e] and ionomycin [Figure 1a]). Further, the partial reduction of invasion is most likely due to the concurrent activity of multiple internalization pathways which are not all targeted by the used compounds.

Invoking an elevation of NAADP as the mediator of calcium release requires measurements of the changes in NAADP concentration in response to the bacteria. This was not performed. Instead, the authors analyzed the possible contribution of putative NAADP-generating systems and reported that the most active of these, CD38, was without effect, while the elimination of SARM1, another potential source of NAADP, had a very modest (≈20%) inhibitory effect that may have been due to clonal variation, which was not ruled out. In view of these data, the conclusion that NAADP is involved in the invasion process seems unwarranted.

Our results from two independent experimental set-ups (Ned19 [Figure 1d] and TPC1 K.O. [Figure 1e & Figure 2f]) indicate the involvement of NAADP in the process. However, the measurement of NAADP concentration is non-trivial. However, we can rule out clonal variation in the SARM1 mutant since experiments were conducted with a cell pool as described above in order to avoid clonal variation of single clones.

The mechanism behind biosynthesis of NAADP is still debated. CD38 was the first enzyme discovered to possess the ability of producing NAADP. However, it requires acidic pH to produce NAADP10 -which does not match the characteristics of a cytosolic NAADP producer. HeLa cells do not express CD38 and hence, it is not surprising that inhibition of CD38 had no effect on S. aureus invasion in HeLa cells. However, NAADP production by HeLa cells was observed in absence of CD3811. Thus CD38-independent NAADP generation is likely. SARM1 can produce NAADP at neutral pH12 and is expressed in HeLa, thus providing a more promising candidate.

We agree with the reviewer that the reduction of S. aureus internalization after ablation of SARM1 is less pronounced than in other experiments of ours. This may be explained by NAADP originating from other enzymes, such as the recently discovered DUOX1, DUOX2, NOX1 and NOX213, which – with exception of DUOX2- possess a low expression even in HeLa cells. We will discuss this in the revised manuscript.

The involvement of lysosomal secretion is, again, predicated largely on the basis of pharmacological evidence. No direct evidence is provided for the insertion of lysosomal components into the plasma membrane, or for the release of lysosomal contents to the medium. Instead, inhibition of lysosomal exocytosis by vacuolin-1 is the sole source of evidence. However, vacuolin-1 is by no means a specific inhibitor of lysosomal secretion: it is now known to act primarily as a PIKfyve inhibitor and to cause massive distortion of the endocytic compartment, including gross swelling of endolysosomes. The modest (20-25%) inhibition observed when using synaptotagmin 7 knockout cells is similarly not convincing proof of the requirement for lysosomal secretion.

We agree that the manuscript will strongly benefit from a functional analysis of lysosomal exocytosis. We therefore will conduct assays to investigate exocytosis in the revision. However, we previously showed i) by addition of specific antisera that LAMP1 transiently is exposed on the plasma membrane during ionomycin and pore-forming toxin challenge and ii) demonstrated the release of ASM activity into the culture medium under these conditions.8 Both measurements are not compatible with S. aureus infection, since LAMP1 antibodies also are non-specifically bound by protein A and another IgG-binding protein on the S. aureus surface, which would bias the results. Since protein A also serves as an adhesin, we cannot simply delete the ORF without changing other aspects of staphylococcal virulence. Further, FBS contains a ASM background activity that impedes activity measurements of cell culture medium. We previously removed this background activity by a specific heat-inactivation protocol.8 However, S. aureus invasion is strongly reduced in culture medium containing this heat-inactivated FBS.

We agree with the reviewer that Vacuolin-1 has unspecific side effects. We will address this in the revised version of the manuscript.

As to the involvement of synaptotagmin 7:

Synaptotagmin 7 is not the only protein possibly involved in Ca-dependent exocytosis. For instance, SYT1 has been shown to possess an overlapping function.14 This may explain the discrepancy between our vacuolin-1 and SYT7 ablation experiments. We will add an according section to the discussion.

ASM is proposed to play a central role in the rapid invasion process. As above, most of the evidence offered in this regard is pharmacological and often inconsistent between inhibitors or among cell types. Some drugs affect some of the cells, but not others. It is difficult to reach general conclusions regarding the role of ASM. The argument is made even more complex by the authors' use of exogenous sphingomyelinase (beta-toxin). Pretreatment with the toxin decreased invasion efficiency, a seemingly paradoxical result. Incidentally, the effectiveness of the added toxin is never quantified/validated by directly measuring the generation of ceramide or the disappearance of SM.

Although pharmacological inhibitors can have unspecific side effects, we want to emphasize that the inhibitors used in our study act on the enzyme ASM by completely different mechanisms. Amitriptyline is a so called functional inhibitor of ASM (FIASMA) which induces the detachment of ASM from lysosomal membranes resulting in degradation of the enzyme.15 By contrast, ARC39 is a competitive inhibitor.16, 17

We do not see inconsistencies in our data obtained with ASM inhibitors. Amitriptyline and ARC39 both reduce the invasion of S. aureus in HuLEC, HuVEC and HeLa cells (Figure 2c). ARC39 needs a longer pre-incubation, since its uptake by host cells is slower (data not shown). We observe a different outcome in 16HBE14o- and Ea.Hy 926 cells, with 16HBE14o- even demonstrating a slightly increased invasion of S. aureus upon ARC39 treatment. Amitriptyline had no effect (Figure 2c). Moreover, both inhibitors affected the invasion dynamics (Figure 3d), phagosomal escape (Figure 4c and Supp. Figure 4e) and Rab7 recruitment (Figure 4a and Supp. Figure 4b) in a similar fashion. Proper inhibition of ASM by both compounds in all cell lines used was validated by enzyme assays (Supp. Figure 2e), which suggests that the ASM-dependent pathway does only exist in specific cell lines. This also may serve as an argument that we here do not observe unspecific side effects of the compounds. We will clarify this in the revised manuscript.

ASM is a key player for SM degradation and recycling. In clinical context, deficiency in ASM results in the so-called Niemann Pick disease type A/B. The lipid profile of ASM-deficient cells is massively altered18, which will result in severe side effects. Short-term inhibition by small molecules therefore poses a clear benefit when compared to the usage of ASM K.O. cells.

As to the treatment with a bacterial sphingomyelinase:

Treatment with the bacterial SMase (bSMase, here: β-toxin) was performed in two different ways:

i) Pretreatment of host cells with β-toxin to remove SM from the host cell surface before infection. This removes the substrate of ASM from the cell surface prior to addition of the bacteria (Figure 2e, Figure 4d-f). Since SM is not present on the extracellular plasma membrane leaflet after treatment, a release of ASM cannot cause localized ceramide formation at the sites of lysosomal exocytosis. Similar observations were made by others.19

ii) Addition of bSMase to host cells together with the bacteria to complement for the absence of ASM (Figure 2f).

Removal of the ASM substrate before infection (i) prevents localized ASM-mediated conversion of SM to Cer during infection and resulted in a decreased invasion, while addition of the SMase during infection resulted in an increased invasion in TPC1 and SYT7 ablated cells. Thus, both experiments are consistent with each other and in line with our other observations.

Removal of SM from the plasma membrane by β-toxin was indirectly demonstrated by the absence of Lysenin recruitment to phagosomes/escaped bacteria when host cells were pretreatment with the toxin before infection (Figure4F). In another publication, we recently quantified the effectiveness of β-toxin treatment, even though with slightly longer treatment times (75 min vs. 3h).20 We will repeat the measurements also for shorter treatment times.

To clarify our experimental approaches to the readership we will add an explanatory section to the revised manuscript.

As to the general conclusions regarding the role of ASM: ASM and lysosomal exocytosis has been shown to be involved in uptake of a variety of pathogens19, 21-25 supporting its role in the process.

The use of fluorescent analogs of sphingomyelin and ceramide is not well justified and it is unclear what conclusions can be derived from these observations. Despite the low resolution of the images provided, it appears as if the labeled lipids are largely in endomembrane compartments, where they would presumably be inaccessible to the secreted ASM. Moreover, considering the location of the BODIPY probe, the authors would be unable to distinguish intact sphingomyelin from its breakdown product, ceramide. What can be concluded from these experiments? Incidentally, the authors report only 10% of BODIPY-positive events after 10 min. What are the implications of this finding? That 90% of the invasion events are unrelated to sphingomyelin, ASM, and ceramide?

During the experiments with fluorescent SM analogues (Figure 3a,b), S. aureus was added to the samples immediately before start of video recording. Hence, bacteria are slowly trickling onto the host cells and we thus can image the initial contact between them and the bacteria, for instance, the bacteria depicted in Figure 3a contact the host cell about 9 min before becoming BODIPY-FL-positive (see Supp. Video 1, 55 min). Hence, we think that in these cases we see the formation of phagosomes around bacteria rather than bacteria in endomembrane compartments. Since generation of phagosomes happens at the plasma membrane, SM is accessible to secreted ASM.

The “trickling” approach for infection is an experimental difference to our invasion measurements, in which we synchronized the infection by a very slow centrifugation. This ensures that all bacteria have contact to host cells and are not just floating in the culture medium. However, live cell imaging of initial bacterial-host contact and synchronization of infection is technically not combinable.

In our invasion measurements -with synchronization-, we typically see internalization of ~20% of all added bacteria after 30 min. Hence, most bacteria that are visible in our videos likely are still extracellular and only a small proportion was internalized. This explains why only 10% of total bacteria are positive for BODIPY-FL-SM after 10 min. The proportion of internalized bacteria that are positive for BODIPY-FL-SM should be way higher but cannot be determined with this method.

We agree with the reviewer that we cannot observe conversion of BODIPY-FL-SM by ASM. In order to do that, we attempted to visualize the conversion of a visible-range SM FRET probe (Supp. Figure 3), but the structure of the probe is not compatible with measurement of conversion on the plasma membrane, since the FITC fluorophore released into the culture medium by the ASM activity thereby gets lost for imaging. In general, the visualization of SM conversion with subcellular resolution is challenging and even with novel tools developed in our lab26 visualization of SM on the plasma membrane is difficult.

The conclusion we draw from these experiments are that i.) S. aureus invasion is associated with SM and ii.) SM-associated invasion can be very fast, since bacteria are rapidly engulfed by BODIPY-FL-SM containing membranes.

It is also unclear how the authors can distinguish lysenin entry into ruptured vacuoles from the entry of RFP-CWT, used as a criterion of bacterial escape. Surely the molecular weights of the probes are not sufficiently different to prevent the latter one from traversing the permeabilized membrane until such time that the bacteria escape from the vacuole.

We here want to clarify that both, the Lysenin as well as the CWT reporter have access to rupture vacuoles (Figure 4b). We used the Lysenin reporter in these experiments for estimation of SM content of phagosomal membranes. If a vacuole is ruptured, both the bacteria and the luminal leaflet of the phagosomal membrane remnants get in contact with the cytosol and hence with the cytosolically expressed reporters YFP-Lysenin as well as RFP-CWT resulting in “Lysenin-positive escape” when phagosomes contained SM (see Figure 4f). By contrast, either β-toxin expression by S. aureus or pre-treatment with the bSMase resulted in absence of Lysenin recruitment suggesting that the phagosomal SM levels were decreased/undetectable (Figure 4f, Supp Figure 5f, g, i, j).

This approach does not enable a quantitative measurement of phagosomal SM and rather gives a “yes or no” answer. However, we think this method is sufficient to show that β-toxin expression and pretreatment markedly decreased phagosomal SM levels in the host cells.

The approach we used here to analyze “Lysenin-positive escape” can clearly be distinguished from Lysenin-based methods that were used by others.27 There Lysenin was used to show trans-bilayer movement of SM before rupture of bacteria-containing phagosomes.

To clarify the function of Lysenin in our approach we will add an additional figure to the revised manuscript.

Both SMase inhibitors (Figure 4C) and SMase pretreatment increased bacterial escape from the vacuole. The former should prevent SM hydrolysis and formation of ceramide, while the latter treatment should have the exact opposite effects, yet the end result is the same. What can one conclude regarding the need and role of the SMase products in the escape process?

