Existing facilities that can filter carbon dioxide out of the air only have the capacity to capture 0.01 million metric tons of CO2 globally today, costing companies like Microsoft as much as $600 per ton of CO2. That’s very little capacity with a very high price tag.

Calma, Justine. “Trying to Reverse Climate Change Won’t Save Us, Scientists Warn.” Msn.Com, 1729, https://www.msn.com/en-us/news/technology/trying-to-reverse-climate-change-won-t-save-us-scientists-warn/ar-AA1sN6OC?ocid=msedgntp&cvid=20987699b6484dd5c9aad7c390f9e4cd&ei=4.

Reviewer #3 (Public review):

Summary:

This work investigated the immune response in the murine retina after focal laser lesions. These lesions are made with close to 2 orders of magnitude lower laser power than the more prevalent choroidal neovascularization model of laser ablation. Histology and OCT together show that the laser insult is localized to the photoreceptors and spares the inner retina, the vasculature, and the pigment epithelium. As early as 1-day after injury, a loss of cell bodies in the outer nuclear layer is observed. This is accompanied by strong microglial proliferation at the site of injury in the outer retina where microglia do not typically reside. The injury did not seem to result in the extravasation of neutrophils from the capillary network constituting one of the main findings of the paper. The demonstrated paradigm of studying the immune response and potentially retinal remodeling in the future in vivo is valuable and would appeal to a broad audience in visual neuroscience. However, there are some issues with the conclusions drawn from the data and analysis that can be addressed to further bolster the manuscript.

Strengths:

Adaptive optics imaging of the murine retina is cutting edge and enables non-destructive visualization of fluorescently labeled cells in the milieu of retinal injury. As may be obvious, this in vivo approach is beneficial for studying fast and dynamic immune processes on a local time scale - minutes and hours, and also for the longer days-to-months follow-up of retinal remodeling as demonstrated in the article. In certain cases, the in vivo findings are corroborated with histology.

The analysis is sound and accompanied by stunning video and static imagery. A few different sets of mouse models are used, (a) two different mouse lines, each with a fluorescent tag for neutrophils and microglia, (b) two different models of inflammation - endotoxin-induced uveitis (EAU) and laser ablation are used to study differences in the immune interaction.

One of the major advances in this article is the development of the laser ablation model for 'mild' retinal damage as an alternative to the more severe neovascularization models. While not directly shown in the article, this model would potentially allow for controlling the size, depth, and severity of the laser injury opening interesting avenues for future study.

Weaknesses:

(1) It is unclear based on the current data/study to what extent the mild laser damage phenotype is generalizable to disease phenotypes. The outer nuclear cell loss of 28% and a complete recovery in 2 months would seem quite mild, thus the generalizability in terms of immune-mediated response in the face of retinal remodeling is not certain, specifically whether the key finding regarding the lack of neutrophil recruitment will be maintained with a stronger laser ablation.

(2) Mice numbers and associated statistics are insufficient to draw strong conclusions in the paper on the activity of neutrophils, some examples are below :

a) 2 catchup mice and 2 positive control EAU mice are used to draw inferences about immune-mediated activity in response to injury. If the goal was to show 'feasibility' of imaging these mouse models for the purposes of tracking specific cell type behavior, the case is sufficiently made and already published by the authors earlier. It is possible that a larger sample size would alter the conclusion.

b) There are only 2 examples of extravasated neutrophils in the entire article, shown in the positive control EAU model. With the rare extravasation events of these cells and their high-speed motility, the chance of observing their exit from the vasculature is likely low overall, therefore the general conclusions made about their recruitment or lack thereof are not justified by these limited examples shown.

c) In Figure 3, the 3-day time point post laser injury shows an 18% reduction in the density of ONL nuclei (p-value of 0.17 compared to baseline). In the case of neutrophils, it is noted that "Control locations (n = 2 mice, 4 z-stacks) had 15 {plus minus} 8 neutrophils per sq.mm of retina whereas lesioned locations (n = 2 mice, 4 z-stacks) had 23 {plus minus} 5 neutrophils per sq.mm of retina (Figure 10b). The difference between control and lesioned groups was not statistically significant (p = 0.19)." These data both come from histology. While the p-values - 0.17 and 0.19 - are similar, in the first case a reduction in ONL cell density is concluded while in the latter, no difference in neutrophil density is inferred in the lesioned case compared to control. Why is there a difference in the interpretation where the same statistical test and methodology are used in both cases? Besides this statistical nuance, is there an alternate possibility that there is an increased, albeit statistically insignificant, concentration of circulating neutrophils in the lesioned model? The increase is nearly 50% (15 {plus minus} 8 vs. 23 {plus minus} 5 neutrophils per sq.mm) and the reader may wonder if a larger animal number might skew the statistic towards significance.

(2) The conclusions on the relative activity of neutrophils and microglia come from separate animals. The reader may wonder why simultaneous imaging of microglia and neutrophils is not shown in either the EAU mice or the fluorescently labeled catchup mice where the non-labeled cell type could possibly be imaged with phase-contrast as has been shown by the authors previously. One might suspect that the microglia dynamics are not substantially altered in these mice compared to the CX3CR1-GFP mice subjected to laser lesions, but for future applicability of this paradigm of in vivo imaging assessment of the laser damage model, including documenting the repeatability of the laser damage model and the immune cell behavior, acquiring these data in the same animals would be critical.

(3) Along the same lines as above, the phase contrast ONL images at time points from 3-day to 2-month post laser injury are not shown and the absence of this data is not addressed. This missing data pertains only to the in vivo imaging mice model but are conducted in histology that adequately conveys the time-course of cell loss in the ONL. It is suggested that the reason be elaborated for the exclusion of this data and the simultaneous imaging of microglia and neutrophils mentioned above. Also, it would be valuable to further qualify and check the claims in the Discussion that "ex vivo analysis confirms in vivo findings" and "Microglial/neutrophil discrimination using label-free phase contrast"

Diaspora: the jews living outside Israel (https://www.merriam-webster.com/dictionary/diaspora)

Interesting musical roots for Reggae... Wonder if this is still present?

Mystical roots.

(Note, I give this the fiction tag because I might want to look into this mystical religion for fiction writing as inspiration)

Logical that marijuana (a drug) is correlated with the mystical concept of communicating with diseased spirits for marijuana makes you hallucinate (or perhaps it's demonic in nature?)

It is important to create a shit tag (literally), to keep track of songs to delete.

Chris M. recommends to use a layered system for music categorization:

• Layer 1) Genres / Subgenres
• Layer 2) Energy
• Layer 3) Vibe

Genre itself is the main overall (and broad) genre. Subgenres are tag-like and related to when you want to play it more granularly.

Energy is a measurement of the average energy of the song.

Vibes refer to the emotions and memories it brings up to you and potentially others you play it for. Some questions he asks: - 1) How does it make me feel? - 2) What does it remind me of? - 3) Where would I play it? - 4) When would I play it? - 5) Why would I play it? - 6) Who would I play it for?

Author response:

Reviewer #1 (Public Review):

Summary

The authors asked if parabrachial CGRP neurons were only necessary for a threat alarm to promote freezing or were necessary for a threat alarm to promote a wider range of defensive behaviors, most prominently flight.

Major Strengths of Methods and Results

The authors performed careful single-unit recording and applied rigorous methodologies to optogenetically tag CGRP neurons within the PBN. Careful analyses show that single-units and the wider CGRP neuron population increases firing to a range of unconditioned stimuli. The optogenetic stimulation of experiment 2 was comparatively simpler but achieved its aim of determining the consequence of activating CGRP neurons in the absence of other stimuli. Experiment 3 used a very clever behavioral approach to reveal a setting in which both cue-evoked freezing and flight could be observed. This was done by having the unconditioned stimulus be a "robot" traveling along a circular path at a given speed. Subsequent cue presentation elicited mild flight in controls and optogenetic activation of CGRP neurons significantly boosted this flight response. This demonstrated for the first time that CGRP neuron activation does more than promote freezing. The authors conclude by demonstrating that bidirectional modulation of CGRP neuron activity bidirectionally aTects freezing in a traditional fear conditioning setting and aTects both freezing and flight in a setting in which the robot served as the unconditioned stimulus. Altogether, this is a very strong set of experiments that greatly expand the role of parabrachial CGRP neurons in threat alarm.

We would like to sincerely thank the reviewer for the positive and insightful comments on our work. We greatly appreciate the acknowledgment of our new behavioral approach, which allowed us to observe a dynamic spectrum of defensive behaviors in animals. Our use of the robot-based paradigm, which enables the observation of both freezing and flight, has been instrumental in expanding our understanding of how parabrachial CGRP neurons modulate diverse threat responses. We are pleased that the reviewer found this methodological innovation to be a valuable contribution to the field.

Weaknesses

In all of their conditioning studies the authors did not include a control cue. For example, a sound presented the same number of times but unrelated to US (shock or robot) presentation. This does not detract from their behavioral findings. However, it means the authors do not know if the observed behavior is a consequence of pairing. Or is a behavior that would be observed to any cue played in the setting? This is particularly important for the experiments using the robot US.

We appreciate the reviewer’s insightful comment regarding the absence of a control cue in our conditioning studies. First, we would like to mention that, in response to the Reviewer 3, we have updated how we present our flight data by following methods from previously published papers (Fadok et al., 2017; Borkar et al., 2024). Instead of counting flight responses, we calculated flight scores as the ratio of the velocity during the CS to the average velocity in the 7 s before the CS on the conditioning day (or 10 s for the retention test). This method better captures both the speed and duration of fleeing during CS. With this updated approach, we observed a significant difference in flight scores between the ChR2 and control groups, even during conditioning, which may partly address the reviewer’s concern about whether the observed behavior is a consequence of CS-US pairing.

However, we agree with the reviewer that including an unpaired group would provide stronger evidence, and in response, we conducted an additional experiment with an unpaired group. In this unpaired group, the CS was presented the same number of times, but the robot US was delivered randomly within the inter-trial interval. The unpaired group did not exhibit any notable conditioned freezing or flight responses. We believe that this additional experiment, now reflected in Figure 3, further strengthens our conclusion that the fleeing behavior is driven by associative learning between the CS and US, rather than a reaction to the cue itself.

The authors make claims about the contribution of CGRP neurons to freezing and fleeing behavior, however, all of the optogenetic manipulations are centered on the US presentation period. Presently, the experiments show a role for these neurons in processing aversive outcomes but show little role for these neurons in cue responding or behavior organizing. Claims of contributions to behavior should be substantiated by manipulations targeting the cue period.

We appreciate the reviewer’s constructive comments. We would like to emphasize that our primary objective in this study was to investigate whether activating parabrachial CGRP neurons—thereby increasing the general alarm signal—would elicit different defensive behaviors beyond passive freezing. To this end, we focused on manipulating CGRP neurons during the US period rather than the cue period.

Previous studies have shown that CGRP neurons relay US signals, and direct activation of CGRP neurons has been used as the US to successfully induce conditioned freezing responses to the CS during retention tests (Han et al., 2015; Bowen et al., 2020). In our experiments, we also observed that CGRP neurons responded exclusively to the US during conditioning with the robot (Figure 1F), and stimulating these neurons in the absence of any external stimuli elicited strong freezing responses (Figure 2B). These findings, collectively, suggest that activation of CGRP neurons during the CS period would predominantly result in freezing behavior.

Therefore, we manipulated the activity of CGRP neurons during the US period to examine whether adjusting the perceived threat level through these neurons would result in diverse dfensive behaivors when paired with chasing robot. We observed that enhancing CGRP neuron activity while animals were chased by the robot at 70 cm/s made them react as if chased at a higher speed (90 cm/s), leading to increased fleeing behaviors. While this may not fully address the role of these neurons in cue responding or behavior organizing, we found that silencing CGRP neurons with tetanus toxin (TetTox) abolished fleeing behavior even when animals were chased at high speeds (90 cm/s), which usually elicits fleeing without CGRP manipulation (Figure 5). This supports the conclusion that CGRP neurons are necessary for processing fleeing responses.

In summary, manipulating CGRP neurons during the US period was essential for effectively investigating their role in adjusting defensive responses, thereby expanding our understanding of their function within the general alarm system. We hope this clarifies our experimental design and addresses the concern the reviewer has raised.

Appraisal

The authors achieved their aims and have revealed a much greater role for parabrachial CGRP neurons in threat alarm.

Discussion

Understanding neural circuits for threat requires us (as a field) to examine diverse threat settings and behavioral outcomes. A commendable and rigorous aspect of this manuscript was the authors decision to use a new behavioral paradigm and measure multiple behavioral outcomes. Indeed, this manuscript would not have been nearly as impactful had they not done that. This novel behavior was combined with excellent recording and optogenetic manipulations - a standard the field should aspire to. Studies like this are the only way that we as a field will map complete neural circuits for threat.

We sincerely thank the reviewer for their positive and encouraging comments. We are grateful for the acknowledgment of our efforts in employing a novel behavioral paradigm to study diverse defensive behaviors. We are pleased that our work contributes to advancing the understanding of neural circuits involved in threat responses.

Reviewer #3 (Public Review):

Strengths:

The study used optogenetics together with in vivo electrophysiology to monitor CGRP neuron activity in response to various aversive stimuli including robot chasing to determine whether they encode noxious stimuli diTerentially. The study used an interesting conditioning paradigm to investigate the role of CGRP neurons in the PBN in both freezing and flight behaviors.

Weakness:

The major weakness of this study is that the chasing robot threat conditioning model elicits weak unconditioned and conditioned flight responses, making it diTicult to interpret the robustness of the findings. Furthermore, the conclusion that the CGRP neurons are capable of inducing flight is not substantiated by the data. No manipulations are made to influence the flight behavior of the mouse. Instead, the manipulations are designed to alter the intensity of the unconditioned stimulus.

We sincerely thank the reviewer for the thoughtful and constructive comments on our manuscript. In response to this feedback, we revisited our analysis of the flight responses and compared our methods with those used in previous literatures examining similar behaviors.

We reviewed a study investigating sex differences in defensive behavior using rats (Gruene et al., 2015). In that study, the CS was presented for 30 s, and active defensive behvaior – referred to as ‘darting’ – was quantified as ‘Dart rate (dart/min)’. This was calculated by doubling the number of darts counted during the 30-s CS presentation to extrapolate to a per-min rate. The highest average dart rate observed was approximatley 1.5. Another relevant studies using mice quantified active defensive behavior by calculating a flight score—the ratio of the average speed during each CS to the average speed during the 10 s pre-CS period (Fadok et al., 2017; Borkar et al., 2024). This method captures multiple aspects of flight behavior during CS presentation, including overall velocity, number of bouts, and duration of fleeing. Moreover, it accounts for each animal’s individual velocity prior to the CS, reflecting how fast the animals were fleeing relative to their baseline activity.

In our original analysis, we quantified flight responses by counting rapid fleeing movements, defined as movements exceeding 8 cm/s. This approach was consistent with our previous study using the same robot paradigm to observe unique patterns of defensive behavior related to sex differences (Pyeon et al., 2023). Based on our earlier findings, where this approach effectively identified significant differences in defensive behaviors, we believed that this method was appropriate for capturing conditioned flight behavior within our specific experimental context. However, prompted by the reviewer's insightful comments, we recognized that our initial method might not fully capture the robustness of the flight responses. Therefore, we re-analyzed our data using the flight score method described by Fadok and colleagues, which provides a more sensitive measure of fleeing during the CS.

Re-analyzing our data revealed a more robust flight response than previously reported, demonstrating that additional CGRP neuron stimulation promoted flight behavior in animals during conditioning, addressing the concern that the data did not substantiate the role of CGRP neurons in inducing flight. In addition, we would like to emphasize the findings from our final experiment, where silencing CGRP neurons, even under high-threat conditions (90 cm/s), prevented animals from exhibiting flight responses. This demonstrates that CGRP neurons are necessary in influencing flight responses.

We have updated all flight data in the manuscript and revised the relevant figures and text accordingly. We appreciate the opportunity to enhance our analysis. The reviewer's insightful observation led us to adopt a better method for quantifying flight behavior, which substantiates our conclusion about the role of CGRP neurons in modulating defensive responses.

Borkar, C.D., Stelly, C.E., Fu, X., Dorofeikova, M., Le, Q.-S.E., Vutukuri, R., et al. (2024). Top- down control of flight by a non-canonical cortico-amygdala pathway. Nature 625(7996), 743-749.

Bowen, A.J., Chen, J.Y., Huang, Y.W., Baertsch, N.A., Park, S., and Palmiter, R.D. (2020). Dissociable control of unconditioned responses and associative fear learning by parabrachial CGRP neurons. Elife 9, e59799.

Fadok, J.P., Krabbe, S., Markovic, M., Courtin, J., Xu, C., Massi, L., et al. (2017). A competitive inhibitory circuit for selection of active and passive fear responses. Nature 542(7639), 96-100.

Gruene, T.M., Flick, K., Stefano, A., Shea, S.D., and Shansky, R.M. (2015). Sexually divergent expression of active and passive conditioned fear responses in rats. Elife 4, e11352.

Han, S., Soleiman, M.T., Soden, M.E., Zweifel, L.S., and Palmiter, R.D. (2015). Elucidating an a_ective pain circuit that creates a threat memory. Cell 162(2), 363-374.

Pyeon, G.H., Lee, J., Jo, Y.S., and Choi, J.-S. (2023). Conditioned flight response in female rats to naturalistic threat is estrous-cycle dependent. Scientific Reports 13(1), 20988.

Résumé de la vidéo [00:00:00][^1^][1] - [00:10:23][^2^][2]:

Cette vidéo explore comment les adolescentes YouTubeuses mettent en scène leur féminité en ligne. Elle présente les recherches de Claire Balle, sociologue, sur les pratiques numériques des jeunes filles sur YouTube.

Points forts : + [00:00:00][^3^][3] Développement de l'identité féminine * Affirmation identitaire en ligne * Étude des vidéos de filles et garçons * Importance des vidéos "je suis bizarre" et "anti-boyfriend tag" + [00:02:47][^4^][4] Proximité et sociabilité * Partage d'expériences personnelles * Attente de soutien des abonnés * Mention fréquente d'autres YouTubeuses + [00:04:46][^5^][5] Utilisation de l'intimité * Validation de l'identité par les pairs * Différences de genre dans l'expression de l'intimité * Sexualité et honte corporelle chez les filles + [00:06:30][^6^][6] Caractéristiques féminines involontaires * Manies et habitudes perçues comme féminines * Exigences dans le domaine amoureux * Perfectionnisme et propreté + [00:07:52][^7^][7] Dramatisation et standardisation * Effets de dramatisation pour représenter la féminité * Standardisation des modes de présentation * Influence des médias et réseaux sociaux

Welcome back and in this demo lesson you're going to learn how to install the Docker engine inside an EC2 instance and then use that to create a Docker image.

Now this Docker image is going to be running a simple application and we'll be using this Docker image later in this section of the course to demonstrate the Elastic Container service.

So this is going to be a really useful demo where you're going to gain the experience of how to create a Docker image.

Now there are a few things that you need to do before we get started.

First as always make sure that you're logged in to the I am admin user of the general AWS account and you'll also need the Northern Virginia region selected.

Now attached to this lesson is a one-click deployment link so go ahead and click that now.

This is going to deploy an EC2 instance with some files pre downloaded that you'll use during the demo lesson.

Now everything's pre-configured you just need to check this box at the bottom and click on create stack.

Now that's going to take a few minutes to create and we need this to be in a create complete state.

So go ahead and pause the video wait for your stack to move into create complete and then we're good to continue.

So now this stack is in a create complete state and we're good to continue.

Now if you're following along with this demo within your own environment there's another link attached to this lesson called the lesson commands document and that will include all of the commands that you'll need to type as you move through the demo.

Now I'm a fan of typing all commands in manually because I personally think that it helps you learn but if you are the type of person who has a habit of making mistakes when typing along commands out then you can copy and paste from this document to avoid any typos.

Now one final thing before we finish at the end of this demo lesson you'll have the opportunity to upload the Docker image that you create to Docker Hub.

If you're going to do that then you should pre sign up for a Docker Hub account if you don't already have one and the link for this is included attached to this lesson.

If you already have a Docker Hub account then you're good to continue.

Now at this point what we need to do is to click on the resources tab of this stack and locate the public EC2 resource.

Now this is a normal EC2 instance that's been provisioned on your behalf and it has some files which have been pre downloaded to it.

So just go ahead and click on the physical ID next to public EC2 and that will move you to the EC2 console.

Now this machine is set up and ready to connect to and I've configured it so that we can connect to it using Session Manager and this avoids the need to use SSH keys.

So to do that just right-click and then select connect.

You need to pick Session Manager from the tabs across the top here and then just click on connect.

Now that will take a few minutes but once connected you should see this prompt.

So it should say SH- and then a version number and then dollar.

Now the first thing that we need to do as part of this demo lesson is to install the Docker engine.

The Docker engine is the thing that allows Docker containers to run on this EC2 instance.

So we need to install the Docker engine package and we'll do that using this command.

So we're using shudu to get admin permissions then the package manager DNF then install then Docker.

So go ahead and run that and that will begin the installation of Docker.

It might take a few moments to complete it might have to download some prerequisites and you might have to answer that you're okay with the install.

So press Y for yes and then press enter.

Now we need to wait a few moments for this install process to complete and once it has completed then we need to start the Docker service and we do that using this command.

So shudu again to get admin permissions and then service and then the Docker service and then start.

So type that and press enter and that starts the Docker service.

Now I'm going to type clear and then press enter to make this easier to see and now we need to test that we can interact with the Docker engine.

So the most simple way to do that is to type Docker space and then PS and press enter.

Now you're going to get an error.

This error is because not every user of this EC2 instance has the permissions to interact with the Docker engine.

We need to grant permissions for this user or any other users of this EC2 instance to be able to interact with the Docker engine and we're going to do that by adding these users to a group and we do that using this command.

So shudu for admin permissions and then user mod -a -g for group and then the Docker group and then EC2 -user.

Now that will allow a local user of this system, specifically EC2 -user, to be able to interact with the Docker engine.

Okay so I've cleared the screen to make it slightly easier to see now that we've added EC2 -user the ability to interact with Docker.

So the next thing is we need to log out and log back in of this instance.

So I'm going to go ahead and type exit just to disconnect from session manager and then click on close and then I'm going to reconnect to this instance and you need to do the same.

So connect back in to this EC2 instance.

Now once you're connected back into this EC2 instance we need to run another command which moves us into EC2 user so it basically logs us in as EC2 -user.

So that's this command and the result of this would be the same as if you directly logged in to EC2 -user.

Now the reason we're doing it this way is because we're using session manager so that we don't need a local SSH client or to worry about SSH keys.

We can directly log in via the console UI we just then need to switch to EC2 -user.

So run this command and press enter and we're now logged into the instance using EC2 -user and to test everything's okay we need to use a command with the Docker engine and that command is Docker space ps and if everything's okay you shouldn't see any output beyond this list of headers.

What we've essentially done is told the Docker engine to give us a list of any running containers and even though we don't have any it's not erred it's simply displayed this empty list and that means everything's okay.

So good job.

Now what I've done to speed things up if you just run an LS and press enter the instance has been configured to download the sample application that we're going to be using and that's what the file container.zip is within this folder.

I've configured the instance to automatically extract that zip file which has created the folder container.

So at this point I want you to go ahead and type cd space container and press enter and that's going to move you inside this container folder.

Then I want you to clear the screen by typing clear and press enter and then type ls space -l and press enter.

Now this is the web application which I've configured to be automatically downloaded to the EC2 instance.

It's a simple web page we've got index.html which is the index we have a number of images which this index.html contains and then we have a docker file.

Now this docker file is the thing that the docker engine will use to create our docker image.

I want to spend a couple of moments just stepping you through exactly what's within this docker file.

So I'm going to move across to my text editor and this is the docker file that's been automatically downloaded to your EC2 instance.

Each of these lines is a directive to the docker engine to perform a specific task and remember we're using this to create a docker image.

This first line tells the docker engine that we want to use version 8 of the Red Hat Universal base image as the base component for our docker image.

This next line sets the maintainer label it's essentially a brief description of what the image is and who's maintaining it in this case it's just a placeholder of animals for life.

This next line runs a command specifically the yum command to install some software specifically the Apache web server.

This next command copy copies files from the local directory when you use the docker command to create an image so it's copying that index.html file from this local folder that I've just been talking about and it's going to put it inside the docker image in this path so it's going to copy index.html to /var/www/html and this is where an Apache web server expects this index.html to be located.

This next command is going to do the same process for all of the jpegs in this folder so we've got a total of six jpegs and they're going to be copied into this folder inside the docker image.

This line sets the entry point and this essentially determines what is first run when this docker image is used to create a docker container.

In this example it's going to run the Apache web server and finally this expose command can be used for a docker image to tell the docker engine which services should be exposed.

Now this doesn't actually perform any configuration it simply tells the docker engine what port is exposed in this case port 80 which is HTTP.

Now this docker file is going to be used when we run the next command which is to create a docker image.

So essentially this file is the same docker file that's been downloaded to your EC2 instance and that's what we're going to run next.

So this is the next command within the lesson commands document and this command builds a container image.

What we're essentially doing is giving it the location of the docker file.

This dot at the end contains the working directory so it's here where we're going to find the docker file and any associated files that that docker file uses.

So we're going to run this command and this is going to create our docker image.

So let's go ahead and run this command.

It's going to download version 8 of UBI which it will use as a starting point and then it's going to run through every line in the docker file performing each of the directives and each of those directives is going to create another layer within the docker image.

Remember from the theory lesson each line within the docker file generally creates a new file system layer so a new layer of a docker image and that's how docker images are efficient because you can reuse those layers.

Now in this case this has been successful.

We've successfully built a docker image with this ID so it's giving it a unique ID and it's tagged this docker image with this tag colon latest.

So this means that we have a docker image that's now stored on this EC2 instance.

Now I'll go ahead and clear the screen to make it easier to see and let's go ahead and run the next command which is within the lesson commands document and this is going to show us a list of images that are on this EC2 instance but we're going to filter based on the name container of cats and this will show us the docker image which we've just created.

So the next thing that we need to do is to use the docker run command which is going to take the image that we've just created and use it to create a running container and it's that container that we're going to be able to interact with.

So this is the command that we're going to use it's the next one within the lesson commands document.

It's docker run and then it's telling it to map port 80 on the container with port 80 on the EC2 instance and it's telling it to use the container of cats image and if we run that command docker is going to take the docker image that we've got on this EC2 instance run it to create a running container and we should be able to interact with that container.

So if you go back to the AWS console if we click on instances so look for a4l-public EC2 that's in the running state.

I'm just going to go ahead and select this instance so that we can see the information and we need the public IP address of this instance.

Go ahead and click on this icon to copy the public IP address into your clipboard and then open that in a new tab.

Now be sure not to use this link to the right because that's got a tendency to open the HTTPS version.

We just need to use the IP address directly.

So copy that into your clipboard open a new tab and then open that IP address and now we can see the amazing application if it fits i sits in a container in a container and this amazing looking enterprise application is what's contained in the docker image that you just created and it's now running inside a container based off that image.

So that's great everything's working as expected and that's running locally on the EC2 instance.

Now in the demo lesson for the elastic container service that's coming up later in this section of the course you have two options.

You can either use my docker image which is this image that I've just created or you can use your own docker image.

If you're going to use my docker image then you can skip this next step.

You don't need a docker hub account and you don't need to upload your image.

If you want to use your own image then you do need to follow these next few steps and I need to follow them anyway because I need to upload this image to docker hub so that you can potentially use it rather than your own image.

So I'm going to move back to the session manager tab and I'm going to control C to exit out of this running container and I'm going to type clear to clear the screen and make it easier to see.

Now to upload this to docker hub first you need to log in to docker hub using your credentials and you can do that using this command.

So it's docker space login space double hyphen username equals and then your username.

So if you're doing this in your own environment you need to delete this placeholder and type your username.

I'm going to type my username because I'll be uploading this image to my docker hub.

So this is my docker hub username and then press enter and it's going to ask for the corresponding password to this username.

So I'm going to paste in my password if you're logging into your docker hub you should use your password.

Once you've pasted in the password go ahead and press enter and that will log you in to docker hub.

Now you don't have to worry about the security message because whilst your docker hub password is going to be stored on the EC2 instance shortly we're going to terminate this instance which will remove all traces of this password from this machine.

Okay so again we're going to upload our docker image to docker hub so let's run this command again and you'll see because we're just using the docker images command we can see the base image as well as our image.

So we can see red hat UBI 8.

We want the container of cats latest though so what you need to do is copy down the image ID of the container of cats image.

So this is the top line in my case container of cats latest and then the image ID.

So then we need to run this command so docker space tag and then the image ID that you've just copied into your clipboard and then a space and then your docker hub username.

In my case it's actrl with 1L if you're following along you need to use your own username and then forward slash and then the name of the image that you want this to be stored as on docker hub so I'm going to use container of cats.

So that's the command you need to use so docker tag and then your image ID for container of cats and then your username forward slash container of cats and press enter and that's everything we need to do to prepare to upload this image to docker hub.

So the last command that we need to run is the command to actually upload the image to docker hub and that command is docker space push so we're going to push the image to docker hub then we need to specify the docker hub username so again this is my username but if you're doing this in your environment it needs to be your username and then forward slash and then the image name in my case container of cats and then colon latest and once you've got all that go ahead and press enter and that's going to push the docker image that you've just created up to your docker hub account and once it's up there it means that we can deploy from that docker image to other EC2 instances and even ECS and we're going to do that in a later demo in this section of the course.

Now that's everything that you need to do in this demo lesson you've essentially installed and configured the docker engine you've used a docker file to create a docker image from some local assets you've tested that docker image by running a container using that image and then you've uploaded that image to docker hub and as I mentioned before we're going to use that in a future demo lesson in this section of the course.

Now the only thing that remains to do is to clear up the infrastructure that we've used in this demo lesson so go ahead and close down all of these extra tabs and go back to the cloud formation console this is the stack that's been created by the one click deployment link so all you need to do is select this stack it should be called EC2 docker and then click on delete and confirm that deletion and that will return the account into the same state as it was at the start of this demo lesson.

Now that is everything you need to do in this demo lesson I hope it's been useful and I hope you've enjoyed it so go ahead and complete the video and when you're ready I look forward to you joining me in the next.

Welcome back.

This is part two of this lesson.

We're going to continue immediately from the end of part one.

So let's get started.

We just need to give this a brief moment to perform that reboot.

So just wait a couple of moments and once you have right click again, select Connect.

We're going to use EC2 instance connect again.

Make sure the user's correct and then click on Connect.

Now, if it doesn't immediately connect you to the instance, if it appears to have frozen for a couple of seconds, that's fine.

It just means that the instance hasn't completed its restart.

Wait for a brief while longer and then attempt another connect.

This time you should be connected back to the instance and now we need to verify whether we can still see our volume attached to this instance.

So do a DF space -k and press Enter and you'll note that you can't see the file system.

That's because before we rebooted this instance, we used the mount command to manually mount the file system on our EBS volume into the EBS test folder.

Now that's a manual process.

It means that while we could interact with that before the reboot, it doesn't automatically mount that file system when the instance restarts.

To do that, we need to configure it to auto-mount when the instance starts up.

So to do that, we need to get the unique ID of the EBS volume, which is attached to this instance.

And to get that, we run a shudu space blkid.

Now press Enter and that's going to list the unique identifier of all of the volumes attached to this instance.

You'll see the boot volume listed as devxvda1 and the EBS volume that we've just attached listed as devxvdf.

So we need the unique ID of the volume that we just added.

So that's the one next to xvdf.

So go ahead and select this unique identifier.

You'll need to make sure that you select everything between the speech marks and then copy that into your clipboard.

Next, we need to edit the FSTAB file, which controls which file systems are mounted by default.

So we're going to run a shudu and then space nano, which is our editor, and then a space, and then forward slash ETC, which is the configuration directory on Linux, another forward slash and then FSTAB and press Enter.

And this is the configuration file for which file systems are mounted by our instance.

And we're going to add a similar line.

So first we need to use uuid, which is the unique identifier, and then the equal symbol.

And then we need to paste in that unique ID that we just copied to our clipboard.

Once that's pasted in, press Space.

This is the ID of the EBS volume, so the unique ID.

Next, we need to provide the place where we want that volume to be mounted.

And that's the folder we previously created, which is forward slash EBS test.

Then a space, we need to tell the OS which file system is used, which is xfs, and then a space.

And then we need to give it some options.

You don't need to understand what these do in detail.

We're going to use defaults, all one word, and then a comma, and then no fail.

So once you've entered all of that, press Ctrl+O to save that file, and Enter, and then Ctrl+X to exit.

Now this will be mounted automatically when the instance starts up, but we can force that process by typing shudu space mount space-a.

And this will perform a mount of all of the volumes listed in the FS tab file.

So go ahead and press Enter.

Now if we do a df space-k and press Enter, you'll see that our EBS volume once again is mounted within the EBS test folder.

So I'm going to clear the screen, then I'm going to move into that folder, press Enter, and then do an ls space-la, and you'll see that our amazing test file still exists within this folder.

And that shows that the data on this file system is persistent, and it's available even after we reboot this EC2 instance, and that's different than instance store volumes, which I'll be demonstrating later on.

At this point, we're going to shut down this instance because we won't be needing it anymore.

So close down this tab, click on Instances, right-click on instance one-AZA, and then select Stop Instance.

You'll need to confirm it, refresh that and wait for it to move into a stopped state.

Once it has stopped, go down and click on Volumes, select the EBS test volume, right-click and detach it.

We're going to detach this volume from the instance that we've just stopped.

You'll need to confirm that, and that will begin the process and it will detach that volume from the instance, and this demonstrates how EBS volumes are completely separate from EC2 instances.

You can detach them and then attach them to other instances, keeping the data that's on that volume.

Just keep refreshing.

We need to wait for that to move into an available state, and once it has, we're going to right-click, select Attach Volume, click inside the instance box, and this time, we're going to select instance two-AZA.

It should be the only one listed now in a running state.

So select that and click on Attach.

Just refresh that and wait for that to move into an in-use state, which it is, then move back to instances, and we're going to connect to the instance that we just attached that volume to.

So select instance two-AZA, right-click, select Connect, and then connect to that instance.

Once we connected to that instance, remember this is an instance that we haven't interacted with this EBS volume with.

So this instance has no initial configuration of this EBS volume, and if we do a DF-K, you'll see that this volume is not mounted on this instance.

What we need to do is do an LS, BLK, and this will list all of the block devices on this instance.

You'll see that it's still using XVDF because this is the device ID that we configured when attaching the volume.

Now, if we run this command, so shudu, file, S, and then the device ID of this EBS volume, notice how now it shows a file system on this EBS volume because we created it on the previous instance.

We don't need to go through all of the process of creating the file system because EBS volumes persist past the lifecycle of an EC2 instance.

You can interact with an EBS volume on one instance and then move it to another and the configuration is maintained.

We're going to follow the same process.

We're going to create a folder called EBSTEST.

Then we're going to mount the EBS volume using the device ID into this folder.

We're going to move into this folder and then if we do an LS, space-LA, and press Enter, you'll see the test file that you created in the previous step.

It still exists and all of the contents of that file are maintained because the EBS volume is persistent storage.

So that's all I wanted to verify with this instance that you can mount this EBS volume on another instance inside the same availability zone.

At this point, close down this tab and then click on Instances and we're going to shut down this second EC2 instance.

So right-click and then select Stop Instance and you'll need to confirm that process.

Wait for that instance to change into a stop state and then we're going to detach the EBS volume.

So that's moved into the stopped state.

We can select Volumes, right-click on this EBSTEST volume, detach the volume and confirm that.

Now next, we want to mount this volume onto the instance that's in Availability Zone B and we can't do that because EBS volumes are located in one specific availability zone.

Now to allow that process, we need to create a snapshot.

Snapshots are stored on S3 and replicated between multiple availability zones in that region and snapshots allow us to take a volume in one availability zone and move it into another.

So right-click on this EBS volume and create a snapshot.

Under Description, just use EBSTESTSNAP and then go ahead and click on Create Snapshot.

Just close down any dialogues, click on Snapshots and you'll see that a snapshot is being created.

Now depending on how much data is stored on the EBS volume, snapshots can either take a few seconds or anywhere up to several hours to complete.

This snapshot is a full copy of all of the data that's stored on our original EBS volume.

But because the snapshot is stored in S3, it means that we can take this snapshot, right-click, create volume and then create a volume in a different availability zone.

Now you can change the volume type, the size and the encryption settings at this point, but we're going to leave everything the same and just change the availability zone from US-EAST-1A to US-EAST-1B.

So select 1B in availability zone, click on Add Tag.

We're going to give this a name to make it easier to identify.

For the value, we're going to use EBS Test Volume-AZB.

So enter that and then create the volume.

Close down any dialogues and at this point, what we're doing is using this snapshot which is stored inside S3 to create a brand new volume inside availability zone US-EAST-1B.

At this point, once the volume is in an available state, make sure you select the right one, then we can right-click, we can attach this volume and this time when we click in the instance box, you'll see the instance that's in availability zone 1B.

So go ahead and select that and click on Attach.

Once that volume is in use, go back to Instances, select the third instance, right-click, select Connect, choose Instance Connect, verify the username and then connect to the instance.

Now we're going to follow the same process with this instance.

So first, we need to list all of the attached block devices using LSBLK.

You'll see the volume we've just created from that snapshot, it's using device ID XVDF.

We can verify that it's got a file system using the command that we've used previously and it's showing an XFS file system.

Next, we create our folder which will be our mount point.

Then we mount the device into this mount point using the same command as we've used previously, move into that folder and then do a listing using LS-LA and you should see the same test file you created earlier and if you cap this file, it should have the same contents.

This volume has the same contents because it's created from a snapshot that we created of the original volume and so its contents will be identical.

Go ahead and close down this tab to this instance, select instances, right click, stop this instance and then confirm that process.

Just wait for that instance to move into the stopped state.

We're going to move back to volumes, select the EBS test volume in availability zone 1B, detach that volume and confirm it and then just move to snapshots and I want to demonstrate how you have the option of right clicking on a snapshot.

You can copy the snapshot and choose a different regions.

So as well as snapshots giving you the option of moving EBS volume data between availability zones, you can also use snapshots to copy data between regions.

Now I'm not going to do this process but I could select a different region, for example, Asia Pacific Sydney and copy that snapshot to the Sydney region.

But there's no point doing that because we just have to remember to clean it up afterwards.

That process is fairly simple and will allow us to copy snapshots between regions.

It might take some time again depending on the amount of data within that snapshot but it is a process that you can perform either as part of data migration or disaster recovery processes.

So go ahead and click on cancel and at this point we're just going to clear things up because this is the end of this first phase of this demo lesson.

So right click on this snapshot and just delete the snapshot and confirm that.

Then go to volumes, select the volume in US East 1A, right click, delete that volume and confirm.

Select the volume in US East 1B, right click, delete volume and confirm.

And that just means we've tidied up both of those EBS volumes within this account.

Now that's the end of this first stage of this set of demo lessons.

All the steps until this point have been part of the free tier within AWS.

What follows won't be part of the free tier.

We're going to be creating a larger instant size and this will have a cost attached but I want to use it to demonstrate instant store volumes and how you can interact with them and some of their unique characteristics.

So I'm going to move into a new video and this new video will have an associated charge.

So you can either watch me perform the steps or you can do it within your own environment.

Now go ahead and complete this video and when you're ready, you can move on to the next video where we're going to investigate instant store volumes.

Welcome back and we're going to use this demo lesson to get some experience of working with EBS and Instant Store volumes.

Now before we get started, this series of demo videos will be split into two main components.

The first component will be based around EBS and EBS snapshots and all of this will come under the free tier.

The second component will be based on Instant Store volumes and will be using larger instances which are not included within the free tier.

So I'm going to make you aware of when we move on to a part which could incur some costs and you can either do that within your own environment or watch me do it in the video.

If you do decide to do it in your own environment, just be aware that there will be a 13 cents per hour cost for the second component of this demo series and I'll make it very clear when we move into that component.

The second component is entirely optional but I just wanted to warn you of the potential cost in advance.

Now to get started with this demo, you're going to need to deploy some infrastructure.

To do that, make sure that you're logged in to the general account, so the management account of the organization and you've got the Northern Virginia region selected.

Now attached to this demo is a one click deployment link to deploy the infrastructure.

So go ahead and click on that link.

That's going to open this quick create stack screen and all you need to do is scroll down to the bottom, check this capabilities box and click on create stack.

Now you're going to need this to be in a create complete state before you continue with this demo.

So go ahead and pause the video, wait for that stack to move into the create complete status and then you can continue.

Okay, now that's finished and the stack is in a create complete state.

You're also going to be running some commands within EC2 instances as part of this demo.

Also attached to this lesson is a lesson commands document which contains all of those commands and you can use this to copy and paste which will avoid errors.

So go ahead and open that link in a separate browser window or separate browser tab.

It should look something like this and we're going to be using this throughout the lesson.

Now this cloud formation template has created a number of resources, but the three that we're concerned about are the three EC2 instances.

So instance one, instance two and instance three.

So the next thing to do is to move across to the EC2 console.

So click on the services drop down and then either locate EC2 under all services, find it in recently visited services or you can use the search box at the top type EC2 and then open that in a new tab.

Now the EC2 console is going through a number of changes so don't be alarmed if it looks slightly different or if you see any banners welcoming you to this new version.

Now if you click on instances running, you'll see a list of the three instances that we're going to be using within this demo lesson.

We have instance one - az a.

We have instance two - az a and then instance one - az b.

So these are three instances, two of which are in availability zone A and one which is in availability zone B.

Next I want you to scroll down and locate volumes under elastic block store and just click on volumes.

And what you'll see is three EBS volumes, each of which is eight GIB in size.

Now these are all currently in use.

You can see that in the state column and that's because all of these volumes are in use as the boot volumes for those three EC2 instances.

So on each of these volumes is the operating system running on those EC2 instances.

Now to give you some experience of working with EBS volumes, we're going to go ahead and create a volume.

So click on the create volume button.

The first thing you'll need to do when creating a volume is pick the type and there are a number of different types available.

We've got GP2 and GP3 which are the general purpose storage types.

We're going to use GP3 for this demo lesson.

You could also select one of the provisioned IOPS volumes.

So this is currently IO1 or IO2.

And with both of these volume types, you're able to define IOPS separately from the size of the volume.

So these are volume types that you can use for demanding storage scenarios where you need high-end performance or when you need especially high performance for smaller volume sizes.

Now IO1 was the first type of provisioned IOPS SSD introduced by AWS and more recently they've introduced IO2 and enhanced it which provides even higher levels of performance.

In addition to that we do have the non-SSD volume types.

So SC1 which is cold HDD, ST1 which is throughput optimized HDD and then of course the original magnetic type which is now legacy and AWS don't recommend this for any production usage.

For this demo lesson we're going to go ahead and select GP3.

So select that.

Next you're able to pick a size in GIB for the volume.

We're going to select a volume size of 10 GIB.

Now EBS volumes are created within a specific availability zone so you have to select the availability zone when you're creating the volume.

At this point I want you to go ahead and select US-EAST-1A.

When creating volume you're also able to specify a snapshot as the basis for that volume.

So if you want to restore a snapshot into this volume you can select that here.

At this stage in the demo we're going to be creating a blank EBS volume so we're not going to select anything in this box.

We're going to be talking about encryption later in this section of the course.

You are able to specify encryption settings for the volume when you create it but at this point we're not going to encrypt this volume.

We do want to add a tag so that we can easily identify the volume from all of the others so click on add tag.

For the key we're going to use name.

For the value we're going to put EBS test volume.

So once you've entered both of those go ahead and click on create volume and that will begin the process of creating the volume.

Just close down any dialogues and then just pay attention to the different states that this volume goes through.

It begins in a creating state.

This is where the storage is being provisioned and then made available by the EBS product.

If we click on refresh you'll see that it changes from creating to available and once it's in an available state this means that we can attach it to EC2 instances.

And that's what we're going to do so we're going to right click and select attach volume.

Now you're able to attach this volume to EC2 instances but crucially only those in the same availability zone.

EBS is an availability zone scoped service and so you can only attach EBS volumes to EC2 instances within that same availability zone.

So if we select the instance box you'll only see instances in that same availability zone.

Now at this point go ahead and select instance 1 in availability zone A.

Once you've selected it you'll see that the device field is populated and this is the device ID that the instance will see for this volume.

So this is how the volume is going to be exposed to the EC2 instance.

So if we want to interact with this instance inside the operating system this is the device that we'll use.

Now different operating systems might see this in slightly different ways.

So as this warning suggests certain Linux kernels might rename SDF to XVDF.

So we've got to be aware that when you do attach a volume to an EC2 instance you need to get used to how that's seen inside the operating system.

How we can identify it and how we can configure it within the operating system for use.

And I'm going to demonstrate that in the next part of this demo lesson.

So at this point just go ahead and click on attach and this will attach this volume to the EC2 instance.

Once that's attached to the instance and you see the state change to in use then just scroll up on the left hand side and select instances.

We're going to go ahead and connect to instance 1 in availability zone A.

This is the instance that we just attached that EBS volume to so we want to interact with this instance and see how we can see the EBS volume.

So right click on this instance and select connect and then you could either connect with an SSH client or use instance connect and to keep things simple we're going to connect from our browser so select the EC2 instance connect option make sure the user's name is set to EC2-user and then click on connect.

So now we connected to this EC2 instance and it's at this point that we'll start needing the commands that are listed inside the lesson commands document and again this is attached to this lesson.

So first we need to list all the block devices which are connected to this instance and we're going to use the LSBLK command.

Now if you're not comfortable with Linux don't worry just take this nice and slowly and understand at a high level all the commands that we're going to run.

So the first one is LSBLK and this is list block devices.

So if we run this we'll be able to see a list of all of the block devices connected to this EC2 instance.

You'll see the root device this is the device that's used to boot the instance it contains the instance operating system you'll see that it's 8 gig in size and then this is the EBS volume that we just attached to this instance.

You'll see that device ID so XVDF and you'll see that it's 10 gig in size.

Now what we need to do next is check whether there is a file system on this block device.

So this block device we've created it with EBS and then we've attached it to this instance.

Now we know that it's blank but it's always safe to check if there's any file system on a block device.

So to do that we run this command.

So we're going to check are there any file systems on this block device.

So press enter and if you see just data that indicates that there isn't any file system on this device and so we need to create one.

You can only mount file systems under Linux and so we need to create a file system on this raw block device this EBS volume.

So to do that we run this command.

So shoo-doo again is just giving us admin permissions on this instance.

MKFS is going to make a file system.

We specify the file system type with hyphen t and then XFS which is a type of file system and then we're telling it to create this file system on this raw block device which is the EBS volume that we just attached.

So press enter and that will create the file system on this EBS volume.

We can confirm that by rerunning this previous command and this time instead of data it will tell us that there is now an XFS file system on this block device.

So now we can see the difference.

Initially it just told us that there was data, so raw data on this volume and now it's indicating that there is a file system, the file system that we just created.

Now the way that Linux works is we mount a file system to a mount point which is a directory.

So we're going to create a directory using this command.

MKDIR makes a directory and we're going to call the directory forward slash EBS test.

So this creates it at the top level of the file system.

This signifies root which is the top level of the file system tree and we're going to make a folder inside here called EBS test.

So go ahead and enter that command and press enter and that creates that folder and then what we can do is to mount the file system that we just created on this EBS volume into that folder.

And to do that we use this command, mount.

So mount takes a device ID, so forward slash dev forward slash xvdf.

So this is the raw block device containing the file system we just created and it's going to mount it into this folder.

So type that command and press enter and now we have our EBS volume with our file system mounted into this folder.

And we can verify that by running a df space hyphen k.

And this will show us all of the file systems on this instance and the bottom line here is the one that we've just created and mounted.

At this point I'm just going to clear the screen to make it easier to see and what we're going to do is to move into this folder.

So cd which is change directory space forward slash EBS test and then press enter and that will move you into that folder.

Once we're in that folder we're going to create a test file.

So we're going to use this command so shudu nano which is a text editor and we're going to call the file amazing test file dot txt.

So type that command in and press enter and then go ahead and type a message.

It can be anything you just need to recognize it as your own message.

So I'm going to use cats are amazing and then some exclamation marks.

Then I'm going to press control o and enter to save that file and then control x to exit again clear the screen to make it easier to see.

And then I'm going to do an LS space hyphen LA and press enter just to list the contents of this folder.

So as you can see we've now got this amazing test file dot txt.

And if we cat the contents of this so cat amazing test file dot txt you'll see the unique message that you just typed in.

So at this point we've created this file within the folder and remember the folder is now the mount point for the file system that we created on this EBS volume.

So the next step that I want you to do is to reboot this EC2 instance.

To do that type sudo space and then reboot and press enter.

Now this will disconnect you from this session.

So you can go ahead and close down this tab and go back to the EC2 console.

Just go ahead and click on instances.

Okay, so this is the end of part one of this lesson.

It was getting a little bit on the long side and so I wanted to add a break.

It's an opportunity just to take a rest or grab a coffee.

Part two will be continuing immediately from the end of part one.

So go ahead complete the video and when you're ready join me in part two.

Author response:

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

New Experiments

(1) Activation-dependent dynamics of PKA with the RIα regulatory subunit, adding to the answer to Reviewers 1 and 2. To determine the dynamics of all PKA isoforms, we have added experiments that used PKA-RIα as the regulatory subunit. We found differential translocation between PKA-C (co-expressed with PKA-RIα) and PKA-RIα (Figure 1–figure supplement 3), similar to the results when PKA-RIIα or PKA-RIβ was used.

(2) PKA-C dynamics elicited by a low concentration of norepinephrine, addressing Reviewer 3’s comment. We have found that PKA-C (co-expressed with RIIα) exhibited similar translocation into dendritic spines in the presence of a 5x lowered concentration (2 μM) of norepinephrine, suggesting that the translocation occurs over a wide range of stimulus strengths (Figure 1-figure supplement 2).

Reviewer #1 (Public Review):

Summary:

This is a short self-contained study with a straightforward and interesting message. The paper focuses on settling whether PKA activation requires dissociation of the catalytic and regulatory subunits. This debate has been ongoing for ~ 30 years, with renewed interest in the question following a publication in Science, 2017 (Smith et al.). Here, Xiong et al demonstrate that fusing the R and C subunits together (in the same way as Smith et al) prevents the proper function of PKA in neurons. This provides further support for the dissociative activation model - it is imperative that researchers have clarity on this topic since it is so fundamental to building accurate models of localised cAMP signalling in all cell types. Furthermore, their experiments highlight that C subunit dissociation into spines is essential for structural LTP, which is an interesting finding in itself. They also show that preventing C subunit dissociation reduces basal AMPA receptor currents to the same extent as knocking down the C subunit. Overall, the paper will interest both cAMP researchers and scientists interested in fundamental mechanisms of synaptic regulation.

Strengths:

The experiments are technically challenging and well executed. Good use of control conditions e.g untransfected controls in Figure 4.

We thank the reviewer for their accurate summarization of the position of the study in the field and for the positive evaluation of our study.

Weaknesses:

The novelty is lessened given the same team has shown dissociation of the C subunit into dendritic spines from RIIbeta subunits localised to dendritic shafts before (Tillo et al., 2017). Nevertheless, the experiments with RII-C fusion proteins are novel and an important addition.

We thank the reviewer for noticing our earlier work. The first part of the current work is indeed an extension of previous work, as we have articulated in the manuscript. However, this extension is important because recent studies suggested that the majority of PKA-RIIβ are axonal localized. The primary PKA subtypes in the soma and dendrite are likely PKA-RIβ or PKA-RIIα. Although it is conceivable that the results from PKA-RIIβ can be extended to the other subunits, given the current debate in the field regarding PKA dissociation (or not), it remains important to conclusively demonstrate that these other regulatory subunit types also support PKA dissociation within intact cells in response to a physiological stimulant. To complete the survey for all PKA-R isoforms, we have now added data for PKA-RIα (New Experiment #1), as they are also expressed in the brain (e.g., https://www.ncbi.nlm.nih.gov/gene/5573). Additionally, as the reviewer points out, our second part is a novel addition to the literature.

Reviewer #2 (Public Review):

Summary:

PKA is a major signaling protein that has been long studied and is vital for synaptic plasticity. Here, the authors examine the mechanism of PKA activity and specifically focus on addressing the question of PKA dissociation as a major mode of its activation in dendritic spines. This would potentially allow us to determine the precise mechanisms of PKA activation and address how it maintains spatial and temporal signaling specificity.

Strengths:

The results convincingly show that PKA activity is governed by the subcellular localization in dendrites and spines and is mediated via subunit dissociation. The authors make use of organotypic hippocampal slice cultures, where they use pharmacology, glutamate uncaging, and electrophysiological recordings.

Overall, the experiments and data presented are well executed. The experiments all show that at least in the case of synaptic activity, the distribution of PKA-C to dendritic spines is necessary and sufficient for PKA-mediated functional and structural plasticity.

The authors were able to persuasively support their claim that PKA subunit dissociation is necessary for its function and localization in dendritic spines. This conclusion is important to better understand the mechanisms of PKA activity and its role in synaptic plasticity.

We thank the reviewer for their positive evaluation of our study.

Weaknesses:

While the experiments are indeed convincing and well executed, the data presented is similar to previously published work from the Zhong lab (Tillo et al., 2017, Zhong et al 2009). This reduces the novelty of the findings in terms of re-distribution of PKA subunits, which was already established. A few alternative approaches for addressing this question: targeting localization of endogenous PKA, addressing its synaptic distribution, or even impairing within intact neuronal circuits, would highly strengthen their findings. This would allow us to further substantiate the synaptic localization and re-distribution mechanism of PKA as a critical regulator of synaptic structure, function, and plasticity.

We thank the reviewer for noticing our earlier work. The first part of the current work is indeed an extension of previous work, as we have articulated in the manuscript. However, this extension is important because recent studies suggested that the majority of PKA-RIIβ are axonal localized. The primary PKA subtypes in the soma and dendrite are likely PKA-RIβ or PKA-RIIα. Although it is conceivable that the results from PKA-RIIβ can be extended to the other subunits, given the current debate in the field regarding PKA dissociation (or not), it remains important to conclusively demonstrate that these other regulatory subunit types also support PKA dissociation within intact cells in response to a physiological stimulant. To complete the survey for all PKA-R isoforms, we have now added data for PKA-RIα (New Experiment #1), as they are also expressed in the brain (e.g., https://www.ncbi.nlm.nih.gov/gene/5573). Additionally, as Reviewer 1 points out, our second part is a novel addition to the literature.

We also thank the reviewer for suggesting the experiments to examine PKA’s synaptic localization and dynamics as a key mechanism underlying synaptic structure and function. We agree that this is a very interesting topic. At the same time, we feel that this mechanistic direction is open ended at this time and beyond what we try to conclude within this manuscript: prevention of PKA dissociation in neurons affects synaptic function. Therefore, we will save the suggested direction for future studies. We hope the reviewer understand.

Reviewer #3 (Public Review):

Summary:

Xiong et al. investigated the debated mechanism of PKA activation using hippocampal CA1 neurons under pharmacological and synaptic stimulations. Examining the two PKA major isoforms in these neurons, they found that a portion of PKA-C dissociates from PKA-R and translocates into dendritic spines following norepinephrine bath application. Additionally, their use of a non-dissociable form of PKC demonstrates its essential role in structural long-term potentiation (LTP) induced by two-photon glutamate uncaging, as well as in maintaining normal synaptic transmission, as verified by electrophysiology. This study presents a valuable finding on the activation-dependent re-distribution of PKA catalytic subunits in CA1 neurons, a process vital for synaptic functionality. The robust evidence provided by the authors makes this work particularly relevant for biologists seeking to understand PKA activation and its downstream effects essential for synaptic plasticity.

Strengths:

The study is methodologically robust, particularly in the application of two-photon imaging and electrophysiology. The experiments are well-designed with effective controls and a comprehensive analysis. The credibility of the data is further enhanced by the research team's previous works in related experiments. The conclusions of this paper are mostly well supported by data. The research fills a significant gap in our understanding of PKA activation mechanisms in synaptic functioning, presenting valuable insights backed by empirical evidence.

We thank the reviewer for their positive evaluation of our study.

Weaknesses:

The physiological relevance of the findings regarding PKA dissociation is somewhat weakened by the use of norepinephrine (10 µM) in bath applications, which might not accurately reflect physiological conditions. Furthermore, the study does not address the impact of glutamate uncaging, a well-characterized physiologically relevant stimulation, on the redistribution of PKA catalytic subunits, leaving some questions unanswered.

We agreed with the Reviewer that testing under physiological conditions is critical especially given the current debate in the literature. That is why we tested PKA dynamics induced by the physiological stimulant, norepinephrine. It has been suggested that, near the release site, local norepinephrine concentrations can be as high as tens of micromolar (Courtney and Ford, 2014). Based on this study, we have chosen a mid-range concentration (10 μM). At the same time, in light of the Reviewer’s suggestion, we have now also tested PKA-RIIα dissociation at a 5x lower concentration of norepinephrine (2 μM; New Experiment #2). The activation and translocation of PKA-C is also readily detectible under this condition to a degree comparable to when 10 μM norepinephrine was used.

Regarding the suggested glutamate uncaging experiment, it is extremely challenging because of finite signal-to-noise ratios in our experiments. From our past studies, we know that activated PKA-C can diffuse three dimensionally, with a fraction as membrane-associated proteins and the other as cytosolic proteins. Although we have evidence that its membrane affinity allows it to become enriched in dendritic spines, it is not known (and is unlikely) that activated PKA-C is selectively targeted to a particular spine. Glutamate uncaging of a single spine presumably would locally activate a small number of PKA-C. It will be very difficult to trace the 3D diffusion of these small number of molecules in the presence of surrounding resting-state PKA-C molecules. Finally, we hope the reviewer agrees that, regardless of the result of the glutamate uncaging experiment, the above new experiment (New Experiment #2) already indicate that certain physiologically relevant stimuli can drive PKA-C dissociation from PKA-R and translocation to spines, supporting our conclusion.

Reviewer #2 (Recommendations For The Authors):

It was a pleasure reading your paper, and the results are well-executed and well-presented.

My main and only recommendations are two ways to further expand the scope of the findings.

First, I believe addressing the endogenous localization of PKA-C subunit before and after PKA activation would be highly important to validate these claims. Overexpression of tagged proteins often shows vastly different subcellular distribution than their endogenous counterparts. Recent technological advances with CRISPR/Cas9 gene editing (Suzuki et al Nature 2016 and Gao et al Neuron 2019 for example) which the Zhong lab recently contributed to (Zhong et al 2021 eLife) allow us to tag endogenous proteins and image them in fixed or live neurons. Any experiments targeting endogenous PKA subunits that support dissociation and synaptic localization following activation would be very informative and greatly increase the novelty and impact of their findings.

We agreed that addressing the endogenous PKA dynamics is important. However, despite recent progress, endogenous labeling using CRISPR-based methods remains challenging and requires extensive optimization. This is especially true for signaling proteins whose endogenous abundance is often low. We have tried to label PKA catalytic subunits and regulatory subunits using both the homologous recombination-based method SLENDR and our own non-homologous end joining-based method CRISPIE. We did not succeed, in part because it is very difficult to see any signal under wide-field fluorescence conditions, which makes it difficult to screen different constructs for optimizing parameters. It is also possible that, at the endogenous abundance, the label is just not bright enough to be seen. Nevertheless, for both PKA type Iβ and type IIα that we studied in this manuscript, we have correlated the measured parameters (specifically, Spine Enrichment Index or SEI) with the overexpression level (Figure 1-figure supplement 1). We found that they are not strongly correlated with the expression level under our conditions. By extrapolating to non-overexpression conditions, our conclusion remains valid.

To overcome the inability to label endogenous PKA subunits using CRISPR-based methods, we have also attempted a conditional knock-in method call ENABLED that we previously developed to label PKA-Cα. In preliminary results, we found that endogenously label PKA were very dim. However, in a subset of cells that are bright enough to be quantified, the PKA catalytic subunit indeed translocated to dendritic spines upon stimulation (see Additional Fig. 1 in the next page), corroborating our results using overexpression. These results, however, are not ready to be published because characterization of the mouse line takes time and, at this moment, the signal-to-noise ratio remains low. We hope that the reviewer can understand.

Author response image 1.

Endogeneous PKA-Cα translocate to dendritic spines upon activation.

Second, experiments which would advance and validate these findings in vivo would be highly valuable. This could be achieved in a number of ways - one would be overexpression of tagged PKA versions and examining sub-cellular distribution before and after physiological activation in vivo. Another possibility is in vivo perturbation - one would speculate that disruption or tethering of PKA subunits to the dendrite would lead to cell-specific functional and structural impairments. This could be achieved in a similar manner to the in vitro experiments, with a PKA KO and replacement strategy of the tethered C-R plasmid, followed by structural or functional examination of neurons.

I would like to state that these experiments are not essential in my opinion, but any improvements in one of these directions would greatly improve and extend the impact and findings of this paper.

We thank the reviewer for the suggestion and the understanding. The suggested in vivo experiments are fascinating. However, in vivo imaging of dendritic spine morphology is already in itself challenging. The difficulty greatly increases when trying to detect partial, likely transient translocation of a signaling protein. It is also very difficult to knock down endogenous PKA while simultaneously expressing the R-C construct in a large number of cells to achieve detectable circuit or behavioral effect (and hope that compensation does not happen over weeks). We hope the reviewer agrees that these experiments would be their own project and go beyond the time and scope of the current study.

Reviewer #3 (Recommendations For The Authors):

Please elaborate on the methods used to visualize PKA-RIIα and PKA-RIβ subunits.

As suggested, we have now included additional details for visualizing PKA-Rs in the text. Specifically, we write (pg. 5): “…, as visualized using expressed PKA-R-mEGFP in separate experiments (Figs. 1A-1C).”.

It seems that you're able to make notes on commits. Since a commit can be referenced by a tag, or branch, you can make notes on those, too -- kind of.

Reviewer #1 (Public review):

Summary:

In their manuscript, Zhou et al. analyze the factors controlling the activation and maintenance of a sustained cell cycle block in response to persistent DNA DSBs. By conditionally depleting components of the DDC using auxin-inducible degrons, the authors verified that some of them are only required for the activation (e.g., Dun1) or the maintenance (e.g., Chk1) of the DSB-dependent cell cycle arrest, while others such as Ddc2, Rad24, Rad9 or Rad53 are required for both processes. Notably, they further show that after a prolonged arrest (>24 h) in a strain carrying two DSBs, the DDC becomes dispensable and the mitotic block is then maintained by SAC proteins such as Mad1, Mad2 or the mitotic exit network (MEN) component Bub2.

Strengths:

The manuscript dissects the specific role of different components of the DDC and the SAC during the induction of a cell cycle arrest induced by DNA damage, as well as their contribution for the short-term and long-term maintenance of a DNA DSB-induced mitotic block. Overall, the experiments are well described and properly executed, and the data in the manuscript are clearly presented. The conclusions drawn are generally well supported by the experimental data. Their observations contribute to drawing a clearer picture of the relative contribution of these factors to the maintenance of genome stability in cells exposed to permanent DNA damage.

Weaknesses:

The main weakness of the study is that it is fundamentally based on the use of the auxin-inducible degron (AID) strategy to deplete proteins. This widely used method allows an efficient depletion of proteins in the cell. However, the drawback is that a tag is added to the protein, which can affect the functionality of the targeted protein or modify its capacity to interact with others. In fact, three of the proteins that are depleted using the AID systems are shown to be clearly hypomorphic, and hence their capacity to induce a strong checkpoint response might be compromised. A corroboration of at least some of the results using an alternative manner to eliminate the proteins would help to strengthen the conclusions of the manuscript.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

To hopefully contribute to more strongly support the conclusions drawn by the authors, I am including a series of concerns regarding the manuscript, as well as some suggestions that could be useful to address these issues:

(1) The main results of this study derive from the use of auxin-inducible degron (AID)-tagged proteins. Despite the great advantages of the AID strategy to conditionally deplete proteins, the AID tag can affect the normal function of a protein. In fact, some of the AID-labeled DDC components generated in this work are shown to be hypomorphic. Hence, the manuscript would have benefited from the additional confirmation of some of the observations using a different way to eliminate the proteins (e.g., temperature-sensitive mutants).

Most ts mutants are also hypomorphic; hence we don’t see there is much advantage to their use. The addition of the AID to these proteins alone does not interfere with the ability to sustain checkpoint arrest as demonstrated in Figure S1. Instead we found that by overexpressing Rad9-AID we could demonstrate that inactivating Rad9 after 15 h behaved the same way as the inactivation of Ddc2, significantly strengthening our finding that the DDC checkpoint becomes dispensable while the SAC takes over. 

(2) In cells depleted of Rad53-AID, the deletion of CHK1 stimulates an earlier release from a mitotic arrest induced by two DSBs (Figures 2D and 3C). Likewise, the authors claim that a faster escape from the cell cycle block can also be observed when upstream factors such as Ddc2, Rad9, or Rad24 are depleted in the absence of CHK1 (Figures 2A-C and Figures 3D-F). However, this earlier release from the cell cycle arrest, if at all, is only slightly noticeable in a Rad9-AID background (Figures 2B and 3E). In this sense, it is also worth pointing out that Rad9-AID chk1Δ (Figure 3E) and Rad24-AID chk1Δ (Figure 3F) cells were only evaluated up to 7 h, while in all other instances, cells were followed for 9 h, which hinders a fair assessment of the differences in the release from the cell cycle arrest.

As noted above, we have now been able to examine Rad9 over the long-time frame.

(3) Although only 25% of the cells depleted for Dun1 remained in G2/M arrest 7 h following the induction of two DSBs, it is shocking that Rad53 was nonetheless still phosphorylated after the cells had escaped the cell cycle blockage (Figure 4A).

This persistence of Rad53 phosphorylation is also seen with the inactivation of Mad2, allowing escape in spite of continued Rad53 phosphorylation.

