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Trong chương trình Bí mật đồng tiền diễn ra trưa ngày 17/5, ông Đào Phúc Tường, một chuyên gia chứng khoán với kinh nghiệm 17 năm trên thị trường, đánh giá rằng thị trường hiện đang đắt trong bối cảnh cơ hội ít hơn rủi ro. Ở khối tài chính, cụ thể với ngành ngân hàng, định giá P/B hiện khoảng 1,4 – 1,5 lần, vị chuyên gia cho rằng đây không hẳn là mức cao nhưng bức tranh phía trước của ngành ngân hàng lại mang một màu xám.

Cụ thể trong năm 2023, tăng trưởng tín dụng sẽ rất khó khăn khiến biên lợi nhuận NIM ngân hàng giảm, các trụ chính liên quan đến thu phí của ngân hàng cũng sẽ giảm. Đặc biệt, thông qua báo cáo tài chính quý I của các ngân hàng có thể thấy định giá tài sản chung bên ngoài thị trường đi xuống nhưng tỷ lệ bao phủ nợ xấu ngân hàng lại giảm.

"Điều này cho thấy ngân hàng đang co kéo để có được tăng trưởng lợi nhuận tốt trong quý I vừa rồi, nếu tình hình không có tiến triển trong quý II, III thì tăng trưởng lợi nhuận của ngành ngân hàng sẽ nằm trong vùng rủi ro.

Ở khối phi tài chính, với mức định giá P/E 15 – 16 lần, trụ chính của khối này liên quan đến bất động sản, các hàng hoá cơ bản,… Đây là những doanh nghiệp có nền lợi nhuận quý I, II thậm chí quý III năm ngoái rất cao. Đến quý II, III năm nay, dự báo P/E sẽ lên 18 – 20 lần, một nhóm ngành chưa nhìn thấy đầu ra với mức định giá này là không hợp lý.", ông Tường đưa ra lời nhận định.

oxitocina

DOI: 10.1016/j.cmet.2017.10.006

Resource: (BioLegend Cat# 109103, RRID:AB_313420)

Curator: @scibot

SciCrunch record: RRID:AB_313420

What is this?

DOI: 10.1038/s41388-024-03094-1

Resource: (Cell Signaling Technology Cat# 4526, RRID:AB_2062663)

Curator: @scibot

SciCrunch record: RRID:AB_2062663

What is this?

"Although these principles are based on recent groundbreaking research from across the social sciences, it is worth emphasizing that the new research, in some ways, brings the discipline of economics full circle to where it began, with Adam Smith in the late 18th century, and to perspectives that were prominent in the early and middle parts of the 20th century (box O.1).

A question I have after reading this is how hard is it for people in poverty to change their thinking philosophy? As a person looking from the outside its easy to point out the things that they are doing wrong, but we also need to understand the why behind their decisions.

This ties back to what we discussed before about the poor. Since they have more jobs, need to migrate, and are working low salary jobs, their minds are scrambled and stressed for a lot of the time. Therefore, they may tend to make more decisions using their "Automatic System" while those in higher economic classes have gone through better education and (generally) have single income flows. This makes their lives more simplified and allows them to make more deicions using their Deliberative System.

Me gustaría saber como lograste procesar el documento de audio, ¿estaba descargado previamente en el computador o copiaste el link?

Precisamente esa idea grandilocuente de qué son un programador y un problema valiosos es lo que deja desatendidas las soluciones que no suenan ambiciosas.

Preferimos terraformar marte, que el depredado Amazonas.

En contraste el software situado nos ha permitido resolver problemas para comunidades pequeñas en HackBo, nuestro hackerspace local o ayudando en la preservación lingüística en el Amazonas.

En los ejemplos, todos los problemas a resolver parecen grandilocuentos: miles de millones de personas, la información del mundo, los astronáutas. Pareciera ser que el vecino, la familia, la comunidad local, están por fuera de esos imaginarios. Al menos pensar que los problemas importantes tan bien son cotidianos y pequeños es algo que vale la pena comunicar más asertiva y reiteradamente.

Una de las cosas interesantes es que Breck cuenta en otra entrada como el software debería ahorrar tiempo a las personas, y allí revela una sensibilidad por los problemas pequeños, que le importaban a su familia y a él como niño/adolescente: tener 20 minutos más para poder jugar o ahorrarle esos 20 minutos a su familia a conectarse a internet.

Lo que creo que necesitamos es una manera de expresar software para el cuidado: de la gente, del planeta, del tiempo. Algo como un software convivial, en las líneas de las tecnologías conviviales de Ivan Illich.

Author response:

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

My main concern is still in place. It is unclear whether the proposed method can find actual goal states, and as a result it is unclear what states it finds. Table S1 mentions the model BIOMD0000000454, which is a small metabolic pathway with known equations given in "Example One" in "Metabolic Control Analysis: Rereading Reder". In this model the goal states can be calculated analytically.

Regarding your statements below: I am not concerned that your method will be less efficient than random search (or any other search..) on small models, but I think it is important for the readers to have evidence that your method is able to discover true goal states at least in small networks, used in your study. You do show that your method scales to complex models. So, in my opinion, the missing part is to show that it is able to find true goal states.

"...For simple models whose true steady-state distribution can be derived numerically and/or analytically, it is very likely that their exploration will be much simpler and this is not where a lot of improvement over random search may be found, which explains our focus on more complex models..."

We thank you for your response and for your concerns on the lack of evidence that our method is able to re-discover the true goal states of simple models when these are known a priori. We acknowledge that adding these simple cases is useful for completeness. We did not include these simple models in our main study because in most cases a basic random search over the initial conditions will lead to the re-discovery of these goal states. For instance for the mentioned model BIOMD0000000454 described in the "Example One" from the "Metabolic Control Analysis: Rereading Reder" paper, several simplifying assumptions are made such that the system only has one steady state (x1=0.056, x2=0.769, x3=4.231) which can be found analytically as shown in the paper. In that simple case, this goal state is also straightforward to find with numerical simulation as any valid initial condition will converge to it.

To address the concerns of the reviewer, we propose to add an additional "sanity check" figure in the supplementary of the revised paper (Figure S4), as well as a “sanity check” subsection in the “Methods”, to present additional experiments made on  simple models such as this one. The novel figure and subsection can be visualized on the paper’s interactive version available online https://developmentalsystems.org/curious-exploration-of-grn-competencies, and we plan to include them as such in the further revision.  We have also included the full code to reproduce this sanity check as a ‘sanity_check.ipynb’  jupyter notebook in the github repository (https://github.com/flowersteam/curious-exploration-of-grn-competencies/blob/main/notebooks/sanity_check.ipynb).

In the novel figure S4-b, we show the results of our exploration pipeline on the suggested model BIOMD0000000454 as described in the "Example One" of the paper. These results provide evidence that the curiosity search is able to find back the correct unique goal state (x1=0.056, x2=0.769, x3=4.231), as expected.

We also include a second sanity check on BIOMD0000000341 which models the dynamics of beta-cell mass, insulin and glucose dynamics. This model has two stable fixed points representing physiological (B=300, I=10, G=100) and pathological (B=0, I=0, G=600) steady states, which are the known ground truth steady states as described in Figure 3 of the "A Model of b-Cell Mass, Insulin, and Glucose Kinetics: Pathways to Diabetes" paper. Again, as expected, curiosity search is able to find back those two steady states (Figure S4-a).

As stated in our previous answer, our main study focuses on more complex models that are not limited to one or few attractors that can easily be discovered with random initial conditions. Regarding the mentioned BIOMD0000000454, maybe something that has been confusing for the reviewer is that we indeed included it in our main study but, as specified in the caption of table S4, at the difference of what is done in the "example one" of the original paper, we let the metabolite concentrations y1,...,y5 evolve in time (instead of enforcing them as constants). When doing so, the resulting dynamics of the system are more complex and exhibit a spectrum of possible steady states (unknown a priori), which differ from the previous case with a single steady state. In that case, the new attractors are not analytically easy to find and the proposed curiosity search becomes interesting as it is able to uncover the distribution of possible steady states much more efficiently than a random search baseline, as shown in the new figures S4-c and S4-d.

We hope that these new results will address the reviewer’s concerns and provide evidence to the readers on the validity of the approach on simple networks.

eLife assessment

This important study develops a machine learning method to reveal hidden unknown functions and behavior in gene regulatory networks by searching parameter space in an efficient way. The evidence for some parts of the paper is still incomplete and needs systematic comparison to other methods and to the ground truth, but the work will be of broad interest to anyone working in biology of all stripes since the ideas reach beyond gene regulatory networks to revealing hidden functions in any complex system with many interacting parts.

We thank the editors and reviewers for their positive assessment and constructive suggestions. In our response, we acknowledge the importance of systematic comparison to other methods and to the ground truth, when available. However we also emphasize the challenges associated with evaluating such methods in the context of uncovering hidden behaviors in complex biological networks as the ground truth is often unknown. We hope that our explanations will clarify the potential of our approach in advancing the exploration of these systems.

Public Reviews:

Reviewer #1 (Public Review):

Summary: This paper suggests to apply intrinsically-motivated exploration for the discovery of robust goal states in gene regulatory networks.

Strengths:

The paper is well written. The biological motivation and the need for such methods are formulated extraordinarily well. The battery of experimental models is impressive.

We thank the reviewer for sharing interest in the research problem and for recognizing the strengths of our work.

Weaknesses:

(1) The proposed method is compared to the random search. That says little about the performance with regard to the true steady-state goal sets. The latter could be calculated at least for a few simple ODE (e.g., BIOMD0000000454, `Metabolic Control Analysis: Rereading Reder'). The experiment with 'oscillator circuits' may not be directly interpolated to the other models.

The lack of comparison to the ground truth goal set (attractors of ODE) from arbitrary initial conditions makes it hard to evaluate the true performance/contribution of the method. A part of the used models can be analyzed numerically using JAX, while there are models that can be analyzed analytically.

"...The true versatility of the GRN is unknown and can only be inferred through empirical exploration and proxy metrics....": one could perform a sensitivity analysis of the ODEs, identifying stable equilibria. That could provide a proxy for the ground truth 'versatility'.

We agree with the reviewer that one primary concern is to properly evaluate the effectiveness of the proposed method. However, as we move toward complex pathways, knowledge of the “true” steady-state goal sets is often unknown which is where the use of machine learning methods as the one we propose are particularly interesting (but challenging to evaluate).

For simple models whose true steady-state distribution can be derived numerically and/or analytically, it is very likely that their exploration will be much simpler and this is not where a lot of improvement over random search may be found, which explains our focus on more complex models. While we agree that it is still interesting to evaluate exploration methods on these simple models for checking their behavior, it is not clear how to scale this analysis to the targeted more complex systems.

For systems whose true steady state distribution cannot be derived analytically or numerically, we believe that random search is a pertinent baseline as it is commonly used in the literature to discover the attractors/trajectories of a biological network. For instance, Venkatachalapathy et al. [1] initialize stochastic simulations at multiple randomly sampled starting conditions (which is called a kinetic Monte Carlo-based method) to capture the steady states of a biological system. Similarly, Donzé et al. [29] use a Monte Carlo approach to compute the reachable set of a biological network «when the number of parameters  is large and their uncertain range  is not negligible». For the considered models, the true steady-state goal set is unknown, which is why we chose comparison with random search. We added a “Statistics” subsection in the Methods section providing additional details about the statistical analyses we perform between our method and the random search baseline.

(2) The proposed method is based on `Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning', which assumes state action trajectories [s_{t_0:t}, a_{t_0:t}], (2.1 Notations and Assumptions' in the IMGEP paper). However, the models used in the current work do not include external control actions, but rather only the initial conditions can be set. It is not clear from the methods whether IMGEP was adapted to this setting, and how the exploration policy was designed w/o actual time-dependent actions. What does "...generates candidate intervention parameters to achieve the current goal....", mean considering that interventions 'Sets the initial state...' as explained in Table 2?

We thank the reviewer for asking for clarification, as indeed the IMGEP methodology originates from developmental robotics scenarios which generally focus on the problem of robotic sequential decision-making, therefore assuming state action trajectories as presented in Forestier et al. [65]. However, in both cases, note that the IMGEP is responsible for sampling parameters which then govern the exploration of the dynamical system. In Forestier et al. [65], the IMGEP also only sets one vector at the start (denoted ) which was specifying parameters of a movement (like the initial state of the GRN), which was then actually produced with dynamic motion primitives which are dynamical system equations similar to GRN ODEs, so the two systems are mathematically equivalent. More generally, while in our case the “intervention” of the IMGEP (denoted ) only controls the initial state of the GRN, future work could consider more advanced sequential interventions simply by setting parameters of an action policy  at the start which could be called during the GRN’s trajectory to sample control actions  where  would be the state of the GRN. In practice this would also require setting only one vector at the start, so it would remain the same exploration algorithm and only the space of parameters would change, which illustrates the generality of the approach.

(3) Fig 2 shows the phase space for (ERK, RKIPP_RP) without mentioning the typical full scale of ERK, RKIPP_RP. It is unclear whether the path from (0, 0) to (~0.575, ~3.75) at t=1000 is significant on the typical scale of this phase space. is it significant on the typical scale of this phase space?

The purpose of Figure 2 is to illustrate an example of GRN trajectory in transcriptional space, and to illustrate what “interventions” and “perturbations” can be in that context. To that end we have used the fixed initial conditions provided in the BIOMD0000000647, replicating Figure 5 of Cho et al. [56].

While we are not sure of what the reviewer means with “typical” scale of this phase space, we would like to point reviewer toward Figure 8 which shows examples of certain paths that indeed reach further point in the same phase space (up to ~10 in RKIPP_RP levels and ~300 in ERK levels). However, while the paths displayed in Figure 8 are possible (and were discovered with the IMGEP), note that they may be “rarer” to occur naturally  in the sense that a large portion of the tested initial conditions with random search tend to converge toward smaller (ERK, RKIPP_RP) steady-state values similar to the ones displayed in Figure 2.

(4) Table 2:

a. Where is 'effective intervention' used in the method?

b. in my opinion 'controllability', 'trainability', and 'versatility' are different terms. If their correspondence is important I would suggest to extend/enhance the column "Proposed Isomorphism". otherwise, it may be confusing.

a) We thank the reviewer for pointing out that “effective intervention” is not explicitly used in the method. The idea here is that as we are exploring a complex dynamical system (here the GRN), some of the sampled interventions will be particularly effective at revealing novel unseen outcomes whereas others will fail to produce a qualitative change to the distribution of discovered outcomes. What we show in this paper, for instance in Figure 3a and Figure 4, is that the IMGEP method is particularly sample-efficient in finding those “effective interventions”, at least more than a random exploration. However we agree that the term “effective intervention” is ambiguous (does not say effective in what) and we have replaced it with “salient intervention” in the revised version.

b) We thank the reviewer for highlighting some confusing terms in our chosen vocabulary, and we have clarified those terms in the revised version. We agree that controllability/trainability and versatility are not exactly equivalent concepts, as controllability/trainability typically refers to the amount to which a system is externally controllable/trainable whereas versatility typically refers to the inherent adaptability or diversity of behaviors that a system can exhibit in response to inputs or conditions. However, they are both measuring the extent of states that can be reached by the system under a distribution of stimuli/conditions, whether natural conditions or engineered ones, which is why we believe that their correspondence is relevant.

I don't see how this table generalizes "concepts from dynamical complex systems and behavioral sciences under a common navigation task perspective".

We have replaced the verb “generalize” with “investigate” in the revised version.

Reviewer #2 (Public Review):

Summary:

Etcheverry et al. present two computational frameworks for exploring the functional capabilities of gene regulatory networks (GRNs). The first is a framework based on intrinsically-motivated exploration, here used to reveal the set of steady states achievable by a given gene regulatory network as a function of initial conditions. The second is a behaviorist framework, here used to assess the robustness of steady states to dynamical perturbations experienced along typical trajectories to those steady states. In Figs. 1-5, the authors convincingly show how these frameworks can explore and quantify the diversity of behaviors that can be displayed by GRNs. In Figs. 6-9, the authors present applications of their framework to the analysis and control of GRNs, but the support presented for their case studies is often incomplete.

Strengths:

Overall, the paper presents an important development for exploring and understanding GRNs/dynamical systems broadly, with solid evidence supporting the first half of their paper in a narratively clear way.

The behaviorist point of view for robustness is potentially of interest to a broad community, and to my knowledge introduces novel considerations for defining robustness in the GRN context.

We thank the reviewer for recognizing the strengths and novelty of the proposed experimental framework for exploring and understanding GRNs, and complex dynamical systems more generally. We agree that the results presented in the section “Possible Reuses of the Behavioral Catalog and Framework” (Fig 6-9) can be seen as incomplete along certain aspects, which we tried to make as explicit as possible throughout the paper, and why we explicitly state that these are “preliminary experiments”. Despite the discussed limitations, we believe that these experiments are still very useful to illustrate the variety of potential use-cases in which the community could benefit from such computational methods and experimental framework, and build on for future work.

Some specific weaknesses, mostly concerning incomplete analyses in the second half of the paper:

(1) The analysis presented in Fig. 6 is exciting but preliminary. Are there other appropriate methods for constructing energy landscapes from dynamical trajectories in gene regulatory networks? How do the results in this particular case study compare to other GRNs studied in the paper?

We are not aware of other methods than the one proposed by Venkatachalapathy et al. [1] for constructing an energy landscape given an input set of recorded dynamical trajectories, although it might indeed be the case. We want to emphasize that any of such methods would anyway depend on the input set of trajectories, and should therefore benefit from a set that is more representative of the diversity of behaviors that can be achieved by the GRN, which is why we believe the results presented in Figure 6 are interesting. As the IMGEP was able to find a higher diversity of reachable goal states (and corresponding trajectories) for many of the studied GRNs, we believe that similar effects should be observable when constructing the energy landscapes for these GRN models, with the discovery of additional or wider “valleys” of reachable steady states.

Additionally, it is unclear whether the analysis presented in Fig. 6C is appropriate. In particular, if the pseudopotential landscapes are constructed from statistics of visited states along trajectories to the steady state, then the trajectories derived from dynamical perturbations do not only reflect the underlying pseudo-landscape of the GRN. Instead, they also include contributions from the perturbations themselves.

We agree that the landscape displayed Fig. 6C integrates contributions from the perturbations on the GRN’s behavior, and that it can shape the landscape in various ways, for instance affecting the paths that are accessible, the shape/depth of certain valleys, etc. But we believe that qualitatively or quantitatively analyzing the effect of these perturbations  on the landscape is precisely what is interesting here: it might help 1) understand how a system respond to a range of perturbations and to visualize which behaviors are robust to those perturbations, 2) design better strategies for manipulating those systems to produce certain behaviors

(2) In Fig. 7, I'm not sure how much is possible to take away from the results as given here, as they depend sensitively on the cohort of 432 (GRN, Z) pairs used. The comparison against random networks is well-motivated. However, as the authors note, comparison between organismal categories is more difficult due to low sample size; for instance, the "plant" and "slime mold" categories each only have 1 associated GRN. Additionally, the "n/a" category is difficult to interpret.

We acknowledge that this part is speculative as stated in the paper: “the surveyed database is relatively small with respect to the wealth of available models and biological pathways, so we can hardly claim that these results represent the true distribution of competencies across these organism categories”. However, when further data is available, the same methodology can be reused and we believe that the resulting statistical analyses could be very informative to compare organismal (or other) categories.

(3) In Fig. 8, it is unclear whether the behavioral catalog generated is important to the intervention design problem of moving a system from one attractor basin to another. The authors note that evolutionary searches or SGD could also be used to solve the problem. Is the analysis somehow enabled by the behavioral catalog in a way that is complementary to those methods? If not, comparison against those methods (or others e.g. optimal control) would strengthen the paper.

We thank the reviewer for asking to clarify this point, which might not be clearly explained in the paper. Here the behavioral catalog is indeed used in a complementary way to the optimization method, by identifying a representative set of reachable attractors which are then used to define the optimization problem. For instance here, thanks to the catalog, we 1) were able to identify a “disease” region and several possible reachable states in that region and 2) use several of these states as starting points of our optimization problem, where we want to find a single intervention that can successfully and robustly reset all those points, as illustrated in Figure 8. Please note that given this problem formulation, a simple random search was used as an optimization strategy. When we mention more advanced techniques such as EA or SGD, it is to say that they might be more efficient optimizers than random search. However, we agree that in many cases optimizing directly will not work if starting from random or bad initial guess, and this even with EA or SGD. In that case the discovered behavioral catalog can be useful to better initialize  this local search and make it more efficient/useful, akin to what is done in Figure 9.

(4) The analysis presented in Fig. 9 also is preliminary. The authors note that there exist many algorithms for choosing/identifying the parameter values of a dynamical system that give rise to a desired time-series. It would be a stronger result to compare their approach to more sophisticated methods, as opposed to random search and SGD. Other options from the recent literature include Bayesian techniques, sparse nonlinear regression techniques (e.g. SINDy), and evolutionary searches. The authors note that some methods require fine-tuning in order to be successful, but even so, it would be good to know the degree of fine-tuning which is necessary compared to their method.

We agree that the analysis presented in Figure 9 is preliminary, and thank the reviewer for the suggestion. We would first like to refer to other papers from the ML literature that have more thoroughly analyzed this issue, such as Colas et al. [74] and Pugh et al. [34], and shown the interest of diversity-driven strategies as promising alternatives.  Additionally, as suggested by the reviewer, we added an additional comparison to the CMA-ES algorithm in the revised version in order to complete our analysis. CMA-ES is an evolutionary algorithm which is self-adaptive in the optimization steps and that is known to be better suited than SGD to escape local minimas when the number of parameters is not too high (here we only have 15 parameters). However, our results showed that while CMA-ES explores more the solution space at the beginning of optimization than SGD does, it also ultimately converges into a local minima similarly to SGD. The best solution converges toward a constant signal (of the target b) but fails to maintain the target oscillations, similar to the solutions discovered by gradient descent. We tried this for a few hyperparameters (init mean and std) but always found similar results.  We have updated the figure 9 image and caption, as well as descriptive text, to include these novel results in the revised version. We also added a reference to the CMA-ES paper in the citations.

Reviewer #1 (Recommendations For The Authors):

I would suggest to conduct a more rigor analysis of the performance by estimating/approximating the ground truth robust goal sets in important GRNs.

Also, the use of terminology from different disciplines can be improved. Please see my comments above. Specifically, the connection between controllability in dynamical control systems and versatility used in this paper is unclear.

We hope to have addressed the reviewer's concerns in our previous answers.

Reviewer #2 (Recommendations For The Authors):

Fig 4b: I'm not sure if DBSCAN is the appropriate method to use here, as the visual focus on the core elements of the clusters downplays the full convex hull of the points that random sampling achieves in Z space. An analysis based on convex hulls or the ball-coverage from Fig. 3b would presumably generate plots that were more similar between random sampling and curiosity search. If the goal is to highlight redundancy/non-linearity in the mapping between Z and I, another approach might be to simply bin Z-space in a grid, or to use a clustering algorithm that is less stringent about core/noise distinctions.

We thank the reviewer for the suggestion. This plot is intended to convey the reader an understanding of why a method that uniformly samples goals in Z (what the  IMGEP is doing), is more efficient than a method that uniformly samples parameters in I (what the random search is doing), in systems for which there is high redundancy/non-linearity in the mapping between I and Z. We agree that binning the Z-space in a grid and counting the number of achieved bins is a way to quantitatively measure this, which is by the way very close to what we do in Figure 3 for measuring the achieved diversity. We believe however that the clustering and coloring provides additional intuitions on why this is the case: it illustrates that large regions of the intervention space map to small regions in the outcome space and vice versa.

Additional changes in the revised version:

We added a sentence in the Methods section as well as in the caption of Table S1 providing additional details about the way we simulate the biological models from the BioModels website

We fixed a wrong reference to Figure 4 in the Methods “Sensitivity measure” subsection with reference to Figure 5.

Reviewer #1 (Public Review):

Summary:

This paper suggests to apply intrinsically-motivated exploration for the discovery of robust goal states in gene regulatory networks.

Strengths:

The paper is well written. The biological motivation and the need for such methods are formulated extraordinarily well. The battery of experimental models is impressive.

Weaknesses:

(1) The proposed method is compared to the random search. That says little about the performance with regard to the true steady-state goal sets. The latter could be calculated at least for a few simple ODE (e.g., BIOMD0000000454, `Metabolic Control Analysis: Rereading Reder'). The experiment with 'oscillator circuits' may not be directly interpolated to the other models.

The lack of comparison to the ground truth goal set (attractors of ODE) from arbitrary initial conditions makes it hard to evaluate the true performance/contribution of the method. A part of the used models can be analyzed numerically using JAX, while there are models that can be analyzed analytically.

"...The true versatility of the GRN is unknown and can only be inferred through empirical exploration and proxy metrics....": one could perform a sensitivity analysis of the ODEs, identifying stable equilibria. That could provide a proxy for the ground truth 'versatility'.

(2) The proposed method is based on `Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning', which assumes state action trajectories [s_{t_0:t}, a_{t_0:t}], (2.1 Notations and Assumptions' in the IMGEP paper). However, the models used in the current work do not include external control actions, but rather only the initial conditions can be set. It is not clear from the methods whether IMGEP was adapted to this setting, and how the exploration policy was designed w/o actual time-dependent actions. What does "...generates candidate intervention parameters to achieve the current goal...."<br /> mean considering that interventions 'Sets the initial state...' as explained in Table 2?

(3) Fig 2 shows the phase space for (ERK, RKIPP_RP) without mentioning the typical full scale of ERK, RKIPP_RP. It is unclear whether the path from (0, 0) to (~0.575, ~3.75) at t=1000 is significant on the typical scale of this phase space. is it significant on the typical scale of this phase space?