As pointed out above, pretreatment of host cells with SMase removes SM from the plasma membrane and hence, ASM does not have access to its substrate. Hence, both treatment with either ASM inhibitors or pretreatment with bacterial SMase prevent ASM from being active on the plasma membrane and hence block the ASM-dependent uptake (Figure 2 c, e). Although overall less bacteria were internalized by host cells under these conditions, the bacteria that invaded host cells did so in an ASM-independent manner.

Since blockage of the ASM-dependent internalization pathway (with ASM inhibitor [Figure 4c], SMase pretreatment [Figure 4e] and Vacuolin-1[Supp. Fig.4f]) always resulted in enhanced phagosomal escape, we conclude that bacteria that were internalized in an ASM-independent fashion cause enhanced escape. Vice versa, bacteria that enter host cells in an ASM-dependent manner demonstrate lower escape rates.

This is supported by comparing the escape rates of “early” and “late” invaders [Figure 4g/h], which in our opinion is a key experiment that supports this hypothesis. The “early” invaders are predominantly ASM-dependent (see e.g. Figure 3e) and thus, bacteria that entered host cell in the first 10 min of infection should have been internalized predominantly in an ASM-dependent fashion, while slower entry pathways are active later during infection. The early ASM dependent invaders possessed lower escape rates, which is in line with the data obtained with inhibitors (e.g. Figure 4c and Supp. Fig. 4f).

We hypothesize that the activity of ASM on the plasma membrane during invasion mediates the recruitment of a specific subset of receptors, which then influence downstream phagosomal maturation and escape. This hypothesis is supported by the fact that the subset of receptors interacting with S. aureus is altered upon inhibition of the ASM-dependent uptake pathway. We describe this in another study that is currently under evaluation elsewhere.

Reviewer #2 (Public review):

Summary:

In this 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.

The evidence provided is solid, methods used are appropriate and results largely support their conclusions, but can be substantiated further as detailed below. The weakness is a reliance on chemical inhibitors that can be non-specific to delineate critical steps.

Specific comments:

A large number of experiments rely on treatment with chemical inhibitors. While this approach is reasonable, many of the inhibitors employed such as amitriptyline and vacuolin1 have other or non-defined cellular targets and pleiotropic effects cannot be ruled out. Given the centrality of ASM for the manuscript, it will be important to replicate some key results with ASM KO cells.

We thank the reviewer for the critical evaluation of our manuscript and plenty of constructive comments.

We agree with the reviewer, that ASM inhibitors such as functional inhibitors of ASM (FIASMA) like amitriptyline used in our study have unspecific side effects given their mode-of-action. FIASMAs induce the detachment of ASM from lysosomal membranes resulting in degradation of the enzyme.15  However, we want to emphasize that we also used the competitive inhibitor ARC39 in our study16, 17 which acts on the enzyme by a completely different mechanism. All phenotypes (reduced invasion [Figure 2c, d], effect on invasion dynamics [Figure 3d], enhanced escape [Figure 4c and Supp Figure 4e] and differential recruitment of Rab7 [Supp. Figure 4b]) were observed with both inhibitors thereby supporting the role of ASM in the process.

We further agree that experiments with genetic evidence usually support and improve scientific findings. However, ASM is a cellular key player for SM degradation and recycling. In a clinical context, deficiency in ASM results in a so-called Niemann Pick disease type A/B. The lipid profile of ASM-deficient cells is massively altered18, which in itself will result in severe side effects. Thus, the usage of inhibitors provides a clear benefit when compared to ASM K.O. cells, since ASM activity can be targeted in a short-term fashion thereby preventing larger alterations in cellular lipid composition.

Most experiments are done in HeLa cells. Given the pathway is projected as generic, it will be important to further characterize cell type specificity for the process. Some evidence for a similar mechanism in other cell types S. aureus infects, perhaps phagocytic cell type, might be good.

Whenever possible we performed the experiments not only in HeLa but also in HuLECs. For example, we refer to experiments concerning the role of Ca2+ (Figure 1c/Supp.Figure1e), lysosomal Ca2+/Ned19 (Figure1d/Supp Figure 1g), lysosomal exocytosis/Vacuolin-1 (Figure 2a/Supp. Figure2a), ASM/ARC39 and amitriptyline (Figure 2c), surface SM/β-toxin (Figure 2e/Supp. Figure 2g), analysis of invasion dynamics (complete Figure 3) and measurement of cell death during infection (Figure 5c-e, Supp. Figure 6a+b).

HuLECs, however, are not really genetically amenable and hence we were not able to generate gene deletions in these cells and upon introduction of the fluorescence escape reporter the cells are not readily growing.

As to ASM involvement in phagocytic cells: a role for ASM during the uptake of S. aureus by macrophages was previously reported by others.23 However, in professional phagocytes S. aureus does not escape from the phagosome and replicates within the vacuole.28

I'm a little confused about the role of ASM on the surface. Presumably, it converts SM to ceramide, as the final model suggests. Overexpression of b-toxin results in the near complete absence of SM on phagosomes (having representative images will help appreciate this), but why is phagosomal SM detected at high levels in untreated conditions? If bacteria are engulfed by SM-containing membrane compartments, what role does ASM play on the surface? If surface SM is necessary for phagosomal escape within the cell, do the authors imply that ASM is tuning the surface SM levels to a certain optimal range? Alternatively, can there be additional roles for ASM on the cell surface? Can surface SM levels be visualized (for example, in Figure 4 E, F)?

We initially hypothesized that we would detect higher phagosomal SM levels upon inhibition of ASM, since our model suggests SM cleavage by ASM on the host cell surface during bacterial cell entry. However, we did not detect any changes in our experiments (Supp. Figure 4d). We currently favor the following explanation: SM is the most abundant sphingolipid in human cells.29 If peripheral lysosomes are exocytosed and thereby release ASM, only a localized and relative small proportion of SM may get converted to Cer, which most likely is below our detection limit. In addition, the detection of cytosolically exposed phagosomal SM by YFP-Lysenin is not quantitative and provides a “Yes or No” measurement. Hence, we think that the rather limited SM to Cer conversion in combination with the high abundance of SM in cellular membranes does not visibly affect the recruitment of the Lysenin reporter.

In our experiments that employ BODIPY-FL-SM (Figure 3a+b), we cannot distinguish between native SM and downstream metabolites such as Cer. Hence, again we cannot make any assumptions on the extent to which SM is converted on the surface during bacterial internalization. Although our laboratory recently used trifunctional sphingolipid analogs to analyze the SM to Cer conversion20, the visualization of this process on the plasma membrane is currently still challenging.

Overall, we hypothesize that the localized generation of Cer on the surface by released ASM leads to generation of Cer-enriched platforms. Subsequently, a certain subset of receptors may be recruited to these platforms and influence the uptake process. These platforms are supposed to be very small, which also would explain that we did not detect changes in Lysenin recruitment.

Related to that, why is ASM activity on the cell surface important? Its role in non-infectious or other contexts can be discussed.

ASM release by lysosomal exocytosis is implied in plasma membrane repair upon injury. We will this discuss this in the revised version of the manuscript.

If SM removal is so crucial for uptake, can exocytosis of lysosomes alone provide sufficient ASM for SM removal? How much or to what extent is lysosomal exocytosis enhanced by initial signaling events? Do the authors envisage the early events in their model happening in localized confines of the PM, this can be discussed.

Ionomycin treatment led to a release of ~10 % of all lysosomes and also increased extracellular ASM activity.7, 8 However, it is currently unclear– to our knowledge -to which extent the released ASM affects surface SM levels. Also, it is unknown which percentage of the lysosomes is released during infection with S. aureus. However, one has to speculate that this will be only a fraction of the “releasable lysosomes” as we assume that the effects (lysosomal Ca2+ liberation, lysosomal exocytosis and ASM activity) are very localized and take place only at host-pathogen contact sites (see also above). In initial experimentation we attempted to visualize the local ASM activity on the cell surface by using a visible range FRET probe (Supp. Fig. 3). Cleavage of the probe by ASM on the surface leads to release of FITC into the cell culture medium which does not contribute a measurable signal at the surface.

How are inhibitor doses determined? How efficient is the removal of extracellular bacteria at 10 min? It will be good to substantiate the cfu experiments for infectivity with imaging-based methods. Are the roles of TPC1 and TPC2 redundant? If so, why does silencing TPC1 alone result in a decrease in infectivity? For these and other assays, it would be better to show raw values for infectivity. Please show alterations in lysosomal Ca2+ at the doses of inhibitors indicated. Is lysosomal Ca2+ released upon S. aureus binding to the cell surface? Will be good to directly visualize this.

Concerning the inhibitor concentrations, we either used values established in published studies or recommendations of the suppliers (e.g. 2-APB, Ned19, Vacuolin-1). For ASM inhibitors, we determined proper inhibition of ASM by activity assays. Concentrations of ionomycin resulting in Ca2+ influx and lysosomal exocytosis was determined in earlier studies of our lab.8, 30

As to the removal of bacteria at 10 min p.i.: Lysostaphin is very efficient for removal of extracellular S. aureus and sterilizes the tissue culture supernatant. It significantly lyses bacteria within a few minutes, as determined by turbidity assays.31

As to imaging-based infectivity assays: We will add an analysis of imaging-based invasion assays in the revised manuscript.

Regarding the roles of TPC1 and TPC2: from our data we cannot conclude whether the roles of TPC1 and TPC2 are redundant. One could speculate that since blockage of TPC1 alone is sufficient to reduce internalization of bacteria, that both channels may have distinct roles. On the other hand, there might be a Ca2+ threshold in order to initiate lysosomal exocytosis that can only be attained if TPC1 and TPC2 are activated in parallel. Thus, our observations are in line with another study that shows reduced Ebola virus infection in absence of either TPC1 or TPC2.32

As to raw CFU counts: whereas the observed effects upon blocking the invasion of S. aureus are stable, the number of internalized bacteria varies between individual biological replicates, for instance, by differences in host cell fitness or growth differences in bacterial cultures, which are prepared freshly for each experiment.

With respect to visualization of lysosomal Ca2+ release: we agree with the reviewer that direct visual demonstration of lysosomal Ca2+ release upon infection will improve the manuscript. We therefore will perform additional experimentation to show alterations of Ca2+ at the lysosomes during infection.

The precise identification of cytosolic vs phagosomal bacteria is not very easy to appreciate. The methods section indicates how this distinction is made, but how do the authors deal with partial overlaps and ambiguities generally associated with such analyses? Please show respective images. The number of events (individual bacteria) for the live cell imaging data should be clearly mentioned.

We apologize for not having sufficiently explained the technology to detect escaped S. aureus. The cytosolic location of S. aureus is indicated by recruitment of RFP-CWT.33 CWT is the cell wall targeting domain of lysostaphin, which efficiently binds to the pentaglycine cross bridge in the peptidoglycan of S. aureus. This reporter is exclusively and homogenously expressed in the host cytosol. Only upon rupture of phagoendosomal membranes the reporter can be recruited to the cell wall of now cytosolically located bacteria. S. aureus mutants, for instance in the agr quorum sensing system, cannot break down the phagosomal membrane in non-professional phagocytes and thus stay unlabeled by the CWT-reporter.33 We will include respective images/movies of escape events and the bacteria numbers for live cell experiments in the revised version of the manuscript.

In the phagosome maturation experiments, what is the proportion of bacteria in Rab5 or Rab7 compartments at each time point? Will the decreased Rab7 association be accompanied by increased Rab5? Showing raw values and images will help appreciate such differences. Given the expertise and tools available in live cell imaging, can the authors trace Rab5 and Rab7 positive compartment times for the same bacteria?

We will include the proportion of Rab7-associated bacteria in the revised manuscript. Usually, we observe that Rab5 is only transiently (for a few minutes) present on phagosomes and only afterwards the phagosomes become positive for Rab7. We do not think that a decrease in Rab7-positive phagosomes would increase the proportion of Rab5-positive phagosomes. However, we cannot exclude this hypothesis with our data.

We can achieve tracing of individual bacteria for recruitment of Rab5/Rab7 only manually, which impedes a quantitative evaluation. However, we will include information that illustrates the consecutive recruitment of the GTPases.