(4) Generation of Rad9-AID2 and Rad24-AID2 strains did not fully restore the function of these proteins, since most cells had adapted 24 h after induction of two DSBs (Figure S1C). Nonetheless, Rad9-AID2 and Rad24-AID2 are still likely more stable than their AID counterparts, and hence the authors could have instead used the AID2 proteins for the experiments in Figure 2 to better evaluate the role of Rad9 and Rad24 in the maintenance of the DDC-dependent arrest.

We note again that we have found a way to study Rad9 up to 24 h. 

(5) Deletion of BFA1 has been shown to promote the escape from a cell cycle arrest triggered by telomere uncapping (Wang et al. 2000, Hu et al. 2001, Valerio-Santiago et al. 2013). Likewise, while cells carrying the cdc5-T238A allele cannot adapt to a checkpoint arrest induced by one irreparable DSB, BFA1 deletion rescues the adaptation defect of this mutant CDC5 allele (Rawal et al., 2016). The authors show how, using AID-degrons of Bfa1 and Bub2, that only Bub2, but not Bfa1, is required to maintain a prolonged cell cycle arrest after the induction of two DSBs. To reinforce this point, and as shown for mad2Δ cells (Figure S6A), the authors could perform a complete time course using both the Bfa1-AID and a bfa1Δ mutant to demonstrate that they do indeed show the same behavior in terms of the adaptation to a two DSB-induced cell cycle arrest.

We thank the reviewer for noting these other instances where bfa1D promoted an escape from arrest. We tested a 2-DSB bfa1 deletion, data has been added to Figure S9E-F. We did not observe a difference in the percentage of cells escaping arrest between the 2-DSB bfa1 deletion and the 2-DSB BFA1-AID strains.

(6) Bypass or adaptation of a checkpoint-induced cell cycle arrest in S. cerevisiae often leads to cells entering a new cell cycle without doing cytokinesis and, hence, to the accumulation of rebudded cells. However, the experiments shown in the manuscript only account for G1 or budded cells with either one or two nuclei. Do any of the mutants show cytokinesis problems and subsequent rebudding of the cells? If so, this should have been also noted and quantified in the corresponding assays.

In the cases we have studied we have not seen instances where the cells re-bud without completing mitosis (at least as assessed by the formation of budded cells with two distinct DAPI staining masses). In the morphological assays we have done, we score the continuation of the cell cycle by the appearance of multiple buds, G1, and small budded cells. In our adaptation assays when cells escaped G2/M arrest they formed microcolonies indicating no short-term deficiency in cell division.

(7) The location of the DSB relative to the centromere of a chromosome seems to be a factor that determines the capacity of the SAC to sustain a prolonged cell cycle arrest. The authors discuss the possibility that the DSB could somehow affect the structure of the kinetochore. Did they evaluate whether Mad1 or Mad2 were more actively recruited to kinetochores in those strains that more strongly trigger the SAC after induction of the DSBs?

We have not attempted to follow Mad1/2 recruitment. ChIP-seq could be used to monitor Mad1/2 localization at the 16 centromeres in response to DSBs and the spread of g-H2AX across the centromere. Our previous data showed that g-H2AX could spread across the centromere region and could create a change that would be detected by Mad1/2.  This change does not, however, affect the mitotic behavior of a strain in which the H2A genes have been modified to the possibly phosphomimetic H2A-S129E allele.

(8) The authors could speculate in the discussion about the reasons that could explain why the DDC is required for the maintenance of checkpoint arrest at early stages but then becomes dispensable for the preservation of a prolonged cell DNA DSB-induced cycle arrest, which is instead sustained at later stages by the SAC.

Our suggestion is that cells would have adapted, but modification of the centromere region engages SAC.

Finally, some minor issues are:

(1) The lines in the graphs that display the results from adaptation assays (e.g., Figures 1B and 1E) or cell and nuclear morphology (e.g., Figures 1D and 1G) are too thick. This makes it sometimes difficult to distinguish the actual percentages of cells in each category, particularly in the experiments monitoring nuclear division.

Fixed

(2) While both the adaptation assay and the analysis of nuclear division in Figures 1E and 1G, respectively, show a complete DDC-dependent arrest at 4h, the Western blot in Figure 1F suggests that Rad53 is not phosphorylated at that time point. Do these figures represent independent experiments? Ideally, the analysis of cell budding and nuclear division, which is performed in liquid cultures, and the Western blot displaying Rad53 phosphorylation should correspond to the same experiment.

Cell budding in liquid cultures and adaptation assays were performed in triplicate with 3 biological replicates and the collective results are shown in each graph showing the percentage of large-budded cells. Western blot samples were collected in each liquid culture experiment. The western blot in 1G is a representative western blot.

(3) It is somewhat confusing that the blots for the proteins are not displayed in the same order in Figures 2A (Rad53 at the top) and 2B or 2C (Rad53 in the middle).

Fixed.  We place Rad53 – the relevant protein - at the top.

Reviewer #2 (Recommendations For The Authors):

(1) Yeast with the two breaks responds to DNA damage checkpoint (DDC) until sometimes 4-15 h post DNA damage. Since the auxin-induced degradation does not completely deplete all the tagged proteins in cells, the results should be more carefully considered and not to interpret if the checkpoint entry or maintenance depends on each target protein's ability to induce Rad53 phosphorylation. It should be theoretically possible if checkpoint maintenance requires only a modest amount of checkpoint factors especially because the experiments involve the induction of one or two DSBs. The low levels of DDC factors may be insufficient for Rad53 activation but could still be effective for cell cycle arrest. Indeed, the Haber group showed that the mating type switch did not induce Rad53 phosphorylation but still invoked detectable DNA damage response. To test such possibilities, the authors might consider employing yet another marker for DDC such as H2A or Chk1 phosphorylation besides Rad53 autophosphorylation. Alternatively, the authors might check if auxin-induced depletion also disrupts break-induced foci formation for checkpoint maintenance or their enrichment at DNA breaks using ChIP assays at various points post-damage.

DAPI staining of Ddc2-AID cells show that when IAA is added 4 h after DSB induction (Figure S3A), cells escape G2/M arrest as evidenced by the increase in large-budded cells with 2 DAPI signals, small budded cells, and G1 cells. Overexpression of Ddc2 can sustain the checkpoint past 24 h, but without SAC proteins like Mad2 they will eventually adapt (Figure S6B).

That Rad9-AID or Rad24-AID in the absence of added auxin (but in the presence of TIR1) is unable to sustain arrest suggests to us that low levels of Rad9 or Rad24 are not sufficient to maintain arrest.  As the reviewer notes, normal MAT switching doesn’t cause Rad53 phosphorylation or arrest, though early damage-induced events such as H2A phosphorylation do occur.  But our point is that Rad9 or Ddc2 is needed to maintain arrest only up to a certain point, after which they become superfluous and a different checkpoint arrest is imposed. At that point apparently a low level of these proteins plays no obvious role.

(2) It is interesting that DDC no longer responds to the damage signaling after 15 h of DSB-induced prolonged checkpoint arrest after two DNA double-strand breaks. Is this also applicable to other adaptation mutants? The results might improve the broad impact of the current conclusions. It is also possible that the transition from DDC to SPC depends on simply the changes in signaling or in part due to the molecular changes in the status of DNA breaks or its flanking regions. Indeed, the proposed model suggests that the spreading of H2A phosphorylation to centromeric regions induces SAC and thus mitotic arrest. The authors could measure H2A phosphorylation near the centromere using ChIP assays at various intervals post-DNA damage. It is particularly interesting if depletion of Ddc2 at 15 h post DNA damage does not alter the level of H2A phosphorylation at or near centromere.

Our previous data have suggested that the involvement of the SAC in prolonging DSB-induced arrest involved post-translational modification of centromeric chromatin such as the Mec1- and Tel1-dependent phosphorylation of the histone H2A (Dotiwala). In budding yeast there is also a similar DSB-induced modification of histone H2B (Lee et al.). To ask if there is an intrinsic activation of the SAC if the regions around centromeres were modified by checkpoint kinase phosphorylation, we examined cell cycle progression in strains in which histone H2A or histone H2B was mutated to their putative phosphomimetic forms (H2A-S129E and H2B-T129E).  As shown in Figure S11, there was no effect on the growth rate of these strains, or of the double mutant, suggesting that cells did not experience a delay in entering mitosis because of these modifications. We note that although histone H2A-S129E is recognized by an antibody specific for the phosphorylation of histone H2A-S129, the mutation to S129E may not be fully phosphomimetic. 

(3) It is puzzling why Rad9-AID or Rad24-AID are proficient for DDC establishment but cannot sustain permanent arrest in the two break cells. It appears Rad53 phosphorylation for DDC is weaker in cells expressing Rad9-AID or Rad24-AID according to Fig.2B and C even though their protein level before IAA treatment is still robust. This might also explain why the results of depleting Rad53 and Rad9 are very different. It also raises concern if the effect of Rad24 depletion on checkpoint maintenance is in part due to the weaker checkpoint establishment. It might be necessary to use the AID2 system to redo Rad24 depletion to exclude such a possibility.

We believe that the AID mutants are very sensitive to the low level of IAA present in yeast.  The instability of the protein is entirely dependent on the TIR1 SCF factor, so the proteins themselves are not intrinsically defective; they are just subject to degradation.  Overexpressing Rad9 allowed us to evaluate its role at late time points. 

(4) It is intriguing that the switch from DDC to SAC might take place at around 12 h when yeasts with a single unrepairable break ignore DDC and resume cell cycling (so-called "adaptation"). Since 4h and 15h are far apart and the transition point from DDC to SAC likely takes place between these two points, it will be very helpful to analyze and compare cell cycle exit after 24 h by treating IAA at multiple points between 4-15h.

When we add IAA to Mad2-AID and Mad1-AID 4 h after DSB induction, cells remain arrested for up to 12 h after DSB induction. At 15 h cells begin to exit checkpoint arrest indicating that the handoff of checkpoint arrest must occur between 12 to 15 h after DSB induction. If we degraded DNA damage checkpoint proteins at any point before Mad2, Mad1, and Bub2 begin to contribute to checkpoint arrest, then arrested cells will likely adapt in a similar manner to when IAA was added 4 h after DSB induction.

(5) Some of the Western blot quality is poor. For instance, in Figure 6C, Mad1-AID level after IAA addition is not compelling especially because the TIR level (the loading control) is also very low.

In Figure 6C, while the relative levels of TIR1 are similar in the IAA treated and untreated samples, there is no detectable amount of Mad1-AID in the IAA treated samples indicating that Mad1-AID was successful degraded with the AID system.

(6) Fig. 8 is complex. It might be helpful to define the different types of arrows in the figure. The legend also has a spelling error, Rad23 should be Rad24.

We’ve defined what each arrow means in the legend and corrected the spelling error in the figure legend.

Reviewer #3 (Recommendations For The Authors):

Major concerns:

Much of the manuscript states that two unrepairable DSBs lead to a long and severe G2/M arrest. Two main cytological approaches are used to make this statement: bud size and number on plates after micromanipulation (microcolony assay), and cell and nuclear morphology in liquid cultures. While the latter gives a clear pattern that can be assigned to a G2/M block as expected by DDC, i.e. metaphase-like mononucleated cells with large buds, the former can only tell whether cells eventually reach a second S phase (large budded cells on the plate can be in a proper G2/M arrest, but can also be in an anaphase block or even in the ensuing G1). The authors always performed the microcolony assay, but there are several cases where the much more informative budding/DAPI assay is missing. These include Dun1-aid and others, but more importantly chk1D and its combinations with DDC proteins. Incidentally, for the microcolony assay, it is more accurate to label the y-axis of the corresponding graphs (and in the figure legends and main text) with something like "large budded cells"; "G2/M arrested cells" is misleading.

Figures have been updated to more accurately reflect what we are measuring.

The results obtained with the Bfa1/Bub2 partner are intriguing. These two proteins form a complex whose canonical function is to prevent exit from mitosis until the spindle is properly aligned, acting in a distinct subpathway within the SAC that blocks MEN rather than anaphase onset. The data presented by the authors suggest that, on the one hand, both SAC subpathways work together to block the cell cycle. However, why does canonical SAC (Mad1/Mad2) inactivation not lead to a transition from G2/M (metaphase-like) arrested cells to anaphase-like arrest maintained by Bfa1-Bub2? Since Bfa1-Bub2 is a target of DDC, is it possible that DDC knockdown also inactivates this checkpoint, allowing adaptation? On the other hand, can the authors provide more data to confirm and strengthen their claim of a Bfa1-independent Bub2 role in prolonged arrest? Perhaps long-term protein localization and PTM changes. Bub2-independent roles for Bfa1 have been reported, but not vice versa, to the best of my knowledge.

In the mitotic exit network Bfa1/Bub2 prime activation of the pathway by bringing Tem1 to spindle pole bodies. Phosphorylation of Bfa1 causes Tem1 to be released and phosphorylate Cdc5 to trigger exit by MEN. It has been shown that DNA damage, in a cdc13-1 ts mutant, phosphorylates Bfa1 in a Rad53 and Dun1 dependent manner. This phosphorylation of Bfa1 could release Tem1 and prime cells to exit checkpoint arrest when cells pass through anaphase. Looking at Tem1 localization to spindle pole bodies and interactions with Bfa1/Bub2 in response to DNA damage might give insight into why cells don’t experience an anaphase-like arrest when they are released by either deactivation of the DNA damage checkpoint or SAC.

We have previously shown that a deletion of bub2 in a 1-DSB background shortens DSB-induced checkpoint arrest. Deletion of bfa1 in a 2-DSB background showed ~80-70% of cells stuck in a large-budded state as measured through an adaptation assay tracking the morphology of G1 cells on a YP-Gal plate and DAPI staining. Deletion or degradation of bfa1 might not release cells from arrest because the Mad2/Mad1 prevent cells from transitioning into anaphase. Our DAPI data for Bub2-AID shows an increase in cells with 2 DAPI signals (transition into anaphase) and small budded cells indicating that degradation of Bub2 is releasing cells into anaphase and allowing cells to complete mitosis.

Further suggestions:

It would be richer if authors could provide more than one experimental replicate in some panels (e.g., S1A,B; S4A; and S6B).

S1C confirms that Rad9-AID and Rad24-AID will adapt by 24 h even with the point mutant TIR1(F74G) which has lower basal degradation than TIR1. S4A has been updated with additional experimental replicates. The 48 h timepoint after DSB induction was to show the importance of Mad2 even when Ddc2 is overexpressed.

Figure 1: Rearrange figure panels when they are first mentioned in the text. For example, it makes more sense to have the plate adaptation assay as panel B for both 1-DSB and 2-DSB strains, budding plus DAPI as panel C, and Rad53 as panel D.

These figures have been rearranged in the order that they are mentioned in the paper.

Figure 5: Correct Ph-5-IAA in the Rad53 WBs (it should be 5-Ph-IAA).

This has been corrected.

Figure S2: The straight line under the "+IAA" text box is misleading. I think it should also cover the "-2" time point, right? Also, check the figure legend. Information is missing and does not correspond to the figure layout.

This has been corrected.

Figure S3: Perhaps "Cell cycle profile as determined by budding and DAPI staining" is a better and more accurate legend title.

The legend title has been updated to “Cell cycle profile as determined by budding and DAPI staining in Ddc2-AID and Rad53-AID mutants ± IAA 4 h after galactose.”

Figure S5: Detection of both Rad53 and Ddc2 in the same blot could lead to misinterpretation as hyperphosphorylated Rad53 appears to coincide with Ddc2 migration.

Figure S5A-B are representative western blots where Rad53 was probed to show activation of the DNA damage checkpoint by Rad53 phosphorylation. When measuring the relative abundance of Ddc2 we did not probe all blots for Rad53.

Table S1: Include the post-hoc test used for comparisons after ANOVA.

A Sidak post-hoc test was used in PRISM for the one-way ANOVA test. PRISM listed the Sidak post-hoc test as the recommended test to correct for multiple comparisons. A column has been added to S. Table 1 to show which post-hoc test was used.

Page 10, line 4: The putative additive effect of chk1 knockout with Dun1 depletion should also be compared to chk1 alone (in Figure 3A).

We address the additive effect of chk1 knockout with Dun1-AID depletion in a later section on Page 11, line 6. Since we had not explored possible effects from downstream targets of Rad53 for prolonging checkpoint arrest when Rad53 was depleted, we did not mention the effect of the chk1 knockout on Dun1 depletion.

Page 14, second paragraph, line 4: "Figure 6A-D", is it not?

Figure S6A is measuring checkpoint arrest in a deletion of mad2 in a 2-DSB strain. Figure 6A-D shows how degradation of Mad2-AID and Mad1-AID after the handoff of arrest causes cells to exit the checkpoint in a Rad53 independent manner.

Author response:

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

Public Review:

Summary:

Bursicon is a key hormone regulating cuticle tanning in insects. While the molecular mechanisms of its function are rather well studied--especially in the model insect Drosophila melanogaster, its effects and functions in different tissues are less well understood. Here, the authors show that bursicon and its receptor play a role in regulating aspects of the seasonal polyphenism of Cacopsylla chinensis. They found that low temperature treatment activated the bursicon signaling pathway during the transition from summer form to winter form and affect cuticle pigment and chitin content, and cuticle thickness. In addition, the authors show that miR-6012 targets the bursicon receptor, CcBurs-R, thereby modulating the function of bursicon signaling pathway in the seasonal polyphenism of C. chinensis. This discovery expands our knowledge of the roles of neuropeptide bursicon action in arthropod biology.

However, the study falls short of its claim that it reveals the molecular mechanisms of a seasonal polyphenism. While cuticle tanning is an important part of the pear psyllid polyphenism, it is not the equivalent of it. First, there are other traits that distinguish between the two morphs, such as ovarian diapause (Oldfield, 1970), and the role of bursicon signaling in regulating these aspects of polyphenism were not measured. Thus, the phenotype in pear psyllids, whereby knockdown bursicon reduces cuticle tanning seems to simply demonstrate the phenotypes of Drosophila mutants for bursicon receptor (Loveall and Deitcher, 2010, BMC Dev Biol) in another species (Fig. 2I, 4H). Second, the study fails to address the threshold nature of cuticular tanning in this species, although it is the threshold response (specifically, to temperature and photoperiod) that distinguishes this trait as a part of a polyphenism. Whereas miR-6012 was found to regulate bursicon expression, there no evidence is provided that this microRNA either responds to or initiates a threshold response to temperature. In principle, miR-6012 could regulate bursicon whether or not it is part of a polyphenism. Thus, the impact of this work would be significantly increased if it could distinguish between seasonal changes of the cuticle and a bona fide reflection of polyphenism.

Thanks for your valuable suggestion. We concur with the review’s comment that cuticle tanning does not equate to the C. chinensis polyphenism. To better reflect the core focus of our research, we have revised the title to "Neuropeptide Bursicon and its receptor mediated the transition from summer-form to winter-form of Cacopsylla chinensis".

In response to the reviewer's inquiry regarding the threshold nature of cuticular tanning in C. chinensis, we have included a detailed analysis of the phenotypic changes (including nymph phenotypes, cuticle pigment absorbance, and cuticle thickness) during the transition from summer-form to winter-form in C. chinensis at distinct time intervals (3, 6, 9, 12, 15 days) under different temperature conditions (10°C and 25°C). As shown in Figure S1, nymphs exhibit a light yellow and transparent coloration at 3, 6, and 9 days, while nymphs at 12 and 15 days display shades of yellow-green or blue-yellow under 25°C conditions. At 10°C conditions, the abdomen end turns black at 3, 6, and 9 days. By the 12 days, numerous light black stripes appear on the chest and abdomen of nymphs at 10°C. At 15 days, nymphs exhibit an overall black-brown appearance, featuring dark brown stripes on the left and right sides of each chest and abdominal section. Furthermore, the end of the abdomen and back display a large black-brown coloration at 10°C (Figure S1A). The UV absorbance of the total pigment extraction at a 300 nm wavelength markedly increases following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1B). Cuticle thicknesses also increased following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1C). The detailed results (L122-143), materials and methods (L647-652), and discussion (L319-322) have been added in our revised manuscript.

Regarding the response of miR-6012 to temperature, we have already determined its expression at 3, 6, 10 days under different temperatures in the previous Figure 5E. We now included additional time intervals (9, 12, 15 days) in the updated Figure 5E. Our results indicate a significant decrease in the expression levels of miR-6012 after 10°C treatment for 3, 6, 9, 12, 15 days compared to the 25°C treatment group. Detailed information regarding this has been integrated into the Materials and Methods (Line 608-610) of our revised manuscript.

Strengths:

This study convincingly identifies homologs of the genes encoding the bursicon subunits and its receptor, showing an alignment with those of another psyllid as well as more distant species. It also demonstrates that the stage- and tissue-specific levels of bursicon follow the expected patterns, as informed by other insect models, thus validating the identity of these genes in this species. They provide strong evidence that the expression of bursicon and its receptor depend on temperature, thereby showing that this trait is regulated through both parts of the signaling mechanism.

Several parallel measurements of the phenotype were performed to show the effects of this hormone, its receptor, and an upstream regulator (miR-6012), on cuticle deposition and pigmentation (if not polyphenism per se, as claimed). Specifically, chitin staining and TEM of the cuticle qualitatively show difference between controls and knockdowns, and this is supported by some statistical tests of quantitative measurements (although see comments below). Thus, this study provides strong evidence that bursicon and its receptor play an important role in cuticle deposition and pigmentation in this psyllid.

The study identified four miRNAs which might affect bursicon due to sequence motifs. By manipulating levels of synthetic miRNA agonists, the study successfully identified one of them (miR-6012) to cause a cuticle phenotype. Moreover, this miRNA was localized (by FISH) to the cuticle, body-wide. To our knowledge, this is the first demonstrated function for this miRNA, and this study provides a good example of using a gene of known function as an entry point to discovering others influencing a trait. Thus, this finding reveals another level of regulation of cuticle formation in insects.

Weaknesses:

(1) The introduction to this manuscript does not accurately reflect progress in the field of mechanisms underlying polyphenism (e.g., line 60). There are several models for polyphenism that have been used to uncover molecular mechanisms in at least some detail, and this includes seasonal polyphenisms in Hemiptera. Therefore, the justification for this study cannot be predicated on a lack of knowledge, nor is the present study original or unique in this line of research (e.g., as reviewed by Zhang et al. 2019; DOI: 10.1146/annurev-ento-011118-112448). The authors are apparently aware of this, because they even provide other examples (lines 104-108); thus the introduction seems misleading as framed.

Thanks for your excellent suggestion. We have added the paper of Zhang et al. 2019 which recommended by reviewer (DOI: 10.1146/annurev-ento-011118-112448) in Line 57 of our revised manuscript. The statement has been revised to “However, the specific molecular mechanism underling temperature-dependent polyphenism still require further clarification” in Line 60-61 of our revised manuscript.

(2) The data in Figure 2H show "percent of transition." However, the images in 2I show insects with tanned cuticle (control) vs. those without (knockdown). Yet, based on the description of the Methods provided, there appears to be no distinction between "percent of transition" and "percent with tanning defects". This an important distinction to make if the authors are going to interpret cuticle defects as a defect in the polyphenism. Furthermore, there is no mention of intermediate phenotypes. The data in 2H are binned as either present or absent, and these are the phenotypes shown in 2I. Was the phenotype really an all-or-nothing response? Instead of binning, which masks any quantitative differences in the tanning phenotypes, the authors should objectively quantify the degree of tanning and plot that. This would show if and to what degree intermediate tanning phenotypes occurred, which would test how bursicon affects the threshold response. This comment also applies to the data in Figures 4G and 6G. Since cuticle tanning is present in more insect than just those with seasonal polyphenism, showing how this responds as a threshold is needed to make claims about polyphenism.

We appreciate your insightful comments. As shown in Figure 1 of our published paper (Zhang et al., 2013; doi.org/10.7554/eLife.88744.3) and Figure 2C-2I of the current manuscript, the transition from summer-form to winter-form entails not only external cuticular tanning but also alterations in internal cuticular chitin levels and cuticle thickness. While external cuticular tanning serves as a prominent and easily observable indicator of this transition, it is crucial to acknowledge that internal changes also play a significant role and should be taken into consideration. Therefore, we propose that the term "percent of transition" may be more suitable than "percent with tanning defects" to describe this process accurately.

In order to provide a more visually comprehensive understanding of the phenotypic changes during the transition from summer-form to winter-form, we have included images at different time points (3, 6, 9, 12, 15 days) under different temperature conditions in Figure S1A of our revised manuscript. Specifically, under the 10°C condition, nymphs exhibit abdomen tanning after 6 and 9 days of treatment, while the thorax remains untanned. By days 12 to 15, both the abdomen and thorax of the nymphs show tanning, resulting in the majority of summer-form nymphs transitioning into winter-form, as depicted in Figure 2I for comparison. This observation indicates the presence of a critical threshold for cuticle tanning of C. chinensis following exposure to 10°C. Nymphs that did not undergo the transition to winter-form succumbed to the cold, highlighting the absence of intermediate phenotypes at 12-15 days under the 10°C condition. The UV absorbance of the total pigment extraction at a 300 nm wavelength markedly increases following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1B). Additionally, cuticle thickness shows an increase following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1C). These results highlight the relationship between the threshold of cuticular tanning and the transition process. The detailed description and information have been added in Results (L122-143), Materials and Methods (L647-652), and Discussion (L319-322) of our manuscript.

(3) This study also does not test the threshold response of cuticle phenotypes to levels of bursicon, its receptor, or miR-6012. Hormone thresholds are the most widespread and, in most systems where polyphenism has been studied, the defining characteristic of a polyphenism (e.g., Nijhout, 2003, Evol Dev). Quantitative (not binned) measurements of a polyphenism marker (e.g., chitin) should be demonstrated to result as a threshold titer (or in the case of the receptor, expression level) to distinguish defects in polyphenism from those of its component trait.

Thanks for your valuable feedback. We have supplemented additional data on the phenotypes (Figure S1A), cuticle pigment absorbance (Figure S1B), cuticle thickness (Figure S1C), expression levels of bursicon (Figure 1E and 1F), its receptors (Figure 3G), and miR-6012 (Figure 5E) corresponding to nymphs treated over different time periods (3, 6, 9, 12, 15 days) under both 10°C and 25°C conditions in our revised manuscript.

While all these identified markers exhibit a strong correlation with the transition from summer-form to winter-form, it is important to note that they are not suitable as definitive thresholds due to the nature of relative gene expression quantification and chitin content assessment, rather than absolute quantitation. Further, given that tanning hormones are neuropeptides present in trace amounts in insects, unlike steroid hormones, determining their titers poses a considerable challenge.

(4) Cuticle issue:

(a) Unlike Fig. 6D and F, Figs. 2D and F do not correspond to each other. Especially the lack and reduction of chitin in ds-a+b! By fluorescence microscopy there is hardly any signal, whereas by TEM there is a decent cuticle. Additionally, the dsGFP control cuticle in 2D is cut obliquely with a thick and a thin chitin layer. This is misleading.

Thanks for your insightful feedback. We have replaced the previous WGA chitin staining images in the dsCcbursα+β treatment of Figure 2D with new representative images aligning with Figure 2F. Furthermore, the presence of both thin and thick chitin layers observed in the dsEGFP treatment of Figure 2D could potentially be ascribed to the chitin content in the insect midgut or fat body as previously discussed (Zhu et al., 2016). It is notable that during the process of cuticle staining, the chitin located in the midgut and fat body of C. chinensis may exhibit green fluorescence, leading to the appearance of a thin chitin layer. A detailed analysis and elucidation of these observations have been added in the discussion section (Lines 347-352) of our revised manuscript.

Zhu KY, Merzendorfer H, Zhang W, Zhang J, Muthukrishnan S. Biosynthesis, Turnover, and Functions of Chitin in Insects. Annu Rev Entomol. 2016;61:177-196. doi:10.1146/annurev-ento-010715-023933.

(b) In Figs. 2F and 4F, the endocuticle appears to be missing, a portion of the procuticle that is produced post-molting. As tanning is also occurring post-molting, there seems to be a general problem with cuticle differentiation at this time point. This may be a timing issue. Please clarify.

Thank you for your suggestion. The insect cuticle typically comprises three distinct layers (endocuticle, exocuticle, and epicuticle), with the thickness of each layer varying among different insect species. Cuticle differentiation is closely linked to the molting cycle of insects (Mrak et al., 2017). In our study, nymphal cuticles exhibited normal differentiation patterns, characterized by a thin epicuticle and comparable widths of the endocuticle and exocuticle following dsEGFP treatment, as illustrated in Figure 2F and 4F. Conversely, nymphs treated with dsCcBurs-α, dsCcBurs-β, and dsCcburs-R displayed impaired development, manifesting only the exocuticle without a discernible endocuticle layer. These findings suggest that bursicon genes and their receptor play a pivotal role in regulating insect cuticle development (Costa et al., 2016). We have added some discussion about these results in Lines 356-367 of our revised manuscript.

Mrak, P., Bogataj, U., Štrus, J., & Žnidaršič, N. (2017). Cuticle morphogenesis in crustacean embryonic and postembryonic stages. Arthropod structure & development, 46(1), 77–95. https://doi.org/10.1016/j.asd.2016.11.001

Costa, C. P., Elias-Neto, M., Falcon, T., Dallacqua, R. P., Martins, J. R., & Bitondi, M. (2016). RNAi-mediated functional analysis of Bursicon genes related to adult cuticle formation and tanning in the Honeybee, Apis mellifera. PloS one, 11(12), e0167421. https://doi.org/10.1371/journal.pone.0167421

(c) To provide background information, it would be useful analyze cuticle formation in the summer and winter morphs of controls separately by light and electron microscopy. More baseline data on these two morphs is needed.

Thanks for your valuable feedback. To provide more background information about cuticle formation, we supplied the results of nymph phenotypes, cuticle pigment absorbance, and cuticle thickness at distinct time intervals (3, 6, 9, 12, 15 days) under different temperatures of 10°C and 25°C in Figure S1 of our revised manuscript. Hope these results can help better understand the baseline data on these two morphs.

(d) For the TEM study, it is not clear whether the same part of the insect's thorax is being sectioned each time, or if that matters. There is not an obvious difference in the number of cuticular layers, but only the relative widths of those layers, so it is difficult to know how comparable those images are. This raises two questions that the authors should clarify. First, is it possible that certain parts of the thoracic cuticle, such as those closer to the intersegmental membrane, are naturally thinner than other parts of the body? Second, is the tanning phenotype based on the thickness or on the number of chitin layers, or both? The data shown later in Figure 4I, J convincingly shows that the biosynthesis pathway for chitin is repressed, but any clarification of what this might mean for deposition of chitin would help to understand the phenotypes reported. Also, more details on how the data in Fig. 2G were collected would be helpful. This also goes for the data in Fig. 4 (bursicon receptor knockdowns).

Thanks for your great comment. The TEM investigation adhered to a standardized protocol was used as previous description (Zhang et al., 2023), Initially, insect heads were uniformly excised and then fixed in 4% paraformaldehyde. Subsequently, a consistent cutting and staining procedure was executed at a uniform distance above the insect's thorax. The dorsal region of the thorax was specifically chosen for subsequent fluorescence imaging or transmission electron microscopy assessments with the specific objective of quantifying cuticle thickness. Regarding the measurement of cuticle thickness, use the built-in measuring ruler on the software to select the top and bottom of the same horizontal line on the cuticle. Measure the cuticle of each nymph at two close locations. Six nymphs were used for each sample. Randomly select 9 values and plot them. The related description has been added in the Materials and Methods (Line 660-668) of our revised manuscript.

Zhang, S.D., Li, J.Y., Zhang, D.Y., Zhang, Z.X., Meng, S.L., Li, Z., & Liu, X.X. (2023). MiR-252 targeting temperature receptor CcTRPM to mediate the transition from summer-form to winter-form of Cacopsylla chinensis. eLife, 12. https://doi.org/10.7554/eLife.88744

(5) Tissue issue:

The timed experiments shown in all figures were done in whole animals. However, we know from Drosophila that Bursicon activity is complex in different tissues. There is, thus, the possibility, that the effects detected on different days in whole animals are misleading because different tissues--especially the brain and the epidermis, may respond differentially to the challenge and mask each other's responses. The animal is small, so the extraction from single tissue may be difficult. However, this important issue needs to be addressed.

Thanks for your excellent suggestion. We express our heartfelt appreciation to the reviewer for their valuable input regarding the challenges involved in dissecting various tissue sections from the diminutive early instar nymphs of C. chinensis. In light of the metamorphic transition of C. chinensis across developmental stages, this study concentrated on examining the extensive phenotypic alterations. Consequently, intact samples of C. chinensis were specifically chosen for for qPCR analysis. The related descriptions have been added in the Materials and Methods (Line 513, 517, 553, 555, and 613) and Discussion (Line 327-329) of our revised manuscript.

(6) No specific information is provided regarding the procedure followed for the rescue experiments with burs-α and burs-β (How were they done? Which concentrations were applied? What were the effects?). These important details should appear in the Materials and Methods and the Results sections.

Thanks for your excellent suggestion. For the rescue experiments, the dsRNA of CcBurs-R and proteins of burs α-α, burs β-β homodimers, or burs α-β heterodimer (200 ng/μL) were fed together. The concentration of heterodimer protein of CcBurs-α+β was 200 ng/μL. The heterodimer protein of CcBurs-α+β fully rescued the effect of RNAi-mediated knockdown on CcBurs-R expression, while α+α or β+β homodimers did not (Figure 3F). Feeding the α+β heterodimer protein fully rescued the defect in the transition percent and morphological phenotype after CcBurs-R knockdown (Figure 4G-4H). We have added the detailed methods of rescued experiments and specific concentrations in the Materials and Methods (Line 561-563), and Results (Line 263) of our revised manuscript.

(7) Pigmentation

(a) The protocol used to assess pigmentation needs to be validated. In particular, the following details are needed: Were all pigments extracted? Were pigments modified during extraction? Were the values measured consistent with values obtained, for instance, by light microscopy (which should be done)?

Thanks for your excellent comment. Our protocol for pigment extracted as detailed in Bombyx mori, the cuticles were pulverized in liquid nitrogen and then dissolved in 30 milliliters of acidified methanol (Futahashi et al., 2012; Osanai-Futahashi et al., 2012). Thus, all cuticle pigments were dissected and treated with acidified methanol. Pigments were not modified during extraction.. The details description have been integrated into the Materials and Methods (Line 630-633) of our revised manuscript.

Futahashi, R., Kurita, R., Mano, H., & Fukatsu, T. (2012). Redox alters yellow dragonflies into red. Proceedings of the National Academy of Sciences of the United States of America, 109(31), 12626–12631. https://doi.org/10.1073/pnas.1207114109

Osanai-Futahashi, M., Tatematsu, K. I., Yamamoto, K., Narukawa, J., Uchino, K., Kayukawa, T., Shinoda, T., Banno, Y., Tamura, T., & Sezutsu, H. (2012). Identification of the Bombyx red egg gene reveals involvement of a novel transporter family gene in late steps of the insect ommochrome biosynthesis pathway. The Journal of biological chemistry, 287(21), 17706–17714. https://doi.org/10.1074/jbc.M111.321331

(b) In addition, pigmentation occurs post-molting; thus, the results could reflect indirect actions of bursicon signaling on pigmentation. The levels of expression of downstream pigmentation genes (ebony, lactase, etc) should be measured and compared in molting summer vs. winter morphs.

Thanks for your valuable suggestion. Actually, we already studied the function of some downstream pigmentation genes, including ebony, Lactase, Tyrosine hydroxylase, Dopa decarboxylase, and Acetyltransferase. The variations in the expression patterns of these genes are closely tied to the molting dynamics of nymphs undergoing transitions between summer-form and winter-form. These findings will put in another manuscript currently being prepared for submission, thus detailed outcomes are not suitable for inclusion in the current manuscript.

(8) L236: "while the heterodimer protein of CcBurs α+β could fully rescue the effect of CcBurs-R knockdown on the transition percent (Figure 4G 4H)". This result seems contradictory. If CcBurs-R is the receptor of bursicon, the heterodimer protein of CcBurs α+β should not be able to rescue the effect of CcBurs-R knockdown insects. How can a neuropeptide protein rescue the effect when its receptor is not there! If these results are valid, then the CcBurs-R would not be the (sole) receptor for CcBurs α+β heterodimer. This is a critical issue for this manuscript and needs to be addressed (also in L337 in Discussion).

Thanks for your insightful suggestion. Following the administration of dsCcBur-R to C. chinensis, the expression of CcBurs-R exhibited a reduction of approximately 66-82% as depicted in Figure 4A, rather than complete suppression. Activation of endogenous CcBurs-R through feeding of the α+β heterodimer protein results in an increase in CcBurs-R expression, with the effectiveness of the rescue effect contingent upon the dosage of the α+β heterodimer protein. Consequently, the capacity of the α+β heterodimer protein to effectively mitigate the impacts of CcBurs-R knockdown on the conversion rate is clearly demonstrated. We have added additional discussion in Line 396-403 of our revised manuscript.

(9) Fig. 5D needs improvement (the magnification is poor) and further explanation and discussion. mi6012 and CcBurs-R seem to be expressed in complementary tissues--do we see internal tissues also (see problem under point 2)? Again, the magnification is not high enough to understand and appreciate the relationships discussed.

Thanks for your valuable suggestion. In order to enhance the resolution of the magnified images, we conducted FISH co-localization of miR-6012 and CcBurs-R in 3rd instar nymphs and obtained detailed zoomed-in images. As shown in the magnified view of Figure 5D, miR-6012 and CcBurs-R appear to exhibit complementary expression patterns in tissues. During the FISH assays, epidermis transparency of C. chinensis was achieved via decolorization treatment. Noteworthy observations from Figure 3G and Figure 5E reveal an inverse correlation in the expression profiles of CcBurs-R and miR-6012. Consequently, the FISH results distinctly highlight a significant disparity in the expression levels of CcBurs-R and miR-6012 within the same tissue. We have added related explanation and discussion in Line 291-293 of our revised manuscript.

(10) The schematic in Fig. 7 is a useful summary, but there is a part of the logic that is unsupported by the data, specifically in terms of environmental influence on cuticle formation (i.e., plasticity). What is the evidence that lower temperatures influence expression of miR-6012? The study measures its expression over life stages, whether with an agonist or not, over a single temperature. Measuring levels of expression under summer form-inducing temperature is necessary to test the dependence of miR-6012 expression on temperature. Otherwise, this result cannot be interpreted as polyphenism control, but rather the control of a specific trait.

Thanks for your great suggestion. We actually conducted the assessment of miR-6012 expression at specific time intervals (3, 6, 9, 12, 15 days) under different temperatures of 10°C and 25°C. As depicted in Figure 5E, the expression levels of miR-6012 were notably reduced at 10°C compared to 25°C. Additionally, the evaluation of agomir-6012 expression level of C. chinensis under 25°C conditions at various time points (3, 6, 9, 12, 15 days) revealed no significant changes. Hence, we suggest that the impact of miR-6012 on the seasonal morphological transition is influenced upon temperature.

Recommendations for the authors:

The authors report a novel role of Bursicon and its receptor in regulating the seasonal polyphenism of Cacopsylla chinensis. They found that low temperature treatment (10°C) activated the Bursicon signaling pathway during the transition from summer-form to winter-form, which influences cuticle pigment content, cuticle chitin content, and cuticle thickness. Moreover, the authors identified miR-6012 and show that it targets CcBurs-R, thereby modulating the function of Bursicon signaling pathway in the seasonal polyphenism of C. chinensis. This discovery expands our knowledge of multiple roles of neuropeptide bursicon action in arthropod biology. However, the m

anuscript does have several major weaknesses, described under "Public review", which the authors need to address.

Major issues:

(1) L152-154 Fig S2E and S2F: Bursicon has been shown to be expressed in the CNS in a specific set of neurons. For example, In the larval CNS of Manduca sexta, bursicon expression is restricted to the subesophageal ganglion (SG), thoracic ganglia, and first abdominal ganglion. Pharate pupae and pharate adults show expression of this heterodimer in all ganglia. In Drosophila larvae, expression of a bursicon heterodimer is confined to abdominal ganglia. The additional neurons in the ventral nerve cord express only burs. In pharate adults, bursicon is produced by neurons in the SG and abdominal ganglia. I am wondering where bursicon subunits are expressed in the C. chinensis CNS? Since the authors have the antibodies, it would be useful to include immunocytochemical staining of bursicon alpha and beta in the CNS. The qPCR results from head or other tissues (Fig S2E and S2F) is not the most informative way to document localization of gene expression. Regarding the qPCR results, they show that the cuticle and the fat body express CcBurs-α and CcBurs-β. Can the authors confirm this unexpected results independently?

Thanks for your insightful comment. In this study, we did not directly used antibodies targeting bursicon subunits, instead, the bursicon subunits along with a histidine tag were integrated into the expression vector pcDNA3.1 using homologous recombination. The experimental procedures were executed as follows: initially, the histidine tag was fused to the pcDNA3.1-mCherry vector through homologous recombination to generate the recombinant plasmid pcDNA3.1-his-mCherry. Subsequently, the amino acid sequences of the two bursicon subunits were introduced into the pcDNA3.1-his-mCherry vector via homologous recombination to produce the recombinant plasmids pcDNA3.1-CcBurs-α-his-mCherry and pcDNA3.1-CcBurs-β-his-mCherry. Finally, the P2A sequence was incorporated into the vector using reverse PCR to yield the recombinant plasmids pcDNA3.1-CcBurs-α-his-P2A-mCherry and pcDNA3.1-CcBurs-β-his-P2A-mCherry. Consequently, the bursicon subunits, along with the histidine tag, were capable of generating fusion proteins with the histidine tag. Western blot analysis was conducted using antibodies targeting the histidine tag, enabling the detection of histidine expression, which corresponds to the expression of the bursicon subunits. However, they are not suitable to conduct the in vivo immunocytochemical staining of bursicon alpha and beta in the CNS.

Due to the diminutive size of the C. chinensis nymphs, dissection of the central nervous system (CNS) was unfeasible, precluding specific assessment of bursicon expression in the CNS. Prior literature has documented the expression of bursicon subunits in the epidermis and fat body of C. chinensis. Studies suggest that bursicon subunits not only play a role in the melanization and sclerotization processes of insect epidermis but also have significant roles in insect immunity (An et al., 2012). The presence of bursicon subunits in the epidermis, gut, and fat body of C. chinensis may indicate their crucial roles in the immune functions of these tissues. Further investigation is required to elucidate the specific immune functions they perform, hinting at the potential expression of these bursicon subunits in these two tissues.

An, S., Dong, S., Wang, Q., Li, S., Gilbert, L. I., Stanley, D., & Song, Q. (2012). Insect neuropeptide bursicon homodimers induce innate immune and stress genes during molting by activating the NF-κB transcription factor Relish. PloS one, 7(3), e34510. https://doi.org/10.1371/journal.pone.0034510

(2) L222: "CcBurs-R is the Bursicon receptor of C. chinensis". Is this statement supported by affinity binding assay results?

Thanks for your excellent suggestion. We employed a fluorescence-based assay to quantify calcium ion concentrations and investigate the binding affinities of bursicon heterodimers and homodimers to the bursicon receptor across varying concentrations. Our findings suggest that activation of the receptor by the burs α-β heterodimer leads to significant alterations in intracellular calcium ion levels, whereas stimulation with burs α-α and burs β-β homodimers, in conjunction with Adipokinetic hormone (AKH), maintains consistent intracellular calcium ion levels. Consequently, this research definitively identifies CcBurs-R as the bursicon receptor. For further details, please refer to the Materials and Methods (Lines 493-504), Results (Lines 231-239), and Discussion (Lines 377-384) of our revised manuscript.

(3) L245 Figure 4I-4J: Since knockdown of bursicon and its receptor cause a decrease pigment accumulation in the cuticle, it would be useful to examine 1-2 rate limiting enzyme-encoding genes in the bursicon regulated cuticle darkening process if possible (as was done for genes involved in cuticle thickening).

Thanks for your excellent comment. Following the further study, a thorough analysis was conducted to evaluate the impact of bursicon and its receptor on the expression levels of Lactase, Tyrosine hydroxylase, Dopa decarboxylase, Acetyltransferase, and the effects of RNA interference targeting these genes on the seasonal morphological transition. The findings underscored their role in the bursicon-mediated cuticle darkening process. However, as this section is slated for inclusion in an upcoming manuscript intended for submission, it is deemed unsuitable for incorporation into the current manuscript.

Minor issues:

(1) L75 "stronger resistance (Ge et al., 2019; Tougeron et al., 2021)". Stronger resistance to what? Stronger resistance to environmental stress or weather condition? Please clarify.

Thanks for your excellent suggestion. We have changed the statement to “stronger resistance to weather condition” in Line 75 of our revised manuscript.

(2) L132 Figure 1A and 1B: Bursicon sequence was first identified and functionally characterized in Drosophila melanogaster: is there any reason why Drosophila bursicon sequences were not included in the comparison?

Thanks for your excellent comment. We have added the sequence of Burs-α and Burs-β of D. melanogaster in the sequence alignment results of Figure 1A and 1B of our revised manuscript.

(3) Although the authors clearly identify and validate the function for the bursicon genes and its receptor's, there is no mention of whether duplicates of this gene are also present in the pear psyllid. This has been known to happen in otherwise conserved hormone pathways (e.g., insulin receptor in some insects), so a formal check of this should be done.

Thanks for your excellent comment. As shown in Figure S2A-S2B and 3B, there are two bursicon subunit genes and only one bursicon receptor gene in our selected insect species, for examples Drosophila melanogaster, Diaphorina citri, Bemisia tabaci, Nilaparvata lugens, and Sogatella furcifera. In our transcriptome database of C. chinensis, we also only identified two bursicon subunit genes and only one bursicon receptor gene.

(4) Line 41: Here, as in the title, "fascinating" is a subjective judgement that does not improve a study's presentation.

Thanks for your great comment. We have changed "fascinating" to "transformation" in Line 41 and also revised the title of our revised manuscript.

(5) Line 44: What makes some fields "cutting-edge" and others not?

Thanks for your excellent suggestion. The expression of "in cutting-edge fields" has been deleted in Line 44 of our revised manuscript.

(6) Line 97: This is a peculiar choice of reference for the concept of slower development in cold temperatures. The concept of degree-days and growth rates is old and widespread in entomology.

Thanks for your insightful comment. The reference of Nyamaukondiwa et al., 2011 in Line 95 has been deleted in our revised manuscript.

(7) Lines 149-150: What justifies the assumption that higher levels of expression mean a more important role? This gene might be just as necessary for development of the summer form, even if expressed at lower levels.

Thanks for your excellent suggestion. This sentence has been revised to “Increased gene expression levels may potentially contribute to the transition from summer-form to winter-form in C. chinensis.” in Line 168-169 of our revised manuscript.

(8) The blue arrow in Fig. 7 is confusing.

Thanks for your excellent suggestion. In Figure 7, the blue arrow represents the down-regulated expression of miR-6012. We have added a description about the blue arrow in Figure 7 of our revised manuscript.

Miles touches on the interplay of knowledge written down on index cards and the knowledge which is kept only in one's mind. Some practitioners in the space from 2013-2024 seem to imply that they're writing almost everything out in far deeper detail than Miles would indicate. In his incarnation, much of the knowledge might be more quickly indicated by a short sentence or heading which the brain can associate to longer explanations.

This sort of indexing is akin to some of the method potentially seen in Marshall Mathers' zettelkasten.

I'm creating a tag here for "card index for productivity" to track the idea of productivity in writing which I'm specifically using separately from the tag "card index as productivity system" which is used to describe their use for project tracking systems in systems like GTD, Memindex, etc.

Author response:

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

Many thanks to the editors for the reviewing of the revised manuscript.

We are very grateful to the Reviewers for their time and for the appreciation of the revision.

We thank the Reviewer 3 for acknowledging the use of sulforhodamine B (SRB) fluorescence as a real-time readout of astrocyte volume dynamics. Experimental data in brain slices were provided to validate this approach.<br /> The incomplete matching of our observation with early reported data in cultured astrocytes (e.g., Solenov et al., AJP-Cell, 2004), might reflect certain of their properties differing from the slice/in vivo counterparts as discussed in the manuscript.<br /> The study (T.R. Murphy et al., Front Cell Neurosci., 2017) showed that AQP4 knockout increased astrocyte swelling extent in response to hypoosmotic solution in brain slices (Fig 9), and discussed '... AQP4 can provide an efficient efflux pathway for water to leave astrocytes.’ Correspondingly, our data suggest that AQP4 mediate astrocyte water efflux in basal conditions.<br /> We have discussed the study (Igarashi et al., NeuroReport 2013); our current data would help to understand the cellular mechanisms underlying the finding of Igarashi et al.

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Pham and colleagues provide an illuminating investigation of aquaporin-4 water flux in the brain utilizing ex vivo and in vivo techniques. The authors first show in acute brain slices, and in vivo with fiber photometry, SRB-loaded astrocytes swell after inhibition of AQP4 with TGN-020, indicative of tonic water efflux from astrocytes in physiological conditions. Excitingly, they find that TGN-020 increases the ADC in DW-MRI in a region-specific manner, potentially due to AQP4 density. The resolution of the DW-MRI cannot distinguish between intracellular or extracellular compartments, but the data point to an overall accumulation of water in the brain with AQP4 inhibition. These results provide further clarity on water movement through AQP4 in health and disease.

Overall, the data support the main conclusions of the article, with some room for more detailed treatment of the data to extend the findings.

Strengths:

The authors have a thorough investigation of AQP4 inhibition in acute brain slices. The demonstration of tonic water efflux through AQP4 at baseline is novel and important in and of itself. Their further testing of TGN-020 in hyper- and hypo-osmotic solutions shows the expected reduction of swelling/shrinking with AQP4 blockade.

Their experiment with cortical spreading depression further highlights the importance of water efflux from astrocytes via AQP4 and transient water fluxes as a result of osmotic gradients. Inhibition of AQP4 increases the speed of tissue swelling, pointing to a role in the efflux of water from the brain.

The use of DW-MRI provides a non-invasive measure of water flux after TGN-020 treatment.

We thank the reviewer for the insightful comments.

Weaknesses:

The authors specifically use GCaMP6 and light sheet microscopy to image their brain sections in order to identify astrocytic microdomains. However, their presentation of the data neglects a more detailed treatment of the calcium signaling. It would be quite interesting to see whether these calcium events are differentially affected by AQP4 inhibition based on their cellular localization (ie. processes vs. soma vs. vascular end feet which all have different AQP4 expressions).

Following the suggestion, we provide new data on the effect of AQP4 inhibition on spontaneous calcium signals in perivascular astrocyte end-feet. As shown now in Fig.S2, acute application of TGN020 induced Ca2+ oscillations in astrocyte end-feet regions where the GCaMP6 labeling lines the profile of the blood vessel. It is noted that on average, the strength of basal Ca2+ signals in the end-feet is higher than that observed across global astrocyte territories (4.65 ± 0.55 vs. 1.45 ± 0.79, p < 0.01), as does the effect of TGN (8.4 ± 0.62 vs. 6.35 ± 0.97, p < 0.05; Fig S2 vs. Fig 2B). This likely reflects the enrichment of AQP4 in astrocyte end-feet. We describe the data in Fig.S2, and on page 8, line 20 – 23.  

We now use the transgenic line GLAST-GCaMP6 for cytosolic GCaMP6 expression in astrocytes. Spontaneous calcium signals, reflected by transient fluorescence rises, occur in discrete micro-domains whereas the basal GCaMP6 fluorescence in the soma is weak. In the present condition, it is difficult to unambiguously discriminate astrocyte soma from the highly intermingled processes. 

The authors show the inhibition of AQP4 with TGN-020 shortens the onset time of the swelling associated with cortical spreading depression in brain slices. However, they do not show quantification for many of the other features of CSD swelling, (ie. the duration of swelling, speed of swelling, recovery from swelling).

Regarding the features of the CSD swelling, we have performed new analysis to quantify the duration of swelling, speed of swelling and the recovery time from swelling in control condition and in the presence of TGN-020. The new analysis is now summarized in Fig. S5. Blocking AQP4 with TGN-020 increases the swelling speed, prolongs the duration of swelling and slows down the recovery from swelling, confirming our observation that acute inhibition of AQP4 water efflux facilitates astrocyte swelling while restrains shrinking. We describe the result on page 11, line 19-21. 

Significance:

AQP4 is a bidirectional water channel that is constitutively open, thus water flux through it is always regulated by local osmotic gradients. Still, characterizing this water flux has been challenging, as the AQP4 channel is incredibly water-selective. The authors here present important data showing that the application of TGN-020 alone causes astrocytic swelling, indicating that there is constant efflux of water from astrocytes via AQP4 in basal conditions. This has been suggested before, as the authors rightfully highlight in their discussion, but the evidence had previously come from electron microscopy data from genetic knockout mice.

AQP4 expression has been linked with the glymphatic circulation of cerebrospinal fluid through perivascular spaces since its rediscovery in 2012 [1]. Further studies of aging[2], genetic models[3], and physiological circadian variation[4] have revealed it is not simply AQP4 expression but AQP4 polarization to astrocytic vascular endfeet that is imperative for facilitating glymphatic flow. Still, a lingering question in the field is how AQP4 facilitates fluid circulation. This study represents an important step in our understanding of AQP4's function, as the basal efflux of water via AQP4 might promote clearance of interstitial fluid to allow an influx of cerebrospinal fluid into the brain. Beyond glymphatic fluid circulation, clearly, AQP4-dependent volume changes will differentially alter astrocytic calcium signaling and, in turn, neuronal activity.

(1) Iliff, J.J., et al., A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med, 2012. 4(147): p. 147ra111.

(2) Kress, B.T., et al., Impairment of paravascular clearance pathways in the aging brain. Ann Neurol, 2014. 76(6): p. 845-61.

(3) Mestre, H., et al., Aquaporin-4-dependent Glymphatic Solute Transport in the Rodent Brain. eLife, 2018. 7.

(4) Hablitz, L., et al., Circadian control of brain glymphatic and lymphatic fluid flow. Nature Communications, 2020. 11(1).

We thank the reviewer in acknowledging the significance of our study and the functional implication in brain glymphatic system. We have now highlighted the mentioned studies as well as the potential implication glymphatic fluid circulation (page 4, line 9-10; page 5, line 1-3; and page 19, line 3-10). 

Reviewer #2 (Public Review):

Summary:

The paper investigates the role of astrocyte-specific aquaporin-4 (AQP4) water channel in mediating water transport within the mouse brain and the impact of the channel on astrocyte and neuron signaling. Throughout various experiments including epifluorescence and light sheet microscopy in mouse brain slices, and fiber photometry or diffusion-weighted MRI in vivo, the researchers observe that acute inhibition of AQP4 leads to intracellular water accumulation and swelling in astrocytes. This swelling alters astrocyte calcium signaling and affects neighboring neuron populations. Furthermore, the study demonstrates that AQP4 regulates astrocyte volume, influencing mainly the dynamics of water efflux in response to osmotic challenges or associated with cortical spreading depolarization. The findings suggest that AQP4-mediated water efflux plays a crucial role in maintaining brain homeostasis, and indicates the main role of AQP4 in this mechanism. However authors highlight that the report sheds light on the mechanisms by which astrocyte aquaporin contributes to the water environment in the brain parenchyma, the mechanism underlying these effects remains unclear and not investigated. The manuscript requires revision.

Strengths:

The paper elucidates the role of the astrocytic aquaporin-4 (AQP4) channel in brain water transport, its impact on water homeostasis, and signaling in the brain parenchyma. In its idea, the paper follows a set of complimentary experiments combining various ex vivo and in vivo techniques from microscopy to magnetic resonance imaging. The research is valuable, confirms previous findings, and provides novel insights into the effect of acute blockage of the AQP4 channel using TGN-020.

We thank the reviewer for the constructive comments.

Weaknesses:

Despite the employed interdisciplinary approach, the quality of the manuscript provides doubts regarding the significance of the findings and hinders the novelty claimed by the authors. The paper lacks a comprehensive exploration or mention of the underlying molecular mechanisms driving the observed effects of astrocytic aquaporin-4 (AQP4) channel inhibition on brain water transport and brain signaling dynamics. The scientific background is not very well prepared in the introduction and discussion sections. The important or latest reports from the field are missing or incompletely cited and missconcluded. There are several citations to original works missing, which would clarify certain conclusions. This especially refers to the basis of the glymphatic system concept and recently published reports of similar content. The usage of TGN-020, instead of i.e. available AER-270(271) AQP4 blocker, is not explained. While employing various experimental techniques adds depth to the findings, some reasoning behind the employed techniques - especially regarding MRI - is not clear or seemingly inaccurate. Most of the time the number of subjects examined is lacking or mentioned only roughly within the figure captions, and there are lacking or wrongly applied statistical tests, that limit assessment and reproducibility of the results. In some cases, it seems that two different statistical tests were used for the same or linked type of data, so the results are contradictory even though appear as not likely - based on the figures. Addressing these limitations could strengthen the paper's impact and utility within the field of neuroscience, however, it also seems that supplementary experiments are required to improve the report.

The current data hint at a tonic water efflux from astrocyte AQP4 in physiological condition, which helps to understand brain water homeostasis and the functional implication for the glymphatic system. The underlying molecular and cellular mechanisms appear multifaceted and functionally interconnected, as discussed (page 14 line 8 –page 15, line 3). We agree that a comprehensive exploration will further advance our understanding.

The introduction and discussion are now strengthened by incorporating the important advances in glymphatic system while highlighting the relevant studies. 

The use of TGN-020 was based on its validation by wide range of ex vivo and in vivo studies including the use of heterologous expression system and the AQP4 KO mice. The validation of AER-270(271, the water soluble prodrug) using AQP4 KO mice is reported recently (Giannetto et al., 2024). AER-271 was noted to impact brain water ADC (apparent diffusion coefficient evaluated by diffusion-weighted MRI) in AQP4 KO mice ~75 min after the drug application (Giannetto et al., 2024). This likely reflects that AER270(271) is also an inhibitor for κΒ nuclear factor (NF-κΒ) whose inhibition could reduce CNS water content independent of AQP4 targeting (Salman et al., 2022). In addition, the inhibition efficiency of AER-270(271) seems lower than TGN-020 (Farr et al., 2019; Giannetto et al., 2024; Huber et al., 2009; Salman et al., 2022). We have now supplemented this information in the manuscript (page 7, line 1-6 and page15, line 7-17).

The description on the DW-MRI is now updated (page 4, line 10-14). 

We also performed new experiments and data analysis as described in a point-to-point manner below in the section ‘Recommendations For The Authors’.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors propose that astrocytic water channel AQP4 represents the dominant pathway for tonic water efflux without which astrocytes undergo cell swelling. The authors measure changes in astrocytic sulforhodamine fluorescence as the proxy for cell volume dynamics. Using this approach, they perform a technically elegant series of ex vivo and in vivo experiments exploring changes in astrocytic volume in response to AQP4 inhibitor TGN-020 and/or neuronal stimulation. The key finding is that TGN-020 produces an apparent swelling of astrocytes and modifies astrocytic cell volume regulation after spreading depolarizations. Additionally, systemic application of TGN-020 produced changes in diffusion-weighted MRI signal, which the authors interpret as cellular swelling. This study is perceived as potentially significant. However, several technical caveats should be strongly considered and perhaps addressed through additional experiments.

Strengths:

(1) This is a technically elegant study, in which the authors employed a number of complementary ex vivo and in vivo techniques to explore functional outcomes of aquaporin inhibition. The presented data are potentially highly significant (but see below for caveats and questions related to data interpretation).

(2) The authors go beyond measuring cell volume homeostasis and probe for the functional significance of AQP4 inhibition by monitoring Ca2+ signaling in neurons and astrocytes (GCaMP6 assay).

(3) Spreading depolarizations represent a physiologically relevant model of cellular swelling. The authors use ChR2 optogenetics to trigger spreading depolarizations. This is a highly appropriate and much-appreciated approach.

We thank the reviewer for the effort in evaluating our work.

Weaknesses:

(1) The main weakness of this study is that all major conclusions are based on the use of one pharmacological compound. In the opinion of this reviewer, the effects of TGN-020 are not consistent with the current knowledge on water permeability in astrocytes and the relative contribution of AQP4 to this process.

Specifically: Genetic deletion of AQP4 in astrocytes reduces plasmalemmal water permeability by ~two-three-fold (when measured a 37oC, Solenov et al., AJP-Cell, 2004). This is a significant difference, but it is thought to have limited/no impact on water distribution. Astrocytic volume and the degree of anisosmotic swelling/shrinkage are unchanged because the water permeability of the AQP4null astrocytes remains high. This has been discussed at length in many publications (e.g., MacAulay et al., Neuroscience, 2004; MacAulay, Nat Rev Neurosci, 2021) and is acknowledged by Solenov and Verkman (2004).

Keeping this limitation in mind, it is important to validate astrocytic cell volume changes using an independent method of cell volume reconstruction (diameter of sulforhodamine-labeled cell bodies? 3D reconstruction of EGFP-tagged cells? Else?)

Solenov and coll. used the calcein quenching assay and KO mice demonstrating AQP4 as a functional water channel in cultured astrocytes (Solenov et al., 2004). AQP4 deletion reduced both astrocyte water permeability and the absolute amplitude of swelling over comparable time, and also slowed down cell shrinking, which overall parallels our results from acute AQP4 blocking. Yet in Solenovr’s study, the time to swelling plateau was prolonged in AQP4 KO astrocytes, differing from our data from the pharmacological acute blocking. This discrepancy may be due to compensatory mechanisms in chronic AQP4 KO, or reflect the different volume responses in cultured astrocytes from brain slices or in vivo results as suggested previously (Risher et al., 2009). 

Soma diameter might be an indicator of cell volume change, yet it is challenging with our current fluorescence imaging method that is diffraction-limited and insufficient to clearly resolve the border of the soma in situ. In addition, the lateral diameter of cell bodies may not faithfully reflect the volume changes that can occur in all three dimensions. Rapid 3D imaging of astrocyte volume dynamics with sufficient high Z-axis resolution appears difficult with our present tools. 

We have now accordingly updated the discussion with relevant literatures being cited (page 17 line 14 – page 18, line 3).

(2) TGN-020 produces many effects on the brain, with some but not all of the observed phenomena sensitive to the genetic deletion of AQP4. In the context of this work, it is important to note that TGN020 does not completely inhibit AQP4 (70% maximal inhibition in the original oocyte study by Huber et al., Bioorg Med Chem, 2009). Thus, besides not knowing TGN-020 levels inside the brain, even

"maximal" AQP4 inhibition would not be expected to dramatically affect water permeability in astrocytes.

This caveat may be addressed through experiments using local delivery of structurally unrelated AQP4 blockers, or, preferably, AQP4 KO mice.

It is an important point that TGN-020 partially blocks AQP4, implying the actual functional impact of AQP4 per se might be stronger than what we observed. TGN provides a means to acutely probe AQP4 function in situ, still we agree, its limitation needs be acknowledged. We mention this now on page 15, line 7-9 and 14-17.

We agree that local delivery of an alternative blocker will provide additional information. Meanwhile, local delivery requires the stereotaxic implantation of cannula, which would cause inflammations to surrounding astrocytes (and neurons). The recently introduced AQP4 blocker AER-270(271) has received attention that it influences brain water dynamics (ADC in DW-MRI) in AQP4 KO mice (Giannetto et al., 2024), recalling that AER-270(271) is also an inhibitor for κΒ nuclear factor (NF-κΒ). This pathway can potentially perturb CNS water content and influence brain fluid circulation, in an AQP4independent manner (Salman et al., 2022). The inhibition efficiency on mouse AQP4 of AER-270 (~20%, Farr et al., 2019; Salman et al., 2022) appears lower than TGN-020 (~70%, Huber et al., 2009).

We chose to use the pharmacological compound to achieve acute blocking of AQP4 thereby avoiding the chronic genetics-caused alterations in brain structural, functional and water homeostasis. Multiple lines of evidence including the recent study (Gomolka et al., 2023), have shown that AQP4 KO mice alters brain water content, extracellular space and cellular structures, which raises concerns to use the transgenic mouse to pinpoint the physiological functions of the AQP4 water channel. 

We have now mentioned the concerns on AQP4 pharmacology by supplementing additional literatures in the field (page 15, line 8-18). 

(3) This reviewer thinks that the ADC signal changes in Figure 5 may be unrelated to cellular swelling. Instead, they may be a result of the previously reported TGN-020-induced hyphemia (e.g., H. Igarashi et al., NeuroReport, 2013) and/or changes in water fluxes across pia matter which is highly enriched in AQP4. To amplify this concern, AQP4 KO brains have increased water mobility due to enlarged interstitial spaces, rather than swollen astrocytes (RS Gomolka, eLife, 2023). Overall, the caveats of interpreting DW-MRI signal deserve strong consideration.

The previous observation show that TGN-020 increases regional cerebral blood flow in wild-type mice but not in AQP4 KO mice (Igarashi et al., 2013). Our current data provide a possible mechanism explanation that TGN-020 blocking of astrocyte AQP4 causes calcium rises that may lead to vasodilation as suggested previously (Cauli and Hamel, 2018). We now add updates to the discussion on page 15, line 3-7.

We are in line with the reviewer regarding the structural deviations observed with the AQP4 KO mice

(Gomolka et al., 2023), now mentioned on page 19, line 3-5. Following the Reviewer’s suggestion, we have also updated the interpretation of the DW-MRI signal and point that in addition to being related to the astrocyte swelling, the ADC signal changes may also be caused by indirect mechanisms, such as the transient upregulation of other water-permeable pathways in compensating AQP4 blocking. We now describe this alternative interpretation and the caveats of the DW-MRI signals (page 20, line 1-8). 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Private recommendations

My more broad experimental suggestions are in the "weaknesses" section. Some minor points that would improve the manuscript are included below:

(1) A more detailed explanation for why SRB fluorescence reflects the astrocyte volume changes, whereas typical intracellular GFP does not.