(4) Table 2:<br /> (a) Where is 'effective intervention' used in the method?<br /> (b) In my opinion 'controllability', 'trainability', and 'versatility' are different terms. If there correspondence is important I would suggest to extend/enhance the column "Proposed Isomorphism". otherwise, it may be confusing. I don't see how this table generalizes generalizes "concepts from dynamical complex systems and behavioral sciences under a common navigation task perspective".

trong một cái cái thị trường khi mà tăng trưởng trở thành một thứ xa xỉ tăng trưởng nền kinh tế rồi tăng trưởng của ngành trở thành một sự xa xỉ thì chỉ có một thì những cái tăng trưởng đột biến ở

00:32:58

một vài doanh nghiệp là sẽ trở thành có thể giúp những cổ phiếu đấy trở thành những ngôi sao Thì đấy là cái cách mà tiếp cận để để tìm ra những cổ phiếu mà có thể tăng trưởng đột biến Ví dụ như ngành nhựa vừa rồi ống nhựa vừa rồi chẳng hạn thì bình minh so với nhựa Tiền Phong thì rõ ràng là nhựa Bình Minh có một cái nền tảng tăng trưởng tốt hơn rất nhiều với những yếu tố nội tại của doanh nghiệp Thì đấy là đấy là cách nhìn thế thì liên

00:33:24

quan đến câu hỏi câu hỏi của Nam là có ngành nào bắt đầu có có tia sáng thì chắc là chúng ta phải đợi đến quý 3 quý 4 Ví dụ như ngành chăn nuôi chẳng hạn thì chăn nuôi chúng ta nhìn thấy một ví dụ rất đơn giản thôi là đàn gà đàn lợn thì 3 đến 6 tháng là chỉ có vòng đời thế thôi mà mình nông dân người ta không nuôi thì sẽ không có thịt mà nhu cầu luộc thịt là nhu cầu thiết yếu của người dân Thế thì khi mà cung giảm một cách đột biến cầu thì vẫn cứ tăng trưởng bền

00:33:52

vững Tất nhiên là cầu cho thầy sản phẩm thịt thì không thể tăng nhanh được các bạn vững thì nó sẽ kích hoạt với giá tăng đặc biệt là với những doanh nghiệp mà đã trích lập dự phòng khá là đầy đủ thì sau đó nó sẽ tạo tiềm năng cho cái chuyện lợi nhuận tăng trưởng mạnh trở lại thì đấy chính là những yếu tố giúp cho giá cổ phiếu tăng thì anh Hưng có kinh nghiệm nhiều chắc là không thì tôi vĩ mô đi từ trên xuống anh còn chưa

chúng ta tách ra thị trường làm hai nhóm nhóm thứ nhất là nhóm tài chính, cụ thể là nhóm ngân hàng thì cái định giá của ngân hàng bây giờ đang tầm khoảng 1,4-1,5 lần P/B đánh giá thực ra nhìn thì không phải là cao, đấy nhưng mà cái bức tranh phía trước của ngành ngân hàng thì chúng ta thấy một màu nó hơi xám

00:22:12

tăng trưởng tín dụng thì năm nay tăng trưởng rất là khó và trong cái môi trường tăng trưởng ví dụ khó thì các ngân hàng sẽ phải gianh giat nhau khách hàng tốt và nó làm cho biết lợi nhuận của cái NIM của ngân hàng nó sẽ sẽ giảm xuống các cái trụ chính liên quan đến thu phí của ngân hàng năm nay cũng sẽ giảm Ví dụ như bánh kem qua một loạt các sự kiện gần đây nó cũng sẽ sẽ giảm và đặc biệt hơn nữa là Nếu chúng ta xem báo cáo quý 1 các ngân hàng chúng ta thấy

00:22:37

rằng là định giá tài sản chung với ngoài thị trường thì đi xuống nhưng mà tỷ lệ bao phủ nợ xấu của ngân hàng lại giảm chứng tỏ rằng là các ngân hàng cũng đang co kéo để có được tốc độ tăng trưởng lợi nhuận tốt trong quý 1 vừa rồi thì nếu mà quý 2 và quý 3 tình hình không có tiến triển thì cái tăng trưởng lợi nhuận của ngành ngân hàng sẽ nằm ở trong vùng rủi ro.

đấy là với tài chỉ tài chính khối Phi Tài chính thì bây giờ đang định giá ở cái tầm PE khoảng tầm 15-16 lần trong khi đó thì cái trụ chính của khối Phi Tài chính thì nó liên quan đến bất động sản, Liên quan đến các mặt hàng liên quan đến đến các hàng hóa cơ bản, đó và những cái doanh nghiệp như thế thì cái quý 1 và quý 2 kể cả quý 3 năm ngoái thì cái nền lợi nhuận nó rất là cao, nên chúng ta nhìn xa một chút đến quý 2 quý 3 năm nay thì nó sẽ dành lên đến tầm 18 đến 20 lần PE Nếu mà chúng ta nhìn dự báo thế thì một cái ngành mà chưa nhìn

00:23:34

thấy đầu ra một cái nhóm ngành mà chưa nhìn thấy đầu ra mà định giá 18 20 lần pi nó nằm ở mid cycle thì có gì đó nó nó không hợp lý

anh nghĩ là có hai cái nguyên tắc mà anh thường thường thực hành, 1) cái nguyên tắc thứ nhất là đa phần những người bạc đầu là đang gọi là cầm chứng khoán Ở Trên Đỉnh Phù Vân sau đó giá nó xuống nhé đó thì cái việc đó là đa phần là do mọi người quá tham lam Cho nên là kiềm chế cái lòng tham của mình, 2) sau khi kiềm chế được lòng tham của mình rồi thì định hình được mình hiểu biết gì về chứng khoán mình hiểu biết gì về cái cổ phiếu mình đang nắm giữ lúc đó mình sẽ có sự tự tin và khi có sự tự tin rồi thì mình chuyển cái bạc đầu thành sự tận hưởng

Neu nghĩ chung về ngành thì lấy một góc hẹp đây thì đúng là ra đặt hai van de cái này không cần nhấn có thèm cái kết quả lợi nhuận quý 1 của công ty chứng khoán được rồi Đúng là chẳng có một sự và là đột phá so với ki vong của thị

00:49:25

trường hoàn toàn ổn định đến thôi là rất không Ngay cả cuộc đời chứng khoán à công bố Thông tin cấp một thị trường nó ra Rất là tranh tối tranh sáng rất có tên là giả cổ phiếu một công cụ không phải là tốt nhưng mà nhìn rộng hơn chút đấy ra lại thấy rằng là anh cũng tương tự như chả có cái này 21 đã được tăng lên quá cao đến Tân lạc của các ngân hàng để làm tham chiếu thấy rằng nửa đầu năm của mà không Mai mốt giá cổ phiếu ngân hàng nên

00:49:51

tốt mà sau đó thì chững di ngang nha vào trong đó phải tập dần là vì lúc đó chưa gái của tiếng nó thăng hơn rất nhiều so với lại tăng trưởng của ngân hàng là lại cho cho 2021 vào cho nên là nó cũng nó cũng còn luc de day nua những cái này nữa thì mới nói cuộc cái quy khóa gia CTCK cũng tương tự lá như thế là đánh đẩy lên quá cao và thể hiện giá đẩy quá cao đó thực sự nó có những điều thú vị thị trường nhỏ đặc biệt là kế hoạch phát hành không chứng khoán chúng ta sẽ nghe đâu đó nói chuyện OK công ty nó

00:50:18

cũng đi phát hành đã bỏ pin nó đã cổ phiếu đã chặn nó thì đó là cái kính cơ gọi là tính động cơ hội trường tăng lên Thế còn đang nghĩ tới mức mà định giá của công ty chứng khoán của các bức ảnh giá cả bị cả nước hàng thì cá nhân tôi cho rằng nó sẽ bật định là sẽ bắt đầu Mỹ tên kìa cao thế rồi mà tôi nói buồn anh em ấy là giá cao nên tránh đạo cũng chứng khoán Nếu nhìn thấy giá thị trường ý thức Cổ phiếu của mình Tại sao tay phát

00:50:44

hành trên giá thấp đã thị trường Long tục thì nó dành cho xã 12 có thể nó có yếu tố gì đó rất hợp lý ở trong phần trong câu chuyện đó thực tế nếu về cái ngôn ngữ có định giá thì giá cổ phiếu này định giá của công minh chứng khoán khó mà cao hơn để tránh ngân hàng được những gì mình sẽ không còn tất nhiên sẽ có những công ty trên bề mặt vụ ngang hàng nói cái là người đã chỉ quản lý làm sau đấy để còn cả cả Magiê tốt thì phải quay tốt thì giá sẽ cao hơn nữa cả vì

00:51:09

cái quản lý rủi ro khi chứng khoán nó dễ hơn rất nhiều có phản ứng gì đâu này thì chắc chắn là nó sẽ cao hơn nhưng mà kết luận từ các yếu tố rất là được Có những khoản vay đến từ hai phần những công ty dịch vụ thì đa phần lợi nhuận đến từ tuần hoàn Dịch vụ thế mạnh đó nó khá là ổn định có ích rất nhiều khi chứng khoán cái phần luận tự do Kiếm 6 75 được thế thì khi vào thị trường khó đi làm sao kiếm tính là sao cả Định sang hàng công ty có thể kiếm thường của nó bình luận

00:51:33

cho nên đã lên rồi thì phải trả lại tôi giá trị của nó thì nhiên đến tương lai Tôi không nghĩ rằng 520 ở nhà sẽ làm 5 tốt cho cổ phiếu linh hoạt mà nó sẽ như hàng nói tôi đồng ý cho sẽ có sự phân hóa nhưng mà thấy giống hóa đấy nó sẽ đến sau khi cả cái bóng này để nó sẽ bị gọi là gì vậy gọi là thức mặc xác tôi cảm ơn ông tưởng nếu mà chúng ta cứ tiếp tục câu chuyện như thế này nó sẽ rất là giải và chương trình của chúng ta thì cố gắng phấn đấu và mỗi xấu chúng ta

DOI: 10.1016/j.celrep.2024.114595

Resource: MultiClamp 700B Microelectrode Amplifier (RRID:SCR_018455)

Curator: @scibot

SciCrunch record: RRID:SCR_018455

What is this?

DOI: 10.1016/j.cell.2024.07.018

Resource: (BioLegend Cat# 329920, RRID:AB_10960742)

Curator: @scibot

SciCrunch record: RRID:AB_10960742

What is this?

In the Mahabharata, Draupadi is portrayed as a powerful and assertive woman who challenges traditional gender norms. As the wife of the five Pandava brothers, Draupadi is expected to conform to the ideal of the submissive and obedient wife. However, she consistently subverts these expectations, demonstrating a strong sense of agency and autonomy. For instance, when her husbands lose her in a game of dice to their cousins, the Kauravas, Draupadi refuses to accept her fate, instead demanding justice and protection from her husbands and the gods. This bold and unyielding attitude is remarkable, given the patriarchal society in which she lives, where women are often relegated to secondary roles and expected to prioritize the needs of their husbands and families above their own.

Draupadi's character also blurs the lines between traditional gender definitions. While she embodies many feminine traits, such as beauty, compassion, and nurturing, she also displays masculine qualities, like courage, strength, and strategic thinking. For example, during the great war, Draupadi takes on a leadership role, advising her husbands and helping to devise military strategies. This fluidity of gender expression is significant, as it challenges the binary oppositions that often govern gender roles in Indian culture. Draupadi's multifaceted personality thus expands our understanding of what it means to be a woman, and highlights the limitations of rigid gender categories.

This is a good example and a good morale too. Every thing has a consequence and even if we can redeem it a little bit, we can not escape it. It is up to us if we want them to be good or bad.

Her father genuinly planned everything for her, from the very strt. Which related quite a lot to the traditions.

Author response:

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

We extend our sincere gratitude to the reviewers for their constructive feedback and valuable suggestions, which have significantly contributed to enhancing the quality of our work. In response to the comments, we have meticulously revised our manuscript with the following updates:

(1) New Data Inclusion: We have incorporated new immunofluorescent staining images, FACS analysis of monocytes, and single-cell RNA sequencing (scRNAseq) expression analysis focusing on genes related to IFNGR, as well as T cell memory subsets (Trm, Tcm, and Tem).

(2) Comparative Analysis: We have conducted a comparative analysis between the active vitiligo dFBs and the ACD pAd (r5) identified in our study, which provides further insight into the immune response mechanisms.

(3) Discussion Expansion: We have expanded the discussion to include the role of tissue-resident memory (Trm) T cells in our model and have addressed the limitations of our animal model and in vitro studies.

(4) Supplemental Material: As requested by the reviewers, we have provided four new supplemental tables (Table S2 ~ S5) and specific information for antibodies used in our study.

Please see our Point-to-Point Responses to Reviewers' comments below:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Liu et al. used scRNA-seq to characterize cell type-specific responses during allergic contact dermatitis (ACD) in a mouse model, specifically the hapten-induced DNFB model. Using the scRNA-seq data, they deconvolved the cell types responsible for the expression of major inflammatory cytokines such as IFNG (from CD4 and CD8 T cells), IL4/13 (from basophils), IL17A (from gd T cells), and IL1B from neutrophils and macrophages. They found the highest upregulation of a type 1 inflammatory response, centering around IFNG produced by CD4 and CD8 T cells. They further identified a subpopulation of dermal fibroblasts that upregulate CXCL9/10 during ACD and provided functional genetic evidence in their mouse model that disrupting IFNG signaling to fibroblasts decreases CD8 T cell infiltration and overall inflammation. They identify an increase in IFNG-expressing CD8 T cells in human patient samples of ACD vs. healthy control skin and co-localization of CD8 T cells with PDGFRA+ fibroblasts, which suggests this mechanism is relevant to human ACD. This mechanism is reminiscent of recent work (Xu et al., Nature 2022) showing that IFNG signaling in dermal fibroblasts upregulates CXCL9/10 to recruit CD8 T cells in a mouse model of vitiligo. Overall, this is a very wellpresented, clear, and comprehensive manuscript. The conclusions of the study are mostly well supported by data, but some aspects of the work could be improved by additional clarification of the identity of the cell types shown to be involved, including the exact subpopulation discovered by scRNA-seq and the subtype of CD8 T cell involved. The study was limited by its use of one ACD model (DNFB), which prevents an assessment of how broadly relevant this axis is. The human sample validation is slightly circumstantial and limited by the multiplexing capacity of immunofluorescence markers.

Strengths:

Through deep characterization of the in vivo ACD model, the authors were able to determine which cell types were expressing the major cytokines involved in ACD inflammation, such as IFNG, IL4/13, IL17A, and IL1B. These analyses are well-presented and thoughtful, showing first that the response is IFNG-dominant, then focusing on deeper characterization of lymphocytes, myeloid cells, and fibroblasts, which are also validated and complemented by FACS experiments using canonical markers of these cell types as well as IF staining. Crosstalk analyses from the scRNA-seq data led the authors to focus on IFNG signaling fibroblasts, and in vitro experiments demonstrate that CXCL9 and CXCL10 are expressed by fibroblasts stimulated by IFNG. In vivo functional genetic evidence demonstrates an important role for IFNG signaling in fibroblasts, as KO of Ifngr1 using Pdgfra-Cre Ifngr1 fl/fl mice, showed a reduction in inflammation and CD8 T cell recruitment.

Weaknesses:

(1) The use of one model limits an understanding of how broad this fibroblast-T cell axis is during ACD. However, the authors chose the most commonly employed model and cited additional work in a vitiligo model (another type 1 immune response).

We thanks the reviewer for pointing out this limitation. Although the DNFB-elicited ACD model is the most commonly used animal model for ACD, our study is limited by the use of only one type 1 immune response model. We have now added new data (Figure 5-figure supplement 1A) showing that the active ACD pAd (r5) and the active IFNγ-responsive vitiligo dFBs (Xu et al., 2022) are enriched with a highly similar panel of IFNγ-inducible genes. Future studies are still needed to determine whether this fibroblast-T cell axis may be broadly applied to other ACD models or to other type-1 immune response-related inflammatory skin diseases.

(2) The identity of the involved fibroblasts and T cells in the mouse model is difficult to assess as scRNA-seq identified subpopulations of these cell types, but most work in the Pdgfra-Cre Ifngr1 fl/fl mice used broad markers for these cell types as opposed to matched subpopulation markers from their scRNA-seq data.

Thanks for the reviewer's constructive comments. To better showcase the dWAT layer where PDGFRA+ pAds are enriched, we have included new histological staining and PLIN1 (adipocyte marker) in new Figure 4 - figure supplement 1F-G. As shown in Figure 4 - figure supplement 1G, the PLIN1+ dWAT layer is located in the lower dermis right above the cartilage layer.  In Figure 4-figure supplement 1I and J, we have shown that phosphor-STAT1 (pSTAT1), a key signaling molecule activated by IFNγ, was detected primarily in PDGFRA+Ly6A+ pAds in the lower dermis where dWAT is located. In addition, we have now included new data showing that the pAd (dFB_r5) cluster preferentially expressed the highest levels of both Ifngr1 and Ifngfr2 among all dFB subclusters (new Figure 5 - figure supplement 1B). Furthermore, we have included new co-staining data showing that CXCL9 largely co-localized with ICAM1(new Figure 4 - figure supplement 1K), a marker for committed pAds (Merrick et al., 2019), in the reticular dermis and dWAT region of the ACD skin, further confirming that CXCL9 is specifically induced in the pAd subset of dFBs. Additionally, we included new staining data showing that ACD-mediated induction of CXCL9 in ICAM1+ dFBs were largely suppressed upon targeted deletion of Ifngr1 in Pdgfra+ dFBs (new Figure 6 - figure supplement 1D-E).

(3) Human patient samples of ACD were co-stained with two markers at a time, demonstrating the presence of CD8+IFNG+ T cells, PDGFRA+CXCL10+ fibroblasts, and co-localization of PDGFRA+ fibroblasts and CD8+ T cells. However, no IF staining demonstrates co-expression of all 4 markers at once; thus, the human validation of co-localization of CD8+IFNG+ T cells and PDGFRA+CXCL10+ fibroblasts is ultimately indirect, although not a huge leap of faith. Although n=3 samples of healthy control and ACD samples are used, there is no quantification of any results to demonstrate the robustness of differences.

Thanks for the reviewer’s constructive comments. We have shown that PDGFRA colocalizes with CXCL10, in the dermal micro-vascular structures, where CD8+ T cells infiltrate around PDGFRA+ dFBs. We are sorry that due to technical issues (antibody compatibility), we cannot provide the four color co-staining as suggested by the reviewers. In order to demonstrate the robustness and reproducibility of the staining presented, we have now supplemented 4 independent images for both Fig. 7A and Fig. 7E in the updated Figure 7-figure supplement 1A-B.

Reviewer #2 (Public Review):

Summary:

The investigators apply scRNA seq and bioinformatics to identify biomarkers associated with DNFB-induced contact dermatitis in mice. The bioinformatics component of the study appears reasonable and may provide new insights regarding TH1-driven immune reactions in ACD in mice. However, the IF data and images of tissue sections are not clear and should be improved to validate the model.

Strengths:

The bioinformatics analysis.

Weaknesses:

The IF data presented in 4H, 6H, 7E and 7F are not convincing and need to be correlated with routine staining on histology and different IF markers for PDGFR. Some of the IF staining data demonstrates a pattern inconsistent with its target.

We are sorry for the confusion, because 4H and 6H are staining on mouse skin sections, and 7E and 7F are staining on human skin sections, therefore the patterns of PDGFRA+ dFBs appeared inconsistent between species. As shown in Fig. 4H, in mouse skin, PDGFRA+CXCL9/10+ dFBs are located between the lower reticular dermis and dWAT region, where preadipocytes are located (Sun et al., 2023). To better showcase the dWAT layer where PDGFRA+ pAds are enriched, we have included new histological staining and PLIN1 (adipocyte marker) in new Figure 4 - figure supplement 1F-G. As shown in Figure 4 - figure supplement 1G, the PLIN1+ dWAT layer is located in the lower dermis right above the cartilage layer. Furthermore, we have included new co-staining data showing that CXCL9 largely co-localized with ICAM1(new Figure 4 - figure supplement 1K), a marker for committed pAds (Merrick et al., 2019), in the reticular dermis and dWAT region of the ACD skin, further confirming that CXCL9 is specifically induced in the pAd subset of dFBs.   

As shown in Fig. 7E, in human skin, PDGFRA+CXCL10+ dFBs are located within the microvascular structures located at the dermal-epidermal junction (DEJ) region, where mesenchymal stem cells are enriched (Russell-Goldman & Murphy, 2020). We have included the corresponding HE histological staining image for Fig. 4H in new Figure 4-supplement 1F. Histological staining for Fig. 6H is the HE staining image in Fig. 6F. The histological staining for Fig. 7E and 7F is shown by Masson’s trichrome staining shown in Fig. 7C (a three-colour histological staining).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major comments:

(1) While the focus on fibroblast and T cell interactions and overall biological findings regarding these interactions (IFNG - CXCL9/10 - CXCR3) is sound, it is slightly confusing about which exact subpopulations of these cells are involved in ACD pathogenesis as both scRNA-seq and IF are used but very broad markers are used for IF. Regarding fibroblasts, the scRNA-seq identifies the pAd (r5) cluster of fibroblasts as the main producer of CXCL9/10. However, the expression of IFNGR1 was not shown for this subpopulation as well as for other fibroblast subpopulations. Figure 6C shows IFNGR1 staining in the Ifngr1 fl/fl control mice which appears quite broad. With the seemingly broad expression of IFNGR1, why is it that only a subpopulation of fibroblasts upregulate CXCL9/10? Is there a specific location of these pAd fibroblasts that help drive this IFNG response? Please show the expression of Ifngr1 in the fibroblast scRNA-seq data.

Thanks for the reviewer’s constructive comments. We have now included new data showing that the pAd (dFB_r5) cluster preferentially expressed higher levels of both Ifngr1 and Ifngfr2 among all dFB subclusters (new Figure 5 - figure supplement 1B). In addition, we included new co-staining data showing that CXCL9 largely co-localized with ICAM1, a marker for committed pAds (Merrick et al., 2019), in the reticular dermis and dWAT region of the ACD skin, further confirming that CXCL9 is specifically induced in the pAd subset of dFBs.

(2) Regarding T cells, it is slightly confusing regarding what role the fibroblast-produced CXCL9/10 plays on T cell migration vs. activation. This is mainly because in vitro work focuses on T cell activation, while in vivo work seems to mainly assess T cell migration into the tissue. The in vivo studies have nicely shown that CD8 T cells are the main cell type affected by Ifngr1 iKO (i.e., a reduction of these cells), but T cell activity in vivo is not assessed (in the form of IFNG production). I have the following related questions:

a. Authors do not discuss whether T cells involved in ACD in their model are tissue-resident memory T cells (Trm) or whether these are recruited from circulation. This may be possible to assess via additional analysis of the scRNA-seq data (looking for expression of Trm markers). 

Thanks for the reviewer’s constructive comments. We have now included new data showing the expression of marker genes of various memory T cells in various T cell subclusters (new Figure 2 - figure supplement 1C-D). Antigen-specific CD8 or CD4 memory T cells can be classified into CD62hi/CCR7hi/CD28hi/CD27hi/CX3CR1lo central memory T cells (Tcm), CX3CR1hi/Cd28hi/Cd27lo/CD62lo/CCR7lo effector memory T cells (Tem), and CD49ahi/CD103hi/ CD69hi/BLIMP1hi tissue-resident memory T cells (Trm) (Benichou, Gonzalez, Marino, Ayasoufi, & Valujskikh, 2017; Cheon, Son, & Sun, 2023; Mackay et al., 2013; Martin & Badovinac, 2018; Park et al., 2023). We observed that in ACD skin, CD4+ and CD8+ T cells predominantly expressed marker genes associated with Tcm including Cd28, Cd27, Ccr7, and S1pr1/Cd62l. In contrast, marker genes associated with Tem (Cx3cr1) and Trm (Itga1/Cd49a, Itgae/Cd103, Cd69 and Prdm1/Blimp1, Cd127/Il7r) were only scarcely expressed in these αβ T cells, suggesting that ACD predominantly triggers a central memory T cell response in the skin.

Furthermore, this hypothesis is supported by new lymph node gene expression results. We showed that the expression of Ifng, but not Il4 or Il17a, was rapidly induced in skin draining lymph nodes at 24 hours after ACD elicitation (new Figure 1-figure supplement 1H). This suggests a robust and systemic activation of type 1 memory T cell response in the early stage of ACD, and the migration of these lymphatic memory T cells to the skin may contribute to the exacerbation of skin inflammation.

b. Authors have focused on CXCR3 axis involvement in IFNG production (Figures 5G-H) without assessing the presumed migratory role of this axis. Presumably, CD8 T cells are recruited to the skin via the CXCL9/10-CXCR3 axis, but this would be important to clarify given other work that has demonstrated Trm involvement in ACD. Authors should at least discuss how their model and findings support, refine, or even contradict the current paradigm of Trm involvement in ACD (Lefevre et al., 2021; PMID: 34155157).

We are grateful for the constructive feedback provided by the reviewer. CXCR3 is a chemokine receptor on T cells and not only plays a pivotal role in the trafficking of type 1 T cells, but also is required for optimal generation of IFNG-secreting type 1 T cells in vivo (Groom et al., 2012). Our in vitro study is limited by only focusing on CXCL9/10-CXCR3 axis involvement in IFNγ production without studying its role in driving T cell migration. We have now addressed this limitation in the discussion section.

In the murine model of ACD, the initial sensitization phase involves exposing mouse skin to a high dose of DNFB to prime effector T cells in lymphoid organs, and this is followed by a later challenge/elicitation phase, during which the mice are re-exposed to a lower dose of DNFB in a different area of the skin, distal from the original sensitization site (Manresa, 2021; Vocanson, Hennino, Rozieres, Poyet, & Nicolas, 2009). Our updated analysis of the expression of marker genes associated with central memory T cells (Tcm), effector memory T cells (Tem), and tissue-resident memory T cells (Trm), as presented in the revised Figure 2-figure supplement 1C-D, indicates that indicate that the type-1 inflammation observed upon ACD elicitation is predominantly driven by memory T cells recruited from lymphoid organs, rather than by skin resident memory T cells. We have read the reference provided by the reviewer along with a few other related studies indicating that Trm is involved in ACD. We found that these studies performed the elicitation phase on the same skin area where the initial sensitization is conducted, and only when it results in a rapid allergen-induced skin inflammatory response, that is primarily mediated by IL17A-producing and IFNγ-producing CD8+ skin resident memory T cells (Gadsboll et al., 2020; Murata & Hayashi, 2020; Schmidt et al., 2017; Wongchang et al., 2023). These studies suggest that Trm cells establish a long-lasting local memory during the initial sensitization, and upon re-exposure to the hapten in the same skin area, these site-specific Trm cells can rapidly contribute to a robust type-1 skin inflammatory response. Therefore, a robust involvement of Trm in ACD requires a repeated exposure of the same hapten to the same skin area. We have now added related discussion in the discussion section.

c. While it may be difficult to assess given reduced numbers of CD8 T cells in the Ifngr1 iKO, is the CXCL9/10-CXCR3 axis affecting IFNG production by T cells in vivo?

Yes, we have shown in Fig. 6G that ACD-mediated induction of Ifng was significantly suppressed in the Ifngr1-iKO mice compared to the control mice.

(3) The authors cite prior work (Xu et al. Nature 2022) that demonstrated a similar mechanism for fibroblasts in recruiting vitiligo-inducing T cells. Are the pAd (r5) cluster of fibroblasts similar to the fibroblast subpopulation that drives vitiligo?