The results with longer-term infection are interesting. Live cell imaging suggests that ASM-inhibited cells show accelerated phagosomal escape that reduces by 6 hpi. Where are the bacteria at this time point ? Presumably, they should have reached lysosomes. The relationship between cytosolic escape, replication, and host cell death is interesting, but the evidence, as presented is correlative for the populations. Given the use of live cell imaging, can the authors show these events in the same cell?

We think that most bacteria-containing phagoendosomes should have fused with lysosomes 6 h p.i. as we have previously shown by acidification to pH of 5 and LAMP1 decoration.34

We will provide images/videos to show the correlation between escape and replication in the revised manuscript.

Given the inherent heterogeneity in uptake processes and the use of inhibitors in most experiments, the distinction between ASM-dependent and independent pathways might not be as clear-cut as the authors suggest. Some caution here will be good. Can the authors estimate what fraction of intracellular bacteria are taken up ASM-dependent?

We agree with the reviewer that an overlap between internalization pathways is likely. A clear distinction is therefore certainly non-trivial. Alternative to ASM-dependent and ASM-independent pathways, the ASM activity may also accelerate one or several internalization pathways. We will address this limitation in the revised manuscript. 

Early in infection (~10 min after contact with the cells), the proportion of bacteria that enter host cells ASM-dependently is relatively high amounting to roughly 75% in HuLEC. After 30 min, this proportion is decreasing to about 50%. We will include this information in the revised version of the manuscript.

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Reviewer #2 (Public review):

The study by Liu et al provides a functional analysis of lnc-FANCI-2 in cervical carcinogenesis, building on their previous discovery of FANCI-2 being upregulated in cervical cancer by HPV E7.

The authors conducted a comprehensive investigation by knocking out (KO) FANCI-2 in CaSki cells and assessing viral gene expression, cellular morphology, altered protein expression and secretion, altered RNA expression through RNA sequencing (verification of which by RT-PCR is well appreciated), protein binding, etc. Verification experiments by RT-PCR, Western blot, etc are notable strengths of the study.

The KO and KD were related to increased Ras signaling and EMT and reduced IFN-y/a responses.

Although the large amount of data is well acknowledged, it is a limitation that most data come from CaSki cells, in which FANCI-2 localization is different from SiHa cells and cancer tissues (Figure 1). The cytoplasmic versus nuclear localization is somewhat puzzling.

DOI: 10.1038/s41418-024-01418-y

Resource: RRID:Addgene_44386

Curator: @scibot

SciCrunch record: RRID:Addgene_44386

What is this?

DOI: 10.1038/s41418-024-01418-y

Resource: None

Curator: @scibot

SciCrunch record: RRID:Addgene_100765

What is this?

quedó claro

De gran ayuda para diferenciar

Muchas gracias por compartirnos su trabajo para complementar nuestro curso Dr. Sinhué

En el análisis de datos algunas veces podemos confundir las variables dependientes con las independientes, yo pienso que a veces varía según lo que queramos encontrar

Entendido

Pienso que si integraran a científicos de diferentes países y todos colaboraran, habría menor error de medición dado el sistema que utilizan en sus países, así todos llegan de acuerdo a trabajar con un sólo sistema y se evitan de este tipo de errores

La hipótesis es muy importante al realizar un experimento porque es la base de la investigación, el punto de partida

En la ingeniería como en medicina, es muy importante tener valores precisos y medir el error para evitar accidentes

Author response:

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

We greatly appreciate the opportunity to submit a revision of our manuscript entitled: "The Autophagy Protein, ATG14 Safeguards Against Unscheduled Pyroptosis Activation to Enable Embryo Transport During Early Pregnancy" by Popli et al. We thank all three Referees for underscoring the importance of our findings as well as the constructive critiques that we used to improve our paper. Most notably, we added the following new data:

· To provide more insight into whether pyroptosis activation occurs distinctly in the oviduct, we looked for GSDMD, (primary executioner of the pyroptosis pathway) expression in the uterus and ovary too. We observed no signs of pyroptosis activation in response to ATG14 loss in either the uterus or ovary of Atg14 cKO mice compared to control ones suggesting that ATG14 plays a distinct role in regulating pyroptosis specifically in the oviduct (Revised Figure 5F).

· To better understand the molecular mechanisms of pyroptosis activation in the oviducts, we examined various key markers of mitochondrial integrity, architecture, and function in control and Atg14 cKO oviducts. Our findings indicate a significant loss of mitochondrial structural and functional integrity, possibly contributing to the embryo retention phenotype via activating the pyroptosis pathway in the oviduct. (Revised Figure 5B & C).

· To address the spatiotemporal and region-specific expression of ATG14 in the oviduct, we performed immunofluorescence analysis and observed the consistent expression of ATG14 in all the cellular compartments of oviducts including ciliary epithelial cells, secretory epithelial cells, and smooth muscle cells. Moreover, the region-specific expression analysis revealed that distinct expression of ATG14 in the ampullary region of cKO mice oviduct helps to preserve its structural integrity. Conversely, its loss in the isthmus region of the oviduct in concordance with active PR-cre activity causes completely distorted epithelial structures with luminal obliteration or narrowing resulting in an unorganized and obstructed lumen leading to embryo retention, suggesting that ATG14 is essential for maintaining the structural integrity of the oviduct (Revised Figure 3F & S2A).

· Considering the expression of PR-cre in the pituitary, which could potentially influence hormonal secretion and ovulation, we evaluated the levels of E2 and P4 during pregnancy. Our findings show that these hormone levels remained unchanged in Atg14 cKO mice, indicating that the absence of ATG14 does not negatively affect the HPG axis or pituitary function (Revised Figure 2F).

· ATG14 is an essential factor for the initiation of autophagy, and its loss can lead to reduced or inhibited autophagic activity. Consistently, we observed elevated levels of LC3b and p62 proteins, two well-known markers of autophagic flux in the oviducts of Atg14-deficient mice implying that loss of ATG14 leads to defective autophagy potentially disturbing the structural integrity of oviductal epithelial cells and impairing embryo transport. (New Supplementary Figure S2B).   

Reviewer #1 (Public Review):

This study by Popli et al. evaluated the function of Atg14, an autophagy protein, in reproductive function using a conditional knockout mouse model. The authors showed that female mice lacking Atg14 were infertile partly due to defective embryo transport function of the oviduct and faulty uterine receptivity and decidualization using PgrCre/+; Atg14f/f mice. The findings from this work are exciting and novel. The authors demonstrated that a loss of Atg14 led to an excessive pyroptosis in the oviductal epithelial cells that compromises cellular integrity and structure, impeding the transport function of the oviduct. In addition, the authors use both genetic and pharmacological approaches to test the hypothesis. Therefore, the findings from this study are high-impact and likely reproducible. However, there are multiple major concerns that need to be addressed to improve the quality of the work.

Major comments:

(1) It is interesting that deletion of Atg14 using PgrCre results in pyroptosis only in the oviduct; the authors should speculate/evaluate why the oviduct, but not the uterus or follicles. Is there any cellular specificity that is sensitive to autophagy/pyroptosis in the oviduct but not in other cell types? This has not been evaluated or discussed in the manuscript. Is it possible to include GSDMD IHC for the uterine section to ensure that there was no pyroptosis event in the cKO uteri?

We performed GSDMD IHC and found that, unlike in the oviduct, the cKO uteri and ovaries do not exhibit detectable pyroptosis (Revised Figure 5F). Additionally, we have added text to the discussion section addressing possible reasons for the differential impact of Atg14 loss on pyroptosis along the reproductive tract continuum (Line number: 532-538)

(2) Please include an explanation of how a loss of Atg14, important for the initiation process of autophagy (as indicated in line 88), can lead to pyroptosis. There was some discussion about inflammation. But the connection is still missing.

We thank the reviewer for noting on this. We have now included a possible explanation of how autophagy could impact pyroptosis in the discussion section (Line number: 532-538)  

(3) No expression data of ATG14 using IHC/IF analysis were included in the manuscript - this is missing. This is needed and important as the authors found that Foxj1Cre/+; Atg14f/f cKO mice had no fertility defect. Is it possible that ATG14 is not present in the ciliated epithelial cells of the oviduct? In addition, the data in Figure 5B also points to this speculation. This is because the GSDMD (the pyroptosis marker) is only observed in the isthmus region but not the ampulla.

We thank the reviewer for this nice suggestion. We performed the immunofluorescence analysis for ATG14 expression in control and Atg14 cKO oviducts and observed the consistent expression of ATG14 in all the cellular compartments of oviducts including ciliary epithelial cells, secretory epithelial cells, and smooth muscle cells (New Supplementary Figure S2A). We also looked for α-tubulin expressions in the oviduct of Foxj1Cre/+; Atg14 f/f mice and control mice and observed that ciliated epithelial cells that were positive for acetylated α-tubulin staining did not appear to be different in Foxj1Cre/+; Atg14 f/f mice oviduct compared to controls (Revised Figure 4C). However, due to the unavailability of reliable fluorescent-labeled antibodies for both Foxj1 and Atg14, we were unable to conduct the co-localization study as intended. This limitation hindered our ability to precisely determine the spatial overlap of these proteins within the tissue.

(4) In line with the previous comment, is ATG14 present in the human Fallopian tube? If so, which cell type? This needs to be addressed.

Author’s Response: We appreciate the reviewer's valuable suggestion. While we currently lack access to human fallopian tube biopsies, the Human Protein Atlas (https://www.proteinatlas.org/ENSG00000126775-ATG14) demonstrates distinct ATG14 expression in various fallopian tube cell types, with localization in the cytoplasm, membrane, and nucleus.

(5) As PgrCre is also expressed in the pituitary, is it possible that the deletion of Atg14 using PgrCre would affect pituitary function – hence a change in the FSH/LH secretion that subsequently affects ovulation? Although the uterine and ovarian histology in the Atg14 cKO looks similar to the controls, is it possible that cyclicity is also affected? The authors should evaluate whether the estrous cycle takes place regularly.

Author’s Response: Thank you for the insightful comment. However, evaluating the estrous cycle requires significant time and effort and is beyond the scope of the current manuscript. Nonetheless, we have now shown that both P4 and E2 levels were not altered in Atg14 cKO mice, indicating that the loss of Atg14 did not adversely impact the HPG axis, and by extension, pituitary function (Revised Figure 2F).

(6) The number of total embryos/oocytes in the cKO compared to the control has not been evaluated - this data must be included. Do the changes in autophagy in Atg14 cKO affect preimplantation embryo development? Please categorize the embryos found in the oviduct/uterus in both genotypes. i.e., % blastocyst, % morula, % developmentally delayed, % non-viable etc. It would be interesting to evaluate if the oviduct with heavy pyroptosis can support preimplantation embryo development.

Author’s Response: We thank the reviewer for this nice suggestion. We categorized the embryos into different categories as suggested and included the data (Revised Figure 3C and Figure 6D).

(7) It is unclear why the superovulation+mating experiment (Figure 3C) was performed. Please provide justification. Why was the data from natural mating (Figure 3A) insufficient?

Author’s Response: In Figure 3C, superovulation was employed to complement the natural mating studies and to provide stronger evidence for the embryo retention phenotype observed in the oviduct.

(8) In lines 297-298, the conclusion that "ATG14 is required for P4-mediated but not for E2-mediated actions during uterine receptivity" is not entirely correct. This is because the authors also observed that the downregulation of MUC1 (E2-target protein) is absent in the PgrCre/+;Atg14f/f cKO female uteri.

We thank the reviewer for noting this. We detected more E2-induced targets in D-4 pregnant uterine samples and found no change in their expression in response to Atg14 depletion in cKO females (Revised Figure 2E).

(9) Figure 3D: Please include an image that also represents the ampulla region. All images are from the isthmus region. It would be informative to see if the loss of cell boundaries also takes place at the ampulla region in the cKO oviduct.

We thank the reviewer for this nice suggestion. We included the ampulla section from the cKO and control female oviducts (Revised Figure 3F). As PR-cre activity is limited to isthmus only [1, 2], we did not see any structural abnormality in ampulla sections of cKO oviducts.

(10) Figure 3E: Please indicate which region the TEM was performed. Isthmus? Ampulla? Were the changes in mitochondrial phenotype observed across all oviductal regions?