As an engineered fluorescence protein, the GFP has been used to tag specific type of cells. Meanwhile, as a relatively big protein (MW, 26.9 kDa), the diffusion rate of EGFP is expected to be much less than SRB, a small chemical dye (MW, 558.7 Da). Also, the IP injection of SRB enables geneticsless labeling of brain astrocytes, so to avoid the influence of protein overexpression on astrocyte volume and water transport responses. We have now stated this point in the manuscript (page 13, line 21 – page 14, line 4).

(2) Figure 1 panel B should have clear labels on the figure and a description in the legend to delineate which part of the panel refers to hyper- or hypo-osmotic treatment.

We have now updated the figure and the legend.  

(3) For Figure 2, what is the rationale for analyzing the calcium signaling data between the cell types differently?

We analyzed calcium micro-domains for astrocytes as their spontaneous signals occur mainly in discrete micro-domains (Shigetomi et al., 2013). While for neurons, we performed global analysis by calculating the mean fluorescence of imaging field of view, because calcium signal changes were only observed at global level rather than in micro-domains. This information is now included (page 24, line1820).

(4) For Figure 3, the authors mention that TGN-020 likely caused swelling prior to the hypotonic solution administration. Do they have any measurements from these experiments prior to the TGN-020 application to use as a "true baseline" volume?

The current method detects the relative changes in astrocyte volume (i.e., transmembrane water transport), which nevertheless is blind to the absolute volume value. We have no readout on baseline volumes.  

(5) For Figures 3 and 4, did the authors see any evidence for regulatory volume decrease? And is this impaired by TGN-020? It is a well-characterized phenomenon that astrocytes will open mechanosensitive channels to extrude ions during hypo-osmotic induced swelling. This process is dependent on AQP4 and calcium signaling [5]

Mola and coll. provided important results demonstrating the role of AQP4 in astrocyte volume regulation (Mola et al., 2016). In the present study in acute brain slices, when we applied hypotonic solution to induce astrocyte swelling, our protocol did not reveal rapid regulatory volume decrease (e.g., Fig. 3D). When we followed the volume changes of SRB-labeled astrocytes during optogenetically induced CSD, we observed the phase of volume decrease following the transient swelling (Fig. 4F), where the peak amplitude and the degree of recovery were both reduced by inhibiting AQP4 with TGN020. These data imply that regulatory astrocyte volume decrease may occur in specific conditions, which intriguingly has been suggested to be absent in brain slices and in vivo (e.g., Risher et al., 2009). We have not specifically investigated this phenomenon, and now briefly discuss this point on page18 line 6-14.

(6) Figure 5 box plots do not show all data points, could the authors modify to make these plots show all the animals, or edit the legend to clarify what is plotted?

We have now updated the plot and the legend. This plot is from all animals (n = 7 per condition).

(7) pg. 9 line 6, there is a sentence that seems incomplete or otherwise unfinished. "We first followed the evoked water efflux and shrinking induced by hypertonic solution while."

Fixed (now, page 9 line 17-18). 

(8)  During the discussion on pg 13 line 11, it may be more clear to describe this as the cotransport of water into the cells with ions/metabolites as reviewed by Macaulay 2021 [6].

We agree; the text is modified following this suggestion (now page14, line 12-13).  

(1) Iliff, J.J., et al., A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med, 2012. 4(147): p. 147ra111.

(2) Kress, B.T., et al., Impairment of paravascular clearance pathways in the aging brain. Ann Neurol, 2014. 76(6): p. 845-61.

(3) Mestre, H., et al., Aquaporin-4-dependent Glymphatic Solute Transport in the Rodent Brain. eLife, 2018. 7.

(4) Hablitz, L., et al., Circadian control of brain glymphatic and lymphatic fluid flow. Nature Communications, 2020. 11(1).

(5) Mola, M., et al., The speed of swelling kinetics modulates cell volume regulation and calcium signaling in astrocytes: A different point of view on the role of aquaporins. Glia, 2016. 64(1).

(6) MacAulay, N., Molecular mechanisms of brain water transport. Nat Rev Neurosci, 2021. 22(6): p. 326-344.

We thank the reviewer. These important literatures are now supplemented to the manuscript together with the corresponding revisions.

Reviewer #2 (Recommendations For The Authors):

In its concept, the paper is interesting and provides additional value - however, it requires revision.

Below, I provide the following remarks for the following sections/ pages/lines:

ABSTRACT/page 2 (remarks here refer to the rest of the manuscript, where these sentences are repeated):

- It seems that the 'homeostasis' provides not only physical protection, but also determines the diffusion of chemical molecules...' Please correct the sentence as it is grammatically incorrect.

It is now corrected (page 2, line 1).

- The term 'tonic water' is not clear. I understand, after reading the paper, that it is about tonicity of the solutes injected into the mouse.

We use the term ‘tonic’ to indicate that in basal conditions, a constant water efflux occurs through the APQ4 channel.

- 'tonic aquaporin water efflux maintains volume equilibrium' - I believe it is about maintaining volume and osmotic equilibrium?

This description is now refined (now page 2, line 10).

- It is not clear whether the tonic water outflow refers to the cellular level or outflow from the brain parenchyma (i.e., glymphatic efflux)

It refers to the cellular level. 

INTRODUCTION/page 3:

- 'clearance of waste molecules from the brain as described in the glymphatic system' - The original papers describing the phenomena are not cited: Iliff et al. 2012, 2013, Mestre et al. 2018, as well as reviews by Nedergaard et al.

Indeed. We have now cited these key literatures (now page 4, line 10).

- 'brain water diffusion is the basis for diffusion-weighted magnetic resonance imaging (DW-MRI)' - The statement is wrong. it is the mobility of the water protons that DWI is based on, but not the diffusion of molecules in the brain. This should be clarified and based on the DW-MRI principle and the original works by Le Bihan from 1986, 1988, or 2015.

This sentence is now updated (page 4, line10-14).

- Similarly, I suggest correcting or removing the citations and the sentence part regarding the clinical use of DWI, as it has no value here. Instead, it would be worth mentioning what actually ADC reflects as a computational score, and what were the results from previous studies assessing glymphatic systems using DWI. This is especially important when considering the mislocalization of the AQP4 channel.

We now states recent studies using DW-MRI to evaluate glymphatic systems (page 4, line16-17).  

- 'In the brain, AQP4 is predominantly expressed in astrocytes'-please review the citations. I suggest reading the work by Nielsen 1997, Nagelhus 2013, Wolburg 2011, and Li and Wang from 2017. To my best knowledge, in the brain AQP4 is exclusively expressed in astrocytes.

Thanks for the reviewer. It is described that while enriched in astrocytes, AQP4 is also expressed in ependymal cells lining the ventricles (e.g., (Mayo et al., 2023; Verkman et al., 2006)). ‘predominantly’ is now removed (page 4, line 21).

- The conclusion: ' Our finding suggests that aquaporin acts as a water export route in astrocytes in physiological conditions, so as to counterbalance the constitutive intracellular water accumulation caused by constant transmitter and ion uptake, as well as the cytoplasmic metabolism processes. This mechanism hence plays a necessary role in maintaining water equilibrium in astrocytes, thereby brain water homeostasis' seems to be slightly beyond the actual findings in the paper. I suggest clarifying according to the described phenomena.

We have now refined the conclusion sticking to the experimental observations (page 5, line16-18).

- The introduction lacks important information on existing AQP4 blockers and their effects, pros and cons on why to use TGN-020. Among others, I would refer to recent work by Giannetto et al 2024, as well as previous work of Mestre et al. 2018 and Gomolka et al. 2023.

We initiated the study by using TGN-020 as an AQP4 blocker because it has been validated by wide range of ex vivo and in vivo studies as documented in the text (page 7, line 1-6). We also update discussions on the recent advances in validating the AQP4 blocker AER-270(271) while citing the relevant studies (page 15, line 7-17).  

RESULTS:

- Page 5, lines 19-20: '...transport, we performed fluorescence intensity translated (FIT) imaging.' - this term was never introduced in the methods so it is difficult for the reader to understand it at first sight. -'To this end,' - it is not clear which action refers to 'this'. (is it about previous works or the moment that the brain samples were ready for imaging? Please clarify, as it is only starting to be clear after fully reading the methods.

We now refine the description give the principle of our imaging method first, then explain the technical steps. To avoid ambiguity, the term ‘To this end’ is removed. The updated text is now on page 6, line 1-3.  

- From page 6 onwards - all references to Figures lack information to which part of the figure subpanel the information refers (top/middle bottom or left/middle/right).

We apologize. The complementary indication is now added for figure citations when applicable.  

- 'whereas water export and astrocyte shrinking upon hyperosmotic manipulation increased astrocyte fluorescence (Figure 1B). Hence, FIT imaging enables real-time recording of astrocyte transmembrane water transport and volume dynamics.' - this part seems to be undescribed or not clear in the methods.

We have now refined this description (page 6, line 19-20).

- Page 6, lines 17-22: TGN-020. In addition to the above, I suggest familiarizing also with the following works by Igarashi 2011. doi: 10.1007/s10072-010-0431-1, and by Sun 2022. doi: 10.3389/fimmu.2022.870029.

These studies are now cited (page 7, line 3-4).

- Page 7: ' AQP4 is a bidirectional channel facilitating... ' - AQP4 water channel is known as the path of least resistance for water transfer, please see Manley, Nature Medicine, 2000 and Papadopoulos, Faseb J, 2004.

This sentence is now updated (page 7, line 12-13).

- ' astrocyte AQP4 by TGN-020 caused a gradual decrease in SRB fluorescence intensity, indicating an intracellular water accumulation' - tissue slice experiment is a very valuable method. However it seems right, the experiment does not comment on the cell swelling that may occur just due to or as a superposition of tissue deterioration and the effect of TGN-020. The AQP4 channel is blocked, and the influx of water into astrocytes should be also blocked. Thus, can swelling be also a part of another mechanism, as it was also observed in the control group? I suggest this should be addressed thoroughly.

We performed this experiment in acute brain slices to well control the pharmacological environment and gain spatial-temporal information. Post slicing, the brain slices recovered > 1hr prior to recording, so that the slices were in a stable state before TGN-020 application as evidenced by the stable baseline. The constant decrease in the control trace is due to photobleaching which did not change its curve tendency in response to vehicle. TGN-020, in contrast, caused a down-ward change suggesting intracellular water accumulation and swelling. 

The experiment was performed at basal condition without active water influx; a decrease in SRB fluorescence hints astrocyteintracellular water buildup. This result shows that in basal condition, astrocyte aquaporin mediates a constant (i.e., tonic) water efflux; its blocking causes intracellular water accumulation and swelling. 

We have accordingly updated the description of this part (page 7, line 15-20).

- From the Figure 1 legend: Only 4 mice were subjected to the experiment, and only 1 mouse as a control. I suggest expanding the experiment and performing statistics including two-way ANOVA for data in panels B, C, and D, as no results of statistical tests confirm the significance of the findings provided.

The panel B confirms that cytosolic SRB fluorescence displays increasing tendency upon water efflux and volume shrinking, and vice versa. As for the panel C, the number of mice is now indicated. Also, the downward change in the SRB fluorescence was now respectively calculated for the phases prior and post to TGN (and vehicle) application, and this panel is accordingly updated. TGN-020 induced a declining in astrocyte SRB fluorescence, which is validated by t-test performed in MATLAB. To clarify, we now add cross-link lines to indicate statistical significance between the corresponding groups (Fig 1C, middle). As for panel D, we calculated the SRB fluorescence change (decrease) relative to the photobleaching tendency illustrated by the dotted line. The significance was also validated by t-test performed in MATLAB.  

- Figure 1: Please correct the figure - pictures in panel A are low quality and do not support the specificity of SRB for astrocytes. Panels B-D are easier to understand if plotted as normal X/Y charts with associated statistical findings. Some drawings are cut or not aligned.

In GFAP-EGFP transgenic, astrocytes are labeled by EGFP. SRB labeling (red fluorescence) shows colocalization with EGFP-positive astrocytes, meanwhile not all EGFP-positive astrocytes are labeled by SRB. The PDF conversion procedure during the submission may also somehow have compromised image quality. We have tried to update and align the figure panels.  

- Page 12: ' TGN-020 increased basal water diffusion within multiple regions including the cortex,

hippocampus and the striatum in a heterogeneous manner (Figure 5C).'

This sentence is updated now (page 12, line 12 – page13, line 2). It reads ‘The representative images reveal the enough image quality to calculate the ADC, which allow us to examine the effect of TGN-020 on water diffusion rate in multiple regions (Fig. 5C).’

- The expression of AQP4 within the brain parenchyma is known to be heterogenous. Please familiarize yourself with works by Hubbard 2015, Mestre 2018, and Gomolka 2023. A correlation between ADC score and AQP4 expression ROI-wise would be useful, but it is not substantial to conduct this experiment.

We thank the reviewer. This point is stressed on page 19, line 12-14.

DISCUSSION:

- Most of the issues are commented on above, so I suggest following the changes applied earlier. -Page 16: 'We show by DW-MRI that water transport by astrocyte aquaporin is critical for brain water homeostasis.' This statement is not clear and does not refer to the actual impact of the findings. DWI is allowed only to verify the changes of ADC fter the application of TGN-020. I suggest commenting on the recent report by Giannetto 2024 here.

This sentence is now refined (page 19, line 1-2), followed by the updates commenting on the recent studies employing DW-MRI to evaluate brain fluid transport, including the work of (Giannetto et al., 2024) (page 19, line 3-10). 

METHODS:

- Page 18: no total number of mice included in all experiments is provided, as well as no clearly stated number of mice used in each experiment. Please correct.

We have now double checked the number of the mice for the data presented and updated the figure legends accordingly (e.g., updates in legends fig1, fig5, etc).

-  Page 18, line 7: 'Axscience' is not a producer of Isoflurane, but a company offering help with scientific manuscript writing. If this company's help was used, it should be stated in the acknowledgments section. Reference to ISOVET should be moved from line 15 to line 7.

We apologize. We did not use external writing help, and now have removed the ‘Axcience’. The Isoflurane was under the mark ‘ISOVET’ from ‘Piramal’. This info is now moved up (page 21, line 11). 

- Page 18, line 9: ' modified artificial cerebrospinal fluid (aCSF)'. Additional information on the reason for the modified aCSF would be useful for the reader.

In this modified solution, the concentration of depolarizing ions (Na+, Ca2+) was reduced to lower the potential excitotoxicity during the tissue dissection (i.e., injury to the brain) for preparing the brain slices. Extra sucrose was added to balance the solution osmolarity. This solution has been used previously for the dissection and the slicing steps in adult mice (Jiang et al., 2016). We now add this justification in the text and quote the relevant reference (page 21, line14-16). 

- Page 19, line 6: a reasoning for using Tamoxifen would be helpful for the reader.

The Glast-CreERT2 is an inducible conditional mouse line that expresses Cre recombinase selectively in astrocytes upon tamoxifen injection. We now add this information in the text (page 22, line 10-11). 

- Line 8 - 'Sigma'

Fixed.

- Line 7/8: It is not clear if ethanol is of 10% solution or if proportions of ethanol+tamoxifen to oil were of 1:9. The reasoning for each performed step is missing.

We have now clarified the procedure (page 22, line 11-15).

- Line 10: '/' means 'or'?

Here, we mean the bigenic mice resulting from the crossing of the heterozygous Cre-dependent GCaMP6f and Glast-CreERT2 mouse lines. We now modify it to ‘Glast-CreERT2::Ai95GCaMP6f//WT’, in consistence with the presentation of other mouse lines in our manuscript (page 22, line 16).

- Lines 22-23: being in-line with legislation was already stated at the beginning of the Methods so I suggest combining for clearance.

Done. 

- Page 21, line 4: it is good to mention which printer was used, but it would be worth mentioning the material the chamber was printed from - was it ABS?

Yes. We add this info in the text now (page 24, line 5).

- Line 9 -'PI' requires spelling out.

It is ‘Physik Instrumente’, now added (page 24, line 10).

- Line 11-12: What is the reason for background subtraction - clearer delineation of astrocytes/ increasing SNR in post-processing, or because SRB signal was also visible and changing in the background over time? Was the background removed in each frame independently (how many frames)? How long was the time-lapse and was the F0 frame considered as the first frame acquired? The background signal should be also measured and plotted alongside the astrocytic signal, as a reference (Figure 1). This should be clarified so that steps are to be followed easily.

We sought to follow the temporal changes in SRB fluorescence signal. The acquired fluorescent images contain not only the SRB signals, but also the background signals consisting of for instance the biological tissue autofluorescence, digital camera background noise and the leak light sources from the environments. The value of the background signal was estimated by the mean fluorescence of peripheral cell-free subregions (15 × 15 µm²) and removed from all frames of time-lapse image stack. The traces shown in the figures reflect the full lengths of the time-lapse recordings. F0 was identified as the mean value of the 10 data points immediately preceding the detected fluorescence changes. The text is now updated (page 24 line 21 - page 25 line 5).

- Line 15: Was astrocyte image delineation performed manually or automatically? Where was the center of the region considered in the reference to the astrocyte image? It would be good to see the regions delineated for reference.

Astrocytes labeled by SRB were delineated manually with the soma taken as the center of the region of interest. We now exemplify the delineated region in Fig 1A, bottom.

- Page 22, line 2: 'x4 objective'.

Added (now, page 25, line 16). 

- Line 3: 'barrels' - reference to publication or the explanation missing.

The relevant reference is now added on barrel cortex (Erzurumlu and Gaspar, 2020) (page 25, line 19-20). 

- Line 19: were the coordinates referred to = bregma?

Yes. This info is now added (page 26, line 12). 

- Line 20: was the habituation performed directly at the acquisition date? It is rather difficult to say that it was a habituation, but rather acute imaging. I suggest correcting, that mice were allowed to familiarize themselves with the setup for 30 minutes prior to the imaging start.

In this context, although it is a very nice idea and experiment, the influence of acute stress in animals familiar with the setup only from the day of acquisition is difficult to avoid. It is a major concern, especially when considering norepinephrine as a master driver of neuronal and vascular activity through the brain, and strong activation of the hypothalamic-adrenal axis in response to acute stress. It is well known, that the response of monoamines is reduced in animals subjected to chronic v.s acute stress, but still larger than that if the stressor is absent.

Major remark: The animals should, preferably, be imaged at least after 3 days of habituation based on existing knowledge. I suggest exploring the topic of the importance of habituation. It is difficult though, to objectively review these findings without considering stress and associated changes in vascular dynamics.

Many thanks for the reviewer to help to precise this information. The text is accordingly updated to describe the experiment (now page 26, line 14). 

- Page 23, line 17: number of animals included in experiments missing.

The number of animals is added in Methods (page 27, line 12) and indicated in the legend of Figure 5. 

- Line 18/19: were the respiratory effects observed after injection of saline or TGN-020? Since DWI was performed, the exclusion of perfusive flow on ADC is impossible.

I suggest an additional experiment in n=3 animals per group, verifying the HR (and if possible BP) response after injection of TGN-020 and saline in mice.

The respiratory rate has been recorded. We added the averaged respiratory rate before and after injection of TGN-020 or saline (now, Fig. S6; page 13, line 5-6).

- Line 22: Please, provide the model of the scanner, the model of the cryoprobe, as well as the model of the gradient coil used, otherwise it is difficult to assess or repeat these experiments.

We have now added the information of MRI system in Methods section (page 27, line17-21).

- Page 24: line 3/4: although the achieved spatial resolution of DWI was good and slightly lower than desired and achievable due to limitations of the method itself as well as cryoprobe, it is acceptable for EPI in mice.

Still, there is no direct explanation provided on the reasoning for using surface instead of volumetric coil, as well as on assuming an anisotropic environment (6 diffusion directions) for DWI measurements. This is especially doubtful if such a long echo-time was used alongside lower-thanpossible spatial resolution. Longer echo time would lower the SNR of the depicted signal but also would favor the depiction of signal from slow-moving protons and larger water pools. On the other hand, only 3 b-values were used, which is the minimum for ADC measurements, while a good research protocol could encompass at least 5 to increase the accuracy of ADC estimation and avoid undersampling between 250 and 1800 b-values. What was the reason for choosing this particular set of b-values and not 50, 600, and 2000? Besides, gradient duration time was optimally chosen, however, I have concerns about the decision for such a long gradient separation times.

If the protocol could have been better optimized, the assessment could have been also performed in respiratory-gated mode, allowing minimization of the effects of one of the glymphatic system driving forces.

Thus, I suggest commenting on these issues.

We chose the cryoprobe to increase the signal-to-noise ratio (SNR) in DW-MRI with long echo-time and high b-value. The volume coil has a more homogeneous SNR in the whole brain rather than the cryoprobe, but SNR should be reduced compared with cryoprobe. We confirmed that, even at the ventral part of the brain, the image quality of DW-MRI images was enough to investigate the ADC with cryoprobe (Fig. 5B-C). This is mentioned now in Methods (page 27, line 17-21).

We performed DW-MRI scanning for 5 min at each time-point using the condition of anisotropic resolution and 3 b-values, to investigate the time-course of ADC change following the injection of TGN020. Because the effect of TGN-020 appears about dozen of minutes post the injection (Igarashi et al., 2011), fast DW-MRI scanning is required. If isotropic DW-MRI with lower echo-time and more direction is used, longer scan time at each time point is required, maybe more than 1h. We agree that three bvalues is minimum to calculate the ADC and more b-values help to increase the accuracy. However, to achieve the temporal resolution so as to better catch the change of water diffusion, we have decided to use the minimum b-values. The previous study also validates the enough accuracy of DW-MRI with three b-values (Ashoor et al., 2019). Furthermore, previous study that used long diffusion time (> 20 ms) and long echo time (40 ms) shows the good mean diffusivity (Aggarwal et al., 2020), supporting that our protocol is enough to investigate the ADC. We have now updated the description (page 28 line 5-9).  The reason why we choose the b = 250 and 1800 s/mm² is that 2000 s/mm² seems too high to get the good quality of image. In the previous study, we have optimized that ADC is measurable with b = 0, 250, and 1800 s/mm² (Debacker et al., 2020). 

- Page 24, line 7: What was the post-processing applied for images acquired over 70 minutes? Did it consider motion-correction, co-registration, or drift-correction crucial to avoid pitfalls and mismatches in concluding data?

The motion correction and co-registration were explained in Methods (page 28, line 12-14).

Also, were these trace-weighted images or magnitude images acquired since DTI software was used for processing - while ADC fitting could be reliably done in Matlab, Python, or other software. Thus, was DSI software considering all 3 b-values or just used 0 and 1800 for the calculation of mean diffusivity for tractography (as ADC). The details should be explained.

DSIstudio was used with all three b values (b = 0, 250, and 1800 s/mm²) to calculate the ADC. We added the description in Methods (page 28, line 16-18).

To make sure that the results are not affected by the MR hardware, I suggest performing 3 control measurements in a standard water phantom, and presenting the results alongside the main findings.

Thanks for this suggestion. We have performed new experiments and now added the control measurement with three phantoms, that is water, undecane, and dodecane. These new data are summarized now in Fig. S7, showing the stability of ADC throughout the 70 min scanning. We have updated the description on Method part (page 28, line 9-11) and on the Results (page 13, line 6-8).  

- Line 13: were the ROI defined manually or just depicted from previously co-registered Allen Brain atlas?

The ROIs of the cortex, the hippocampus, and the striatum were depicted with reference to Allen mouse brain atlas (https://scalablebrainatlas.incf.org/mouse/ABA12). This is explained in Methods (page 28, line 14-16).

- Line 10: why the average from 1st and 2nd ADC was not considered, since it would reduce the influence of noise on the estimation of baseline ADC?

We are sorry that it was a typo. The baseline was the average between 1st and 2nd ADC. We corrected the description (page 28, line 20).

STATISTIC:

Which type of t-test - paired/unpaired/two samples was used and why? Mann-Whitney U-tets are used as a substitution for parametric t-tests when the data are either non-parametric or assuming normal distribution is not possible. In which case Bonferroni's-Holm correction was used? - I couldn't find any mention of any multiple-group analysis followed by multiple comparisons. Each section of the manuscript should have a description of how the quantitative data were treated and in which aim. I suggest carefully correcting all figures accordingly, and following the remarks given to the Figure 1.

We used unpaired t-test for data obtained from samples of different conditions. Indeed, MannWhitney U-test is used when the data are non-parametric deviating from normal distributions.  Bonferroni-Holm correction was used for multiple comparisons (e.g., Fig. 4D-E).

Reviewer #3 (Recommendations For The Authors):

I think that the following statement is insufficient: "The authors commit to share data, documentation, and code used in analysis". My understanding is eLife expects that all key data to be provided in a supplement.

We thank the reviewer; we follow the publication guidelines of eLife. 

References

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Ashoor, M., Khorshidi, A., and Sarkhosh, L. (2019). Estimation of microvascular capillary physical parameters using MRI assuming a pseudo liquid drop as model of fluid exchange on the cellular level. Rep Pract Oncol Radiother 24, 3-11.

Cauli, B., and Hamel, E. (2018). Brain Perfusion and Astrocytes. Trends in neurosciences 41, 409-413.

Debacker, C., Djemai, B., Ciobanu, L., Tsurugizawa, T., and Le Bihan, D. (2020). Diffusion MRI reveals in vivo and non-invasively changes in astrocyte function induced by an aquaporin-4 inhibitor. PLoS One 15, e0229702.

Erzurumlu, R.S., and Gaspar, P. (2020). How the Barrel Cortex Became a Working Model for Developmental Plasticity: A Historical Perspective. J Neurosci 40, 6460-6473.

Farr, G.W., Hall, C.H., Farr, S.M., Wade, R., Detzel, J.M., Adams, A.G., Buch, J.M., Beahm, D.L., Flask, C.A., Xu, K., et al. (2019). Functionalized Phenylbenzamides Inhibit Aquaporin-4 Reducing Cerebral Edema and Improving Outcome in Two Models of CNS Injury. Neuroscience 404, 484-498.

Giannetto, M.J., Gomolka, R.S., Gahn-Martinez, D., Newbold, E.J., Bork, P.A.R., Chang, E., Gresser, M., Thompson, T., Mori, Y., and Nedergaard, M. (2024). Glymphatic fluid transport is suppressed by the aquaporin-4 inhibitor AER-271. Glia.

Gomolka, R.S., Hablitz, L.M., Mestre, H., Giannetto, M., Du, T., Hauglund, N.L., Xie, L., Peng, W., Martinez, P.M., Nedergaard, M., et al. (2023). Loss of aquaporin-4 results in glymphatic system dysfunction via brain-wide interstitial fluid stagnation. eLife 12.

Huber, V.J., Tsujita, M., and Nakada, T. (2009). Identification of aquaporin 4 inhibitors using in vitro and in silico methods. Bioorg Med Chem 17, 411-417.

Igarashi, H., Huber, V.J., Tsujita, M., and Nakada, T. (2011). Pretreatment with a novel aquaporin 4 inhibitor, TGN-020, significantly reduces ischemic cerebral edema. Neurol Sci 32, 113-116.

Igarashi, H., Tsujita, M., Suzuki, Y., Kwee, I.L., and Nakada, T. (2013). Inhibition of aquaporin-4 significantly increases regional cerebral blood flow. Neuroreport 24, 324-328.

Jiang, R., Diaz-Castro, B., Looger, L.L., and Khakh, B.S. (2016). Dysfunctional Calcium and Glutamate Signaling in Striatal Astrocytes from Huntington's Disease Model Mice. J Neurosci 36, 3453-3470.

Mayo, F., Gonzalez-Vinceiro, L., Hiraldo-Gonzalez, L., Calle-Castillejo, C., Morales-Alvarez, S., Ramirez-Lorca, R., and Echevarria, M. (2023). Aquaporin-4 Expression Switches from White to Gray Matter Regions during Postnatal Development of the Central Nervous System. Int J Mol Sci 24.

Mola, M.G., Sparaneo, A., Gargano, C.D., Spray, D.C., Svelto, M., Frigeri, A., Scemes, E., and Nicchia, G.P. (2016). The speed of swelling kinetics modulates cell volume regulation and calcium signaling in astrocytes: A different point of view on the role of aquaporins. Glia 64, 139-154.

Risher, W.C., Andrew, R.D., and Kirov, S.A. (2009). Real-time passive volume responses of astrocytes to acute osmotic and ischemic stress in cortical slices and in vivo revealed by two-photon microscopy. Glia 57, 207-221.

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Shigetomi, E., Bushong, E.A., Haustein, M.D., Tong, X., Jackson-Weaver, O., Kracun, S., Xu, J., Sofroniew, M.V., Ellisman, M.H., and Khakh, B.S. (2013). Imaging calcium microdomains within entire astrocyte territories and endfeet with GCaMPs expressed using adeno-associated viruses. J Gen Physiol 141, 633-647.

Solenov, E., Watanabe, H., Manley, G.T., and Verkman, A.S. (2004). Sevenfold-reduced osmotic water permeability in primary astrocyte cultures from AQP-4-deficient mice, measured by a fluorescence quenching method. Am J Physiol Cell Physiol 286, C426-432.

Verkman, A.S., Binder, D.K., Bloch, O., Auguste, K., and Papadopoulos, M.C. (2006). Three distinct roles of aquaporin-4 in brain function revealed by knockout mice. Biochim Biophys Acta 1758, 10851093.

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Reviewer #1 (Public review):

Summary:

The manuscript by Zhou et al offers new high resolution Cryo-EM structures of two human biotin-dependent enzymes: propionyl-CoA carboxylase (PCC) and methycrotonyl-CoA carboxylase (MCC). While X-ray crystal structures and Cryo-EM structures have previously been reported for bacterial and trypanosomal versions of MCC and for bacterial versions of PCC, this marks one of the first high resolution Cryo-EM structures of the human version of these enzymes. Using the biotin cofactor as an affinity tag, this team purified a group of four different human biotin-dependent carboxylases from cultured human Expi 293F (kidney) cells (PCC, MCC, acetyl-CoA carboxylase (ACC), and pyruvate carboxylase). Following further enrichment by size-exclusion chromatography, they were able to vitrify the sample and pick enough particles of MCC and PCC to separately refine the structures of both enzymes to relatively high average resolutions (the Cryo-EM structure of ACC also appears to have been determined from these same micrographs, though this is the subject of a separate publication). To determine the impact of substrate binding on the structure of these enzymes and to gain insights into substrate selectivity, they also separately incubated with propionyl-CoA and acetyl-CoA and vitrified the samples under active turnover conditions, yielding a set of cryo-EM structures for both MCC and PCC in the presence and absence of substrates and substrate analogues.

Strengths:

The manuscript has several strengths. It is clearly written, the figures are clear and the sample preparation methods appear to be well described. This study demonstrates that Cryo-EM is an ideal structural method to investigate the structure of these heterogeneous samples of large biotin-dependent enzymes. As a consequence, many new Cryo-EM structures of biotin-dependent enzymes are emerging, thanks to the natural inclusion of a built-in biotin affinity tag. While the authors report no major differences between the human and bacterial forms of these enzymes, it remains an important finding that they demonstrate how/if the structure of the human enzymes are or are not distinct from the bacterial enzymes. The MCC structures also provide evidence for a transition for BCCP-biotin from an exo-binding site to an endo-binding site in response to acetyl-CoA binding. This contributes to a growing number of biotin-dependent carboxylase structures that reveal BCCP-biotin binding at locations both inside (endo-) and outside (exo-) of the active site.

Weaknesses:

There are some minor weaknesses. Notably, there are not a lot of new insights coming from this paper. The structural comparisons between MCC and PCC have already been described in the literature and there were not a lot of significant changes (outside of the exo- to endo- transition) in the presence vs. absence of substrate analogues. There are sections of this manuscript that do not sufficiently clarify what represents a new insight from the current set of structures (there are few of them), vs. what is largely recapitulating what has been seen in previous structures.

There is not a great deal of depth of analysis in the discussion. For example, no new insights were gained with respect to the factors contributing to substrate selectivity (the factors contributing to selectivity for propionyl-CoA vs. acetyl-CoA in PCC). The authors acknowledge that they are limited in their interpretations as a consequence of the acyl groups being unresolved in all of the structures. They offer a simple, overarching and not particularly insightful explanation that the longer acyl group in propionyl-CoA may mediate stronger hydrophobic interactions that stabilize the alpha carbon of the acyl group at the proper position. The authors did not take the opportunity to describe the specific interactions that may be responsible for the stronger hydrophobic interaction nor do they offer any plausible explanation for how these might account for an astounding difference in the selectivity for propionyl-CoA vs. acetyl-CoA. Essentially, the authors concede that these cryo-EM structures offer no new insights into the structural basis for substrate selectivity in PCC, confirming that these structures do not yet fully capture the proper conformational states.

Some of these minor deficiencies aside, the overall aim of contributing new cryo-EM structures of the human MCC and PCC has been achieved. While I am not a cryo-EM expert, I see no flaws in the methodology or approach. While the contributions from these structures are somewhat incremental, it is nevertheless important to have these representative examples of the human enzymes and it is noteworthy to see a new example of the exo-binding site in a biotin-dependent enzyme.

eLife Assessment

This work presents a valuable finding on how the interplay between transcription factors SOX2 and OCT4 establishes the pluripotency network in early mouse embryos. Despite the high quality of the data, the evidence supporting the claims of the authors is currently incomplete and would benefit from more omics analysis such as H3K4me1 and H3K27ac CUT&Tag. The work will be of interest to biologists working on embryonic development.

Reviewer #1 (Public review):

Hotinger et al. explore the population dynamics of Salmonella enterica serovar Typhimurium in mice using genetically tagged bacteria. In addition to physiological observations, pathology assessments, and CFU measurements, the study emphasizes quantifying host bottleneck sizes that limit Salmonella colonization and dissemination. The authors also investigate the genetic distances between bacterial populations at various infection sites within the host.

Initially, the study confirms that pretreatment with the antibiotic streptomycin before inoculation via orogastric gavage increases the bacterial burden in the gastrointestinal (GI) tract, leading to more severe symptoms and heightened fecal shedding of bacteria. This pretreatment also significantly reduces between-animal variation in bacterial burden and fecal shedding. The authors then calculate founding population sizes across different organs, discovering a severe bottleneck in the intestine, with founding populations reduced by approximately 10^6-fold compared to the inoculum size. Streptomycin pretreatment increases the founding population size and bacterial replication in the GI tract. Moreover, by calculating genetic distances between populations, the authors demonstrate that, in untreated mice, Salmonella populations within the GI tract are genetically dissimilar, suggesting limited exchange between colonization sites. In contrast, streptomycin pretreatment reduces genetic distances, indicating increased exchange.

In extraintestinal organs, the bacterial burden is generally not substantially increased by streptomycin pretreatment, with significant differences observed only in the mesenteric lymph nodes and bile. However, the founding population sizes in these organs are increased. By comparing genetic distances between organs, the authors provide evidence that subpopulations colonizing extraintestinal organs diverge early after infection from those in the GI tract. This hypothesis is further tested by measuring bacterial burden and founding population sizes in the liver and GI tract at 5 and 120 hours post-infection. Additionally, they compare orogastric gavage infection with the less injurious method of infection via drinking, finding similar results for CFUs, founding populations, and genetic distances. These results argue against injuries during gavage as a route of direct infection.

To bypass bottlenecks associated with the GI tract, the authors compare intravenous (IV) and intraperitoneal (IP) routes of infection. They find approximately a 10-fold increase in bacterial burden and founding population size in immune-rich organs with IV/IP routes compared to orogastric gavage in streptomycin-pretreated animals. This difference is interpreted as a result of "extra steps required to reach systemic organs."

While IP and IV routes yield similar results in immune-rich organs, IP infections lead to higher bacterial burdens in nearby sites, such as the pancreas, adipose tissue, and intraperitoneal wash, as well as somewhat increased founding population sizes. The authors correlate these findings with the presence of white lesions in adipose tissue. Genetic distance comparisons reveal that, apart from the spleen and liver, IP infections lead to genetically distinct populations in infected organs, whereas IV infections generally result in higher genetic similarity.

Finally, the authors investigate GI tract reseeding, identifying two distinct routes. They observe that the GI tracts of IP/IV-infected mice are colonized either by a clonal or a diversely tagged bacterial population. In clonally reseeded animals, the genetic distance within the GI tract is very low (often zero) compared to the bile population, which is predominantly clonal or pauciclonal. These animals also display pathological signs, such as cloudy/hardened bile and increased bacterial burden, leading the authors to conclude that the GI tract was reseeded by bacteria from the gallbladder bile. In contrast, animals reseeded by more complex bacterial populations show that bile contributes only a minor fraction of the tags. Given the large founding population size in these animals' GI tracts, which is larger than in orogastrically infected animals, the authors suggest a highly permissive second reseeding route, largely independent of bile. They speculate that this route may involve a reversal of known mechanisms that the pathogen uses to escape from the intestine.

The manuscript presents a substantial body of work that offers a meticulously detailed understanding of the population dynamics of S. Typhimurium in mice. It quantifies the processes shaping the within-host dynamics of this pathogen and provides new insights into its spread, including previously unrecognized dissemination routes. The methodology is appropriate and carefully executed, and the manuscript is well-written, clearly presented, and concise. The authors' conclusions are well-supported by experimental results and thoroughly discussed. This work underscores the power of using highly diverse barcoded pathogens to uncover the within-host population dynamics of infections and will likely inspire further investigations into the molecular mechanisms underlying the bottlenecks and dissemination routes described here.

Major point:

Substantial conclusions in the manuscript rely on genetic distance measurements using the Cavalli-Sforza chord distance. However, it is unclear whether these genetic distance measurements are independent of the founding population size. I would anticipate that in populations with larger founding population sizes, where the relative tag frequencies are closer to those in the inoculum, the genetic distances would appear smaller compared to populations with smaller founding sizes independent of their actual relatedness. This potential dependency could have implications for the interpretation of findings, such as those in Figures 2B and 2D, where antibiotic-pretreated animals consistently exhibit higher founding population sizes and smaller genetic distances compared to untreated animals.

Author response:

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

Public reviews:

Reviewer #1 (Public Review):

Summary:

The authors have developed a valuable method based on a fully cell-free system to express a channel protein and integrate it into a membrane vesicle in order to characterize it biophysically. The study presents a useful alternative to study channels that are not amenable to being studied by more traditional methods.

Strengths:

The evidence supporting the claims of the authors is solid and convincing. The method will be of interest to researchers working on ionic channels, allowing them to study a wide range of ion channel functions such as those involved in transport, interaction with lipids, or pharmacology.

Weaknesses:

The inclusion of a mechanistic interpretation of how the channel protein folds into a protomer or a tetramer to become functional in the membrane would strengthen the study.

Work from other labs has described key factors which can improve expression and artificial lipid integration of cellfree derived transmembrane proteins (PMIDs: 35520093, 29625253, 26270393) . However, a significant number of additional experiments would be needed to elucidate the exact biophysical properties governing channel assembly of synthetically derived polycystins. We carried out additional biochemical experiments to address these concerns (see new Figure 1— figure supplement 1 D, E). We used fluorescence-detection size-exclusion chromatography (FSEC) with the goal of understanding how much of the CFE-derived protomers are biochemically folding and assembly into functional tetramers upon incorporation into SUVs. When compared to protein recombinant sources from HEK cells, the production of assembled channels is less than 4% when using the CFE+SUV approach, an estimate based on the oligomer peak fluorescence. In the absence of chaperones found in cells, the assembly of synthetically derived protomers into tetramers is likely intrinsic to the chemical properties of the proteins, and the biophysical principles governing helical membrane protein when inserted into the lipid membrane  (PMID:35133709). We have added our interpretation in lines 111-121.

Reviewer #2 (Public Review):

It is challenging to study the biophysical properties of organelle channels using conventional electrophysiology. The conventional reconstitution methods require multiple steps and can be contaminated by endogenous ionophores from the host cell lines after purification. To overcome this challenge, in this manuscript, Larmore et al. described a fully synthetic method to assay the functional properties of the TRPP channel family. The TRPP channels are an important organelle ion channel family that natively traffic to primary cilia and ER organelles. The authors utilized cell-free protein expression and reconstitution of the synthetic channel protein into giant unilamellar vesicles (GUV), the single channel properties can be measured using voltage-clamp electrophysiology. Using this innovative method, the authors characterized their membrane integration, orientation, and conductance, comparing the results to those of endogenous channels. The manuscript is well-written and may present broad interest to the ion channel community studying organelle ion channels. Particularly because of the challenges of patching native cilia cells, the functional characterization is highly concentrated in very few labs. This method may provide an alternative approach to investigate other channels resistant to biophysical analysis and pharmacological characterization.

Thank you for evaluating our manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) It would be useful to explain how the Polycystin protein is folded under the experimental conditions used. The expression data shown in Figure 1 Supplement 1B show different protein concentrations of protomer or tetramer. However, it is not described how each form is identified and distinguished. It is also important to mention in the manuscript that this method is only applicable to membrane channels that do not require chaperons for its folding and expression into the membrane. How is the tetramer mechanistically conformed? In line 184, it is stated that this method can be leveraged for studying the effects of channel subunit composition. Would this method allow the expression of two different subunit proteins in order to produce a heteromeric channel?

In Figure 1—figure supplement 1B, total fluorescence from the synthesized channel-GFP was measured. Protein concentration was calculated based on the linear regression of the GFP standards. Monomeric protein concentration was reported directly from total fluorescence. Tetrameric protein concentration was calculated by dividing the fluorescence by four, and subsequently calculating the concentration based off the GFP standards. 

This is a good point. Based on your suggestion, we carried out additional biochemical experiments (see new Figure 1— figure supplement 1 D, E). We used fluorescence-detection size-exclusion chromatography (FSEC) with the goal of understanding how much of the CFE-derived protomers are biochemically folding and assembly into functional tetramers upon incorporation into SUVs. As controls we produced recombinant PKD2-GFP and PKD2L1GFP channels as elution time standards and to compare the relative production of tetrameric channels generated when using the two expression systems. The synthetically derived polycystin channels indeed produced tetramers and protomers, which supports feasibility of using this method to assay their functional properties.  When compared to protein recombinant sources from HEK cells, the production of assembled channels is less than 4% when using the CFE+SUV approach, an estimate based on the oligomer peak fluorescence. We speculate that assembly of synthetically derived protomers into tetramers is likely intrinsic to the chemical properties of the proteins, and the biophysical principles governing helical membrane protein when inserted into the lipid membrane (PMID: 35133709). Although an interesting question, a systematic analysis of these channel-lipid interactions is beyond the scope of this eLife Report but can be addressed in future studies. The limitation of using this method to characterize channels which fold and membrane integrate without the aid of molecular chaperones is now stated in lines 201205. In principle, the CFE-GUV method can be deployed to co-express different subunits to produce heteromeric channels. We have modified the text lines 192-197 to be clearer on this point.

(2) The type of plasmid (and promoter) required for this methodology should be mentioned.

Added to the methods (lines 210-211). “PKD2 and PKD2L1 are in pET19b plasmid under T7 promoter.”

(3) Since this paper is methodological, it would be useful to have some information about the stability of the GUVs containing the synthetic channel. In Methods, it is stated that GUV vesicles are used on the same day (line 207). And in line 193 it says that the reactions (?) are placed at 4{degree sign}C for storage.

Restated in lines 226-228: GUVs are electroformed and used for electrophysiology the same day. SUVs with channel incorporated are stored at 4°C for 3 days.

(4) A comment reasoning why the PKD2 protein is more frequently incorporated into the membrane in comparison to PKD2L1 should be included. A brief description of the differences between these two proteins would also be helpful for the reader.

In terms of overall protein production and oligomeric assembly— more PKD2L1 channels are produced compared to PKD2 (see new Figure 1C, and Figure 1— figure supplement 1 D, E). In lines 149-155 we note single channel openings were frequently observed for the high expressing PKD2L1 channels, but this often resulted in patch instability. As a result, GUV patches with lower expressing PKD2-GFP channel were more stable and thus more successfully recorded from. We have revised the text to be clearer on this point.

(5) There are no methods for preparing hippocampal neurons or IMCD cells shown in Figure 4 Supplement 1. Instead, the method of mammalian cultures provided corresponds to HEK 293T cells.

This information has been added to lines 273-284.

(6) Minor:

In Figure 2C, please include the actual % of the Cell488+Surface647+Clear lumen vesicles.

Added

Line 99, 108: Figures 1B and 1C are swapped. Please correct.

Corrected in figure and figure legends.

Line 108: misspelling: effect.

Done

Line 109: check sentence: verb is missing.

Sentence now reads “Minimal changes in fluorescence were detected when a control plasmid (Ctrl) encoding a non- fluorescent protein (dihyrofolate reductase) was used in the reaction.”

Line 145: recoding. Correct.

Recoding changed to recordings

Line 169: "from" is missing (recorded from MCD cilia).

Added

Line 169: In Table 1, the PKD2 K+ conductance magnitudes recorded from IMCD cilia were significantly smaller, not larger as stated, than those assayed using CFE-GUV system. Please correct.

Corrected

Line 180: "of" is missing (adaptation of CFE derived...).

Corrected

Line 182: "to" is missing (generalized to other channels).

Corrected

Line 193: "in" 4ºC, correct at.

Corrected

Line 197: replace "mole" for "mol".

Corrected

Line 207: are used "within the" same day.

Corrected

Line 210: c-terminally. C-should be capital letter.

Corrected

Line 231: n-terminally. N- should be capital letter.

Corrected

Reviewer #2 (Recommendations For The Authors):

The authors validated their method using PKD2 and PKD2L1 channels, demonstrating the potential of this approach. However, a few points merit further clarification or validation:

(1) Stability of the protein vesicles for recording. The authors observed membrane instability during voltage transitions. It would be beneficial to discuss potential solutions to enhance stability.

In lines 197-202, we have added a discussion of potential solutions to enhance stability. CsF in the intracellular saline could be added to stabilize the GUV membranes. CsF is frequently added to stabilize whole cell membranes in HTS planer patch clamp recording. We did not explore this formulation because Cs+ would limit outward polycystin conductance. We also suggest but did not test altering the membrane formulation of GUVs with additional cholesterol to stabilize these recordings.

(2) Validation. Further discussion on how broadly this method can be applied to other channels would strengthen the manuscript.

We have included further discussion on this point in lines 190-206. 

(3) Protein production estimated by a standard GFP absorbance assay. The estimation of protein production using GFP absorption may be affected by improperly folded protein. Additional validation methods could be considered.

C-terminal GFP fluorescence has been widely used in expression systems to designate proper folding of the target protein upstream of the GFP-tag (PMID: 22848743, PMID: 21805523, PMID: 35520093). Nonetheless we have conducted additional experiments designed to estimate the amount of assembled PKD2 and PKD2L1 channels generated using the CFE method. In the new Figure 1— figure supplement 1 D, E, we carried out fluorescencedetection size-exclusion chromatography and compared channel assembly of recombinant and CFE+SUV derived PKD2-GFP and PKD2L1-GFP. Here, we clearly observed tetrameric and protomeric forms of the channels using the synthetic approach, which supports feasibility of using this method to assay their functional properties (see new Figure 1— figure supplement 1 D, E).  When compared to protein recombinant sources from HEK cells, the production of assembled channels is less than 4% when using the CFE+SUV approach, an estimate based on the oligomer peak fluorescence. 

(4) Single channels were observed more frequently from PKD2 incorporated GUVs compared to PKD2L1. Does this just randomly happen or is there a reason behind this difference?

In terms of overall protein production and oligomeric assembly— more PKD2L1 channels are produced compared to PKD2 (Figure 1C, and Figure 1— figure supplement 1 D, E). This is apparent whether the channels are produced recombinantly in cells or when using the cell-free method (Figure 1— figure supplement 1 D, E). In lines 149-155, we note single channel openings were frequently observed but that the high expression of the PKD2L1 often resulted in patch instability. As a result, GUV patches the lower expressing PKD2-GFP channel were more stable and thus more successfully recorded from. As requested, we have included a brief description of the two proteins in lines 76-78. 

(5) Additional validation or clarification for examining the channel orientation may strengthen the manuscript.

We have modified the text to make this point clearer. 

(6) Advantage and limitations. The authors compared the recordings from hippocampal primary cilia membranes, noting differences in conductance magnitudes compared to the GUV method. Further discussing the limitations and advantages of this approach for the biophysical properties of organelle channels would be beneficial.

We have revised the final paragraph to discuss the limitations of this method.

(7) Including experiments that demonstrate ligand-induced activation or inhibition to further validate the current using this method would strengthen the manuscript (optional, not required).

Despite our best attempts, exchange of the external bath to apply inhibitors (Gd3+, La3+) resulted in GUV patch instability. Our plans are to investigate ways to stabilize the high resistance seals to develop pharmacological screening using the CFE+GUV method.

Reviewer #1 (Public review):

Summary:

The authors aim to investigate the role of ORMDL3 in regulating Type 1 interferon (IFN) responses and its effect on tumor growth inhibition. The study focuses on the mechanisms involving the RIG-I pathway and USP10-mediated degradation and attempts to establish a link between ORMDL3 expression and the effectiveness of cancer therapy. The authors also explore the broader implications of ORMDL3 in immune signaling, particularly within the context of Type 1 IFN signaling and its therapeutic potential.

Strengths:

• The manuscript explores a novel aspect of cancer immunology by examining the relationship between ORMDL3 and Type 1 IFN signaling, potentially offering new therapeutic avenues.<br /> • A variety of experimental approaches are employed, including knockdown models, overexpression assays, and protein interaction analyses, to elucidate the role of ORMDL3 in modulating immune responses.<br /> • The findings suggest a potential mechanism by which ORMDL3 affects the tumor microenvironment and immune responses, which could have significant implications for understanding cancer progression and therapy.

Weaknesses:

• The study does not clearly establish the relationship between Type 1 IFN and cancer therapy, and more robust data are needed to support the claim that tumor growth inhibition occurs via Type 1 IFN upregulation following ORMDL3 knockdown.<br /> • There is ambiguity regarding whether ORMDL3 has a positive or negative role in the Type 1 IFN pathway, especially given conflicting findings in the literature that link higher ORMDL3 levels to increased Type 1 IFN expression.<br /> • The use of certain experimental models, such as HEK293T cells (which are not typical Type 1 IFN producers), raises concerns about the validity and generalizability of the results. Further clarity is needed regarding the rationale for using the same tag in overexpression experiments.<br /> • The manuscript contains several inconsistencies and lacks detailed explanations of critical areas, such as the mechanism by which ORMDL3 facilitates USP10 transfer to RIG-I despite no direct interaction between ORMDL3 and RIG-I.

Author response:

• The study does not clearly establish the relationship between Type 1 IFN and cancer therapy, and more robust data are needed to support the claim that tumor growth inhibition occurs via Type 1 IFN upregulation following ORMDL3 knockdown.

We thank the reviewer’s concern. In Figure 6 we detected the expression of IFNB1 and ISGs in MC38 and LLC tumor upon ORMDL3 knockdown. At the mean time, we also used IHC to explore the abundance of RIG-I and ORMDL3 in these tumors. In addition, in figure S5 we performed western blots to detect the expression of RIG-I with or without ORMDL3 knockdown. All these results support our hypothesis that that ORMDL3 is a negative regulator of interferon via modulating RIG-I abundance.

• There is ambiguity regarding whether ORMDL3 has a positive or negative role in the Type 1 IFN pathway, especially given conflicting findings in the literature that link higher ORMDL3 levels to increased Type 1 IFN expression.

We appreciate the reviewer’s concern. In our system and experiments, we validated that ORMDL3 is a negative regulator of interferon, although there is also literature that links higher ORMDL3 levels to increased type-I IFN response. ORMDL3 has been reported associated with rhinovirus-induced childhood asthma (Nature.  2007;448(7152):470-473; N Engl J Med. 2013 Apr 11;368(15):1398-407), and ORMDL3 level is positively associated with rhinovirus abundance (N Engl J Med. 2013 Apr 11;368(15):1398-407).  There are reports indicating that ORMDL3 supports the replication of rhinovirus (for example, Am J Respir Cell Mol Biol. 2020 Jun;62(6):783-792). This phenomenon is consistent with our findings that higher ORMDL3 expression leads to lower interferon production, which facilitates viral replication. We believe that the different experimental conclusions obtained in these experiments are due to different experiment condition and different stimulation. In our research, we provided comprehensive studies at the molecular, cellular, and animal levels to support the conclusion that ORMDL3 is a negative regulator of type-I interferon.

• The use of certain experimental models, such as HEK293T cells (which are not typical Type 1 IFN producers), raises concerns about the validity and generalizability of the results. Further clarity is needed regarding the rationale for using the same tag in overexpression experiments.

We thank the reviewer’s suggestion. Besides HEK293T, in Figure 1C and 1D we also used A549 and BMDM to overexpress ORMDL3 and stimulate them with polyI:C or polyG:C, Our results showed that ORMDL3 especially inhibits RLR signaling. Additionally, in Figure 3H we found that the endogenous RIG-I expression decreased when we overexpressed ORMDL3 in BMDM. Regarding the issue of using different protein tags, we plan to use different tags to validate our results.

• The manuscript contains several inconsistencies and lacks detailed explanations of critical areas, such as the mechanism by which ORMDL3 facilitates USP10 transfer to RIG-I despite no direct interaction between ORMDL3 and RIG-I.

There are some ERMC (ER-mitochondria contact) proteins that mediate the interaction between ER and mitochondria. ORMDL3 locates in ER, and it has been reported to be associated with calcium transportation. At the meantime, the calcium transfer between ER and mitochondria plays an important role in protein synthesis. It is possible that some ERMC proteins mediate the interaction between ORMDL3 and MAVS. In addition,  we also validated that ORMDL3 interacts with USP10 (Figure 5B). Although ORMDL3 and RIG-I do not interact directly, we generated a mechanistic model that ORMDL3 and MAVS recruit USP10 and RIG-I to ERMCS respectively, thus USP10 could form a complex with RIG-I (Figure 5C) and regulate the stability of RIG-I upon RNA sensing.

Not that it couldn't be done, but I'll suggest that following the structure/order of a Luhmann-artig zettelkasten may be a bit more limiting or difficult for creating fiction.

There's a rich history of researching, outlining, and writing with card indexes as part of the creative process. Perhaps looking briefly at some examples particularly focusing on fiction may be helpful? Once you've done this, you can pick and choose the portions and affordances that work best for your preferred way of thinking and working.

Some quick examples:

• Vladimir Nabokov https://www.openculture.com/2014/02/the-notecards-on-which-vladimir-nabokov-wrote-lolita.html
• David Lynch process via Frank Daniel: https://hypothes.is/users/chrisaldrich?q=tag%3A%27David+Lynch%27
• Variations of this method include:
  • Dustin Lance Black https://www.youtube.com/watch?v=vrvawtrRxsw
  • Ben Rowland https://www.youtube.com/watch?v=mwKjuBvNi40
• Randy Ingermanson. “The Snowflake Method For Designing A Novel.” Advanced Fiction Writing, circa 2013. https://www.advancedfictionwriting.com/articles/snowflake-method/

Perhaps querying my digital zettelkasten may be helpful for you? Start with: https://hypothes.is/users/chrisaldrich?q=tag%3A%27card+index+for+writing%27

Ultimately, you can only spend so much time going down the rabbit hole of how you ought to do this work and taking suggestions or reading about how others have done it. The more difficult but more fruitful portion is to pick a method which seems like it will work for you and experiment with it by actually using or evolving it for yourself. How you start may not necessarily be how you end, but you won't know what's best for you if you don't start. Practice, practice, practice will get you much farther faster.

reply to u/Atreides_Lion at https://reddit.com/r/Zettelkasten/comments/1ft4r3z/a_very_important_matter_for_me/

Author response:

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

Reviewer #1 (Public Review):

(1) The overall writing is very difficult to follow and the authors need to work on significant re-writing. 

Thank you for your comment. We have rewritten the text and asked an immunology expert, who is also a native English speaking editor, to review it.

(2) The paper in its current form really lacks detail and it is NOT possible for readers to repeat or follow their methods. For example: a) It is not clear whether the authors checked the serum to see if the mice were producing antibodies before they sacrificed them to harvest spleen/blood i.e. using ELISA? b) How long after administration of the second dose were the mice sacrificed? c) What cell types are taken for single B cell sorting? Splenocytes or PBMC?

Thank you for your comment. We have revised the methodology section thoroughly to ensure that the readers can follow and replicate the method. Our responses to the specific examples raised are as follows:

a) We did not examine the serum titer after immunization. An increased serum titer, as determined by ELISA, does not always reflect the number of cross-reactive B cells because we expected the serum titer to consist of polyclonal antibodies, which are a mixture of PR8-reactive, H2-reactive, and cross-reactive clones. We thus anticipated that we would not obtain enough cross-reactive B cells after a series of immunizations. After comparing various immunization methods, including different adjuvants and immunization sites, using the readout of the number of cross-reactive B cells, we decided to adopt the immunization protocol presented in this paper.

b) We sacrificed the mice two weeks after the second immunization (see Supplementary Figure 5).

c) For this experiment, we used CD43 MACS B cells from the spleen purified with negatively charged beads (see Supplementary Figure 6).

(3) According to the authors, 77 clones were sorted from the PR8+ and H2+ double positive quadrant. It is surprising that after transfection and re-analyzing of bulk antibody presenting EXPI cells on FACS, only 13 clones (or 8 clones? - unclear) seemed to be truly cross-reactive. If that is the case, the approach is not as efficient as the authors claimed.

Thank you for your comment. To isolate high affinity antibodies, we gated the high fluorescent intensity population of cross-reactive B cells during Ig-expressing 293 cell sorting, as shown in Fig 2B, while we collected a wide intensity population of cross-reactive cells during splenocyte sorting. The narrow gating reduced the number of clones. We, however, cannot quantify how many clones we lost in the process, but we achieved a cloning efficiency exceeding 75%. To avoid any confusion, we have clarified this point by attaching additional supplementary figures (Supplementary Figures 5 and 6).

Reviewer #2 (Public Review):

(4) A His tagged antigen was used for immunization and H1-his was used in all assays. Either the removal of His specific clones needs to be done before selection, or a different tag needs to be used in the subsequent assays.

Thank you for your comment. As pointed out, the possibility of antibody generation in regions other than HA cannot be ruled out since the immunized antigen and the detection antigen were the same. However, as shown in Table 1, the cross-reactive antibodies obtained in this study exhibited characteristic binding abilities to each of the six types of HA. If these were antibodies recognizing His, they would bind to all six types of HA. This indicates that these cross-reactive antibodies were not His-specific clones.

We have incorporated information on this potential caveat into the discussion (page 12, lines 4-9).

(5) This assay doesn't directly test the neutralization of influenza but rather equates viral clearance to competitive inhibition. The results would be strengthened with the demonstration of a functional antibody in vivo with viral clearance.

Thank you for your constructive comment. While we agree that demonstration of a functional antibody in vivo with viral clearance would strengthen our results, this is clearly out of the scope of our current study and will be subject of future research.

(6) Limitations of this new technique are as follows: there is a significant loss of cells during FACs, transfection and cloning efficiency are critical to success, and well-based systems limit the number of possible clones (as the author discussed in the conclusions). Early enrichment of the B cells could improve efficiency, such as selection for memory B cells.

Thank you for your comment. Our cloning efficiency for sorted B cells exceeded 75%. However, we selected high binders of cross-reactive B cells during Ig-expressing 293 functional screening on purpose, as shown in Figure 2B, while we collected all cross-reactive B cells during B cell sorting (see attached Supplementary Figure 5). This functional selection step reduced the number of clones. We clarified this point by attaching additional supplementary figures (Supplementary Figures 5 and 6).

Our sorted cross-reactive B cells are most likely CD38+ memory B cells, as shown in Supplementary Figure 6.

Reviewer #1 (Recommendations For The Authors):

a) It is advised for the authors to provide a flow chart with time stamps to prove the many statements made in the paper. For example, it is stated that "we demonstrated efficient isolation of influenza cross-reactive antibodies with high affinity from mouse germinal B cells over 4 days". It is not clear how this was calculated.

Thank you for your comment. We have prepared a time-stamped flow chart (Supplementary Figure 5).

b) The papers cited by the authors are relatively old if not outdated. There are many papers published focusing on efficient isolation of mAbs for SARS-CoV-2 research. For example, the paper by Lima et al (Nat Comm 2022, 13:7733) used a very similar strategy for rapid isolation of cross-reactive mAbs by FACS sorting followed by cloning of paired heavy and light chains from single B cells. The authors need to incorporate citations from the latest publications in this field.

Thank you for your comment. The paper by Lima et al. (Nat Comm 2022, 13:7733) has been cited in the Discussion as ref 28.

c) Figure 2 needs much more detail for readers to follow.

Thank you for your comment. We have revised the legend of Figure 2 accordingly and added additional supplementary figures (Supplementary Figures 5 and 6) to increase clarity.

bool skip_kasan_poison = should_skip_kasan_poison(page, fpi_flags);

bool should_skip_kasan_poison(...) { bool skip_kasan_poison = should_skip_kasan_poison(page, fpi_flags); }

Skip KASAN memory poisoning is based on the configuration (depending on which kind of KASAN: generic or tag-based)

my comment, in response to someone getting lost in up front organising of notes, and ending up in a 'mess'. Embrace the mess, lengthen traces to stumble upon, earn structure (they're a personal tool not an outside standard or demand). Organise during note usage rather than during note taking, except for curation when saving something external with a remark (tags sometimes) and an internal link.

It points out that when students are asked about tracking programs in their schools, many say there aren't any. However, when asked about gifted and talented programs, they can easily describe how different those programs are. This shows that students might not realize how tracking affects their education, even though they notice the differences in opportunities for gifted students. It raises questions about fairness in education and how these experiences shape their views on learning. Why do you think some students don't see tracking as an issue?

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study provides an incremental advance to the scavenger receptor field by reporting the crystal structures of the domains of SCARF1 that bind modified LDL such as oxidized LDL and acylated LDL. The crystal packing reveals a new interface for the homodimerization of SCARF1. The authors characterize SCARF1 binding to modified LDL using flow cytometry, ELISA, and fluorescent microscopy. They identify a positively charged surface on the structure that they predict will bind the LDLs, and they support this hypothesis with a number of mutant constructs in binding experiments.

Strengths:

The authors have crystallized domains of an understudied scavenger receptor and used the structure to identify a putative binding site for modified LDL particles. An especially interesting set of experiments is the SCARF1 and SCARF2 chimeras, where they confer binding of modified LDLs to SCARF2, a related protein that does not bind modified LDLs, and use show that the key residues in SCARF1 are not conserved in SCARF2.

Weaknesses:

While the data largely support the conclusions, the figures describing the structure are cursory and do not provide enough detail to interpret the model or quality of the experimental X-ray structure data. Additionally, many of the flow cytometry experiments lack negative controls for non-specific LDL staining and controls for cell surface expression of the SCARF constructs. In several cases, the authors interpret single data points as increased or decreased affinity, but these statements need dose-response analysis to support them. These deficiencies should be readily addressable by the authors in the revision.

The paper is a straightforward set of experiments that identify the likely binding site of modified LDL on SCARF1 but adds little in the way of explaining or predicting other binding interactions. That a positively charged surface on the protein could mediate binding to LDL particles is not particularly surprising. This paper would be of greater importance if the authors could explain the specificity of the binding of SCARF1 to the various lipoparticles that it does or does not bind. Incorporating these mutants into an assay for the biological role of SCARF1 would be powerful.

Reviewer #2 (Public Review):

Summary:

The manuscript by Wang and colleagues provided mechanistic insights into SCARF1 and its interactions with the lipoprotein ligands. The authors reported two crystal structures of the N-terminal fragments of SCARF1 ectodomain (ECD). On the basis of the structural analysis, the authors further investigated the interactions between SCARF1 and modified LDLs using cell-based assays and biochemical experiments. Together with the two structures and supporting data, this work provided new insights into the diverse mechanisms of scavenger receptors and especially the crucial role of SCARF1 in lipid metabolism.

Strengths:

The authors started by determining the crystal structures of two fragments of SCARF1 ECD. The superposition of the two high-resolution structures, together with the predicted model by AlphaFold, revealed that the ECD of SCARF1 adopts a long-curved conformation with multiple EGF-like domains arranged in tandem. Non-crystallographic and crystallographic two-fold symmetries were observed in crystals of f1 and f2 respectively, indicating the formation of SCARF1 homodimers. Structural analysis identified critical residues involved in dimerization, which were validated through mutational experiments. In addition, the authors conducted flow cytometry and confocal experiments to characterize cellular interactions of SCARF1 with lipoproteins. The results revealed the vital role of the 133-221aa region in the binding between SCARF1 and modified LDLs. Moreover, four arginine residues were identified as crucial for modified LDL recognition, highlighting the contribution of charge interactions in SCARF1-lipoprotein binding. The lipoprotein binding region is further validated by designing SCARF1/SCARF2 chimeric molecules. Interestingly, the interaction between SCARF1 and modified LDLs could be inhibited by teichoic acid, indicating potential overlap in or sharing of binding sites on SCARF1 ECD.

The author employed a nice collection of techniques, namely crystallographic, SEC, DLS, flow cytometry, ELISA, and confocal imaging. The experiments are technically sound and the results are clearly written, with a few concerns as outlined below. Overall, this research represents an advancement in the mechanistic investigation of SCARF1 and its interaction with ligands. The role of scavenger receptors is critical in lipid homeostasis, making this work of interest to the eLife readership.

Reviewer #3 (Public Review):

Summary:

The manuscript by Wang et. al. described the crystal structures of the N-terminal fragments of Scavenger receptor class F member 1 (SCARF1) ectodomains. SCARF1 recognizes modified LDLs, including acetylated LDL and oxidized LDL, and it plays an important role in both innate and adaptive immune responses. They characterized the dimerization of SCARF1 and the interaction of SCARF1 with modified lipoproteins by mutational and biochemical studies. The authors identified the critical residues for dimerization and demonstrated that SCARF1 may function as homodimers. They further characterized the interaction between SCARF1 and LDLs and identified the lipoprotein ligand recognition sites, the highly positively charged areas. Their data suggested that the teichoic acid inhibitors may interact with SCARF1 in the same areas as LDLs.