The study on mouse model of vitiligo (Xu et al. Nature 2022) did not perform single-cell RNAseq of the vitiligo mouse skin. Instead, they conducted RNAseq analysis on the sorted PDGFRA+ dFBs. Therefore, we cannot directly compare our pAd (r5) cluster with the fibroblast subpopulation that drives vitiligo. Nevertheless, by utilizing a Venn diagram to compare the top 100 lFNγ signaling dependent genes upregulated in the active vitiligo mouse dFBs and the top 100 genes enriched in our ACD pAd (dFB_r5) cells, we identified 29 commonly upregulated genes between the two conditions (Figure 5-figure supplement 1A). Furthermore, all these 29 genes were among the top IFNγ-inducible genes in primary dFBs. These shared genes include CXCL9, CXCL10, and several other downstream targets of IFNγ signaling, such as B2M, BST2, CD274, as well as the GBP family members GBP3, GBP4, GBP5, GBP7, and additional genes like H2-K1, H2-Q4, H2-Q7, H2-T23, IFIT3, ISG15, and STAT1. This result suggests that the pAd (dFB_r5) cells possess a common IFNγ-pathway gene signature with the active vitiligo mouse dFBs, indicating a potential overlap in molecular pathways.

(4) The authors should include bulk RNA-seq data from fibroblast stimulation (Figure 5b) at a minimum in the GEO submission. They should ideally include the differentially expressed genes in a supplementary table.

Thanks for the reviewer’s constructive comments. We have now included the raw FPKM file for the bulk RNAseq data shown in Fig. 5 in Supplemental Table S3, and the list for differentially expressed genes in Supplemental Table S4.

(5) The authors state that human sample stainings were n = 3 per group for healthy control and ACD (Figure 7), but no quantification or statistical testing is provided to demonstrate significant differences in findings such as co-localization of fibroblasts and T cells, IFNG+CD8+ T cells, etc.

Thanks for the reviewer’s constructive comments. We have now supplemented 4 independent images for both Fig. 7A and Fig. 7E in the new Figure 7-figure supplement 1A-B to demonstrate the robustness and reproducibility of the staining presented.

Minor comments:

(1) Figure 1G, possible typos, Il14 and Il11b are on the violin plots when I believe authors meant Il4 and Il1b.

Thank a lot for pointing out these typos. We have now made the correction in the updated manuscript figure 1.

(2) The authors label cluster 27 as neutrophils based on the expression of Ly6g and S100a8. These markers are also expressed by Cd14+ inflammatory monocytes. I believe the authors need to additionally validate that these cells are neutrophils (via staining or additional analyses). Neutrophils are notoriously difficult to capture in scRNA-seq given low RNA content. Later, they are quantified by FACS using CD11b+Ly6G+ markers, but I do not believe this would distinguish them from CD14+ monocytes. As this is a relatively minor aspect of the manuscript, I consider this a minor concern, but a finding that should be as accurate as possible as Il1b is likely important, and identifying its accurate source likewise.

Thanks a lot for reviewer’s constructive comments. According to the reviewer’s suggestion, we have now added Cd14 expression in Figure 1C, and found that indeed cluster 27 express not only expressed Ly6G but also expressed Cd14. Based on literatures, the expression of Ly6G in circulating blood, spleen, and peripheral tissues is limited to neutrophils, whereas monocytes, macrophages, and lymphocytes are negative of Ly6G (Ikeda et al., 2023; Lee, Wang, Parisini, Dascher, & Nigrovic, 2013). Therefore, Ly6G can be used as a marker to distinguish neutrophils and monocytes. Although CD14 is highly expressed in monocytes, neutrophils can also express CD14 at lower level (Antal-Szalmas, Strijp, Weersink, Verhoef, & Van Kessel, 1997). Therefore, the cluster 27 is likely a mixed population of neutrophils and monocytes. So we have changed the definition of this cluster as NEU/Mon in the updated manuscript.

To confirm the presence of neutrophils and monocytes in ACD, we have included new FACS analysis of inflammatory monocytes, which are gated as CD11B+Ly6G-F4/80-CD11C-Ly6Chi, according to published FACS protocol(Rose, Misharin, & Perlman, 2012). We found that elicitation of ACD led to a transient influx of monocytes at 24 hrs post treatment, whereas the percentage of neutrophils continued to increase by 60 hours post-treatment (Figure 3L, and Figure 3-figure supplement 1G). In addition, at 60 hrs, the percentage of neutrophils (~5%) was > 10 times greater than the percentage of monocytes (~0.4%), indicating that neutrophils are the dominant granulocytes at 60 hours post ACD elicitation.

(3) The authors should include a cluster marker table as a supplementary file to accompany Figure 1C. Only top cluster markers are shown in 1C.

Thanks a lot for reviewer’s constructive comments. We have now included the top 5 enriched genes in each cell clusters shown in Fig. 1C in supplementary Table S2.

(4) Figures 2A/B have mismatched labels. There is a gdT/ILC2 label in the 2B, but not in 2A. Please match these. Along these lines, which gdT cluster is the IL17A expressing cluster as shown in 1D? Matching these labels will clarify which population is doing what.

Thanks a lot for reviewer to point out this mistake. To avoid confusion about the T cell clusters, we have added a specific recluster# for the T cell clusters as r0~r7 (Figure 2A-B). The r4 cluster is a mixed population of δγT and ILC2, therefore termed as δγT/ILC2. As shown in Figure 2-figure supplement 1E, IL17A is primarily expressed in the δγT cell (r5). We have now corrected δγT2 to δγT/ILC2 throughout the manuscript. To avoid confusion, we have now added cluster # in updated Figure 2D.

(5) In Figure 3E, the authors used CD11B as a distinguishing marker for basophils (CD11B+) vs. mast cells (CD11B-). Mcpt8 is a better distinguishing marker, so I am wondering why the authors chose CD11B.

Thanks a lot for reviewer’s comments. In scRNAseq, we did use Mcpt8 as a basophil specific marker to distinguish basophils and mast cells (see Figure 1C). However, Mcpt8 is not a surface receptor that can be used in FACS analysis. Therefore, to distinguish basophils from mast cells by FACS, we have to choose surface markers expressed on these cells. FcεR1a is a highly specific markers expressed exclusively on basophils and mast cells, and CD11B is expressed on basophils but not in mature mast cells (Hamey et al., 2021). As a result, FACS analysis of the surface expression of CD11B and FceR1a can distinguish basophils (CD11B+ FcεR1a+) from mast cells (CD11B- FcεR1a+). The use of CD11B and FcεR1a to distinguish basophils and mast cells can also been see in a published reference study (Arinobu et al., 2005).

(6) Antibody information is missing for IF studies. No clones, catalog numbers, vendors, RRIDs, or dilutions are included in the Methods section for any of the IF data.

Thanks a lot for reviewer’s constructive comments. We have now added related information for all the antibodies we used for FACS or IF data in the method section.

(7) Figure 3 supplement E and F appear to be reversed based on legend descriptions.

Thank a lot for pointing this out. We have now made the correction in the updated Supplementary file.

References:

Antal-Szalmas, P., Strijp, J. A., Weersink, A. J., Verhoef, J., & Van Kessel, K. P. (1997). Quantitation of surface CD14 on human monocytes and neutrophils. J Leukoc Biol, 61(6), 721-728. doi:10.1002/jlb.61.6.721

Arinobu, Y., Iwasaki, H., Gurish, M. F., Mizuno, S., Shigematsu, H., Ozawa, H., . . . Akashi, K. (2005). Developmental checkpoints of the basophil/mast cell lineages in adult murine hematopoiesis. Proc Natl Acad Sci U S A, 102(50), 18105-18110. doi:10.1073/pnas.0509148102

Benichou, G., Gonzalez, B., Marino, J., Ayasoufi, K., & Valujskikh, A. (2017). Role of Memory T Cells in Allograft Rejection and Tolerance. Front Immunol, 8, 170. doi:10.3389/fimmu.2017.00170

Cheon, I. S., Son, Y. M., & Sun, J. (2023). Tissue-resident memory T cells and lung immunopathology. Immunol Rev, 316(1), 63-83. doi:10.1111/imr.13201

Gadsboll, A. O., Jee, M. H., Funch, A. B., Alhede, M., Mraz, V., Weber, J. F., . . . Bonefeld, C. M. (2020). Pathogenic CD8(+) Epidermis-Resident Memory T Cells Displace Dendritic Epidermal T Cells in Allergic Dermatitis. J Invest Dermatol, 140(4), 806-815 e805. doi:10.1016/j.jid.2019.07.722

Groom, J. R., Richmond, J., Murooka, T. T., Sorensen, E. W., Sung, J. H., Bankert, K., . . . Luster, A. D. (2012). CXCR3 chemokine receptor-ligand interactions in the lymph node optimize CD4+ T helper 1 cell differentiation. Immunity, 37(6), 1091-1103. doi:10.1016/j.immuni.2012.08.016

Hamey, F. K., Lau, W. W. Y., Kucinski, I., Wang, X., Diamanti, E., Wilson, N. K., . . . Dahlin, J. S. (2021). Single-cell molecular profiling provides a high-resolution map of basophil and mast cell development. Allergy, 76(6), 1731-1742. doi:10.1111/all.14633

Ikeda, N., Kubota, H., Suzuki, R., Morita, M., Yoshimura, A., Osada, Y., . . . Asano, K. (2023). The early neutrophil-committed progenitors aberrantly differentiate into immunoregulatory monocytes during emergency myelopoiesis. Cell Rep, 42(3), 112165. doi:10.1016/j.celrep.2023.112165

Lee, P. Y., Wang, J. X., Parisini, E., Dascher, C. C., & Nigrovic, P. A. (2013). Ly6 family proteins in neutrophil biology. J Leukoc Biol, 94(4), 585-594. doi:10.1189/jlb.0113014

Mackay, L. K., Rahimpour, A., Ma, J. Z., Collins, N., Stock, A. T., Hafon, M. L., . . . Gebhardt, T. (2013). The developmental pathway for CD103(+)CD8+ tissue-resident memory T cells of skin. Nat Immunol, 14(12), 1294-1301. doi:10.1038/ni.2744

Manresa, M. C. (2021). Animal Models of Contact Dermatitis: 2,4-Dinitrofluorobenzene-Induced Contact Hypersensitivity. Methods Mol Biol, 2223, 87-100. doi:10.1007/978-1-0716-1001-5_7

Martin, M. D., & Badovinac, V. P. (2018). Defining Memory CD8 T Cell. Front Immunol, 9, 2692. doi:10.3389/fimmu.2018.02692

Merrick, D., Sakers, A., Irgebay, Z., Okada, C., Calvert, C., Morley, M. P., . . . Seale, P. (2019). Identification of a mesenchymal progenitor cell hierarchy in adipose tissue. Science, 364(6438). doi:10.1126/science.aav2501

Murata, A., & Hayashi, S. I. (2020). CD4(+) Resident Memory T Cells Mediate Long-Term Local Skin Immune Memory of Contact Hypersensitivity in BALB/c Mice. Front Immunol, 11, 775. doi:10.3389/fimmu.2020.00775

Park, S. L., Christo, S. N., Wells, A. C., Gandolfo, L. C., Zaid, A., Alexandre, Y. O., . . . Mackay, L. K. (2023). Divergent molecular networks program functionally distinct CD8(+) skin-resident memory T cells. Science, 382(6674), 1073-1079. doi:10.1126/science.adi8885

Rose, S., Misharin, A., & Perlman, H. (2012). A novel Ly6C/Ly6G-based strategy to analyze the mouse splenic myeloid compartment. Cytometry A, 81(4), 343-350. doi:10.1002/cyto.a.22012

Russell-Goldman, E., & Murphy, G. F. (2020). The Pathobiology of Skin Aging: New Insights into an Old Dilemma. Am J Pathol, 190(7), 1356-1369. doi:10.1016/j.ajpath.2020.03.007

Schmidt, J. D., Ahlstrom, M. G., Johansen, J. D., Dyring-Andersen, B., Agerbeck, C., Nielsen, M. M., . . . Bonefeld, C. M. (2017). Rapid allergen-induced interleukin-17 and interferon-gamma secretion by skin-resident memory CD8(+) T cells. Contact Dermatitis, 76(4), 218-227. doi:10.1111/cod.12715

Sun, L., Zhang, X., Wu, S., Liu, Y., Guerrero-Juarez, C. F., Liu, W., . . . Zhang, L. J. (2023). Dynamic interplay between IL-1 and WNT pathways in regulating dermal adipocyte lineage cells during skin development and wound regeneration. Cell Rep, 42(6), 112647. doi:10.1016/j.celrep.2023.112647

Vocanson, M., Hennino, A., Rozieres, A., Poyet, G., & Nicolas, J. F. (2009). Effector and regulatory mechanisms in allergic contact dermatitis. Allergy, 64(12), 1699-1714. doi:10.1111/j.1398-9995.2009.02082.x

Wongchang, T., Pluangnooch, P., Hongeng, S., Wongkajornsilp, A., Thumkeo, D., & Soontrapa, K. (2023). Inhibition of DYRK1B suppresses inflammation in allergic contact dermatitis model and Th1/Th17 immune response. Sci Rep, 13(1), 7058. doi:10.1038/s41598-023-34211-x

Xu, Z., Chen, D., Hu, Y., Jiang, K., Huang, H., Du, Y., . . . Chen, T. (2022). Anatomically distinct fibroblast subsets determine skin autoimmune patterns. Nature, 601(7891), 118-124. doi:10.1038/s41586-021-04221-8

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

As adult-born granule neurons have been shown to play diverse roles, both positive and negative, to modulate hippocampal circuitry and function in epilepsy, understanding the mechanisms by which altered neurogenesis contributes to seizures is important for future therapeutic strategies. The work by Jain et al. demonstrates that increasing adult neurogenesis before status epilepticus (SE) leads to a suppression of chronic seizures in the pilocarpine model of temporal lobe epilepsy. This work is potentially interesting because previous studies showed suppressing neurogenesis led to reduced chronic seizures.

To increase neurogenesis, the authors conditionally delete the pro-apoptotic gene Bax using a tamoxifen-inducible Nestin-CreERT2 which has been previously published to increase proliferation and survival of adult-born neurons by Sahay et al. After 6 weeks of tamoxifen injection, the authors subjected male and female mice to pilocarpine-induced SE. In the first study, at 2 hours after pilocarpine, the authors examine latency to the first seizure, severity and total number of acute seizures, and power during SE. In the second study in a separate group of mice, at 3 weeks after pilocarpine, the authors examine chronic seizure number and frequency, seizure duration, postictal depression, and seizure distribution/cluster seizures. Overall, the study concludes that increasing adult neurogenesis in the normal adult brain can reduce epilepsy in females specifically. However, important BrdU birthdating experiments in both male and female mice need to be included to support the conclusions made by the authors. Furthermore, speculative mechanisms lacking direct evidence reduce enthusiasm for the findings.

There are two suggestions. First, BrdU birthdating of newborn neurons is important to add to the paper so that there is support for the conclusions. Second, speculative text reduced enthusiasm. In response, we clarified the conclusions. We do not think that the clarified conclusions require BrdU birthdating (discussed further below). We also removed two schematics (and associated text) that we think the reviewer was referring to when speculation was mentioned.

We also want to point out something minor -that the times of injections listed above are not correct.

a. Seizures were not measured 2 hrs after pilocarpine; that is when the anticonvulsant diazepam was administered to males. 

b. Seizures were not measured 3 weeks after pilocarpine; the duration of recording was 3 weeks.  

(1) BrdU birthdating is required for conclusions.

We think that the Reviewer was suggesting birthdating because we were not clear about our conclusions, and we apologize for the confusion. The Reviewer stated that we concluded: “conditionally deleting Bax in Nestin-Cre+ cells leads to increased neurogenesis and hilar ectopic granule cells, thereby reducing chronic seizures.”  (Note this is a quote from the review).

However, we did not intend to conclude that. We intended to conclude that conditionally deleting Bax in Nestin-Cre+ mice reduced chronic seizures in the mouse model of epilepsy that we used. Also, that conclusion only pertained to females. Please note we did not conclude that hilar ectopic granule cells led to reduced seizures. We also concluded that Bax deletion increased neurogenesis in female mice. We have revised the text to make the conclusions clear.

Abstract, starting on line 67:

The results suggest that selective Bax deletion to increase adult neurogenesis can reduce experimental epilepsy, and the effect shows a striking sex difference.

Results, starting on line 448:

Because Cre+ epileptic females had increased numbers of immature neurons relative to Cre- females at the time of SE, and prior studies show that Cre+ females had less neuronal damage after SE (Jain et al., 2019), female Cre+ mice might have had reduced chronic seizures because of high numbers of immature neurons. However, the data do not prove a causal role.

Starting on line 477:

...we hypothesized that female Cre+ mice would have fewer hilar ectopic GCs than female Cre- mice. However, that female Cre+ mice did not have fewer hilar ectopic GCs.

Discussion, starting on line 563:

The chronic seizures, measured 4-7 weeks after pilocarpine, were reduced in frequency by about 50% in females. Therefore, increasing young adult-born neurons before the epileptogenic insult can protect against epilepsy. However, we do not know if the protective effect was due to the greater number of new neurons before SE or other effects. Past data would suggest that increased numbers of newborn neurons before SE leads to a reduced SE duration and less neuronal damage in the days after SE. That would be likely to lessen the epilepsy after SE. However, there may have been additional effects of larger numbers of newborn neurons prior to SE.

Conclusions, starting on line 745:

In the past, suppressing adult neurogenesis before SE was followed by fewer hilar ectopic GCs and reduced chronic seizures. Here, we show that the opposite - enhancing adult neurogenesis before SE and increased hilar ectopic GCs - do not necessarily reduce seizures. We suggest instead that protection of the hilar neurons from SE-induced excitotoxicity was critical to reducing seizures. The reason for the suggestion is that the survival of hilar neurons would lead to persistence of the normal inhibitory functions of hilar neurons, protecting against seizures. However, this is only a suggestion at the present time because we do not have data to prove it. Additionally, because protection was in females, sex differences are likely to have played an important role. Regardless, the results show that enhancing neurogenesis of young adult-born neurons in Nestin-Cre+ mice had a striking effect in the pilocarpine model, reducing chronic seizures in female mice.

The Reviewer is correct that it would be interesting to know when the increase in adult neurogenesis occurred that was critical to the effect. For example, was it the initial increase following Bax deletion but before pilocarpine-induced SE, or the increase in neurogenesis following SE, or increased adult neurogenesis in the chronic stage of epilepsy. It also might be that related aspects of neurogenesis played a role such as the degree that maturation was normal in adult-born neurons. We have not pursued the experiments to identify these aspects of neurogenesis because of how much work it would entail. Also, approaches to conclude cause-effect relationships are going to be difficult. 

(2) Speculation.

We removed the text and supplemental figures with schematics that we think were the overly speculative parts of the paper the Reviewer mentioned.

Strengths:

(1) The study is sex-matched and reveals differences in response to increasing adult neurogenesis in chronic seizures between males and females.

(2) The EEG recording parameters are stringent, and the analysis of chronic seizures is comprehensive. In two separate experiments, the electrodes were implanted to record EEG from the cortex as well as the hippocampus. The recording was done for 10 hours post pilocarpine to analyze acute seizures, and for 3 weeks continuous video EEG recording was done to analyze chronic seizures.

Weaknesses:

(1) Cells generated during acute seizures have different properties to cells generated in chronic seizures. In this study, the authors employ two bouts of neurogenesis stimuli (Bax deletion dependent and SE dependent), with two phases of epilepsy (acute and chronic). There are multiple confounding variables to effectively conclude that conditionally deleting Bax in Nestin-Cre+ cells leads to increased neurogenesis and hilar ectopic granule cells, thereby reducing chronic seizures.

As mentioned above, with a clarification of our conclusions we think we have addressed the concern. We believe that we conditionally deleted Bax in Nestin-expressing cells. We believe we found that female mice had reduced loss of hilar mossy cells and somatostatin-expressing neurons after SE, and fewer chronic seizures after SE. While it makes sense that increased neurogenesis caused the reduced seizures, we acknowledge it was not proved.

We do not make conclusions about the role of hilar ectopic granule cells. However, we note that they appear to have been similar in number across groups, which suggests they played no role in the results. This is very surprising and therefore adds novelty.

(2) Related to this is the degree of neurogenesis between Cre+ and Cre- mice and the nature of the sex differences. It is crucial to know the rate/fold change of increased neurogenesis before pilocarpine treatment and whether it is different between male and female mice.

We agree that if sex differences in adult neurogenesis could be shown by a sex difference in rate, fold change, maturation, and other characteristics.  However, sex differences can also be shown by a change in doublecortin (DCX), which is what we did. We respectfully submit that we do not see an exhaustive study is critical.

As a result, we have clarified DCX was studied either before SE or in the period of chronic seizures:

Results, starting on line 406:

III. Before and after epileptogenesis, Cre+ female mice exhibited more immature neurons than Cre- female mice but that was not true for male mice.

Starting on line 446:

Therefore, elevated DCX occurred after chronic seizures had developed in Cre+ mice but the effect was limited to females.

Discussion, starting on line 592:

This study showed that conditional deletion of Bax from Nestin-expressing progenitors increased young adult-born neurons in the DG when studied 6 weeks after deletion and using DCX as a marker of immature neurons.

(3) The authors observe more hilar Prox1 cells in Cre+ mice compared to Cre- mice. The authors should confirm the source of the hilar Prox1+ cells.

This is an excellent question but it is unclear that it is critical to the seizures since both sexes showed more hilar Prox1 cells in Cre+ mice but only the females had fewer seizures than Cre- mice. This is the additional text to describe the results (starting on Line 493):

In past studies, hilar ectopic GCs have been suggested to promote seizures (Scharfman et al., 2000; Jung et al., 2006; Cho et al., 2015). Therefore, we asked if the numbers of hilar ectopic GCs correlated with the numbers of chronic seizures. When Cre- and Cre+ mice were compared (both sexes pooled), there was a correlation with numbers of chronic seizures (Fig. 6D1) but it suggested that more hilar ectopic GCs improved rather than worsened seizures. However, the correlation was only in Cre- mice, and when sexes were separated there was no correlation (Fig. 6D3).

When seizure-free interval was examined with sexes pooled, there was a correlation for Cre+ mice (Fig. 6D2) but not Cre- mice. Strangely, the correlations of Cre+ mice with seizure-free interval (Fig. 6D2, D4) suggest ectopic GCs shorten the seizure-free interval and therefore worsen epilepsy, opposite of the correlative data for numbers of chronic seizures. In light of these inconsistent results it seems that hilar ectopic granule cells had no consistent effect on chronic seizures.

(4) The biggest weakness is the lack of mechanism. The authors postulate a hypothetical mechanism to reconcile how increasing and decreasing adult-born neurons in GCL and hilus and loss of hilar mossy and SOM cells would lead to opposite effects - more or fewer seizures. The authors suggest the reason could be due to rewiring or no rewiring of hilar ectopic GCs, respectively, but do not provide clear-cut evidence.

As we mention above, we removed the supplemental figures with schematics because they probably were what seemed overly speculative.

We acknowledge that mechanism is not proven by our study. However, we would like to mention that in our view, showing preservation of hilar mossy cells and SOM cells, but not PV cells, does add mechanistic data to the paper. We understand more experiments are necessary.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Jain et al explore whether increasing adult neurogenesis is protective against status epilepticus (SE) and the development of spontaneous recurrent seizures (chronic epilepsy) in a mouse pilocarpine model of TLE. The authors increase adult neurogenesis via conditional deletion of Bax, a pro-apoptotic gene, in Nestin-CreERT2Baxfl/fl mice. Cre- littermates are used as controls for comparisons. In addition to characterizing seizure phenotypes, the authors also compare the abundance of hilar ectopic granule cells, mossy cells, hilar SOM interneurons, and the degree of neuronal damage between mice with increased neurogenesis (Cre+) vs Cre- controls. The authors find less severe SE and a reduction in chronic seizures in female mice with pre-insult increased adult-born neurons. Immunolabeling experiments show these females also have preservation of hilar mossy cells and somatostatin interneurons, suggesting the pre-insult increase in adult neurogenesis is protective.

Strengths:

(1) The finding that female mice with increased neurogenesis at the time of pilocarpine exposure have fewer seizures despite having increased hilar ectopic granule cells is very interesting.

(2) The work builds nicely on the group's prior studies.

(3) Apparent sex differences are a potentially important finding.

(4) The immunohistochemistry data are compelling.

(5) Good controls for EEG electrode implantation effects.

(6) Nice analysis of most of the SE EEG data.

Weaknesses:

(1) In addition to the Cre- littermate controls, a no Tamoxifen treatment group is necessary to control for both insertional effects and leaky expression of the Nestin-CreERT2 transgene.

About “leaky” expression, we have not found expression to be leaky. We checked by injecting a Cre-dependent virus so that mCherry would be expressed in those cells that had Cre.  The results were published as Supplemental Figure 9 in Jain et al. (2019).

In the revised manuscript we also mention a study that examined three Nestin-CreERT2 mouse lines (Sun et al., 2014). One of the mouse lines was ours. The leaky expression was not in the mouse line we use. We have added these points to the revised manuscript:

Methods, section II starting on line 791:

Although Nestin-Cre-ERT2 mouse lines have been criticized because  they can have leaky expression, the mouse line used in the present study did not (Sun et al., 2014), which we confirmed (Jain et al., 2019).

(2) The authors suggest sex differences; however, experimental procedures differed between male and female mice (as the authors note). Female mice received diazepam 40 minutes after the first pilocarpine-induced seizure onset, whereas male mice did not receive diazepam until 2 hours post-onset. The former would likely lessen the effects of SE on the female mice. Therefore, sex differences cannot be accurately assessed by comparing these two groups, and instead, should be compared between mice with matching diazepam time courses.

We agree that a shorter delay between pilocarpine and diazepam would be likely to lead to less damage. However, the latency from pilocarpine to SE varied, making the time from the onset of SE to diazepam variable. Most of the variability was in females. By timing the diazepam injection differently in males and females, we could make the time from the onset of SE to diazepam similar between females and males. We had added a supplemental figure to show that our approach led to no significant differences between females and males in the latency to SE, time between SE and diazepam injection, and time between pilocarpine and diazepam injection. We also show that Cre+ females and Cre- females were not different in these times, so it could not be related to the neuroprotection of Cre+ females.

Additionally, the authors state that female mice that received diazepam 2 hours post-onset had severe brain damage. This is concerning as it would suggest that SE is more severe in the female than in the male mice.

We regret that our language was misleading. We intended to say females had more morbidity and mortality than males (lack of appetite and grooming, death in the days after SE) when we gave DZP 2 hrs after Pilo. We actually don’t know why because there were no differences in severity of SE. We think the females had worse outcome when they had a short latency to SE.  These females had a longer period of SE before DZP than males, probably leading to worse outcome. To correct this we gave DZP to females sooner. Then morbidity and mortality was improved in females. 

Interestingly, after we did this we saw females did not always have a short latency to SE. We maintained the same regimen however, to be consistent. As the new supplemental figure (above) shows, there were significant sex differences in the latency to SE, time between SE and DZP, and time between pilocarpine and DZP.

(3) Some sample sizes are low, particularly when sex and genotypes are split (n=3-5), which could cause a type II statistical error.