The TEM imaging was performed by the WashU Core services. Although we clearly mentioned the core person to look into the isthmus region only, we are not sure if they accurately follow the instructions.

(11) Figure 4B; the evaluation of FOXJ1 IHC. The authors need to include sections that also have an ampulla region-especially in the cKO. In addition, it is misleading to state that there were fewer FOXJ1+ cells (line 361) in the cKO if the region being evaluated is the isthmus (which has a lot fewer ciliated epithelial cells in general) while the control image showed an ampulla where the abundancy of ciliated epithelial cells (FOXJ1+) is higher than that of the isthmus. The authors also need to include a higher resolution image (a zoom-in at the ciliated epithelial cells with FOXJ1+ signal) as well as the quantification of FOXJ1+ cells.

We appreciate the reviewer for the suggestion. In Figure 4A, we have already shown the ampulla region from both control and cKO oviducts, wherein alpha-tubulin staining was evident in both oviducts.  

We agree with the reviewer that the isthmus usually has fewer ciliary epithelial cells than the ampulla, however, as illustrated in Figures 4A and 4B, Atg14 depletion causes a marked disruption of structural integrity with loss of cell boundaries specifically in the isthmus, which is far more pronounced than in the ampulla. One reason for this is the reported Pgr Cre activity, which is much more robust in the isthmus than in the ampulla [1, 2] . This disruption leads to the substantial loss of both ciliated and secretory cells, compromising the epithelial architecture to such an extent that it is impossible to accurately quantify the Foxj1 signal as can be seen in higher resolution images in New Supplementary Figure S3.

For more clarity, we modified the statement in the revised file (Line Number: 393-396)

(12) All IHC/IF and embryo images need to include the scale bars.

We thank the reviewer for this suggestion. We now included the scale bar in all the images.

(13) Figure 5H: although IL1B is being discussed, there was no data in this study to support the figure.

In Figure 5H, IL1B is presented as part of the pyroptosis signaling pathway. As we have already shown other key executioners of this pathway: Caspase 1 and GSDMD, we believe that additional IL1B data would not provide new insights beyond what has already been shown.

Minor comments:

(1) Please include n (sample size) for all data, including the histology image in the figure legends for all studies.

We now included the sample size in figure legends for all data shown in the manuscript.

(2) Line 32, did the authors mean to say, "Self-digestion of..." instead of "Self-digestion for..."?

In Line 32, we meant, “Cellular self-digestion for female reproductive tract functions”. We have now corrected the statement.

Fig. 1A - please include negative control.

We included the negative control (Revised Figure 1)

(3) Figure 1E left panel and Figure 4C - please label "Average no. of pups/female/litter" as each female has more than one litter over her reproductive lifespan. If the authors represent pups/females, then the number should be accumulative in the range of 35-40pups/females in the control group.

We thank the reviewer for noting this. We now corrected the label in both Revised Figure 1E and Revised Figure 4E.

(4) Line 273: please remove "& F" as there is no Figure F in the image.

We removed “&F” from the Line 273.

(5) The presence of CL is not always indicative of normal hormonal levels; therefore, the authors should include the measurement of progesterone levels at 3.5 dpc in the cKO compared to the control group. Hormonal regulation is also crucial for embryo transport.

We thank the reviewer for this suggestion. We measured not only P4 but also E2 levels in D4 pregnant females and found no significant difference in their levels compared to corresponding controls (Revised Figure 2F).

(6) Figure 2A shows that KRT expression is not present in the control uteri. Although the KRT8 levels may have decreased at 4 dpc, they should be present (see Figure S2A).

We observed no decrease in KRT expression in control uteri on 5 dpc. We included better-resolution images for KRT expression (Revised Figure 2A).

(7) The dotted white lines in Figure 2A are too thick. It's difficult to see the Ki67 positive signal in the luminal epithelial cells. Please also add a quantitative analysis of Ki67+ cells in the luminal epithelium vs. stromal cells.

We now corrected the dotted lines in Revised Figure 2B. However, as the Ki-67 proliferation is evident in the representative images, we believe quantification analysis will not add anything new to the existing conclusion.

(8) Figure 2D - the y-axis mentions the weight ratio. However, the figure legend describes the transcript levels of Atg14 - please correct this.

We corrected the label in the revised manuscript.

(9) Line 294 - Please correct Figure 2C to Figure 2B.

We corrected it.

(10) Line 308 - Please correct Figure 2E to Figure 2F.

We corrected it.

(11) Line 310 - Please correct Figure 2F to Figure 2G.

We corrected it.

(12) Line 311 - Please correct Figure 2F to Figure 2G.

We corrected it.

(13) Information in Figure S2A and S2B should be included in the main figure.

We thank the reviewer for this nice suggestion. We now included the figures S2A and S2B in the main figure (Revised Figure 2C & D).

(14) Figure 3C - due to a lot of cellular debris after flushing, it's difficult to see. But it seems like there are secondary follicles in the flushing of control oviducts - this is highly unlikely. This could be due to an artifact of an accidental poking of the ovaries during collection.

We agree with the reviewer. It might be due to the unintentional poking of the ovaries. We will take extra care in future experiments to avoid this and ensure clean flushing to prevent any confusion from debris or artifacts.

(15) Figure 2B and Figure 3D signals from DAPI are missing - it's black with no blue signal. This could be the data loss during file compression for manuscript submission.

We included better-resolution pictures for the DAPI signal in Revised Figure 2B & Figure 3F.

(16) Explain why some embryos in the cKO make it to the uterus when the females are superovulated.

It might be due to the heightened hormonal stimulation provided by the superovulation which could facilitate the movement of some embryos through the oviduct despite any defects or abnormalities caused by the loss of ATG14 in the oviduct.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Popli et al investigated the roles of the autophagy-related gene, Atg14, in the female reproductive tract (FRT) using conditional knockout mouse models. By ablation of Atg14 in both oviduct and uterus with PR-Cre (Atg14 cKO), the authors discovered that such females are completely infertile. They went on to show that Atg14 cKO females have impaired embryo implantation and uterus receptivity due to impaired response to P4 stimulation and stromal decidualization. In addition to the uterus defect, the authors also discovered that early embryos are trapped inside the oviduct and cannot be efficiently transported to the uterus in these females. They went on to show that oviduct epithelium in Atg14 cKO females showed increased pyroptosis, which disrupts oviduct epithelial integrity and leads to obstructive oviduct lumen and impaired embryo transport. Therefore, the authors concluded that autophagy is critical for maintaining the oviduct homeostasis and keeping the inflammation under check to enable proper embryo transport.

Strengths:

This study revealed an important and unexpected role of the autophagy-related gene Atg14 in preventing pyroptosis and maintaining oviduct epithelial integrity, which is poorly studied in the field of reproductive biology. The study is well designed to test the roles ofATG14 in mouse oviduct and uterus. The experimental data in general support the conclusion and the interpretations are mostly accurate. This work should be of interest to reproductive biologists and scientists in the field of autophagy and pyroptosis.

Weaknesses:

Despite the strengths, there are several major weaknesses raising concerns. In addition, the mismatched figure panels, the undefined acronyms, and the poor description/presentation of some of the data significantly hinder the readability of the manuscript.

(1) In the abstract, the authors stated that "autophagy is critical for maintaining the oviduct homeostasis and keeping the inflammation under check to enable embryo transport". This statement is not substantiated. Although Atg14 is an autophagy-related gene and plays a critical role in oviduct homeostasis, the authors did not show a direct link between autophagy and pyroptosis/oviduct integrity. In addition, the authors pointed out in the last paragraph of the introduction that none of the other autophagy-related genes (ATG16L, FIP200, BECN1) exhibited any discernable impact on oviduct function. Therefore, the oviduct defect is caused by Atg14 specifically, not necessarily by autophagy.

We thank the reviewer for noting this. We corrected the statement in the revised manuscript (Line number: 53-54).

(2) In lines 412-414, the authors stated that "Atg14 ablation in the oviduct causes activation of pyroptosis", which is also not supported by the experimental data. The authors did not show that Atg14 is expressed in oviduct cells. PR-Cre is also not specific in oviduct cells. It is possible that Atg14 knockout in other PR-expressing tissues (such as the uterus) indirectly activates pyroptosis in the oviduct. More experiments will be required to support this claim. In line with the no defect when Atg14 has knocked out in oviduct ciliary cells, it will be good to use the secretory cells Cre, such as Pax8-Cre, to demonstrate that Atg14 functions in the secretory cells of the oviduct thus supporting this conclusion.

We now included the ATG14 expression data in the oviduct (New Supplementary Figure S2A). Consistent with previous studies reporting PR-cre activity in the isthmus [1, 2] , we observed that Atg14 depletion was more pronounced in the isthmus compared to the ampulla. However, generating a secretory Pax-8 cell Cre mice model will require a substantial amount of time and effort, and we respectfully note that this is beyond the scope of the current manuscript.

(3) With FOXJ1-Cre, the authors attempted to specifically knockout Atg14 in ciliary cells, but there are no clear fertility and embryo implantation defects in Foxj1/Atg14 cKO mice. The author should provide verification data to show that Atg14 had been effectively depleted in ciliary cells if Atg14 is normally expressed.

We understand the reviewer’s concern. We included new data for ATG14 expression in control and Atg14 cKO mice oviducts (New Supplementary Figure S2A). However, due to the unavailability of reliable fluorescent-labeled antibodies for both Foxj1 and Atg14, we could not conduct the co-localization studies as intended, and this limitation hindered our ability to precisely determine the spatial overlap of these proteins within the oviduct. Nonetheless, Foxj1-cre is a widely used mice model with reported cre-activity in ciliary epithelial cells including oviduct tissues [3]. Given the widespread expression of ATG14 in all the ciliary and secretory cells (New Supplementary Figure S2A) and distinct FOXJ1 expression in the oviduct (New Supplementary Figure S3), we are confident that Atg14 is deleted in the ciliary epithelial cells of Foxj1/Atg14 cKO mice oviducts.

(4) In lines 307-313, the author tested whether ATG14 is required for the decidualization of HESCs. The author stated that "Control siRNA transfected cells when treated with EPC seemed to change their morphological transformation from fibroblastic to epithelioid (Fig. 2E) and had increased expression of the decidualization markers IGFBP1 and PRL by day three only (Fig. 2F)". First, the labels in Figure 2 are not corresponding to the description in the text. Second, the morphology of the HESCs in the control and Atg14 siRNA group showed no obvious difference even at day 3 and day 6. The author should point out the difference in each panel and explain in the text or figure legend.

Decidualization is a post-implantation event, whereas our study primarily focuses on pre-implantation events in the oviduct. Therefore, we have removed all data related to human and mouse decidualization to enhance the clarity and precision of our study.

(5) In lines 332-336, the authors pointed out that the cKO mice oviduct lining shows marked eosinophilic cytoplasmic change, but there's no data to support the claim. In addition, the authors further described that "some of the cells showed degenerative changes with cytoplasmic vacuolization and nuclear pyknosis, loss of nuclear polarity, and loss of distinct cell borders giving an appearance of fusion of cells (Fig. 3D)". First, Figure 3D did not show all these phenotypes, and it is likely a mismatch to Figure 3E. Even in Figure 3E, it is not obvious to notice all the phenotypes described here. The figure legend is overly simple, and there's no explanation of the arrowheads in the panel. More data/images are required to support the claim here and provide a clear indication and explanation in the figure legend.

Dr. Ramya Masand, Chief pathologist in the Pathology Department at the Baylor College of Medicine, and a contributing author, assessed the H&E-stained oviduct sections from control and cKO mice. We have now included a new Supplementary Figure S3 with previous representative H&E images that depict the cellular alterations described in lines 332–336.

(6) In lines 317-325, it is rather confusing about the description of the portion of embryos from the oviduct and uterus. In addition, the total number of embryos was not provided. I would recommend presenting the numerical data to show the average embryos from the oviduct and uterus instead of using the percentage data in Figures 3A and 5G.

We thank the reviewer for this nice suggestion. We calculated the average number of embryos and found no difference in the number of embryos recovered from cKO or polyphyllin-treated pregnant mice at 4 dpc compared to their controls. (New Supplementary Figure S4A & B).