Strengths:

The crystal structures of SCARF1 were high quality. The authors performed extensive site-specific mutagenesis studies using soluble proteins for ELISA assays and surface-expressed proteins for flow cytometry.

Weaknesses:

(1) The schematic drawing of human SCARF1 and SCARF2 in Fig 1A did not show the differences between them. It would be useful to have a sequence alignment showing the polymorphic regions.

The schematic drawing in Fig.1A is to give a brief idea about the two molecules, the sequence alignment may take too much space in the figure. A careful alignment between SCARF1 and SCARF2 can be found in Ref. 24 (Ishii, et al., J Biol Chem, 2002. 277, 39696-702) an also mentioned in p.4.

(2) The description of structure determination was confusing. The f1 crystal structure was determined by SAD with Pt derivatives. Why did they need molecular replacement with a native data set? The f2 crystal structure was solved by molecular replacement using the structure of the f1 fragment. Why did they need to use EGF-like fragments predicted by AlphaFold as search models?

The crystal structure of f1 was first determined by SAD using Pt derivatives, but soaking of Pt reduced the resolution of the crystals, therefore we use this structure as a search model for a native data set that had higher resolution for further refinement. For the structural determination of f2, the molecular replacement using f1 structure was not able to show the initial density of the extra region in f2 (residues 133-209), which was missing in f1. Therefore, the EGF-like domains of SCARF1 modeled by AlphaFold were applied as search models for this region (p.18).

(3) It's interesting to observe that SCARA1 binds modified LDLs in a Ca2+-independent manner. The authors performed the binding assays between SCARF1 and modified LDLs in the presence of Ca2+ or EDTA on Page 9. However, EDTA is not an efficient Ca2+ chelator. The authors should have performed the binding assays in the presence of EGTA instead.

The binding assays in the presence of EGTA are included in the revised manuscript (Fig. S7) (p.9), which also suggest that SCARA1 binds OxLDL in a Ca2+-independent manner.

(4) The authors claimed that SCARF1Δ353-415, the deletion of a C-terminal region of the ectodomain, might change the conformation of the molecule and generate hinderance for the C-terminal regions. Why didn't SCARF1Δ222-353 have a similar effect? Could the deletion change the interaction between SCARF1 and the membrane? Is SCARF1Δ353-415 region hydrophobic?

The truncation mutants were constructed to roughly locate the binding region of lipoproteins on SCARF1, and the overall results showed that the sites might locate at the region of 133-221. Mutant Δ222-353 may also affect the conformation, but it still had binding with OxLDL like wild type, suggesting the binding sites were retained in this mutant. Mutant Δ353-415 showed a reduction of binding, implying that the binding sites might be retained but binding was affected, we think it might be due to the conformational change that could reduce the binding or accessibility of lipoproteins. Since this region locates closer to the membrane, it’s possible that it may change the interaction with the membrane. In the AF model, Δ353-415 region does not seem to be more hydrophobic than other regions (Fig. S2C).

(5) What was the point of having Figure 8? Showing the SCARF1 homodimers could form two types of dimers on the membrane surface proposed? The authors didn't have any data to support that.

Fig. 8 shows a potential model of the SCARF1 dimers on the cell surface by combining the structural information from crystals and AF predictions. The two dimers in the figure are identical but with different viewing angles. The lipoprotein binding sites are also indicated (Fig. 8).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The authors need to show examples of the electron density for both structures.

Electron density examples of the two structures are shown in Fig. S2A.

Figure 1)

The figure does not show enough details of the structure. The text mentions hydrogen-bond and disulfide bonds that stabilize the loops, these should be shown.

Disulfide bonds of the two structures are shown in Fig. 1.

Figure 2)

D) The full gel should be shown.

E) Rather than just relying on changes in gel filtration elution volumes, the authors do the appropriate experiment and measure the hydrodynamic radius of the WT and mutant ectodomains by DLS. However, they need to show plots of the size distribution, not just mean radial values, in order to show if the sample is monodisperse.

The full gel and plots of DLS are shown in Fig. S3A-B.

Figure 3)

I have concerns about the rigor of the experiments in panels A-D. The authors include a non-transfected control but do not appear to have treated non-transfected cells with the lipoproteins to evaluate the specificity of binding. Every cell binding assay (flow  or confocal) must show the data from non-transfected cells treated with each lipoprotein, as each lipoprotein species could have a unique non-specific binding pattern. The authors show these controls in Figure 6, but these controls are necessary in every experiment.

In Fig. 3A, since several lipoproteins were included in the figure, we use non-transfected cells without lipoprotein treatment as a negative control. The OxLDL or AcLDL treated non-transfected cells were also used as negative controls and shown in Fig. 3B-C. LDL, HDL or OxHDL may have their own non-specific binding patterns, the treatment of LDL, HDL or OxHDL with the transfected cells all gave negative results (Fig. 3A and D).

Cell-surface of the SCARF1 variants is a major concern. The constructs the authors use are tagged with a GFP on the cytosolic side. However, the Methods to do indicate if they gate on GFP+ transfected cells for analytical flow. Such gating may have been used because the staining experiments in Figures 3 and 4 show uniform cell populations, whereas the staining done with an anti-SCARF1 Ab in S4 shows most of the cells not expressing the protein on the surface. Please clarify.

Data for the anti-SCARF1 Ab assay is gated for GFP in the revised Fig. S4, and  the non-transfected cells are included as a control.

The authors must demonstrate cell-surface staining with an epitope tag on the extracellular side and clarify if the analyzed cells are gated for surface expression. The anti-SCARF antibody used in S4 may not recognize the truncated or mutant SCARFs equally. Cell-surface expression in the flow experiments cannot be inferred from confocal experiments because the flow experiments have a larger quantitative range.

Anti-SCARF1 antibody assay provides an estimation of the surface expression of the proteins. If the epitope of the antibody was mutated or removed in the mutants, most likely it would lose binding activity. Including an epitope tag on the ectodomain could be an option, but if truncation or mutation changes the conformation of the ectodomain, the accessibility of the epitope may also be affected, and addition of an extra sequence or domain, such as an epitope tag, may affect the surface expression of proteins sometimes.

In several places, the authors infer increased or decreased affinity from mean fluorescent intensity values of a single concentration point without doing appropriate dose-curves. These experiments need to be done or else the mentions of changes in apparent affinities should be removed.

We add a concentration for the WT interaction with OxLDL (Fig. S6, p.9) and the manuscript is also modified accordingly.

Figure 7

The concentration of teichoic acid used to inhibit modified LDL binding should be indicated and a dose-curve analysis should be done comparing teichoic acid to some non-inhibitory bacterial polymer.

The concentration of teichoic acids used in the inhibition assays is 100 mg/ml (p.21). Unfortunately, we don’t have other bacterial polymers in the lab and not sure about the potential inhibitory effects.

Reviewer #2 (Recommendations For The Authors):

Major points:

(1) The SCARF1 ECD contains three N-linked glycosylation sites (N289, N382, N393). It remains unclear whether these modifications are involved in SCARF1 binding to modified LDLs. Is it possible to design some experiments to investigate the effect of N-glycans on the recognition of modified LDLs? In particular, N382 and N393 are included in 353-415aa and the truncation mutant of SCARF1Δ353-415aa resulted in reduced binding with OxLDL in Fig.3G. Or whether the reduced binding is only due to the potential conformational changes caused by the deletion of the C-terminal region of the ECD?

A previous study regarding the N-glycans (N289, N382, N393) of SCARF1 (ref.17) has shown that they may affect the proteolytic resistance, ligand-binding affinity and subcellular localization of SCARF1, which is not quite surprising as lipoproteins are large particles, the N-glycans on the surface of SCARF1 could affect accessibility or affinity for lipoproteins. But the exact roles of each glycan could be difficult to clarify as they might also be involved in protein folding and trafficking.

The reduction of the binding of OxLDL for the mutant SCARF1 Δ353-415aa may be due to the conformational change or the loss of the glycans or both.

(2) The authors speculated that the dimeric form of SCARF1 may be more efficient in recognizing lipoproteins on the cell surface. Please highlight the critical region/sites for ligand binding in Figure 8 and discuss the structural basis of dimerization improving the binding.

The binding sites for lipoproteins on SCARF1 are indicated in Fig. 8. According to our data, it might be possible the conformation of the dimeric form of SCARF1 makes it more accessible to the ligands on the cell surface as implied by flow cytometry (p.14-15), but still needs further evidence on this.

(3) Could the two salt bridges (D61-K71, R76-D98) observed in f1 crystals be found in f2 crystals? They seemed to be a little far from the defined dimeric interface (F82, S88, Y94) and how important are these to SCARF1 dimerization?

The two salt bridges observed in f1 crystal are not found in f2 crystal (distances are larger than 5.0 Å), suggesting they are not required for dimerization (p. 7-8), but may be helpful in some cases.

(4) The monomeric mutants (S88A/Y94A, F82A/S88A/Y94A) exhibited opposite affinity trends to OxLDL in ELISA and flow cytometry. The authors proposed steric hinderance of the dimers coated onto the plates as the potential explanation for this observation. However, the method of ELISA stated that OxLDLs, instead of SCARF1 ECD, were coated onto the plates. So what's the underlying reason for the inconsistency in different assays?

Thanks. ELISA was done by coating OxLDLs on the plates as described in the Methods. But still, a dimeric form of SCARF1 may only bind one OxLDL coated on the plates due to steric hinderance. We correct this on p.12.

Minor points:

(1) Figure 2D and Figure S3 - please label the molecular weight marker on the SEC traces to indicate the native size of various purified proteins.

The elution volume of SEC not only reflects the molecular weight, but it’s also affected by the conformation or shape of protein. The ectodomain of SCARF1 has a long curved conformation, the elution volumes of the monomeric or dimeric forms of SCARF1 do not align well with the standard molecular weight marker and elute much earlier in SEC. We include the standard molecular weight marker in Fig. S3C-D.

(2) Could the authors provide SEC profiles of f1 and f2 that were used in crystallographic study?

The SEC profiles of f1 and f2 for crystallization are shown in Fig. S5 (p.6).

(3) The legend of Figure 3A states that the NC in flow cytometry assay represents the non-transfected cells, but please confirm whether the NC in Fig. 3A-C corresponds to non-transfected cells or no lipoprotein.

NC in Fig. 3A represents the non-transfected cells, and no lipoproteins were added in this case as several lipoproteins are included in Fig. 3A. The lipoprotein (OxLDL or AcLDL) treated non-transfected cells (NC) were shown in Fig. 3B-C as negative controls.

Reviewer #2 (Public review):

The manuscript by Yorek et al explores the role of fatty acids, particularly unsaturated fatty acids, in lipid droplet accumulation and lipolysis in tumor-associated macrophages (TAMs). Using flow cytometry, immunofluorescent imaging, and TEM, the authors observed that unsaturated fatty acids, such as linoleic acids (LA), tend to induce lipid droplet accumulation in the ER of macrophages, but not in the lysosomes. This phenomenon led them to examine the key enzymes involved in lipid droplet/TAG biosynthesis, where they found incubation of LA upregulates GPAT/DGAT and C/EBPα. In vitro studies, data from public databases, single-cell RNA sequencing of splenic macrophages, and more show that FABP4 emerges as an important mediator for C/EBPα activation. This is further confirmed by FABP4-knockout macrophages, where lipid accumulation and utilization of unsaturated fatty acids were compromised in macrophages through inhibition of LA-induced lipolysis. Using the co-culture system and immunohistochemical analysis, they found that the high FABP4 expression in TAMs, which are observed in metastatic breast cancer tissue, promotes breast cancer cell migration in vitro.

This study is important since the impact of tumor microenvironment is crucial for the development of breast cancer. The individual experiments are well-designed and structured. However, the logic connecting to the next step is a bit difficult to follow, especially when combined with incomplete statistical analysis in some figures, making the conclusion less convincing. For instance, the comparison of macrophage FABP4 expression between breast cancer patients with or without metastasis illustrates the importance of FABP4 expression in metastasis, yet there is no examination of the expression of other key enzymes in the lipolysis or lipid biosynthesis pathway nor there is any correlation with parameters that would reflect patients' consumption of fatty acids. Similarly, an in vivo study comparing FABP4 knockout mice with or without unsaturated fatty acids would yield more compelling evidence. The statistical analysis was largely focused on the sets of unsaturated fatty acids when data from both types of fatty acids were present. In some cases, significant changes are observed in the sets of saturated fat, but there is no explanation of why only the data from unsaturated fats are important for investigation.

Overall, there is solid evidence for the importance of FABP4 expression in TAMs on metastatic breast cancer as well as lipid accumulation by LA in the ER of macrophages. A stronger rationale for the exclusive contribution of unsaturated fatty acids to the utilization of TAMs in breast cancer and a more detailed description and statistical analysis of data will strengthen the findings and resulting claims.

Reviewer #3 (Public review):

Summary:

Regulated metabolism has only recently been recognized as a key component of cancer biology, and even more recently recognized as a significant modulator of the tumor microenvironment (TME). TAMs in the TME play a major role in supporting cancer cell survival and growth/spread, as well as generating an immunosuppressive ME to suppress anti-tumor immunity. Specific regulation of lipid metabolism in this context, in particular how lipids are stored and subsequently mobilized for metabolism, is largely unexplored - especially in the immunological components of the TME.

In this manuscript, the authors build on their previous observations that the fatty acid-binding protein FABP4 plays an important role in macrophage function and that FABP4 expression in tumor associated macrophages (TAM) promotes breast cancer progression. They demonstrate:

(1) Unlike saturated fatty acids (FA), unsaturated FA promotes lipid droplet (LD) accumulation in murine macrophages. LD is the primary intracellular storage depot for FA.

(2) Unsaturated FA activates the FABP4-C/EBPalpha axis to upregulate transcription of the enzymes involved in the synthesis of neutral triacylglycerol (TAG) is an essential step in the formation of the neutral lipid core of LD. It should be noted that the authors speculate that UFA-activated FABP4 translocates to the nucleus to activate PPARgamma, which is known to induce C/EBPalpha expression, but do not directly test the involvement of PPARgamma in this axis.

(3) FABP4 deficiency compromises unsaturated FA-mediated lipid accumulation and utilization in murine macrophages.

(4) FABP4-mediated lipid metabolism in macrophages (TAMS) contributes to breast cancer metastasis, in in-vitro of tumor migration induced by murine macrophages and in correlative studies from human patient breast cancer biopsies.

From these studies, the manuscript concludes that FABP4 plays a pivotal role in mediating lipid droplet formation and lipolysis in TAM, which provides lipids to breast cancer cells that contribute to their growth and metastasis.

These are significant findings, as they provide new insight into the mechanistic regulation of TAM biology via regulation of lipid metabolism, as well as define new biomarkers and potential novel therapeutic targets.

The findings are strong in the studies that mechanistically define the role of FAB4 in lipid accumulation and utilization in murine macrophages. However, evidence is less compelling regarding TAM biology and human breast cancer in 3 main areas:

First, while there is clear in vitro evidence that co-cultured murine macrophages genetically deficient in FABP4 (or their conditioned media) do not enhance breast cancer cell motility and invasion, these macrophages are not bonafide TAM - which may have different biology. The use of actual TAM in these experiments would be more compelling. Perhaps more importantly, there is no in vivo data in tumor-bearing mice that macrophage deficiency of FABP4 affects tumor growth or metastasis - which are doable experiments given the availability of the FABP4 KO mice.

Second, no data is presented that the mechanisms/biology that are elegantly demonstrated in the murine macrophages also occur in human macrophages - which would be foundational to translating these findings into human breast cancer. It seems like straightforward in vitro studies in human monocytes/macrophages could be done to recapitulate the main characteristics seen in the murine macrophages.

Third, while the data from the human breast cancer specimens is very intriguing, it is difficult to ascertain how accurate IHC is in determining that the CD163+ cells (TAM) are in fact the same cells expressing FABP4 - which is the central premise of these studies. Demonstrating that IHC has the resolution to do this would be important. Additionally, the in vitro characterization of FABP4 expression in human macrophages would also add strength to these findings.

In summary, the strengths of this manuscript are the significance of metabolic regulation of the immune tumor microenvironment (TME), and the careful mechanistic delineation of FABP4 involvement in mediating lipid droplet formation and lipolysis in murine macrophages. The weaknesses of the work are the lack of direct experimental evidence that human macrophages behave in the same way as murine macrophages, the incomplete characterization of the role of FABP4 expression in TAM in modulating tumor growth in vivo (in murine models), and whether it can be definitively determined that FABP4 is being primarily expressed in the CD163+ macrophages in human breast cancer samples.

Strengths:

(1) Regulated metabolism has only recently been recognized as a key component of cancer biology, and even more recently recognized as a significant modulator of the tumor microenvironment (TME). TAMs in the TME play a major role in supporting cancer cell survival and growth/spread, as well as generating an immunosuppressive ME to suppress anti-tumor immunity.

(2) Regulation of lipid metabolism in this context is largely unexplored, especially in the immunological components of the TME.

(3) The work builds on the authors' previous work on the role of FABP4 plays an important role in macrophage function including FABP4 expression in TAM promotes breast cancer progression (Hao et al, Cancer Res 2018). This paper identified FABP4-expressing macrophages as being pro-tumorigenic via upregulation of IL-6STAT3 signaling.

(4) The careful and thorough mechanistic delineation of FABP4 involvement in mediating lipid droplet formation and lipolysis in murine macrophages.

(5) The intriguing observations that FABP4-mediated lipid metabolism in macrophages contributes to breast cancer metastasis, in in vitro of tumor migration induced by murine macrophages and in correlative studies from human patient breast cancer biopsies that CD163+ cell numbers (putatively TAM) and FABP4 expression was associated with increased metastatic disease and poor overall survival.

(6) Identification of FABP4 both a prognostic biomarker and a potential therapeutic target to modulate the pro-tumor immune TME.

Weaknesses:

(1) While the authors speculate that UFA-activated FABP4 translocates to the nucleus to activate PPARgamma, which is known to induce C/EBPalpha expression, they do not directly test involvement of PPARgamma in this axis.

(2) While there is clear in vitro evidence that co-cultured murine macrophages genetically deficient in FABP4 (or their conditioned media) do not enhance breast cancer cell motility and invasion, these macrophages are not bonafide TAM - which may have different biology. Use of actual TAM in these experiments would be more compelling. Perhaps more importantly, there is no in vivo data in tumor bearing mice that macrophage-deficiency of FABP4 affects tumor growth or metastasis.

(3) Related to this, the authors find FABP4 in the media and propose that macrophage secreted FABP4 is mediating the tumor migration - but don't do antibody neutralizing experiments to directly demonstrate this.

(4) No data is presented that the mechanisms/biology that are elegantly demonstrated in the murine macrophages also occurs in human macrophages - which would be foundational to translating these findings into human breast cancer.

(5) While the data from the human breast cancer specimens is very intriguing, it is difficult to ascertain how accurate IHC is in determining that the CD163+ cells (TAM) are in fact the same cells expressing FABP4 - which is central premise of these studies. Demonstration that IHC has the resolution to do this would be important. Additionally, the in vitro characterization of FABP4 expression in human macrophages would also add strength to these findings.

Author response:

Reviewer #1:

(1) After Figure 1, a single saturated (palmitic acid; PA) and a single unsaturated (linoleic acid; LA) fatty acid are used for the remaining studies, bringing into question whether effects are in fact the result of a difference in saturation vs. other potential differences.

PA, SA, OA and LA are the most common FA species in humans (Figure 1A in manuscript). Among them, PA predominantly represents saturated FAs while LA is the main unsaturated FAs, respectively. Of note, although both SA and OA were included in our studies, their effects were comparable to those of PA and LA, respectively. Due to space constraints, the data of SA and OA are not presented in the figures.  

(2) While primary macrophages are used in several mechanistic studies, tumor-associated macrophages (TAMs) are not used. Rather, correlative evidence is provided to connect mechanistic studies in macrophage cell lines and primary macrophages to TAMs.

The roe of FABP4 in TAMs has been demonstrated in our previous studies using in vivo animal models1. Therefore, we did not include TAM-specific data in the current study.

(3) CEBPA and FABP4 clearly regulate LA-induced changes in gene expression. However, whether these two key proteins act in parallel or as a pathway is not resolved by presented data.

Multiple lines of evidence in our studies suggest that FABP4 and CEBPA act as a pathway in LA-induced changes: 1) FABP4-negative macrophages exhibit reduced expression of CEBPA in single cell sequencing data; 2) FABP4 KO macrophages exhibited reduced CEBPA expression; 3) LA-induced CEBPA expression in macrophages was compromised when FABP4 was absent.

(4) It is very interesting that FABP4 regulates both lipid droplet formation and lipolysis, yet is unclear if the regulation of lipolysis is direct or if the accumulation of lipid droplets - likely plus some other signal(s) - induces upregulation of lipolysis genes.

Yes, it is likely that tumor cells induce lipolysis signals. Multiple studies have shown that various tumor types stimulate lipolysis to support their growth and progression2-4.  In this process, lipid-loaded macrophages have emerged as a promising therapeutic target in cancer5, 6. Consistent with findings that lipolysis is essential for tumor-promoting M2 alternative macrophage activation7, our data using FABP4 WT and KO macrophages demonstrate that FABP4 plays a critical role in LA-induced lipid accumulation and lipolysis for tumor metastasis. 

(5) In several places increased expression of genes coding for enzymes with known functions in lipid biology is conflated with an increase in the lipid biology process the enzymes mediate. Additional evidence would be needed to show these processes are in fact increased in a manner dependent on increased enzyme expression.

We fully agree with the reviewer that increased gene expression does not necessarily equate to increased activity. The key finding of this study is that FABP4 plays a pivotal role in linoleic acid (LA)-mediated lipid accumulation and lipolysis in macrophages that promote tumor metastasis. Numerous lipid metabolism-related genes, including FABP4, CEBPA, GPATs, DGATs, and HSL, are involved in this process. While it was not feasible to verify the activity of all these genes, we confirmed the functional roles of key genes like FABP4 and CEBPA through various functional assays, such as gene silencing, knockout cell lines, lipid droplet formation, and tumor migration assays. Supported by established lipid metabolism pathways, our data provide compelling evidence that FABP4 functions as a crucial lipid messenger, facilitating unsaturated fatty acid-driven lipid accumulation and lipolysis in tumor-associated macrophages (TAMs), thus promoting breast cancer metastasis.   

Reviewer #2:

Overall, there is solid evidence for the importance of FABP4 expression in TAMs on metastatic breast cancer as well as lipid accumulation by LA in the ER of macrophages. A stronger rationale for the exclusive contribution of unsaturated fatty acids to the utilization of TAMs in breast cancer and a more detailed description and statistical analysis of data will strengthen the findings and resulting claims.

We greatly appreciated the positive comments from Reviewer #2. In our study, we evaluated the effects of both saturated and unsaturated fatty acids (FA) on lipid metabolism in macrophages.  Our results showed that unsaturated FAs exhibited a preference for lipid accumulation in macrophages compared to saturated FAs. Further analysis revealed that unsaturated LA, but not saturated PA, induced FABP4 nuclear translocation and CEBPA activation, driving the TAG synthesis pathway. For in vitro experiments, statistical analyses were performed using a two-tailed, unpaired student t-test, two-way ANOVA followed by Bonferroni’s multiple comparison test, with GraphPad Prism 9. For experiments analyzing associations of FABP4, TAMs and other factors in breast cancer patients, the Kruskal-Wallis test was applied to compare differences across levels of categorical predictor variable. Additionally, multiple linear regression models were used to examine the association between the predictor variables and outcomes, with log transformation and Box Cox transformation applied to meet the normality assumptions of the model. It is worth noting that in some experiments, only significant differences were observed in groups treated with unsaturated fatty acids. Non-significant results from groups treated with saturated fatty acids were not included in the figures.

Reviewer #3

(1) While the authors speculate that UFA-activated FABP4 translocates to the nucleus to activate PPARgamma, which is known to induce C/EBPalpha expression, they do not directly test involvement of PPARgamma in this axis.

Yes, LA induced FABP4 nuclear translocation and activation of PPARgamma in macrophages (see Figure below). Since these findings have been reported in multiple other studies 8, 9, we did not include the data in the current manuscript.

Author response image 1.

LA induced PPARg expression in macrophages. Bone-marrow derived macrophages were treated with 400μM saturated FA (SFA), unsaturated FA (UFA) or BSA control for 6 hours. PPARg expression was measured by qPCR (***p<0.001).

(2) While there is clear in vitro evidence that co-cultured murine macrophages genetically deficient in FABP4 (or their conditioned media) do not enhance breast cancer cell motility and invasion, these macrophages are not bonafide TAM - which may have different biology. Use of actual TAM in these experiments would be more compelling. Perhaps more importantly, there is no in vivo data in tumor bearing mice that macrophage-deficiency of FABP4 affects tumor growth or metastasis.

In our previous studies, we have shown that macrophage-deficiency of FABP4 reduced tumor growth and metastasis in vivo in mouse models1.

(3) Related to this, the authors find FABP4 in the media and propose that macrophage secreted FABP4 is mediating the tumor migration - but don't do antibody neutralizing experiments to directly demonstrate this.

Yes, we have recently published a paper of developing anti-FABP4 antibody for treatment of breast cancer in moue models10.

(4) No data is presented that the mechanisms/biology that are elegantly demonstrated in the murine macrophages also occurs in human macrophages - which would be foundational to translating these findings into human breast cancer.

Thanks for the excellent suggestions. Since this manuscript primarily focuses on mechanistic studies using mouse models, we plan to apply these findings in our future human studies. 

(5) While the data from the human breast cancer specimens is very intriguing, it is difficult to ascertain how accurate IHC is in determining that the CD163+ cells (TAM) are in fact the same cells expressing FABP4 - which is central premise of these studies. Demonstration that IHC has the resolution to do this would be important. Additionally, the in vitro characterization of FABP4 expression in human macrophages would also add strength to these findings.

The expression of FABP4 in CD163+ TAM observed through IHC is consistent with our previous findings, where we confirmed FABP4 expression in CD163+ TAMs using confocal microscopy. Emerging evidence further supports the pro-tumor role of FABP4 expression in human macrophages across various types of obesity-associated cancers11-13. 

References

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Reviewer #2 (Public review):

Summary:

The authors tried to determine how PA28g functions in oral squamous cell carcinoma (OSCC) cells. They hypothesized it may act through metabolic reprogramming in the mitochondria.

Strengths:

They found that the genes of PA28g and C1QBP are in an overlapping interaction network after an analysis of a genome database. They also found that the two proteins interact in coimmunoprecipitation and pull-down assays using the lysate from OSCC cells with or without expression of the exogenous genes. They used truncated C1QBP proteins to map the interaction site to the N-terminal 167 residues of C1QBP protein. They observed the levels of the two proteins are positively correlated in the cells. They provided evidence for the colocalization of the two proteins in the mitochondria, the effect on mitochondrial form and function in vitro and in vivo OSCC models, and the correlation of the protein expression with the prognosis of cancer patients.

Weaknesses:

Many data sets are shown in figures that cannot be understood without more descriptions, either in the text or the legend, e.g., Figure 1A. Similarly, many abbreviations are not defined.

Some of the pull-down and coimmunoprecipitation data do not support the conclusion about the PA28g-C1QBP interaction. For example, in Appendix Figure 1B the Flag-C1QBP was detected in the Myc beads pull-down when the protein was expressed in the 293T cells without the Myc-PA28g, suggesting that the pull-down was not due to the interaction of the C1QBP and PA28g proteins. In Appendix Figure 1C, assume the SFB stands for a biotin tag, then the SFB-PA28g should be detected in the cells expressing this protein after pull-down by streptavidin; however, it was not. The Western blot data in Figure 1E and many other figures must be quantified before any conclusions about the levels of proteins can be drawn.

The immunoprecipitation method is flawed as it is described. The antigen (PA28g or C1QBP) should bind to the respective antibody that in turn should binds to Protein G beads. The resulting immunocomplex should end up in the pellet fraction after centrifugation and be analyzed further by Western blot for coprecipitates. However, the method in the Appendix states that the supernatant was used for the Western blot.

To conclude that PA28g stabilizes C1QBP through their physical interaction in the cells, one must show whether a protease inhibitor can substitute PA28q and prevent C1QBP degradation, and also show whether a mutation that disrupts the PA28g-C1QBP interaction can reduce the stability of C1QBP. In Figure 1F, all cells expressed Myc-PA28g. Therefore, the conclusion that PA28g prevented C1QBP degradation cannot be reached. Instead, since more Myc-PA28g was detected in the cells expressing Flag-C1QBP compared to the cells not expressing this protein, a conclusion would be that the C1QBP stabilized the PA28g. Figure 1G is a quantification of Western blot data that should be shown.

The binding site for PA28g in C1QBP was mapped to the N-terminal 167 residues using truncated proteins. One caveat would be that some truncated proteins did not fold correctly in the absence of the sequence that was removed. Thus, the C-terminal region of the C1QBP with residues 168-283 may still bind to the PA29g in the context of full-length protein. In Figure 1I, more Flag-C1QBP 1-167 was pulled down by Myc-PA28g than the full-length protein or the Flag-C1QBP 1-213. Why?

The interaction site in PA28g for C1QBP was not mapped, which prevents further analysis of the interaction. Also, if the interaction domain can be determined, structural modeling of the complex would be feasible using AlphaFold2 or other programs. Then, it is possible to test point mutations that may disrupt the interaction and if so, the functional effect

Gemignani, Michael C. Elementary Topology. 2nd ed. Addison-Wesley Series in Mathematics. Reading, MA: Addison-Wesley Publishing Company, 1971.

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Welcome back and in this lesson I want to cover a few really important topics which will be super useful as you progress your general IT career, but especially so for anyone who is working with traditional or hybrid networking.

Now I want to start by covering what a VLAN is and why you need them, then talk a little bit about Trump connections and finally cover a more advanced version of VLANs called Q in Q.

Now I've got a lot to cover so let's just jump in and get started straight away.

Let's start with what I've talked about in my technical fundamentals lesson so far.

This is a physical network segment.

It has a total of eight devices, all connected to a single, layer 2 capable device, a switch.

Each LAN, as I talked about before, is a shared broadcast domain.

Any frames which are addressed to all Fs will be broadcast on all ports of the switch and reach all devices.

Now this might be fine with eight devices but it doesn't scale very well.

Every additional device creates yet more broadcast traffic.

Because we're using a switch, each port is a different collision domain and so by using a switch rather than a layer 1 hub we do improve performance.

Now this local network also has three distinct groups of users.

We've got the game testers in orange, we've got sales in blue and finance in green.

Now ideally we want to separate the different groups of devices from one another.

In larger businesses you might have a requirement for different segments of the network from normal devices, for servers and for other infrastructure.

Different segments for security systems and CCTV and maybe different ones for IoT devices and IP telephony.

Now if we only had access to physical networks this would be a challenge.

Let's have a look at why.

Let's say that we talk each of the three groups and split them into either different floors or even different buildings.

On the left finance, in the middle game testers and on the right sales.

Each of these buildings would then have its own switch and the switches in those buildings would be connected to devices also in those buildings.

Which for now is all the finance, all the game tester and all the sales teams and machines.

Now these switches aren't connected and because of that each one is its own broadcast domain.

This would be how things would look in the real world if we only had access to physical networking.

And this is fine if different groups don't need to communicate with us so we don't require cross domain communication.

The issue right now is that none of these switches are connected so the switches have no layer 2 communications between them.

If we wanted to do cross building or cross domain communications then we could connect the switches.

But this creates one larger broadcast domain which moves us back to the architecture on the previous screen.

What's perhaps more of a problem in this entirely physical networking world is what happens if a staff member changes role but not building.

In this case moving from sales to game tester.

In this case you need to physically run a new cable from the middle switch to the building on the right.

If this happens often it doesn't scale very well and that is why some form of virtual local area networking is required.

And that's why VLANs are invaluable.

Let's have a look at how we support VLANs using layer 2 as the OSI 7-Line model.

This is a normal Ethernet frame.

In the context of this lesson what's important is that it has a source and destination MAC address fields together with a payload.

Now the payload carries the data.

The source MAC address is the MAC address of the device which is creating and sending the frame.

The destination MAC address can contain a specific MAC address which means that it's a unique S-frame to a frame that's destined for one other device.

Or it can contain all F's which is known as a broadcast.

And it means that all of the devices on the same layer 2 network will see that frame.

What a standard frame doesn't offer us is any way to isolate devices into different parts, different networks.

And that's where a new standard comes in handy which is known as 802.1Q, also known as .1Q. .1Q changes the frame format of the standard Ethernet frame by adding a new field, a 32-bit field in the middle in Scion.

The maximum size of the frame as a result can be larger to accommodate this new data. 12 bits of this 32-bit field can be used to store values from 0 through to 4095.

This represents a total of 4096 values.

This is used for the VLAN ID or VID.

A 0 in this 12-bit value signifies no VLAN and 1 is generally used to signify the management VLAN.

The others can be used as desired by the local network admin.

What this means is that any .1Q frames can be a member of over 4,000 VLANs.

And this means that you can create separate virtual LANs or VLANs in the same layer 2 physical network.

A broadcast frame so anything that's destined to all PEPs would only reach all the devices which are in the same VLAN.

Essentially, it creates over 4,000 different broadcast domains in the same physical network.

You might have a VLAN for CCTV, a VLAN for servers, a VLAN for game testing, a VLAN for guests and many more.

Anything that you can think of and can architect can be supported from a networking perspective using VLANs.

But I want you to imagine even bigger.

Think about a scenario where you as a business have multiple sites and each site is in a different area of the country.

Now each site has the same set of VLANs.

You could connect them using a dedicated wide area network and carry all of those different company specific VLANs and that would be fine.

But what if you wanted to use a comms provider, a service provider who could provide you with this wide area network capability?

What if the comms provider also uses VLANs to distinguish between their different clients?

Well, you might face a situation where you use VLAN 1337 and another client of the comms provider also uses VLAN 1337.

Now to help with this scenario, another standard comes to the rescue, 802.1AD.

And this is known as Q in Q, also known as provider bridging or stacked VLANs.

This adds another space in the frame for another VLAN field.

So now instead of just the one field for 802.1Q VLANs, now you have two.

You keep the same customer VLAN field and this is known as the C tag or customer tag.

But you add another VLAN field called the service tag or the S tag.

This means that the service provider can use VLANs to isolate their customer traffic while allowing each customer to also use VLANs internally.

As the customer, you can tag frames with your VLANs and then when those frames move onto the service provider network, they can tag with the VLAN ID which represents you as a customer.

Once the frame reaches another of your sites over the service provider network, then the S tag is removed and the frame is passed back to you as a standard .1Q frame with your customer VLAN still tagged.

Q in Q tends to be used for larger, more complex networks and .1Q is used in smaller networks as well as cloud platforms such as AWS.

For the remainder of this lesson, I'm going to focus on .1Q though if you're taking an advanced networking course of mine, I will be returning to the Q in Q topic in much more detail.

For now though, let's move on and look visually at how .1Q works.

This is a cut down version of the previous physical network I talked about, only this time instead of the three groups of devices we have two.

So on the left we have the finance building and on the right we have game testers.

Inside these networks we have switches and connected to these switches are two groups of machines.

These switches have been configured to use 802.1Q and ports have been configured in a very specific way which I'm going to talk about now.

So what makes .1Q really cool is that I've shown these different device types as separate buildings but they don't have to be.

Different groupings of devices can operate on the same layer to switch and I'll show you how that works in a second.

With 802.1Q ports and switches are defined as either access ports or trunk ports and access ports generally has one specific VLAN ID or vid associated with it.

A trunk conceptually has all VLAN IDs associated with it.

So let's say that we allocate the finance team devices to VLAN 20 and the game tester devices to VLAN 10.

We could easily hit any other numbers, remember we have over 4,000 to choose from, but for this example let's keep it simple and keep 10 and 20.

Now right now these buildings are separate broadcast domains because they have separate switches which are not connected and they have devices within them.

Two laptops connected to switch number one for the finance team and two laptops connected to switch number two for the game tester team.

Now I mentioned earlier that we have two types of switch ports in a VLAN cable network.

The first are access ports and the ports which the orange laptops on the right are connected to are examples of access ports.

Access ports communicate with devices using standard Ethernet which means no VLAN tags are applied to the frames.

So in this case the laptop at the top right sends a frame to the switch and let's say that this frame is a broadcast frame.

When the frame exits an access port it's tagged with a VLAN that the access port is assigned to.

In this case VLAN 10 which is the orange VLAN.

Now because this is a broadcast frame the switch now has to decide what to do with the frame and the default behaviour for switches is to forward the broadcast out of all ports except the one that it was received on.

For switches using VLANs this is slightly different.

First it forwards to any other access ports on the same VLAN but the tagging will be removed.

This is important because devices connected to access ports won't always understand 802.1Q so they expect normal untagged frames.

In addition the switch will fold frames over any trunk ports.

A trunk port in this context is a port between two switches for example this one between switch two and switch one.

Now a trunk port is a connection between two dot 1Q capable devices.

It forwards all frames and it includes the VLAN tagging.

So in this case the frame will also be forwarded over to switch one tagged as VLAN 10 which is the gain tester VLAN.

So tagged dot 1Q frames they only get forwarded to other access ports with the same VLAN but they have the tag stripped or they get forwarded across trunk ports with the VLAN tagging intact.

And this is how broadcast frames work.

For unicast ones which go to a specific single MAC address well these will be either forwarded to an access port in the same VLAN that the specific device is on or if the switch isn't aware of the MAC address of that device in the same VLAN then it will do a broadcast.

Now let's say that we have a device on the finance VLAN connected to switch two.

And let's say that the bottom left laptop sends a broadcast frame on the finance VLAN.

Can you see what happens to this frame now?

Well first it will go to any other devices in the same VLAN using access ports meaning the top left laptop and in that case the VLAN tag will be removed.

It will also be forwarded out of any trunk ports tagged with VLAN 20 so the green finance VLAN.

It will arrive at switch two with the VLAN tag still there and then it will be forwarded to any access ports on the same VLAN so VLAN 20 on that switch but the VLAN tagging will be removed.

Using virtual LANs in this way allows you to create multiple virtual LANs or VLANs.

With this visual you have two different networks.

The finance network in green so the two laptops on the left and the one at this middle bottom and then you have the gain testing network so VLAN 10 meaning the orange one on the right.

Both of these are isolated.

Devices cannot communicate between VLANs which are led to networks without a device operating between them such as a layer 3 router.

Both of these virtual networks operate over the top of the physical network and it means that now we can configure this network in using virtual configuration software which can be configured on the switches.

Now VLANs are how certain things within AWS such as public and private vifs on direct connect works so keep this lesson in mind when I'm talking about direct connect.

A few summary points though that I do want to cover before I finish up with this lesson.

First VLANs allow you to create separate layer 2 network segments and these provide isolation so traffic is isolated within these VLANs.

If you don't configure and deploy a router between different VLANs then frames cannot leave that VLAN boundary so they're virtual networks and these are ideal if you want to configure different virtual networks for different customers or if you want to access different networks for example when you're using direct connect to access VPCs.

VLANs offer separate broadcast domains and this is important.

They create completely separate virtual network segments so any broadcast frames within a VLAN won't leave that VLAN boundary.

If you see any mention of 802.1Q then you know that means VLANs.

If you see any mention of VLANs stacking or provide a bridging or 802.1AD or Q in Q this means nested VLANs.

So having a customer tag and a service tag allowing you to have VLANs in VLANs and these are really useful if you want to use VLANs on your internal business network and then use a service provider to provide wide area network connectivity who also uses VLANs and if you are doing any networking exams then you will need to understand Q in Q as well as 802.1Q.

So with that being said that's everything I wanted to cover.

Go ahead and complete this video and when you're ready I'll look forward to you joining me in the next.

This breakdown of classical aggression tactics I believe helped mitigate the lack of individual-specific behaviors being observed. I also think it highlights the fact that within the crow social circle there are both hierarchies as well as the fascinating (albeit strange) existence of clique-like behavior and tag-teaming to agitate or attack other birds.

-Video might be overstimulating, containing fast moving clips, different colours)

-Video also is missing a descriptive <ALT> tag.

-No closed caption in video needed as there is no audio

Pictures have links attached, however, no descriptive <ALT> tag to describe what is the image

Author response:

We sincerely thank the reviewers for their thoughtful, critical, and constructive comments, which will help us in further exploring the mechanisms by which LDH regulates glycolysis, the tricarboxylic acid cycle, and oxidative phosphorylation future studies. The following is our responses to the reviewers' comments.

Reviewer #1 (Public Review):

Summary:

Zeng et al. have investigated the impact of inhibiting lactate dehydrogenase (LDH) on glycolysis and the tricarboxylic acid cycle. LDH is the terminal enzyme of aerobic glycolysis or fermentation that converts pyruvate and NADH to lactate and NAD+ and is essential for the fermentation pathway as it recycles NAD+ needed by upstream glyceraldehyde-3-phosphate dehydrogenase. As the authors point out in the introduction, multiple published reports have shown that inhibition of LDH in cancer cells typically leads to a switch from fermentative ATP production to respiratory ATP production (i.e., glucose uptake and lactate secretion are decreased, and oxygen consumption is increased). The presumed logic of this metabolic rearrangement is that when glycolytic ATP production is inhibited due to LDH inhibition, the cell switches to producing more ATP using respiration. This observation is similar to the well-established Crabtree and Pasteur effects, where cells switch between fermentation and respiration due to the availability of glucose and oxygen. Unexpectedly, the authors observed that inhibition of LDH led to inhibition of respiration and not activation as previously observed. The authors perform rigorous measurements of glycolysis and TCA cycle activity, demonstrating that under their experimental conditions, respiration is indeed inhibited. Given the large body of work reporting the opposite result, it is difficult to reconcile the reasons for the discrepancy. In this reviewer's opinion, a reason for the discrepancy may be that the authors performed their measurements 6 hours after inhibiting LDH. Six hours is a very long time for assessing the direct impact of a perturbation on metabolic pathway activity, which is regulated on a timescale of seconds to minutes. The observed effects are likely the result of a combination of many downstream responses that happen within 6 hours of inhibiting LDH that causes a large decrease in ATP production, inhibition of cell proliferation, and likely a range of stress responses, including gene expression changes.

Strengths:

The regulation of metabolic pathways is incompletely understood, and more research is needed, such as the one conducted here. The authors performed an impressive set of measurements of metabolite levels in response to inhibition of LDH using a combination of rigorous approaches.

Weaknesses:

Glycolysis, TCA cycle, and respiration are regulated on a timescale of seconds to minutes. The main weakness of this study is the long drug treatment time of 6 hours, which was chosen for all the experiments. In this reviewer's opinion, if the goal was to investigate the direct impact of LDH inhibition on glycolysis and the TCA cycle, most of the experiments should have been performed immediately after or within minutes of LDH inhibition. After 6 hours of inhibiting LDH and ATP production, cells undergo a whole range of responses, and most of the observed effects are likely indirect due to the many downstream effects of LDH and ATP production inhibition, such as decreased cell proliferation, decreased energy demand, activation of stress response pathways, etc.

We appreciate the reviewer’s critical comments. The main argument is whether the inhibition of LDH induces a temporal perturbation in glycolysis, the TCA cycle, and OXPHOS, or if it leads to a shift to a new steady state. We argue that this shift represents a transition between two steady states; specifically, GNE-140 treatment drives metabolism from one steady state to another.

Before conducting the experiment, we performed a time course experiment, measuring glucose consumption and lactate production in cells treated with GNE-140. The results demonstrated a very good linearity, indicating that the glycolytic rate remained constant—thus confirming that glycolysis was at steady state. Given the tight coupling between glycolysis, the TCA cycle, and OXPHOS, we infer that the TCA cycle and OXPHOS were also at steady state. However, this ‘infer’ requires further confirmation.

Multiple published reports have shown that LDH inhibition in cancer cells causes a shift from fermentative ATP production to respiratory ATP production. This notion persists because it is often compared to the well-established Crabtree and Pasteur effects, where cells toggle between fermentation and respiration based on glucose and oxygen availability. However, in the Pasteur or Crabtree effects, the deprivation of oxygen—the terminal electron acceptor—drives the switch, which is fundamentally different from LDH inhibition.

Reviewer #2 (Public Review):

Summary:

Zeng et al. investigated the role of LDH in determining the metabolic fate of pyruvate in HeLa and 4T1 cells. To do this, three broad perturbations were applied: knockout of two LDH isoforms (LDH-A and LDH-B), titration with a non-competitive LDH inhibitor (GNE-140), and exposure to either normoxic (21% O2) or hypoxic (1% O2) conditions. They show that knockout of either LDH isoform alone, though reducing both protein level and enzyme activity, has virtually no effect on either the incorporation of a stable 13C-label from a 13C6-glucose into any glycolytic or TCA cycle intermediate, nor on the measured intracellular concentrations of any glycolytic intermediate (Figure 2). The only apparent exception to this was the NADH/NAD+ ratio, measured as the ratio of F420/F480 emitted from a fluorescent tag (SoNar).

The addition of a chemical inhibitor, on the other hand, did lead to changes in glycolytic flux, the concentrations of glycolytic intermediates, and in the NADH/NAD+ ratio (Figure 3). Notably, this was most evident in the LDH-B-knockout, in agreement with the increased sensitivity of LDH-A to GNE-140 (Figure 2). In the LDH-B-knockout, increasing concentrations of GNE-140 increased the NADH/NAD+ ratio, reduced glucose uptake, and lactate production, and led to an accumulation of glycolytic intermediates immediately upstream of GAPDH (GA3P, DHAP, and FBP) and a decrease in the product of GAPDH (3PG). They continue to show that this effect is even stronger in cells exposed to hypoxic conditions (Figure 4). They propose that a shift to thermodynamic unfavourability, initiated by an increased NADH/NAD+ ratio inhibiting GAPDH explains the cascade, calculating ΔG values that become progressively more endergonic at increasing inhibitor concentrations.

Then - in two separate experiments - the authors track the incorporation of 13C into the intermediates of the TCA cycle from a 13C6-glucose and a 13C5-glutamine. They use the proportion of labelled intermediates as a proxy for how much pyruvate enters the TCA cycle (Figure 5). They conclude that the inhibition of LDH decreases fermentation, but also the TCA cycle and OXPHOS flux - and hence the flux of pyruvate to all of those pathways. Finally, they characterise the production of ATP from respiratory or fermentative routes, the concentration of a number of cofactors (ATP, ADP, AMP, NAD(P)H, NAD(P)+, and GSH/GSSG), the cell count, and cell viability under four conditions: with and without the highest inhibitor concentration, and at norm- and hypoxia. From this, they conclude that the inhibition of LDH inhibits the glycolysis, the TCA cycle, and OXPHOS simultaneously (Figure 7).

Strengths:

The authors present an impressively detailed set of measurements under a variety of conditions. It is clear that a huge effort was made to characterise the steady-state properties (metabolite concentrations, fluxes) as well as the partitioning of pyruvate between fermentation as opposed to the TCA cycle and OXPHOS.

A couple of intermediary conclusions are well supported, with the hypothesis underlying the next measurement clearly following. For instance, the authors refer to literature reports that LDH activity is highly redundant in cancer cells (lines 108 - 144). They prove this point convincingly in Figure 1, showing that both the A- and B-isoforms of LDH can be knocked out without any noticeable changes in specific glucose consumption or lactate production flux, or, for that matter, in the rate at which any of the pathway intermediates are produced. Pyruvate incorporation into the TCA cycle and the oxygen consumption rate are also shown to be unaffected.

They checked the specificity of the inhibitor and found good agreement between the inhibitory capacity of GNE-140 on the two isoforms of LDH and the glycolytic flux (lines 229 - 243). The authors also provide a logical interpretation of the first couple of consequences following LDH inhibition: an increased NADH/NAD+ ratio leading to the inhibition of GAPDH, causing upstream accumulations and downstream metabolite decreases (lines 348 - 355).

Weaknesses:

Despite the inarguable comprehensiveness of the data set, a number of conceptual shortcomings afflict the manuscript. First and foremost, reasoning is often not pursued to a logical conclusion. For instance, the accumulation of intermediates upstream of GAPDH is proffered as an explanation for the decreased flux through glycolysis. However, in Figure 3C it is clear that there is no accumulation of the intermediates upstream of PFK. It is unclear, therefore, how this traffic jam is propagated back to a decrease in glucose uptake. A possible explanation might lie with hexokinase and the decrease in ATP (and constant ADP) demonstrated in Figure 6B, but this link is not made.

We appreciate the reviewer's critical comment. In Figure 3C, there is no accumulation of F6P or G6P, which are upstream of PFK1. This is because the PFK1-catalyzed reaction sets a significant thermodynamic barrier. Even with treatment using 30 μM GNE-140, the ∆GPFK1 (Gibbs free energy of the PFK1-catalyzed reaction) remains -9.455 kJ/mol (Figure 3D), indicating that the reaction is still far from thermodynamic equilibrium, thereby preventing the accumulation of F6P and G6P.

We agree with the reviewer that hexokinase inhibition may play a role, this requires further investigation.

The obvious link between the NADH/NAD+ ratio and pyruvate dehydrogenase (PDH) is also never addressed, a mechanism that might explain how the pyruvate incorporation into the TCA cycle is impaired by the inhibition of LDH (the observation with which they start their discussion, lines 511 - 514).

We agree with the reviewer’s comment. In this study, we did not explore how the inhibition of LDH affects pyruvate incorporation into the TCA cycle. As this mechanism was not investigated, we have titled the study: "Elucidating the Kinetic and Thermodynamic Insights into the Regulation of Glycolysis by Lactate Dehydrogenase and Its Impact on the Tricarboxylic Acid Cycle and Oxidative Phosphorylation in Cancer Cells."

It was furthermore puzzling how the ΔG, calculated with intracellular metabolite concentrations (Figures 3 and 4) could be endergonic (positive) for PGAM at all conditions (also normoxic and without inhibitor). This would mean that under the conditions assayed, glycolysis would never flow completely forward. How any lactate or pyruvate is produced from glucose, is then unexplained.

This issue also concerned me during the study. However, given the high reproducibility of the data, we consider it is true, but requires explanation.

The PGAM-catalyzed reaction is tightly linked to both upstream and downstream reactions in the glycolytic pathway. In glycolysis, three key reactions catalyzed by HK2, PFK1, and PK are highly exergonic, providing the driving force for the conversion of glucose to pyruvate. The other reactions, including the one catalyzed by PGAM, operate near thermodynamic equilibrium and primarily serve to equilibrate glycolytic intermediates rather than control the overall direction of glycolysis, as previously described by us (J Biol Chem. 2024 Aug 8;300(9):107648).

The endergonic nature of the PGAM-catalyzed reaction does not prevent it from proceeding in the forward direction. Instead, the directionality of the pathway is dictated by the exergonic reaction of PFK1 upstream, which pushes the flux forward, and by PK downstream, which pulls the flux through the pathway. The combined effects of PFK1 and PK may account for the observed endergonic state of the PGAM reaction.

However, if the PGAM-catalyzed reaction were isolated from the glycolytic pathway, it would tend toward equilibrium and never surpass it, as there would be no driving force to move the reaction forward.

Finally, the interpretation of the label incorporation data is rather unconvincing. The authors observe an increasing labelled fraction of TCA cycle intermediates as a function of increasing inhibitor concentration. Strangely, they conclude that less labelled pyruvate enters the TCA cycle while simultaneously less labelled intermediates exit the TCA cycle pool, leading to increased labelling of this pool. The reasoning that they present for this (decreased m2 fraction as a function of DHE-140 concentration) is by no means a consistent or striking feature of their titration data and comes across as rather unconvincing. Yet they treat this anomaly as resolved in the discussion that follows.

GNE-140 treatment increased the labeling of TCA cycle intermediates by [13C6]glucose but decreased the OXPHOS rate, we consider the conflicting results as an 'anomaly' that warrants further explanation. To address this, we analyzed the labeling pattern of TCA cycle intermediates using both [13C6]glucose and  [13C5]glutamine. Tracing the incorporation of glucose- and glutamine-derived carbons into the TCA cycle suggests that LDH inhibition leads to a reduced flux of glucose-derived acetyl-CoA into the TCA cycle, coupled with a decreased flux of glutamine-derived α-KG, and a reduction in the efflux of intermediates from the cycle. These results align with theoretical predictions. Under any condition, the reactions that distribute TCA cycle intermediates to other pathways must be balanced by those that replenish them. In the GNE-140 treatment group, the entry of glutamine-derived carbon into the TCA cycle was reduced, implying that glucose-derived carbon (as acetyl-CoA) entering the TCA cycle must also be reduced, or vice versa.

This step-by-step investigation is detailed under the subheading "The Effect of LDHB KO and GNE-140 on the Contribution of Glucose Carbon to the TCA Cycle and OXPHOS" in the Results section in the manuscript.

In the Discussion, we emphasize that caution should be exercised when interpreting isotope tracing data. In this study, treatment of cells with GNE-140 led to an increase labeling percentage of TCAC intermediates by [13C6]glucose (Figure 5A-E). However, this does not necessarily imply an increase in glucose carbon flux into TCAC; rather, it indicates a reduction in both the flux of glucose carbon into TCAC and the flux of intermediates leaving TCAC. When interpreting the data, multiple factors must be considered, including the carbon-13 labeling pattern of the intermediates (m1, m2, m3, ---) (Figure 5G-K), replenishment of intermediates by glutamine (Figure 5M-V), and mitochondrial oxygen consumption rate (Figure 5W). All these factors should be taken into account to derive a proper interpretation of the data. 

Reviewer #3 (Public Review):

Hu et al in their manuscript attempt to interrogate the interplay between glycolysis, TCA activity, and OXPHOS using LDHA/B knockouts as well as LDH-specific inhibitors. Before I discuss the specifics, I have a few issues with the overall manuscript. First of all, based on numerous previous studies it is well established that glycolysis inhibition or forcing pyruvate into the TCA cycle (studies with PDKs inhibitors) leads to upregulation of TCA cycle activity, and OXPHOS, activation of glutaminolysis, etc (in this work authors claim that lowered glycolysis leads to lower levels of TCA activity/OXPHOS). The authors in the current work completely ignore recent studies that suggest that lactate itself is an important signaling metabolite that can modulate metabolism (actual mechanistic insights were recently presented by at least two groups (Thompson, Chouchani labs). In addition, extensive effort was dedicated to understanding the crosstalk between glycolysis/TCA cycle/OXPHOS using metabolic models (Titov, Rabinowitz labs). I have several comments on how experiments were performed. In the Methods section, it is stated that both HeLa and 4T1 cells were grown in RPMI-1640 medium with regular serum - but under these conditions, pyruvate is certainly present in the medium - this can easily complicate/invalidate some findings presented in this manuscript. In LDH enzymatic assays as described with cell homogenates controls were not explained or presented (a lot of enzymes in the homogenate can react with NADH!). One of the major issues I have is that glycolytic intermediates were measured in multiple enzyme-coupled assays. Although one might think it is a good approach to have quantitative numbers for each metabolite, the way it was done is that cell homogenates (potentially with still traces of activity of multiple glycolytic enzymes) were incubated with various combinations of the SAME enzymes and substrates they were supposed to measure as a part of the enzyme-based cycling reaction. I would prefer to see a comparison between numbers obtained in enzyme-based assays with GC-MS/LC-MS experiments (using calibration curves for respective metabolites, of course). Correct measurements of these metabolites are crucial especially when thermodynamic parameters for respective reactions are calculated. Concentrations of multiple graphs (Figure 1g etc.) are in "mM", I do not think that this is correct.

While the roles of lactate as a signaling metabolite and metabolic models are important areas of research, our work focuses on different aspects.

It is true that cell homogenates contain many enzymes that use NAD as a hydride acceptor or NADH as a hydride donor. However, in our assay system, the substrates are pyruvate and NADH, meaning only enzymes that catalyze the conversion of pyruvate + NADH to NAD + lactate can utilize NADH. Other enzymes do not interfere with this reaction. Although some enzymes may also catalyze this reaction, their catalytic efficiency is markedly lower than that of LDH, ensuring the validity of this assay.

Similarly, the assays for glycolytic intermediates are validated by the substrate specificity.

We have developed an LC-MS methodology for some glycolytic intermediates, but the accuracy of quantification remains unsatisfactory due to inherent limitations of this methodology.

Reviewer #2 (Public Review):

Summary:

Zeng et al. investigated the role of LDH in determining the metabolic fate of pyruvate in HeLa and 4T1 cells. To do this, three broad perturbations were applied: knockout of two LDH isoforms (LDH-A and LDH-B), titration with a non-competitive LDH inhibitor (GNE-140), and exposure to either normoxic (21% O2) or hypoxic (1% O2) conditions. They show that knockout of either LDH isoform alone, though reducing both protein level and enzyme activity, has virtually no effect on either the incorporation of a stable 13C-label from a 13C6-glucose into any glycolytic or TCA cycle intermediate, nor on the measured intracellular concentrations of any glycolytic intermediate (Figure 2). The only apparent exception to this was the NADH/NAD+ ratio, measured as the ratio of F420/F480 emitted from a fluorescent tag (SoNar).

The addition of a chemical inhibitor, on the other hand, did lead to changes in glycolytic flux, the concentrations of glycolytic intermediates, and in the NADH/NAD+ ratio (Figure 3). Notably, this was most evident in the LDH-B-knockout, in agreement with the increased sensitivity of LDH-A to GNE-140 (Figure 2). In the LDH-B-knockout, increasing concentrations of GNE-140 increased the NADH/NAD+ ratio, reduced glucose uptake, and lactate production, and led to an accumulation of glycolytic intermediates immediately upstream of GAPDH (GA3P, DHAP, and FBP) and a decrease in the product of GAPDH (3PG). They continue to show that this effect is even stronger in cells exposed to hypoxic conditions (Figure 4). They propose that a shift to thermodynamic unfavourability, initiated by an increased NADH/NAD+ ratio inhibiting GAPDH explains the cascade, calculating ΔG values that become progressively more endergonic at increasing inhibitor concentrations.

Then - in two separate experiments - the authors track the incorporation of 13C into the intermediates of the TCA cycle from a 13C6-glucose and a 13C5-glutamine. They use the proportion of labelled intermediates as a proxy for how much pyruvate enters the TCA cycle (Figure 5). They conclude that the inhibition of LDH decreases fermentation, but also the TCA cycle and OXPHOS flux - and hence the flux of pyruvate to all of those pathways. Finally, they characterise the production of ATP from respiratory or fermentative routes, the concentration of a number of cofactors (ATP, ADP, AMP, NAD(P)H, NAD(P)+, and GSH/GSSG), the cell count, and cell viability under four conditions: with and without the highest inhibitor concentration, and at norm- and hypoxia. From this, they conclude that the inhibition of LDH inhibits the glycolysis, the TCA cycle, and OXPHOS simultaneously (Figure 7).

Strengths:

The authors present an impressively detailed set of measurements under a variety of conditions. It is clear that a huge effort was made to characterise the steady-state properties (metabolite concentrations, fluxes) as well as the partitioning of pyruvate between fermentation as opposed to the TCA cycle and OXPHOS.

A couple of intermediary conclusions are well supported, with the hypothesis underlying the next measurement clearly following. For instance, the authors refer to literature reports that LDH activity is highly redundant in cancer cells (lines 108 - 144). They prove this point convincingly in Figure 1, showing that both the A- and B-isoforms of LDH can be knocked out without any noticeable changes in specific glucose consumption or lactate production flux, or, for that matter, in the rate at which any of the pathway intermediates are produced. Pyruvate incorporation into the TCA cycle and the oxygen consumption rate are also shown to be unaffected.

They checked the specificity of the inhibitor and found good agreement between the inhibitory capacity of GNE-140 on the two isoforms of LDH and the glycolytic flux (lines 229 - 243). The authors also provide a logical interpretation of the first couple of consequences following LDH inhibition: an increased NADH/NAD+ ratio leading to the inhibition of GAPDH, causing upstream accumulations and downstream metabolite decreases (lines 348 - 355).

Weaknesses:

Despite the inarguable comprehensiveness of the data set, a number of conceptual shortcomings afflict the manuscript. First and foremost, reasoning is often not pursued to a logical conclusion. For instance, the accumulation of intermediates upstream of GAPDH is proffered as an explanation for the decreased flux through glycolysis. However, in Figure 3C it is clear that there is no accumulation of the intermediates upstream of PFK. It is unclear, therefore, how this traffic jam is propagated back to a decrease in glucose uptake. A possible explanation might lie with hexokinase and the decrease in ATP (and constant ADP) demonstrated in Figure 6B, but this link is not made.

The obvious link between the NADH/NAD+ ratio and pyruvate dehydrogenase (PDH) is also never addressed, a mechanism that might explain how the pyruvate incorporation into the TCA cycle is impaired by the inhibition of LDH (the observation with which they start their discussion, lines 511 - 514).

It was furthermore puzzling how the ΔG, calculated with intracellular metabolite concentrations (Figures 3 and 4) could be endergonic (positive) for PGAM at all conditions (also normoxic and without inhibitor). This would mean that under the conditions assayed, glycolysis would never flow completely forward. How any lactate or pyruvate is produced from glucose, is then unexplained.

Finally, the interpretation of the label incorporation data is rather unconvincing. The authors observe an increasing labelled fraction of TCA cycle intermediates as a function of increasing inhibitor concentration. Strangely, they conclude that less labelled pyruvate enters the TCA cycle while simultaneously less labelled intermediates exit the TCA cycle pool, leading to increased labelling of this pool. The reasoning that they present for this (decreased m2 fraction as a function of DHE-140 concentration) is by no means a consistent or striking feature of their titration data and comes across as rather unconvincing. Yet they treat this anomaly as resolved in the discussion that follows.

DOI: 10.1016/j.immuni.2021.03.008

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DOI: 10.1016/j.cell.2021.01.050

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DOI: 10.1016/j.celrep.2020.107941

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DOI: 10.7554/eLife.46043

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What is this?

Author response:

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

Reviewer 2:

In addition, it is still unacceptable for me that the number of ovulated oocytes in mice at 6 months of age is only one third of young mice (10 vs 30; Fig. S1E). The most of published literature show that mice at 12 months of age still have ~10 ovulated oocytes.

We disagree with the reviewer’s comment, and the concerns raised were not shared by the other reviewers.  We have reported our data with full transparency (each data point is plotted). In the current study, we observed an intermediate phenotype in gamete number (assessed by both ovarian follicle counts and ovulated eggs) when comparing 6 month old mice to 6 week or 10 month old mice; this is as expected. It is well accepted that follicle counts are highly mouse strain dependent.  Although the reviewer mentions that mice at 12 months have ~10 ovulated oocytes, no actual references are provided nor are the mouse strain or other relevant experimental details mentioned.  Therefore, we do not know how these quoted metrics relate to the female FVB mice used in our current study.   As clearly explained and justified in our manuscript, we used mice at 6 months and 10 months to represent a physiologic aging continuum. 

Moreover, based on the follicle counting method used in the present study (Fig. S1D), there are no antral follicles observed in mice at 6 months and 10 months of age, which is not reasonable.

This statement is incorrect. Antral follicles were present at 6 and 10 months of age, but due to the scale of the y-axis and the normalization of follicle number/area in Fig. S1D, the values are small.  The absolute number of antral follicles per ovary (counted in every 5th section) was 31.3 ± 3.8 follicles for 6-week old mice, 9.3 ± 2.3 follicles for 6-month old mice, and 5.3 ± 1.8 follicles for 10-month old mice.  Moreover, it is important to note that these ovaries were not collected in a specific stage of the estrous cycle, so the number of antral follicles may not be maximal.  In addition, as described in the Materials and Methods, antral follicles were only counted when the oocyte nucleus was present in a section to avoid double counting.  Therefore, this approach (which was applied consistently across samples) could potentially underestimate the total number.

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript by Bomba-Warczak describes a comprehensive evaluation of long-lived proteins in the ovary using transgenerational radioactive labelled 15N pulse-chase in mice. The transgenerational labeling of proteins (and nucleic acids) with 15N allowed the authors to identify regions enriched in long-lived macromolecules at the 6 and 10-month chase time points. The authors also identify the retained proteins in the ovary and oocyte using MS. Key findings include the relative enrichment in long-lived macromolecules in oocytes, pregranulosa cells, CL, stroma, and surprisingly OSE. Gene ontology analysis of these proteins revealed enrichment for nucleosome, myosin complex, mitochondria, and other matrix-type protein functions. Interestingly, compared to other post-mitotic tissues where such analyses have been previously performed such as the brain and heart, they find a higher fractional abundance of labeled proteins related to the mitochondria and myosin respectively.

Response: We thank the reviewer for this thoughtful summary of our work.  We want to clarify that our pulse-chase strategy relied on a two-generation stable isotope-based metabolic labelling of mice using 15N from spirulina algae (for reference, please see (Fornasiero & Savas, 2023; Hark & Savas, 2021; Savas et al., 2012; Toyama et al., 2013)).  We did not utilize any radioactive isotopes.

Strengths:

A major strength of the study is the combined spatial analyses of LLPs using histological sections with MS analysis to identify retained proteins.

Another major strength is the use of two chase time points allowing assessment of temporal changes in LLPs associated with aging.

The major claims such as an enrichment of LLPs in pregranulosa cells, GCs of primary follicles, CL, stroma, and OSE are soundly supported by the analyses, and the caveat that nucleic acids might differentially contribute to this signal is well presented.

The claims that nucleosomes, myosin complex, and mitochondrial proteins are enriched for LLPs are well supported by GO enrichment analysis and well described within the known body of evidence that these proteins are generally long-lived in other tissues.

Weaknesses:

Comment 1: One small potential weakness is the lack of a mechanistic explanation of if/why turnover may be accelerating at the 6-10 month interval compared to 1-6.