We agree and have noted this limitation in the Discussion:

Additional considerations, starting on line 739:

This study is limited by the possibilities of type II statistical errors in those instances where we divided groups by genotype and sex, leading to comparisons of 3-5 mice/group.

(4) Several figures show a datapoint in the sex and genotype-separated graphs that is missing from the corresponding male and female pooled graphs (Figs. 2C, 2D, 4B).

We are very grateful to the Reviewer for pointing out the errors. They are corrected.

(5) In Suppl Figs. 1B & 1C, subsections 1c and 2c, the EEG trace recording is described as the end of SE; however, SE appears to still be ongoing in these traces in the form of periodic discharges in the EEG.

The Reviewer is correct.  It is a misconception that SE actually ends completely. The most intense seizure activity may, but what remains is abnormal activity that can last for days. Other investigators observe the same and have suggested that it argues against the concept of a silent period between SE and chronic epilepsy. We had discussed this in our prior papers and had referenced how we define SE.  In the revised manuscript we add the information to the Methods section instead of referencing a prior study:

Methods, starting on line 899:

SE duration was defined in light of the fact that the EEG did not return to normal after the initial period of intense activity. Instead, intermittent spiking occurred for at least 24 hrs, as we previously described (Jain et al., 2019) and has been described by others (Mazzuferi et al., 2012; Bumanglag and Sloviter, 2018; Smith et al., 2018). We therefore chose a definition that captured the initial, intense activity. We defined the end of this time as the point when the amplitude of the EEG deflections were reduced to 50% or less of the peak deflections during the initial hour of SE. Specifically, we selected the time after the onset of SE when the EEG amplitude in at least 3 channels had dropped to approximately 2 times the amplitude of the EEG during the first hour of SE, and remained depressed for at least 10 min (Fig. S2 in (Jain et al., 2019). Thus, the duration of SE was defined as the time between the onset and this definition of the "end" of SE.

(6) In Results section II.D and associated Fig.3, what the authors refer to as "postictal EEG depression" is more appropriately termed "postictal EEG suppression". Also, postictal EEG suppression has established criteria to define it that should be used.

We find suppression is typical in studies of ECT or humans (Esmaeili et al., 2023; Gascoigne et al., 2023; Hahn et al., 2023; Kavakbasi et al., 2023; Langroudi et al., 2023; Karl et al., 2024; Vilan et al., 2024; Zhao et al., 2024) and animal research uses the term postictal depression(Kanner et al., 2010; Krishnan and Bazhenov, 2011; Riljak et al., 2012; Singh et al., 2012; Carballosa-Gonzalez et al., 2013; Kommajosyula et al., 2016; Smith et al., 2018; Uva and de Curtis, 2020; Medvedeva et al., 2023). Therefore we think depression is a more suitable term.

The example traces in Fig. 3A and B should also be expanded to better show this potential phenomenon.

We expanded traces in Fig. 3 as suggested. They are in Fig 3A.

(7) In Fig.5D, the area fraction of DCX in Cre+ female mice is comparable to that of Cre- and Cre+ male mice. Is it possible that there is a ceiling effect in DCX expression that may explain why male Cre+ mice do not have a significant increase compared to male Cre- mice?

We thank the Reviewer for the intriguing possibility. We now mention it in the manuscript:

Results, starting on line 456:

It is notable that the Cre+ male mice did not show increased numbers of immature neurons at the time of chronic seizures but Cre+ females did. It is possible that there was a “ceiling” effect in DCX expression that would explain why male Cre+ mice did not have a significant increase in immature neurons relative to male Cre- mice.

(8) In Suppl. Fig 6, the authors should include DCX immunolabeling quantification from conditional Cre+ male mice used in this study, rather than showing data from a previous publication.

We have made this revision.

(9) In Fig 8, please also include Fluorojade-C staining and quantification for male mice.

The additional data for males have been added to part D.

(10) Page 13: Please specify in the first paragraph of the discussion that findings were specific to female mice with pre-insult increases in adult-born neurogenesis.

This has been done.

Minor:

(11) In Fig. 1 and suppl. figure 1, please clarify whether traces are from male or female mice.

We have clarified.

(12) Please be consistent with indicating whether immunolabeling images are from female or male mice.

a. Fig 5B images labeled as from "Cre- Females" and "Cre+ Females".

b. Suppl. Fig 8: Images labeled as "Cre- F" and "Cre+ F".

c. Fig 6: sex not specified.

d. Fig. 7: sex only specified in the figure legend.

e. Fig 8: only female mice were included in these experiments, but this is not clear from the figure title or legend.

We revised all figures according to the comments.

(13) Page 4: the last paragraph of the introduction belongs within the discussion section.

We recognize there is a classic view that any discussion of Results should not be in the Introduction. However, we find that view has faded and more authors make a brief summary statement about the Results at the end of the Introduction. We would like to do so because it allow Readers to understand the direction of the study at the outset, which we find is helpful.

(14) Page 6: The sentence "The data are consistent with prior studies..." is unnecessary.

We have removed the text.

(15) Suppl. Fig 6A: Please include representative images of normal condition DCX immunolabeling.

We have added these data. There is an image of a Cre- female, Cre+ female, Cre- male and Cre+ male in the new figure, Supplemental Figure 6. All mice had tamoxifen at 6 weeks of age and were perfused 6 weeks later. None of the mice had pilocarpine.

(16) In Suppl. Fig 7C, I believe the authors mean "no loss of hilar mossy and SOM cells" instead of "loss of hilar mossy and SOM cells".

This Figure was removed because of the input from Reviewer 1 suggesting it was too speculative.

Reviewer #1 (Recommendations For The Authors):

(1) The main claim of the study is that increasing adult neurogenesis decreases chronic seizures. However, to quantify adult-born neurons, DCX immunoreactivity is used as the sole metric to determine neurogenesis. This is insufficient as changes in DCX-expressing cells could also be an indicator of altered maturation, survival, and/or migration, not proliferation per se. To claim that increasing adult neurogenesis is associated with a reduction of chronic seizures, the authors should perform a pulse/chase (birth dating) experiment with BrdU and co-labeling with DCX.

We think that increased DCX does reflect increased adult neurogenesis. However, we agree that one does not know if it was due to increased proliferation, survival, etc. We also note that this mouse line has been studied thoroughly to show there was increased neurogenesis with BrdU, Ki67 and DCX. We mention that paper in the revised text:

Methods, starting on line 786:

It was shown that after tamoxifen injection in adult mice there is an increase in dentate gyrus neurogenesis based on studies of bromo-deoxyuridine, Ki67, and doublecortin (Sahay et al., 2011).

(2) As mentioned above, analysis of DCX staining alone months after TAM injections is limited. Instead, the cells could be labelled by BrdU prior to TAM injection, following which quantification of BrdU+/Prox1+ cells at 6 weeks post TAM injection should be performed in Cre+ and Cre- mice (males and females) to yield the rate of neurogenesis increase.

We respectfully disagree that birthdating cells is critical. Using DCX staining just before SE, we know the size of the population of cells that are immature at the time of SE. This is what we think is most important because these immature neurons are those that appear to affect SE, as we have already shown.

(3) To confirm the source of the hilar Prox1+ cells, a dual BrdU/EdU labeling approach would be beneficial. BrdU injection could be given before TAM injection and EdU injection before pilocarpine to label different cohorts of neural stem cells. Co-staining with Prox1 at different time points will help in identifying the origin of hilar ectopic cells.

We are grateful for the ideas of the Reviewer. We hesitate to do these experiments now because it seems like a new study to find out where hilar granule cells come from.

REFERENCES

Bumanglag AV, Sloviter RS (2018) No latency to dentate granule cell epileptogenesis in experimental temporal lobe epilepsy with hippocampal sclerosis. Epilepsia 59:2019-2034.

Carballosa-Gonzalez MM, Munoz LJ, Lopez-Alburquerque T, Pardal-Fernandez JM, Nava E, de Cabo C, Sancho C, Lopez DE (2013) EEG characterization of audiogenic seizures in the hamster strain gash:Sal. Epilepsy Res 106:318-325.

Cho KO, Lybrand ZR, Ito N, Brulet R, Tafacory F, Zhang L, Good L, Ure K, Kernie SG, Birnbaum SG, Scharfman HE, Eisch AJ, Hsieh J (2015) Aberrant hippocampal neurogenesis contributes to epilepsy and associated cognitive decline. Nat Commun 6:6606.

Esmaeili B, Weisholtz D, Tobochnik S, Dworetzky B, Friedman D, Kaffashi F, Cash S, Cha B, Laze J, Reich D, Farooque P, Gholipour T, Singleton M, Loparo K, Koubeissi M, Devinsky O, Lee JW (2023) Association between postictal EEG suppression, postictal autonomic dysfunction, and sudden unexpected death in epilepsy: Evidence from intracranial EEG. Clin Neurophysiol 146:109-117.

Gascoigne SJ, Waldmann L, Schroeder GM, Panagiotopoulou M, Blickwedel J, Chowdhury F, Cronie A, Diehl B, Duncan JS, Falconer J, Faulder R, Guan Y, Leach V, Livingstone S, Papasavvas C, Thomas RH, Wilson K, Taylor PN, Wang Y (2023) A library of quantitative markers of seizure severity. Epilepsia 64:1074-1086.

Hahn T et al. (2023) Towards a network control theory of electroconvulsive therapy response. PNAS Nexus 2:pgad032.

Jain S, LaFrancois JJ, Botterill JJ, Alcantara-Gonzalez D, Scharfman HE (2019) Adult neurogenesis in the mouse dentate gyrus protects the hippocampus from neuronal injury following severe seizures. Hippocampus 29:683-709.

Jung KH, Chu K, Lee ST, Kim J, Sinn DI, Kim JM, Park DK, Lee JJ, Kim SU, Kim M, Lee SK, Roh JK (2006) Cyclooxygenase-2 inhibitor, celecoxib, inhibits the altered hippocampal neurogenesis with attenuation of spontaneous recurrent seizures following pilocarpine-induced status epilepticus. Neurobiol Dis 23:237-246.

Kanner AM, Trimble M, Schmitz B (2010) Postictal affective episodes. Epilepsy Behav 19:156-158.

Karl S, Sartorius A, Aksay SS (2024) No effect of serum electrolyte levels on electroconvulsive therapy seizure quality parameters. J ECT 40:47-50.

Kavakbasi E, Stoelck A, Wagner NM, Baune BT (2023) Differences in cognitive adverse effects and seizure parameters between thiopental and propofol anesthesia for electroconvulsive therapy. J ECT 39:97-101.

Kommajosyula SP, Randall ME, Tupal S, Faingold CL (2016) Alcohol withdrawal in epileptic rats - effects on postictal depression, respiration, and death. Epilepsy Behav 64:9-14.

Krishnan GP, Bazhenov M (2011) Ionic dynamics mediate spontaneous termination of seizures and postictal depression state. J Neurosci 31:8870-8882.

Langroudi ME, Shams-Alizadeh N, Maroufi A, Rahmani K, Rahchamani M (2023) Association between postictal suppression and the therapeutic effects of electroconvulsive therapy: A systematic review. Asia Pac Psychiatry 15:e12544.

Mazzuferi M, Kumar G, Rospo C, Kaminski RM (2012) Rapid epileptogenesis in the mouse pilocarpine model: Video-EEG, pharmacokinetic and histopathological characterization. Exp Neurol 238:156-167.

Medvedeva TM, Sysoeva MV, Sysoev IV, Vinogradova LV (2023) Intracortical functional connectivity dynamics induced by reflex seizures. Exp Neurol 368:114480.

Riljak V, Maresova D, Jandova K, Bortelova J, Pokorny J (2012) Impact of chronic ethanol intake of rat mothers on the seizure susceptibility of their immature male offspring. Gen Physiol Biophys 31:173-177.

Sahay A, Scobie KN, Hill AS, O'Carroll CM, Kheirbek MA, Burghardt NS, Fenton AA, Dranovsky A, Hen R (2011) Increasing adult hippocampal neurogenesis is sufficient to improve pattern separation. Nature 472:466-470.

Scharfman HE, Goodman JH, Sollas AL (2000) Granule-like neurons at the hilar/CA3 border after status epilepticus and their synchrony with area CA3 pyramidal cells: Functional implications of seizure-induced neurogenesis. J Neurosci 20:6144-6158.

Singh B, Singh D, Goel RK (2012) Dual protective effect of passiflora incarnata in epilepsy and associated post-ictal depression. J Ethnopharmacol 139:273-279.

Smith ZZ, Benison AM, Bercum FM, Dudek FE, Barth DS (2018) Progression of convulsive and nonconvulsive seizures during epileptogenesis after pilocarpine-induced status epilepticus. J Neurophysiol 119:1818-1835.

Sun MY, Yetman MJ, Lee TC, Chen Y, Jankowsky JL (2014) Specificity and efficiency of reporter expression in adult neural progenitors vary substantially among nestin-creer(t2) lines. J Comp Neurol 522:1191-1208.

Uva L, de Curtis M (2020) Activity- and ph-dependent adenosine shifts at the end of a focal seizure in the entorhinal cortex. Epilepsy Res 165:106401.

Vilan A, Grangeia A, Ribeiro JM, Cilio MR, de Vries LS (2024) Distinctive amplitude-integrated EEG ictal pattern and targeted therapy with carbamazepine in kcnq2 and kcnq3 neonatal epilepsy: A case series. Neuropediatrics 55:32-41.

Zhao C, Tang Y, Xiao Y, Jiang P, Zhang Z, Gong Q, Zhou D (2024) Asymmetrical cortical surface area decrease in epilepsy patients with postictal generalized electroencephalography suppression. Cereb Cortex 34.

https://hyp.is/ooPwuFy1Ee-caEcVuPaVIA/kaizenbatter.com/the-kaizen-philosophy/

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Zen means “to make good.”

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auto-poietic self-improvement

Humidity control is achieved through monitoring the negative pressure differential behind the top fan wheel , between the centre and periphery of the fan. The pressure differential behind the fan varies as the atmosphere in the cook chamber varies.

Generally:

• The less humid, the higher the voltage of P1

• The higher the fan rpm, the higher the voltage of P1

During the calibration process the CPU is educated with specific voltages from the P1 pressure switch for known fan speeds, temperature and humidity.

• The Fan motor inverter sends fan speed data to the CPU through the Bus system.

• B4 (Humidity) Thermocouple connected to terminal X5 on the A10 I/O board and situated behind the top fan (secured on the outside of the oven liner) measures the air temp behind the fan and sends this data to the CPU from A10 via the Bus system. *

• P1 pressure sensor connected to terminal X1 on the A10 I/O board measures the differential pressure across the back of the fan wheel, converts the pressure reading to a voltage and delivers the results to the CPU from A10 via the Bus system, where they are stored both in the CPU and on the SD card (iCombi Pro) and External Eprom (iCombi Classic). These clearly defined reference voltages, created during the calibration process and stored in the CPU, enable the iCombi to create any specific climate requested by the chef.

Example: 160 degrees C – 60% humidity – Fan speed 1000 rpm

• The processor will ensure a fan speed of 1000 rpm via the communication with the fan motor inverter, via the bus system.

• Y5 humidity valve is closed, and heat is applied via the hot air heating elements or Gas heat exchanger, to 160 deg C. Communicated to the CPU by B4 thermocouple via A10 and the Bus system.

• Humidity is delivered to the chamber via the steam generator and pressure sensor P1 monitors the changing negative differential pressure behind the fan until a predetermined value calculated from data stored during the calibration process is reached.

If the signal from P1 pressure sensor overruns the predetermined value, the steam generator will shut down, Y5 Humidity valve will open and negative pressure behind the fan wheel will draw ambient air into the oven pushing humidity down into the control box and out through the vent stack..

As this occurs the P1 sensor voltage signal will indicate the reduction in humidity, Y5 will close again ad the steam generator will start again.

Remember the PT1000 probes operate at mains voltage, so do not unplug or reconnect PT 1000 probes whilst the oven is connected to mains power !

If plug connections between the PT1000 probes and the ESTL are loose, the potential for high resistance joints increases.

Additional resistance in the circuit will change the ESTLs understanding of the actual temperature at the required point of sensing and can actually promote incorrect Service / Error 72 indications.

The input to L1 on the ESTL comes from the A10 I/O PCB Terminal X22 pin number 1 (marked with an arrow on the pcb). This is the 240V output from A10 to the safety chain.

The output from the ESTL (Terminal L*) terminates on connection A1 of the K1 contactor coil, thus potentially switching K1 on or off. Dependent of course upon the condition of the safety chain.

At the same time the output from the ESTL (Terminal L*) also returns to pin 3 of A10 terminal X22 to signal to A10 the condition of the safety chain, enabling the processor to provide appropriate indications of safety chain status. (Service 72 or Error 72).

Where two ESTLs are employed (20-2/1E models) the output terminal L* on the first ESTL is connected directly to the input terminal L1 on the second ESTL thus connecting their outputs in series, meaning both ESTL ouputs must be closed for the K1 contactor to energise.

See section 24 of the training manual for electrical wiring diagrams - commencing page 181.

Known as Forced Update!

Additional example!

When the A11 CPU is replaced it requires loading with the correct operating software as it is supplied without software.

This is because the CPU, part number 42.00.251P, is used both in iVario Pro and iCombi Pro control panels.

To upload software to the CPU, make sure the SD card is tranferred from the old CPU and complete a forced update using Rational USB stick 2.0 (Black tip) as detailed in the manual.

Once the update is completed, check that the serial number is present on the machine in Service Level and also that calibration values are present.

The CDS (Calcium Diagnostic Sensor) S11 is a hall sensor device, sensing movement of water though any of the five solenoid valves in the 5 port valve block.

In Basic Settings the number of pulses per litre can be adjusted.

Must be set at 1350 pulses per litre for 6-1/1 to 20-2/1

Must be set at 1000 pulses per litre for 6-2/3 (XS model)

The CDS sensor is connected to the A10 I/O board at connection X15 and when water is flowing, measures the volume by sending electrical pulses via the A10 I/O board through the bus system (Data Highway) to the main processor (A11).

The CDS provides a preset number of pulses for each litre of water passing through the valve block.

The processor (A11) uses this information to confirm:

a) That any specific valve is in fact working by sending a signal to open the valve and then waiting for pulses to appear from the CDS sensor as in confirmation that the valve has in fact opened.

b) The volume of water stored in the steam tank each time the SC Automaitic system operates..

Error code description:

Faulty data communication to the I/O board.

Condition for error detection:

BUS signal from I/O board is missing or is not transmitted for at least 5 seconds at a time.

Error area:

Data transfer cable, I/O board

Relevant causes/components:

• Electrical connection to components
• I/O board

240Vac supply to A13 pump board iCombi Pro and Classic

240Vac supply to A18 pump board for iCombi XS units.

The iCombi Pro XS does not have the new A13 as it still has the original hardware from index I (i.e. Drain valve, SC pump, Care pump, CleanJet pump). Therefore it needs a special pump adapter pcb A18 to be able to control the older hardware with the new A10 I/O board.

X51 is a 12 pole female connector from which a 12 core cable carries data to and from all Bus members.

The 12 wire cable also caries a 5V "stand by" voltage from I/O board A10 to CPU (A11 iCombi Pro) and the ICP capacitor switch pcb (A19).

The "stand by" voltage enables operation of the capacitor on/off switch and the CPU as soon as power is supplied to the oven (as the isolator is switched on).

When the capacitor switch is operated, ESTL completes its safety checks and providing no high temperature errors or component errors exist, contactor K1 energises and a 12 V supply is also sent to the CPU via this connector and its associated cable.

Error code description:

Incorrect water quantity/flow measurement when filling the steam generator.

Condition for error detection:

A flow rate in a corresponding tolerance range is expected on the CDS sensor. The water also actively enters the steam generator, the level electrode indicates a full water level, but the CDS sensor passes on an error/implausible flow or no signal at all.

Error area:

Water supply, water flow detection, fill level detection

Relevant causes/components:

• Water supply
• Solenoid valve block with CDS sensor
• Electrical connection (CDS sensor – I/O module)
• Level electrode
• Steam generator reference volume
• Incorrect CDS pulse saved

X51 is a 12 pole female connector from which a 12 core cable carries data to and from A10 and the CPU (A12).

The 12 wire cable also caries a 5V "stand by" voltage from I/O board A10 to on/off switch on CPU (A12) .

The "stand by" voltage enables operation of the on/off switch and the CPU as soon as power is supplied to the oven (as the isolator is switched on).

When the on/off switch is operated, ESTL completes its safety checks and providing no high temperature errors or component errors exist, contactor K1 energises and a 12 V supply is also sent to the CPU via this connector and its associated cable.

B5 Thermocouple is situated in the steam generator directly against the heat source, it is connected to pins 1 & 2 terminal X6.

B9 Thermocouple is situated in the cooking cabinet at low level, it is connected to pins 3 & 4 terminal; X6.

The function of B5 s to measure / monitor preheat of the steam generatorfor timing of any cooking program start.

The function of B9 is to work in conjunction with B1 to ensure accurate chamber temperature monitoring in a larger chamber with more circulation fans

The combi-steamer checks the temperature of sensor thermocouple B5 and B9 every second (in all modes).

As soon as an implausible temperature value is measured on the thermocouple sensor B5, a service error (Service 20.8) is indicated.

Check the value of temperature sensor B5 in the service menu:

For iCombi Pro: Select the steam launcher - go to Diagnosis.

For iCombi Classic: Select Diagnostics and B5

As soon as an implausible temperature value is measured on the thermocouple sensor B9 cooking cabinet, a service error (Service 20.16) is indicated.

Check the value of temperature sensor B9 in the service menu:

For iCombi Pro: Select the hot air or humidity launcher - go to Diagnosis.

For iCombi Classic: Select Diagnostic and B9

If this value is approx. 615°C [1140°F], either the sensor is defective (Open Circuit) or the electrical connection between the temperature sensor and the I/O PCB is faulty

B5 Thermocouple is situated in the steam generator directly against the heat source, it is connected to pins 1 & 2 terminal X6.

B10 Thermocouple is situated in the electrcal compartment at the left of the oven, behind the inverter of M1 Fan, it is connected to pins 3 & 4 terminal; X6.

B5 function is to measure / monitor preheating of the steam generator for timing of any cooking program start.

B10 controls the speed of the cooling fan M5

The combi-steamer checks the temperature of sensor thermocouple B5 and B10 every second (in all modes).

As soon as an implausible temperature value is measured on the thermocouple sensor B5, a service error (Service 20.8) is indicated.

Check the value of temperature sensor B5 in the service menu:

For iCombi Pro: Select the steam launcher - go to Diagnosis.

For iCombi Classic: Select Diagnostics and B5

As soon as an implausible temperature value is measured on the thermocouple sensor B10, a service error (Service 20.32) is indicated.

Check the value of temperature sensor B10 in the service menu:

For iCombi Pro:Select Launcher General – Diagnosis tab – PCB temperatures.

For iCombi Classic under “Diagnostic B10

If either value is approx. 615°C [1140°F], either the sensor is defective (Open Circuit) or the electrical connection between the temperature sensor and the I/O PCB is faulty

B5 Thermocouple is situated in the steam generator directly against the heat source, it is connected to pins 1 & 2 terminal X6.

Its function is to measure / monitor preheat of the steam generator for timing of cooking program start.

The combi-steamer checks the temperature of sensor thermocouple B5 every second (in all modes).

As soon as an implausible temperature value is measured on the thermocouple sensor B5 steam generator, a service error (Service 20.8) is indicated.

*Check the value of temperature sensor B5 in the service menu:

For iCombi Pro: Select the steam launcher - go to Diagnosis.

For iCombi Classic: Select Diagnostics and B5

If this value is approx. 615°C [1140°F], either the sensor is defective (Open Circuit) or the electrical connection between the temperature sensor and the I/O PCB is faulty

Thermocouple B4 measures the temperature behind the top circulation fan during the initial Self test calibration process and during any subsequent manual calibration. It also monitors the cabinet temperature for humidity control above the local boiling point.

The combi-steamer checks the temperature of thermocouple B4 every second (in all modes). As soon as an implausible temperature value is measured service error code Service 20.4 is indicated..

Check the value of temperature sensor B4 in the service menu:

For iCombi Pro: Select the steam or humidity launchers - go to Diagnosis.

For iCombi Classic: Select Diagnostics and B4

If this value is approx. 615°C [1140°F], either the sensor is defective (Open Circuit) or the screw connection on the inner cabinet is loose or not adequately insulated or the electrical connection between the temperature sensor and the I/O PCB is faulty.

B2 is situated in the Control box (formerly known as the quench box) and its function is to reduce the volume of steam emmissions from the vent stack and also to reduce the temperature of waste water through the drain to a level below 65 degrees during normal oven cooking processes.

The combi-steamer checks the temperature of thermocouple B2 every second (in all modes).

As soon as an implausible temperature value is measured, service error code Service 20.2 is indicated.

Check the value of temperature sensor B2 in the service menu:

For iCombi Pro: Select the steam or humidity launchers - go to Diagnosis.

For iCombi Classic: Select Diagnostics and B2

If this value is approx. 615°C [1140°F], either the sensor is defective (Open Circuit) or the electrical connection between the temperature sensor and the I/O PCB is faulty.

DOI: 10.1098/rsob.230355

Resource: RRID:BDSC_20793

Curator: @anisehay

SciCrunch record: RRID:BDSC_20793

What is this?

DOI: 10.1016/j.isci.2024.110558

Resource: (SouthernBiotech Cat# 9052-09, RRID:AB_2796621)

Curator: @scibot

SciCrunch record: RRID:AB_2796621

What is this?

DOI: 10.1016/j.isci.2024.110568

Resource: (Affinity Biosciences Cat# AF4022, RRID:AB_2845464)

Curator: @scibot

SciCrunch record: RRID:AB_2845464

What is this?

Given that: h^2 = o^2 + a^2 a = sqrt(h^2 - o^2) then x = sqrt(1^2 - 0.5^2) = sqrt(1-0.25) = sqrt(0.75)

DOI: 10.1038/s44318-024-00136-y

Resource: (DSHB Cat# f59, RRID:AB_528373)

Curator: @scibot

SciCrunch record: RRID:AB_528373

What is this?

DOI: 10.1083/jcb.202401140

Resource: (Synaptic Systems Cat# 377 308, RRID:AB_2924929)

Curator: @scibot

SciCrunch record: RRID:AB_2924929

What is this?

DOI: 10.1083/jcb.202401140

Resource: (UC Davis/NIH NeuroMab Facility Cat# 75-146, RRID:AB_10673030)

Curator: @scibot

SciCrunch record: RRID:AB_10673030

What is this?

DOI: 10.1016/j.cell.2024.07.006

Resource: DANPOS2 (RRID:SCR_015527)

Curator: @scibot

SciCrunch record: RRID:SCR_015527

What is this?

DOI: 10.1101/2023.09.25.559303

Resource: Fiji (RRID:SCR_002285)

Curator: @scibot

SciCrunch record: RRID:SCR_002285

What is this?