(7) In lines 389-391, authors tested whether Polyphyllin VI treatment led to activated pyroptosis and blocked embryo transport. Although Figures 5F-G showed the expected embryo transport defect, the authors did not show the pyroptosis and oviduct morphology. It will be important to show that the Polyphyllin VI treatment indeed led to oviduct pyroptosis and lumen disruption.

We performed the GSDMD staining IHC in Polyphyllin VI or vehicle-treated mice oviducts and observed elevated GSDMD expression with Polyphyllin V (New Figure 6E). However, no significant lumen disruption was detected, which may be attributed to the short-term exposure of the oviducts to pyroptosis induction, in contrast to the more pleiotropic effects observed in genetically induced models. Nonetheless, this observation clearly indicates that unscheduled or unwarranted activation of pyroptosis impedes embryo transport.

(8) In line 378, it would be better to include a description of pyroptosis and its molecular mechanisms to help readers better understand your experiments. Alternatively, you can add it in the introduction.

We thank the reviewer for this nice suggestion. We included literature on the pyroptosis pathway in the introduction section (Line Number: 105-118).

(9) Please make sure to provide definitions for the acronyms such as FRT, HESCs, GSDMD, etc.

We added definitions for the acronyms such as FRT, HESCs, and GSDMD used in the study.

(10) It is rather confusing to use oviducal cell plasticity in this manuscript. The work illustrated the oviducal epithelial integrity, not the plasticity.

We thank the reviewer for the suggestion. We have revised the manuscript accordingly to ensure clarity and precision in describing the oviductal epithelial structural changes observed in the absence of ATG14.

A few of the additional comments for authors to consider improving the manuscript are listed below.

(1) Some of the figures are missing scale bars, while others have inconsistent scale bars. It would be better to be consistent.

We now included the scale bars in all images.

(2) On a couple of occasions, the DAPI signal cannot be seen, such as in Figure 2B and Figure 3D.

We now included better-resolution images for the DAPI signal in all fluorescent images shown in the revised manuscript.

(3) Overall, the figure legends can be improved to provide more detailed information to help the reader to interpret the data.

We included additional details in all the figure legends in the revised manuscript.

(4) In Figure 2D, the Y-axis showed the stimulated/unstimulated uterine weight ratio, why did the author put "Atg14" at the top of the graph? At the same time, the X-axis title is missing in Figure 2D.

We apologize for the typo error. We removed “Atg14” from the top of the graph and included the X-axis title in the revised manuscript.

(5) In the left panel of Figure 2G, "ATG14" at the top should be "Atg14" to be consistent.

In Figure 2G, we are representing “ATG14” according to human gene annotation.

(6) In line 559, there miss "(A)" in front of Immunofluorescence analysis of GSDMD.

We thank the reviewer for noting this. We corrected it in the revised manuscript.

Reviewer #3 (Public Review):

Summary:

The manuscript by Pooja Popli and co-authors tested the importance of Atg14 in the female reproductive tract by conditionally deleting Atg14 using Pr Cre and also Foxj1cre. The authors showed that loss of Atg14 leads to infertility due to the retention of embryos within the oviduct. The authors further concluded that the retention of embryos within the oviduct is due to pyroptosis in oviduct cells leading to defective cellular integrity. The manuscript has some interesting findings, however there are also areas that could be improved.

Strengths:

The importance of Atg14 and autophagy in the female reproductive tract is incompletely understood. The manuscript also provide spatial evidence about a new mechanism linking Atg14 to pyroptosis.

We thank the reviewer for the positive statements and constructive comments on our manuscript.

Weaknesses:

(1) It is not clear why the loss of Atg14 selectively induces Pyroptosis within oviduct cells but not in other cellular compartments. The authors should demonstrate that these events are not happening in uterine cells.

We thank the reviewer for this nice suggestion. We performed GSDMD IHC and found that, unlike in the oviduct, the cKO uteri and ovaries do not exhibit detectable pyroptosis (Revised Figure 5F). Additionally, we have added text to the discussion section addressing possible reasons for the differential impact of Atg14 loss on pyroptosis along the reproductive tract continuum (Line number: 532-538)

(2) The manuscript never showed any effect on the autophagy upon loss of Atg14. Is there any effect on autophagy upon Atg14 loss? If so, does that contribute to the observation?

We thank the reviewer for the nice suggestion. We found LC3b and p62 protein levels, two well-known markers of autophagic flux are elevated due to Atg14 loss in the oviduct (New Supplementary Figure S2B).  Since, p62 accumulation is an indicative of the reduced autophagic flux [4], we posit loss of Atg14 results in defective autophagy in the oviduct. Importantly, this defective autophagy adversely impacted the structural integrity of oviductal epithelial cells, causing impairment in embryo transport.

(3) It is not clear what the authors meant by cellular plasticity and integrity. There is no evidence provided in that aspect that the plasticity of oviduct cells is lost. Similarly, more experimental evidence is necessary for the conclusion about cellular integrity.

We thank the reviewer for the suggestion. We have revised the text for clarity and precision in describing the oviductal epithelial structural changes observed in the absence of ATG14. To avoid ambiguity, we have removed the term "cellular plasticity." We have already provided extensive evidence, including multiple H&E stains and immunofluorescence analyses for KRT8 and smooth muscle actin to illustrate cellular integrity in both control and cKO oviducts. However, we respectfully believe that performing additional experiments on cellular integrity would not contribute further to the conclusions already drawn.

(4) The mitochondrial phenotype shown in Figure 3 didn't appear as severe as it is described in the results section. The analyses should be more thorough. They should include multiple frames (in supplemental information) showing mitochondrial morphology in multiple cells. The authors should also test that aspect in uterine cells. The authors should measure Feret's diagram. Diff erence in membrane potential etc. for a definitive conclusion.

We appreciate the reviewer’s suggestion. We carried out the TOM20 (mitochondrial structural marker) and cytochrome C (mitochondrial damage and cell death marker) immune-colocalization study and found loss of TOM20 signal with concomitant cytochrome c leakage into the peri-nuclear space (Revised Figure 5B). Additionally, we also observed reduced expression of mitochondrial structural and functional markers by qPCR analysis (Revised Figure 5C). However, we respectfully argue that conducting membrane potential studies on murine oviducts is extremely complex and is beyond the scope of this study.

(5) The comment that the loss of Atg14 and pyroptosis leads to the narrowing of the lumen in the oviduct should be experimentally shown.

We have now included a New Supplementary Figure S3 with representative previous immunofluorescence images that clearly show the narrowing of the lumen with Atg14 loss in the oviduct.

(6) The manuscript never showed the proper mechanism through which Atg14 loss induces pyroptosis. The authors should link the mechanism.

We respectfully disagree with the reviewer on this point. We have provided substantial evidence regarding the cellular mechanisms through which the loss of Atg14 may lead to the activation of pyroptosis as outlined below:

(1) Cellular Changes: Loss of ATG14 in the oviduct results in cellular swelling and the formation of fused membranous structures, which are characteristic features of pyroptosis activation.

(2) Expression of Key Pyroptosis Proteins: We observed an induced expression of GSDMD and Caspase-1, primary executioners of the pyroptotic pathway, in response to Atg14 loss.

(3) Inflammatory Markers: Elevated levels of inflammatory markers such as TNF-α and CXCR3 were detected, both of which are known to promote pyroptosis [5, 6].

(4) Mitochondrial Damage: We have added new data demonstrating disrupted colocalization of TOM20 (a mitochondrial structural marker) and Cytochrome c (a cell death marker), resulting in Cytochrome c leakage into the perinuclear space (Revised Figure 5B). Additionally, qPCR analysis revealed reduced expression of mitochondrial structural and functional markers in cKO oviduct tissues (Revised Figure 5C).

Based on these evidences, we can clearly say that Atg14 has some direct or indirect link to inflammasome activation. However, understanding the complex rheostat between the Atg14-mediated autophagy and inflammation regulatory axis will necessitate future studies employing sophisticated models, such as combined knockout mice where ATG14 is deleted alongside key inflammatory regulators (e.g., NLRP3, GSDMD, or CASPASE-1). These dual knockout models could provide crucial insights into how ATG14 modulates inflammatory pathways.

References:

(1) Herrera, G.G.B., et al., Oviductal Retention of Embryos in Female Mice Lacking Estrogen Receptor alpha in the Isthmus and the Uterus. Endocrinology, 2020. 161(2).

(2) Soyal, S.M., et al., Cre-mediated recombination in cell lineages that express the progesterone receptor. Genesis, 2005. 41(2): p. 58-66.

(3) Zhang, Y., et al., A transgenic FOXJ1-Cre system for gene inactivation in ciliated epithelial cells. Am J Respir Cell Mol Biol, 2007. 36(5): p. 515-9.

(4) Mizushima, N., T. Yoshimori, and B. Levine, Methods in mammalian autophagy research. Cell, 2010. 140(3): p. 313-26.

(5) Vaher, H., Expanding the knowledge of tumour necrosis factor-alpha-induced gasdermin E-mediated pyroptosis in psoriasis. Br J Dermatol, 2024. 191(3): p. 319-320.

(6) Liu, C., et al., CXCR4-BTK axis mediate pyroptosis and lipid peroxidation in early brain injury after subarachnoid hemorrhage via NLRP3 inflammasome and NF-kappaB pathway. Redox Biol, 2023. 68: p. 102960.

DOI: 10.1038/s41589-024-01628-y

Resource: University of Texas at Austin Center for Biomedical Research Support Microscopy and Imaging Facility (RRID:SCR_021756)

Curator: @scibot

SciCrunch record: RRID:SCR_021756

What is this?

Author response:

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

Reviewer #1 (Public Review):

Summary:

Qin and colleagues analysed data from the Human Connectome Project on four right-handed subgroups with different gyrification patterns in Heschl's gyrus. Based on these groups, the authors highlight the structure-function relationship of planum temporale asymmetry in lateralised language processing at the group level and next at the individual level. In particular, the authors propose that especially microstructural asymmetries are related to functional auditory language asymmetries in the planum temporale.

Strengths:

The study is interesting because of an ongoing and long-standing debate about the relationship between structural and functional brain asymmetries, and in particular whether structural brain asymmetries can be seen as markers of functional language brain lateralisation.

In this debate, the relationship between Heschl's gyrus asymmetry and planum temporale asymmetry is rare and therefore valuable here. A large sample size and inter-rater reliability support the findings.

Weaknesses:

In this case of multiple brain measures, it would be important to provide the reader with some sort of effect size (e.g. Cohen's d) to help interpret the results.

Thank you for pointing this out. In the revised version, the effect size, i.e., Cohen's d, has been incorporated into the results (page 8, line 159-160; page 9, line 181-186, supplementary page 14, Table S14).

In addition, the authors highlight the microstructural results in spite of the macrostructural results. However, the macrostructural surface results are also strong. I would suggest either reducing the emphasis on micro vs macrostructural results or adding information to justify the microstructural importance.

In the original manuscript, we highlighted the results of microstructural measures because the correlations between PT microstructural and functional measures were more pronounced both within the hemispheres and in terms of asymmetry, compared with the significant results of surface area. Following your comments here, we now lowered the tone of microstructure results (page 2, line 40; page 14, line 267), and added relevant discussion regarding the macrostructural results in the revised version (page 18, line 363-370; as copied below):

“As for macrostructural measures, the asymmetric PT surface area was also associated with speech comprehension AI. Given that the within-hemispheric coupling tendency between surface and speech comprehension existed only in the left PT, it was possible that the larger surface area of the left PT led to a less recruitment of its right homologous, and therefore the lateralization of functional activity would be more pronounced. Additionally, an opposite tendency was found between the correlation of speech perception and comprehension with surface area, potentially implying the segregation of the different speech processing in the PT area.”

Recommendations for the authors:

I have only some comments that I wish to be addressed by the authors:

(1) Please always specify "structural" or "functional" asymmetry or lateralisation, as the reader may be confused.

This has been done in relevant places.

(2) Please state that the scale is not the same between the results in Figure 3.

This have been specified, as suggested (see below).

“Notably, we did not standardize these structural measures, so the scales differed between indicators.”