Response 1: At the 6-month time point, we detected more long lived proteins than the 10 month time point in both the ovary and the oocyte.  We anticipated this because proteins are degraded over time, and substantially more time has elapsed at the later time point.  Moreover, at the 6–10-month time point, age-related tissue dysfunction is already evident in the ovary.  For example, in 6-9 month old mice, there is already a deterioration of chromosome cohesion in the egg which results in increased interkinetochore distances (Chiang et al., 2010), and by 10 months, there are multinucleated giant cells present in the ovarian stroma which is consistent with chronic inflammation (Briley et al., 2016).  Thus, the observed changes in protein dynamics may be another early feature of aging progression in the ovary.  

Comment 2: A mild weakness is the open-ended explanation of OSE label retention. This is a very interesting finding, and the claims in the paper are nuanced and perfectly reflect the current understanding of OSE repair. However, if the sections are available and one could look at the spatial distribution of OSE signal across the ovarian surface it would interesting to note if label retention varied by regions such as the CLs or hilum where more/less OSE division may be expected. 

Response 2: We agree that the enrichment of long-lived molecules in the OSE is interesting. To make interpretable conclusions about the dynamics of long-lived molecules in the OSE, we would need to generate a series of samples at precise stages of the estrous cycle or ideally across a timecourse of ovulation to capture follicular rupture and repair.  These samples do not currently exist and are beyond the scope of this study. However, this idea is an important future direction and it has been added to the discussion (lines 221-223). Furthermore, from a practical standpoint, MIMS imaging is resource and time intensive. Thus, we are not able to readily image entire ovarian sections.  Instead, we focused on structures within the ovary and took select images of follicles, stroma, and OSE.  We, therefore, do not have a comprehensive series of images of the OSE from the entire ovarian section for each mouse analyzed.

Reviewer #2 (Public Review):

Summary:

The manuscript by Bomba-Warczak et al. applied multi-isotope imaging mass spectrometry (MIMS) analysis to identify the long-lived proteins in mouse ovaries during reproductive aging, and found some proteins related to cytoskeletal and mitochondrial dynamics persisting for 10 months.

Response: We thank the reviewer for their summary and feedback.

Strengths:

The manuscript provides a useful dataset about protein turnover during ovarian aging in mice.

Weaknesses:

Comment 1: The study is pretty descriptive and short of further new findings based on the dataset. In addition, some results such as the numbers of follicles and ovulated oocytes in aged mice are not consistent with the published literature, and the method for follicle counting is not accurate. The conclusions are not fully supported by the presented evidence.

Response 1: We agree with the reviewer that this study is descriptive. Our goal, as stated, was to use a discovery-based approach to define the long-lived proteome of the ovary and oocyte across a reproductive aging continuum.  As the prominent aging researcher, Dr. James Kirkland, stated: “although ‘descriptive’ is sometimes used as a pejorative term…descriptive or discovery research leading to hypothesis generation has become highly sophisticated and of great relevance to the aging field (Kirkland, 2013).”  We respectfully disagree with the reviewer that our study is short of new findings. In fact, this is the first time that a stable two-generation stable isotope-based metabolic labelling of mice in combination with two different state-of-the-art mass spectrometry methods has been used to identify and localize long lived molecules in the ovary and oocyte along this particular reproductive aging continuum in an unbiased manner.  We have identified proteins groups that were previously not known to be long lived in the ovary and oocyte.  Our hope is that this long-lived proteome will become an important hypothesis-generating resource for the field of reproductive aging.

The age-dependent decline in number of follicles and eggs ovulated in mice has been well established by our group as well as others (Duncan et al., 2017; Mara et al., 2020).  Thus, we are unclear about the reviewer’s comments that our results are not consistent with the published literature.  The absolute numbers of follicles and eggs ovulated as well as the rate of decline with age are highly strain dependent.  Moreover, mice can have a very small ovarian reserve and still maintain fertility (Kerr et al., 2012).  In our study, we saw a consistent age-dependent decrease in the ovarian reserve (Figure 1 – figure supplement 1 D), the number of oocytes collected from large antral follicles following hyperstimulation with PMSG (used for LC-MS/MS), and the number of eggs collected from the oviduct following hyperstimulation and superovulation with PMSG and hCG (Figure 1 – figure supplement 1 E and F).  In all cases, the decline was greater in 10 month old compared to 6 month old mice demonstrating a relative reproductive aging continuum even at these time points.

Our research team has significant expertise in follicle classification and counting as evidenced by our publication record (Duncan et al., 2017; Kimler et al., 2018; Perrone et al., 2023; Quan et al., 2020).  We used our established methods which we have further clarified in the manuscript text (lines 395-397).  Follicle counts were performed on every 5th tissue section of serial sectioned ovaries, and 1 ovary from 3 mice per timepoint were counted. Therefore, follicle counts were performed on an average of 48-62 total sections per ovary. The number of follicles was then normalized per total area (mm2) of the tissue section, and the counts were averaged. Figure 1 – figure supplement 1 C and D represents data averaged from all ovarian sections counted per mouse.   It is important to note that the same criteria were applied consistently to all ovaries across the study, and thus regardless of the technique used, the relative number of follicles or oocytes across ages can be compared.

Reviewer #3 (Public Review):

Summary:

In this study, Bomba-Warczak et al focused on reproductive aging, and they presented a map for long-lived proteins that were stable during reproductive lifespan. The authors used MIMS to examine and show distinct molecules in different cell types in the ovary and tissue regions in a 6 month mice group, and they also used proteomic analysis to present different LLPs in ovaries between these two timepoints in 6-month and 10-month mice. The authors also examined the LLPs in oocytes in the 6-months mice group and indicated that these were nuclear, cytoskeleton, and mitochondria proteins.

Response: We thank the reviewer for their summary and feedback.

Strengths:

Overall, this study provided basic information or a 'map' of the pattern of long-lived proteins during aging, which will contribute to the understanding of the defects caused by reproductive aging.

Weaknesses:

Comment 1: The 6-month mice were used as an aged model; no validation experiments were performed with proteomics analysis only.  

Response 1:  We did not select the 6-month time point to be representative of the “aged model” but rather one of two timepoints on the reproductive aging continuum – 6 and 10 months.  In the manuscript (Figure 1 – figure supplement 1) we have demonstrated the relevance of the two timepoints by illustrating a decrease in follicle counts, number of fully grown oocytes collected, and number of eggs ovulated as well as a tendency towards increased stromal fibrosis (highlighted in the main text lines 78-85).  Inclusion of the 6-month timepoint ultimately turned out to be informative and essential as many long-lived proteins were absent by the 10 month timepoint. These results suggest that important shifts in the proteome occur during mid to advanced reproductive age.  The relevance of these timepoints is mentioned in the discussion (lines 247-270).

Two independent mass spectrometry approaches (MIMS and LC-MS/MS) were used to validate the presence of long-lived macromolecules in the ovary and oocyte. Studies focused on the role of specific long-lived proteins in oocyte and ovarian biology as well as how they change with age in terms of function, turnover, and modification are beyond the scope of the current study but are ongoing.  We have acknowledged these important next steps in the manuscript text (lines 286-288, 311-312).

It is important to note, that oocytes are biomass limited cells, and their numbers decrease with age.  Thus, we had to select ages where we could still collect enough from the mice available to perform LC-MS/MS. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Comment 1: The writing and figures are beautiful - it would be hard to improve this manuscript.

Response 1: We greatly appreciate this enthusiastic evaluation of our work.

Comment 2: In Fig S1E/F it would help to list the N number here. Why are there 2 groups at 6-12 wk?

Response 2:  We did not have 6 month and 10-month-old mice available at the same time to be able to run the hyperstimulation and superovulation experiment in parallel.  Therefore, we performed independent experiments comparing the number of eggs collected from either 6-month-old or 10 month old mice relative to 6-12 week old controls.  In each trial, eggs were collected from pooled oviducts from between 3-4 mice per age group, and the average total number of eggs per mouse was reported.  Each point on the graph corresponds to the data from an individual trial, and two trials were performed.  This has been clarified in the figure legend (lines 395-397).  Of note, while addressing this reviewer’s comments, we noticed that we were missing Materials and Methods regarding the collection of eggs from the oviduct following hyperstimulation and superovulation with PMSG and hCG.  This information has now been added in Methods Section, lines 477-481.

Comment 3: The manuscript would benefit from an explanation of why the pups were kept on a 1-month N15 diet after birth, since the oocytes are already labeled before birth, and granulosa at most by day 3-4. Would ZP3 have not been identified otherwise?

Response 3:   The pups used in this study were obtained from fully labeled female dams that were maintained on an15N diet.  These pups had to be kept with their mothers through weaning.  To limit the pulse period only through birth, the pups would have had to be transferred to unlabeled foster mothers.  However, this would have risked pup loss which would have significantly impacted our ability to conduct the studies given that we only had 19 labeled female pups from three breeding pairs.  We have clarified this in the manuscript text in lines 78-80.  It is hard to know, without doing the experiment, whether we would have detected ZP3 if we only labeled through birth.  The expression of ZP3 in primordial follicles, albeit in human, would suggest that this protein is expressed quite early in development.

Comment 4: What is happening to the mitochondria at 6-10 months? Does their number change in the oocyte? Is there a change in the rate of fission? Any chance to take a stab at it with these or other age-matched slides?

Response 4:  The reviewer raises an excellent point.  As mentioned previously in the Discussion (lines 290-301), there are well documented changes in mitochondrial structure and function in the oocyte in mice of advanced reproductive age.  However, there is a paucity of data on the changes that may happen at earlier mid-reproductive age time points.  From the oocyte mitochondrial proteome perspective, our data demonstrate a prominent decline in the persistence of long-lived proteins between 6 and 10 months, and this occurs in the absence of a change in the total pool of mitochondrial proteins (both long and short lived populations) as assessed by spectral counts or protein IDs (figure below).  These data, which we have added into Figure 3 – figure supplement 1 and in the manuscript text (lines 164-170) are suggestive of similar numbers of mitochondria at these two timepoints. It would be informative to do a detailed characterization of oocyte mitochondrial structure and function within this window to see if there is a correlation with this shift in long lived mitochondrial proteins.  Although this analysis is beyond the scope of the current manuscript, it is an important next line of inquiry which we have highlighted in the manuscript text (lines 255-257 and 311-312).

Reviewer #2 (Recommendations For The Authors):

Several concerns are raised as shown below.

Comment 1: In Fig. 2F, it is surprising that ZP3 disappeared in the ovary from mice at the age of 10 months by MIMS analysis, because quite a few oocytes with intact zona pellucida can still be obtained from mice at this age. Notably, ZP would not be renewed once formed.

Response 1: To clarify, Figure 2F shows LC-MS/MS data and not MIMS data.  As mentioned in the Discussion, the detection of long-lived pools of ZP3 at 6 months cannot be derived from newly synthesized zona pellucidae in growing follicles because they would not have been present during the pulse period.  The only way we could detect ZP3 at 6 months is if it forms a primitive zona scaffold in the primordial follicle or if ZPs from atretic follicles of the first couple of waves of folliculogenesis incorporate into the extracellular matrix of the ovary.  The lack of persistence of ZP3 at 10 months could be due to protein degradation. Should ZP3 indeed form a primitive zona, its loss at 10 months would be predicted to result in poor formation of a bona fide zona pellucida upon follicle growth.  Interestingly, aging has been associated with alterations in zona pellucida structure and function.   These data open novel hypotheses regarding the zona pellucida (e.g. a primitive zona scaffold and part of the extracellular matrix) and will require significant further investigation to test. These points are highlighted in the Discussion lines 227-245.

Comment 2: To determine whether those proteins that can not be identified by MIMS at the time point of 10 months are degraded or renewed, the authors should randomly select some of them to examine their protein expression levels in the ovary by immunoblotting analysis.

Response 2: To clarify, proteins were identified by LC-MS/MS and not MIMS which was used to visualize long lived macromolecules.   Each protein will be comprised of old pools (15N containing) and newly synthesized pools (14N containing).  Degradation of the old pool of protein does not mean that there will be a loss of total protein.  Moreover, immunoblotting cannot distinguish old and newly synthesized pools of protein. Where overall peptide counts are listed for each protein identified at both time points.  As peptides derive from proteins, the table provided with the manuscript reflects what immunoblotting would, but on a larger and more precise scale.

Comment 3: I think those proteins that can be identified by MIMS at the time point of 6 months but not 10 months deserve more analyses as they might be the key molecules that drive ovarian aging.

Response 3:  This comment conflicts with comment 2 from Reviewer #3 (Recommendations For The Authors).  This underscores that different researchers will prioritize the value and follow up of such rich datasets differently.  We agree that the LLP identified at 6 months are of particular interest to reproductive aging, and we are planning to follow up on these in future studies.

Comment 4:  Figure 1 – figure supplement 1 C-F, compared with the published literature, the numbers of follicles at different developmental stages and ovulated oocytes at both ages of 6 months and 10 months were dramatically low in this study. For 6-month-old female mice, the reproductive aging just begins, thus these numbers should not be expected to decrease too much. In addition, follicle counting was carried out only in an area of a single section, which is an inaccurate way, because the numbers and types of follicles in various sections differ greatly. Also, the data from a single section could not represent the changes in total follicle counts.

Response 4: We have addressed these points in response to Comment 1 in the Reviewer #2 Public Review, and corresponding changes in the text have been noted.    

Comment 5:  The study lacks follow-up verification experiments to validate their MIMS data.

Response 5: Two independent mass spectrometry approaches (MIMS and LC-MS/MS) were used to validate the presence of long-lived macromolecules in the ovary and oocyte. Studies focused on the role of specific long-lived proteins in oocyte and ovarian biology as well as how they change with age in terms of function, turnover, and modification are beyond the scope of the current study but ongoing.  We have acknowledged these important next steps in the manuscript text (lines 286-288 and 311-312).

Reviewer #3 (Recommendations For The Authors):

Comment 1: The authors used the 6-month mice group to represent the aged model, and examined the LLPs from 1 month to 6 months. Indeed, 6-month-old mice start to show age-related changes; however, for the reproductive aging model, the most widely accepted model is that 10-month-old age mice start to show reproductive-related changes and 12-month-old mice (corresponding to 35-40 year-old women) exhibit the representative reproductive aging phenotypes. Therefore, the data may not present the typical situation of LLPs during reproductive aging.

Response 1: As described in the response to Comment 1 in the Reviewer #3 Public Review, there were clear logistical and technical feasibility reasons why the 6 month and 10-month timepoints were selected for this study.  Importantly, however, these timepoints do represent a reproductive aging continuum as evidenced by age-related changes in multiple parameters.  Furthermore, there were ultimately very few LLPs that remained at 10 months in both the oocyte and ovary, so inclusion of the 6-month time point was an important intermediate.  Whether the LLPs at the 6-month timepoint serve as a protective mechanism in maintaining gamete quality or whether they contribute to decreased quality associated with reproductive aging is an intriguing dichotomy which will require further investigation.  This has been added to the discussion (lines 247-257).

Comment 2:  Following the point above, the authors examined the ovaries in 6 months and 10 months mice by proteomics, and found that 6 months LLPs were not identical compared with 10 months, while there were Tubb5, Tubb4a/b, Tubb2a/b, Hist2h2 were both expressed at these two time points (Fig 2B), why the authors did not explore these proteins since they expressed from 1 month to 10 months, which are more interesting.

Response 2:  The objective of this study was to profile the long-lived proteome in the ovary and oocyte as a resource for the field rather than delving into specific LLPs at a mechanistic level.  That being said, we wholeheartedly agree with the reviewer that the proteins that were identified at both 6 month and 10 months are the most robust and long lived and worthy of prioritizing for further study.  Interestingly, Tubb5 and Tubb4a have high homology to primate-specific Tubb8, and Tubb8 mutations in women are associated with meiosis I arrest in oocytes and infertility (Dong et al., 2023; Feng et al., 2016).  Thus, perturbation of these specific proteins by virtue of their long-lived nature may be associated with impaired function and poor reproductive outcomes.  We have highlighted the importance of these LLPs which are present at both timepoints and persist to at least 10 months in the manuscript text (lines 259-270).

Comment 3:  The authors also need to provide a hypothesis or explanation as to why LLDs from 6 months LLPs were not identical compared with 10 months.

Response 3:  We agree that LLDs identified at 10 months should be also identified as long-lived at 6 months. This is a common limitation of mass spectrometry-based proteomics where each sample is prepared and run individually, which introduces variability between biological replicates, especially when it comes to low abundant proteins. It is key to note that just because we do not identify a protein, it does not mean the protein is not there – it merely means that we were not able to detect it in this particular experiment, but low levels of the protein may still be there. To compensate for this known and inherent variability, we have applied stringent filtering criteria where we required long-lived peptides to be identified in an independent MS scan (alternative is to identify peptide in either heavy or light scan and use modeling to infer FA value based on m/z shift), which gave us peptides of highest confidence. Ideally, these experiments would be done using TMT (tandem mass tag) approach. However, TMT-based experiments typically require substantial amount of input (80-100ug per sample) which unfortunately is not feasible with oocytes obtained from a limited number of pulse-chased animals.  We have added this explanation to the discussion (lines 265-270).

Comment 4:  The reviewer thinks that LLPs from 6 months to 10 months may more closely represent the long-lived proteins during reproductive aging.

Response 4:  We fully agree that understanding the identity of LLPs between the 6 month and 10 month period will be quite informative given that this is a dynamic period when many of LLPs get degraded and thus might be key to the observed decline in reproductive aging. This is a very important point that we hope to explore in future follow-up studies.

Comment 5: The authors used proteomics for the detection of ovaries and oocytes, however, there are no validation experiments at all. Since proteomics is mainly for screening and prediction, the authors should examine at least some typical proteins to confirm the validity of proteomics. For example, the authors specifically emphasized the finding of ZP3, a protein that is critical for fertilization.

Response 5:  Thank you, we agree that closer examination of proteins relevant and critical for fertilization is of importance.  However, a detailed analysis of specific proteins fell outside of the scope of this study which aimed at unbiased identification of long-lived macromolecules in ovaries and oocytes. We hope to continue this important work in near future.

Comment 6: For the oocytes, the authors indicated that cytoskeleton, mitochondria-related proteins were the main LLPs, however, previous studies reported the changes of the expression of many cytoskeleton and mitochondria-related proteins during oocyte aging. How do the authors explain this contrary finding?   

Response 6:  Our findings are not contrary to the studies reporting changes in protein expression levels during oocyte aging – the two concepts are not mutually exclusive. The average FA value at 6-month chase for oocyte proteins is 41.3 %, which means that while 41.3% of long-lived proteins pool persisted for 6 months, the other 58.7% has in fact been renewed. With the exception of few mitochondrial proteins (Cmkt2 and Apt5l), and myosins (Myl2 and Myh7), which had FA values close to 100% (no turnover), most of the LLPs had a portion of protein pools that were indeed turned over. Moreover, we included new data analysis illustrating that we identify comparable number of mitochondrial proteins between the two time points, indicating that while the long-lived pools are changing over time, the total content remains stable (Figure 3 – figure supplement 1E-G).

Comment 7:  The authors also should provide in-depth discussion about the findings of the current study for long-lived proteins. In this study, the authors reported the relationship between these "long-lived" proteins with aging, a process with multiple "changes". Do long-lived proteins (which are related to the cytoskeleton and mitochondria) contribute to the aging defects of reproduction? or protect against aging?

Response 7: This is a very important comment and one that needs further exploration. The fact is – we do not know at this moment whether these proteins are protective or deleterious, and such a statement would be speculative at this stage of research into LLPs in ovaries and oocytes. Future work is needed to address this question in detail.

Briley, S. M., Jasti, S., McCracken, J. M., Hornick, J. E., Fegley, B., Pritchard, M. T., & Duncan, F. E. (2016). Reproductive age-associated fibrosis in the stroma of the mammalian ovary. Reproduction, 152(3), 245-260. https://doi.org/10.1530/REP-16-0129

Chiang, T., Duncan, F. E., Schindler, K., Schultz, R. M., & Lampson, M. A. (2010). Evidence that Weakened Centromere Cohesion Is a Leading Cause of Age-Related Aneuploidy in Oocytes. Current Biology, 20(17), 1522-1528. https://doi.org/10.1016/j.cub.2010.06.069

Dong, J., Jin, L., Bao, S., Chen, B., Zeng, Y., Luo, Y., Du, X., Sang, Q., Wu, T., & Wang, L. (2023). Ectopic expression of human TUBB8 leads to increased aneuploidy in mouse oocytes. Cell Discov, 9(1), 105. https://doi.org/10.1038/s41421-023-00599-z

Duncan, F. E., Jasti, S., Paulson, A., Kelsh, J. M., Fegley, B., & Gerton, J. L. (2017). Age-associated dysregulation of protein metabolism in the mammalian oocyte. Aging Cell, 16(6), 1381-1393. https://doi.org/10.1111/acel.12676

Feng, R., Sang, Q., Kuang, Y., Sun, X., Yan, Z., Zhang, S., Shi, J., Tian, G., Luchniak, A., Fukuda, Y., Li, B., Yu, M., Chen, J., Xu, Y., Guo, L., Qu, R., Wang, X., Sun, Z., Liu, M., . . . Wang, L. (2016). Mutations in TUBB8 and Human Oocyte Meiotic Arrest. N Engl J Med, 374(3), 223-232. https://doi.org/10.1056/NEJMoa1510791

Fornasiero, E. F., & Savas, J. N. (2023). Determining and interpreting protein lifetimes in mammalian tissues. Trends Biochem Sci, 48(2), 106-118. https://doi.org/10.1016/j.tibs.2022.08.011

Hark, T. J., & Savas, J. N. (2021). Using stable isotope labeling to advance our understanding of Alzheimer's disease etiology and pathology. J Neurochem, 159(2), 318-329. https://doi.org/10.1111/jnc.15298

Kerr, J. B., Hutt, K. J., Michalak, E. M., Cook, M., Vandenberg, C. J., Liew, S. H., Bouillet, P., Mills, A., Scott, C. L., Findlay, J. K., & Strasser, A. (2012). DNA damage-induced primordial follicle oocyte apoptosis and loss of fertility require TAp63-mediated induction of Puma and Noxa. Mol Cell, 48(3), 343-352. https://doi.org/10.1016/j.molcel.2012.08.017

Kimler, B. F., Briley, S. M., Johnson, B. W., Armstrong, A. G., Jasti, S., & Duncan, F. E. (2018). Radiation-induced ovarian follicle loss occurs without overt stromal changes. Reproduction, 155(6), 553-562. https://doi.org/10.1530/REP-18-0089

Kirkland, J. L. (2013). Translating advances from the basic biology of aging into clinical application. Exp Gerontol, 48(1), 1-5. https://doi.org/10.1016/j.exger.2012.11.014

Mara, J. N., Zhou, L. T., Larmore, M., Johnson, B., Ayiku, R., Amargant, F., Pritchard, M. T., & Duncan, F. E. (2020). Ovulation and ovarian wound healing are impaired with advanced reproductive age. Aging (Albany NY), 12(10), 9686-9713. https://doi.org/10.18632/aging.103237

Perrone, R., Ashok Kumaar, P. V., Haky, L., Hahn, C., Riley, R., Balough, J., Zaza, G., Soygur, B., Hung, K., Prado, L., Kasler, H. G., Tiwari, R., Matsui, H., Hormazabal, G. V., Heckenbach, I., Scheibye-Knudsen, M., Duncan, F. E., & Verdin, E. (2023). CD38 regulates ovarian function and fecundity via NAD(+) metabolism. iScience, 26(10), 107949. https://doi.org/10.1016/j.isci.2023.107949

Quan, N., Harris, L. R., Halder, R., Trinidad, C. V., Johnson, B. W., Horton, S., Kimler, B. F., Pritchard, M. T., & Duncan, F. E. (2020). Differential sensitivity of inbred mouse strains to ovarian damage in response to low-dose total body irradiationdagger. Biol Reprod, 102(1), 133-144. https://doi.org/10.1093/biolre/ioz164

Savas, J. N., Toyama, B. H., Xu, T., Yates, J. R., 3rd, & Hetzer, M. W. (2012). Extremely long-lived nuclear pore proteins in the rat brain. Science, 335(6071), 942. https://doi.org/10.1126/science.1217421

Toyama, B. H., Savas, J. N., Park, S. K., Harris, M. S., Ingolia, N. T., Yates, J. R., 3rd, & Hetzer, M. W. (2013). Identification of long-lived proteins reveals exceptional stability of essential cellular structures. Cell, 154(5), 971-982. https://doi.org/10.1016/j.cell.2013.07.037

Reviewer #1 (Public review):

Summary:

The manuscript by Jang et al. describes the application of new methods to measure the localization GTP-binding signaling proteins (G proteins) on different membrane structures in a model mammalian cell line (HEK293). G proteins mediate signaling by receptors found at the cell surface (GPCRs), with evidence from the last 15 years suggesting that GPCRs can induce G-protein mediated signaling from different membrane structures within the cell, with variation in signal localization leading to different cellular outcomes. While it has been clearly shown that different GPCRs efficiently traffic to various intracellular compartments, it is less clear whether G proteins traffic in the same manor, and whether GPCR trafficking facilitates "passenger" G protein trafficking. This question was a blind spot in the burgeoning field of GPCR localized signaling in need of careful study, and the results obtained will serve as an important guide post for further work in this field.<br /> The extent to which G proteins localize to different membranes within the cell is the main experimental question tested in this manuscript. This question is pursued by through two distinct methods, both relying on genetic modification of the G-beta subunit with a tag. In one method, G-beta is modified with a small fragment of the fluorescent protein mNG, which combines with the larger mNG fragment to form a fully functional fluorescent protein to facilitate protein trafficking by fluorescent microscopy. This approach was combined with expression of fluorescent proteins directed to various intracellular compartments (different types of endosomes, lysosome, endoplasmic reticulum, golgi, mitochondria) to look for colocalization of G-beta with these markers. These experiments showed compelling evidence that G-beta co-localizes with markers at the plasma membrane and the lysosome, with weak or absent co-localization for other markers. A second method for measuring localization relied on fusing G-beta with a small fragment from a miniature luciferase (HiBit) that combines with a larger luciferase fragment (LgBit) to form an active luciferase enzyme. Localization of G-beta (and luciferase signal) was measured using a method known as bystander BRET, which relies on expression of a fluorescent protein BRET acceptor in different cellular compartments. Results using bystander BRET supported findings from fluorescence microscopy experiments. These methods for tracking G protein localization were also used to probe other questions. The activation of GPCRs from different classes had virtually no impact on the localization of G-beta, suggesting that GPCR activation does not result in shuttling of G proteins through the endosomal pathway with activated receptors.

In the revised version of this manuscript the authors have performed informative and important new experiments in addition to adding new text to address conceptual questions. These new data and discussions are commendable and address most or all of the weaknesses listed in the initial review.

Strengths:

The question probed in this study is quite important and, in my opinion, understudied by the pharmacology community. The results presented here are an important call to be cognizant of the localization of GPCR coupling partners in different cellular compartments. Abundant reports of endosomal GPCR signaling need to consider how the impact of lower G protein abundance on endosomal membranes will affect the signaling responses under study.

*The work presented is carefully executed, with seemingly high levels of technical rigor. These studies benefit from probing the experimental questions at hand using two different methods of measurement (fluorescent microscopy and bystander BRET). The observation that both methods arrive at the same (or a very similar) answer inspires confidence about the validity of these findings.

Weaknesses:

*As noted by the authors, they do not demonstrate that the tagged G-beta is predominantly found within heterotrimeric G protein complexes. In the revised manuscript the authors have added new discussion text on why it is likely that G-beta is mostly found in complexes. This line of reasoning is convincing, although more robust experimental methods for assessing the assembly status of G-beta could be a valuable target for future experimental developments.

Reviewer #3 (Public review):

Summary:

This article addresses an important and interesting question concerning intracellular localization and dynamics of endogenous G proteins. The fate and trafficking of G protein-coupled receptors (GPCRs) have been extensively studied but so far little is known about the trafficking routes of their partner G proteins that are known to dissociate from their respective receptors upon activation of the signaling pathway. Authors utilize modern cell biology tools including genome editing and bystander bioluminescence resonance energy transfer (BRET) to probe intracellular localization of G proteins in various membrane compartments in steady state and also upon receptor activation. Data presented in this manuscript shows that while G proteins are mostly present on the plasma membrane, they can be also detected in endosomal compartments, especially in late endosomes and lysosomes. This distribution, according to data presented in this study, seems not to be affected by receptor activation. These findings will have implications in further studies addressing GPCR signaling mechanisms from intracellular compartments.

Strengths:

The methods used in this study are adequate for the question asked. Especially use of genome-edited cells (for addition of the tag on one of the G proteins) is a great choice to prevent effects of overexpression. Moreover, use of bystander BRET allowed authors to probe intracellular localization of G proteins in a very high-throughput fashion. By combining imaging and BRET authors convincingly show that G proteins are very low abundant on early endosomes (also ER, mitochondria, and medial Golgi), however seem to accumulate on membranes of late endosomal compartments. Moreover, authors also looked at the dynamics of G protein trafficking by tracking them over multiple time points in different compartments.

Weaknesses:

While authors provide a novel dataset, many questions regarding G protein trafficking remain open. For example, it is not entirely clear which pathway is utilized to traffic G proteins from the plasma membrane to intracellular compartments. Additionally, future studies should also include more quantitative details considering G-protein distribution in different compartments as well as more detailed dynamic data on G protein internalization as well as intracellular trafficking kinetics.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript by Jang et al. describes the application of new methods to measure the localization of GTP-binding signaling proteins (G proteins) on different membrane structures in a model mammalian cell line (HEK293). G proteins mediate signaling by receptors found at the cell surface (GPCRs), with evidence from the last 15 years suggesting that GPCRs can induce G-protein mediated signaling from different membrane structures within the cell, with variation in signal localization leading to different cellular outcomes. While it has been clearly shown that different GPCRs efficiently traffic to various intracellular compartments, it is less clear whether G proteins traffic in the same manner, and whether GPCR trafficking facilitates "passenger" G protein trafficking. This question was a blind spot in the burgeoning field of GPCR localized signaling in need of careful study, and the results obtained will serve as an important guidepost for further work in this field. The extent to which G proteins localize to different membranes within the cell is the main experimental question tested in this manuscript. This question is pursued through two distinct methods, both relying on genetic modification of the G-beta subunit with a tag. In one method, G-beta is modified with a small fragment of the fluorescent protein mNG, which combines with the larger mNG fragment to form a fully functional fluorescent protein to facilitate protein trafficking by fluorescent microscopy. This approach was combined with the expression of fluorescent proteins directed to various intracellular compartments (different types of endosomes, lysosome, endoplasmic reticulum, Golgi, mitochondria) to look for colocalization of G-beta with these markers. These experiments showed compelling evidence that G-beta co-localizes with markers at the plasma membrane and the lysosome, with weak or absent co-localization for other markers. A second method for measuring localization relied on fusing G-beta with a small fragment from a miniature luciferase (HiBit) that combines with a larger luciferase fragment (LgBit) to form an active luciferase enzyme. Localization of Gbeta (and luciferase signal) was measured using a method known as bystander BRET, which relies on the expression of a fluorescent protein BRET acceptor in different cellular compartments. Results using bystander BRET supported findings from fluorescence microscopy experiments. These methods for tracking G protein localization were also used to probe other questions. The activation of GPCRs from different classes had virtually no impact on the localization of G-beta, suggesting that GPCR activation does not result in the shuttling of G proteins through the endosomal pathway with activated receptors.

Strengths:

The question probed in this study is quite important and, in my opinion, understudied by the pharmacology community. The results presented here are an important call to be cognizant of the localization of GPCR coupling partners in different cellular compartments. Abundant reports of endosomal GPCR signaling need to consider how the impact of lower G protein abundance on endosomal membranes will affect the signaling responses under study.

The work presented is carefully executed, with seemingly high levels of technical rigor. These studies benefit from probing the experimental questions at hand using two different methods of measurement (fluorescent microscopy and bystander BRET). The observation that both methods arrive at the same (or a very similar) answer inspires confidence about the validity of these findings.

Weaknesses:

The rationale for fusing G-beta with either mNG2(11) or SmBit could benefit from some expansion. I understand the speculation that using the smallest tag possible may have the smallest impact on protein performance and localization, but plenty of researchers have fused proteins with whole fluorescent proteins to provide conclusions that have been confirmed by other methods. Many studies even use G proteins fused with fluorescent proteins or luciferases. Is there an important advantage to tagging G-beta with small tags? Is there evidence that G proteins with full-size protein tags behave aberrantly? If the studies presented here would not have been possible without these CRISPR-based tagging approaches, it would be helpful to provide more context to make this clearer. Perhaps one factor would be interference from newly synthesized G proteins-fluorescent protein fusions en route to the plasma membrane (in the ER and Golgi).

There are several advantages to using small peptide tags that we did not fully explain. From a practical standpoint the most important advantage of using the HiBit tag instead of full-length Nanoluc is that it allows us to restrict luminescence output to cells transiently transfected with LgBit. In this way untransfected cells contribute no background signal. Although we did not take advantage of it here, this also applies to fluorescent protein complementation, and will be useful for visualizing proteins in individual cells within tissues. The HiBit tag also allows PAGE analysis by probing membranes with LgBit (as in Fig. 1). We are not aware of evidence that tagging Gb or Gg subunits on the N terminus results in aberrant behavior, while there is some evidence that Ga subunits tagged with full-size protein tags (in some positions) have altered functional properties (PMID: 16371464). We do think that editing endogenous genes is critical, as studies using transient overexpression (usually driven by strong promoters) have sometimes reported accumulation of tagged G proteins in the biosynthetic pathway (e.g., PMID: 17576765), as the reviewer suggests. Ga and Gbg appear to be mutually dependent on each other for appropriate trafficking to the plasma membrane (reviewed in PMID: 23161140), therefore the native (presumably matched) stoichiometry is likely to be critical.

To clarify this context the revised manuscript includes the following:

“For bioluminescence experiments we added the HiBit tag (Schwinn et al., 2018) and isolated clonal “HiBit-b1“ cell lines. An advantage of this approach over adding a full-length Nanoluc luciferase is that it requires coexpression of LgBit to produce a complemented luciferase. This limits luminescence to cotransfected cells and thus eliminates background from untransfected cells.”

“Some studies using overexpressed G protein subunits have suggested that a large pool of G proteins is located on intracellular membranes, including the Golgi apparatus (Chisari et al., 2007; Saini et al., 2007; Tsutsumi et al., 2009), whereas others have indicated a distribution that is dominated by the plasma membrane (Crouthamel et al., 2008; Evanko, Thiyagarajan, & Wedegaertner, 2000; Marrari et al., 2007; Takida & Wedegaertner, 2003). A likely factor contributing to these discrepant results is the stoichiometry of overexpressed subunits, as neither Ga nor Gbg traffic appropriately to the plasma membrane as free subunits (Wedegaertner, 2012). Our gene-editing approach presumably maintains the native subunit stoichiometry, providing a more accurate representation of native G protein distribution.”

As noted by the authors, they do not demonstrate that the tagged G-beta is predominantly found within heterotrimeric G protein complexes. If there is substantial free G-beta, then many of the conclusions need to be reconsidered. Perhaps a comparison of immunoprecipitated tagged G beta vs immunoprecipitated supernatant, with blotting for other G protein subunits would be informative.

We do think that HiBit-b1 exists predominantly within heterotrimeric complexes, for several reasons. First, overexpression studies have shown that Gbg requires association with Ga to traffic to the plasma membrane, and that by itself Gbg is retained on the endoplasmic reticulum

(PMID: 12609996; PMID: 12221133). We find almost no endogenous Gb1 on the endoplasmic reticulum, and a high density on the plasma membrane. Second, we are able to detect large increases in free HiBit-Gbg after G protein activation using free Gbg sensors (e.g. Fig. 1). Third, many proteins that bind to free Gbg are found entirely in the cytosol of HEK 293 cells (e.g. PMID: 10066824), suggesting there is not a large population of free Gbg. We have added discussion of these points to the revised manuscript as follows:

“Endogenous Ga and Gb subunits are expressed at approximately a 1:1 ratio, and Gb subunits are tightly associated with Gg and inactive Ga subunits (Cho et al., 2022; Gilman, 1987; Krumins & Gilman, 2006). Moreover, proteins that bind to free Gbg dimers are found in the cytosol of unstimulated HEK 293 cells, suggesting at most only a small population of free Gbg in these cells. Therefore, we assume that the large majority of mNG-b1 and HiBit-b1 subunits in unstimulated cells are part of heterotrimers.”

“Notably, when Gbg dimers are expressed alone they accumulate on the endoplasmic reticulum

(Michaelson et al., 2002; Takida & Wedegaertner, 2003). That we detect almost no endogenous Gbg on the endoplasmic reticulum supports our conclusion that the large majority of Gbg in unstimulated HEK 293 cells is associated with Ga, although we cannot rule out a small population of free Gbg.”

We do not entirely understand the suggested experiment, as free Gbg will still be largely associated with the membrane fraction. Notably, we find almost no HiBit-b1 in the supernatant after lysis in hypotonic buffer and preparation of membrane fractions, and the small amount that we do find does not change if Ga is overexpressed.

Additional context and questions:

(1) There exists some evidence that certain GPCRs can form enduring complexes with G-betagamma (PubMed: 23297229, 27499021). That would seem to offer a mechanism that would enable receptor-mediated transport of G protein subunits. It would be helpful for the authors to place the findings of this manuscript in the context of these previous findings since they seem somewhat contradictory.

We agree. In our original submission we noted “It is possible that other receptors will influence G protein distribution using mechanisms not shared by the receptors we studied.” In the revised manuscript we have added:

“For example, a few receptors are thought to form relatively stable complexes with Gbg, which could provide a mechanism of trafficking to endosomes (Thomsen et al., 2016; Wehbi et al., 2013).”

(2) There is some evidence that GaS undergoes measurable dissociation from the plasma membrane upon activation (see the mechanism of the assay in PubMed: 35302493). It seems possible that G-alpha (and in particular GaS) might behave differently than the G-beta subunit studied here. This is not entirely clear from the discussion as it now stands.

Indeed, there is abundant evidence that some Gas translocates away from the plasma membrane upon activation. We referred to translocation of “some Ga subunits” in the introduction, although we did not specify that Gas is by far the most studied example. In a previous study (PMID: 27528603) we found that overexpressed Gas samples many intracellular membranes upon activation and returns to the plasma membrane when activation ceases. This is similar to activation-dependent translocation of free Gbg dimers. Because these translocation mechanisms depend on activation and are reversible they are unlikely to be a major source of inactive heterotrimers for intracellular membranes.

We did a poor job of making it clear that we intentionally avoided translocation mechanisms that operate only during receptor and G protein stimulation. In the revised manuscript we have added new data showing reversible activation-dependent translocation of endogenous HiBitGb1.

(3) The authors say "The presence of mNG-b1 on late endosomes suggested that some G proteins may be degraded by lysosomes". The mechanism of lysosomal degradation by proteins on the outside of the lysosome is not clear. It would be helpful for the authors to clarify.

We agree we didn’t connect the dots here. Our initial idea was that G proteins on the surface of late endosomes might reach the interior of late endosomes and then lysosomes by involution into multivesicular bodies. However, the reviewer correctly points out that much of the G protein associated with lysosomes still appears to be on the cytosolic surface, where it would not be subject to degradation. In fact, since lysosomes can fuse with the plasma membrane under certain circumstances, this could even represent a pathway for recycling G proteins to the plasma membrane.

We have revised the text to avoid giving the impression that lysosomes degrade G proteins, since we have scant evidence that this occurs. In the revised discussion we point out that we do not know the fate of G proteins located on the surface of lysosomes and speculate that these could be returned to the plasma membrane:

“We do not know the fate of G proteins located on the surface of lysosomes. Since lysosomes may fuse with the plasma membrane under certain circumstances (Xu & Ren, 2015), it is possible that this represents a route of G protein recycling to the plasma membrane.”

(4) Although the authors do a good job of assessing G protein dilution in endosomal membranes, it is unclear how this behavior compares to the measurement of other lipidanchored proteins using the same approach. Is the dilution of G proteins what we would expect for any lipid-anchored protein at the inner leaflet of the plasma membrane?

This is a great question. To begin to address it we have studied a model lipid-anchored protein consisting of mNeongreen2 anchored to the plasma membrane by the C terminus of HRas, which is palmitoylated and prenylated. We find that this protein is also diluted on endocytic vesicles, although to a lesser degree than heterotrimeric G proteins. We have added a section to the results and a new figure supplement describing these results:

“To test if other peripheral membrane proteins are similarly depleted from endocytic vesicles, we performed analogous experiments by overexpressing mNG bearing the C-terminal membrane anchor of HRas (mNG-HRas ct). We found that mNG-HRas ct was also less abundant on FM464-positive endocytic vesicles than expected based on plasma membrane abundance, although not to the same extent as mNG-b1 (Figure 4 - figure supplement 2); mNG-HRas ct density on FM4-64-positive vesicles was 64 ± 17% (mean ± 95% CI; n=78) of the nearby plasma membrane.”

Reviewer #2 (Public Review):

This is an interesting method that addresses the important problem of assessing G protein localization at endogenous levels. The data are generally convincing.

Specific comments

Methods:

The description of the gene editing method is unclear. There are two different CRISPR cell lines made in two different cell backgrounds. The methods should clearly state which CRISPR guides were used on which cell line. It is also not clear why HiBit is included in the mNG-β1 construct. Presumably, this is not critical but it would be helpful to explicitly note. In general, the Methods could be more complete.

We have added the following to the methods to clarify that the same gRNA was used to produce both cell lines:

“The human GNB1 gene was targeted at a site corresponding to the N-terminus of the Gb1 protein; the sequence 5’-TGAGTGAGCTTGACCAGTTA-3’ was incorporated into the crRNA, and the same gRNA was used to produce both HiBit-b1 and mNG-b1 cell lines.”

We have added the following to the methods to clarify why HiBit is included in the mNG-b1 construct:

“HiBit was included in the repair template for producing mNG-b1 cells to enable screening for edited clones using luminescence.”

Results:

The explanation of validation experiments in Figures 1 C and D is incomplete and difficult to follow. The rationale and explanation of the experiments could be expanded. In addition, because this is an interesting method, it would be helpful to know if the endogenous editing affects normal GPCR signaling. For example, the authors could include data showing an Isoinduced cAMP response. This is not critical to the present interpretation but is relevant as a general point regarding the method. Also, it may be relevant to the interpretation of receptor effects on G protein localization.

We have expanded the rationale and explanation of experiments in Figures 1C and D by adding:

“For example, we observed agonist-induced BRET between the D2 dopamine receptor and mNG-b1, an interaction that requires association with endogenous Ga subunits (Figure 1C). Similarly, we observed BRET between HiBit-b1 and the free Gbg sensor memGRKct-Venus after activation of receptors that couple Gi/o, Gs, and Gq heterotrimers, indicating that HiBit-b1 associated with endogenous Ga subunits from these three families (Figure 1D).”

We have done the suggested cAMP experiment and provide the data in a new figure supplement:

“We also found that cyclic AMP accumulation in response to stimulation of endogenous b adrenergic receptors was similar in edited cell lines and their unedited parent lines (Figure 1 - figure supplement 1).”

Discussion:

The conclusion that beta-gamma subunits do not redistribute after GPCR activation seems new and different from previous reports. Is this correct? Can the authors elaborate on how the results compare to previous literature?

Many previous studies have indeed shown that free Gbg dimers can redistribute after GPCR activation and sample intracellular membranes. Our initial focus was on possible changes in heterotrimer distribution after GPCR activation, but in retrospect we should have directly addressed free Gbg translocation and made the distinction clear. 

In the revised manuscript we show that during stimulation we observe changes consistent with modest translocation of endogenous Gbg from the plasma membrane and sampling of intracellular compartments. To our knowledge this is the first demonstration of endogenous Gbg translocation.

We have added:

“With overexpressed G proteins free Gbg dimers translocate from the plasma membrane and sample intracellular membrane compartments after activation-induced dissociation from Ga subunits. Consistent with this, we observed small decreases in bystander BRET at the plasma membrane and small increases in bystander BRET at intracellular compartments during activation of GPCRs, suggesting that endogenous Gbg subunits undergo similar translocation (Figure 5- figure supplement 1). Notably, these changes occurred at room temperature, suggesting that endocytosis was not involved, and developed over the course of minutes. The latter observation and the small magnitude of agonist-induced changes are both consistent with expression of primarily slowly-translocating endogenous Gg subtypes in HEK 293 cells. Moreover, as shown previously for overexpressed Gbg, the changes we observed with endogenous Gbg were readily reversible (Figure 5- figure supplement 1), suggesting that most heterotrimers reassemble at the plasma membrane after activation ceases.”

Can the authors note that OpenCell has endogenously tagged Gβ1 and reports more obvious internal localization? Can the authors comment on this point?

OpenCell has tagged GNB1 and the Leonetti group kindly provided a parent cell line we used to add a slightly different tag. Although their study did not identify any specific intracellular compartments, our impression is that most of the internal structures visible in their images are likely to be lysosomes, as they are large, round and often have a clear lumen. Overall their images and ours are comfortingly similar. We have added:

“Unsurprisingly, our images are quite similar to those made as part of previous study that labeled Gb1 subunits with mNG2 (Cho et al., 2022).”

Notably, the Leonetti group has recently reported the subcellular distribution of many untagged proteins using a proteomic approach. They find that Gb1 is enriched on the plasma membrane and lysosomes but is not enriched on endosomes, the Golgi apparatus, endoplasmic reticulum or mitochondria (https://www.biorxiv.org/content/10.1101/2023.12.18.572249v1). We have cited this work in the revised manuscript.

Is this the first use of CRISPR / HiBit for BRET assay? It would be helpful to know this or cite previous work if not. Also, as this is submitted as a tools piece, the authors might say a little more about the potential application to other questions.

The only previous study we are aware of utilizing a similar combination of methods is a 2020 report from the group of Dr. Stephen Hill, in which the authors studied binding of fluorescent ligands to HiBit-tagged GPCRs. This work is now cited.

We have also added the following to our previous brief statement about potential applications:

“In addition, it may also be possible to use these cells in combination with targeted sensors to study endogenous G protein activation in different subcellular compartments. More broadly, our results show that subcellular localization of endogenous membrane proteins can be studied in living cells by adding a HiBit tag and performing bystander BRET mapping. Applied at large scale this approach would have some advantages over fluorescent protein complementation, most notably the ability to localize endogenous membrane proteins that are expressed at levels that are too low to permit fluorescence microscopy.”

Reviewer #3 (Public Review):

Summary:

This article addresses an important and interesting question concerning intracellular localization and dynamics of endogenous G proteins. The fate and trafficking of G protein-coupled receptors (GPCRs) have been extensively studied but so far little is known about the trafficking routes of their partner G proteins that are known to dissociate from their respective receptors upon activation of the signaling pathway. The authors utilize modern cell biology tools including genome editing and bystander bioluminescence resonance energy transfer (BRET) to probe intracellular localization of G proteins in various membrane compartments in steady state and also upon receptor activation. Data presented in this manuscript shows that while G proteins are mostly present on the plasma membrane, they can be also detected in endosomal compartments, especially in late endosomes and lysosomes. This distribution, according to data presented in this study, seems not to be affected by receptor activation. These findings will have implications in further studies addressing GPCR signaling mechanisms from intracellular compartments.

Strengths:

The methods used in this study are adequate for the question asked. Especially, the use of genome-edited cells (for the addition of the tag on one of the G proteins) is a great choice to prevent the effects of overexpression. Moreover, the use of bystander BRET allowed authors to probe the intracellular localization of G proteins in a very high-throughput fashion. By combining imaging and BRET authors convincingly show that G proteins are very low abundant on early endosomes (also ER, mitochondria, and medial Golgi), however seem to accumulate on membranes of late endosomal compartments.

Weaknesses:

While the authors provide a novel dataset, many questions regarding G protein trafficking remain open. For example, it is not entirely clear which pathway is utilized to traffic G proteins from the plasma membrane to intracellular compartments. Additionally, future studies should also address the dynamics of G protein trafficking, for example by tracking them over multiple time points.

We agree, there is much more to do.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

On page 7 the text says "the difference did reach significance (Figure 5D)". It looks like the difference did not reach significance. Please check on this.

Thank you, this was an unfortunately significant typo.

Reviewer #3 (Recommendations For The Authors):

This article addresses an important and interesting question concerning intracellular localization and dynamics of endogenous G proteins. While the posed question is indeed a grand one and the methods used by the authors are novel, I believe that the data presented in this manuscript are still insufficient to support all claims posed by the authors. Below I list my major concerns:

(1) The authors claim that they provide a "detailed subcellular map of endogenous G protein distribution", however, the map is in my opinion not sufficiently detailed (e.g. trans-Golgi network is not included) and not quantitative enough (e.g. % of proteins present on one compartment vs. the other as authors claim that BRET signals "cannot be directly compared between different compartments"). To strengthen this statement, except for providing more extensive and quantitative data, it would be beneficial to provide such a "map" as an illustration based on the findings presented in this article.

“Detailed” is certainly a subjective term. While we maintain that our description of endogenous G protein distribution is far more detailed than any previous study, we now simply claim to provide a “subcellular map”. We have added images of TGNP (TGN46; TGOLN2), showing that endogenous G proteins are readily detectable on the structures labeled by this marker. These data are now provided in Figure 3 – figure supplement 7.

We did not claim that our study was quantitative- we did not try to count G proteins. However, if we use published estimates of total G proteins and surface area for HEK 293 cells we estimate that there are roughly 2,500 G proteins µm-2 on the plasma membrane and 500 G proteins µm-2 on endocytic vesicles. For other intracellular compartments relative density can be approximated by inspecting images, but a truly quantitative estimate would require a surface area standard analogous to FM4-64 for each compartment. The percentage of the total G protein pool on a given compartment is, in our opinion, less important than the density of G proteins on that compartment, as the latter is more likely to affect the efficiency of local signal transduction. Since we do not claim to have accurate G protein density estimates for many intracellular compartments, we prefer to provide several raw images for each compartment rather than a schematized map.

Bystander BRET values cannot be compared directly across compartments due to differences in expression and energy transfer efficiency of different markers and compartment surface area. This method is well suited for following changes in distribution as a function of time or after perturbations and for sensitive detection of weak colocalization but can only provide approximate “maps” of absolute distribution.

(2) Probing of the intracellular distribution of these proteins, especially after GPCR activation, includes a single chosen timepoint. I believe that the manuscript would greatly benefit from including some dynamic data on internalization and intracellular trafficking kinetics. What is the turnover of tested G proteins? What is the fraction that is going to recycling compartments and/or lysosomes? Authors could perhaps turn to other methods to be able to dynamically track proteins over time e.g. via photoconversion techniques.

Because G protein trafficking appears to be largely constitutive there is no easy way for us to assess how long it takes G proteins to transit various intracellular compartments, although we agree this would be interesting. As the reviewer suggests, dynamic data on constitutive trafficking would require methods (such as photoconversion) not currently available to us for endogenous G proteins. Accordingly, we have made no claims regarding the kinetics of G protein trafficking. As for possible redistribution after GPCR activation, in the revised manuscript we have added 5- and 15-minute timepoints after agonist stimulation for our bystander BRET mapping (Figure 5- figure supplement 2). These timepoints were chosen to correspond to persistent signaling mediated by internalized receptors. 

(3) Exemplary images with cells showing significant colocalization with lysosomal compartments seem to contain more intracellular vesicles visible in the mNG channel than in the case of the other compartment. Is it an effect of the treatment to stain lysosomes? It would be beneficial to compare it with some endogenous marker e.g. LAMP1 without additional treatments.

The visibility of intracellular vesicles in our lysosome images likely reflects our selection of cells and regions with visible and abundant lysosomes, specifically peripheral regions directly adhered to the coverslip, rather than treatment with lysosomal stains (LV 633 and dextran). As suggested, we now include images of cells expressing LAMP1 as an alternative lysosome marker (Figure 3 - figure supplement 6).

(4) The authors probe an abundance of G proteins along the constitutive endocytic pathway. However, to prove that G proteins are not de-palmitoylated rather than endocytosed authors should perform control experiments where endocytosis is blocked e.g. pharmacologically or via a knockdown approach. Additionally, various endocytic pathways can be probed.

We did not claim that depalmitoylation plays no role in delivery of G proteins to internal compartments. In fact, we pointed out that we cannot at present rule out other pathways and delivery mechanisms. Importantly, if some of the G proteins that we detect along the endocytic pathway do arrive there by trafficking through the cytosol this would only strengthen our major conclusion that endocytosis is inefficient.

Having said this, we have now conducted extensive experiments investigating the role of palmitate cycling in the trafficking of heterotrimeric G proteins and the small G protein H-Ras. Our results suggest that a depalmitoylation-repalmitoylation cycle is not important for the distribution of heterotrimers, but these findings will be the subject of a separate publication focused on this specific question for both large and small G proteins.

We agree that it will be interesting to probe different endocytic pathways, as suggested using a genetic approach. Our main interest here was in endocytic membranes that were defined functionally (with FM4-64 or internalized receptors) rather than biochemically.

Minor comments:

(5) "Imaging" paragraph in the Methods section refers to a non-existent figure called "SI Appendix S9".

Thank you.

(6) It is not clear what was used as a "control" in Figure 5E.

“Control” refers to DPBS vehicle alone. This information is now added to the legend for Figure 5E.

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For example, even as someone without visual impairments, I find it challenging to understand the image below without any context. If I find it frustrating, it must be even more difficult for those with physical disabilities. Therefore, it is essential to ensure that every image has a descriptive alt tag of 125 characters or less. This description should be concise yet detailed enough to effectively convey the image’s content.

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Reviewer #1 (Public Review):

The study identifies the epigenetic reader SntB as a crucial transcriptional regulator of growth, development, and secondary metabolite synthesis in Aspergillus flavus, although the precise molecular mechanisms remain elusive. Using homologous recombination, researchers constructed sntB gene deletion (ΔsntB), complementary (Com-sntB), and HA tag-fused sntB (sntB-HA) strains. Results indicated that deletion of the sntB gene impaired mycelial growth, conidial production, sclerotia formation, aflatoxin synthesis, and host colonization compared to the wild type (WT). The defects in the ΔsntB strain were reversible in the Com-sntB strain.

Further experiments involving ChIP-seq and RNA-seq analyses of sntB-HA and WT, as well as ΔsntB and WT strains, highlighted SntB's significant role in the oxidative stress response. Analysis of the catalase-encoding catC gene, which was upregulated in the ΔsntB strain, and a secretory lipase gene, which was downregulated, underpinned the functional disruptions observed. Under oxidative stress induced by menadione sodium bisulfite (MSB), the deletion of sntB reduced catC expression significantly. Additionally, deleting the catC gene curtailed mycelial growth, conidial production, and sclerotia formation, but elevated reactive oxygen species (ROS) levels and aflatoxin production. The ΔcatC strain also showed reduced susceptibility to MSB and decreased aflatoxin production compared to the WT.

This study outlines a pathway by which SntB regulates fungal morphogenesis, mycotoxin synthesis, and virulence through a sequence of H3K36me3 modification to peroxisomes and lipid hydrolysis, impacting fungal virulence and mycotoxin biosynthesis.

The authors have achieved the majority of their aims at the beginning of the study, finding target genes, which led to catC mediated regulation of development, growth and aflatoxin metabolism. Overall most parts of the study are solid and clear.

Comments on revision:

The authors have thoroughly addressed all the concerns I raised. The current manuscript is robust and effectively presents evidence supporting its claims. The overall quality of the manuscript has significantly improved.

Reviewer #3 (Public review):

Summary:

This paper identifies GTSE1 as a potential substrate of cyclin D1-CDK4/6 and shows that GTSE1 correlates with cancer prognosis, probably through an effect on cell proliferation. The main problem is that the phosphorylation analysis relies on the over-expression of cyclin D1. It is unclear if the endogenous cyclin D1 is responsible for any phosphorylation of GTSE1 in vivo, and what, if anything, this moderate amount of GTSE1 phosphorylation does to drive proliferation.

Strengths:

There are few bonafide cyclin D1-Cdk4/6 substrates identified to be important in vivo so GTSE1 represents a potentially important finding for the field. Currently, the only cyclin D1 substrates involved in proliferation are the Rb family proteins.

Weaknesses:

The main weakness is that it is unclear if the endogenous cyclin D1 is responsible for phosphorylating GTSE1 in the G1 phase. For example, in Figure 2G there doesn't seem to be a higher band in the phos-tag gel in the early time points for the parental cells. This experiment could be redone with the addition of palbociclib to the parental to see if there is a reduction in GTSE1 phosphorylation and an increase in the amount in the G1 phase as predicted by the authors' model.

The experiments involving palbociclib do not disentangle cell cycle effects. Adding Cdk4 inhibitors will progressively arrest more and more cells in the G1 phase and so there will be a reduction not just in Cdk4 activity but also in Cdk2 and Cdk1 activity. More experiments, like the serum starvation/release in Figure 2G, with synchronized populations of cells would be needed to disentangle the cell cycle effects of palbociclib treatment.

It is unclear if GTSE1 drives the G1/S transition. Presumably, this is part of the authors' model and should be tested.

The proliferation assays need to be more quantitative. Figure 4B should be plotted on a log scale so that the slope can be used to infer the proliferation rate of an exponentially increasing population of cells. Figure 4c should be done with more replicates and error analysis since the effects shown in the lower right-hand panel are modest.

Author response:

Reviewer #1:

Summary:

García-Vázquez et al. identify GTSE1 as a novel target of the cyclin D1-CDK4/6 kinases. The authors show that GTSE1 is phosphorylated at four distinct serine residues and that this phosphorylation stabilizes GTSE1 protein levels to promote proliferation.

Strengths:

The authors support their Kindings with several previously published results, including databases. In addition, the authors perform a wide range of experiments to support their Kindings.

Weaknesses:

I feel that important controls and considerations in the context of the cell cycle are missing. Cyclin D1 overexpression, Palbociclib treatment and apparently also AMBRA1 depletion can lead to major changes in cell cycle distribution, which could strongly inKluence many of the observed effects on the cell cycle protein GTSE1. It is therefore important that the authors assess such changes and normalize their results accordingly.

We have approached the question of GTSE1 phosphorylation to account for potential cell cycle effects from multiple angles:  

(i) We conducted in vitro experiments with puriIied, recombinant proteins and shown that GTSE1 is phosphorylated by cyclin D1-CDK4 in a cell-free system (Figure 2A-C). This experiment provides direct evidence of GTSE1 phosphorylation by cyclin D1-CDK4 without the inIluence of any other cell cycle effectors.  

(ii) We present data using synchronized AMBRA1 KO cells (Figure 2G and Supplementary Figure 3B).  As shown previously (Simoneschi et al., Nature 2021, PMC8875297), AMBRA1 KO cells progress faster in the cell cycle but they are still synchronized as shown, for example by the mitotic phosphorylation of Histone H3. Under these conditions we observed that while phosphorylation of GTSE1 in parental cells peaks at the G2/M transition, AMBRA1 KO cells exhibited sustained phosphorylation of GTSE1 across all cell cycle phases.  This is evident when using Phos-tag gels as in the current top panel of Figure 2G. We now re-run one the biological triplicates of the synchronized cells using higher concentration of Zn+2-Phos-tag reagent and lower voltage to allow better separation.  Under these conditions, GTSE1 phosphorylation is more apparent. In the new version of the paper, we will either show both blots or substitute the old panel with the new one. This experiment provides evidence that high levels of cyclin D1 in AMBRA1 KO cells affect GTSE1 independently of the speciIic points in the cell cycle.  

(iii) The relative short half-life of GTSE1 (<4 hours) makes its levels sensitive to acute treatments such as Palbococlib or AMBRA1 depletion. The effects of these treatments on GTSE1 levels are measurable within a time frame too short to affect cell cycle progression in a meaningful way. For example, we used cells with fusion of endogenous AMBRA1 to a mini-Auxin Inducible Degron (mAID) at the N-terminus. This system allows for rapid and inducible degradation of AMBRA1 upon addition of auxin, thereby minimizing compensatory cellular rewiring. Again, we observed an increase in GTSE1 levels upon acute ablation of AMBRA1 (i.e., in 8 hours) (Figure 3B), when no signiIicant effects on cell cycle distribution are observed (please see Simoneschi et al., Nature 2021, PMC8875297 and Rona et al., Mol. Cell 2024, PMC10997477). 

All together, these lines of evidence support our conclusion that GTSE1 is a target of cyclin D1-CDK4, independent of cell cycle effects. In conclusion, as stated in the Discussion section, GTSE1 has been established as a substrate of mitotic cyclins, but we observed that overexpression of cyclin D1-CDK4 induce GTSE1 phosphorylation at any point of the cell cycle. Thus, we propose that GTSE1 is phosphorylated by CDK4 and CDK6 particularly in pathological states, such as cancers displaying overexpression of D-type cyclins beyond the G1 phase. In turn, GTSE1 phosphorylation induces its stabilization, leading to increased levels that, as expected based on the existing literature, contribute to enhanced cell proliferation. So, the cyclin D1-CDK4/6 kinase-dependent phosphorylation of GTSE1 induces its stabilization independently of the cell cycle.  

Reviewer #2:

Summary:

The manuscript by García-Vázquez et al identifies the G2 and S phases expressed protein

1(GTSE1) as a substrate of the CycD-CDK4/6 complex. CycD-CDK4/6 is a key regulator of the G1/S cell cycle restriction point, which commits cells to enter a new cell cycle. This kinase is also an important therapeutic cancer target by approved drugs including Palbocyclib. Identification of substrates of CycD-CDK4/6 can therefore provide insights into cell cycle regulation and the mechanism of action of cancer therapeutics. A previous study identified GTSE1 as a target of CycB-Cdk1 but this appears to be the first study to address the phosphorylation of the protein by Cdk4/6.

The authors identified GTSE1 by mining an existing proteomic dataset that is elevated in AMBRA1 knockout cells. The AMBRA1 complex normally targets D cyclins for degradation. From this list, they then identified proteins that contain a CDK4/6 consensus phosphorylation site and were responsive to treatment with Palbocyclib. 

The authors show CycD-CDK4/6 overexpression induces a shift in GTSE1 on phostag gels that can be reversed by Palbocyclib. In vitro kinase assays also showed phosphorylation by CDK4. The phosphorylation sites were then identified by mutagenizing the predicted sites and phostag got to see which eliminated the shift. 

The authors go on to show that phosphorylation of GTSE1 affects the steady state level of the protein. Moreover, they show that expression and phosphorylation of GTSE1 confer a growth advantage on tumor cells and correlate with poor prognosis in patients.

Strengths:

The biochemical and mutagenesis evidence presented convincingly show that the GTSE1 protein is indeed a target of the CycD-CDK4 kinase. The follow-up experiments begin to show that the phosphorylation state of the protein affects function and has an impact on patient outcomes. 

Weaknesses:

It is not clear at which stage in the cell cycle GTSE1 is being phosphorylated and how this is affecting the cell cycle. Considering that the protein is also phosphorylated during mitosis by CycB-Cdk1, it is unclear which phosphorylation events may be regulating the protein.

In cells that do not overexpress cyclin D1, GTSE1 is phosphorylated at the G2/M transition, consistent with the known cyclin B1-CDK1-mediated phosphorylation of this protein. However, AMBRA1 KO cells exhibited high levels of cyclin D1 throughout the cell cycle and sustained phosphorylation of GTSE1 across all cell cycle points (Figure 2G and Supplementary Figure 3B). Please see also answer to Reviewer #1.  Moreover, we show that, compared to the amino acids phosphorylated by cyclin D1-CDK4, cyclin B1-CDK1 phosphorylates GTSE1 on either additional residues or different sites (Figure 2H). Finally, we show that expression of a phospho-mimicking GTSE1 mutant leads to accelerated growth and an increase in the cell proliferative index (Figure 4C).  However, we have not evaluated how phosphorylation affects the cell cycle distribution.  We will perform FACS analyses and include them in the new version. 

Reviewer #3:

Summary:

This paper identifies GTSE1 as a potential substrate of cyclin D1-CDK4/6 and shows that GTSE1 correlates with cancer prognosis, probably through an effect on cell proliferation. The main problem is that the phosphorylation analysis relies on the over-expression of cyclin D1. It is unclear if the endogenous cyclin D1 is responsible for any phosphorylation of GTSE1 in vivo, and what, if anything, this moderate amount of GTSE1 phosphorylation does to drive proliferation.

Strengths: 

There are few bonafide cyclin D1-Cdk4/6 substrates identified to be important in vivo so GTSE1 represents a potentially important finding for the field. Currently, the only cyclin D1 substrates involved in proliferation are the Rb family proteins.

Weaknesses:

The main weakness is that it is unclear if the endogenous cyclin D1 is responsible for phosphorylating GTSE1 in the G1 phase. For example, in Figure 2G there doesn't seem to be a higher band in the phos-tag gel in the early time points for the parental cells. This experiment could be redone with the addition of palbociclib to the parental to see if there is a reduction in GTSE1 phosphorylation and an increase in the amount in the G1 phase as predicted by the authors' model. The experiments involving palbociclib do not disentangle cell cycle effects. Adding Cdk4 inhibitors will progressively arrest more and more cells in the G1 phase and so there will be a reduction not just in Cdk4 activity but also in Cdk2 and Cdk1 activity. More experiments, like the serum starvation/release in Figure 2G, with synchronized populations of cells would be needed to disentangle the cell cycle effects of palbociclib treatment.    