DOI: 10.1101/2023.09.25.559303

Resource: RRID:BDSC_28718

Curator: @scibot

SciCrunch record: RRID:BDSC_28718

What is this?

DOI: 10.1038/s41598-023-50552-z

Resource: RRID:BDSC_22680

Curator: @scibot

SciCrunch record: RRID:BDSC_22680

What is this?

DOI: 10.3389/fnins.2020.00068

Resource: RRID:BDSC_36062

Curator: @scibot

SciCrunch record: RRID:BDSC_36062

What is this?

DOI: 10.2147/JIR.S457570

Resource: (BD Biosciences Cat# 553046, RRID:AB_394582)

Curator: @scibot

SciCrunch record: RRID:AB_394582

What is this?

In this excerpt from the story of Joseph, gender roles are sharply delineated, revealing the power dynamics and societal expectations of men and women within the cultural context. Joseph (as the male protagonist) embodies the traits of the HERO as—virtue, piety, and steadfastness in the face of temptation. His resistance to the advances of the governor's wife is a key moment that defines his heroism. The narrative portrays Joseph's refusal as not just a personal victory but as a demonstration of his commitment to his moral principles, which are attributes traditionally associated with male heroes in many cultures (such as duty or ‘dharma’ in Hinduism). The governor's wife, whose actions are central to the plot, represents the dangers of unchecked female desire. Her attempt to seduce Joseph is depicted as a moral failing, and her eventual confession reinforces the narrative that women’s desires must be controlled. This portrayal aligns with patriarchal views where female sexuality is often portrayed as dangerous or destructive unless it is confined within socially acceptable boundaries. When comparing the different segments of the same text, particularly the lines where the governor’s wife attempts to seduce Joseph (31-33) and later when she confesses her wrongdoing (51-53), we see a shift in the narrative focus from her initial power and agency to a more repentant and submissive role. Initially, she wields considerable power, using her position to try and manipulate Joseph. However, her eventual confession and the exoneration of Joseph highlight the underlying patriarchal values, where the woman’s role is to recognize her transgression and submit to the moral authority of the male hero. This shift reflects the gender dynamics at play—while the woman exercises agency, it is ultimately curtailed by the moral and social expectations of her gender. Joseph’s steadfastness, in contrast, remains unchallenged, further cementing his role as the HERO, whose righteousness is never in doubt. Comparing this story with other narratives of male chastity and female temptation, such as the story of Hippolytus and Phaedra, reveals a similar pattern in the portrayal of gender roles. In both stories, the male figure’s heroism is defined by his resistance to female desire. However, the outcomes for the male characters differ—Hippolytus meets a tragic end despite his virtue, while Joseph is ultimately rewarded with power and security. This difference highlights the cultural variations in the construction of the HERO: in the Greek context, the hero’s virtues do not necessarily shield him from a tragic fate, whereas in the Biblical and Quranic context, the hero’s righteousness leads to his eventual elevation. In terms of gender definitions, both stories depict female desire as a source of chaos and disorder. In both narratives, the women’s roles are largely defined by their relationships to the male protagonists, reinforcing a patriarchal worldview where female agency is limited and often portrayed as dangerous when it transgresses societal norms. From a linguistic perspective, the language used to describe the governor's wife—her scheming, her eventual confession, and her acknowledgment of guilt—emphasizes her role as a transgressor who must be brought back in line with societal expectations. The repeated references to “scheming” and “betrayal” in the context of the women involved in the story further highlight the narrative’s focus on controlling and condemning female agency that steps outside prescribed bounds. I believe the high points of this version lie in its clear moral message and the elevation of Joseph as a paragon of virtue. However, this comes at the cost of a more nuanced portrayal of the female characters, who are largely depicted in a negative light. The story’s manipulation of gender roles to reinforce the HERO’s virtue reflects the broader cultural and political context in which it was written—one where patriarchal values dominated. However, the translation and interpretation of this text over time may have further reinforced these patriarchal elements, as translators and scholars may have emphasized certain aspects of the story to align with their own cultural and moral frameworks. This is evident in the way the text frames the governor's wife’s confession, where her recognition of Joseph’s innocence and her own guilt is portrayed as a necessary and redemptive act, reinforcing the idea that true virtue lies in submission to male authority. CC BY Aarushi Attray (contact)

This passage is from the story of Joseph (Yusuf) in the Quran, specifically focusing on the moment when Joseph's brothers conspire to get rid of him due to their jealousy. Verse 14 highlights their rationalization for their actions, implying that if they let Joseph be harmed while they were many, they would be useless. In verse 15, they carry out their plan, throwing Joseph into a well and leaving him there, while verse 16 shows them returning to their father, Jacob (Yaqub), with a fabricated story of a wolf attack. Their pretense of innocence and false sorrow in verse 17 is meant to deceive Jacob, despite their knowledge that their actions are deeply wrong. This passage illustrates themes of jealousy, betrayal, and the complexity of human emotions and actions. It sets the stage for Joseph's trials and eventual rise to prominence, emphasizing divine wisdom and justice in the face of human wrongdoing. #worldlit-lit211-SS2024

DOI: 10.1534/g3.119.400581

Resource: RRID:BDSC_61978

Curator: @scibot

SciCrunch record: RRID:BDSC_61978

What is this?

DOI: 10.1534/g3.119.400581

Resource: RRID:BDSC_39041

Curator: @scibot

SciCrunch record: RRID:BDSC_39041

What is this?

DOI: 10.1534/g3.119.400581

Resource: RRID:BDSC_52935

Curator: @scibot

SciCrunch record: RRID:BDSC_52935

What is this?

DOI: 10.1534/g3.119.400581

Resource: RRID:BDSC_36623

Curator: @scibot

SciCrunch record: RRID:BDSC_36623

What is this?

DOI: 10.1534/g3.119.400581

Resource: RRID:BDSC_57801

Curator: @scibot

SciCrunch record: RRID:BDSC_57801

What is this?

DOI: 10.1534/g3.119.400581

Resource: RRID:BDSC_51913

Curator: @scibot

SciCrunch record: RRID:BDSC_51913

What is this?

DOI: 10.1534/g3.119.400581

Resource: RRID:BDSC_61854

Curator: @scibot

SciCrunch record: RRID:BDSC_61854

What is this?

DOI: 10.1534/g3.119.400581

Resource: RRID:BDSC_60074

Curator: @scibot

SciCrunch record: RRID:BDSC_60074

What is this?

DOI: 10.1534/g3.119.400581

Resource: RRID:BDSC_55731

Curator: @scibot

SciCrunch record: RRID:BDSC_55731

What is this?

,

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Smith, Allan H., Elena O. Lingas, and Mahfuzar Rahman. "Contamination of drinking-water by arsenic in Bangladesh: a public health emergency." Bulletin of the world health organization 78, no. 9 (2000): 1093-1103.

Welcome back! In this demo lesson, you're going to create the AWS account structure which you'll use for the remainder of the course. At this point, you need to log in to the general AWS account. I’m currently logged in as the IAM admin user of my general AWS account, with the Northern Virginia region selected.

You’ll need either two different web browsers or a single web browser like Firefox that supports different sessions because we’ll be logged into multiple AWS accounts at once. The first task is to create the AWS organization. Since I'm logged in to a standard AWS account that isn’t part of an AWS organization, it’s neither a management account nor a member account. We need to move to the AWS Organizations part of the console and create the organization.

To start, go to "Find Services," type "Organizations," and click to move to the AWS Organizations console. Once there, click "Create Organization." This will begin the process of creating the AWS organization and convert the standard account into the management account of the organization. Click on "Create Organization" to complete the process. Now, the general account is the management account of the AWS organization.

You might see a message indicating that a verification email has been sent to the email address associated with the general AWS account. Click the link in that email to verify the address and continue using AWS Organizations. If you see this notification, verify the email before proceeding. If not, you can continue.

Now, open a new web browser or a browser session like Firefox and log in to the production AWS account. Ensure this is a separate session; if unsure, use a different browser to maintain logins to both the management and production accounts. I’ll log in to the IAM admin user of the production AWS account.

With the production AWS account logged in via a separate browser session, copy the account ID for the production AWS account from the account dropdown. Then, return to the browser session with the general account, which is now the management account of the organization. We’ll invite the production AWS account into this organization.

Click on "Add Account," then "Invite Account." Enter either the email address used while signing up or the account ID of the production account. I’ll enter the account ID. If you’re inviting an account you administer, no notes are needed. However, if the account is administered by someone else, you may include a message. After entering the email or account ID, scroll down and click "Send Invitation."

Depending on your AWS account, you might receive an error message about too many accounts within the organization. If so, log a support request to increase the number of allowed accounts. If no error message appears, the invite process has begun.

Next, accept the invite from the production AWS account. Go back to the tab with the general AWS account, move to the Organizations console, and click "Invitations" on the middle left. You should see an overview of all invitations related to the production AWS account. Click "Accept" to complete the process of joining the organization. Now, the production account is a member of the AWS organization.

To verify, return to the general account tab and refresh. You should now see two AWS accounts: the general and the production accounts. Next, I’ll demonstrate how to role switch into the production AWS account, now a member of the organization.

When adding an account to an organization, you can either invite an existing account or create a new one within the organization. If creating a new account, a role is automatically created for role switching. If inviting an existing account, you need to manually add this role.

To do this, switch to the browser or session where you're logged into the production AWS account. Click on the services search box, type IAM, and move to the IAM console to create IAM roles. Click on "Create Role," select "Another AWS Account," and enter the account ID of the general AWS account, which is now the management account.

Copy the account ID of the general AWS account into the account ID box, then click "Next." Attach the "AdministratorAccess" policy to this role. On the next screen, name the role "OrganizationAccountAccessRole" with uppercase O, A, A, and R, and note that "Organization" uses the U.S. spelling with a Z. Click "Create Role."

In the role details, select "Trust Relationships" to verify that the role trusts the account ID of your general AWS account, which allows identities within the general account to assume this role.

Next, switch back to the general AWS account. Copy the account ID for the production AWS account because we will switch into it using role switch. In the AWS console, click on the account dropdown and select "Switch Roles." Paste the production account ID into the account ID box, and enter the role name "OrganizationAccountAccessRole" with uppercase O, A, A, and R.

For the display name, use "Prod" for production, and pick red as the color for easy identification. Click "Switch Role" to switch into the production AWS account. You’ll see the red color and "Prod" display name indicating a successful switch.

To switch back to the general account, click on "Switch Back." In the role history section, you can see shortcuts for switching roles. Click "Prod" to switch back to the production AWS account using temporary credentials granted by the assumed role.

Now, let’s create the development AWS account within our organization. Close the browser window or tab with the production AWS account as it’s no longer needed. Return to the AWS Organizations console, click "Add Account," and then "Create Account." Name the account "Development," following the same naming structure used for general and production accounts.

Provide a unique email address for the development AWS account. Use the same email structure you’ve used for previous accounts, such as "Adrian+TrainingAWSDevelopment" for consistency.

In the box for the role name, use "OrganizationAccountAccessRole" with uppercase O, A, A, and R, and the U.S. spelling. Click "Create" to create the development account. If you encounter an error about too many accounts, you might need to request an increase in the account limit.

The development account will be created within the organization, and this may take a few minutes. Refresh to see the new development account with its own account ID. Copy this account ID for the switch role dialogue.

Click on the account dropdown, select "Switch Roles," and enter the new development account ID. For the role name, use "OrganizationAccountAccessRole" and for the display name, use "Dev" for development with yellow as the color for distinction. Click "Switch Role" to switch into the development AWS account.

In the AWS console, you’ll see the new development account. You can switch directly between the general, production, and development accounts using role switch shortcuts. AWS automatically created the "OrganizationAccountAccessRole" in the development account.

In summary, you now have three AWS accounts: the general AWS account (management account), the production AWS account, and the development AWS account. This completes the account structure for the course. Complete this video, and I'll look forward to seeing you in the next lesson.

The daily cards and journal entries are obviously indexed by chronological date and then within tabbed sections by month and year.

The rest of the other cards with notes are given individual (decimal) numbers and and then are put into numerical order. These numbered cards are then indexed by putting related subject/topic/category words from them onto a separate index card which cross references either a dated card or the numbered card to which it corresponds. These index cards with topical words/phrases are then filed alphabetically into a tabbed alphabetical section (A-Z).

As an example with the card in this post, if I wanted to remember all the books I buy from Octavia's Bookshelf, then I'd create a card titled "Octavia's Bookshelf" and list the title along with the date 2024-08-13 and file it alphabetically within the "O" tab section of the index. Obviously this might be more useful if I had more extensive notes about the book or its purchase on the 2024-08-13 card. I did create a short journal card entry about the bookstore on 08-13 because it was the first time I visited the bookstore in it's new location and decor, so there are some scant notes about my impressions of that which are cross-indexed to that Octavia's Bookshelf card. Thus my Octavia's Bookshelf card has an entry with "The Book Title, 2024-08-13 (J)(R)" where the '(J)' indicates there's a separate journal entry for that day and the '(R)' indicates there's also a receipt filed next to that day's card.

I also created an "Author Card" with the author of the book's name, the title, publication date, etc. I included the purchase date and the reason why I was interested in the book. I'll use that same card to write notes on that particular book as I read it. These author cards are filed in a separate A-Z tabbed 'Bibliography' section for easily finding them as well. (I suppose I could just put them into the primary A-Z index, but I prefer having all the authors/books (I have thousands) in the same section.)

I also have a rolodex section of people filed alphabetically, so I can easily look up Steve and Sonia separately and see what I might have gotten them on prior birthdays as well as notes about potential future gift ideas. I had tickler cards with their names on them filed in early August and now that they're in my to do list, I've moved those cards to August 2025, ready for next year's reminder. Compared to a typical Future Log I don't do nearly as much writing and rewriting when migrating. I just migrate a card forward until it's done or I don't need it anymore.

If you've used a library card index before, the general idea is roughly the same, you're just cross-indexing more than books by title, author and subject. You can index by day, idea, project, or any other thing you like. My card index cabinet is really just a large personal database made out of paper and metal.

The secret isn't to index everything—just the things you either want to remember or know you'll want to look up later and use/re-use.

for - quote - awakening experience - Federico Faggin

quote - awakening experience - Federico Faggin - (see below)

• What I was observing was energy that previously had come out of my chest and
• It was physical energy
• It was not an imagination
• It was physical energy was
• It was a white light that
• It felt like a love that I never felt before and
• It was love, joy and peace
• I never I never had experienced peace before
• It was like like that's me this is my home this is this is who - I am that energy then now exploded now is everywhere and now I am, my consciousness is in that energy
• My feelings are in that consciousness, which is also outside inside your body and o
• Outside your body is everywhere well that experience can change your idea of who you are very quickly because
  • Apart from the fact that
    • it was so profound and
    • so deeply felt to be true
  • it was a direct experience of Consciousness that I never had before and
  • it revealed that I am the totality of reality observing itself from a one point of view

Author response:

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

Reviewer #1 (Public Review): 

Summary:

The authors profile gene expression, chromatin accessibility, and chromosomal architecture (by Hi-C) in activated CD4 T cells and use this information to link non-coding variants associated with autoimmune diseases with putative target genes. They find over 1000 genes physically linked with autoimmune disease loci in these cells, many of which are upregulated upon T cell activation. Focusing on IL2, they dissect the regulatory architecture of this locus, including the allelic effects of GWAS variants. They also intersect their variant-to-gene lists with data from CRISPR screens for genes involved in CD4 T cell activation and expression of inflammatory genes, finding enrichments for regulators. Finally, they showed that pharmacological inhibition of some of these genes impacts T-cell activation. 

This is a solid study that follows a well-established canvas for variant-to-gene prioritisation using 3D genomics, applying it to activated T cells. The authors go some way in validating the lists of candidate genes, as well as exploring the regulatory architecture of a candidate GWAS locus. Jointly with data from previous studies performing variant-to-gene assignment in activated CD4 T cells (and other immune cells), this work provides a useful additional resource for interpreting autoimmune disease-associated genetic variation. 

Suggestions for improvement:

Autoimmune disease variants were already linked with genes in CD28-stimulated CD4 T cells using chromosome conformation capture, specifically Promoter CHi-C and the COGS pipeline (Javierre et al., Cell 2016; Burren et al., Genome Biol 2017; Yang et al., Nat Comms 2020). The authors cite these papers and present a comparative analysis of their variant-to-gene assignments (in addition to scRNA-seq eQTL-based assignments). Furthermore, they find that the Burren analysis yields a higher enrichment for gold standard genes. 

The obvious question that the authors don't venture into is why the results are quite different. In principle, this could be due to the differences between: 

(a) the cell stimulation procedure 

(b) the GWAS datasets used 

(c)  the types of assay (Hi-C vs Capture Hi-C) 

(d) approaches for defining gene-linked regions (loops vs neighbourhoods) 

(e) how the GWAS signals at gene-linked regions are aggregated (e.g., the flavours of COGS in Javierre and Burren vs the authors' approach)

Re (a), I'm not sure the authors make it explicitly clear in the main text that the Capture Hi-Cbased studies also use *stimulated* CD4 T cells, particularly in the section "Comparative predictive power...". So the cells used are pretty much the same, and the differences likely arise from points (b) to (e).

It would be useful for the community to understand more clearly what is driving these differences, ideally with some added data. Could the authors, for example, take the PCHi-C data from Javierre/Burren and use their GWAS data and variant-to-gene assignment algorithms? 

We greatly appreciate the referee’s expert assessment of our work and its value to the field, and we are glad that the referee was enthused by our comparison of the predictive power of the various V2G approaches. A point not emphasized enough in the original version of the manuscript is that we actually did harmonize the various datasets in the way the referee suggests for the precision/recall analysis. We took the contact maps presented from each paper, mapped genes using the same set of GWAS SNPs, and defined all gene-linked regions using our loop calling approach. This has been clarified in the revised version of the manuscript. We have now included a more thoughtful discussion of the possible sources of discrepancy between the different studies included in the comparison, and our thoughts on the potential sources raised by the referee are outlined below:

(a) The modes of stimulation used are similar between studies, but timepoints and donors did vary, and ours was the only study that sorted naïve CD4+ T cells before stimulation. These aspects could represent a source of variability. 

(b) The GWAS is not a source of variability because we re-ran the raw data from all the orthogonal studies through our V2G pipeline using the same GWAS as in the current manuscript. 

(c) The use of HiC vs. Capture HiC is a likely source of variability. The Capture-HiC datasets included in our comparison are lower resolution (i.e. HindIII) but focus higher sequencing depth at promoters compared to our HiC datasets – i.e., Capture-HiC may mis-call loops to the wrong promoters due to lower resolution as we have shown in our previous study [Su, Human Genetics, 2021], and will miss distal SNP interactions at promoters not included in the capture set. While HiC is unbiased in this regard, HiC will fail to call some SNP-promoter loops called by CaptureHiC because the sequencing power is not specifically focused at promoters. 

(d) For studies using neighborhood approaches, we re-ran the raw data through our loop calling algorithm to connect distal SNP to gene promoters, and regarding (e) above, we ran the raw data through our V2G pipeline to allow a better comparison.

In addition, given that the authors use Hi-C, a popular method for V2G prioritisation for this type of data is currently ABC (Nasser et al, Nature 2021). Could the authors provide a comparative analysis with respect to the V2G assignments in the paper and, if they see it appropriate, also run ABC-based GWAS integration on their own Hi-C data?

This is an excellent suggestion, which we have followed in the revised version of our manuscript. It should be noted (and we do so in the text of the revision) that there is an important caveat to bringing in the ABC model. Chromosome conformation-based approaches are biologically constrained (i.e., informed) by the natural structure of chromatin in the nucleus that controls how gene transcription is regulated in cis, and it does so in a way that brings value to GWAS data. However, the ABC model further constrains the input data by imposing non-biological filters that allow the algorithm to be applied, but impose artifactual limitations that may negatively impact interpretation and discovery. In addition to filtering out pseudogenes, bidirectional RNA, antisense RNAs, and small RNAs, the ABC model gene set eliminates genes ubiquitously expressed across tissues (based on the assumption that these genes are driven primarily by elements adjacent to their promoters) and only allows annotation of one promoter per gene, even though the median number of promoters per gene in the human genome is three. In contrast, our chromatin-based V2G removes pseudogenes, but includes lincRNA and small RNAs, and includes all alternative transcription start sites annotated by gencode. 

To apply the ABC GWAS gene nomination model to our CD4+ T cell chromatin-based V2G data, we used our ATAC-seq data and publicly available CD4+ T cell H3K27ac ChIP-seq data as input, and integrated this with GWAS and the average ENCODE-derived HiC dataset from the original ABC paper. The activity-by-contact model nominated 650 genes, compared to 1836 genes when using our cell type-matched HiC data and analysis pipeline. Only 357 of these genes were nominated by both approaches; 1479 genes nominated by our approach were not nominated by ABC, while 293 genes not implicated by our approach were newly implicated by ABC. To determine how the ABC-constrained approach performs against the HIEI gold standard set, we subjected all datasets used for the comparison depicted in the new Figure 5D to the same promoter filter used by the ABC model prior as part of the precision-recall re-analysis. Firstly, we found that applying the restricted ABC model promoter annotation to all datasets did not have a large effect on recall, however, the precision of several of the datasets were affected. For example, using the restricted promoter set reduced the precision of our (Pahl) V2G approach and inflated the precision of the nearest gene to SNP metric. Second, the new precision-recall analysis shows that the ABC score-based approach is only half as sensitive at predicting HIEI genes as the chromatin-based V2G approaches. This indicates that constraining GWAS data with cell type- and state-specific 3D chromatin-based data brings more GWAS target gene predictive power than application of the multi-tissue-averaged HiC used by the ABC model. We thank the reviewer for helpful suggestions that have improved the quality of our study.

Reviewer #2 (Public Review): 

Summary:

There is significant interest in characterizing the mechanisms by which genetic mutations linked to autoimmunity perturb immune processes. Pahl et al. collect information on dynamic accessible regions, genes, and 3D contacts in primary CD4+ T cell samples that have been stimulated ex vivo. The study includes a variety of analyses characterizing these dynamic changes. With TF footprinting they propose factors linked to active regulatory elements. They compare the performance of their variant mapping pipeline that uses their data versus existing datasets. Most compelling there was a deep dive into additional study of regulatory elements nearby the IL2 gene. Finally, they perform a pharmacological screen targeting several genes they suggest are involved in T cell proliferation. 

Strengths:

The work done characterizing elements at the IL2 locus is impressive. 

Weaknesses:

Missing critical context to evaluate claims. There are extensive studies performed on resting and activated immune cell states (CD4+ T cells and other cell types) and some at multiple time points or concentrations of stimuli that collect ATAC-seq and/or RNA-seq that have been ignored by this study. How do conclusions from previous studies compare to what the authors conclude here? It is impossible to evaluate the claims without this additional context. These are a few studies I am familiar with (the authors should perform a more comprehensive search to be sure they're not ignoring existing observations) that would be important to compare/contrast conclusions:  o Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424-431 (2018). 

- Calderon, D., Nguyen, M.L.T., Mezger, A. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat Genet 51, 1494-1505 (2019). 

- Gate, R.E., Cheng, C.S., Aiden, A.P. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat Genet 50, 1140-1150 (2018).  o Glinos, D.A., Soskic, B., Williams, C. et al. Genomic profiling of T-cell activation suggests increased sensitivity of memory T cells to CD28 costimulation. Genes Immun 21, 390-408 (2020).  o Gutierrez-Arcelus, M., Baglaenko, Y., Arora, J. et al. Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci. Nat Genet 52, 247-253 (2020). 

- Kim-Hellmuth, S. et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. Nat. Commun. 8, 266 (2017).  o Ye, C. J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014). 

- As a general point, I appreciate it when each claim includes a corresponding effect size and p-value, which helps me evaluate the strength of significance of supporting evidence. 

We greatly appreciate the referee’s expert assessment of our work and emphasis on the value of our functional follow-up studies. Our precision-recall analyses were not meant to represent an exhaustive comparison of all prior GWAS gene nomination studies, although we agree that this could (and should) be done as part of a separate study in a future manuscript. Instead, we focused on gene nomination studies that 1) analyzed resting and activated human CD4+ T cells, 2) whose experimental design was most comparable to our own studies, and 3) had raw data readily available in the appropriate formats to allow re-analysis and harmonization before comparison. This is a point we did not make sufficiently clear in the original version of the manuscript, but have clarified in the revision. 

Based on this rationale, we agree that the studies by Gate et al. and Ye et al. should be included in our comparative precision-recall analysis, and we have done so in the revised manuscript. The Gate study reported ATAC-seq peak co-accessibility, caQTL, eQTL, and HiC data, and we now include the resulting gene nominations from these datasets in the precision-recall analysis. These datasets performed poorly with respect to nomination of HIEI genes, likely due to small sample numbers and low sequencing depth compared to the other eQTL and chromatin capture-based studies. The eQTL reported by Ye et al. nominated 15 genes for autoimmune traits, two of which were in the ‘truth’ HIEI set (IL7R and IL2RB). This resulted low predictive power but a high precision due to the low number of nominated genes compared to the other V2G datasets. As suggested by referee 1, we have also subjected our data to the ‘activity-by-contact’ (ABC) algorithm and have included this dataset in the comparison as well. Please see Figure 5 in the revised manuscript. 

We have elected not to include data from the other studies suggested by the referee for the following reasons: The stimulation paradigm used in the Glinos study is very different from that used in other studies. Also, this study and the study by Calderon did not nominate genes. The studies by Alasoo et al. and Kim-Hellmuth et al. analyzed macrophages, which are not a comparable cell type to CD4+ T cells. The allele-specific eQTL study by Gutierrez-Arcelus et al. included relevant the cell type and activation states, but included a relatively small number of samples (24) and variants (561), and the raw data in dbGAP does not readily allow for re-analysis and harmonization with the other studies. We thank the reviewer for helpful suggestions that have improved the quality of our study.

Reviewer #3 (Public Review): 

Summary:

This paper used RNAseq, ATACseq, and Hi-C to assess gene expression, chromatin accessibility, and chromatin physical associations for native CD4+ T cells as they respond to stimulation through TCR and CD28. With these data in hand, the authors identified 423 GWAS signals to their respective target genes, where most of these were not in the proximal promoter, but rather distal enhancers. The IL-2 gene was used as an example to identify new distal cisregulatory regions required for optimal IL-2 gene transcription. These distal elements interact with the proximal IL2 promoter region. When the distal enhancer contained an autoimmune SNP, it affected IL-2 gene transcription. The authors also identified genetic risk variants that were associated with genes upon activation. Some of these regulate proliferation and cytokine production, but others are novel. 

Strengths:

This paper provides a wealth of data related to gene expression after CD4 T cells are activated through the TCR and CD28. An important strength of this paper is that these data were intensively analyzed to uncover autoimmune disease SNPs in cis-acting regions. Many of these could be assigned to likely target genes even though they often are in distal enhancers. These findings help to provide a better understanding concerning the mechanism by which GWAS risk elements impact gene expression. 