(3) It may be of interest to the reader to learn more about interpretations of how Heschl's gyrus and planum temporale asymmetries are related.

Thank you for this comment. Given that the asymmetry of Heschl's gyrus was not analyzed in the present study, we do not have direct data/results for such an interpretation. Also, we reviewed the literature but found no relevant results on how Heschl's gyrus and planum temporale asymmetries are related. To address this, specific investigation targeting on this topic is needed. This has now been added in the discussion (page 20, line 415-417).

(4) As this manuscript builds somewhat on the Science Advances article by Ocklenburg et al. (2018), it would be important to discuss how this more liberal planum temporale definition might (or might not) affect the results compared to the more conservative planum temporale definition described here.

Yes, the definition of planum temporale varies across studies. Our current manual one is relatively more conservative than the Ocklenburg et al. (2018), in which the planum temporale was automatically derived from the Destrieux atlas. We believe that the definition of the planum temporale likely have non-trivial impact on the results, and our current manual definition with the consideration of the HG duplication should be more reliable and accurate, therefore favored, relative to the other ones. This has been briefly discussed in the revision (page 15-16, line 300-304).

(5) I would like the authors to briefly but critically discuss what exactly the MRI NODDI model measures and how this is interpreted as measuring microstructural properties of tissue.

We now provided relevant information regarding the NODDI measures (page 26, line 552-558; as copied below).

“NODDI is a highly effective method for detecting key features of neurite morphology, which employs a tissue model that detects three microstructural environments: the intracellular, extracellular and cerebrospinal fluid compartments (Zhang et al., 2012). In the grey matter of the cerebral cortex, the neurite density index (NDI) is an estimated volume fraction of the intracellular microstructural environment, with higher NDIs indicating greater neurite density (Jespersen et al., 2010; Zhang et al., 2012). The orientation dispersion index (ODI) is a measure of the alignment or dispersion of neurite, with higher ODIs indicating more dispersed neurite and lower ODIs indicating more aligned neurite (Jespersen et al., 2012; Zhang et al., 2012).”

(6) While not mandatory, I would be interested to read the authors' thoughts on the evolution of such a functional/(micro)structural lateralisation link of the planum temporale, in light of the literature on planum temporale asymmetries in (newborn) non-human primate species.

Thank you for this inspiring suggestion. We have incorporated relevant discussion into the revised version (page 15, line 281-288; as copied below).

“Moreover, there exist evolutionary evidence supporting the role of the PT as an anatomical substrate for language lateralization. For example, the leftward structural asymmetry of the PT have been observed in multiple non-human primates, including chimpanzees, macaques, and baboons (Becker et al., 2024; Gannon et al., 1998; Xia et al., 2019). Particularly, recent studies on baboons further demonstrated that PT structural leftward asymmetry in newborn baboons could predict future development of communicative gestures, implying a key role of PT structural asymmetry in the lateralized communication system for human and non-human brain evolution (Becker et al., 2024, 2021).”

Reference

Becker Y, Phelipon R, Marie D, Bouziane S, Marchetti R, Sein J, Velly L, Renaud L, Cermolacce A, Anton J-L, Nazarian B, Coulon O, Meguerditchian A. 2024. Planum temporale asymmetry in newborn monkeys predicts the future development of gestural communication’s handedness. Nat Commun 15:4791. doi:10.1038/s41467-024-47277-6

Becker Y, Sein J, Velly L, Giacomino L, Renaud L, Lacoste R, Anton J-L, Nazarian B, Berne C, Meguerditchian A. 2021. Early Left-Planum Temporale Asymmetry in newborn monkeys (Papio anubis): A longitudinal structural MRI study at two stages of development. NeuroImage 227:117575. doi:10.1016/j.neuroimage.2020.117575

Gannon PJ, Holloway RL, Broadfield DC, Braun AR. 1998. Asymmetry of Chimpanzee Planum Temporale: Humanlike Pattern of Wernicke’s Brain Language Area Homolog. Science 279:220–222. doi:10.1126/science.279.5348.220

Jespersen SN, Bjarkam CR, Nyengaard JR, Chakravarty MM, Hansen B, Vosegaard T, Østergaard L, Yablonskiy D, Nielsen NChr, Vestergaard-Poulsen P. 2010. Neurite density from magnetic resonance diffusion measurements at ultrahigh field: Comparison with light microscopy and electron microscopy. NeuroImage 49:205–216. doi:10.1016/j.neuroimage.2009.08.053

Jespersen SN, Leigland LA, Cornea A, Kroenke CD. 2012. Determination of Axonal and Dendritic Orientation Distributions Within the Developing Cerebral Cortex by Diffusion Tensor Imaging. IEEE Trans Med Imaging 31:16–32. doi:10.1109/TMI.2011.2162099

Xia J, Wang F, Wu Z, Wang L, Zhang C, Shen D, Li G. 2019. Mapping hemispheric asymmetries of the macaque cerebral cortex during early brain development. Hum Brain Mapp. doi:10.1002/hbm.24789

Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. 2012. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61:1000–1016. doi:10.1016/j.neuroimage.2012.03.072

Reviewer #2 (Public Review):

Summary:

The authors assessed the link between structural and functional lateralization in area PT, one of the brain areas that is known to exhibit strong structural lateralization, and which is known to be implicated in speech processing. Importantly, they included the sulcal configuration of Heschl's gyrus (HG), presenting either as a single or duplicated HG, in their analysis. They found several significant associations between microstructural indices and task-based functional lateralization, some of which depended on the sulcal configuration.

Strengths:

A clear strength is the large sample size (n=907), an openly available database, and the fact that HG morphology was manually classified in each individual. This allows for robust statistical testing of the effects across morphological categories, which is not often seen in the literature.

Weaknesses:

- Unfortunately, no left-handers were included in the study. It would have been a valuable addition to the literature, to study the effect of handedness on the observed associations, as many previous studies on this topic were not adequately powered. The fact that only right-handers were studied should be pointed out clearly in the introduction or even the abstract.

Thank for pointing this out. We have explicitly specified this in the Abstract and Introduction.

- The tasks to quantify functional lateralization were not specifically designed to pick up lateralization. In the interest of the sample size, it is understandable that the authors used the available HCP-task-battery results, however, it would have been feasible to access another dataset for validation. A targeted subset of results, concerning for example the relationship between sulcal morphology and task-based functional lateralization, could be re-assessed using other open-access fMRI datasets.

Yes, the fMRI task was not specifically designed to evaluate PT functional lateralization, which has been acknowledged in the discussion (page 17, line 330-342). Given the observed small effect size of our current structural-functional relationship, reproducing similar results with other datasets would require a cohort with a large sample size. This would induce a quite labor-intensive work given our current manual protocol for outlining PT and HG for everyone. The lack of validation with independent dataset has been discussed as a limitation in the revised version. We will try to conduct such a validation in future work, likely after developing an automatic pipeline for accurately extracting the PT and HG in the individual space (like the manual outlining protocol).

- The study is mainly descriptive and the general discussion of the findings in the larger context of brain lateralization comes a bit short. For example, are the observed effects in line with what we know from other 'language-relevant' areas? What could be the putative mechanisms that give rise to functional lateralization based on the microstructural markers observed? And which mechanisms might be underlying the formation of a duplicated HG?

Thank you for these insightful comments. As suggested, we strengthened the discussion as below:

“Another possible explanation could be that higher myelin content and larger surface area in left PT potentially indicated more white matter connection with other language-related regions such as Broca’s area, and therefore is more involved in language tasks than its right homolog (Allendorfer et al., 2016; Catani et al., 2005; Giampiccolo and Duffau, 2022).

The distinct roles of left and right PT in speech processing have been well-documented. A number of studies substantiated that PT of the left hemisphere responded more strongly to lexical-semantic and syntactic aspects of sentence processing, whereas the right hemisphere demonstrated a greater involvement in the speech melody (Albouy et al., 2020; Meyer et al., 2002).

These findings are consistent with those reported for the arcuate fasciculus (AF). The left AF has been identified as a crucial structure for language function (Giampiccolo and Duffau, 2022; Zhang et al., 2021). Disruption to this pathway has been linked to multimodal phonological and semantic deficits (Agosta et al., 2010), while injuries in the right AF did not affect language function (Zeineh et al., 2015).”

Regarding the mechanism underlying the formation of a duplicated HG, we did not come up with good thoughts after careful literature review. Also, we feel that this is kind of out of the scope of the present study and therefore did not add more discussion on this topic.

Recommendations for the authors:

(1) The data availability statement makes no explicit mention of the manual labels of HG configuration. Would the authors consider making available a list of HCP-subject-ID with a morphological group (L1/R1, L1/R2, etc.) for replicability and for re-use by other researchers?

The list of HCP-subject-ID with a morphological group (L1/R1, L1/R2, etc.) is now available in the supplementary material 2. We have specified this in the revised version.

(2) It would be helpful to state again the statistical tests associated with the p-value in the figure/table caption, e.g. Table 2.

As suggested, we now specified the statistical method in the figure/table caption.

(3) Sometimes, the y-axis labels are missing or not clear, for example in Figure S2.

Sorry about these. We double-checked all the figures, and corrected the missing or unclear labels for Figure S2 and S3 in the revised version.

(4) In a few instances the font sizes vary within a figure caption.

This has been corrected in the revision.

Reference

Agosta F, Henry RG, Migliaccio R, Neuhaus J, Miller BL, Dronkers NF, Brambati SM, Filippi M, Ogar JM, Wilson SM, Gorno-Tempini ML. 2010. Language networks in semantic dementia. Brain J Neurol 133:286–299. doi:10.1093/brain/awp233

Albouy P, Benjamin L, Morillon B, Zatorre RJ. 2020. Distinct sensitivity to spectrotemporal modulation supports brain asymmetry for speech and melody. Science 367:1043–1047. doi:10.1126/science.aaz3468

Allendorfer JB, Hernando KA, Hossain S, Nenert R, Holland SK, Szaflarski JP. 2016. Arcuate fasciculus asymmetry has a hand in language function but not handedness. Hum Brain Mapp 37:3297–3309. doi:10.1002/hbm.23241

Catani M, Jones DK, Ffytche DH. 2005. Perisylvian language networks of the human brain. Ann Neurol 57:8–16. doi:10.1002/ana.20319

Giampiccolo D, Duffau H. 2022. Controversy over the temporal cortical terminations of the left arcuate fasciculus: a reappraisal. Brain J Neurol 145:1242–1256. doi:10.1093/brain/awac057

Meyer M, Alter K, Friederici AD, Lohmann G, von Cramon DY. 2002. FMRI reveals brain regions mediating slow prosodic modulations in spoken sentences. Hum Brain Mapp 17:73–88. doi:10.1002/hbm.10042

Zeineh MM, Kang J, Atlas SW, Raman MM, Reiss AL, Norris JL, Valencia I, Montoya JG. 2015. Right arcuate fasciculus abnormality in chronic fatigue syndrome. Radiology 274:517–526. doi:10.1148/radiol.14141079

Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. 2012. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61:1000–1016. doi:10.1016/j.neuroimage.2012.03.072

Zhang J, Zhong S, Zhou L, Yu Yamei, Tan X, Wu M, Sun P, Zhang W, Li J, Cheng R, Wu Y, Yu Yanmei, Ye X, Luo B. 2021. Correlations between Dual-Pathway White Matter Alterations and Language Impairment in Patients with Aphasia: A Systematic Review and Meta-analysis. Neuropsychol Rev 31:402–418. doi:10.1007/s11065-021-09482-8

Reviewing Editor:

I encourage the authors to incorporate the suggestions of the reviewers, such as:

(1) to provide more in-depth interpretations about how and why structural and functional lateralization relate,

Done.

(2) to provide statistical effect sizes,

Done.

(3) to make their sulcal-morphology classification openly available,

Done.

(4) to provide statistical effect sizes,

Done

(5) to discuss the possible impact of diverging PT definitions with regard to previous studies,

Done.

(6) to provide more in-depth interpretations about how and why structural and functional lateralization relate.

Done.

Detailed comments:

In an impressive cohort of 907 human participants, the present paper presents a very interesting set of data on PT asymmetries not only at the macro-structural but also at the microstructural levels in order to investigate their potential correlates with PT functional asymmetry in relation to perceptual acoustic language tasks.