In normal cells, GTSE1 is phosphorylated at the G2/M transition in a cyclin B1-CDK1dependent manner.  During G1, when the levels of cyclin D1 peak, GTSE1 is not phosphorylated. This could be due to a higher affinity between GTSE1 and mitotic cyclins as compared to G1 cyclins or to a higher concentration of mitotic cyclins compared to G1 cyclins.  We show that higher levels of cyclin D1 induce GTSE1 phosphorylation during interphase, but we do not rely only on the overexpression of exogenous cyclin D1. In fact, we observe similar effect when we deplete endogenous AMBRA1, resulting in the stabilization of endogenous cyclin D1.  As mentioned in the Discussion section, we propose that GTSE1 is phosphorylated by CDK4 and CDK6 particularly in pathological states, such as cancers displaying overexpression of D-type cyclins (i.e., the overexpression appears to overcome the lower afIinity of the cyclin D1-GTSE1 complex). In sum, our study suggests that overexpression of cyclin D1, which is often observed in cancers cells beyond the G1 phase, induces phosphorylation of GTSE1 at all points in the cell cycle displaying high levels of cyclin D1.  Please see also response to Reviewer #1.  Concerning the experiments involving palbociclib, we limited confounding effects on the cell cycle by treating cells with palbociclib for only 4-6 hours. Under these conditions, there is simply not enough time for the cells to arrest in G1.

It is unclear if GTSE1 drives the G1/S transition. Presumably, this is part of the authors' model and should be tested.

We are not claiming that GTSE1 drives the G1/S transition.  GTSE1 is known to promote cell proliferation, but how it performs this task is not well understood.  Our experiments indicate that, when overexpressed, cyclin D1 promotes GTSE1 phosphorylation and its consequent stabilization.  In agreement with the literature, we show that higher levels of GTSE1 promote cell proliferation.  To measure cell cycle distribution upon expressing various forms of GTSE1, we will now perform FACS analyses and include them in the new version. 

The proliferation assays need to be more quantitative. Figure 4B should be plotted on a log scale so that the slope can be used to infer the proliferation rate of an exponentially increasing population of cells. Figure 4c should be done with more replicates and error analysis since the effects shown in the lower right-hand panel are modest.

In Figure 4B, we plotted data in a linear scale as done in the past (Donato et al. Nature Cell Biol. 2017, PMC5376241) to better represent the changes in total cell number overtime.  The experiments in Figure 4C were performed in triplicate. Error analysis was not included for simplicity, given the complexity of the data. We will include the other two sets of experiments in the revised version.  While the effects shown in the lower right-hand panel of Figure 4C are modest, they demonstrate the same trend as those observed in the AMBRA KO cells (Figure 4C and Simoneschi et al., Nature 2021, PMC8875297). It's important to note that this effect is achieved through the stable expression of a single phosphomimicking protein, whereas AMBRA KO cells exhibit changes in numerous cell cycle regulators.

We appreciate the constructive comments and suggestions made by the reviewers, and we believe that the resulting additions and changes will improve the clarity and message of our study.

• goal: protect user rights/freedoms
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what pitfalls come with putting a price tag on trauma / harm? when is the debt paid?

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Reply to the reviewers

We thank both reviewers for their detailed and critical assessment of our work. Below we provide a step-by-step response to your concerns.

Reviewer #1

Evidence, reproducibility and clarity

The manuscript presents data demonstrating the function of BRI1 in removing the H3K27me3 epigenetic marks in genes involved in seed coat development in Arabidopsis. The results support that BRI1 may function here independently of brassinosteroid. The work combines genetics with a large panel of mutant lines, phenotyping by quantitative microscopy and chemical treatment, H3K27me3 profiling by CUT&TAG, and data mining for published gene profiling. The introduction is adequately informative, complete and explaining the state-of-the-art to the readers. The result part may be a bit lengthy (especially the first part) and some parts may be a bit repetitive.

Thank you for your positive assessment of our work and for the constructive criticism. Below we respond to each of your points.

Major

1. Seed size is dependent of multiple factors. And few are explained here, notably the number of seeds per silique, the number of ovule per silique, the position of the silique of the branch (related to the age of the meristem), the total number of produced siliques (fertilised flowers) by the inflorescence meristem and the plant. And maybe if produced by the main and lateral branches. Were the authors consistent in the evaluation of analyzed siliques coming from the same type of branches, same age of the meristem, etc? Especially as some of the analysed mutants are dwarf, which is a sign of different plant fitness compared to WT.

This is a valid point. We did aim to analyze seeds coming from the main inflorescences of the plants and at similar stages of shoot development. This was harder to achieve in some genotypes, as indeed some BR and JMJ mutants have different plant architectures. However, we did repeat those experiments multiple times and always found consistent differences between the WTs and the mutants. See also our response to your next point and to the first point raised by Reviewer 2, as well as our new Fig. S6.

1. The seed perimeter measurements in BR mutant seeds (Figure S6) are variable. Are you sue the ovule size does not have any influence? What about presenting the relative size as earlier in the text?

Yes, this is particularly true for the Col-0 vs dwf5 comparison. The reason for this is that a different growth chamber was used for this experiment (greenhouse vs a climate chamber). We have observed that absolute seed growth phenotypes can change depending on the environmental conditions, which is something we are currently studying. However, importantly, we do not see changes in relative growth of the mutants when compared to the WT, independently of the growth conditions. That is, BR mutants produce consistently smaller seeds than the WT, independently of the conditions in which the plants are grown. To illustrate this point, we now add a new Figure, Fig. S6, where we show four independent biological replicates of assays comparing seed size between WT and det2 or bri1. These replicates were done in different growth chambers.

Indeed, presenting the data as relative size would solve this issue, but we worried that we would be hiding the "real" values by doing so. However, if the Reviewer and Editor deem it necessary, we could replot the data as relative to WT.

1. The number of evaluated samples is often {plus minus} n = 30, sometimes less, meaning less than what a silique contains of seeds. Did the authors evaluate the variability and reproductibility of their measurements, e.g, how many siliques per plant, how many plants, how many biological repeats? For example, in Figure S6, the number of measured ovules were as low as 16, which could be the reason why no significant difference in size were observed (low statitical strength). The variation in the Col WT is already visible. Is this variation significant?

On average we pooled seeds from 6-10 siliques coming from 2-3 different plants of the same genotype. We then took microscopic photos of 60 to 100 random seeds in those pools. Out of those, 30 random photos were used for the measurements. You are right this is an important point. We now added this information to the Methods section.

Moreover, we did calculate whether the sample size we were using provided enough statistical power. For the differences that we see, of around 50 um in perimeter, 26 samples would have been enough to achieve 80% statistical power, which most studies use as standard. In most of our experiments we used closer to 30 samples, which gives us 95% power.

Indeed, the left-most panel on Fig S6B is the exception. With that plot we mostly wanted to test if ovules produced by BR mutants were smaller than those of WT plants. That does not seem to be the case, even if the sample size is small. However, if deemed necessary, we can repeat those measurements with a higher sample number.

1. You indicate (line 149) that REF6 is not expressed in the gametophyte but GFP signal is observed in the cytoplasm for the central cell in Fig 1. The same goes for the expression pattern with the GUS line in Figure S2. (Line 290) One can not exclude expression in the endosperm or embryo with the presented pictures, or in the seed coat in older seeds.

We interpreted those diffuse signals in the cytoplasm of the gametophyte as background noise, as REF6 should be nuclearly localized. But we could be wrong. We therefore made changes to the text in lines 150-152 to reflect this.

And you are right that REF6 is expressed in the endosperm and embryo in later stages of development. We mention this in lines 157-159.

1. Make sure that you do not overstate your result conclusions, or add a reference to some of the statements. For example, line 185, for the choice of 3 DAP time point and the fact that seed coat development is based on cell expansion and interaction with the endosperm. Another example, in line 262, is where it is stated that the jmj mutants are compromised in ovule and pollen development. This was not assessed. You only checked the reduced seed set, not the fitness of the gametophytes. Or in line 337, where you indicate that KLUH is not expressed in all integument layers.

Thank you for pointing this out. For the claim that seed size at early time points is dictated only by the seed coat and endosperm, and not by the embryo, we added the appropriate reference. For the claim that jmj mutants are compromised in ovule development, this was based on our observations of Fig. S3C. We do see malformed or absent megagametophytes in jmj mutants. For pollen development, you are correct that we did not formally address this. We rephrased the sentence to reflect that. For the statement that KLU is not expressed in all integument cell layers, we added the reference.

1. Another example of this is in line 289 where you stated "a sporophytic function of JMJs at early stages of seed development, [..] and to a zygotic function at the later stages of seed development". I am not sure on what data do you base this conclusion as in all three categories (endosperm, embryo, seed coat) in Fig 2 and S5, genes are expressed in pre-globular stages. And again in line 475: "seed coat growth genes are expressed independentlyof fertilization". Do you have any evidence, a reference?

The evidence for a sporophytic JMJ function at early stages of seed development, and zygotic function at later stages, comes from our observations that jmj seed phenotypes are maternal in origin at early stages, but become zygotic later in development. But you are correct that we have to be careful with this interpretation. We now modified that sentence accordingly.

For the data of Fig 2E and Fig S5, we cannot rule out that some putative REF6 target genes are also expressed even when in the absence of REF6. The expression of those genes is also likely controlled by other factors. The point we wanted to make with those plots is that REF6 may have different target genes in different seed tissues, thus potentially regulating different developmental processes in a tissue-specific manner. We mention this in lines 288-290.

For your second point, we added the adequate reference.

1. (around lines 461) I understand that using a 35S promoter is not a good strategy as it would affect many other tissues. Did you consider using a tissue-specific approach as presented in Figure 4?

We suppose you mean the 35S::ELF6 construct. Yes, this makes sense and we did spend quite some time trying to come up with a good strategy. However, we failed to find a suitable promoter. The issue is that we would need a promoter that is active in all (or most) seed coat layers, but only after fertilization. There are promoters like those of TT genes which are active post-fertilization, but only in one cell layer, and thus likely not useful for our purpose. And there are other promoters, like those of STK or ANT, which are expressed in most integument cell layers, but are also expressed during integument development, and not just after fertilization. So they would have the same issue as the 35S promoter. Unfortunately, so far we have not identified a promoter that would be useful for this kind of experiment, which is why we went with a constitutive promoter, but which is specific to the sporophytic tissues.

1. You observed that the triple swn clf bri mutant is less dwarf than bri1 mutant and stated in line 483 that it is larger, has more leaves, grow tallerm and flower later and longer. Do you have any qunatitative data? If not, I would state that these observations are qualitative from growing plant aside.

You are correct that this was based on qualitative assessments, rather than on quantitative data (as it was not the point of the manuscript). We now indicate this in lines 489-490.

Minor: 1. The title should precise the studies species, here Arabidopsis thaliana. Also the title of one of the part could be rephrased. "in a zygotic manner" sounds strange.

We modified both title and subtitle, as suggested.

1. Scale bars are missing in many figures.

Fixed.

1. The font size in the graphs is small. The authors may use the empty space of the figures to increase the size of the graphs for clarity. Guidelines could be found here https://tpc.msubmit.net/html/TPC_Detailed_Figure_Guidelines.pdf, as example of good practices.

You are right. We revised all the figures and increased the font size, especially in the plot labels.

1. Be consitent in the mutant name, e.g., brz1-D is also presented as brz1-d.

Fixed.

1. Figure legend S1: I would not use the word "extremely" while you still have 30% seed set. Extremely would qualifiy for

We suppose you mean Fig. S3. We corrected the legend.

1. Figure S8 is missing the WT control for comparison.

Fixed.

1. Figure S12, stats are missing

Fixed.

1. I would recommend to add a line in the Supplemental tables with the name as this name disappears from the file name during upload. It would help the readers to navigate the data.

We now made it so the top line is static and is always visible.

1. Methods: Are all the lines listed used in the study? SR2200 is missing for the method, and please indicate the selection marker for each of the generated lines for open-access of the data if other researchers later use your lines.

You are right that some references had been left over from a previous document. We now updated the list of lines.

And indeed, we forgot to mention the use of SR2200. It is now added to the Methods section. We also added the information on the selection markers for the lines we generated.

1. You have a duplicate for reference Vukašinovíc et al.

Fixed.

1. Line 393, remove "s" in embryo and endosperm, in coat (line 674), in size (lines 684, 686

Fixed.

1. Line 410, write RPS5A in upper case.

Fixed throughout the manuscript.

1. LIne 676, the sentence "...H3K27me3 to be removed from the integuments." I would recomend to be more precise. For example "H3K27mme3 marks to be removed from genes to be expressed in the integuments" or something like that.

We rephrased this sentence to "We thus hypothesized that BR signaling would be required for JMJ function, allowing for H3K27me3 to be removed from genes necessary for seed coat formation."

Significance

The authors provide novel information on the step-wise regulation of seed coat development and its influence on seed size. This is a topic of general interest, beyond the plant model Arabidopsis, especially in the context of reduced seed set caused by (a)biotic stress. The results of this study are valuable to understand seed size regulation in differnet growth context or species. The group previously showed that the auxin phytohormone is necessary after fertilization to initiate seed coat differentiation by inhibiting PRC2. However, as seed coat develops mainly as cell elongation, the epigenetic marks are not diluted by cell division and needs to be actively removed. This study provides insight into this process by identifcation 2 JMJ proteins responsible for removing H3K27me3 marks in the seed coat after fertilization to initiation seed coat development and regulating seed size. BRI1, BES1 and BZR1 are involved in this process, indepently of brassinosteroid, to guide JMJ to their target loci. While the study bring some genetic evidence of this process, molecular insight is still missing. Notably the identification of the target genes and how BRI1 is regulated/activated upon fertilization. Or how auxin and BRI1 co-regulate the process. These questions appear how of scope of this current study.

Thank you for the assessment. Indeed, the identification of BRI1 downstream genes is out of scope of this work. As you point out earlier in the review, the manuscript is already quite long, and adding such data would make it even more so.

Reviewer #2

In this study, Pankaj et al. investigate the role of brassinosteroids and H3K27me3 in seed development, particularly in controlling seed size. They demonstrate that defects in these pathways affect seed size control and suggest that this control occurs in the maternal seed coat. This paper presents novel findings that merit publication and would be of interest to the plant community. However, the data interpretation and presentation could be improved. Additionally, I have a few comments that necessitate further analysis and revision.

Thank you for the careful and critical assessment of our work. Below we respond to each of the points you raised.

Major Comments

1. My main concern is the use of seed size measurement as a proxy for seed coat development. Mature seed size measurements can vary significantly with growth conditions, so it is crucial that the authors present at least three independent experiments (wild type and mutant grown in parallel) in a single box plot to ensure data reliability. Additionally, due to the high number of seeds analyzed, significant changes are often observed, though they are not always reproducible. The authors should standardize their seed measurements, using either seed area or seed perimeter.

You are right that we do see some variation in seed size between experiments. And, indeed, we suspect this is due to slightly different plant growth conditions, for example when different growth chambers are used. As you suggest, we now show data from four independent biological replicates of seed size comparisons of WT and BR mutants. This is in the new Fig. S6. As you can see, although we do see variations in absolute seed sizes, depending on the growth conditions, there is a consistent difference between WT and mutant seeds across experiments.

1. It would be beneficial to include data on cell division and cell elongation in the seed coat if the authors aim to extend the seed size phenotype to a seed coat phenotype.

This is indeed a good point. However, we already showed in a previous publication that seed coat growth is driven by cell elongation and not cell division (https://elifesciences.org/articles/20542). But you are right that this is important to point out. We mention it in lines 66-67.

1. It is challenging to be fully convinced by the seed coat specificity of the phenotype, as the authors observe variations in total seed set and phenotypic differences in self-crosses and when the mutants are used paternally. Some of the observed phenotypes do not support their hypothesis. In all mutant analyses, the authors should complement their phenotype analysis using seed coat-specific promoters and include heterozygote measurements, as done in some figures.

We assume you mean the effect of jmj mutations. For BR mutants, we do show data supporting a seed coat effect (Fig. 4). For PRC2 mutants, that has also been previously described (doi.org/10.7554/eLife.20542 and doi.org/10.1073/pnas.1117111108).

For the JMJ mutants, you are right that we cannot be 100% sure that their effect is purely sporophytic. We now modified the text accordingly to reflect this (see also the response to point 6 of Reviewer 1). We indeed show that REF6 and ELF6 are expressed in the sporophytic tissues of the ovule and that the double mutant has seed coat defects (smaller seed coats and defects in accumulation of proanthocyanidins). And although we can say that those defects are maternal in nature, we can not 100% conclude that they are simply due to the effect of those JMJs in the sporophyte. There may be gametophytic effects that we cannot rule out, even though we do not see either protein expressed in embryo sacs. Thank you for pointing this out.

Doing a tissue-specific rescue of these phenotypes would be very informative indeed, but also very hard. As we mention in the response to point 7 of Reviewer 1, we do not currently have suitable promoters for this. So we simply cannot run such experiments in a reasonable time frame.

Overall, we now tried to be more careful in our conclusions and avoid claiming that the effect of JMJs is purely sporophytic. We can make that argument for the BR machinery and for PRC2, but not necessarily for JMJs. You are correct in that assessment.

1. The authors need to include a fluorescent reporter for ELF6; tissue-specific expression cannot be conclusively determined with the GUS reporter.

We did obtain an ELF6::GFP line from Caroline Dean's lab (https://www.pnas.org/doi/full/10.1073/pnas.1605733113), but could not see much expression during endosperm or seed coat development. As you can see from that publication, even in embryos and in roots the expression of ELF6:GFP is very blurry. It seems ELF6 is simply expressed at very low levels. We therefore used the GUS reporter, as a more sensitive means to visualize where ELF6 is expressed. You are right that the results are not as precise as that obtained with a fluorescent reporter. However, note that we simply claim that ELF6 is expressed in the integuments and seed coat (line 155). This can be clearly seen in Fig. 1B. The blue product of the β-glucuronidase reaction should be immotile and not travel between tissues (also note that there are no plasmodesmata between endosperm and seed coat). Therefore, we believe that GUS is a suitable reporter to test the seed coat expression of ELF6.

1. Text editing: In some places, the text is unclear and could benefit from simplification. The authors should replace the term "seed coat formation," as developmentally, integuments are already present before fertilization. The authors are not studying the formation of the seed coat but rather its growth. They should also clarify the term "PRC2 removal." It is unclear whether the authors mean PRC2 lack of expression in the integument, PRC2 eviction from chromatin, or removal of H3K27me3.

Thank you for noting that. It is very important to us that the text is clear to the reader. If you could indicate where the text is unclear, we are happy to simplify it.

Regarding the wording, we refer to "seed coat formation" because the seed coat only indeed forms after fertilization. Before fertilization, the sporophytic tissues that cover the megagametophyte are called integuments, and not seed coat. Therefore, we see the seed coat as "forming" from the integuments (i.e., the integuments become seed coat via growth and differentiation).

With PRC2 removal we indeed mean reduction of expression of PRC2 components. We now make this clear in lines 54-55.

Minor Comments

1. L151: Is REF6 expressed in zygotic tissues?

Reviewer 1 also raised this question. We now added this information to lines 148-150.

1. Confirm mutant complementation with the different reporter lines.

All mutant lines that we used have been previously described to be either loss-of-function or hypomorphic mutants. We did not use any mutant line that has not been previously described. We added all references to the corresponding publications in the Methods.

1. Confirm by qPCR that JMJ13 is indeed not expressed in seeds.

We tested JMJ13 as a possible factor involved in H3K27me3 removal in the seed coat due to it being described, together with ELF6 and REF6, as one of the three main H3K27 demethylases. But there are, in fact, transcriptomic datasets showing that the expression of JMJ13 is indeed very low or absent in seeds: see RNAseq data in Table S3 in doi.org/10.3389/fpls.2022.998664. Moreover we checked CPMs on published seed scRNAseq datasets (doi.org/10.1038/s41477-021-00922-0) and JMJ13 (AT5G46910) has zero transcript counts in these datasets.

Because of these two independent instances showing that the expression of JMJ13 is extremely low in seeds (or even totally absent), together with the analysis that we did of the fluorescent reporter line, we believe this is sufficient evidence that this JMJ is specific to the pollen during reproductive development. Note that the reporter that we used is strongly expressed in pollen grains, as had been previously described (doi.org/10.1038/s41556-020-0515-y).

Even so, if the Reviewer and the Editor deem it necessary that we check JMJ13 expression by qPCR, we can of course do so.

1. Fig1a and Fig1b: Align the panels in the figure.

Done.

1. L183-189: This section is unclear.

I am sorry that the section is not clear. If you direct us to the points that need to be cleared, we are happy to make changes.

1. There may be a PDF artifact, but most figures have unattractive misaligned boxes.

We went through every figure and made slight modifications to avoid such artifacts. We hope they now appear more clear in the new version.

1. Change the color in Fig 2a.

Fixed.

1. The introduction is heavily self-cited. The authors should try to include a broader range of literature.

It is not clear to us why the Reviewer sees it like that. We only refer to three of our publications in the Introduction. One review manuscript and two research manuscripts. We cite almost 40 manuscripts in the introduction. Therefore, citing three of our works does not seem out of line to us, especially since those manuscripts laid the foundation for this work.

1. Fig3F: Typo in "microM."

Fixed.

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Referee #1

Evidence, reproducibility and clarity

The manuscript presents data demonstrating the function of BRI1 in removing the H3K27me3 epigenetic marks in genes involved in seed coat development in Arabidopsis. The results support that BRI1 may function here independently of brassinosteroid. The work combines genetics with a large panel of mutant lines, phenotyping by quantitative microscopy and chemical treatment, H3K27me3 profiling by CUT&TAG, and data mining for published gene profiling. The introduction is adequately informative, complete and explaining the state-of-the-art to the readers. The result part may be a bit lengthy (especially the first part) and some parts may be a bit repetitive.

Major comments:

1. Seed size is dependent of multiple factors. And few are explained here, notably the number of seeds per silique, the number of ovule per silique, the position of the silique of the branch (related to the age of the meristem), the total number of produced siliques (fertilised flowers) by the inflorescence meristem and the plant. And maybe if produced by the main and lateral branches. Were the authors consistent in the evaluation of analyzed siliques coming from the same type of branches, same age of the meristem, etc? Especially as some of the analysed mutants are dwarf, which is a sign of different plant fitness compared to WT.

2. The seed perimeter measurements in BR mutant seeds (Figure S6) are variable. Are you sue the ovule size does not have any influence? What about presenting the relative size as earlier in the text?

3. The number of evaluated samples is often {plus minus} n = 30, sometimes less, meaning less than what a silique contains of seeds. Did the authors evaluate the variability and reproductibility of their measurements, e.g, how many siliques per plant, how many plants, how many biological repeats? For example, in Figure S6, the number of measured ovules were as low as 16, which could be the reason why no significant difference in size were observed (low statitical strength). The variation in the Col WT is already visible. Is this variation significant?

4. You indicate (line 149) that REF6 is not expressed in the gametophyte but GFP signal is observed in the cytoplasm for the central cell in Fig 1. The same goes for the expression pattern with the GUS line in Figure S2. (Line 290) One can not exclude expression in the endosperm or embryo with the presented pictures, or in the seed coat in older seeds.

5. Make sure that you do not overstate your result conclusions, or add a reference to some of the statements. For example, line 185, for the choice of 3 DAP time point and the fact that seed coat development is based on cell expansion and interaction with the endosperm. Another example, in line 262, is where it is stated that the jmj mutants are compromised in ovule and pollen development. This was not assessed. You only checked the reduced seed set, not the fitness of the gametophytes. Or in line 337, where you indicate that KLUH is not expressed in all integument layers.

6. Another example of this is in line 289 where you stated "a sporophytic function of JMJs at early stages of seed development, [..] and to a zygotic function at the later stages of seed development". I am not sure on what data do you base this conclusion as in all three categories (endosperm, embryo, seed coat) in Fig 2 and S5, genes are expressed in pre-globular stages. And again in line 475: "seed coat growth genes are expressed independentlyof fertilization". Do you have any evidence, a reference?

7. (around lines 461) I understand that using a 35S promoter is not a good strategy as it would affect many other tissues. Did you consider using a tissue-specific approach as presented in Figure 4?

8. You observed that the triple swn clf bri mutant is less dwarf than bri1 mutant and stated in line 483 that it is larger, has more leaves, grow tallerm and flower later and longer. Do you have any qunatitative data? If not, I would state that these observations are qualitative from growing plant aside.

Minor comments:

1. The title should precise the studies species, here Arabidopsis thaliana. Also the title of one of the part could be rephrased. "in a zygotic manner" sounds strange.

2. Scale bars are missing in many figures.

3. The font size in the graphs is small. The authors may use the empty space of the figures to increase the size of the graphs for clarity. Guidelines could be found here https://tpc.msubmit.net/html/TPC_Detailed_Figure_Guidelines.pdf, as example of good practices.

4. Be consitent in the mutant name, e.g., brz1-D is also presented as brz1-d.

5. Figure legend S1: I would not use the word "extremely" while you still have 30% seed set. Extremely would qualifiy for <5%, I guess.

6. Figure S8 is missing the WT control for comparison.

7. Figure S12, stats are missing

8. I would recommend to add a line in the Supplemental tables with the name as this name disappears from the file name during upload. It would help the readers to navigate the data.

9. Methods: Are all the lines listed used in the study? SR2200 is missing for the method, and please indicate the selection marker for each of the generated lines for open-access of the data if other researchers later use your lines.

10. You have a duplicate for reference Vukašinovíc et al.

11. Line 393, remove "s" in embryo and endosperm, in coat (line 674), in size (lines 684, 686

12. Line 410, write RPS5A in upper case.

13. LIne 676, the sentence "...H3K27me3 to be removed from the integuments." I would recomend to be more precise. For example "H3K27mme3 marks to be removed from genes to be expressed in the integuments" or something like that.

Cross-commenting:

I have been comparing our peer-review reports of the manuscript and found much similarity on our assessment:

1. The seed size assemment and how this relates to seed coat development

2. The GUS expression of ELF6 is not sufficient for the provided conclusion of the ELF6 expression

3. The same would be for REP6

4. Use of tissue-specific (seed coat specific) promoters to confirm the conclusion.

Significance

The authors provide novel information on the step-wise regulation of seed coat development and its influence on seed size. This is a topic of general interest, beyond the plant model Arabidopsis, especially in the context of reduced seed set caused by (a)biotic stress. The results of this study are valuable to understand seed size regulation in differnet growth context or species. The group previously showed that the auxin phytohormone is necessary after fertilization to initiate seed coat differentiation by inhibiting PRC2. However, as seed coat develops mainly as cell elongation, the epigenetic marks are not diluted by cell division and needs to be actively removed. This study provides insight into this process by identifcation 2 JMJ proteins responsible for removing H3K27me3 marks in the seed coat after fertilization to initiation seed coat development and regulating seed size. BRI1, BES1 and BZR1 are involved in this process, indepently of brassinosteroid, to guide JMJ to their target loci. While the study bring some genetic evidence of this process, molecular insight is still missing. Notably the identification of the target genes and how BRI1 is regulated/activated upon fertilization. Or how auxin and BRI1 co-regulate the process. These questions appear how of scope of this current study.

My filed of expertise: hormones, plant reproduction, Arabidopis, oilseed rape, microscopy, transformation

Marx's tag- a little hazy on alienation.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review):

In the article by Dearlove et al., the authors present evidence in strong support of nucleotide ubiquitylation by DTX3L, suggesting it is a promiscuous E3 ligase with capacity to ubiquitylate ADP ribose and nucleotides. The authors include data to identify the likely site of attachment and the requirements for nucleotide modification. 

While this discovery potentially reveals a whole new mechanism by which nucleotide function can be regulated in cells, there are some weaknesses that should be considered. Is there any evidence of nucleotide ubiquitylation occurring cells? It seems possible, but evidence in support of this would strengthen the manuscript. The NMR data could also be strengthened as the binding interface is not reported or mapped onto the structure/model, this seems of considerable interest given that highly related proteins do have the same activity. 

The paper is for the most part well well-written and is potentially highly significant 

Comments on revised version: 

The revised manuscript has addressed many of the concerns raised and clarified a number of points. As a result the manuscript is improved. 

The primary concern that remains is the absence of biological function for Ub-ssDNA/RNA and the inability to detect it in cells. Despite this the manuscript will be of interest to those in the ubiquitin field and will likely provoke further studies and the development of tools to better assess the cellular relevance. As a result this manuscript is important. 

We agree with the reviewer’s assessment.

Minor issue: 

Figure 1A - the authors have now included the constructs used but it would be more informative if the authors lined up the various constructs under the relevant domains in the full-length protein. 

Figure 1 will be fixed in the Version of Record.

Reviewer #2 (Public Review):

The manuscript by Dearlove et al. entitled "DTX3L ubiquitin ligase ubiquitinates single-stranded nucleic acids" reports a novel activity of a DELTEX E3 ligase family member, DTX3L, which can conjugate ubiquitin to the 3' hydroxyl of single-stranded oligonucleotides via an ester linkage. The findings that unmodified oligonucleotides can act as substrates for direct ubiquitylation and the identification of DTX3 as the enzyme capable of performing such oligonucleotide modification are novel, intriguing, and impactful because they represent a significant expansion of our view of the ubiquitin biology. The authors perform a detailed and diligent biochemical characterization of this novel activity, and key claims made in the article are well supported by experimental data. However, the studies leave room for some healthy skepticism about the physiological significance of the unique activity of DTX3 and DTX3L described by the authors because DTX3/DTX3L can also robustly attach ubiquitin to the ADP ribose moiety of NAD or ADP-ribosylated substrates. The study could be strengthened by a more direct and quantitative comparison between ubiquitylation of unmodified oligonucleotides by DTX3/DTX3L with the ubiquitylation of ADP-ribose, the activity that DTX3 and DTX3L share with the other members of the DELTEX family.

Comment on revised version:

In my opinion, reviewers' comments are constructively addressed by the authors in the revised manuscript, which further strengthens the revised submission and makes it an important contribution to the field. Specifically, the authors perform a direct quantitative comparison of two distinct ubiquitylation substrates, unmodified oligonucleotides and fluorescently labeled NADH and report that kcat/Km is 5-fold higher for unmodified oligos compared to NADH. This observation suggests that ubiquitylation of unmodified oligos is not a minor artifactual side reaction in vitro and that unmodified oligonucleotides may very well turn out to be the physiological substrates of the enzyme. However, the true identity of the physiological substrates and the functionally relevant modification site(s) remain to be established in further studies. 

We agree with the reviewer’s assessment.

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

Public Reviews: 

Reviewer #1 (Public Review): 

In the article by Dearlove et al., the authors present evidence in strong support of nucleotide ubiquitylation by DTX3L, suggesting it is a promiscuous E3 ligase with capacity to ubiquitylate ADP ribose and nucleotides. The authors include data to identify the likely site of attachment and the requirements for nucleotide modification. 

While this discovery potentially reveals a whole new mechanism by which nucleotide function can be regulated in cells, there are some weaknesses that should be considered. Is there any evidence of nucleotide ubiquitylation occurring cells? It seems possible, but evidence in support of this would strengthen the manuscript. The NMR data could also be strengthened as the binding interface is not reported or mapped onto the structure/model, this seems of considerable interest given that highly related proteins do have the same activity. 

The paper is for the most part well well-written and is potentially highly significant, but it could be strengthened as follows: 

(1) The authors start out by showing DTX3L binding to nucleotides and ubiquitylation of ssRNA/DNA. While ubiquitylation is subsequently dissected and ascribed to the RD domains, the binding data is not followed up. Does the RD protein alone bind to the nucleotides? Further analysis of nucleotide binding is also relevant to the Discussion where the role of the KH domains is considered, but the binding properties of these alone have not been analysed. 

We thank the reviewer for the suggestion. We have tested DTX3L RD for ssDNA binding using NMR (see Figure 4A and Figure S2), which showed that DTX3L RD binds ssDNA. We have now tested the DTX3L KH domains for RNA/ssDNA binding using an FP experiment. However, the FP experiment did not show significant changes upon titrating RNA/ssDNA, suggesting that the KH domains alone are not sufficient to bind RNA/ssDNA. We have opted to put this data in the response-to-review as future investigation will be required to examine whether other regions of DTX3L cooperate with RD to bind RNA/ssDNA. We have revised the Discussion on the KH domains. We now state that “Our findings show the DTX3L DTC domain binds nucleic acids but whether the KHL domains contribute to nucleic acid binding requires further investigation.”

Author response image 1.

Fold change of fluorescence polarisation of 6-FAM-labelled ssDNA D4 upon titrating with DTX3L variants. DTX3L KH domain fragments were expressed with a N-terminal His-MBP tag to increase the molecular weight to enhance the signal.

(2) With regard to the E3 ligase activity, can the authors account for the apparent decreased ubiquitylation activity of the 232-C protein in Figure 1/S1 compared to FL and RD? 

We found that the 232-C protein batch used in the assay was not pure and have subsequently re-purified the protein. We have repeated the ubiquitination of ssDNA and RNA (Fig. 1H and 1I) and 232-C exhibited similar activity as WT. Furthermore, we performed autoubiquitination (Fig. S1G) and E2~Ub discharge assay (Fig. S1H) to compare the activity. 232-C was slower in autoubiquitination (Fig. S1G), but showed similar activity in the E2~Ub discharge assay as WT. These findings suggest that the RING domain in 232-C is functional and 232-C likely lacks ubiquitination site(s) present in 1-231 region necessary for autoubiquitination.

(3) Was it possible to positively identify the link between Ub and ssDNA/RNA using mass spectrometry? This would overcome issues associated with labels blocking binding rather than modification. 

We have tried to use mass spectrometry to detect the linkage between Ub and ssDNA/RNA, but was unable to do so. We suspect that the oxyester linkage might be labile, posing a challenge for mass spectrometry techniques. Similarly, a recent preprint from Ahel lab, which utilises LC-MS, detects the Ub-NMP product rather than the linkage (https://www.biorxiv.org/content/10.1101/2024.04.19.590267v1.full.pdf).

(4) Furthermore, can a targeted MS approach be used to show that nucleotides are ubiquitylated in cells? 

This will require future development and improvement of the MS approach, specifically the isolation of labile oxyester-linked products from cells and the optimisation of the MS detection method.

(5) Do the authors have the assignments (even partial?) for DTX3L RD? In Figure 4 it would be helpful to identify the peaks that correspond to the residues at the proposed binding site. Also do the shifts map to a defined surface or do they suggest an extended site, particularly for the ssDNA.

We only collected HSQC spectra which was insufficient for assignments. We have performed a competition experiment using ADPr and labelled ssDNA, showing that ADPr competes against the ubiquitination of ssDNA (Figure 4D). We have also provided an additional experiment showing that ssDNA with a blocked 3’-OH can compete against ubiquitination of ADPr (Figure 4E). These data, together with our NMR analysis, further strengthen the evidence that ssDNA and ADPr compete the same binding pocket in DTX3L RD. Understanding how DTX3L RD binds ssDNA/RNA is an ongoing research in the lab.

(6) Does sequence analysis help explain the specificity of activity for the family of proteins? 

We have performed sequence alignment and structure comparison of DTX proteins using both RING and DTC domains (Fig. S3). These analyses showed that DTX3 and DTX3L RING domains lack a N-terminal helix and two loop insertions compared to DTX1, DTX2 and DTX4. These additions make DTX1, DTX2 and DTX4 RING domain larger than DTX3L and DTX3. It is not clear how these would influence the orientation of the recruited E2~Ub. Comparison of the DTC domain showed that DTX1, DTX2 and DTX4 contain an Ala-Arg motif, which causes a bulge at one end of DTC pocket. In the absence of Ala-Arg motif, DTC pockets of DTX3 and DTX3L contain an extended groove which might accommodate one or more of the nucleotides 5' to the targeted terminal nucleotide. It seems that both features of RING and DTC domains might attribute to the specificity of DTX3L and DTX3. We have included these comparisons in the discussion and suggested that future structural characterization is necessary to unveil the specificity.

(7) While including a summary mechanism (Figure 5I) is helpful, the schematic included does not necessarily make it easier for the reader to appreciate the key findings of the manuscript or to account for the specificity of activity observed. While this figure could be modified, it might also be helpful to highlight the range of substrates that DTX3L can modify - nucleotide, ADPr, ADPr on nucleotides etc. 

We have modified this Figure to include the range of substrates.

Reviewer #2 (Public Review): 

Summary: 

The manuscript by Dearlove et al. entitled "DTX3L ubiquitin ligase ubiquitinates single-stranded nucleic acids" reports a novel activity of a DELTEX E3 ligase family member, DTX3L, which can conjugate ubiquitin to the 3' hydroxyl of single-stranded oligonucleotides via an ester linkage. The findings that unmodified oligonucleotides can act as substrates for direct ubiquitylation and the identification of DTX3 as the enzyme capable of performing such oligonucleotide modification are novel, intriguing, and impactful because they represent a significant expansion of our view of the ubiquitin biology. The authors perform a detailed and diligent biochemical characterization of this novel activity, and key claims made in the article are well supported by experimental data. However, the studies leave room for some healthy skepticism about the physiological significance of the unique activity of DTX3 and DTX3L described by the authors because DTX3/DTX3L can also robustly attach ubiquitin to the ADP ribose moiety of NAD or ADP-ribosylated substrates. The study could be strengthened by a more direct and quantitative comparison between ubiquitylation of unmodified oligonucleotides by DTX3/DTX3L with the ubiquitylation of ADP-ribose, the activity that DTX3 and DTX3L share with the other members of the DELTEX family. 

Strengths: 

The manuscript reports a novel and exciting observation that ubiquitin can be directly attached to the 3' hydroxyl of unmodified, single-stranded oligonucleotides by DTX3L. The study builds on the extensive expertise and the impactful previous studies by the Huang laboratory of the DELTEX family of E3 ubiquitin ligases. The authors perform a detailed and diligent biochemical characterization of this novel activity, and all claims made in the article are well supported by experimental data. The manuscript is clearly written and easy to read, which further elevates the overall quality of submitted work. The findings are impactful and will help illuminate multiple avenues for future follow-up investigations that may help establish how this novel biochemical activity observed in vitro may contribute to the biological function of DTX3L. The authors demonstrate that the activity is unique to the DTX3/DTX3L members of the DELTEX family and show that the enzyme requires at least two single-stranded nucleotides at the 3' end of the oligonucleotide substrate and that the adenine nucleotide is preferred in the 3' position. Most notably, the authors describe a chimeric construct containing RING domain of DTX3L fused to the DTC domain DTX2, which displays robust NAD ubiquitylation, but lacks the ability to ubiquitylate unmodified oligonucleotides. This construct will be invaluable in the future cell-based studies of DTX3L biology that may help establish the physiological relevance of 3' ubiquitylation of nucleic acids. 

Weaknesses: 

The main weakness of the study is in the lack of direct evidence that the ubiquitylation of unmodified oligonucleotides reported by the authors plays any role in the biological function of DTX3L. The study leaves plenty of room for natural skepticism regarding the physiological relevance of the reported activity, because, akin to other DELTEX family members, DTX3 and DTX3L can also catalyze attachment of ubiquitin to NAD, ADP ribose and ADP-ribosylated substrates. Unfortunately, the study does not offer any quantitative comparison of the two distinct activities of the enzyme, which leaves plenty of room for doubt. One is left wondering, whether ubiquitylation of unmodified oligonucleotides is just a minor and artifactual side activity owing to the high concentration of the oligonucleotide substrates and E2~Ub conjugates present in the in-vitro conditions and the somewhat lower specificity of the DTX3 and DTX3L DTC domains (compared to DTX2 and other DELTEX family members) for ADP ribose over other adenine-containing substrates such as unmodified oligonucleotides, ADP/ATP/dADP/dATP, etc. The intriguing coincidence that DTX3L, which is the only DTX protein capable of ubiquitylating unmodified oligonucleotides, is also the only family member that contains nucleic acid interacting domains in the N-terminus, is suggestive but not compelling. A recently published DTX3L study by a competing laboratory (PMID: 38000390), which is not cited in the manuscript, suggests that ADP-ribose-modified nucleic acids could be the physiologically relevant substrates of DTX3L. That competing hypothesis appears more convincing than ubiquitylation of unmodified oligonucleotides because experiments in that study demonstrate that ubiquitylation of ADP-ribosylated oligos is quite robust in comparison to ubiquitylation of unmodified oligos, which is undetectable. It is possible that the unmodified oligonucleotides in the competing study did not have adenine in the 3' position, which may explain the apparent discrepancy between the two studies. In summary, a quantitative comparison of ubiquitylation of ADP ribose vs. unmodified oligonucleotides could strengthen the study. 

We thank the reviewer for the constructive feedback. We agree that evidence for the biological function is lacking. While we have tried to detect Ub-ssDNA/RNA from cells, we found that isolating and detecting labile oxyester-linked Ub-ssDNA/RNA products remain challenging due to (1) low levels of Ub-ssDNA/RNA products, (2) the presence of DUBs and nucleases that rapidly remove the products during the experiments, and (3) our lack of a suitable MS approach to detect the product. For these reasons, we feel that discovering the biological function will require future effort and expertise and is beyond the scope of our current manuscript.

In the manuscript (PMID: 38000390), the authors used PARP10 to catalyse ADP-ribosylation onto 5’-phosphorylated ssDNA/RNA. They used the following sequences which lacks 3’-adenosine, which could explain the lack of ubiquitination.

E15_5′P_RNA [Phos]GUGGCGCGGAGACUU

E15_5′P_DNA [Phos]GTGGCGCGGAGACTT

We have performed the experiment using this sequence to verify this (see Author response image 2 below). We have cited this manuscript but for some reasons, Pubmed has updated its published date from mid 2023 to Jan 2024. We have updated the Endnote in the revised manuscript.

Author response image 2.

Fluorescently detected SDS-PAGE gel of in vitro ubiquitination catalysed by DTX3L-RD in the presence ubiquitination components and 6-FAM-labelled ssDNA D4 or D31.

We agree that it is crucial to compare ubiquitination of oligonucleotides and ADPr by DTX3L to find its preferred substrate. We have challenged oligonucleotide ubiquitination by adding excess ADPr and found that ADPr efficiently competes with oligonucleotide (Figure 4D). We have also performed an experiment showing that ssDNA with a blocked 3’-OH can compete against ubiquitination of ADPr (Figure 4E). These data support that ADPr and ssDNA compete for the same binding site on DTX3L.

We also performed kinetic analysis of ubiquitination of fluorescently labelled ssDNA (D4) and NAD+ by DTX3L-RD (Fig. 4F and Fig. S2D–G) to assess substrate preferences. Here, we used fluorescent-labelled NAD+ (F-NAD+) in place of ADPr as labelled NAD+ is commercially available. With the known concentration of fluorescently labelled ssDNA and NAD+ as the standard, we could estimate the rate of ubiquitinated product formation across different substrate concentrations. We have included this finding in the main text “DTX3L-RD displayed _k_cat value of 0.0358 ± 0.0034 min-1 and a _K_m value of 6.56 ± 1.80 mM for Ub-D4 formation, whereas the Michaelis-Menten curve did not reach saturation for Ub-F-NAD+ formation (Fig. 4F and fig. S2, D-G). Comparison of the estimated catalytic efficiency (_k_cat/_K_m = 5457  M-1 min-1 for D4 and estimated _k_cat/_K_m = 1190  M-1 min-1 for F-NAD+; Fig. 4F) suggested that DTX3L-RD exhibited 4.5-fold higher catalytic efficiency for D4 than F-NAD+. This difference primarily results from a better _K_m value for D4 compared to F-NAD+. Although DTX3L-RD showed weak _K_m for F-NAD+, it displays a higher rate for converting F-NAD+ to Ub-F-NAD+ at higher substrate concentration (Fig. 4F). Thus, substrate concentration will play a role in determining the preference.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Writing/technical points: 

(1) The introduction is relatively complex and the last paragraph, which reviews the discoveries on the paper, is long. It may be helpful to highlight the significance and frame the experiments as what they have addressed, rather than detailing each set of experiments completed. 

We have modified the last paragraph in the introduction to highlight the major discovery of our work.

(2) Line 24, Abstract. 'Its N-terminal region' is not obvious 

We have changed “Its N-terminal region” to “the N-terminal region of DTX3L”.

(3) Line 44 - split sentence to emphasize E3 ligase point? 

We have modified the sentence as suggested.

(4) Figures 1B and 1C could be larger - currently they are not very helpful. Also atoms (ADPr?) are shown, but not indicated in the legend or labelled on the panel. 

We have enlarged Figures 1B and 1C and indicated RNA on the structure.

(5) The structure of the D2 domain of DTX3L has recently been reported (Vela-Rodriguez et al). It might be helpful to comment on this manuscript. 

We have now commented on D2 domain in the results section and in the discussion.

(6) It would be helpful to indicate the DTX3L constructs used in Figure 1a. 

We have included all DTX3L constructs used in Figure 1a.

(7) Interpretation of Figure 4A is difficult, the authors may wish to consider other ways to visualize the data. 

We have now removed the black arrow in Figure 4A as it was confusing. Instead, we drew a black box on the cross-peak where the close-up views are shown in Figures 4B and 4C.

(8) Figure 4A. Please indicate which binding partner is highlighted by red/black arrows. 

We have removed black arrow. The red arrows indicate cross-peaks which undergo chemical shift perturbation when DTX3L-RD was titrated with ssDNA or ADPr, highlighting their binding sites on DTX3L-RD overlap.

(9) Line 284 - please indicate the bulge in Figure S3. 

We have indicated the bulge on Figure S3.

(10) Aspects of the discussion are speculative, given that evidence of Ub conjugated to nucleotides in cells is yet to be obtained and the functional consequences of modification are uncertain. 

We understand that the discussion on the potential roles of ubiquitination of ssNAs is speculative. We have now modified it to: “Based on the known functions of the DTX3L/PARP9 complex and the findings of this study, we propose several hypotheses for future research”, so that readers will understand that these are speculative.

(11) Line 295 onwards - this paragraph discusses the role of the KH domains in nucleotide binding, but it is not clear that the authors have directly demonstrated that the KH domains bind nucleotides as all constructs used in the binding experiments in Figure 1/S1 include the RING-DTC domains. 

We found that KH domains alone did not bind ssDNA or RNA. We have modified line 295. This section now reads “Typically, KH domains contain a GXXG motif within the loop between the first and second α helix (22). However, analysis of the sequence of the KHL domains in DTX3L shows these domains lack this motif. Multiple studies have shown that mutation in this motif abolishes binding to nucleic acids (23-26). Our findings show the DTX3L DTC domain binds nucleic acids but whether the KHL domains contribute to nucleic acid binding requires further investigation. Additionally, the structure of the first KHL domain was recently reported and shown to form a tetrameric assembly (20). Our analysis with DTX3L 232-C, which lacks the first KHL domain and RRM, indicate that it can still bind ssDNA and ssRNA. Despite this, a more detailed analysis will be required to determine whether oligomerization plays a role in nucleic acid binding and ubiquitination.”

Reviewer #1 (Public review):

This study describes a useful antibody-free method to map G-quadruplexes in vertebrate cells. The analysis of the data is solid but it remains primarily descriptive and does not substantially add to existing publications (such as PMID:34792172 for example). Nevertheless, the datasets generated here might constitute a good starting point for more functional studies.

Comments on revised version:

It is disappointing to see that the authors decided to brush aside most of the comments made by the three referees, even though these comments were largely consistent with each other. As a result, the revised manuscript is not substantially changed or improved. Legitimate concerns regarding the specificity of the Cut&Tag signals were not addressed and therefore remain. The sensitivity of the HBD-seq signals to a combination of RNase A and RNase H does not demonstrate that HBD-seq specifically reports the presence of RNA:DNA hybrids. The new Figure 9 comparing HepG4-seq to existing datasets does not unequivocally demonstrate the superiority of the Hemin-based strategy to map G4s.

Reviewer #3 (Public review):

Summary:

The authors developed and optimized the methods for detecting G4s and R-loops independent of BG4 and S9.6 antibody, and mapped genomic native G4s and R-loops by HepG4-seq and HBD-seq, revealing that co-localized G4s and R-loops participate in regulating transcription and affecting the self-renewal and differentiation capabilities of mESCs.

Strengths:

By utilizing the peroxidase activity of G4-hemin complex and combining proximity labeling technology, the authors developed HepG4-seq (high throughput sequencing of hemin-induced proximal labelled G4s) , which can detect the dynamics of G4s in vivo. Meanwhile, the "GST-His6-2xHBD"-mediated CUT&Tag protocol (Wang et al., 2021) was optimized by replacing fusion protein and tag, the optimized HBD-seq avoids the generation of GST fusion protein aggregates and can reflect the genome-wide distribution of R-loops in vivo.

The authors employed HepG4-seq and HBD-seq to establish comprehensive maps of native co-localized G4s and R-loops in human HEK293 cells and mouse embryonic stem cells (mESCs). The data indicate that co-localized G4s and R-loops are dynamically altered in a cell type-dependent manner and are largely localized at active promoters and enhancers of transcriptional active genes.

Combined with Dhx9 ChIP-seq and co-localized G4s and R-loops data in wild-type and dhx9KO mESCs, the authors found that the helicase Dhx9, a major regulator of co-localized G4s and R-loops, affects the self-renewal and differentiation capacities of mESCs.

In conclusion, the authors provide an approach to study the interplay between G4s and R-loops, shedding light on the important roles of co-localized G4s and R-loops in development and disease by regulating the transcription of related genes.

Weaknesses:

As we know, there are at least two structure data of S9.6 antibody very recently, and the questions about the specificity of the S9.6 antibody on RNA:DNA hybrids should be finished. The authors referred (Hartono et al., 2018; Konig et al., 2017; Phillips et al., 2013) need to be updated, and the author's bias against S9.6 antibodies needs also to be changed. In contrast to S9.6 CUT&Tag and other inactive ribonucleotide H1-based methods including MapR (inactive ribonucleotide H1-mediated CUT&Run) (Yan et al., 2019)and GST-2xHBD CUT&Tag (Wang et al., 2021), HBD-seq did not perform satisfactorily and its binding specificity was questionable.

Although HepG4-seq is an effective G4s detection technique, and the authors have also verified its reliability to some extent, given the strong link between ROS homeostasis and G4s formation, hemin's affinity for different types of G4s and their differences in peroxidase activities, whether HepG4-seq reflects the dynamics of G4s in vivo more accurately than existing detection techniques still needs to be more carefully corroborated.

The authors focus on the interaction of non-B DNA structures G4s and R-loops and their roles in development and disease by regulating the transcription of related genes. Compared to the complex regulatory network of G4s and R-loops, the authors provide limited mechanistic insight into the major regulator of co-localized G4s and R-loops, helicase Dhx9. However, the authors propose that "A degron system-mediated simultaneous and/or stepwise degradation system of multiple regulators will help us elucidate the interplaying effects between G4s and R-loops." is attractive. The main innovations of this article are the proposal of new antibody-independent methods for detecting G4s and the optimization of the GST-2xHBD CUT&Tag (Wang et al., 2021) method for detecting R-loops. Unfortunately, however, the reliability and accuracy of these methods are still debatable, and the reference value of the G4s and R-loops datasets based on these methods is relatively limited.

Author response:

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

eLife assessment

This useful study describes an antibody-free method to map G-quadruplexes (G4s) in vertebrate cells. While the method might have potential, the current analysis is primarily descriptive and does not add substantial new insights beyond existing data (e.g., PMID:34792172). While the datasets provided might constitute a good starting point for future functional studies, additional data and analyses would be needed to fully support the major conclusions and, at the same time, clarify the advantage of this method over other methods. Specifically, the strength of the evidence for DHX9 interfering with the ability of mESCs to differentiate by regulating directly the stability of either G4s or R-loops is still incomplete.

We thank the editors for their helpful comments.

Given that antibody-based methods have been reported to leave open the possibility of recognizing partially folded G4s and promoting their folding, we have employed the peroxidase activity of the G4-hemin complex to develop a new method for capturing endogenous G4s that significantly reduces the risk of capturing partially folded G4s. We have included a new Fig. 9 and a new section “Comparisons of HepG4-seq and HBD-seq with previous methods” to carefully compare our methods to other methods.

In the Fig. 7, we applied the Dhx9 CUT&Tag assay to identify the G4s and R-loops directly bound by Dhx9 and further characterized the differential Dhx9-bound G4s and R-loops in the absence of Dhx9. Dhx9 is a versatile helicase capable of directly resolving R-loops and G4s or promoting R-loop formation (PMID: 21561811, 30341290, 29742442, 32541651, 35905379, 34316718). Furthermore, we showed that depletion of Dhx9 significantly altered the levels of G4s or R-loops around the TSS or gene bodies of several key regulators of mESC and embryonic development, such as Nanog, Lin28a, Bmp4, Wnt8a, Gata2, and Lef1, and also their RNA levels (Fig.7 I). The above evidence is sufficient to support the transcriptional regulation of mESCs cell fate by directly modulating the G4s or R-loops within the key regulators of mESCs.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Non-B DNA structures such as G4s and R-loops have the potential to impact genome stability, gene transcription, and cell differentiation. This study investigates the distribution of G4s and R-loops in human and mouse cells using some interesting technical modifications of existing Tn5-based approaches. This work confirms that the helicase DHX9 could regulate the formation and/or stability of both structures in mouse embryonic stem cells (mESCs). It also provides evidence that the lack of DHX9 in mESCs interferes with their ability to differentiate.

Strengths:

HepG4-seq, the new antibody-free strategy to map G4s based on the ability of Hemin to act as a peroxidase when complexed to G4s, is interesting. This study also provides more evidence that the distribution pattern of G4s and R-loops might vary substantially from one cell type to another.

We appreciate your valuable points.

Weaknesses:

This study is essentially descriptive and does not provide conclusive evidence that lack of DHX9 does interfere with the ability of mESCs to differentiate by regulating directly the stability of either G4 or R-loops. In the end, it does not substantially improve our understanding of DHX9's mode of action.

In this study, we aimed to report new methods for capturing endogenous G4s and R-loops in living cells. Dhx9 has been reported to directly unwind R-loops and G4s or promote R-loop formation (PMID: 21561811, 30341290, 29742442, 32541651, 35905379, 34316718). To understand the direct Dhx9-bound G4s and R-loops, we performed the Dhx9 CUT&Tag assay and analyzed the co-localization of Dhx9-binding sites and G4s or R-loops. We found that 47,857 co-localized G4s and R-loops are directly bound by Dhx9 in the wild-type mESCs and 4,060 of them display significantly differential signals in absence of Dhx9, suggesting that redundant regulators exist as well. We showed that depletion of Dhx9 significantly altered the RNA levels of several key regulators of mESC and embryonic development, such as Nanog, Lin28a, Bmp4, Wnt8a, Gata2, and Lef1, which coincides with the significantly differential levels of G4s or R-loops around the TSS or gene bodies of these genes (Fig.7). The comprehensive molecular mechanism of Dhx9 action is indeed not the focus of this study. We will work on it in the future studies. Thank you for the comments.

There is no in-depth comparison of the newly generated data with existing datasets and no rigorous control was presented to test the specificity of the hemin-G4 interaction (a lot of the hemin-dependent signal seems to occur in the cytoplasm, which is unexpected).

The specificity of hemin-G4-induced peroxidase activity and self-biotinylation has been well demonstrated in previous studies (PMID: 19618960, 22106035, 28973477, 32329781). In the Fig.1A, we compared the hemin-G4-induced biotinylation levels in different conditions. Cells treated with hemin and Bio-An exhibited a robust fluorescence signal, while the absence of either hemin or Bio-An almost completely abolished the biotinylation signals, suggesting a specific and active biotinylation activity. To identify the specific signals, we have included the non-label control and used this control to call confident HepG4 peaks in all HepG4-seq assays.

The hemin-RNA G4 complex has also been reported to have mimic peroxidase activity and trigger similar self-biotinylation signals as DNA G4s (PMID: 32329781, 31257395, 27422869). Therefore, it is not surprising to observe hemin-dependent signals in the cytoplasm generated by cytoplasmic RNA G4s.

In the revised version, we have included a new Fig. 9 and a new section “Comparisons of HepG4-seq and HBD-seq with previous methods” to carefully compare our methods to other methods.

The authors talk about co-occurrence between G4 and R-loops but their data does not actually demonstrate co-occurrence in time. If the same loci could form alternatively either R-loops or G4 and if DHX9 was somehow involved in determining the balance between G4s and R-loops, the authors would probably obtain the same distribution pattern. To manipulate R-loop levels in vivo and test how this affects HEPG4-seq signals would have been helpful.

Single-molecule fluorescence studies have shown the existence of a positive feedback mechanism of G4 and R-loop formation during transcription (PMID: 32810236, 32636376), suggesting that G4s and Rloops could co-localize at the same molecule. Dhx9 is a versatile helicase capable of directly resolving R-loops and G4s or promoting R-loop formation (PMID: 21561811, 30341290, 29742442, 32541651, 35905379, 34316718). Although depletion of Dhx9 resulted in 6,171 Dhx9-bound co-localized G4s and R-loops with significantly altered levels of G4s or R-loops, only 276 of them (~4.5%) harbored altered G4s and R-loops, suggesting that the interacting G4s and R-loops are rare in living cells. Nowadays, the genome-wide co-occurrence of two factors are mainly obtained by bioinformatically intersection analysis. We agreed that F We will carefully discuss this point in the revised version. At the same time, we will make efforts to develop a new method to map the co-localized G4 and R-loop in the same molecule in the future study.

This study relies exclusively on Tn5-based mapping strategies. This is a problem as global changes in DNA accessibility might strongly skew the results. It is unclear at this stage whether the lack of DHX9, BLM, or WRN has an impact on DNA accessibility, which might underlie the differences that were observed. Moreover, Tn5 cleaves DNA at a nearby accessible site, which might be at an unknown distance away from the site of interest. The spatial accuracy of Tn5-based methods is therefore debatable, which is a problem when trying to demonstrate spatial co-occurrence. Alternative mapping methods would have been helpful.

In this study, we used the recombinant streptavidin monomer and anti-GP41 nanobody fusion protein (mSA-scFv) to specifically recognize hemin-G4-induced biotinylated G4 and then recruit the recombinant GP41-tagged Tn5 protein to these G4s sites. Similarly, the recombinant V5-tagged N-terminal hybrid-binding domain (HBD) of RNase H1 specifically recognizes R-loops and recruit the recombinant protein G-Tn5 (pG-Tn5) with the help of anti-V5 antibody. Therefore, the spatial distance of Tn5 to the target sites is well controlled and very short, and also the recruitment of Tn5 is specifically determined by the existence of G4s in HepG4-seq and R-loops in HBD-seq. In addition, RNase treatment markedly abolished the HBD-seq signals and the non-labeled controls exhibit obviously reduction of HepG4-seq signals, demonstrating that HBD-seq and HepG4-seq were not contamination from tagmentation of asccessible DNA.

Reviewer #2 (Public Review):

Summary:

In this study, Liu et al. explore the interplay between G-quadruplexes (G4s) and R-loops. The authors developed novel techniques, HepG4-seq and HBD-seq, to capture and map these nucleic acid structures genome-wide in human HEK293 cells and mouse embryonic stem cells (mESCs). They identified dynamic, cell-type-specific distributions of co-localized G4s and R-loops, which predominantly localize at active promoters and enhancers of transcriptionally active genes. Furthermore, they assessed the role of helicase Dhx9 in regulating these structures and their impact on gene expression and cellular functions.

The manuscript provides a detailed catalogue of the genome-wide distribution of G4s and R-loops. However, the conceptual advance and the physiological relevance of the findings are not obvious. Overall, the impact of the work on the field is limited to the utility of the presented methods and datasets.

Strengths:

(1) The development and optimization of HepG4-seq and HBD-seq offer novel methods to map native G4s and R-loops.

(2) The study provides extensive data on the distribution of G4s and R-loops, highlighting their co-localization in human and mouse cells.

(3) The study consolidates the role of Dhx9 in modulating these structures and explores its impact on mESC self-renewal and differentiation.

We appreciate your valuable points.

Weaknesses:

(1) The specificity of the biotinylation process and potential off-target effects are not addressed. The authors should provide more data to validate the specificity of the G4-hemin.

The specificity of hemin-G4-induced peroxidase activity and self-biotinylation has been well demonstrated in previous studies (PMID: 19618960, 22106035, 28973477, 32329781). In the Fig.1A, we compared the hemin-G4-induced biotinylation levels in different conditions. Cells treated with hemin and Bio-An exhibited a robust fluorescence signal, while the absence of either hemin or Bio-An almost completely abolished the biotinylation signals, suggesting a specific and active biotinylation activity.

(2) Other methods exploring a catalytic dead RNAseH or the HBD to pull down R-loops have been described before. The superior quality of the presented methods in comparison to existing ones is not established. A clear comparison with other methods (BG4 CUT&Tag-seq, DRIP-seq, R-CHIP, etc) should be provided.

Thank you for the suggestions. We have included a new Fig. 9 and a new section “Comparisons of HepG4-seq and HBD-seq with previous methods” to carefully compare our methods to other methods.

(3) Although the study demonstrates Dhx9's role in regulating co-localized G4s and R-loops, additional functional experiments (e.g., rescue experiments) are needed to confirm these findings.

Dhx9 has been demonstrate as a versatile helicase capable of directly resolving R-loops and G4s or promoting R-loop formation in previous studies (PMID: 21561811, 30341290, 29742442, 32541651, 35905379, 34316718). We believe that the current new dataset and previous studies are enough to support the capability of Dhx9 in regulating co-localized G4s and R-loops.

(4) The manuscript would benefit from a more detailed discussion of the broader implications of co-localized G4s and R-loops.

Thank you for the suggestions. We have included the discussion in the revised version.

(5) The manuscript lacks appropriate statistical analyses to support the major conclusions.

We apologized for this point. Whereas we have applied careful statistical analyses in this study, lacking of some statistical details make people hard to understand some conclusions. We have carefully added details of all statistical analysis.

(6) The discussion could be expanded to address potential limitations and alternative explanations for the results.

Thank you for the suggestions. We have included the discussion about this point in the revised version.

Reviewer #3 (Public Review):

Summary:

The authors developed and optimized the methods for detecting G4s and R-loops independent of BG4 and S9.6 antibody, and mapped genomic native G4s and R-loops by HepG4-seq and HBD-seq, revealing that co-localized G4s and R-loops participate in regulating transcription and affecting the self-renewal and differentiation capabilities of mESCs.

Strengths:

By utilizing the peroxidase activity of G4-hemin complex and combining proximity labeling technology, the authors developed HepG4-seq (high throughput sequencing of hemin-induced proximal labelled G4s), which can detect the dynamics of G4s in vivo. Meanwhile, the "GST-His6-2xHBD"-mediated CUT&Tag protocol (Wang et al., 2021) was optimized by replacing fusion protein and tag, the optimized HBD-seq avoids the generation of GST fusion protein aggregates and can reflect the genome-wide distribution of R-loops in vivo.

The authors employed HepG4-seq and HBD-seq to establish comprehensive maps of native co-localized G4s and R-loops in human HEK293 cells and mouse embryonic stem cells (mESCs). The data indicate that co-localized G4s and R-loops are dynamically altered in a cell type-dependent manner and are largely localized at active promoters and enhancers of transcriptionally active genes.

Combined with Dhx9 ChIP-seq and co-localized G4s and R-loops data in wild-type and dhx9KO mESCs, the authors confirm that the helicase Dhx9 is a direct and major regulator that regulates the formation and resolution of co-localized G4s and R-loops.

Depletion of Dhx9 impaired the self-renewal and differentiation capacities of mESCs by altering the transcription of co-localized G4s and R-loops-associated genes.

In conclusion, the authors provide an approach to studying the interplay between G4s and R-loops, shedding light on the important roles of co-localized G4s and R-loops in development and disease by regulating the transcription of related genes.

We appreciate your valuable points.

Weaknesses:

As we know, there are at least two structure data of S9.6 antibody very recently, and the questions about the specificity of the S9.6 antibody on RNA:DNA hybrids should be finished. The authors referred to (Hartono et al., 2018; Konig et al., 2017; Phillips et al., 2013) need to be updated, and the authors' bias against S9.6 antibodies needs also to be changed. However, as the authors had questioned the specificity of the S9.6 antibody, they should compare it in parallel with the data they have and the data generated by the widely used S9.6 antibody.

Thank you for the updating information about the structure data of S9.6 antibody. We politely disagree the specificity of the S9.6 antibody on RNA:DNA hybrids. The structural studies of S9.6 (PMID: 35347133, 35550870) used only one RNA:DNA hybrid to show the superior specificity of S9.6 on RNA:DNA hybrid than dsRNA and dsDNA. However, Fabian K. et al has reported that the binding affinities of S9.6 on RNA:DNA hybrid exhibits obvious sequence-dependent bias from null to nanomolar range (PMID: 28594954). We have included the comparison between S9.6-derived data and our HBD-seq data in the Fig.9 and the section “Comparisons of HepG4-seq and HBD-seq with previous methods”.

Although HepG4-seq is an effective G4s detection technique, and the authors have also verified its reliability to some extent, given the strong link between ROS homeostasis and G4s formation, and hemin's affinity for different types of G4s, whether HepG4-seq reflects the dynamics of G4s in vivo more accurately than existing detection techniques still needs to be more carefully corroborated.

Thank you for pointing out this issue. In the in vitro hemin-G4 induced self-biotinylation assay, parallel G4s exhibit higher peroxidase activities than anti-parallel G4s. Thus, the dynamics of G4 conformation could affect the HepG4-seq signals (PMID: 32329781). In the future, people may need to combine HepG4-seq and BG4s-eq to carefully explain the endogenous G4s. We have discussed this point in the revised version.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Figures 1A&1G. Although no merge images were provided, it seems that the biotin signals are strongly enriched outside the nucleus. This suggests that hemin is not specific for G4s in DNA. Does it mean that Hemin can also recognise G4 on RNAs? How do the authors understand the cytoplasmic signal?

Hemin indeed could interact with RNA G4 to obtain the peroxidase activity like DNA G4-hemin complex (PMID: 27422869, 32329781, 31257395). The cytoplasmic signals in Figure 1A&1G were derived from RNA G4.

Figure 1A: The fact that there is no Alexa647 signal without hemin or Bio-An does not actually demonstrate that the signals are specific. These controls do not actually test for the specificity of the G4-Hemin interaction.