Another strength of this study was the proof-of-principle studies examining the IL-2 gene. Not only were new cis-acting enhancers discovered, but they were functionally shown to be important in regulating IL-2 expression, including susceptibility to colitis. Their importance was also established with respect to such distal enhancers harboring disease-relevant SNPs, which were shown to affect IL-2 transcription. 

The data from this study were also mined against past CRISPR screens that identified genes that control aspects of CD4 T cell activation. From these comparisons, novel genes were identified that function during T cell activation. 

Weaknesses:

A weakness of this study is that few individuals were analyzed, i.e., RNAseq and ATACseq (n=3) and HiC (n=2). Thus, the authors may have underestimated potentially relevant risk associations by their chromatin capture-based methodology. This might account for the low overlap of their data with the eQTL-based approach or the HIEI truth set. 

Impact:

This study indicates that defining distal chromatin interacting regions helps to identify distal genetic elements, including relevant variants, that contribute to gene activation. 

We greatly appreciate the referee’s expert assessment of our work and emphasis on the value of our functional follow-up studies. We have ensured that all sample sizes, effect sizes, p values and FDR statistics are included in the figures and figure legends. We agree that including more donors for the HiC studies would increase the number of implicated variants and genes, however, all the chromatin-based V2G approaches described in our manuscript use relatively small sample sizes, but implicate more variants and genes than the comparable eQTL studies. I.e., the low overlap is not driven by a paucity of GWAS-chromatin-based associations. An alternative explanation for the low overlap between GWAS-chromatin-based approaches and eQTL approaches was recently by Pritchard and colleagues, who reported that GWAS and eQTL studies systematically implicate different types of variants (Mostafavi et al., Nature Genetics 2023). Among other differences, eQTL tend to implicate nearby genes while GWAS variants implicate distant genes, and our results support this contention. We referred to this study in the original version of the manuscript, but have included a more extensive discussion of potential explanations in the revised version. We thank the reviewer for helpful suggestions that have improved the quality of our study.

first mention of israel

This passage highlights Sita’s duty as a wife to share her husband’s fate and accompany him in exile. She argues that a wife must share with her husband. Rama’s fate, as she cannot find refuge or protection from anyone else but him. Throughout the Book, Rama tries to dissuade by describing the difficulties and horrors of the wilderness; however, Sita emphasizes that her love and commitment transcend fear and discomfort while emphasizing that her happiness stems from being benign with him rather than living in luxury. Sita’s speech simultaneously highlights the traditional gender roles and stereotypical expectations placed on both men and women. The idea of a ‘hero’ is identified with masculinity and being warrior-like (physical toughness). Sita refers to Rama as the ‘best of heroes’ and dismisses the idea of leaving the hand as suggesting that it would be "unworthy of a warrior's fame" and bring "shame" to a "monarch's son." This emphasizes the societal expectation that a hero must uphold his honor and strength, particularly in the context of his relationships and duties. Additionally, Sita's declaration that "the wife alone, whate'er await, must share on earth her husband's fate" underscores the patriarchal norm that a woman's place is with her husband, highlighting her role as a devoted and submissive partner. This builds on the cultural- and somewhat universal- stereotype that a woman’s role, as a wife, heavily resides in her being a devoted and submissive partner to her husband. When comparing different translations and adaptations of the Ramayana, variations in the portrayal of gender roles can be observed. For instance, in some modern adaptations, there may be a subtle shift towards portraying Sita with more agency and independence, reflecting contemporary views on gender equality. However, in traditional versions, such as those by Valmiki and other ancient translators, the patriarchal mindset is more pronounced. Yet, Sita's role is predominantly defined by her loyalty and subservience to Rama. The language used to describe Rama and Sita's roles reflects the societal norms and expectations of the time. Phrases such as "unworthy of a warrior's fame" and "the wife alone, whate'er await, must share on earth her husband's fate" reveal the deeply ingrained gender roles and the emphasis on male heroism and female subservience. However, the linguistic value of the work also lies in its expressive qualities as Sita’s heart-touching lines: "through tangled thorn and matted grass," illustrate the depth of her love for Rama. Ultimately, the translations differ based on the politics of the time and culture. CC BY Aarushi Attray (contact)

Valmiki. The Ramayana. Translated by Ralph T.H. Griffith, Project Gutenberg, 2009, Book II: Canto XXVII.: Sítá’s Speech, https://sacred-texts.com/hin/rama/ry105.htm. Accessed 4 Aug. 2024.

Valmiki. The Ramayana of Valmiki. Translated by Hari Prasad Shastri, Shanti Sadan, 1952.

Author response:

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

(1) Please provide more background about Rpgrip1l in the introduction, particularly the past studies of mammalian homolog of Rpgrip11, if any? Is there any human disease associated with Rpgrip1l? Do these patients have scoliosis phenotype? 

• We have added more background on the human ciliopathies caused by RPGRIP1L mutations and on their occasional association with early onset scoliosis (lines 45-54 page 2 in the introduction, see cited references). 

(2) The allele is a large deficiency of most of the coding region of rpgrip1l, can you give details in the Supplementary data of how you show this by genotyping? It would be good to explain that this mutation is most likely behaving as a null, if you have RNAseq data that supports this please note that. Otherwise, it may be incorrect to assume it is a null allele as your shorthand nomenclature states. If you do not have stronger evidence that the deficiency allele is behaving as a null allele, then please think about using an allele nomenclature as outlined at ZFIN:  

• We now describe in the results section (Lines 72-76, page 3) the extent of the deletion of rpgrip1l ∆/∆ (22 exons out of 26) that creates an early stop at position 88 of 1256 aas. We have submitted to ZFIN our two novel mutant lines: rpgrip1l∆  is recorded as rpgrip1l bps1 and rpgrip1l ex4 as rpgrip1l bps2 , and we provide this information in the text. Transcriptomics data confirmed this allele is behaving as a null as the most down-regulated transcript found in the brain of rpgrip1l ∆/∆ is rpgrip1l transcript itself, (volcano plot in Fig 5A, described in the results, Line 270-71, page 9).

• We also have provided in Supplementary Figure 1 A’ a picture of a typical genotyping gel for the rpgrip1l∆ allele. Sequences of both CRISPR guide RNAs and genotyping primers are provided in the Math & Meth section. 

(3) Throughout the manuscript, the authors refer to zebrafish mutant phenotypes as "juvenile scoliosis". However, scoliosis may not appear until 11 weeks post-fertilization in some animals. After 6-8 weeks of age, it would be more appropriate to describe the phenotype as "late-onset or adult scoliosis" to differentiate between other reported scoliosis mutants (such as hypomorphic or dominant negative alleles of scospondin) that start body curvatures at 3-5 dpf .

• We think we can really qualify rpgrip1l-/- scoliosis as being a “juvenile scoliosis” as shown by the time course displayed in Fig 1B: rpgrip1l-/- scoliosis develops asynchronously between 4 weeks and 9 weeks (from 0.8 cm/1 cm to 1.6 cm, corresponding to juvenile stages according to Parichy et al, 2009 PMID: 19891001), after which it reaches a plateau. Half of the mutants are already scoliotic by 5 weeks and no scoliosis develops at adult stage, ie from 10 weeks on. We have acknowledged the late onset scoliosis in page 3 line 93.

(4) A more careful demonstration of the individual vertebrae, using magnified high-resolution pictures in Figures 1D-G, should be made to more clearly show no obvious vertebral malformations are present. 

• We now provide a movie in Sup Data that presents 3D views of controls and mutant spines, which show the intervertebral spaces as well as vertebral shape and size. With these images we could exclude vertebral fusion and the presence of dysmorphic vertebrae.

(5) On page 5: the authors comment on transgenic expression of RPGRIP1L in foxj1a-lineages as "rescuing" scoliosis. This terminology is confusing, as rescuing a condition could be interpreted as inducing it where it was once absent. "Suppressing" scoliosis may be a more appropriate term. 

• We agree with the reviewers, the “rescue” term is confusing, we changed it for “suppress” in the title of the paragraph (line 95 page 3) and within the text (line 115 page 3).

(6) On page 5, lines 155-156: the authors state that "Indeed, no tissue-specific rescue has been performed yet in zebrafish ciliary gene mutants". This is misleading, as ptk7a and katnb1 mutations both disrupt cilia, and transgenic reintroduction of both ptk7a and katnb1 in foxj1a- expressing lineages has previously been shown to suppress cilia defects as well as scoliosis in these models. The statement should be removed for accuracy. 

• We agree that we were not precise enough in our sentence: when we mentioned “ciliary gene” mutants, we were referring to genes whose products are enriched within cilia and directly affecting ciliogenesis, cilia content and maintenance such as TZ or BBS genes, without encompassing genes like ptk7 and katnb1 whose products perform multiple functions on top of cilia maintenance such as Wnt signalling and remodelling of the whole microtubule network respectively. We have therefore modified our sentence by adding zebrafish ciliary “TZ and BBS” genes (line 104, page 4).

(7) Figure 2: panels A-B: In the text (line 196) you state that cilia length was increased and that Arl13b content was severely reduced. However, Panel B shows no significant length difference between scoliotic mutants and controls. This statement and graph should be corrected for accuracy. Also, the Arl13b staining is difficult to see in panel A - can channels be split, and/or quantified? 

• We have now split the Arl13b and glutamylated tubulin channels (Fig 2 A-C”). We think that the reduction of Arl13b staining intensity is now obvious in both straight and scoliotic mutants (Compare 2A” with 2B” and 2C”). We were not able to quantify Arl13b staining using ciliary masks from glutamylated tubulin staining since both staining only partially overlap along the length of the cilium, Arl13b being more distal than glutamylated tubulin (Fig 2A’). 

• Ciliary length was significantly increased (from 3.4 to 5.3 µ) in straight rpgrip1l-/-, while the average mean values for scoliotic rpgrip1l-/- were heterogenous (mean 4.1µ) and therefore not significantly different when compared to controls. This heterogeneity stems from the combined presence of both shorter and longer cilia in scoliotic fish, a finding we interpreted by the potential breakage over time of extra-long and thin cilia observed in scoliotic fish (as in Sup figure 1 H’’’, Sup Fig 2M’ and 2O’). 

• We changed the text to be more accurate: we now state that cilia length increased in straight mutants, and became more heterogenous than controls in scoliotic mutants (line 143-144, page 5). 

(8) Figure 3: Page 7, line 206: authors state that SCO-spondin secreting cells varied in number along SCO length. What is the evidence that these cells secrete SCO-spondin? The staining shown in Figure 3L-O appears to demonstrate extracellular accumulation of sspo:GFP. What is the evidence that this staining originated from cells in proximity to it? 

The claim of SCO-secreting cells in Figure 2E-J is confusing. I assume you are using anatomy to infer the SCO is captured in these sections. This should be done in sspo-GFP animals (as in Figure 3) and/or dual anti-body labeling can be done to show SCO-secreting cells and cilia. 

• We now show in Supplementary Figure 2 A-D a double staining for Sco-spondin-GFP and cilia (Ac-tub, Glu-Tub). Analyzing GFP staining along SCO length on successive sections, we identified the SCO producing cells on the diencephalic dorsal midline by their position under the posterior commissure (PC), which forms an Acetylated Tubulin positive arch), and counted the nuclei surrounded by cytoplasmic GFP from the most anterior region ( 24 cells wide, Sup Fig 2A-A’) to the most posterior region (4-8 cells wide, Sup Fig 2 C).` 

• Furthermore, the close-ups presented on Fig 2A’ and 2B’ allow to detect the cytoplasmic Sspo-GFP staining around SCO nuclei, above the region presenting primary cilia pointing towards the diencephalic ventricle, both in controls and mutants at scoliosis onset (tail-up mutants), showing that the extracellular staining in B’ very likely originates from these cells. In these tail-up mutants, extracellular Sspo aggregates have not yet filled the whole diencephalic ventricle as in Fig 3 N and Q. 

(9) Figure 5: Is the transcriptome data and proteomic data consistent for any transcripts and encoded protein products? Please highlight those consistent targets in both analyses. 

• We would like to emphasize that the transcriptomic study was performed at scoliosis onset, at 5 weeks, while the proteomics analysis was performed at adult stage (3 months) so they cannot be directly compared.

Moreover, low abundance proteins (such as centrosomal proteins and transcription factors like Foxj1a ) are not detected by label-free proteomics, without prior subcellular fractionation procedure (Lindemann et al, 2017 PMID: 28282288). The extraction protocol also does not allow to purify short neuropeptides such as Urp1-2.

Nevertheless, we found four targets in common, now highlighted in red in Fig 5, Panel E: Anxa2, complement proteins

C4 and C7a, and Stat3, all related to immune response, a GO term enriched in both studies as explained in the text (Lines 308-311, page 10). 

The absence of many inflammation markers or immune response proteins at adult stage in scoliotic mutants most probably indicates a transient inflammatory episode at scoliosis onset, while astrogliosis, as detected by GFAP staining, increases with scoliosis severity. Along the same lines, the two-fold increase of Lcp1 cells within the tectum is present before axis curvature (in straight mutants) and disappears in scoliotic fish (Graph G in Sup Figure S5) as explained in the text, Lines 378-381, page 12, 

(10) Supplementary Figure 1 F-H: What stage/age samples were used for SEM? It is only stated that they were 'adults'. It is also stated that cilia tufts in straight rpgrip1l-/- fish were morphologically normal but 'less dense'- this was not obvious from the figure. Can density be quantified? (otherwise, data does not support the statement). Similarly, can the statement that "cilia of mono-ciliated ependymal cells showed abnormal irregular structures compared to controls, with either bulged or thinner parts" be supported with measurements/quantification? 

• The SEM study was performed on 3 months old fish, 3 controls and 5 mutants. We added this information in the figure legend. We could not quantify the number of ciliary tufts in the brain ventricle of the sole straight mutant that was analyzed. We therefore removed the statement that cilia were less dense in the straight mutant. Along the same lines, we mentioned that we could find mutant cilia of irregular shape as shown in Supplementary Figure S1, F”,G’’, H’’ and H’’’) (page 4, lines 124-129). 

(11) Supplementary Figure 1D-E is never mentioned in the text. The Supplemental Figure legend also refers to a graph of cilia length that is not in the figure itself. As a result, many of the subsequent panel references are out of register. 

• We now provide the correct version of the legend and refer to Sup Fig 1D-E in the text (page 3, lines 79-81) and its legend, page 53, lines 1616-1620.

(12) Supplementary Figure 2A-F: Of interest, in panels C and F, it looks as though sspo:GFP is accumulating on cilia within the ventricles of rpgrip1l mutants. Can this be explored? Is it possible that abnormal aggregation of SSPO on cilia is ultimately leading to cilia loss, as you report for multi-ciliated cells surrounding the subcommissural organ? This could be a very interesting finding and possible mechanism for cilia loss.

• Our observation of all brain sections led us to conclude that the majority of Sspo-GFP aggregates were floating within the brain ventricles of rpgrip1l-/- fish while a portion of aggregates were stuck on ventricle walls, in close contact with cilia as now shown on Supplementary figure S2 B’, outlined in legend page 54, lines 1634-1637. We agree that the contact between Sspo aggregates and cilia might have damaging consequences, either on cilia maintenance or on immune reaction induction and we now mention these possibilities in the discussion page16, lines 524-526. These research lines will be explored in the near future.

(13) Supplementary Figure 5A-F is not mentioned in the manuscript. Please clarify the role of Anxa2 in neuroinflammation. Is increased Anxa2 expression in rpgrip1l mutant zebrafish reduced after anti-inflammatory drug treatment? What is the expression level of anxa2 in cep290 mutant zebrafish? 

• We have now added mention to Supplementary Figure 5A-F in the text page 10 lines 328-331. 

• We unfortunately did not have enough histological material to test Anxa2 staining on NACET treated fish after performing GFAP and Lcp1 staining, neither for dilatation measurement or multiciliated cells quantification. We agree this would have helped to better define which defect might be an indirect consequence of an inflammatory environment.

• We tested the expression level of Anxa2 in cep290-/- fish. No labelling above control level was detected on cep290-/- brain sections that were positive for GFAP (N = 5). As GFAP staining in 3-4 weeks cep290-/- was not as intense and widespread as in adult rpgrip1l-/- (50% of GFAP + cells compared to 100% in the SCO for example), we concluded that Anxa2 expression may be upregulated after widespread or long-term astrogliosis/inflammation. Alternatively, Anxa2 overexpression could be specific to rpgrip1l-/- fish. 

(14) A summary diagram at the end would be helpful for understanding the main findings. 

We added a Graphical Abstract summarizing the main conclusions and hypotheses of this study. It is mentioned and explained in the Discussion section, p. 16 lines 504-508 and 516-529. 

(15) The sspo-GFP zebrafish line should be listed in the STAR methods section: 

The sspo-GFP line is now listed in the STAR methods, Scospondin-GFPut24, (Troutwine et al., 2020 PMID: 32386529), p.43, last line.

Lacan states that the unconscious is made of language because the states of knowledge within the unconscious can only be understood by way of language? through articulation?

Reviewer #2 (Public Review):

Summary:

This manuscript describes a comprehensive analysis of signalling downstream of the chemokine receptor CCR7. A comprehensive dataset supports the authors' hypothesis that G protein and beta-arrestin signalling can occur simultaneously at CCR7 with implications for continued signalling following receptor endocytosis.

Strengths:

The experiments are well controlled and executed, employing a wide range of assays using - in the main - CCR7 transfectants. Data are well presented, with the authors' claims supported by the data. The paper also has an excellent narrative which makes it relatively easy to follow. I think this would certainly be of interest to the readership of the journal.

Weaknesses:

Since the authors show a differential enrichment of RhoGTPases by CCR7 stimulation with CCL19 versus CCL21, I think that they also need to show that the Gi/o coupling of HEK-292-CCR7-APEX2 cells to both CCL19 and CCL21 is not perturbed by the modification. Currently, the authors only show data for CCL19 signalling, which leaves the potential for a false negative finding in terms of CCL21 signalling being selectively impaired. This should be relatively easy to do and should strengthen the authors' conclusions.

The authors conclude the discussion by suggesting that their findings highlight endosomal signalling as a general mechanism for chemokine receptors in cell migration. I think this is an overreach. The authors chose several studies of CXC chemokine receptors to support their argument that C-terminal truncation or mutation of the C-terminal phosphorylation sites impairs endocytosis and chemotaxis (refs 40-42). However, in some instances e.g. at the related chemokine receptor CCR4, C-terminal removal of these sites impairs endocytosis but promotes chemotaxis (Nakagawa et al, 2014); Anderson et al, 2020). I therefore think that either the final statement needs to be tempered down or the counterargument discussed a little.

References:

Anderson, C. A. et al. A degradatory fate for CCR4 suggests a primary role in Th2 inflammation. J Leukocyte Biol 107, 455-466 (2020).

Nakagawa, M. et al. Gain-of-function CCR4 mutations in adult T cell leukaemia/lymphoma. Journal of Experimental Medicine 211, 2497-2505 (2014).

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Reply to the reviewers

We would first like to thank the reviewers for their careful reading and thoughtful feedback.

We have substantially revised the manuscript and included additional experimental evidence on O-GlcNAc and OGT/OGA protein levels in the placenta of embryos bearing the OGT-Y851A hypomorphic mutation.

Overall, we believe our improved manuscript provides compelling evidence that the glycosyltransferase activity of OGT, and thus the O-GlcNAc modification itself, plays a sexually dimorphic function in placental development and the developmental repression of retrotransposons in the developing embryo.

We have addressed each of the reviewers' comments below. The original comments (C) are in italic, our responses (R) in Roman font.

Reviewer #1

Evidence, reproducibility and clarity

C1: Formichetti at el. developed mice with OGT catalytic dead mutations and then studied their function during early embryogenesis. Not surprisingly, dramatic reduction in OGT activity failed to produce embryos; however, mild reduction in OGT did produce animals. The authors then use the T931 animals that have a mild reduction in activity to further characterize the function in the early embryo. Not surprisingly, male mice showed changes in gene expression, implantation sub-lethality, and an uptick in loss of retrotransposon silencing. The authors also show that an even milder reduction in OGT activity (Y851A) effects male placenta function and chromatin remodeling. Finally, the authors make a less stable OGT transgene within the mouse and again found embryogenesis issues in the males and alterations in numerous gene families including mTOR signaling and p53 function. All in all, this is an interesting study that track functions of OGT in early embryonic development. The studies are well-controlled and rigorous.

R1: We thank the reviewer for their clear understanding and their appreciation of the rigor and impact of this work.

Significance

C2: This is a good study and novel. Not only is it of interest to reproductive biologist, but it echos themes found in O-GlcNAc biology.

R1: We are pleased that the reviewer underlined the novelty of the study and its impact across fields.

Reviewer #2

Evidence, reproducibility and clarity

Comments to authors

C3: To investigate the function of OGT at specific developmental stages, the authors perturbed OGT's function in vivo by creating a murine allelic series featuring four single amino acid substitutions that variably reduced OGT's catalytic activity. The goal was to identify the direct effect of O-GlcNAcylation, using a sophisticated collection of genetic mutants to evaluate in vivo the role of this modification at early stages of development. Overall, the severity of embryonic lethality correlated with the extent of catalytic impairment of OGT, demonstrating that the O-GlcNAc modification is essential for early development.The study represents a substantial advance in our understanding of OGT and O-GlcNAcylation in mammalian development. The creation of novel murine models and inducible systems is an important contribution, providing powerful tools for future research in this field. The insights into the role of OGT's catalytic activity and its involvement in epigenetic regulation during embryonic development are noteworthy, opening new avenues for research.

R3: We thank the reviewer for their insightful comments. We are grateful for the supporting statements. Please find below detailed response to all your comments.

However, there are a few considerations and concerns:

Major:

C4: 1. An assumption of the study is that different mutations cause different levels of O-GlcNAcylation rather than alterations in substrate specificity. It might be important to test, at least in cultured cells, that the different mutations do not change the preference of OGT to modify certain proteins rather than others, which can provide alternative explanations for their findings.

R4: Thanks for asking this question, it helped us to better explain the rationale behind the choice of the Ogt amino-acid substitutions.

This is a critical point that we carefully considered in the design of the single amino-acid substitutions. Two lines of evidence support that the precise mutations created impact the catalytic rate without modifying the substrate specificity:

First, as explained in the text, the choice of the single amino-acid substitutions was driven by previous structural and enzymology knowledge. The impact of the four point mutations selected on OGT protein stability and on the Michaelis-Menten kinetic values had previously been determined experimentally (Fig. 1A legend and Martinez-Fleites, C. et al. Nature Structure Molecular Biology 2008; https://doi.org/10.1038/nsmb.1443).

There is a second important rationale that we added in the revised manuscript: the four point mutations selected are all located in the catalytic domain (specifically, H568A in the N-Cat domain and Y851A, T931A and Q849A in the C-Cat domain), while the substrate recognition is operated via two other domains namely the intervening domain (Int-D) https://doi.org/10.1038/s41589-023-01422-2) and the tetratricopeptide Repeat (TPR) superhelix (10.1021/jacs.7b13546; https://doi.org/10.1073/pnas.2303690120). Therefore, for both these reasons, it is extremely unlikely that these mutations could influence the substrate specificity.

C5.1: 2. In Fig 1D and 1H, the thresholds to define a gene or TE as differentially expressed are not strong. According to the figure legends, "any" change in terms of log2Fc was considered as DE and colored. I think the figures should illustrate better that the changes are subtle, by for example adding a dotted line (at least) in the value 0.5 of the y-axis. These subtle transcriptional changes should be reflected better in certain paragraphs where the expression of TEs are presented/and discussed as a hallmark of the absence of O-GlcNAcylation in the OGT-mutants. The same happens with Suppl Fig 3C (changes are very minor). {. Applying a stronger threshold, among the upregulated genes, only Xist will be significantly overexpressed. If a gentle threshold needs to be applied to this data, authors should at least justify the reasons behind doing so. Same for Fig2D.

R5.1: The reviewer means Figure 2D for MA plot of gene expression and Figure 2H for retrotransposons expression. These figures now include a dash line to indicate Log2FC = 0.5 (as all MA plots).

The text is explicit on the subtle changes in transcription, it reads "with 2/3 of the genes downregulated and 90% of the significant changes below 1 log__2__FC"; "most of the Ogt__T931del/Y embryos showed a low magnitude upregulation of retrotransposons".

The revised text states "Notably, most of the OgtT931__del/Y embryos showed a low magnitude (log2FC < 1) upregulation of retrotransposons".

We expand on this topic in the next response (R5.2) noting that changes in gene expression upon O-GlcNAc perturbation in different systems were previously characterized as subtle and widespread. We suggest that this phenotype may arise from the scarcely understood pleiotropic function of O-GlcNAc in fine-tuning gene expression; this phenotype could have a biological significance.

C5.2: If a gentle threshold needs to be applied to this data, authors should at least justify the reasons behind doing so. Same for Fig2D.

R5.2: Previous studies in different systems reported that O-GlcNAc perturbation causes a widespread change in gene expression of low magnitude (https://doi.org/10.1101/2024.01.22.576677, https://www.pnas.org/doi/10.1073/pnas.2218332120). We use the same thresholds as a recent functional Ogt study in ES cells to call differentially expressed genes, specifically: p<0.05 (Wald test), any FC (Li et al. PNAS 2023, https://www.pnas.org/doi/10.1073/pnas.2218332120). The p value threshold is standard; the absence of FC threshold is dictated by the insufficient knowledge of the significance of the low magnitude changes observed across many transcripts.

C6: 3. In Figure 2B, the T931del allele was recovered in the blastocyst population with a very high frequency, even higher than the male WT group (T931del: 10; WT: 3). This observation suggests that the T931del allele did not significantly affect blastocyst survival. Further clarification or additional experiments might be necessary to understand the implications of this finding on early developmental stages.

R6: This is only a hint as the numbers of blastocysts recovered were too small to perform statistics on Mendelian distribution. Thus, more experiments are needed to perform these statistical tests. These experiments are onerous because the low frequency of germline transmission is incompatible with maintaining this mutation by breeding heterozygous animals. Because of this, a new mouse line needs to be created by CRISPR-HDR targeting in the zygote in order to compute statistics on Mandelian ratios. Importantly, this question - does T931del affect blastocyst survival? - is peripheral, and the results of these experiments would not affect our conclusions in any way.

C7: 4. Similarly, in Figure 2G, there is an apparent higher expression of TE expression in the T931A/Y embryos group than in the T931del/Y group, which combined with the higher frequency of blastocyst generated in this latest group it may indicate a deeper molecular consequence after the deletion of the T931. A comparison of the transcriptome between these two cell lines help to address this possibility. Also, the authors should compare the O-GlcNAc levels of WT, T931A, and T931del mutant blastocysts by immunostaining, similar to what was done in Figure S5F.