I believe this is a key paper for the following reasons:

(1) it provides critical data and results for addressing a controversial but important question: the relevance of measures of anatomical asymmetry for inferring its language-related functional hemispheric specialization;

(2) to do so, the authors made a very impressive effort to manually trace the anatomical delineation of the planum temporale at different levels in every participant, the best (but crazy time-consuming) approach so far to document interindividual variability of the PT and to address such a question;

(3) the contribution is particularly relevant regarding the statistical power of the study, the study and measures having been done in 907 participants!

(4) I also found the study well designed and well written with great relevance of the findings for the field.

As the results, the authors reported asymmetric measures of microstructural asymmetry (including intracortical myelin content, neurite density, and neurite orientation) but also of macrostructural asymmetries in relation to functional lateralization for language.

Comments：

I have only 2 additional minor comments of my own:

(1) In agreement with reviewer 2, I don't understand why the authors seem to downplay the links they found between gross PT asymmetry and functional lateralization. I recommend the authors to highlight and discuss this important result, just as the microstructural PT asymmetries and their functional links.

This has been done (page 18, line 363-370).

(2) PT structural asymmetry (both micro & macro) has been well documented in nonhuman primates (and their functional link with manual lateralization for gestural communication). Without detailing this literature, I recommend the authors at least mention this literature as a comparative perspective in the introduction and/or discussion in order to make the question of PT asymmetry less anthropocentric.

This has been done (page 15, line 281-288).

eLife Assessment

This study investigates the molecular mechanisms underlying chronic pain-related memory impairment by focusing on S1P/S1PR1 signaling in the dentate gyrus (DG) of the hippocampus. Through behavioral tests (Y-maze and Morris water maze) and RNA-seq analysis, the researchers discovered that S1P/S1PR1 signaling is crucial for determining susceptibility to memory impairment, with decreased S1PR1 expression linked to structural plasticity changes and memory deficits. This work has important significance and a convincing level of evidence, thus offering new insights into the mechanisms underlying chronic pain-related memory impairment.

Reviewer #2 (Public review):

Summary:

The study investigates the molecular mechanisms underlying chronic pain-related memory impairment by focusing on S1P/S1PR1 signaling in the dentate gyrus (DG) of the hippocampus. Through behavioural tests (Y-maze and Morris water maze) and RNA-seq analysis, the researchers segregated chronic pain mice into memory impairment-susceptible and -unsusceptible subpopulations. They discovered that S1P/S1PR1 signaling is crucial for determining susceptibility to memory impairment, with decreased S1PR1 expression linked to structural plasticity changes and memory deficits.

Knockdown of S1PR1 in the DG induced a susceptible phenotype, while overexpression or pharmacological activation of S1PR1 promoted resistance to memory impairment and restored normal synaptic structure. The study identifies actin cytoskeleton-related pathways, including ITGA2 and its downstream Rac1/Cdc42 signaling, as key mediators of S1PR1's effects, offering new insights and potential therapeutic targets for chronic pain-related cognitive dysfunction.

This manuscript consists of a comprehensive investigation and significant findings. The study provides novel insights into the molecular mechanisms of chronic pain-related memory impairment, highlighting the critical role of S1P/S1PR1 signaling in the hippocampal dentate gyrus. The clear identification of S1P/S1PR1 as a potential therapeutic target offers promising avenues for future research and treatment strategies. The manuscript is well-structured, methodologically sound, and presents valuable contributions to the field.

Strengths:

(1) The manuscript is well-structured and written in clear, concise language. The flow of information is logical and easy to follow.

(2) The segregation of mice into memory impairment-susceptible and -unsusceptible subpopulations is innovative and well-justified. The statistical analyses are robust and appropriate for the data.

(3) The detailed examination of S1PR1 expression and its impact on synaptic plasticity and actin cytoskeleton reorganization is impressive. The findings are significant and contribute to the understanding of chronic pain-related memory impairment.

Comments on revisions:

The authors have satisfactorily addressed all the issues raised.

Author response:

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

This study presents a valuable finding on sperm flagellum and HTCA stabilization. The evidence supporting the authors' claims is incomplete. The work will be of broad interest to cell and reproductive biologists working on cilium and sperm biology.

We thank the Editor and the two reviewers for their time and thorough evaluation of our manuscript. We greatly appreciate their valuable guidance on improving our study. In the revised manuscript, we have conducted additional experiments and provided quantitative data in response to the reviewers' comments. Furthermore, we have refined the manuscript and added further context to elucidate the significance of our findings for the readers.

Public Reviews:

Reviewer #1 (Public Review):

In this paper, Wu et al. investigated the physiological roles of CCDC113 in sperm flagellum and HTCA stabilization by using CRISPR/Cas knockouts mouse models, co-IP, and single sperm imaging. They find that CCDC113 localizes in the linker region among radial spokes, the nexin-dynein regulatory complex (N-DRC), and doublet microtubules (DMTs) RS, N-DRC, and DMTs and interacts with axoneme-associated proteins CFAP57 and CFAP91, acting as an adaptor protein that facilitates the linkage between RS, N-DRC, and DMTs within the sperm axoneme. They show the disruption of CCDC113 produced spermatozoa with disorganized sperm flagella and CFAP91, DRC2 could not colocalize with DMTs in Ccdc113-/- spermatozoa. Interestingly, the data also indicate that CCDC113 could localize on the HTCA region, and interact with HTCA-associated proteins. The knockout of Ccdc113 could also produce acephalic spermatozoa. By using Sun5 and Centlein knockout mouse models, the authors further find SUN5 and CENTLEIN are indispensable for the docking of CCDC113 to the implantation site on the sperm head. Overall, the experiments were designed properly and performed well to support the authors' observation in each part. Furthermore, the study's findings offer valuable insights into the physiological and developmental roles of CCDC113 in the male germ line, which can provide insight into impaired sperm development and male infertility. The conclusions of this paper are mostly well supported by data, but some points need to be clarified and discussed.

We thank Reviewer #1 for his or her critical reading and the positive assessment.

(1) In Figure 1, a sperm flagellum protein, which is far away from CCDC113, should be selected as a negative control to exclude artificial effects in co-IP experiments.

We greatly appreciate Reviewer #1’s insightful suggestion. In response, we selected two sperm outer dense fiber proteins, ODF1 and ODF2, which are located distant from the sperm axoneme, as negative controls in the co-IP experiments. As shown in Figure 1- figure supplement 1A and B, neither ODF1 nor ODF2 bound to CCDC113, indicating the interaction observed in Figure 1 is not an artifact.

(2) Whether the detachment of sperm head and tail in Ccdc113-/- mice is a secondary effect of the sperm flagellum defects? The author should discuss this point.

Good question. Considering that CCDC113 is localized in the sperm neck region and interacts with SUN5 and CENTLEIN, it may play a direct role in connecting the sperm head and tail. Indeed, PAS staining revealed that Ccdc113–/– sperm heads exhibit abnormal orientation in stages V–VIII of the seminiferous epithelia (Figure 6C-D). Furthermore, transmission electron microscopy (TEM) analysis indicated that the absence of CCDC113 caused detachment of the damaged coupling apparatus from the sperm head in step 9–11 spermatids (Figure 6E). These results suggest that the detachment of the sperm head and tail in Ccdc113–/– mice may not be a secondary effect of sperm flagellum defects. We have discussed this point further below:

“CCDC113 can interact with SUN5 and CENTLEIN, but not PMFBP1 (Figure 7A-C), and left on the tip of the decapitated tail in Sun5–/– and Centlein–/– spermatozoa (Figure 7K and L). Furthermore, CCDC113 colocalizes with SUN5 in the HTCA region, and immunofluorescence staining in spermatozoa shows that SUN5 is positioned closer to the sperm nucleus than CCDC113 (Figure 7G and H). Therefore, SUN5 and CENTLEIN may be closer to the sperm nucleus than CCDC113. PAS staining revealed that Ccdc113–/– sperm heads are abnormally oriented in stages V–VIII seminiferous epithelia (Figure6 C and D), and TEM analysis further demonstrated that the disruption of CCDC113 causes the detachment of the destroyed coupling apparatus from the sperm head in step 9–11 spermatids (Figure 6E). All these results suggest that the detachment of sperm head and tail in Ccdc113–/– mice may not be a secondary effect of sperm flagellum defects.”

(3) Given that some cytoplasm materials could be observed in Ccdc113-/- spermatozoa (Fig. 5A), whether CCDC113 is also essential for cytoplasmic removal?

Good question. Unremoved cytoplasm could be detected in spermatozoa by using transmission electron microscopy (TEM) analysis, including disrupted mitochondria, damaged axonemes, and large vacuoles. These observations indicate defects in cytoplasmic removal in Ccdc113–/– mice. We have discussed this point as below:

“Moreover, TEM analysis detected excess residual cytoplasm in spermatozoa, including disrupted mitochondria, damaged axonemes, and large vacuoles, indicating defects in cytoplasmic removal in Ccdc113–/– mice (Figure 5A).”

(4) Although CCDC113 could not bind to PMFBP1, the localization of CCDC113 in Pmfbp1-/- spermatozoa should be also detected to clarify the relationship between CCDC113 and SUN5-CENTLEIN-PMFBP1.

We appreciate Reviewer #1’s suggestion. We have analyzed the localization of CCDC113 in Pmfbp1-/- spermatozoa and found that CCDC113 was located at the tip of the decapitated tail in Pmfbp1-/- spermatozoa (Figure 7K and L). This finding has been incorporated into the revised manuscript as below:

“To further elucidate the functional relationships among CCDC113, SUN5, CENTLEIN, and PMFBP1 at the sperm HTCA, we examined the localization of CCDC113 in Sun5-/-, Centlein–/–, and Pmfbp1–/– spermatozoa. Compared to the control group, CCDC113 was predominantly localized on the decapitated flagellum in Sun5-/-, Centlein–/–, and Pmfnp1–/– spermatozoa (Figure 7K and L), indicating SUN5, CENTLEIN, and PMFBP1 are crucial for the proper docking of CCDC113 to the implantation site on the sperm head. Taken together, these data demonstrate that CCDC113 cooperates with SUN5 and CENTLEIN to stabilize the sperm HTCA and anchor the sperm head to the tail.”

Reviewer #2 (Public Review):

Summary:

In the present study, the authors select the coiled-coil protein CCDC113 and revealed its expression in the stages of spermatogenesis in the testis as well as in the different steps of spermiogenesis with expression also mapped in the different parts of the epididymis. Gene deletion led to male infertility in CRISPR-Cas9 KO mice and PAS staining showed defects mapped in the different stages of the seminiferous cycle and through the different steps of spermiogenesis. EM and IF with several markers of testis germ cells and spermatozoa in the epididymis indicated defects in flagella and head-to-tail coupling for flagella as well as acephaly. The authors' co-IP experiments of expressed CCDC113 in HEK293T cells indicated an association with CFAP91 and DRC2 as well as SUN5 and CENTLEIN.

The authors propose that CCDC113 connects CFAP91 and DRC2 to doublet microtubules of the axoneme and CCDC113's association with SUN5 and CENTLEIN to stabilize the sperm flagellum head-to-tail coupling apparatus. Extensive experiments mapping CCDC13 during postnatal development are reported as well as negative co-IP experiments and studies with SUN5 KO mice as well as CENTLEIN KO mice.

Strengths:

The authors provide compelling observations to indicate the relevance of CCDC113 to flagellum formation with potential protein partners. The data are relevant to sperm flagella formation and its coupling to the sperm head.

We are grateful to Reviewer #2 for his or her recognition of the strength of this study.

Weaknesses:

The authors' observations are consistent with the model proposed but the authors' conclusions for the mechanism may require direct demonstration in sperm flagella. The Walton et al paper shows human CCDC96/113 in cilia of human respiratory epithelia. An application of such methodology to the proteins indicated by Wu et al for the sperm axoneme and head-tail coupling apparatus is eagerly awaited as a follow-up study.