The specificity of hemin-G4-induced peroxidase activity and self-biotinylation has been well demonstrated in previous studies (PMID: 19618960, 22106035, 28973477, 32329781). In this study, we performed the IF to confirm this phenomena.

Figure 1C: It looks like the HepG4-seq signals are simply an amplification of the noise given by the Tn5 (the non-label ctrl has the same pattern, albeit weaker). It is unclear why this happens but it might happen if somehow hemin increased the probability that the Tn5 is close to chromatin in an unspecific manner (it would cut G-rich, nucleosome-poor, accessible sites in an unspecific manner). To discard this possibility, it would be interesting to investigate directly which loci are biotinylated. For this, the authors could extract and sonicate the genomic DNA and use streptavidin to enrich for biotinylated fragments. Strand-specific DNA sequencing could then be used to map the biotinylated loci.

In the cell culture medium, there were a certain amount of hemin from serum and a low dosage of biotin from the basal medium DMEM, which could not be avoid. Thus, these contaminated hemin and biotin would generate the background signals observed in the Non-label control samples. The biotinylated sites were specifically recognized by the recombinant Streptavidin monomer which further recruits Tn5 to the biotinylated sites with the help of Moon-tag. Different from the signals in the HEK293 samples, a much more robust HepG4-seq signals were observed in the mESC samples and the signals were also abolished in the non-label control samples. Thus, the relatively small signal-to-noise ratio in the HEK293 samples suggest the week abundance of endogenous G4s in the HEK293 cells. Thus, we politely disagree that hemin increased the non-specific recruitment of Th5. In addition, the CUT&Tag technology has been wildly demonstrated to have a much lower background, high signal-to-noise ratio and high sensitivity. Thus, we also politely disagree to replace the CUT&Tag with the traditional DNA library preparation method.

Figure 1H: No spike-in was added and the data are not quantitative. The number of replicates is unclear. 70000 extra peaks (10x) after inhibition of BLM or WRN seems enormous. These extra peaks should be better characterised: do they contain G4 motifs? Are they transcribed? etc...; again what kind of controls should be used here, in case the inhibition of BLP and WRN has a global impact on chromatin accessibility?

To quantitatively compare different samples, we have normalized all samples according their de-duplicated uniquely mapping reads numbers. Given that the inhibitors were dissolved in the DMSO, we used the DMSO as the control. Since the Tn5 were specifically recruited the biotinylated G4 sites through the recombinant Streptavidin monomer protein and the moon tag system, the chromatin accessibility will not affect the Tn5, which were normally observed in the ATAT-seq.

As suggested, we have analyzed the enriched motifs of the extra peaks induced by BLM or WRN inhibition and showed that the top enriched motifs are also G-rich in the supplementary Fig.1E. In addition, we analyzed the RNA-seq levels of genes-associated with these extra peaks. As shown in the figure below, the majority of these genes are actively transcribed.

Author response image 1.

Figure 2: The mutated version of HBD should have been used as a control. As shown clearly in PMID: 37819055, the HBD domain does interact in an unspecific manner with chromatin at low levels. As above, this might be enough to increase the local concentration of the Tn5 close to chromatin in the Cut&Tag approach and to cleave accessible sites close to TSS in an unspecific manner.

As shown in Fig.2B and Fig.4A, we have included the RNase treatment as the control and showed that the HBD-seq-identified R-loops signals are dramatically attenuated (Fig.2B) or almost completely abolished after the RNase treatment (Fig.4A). These data demonstrate the specificity of HBD-seq.

Figure 2: What fraction of the HEPG4-seq signal is sensitive to RNase treatment? The authors used a combination of RNase A and RNase H but previous data have shown that the RNase A treatment is sufficient to remove the HBD-seq signal (which means that it is not actually possible on this sole basis to claim or disclaim that the signals do correspond to genuine R-loops). Do the authors have evidence that the RNase H treatment alone does impact their HBD-seq or HEPG4-seq signals?

As shown in Fig.2B and Fig.4A, the HBD-seq-identified R-loops signals are all dramatically attenuated (Fig.2B) or almost completely abolished after the RNase treatment (Fig.4A). The specificity of HBD on recognizing R-loops has been carefully demonstrated in the previous study (PMID: 33597247). In this study, we used the same two copies of HBD (2xHBD) and replaced the GST tag to EGFP-V5 to reduce the possibility of variable high molecular-weight aggregates caused by GST tag. In addition, RNase H treatment has been shown to fail to completely abolish the CUT&Tag signals since a subset of DNA-RNA hybrids with high GC skew are partially resistant to RNase H (PMID: 32544226, 33597247). In consideration of the high GC skew of co-localized G4s and R-loops, we combined the RNase A and RNase H. We currently did not have the RNaseH alone samples.

Figure 3A: "RNA-seq analysis revealed that the RNA levels of co-localized G4s and R-loops-associated genes are significantly higher": the differences are not very convincing.

In the Figure 3A, we have performed the Mann-Whitney test to examine the significance in the revised manuscript. RNA levels of co-localized G4s and R-loops-associated genes are indeed significantly higher than all genes, G4s or R-loops- associated genes with the Mann-Whitney test p < 2.2E-16.

Figure 3B: the patterns for "G4" and "co-localised G4 and R-loop" are extremely similar, suggesting that nearly all G4s mapped here could also form R-loops. If this is the case, most of the HEPG4-seq signals should be sensitive to exogenous RNase H treatment or to the in vivo over-expression of RNase H1. This should be tested (see above).

The percentage of co-localized G4 and R-loop in G4 peaks is 80.3% ( 5,459 out of 6,799) in HEK293 cells and 72.0% (68,482 out of 95,128) in mESC cells, respectively. The co-localization does not mean that G4 and R-loop interact with each other. We have showed that only small proportion of co-localized G4s and R-loops displayed differential G4s and R-loops at the same time in the dhx9KO mESCs (Fig. 6D, Supplementary Fig. 3B), suggesting that the majority of co-localized G4s and R-loops do not interact with each other. Thus, we thought that it is not necessary to perform the RNase H test.

Figure 3C: there is no correlation between the FC of G4 and the FC of RNA; this is not really consistent with the idea that the stabilisation of G4 is the driver rather than a consequence of the transcriptional changes.

Given that the treatment of WRN or BLM inhibition induced a large mount of G4 accumulation (Fig.1H-I), we examined the transcription effect on genes associated with these accumulated G4s in Fig.3C. We indeed observed the effect of G4 accumulation on transcription of G4-associated genes. Given that G4 stabilization triggers the transcriptional changes, it does not mean that the transcriptional changes should be highly correlated with the increase levels of G4s. To our knowledge, we have not observed this type of connections in the previous studies. 

l279: the overlap with H3K4me1 is really not convincing.

For all G4 peaks, the signals of H3K4me1 indeed exhibit a high background around the center of G4 peaks but we still could observe a clear peak in the center.

Figure 5C: it should be clearly indicated here that the authors compare Cut&Tag and ChIP data. The origin of the ChIP-seq data is also unclear and should be indicated.

Thank you for the suggestions. We have clarified this point.

For the ChIP data, we have described the origin of ChIP-seq data in the “Data availability” section as below: “The ChIP-seq data of histone markers and RNAP are openly available in GNomEx database (accession number 44R) (Wamstad et al., 2012).”

Reviewer #2 (Recommendations For The Authors):

(1) Figure 1A. An experimental condition lacking H2O2 (-H2O2) should be included.

We have added this control in Fig.1A

(2) Does RNAse H affect G4 profiles?

We have not tested the effect of RNase H on G4 forming. However, we have showed that only small proportion of co-localized G4s and R-loops displayed differential G4s and R-loops at the same time in the dhx9KO mESCs (Fig. 6D, Supplementary Fig. 3B), suggesting that the majority of co-localized G4s and R-loops do not interact with each other. Thus, we thought that it is not necessary to perform the RNase H test on G4. In addition, to treat cells wit RNase H, we have to permeabilize cells first to let RNase H enter the nuclei. If so, we will lose the pictures of endogenous G4s.

(3) Figure 2G. R-loops are detected upstream of the KPNB1 gene. What is this region? Is it transcribed?

We are so sorry to make a mistake when we prepared this figure. We have change it to the correct one in Fig. 2G. The R-loop is around the TSS of KPNB1. We also showed the RNA-seq data in this region in Author response image 2 below. This region is indeed transcribed.

Author response image 2.

(4) Did BLM and WRN inhibition specifically affect the expression of genes containing colocalized G4s and R-loops? Was the effect seen in other genes as well? Appropriate statistical analyses are needed.

In the Fig.3, we have shown that the accumulation of co-localized G4 and R-loops induced by the inhibition of BLM or WRN significantly caused the changes of genes (480 in BLM inhibition, 566 in WRN inhibition) containing these structures most of which are localized at the promoter-TSS regions. We indeed detected the effect in other genes as well. There were 918 and 1020 genes with significantly changes (padjust <0.05 & FC >=2 or FC <=0.5) in BLM and WRN inhibition, respectively.

(5) The claim that "The co-localized G4s and R-loops-mediated transcriptional regulation in HEK293 cells" (title of Figure 3) is not supported by the presented data. A causality link is not established in this study, which only reports correlations between G4s/R-loops and transcription regulation.

We politely disagree with this point. BLM and WRN are the best characterized DNA G4-resolving helicase ((Fry and Loeb, 1999; Mendoza et al., 2016; Mohaghegh et al., 2001). Here, we used the selective small molecules to specifically inhibit their ATPase activity and observed dramatical induction of G4 accumulation. Notably, the accumulated G4s that trigger the transcriptional changes are mainly located at the promoter-TSS region. If the transcriptional changes trigger the G4 accumulations, we should not observe such a biased distribution and more accumulated G4s should be detected in the gene body.

(6) The effect of Dhx9 KO on colocalized G4s/R-loops and transcription is not clear. The suggestion that Dhx9 could regulate transcription by modulating G4s, R-loops, and co-localized G4s and R-loops is not supported by the presented data. Additional experiments and statistical analyses are needed to conclude the role of Dhx9 on colocalized G4s/Rloops and transcription.

Dhx9 has been extensively studied and reported to directly unwind R-loops and G4s or promote R-loop formation (PMID: 21561811, 30341290, 29742442, 32541651, 35905379, 34316718). Thus, it is not necessary to repeat these assays again. To understand the direct Dhx9-bound G4s and R-loops, we performed the Dhx9 CUT&Tag assay and analyzed the co-localization of Dhx9-binding sites and G4s or R-loops. 47,857 co-localized G4s and R-loops are directly bound by Dhx9 in the wild-type mESCs and 4,060 of them display significantly differential signals in absence of Dhx9, suggesting that redundant regulators exist as well. These data have clearly shown the roles of Dhx9 directly modulating the stabilities of G4s and R-loops. Furthermore, we showed that loss of Dhx9 caused 816 Dhx9 directly bound colocalized G4 and R-loop associated genes significantly differentially expressed, supporting the transcriptional regulation of Dhx9. We performed the differential analysis following the standard pipeline: DESeq2 for RNA-seq and DiffBind for HepG4-seq and HBD-seq. The statistical details have been described in the figure legends.

(7) The conclusion that Dhx9 regulates the self-renewal and differentiation capacities of mESCs is vague. Additional experiments are needed to elucidate the exact contribution of Dhx9.

In this study, we aimed to report new methods for capturing endogenous G4s and R-loops in living cells. In this study, we have shown that depletion of Dhx9 significantly attenuated the proliferation of the mESCs and also influenced the capacity of mESCs differentiation into three germline lineages during the EB assay. In addition, we showed that depletion of Dhx9 significantly reduced the protein levels of mESCs pluripotent markers Nanog and Lin28a. The comprehensive molecular mechanism of Dhx9 action is indeed not the focus of this study. We will work on it in the future studies. Thank you for the comments.

Reviewer #3 (Recommendations For The Authors):

The study on the involvement of native co-localized G4s and R-loops in transcriptional regulation further enriches the readers' understanding of genomic regulatory networks, and the functional dissection of Dhx9 also lays a good foundation for the study of the dynamic regulatory mechanisms of co-localized G4s and R-loops. Unfortunately, however, the authors lack a strong basis for questioning the widely used BG4 and S9.6 antibodies, and the co-localized G4s and R-loops sequencing data obtained by the developed and optimized method also lack parallel comparison with existing sequencing technologies, which cannot indicate that HepG4-seq and HBD-seq are more reliable and superior than BG4 and S9.6 antibody-based sequencing technologies. There are also some minor errors in the manuscript that need to be corrected.

Thank you for the constructive comments. We have added a new section (Comparisons of HepG4-seq and HBD-seq with previous methods) and a new figure 9 to parallelly compare our methods to other widely-used methods.

(1) This work mainly focuses on co-localized G4s and R-loops, but in the introduction section, the interplay between G4s and R-loops is only briefly mentioned. It is suggested that the importance of the interplay of G4s and R-loops for gene regulation should be further expanded to help readers better understand the significance of studying co-localized G4s and R-loops.

Thank you for the comments. The current studies about the interplay between G4s and R-loops are limited. We have summarized all we could find in the literatures.

(2) The authors mentioned that "a steady state equilibrium is generally set at low levels in living cells under physiological conditions (Miglietta et al., 2020) and thus the addition of high-affinity antibodies may pull the equilibrium towards folded states", in my understanding this is one of the important reasons why the authors optimized the G4s and R-loops detection assays, I wonder if there is a reliable basis for this statement. If there is, I suggest that the authors can supplement it in the manuscript.

The main reason we develop the new method is to develop an antibody-free method to label the endogenous G4s in living cells. We ever tried to capture endogenous G4s using the tet-on controlled BG4. Unfortunately, we found that even a short time induction of BG4 in living cells was toxic. The traditional antibody-based methos rely on permeabilizing cells first to let the antibodies enter the nuclei. In this case, it is easy to lost the physiological pictures of endogenous G4s. We will add more discussion about this point. For R-loops, we just further optimized the GST-2xHBD-mediated method to avoid the problem of GST-tag. GST-fusion proteins are prone to form variable high molecular-weight aggregates and these aggregates often undermine the reliability of the fusion proteins.

(3) Some questions about HepG4-seq:

Is there a difference in hemin affinity for intramolecular G quadruplexes, interstrand G quadruplexes, and their different topologies? If so, does this bias affect the accuracy of sequencing results based on G4-hemin complexes?

Thank you for pointing out this issue. In the in vitro hemin-G4 induced self-biotinylation assay, parallel G4s exhibit higher peroxidase activities than anti-parallel G4s (PMID: 32329781). Thus, the dynamics of G4 conformation possibly affect the HepG4-seq signals. In the future, people may need to combine HepG4-seq and BG4s-eq to carefully explain the endogenous G4s. We have discussed this point in the revised version.

HepG4-seq is based on proximity labeling and peroxidase activity of the G4-hemin complex. The authors tested and confirmed that the addition of hemin and Bio-An in the experiment had no significant influences on sequencing results, but the effect of exogenous H2O2 treatment may also need to be taken into account since ROS can mediate the formation of G4s.

For HepG4-seq protocol, we only treat cells with H2O2 for one minute. Thus, we thought that the side effect of H2O2 treatment should be limited in such a short time.

(4) As we know, there have been at least two structure data of the S9.6 antibody very recently, and the questions about the specificity of the S9.6 antibody on RNA:DNA hybrids should be finished. The authors referred to (Hartono et al., 2018; Konig et al., 2017; Phillips et al., 2013) need to be updated, and the author's bias against S9.6 antibodies needs also to be changed. However, as the authors had questioned the specificity of the S9.6 antibody, they should compare in parallel with the data they have and the data generated by the widely used S9.6 antibody.

Thank you for the updating information about the structure data of S9.6 antibody. We politely disagree the specificity of the S9.6 antibody on RNA:DNA hybrids. The structural studies of S9.6 (PMID: 35347133, 35550870) used only one RNA:DNA hybrid to show the superior specificity of S9.6 on RNA:DNA hybrid than dsRNA and dsDNA. However, Fabian K. et al has reported that the binding affinities of S9.6 on RNA:DNA hybrid exhibits obvious sequence-dependent bias from null to nanomolar range (PMID: 28594954). We have included the comparison between S9.6-derived data and our HBD-seq data in the Fig.9 and the section “Comparisons of HepG4-seq and HBD-seq with previous methods”.

(5) It is hoped that the results of immunofluorescence experiments can be statistically analyzed.

We have performed the statistical analysis and included the data in the new figure.

(6) Some minor errors:

Line 168, "G4-froming" should be "G4-forming";

Figure 5E, the color of the "Repressed" average signal at the top of the HepG4-seq heatmap should be blue;

Figure 7C, the abbreviation "Gloop" should be indicated in the text or in the figure caption.

Thank you for pointing out these issues. We are sorry for these mistakes. We have corrected them in the revised version.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The manuscript proposes an alternative method by SDS-PAGE calibration of Halo-Myo10 signals to quantify myosin molecules at specific subcellular locations, in this specific case filopodia, in epifluorescence datasets compared to the more laborious and troublesome single molecule approaches. Based on these preliminary estimates, the authors developed further their analysis and discussed different scenarios regarding myosin 10 working models to explain intracellular diffusion and targeting to filopodia. 

Strengths: 

I confirm my previous assessment. Overall, the paper is elegantly written and the data analysis is appropriately presented. Moreover, the novel experimental approach offers advantages to labs with limited access to high-end microscopy setups (super-resolution and/or EM in particular), and the authors proved its applicability to both fixed and live samples. 

Weaknesses: 

Myself and the other two reviewers pointed to the same weakness, the use of protein overexpression in U2OS. The authors claim that Myosin10 is not expressed by U2OS, based on Western blot analysis. Does this completely rule out the possibility that what they observed (the polarity of filopodia and the bulge accumulation of Myo10) could be an artefact of overexpression? I am afraid this still remains the main weakness of the paper, despite being properly acknowledged in the Limitations.

Respectfully, our observations do not capture an “artefact” of overexpression but rather the “response” to overexpression. Our goal in this project was to overexpress Myo10 in a situation where it is the limiting reagent for generating filopodia. As Reviewer 3 notes below, overexpression shows that filopodial tips “can accommodate a surprisingly (shockingly) large number of motors.” This is exactly the point. Reviewer 2 considered our handling of this issue to be a strength of the paper. As far as whether bulges occur in endogenous Myo10 systems, please see our comments to Reviewer 3. 

I consider all the remaining issues I expressed during the first revision solved. 

Reviewer #2 (Public Review): 

Summary: 

The paper sought to determine the number of myosin 10 molecules per cell and localized to filopodia, where they are known to be involved in formation, transport within, and dynamics of these important actin-based protrusions. The authors used a novel method to determine the number of molecules per cell. First, they expressed HALO tagged Myo10 in U20S cells and generated cell lysates of a certain number of cells and detected Myo10 after SDS-PAGE, with fluorescence and a stained free method. They used a purified HALO tagged standard protein to generate a standard curve which allowed for determining Myo10 concentration in cell lysates and thus an estimate of the number of Myo10 molecules per cell. They also examined the fluorescence intensity in fixed cell images to determine the average fluorescence intensity per Myo10 molecule, which allowed the number of Myo10 molecules per region of the cell to be determined. They found a relatively small fraction of Myo10 (6%) localizes to filopodia. There are hundreds of Myo10 in each filopodia, which suggests some filopodia have more Myo10 than actin binding sites. Thus, there may be crowding of Myo10 at the tips, which could impact transport, the morphology at the tips, and dynamics of the protrusions themselves. Overall, the study forms the basis for a novel technique to estimate the number of molecules per cell and their localization to actin-based structures. The implications are broad also for being able to understand the role of myosins in actin protrusions, which is important for cancer metastasis and wound healing. 

Strengths: 

The paper addresses an important fundamental biological question about how many molecular motors are localized to a specific cellular compartment and how that may relate to other aspects of the compartment such as the actin cytoskeleton and the membrane. The paper demonstrates a method of estimating the number of myosin molecules per cell using the fluorescently labeled HALO tag and SDS-PAGE analysis. There are several important conclusions from this work in that it estimates the number of Myo10 molecules localized to different regions of the filopodia and the minimum number required for filopodia formation. The authors also establish a correlation between number of Myo10 molecules filopodia localized and the number of filopodia in the cell. There is only a small % of Myo10 that tip localized relative to the total amount in the cell, suggesting Myo10 have to be activated to enter the filopodia compartment. The localization of Myo10 is log-normal, which suggests a clustering of Myo10 is a feature of this motor. 

One of the main critiques of the manuscript was that the results were derived from experiments with overexpressed Myo10 and therefore are hard to extrapolate to physiological conditions. The authors counter this critique with the argument that their results provide insight into a system in which Myo10 is a limiting factor for controlling filopodia formation. They demonstrate that U20S cells do not express detectable levels of Myo10 (supplementary Figure 1E) and thus introducing Myo10 expression demonstrates how triggering Myo10 expression impacts filopodia. An example is given how melanoma cells often heavily upregulate Myo10. 

In addition, the revised manuscript addresses the concerns about the method to quantitate the number of Myo10 molecules per cell and therefore puncta in the cell. The authors have now made a good faith effort to correct for incomplete labeling of the HALO tag (Figure 2A-C, supplementary Figure 2D-E). The authors also address the concerns about variability in transfection efficiency (Figure 1D-E). 

A very interesting addition to the revised manuscript was the quantitation of the number of Myo10 molecules present during an initiation event when a newly formed filopodia just starts to elongate from the plasma membrane. They conclude that 100s of Myo10 molecules are present during an initiation event. They also examined other live cell imaging events in which growth occurs from a stable filopodia tip and correlated with elongation rates. 

Weaknesses: 

The authors acknowledge that a limitation of the study is that all of the experiments were performed with overexpressed Myo10. They address this limitation in the discussion but also provide important comparisons for how their work relates to physiological conditions, such as melanoma cells that only express large amounts of Myo10 when they are metastatic. Also, the speculation about how fascin can outcompete Myo10 should include a mechanism for how the physiological levels of fascin can complete with the overabundance of Myo10 (page 10, lines 401-408). 

We have expanded the discussion about fascin competing with high concentrations of Myo10 in filopodial tips on pg. 15. The key feature is that fascin binding in a bundle is essentially irreversible, so it wins if any space opens up and it manages to bind before the next Myo10 arrives.

Reviewer #3 (Public Review): 

Summary 

The work represents progress in quantifying the number of Myo10 molecules present in the filopodia tip. It reveals that cells overexpressing fluorescently labeled Myo10 that the tip can accommodate a wide range of Myo10 motors, up to hundreds of molecules per tip. 

The revised, expanded manuscript addresses all of this reviewer's original comments. The new data, analysis and writing strengthen the paper. Given the importance of filopodia in many cellular/developmental processes and the pivotal, as yet not fully understood role of Myo10 in their formation and extension, this work provides a new look at the nature of the filopodial tip and its ability to accommodate a large number of Myo10 motor proteins through interactions with the actin core and surrounding membrane. 

Specific comments - 

(1) One of the comments on the original work was that the analysis here is done using cells ectopically expressing HaloTag-Myo10. The author's response is that cells express a range of Myo10 levels and some metastatic cancer cells, such as breast cancer, have significantly increased levels of Myo10 compared to non-transformed cell lines. It is not really clear how much excess Myo10 is present in those cells compared to what is seen here for ectopic expression in U2OS cells, making a direct correspondence difficult.

We agree, a direct correspondence is difficult, and is further complicated by other variables (e.g., expression levels of Myo10 activators, cargoes, fascin, or other filopodial components) that may differ among cell lines. Properly sorting this out will require additional work in a few key cellular systems.

However, there are two points to keep in mind that somewhat mitigate this concern. First, because ectopic expression of Myo10 causes an ~30x increase in the number of filopodia, the activated Myo10 population is divided over that larger filopodial population. Second, the log-normal distribution of Myo10 across filopodia has a long tail, which means that some cells with low levels of Myo10 will concentrate that Myo10 in a few filopodia. 

In response to comments about the bulbous nature of many filopodia tips the authors point out that similar-looking tips are seen when cells are immunostained for Myo10, citing Berg & Cheney (2002). In looking at those images as well as images from papers examining Myo10 immunostaining in metastatic cancer cells (Arjonen et al, 2014, JCI; Summerbell et al, 2020, Sci Adv) the majority of the filopodia tips appear almost uniformly dot-like or circular. There is not too much evidence of the elongated, bulbous filopodial tips seen here.

Yes, the tips in Berg and Cheney are circular, but their size varies considerably (just as a balloon is roughly circular, its size varies with the amount of air it contains). Non-bulbous filopodial tips have a theoretical radius of ~100 nm, which is below the diffraction limit. However, many of the filopodial tips are larger than the diffraction limit in Berg and Cheney, Fig. 1a. We cropped and zoomed in the images to show each fully visible filopodial tip

We attempted to perform a similar analysis of the images in Arjonen and Summerbell. Unfortunately, their images are too small to do so. 

However, in reconsidering the approach and results, it is the case that the finding here do establish the plasticity of filopodia tips that can accommodate a surprisingly (shockingly) large number of motors. The authors discuss that their results show that targeting molecules to the filopodia tip is a relatively permissive process (lines 262 - 274). That could be an important property that cells might be able to use to their advantage in certain contexts. 

(2) The method for arriving at the intensity of an individual filopodium puncta (starting on line 532 and provided in the Response), and how this is corrected for transfection efficiency and the cell-to-cell variation in expression level is still not clear to this reviewer. The first part of the description makes sense - the authors obtain total molecules/cell based on the estimation on SDS-PAGE using the signal from bound Halo ligand. It then seems that the total fluorescence intensity of each expressing cell analyzed is measured, then summed to get the average intensity/cell. The 'total pool' is then arrived at by multiplying the number of molecules/cell (from SDS-PAGE) by the total number of cells analyzed. After that, then: 'to get the number of molecules within a Myo10 filopodium, the filopodium intensity was divided by the bioreplicate signal intensity and multiplied by 'total pool.' ' The meaning of this may seem simple or straightforward to the authors, but it's a bit confusing to understand what the 'bioreplicate signal intensity' is and then why it would be multiplied by the 'total pool'. This part is rather puzzling at first read.

We agree, such information is critical. We have now revised this description with more precise terms and have included a formula on pg. 20.

Since the approach described here leads the authors to their numerical estimates every effort should be made to have it be readily understood by all readers. A flow chart or diagram might be helpful. 

We have added a diagram of the calculations to the supplemental material (Figure 1—figure supplement 3). We hope that both changes will make it easier for others to follow our work.

(3) The distribution of Myo10 punctae around the cell are analyzed (Fig 2E, F) and the authors state that they detect 'periodic stretches of higher Myo10 density along the plasma membrane' (line 123) and also that there is correlation and anti-correlation of molecules and punctae at opposite ends of the cells. 

In the first case, it is hard to know what the authors really mean by the phrase 'periodic stretches'. It's not easy to see a periodicity in the distribution of the punctae in the many cells shown in Supp Fig 3. Also, the correlation/anti-correlation is not so easily seen in the quantification shown in Fig 2F. Can the authors provide some support or clarification for what they are stating? 

The periodic pattern that we refer to is most apparent in the middle panels of Fig. 2E, F. These panels show the density of Myo10 puncta. These puncta numbers closely correspond to filopodia counts, with the caveat that some filopodia might have multiple puncta. This periodic density might not be as apparent in the raw data shown in Supp. Fig. 3. We have therefore rewritten this paragraph to clarify our observations (pg. 6).

(4) The authors are no doubt aware that a paper from the Tyska lab that employs a completely different method of counting molecules arrives at a much lower number of Myo10 molecules at the filopodial tip than is reported here was just posted (Fitz & Tyska, 2024, bioRxiv, DOI: 10.1101/2024.05.14.593924). 

While it is not absolutely necessary for the authors to provide a detailed discussion of this new work given the timing, they may wish to consider adding a note briefly addressing it. 

We are aware of this manuscript and that it uses a different approach for calibrating the fluorescence signal in microscopy. However, we are not comfortable commenting on that manuscript at this time, given that it has not yet been peer reviewed with the chance for author revisions.

Recommendations for the authors: 

Reviewer #1 (Recommendations For The Authors): 

The manuscript the authors are now presenting does not comply with the formatting limits of a Short report, but it is instead presented as a full article type. I believe the authors could shorten the Discussion, and meet the criteria for a more appropriate Short Report format. 

For instance, I continue to believe that the study of truncation variants could sustain the claim that membrane binding represents the driving force that leads to Myo10 accumulation. I understand the authors want to address these mechanisms in a follow-up story, for this reason, I encourage them to shorten the discussion, which seems unnecessarily long for a technique-based manuscript.

In the first round of review, Reviewer 3 asked us to expand the discussion. Given that, we are happy with where we have landed on the length of the discussion.

Figure 2, could include some images to facilitate the readers on the different messages of the two rose plots E and F, by picking one of the examples from the supplementary Figure 3 

We have now added a supplemental figure showing an example cell (Fig. 2 figure supplement 2). But please note that the averaging of ~150 cells (Fig. 2E, F) should be more reliable to show these overall trends.

Reviewer #2 (Recommendations For The Authors): 

Also, the speculation about how fascin can outcompete Myo10 should include a mechanism for how the physiological levels of fascin can complete with the overabundance of Myo10 (page 10, lines 401-408). 

As noted above, we have now clarified this point. 

Reviewer #3 (Recommendations For The Authors): 

line 495 - what is GOC? 

We have now defined this oxygen scavenger system in the main text.

lines 603/604 - it is stated that 'velocity analysis does not only account for Myo10 punctum that moved away from the starting point of the trajectory.' It's not clear what this really means. 

The sentence now reads: "For Figure 4 parts G-H, note that velocity analysis includes a few Myo10 puncta that switch direction within a single trajectory (e.g., a retracting punctum that then elongates)."

References #4 and #14 are the same. 

Thank you for catching that; it has now been corrected.

Fig 1C - the plot for signal intensity versus fmol of protein has numbers for the standard and then live and fixed cells. While the R2 value is quite good, it seems a bit odd that the three (?) data points for live cells are all quite small relative to the fixed cells and all bunched together at the left side of the plot. 

As mentioned in the main text, the time post-transfection has a noticeable effect on the level of Myo10 expression. The three fixed-cell bioreplicates had higher Myo10 expression because they were analyzed 48 hours post-transfection compared to the three live-cell bioreplicates (24 hours). Therefore, the fixed cell data points are larger in value because they represent more molecules, and the live cell data points are on the left side of the plot because they represent fewer molecules.

At work we have this sprint retrospect task we do every two weeks, everyone says what they think went well or not so well or saying thanks to someone as anons, then we collectively group the same things said by people together is they are the same thing, then we all get 4 votes we use as anon's to signal what we think was most important

Interesante esta mezcla de digital a análogo y las herramientas que en el domino digital continúan usando (drives, Notion, etc). Por supuesto, el grupo está enfocado en la segunda parte y sus innovaciones (Realtak, etc) y no en las innovaciones en la primera (drive, Notion). Dado que nosotros sí nos enfocamos en esta gestión alterna de conocimiento en lo digital, usando infraestructuras de bolsillo, metaherramientas y programación intersticial, cómo esto podría tener una contraparte y puente en análogo, lowtech, similar a Hypertalk in the world

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Chen et al. identified the role of endocardial id2b expression in cardiac contraction and valve formation through pharmaceutical, genetic, electrophysiology, calcium imaging, and echocardiography analyses. CRISPR/Cas9 generated id2b mutants demonstrated defective AV valve formation, excitation-contraction coupling, reduced endocardial cell proliferation in AV valve, retrograde blood flow, and lethal effects.

Strengths:

Their methods, data and analyses broadly support their claims.

Weaknesses:

The molecular mechanism is somewhat preliminary.

We thank the reviewer for the constructive comments. To further elucidate the molecular mechanisms underlying the observed phenotypes, we will conduct the following experiments: (1) perform qRT-PCR to analyze the expression of id2a in hearts isolated from tricane-treated embryos and in id2b-deleted embryos; (2) use RNAscope to detect the expression of id2b in developing embryos; (3) validate the interaction between Id2b and Tcf3b in vivo; and (4) conduct CUT&Tag experiments in developing zebrafish embryos to further validate the Tcf3b binding sites upstream of nrg1.

Reviewer #2 (Public review):

Summary:

Biomechanical forces, such as blood flow, are crucial for organ formation, including heart development. This study by Shuo Chen et al. aims to understand how cardiac cells respond to these forces. They used zebrafish as a model organism due to its unique strengths, such as the ability to survive without heartbeats, and conducted transcriptomic analysis on hearts with impaired contractility. They thereby identified id2b as a gene regulated by blood flow and is crucial for proper heart development, in particular, for the regulation of myocardial contractility and valve formation. Using both in situ hybridization and transgenic fish they showed that id2b is specifically expressed in the endocardium, and its expression is affected by both pharmacological and genetic perturbations of contraction. They further generated a null mutant of id2b to show that loss of id2b results in heart malformation and early lethality in zebrafish. Atrioventricular (AV) and excitation-contraction coupling were also impaired in id2b mutants. Mechanistically, they demonstrate that Id2b interacts with the transcription factor Tcf3b to restrict its activity. When id2b is deleted, the repressor activity of Tcf3b is enhanced, leading to suppression of the expression of nrg1 (neuregulin 1), a key factor for heart development. Importantly, injecting tcf3b morpholino into id2b-/- embryos partially restores the reduced heart rate. Moreover, treatment of zebrafish embryos with the Erbb2 inhibitor AG1478 results in decreased heart rate, in line with a model in which Id2b modulates heart development via the Nrg1/Erbb2 axis. The research identifies id2b as a biomechanical signaling-sensitive gene in endocardial cells that mediates communication between the endocardium and myocardium, which is essential for heart morphogenesis and function.

Strengths:

The study provides novel insights into the molecular mechanisms by which biomechanical forces influence heart development and highlights the importance of id2b in this process.

Weaknesses:

The claims are in general well supported by experimental evidence, but the following aspects may benefit from further investigation:

(1) In Figure 1C, the heatmap demonstrates the up-regulated and down-regulated genes upon tricane-induced cardiac arrest. Aside from the down-regulation of id2b expression, it was also evident that id2a expression was up-regulated. As a predicted paralog of id2b, it would be interesting to see whether the up-regulation of id2a in response to tricaine treatment was a compensatory response to the down-regulation of id2b expression.

As suggested by the reviewer, we will perform qRT-PCR to analyze the expression of id2a in hearts isolated from tricane-treated embryos, as well as in id2b-deleted embryos.

(2) The study mentioned that id2b is tightly regulated by the flow-sensitive primary cilia-klf2 signaling axis; however aside from showing the reduced expression of id2b in klf2a and klf2b mutants, there was no further evidence to solidify the functional link between id2b and klf2. It would therefore be ideal, in the present study, to demonstrate how Klf2, which is a transcriptional regulator, transduces biomechanical stimuli to Id2b.

We have examined the expression levels of id2b in both klf2a and klf2b mutants. The whole mount in situ results clearly demonstrate a decrease in id2b signal in both mutants. As noted by the reviewer, klf2 is a transcriptional regulator, suggesting that the regulation of id2b may occur at the transcriptional level. However, dissecting the molecular mechanisms underling the crosstalk between klf2 and id2b is beyond the scope of the present study.

(3) The authors showed the physical interaction between ectopically expressed FLAG-Id2b and HA-Tcf3b in HEK293T cells. Although the constructs being expressed are of zebrafish origin, it would be nice to show in vivo that the two proteins interact.

We agree with the reviewer and will perform additional experiments to validate the interaction between Id2b and Tcf3b in vivo. Due to the lack of antibodies targeting these proteins, we will overexpress Flag-id2b and HA-Tcf3b in zebrafish embryos and conduct a co-IP analysis.

Reviewer #3 (Public review):

Summary:

How mechanical forces transmitted by blood flow contribute to normal cardiac development remains incompletely understood. Using the unique advantages of the zebrafish model system, Chen et al make the fundamental discovery that endocardial expression of id2b is induced by blood flow and required for normal atrioventricular canal (AVC) valve development and myocardial contractility by regulating calcium dynamics. Mechanistically, the authors suggest that Id2b binds to Tcf3b in endocardial cells, which relieves Tcf3b-mediated transcriptional repression of Neuregulin 1 (NRG1). Nrg1 then induces expression of the L-type calcium channel component LRRC1. This study significantly advances our understanding of flow-mediated valve formation and myocardial function.

Strengths:

Strengths of the study are the significance of the question being addressed, use of the zebrafish model, and data quality (mostly very nice imaging). The text is also well-written and easy to understand.

Weaknesses:

Weaknesses include a lack of rigor for key experimental approaches, which led to skepticism surrounding the main findings. Specific issues were the use of morpholinos instead of genetic mutants for the bmp ligands, cilia gene ift88, and tcf3b, lack of an explicit model surrounding BMP versus blood flow induced endocardial id2b expression, use of bar graphs without dots, the artificial nature of assessing the physical interaction of Tcf3b and Id2b in transfected HEK293 cells, and artificial nature of examining the function of the tcf3b binding sites upstream of nrg1.

We thank the reviewer for the constructive assessments. Our specific responses are as follows:

(1) As all the morpholinos used in this study, including those targeting bmp ligands, the cilia gene ift88, and tcf3b, have been published and validated using genetic mutants in previous studies, we believe these loss-of-function analyses are sufficient to delineate their role in regulating id2b expression or function.

(2) To assess the role of BMP versus blood flow in regulating endocardial id2b expression, we plan to perform live imaging in the id2b:GFP knockin line prior to the initiation of the heartbeat, with or without of BMP inhibitors.

(3) We will revise the data presentation and use bar graphs with individual data points.

(4) We plan to perform additional Co-IP experiment in zebrafish embryos to assess the interaction between Tcf3b and Id2b.

(5) To further validate the tcf3b binding sites upstream of nrg1, we will conduct CUT&Tag experiments in developing zebrafish embryos.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

UGGTs are involved in the prevention of premature degradation for misfolded glycoproteins, by utilizing UGGT-KO cells and a number of different ERAD substrates. They proposed a concept by which the fate of glycoproteins can be determined by a tug-of-war between UGGTs and EDEMs.

Strengths:

The authors provided a wealth of data to indicate that UGGT1 competes with EDEMs, which promotes glycoprotein degradation.

Weaknesses:

Less clear, though, is the involvement of UGGT2 in the process. Also, to this reviewer, some data do not necessarily support the conclusion.

Major criticisms:

(1) One of the biggest problems I had on reading through this manuscript is that, while the authors appeared to generate UGGTs-KO cells from HCT116 and HeLa cells, it was not clearly indicated which cell line was used for each experiment. I assume that it was HCT116 cells in most cases, but I did not see that it was clearly mentioned. As the expression level of UGGT2 relative to UGGT1 is quite different between the two cell lines, it would be critical to know which cells were used for each experiment.

Thank you for this comment. We have clarified this point, especially in the figure legends.

(2) While most of the authors' conclusion is sound, some claims, to this reviewer, were not fully supported by the data. Especially I cannot help being puzzled by the authors' claim about the involvement of UGGT2 in the ERAD process. In most of the cases, KO of UGGT2 does not seem to affect the stability of ERAD substrates (ex. Fig. 1C, 2A, 3D). When the author suggests that UGGT2 is also involved in the ERAD, it is far from convincing (ex. Fig. 2D/E). Especially because now it has been suggested that the main role of UGGT2 may be distinct from UGGT1, playing a role in lipid quality control (Hung, et al., PNAS 2022), it is imperative to provide convincing evidence if the authors want to claim the involvement of UGGT2 in a protein quality control system. In fact, it was not clear at all whether even UGGT1 is also involved in the process in Fig. 2D/E, as the difference, if any, is so subtle. How the authors can be sure that this is significant enough? While the authors claim that the difference is statistically significant (n=3), this may end up with experimental artifacts. To say the least, I would urge the authors to try rescue experiments with UGGT1 or 2, to clarify that the defect in UGGT-DKO cells can be reversed. It may also be interesting to see that the subtle difference the authors observed is indeed N-glycan-dependent by testing a non-glycosylated version of the protein (just like NHK-QQQ mutants in Fig. 2C).

We appreciate this comment. According to this comment, we reevaluated the importance of UGGT2 for ER-protein quality control. As this reviewer mentioned, KO of UGGT2 does not affect the stability of ATF6a, NHK, rRI332-Flag or EMC1-△PQQ-Flag (Fig. 1E, 2A, and 3DE). Furthermore, we tested whether overexpression of UGGT2 reverses the phenotype of UGGT-DKO regarding the degradation rate of NHK, and we found that it did not affect the degradation rate of NHK, whereas overexpression of UGGT1 restored the degradation rate to that in WT cells.

Author response image 1.

Collectively, these facts suggest that the role of UGGT2 in ER protein quality control is rather limited in HCT116 cells. Therefore, we have decided not to mention UGGT2 in the title, and weakened the overall claim that UGGT2 contributes to ER protein quality control. Tissues with high expression of UGGT2 or cultured cells other than HCT116 would be appropriate for revealing the detailed function of UGGT2.

To this reviewer, it is still possible that the involvement of UGGT1 (or 2, if any) could be totally substrate-dependent, and the substrates used in Fig 2D or E happen not to be dependent to the action of UGGTs. To the reviewer, without the data of Fig. 2D and E the authors provide enough evidence to demonstrate the involvement of UGGT1 in preventing premature degradation of glycoprotein ERAD substrates. I am just afraid that the authors may have overinterpreted the data, as if the UGGTs are involved in stabilization of all glycoproteins destined for ERAD.

Based on the point this reviewer mentioned, we decided to delete previous Fig. 2D and 2E. There may be more or less efficacy of UGGT1 for preventing early degradation of substrates.

(3) I am a bit puzzled by the DNJ treatment experiments. First, I do not see the detailed conditions of the DNJ treatment (concentration? Time?). Then, I was a bit surprised to see that there were so little G3M9 glycans formed, and there was about the same amount of G2M9 also formed (Figure 1 Figure supplement 4B-D), despite the fact that glucose trimming of newly syntheized glycoproteins are expected to be completely impaired (unless the authors used DNJ concentration which does not completely impair the trimming of the first Glc). Even considering the involvement of Golgi endo-alpha-mannosidase, a similar amount of G3M9 and G2M9 may suggest that the experimental conditions used for this experiment (i.e. concentration of DNJ, duration of treatment, etc) is not properly optimized.

We think that our experimental condition of DNJ treatment is appropriate to evaluate the effect of DNJ. Referring to the other papers (Ali and Field, 2000; Karlsson et al., 1993; Lomako et al., 2010; Pearse et al., 2010; Tannous et al., 2015), 0.5 mM DNJ is appropriate. In our previously reported experiment, 16 h treatment with kifunensine mannosidase inhibitor was sufficient for N-glycan composition analysis prior to cell collection (Ninagawa et al., 2014), and we treated cells for a similar time in Figure 1-Figure Supplement 4 and 5 (and Figure 1-Figure Supplement 6). We could see the clear effect of DNJ to inhibit degradation of ATF6a with 2 hours of pretreatment (Fig. 1G). Furthermore, our results are very reasonable and consistent with previous findings that DNJ increased GM9 the most (Cheatham et al., 2023; Gross et al., 1983; Gross et al., 1986; Romero et al., 1985). In addition to DNJ, we used CST for further experiments in new figures (Fig. 1H and Figure 1-Figure supplement 6). DNJ and CST are inhibitors of glucosidase; DNJ is a stronger inhibitor of glucosidase II, while CST is a stronger inhibitor of glucosidase I (Asano, 2000; Saunier et al., 1982; Szumilo et al., 1987; Zeng et al., 1997). An increase in G3M9 and G2M9 was detected using CST (Figure1-Figure Supplement 6). Like DNJ, CST also inhibited ATF6a degradation in UGGT-DKO cells (Fig. 1H). These findings show that our experimental condition using glucosidase inhibitor is appropriate and strongly support our model (Fig. 5). Differences between the effects of DNJ and CST are now described in our manuscript pages 8 to 10.

Reviewer #2 (Public Review):

In this study, Ninagawa et al., shed light on UGGT's role in ER quality control of glycoproteins. By utilizing UGGT1/UGGT2 DKO cells, they demonstrate that several model misfolded glycoproteins undergo early degradation. One such substrate is ATF6alpha where its premature degradation hampers the cell's ability to mount an ER stress response.

While this study convincingly demonstrates early degradation of misfolded glycoproteins in the absence of UGGTs, my major concern is the need for additional experiments to support the "tug of war" model involving UGGTs and EDEMs in influencing the substrate's fate - whether misfolded glycoproteins are pulled into the folding or degradation route. Specifically, it would be valuable to investigate how overexpression of UGGTs and EDEMs in WT cells affects the choice between folding and degradation for misfolded glycoproteins. Considering previous studies indicating that monoglucosylation influences glycoprotein solubility and stability, an essential question is: what is the nature of glycoproteins in UGGTKO/EDEMKO and potentially UGGT/EDEM overexpression cells? Understanding whether these substrates become more soluble/stable when GM9 versus mannose-only translation modification accumulates would provide valuable insights.

In the new figure 2DE, we conducted overexpression experiments of structure formation factors UGGT1 and/or CNX, and degradation factors EDEMs. While overexpression of structure formation factors (Fig. 2DE) and KO of degradation factors (Ninagawa et al., 2015; Ninagawa et al., 2014) increased stability of substrates, KO of UGGT1 (Fig. 1E, 2A and 3DF) and overexpression of degradation factors (Fig. 2DE) (Hirao et al., 2006; Hosokawa et al., 2001; Mast et al., 2005; Olivari et al., 2005) accelerated degradation of substrates. A comparison of the properties of N-glycan with the normal type and the type without glucoses was already reported (Tannous et al., 2015). The rate of degradation of substrate was unchanged, but efficiency of secretion of substrates was affected.

The study delves into the physiological role of UGGT, but is limited in scope, focusing solely on the effect of ATF6alpha in UGGT KO cells' stress response. It is crucial for the authors to investigate the broader impact of UGGT KO, including the assessment of basal ER proteotoxicity levels, examination of the general efflux of glycoproteins from ER, and the exploration of the physiological consequences due to UGGT KO. This broader perspective would be valuable for the wider audience. Additionally, the marked increase in ATF4 activity in UGGTKO requires discussion, which the authors currently omit.

We evaluated the sensitivity of WT and UGGT1-KO cells to ER stress (Figure 4G). KO of UGGT1 increased the sensitivity to ER stress inducer Tg, indicating the importance of UGGT1 for resisting ER stress.

We add the following description in the manuscript about ATF4 activity in UGGT1-KO: “In addition to this, UGGT1 is necessary for proper functioning of ER resident proteins such as ATF6a (Fig. 4B-F). It is highly possible that ATF6a undergoes structural maintenance by UGGT1, which could be necessary to avoid degradation and maintain proper function, because ATF6a with more rigid in structure tended to remain in UGGT1-KO cells (Fig. 4C). Responses of ERSE and UPRE to ER stress, which require ATF6a, were decreased in UGGT1-KO cells (Fig. 4DE). In contrast, ATF4 reporter activity was increased in UGGT1-KO cells (Fig. 4F), while the basal level of ATF4 in UGGT1-KO cells was comparable with that in WT (Figure 1-Figure supplement 2B). The ATF4 pathway might partially compensate the function of the ERSE and UPRE pathways in UGGT1-KO cells in acute ER stress. This is now described on Page 17 in our manuscript.

The discussion section is brief and could benefit from being a separate section. It is advisable for the authors to explore and suggest other model systems or disease contexts to test UGGT's role in the future. This expansion would help the broader scientific community appreciate the potential applications and implications of this work beyond its current scope.

Thank you for making this point. The DISCUSSION part has now been separated in our manuscript. We added some points in the manuscript about other model organisms and diseases in the DISCUSSION as follows: “ Our work focusing on the function of mammalian UGGT1 greatly advances the understanding how ER homeostasis is maintained in higher animals. Considering that Saccharomyces cerevisiae does not have a functional orthologue of UGGT1 (Ninagawa et al., 2020a) and that KO of UGGT1 causes embryonic lethality in mice (Molinari et al., 2005), it would be interesting to know at what point the function of UGGT1 became evolutionarily necessary for life. Related to its importance in animals, it would also be of interest to know what kind of diseases UGGT1 is associated with. Recently, it has been reported that UGGT1 is involved in ER retention of Trop-2 mutant proteins, which are encoded by a causative gene of gelatinous drop-like corneal dystrophy (Tax et al., 2024). Not only this, but since the ER is known to be involved in over 60 diseases (Guerriero and Brodsky, 2012), we must investigate how UGGT1 and other ER molecules are involved in diseases.”

Reviewer #3 (Public Review):

This manuscript focuses on defining the importance of UGGT1/2 in the process of protein degradation within the ER. The authors prepared cells lacking UGGT1, UGGT2, or both UGGT1/UGGT2 (DKO) HCT116 cells and then monitored the degradation of specific ERAD substrates. Initially, they focused on the ER stress sensor ATF6 and showed that loss of UGGT1 increased the degradation of this protein. This degradation was stabilized by deletion of ERAD-specific factors (e.g., SEL1L, EDEM) or treatment with mannose inhibitors such as kifunesine, indicating that this is mediated through a process involving increased mannose trimming of the ATF6 N-glycan. This increased degradation of ATF6 impaired the function of this ER stress sensor, as expected, reducing the activation of downstream reporters of ER stress-induced ATF6 activation. The authors extended this analysis to monitor the degradation of other well-established ERAD substrates including A1AT-NHK and CD3d, demonstrating similar increases in the degradation of destabilized, misfolding protein substrates in cells deficient in UGGT. Importantly, they did experiments to suggest that re-overexpression of wild-type, but not catalytically deficient, UGGT rescues the increased degradation observed in UGGT1 knockout cells. Further, they demonstrated the dependence of this sensitivity to UGGT depletion on N-glycans using ERAD substrates that lack any glycans. Ultimately, these results suggest a model whereby depletion of UGGT (especially UGGT1 which is the most expressed in these cells) increases degradation of ERAD substrates through a mechanism involving impaired re-glucosylation and subsequent re-entry into the calnexin/calreticulin folding pathway.

I must say that I was under the impression that the main conclusions of this paper (i.e., UGGT1 functions to slow the degradation of ERAD substrates by allowing re-entry into the lectin folding pathway) were well-established in the literature. However, I was not able to find papers explicitly demonstrating this point. Because of this, I do think that this manuscript is valuable, as it supports a previously assumed assertion of the role of UGGT in ER quality control. However, there are a number of issues in the manuscript that should be addressed.

Notably, the focus on well-established, trafficking-deficient ERAD substrates, while a traditional approach to studying these types of processes, limits our understanding of global ER quality control of proteins that are trafficked to downstream secretory environments where proteins can be degraded through multiple mechanisms. For example, in Figure 1-Figure Supplement 2, UGGT1/2 knockout does not seem to increase the degradation of secretion-competent proteins such as A1AT or EPO, instead appearing to stabilize these proteins against degradation. They do show reductions in secretion, but it isn't clear exactly how UGGT loss is impacting ER Quality Control of these more relevant types of ER-targeted secretory proteins.

We appreciate your comment. It is certainly difficult to assess in detail how UGGT1 functions against secretion-competent proteins, but we think that the folding state of these proteins is improved, which avoids their degradation and increases their secretion. In Figure 1-Figure supplement 2E, there is a clear decrease in secretion of EPO in UGGT1-KO cells, suggesting that UGGT1 also inhibits degradation of such substrates. Note that, as shown in Fig. 3A-C, once a protein forms a solid structure, it is rarely degraded in the ER.

Lastly, I don't understand the link between UGGT, ATF6 degradation, and ATF6 activation. I understand that the idea is that increased ATF6 degradation afforded by UGGT depletion will impair activation of this ER stress sensor, but if that is the case, how does UGGT2 depletion, which only minimally impacts ATF6 degradation (Fig. 1), impact activation to levels similar to the UGGT1 knockout (Fig 4)? This suggests UGGT1/2 may serve different functions beyond just regulating the degradation of this ER stress sensor. Also, the authors should quantify the impaired ATF6 processing shown in Fig 4B-D across multiple replicates.

According to this valuable comment, we reevaluated our manuscript. As this reviewer mentioned, involvement of UGGT2 in the activation of ATF6a cannot be explained only by the folding state of ATF6a. Thus, the part about whether UGGT2 is effective in activating ATF6 is outside the scope of this paper. The main focus of this paper is the contribution of UGGT1 to the ER protein quality control mechanism.

Ultimately, I do think the data support a role for UGGT (especially UGGT1) in regulating the degradation of ERAD substrates, which provides experimental support for a role long-predicted in the field. However, there are a number of ways this manuscript could be strengthened to further support this role, some of which can be done with data they have in hand (e.g., the stats) or additional new experiments.

In this revision period, to further elucidate the function of UGGT, we did several additional experiments (new figures Fig. 1H, 2DE, 4G and, Figure 1-Figure Supplement 6). We hope that these will bring our papers up to the level you have requested.

Reviewer #1 (Recommendations For The Authors):

Minor points:

(1) Abbreviations: GlcNAc, N-acetylglucosamines -> why plural?

Corrected.

(2) Abstract: to this reviewer, it may not be so common to cite references in the abstract.

We submit this manuscript to eLife as “Research Advances”. In the instructions of eLife for “Research Advances”, there is the description: “A reference to the original eLife article should be included in the abstract, e.g. in the format “Previously we showed that XXXX (author, year). Here we show that YYYY.” We follow this.

(3) Introduction: "as the site of biosynthesis of approximately one-third of all proteins." Probably this statement needs a citation?

We added the reference there. You can also confirm this in “The Human Protein Atlas” website. https://www.proteinatlas.org/humanproteome/tissue/secretome

(4) Figure 1F - the authors claimed that maturation of HA was delayed also in UGGT2 cells, but it was not at all clear to me. Rescue experiments with UGGT2 would be desired.

We agree with this reviewer, but there was a statistically significant difference in the 80 min UGGT2-KO strain. Previously, it was reported that HA maturation rate was not affected by UGGT2 (Hung et al., 2022). We think that the difference is not large. A rescue experiment of UGGT2 on the degradation of NHK was conducted, and is shown in this response to referees.

(5) Figure 4A, here also the authors claim that UGGT2 is "slightly" involved in folding of ATF6alpha(P) but it is far from convincing to this reviewer.

Now we also think that involvement of UGGT2 in ER protein quality control should be examined in the future.

(6) Page 11, line 7 from the bottom: "peak of activation was shifted from 1 hour to 4 hours after the treatment of Tg in UGGT-KO cells". I found this statement a bit awkward; how can the authors be sure that "the peak" is 4 hours when the longest timing tested is 4 hours (i.e. peak may be even later)?

Corrected. We deleted the description.

(7) Page 11, line 4 "a more rigid structure that averts degradation" Can the authors speculate what this "rigid" structure actually means? The reviewer has to wonder what kind of change can occur to this protein with or without UGGT1. Binding proteins? The difference in susceptibility against trypsin appears very subtle anyway (Figure 4 Figure Supplement 1).

Let us add our thoughts here: Poorly structured ATF6a is immediately routed for degradation in UGGT1-KO cells. As a result, ATF6a with a stable or rigid structure have remained in the UGGT1-KO strain. ATF6a with a metastable state is tended to be degraded without assistance of UGGT1.

(8) Figure 1 Figure supplement 2; based on the information provided, I calculate the relative ratio of UGGT2/UGGT1 in HCT116 which is 4.5%, and in HeLa 26%. Am I missing something? Also significant figure, at best, should be 2, not 3 (i.e. 30%, not 29.8%).

Corrected. Thank you for this comment.

Reviewer #2 (Recommendations For The Authors):

(1) The effect in Fig. 2B with UGGT1-D1358A add-back is minimal. Testing the inactive and active add-back on other substrates, such as ATF6alpha, which undergoes a more rapid degradation, would provide a more comprehensive assessment.

To examine the effect of full length and inactive mutant of UGGT1 in UGGT1-KO and UGGT2-KO on the rate of degradation of endogenous ATF6a, we tried to select more than 300 colonies stably expressing full-length Myc-UGGT1/2, UGGT1/2-Flag, and UGGT1/2 (no tag), and their point mutant of them. However, no cell lines expressing nearly as much or more UGGT1/2 than endogenous ones were obtained. The expression level of UGGT1 seemed to be tightly regulated. A low-expressing stable cell line could not recover the phenotype of ATF6a degradation.

We also tried to measure the degradation rate of exogenously expressed ATF6a. But overexpressed ATF6a is partially transported to the Golgi and cleaved by proteases, which makes it difficult to evaluate only the effect of degradation.

(2) In reference to this statement on pg. 11:

"This can be explained by the rigid structure of ATF6(P) lacking structural flexibility to respond to ER stress because the remaining ATF6(P) in UGGT1-KO cells tends to have a more rigid structure that averts degradation, which is supported by its slightly weaker sensitivity to trypsin (Figure 4-figure supplement 1A). "

The rationale for testing ATF6(P) rigidity via trypsin digestion needs clarification. The authors should provide more background, especially if it relates to previous studies demonstrating UGGT's influence on substrate solubility. If trypsin digestion is indeed addressing this, it should be applied consistently to all tested misfolded glycoproteins, ensuring a comprehensive approach.

We now provide more background with three references about trypsin digestion. Trypsin digestion allows us to evaluate the structure of proteins originated from the same gene, but it can sometimes be difficult to comparatively evaluate the structure of proteins originated from different genes. For example, antitrypsin is resistant to trypsin by its nature, which does not necessarily mean that antitrypsin forms a more stable structure than other proteins. NHK, a truncated version of antitrypsin, is still resistant to trypsin compared with other substrates.

(3) Many of the figures described in the manuscript weren't referred to a specific panel. For example, pg. 12 "Fig. 1E and Fig.5," the exact panel for Fig. 5 wasn't referenced.

Thank you for this comment. Corrected.

(4) For experiments measuring the composition of glycoproteins in different KO lines, it is necessary to do the experiment more than once for conducting statistical analysis and comparisons. Moreover, the authors did not include raw composition data for these experiments. Statistical analysis should also be done for Fig. 4E-F.

Our N-glycan composition data (Figure 1-Figure supplement 5 and 6C) is consistent with previous our papers (George et al., 2021; George et al., 2020; Ninagawa et al., 2015; Ninagawa et al., 2014). We did it twice in the previous study and please refer to it regarding statistical analysis (George et al., 2020). We add the raw composition data of N-glycan (Figure 1-Figure supplement 4 and 6B). In Fig. 4D-F, now statistical analysis is included.

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Author response:

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

We would like to thank the reviewers and editor for their helpful comments and suggestions. In response, we have revised the manuscript in two main ways:

(1) To address the comments about rearranging figures and tables, we added a new Figure 3 that summarizes neurotransmitter assignments across all neuron classes. Our rationale for this change is detailed below.

(2) To address the comment on clarifying neurotransmitter synthesis versus uptake, we analyzed two additional reporter alleles that tag the monoamine uptake transporters for 5-HT and potentially tyramine. These results are now presented in a new Figure 8 and corresponding sections in the manuscript. Related tables have been updated to include this expression data. Two more authors have been added due to their contributions to these experiments.

For more detailed changes, please see our responses to the specific reviewer's comments as well as the revised manuscript.

Public Reviews:

Reviewer #1 (Public Review): 

Wang and colleagues conducted a study to determine the neurotransmitter identity of all neurons in C. elegans hermaphrodites and males. They used CRISPR technology to introduce fluorescent gene expression reporters into the genomic loci of NT pathway genes. This approach is expected to better reflect in vivo gene expression compared to other methods like promoter- or fosmid-based transgenes, or available scRNA datasets. The study presents several noteworthy findings, including sexual dimorphisms, patterns of NT co-transmission, neuronal classes that likely use NTs without direct synthesis, and potential identification of unconventional NTs (e.g. betaine releasing neurons). The data is well-described and critically discussed, including a comparison with alternative methods. Although many of the observations and proposals have been previously discussed by the Hobert lab, the current study is particularly valuable due to its comprehensiveness. This NT atlas is the most complete and comprehensive of any nervous system that I am aware of, making it an extremely useful tool for the community. 

Reviewer #2 (Public Review):

Summary: 

Together with the known anatomical connectivity of C. elegans, a neurotransmitter atlas paves the way toward a functional connectivity map. This study refines the expression patterns of key genes for neurotransmission by analyzing the expression patterns from CRISPR-knocked-in GFP reporter strains using the color-coded Neuropal strain to identify neurons. Along with data from previous scRNA sequencing and other reporter strains, examining these expression patterns enhances our understanding of neurotransmitter identity for each neuron in hermaphrodites and the male nervous system. Beyond the known neurotransmitters (GABA, Acetylcholine, Glutamate, dopamine, serotonin, tyramine, octopamine), the atlas also identifies neurons likely using betaine and suggests sets of neurons employing new unknown monoaminergic transmission, or using exclusively peptidergic transmission. 

Strengths: 

The use of CRISPR reporter alleles and of the Neuropal strain to assign neurotransmitter usage to each neuron is much more rigorous than previous analysis and reveals intriguing differences between scRNA seq, fosmid reporter, and CRISPR knock-in approaches. Among other mechanisms, these differences between approaches could be attributed to 3'UTR regulatory mechanisms for scRNA vs. knockin or titration of rate-limited negative regulatory mechanisms for fosmid vs. knockin. It would be interesting to discuss this and highlight the occurrences of these potential phenomena for future studies.  

We recognize that readers of this study may be interested in understanding the differences between the three approaches. Therefore, in the Introduction, we addressed the potential risk of overexpression artifacts associated with multicopy transgenes, such as fosmid-based reporters, which can affect rate-limiting negative regulatory mechanisms. Additionally, in the Discussion, we included a section titled 'Comparing approaches and caveats of expression pattern analysis' to further explore these comparative methods and their associated nuances.

Weaknesses: 

For GABAergic transmission, one shortcoming arises from the lack of improved expression pattern by a knockin reporter strain for the GABA recapture symporter snf-11. In its absence, it is difficult to make a final conclusion on GABA recapture vs GABA clearance for all neurons expressing the vesicular GABA transporter neurons (unc-47+) but not expressing the GAD/UNC-25 gene e.g. SIA or R2A neurons. At minima, a comparison of the scRNA seq predictions versus the snf-11 fosmid reporter strain expression pattern would help to better judge the proposed role of each neuron in GABA clearance or recycling. 

The snf-11 fosmid-based reporter data shows very good overlap with scRNA seq predictions (now included in Supp. Table S1). 

But there are two much stronger reasons why we did not seek to further the analysis of expression of the snf-11 GABA uptaker:

(1) Due to available anti-GABA staining data, we do know which neurons have the potential to take up GABA (via SNF-11).

(2) Focusing on SNF-11 function rather than expression, we can ask which neurons lose anti-GABA staining in snf-11 mutants.

Both of these types of analyses have been done in an earlier study from our lab (Gendrel et al., 2016, PMID 27740909), which, among other things, investigated GABA uptake mechanisms via SNF-11. Apart from analyzing the expression of a fosmid-based snf-11 reporter, we immunostained worms for GABA in both snf-11 mutant and wild type backgrounds (results summarized in Tables 1 and 2 of Gendrel et al.). Of the neurons that typically stain for GABA (Table 1, Gendrel et al.), two neuron classes (ALA and AVF) lost the staining in snf-11 mutants, suggesting that these neurons likely uptake GABA via SNF-11. Importantly, one of the neurons the reviewer mentioned, R2A, stains for GABA in both wild type and snf-11 mutants, indicating that it likely does not uptake GABA via SNF-11. The other neuron mentioned, SIA, does not stain for GABA in wild type (Table 2, Gendrel et al.), hence not a GABA uptake neuron. In cases like SIA and other neurons, where a neuron does not express unc-25 but does express unc-47 reporters (either fosmid or CRISPR reporter alleles), we speculate that UNC-47 transport another neurotransmitter.

Considering the complexities of different tagging approaches, like T2A-GFP and SL2-GFP cassettes, in capturing post-translational and 3'UTR regulation is important. The current formulation is simplistic. e.g. after SL2 trans-splicing the GFP RNA lacks the 5' regulatory elements, T2A-GFP self-cleavage has its own issues, and the his-44-GFP reporter protein does certainly have a different post-translational life than vesicular transporters or cytoplasmic enzymes. 

Yes, agreed, these points are mentioned in the Introduction and discussed in "Comparing approaches and caveats of expression pattern analysis" in the Discussion.

Do all splicing variants of neurotransmitter-related genes translate into functional proteins? The possibility that some neurons express a non-functional splice variant, leading to his-74-GFP reporter expression without functional neurotransmitter-related protein production is not addressed. 

We thank the reviewer for bringing up this really interesting point, which we had not considered. First and foremost, with the exception of unc-25 (discussed in the next point), for all other genes that produce multiple splice forms, we made sure to append our tag (at 5’ or 3’ end) such that the expression of all splice forms is captured. The reviewer raises the interesting point that in an alternative splicing scenario, some of the cells that express the primary transcript may “switch” to an inactive form. While we cannot exclude this possibility, we have confirmed by sequence analysis in WormBase that in five of the six cases where there is alternative splicing, the alternatively spliced exon lies outside the conserved, functionally relevant (enzymatic or structural) domain. In one case, unc-25, a shorter isoform is produced that does cut into the functionally relevant domain; however, since all unc-25 reporter allele expression cells are also staining positive for GABA, this may not be an issue. 

Also, one tagged splice variant of unc-25 is expected to fail to produce a GFP reporter, can this cause trouble? 

Yes, there is indeed a third splice variant of unc-25 with an alternative C-terminus. To address potential expression of this isoform, we CRISPR-engineered another reporter, unc-25(ot1536[unc-25b.1::t2a::gfp::h2b]), in which the inserted t2a::gfp::h2b sequences are fused to the C-terminus of the alternative splice form, but we did not observe any expression of this reporter. Now included in the manuscript.