R7: We agree that a direct comparison between the two mutations of the T931 residue would be interesting; however, this comment is very difficult to address experimentally for the reasons outlined below:

Firstly, it is not possible to perform a statistical comparison of the transcriptome T931A/Y VS. T931del/Y with the data generated because the number of hemizygous T931A/Y (n=2) is too small. Hence, it cannot be ruled out that the seemingly milder retrotransposon reactivation in one of the T931A/Y embryos could have occurred by chance.

Secondly, considering the low magnitude effect on gene expression changes upon O-GlcNAc genetic perturbation, to statistically assess the penetrance of the molecular phenotype and perform the differential expression analysis, numerous (>>3) hemizygous blastocysts of each genotype would be needed. Because females heterozygous for the T931 mutations transmit the mutant allele at very low frequency, these experiments require numerous de novo CRISPR injection sessions.

Thirdly, for the immunostaining of O-GlcNAc to be semi-quantitative, a large number of hemizygous blastocysts for each genotype would be required (note that in Figure S5F, 29 morulae per condition were imaged), thus requiring numerous CRISPR injection experiments as discussed above. Moreover, O-GlcNAc changes could be subtler than what expected based on the strong reduction of OGT activity, since as a compensatory mechanism Ogt expression is upregulated in the Ogt__T931A/del blastocysts (Fig. S2D), making a quantification even more challenging despite a high number of stained embryos.

In sum, these in vivo experiments are difficult and require sacrificing many animals (about 20 females per CRISPR injection experiment). Because the results would bring refinement to the study but would not change our conclusions, we suggest that the cost/benefit is too high.

C8: 5. In Boulard et al. 2019 O-GlcNAcylation was shown to be sufficient to modulate expression of DNA methylation-dependent TEs. It would be interesting to know (or at least discuss) if the changes in TE expression observed in OGT-mutant embryos in this study involve changes in DNA methylation. Ideally, some DNA methylation measurement optimized for low input numbers of cells would be useful.

R8: Thank you for making the link with our previous study. In the PNAS paper, we report that targeted removal of O-GlcNAc at proteins bound to specific TEs (e.g. IAPez) causes their full-blown reactivation without detectable changes in DNA methylation, thus suggesting a role of the O-GlcNAc modification for the silencing of methylated TEs downstream or independent of DNA methylation. We agree that it would be informative to quantify DNA methylation in the T931-mutant blastocysts to test if the in vitro result is the same in vivo, but this would require performing onerous microinjection sessions as explained above.

C9: 6. The data related with the OGT-degron system in MEs seem disconnected with the rest of the manuscript. While the developmental models (blastocyst, etc) elegantly assess the contribution of O-GlcNAcylation to the control of cell survival and gene expression through the use of different OGT mutants, the degron system is a system of graded depletion that unfortunately was only possible to be used in MEFs (instead of embryos). Thus, the results obtained with the degron system in MEFs are difficult to intersect with the data from the use of OGT-mutants in embryos. Even though there are obvious interesting questions that one may want to know about this OGT degron MEF system, none of them would demonstrate a direct role for O-GlcNAcylation in cellular function, the major point addressed in the developmental system. Using the degron system in embryonic stem cells might have provided a more parallel comparison. The authors should discuss this point in more detail and either use ESC instead of MEFs or provide a stronger justification for the use of MEFs over ESC.

R9: We thank the reviewer for their clear understanding of the system. The choice of primary MEF as an in vitro model was imposed by technical limitations we encountered during the study. We fully agree that ES cells is the model of choice for preimplantation embryos; thus we initially derived ES cells and obtained only one male clone bearing the AID degron system. Upon auxin addition to the culture media, OGT's level remained unchanged in ES cells. Thus, the ES cells model was not usable. To test the AID degron in a different cell type, we then derived MEFs and showed its effectiveness (Figures 4C and S4C-E), which also allowed to collect functional data on OGT's cellular function (Figures 4D-F). We took the comment on board and clarified the rationale of studying MEFs in the revised manuscript. We agree that it remains to be verified that the OGT-dependent pathways uncovered in MEFs are relevant in the preimplantation embryo. Despite this caveat, we feel the mouse model for endogenous OGT-degron, as well as the negative results in vivo and conclusions in MEFs should be shared with the community, which could take advantage of our results to refine the system.

Minor:C10: 7. In Fig 2C the color and shape codes are confusing to understand - there are some colors/shapes that are not represented in the PCA plot. The same in Fig 3H, where in the PCA plot there are pink triangles that do not match with the code legends.

R10: We apologize for the confusion with the legends of Figures 2C and 3H, that we have made unambiguous in the revised version (as well as Figures S2B,C and S3C).

C11: 8. In the figure legends of Figures 2D, 2E, 2F, and 2H, the notation should be corrected from "OgtT931A/Y" to "OgtT931del/Y".

R11: This has been corrected; many thanks for bringing it to our attention.

Significance

C12: To investigate the function of OGT at specific developmental stages, the authors perturbed OGT's function in vivo by creating a murine allelic series featuring four single amino acid substitutions that variably reduced OGT's catalytic activity. The goal was to identify the direct effect of O-GlcNAcylation, using a sophisticated collection of genetic mutants to evaluate in vivo the role of this modification at early stages of development. Overall, the severity of embryonic lethality correlated with the extent of catalytic impairment of OGT, demonstrating that the O-GlcNAc modification is essential for early development.

R12: We thank the reviewer for their clear understanding of our work and their appreciation of the biological importance of the findings.

Reviewer #3

Evidence, reproducibility and clarity

C13: This is a conceptually interesting paper that attempts to leverage the knowledge of OGT catalysis to begin to dissect OGT function. The evidence is presented I a straightforward fashion and is in general well documented. The breeding strategies are well informed and the paper draws heavily on previous work carried out in the mouse.

R13: We greatly appreciate the overall supporting review. However, we fail to understand what they mean with "the paper draws heavily on previous work carried out in the mouse". This comment may stem from a misunderstanding because this work is not based on any previously published study. Specifically, neither the seven murine alleles presented and analyzed nor the single embryo-transcriptomic data sets on which our conclusions are based have been published elsewhere.

To put this work into context, before our study there were two seminal studies published two decades ago that reported the essential role of Ogt for mouse development, but no molecular profiling was performed (10.1073/pnas.100471497, 10.1128/mcb.24.4.1680-1690.2004). The two Ogt loss-of-function alleles studied in these papers were deemed as not suitable for interrogating molecular phenotypes because they caused cell death that confounds molecular profiling and embryonic lethality at implantation, thus preventing study of the sexually-dimorphic role of Ogt placenta. To overcome this long-standing problem, we created new seven murine alleles, which allowed us to tease apart molecular phenotypes at key stages of mouse embryonic development, focusing on the blastocyst and the placenta.

Significance

C14: The paper describes tools which will help dissect the many potential roles of O-GlcNAc addition in early development. As it stands, this is a descriptive manuscript that will lead to hypothesis generation and testing and this should not be undervalued. The biological reagents produced and characterized will be of general interest to the field. Most of the findings presented represented a verification of existing ideas in the field but this is not meant as a criticism since part of the motivation for the approach was to generate a reproducible system for analyzing the biological phenomena.

R14: We thank the reviewer for their appreciation of the importance of experimentally testing ideas shared in the field without direct evidence.

However, we must respectfully disagree with the qualification of "descriptive manuscript". This qualification may stem from the particularly difficult challenge to accessing the molecular details on how the O-GlcNAc modification exerts the biological functions we report. We are fully cognizant of the limitations of the study that we discussed in the discussion section and in R20.2. However, we feel that the adjective "descriptive" is not a fair qualification because we provide numerous novel functional evidence. Specifically, we introduce two novel orthogonal in vivo perturbations for endogenous Ogt that allowed us to interrogate for the first time its function in the developing mouse embryo. These perturbations allow us to draw causative conclusions (not descriptive) on the essential role of the O-GlcNAc modification itself for preimplantation development, its sexually-dimorphic role in the placenta and its requirement in vivo for the stable repression of retrotransposons.

C15: There are perhaps some bioinformatic shortcuts taken that may need to be corrected upon thorough review. These do not lessen the overall impact of the contribution.

R15: All the code written for the bioinformatic analyses performed in this study is publicly available: https://github.com/boulardlab/Ogt_mouse_models_Formichetti2024. The reviewer needs to specify which bioinformatic analysis they suggest could be improved.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Summary

C16: O-GlcNAcylation is the fundamental post-translational modification of numerous nuclear and cytosolic proteins. OGT is the sole enzyme catalyzing O-GlcNAc addition onto the proteins. The essentiality of OGT for early development and cellular viability has been established by using OGT-KO mice and cell lines. However, it remains to be elucidated whether the catalytic activity of OGT is required for the early development, and if the catalytic activity of OGT is required what are the functions of OGT or O-GlcNAcylation in early development due to a lack of appropriate mouse models. To overcome the technical difficulty of manipulating the levels of O-GlcNAcylation in early embryos, Formichetti et al. created the series of four mouse models (OgtY851A, OgtT931A, OgtQ849N, and OgtH568A) with different OGT activity by introducing single amino acid substitution in the catalytic domain. By analyzing the inheritance of the hypomorphic OGT alleles and the lethality of mouse embryos, they discovered OGT activity is a critical factor for early development. Subsequently, RNA-seq analyses with two mouse models showing the maternal inheritance of the hypomorphic OGT alleles indicated that sever hypo-OGT activity altered transcription and silencing of retrotransposon in preimplantation development while mild reduction of OGT's activity affected placental development in a sexually dimorphic manner rather than preimplantation development. Furthermore, to study the function of OGT at specific developmental stages, they developed a mouse model bearing endogenously AID-tagged OGT for acute degradation of OGT. Although the degron system wasn't efficient in preimplantation embryos, they discovered quick transcriptional changes upon OGT deletion in MEFs. The quality of the manuscript is good because the question to be solved was appropriately set, the approach was well designed, and their findings were interesting, although their writing was sometimes hard to understand as I raised in my following comments. Nevertheless, there are several points to be fixed before being published.

R16: We thank the reviewer for their clear understanding of our work and their appreciation of the biological importance of the findings. Your comprehensive review of the manuscript and the questions you raised were extremely helpful in improving the manuscript and fully addressing its limitations. Below, we respond to comments in full, have revised the manuscript to improve clarity and have included novel results.

Major Comments

C17: 1. Although the authors showed in vitro activity of each mutant of OGT used in this manuscript by referencing the previous literature, they never showed the levels of global O-GlcNAcylation (and OGT itself) in their established mouse embryos. Although it could be impossible to determine O-GlcNAc levels in OgtQ849N and OgtH568A embryos because of the lack of germline transmission and founder line, respectively, they could do that in OgtY851A and OgtT931A embryos. Given that Y851A and T931A mutants had similar VMAX/KM with different VMAX, it is possible that their activity is comparable or Y851A has even lower activity in vivo depending on the concentration of UDP-GlcNAc in embryos. Therefore, it is critical to assess whether in vivo OGT activity is correlated with that in vitro as expected to conclude that severity of sub-Mendelian inheritance is proportional to the reduction of activity of OGT in vivo. Moreover, since the authors developed the elegant system to deplete OGT, the activity of Q849N and H568A mutant OGT can be examined at least in cells by expressing them in MEFs with OGT-degron system. Thus, I propose determination of global O-GlcNAc levels compensated by OGT levels by western blotting in OgtY851A, and OgtT931A embryos or MEFs with the OGT degron system re-expressing the individual four mutant OGTs. If the protein amount is insufficient for western blotting in the embryos because of the sizes of the earlier stages of embryos, I believe the author could address this by utilizing immunofluorescence as shown in Figure S5.

R17: We fully agree that this is an important point that requires revision. The only mutation for which the level of O-GlcNAc and OGT can be assessed by western blot in vivo is Y851A, the other mutations resulting in embryonic lethality before the blastocyst stage.

We have included in the revised manuscript western blot analyses of protein expression for OGT, OGA and O-GlcNAc levels in the placenta of the OgtY851A mutants (new Figures 3C,D). The new data show that OGT is upregulated at the protein level in homozygous females, in good agreement with our transcriptomic analysis. Furthermore, O-GlcNAc levels were slightly reduced in homozygous and hemizygous placentae thus showing the impact of the point mutation on global O-GlcNAc levels in the placentae. Moreover, the analysis of OGA protein level unexpectedly revealed the enrichment of a previously uncharacterized OGA fast migrating isoform in hemizygous and homozygous placentae.

We agree that it would be informative to compare O-GlcNAc levels in OgtT931A versus OgtY851A embryos. A comparison implies performing the experiment at the same developmental stage, which has to be the blastocyst stage or prior because T931A/Y embryos die around implantation. The blastocyst being made of approximately 140 cells, it would require to pool many single blastocysts to obtain the necessary protein input for western blot. We are not aware of another study performing western blot with pooled blastocysts. An additional great challenge for this experiment is the necessity to genotype and sex the blastocysts before pooling. Thus, the feasibility of this experiment is uncertain.

As an alternative, the reviewer suggests measuring O-GlcNAc levels in the degron MEFs after introduction of OGT transgenes bearing the mutation studied. This experiment would not be conclusive because of residual O-GlcNAc after OGT degradation (Figure S4E). Furthermore, the O-GlcNAc proteome is dynamic during development (as shown in the developing brain by Liu et al. https://doi.org/10.1371/journal.pone.0043724), therefore the MEFs results would have limited value to explain our results in the early embryo.

In sum, available technologies to quantify O-GlcNAc (e.g. western bot, mass spectrometry) are inadequate for low input samples as the early embryo. However, our series of hypomorphic alleles backed up with in vitro enzymology measurements brings indirect evidence to this question. Specifically, the qualitative correlation between the measured OGT activity in vitro and the developmental phenotype indicates that the resulting relative levels of O-GlcNAc are consistent with in vitro measurements.

C18.1 : 2. I didn't understand why the authors couldn't find any founder lines of the OgtH568A mutant. Was that because mosaic mice with OgtH568A mutation are lethal?

R18.1: To answer to this question, it is important to recall two key features of the biological system:

1) The mutation H568A was reported to disrupt the glycosyltransferase activity completely (10.1038/nsmb.1443). Hence, OGT-H588A is catalytic dead.

2) We performed the CRISPR-HDR targeting in the 1-cell embryo.

Based on these premises, the absence of F0 with the OgtH568A mutation (0/31) suggests that introducing this mutation causes embryonic lethality in both males and females. This hypothesis is consistent with the previously reported lethality around implementation of Ogt-null alleles (10.1128/mcb.24.4.1680-1690.2004). It is possible that the sgRNA is very efficient and results in homozygous mutations in all female zygotes injected (as we have not obtained heterozygous females bearing these mutations). High efficiency of the targeted mutagenesis in the zygote results in mutants where all or the majority of cells bear the mutation (no or low mosaicism). The high number of microinjections performed (416 embryos over the 3 injection sessions) allows us to make these claims.

C18.2 : Also, I believe there was no explanation why the OgtQ849N allele showed no maternal inheritance. Was that because Q849N possesses enough activity for sustaining mosaic embryos, but not oocytes? The authors should better explain these points in the manuscript text.

R18.2: Thanks for this comment, we agree that this maternal effect phenotype demands further explanation.

The phenotype observed suggests two possibilities: either that the oocyte cannot maturate or that the cleavage-stage embryo cannot develop with the resulting lower levels of O-GlcNAc. The cleavage-stage embryo does not transcribe a catalytically active OGT before the 8-cell stage and thus relies on the OGT protein inherited from the oocyte until this stage (https://doi.org/10.1101/2024.01.22.576677).

Thank you for this comment, we added this interpretation of the result in the text:<br /> "The lack of maternal transmission of the Q849N allele from seemingly mosaic founder females is likely explained by the reliance of the cleavage stage embryo onto the oocyte payload of OGT and O-GlcNAc modified proteins. Specifically, Ogt's exons encoding for the catalytic domains are not detectable before the 8-cell stage, while OGT full-length protein is present and thus maternally inherited (Formichetti et al, 2024)."

C19: 3. The authors serendipitously found a T931del-allele in the "WT" allele of the OgtT931A line, and suggested that T931del had milder activity loss, although the lethality of embryos was greatly mitigated. Nevertheless, transcriptome analyses in male blastocysts revealed that 120 genes' expression was changed in T931del/Y males. This raised the question about which mutant OGT has higher activity, Y851A or T931del. I think comparing the activity of Y851A and T931del mutants in MEFs with OGT-degron system is important to confirm the proportional relationship between activity and phenotypic severity.

R19: We agree that it is a limitation that the effect of the T931del mutation on OGT activity has not been biochemically characterized. However, the important point here is that our assessment of phenotypic severity based on maternal inheritance of the mutant allele and embryonic lethality is based on the point mutations for which the catalytic activity has been determined, namely Y851A, T931A, Q849N and H568A, but not T931del.

We studied the serendipitously discovered T931del mutation to obtain transcriptional insights in the blastocyst. Because the deleted residue T931 is key for the binding to the donor substrate, we can reasonably assume that this mutation affects the catalytic activity, albeit to an undetermined level.

Hence, our conclusions regarding the requirement of O-GlcNAcylation for development are unaffected by the lack of biochemical knowledge on T931del.

C20.1: 4. Regarding transcriptomes of T931del/Y, the authors found the upregulation of proteasomal activity and stress granules along with the downregulation of amino acid metabolism, mitochondrial respiration, and so on. To validate the results, the authors should perform qPCR on several up- or down-regulated genes.

R20.1 : We agree that, in principle, qPCR validation is suitable. However, this validation experiment is particularly expensive in this case because of the requirement of numerous CRISPR zygote pronuclear injection sessions.

The conclusions of the RNA-seq analysis are strongly supported by a high number of biological replicates (n=10). This high number of biological replicates was essential to obtain sufficient statistical power to quantify with a high level of confidence transcriptional changes of low magnitudes (below 2-fold change, see R5.1 and R5.2).

Therefore, the qPCR validation experiment would require to repeat the CRISPR zygote pronuclear injection sessions with the same high number of animals. This represents a major investment in experimental work and the sacrificing of about 40 animals. Importantly, the RNA-seq results presented are authoritative because of a high number of biological replicates and high number of sequencing reads per sample. Thus, we argue that qPCR validation is not essential and thus the high cost of this experiment is difficult to justify.

C20.2: In addition, according to Figure S2E, the authors pointed out that at least for genes upregulated in OgtT931A embryos, the changes were not explained by a developmentally delayed transcriptome, suggesting that upregulation of these genes was the cause of developmental delay. Therefore, I strongly encourage them to discuss in the manuscript text how up-regulated genes could contribute to developmental delay.

R20.2: Throughout the manuscript, we have been cautious to avoid establishing causal relationships between the differentially expressed genes uncovered and the developmental phenotypes (e.g. delayed development). There are two main obstacles which we believe prevent us from establishing causality with the data available. Firstly, it is not possible to disentangle differentially expressed genes and developmental delay (in other words, we have no way to tell which is the cause and which is the consequence). Secondly, O-GlcNAc modifies over 5000 proteins and the developing embryo is a particularly dynamic system; thus we cannot know whether the differentially expressed promoters are direct targets of O-GlcNAc modified proteins (or alternatively secondary effect of another molecular alteration, for example of the proteome). We discuss this limitation of the study in the discussion section.

C21: 5. Regarding the transcriptome in OgtY851A mice, Y851A/Y male mice had huge transcriptomic differences, while Y851A/Y851A female mice barely had any. Although it seems to agree with the number of Ogt alleles, I wonder whether other X-linked genes expressed higher in female placenta as shown in Figure 3C could attenuate the effects of decreased OGT activity. I don't think this possibility can be excluded, unless the authors further decrease OGT activity in Y851A/Y851A female placenta and obtain the similar results as for male placenta. Or if they compared the levels of global O-GlcNAcylation between Y851A/Y and Y851A/Y851A mouse placentas and discovered they had similar levels of O-GlcNAcylation, then the authors could conclude that the number of Ogt alleles was not the reason of sexual-dimorphism. The authors should determine the levels of O-GlcNAcylation in Y851A/Y and Y851A/Y851A mouse placentas and/or at least discuss the above possibilities in the manuscript text.

R21: Thank you for the thoughtful feedback. We agree that the most likely explanation for the higher sensitivity of males placenta as compared to females to OGT reduced activity is the difference in Ogt copy number, especially because Ogt escapes X-chromosome inactivation in the placenta (new Figure S3A).

Western blot quantification of global O-GlcNAc levels was now performed (new Figures 3C,D). We measured similar level of O-GlcNAc in Y851A/Y and Y851A/Y851A placentas (lowered than WT males in both cases), but we cannot exclude that the WB does not have the dynamic range required to detect a subtle difference. In fact, female homozygous were expected to have an intermediate level between WT males and hemizygous males, and the difference between the two male genotypes (also considering sample-to-sample variability) is already small when quantified from the blot (new Figure 3D). It is possible that a X-linked modifier attenuates the impact of hypo-O_GlcNAcylation in female mutant placenta in the case of identical O-GlcNAc levels in homozygous females and hemizygous males. Thank you for the idea that we included in the revised manuscript:

"Of note, the lower sensitivity of the homozygous females' transcriptome to Ogt disruption (Fig. 3F,I and S3B) seems difficult to reconcile with their lower O-GlcNAc level comparable (lower) O-GlcNAc level to the hemizygous males (Fig. 3C). It is possible that the western blot technique is not sensitive enough to detect subtle differences in O-GlcNAcylation. An alternative hypothesis, if O-GlcNAc levels were truly identical between Y851A/Y and Y851A/Y851A, could be the existence of a modifier in female that could be a XCI-escapee."

C22: 6. In terms of the transcriptome in OgtY851A mice, similar to comment 4, the authors should confirm their transcriptomics data shown as Figure 3D by qPCR. In addition, the authors should describe the potential mechanisms by which the differentiation of precursor cells of LaTPs and JZPs were disrupted. Were master regulators of the differentiation known to be O-GlcNAcylated and loss of O-GlcNAcylation perturbed the function?

R22: As for the whole embryo discussed in R20.2, we also interpret cautiously the gene expression phenotype observed in the placenta. Specifically, we state in the manuscript that it could either be caused by an impact of lower O-GlcNAcylation on placental differentiation or by a general delay in placentation or in the development of the embryo as a whole. The hypothesis of a general delay (of the whole embryo and/or of placental formation specifically) is supported by the downregulation of essentially all markers of more differentiated cell types and the upregulation of the precursor marker. We favor this hypothesis because it is consistent with what observed with the T931 mutants and also with the enzymatic removal of O-GlcNAc in the zygote (Formichetti et al., 2024 BioRxiv). Because of the thousands of O-GlcNAcylated proteins present in the cell, it is impossible to know which is the responsible molecular mechanism, which could even start at much earlier stages.

Minor Comments

C23: 1. Regarding DFP461-463 mutant, I couldn't understand the point of this figure because the results had no difference, and the meaning of the mutation was quite different from the others. Thus, the figure was awkward and a little confusing to me. If the authors still want to include the figures, I would suggest that they should reorganize the position of the figure (maybe after figure 3 is better to show you had tried to investigate the effects of nuclear localization of OGT on the changes of transcriptomes) and add some results. Since WT OGT seems to be localized mainly in the cytosol at steady state (Figure S1B and S1C), the effect of mutation on its nuclear localization should not be obvious. Therefore, it is difficult to conclude the mutation had no effect on the nuclear localization unless the ratio of nuclear and cytosol localization is quantified. Also, I wonder whether the O-GlcNAc levels of nuclear and cytosolic proteins in the mutant cells were comparable to those in WT cells. If this is the case, the results would also support the authors' conclusion.

R23: We took the comments on board and made it clearer that the rationale for the DFP461-463 mutant was an attempt to separate OGT's nuclear and cytosolic functions. We fully agree that these results are peripheral, and thus we presented these results in Supplementary Figure 1 (not in the main figure).

The biochemical evidence presented in Fig S1C shows that the genetic substitution of DFP to AAA on endogenous OGT has no detectable impact on its nuclear localization in primary MEFs. This result is far more authoritative than the evidence provided by Seo et al. 2016 (doi: 10.1038/srep34614), which is based on the overexpression of OGT transgenes in HeLa cells. Importantly, Seo et al. 2016 did not assess the impact of their mutations on endogenous OGT.

We believe that the negative results we obtained with the DFP461-463 mouse model shall be extremely valuable for the field. Firstly, science can move forward only if both negative and positive results are shared. In this specific case, we found that mutation of endogenous OGT in MEFs yielded to a different result than previously reported overexpression of the same mutant construct in HeLa cells. Secondly, we want to make the Ogt-NLS- mouse model available for further investigations.

C24: 2. Since OGT or O-GlcNAcylation regulates chromatin status, the authors analyzed the gene expression profiles of retrotransposons in T931del/Y or T931A/Y mice. Is it possible to investigate if the release of gene silencing is also seen in non-retrotransposon genes? I assumed retrotransposons might be a well-established system to analyze gene silencing status, however, if the authors could find similar effects on genes other than retrotransposons, that would be highly valuable.

R24: This is an interesting idea. This notion refers to the activation of promoters that are normally epigenetically repressed (e.g. silent despite the presence of all trans-active factors required for their expression). Epigenetically repressed promoters include retrotransposons, imprinted genes and germline specific genes that are normally expressed in germ cells and maintained in a repressed state in somatic cells (10.1038/s41580-019-0159-6). Testing of mono-allelic expression of imprinted genes required F1-hybrid. Thus, we assessed whether well-studied germline specific genes could be realized from silencing in T931del/Y or T931A/Y blastocyst and found no evidence for it (see dot plot below). The unbiased transcriptomic analysis presented in the manuscript shows that the product of upregulated genes are enriched in mRNA processing (Figure 2E), but these genes are not normally epigenetically repressed. Thus, contrary to retrotransposons, the role of O-GlcNAc at cellular gene promoters appears not to be linked to epigenetic silencing. This could be explained by the many different protein substrates for O-GlcNAc.

C25: 3. OgtY851A mice with milder OGT activity loss didn't exhibit impaired preimplantation development, but did display postimplantation development such as placental development, suggesting that O-GlcNAcylation of proteins required for preimplantation and postimplantation development relies on different degrees of OGT activity. I wonder whether global O-GlcNAc levels in embryos in preimplantation and postimplantation developmental stages are different or not. This might include both the pattern of blotting and intensities. The results would give the authors an explanation why the dependency on OGT activity was different in two developmental stages. Can the authors provide data? If not, then the authors should at least describe hypotheses in the manuscript to address these questions.

R25: We recently reported that the subcellular patterns of O-GlcNAc are highly dynamic during preimplantation development (Formichetti et al. 2024, BioRxiv). The most striking O-GlcNAc remodeling we observed is the enrichment of nuclear O-GlcNAc as compared to cytoplasmic O-GlcNAc that is concomitant to embryonic genome activation (Formichetti et al. 2024, BioRxiv). We quantified the ratio of the nuclear/cytoplasmic signal by immunofluorescence, but absolute quantification is not possible with this method. Due to the limited number of cells of the preimplantation embryo, this analysis cannot be performed by western blot. Hence, there is no appropriate method to quantitatively compare O-GlcNAc levels between preimplantation and postimplantation embryos.