We thank Reviewer 2 for his/her kindly help in improving the manuscript.  We now understand that directly detection of CCDC113 precise localization in sperm axoneme and head-tail coupling apparatus (HTCA) using cryo-electron microscopy (cryo-EM) could powerfully strengthen our model. Recent advances in cryo-EM have indeed advanced our understanding of axonemal structures analysis of axonemal structures and determined the structures of native axonemal DMTs from mouse, bovine, and human sperm (Leung et al., 2023; Zhou et al., 2023). However, high-resolution structures of sperm axoneme and HTCA regions, including those involving CCDC113, have yet to be fully characterized. Thus, we would like to discuss this point and consider it a valuable direction for future research.

“Given that the cryo-EM of sperm axoneme and HTCA could powerfully strengthen the role of CCDC113 in stabilizing sperm axoneme and head-tail coupling apparatus, it a valuable direction for future research.”

References:

Bazan, R., Schröfel, A., Joachimiak, E., Poprzeczko, M., Pigino, G., & Wloga, D. (2021). Ccdc113/Ccdc96 complex, a novel regulator of ciliary beating that connects radial spoke 3 to dynein g and the nexin link. PLoS Genet, 17(3), e1009388.

Ghanaeian, A., Majhi, S., McCafferty, C. L., Nami, B., Black, C. S., Yang, S. K., Legal, T., Papoulas, O., Janowska, M., Valente-Paterno, M., Marcotte, E. M., Wloga, D., & Bui, K. H. (2023). Integrated modeling of the Nexin-dynein regulatory complex reveals its regulatory mechanism. Nat Commun, 14(1), 5741.

Leung, M. R., Zeng, J., Wang, X., Roelofs, M. C., Huang, W., Zenezini Chiozzi, R., Hevler, J. F., Heck, A. J. R., Dutcher, S. K., Brown, A., Zhang, R., & Zeev-Ben-Mordehai, T.  (2023). Structural specializations of the sperm tail. Cell, 186(13), 2880-2896.e2817

Walton, T., Gui, M., Velkova, S., Fassad, M. R., Hirst, R. A., Haarman, E., O'Callaghan, C., Bottier, M., Burgoyne, T., Mitchison, H. M., & Brown, A. (2023). Axonemal structures reveal mechanoregulatory and disease mechanisms. Nature, 618(7965), 625-633.

Zhou, L., Liu, H., Liu, S., Yang, X., Dong, Y., Pan, Y., Xiao, Z., Zheng, B., Sun, Y., Huang, P., Zhang, X., Hu, J., Sun, R., Feng, S., Zhu, Y., Liu, M., Gui, M., & Wu, J. (2023). Structures of sperm flagellar doublet microtubules expand the genetic spectrum of male infertility. Cell, 186(13), 2897-2910.e2819.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Please provide full gel for the Figure 2C experiment (could be as a supplementary file).

Thanks for your insightful suggestions. We have replaced Figure 2C and provided the full gel in Figure 2-figure supplement 1A.

(2) The authors write on Line 163 "In contrast, the flagellum staining appeared reduced in Ccdc113-/- seminiferous tubules (Fig. 2J, red asterisk)." However, the magnification of the pictures is not sufficient to distinguish anything in the panel mentioned, please provide others.

Many thanks for pointing this out. We have provided the iconic figure to show the flagella defect in seminiferous tubules.

(3) Please add statistical p-values for figures.

Thanks for your valuable advice. We have added statistical p-values to the figures in the revised manuscript.

(4) Line 128: Should "speculate" be "speculated"?

Thank you for pointing out this problem. We have corrected it in the revised manuscript, as shown below:

“Given that CFAP91 has been reported to stabilize RS on the DMTs (Bicka et al., 2022; Dymek et al., 2011; Gui et al., 2021) and cryo-EM analysis shows that CCDC113 is closed to DMTs, we speculated that CCDC113 may connect RS to DMTs by binding to CFAP91 and microtubules.”

(5) In lines 384-385, more "-" is typed.

Thank you for pointing out this problem. We have corrected it in the revised manuscript, as shown below:

“Furthermore, CCDC113 colocalizes with SUN5 in the HTCA region, and immunofluorescence staining in spermatozoa shows that SUN5 is closer to the sperm nucleus than CCDC113 (Figure 7G and H). Therefore, SUN5 and CENTLEIN may be closer to the sperm nucleus than CCDC113.”

(6) In general, the article has many typos and should be professionally proofread.

Many thanks for pointing this out. We have thoroughly revised the manuscript with the assistance professional proofreading.

Reviewer #2 (Recommendations For The Authors):

Can the authors indicate in the Materials and Methods if n=3 biological replicates were done for all co-IP, EM, LM, and IF studies? The statistical analysis section indicates this but quantification is missing for most figures including co-IP, most IF, PAS staining, EM, etc.

We thank Reviewer 2 for the insightful comments and guidance to improve our data quality. All the experiments in this study were repeated at least three times to ensure reproducibility. We have quantified the co-IP experiments in Figures 1C-H and 7A-F, the IF data in Figures 2K, 5C, and 5D, as well as the PAS staining in Figure 6C. Since electron microscopy samples require very little testicular tissue and the sections obtained are very thin, the likelihood of capturing sections specifically at the sperm head-tail junction is considerably low. This challenge makes it difficult to perform quantitative analysis and statistical evaluation in the TEM experiment. To address this limitation, we have quantified the percentage of _Ccdc113-/-_sperm heads with abnormal orientation in stages V–VIII of the seminiferous epithelium to indicate impaired head-to-tail anchorage.

Figure S2 is compelling and might be indicated as a major figure instead of a supplementary figure.

We appreciate the positive comment. We have included it as a major figure in Figure 3F.

Figure 4A may be incomplete. Data sets for RNA expression suggest high expression in the ovary and other organs in males and females including the brain and are not indicated by the authors. Figure 4A may be considered for removal with a more complete study for another paper.

Thank you for pointing out this issue. We reviewed RNA expression data from various tissues using RNA-Seq data from Mouse ENCODE (https://www.ncbi.nlm.nih.gov/gene/244608) and found that CCDC113 is highly expressed in the testis, but not significantly in the ovary and brain (Figure 4- figure supplement 1A). Additionally, we re-evaluated CCDC113 protein levels in the spleen, lung, kidney, testis, intestine, stomach, brain, and ovary, confirming that it is highly expressed in the testes, with negligible expression in the ovary and brain (Figure 4- figure supplement 1B). In line with Reviewer 2's suggestion, we have removed Figure 4A in the revised manuscript.

There are grammatical errors throughout the manuscript and Figure 7 is truncated.

Thank you for pointing out this problem. We have thoroughly revised the manuscript with the assistance professional proofreading.

The Introduction and Discussion parts of the paper may need some clarification for the general reader. The material in the "Additional Context " section of the critique below may be a helpful place to introduce what a stage is, and the steps in germ cell development in the testis with the latter of course where and when the flagellum develops.

We appreciate your valuable suggestions. We have referred to the material in the “Additional Context” section to introduce the stages of spermatogenesis and the steps in germ cell development in the testis in the introduction and results.

“Male fertility relies on the continuous production of spermatozoa through a complex developmental process known as spermatogenesis. Spermatogenesis involves three primary stages: spermatogonia mitosis, spermatocyte meiosis, and spermiogenesis. During spermiogenesis, spermatids undergo complex differentiation processes to develop into spermatozoa, which includes nuclear elongation, chromatin remodeling, acrosome formation, cytoplasm elimination, and flagellum development (Hermo et al., 2010).”

Hermo, L., Pelletier, R. M., Cyr, D. G., & Smith, C. E. (2010). Surfing the wave, cycle, life history, and genes/proteins expressed by testicular germ cells. Part 1: background to spermatogenesis, spermatogonia, and spermatocytes. Microscopy research and technique, 73(4), 241–278. https://doi.org/10.1002/jemt.20783

“Pioneering work in the mid-1950s used the PAS stain in histologic sections of mouse testis to visualize glycoproteins of the acrosome and Golgi in seminiferous tubules (Oakberg, 1956). The pioneers discovered in cross-sectioned seminiferous tubules the association of differentiating germ cells with successive layers to define different stages that in mice are twelve, indicated as Roman numerals (XII). For each stage, different associations of maturing germ cells were always the same with early cells in differentiation at the periphery and more mature cells near the lumen. In this way, progressive differentiation from stem cells to mitotic, meiotic, acrosome-forming, and post-acrosome maturing spermatocytes was mapped to define spermatogenesis with the XII stages in mice representing the seminiferous cycle. The maturation process from acrosome-forming cells to mature spermatocytes is defined as spermiogenesis with 16 different steps that are morphologically distinct spermatids (O'Donnell L, 2015).”

Oakberg, E. F. (1956). A description of spermiogenesis in the mouse and its use in analysis of the cycle of the seminiferous epithelium and germ cell renewal. The American journal of anatomy, 99(3), 391-413. https://doi.org/10.1002/aja.1000990303

O'Donnell L. (2015). Mechanisms of spermiogenesis and spermiation and how they are disturbed. Spermatogenesis, 4(2), e979623. https://doi.org/10.4161/21565562.2014.979623

For the Discussion, the authors indicate that the function of CCDC113 in mammals is unknown yet the authors point to the work of Walton et al on human respiratory epithelia that points to a function for CCDC96/113. The work in the manuscript here does indicate a role in sperm flagella and the head-to-tail coupling apparatus but remains descriptive until the methodology of Walton et al is applied. Hopefully, the authors will consider it for a follow-up study.

Thank you for pointing out this problem. We have revised this part and highlighted the Walton et al’s work in the Discussion.

“CCDC113 is a highly evolutionarily conserved component of motile cilia/flagella. Studies in the model organism, Tetrahymena thermophila, have revealed that CCDC113 connects RS3 to dynein g and the N-DRC, which plays essential role in cilia motility (Bazan et al., 2021; Ghanaeian et al., 2023). Recent studies have also identified the localization of CCDC113 within the 96-nm repeat structure of the human respiratory epithelial axoneme, and localizes to the linker region among RS, N-DRC and DMTs (Walton et al., 2023). In this study, we reveal that CCDC113 is indispensable for male fertility, as Ccdc113 knockout mice produce spermatozoa with flagellar defects and head-tail linkage detachment (Figure 3D).”

“Overall, we identified CCDC113 as a structural component of both the flagellar axoneme and the HTCA, where it performs dual roles in stabilizing the sperm axonemal structure and maintaining the structural integrity of HTCA. Given that the cryo-EM of sperm axoneme and HTCA could powerfully strengthen the role of CCDC113 in stabilizing sperm axoneme and head-tail coupling apparatus, it a valuable direction for future research.”

The Discussion may be focused on the key aspects of CCDC113 related to sperm flagella and the head-to-tail coupling apparatus that represent a genuine advance. The more speculative parts of the Discussion that have not been addressed by experimentation in the Results section may be considered for removal in the Discussion section.

Thank you for pointing out this. We have removed the speculative parts of the Discussion that have not been addressed by experimentation in the Results section.

Additional Context to help readers understand the significance of the work:

Pioneering work in the mid-1950s used the periodic acid Schiff (PAS) stain in histologic sections of rodent testis to visualize glycoproteins of the acrosome and Golgi in seminiferous tubules. The pioneers discovered in cross-sectioned seminiferous tubules the association of differentiating germ cells with successive layers to define different stages that in mice are twelve, indicated as Roman numerals (XII). For each stage, different associations of maturing germ cells were always the same with early cells in differentiation at the periphery and more mature cells near the lumen. In this way, progressive differentiation from stem cells to mitotic, meiotic, acrosome-forming, and post-acrosome maturing spermatocytes was mapped to define spermatogenesis with the XII stages in mice representing the seminiferous cycle. The maturation process from acrosome-forming cells to mature spermatocytes is defined as spermiogenesis with 19 different steps that are morphologically distinct spermatids. It is from steps 8-19 of spermiogenesis that the formation of the flagellum takes place. Final maturation occurs in the epididymis as sperm move through the caput, corpus, and cauda of the organ with motile spermatozoa generated.

Thank you very much!

DOI: 10.1038/s41467-024-49439-y

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DOI: 10.1186/s12964-024-01941-y

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DOI: 10.1186/s12964-024-01941-y

Resource: RRID:Addgene_27371

Curator: @scibot

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