Reviewer #3 (Public Review): 

Summary: 

In this paper, Wang et al. provide the most comprehensive description and comparison of the expression of the different genes required to synthesize, transport, and recycle the most common neurotransmitters (Glutamate, Acetylcholine, GABA, Serotonin, Dopamine, Octopamine, and Tyramine) used by hermaphrodite and male C. elegans. This paper will be a seminal reference in the field. Building and contrasting observations from previous studies using fosmid, multicopy reporters, and single-cell sequencing, they now describe CRISPR/Cas-9-engineered reporter strains that, in combination with the multicolor pan-neuronal labeling of all C. elegans neurons (NeuroPAL), allows rigorous elucidation of neurotransmitter expression patterns. These novel reporters also illuminate previously unappreciated aspects of neurotransmitter biology in C. elegans, including sexual dimorphism of expression patterns, cotransmission, and the elucidation of cell-specific pathways that might represent new forms of neurotransmission. 

Strengths: 

The authors set out to establish neurotransmitter identities in C. elegans males and hermaphrodites via varying techniques, including integration of previous studies, examination of expression patterns, and generation of endogenous CRISPR-labeled alleles. Their study is comprehensive, detailed, and rigorous, and achieves the aims. It is an excellent reference for the field, particularly those interested in biosynthetic pathways of neurotransmission and their distribution in vivo, in neuronal and non-neuronal cells. 

Weaknesses: 

No weaknesses were noted. The authors do a great job linking their characterizations with other studies and techniques, giving credence to their findings. As the authors note, there are sexually dimorphic differences across animals and varying expression patterns of enzymes. While it is unlikely there will be huge differences in the reported patterns across individual animals, it is possible that these expression patterns could vary developmentally, or based on physiological or environmental conditions. It is unclear from the study how many animals were imaged for each condition, and if the authors noted changes across individuals during development (could be further acknowledged in the discussion?)  

We have updated the Methods section to specify the number of animals used for imaging. We agree with the reviewer that documenting the developmental dynamics of neurotransmitter expression would be interesting. However, except for one gene (tph-1, Fig. S2), we did not analyze the expression during different developmental stages for most genes in this study. Following the reviewer's suggestion, we have included this as a potential future direction in "Conclusions" at the end of the revised manuscript.

Recommendations for the authors:

After the consultation session, a common suggestion from the reviewers is to bring the tables more upfront, perhaps even in the form of legible main Figures and in alphabetical order of neurons; since we believe that the study will be in the long-term often used for these data; while the Figures with fluorescent expression patterns could be moved to the supplemental information. 

We appreciate the reviewers' and editor's acknowledgment of the tables' possibly frequent usage by the field. We have considered carefully how to order the data presentation. We prefer to keep most of the fluorescent figures in the main text because they convey important subtleties that we want the reader to be aware of.

To address the suggestions to bring key data more upfront, we have added an entirely new figure (Figure 3) before the ensuing data figures that summarized expression patterns of the fluorescent reporters. This new figure (A) summarizes the neurotransmitter use for all neuron classes and (B) illustrates this information within worm schematics, showing the position of neurons in the whole worm. This figure serves as a good overview of neurotransmitter assignments but also specifically refers to the more extensive data and supplementary tables with detailed notes. We believe this solution effectively balances the need for comprehensive information and ease of reference.

Reviewer #1 (Recommendations for The Authors):

Suggestions: 

(1) The study contains up to 10 Figures with gene expression patterns; however, I believe the community will use this paper mostly in the future for its summarizing tables. I wonder if it would be more useful to edit the tables and move them to the main figures while most fluorescent reporter images could be moved to the supplementary part. 

Yes, as mentioned above, we made new summary table & schematic upfront. We do prefer to keep primary data in main figure body. Please see above (Public Review & Response).

(2) In the section titled 'Neurotransmitter Synthesis versus Uptake', the author's wording could be more careful. The data rather suggests functions for individual neuronal classes, such as clearance neurons or signaling neurons. However, these functions remain hypotheses until further detailed studies are conducted to test them. 

These are fair points. We have made several improvements: 

(1) In the referenced section, we added a sentence at the end of the paragraph on betaine to suggest the importance of future functional studies.

(2) We analyzed reporter allele expression for two additional genes: the known uptake transporter for 5-HT (mod-5, reporter allele vlc47) and the predicted uptake transporter for tyramine (oct-1, reporter allele syb8870). The results from these experiments are presented in the new Figure 8 and discussed in Results and Discussion correspondingly. We also collaborated with Curtis Loer, who conducted anti-5-HT staining in wild type and mod-5 mutant animals (results shown in Figure 12). These experiments have enhanced our understanding of 5-HT uptake mechanisms and potential tyramine uptake mechanisms.

(3) At the end of the Conclusions, we emphasized the need for future detailed studies to test the functions of neurotransmitter synthesis and uptake.

(3) Page 21; add to the discussion: neurons could use mainly electrical synapses for communication. Especially for RMG neurons, this might be the case (in addition to neuropeptide communication). 

“Main usage” is a difficult term to use. If there were neurons that are clearly devoid of any form of synaptic vesicle (small or DCV; note that RMG has plenty of DCVs), but show robust and reproducible electrical synapses, we would agree that such neurons could primarily be a “coupling” neuron. But this call is very hard to make for any C. elegans neuron (RMG included) and hence we prefer to not add further to an already quite long Discussion section.

(4) Page 23: I believe that multi-copy promoter-based transgenes (despite array suppression mechanisms) could be potentially more sensitive than single-copy insertion of fluorescent reporters. In our lab, we observed this a couple of times. This could be discussed. 

We discuss this in "Comparing approaches and caveats of expression pattern analysis" in the Discussion.

We have also added a third possibility (i.e. technical issues related to neuron-ID) in the revised manuscript.   

Reviewer #2 (Recommendations For The Authors): 

Comment during consultation session: As for my feedback on the lack of an SNF-11 reporter strain, exercising more caution in their conclusions would suffice for me. Other comments are simple edits/discussion.  

Please see above.  

Several neurotransmitter symporters exist in the C. elegans genome, does any express specifically in the "orphan" UNC-47+ neurons? 

Yes, good point, we considered this possibility, but of the >10 SLC6-family of neurotransmitter reporters, only the classic, de-orphanized ones that we discuss here in the paper show robust scRNA signals (as discussed in the paper) and none of those give clues about the orphan unc-47(+) neurons.

Based on UNC-47+ expression the article suggests a "Novel inhibitory neurotransmitter". Why would any new neurotransmitter using UNC-47 be necessarily inhibitory? The presence of one potential glycine-gated anion channel and one GPCR in C. elegans genome sounds poor evidence to suggest a sign of glycine or b-alanine transmission. 

Yes, agreed, it does not need to be inhibitory. Fixed in Results and Discussion. 

To help readers the expression of the knocked in GFP in neurons should not be reported as binary in table S1 which leads to a feeling of strong discrepancy between scRNA seq and CRISPR GFP, which is not the case.  

There might be some misunderstanding regarding the coloring in this table. To clarify, the green-filled Excel cells denote the expression of reporters utilized in prior studies, rather than the CRISPR reporter alleles. Expression of the CRISPR alleles is instead indicated on the left side of the neuron names, marked as "CRISPR+" in green font. For signifying absence of expression, we used "no CRISPR" in red font in the first submission. We have now changed it into "CRISPR-" for greater clarity.

The variable expression of reporter GFP between individuals for the same neuron is intriguing. It is unclear if this is observed only for dim neurons or can be more of an ON/OFF expression. 

Variability only occurs for dim expression. We have now clarified this point in Discussion, "Comparing approaches and caveats of expression pattern analysis".

The multiple occurrences of co-transmission, especially in male neurons, are interesting. It will be interesting in the future to establish whether the neurotransmitters are synaptically segregated or coreleased. As the section on sexual dimorphism of neurotransmitter usage does not discuss novel information coming from this study, it is not very necessary. 

Agreed. We added this perspective to the Discussion, "Co-transmission of multiple neurotransmitters".  

In the abstract, dopamine is missing in the main known transmitter.  

Fixed. Thanks for spotting this.

Reviewer #3 (Recommendations For The Authors): 

Great article. Minor suggestions to strengthen presentation: 

Figure 1B is hard to interpret. There could be more intuitive ways of representing the data and the methodologies that support a given expression pattern. Neurons should also be reordered by alphabetical order rather than expression levels to facilitate finding them.  

We considered alternative ways of presenting this data, but, regrettably, did not come up with a better approach. To clarify, the primary focus of Fig. 1B is to compare expression of previously reported reporters and scRNA data, which was quite literally the initial impetus for our analysis, i.e. we noted strong scRNA signals that had not previously been supported by transgenic reporter data. For a comprehensive version of the table that includes more details on the expression of CRISPR reporter alleles, please refer to Table S1, which we referenced in the figure legend.   

GFP-only channel images in Figures 3, 4, 5, and 9 sometimes show dim signals that the authors are highlighting as new findings. We recommend using the inverted grayscale version of that channel since the contrast of dim signals is more noticeable to the human eye rather than when the image is colorized. 

Good point, we implemented these suggestions in the figures the reviewer mentioned, now re-numbered Figures 4, 5, 6, and 12. For Figure 6 (tph-1, bas-1, and cat-1 expression in hermaphrodites), we used a new cat-1 head image to reflect the newly identified ASI and AVL expression that wasn’t readily visible in the original projection used in the earlier version of this manuscript. We also added grayscale images in Figure 13 to reflect dim tbh-1 expression in IL2 neurons more clearly.

A plan to integrate this new information into WormAtlas. The C. elegans community is characterized by the open sharing of information on platforms that are user-friendly and accessible. Ideally, the new information would not just 'erase' what was observed before but will describe the new observations and will let the community reach their own conclusions since there is no perfect method and even these CRISPR/Cas9 reporter strains are only proxy for gene expression that subject to post-transcriptional regulation since they depend on T2A and SL2 sequences. 

We completely agree with the reviewer’s suggestion. We will coordinate with WormAtlas on integrating this new information. 

In the case of neurons that were removed from using a specific neurotransmitter, like PVQ. What do the authors conclude overall, if it does not use glutamate, are there any new hypotheses to what it could be using?

Since all neurons express multiple neuropeptides, we hypothesize neurons such as PVQ may be primarily peptidergic. This is included in Discussion, "Neurons devoid of canonical neurotransmitter pathway genes may define neuropeptide-only neurons".  

In Table S5, the I4 neuron is listed as a variable for eat-4 expression but in Table S1 it says that there was no CRISPR expression detected. Which one is correct? 

Thanks for spotting this. Table S5 is correct, we saw very dim and variable expression of the eat-4 reporter allele in I4. Table S1 is fixed now.

Additional discussion points that might be important for the community: 

CRIPSR strains used here should be deposited in the CGC. 

Yes, all strains generated in this study have already been deposited to CGC. 

It would be great to have an additional discussion point on how the neural clusters in CenGEN were defined based on the fosmid reporter expression, so in a way using the defining factor as one that was already defined by it might make results confusing. 

Neural cluster definition in CeNGEN did not rely on isolated data points but on the combination of many expression reagents, each with its own shortcomings, but in combination providing reliable identification. Since one feedback we have gotten from many readers of our manuscript is that it is already very long as is, we prefer not to dilute the discussion further.

It would be important to discuss the rate of neurotransmitter genes that have variable expression patterns. Are any of those genes used in NeuroPAL to define specific neuronal classes? This is important to describe as NeuroPAL labeling is being used to define neuronal identity. 

All the reporters used in NeuroPAL are promoter-based, very robust and do not include the full loci of genes, so they are not directly comparable with the CRISPR reporter alleles in this study. However, we recognize that some expression pattern variability could be confusing. We have discussed this more in the section "Comparing approaches and caveats of expression pattern analysis" in the Discussion.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, James Lee, Lu Bai, and colleagues use a multifaceted approach to investigate the relationship between transcription factor condensate formation, transcription, and 3D gene clustering of the MET regulon in the model organism S. cerevisiae. This study represents a second clear example of inducible transcriptional condensates in budding yeast, as most evidence for transcriptional condensates arises from studies of mammalian systems. In addition, this study links the genomic location of transcriptional condensates to the potency of transcription of a reporter gene regulated by the master transcription factor contained in the condensate. The strength of evidence supporting these two conclusions is strong. Less strong is evidence supporting the claim that Met4-containing condensates mediate the clustering of genes in the MET regulon.

Strengths:

The manuscript is for the most part clearly written, with the overriding model and specific hypothesis being tested clearly explained. Figure legends are particularly well written. An additional strength of the manuscript is that most of the main conclusions are supported by the data. This includes the propensity of Met4 and Met32 to form puncta-like structures under inducing conditions, formation of Met32-containing LLPS-like droplets in vitro (within which Met4 can colocalize), colocalization of Met4-GFP with Met4-target genes under inducing conditions, enhanced transcription of a Met3pr-GFP reporter when targeted within 1.5 - 5 kb of select Met4 target genes, and most impressively, evidence that several MET genes appear to reposition under transcriptionally inducing conditions. The latter is based on a recently reported novel in vivo methylation assay, MTAC, developed by the Bai lab.

Weaknesses:

My principal concern is that the authors fail to show convincing evidence for a key conclusion, highlighted in the title, that nuclear condensates per se drive MET gene clustering. Figure 4E demonstrates that Met4 molecules, not condensates per se, are necessary for fostering distant cis and trans interactions between MET6 and three other Met4 targets under -met inducing conditions. In addition, the paper would be strengthened by discussing a recent study conducted in yeast that comes to many of the same conclusions reported here, including the role of inducible TF condensates in driving 3D genome reorganization (Chowdhary et al, Mol. Cell 2022).

Following the reviewer’s advice, we carried out MTAC with the VP near MET6 in WT Met4 and ΔIDR2.3 strains (results shown below). The conclusions are somewhat ambiguous. For long-distance interactions with MUP1, YKG9, STR3, and MET13, we indeed observe decreased MTAC signals close to background levels in the ΔIDR2.3 strain, which aligns with the model suggesting that Met4 condensation promotes clustering among Met4 targeted genes. However, we also noticed significant decreases in the local MTAC signals (HIS3 and MET6). It is possible that the changes in Met4 condensates alter the chromosomal folding near MET6, thereby affecting the local MTAC signals. Alternatively, LacI-M.CviPI (the methyltransferase) could be induced to a lesser extent in the ΔIDR2.3 strain, leading to a genome-wide decrease in MTAC signals. Due to this ambiguity, we decided not to include the following plot in the main figure.

Author response image 1.

We discussed Hsf1 and added the suggested reference on page 13.

Other concerns:

(1) A central premise of the study is that the inducible formation of condensates underpins the induction of MET gene transcription and MET gene clustering. Yet, Figure 1 suggests (and the authors acknowledge) that puncta-like Met4-containing structures pre-exist in the nuclei of non-induced cells. Thus, the transcription and gene reorganization observed is due to a relatively modest increase in condensate-like structures. Are we dealing with two different types of Met4 condensates? (For example, different combinations of Met4 with its partners; Mediator- or Pol II-lacking vs. Mediator- or Pol II-containing; etc.?) At the very least, a comment to this effect is necessary.

Although Met4 can form smaller puncta in the +met condition (Figure 1A), it cannot be recruited to its target genes due to the absence of its sequence-specific binding partners, Met31 and Met32 (these two factors are actively degraded in the +met condition). Consistently, in the +met condition, Met4 shows extremely low genome-wide ChIP signals (Figure 3C). Therefore, these Met4 puncta in +met do not have organize the 3D genome or have gene regulatory functions. This discussion is added on page 12.

(2) Using an in vitro assay, the authors demonstrate that Met4 colocalizes with Met32 LLPS droplets (Figure 2F). Is the same true in vivo - that is, is Met32 required for Met4 condensation? This could be readily tested using auxin-induced degradation of Met32. Along similar lines, the claim that Met32 is required for MET gene clustering (line 250) requires auxin-induced degradation of this protein.

As the reviewer pointed out above, cells in the +met condition also show small Met4 puncta. In this condition, Met32 is essentially undetectable (Met31 level is even lower and remains undetectable even in the -met conditions). Therefore, Met4 does not strictly require the presence of Met32 in vivo (may require other factors or modifications). Met4 does not have DNA-binding activity, and therefore it cannot target and organize chromosomes on its own. Although we did not do the Met32 degradation experiment, we measured the 3D genome conformation in +met and showed that there are no detectable interactions among Met4 target genes.

(3) The authors use a single time point during -met induction (2 h) to evaluate TF clustering, transcription (mRNA abundance), and 3D restructuring. It would be informative to perform a kinetic analysis since such an analysis could reveal whether TF clustering precedes transcriptional induction or MET gene repositioning. Do the latter two phenomena occur concurrently or does one precede the other?

We appreciate the reviewer’s insightful question. It is indeed intriguing to consider whether TF clustering precedes transcriptional induction and MET gene clustering. However, as mentioned on page 12 of our manuscript, this experiment poses significant challenges. The low intensities of the Met4 and Met32 signals necessitate high excitation for imaging, which also makes them prone to photo-bleaching. Consequently, we have been unable to measure the dynamics of Met4 and Met32 puncta in vivo, let alone co-image them with DNA/RNA. Undertaking this experiment will require considerable effort, which we plan to pursue in the future.

(4) Based on the MTAC assay, MET13 does not appear to engage in trans interactions with other Met4 targets, whereas MET6 does (Figures 4C and 4E). Does this difference stem from the greater occupancy of Met4 at MET6 vs. MET13, greater association of another Met co-factor with the chromatin of MET6 vs. MET13, or something else?

We were also surprised by this result, given that MET13 emerged as one of the strongest transcriptional hotspots in our previous screen. It also exhibits one of the highest Met4 ChIP signals and is closely associated with the nuclear pore complex. Our earlier findings indicate that DNA dynamics near the VP significantly influence the MTAC signal; specifically, a VP with constrained motion is less effective at methylating interacting sites (Li et al., 2024). Therefore, it is plausible that MET13 is associated with a large Met4 condensate, which constrains the motion of nearby chromatin and diminishes MTAC efficiency.

Reviewer #2 (Public Review):

Summary:

This manuscript combines live yeast cell imaging and other genomic approaches to study how transcription factor (TF) condensates might help organize and enhance the transcription of the target genes in the methionine starvation response pathway. The authors show that the TFs in this response can form phase-separated condensates through their intrinsically disordered regions (IDRs), and mediate the spatial clustering of the related endogenous genes as well as reporter inserted near the endogenous target loci.

Strengths:

This work uses rigorous experimental approaches, such as imaging of endogenously labeled TFs, determining expression and clustering of endogenous target genes, and reporter integration near the endogenous target loci. The importance of TFs is shown by rapid degradation. Single-cell data are combined with genomic sequencing-based assays. Control loci engineered in the same way are usually included. Some of these controls are very helpful in showing the pathway-specific effect of the TF condensates in enhancing transcription.

Weaknesses:

Perhaps the biggest weakness of this work is that the role of IDR and phase separation in mediating the target gene clustering is unclear. This is an important question. TF IDRs may have many functions including mediating phase separation and binding to other transcriptional molecules (not limited to proteins and may even include RNAs). The effect of IDR deletion on reduced Fano number in cells could come from reduced binding with other molecules. This should be tested on phase separation of the purified protein after IDR deletion. Also, the authors have not shown IDR deletion affects the clustering of the target genes, so IDR deletion may affect the binding of other molecules (not the general transcription machinery) that are specifically important for target gene transcription. If the self-association of the IDR is the main driving force of the clustering and target gene transcription enhancement, can one replace this IDR with totally unrelated IDRs that have been shown to mediate phase separation in non-transcription systems and still see the gene clustering and transcription enhancement effects? This work has all the setup to test this hypothesis.

We thank the reviewer for raising this point, and we tried more in vitro and in vivo experiments with Met4 IDR deletions. See the answer to Reviewer 1 for the in vivo 3D mapping experiment.

We purified Met4-ΔIDR2 with an MBP tag, but its low yield made labeling and conducting thorough experiments challenging. At concentrations above ~10 μM, the protein tends to aggregate, while at lower concentrations, it remains diffusive in solution and does not form condensates. When we mixed purified Met4-ΔIDR2 with Met32, we observed reduced partitioning inside Met32 condensates compared to the full-length Met4. As the reviewer noted, this diminished interaction may contribute to the decreased puncta formation observed in vivo. This result is added to the manuscript on page 11 and supplementary figure 5.

The Met4 protein was tagged with MBP but Met 32 was not. MBP tag is well known to enhance protein solubility and prevent phase separation. This made the comparison of their in vitro phase behavior very different and led the authors to think that maybe Met32 is the scaffold in the co-condensates. If MBP was necessary to increase yield and solubility during expression and purification, it should be cleaved (a protease cleavage site should be engineered) to allow phase separation in vitro.

Following the reviewer’s advice, we purified Met4-TEV-MBP so that the MBP can be cleaved off. Unfortunately, concentrated Met4-TEV-MBP needs to be stored at high salt (400mM) to be soluble. When exchanged into a suitable buffer for TEV cleavage (≤200 mM NaCl), nearly all soluble protein aggregates. Attempts to digest the protein in storage buffer results in observable aggregation before significant cleavage (see below).  

Author response image 2.

Are ATG36 and LDS2 also supposed to be induced by -met? This should be explained clearly. The signals are high at -met.

Genomic loci ATG36 and LDS2 were chosen as controls because they are not bound by Met TFs (ChIP-seq tracks) and their expressions are not induced by -met (RNA-seq data). This information is added to the manuscript on page 9. When MET3pr-GFP reporter is inserted into these loci, GFP is induced by -met (because it is driven by the MET3 promoter), but the induction level is less than the same reporter inserted into the transcriptional hotspot like MET13 and MET6 (Figure 6E, also see Du et al., Plos Genetics, 2017).

ChIP-seq data:

Author response image 3.

RNA-seq counts:

Author response table 1.

Figure 6B, the Met4-GFP seems to form condensates at all three loci without a very obvious difference, though 6C shows a difference. 6C is from only one picture each. The authors should probably quantify the signals from a large number of randomly selected pictures (cells) and do statistics.

If we understand this comment correctly, the reviewer is referring to the fact that all three loci in Figure 6B appear to show a peak in GFP intensity. This pattern emerges because these images are averaged among many cells (number of cells analyzed in 6B has been added to the Figure legends). GFP intensities near the center will always be higher because peripheral pixels are more likely to fall outside the nuclei boundaries, where Met4 signals are absent (same as in Figure 3F). Importantly, MET6 locus shows higher intensity near the center in comparison to PUT1 and ATG36, indicating its co-localization with Met4 condensates.

Reviewer #3 (Public Review):

Summary:

In this study, the authors probe the connections between clustering of the Met4/32 transcription factors (TFs), clustering of their regulatory targets, and transcriptional regulation. While there is an increasing number of studies on TF clustering in vitro and in vivo, there is an important need to probe whether clustering plays a functional role in gene expression. Another important question is whether TF clustering leads to the clustering of relevant gene targets in vivo. Here the authors provide several lines of evidence to make a compelling case that Met4/32 and their target genes cluster and that this leads to an increase in transcription of these genes in the induced state. First, they found that, in the induced state, Met4/32 forms co-localized puncta in vivo. This is supported by in vitro studies showing that these TFs can form condensates in vitro with Med32 being the driver of these condensates. They found that two target genes, MET6 and MET13 have a higher probability of being co-localized with Met4 puncta compared with non-target loci. Using a targeted DNA methylation assay, they found that MET13 and MET6 show Met4-dependent long-range interactions with other Met4-regulated loci, consistent with the clustering of at least some target genes under induced conditions. Finally, by inserting a Met4-regulated reporter gene at variable distances from MET6, they provide evidence that insertion near this gene is a modest hotspot for activity.

Weaknesses:

(1) Please provide more information on the assay for puncta formation (Figure 1). It's unclear to me from the description provided how this assay was able to quantitate the number of puncta in cells.

Due to the variation in puncta size and intensity (as illustrated in Figure 1A), counting the number of puncta would be highly subjective with arbitrary cutoffs. Therefore, we chose to calculate the CV and Fano values instead, which are unbiased measures. Proteins that form puncta will exhibit greater pixel-to-pixel variations in GFP intensity, resulting in higher CV and Fano values.

(2) How does the number of puncta in cells correspond with the number of Met-regulated genes? What are the implications of this calculation?

As previously mentioned, defining the exact number of Met4 puncta is challenging. The number of puncta does not necessarily have one-to-one correspondence to the number of Met4 target genes. Some puncta may not be associated with chromosomes, while others may interact with multiple genes.

(3) A control for chromosomal insertion of the Met-regulated reporter was a GAL4 promoter derivative reporter. However, this control promoter seems 5-10 fold more active than the Met-regulated promoter (Figure 6). It's possible that the high activity from the control promoter overcomes some other limiting step such that chromosomal location isn't important. It would be ideal if the authors used a promoter with comparable activity to the Met-reporter as a control.

We agree with the reviewer that it will be better to use another promoter with comparable activity. Indeed, this was our rationale for selecting the attenuated GAL1 promoter over the WT version; however, it still exhibited substantially higher activity than the MET3pr. Unfortunately, we do not have a promoter from a different pathway that is calibrated to match the activity level of MET3pr. Nonetheless, MET17pr has much higher activity (~3 fold) than MET3pr, and we observed similar degree of stimulus from the hotspot in comparison to the control locus for both promoters (1.5-2-fold increase in GFP expression) (Figure 6E & F). This suggests that the observed effects are more likely to depend on the activation pathway and TF identity rather than the promoter strength.

(4) It seems like transcription from a very large number of genes is altered in the Met4 IDR mutant (Figure 7F). Why is this and could this variability affect the conclusions from this experiment?

We agree with the reviewer that ΔIDR 2.3 truncation affects the expression of 2711 (P-adj <0.05) genes (1339 up,1372 down). We suspect that this is due to the decreased expression of Met4 target genes, leading to altered levels of methionine and other sulfur-containing metabolites. Such changes would have a global impact on gene expression. Importantly, despite the similar number of genes that show up vs down regulation in the ΔIDR 2.3 strain, almost all Met4 targets showed decreased expression (Fig 7F). This supports the model where Met4 condensates lead to increased expression in its target genes.

Recommendations for the authors:

Reviewer #1 (Recommendations for The Authors):

(1) The introduction contains multiple miscitations. Rather than gene clustering, most of the studies and reviews cited (e.g., lines 35-39) report interactions between genomic loci (E-E, E-P, and P-P). There are other claims not supported by the papers cited. Moreover, the authors lump together original research papers and reviews within a given group without distinguishing which is which.

We thank the reviewer for pointing this out. We reorganized the references in the introduction.

(2) One option to address the concern regarding the lack of evidence that nuclear condensates per se drive MET gene clustering is to test the impact of Met4 ΔIDR2.3 on MTAC signals.

We carried out the suggested experiment. See answer above (Reviewer #1, Question #1).

(3) Authors claim that there are significant differences between values depicted in Figures 1B and 3G. Statistical tests are necessary to show this.

Significance values were calculated in comparison to free GFP using two-tailed Student’s t-test in 1B,1C, and 3G. The corresponding figure legends are updated.

(4) How are the data in Figures 3F, G, and 6B, C generated? This is unclear from the information provided in the Figure legends and Materials and Methods.

For each cell, we projected the highest mCherry and GFP intensity at each pixel for all z positions onto a 2D plane (MIP). The MIP images were aligned with the mCherry dot at the center and averaged among all cells. To calculate the GFP intensities like in Figure 3G and 6C, a single line was drawn across the center and the GFP profile was analyzed by ImageJ. We now describe this in the corresponding figure legends, and the Materials and Methods are also updated.

(5) Typos/ unclear writing: lines 24, 58, 79, 82, 84, 96, 117, 121, 131, 142, 147, 161 (terminus, not "terminal"), 250, 325, 349, 761 (was, not "are"). For several of these: "condense" is not "condensate"; for many others: inappropriate use of "the". Supplementary Figure 1 legend: not "a single nuclei" instead "a single nucleus".

We thank the reviewer for pointing this out. We tried our best to correct grammatical errors.

(6) Define GAL1Spr (Figure 6F).

The GAL1S promoter is an attenuated GAL1 promoter that lacks two out of the four Gal4 binding site. The original paper is now cited in the manuscript on page 10.  

(7) Figure 7B, C: there appears to be an inconsistency between the image and bar graph value for ΔIDR3.

The Fano values calculated in 7C are averaged among a population of cells (we added the cell numbers to the legend), while the image in 7B is an example of an individual nucleus. There is some cell-to-cell variability in how the Met4 appears. To be more representative, we chose a different image for ΔIDR3.

(8) Supplementary Tables: use descriptive titles for file names.

This is corrected.

Reviewer #2 (Recommendations For The Authors):

Minor:

Figure 4F is not cited in the text, and the color legend seems wrong for targeted and control.

Figure 4F is now cited in the text. The labels were corrected.

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Reply to the reviewers

1. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

Reviewer #1 (Evidence, reproducibility, and clarity (Required)): This is an interesting manuscript from the Jagannathan laboratory, which addresses the interaction proteome of two satellite DNA-binding proteins, D1 and Prod. To prevent a bias by different antibody affinities they use GFP-fusion proteins of D1 and Prod as baits and purified them using anti GFP nanobodies. They performed the purifications in three different tissues: embryo, ovary and GSC enriched testes. Across all experiments, they identified 500 proteins with surprisingly little overlap among tissues and between the two different baits. Based on the observed interaction of prod and D1 with members of the canonical piRNA pathway the authors hypothesized that both proteins might influence the expression of transposable elements. However, neither by specific RNAi alleles or mutants that lead to a down regulation of D1 and Prod in the gonadal soma nor in the germline did they find an effect on the repression of transposable elements. They also did not detect an effect of a removal of piRNA pathway proteins on satellite DNA clustering, which is mediated by Prod and D1. However, they do observe a mis-localisation of the piRNA biogenesis complex to an expanded satellite DNA in absence of D1, which presumably is the cause of a mis-regulation of transposable elements in the F2 generation.This is an interesting finding linking satellite DNA and transposable element regulation in the germline. However, I find the title profoundly misleading as the link between satellite DNA organization and transgenerational transposon repression in Drosophila has not been identified by multi-tissue proteomics but by a finding of the Brennecke lab that the piRNA biogenesis complex has a tendency to localise to satellite DNA when the localisation to the piRNA locus is impaired. Nevertheless, the investigation of the D1 and Prod interactome is interesting and might reveal insights into the pathways that drive the formation of centromeres in a tissue specific manner.

We thank the reviewer for the overall positive comments on our manuscript. As noted above, we have performed a substantial number of revision experiments and improved our text. We believe that our revised manuscript demonstrates a clear link between our proteomics data and the transposon repression. We would like to make three main points,

1. Our proteomics data identified that D1 and Prod co-purified transposon repression proteins in embryos. To test the functional significance of this association, we have used Drosophila genetics to generate flies lacking embryonic D1. In adult ovaries from these flies, we observe a striking elevation in transposon expression and Chk2-dependent gonadal atrophy. Along with the results from the control genotypes (F1 D1 mutant, F2 D1 het), our data clearly indicate that embryogenesis (and potentially early larval development) are a period when D1 establishes heritable TE silencing that can persist throughout development.
2. Based on the newly acquired RNA-seq and small RNA seq data, we have edited our title to more accurately reflect our data. Specifically, we have substituted the word 'transgenerational' with 'heritable', meaning that the presence of D1 during early development alone is sufficient to heritably repress TEs at later stages of development.
3. In addition, our RNA seq and small RNA seq experiments demonstrate that D1 makes a negligible contribution to piRNA biogenesis and TE repression in adults, despite the significant mislocalization of the RDC complex. In this regard, our results are substantially different from the recent Kipferl study from the Brennecke lab (Baumgartner et al. 2022).

   Major comments Unfortunately, the proteomic data sets are not very convincing. Not even the corresponding baits are detected in all assays. I wonder whether the extraction procedure really allows the authors to analyze all functionally relevant interactions of Prod and D1. It would be good to see a western blot or an MS analysis of the soluble nuclear extract they use for purification compared to the insoluble chromatin. It may well be that a large portion of Prod or D1 is lost in this early step. I also find the description of the proteomic results hard to follow as the authors mostly list which proteins the find as interactors of Prod and D1 without stating in which tissue or with what bait they could purify them (i.e. p7: Importantly, our hits included known chromocenter-associated or pericentromeric heterochromatin-associated proteins, such as Su(var)3-9[52], ADD1[23,24], HP5[23,24],mei-S332[53], Mes-4[23], Hmr[24,39,54], Lhr[24,39], and members of the chromosome passenger complex, such as borr and Incenp[55]). It would be interesting to at least discuss tissue specific interactions.

Out of six total AP-MS experiments in this manuscript (D1 x 3, Prod x 2 and Piwi), we observe a strong enrichment of the bait in 5/6 attempts (log2FC between 4-12). In the initial submission, the lack of a third high-quality biological replicate for the D1 testis sample meant that only the p-value (0.07) was not meeting the cutoff. To address this, we have repeated this experiment with an additional biological replicate (Fig. S2A), and our data now clearly show that D1 is significantly enriched in the testis sample.

As suggested by the reviewer, we have also assessed our lysis conditions (450mM NaCl and benzonase) and the solubilization of our baits post-lysis. In Fig. S1D, we have blotted equivalent fractions of the soluble supernatant and insoluble pellet from GFP-Piwi embryos and show that both GFP-Piwi and D1 are largely solubilized following lysis. In Fig. S1E, we also show that our IP protocol works efficiently.

GFP-Prod pulldown in embryos is the only instance in which we do not detect the bait by mass spectrometry. Here, one reason could be relatively low expression of GFP-Prod in comparison to GFP-D1 (Fig. S1E), which may lead to technical difficulties in detecting peptides corresponding to Prod. However, we note that Prod IP co-purified proteins such as Bocks that were previously suggested as Prod interactors from high-throughput studies (Giot et al. 2003; Guruharsha et al. 2011). In addition, Prod IP from embryos also co-purified proteins known to associate with chromocenters such as Hcs and Saf-B. Finally, the concordance between D1 and Prod co-purified proteins from embryo lysates (Fig. 2A, C) suggest that the Prod IP from embryos is of reasonable quality.

We also acknowledge the reviewer's comment that the description of the proteomic data was hard to follow. Therefore, we have revised our text to clearly indicate which bait pulled down which protein in which tissue (lines 148-156). We have also highlighted and discussed bait-specific and tissue-specific interactions in the text (lines 162-173).

Minor comments The authors may also want to provide a bit more information on the quantitation of the proteomic data such as how many values were derive from the match-between runs function and how many were imputed as this can severely distort the quantification.

Figure 1: Distribution of data after imputation in embryo (left), ovary (middle) and testis (right) datasets. Imputation is performed with random sampling from the 1% least intense values generated by a normal distribution.

To ensure the robustness of our data analysis, we considered only those proteins that were consistently identified in all replicates for at least one bait (GFP-D1, GFP-Prod or NLS-GFP). This approach resulted in a relative low number of missing values. However, it is also important to bear in mind that in an AP-MS experiment, the number of missing values is higher, as interactors are not identified in the control pulldown. Importantly, the imputation of missing values during the data analysis did not alter the normal distribution of the dataset (Fig. 1, this document). Detailed information regarding the imputed values is also provided (Table 1, this document). The coding script used for the data analysis is available in the PRIDE submission of the dataset (Table 2, this document). This information has been added to our methods section under data availability.

Table 1: ____Number of match-between-runs and imputations for embryo, ovary and testis datasets

Dataset

#match-between-runs

%match-between-runs

%imputation

Embryo

5541/27543

20.11%

8.36%

Ovary

1936/9530

20.30%

8.18%

Testis

1748/7168

24.39%

3.12%

Table 2: ____Access to the PRIDE submission of the datasets

Name

ID PRIDE

UN reviewer

PW reviewer

IP-MS of D1 from Testis tissue

PXD044026

reviewer_pxd044026@ebi.ac.uk

ydswDQVW

IP-MS of Piwi from Embryo tissue

PXD043237

reviewer_pxd043237@ebi.ac.uk

TMCoDsdx

IP-MS of Prod and D1 proteins from Ovary tissue

PXD043236

reviewer_pxd043236@ebi.ac.uk

VOHqPmaS

IP-MS of Prod and D1 proteins from Embryo tissue

PXD043234

reviewer_pxd043234@ebi.ac.uk

L77VXdvA

**Referee Cross-Commenting** I agree with the two other reviewers that the connection between the interactome and the transgenerational phenotype is unclear. This is also what I meant i my comment that the title is somewhat misleading. A systematic analysis of the D1 and Prod knock down effect on mRNAs and small Rnas would indeed be helpful to better understand the interesting effect.

As suggested by the reviewer, we have performed RNA seq and small RNA seq in control and D1 mutant ovaries (Fig. 4) to fully understand the contribution of D1 in piRNA biogenesis and TE repression. Briefly, the mislocalization of RDC complex in D1 mutant ovaries does not significantly affect TE-mapping piRNA biogenesis (Fig. 4C, E). In addition, loss of D1 does not substantially alter TE expression in the ovaries (Fig. 4B) or alter the expression of genes involved in TE repression (Fig. 4F). Along with the results presented in Fig. 5 and Fig. 6, our data clearly indicate that embryogenesis (and potentially early larval development) is a critical period during which D1 makes an important contribution to TE repression.

Reviewer #1 (Significance (Required)): Nevertheless, the investigation of the D1 and Prod interactome is interesting and might reveal insights into the pathways that drive the formation of centromeres in a tissue specific manner. It may be mostly interesting for the Drosophila community but could also be exiting for a broader audience interested in the connection of heterochromatin and its indirect effect on the regulation of transposable elements.

We thank the reviewer again for the helpful and constructive comments, which have enabled us to significantly improve our study. We are excited by the results from our study, which illuminate unappreciated aspects of transcriptional silencing in constitutive heterochromatin.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): Chavan et al. set out to enrich our compendium of pericentric heterochromatin-associated proteins - and to learn some new biology along the way. An ambitious AP-Mass baited with two DNA satellite-binding proteins (D1 and Prod), conducted across embryos, ovaries, and testes, yielded hundreds of candidate proteins putatively engaged at chromocenters. These proteins are enriched for a restricted number of biological pathways, including DNA repair and transposon regulation. To investigate the latter in greater depth, the authors examine D1 and prod mutants for transposon activity changes using reporter constructs for multiple elements. These reporter constructs revealed no transposon activation in the adult ovary, where many proteins identified in the mass spec experiments restrict transposons. However, the authors suggest that the D1 mutant ovaries do show disrupted localization of a key member of a transposon restriction pathway (Cuff), and infer that this mislocalization triggers a striking, transposon derepression phenotype in the F2 ovaries.

The dataset produced by the AP-Mass Spec offers chromosome biologists an unprecedented resource. The PCH is long-ignored chromosomal region that has historically received minimal attention; consequently, the pathways that regulate heterochromatin are understudied. Moreover, attempting to connect genome organization to transposon regulation is a new and fascinating area. I can easily envision this manuscript triggering a flurry of discovery; however, there is quite a lot of work to do before the data can fully support the claims.

We appreciate the reviewer taking the time to provide thoughtful comments and constructive suggestions to improve the manuscript. We believe that we have addressed all the comments raised to the significant advantage of our paper.

Major comments 1. The introduction requires quite a radical restructure to better highlight the A) importance of the work and B) limit information whose relevance is not clear early in the manuscript. A. Delineating who makes up heterochromatin is a long-standing problem in chromosome biology. This paper has huge potential to contribute to this field; however, it is not the first. Others are working on this problem in other systems, for example PMID:29272703. Moreover, we have some understanding of the distinct pathways that may impact heterochromatin in different tissues (e.g., piRNA biology in ovaries vs the soma). Also, the mutant phenotypes of prod and D1 are different. Fleshing out these three distinct points could help the reader understand what we know and what we don't know about heterochromatin composition and its special biology. Understanding where we are as a field will offer clear predictions about who the interactors might be that we expect to find. For example, given the dramatically different D1 and prod mutant phenotypes (and allele swap phenotypes), how might the interactors with these proteins differ? What do we know about heterochromatin formation differences in different tissues? And how might these differences impact heterochromatin composition?

The reviewer brings up a fair point and we have significantly reworked our introduction. We share the reviewer's opinion that improved knowledge of the constitutive heterochromatin proteome will reveal novel biology.

1. The attempt to offer background on the piRNA pathway and hybrid dysgenesis in the Introduction does not work. As a naïve reader, it was not clear why I was reading about these pathways - it is only explicable once the reader gets to the final third of the Results. Moreover, the reader will not retain this information until the TE results are presented many pages later. I strongly urge the authors to shunt the two TE restriction paragraphs to later in the manuscript. They are currently a major impediment to understanding the power of the experiment - which is to identify new proteins, pathways, and ultimately, biology that are currently obscure because we have so little handle on who makes up heterochromatin.

We agree with this suggestion. We have introduced the piRNA pathway in the results section (lines 205 - 216), right before this information is needed. We've also removed the details on hybrid dysgenesis, since our new data argues against a maternal effect from the D1 mutant.

The implications of the failure to rescue female fertility by the tagged versions of both D1 and Prod are not discussed. Consequently, the reader is left uneasy about how to interpret the data.

We understand this point raised by the reviewer. However, in our proteomics experiments, we have used GFP-D1 and GFP-Prod ovaries from ~1 day old females (line 579, methods). These ovaries are morphologically similar to the wild type (Fig. S1C) and their early germ cell development appears to be intact. Moreover, chromocenter formation in female GSCs is comparable to the wildtype (Fig. 1C-D). These data, along with the rescue of the viability of the Prod mutant (Fig. S1A-B), suggest that the presence of a GFP tag is not compromising D1 or Prod function in the early stages of germline development and is consistent with published and unpublished data from our lab. In addition, D1 and Prod from ovaries co-purify proteins such as Rfc38 (D1), Smn (D1), CG15107 (Prod), which have been identified in previous high-throughput screens (Guruharsha et al. 2011; Tang et al. 2023). For these reasons, we believe that GFP-D1 and GFP-Prod ovaries are a good starting point for the AP-MS experiment. We speculate that the failure to completely rescue female fertility may be due to improper expression levels of GFP-D1 or GFP-Prod flies at later stages of oogenesis, which are not present in ovaries from newly eclosed females and therefore unlikely to affect our proteomic data.

1. How were the significance cut-offs determined? Is the p-value reported the adjusted p-value? As a non-expert in AP-MS, I was surprised to find that the p-value, at least according to the Methods, was not adjusted based on the number of tests. This is particularly relevant given the large/unwieldy(?) number of proteins that were identified as signficant in this study. Moreover, the D1 hit in Piwi pull down is actually not significant according to their criteria of p We used a standard cutoff of log2FC>1 and p2FC and low p-value) since these are more likely to be bona fide interactors. Third, we have used string-DB for functional analyses where we can place our hits in existing protein-protein interaction networks. Using this approach, we detect that Prod (but not D1) pulls down multiple members of the RPA complex in the embryo (RPA2 and RpA-70, Fig. S2B) while embryonic D1 (but not Prod) pulls down multiple components of the origin recognition complex (Orc1, lat, Orc5, Orc6, Fig. S2C) and the condensin I complex (Cap-G, Cap-D2, barr, Fig. S2D). Altogether, these filtering strategies allow us to eliminate as many false positives as possible while making sure to minimize the loss of true hits through multiple testing correction.

How do we know if the lack of overlap across tissues is indeed germline- or soma-specialization rather than noise?

To address this part of the comment, we have amended our text (lines 162-173) as follows,

'We also observed a substantial overlap between D1- and Prod-associated proteins (yellow points in Fig. 2A, B, Table S1-3), with 61 hits pulled down by both baits (blue arrowheads, Fig. 2C) in embryos and ovaries. This observation is consistent with the fact that both D1 and Prod occupy sub-domains within the larger constitutive heterochromatin domain in nuclei. Surprisingly, only 12 proteins were co-purified by the same bait (D1 or Prod) across different tissues (magenta arrowheads, Fig. 2C, Table S1-3). In addition, only a few proteins such as an uncharacterized DnaJ-like chaperone, CG5504, were associated with both D1 and Prod in embryos and ovaries (Fig. 2D). One interpretation of these results is that the protein composition of chromocenters may be tailored to cell- and tissue-specific functions in Drosophila. However, we also note that the large number of unidentified peptides in AP-MS experiments means that more targeted experiments are required to validate whether certain proteins are indeed tissue-specific interactors of D1 and Prod.'

To make this inference, conducting some validation would be required. More generally, I was surprised to see no single interactor validated by reciprocal IP-Westerns to validate the Mass-Spec results, though I am admittedly only adjacent to this technique. Note that colocalization, to my mind, does not validate the AP-MS data - in fact, we would a priori predict that piRNA pathway members would co-localize with PCH given the enrichment of piRNA clusters there.

Here, we would point out that we have conducted multiple validation experiments with a specific focus on the functional significance of the associations between D1/Prod and TE repression proteins in embryos. While the reviewer suggests that piRNA pathway proteins may be expected to enrich at the pericentromeric heterochromatin, this is not always the case. For example, Piwi and Mael are present across the nucleus (pulled down by D1/Prod in embryos) while Cutoff, which is present adjacent to chromocenters in nurse cells, was not observed to interact with either D1 or Prod in the ovary samples.

Therefore, for Piwi, we performed a reciprocal AP-MS experiment in embryos due to the higher sensitivity of this method (GFP-Piwi AP-MS, Fig. 3B). Excitingly, this experiment revealed that four largely uncharacterized proteins (CG14715, CG10208, Ugt35D1 and Fit) were highly enriched in the D1, Prod and Piwi pulldowns and these proteins will be an interesting cohort for future studies on transposon repression in Drosophila (Fig. 3C).

Furthermore, we believe that determining the localization of proteins co-purified by D1/Prod is an important and orthogonal validation approach. For Sov, which is implicated in piRNA-dependent heterochromatin formation, we observe foci that are in close proximity to D1- and Prod-containing chromocenters (Fig. 3A).

As for suggestion to validate by IP-WBs, we would point out that chromocenters exhibit properties associated with phase separated biomolecular condensates. Based on the literature, these condensates tend to associate with other proteins/condensates through low affinity or transient interactions that are rarely preserved in IP-WBs, even if there are strong functional relationships. One example is the association between D1 and Prod, which do not pull each other down in an IP-WB (Jagannathan et al. 2019), even though D1 and Prod foci dynamically associate in the nucleus and mutually regulate each other's ability to cluster satellite DNA repeats (Jagannathan et al. 2019). Similarly, IP-WB using GFP-Piwi embryos did not reveal an interaction with D1 (Fig. S4B). However, our extensive functional validations (Figures 4-6) have revealed a critical role for D1 in heritable TE repression.

The AlphaFold2 data are very interesting but seem to lack of negative control. Is it possible to incorporate a dataset of proteins that are not predicted to interact physically to elevate the impact of the ones that you have focused on? Moreover, the structural modeling might suggest a competitive interaction between D1 and piRNAs for Piwi. Is this true? And even if not, how does the structural model contribute to your understanding for how D1 engages with the piRNA pathway? The Cuff mislocalization?

In the revised manuscript, we have generated more structural models using AlphaFold Multimer (AFM) for proteins (log2FC>2, p0.5 and ipTM>0.8), now elaborated in lines 175-177. Despite the extensive disorder in D1 and Prod, we identified 22 proteins, including Piwi, that yield interfaces with ipTM scores >0.5 (Table S4, Table S8). These hits are promising candidates to further understand D1 and Prod function in the future.

For the predicted model between Prod/D1 and Piwi (Fig. S4A), one conclusion could indeed be competition between D1/Prod and piRNAs for Piwi. Another possibility is that a transient interaction mediated by disordered regions on D1/Prod could recruit Piwi to satellite DNA-embedded TE loci in the pericentromeric heterochromatin. These types of interactions may be especially important in the early embryonic cycles, where repressive histone modifications such as H3K9me2/3 must be deposited at the correct loci for the first time. We suggest that mutating the disordered regions on D1 and Prod to potentially abrogate the interaction with Piwi will be important for future studies.

The absence of a TE signal in D1 and Prod mutant ovaries would be much more compelling if investigated more agnostically. The observation that not all TE reporter constructs show a striking signal in the F2 embryos makes me wonder if Burdock and gypsy are not regulated by these two proteins but possibly other TEs are. Alternatively, small RNA-seq would more directly address the question of whether D1 and Prod regulate TEs through the piRNA pathway.

We completely agree with this comment from the reviewer. We have performed RNA seq on D1 heterozygous (control) and D1 mutant ovaries in a chk26006 background. Since Chk2 arrests germ cell development in response to TE de-repression and DNA damage(Ghabrial and Schüpbach 1999; Moon et al. 2018), we reasoned that the chk2 mutant background would prevent developmental arrest of potential TE-expressing germ cells and we observed that both genotypes exhibited similar gonad morphology (Fig. 4A). From our analysis, we do not observe a significant effect on TE expression in the absence of D1, except for the LTR retrotransposon tirant (Fig. 4B). We also do not observe differential expression of TE repression genes (Fig. 4F).

We have complemented our RNA seq experiment with small RNA profiling from D1 heterozygous (control) and D1 mutant ovaries. Here, overall piRNA production and antisense piRNAs mapping to TEs were largely unperturbed (Fig. 4C-E).

Overall, our data is consistent with the TE reporter data (Fig. S7) and suggests that zygotic depletion of D1 does not have a prominent role in TE repression. However, we have uncovered that the presence of D1 during embryogenesis is critical for TE repression in adult gonads (Fig. 6, Fig. S9).

I had trouble understanding the significance of the Cuff mis-localization when D1 is depleted. Given Cuff's role in the piRNA pathway and close association with chromatin, what would the null hypothesis be for Cuff localization when a chromocenter is disrupted? What is the null expectation of % Cuff at chromocenter given that the chromocenter itself expands massively in size (Figure 4D). The relationship between these two factors seems rather indirect and indeed, the absence of Cuff in the AP would suggest this. The biggest surprise is the absence of TE phenotype in the ovary, given the Cuff mutant phenotype - but we can't rule out given the absence of a genome-wide analysis. I think that these data leave the reader unconvinced that the F2 phenotype is causally linked to Cuff mislocalization.

We apologize that this data was not more clearly represented. In a wild-type context, Cuff is distributed in a punctate manner across the nurse cell nuclei, with the puncta likely representing piRNA clusters (Fig. 5A-B). We find that a small fraction of Cuff (~5%) is present adjacent to the nurse cell chromocenter (inset, Fig. 5A and Fig. 5D). In the absence of D1, the nurse cell chromocenters increase ~3-4 fold in size. However, the null expectation is still that ~5% of total Cuff would be adjacent to the chromocenter, since the piRNA clusters are not expected to expand in size. In reality, we observe ~27% of total Cuff is mislocalized to chromocenters. Our data indicate that the satellite DNA repeats at the larger chromocenters must be more accessible to Cuff in the D1 mutant nurse cells. This observation is corroborated by the significant increase in piRNAs corresponding to the 1.688 satellite DNA repeat family (Fig. 4E).

The lack of TE expression in the F1 D1 mutant was indeed surprising based on the Cuff mislocalization but as the reviewers pointed out, we only analyzed two TE reporter constructs in the initial submission. In the revised manuscript, we have performed both RNA seq and small RNA seq. Surprisingly, our data reveal that the Cuff mislocalization does not significantly affect piRNA biogenesis (Fig. 4C, D) and piRNAs mapping to TEs. As a result, both TE repression (Fig. 4B) and fertility (Fig. 6D) are largely preserved in the absence of D1 in adult ovaries.

Finally, we thank the reviewer for their excellent suggestion to incorporate the F2 D1 heterozygote (Fig. S9) in our analysis! This important control has revealed that the maternal effect of the D1 mutant is negligible for gonad development and fertility (Fig. 6B-D). Rather, our data clearly emphasize embryogenesis (or early larval development) as a key period during which D1 can promote heritable TE repression. Essentially, D1 is not required during adulthood for TE repression if it was present in the early stages of development.

Apologies if I missed this, but Figure 5 shows the F2 D1 mutant ovaries only. Did you look at the TM6 ovaries as well? These ovaries should lack the maternally provisioned D1 (assuming that females are on the right side) but have the zygotic transcription.

As mentioned above, this was a great suggestion and we've now characterized this important control in the context of germline development and fertility, to the significant advantage of our paper.

Minor comments 9. Add line numbers for ease of reference

We apologize for this. Line numbers have been added in the full revision.

1. The function of satellite DNA itself is still quite controversial - I would recommend being a bit more careful here - the authors could refer instead to genomic regions enriched for satellite DNA are linked to xyz function (see Abstract line 2 and 7, for example.)

The abstract has been rewritten and does not directly implicate satellite DNA in a specific cellular function. However, we have taken the reviewer's suggestion in the introduction (line 57-58).

"Genetic conflicts" in the introduction needs more explanation.

This sentence has been removed from the introduction in the revised manuscript.

"In contrast" is not quite the right word. Maybe "However" instead (1st line second paragraph of Intro)

Done. Line 57 of the revised manuscript.

Results: what is the motivation for using GSC-enriched testis?

We use GSC-enriched testes for practical reasons. First, they contain a relatively uniform population of mitotically dividing germ cells unlike regular testes which contain a multitude of post-mitotic germ cells such as spermatocytes, spermatids and sperm. Second, GSC-enriched testes are much larger than normal testes and reduced the number of dissections that were needed for each replicate.

1. Clarify sentence about the 500 proteins in the Results section - it's not clear from context that this is the union of all experiments.

Done. Lines 145-149 in the revised manuscript.

The data reported are not the first to suggest that satellite DNA may have special DNA repair requirements. e.g., PMID: 25340780

We apologize if we gave the impression that we were making a novel claim. Specialized DNA repair requirements at repetitive sequences have indeed been previously hypothesized(Charlesworth et al. 1994) and studied and we have altered the text to better reflect this (lines 193-195). We have cited the study suggested by the reviewer as well as studies from the Chiolo(Chiolo et al. 2011; Ryu et al. 2015; Caridi et al. 2018) and Soutoglou(Mitrentsi et al. 2022) labs, which have also addressed this fascinating question.

Page 10: indicate-> indicates.

Done.

1. Page 14: revise for clarity: "investigate a context whether these interactions could not take place"

We've incorporated this suggestion in the revised text (lines 383-386).

1. Might be important to highlight the 500 interactions are both direct and indirect. "Interacting proteins" alone suggests direct interactions only.

Done. Lines 145-149.

The effect of the aub mutant on chromocenter foci did not seem modest to me - however, the bar graphs obscure the raw data - consider plotting all the data not just the mean and error?

Done. This data is now represented by a box-and-whisker plot (Fig. S5), which shows the distribution of the data.

Reviewer #2 (Significance (Required)):

The dataset produced by the AP-Mass Spec offers chromosome biologists an unprecedented resource. The PCH is long-ignored chromosomal region that has historically received minimal attention; consequently, the pathways that regulate heterochromatin are understudied. Moreover, attempting to connect genome organization to transposon regulation is a new and fascinating area. I can easily envision this manuscript triggering a flurry of discovery; however, there is quite a lot of work to do before the data can fully support the claims.

This manuscript represents a significant contribution to the field of chromosome biology.

We thank the reviewer for the positive evaluation of our manuscript, and we really appreciate the great suggestion for the F2 D1 heterozygote control! Overall, we believe that our manuscript is substantially improved with the addition of RNA seq, small RNA seq and important genetic controls. Moreover, we are excited by the potential of our study to open new doors in the study of pericentromeric heterochromatin.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): In the manuscript entitled "Multi-tissue proteomics identifies a link between satellite DNA organization and transgenerational transposon repression", the authors used two satellite DNA-binding proteins, D1 and Prod, as baits to identify chromocenter-associated proteins through quantitative mass spectrometry. The proteomic analysis identified ~500 proteins across embryos, ovaries, and testes, including several piRNA pathways proteins. Depletion of D1 or Prod did not directly contribute to transposon repression in ovary. However, in the absence of maternal and zygotic D1, there was a dramatic increase of agametic ovaries and transgenerational transposon de-repression. Although the study provides a wealth of proteomic data, it lacks mechanistic insights into how satellite DNA organization influence the interactions with other proteins and their functional consequences.

We thank the reviewer for highlighting that this study will be a valuable resource for future studies on the composition and function of pericentromeric heterochromatin. Based on the reviewer's request for more mechanistic knowledge into how satellite DNA organization affects transposon repression, we have performed RNA seq and small RNA seq, added important genetic controls and significantly improved our text. In the revised manuscript, our data clearly demonstrate that embryogenesis (and potentially early larval development) is a critical time period when D1 contributes to heritable TE repression. Flies lacking D1 during embryogenesis exhibit TE expression in germ cells as adults, which is associated with Chk2-dependent gonadal atrophy. We are particularly excited by these data since TE loci are embedded in the satellite DNA-rich pericentromeric heterochromatin and our study demonstrates an important role for a satellite DNA-binding protein in TE repression.

Major____ comments 1. While the identification of numerous interactions is significant, it would be helpful to acknowledge that lots of these proteins were known to bind DNA or heterochromatin regions. To strengthen the study, I recommend conducting functional validation of the identified interactions, in addition to the predictions made by Alphfold 2.

We are happy to take this comment on board. In fact, we believe that the large number of DNA-binding and heterochromatin-associated proteins identified in this study are a sign of quality for the proteomic datasets. In the revised manuscript, we have highlighted known heterochromatin proteins co-purified by D1/Prod in lines 148-151 as well as proteins previously suggested to interact with D1/Prod from high-throughput studies in lines 153-156 (Guruharsha et al. 2011; Tang et al. 2023). In this study, we have focused on the previously unknown associations between D1/Prod and TE repression proteins and functionally validated these interactions as presented in Figures 3-6.

The observation of transgenerational de-repression is intriguing. However, to better support this finding, it would be better to provide a mechanistic explanation based on the data presented.

We appreciate this comment from the reviewer, which is similar to major comment #6 raised by reviewer #2. To generate mechanistic insight into the underlying cause of gonad atrophy in the F2 D1 mutant, we have performed RNA seq, small RNA seq and analyzed germline development and fertility in the F2 D1 heterozygous control (Fig. S9).

For the RNA seq, we used D1 heterozygous (control) and D1 mutant ovaries in a chk26006 background. Since Chk2 arrests germ cell development in response to TE de-repression and DNA damage(Ghabrial and Schüpbach 1999; Moon et al. 2018), we reasoned that the chk2 mutant background would prevent developmental arrest of potential TE-expressing germ cells and we observed that both genotypes exhibited similar gonad morphology (Fig. 4A). From our analysis, we do not observe a significant effect on TE expression in the absence of D1, except for the LTR retrotransposon tirant (Fig. 4B). We also do not observe differential expression of TE repression genes (Fig. 4F).

We have complemented our RNA seq experiment with small RNA profiling from D1 heterozygous (control) and D1 mutant ovaries. Here, overall piRNA production and antisense piRNAs mapping to TEs were largely unperturbed (Fig. 4C-E). Together, these data are consistent with the TE reporter data (Fig. S7) and suggests that zygotic depletion of D1 does not have a prominent role in TE repression.

However, we have uncovered that the presence of D1 during embryogenesis is critical for TE repression in adult gonads (Fig. 6, Fig. S9). Essentially, either only maternal deposited D1 (F1 D1 mutant) or only zygotically expressed D1 (F2 D1 het) were sufficient to ensure TE repression and fertility. In contrast, a lack of D1 during embryogenesis (F2 D1 mutant) led to elevated TE expression and Chk2-mediated gonadal atrophy.

Our results also explain why previous studies have not implicated either D1 or Prod in TE repression, since D1 likely persists during embryogenesis when using depletion approaches such as RNAi-mediated knockdown or F1 generation mutants.

Minor____ comments 3. Given the maternal effect of the D1 mutant, in Figure 4, I suggest analyzing not only nurse cells but also oocytes to gain a more comprehensive understanding.

We agree with the reviewer that this experiment can be informative. In the F2 D1 mutant ovaries, germ cell development does not proceed to a stage where oocytes are specified, thus limiting microscopy-based approaches. Nevertheless, we have gauged oocyte quality in all the genotypes using a fertility assay (Fig. 6D) since even ovaries that have a wild-type appearance can produce dysfunctional gametes. In this experiment, we observe that fertility is largely intact in the F1 D1 mutant and F2 D1 heterozygote strains, suggesting that it is the presence of D1 during embryogenesis (or early larval development) that is critical for heritable TE repression and proper oocyte development.

The conclusion that D1 and Prod do not directly contribute to the repression of transposons needs further analysis from RNA-seq data. Alternatively, the wording could be adjusted to indicate that D1 and Prod are not required for specific transposon silencing, such as Burdock and gypsy.

Agreed. We have performed RNA-seq in D1 heterozygous (control) and D1 mutant ovaries in a chk26006 background (Fig. 4A, B) as described above.

As D1 mutation affects Cuff nuclear localization, it would be insightful to sequence the piRNA in the ovaries.

Agreed. We have performed small RNA profiling from D1 heterozygous (control) and D1 mutant ovaries. Despite the significant mislocalization of the RDC complex, overall piRNA production and antisense piRNAs mapping to TEs were largely unaffected (Fig. 4C-E). However, we observed significant changes in piRNAs mapping to complex satellite DNA repeats (Fig. 4D), but these changes were not associated with a maternal effect on germline development or fertility (F2 D1 heterozygote, Fig. 6B-D).

**Referee Cross-Commenting**

I appreciate the valuable insights provided by the other two reviewers regarding this manuscript. I concur with their assessment that substantial improvements are needed before considering this manuscript for publication.

1. The proteomics data has the potential to be a valuable resource for other scientific community. However, I share the concerns raised by reviewer 1 about the current quality of the data sets. Addressing this, it will augment the manuscript with additional data to show the success of the precipitation. Moreover, as reviewer 2 and I suggested, additional co-IP validations of the IP-MS results are needed to enhance the reliability.

In the revised manuscript, we have performed multiple experiments to address the quality of the MS datasets based on comments raised by reviewer #1. They are as follows,

Out of six total AP-MS experiments in this manuscript (D1 x 3, Prod x 2 and Piwi), we observe a strong enrichment of the bait in 5/6 attempts (log2FC between 4-12, Fig. 2A, B, Fig. S2A, Table S1-S3, Table S7). In the D1 testis sample from the initial submission, the lack of a third biological replicate meant that only the p-value (0.07) was not meeting the cutoff. To address this, we have repeated this experiment with an additional biological replicate (Fig. S2A), and our data now clearly show that D1 is also significantly enriched in the testis sample.

As suggested by the reviewer #1, we have assessed our lysis conditions (450mM NaCl and benzonase) and the solubilization of our baits post-lysis. In Fig. S1D, we have blotted equivalent fractions of the soluble supernatant and insoluble pellet from GFP-Piwi embryos and show that both GFP-Piwi and D1 are largely solubilized following lysis. In Fig. S1E, we also show that our IP protocol works efficiently.

The only instance in which we do not detect the bait by mass spectrometry is for GFP-Prod pulldown in embryos. Here, one reason could be relatively low expression of GFP-Prod in comparison to GFP-D1 (Fig. S1E), which may lead to technical difficulties in detecting peptides corresponding to Prod. However, we note that Prod IP from embryos co-purified proteins such as Bocks that were previously suggested as Prod interactors from high-throughput studies (Giot et al. 2003; Guruharsha et al. 2011). In addition, Prod IP from embryos also co-purified proteins known to associate with chromocenters such as Hcs(Reyes-Carmona et al. 2011) and Saf-B(Huo et al. 2020). Finally, the concordance between D1 and Prod co-purified proteins from embryo lysates (Fig. 2A, C) suggest that the Prod IP from embryos is of reasonable quality.

As for the IP-WB validations, we would point out that chromocenters exhibit properties associated with phase separated biomolecular condensates. In our experience, these condensates tend to associate with other proteins/condensates through low affinity or transient interactions that are rarely preserved in IP-WBs, even if there are strong functional relationships. One example is the association between D1 and Prod, which do not pull each other down in an IP-WB (Jagannathan et al. 2019), even though D1 and Prod foci dynamically associate in the nucleus and mutually regulate each other's ability to cluster satellite DNA repeats (Jagannathan et al. 2019). Similarly, IP-WB using GFP-Piwi embryos did not reveal an interaction with D1 (Fig. S4B). However, our extensive functional validations (Figures 4-6) have revealed a critical role for D1 in heritable TE repression.

I agree with reviewer 2 that the present conclusion is not appropriate regarding D1 and Prod do not contribute to transposon silencing. As reviewer 2 and I suggested, the systematical analysis by using both mRNA-seq and small RNA-seq is required to draw the conclusion.

Agreed. We have performed RNA seq and small RNA seq as elaborated above. Our conclusions on the role of D1 in TE repression are now much stronger.

1. The transgenerational phenotype is an intriguing aspect of the study. I agree with reviewer 2 that the inclusion of TM6 ovaries would be a nice control. Further, it is hard to connect this phenotype with the interactions identified in this manuscript. It would be appreciated if the author could provide a mechanistic explanation.

We have significantly improved these aspects of our study in the revised manuscript. Through the analysis of germline development in the F2 D1 heterozygotes as suggested by reviewer #2, in addition to the recommended RNA seq and small RNA seq, we have now identified embryogenesis (and potentially early larval development) as a time period when D1 makes an important contribution to TE repression. Loss of D1 during embryogenesis leads to TE expression in adult germline cells, which is associated with Chk2-dependent gonadal atrophy. Taken together, we have pinpointed the specific contribution of a satellite DNA-binding protein to transposon repression.

Reviewer #3 (Significance (Required)):

Although this study successfully identified several interactions, the authors did not fully elucidate how these interactions contribute to the transgenerational silencing of transposons or ovary development.

We thank the reviewer for the thoughtful comments and overall positive evaluation of our study, especially the proteomic dataset. We are confident that the revised manuscript has improved our mechanistic understanding of the contribution made by a satellite DNA-binding protein in TE repression.

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