C26: 4. The authors' AID-degron system elegantly worked in MEFs but was inefficient in preimplantation embryos. I wonder if this was because of the high expression of the shorter isoform of OGT detected as OGTp78 in the author's western blot. Is it possible to examine this possibility in the embryos? Either way, the authors should describe a potential explanation for why the efficiency in the embryos was low. In addition, the authors should describe why they inserted the AID tag only into the longest OGT isoform.

R26: This is a good point. The smallest isoform OGTp78 bears the catalytic domain and thus can partially compensate for the degradation of OGTp110. Note that the level of OGTp78 is low and does not increase upon OGTp110 degradation; thus a compensation can only be partial (Figures S4A and S4D). Alternative hypotheses for the ineffectiveness of the degron system in ex vivo grown embryos include: i) the expression level of OsTIR that may be too low in the early embryo (Rosa26 promoter not being activated at EGA), ii) a possible steric hindrance of the N-ter AID tag in these cells, iii) the lower concentration of Auxin imposed by toxicity on the embryo is likely suboptimal. Testing these possibilities is very difficult in preimplantation embryos.

It is unclear how the OGTp78 isoform is produced; it was hypothesized to originate from an alternative transcription start site (https://doi.org/10.1007/s00335-001-2108-9). We initially attempted to target both isoforms by inserting the AID tag at the C-terminus, but we were unsuccessful in producing this mouse model. It is possible that the C-terminus that is near the catalytic site cannot tolerate the AID knock-in.

C27: 5. In Figure S1C, is the band detected right below OGTp78 in nuclei fractions non-specific or do both bands correspond to OGTp78 ?

R27: To answer this question, a knockout control would be needed. OGTp78 being not targeted by our AID-degron, we cannot test the specificity of these bands using our perturbation tool kit.

C28: 6. Figure 1D top row third column: hemizgous -> hemizygous

R28: Many thanks; the embarrassing typo has been corrected.

C29: 7. Figure 1D second row third column: hemyzygous -> hemizygous

R29: Thanks for bringing this other typo to our attention, it is now corrected.

Reviewer #4 (Significance (Required)):

General assessment: strengths and limitations

C30: Strength: This manuscript elegantly revealed the requirement of OGT in mammalian development by taking advantage knock-in mouse models with different OGT activity. In addition, the manuscript provided the interesting and important transcriptomics data in both pre- and post-implantation embryos of OGT mutant mice. These data sets could explain detailed mechanisms how OGT or O-GlcNAcylation regulates mammalian development in the future. Furthermore, development of AID-tagged OGT system would be a useful tool for other researchers studying OGT function.

Limitation: Although they found interesting changes in terms transcriptomes in developing mice with different OGT activity, they lack the data showing how these changes caused the observed phenotypes. In other words, there are less mechanistic insights behind the developmental problems seen in mice with different OGT activity.

In addition, although I agree the question about whether OGT activity itself is crucial for the early development of mammals has not been completely solved for a long time, I assume people thought OGT activity is actually important for the mammalian development thorough the observation of OGT-linked congenital disorders of glycosylation.

Therefore, I would say the novelty of the manuscript is a little less impactful. Furthermore, although AID-tagged OGT system revealed fundamental questions regarding the transcriptional changes upon acute depletion of OGT in cellular levels, the system was inefficient in mouse embryos. So, they showed nothing about developmental-stage specific requirements of OGT.

Advance: The manuscript can fill a current gap regarding requirement of OGT in mammalian development. Also, the manuscript developed a series of mutant mice with different OGT activity and an AID-tagged OGT mouse line. These mice provide technical advances.

Audience: The manuscript will be interested in researchers in specific fields such as glycobiology, developmental biology, and clinical fields.

Describe your expertise: Biochemistry, Glycobiology, Cell biology

R30: We are thankful for the constructive and supportive review.

We fully agree with the limitations of the study and discussed them in the manuscript. Our in vivo approach revealed the most phenotypically relevant transcriptional phenotypes resulting from OGT catalytic impairment during embryonic development. We make the mouse models created for this study available to the community to facilitate follow-up studies aiming at exploring the underlying molecular details.

As pointed out in the comments, the requirement of OGT glycosyltransferase activity for mammalian development was widely assumed by the field, but this belief was without direct experimental evidence. This study provides the first in vivo evidence for this important conclusion.

Conclusion: The reviewers' comments were tremendously useful to improving the clarity of the manuscript and adding important new in vivo evidence. We note that none of the reviewers provided any reason to doubt our important conclusions:

• The demonstration that the enzymatic activity of Ogt, thus the O-GlcNAc modification itself, is essential for preimplantation development.
• The finding that a mild reduction of OGT's activity is sufficient to perturb the silencing of multiple families of retrotransposons in the growing embryo.

• The indication, from transcriptomes of hypo-O-GlcNAcylated embryos, of a developmental retardation upon a mild O-GlcNAc perturbation.

• The discovery that OGT's rapid depletion in vitro downregulates basal cellular function, including translation. This result provides mechanistic support to the embryonic growth delay resulting from decreasing O-GlcNAc in vivo.

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

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Referee #4

Evidence, reproducibility and clarity

Summary

O-GlcNAcylation is the fundamental post-translational modification of numerous nuclear and cytosolic proteins. OGT is the sole enzyme catalyzing O-GlcNAc addition onto the proteins. The essentiality of OGT for early development and cellular viability has been established by using OGT-KO mice and cell lines. However, it remains to be elucidated whether the catalytic activity of OGT is required for the early development, and if the catalytic activity of OGT is required what are the functions of OGT or O-GlcNAcylation in early development due to a lack of appropriate mouse models. To overcome the technical difficulty of manipulating the levels of O-GlcNAcylation in early embryos, Formichetti et al. created the series of four mouse models (OgtY851A, OgtT931A, OgtQ849N, and OgtH568A) with different OGT activity by introducing single amino acid substitution in the catalytic domain. By analyzing the inheritance of the hypomorphic OGT alleles and the lethality of mouse embryos, they discovered OGT activity is a critical factor for early development. Subsequently, RNA-seq analyses with two mouse models showing the maternal inheritance of the hypomorphic OGT alleles indicated that sever hypo-OGT activity altered transcription and silencing of retrotransposon in preimplantation development while mild reduction of OGT's activity affected placental development in a sexually dimorphic manner rather than preimplantation development. Furthermore, to study the function of OGT at specific developmental stages, they developed a mouse model bearing endogenously AID-tagged OGT for acute degradation of OGT. Although the degron system wasn't efficient in preimplantation embryos, they discovered quick transcriptional changes upon OGT deletion in MEFs. The quality of the manuscript is good because the question to be solved was appropriately set, the approach was well designed, and their findings were interesting, although their writing was sometimes hard to understand as I raised in my following comments. Nevertheless, there are several points to be fixed before being published.

Major Comments

1. Although the authors showed in vitro activity of each mutant of OGT used in this manuscript by referencing the previous literature, they never showed the levels of global O-GlcNAcylation (and OGT itself) in their established mouse embryos. Although it could be impossible to determine O-GlcNAc levels in OgtQ849N and OgtH568A embryos because of the lack of germline transmission and founder line, respectively, they could do that in OgtY851A and OgtT931A embryos. Given that Y851A and T931A mutants had similar VMAX/KM with different VMAX, it is possible that their activity is comparable or Y851A has even lower activity in vivo depending on the concentration of UDP-GlcNAc in embryos. Therefore, it is critical to assess whether in vivo OGT activity is correlated with that in vitro as expected to conclude that severity of sub-Mendelian inheritance is proportional to the reduction of activity of OGT in vivo. Moreover, since the authors developed the elegant system to deplete OGT, the activity of Q849N and H568A mutant OGT can be examined at least in cells by expressing them in MEFs with OGT-degron system. Thus, I propose determination of global O-GlcNAc levels compensated by OGT levels by western blotting in OgtY851A, and OgtT931A embryos or MEFs with the OGT degron system re-expressing the individual four mutant OGTs. If the protein amount is insufficient for western blotting in the embryos because of the sizes of the earlier stages of embryos, I believe the author could address this by utilizing immunofluorescence as shown in Figure S5.
2. I didn't understand why the authors couldn't find any founder lines of the OgtH568A mutant. Was that because mosaic mice with OgtH568A mutation are lethal? Also, I believe there was no explanation why the OgtQ849N allele showed no maternal inheritance. Was that because Q849N possesses enough activity for sustaining mosaic embryos, but not oocytes? The authors should better explain these points in the manuscript text.
3. The authors serendipitously found a T931del-allele in the "WT" allele of the OgtT931A line, and suggested that T931del had milder activity loss, although the lethality of embryos was greatly mitigated. Nevertheless, transcriptome analyses in male blastocysts revealed that 120 genes' expression was changed in T931del/Y males. This raised the question about which mutant OGT has higher activity, Y851A or T931del. I think comparing the activity of Y851A and T931del mutants in MEFs with OGT-degron system is important to confirm the proportional relationship between activity and phenotypic severity.
4. Regarding transcriptomes of T931del/Y, the authors found the upregulation of proteasomal activity and stress granules along with the downregulation of amino acid metabolism, mitochondrial respiration, and so on. To validate the results, the authors should perform qPCR on several up- or down-regulated genes. In addition, according to Figure S2E, the authors pointed out that at least for genes upregulated in OgtT931A embryos, the changes were not explained by a developmentally delayed transcriptome, suggesting that upregulation of these genes was the cause of developmental delay. Therefore, I strongly encourage them to discuss in the manuscript text how up-regulated genes could contribute to developmental delay.
5. Regarding the transcriptome in OgtY851A mice, Y851A/Y male mice had huge transcriptomic differences, while Y851A/Y851A female mice barely had any. Although it seems to agree with the number of Ogt alleles, I wonder whether other X-linked genes expressed higher in female placenta as shown in Figure 3C could attenuate the effects of decreased OGT activity. I don't think this possibility can be excluded, unless the authors further decrease OGT activity in Y851A/Y851A female placenta and obtain the similar results as for male placenta. Or if they compared the levels of global O-GlcNAcylation between Y851A/Y and Y851A/Y851A mouse placentas and discovered they had similar levels of O-GlcNAcylation, then the authors could conclude that the number of Ogt alleles was not the reason of sexual-dimorphism. The authors should determine the levels of O-GlcNAcylation in Y851A/Y and Y851A/Y851A mouse placentas and/or at least discuss the above possibilities in the manuscript text.
6. In terms of the transcriptome in OgtY851A mice, similar to comment 4, the authors should confirm their transcriptomics data shown as Figure 3D by qPCR. In addition, the authors should describe the potential mechanisms by which the differentiation of precursor cells of LaTPs and JZPs were disrupted. Were master regulators of the differentiation known to be O-GlcNAcylated and loss of O-GlcNAcylation perturbed the function?

Minor Comments

1. Regarding DFP461-463 mutant, I couldn't understand the point of this figure because the results had no difference, and the meaning of the mutation was quite different from the others. Thus, the figure was awkward and a little confusing to me. If the authors still want to include the figures, I would suggest that they should reorganize the position of the figure (maybe after figure 3 is better to show you had tried to investigate the effects of nuclear localization of OGT on the changes of transcriptomes) and add some results. Since WT OGT seems to be localized mainly in the cytosol at steady state (Figure S1B and S1C), the effect of mutation on its nuclear localization should not be obvious. Therefore, it is difficult to conclude the mutation had no effect on the nuclear localization unless the ratio of nuclear and cytosol localization is quantified. Also, I wonder whether the O-GlcNAc levels of nuclear and cytosolic proteins in the mutant cells were comparable to those in WT cells. If this is the case, the results would also support the authors' conclusion.
2. Since OGT or O-GlcNAcylation regulates chromatin status, the authors analyzed the gene expression profiles of retrotransposons in T931del/Y or T931A/Y mice. Is it possible to investigate if the release of gene silencing is also seen in non-retrotransposon genes? I assumed retrotransposons might be a well-established system to analyze gene silencing status, however, if the authors could find similar effects on genes other than retrotransposons, that would be highly valuable.
3. OgtY851A mice with milder OGT activity loss didn't exhibit impaired preimplantation development, but did display postimplantation development such as placental development, suggesting that O-GlcNAcylation of proteins required for preimplantation and postimplantation development relies on different degrees of OGT activity. I wonder whether global O-GlcNAc levels in embryos in preimplantation and postimplantation developmental stages are different or not. This might include both the pattern of blotting and intensities. The results would give the authors an explanation why the dependency on OGT activity was different in two developmental stages. Can the authors provide data? If not, then the authors should at least describe hypotheses in the manuscript to address these questions.
4. The authors' AID-degron system elegantly worked in MEFs but was inefficient in preimplantation embryos. I wonder if this was because of the high expression of the shorter isoform of OGT detected as OGTp78 in the author's western blot. Is it possible to examine this possibility in the embryos? Either way, the authors should describe a potential explanation for why the efficiency in the embryos was low. In addition, the authors should describe why they inserted the AID tag only into the longest OGT isoform.
5. In Figure S1C, is the band detected right below OGTp78 in nuclei fractions non-specific or do both bands correspond to OGTp78 ?
6. Figure 1D top row third column: hemizgous -> hemizygous
7. Figure 1D second row third column: hemyzygous -> hemizygous

Significance

General assessment: strengths and limitations

Strength: This manuscript elegantly revealed the requirement of OGT in mammalian development by taking advantage knock-in mouse models with different OGT activity. In addition, the manuscript provided the interesting and important transcriptomics data in both pre- and post-implantation embryos of OGT mutant mice. These data sets could explain detailed mechanisms how OGT or O-GlcNAcylation regulates mammalian development in the future. Furthermore, development of AID-tagged OGT system would be a useful tool for other researchers studying OGT function.

Limitation: Although they found interesting changes in terms transcriptomes in developing mice with different OGT activity, they lack the data showing how these changes caused the observed phenotypes. In other words, there are less mechanistic insights behind the developmental problems seen in mice with different OGT activity. In addition, although I agree the question about whether OGT activity itself is crucial for the early development of mammals has not been completely solved for a long time, I assume people thought OGT activity is actually important for the mammalian development thorough the observation of OGT-linked congenital disorders of glycosylation. Therefore, I would say the novelty of the manuscript is a little less impactful. Furthermore, although AID-tagged OGT system revealed fundamental questions regarding the transcriptional changes upon acute depletion of OGT in cellular levels, the system was inefficient in mouse embryos. So, they showed nothing about developmental-stage specific requirements of OGT.

Advance: The manuscript can fill a current gap regarding requirement of OGT in mammalian development. Also, the manuscript developed a series of mutant mice with different OGT activity and an AID-tagged OGT mouse line. These mice provide technical advances.

Audience: The manuscript will be interested in researchers in specific fields such as glycobiology, developmental biology, and clinical fields.

Describe your expertise: Biochemistry, Glycobiology, Cell biology

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Referee #3

Evidence, reproducibility and clarity

This is a conceptually interesting paper that attempts to leverage the knowledge of OGT catalysis to begin to dissect OGT function. The evidence is presented I a straightforward fashion and is in general well documented. The breeding strategies are well informed and the paper draws heavily on previous work carried out in the mouse.

Significance

The paper describes tools which will help dissect the many potential roles of O-GlcNAc addition in early development. As it stands, this is a descriptive manuscript that will lead to hypothesis generation and testing and this should not be undervalued. The biological reagents produced and characterized will be of general interest to the field. Most of the findings presented represented a verification of existing ideas in the field but this is not meant as a criticism since part of the motivation for the approach was to generate a reproducible system for analyzing the biological phenomena.

There are perhaps some bioinformatic shortcuts taken that may need to be corrected upon thorough review. These do not lessen the overall impact of the contribution.

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Referee #2

Evidence, reproducibility and clarity

Comments to authors

To investigate the function of OGT at specific developmental stages, the authors perturbed OGT's function in vivo by creating a murine allelic series featuring four single amino acid substitutions that variably reduced OGT's catalytic activity. The goal was to identify the direct effect of O-GlcNAcylation, using a sophisticated collection of genetic mutants to evaluate in vivo the role of this modification at early stages of development. Overall, the severity of embryonic lethality correlated with the extent of catalytic impairment of OGT, demonstrating that the O-GlcNAc modification is essential for early development.<br /> The study represents a substantial advance in our understanding of OGT and O-GlcNAcylation in mammalian development. The creation of novel murine models and inducible systems is an important contribution, providing powerful tools for future research in this field. The insights into the role of OGT's catalytic activity and its involvement in epigenetic regulation during embryonic development are noteworthy, opening new avenues for research. However, there are a few considerations and concerns:

Major:

1. An assumption of the study is that different mutations cause different levels of O-GlcNAcylation rather than alterations in substrate specificity. It might be important to test, at least in cultured cells, that the different mutations do not change the preference of OGT to modify certain proteins rather than others, which can provide alternative explanations for their findings.
2. In Fig 1D and 1H, the thresholds to define a gene or TE as differentially expressed are not strong. According to the figure legends, "any" change in terms of log2Fc was considered as DE and colored. I think the figures should illustrate better that the changes are subtle, by for example adding a dotted line (at least) in the value 0.5 of the y-axis. These subtle transcriptional changes should be reflected better in certain paragraphs where the expression of TEs are presented/and discussed as a hallmark of the absence of O-GlcNAcylation in the OGT-mutants. The same happens with Suppl Fig 3C (changes are very minor). Similarly, in Fig2C, the changes in gene expression are lower than log2FC 1 (which represent the double in absolute expression). Applying a stronger threshold, among the upregulated genes, only Xist will be significantly overexpressed. If a gentle threshold needs to be applied to this data, authors should at least justify the reasons behind doing so. Same for Fig2D.
3. In Figure 2B, the T931del allele was recovered in the blastocyst population with a very high frequency, even higher than the male WT group (T931del: 10; WT: 3). This observation suggests that the T931del allele did not significantly affect blastocyst survival. Further clarification or additional experiments might be necessary to understand the implications of this finding on early developmental stages.
4. Similarly, in Figure 2G, there is an apparent higher expression of TE expression in the T931A/Y embryos group than in the T931del/Y group, which combined with the higher frequency of blastocyst generated in this latest group it may indicate a deeper molecular consequence after the deletion of the T931. A comparison of the transcriptome between these two cell lines help to address this possibility. Also, the authors should compare the O-GlcNAc levels of WT, T931A, and T931del mutant blastocysts by immunostaining, similar to what was done in Figure S5F.
5. In Boulard et al. 2019 O-GlcNAcylation was shown to be sufficient to modulate expression of DNA methylation-dependent TEs. It would be interesting to know (or at least discuss) if the changes in TE expression observed in OGT-mutant embryos in this study involve changes in DNA methylation. Ideally, some DNA methylation measurement optimized for low input numbers of cells would be useful.
6. The data related with the OGT-degron system in MEs seem disconnected with the rest of the manuscript. While the developmental models (blastocyst, etc) elegantly assess the contribution of O-GlcNAcylation to the control of cell survival and gene expression through the use of different OGT mutants, the degron system is a system of graded depletion that unfortunately was only possible to be used in MEFs (instead of embryos). Thus, the results obtained with the degron system in MEFs are difficult to intersect with the data from the use of OGT-mutants in embryos. Even though there are obvious interesting questions that one may want to know about this OGT degron MEF system, none of them would demonstrate a direct role for O-GlcNAcylation in cellular function, the major point addressed in the developmental system. Using the degron system in embryonic stem cells might have provided a more parallel comparison. The authors should discuss this point in more detail and either use ESC instead of MEFs or provide a stronger justification for the use of MEFs over ESC.

Minor:

1. In Fig 2C the color and shape codes are confusing to understand - there are some colors/shapes that are not represented in the PCA plot. The same in Fig 3H, where in the PCA plot there are pink triangles that do not match with the code legends.
2. In the figure legends of Figures 2D, 2E, 2F, and 2H, the notation should be corrected from "OgtT931A/Y" to "OgtT931del/Y".

Significance

To investigate the function of OGT at specific developmental stages, the authors perturbed OGT's function in vivo by creating a murine allelic series featuring four single amino acid substitutions that variably reduced OGT's catalytic activity. The goal was to identify the direct effect of O-GlcNAcylation, using a sophisticated collection of genetic mutants to evaluate in vivo the role of this modification at early stages of development. Overall, the severity of embryonic lethality correlated with the extent of catalytic impairment of OGT, demonstrating that the O-GlcNAc modification is essential for early development.

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Referee #1

Evidence, reproducibility and clarity

Formichetti at el. developed mice with OGT catalytic dead mutations and then studied their function during early embryogenesis. Not surprisingly, dramatic reduction in OGT activity failed to produce embryos; however, mild reduction in OGT did produce animals. The authors then use the T931 animals that have a mild reduction in activity to further characterize the function in the early embryo. Not surprisingly, male mice showed changes in gene expression, implantation sub-lethality, and an uptick in loss of retrotransposon silencing. The authors also show that an even milder reduction in OGT activity (Y851A) effects male placenta function and chromatin remodeling. Finally, the authors make a less stable OGT transgene within the mouse and again found embryogenesis issues in the males and alterations in numerous gene families including mTOR signaling and p53 function. All in all, this is an interesting study that track functions of OGT in early embryonic development. The studies are well-controlled and rigorous.

Significance

This is a good study and novel. Not only is it of interest to reproductive biologist, but it echos themes found in O-GlcNAc biology.

Kay-Khosrow coming to find out his true heritage is an interesting moment because there is so much internal conflict as he is young and now is confused about his upbringing. He has come to express his own dissatisfaction with his simple upbringing despite being from loyalty and is now told the truth. The feeling must be bittersweet because he knows the truth on one hand but feels like he must have been lied to for his entire life. He feels a disconnect between his lowly origins in comparison to the elevated treatment he can get now being of royalty. It is similar to the feeling someone must feel if they are told that they were adopted later in life. They feel like they lose a part of themselves as they think they are not true members of the family and face a lot of internal conflict about their upbringing and why they were lied to for such a long time. The reference of "son of a shepherd" is what allows the readers to understand that he had a simple life growing up which is why it must feel out of place for him to be associated with royalty. An interesting theme that this brings up is the idea of social status and how that plays into someone's personal identity. People are judged by the social status they belong in so it must be an interesting transition for him to move up social status where he will be treated more favorably as well. It raises the question as to why people in higher social status gets to be treated better when all people should inherently be treated equally. CC BY Ajey Sasimugunthan (contact)

Siawosh's deep sense of honor, sacrifice, and duty can be highlighted here as he faces death soon but still shows his character as a loyal person. The horse itself symbolizes Siawosh's personal and martial identity in which it captures his own heroism. His loyalty and persistence in his beliefs reflects how noble he is and how selfless he is as he prioritizes the future of his lineage rather than himself. The hope that Siawosh's son can avenge his death ties into the idea of legacy as he wants family to correct the wrongdoing of his death. It is also interesting that he wants his son to continue this natural cycle of revenge because it will keep happening until someone stops or a mutual agreement comes into place. While this may be the case, the big reason he wants to avenge his loss is because of justice. It is a universal theme that everyone mostly believes as we all want what is right. In the case of Siawosh, the taking of his life was unjust and revenge in his mind is the best way to enact justice especially considering the betrayal that he just faced. CC BY Ajey Sasimugunthan (contact)

DOI: 10.7717/peerj.12175

Resource: SCR_00645

Curator: @anisehay

SciCrunch record: RRID:SCR_00645

What is this?

DOI: 10.1126/sciadv.adh2301

Resource: RRID:BDSC_24774

Curator: @anisehay

SciCrunch record: RRID:BDSC_24774

What is this?

DOI: 10.1016/j.celrep.2024.114431

Resource: (CLS Cat# 300168/p708_DU-145, RRID:CVCL_0105)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0105

What is this?

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Definición breve historia digital

Reviewer #2 (Public Review):

The study by Setogawa et al. aims to understand the role that different striatal subregions belonging to parallel brain circuits have in associative learning and discrimination learning (S-O-R and S-R tasks). Strengths of the study are the use of multiple methodologies to measure and manipulate brain activity in rats, from microPET imaging to excitotoxic lesions and multielectrode recordings across anterior dorsolateral (aDLS), posterior ventral lateral (pVLS)and dorsomedial (DMS) striatum.

The main conclusions are that the aDLS promotes stimulus-response association and suppresses response-outcome associations. The pVLS is engaged in the formation and maintenance of the stimulus-response association. There is a lot of work done and some interesting findings however, the manuscript can be improved by clarifying the presentation and reasoning. The inclusion of important controls will enhance the rigor of the data interpretation and conclusions.

Se aborda una crítica a una vision sesgada de los conflictos y actos de violencia global. Se cuestiona la tendencia a "satanizar" al Estado Islámico, destacando la influencia de los medios de comunicación en nuestra percepción de estos eventos. Tambien una falta de atención hacia las víctimas de los bombardeos occidentales en Siria e Irak y se argumenta que la violencia y barbarie tienden a engendrar más violencia. Además menciona que la historia ha demostrado repetidamente cómo las subjetividades y sus producciones racionales, por ser inherentemente conflictivas y subjetivas, pueden conducir a la barbarie. Esta perspectiva invita a reflexionar sobre las causas subyacentes y la responsabilidad compartida en los conflictos globales

Again, wasn't all of this covered in the background?

Again, wasn't all of this covered in the background?

PE chart

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Reply to the reviewers

Answers to reviewers

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Reviewer #1

Sagia et al. present a manuscript using A. nidulans as model to study different transport routes of membrane proteins from the ER to the plasma membrane. They showed in earlier work that apparently at least two different transport routes exist, one involving the classical ER-ERES-ERGIC-Golgi route, one bypassing the Golgi. Unpolarized membrane proteins use the former, apically sorted membrane proteins the latter route. The study here confirms their earlier findings, uses a better model (co-expression of representatives for both routes in the same cell) and provides additional mechanistic insights about the roles of rabs, SNARES and other important proteins of the secretory pathway. The study is thoroughly done, figures are of high quality, data and methods well described and adequately replicated.

Thank you for your positive comments

I do have, however, a number of comments that could help to improve the manuscript.

-I suggest using the term polarized or apical rather than polar. Polar alone to me refers more to physico-chemical properties like water-solubility.

Amended in most parts of the revised text.

-introduction and discussion: I don’t think the literature about unconventional secretion bypassing the Golgi is complete, for example studies about TMED10 like Zhang, M. et al. Cell 181, 637-652 e615 (2020) or Zhang et al. Elife 4 (2015) are missing, there might be others. Is UapA a leader-less cargo that could be inserted via TMED10 translocation?

Thank you for letting us know, we have missed these articles. More references on UPS are now added, including the Zhang et all publications. UapA, as all transporters, is a multispan transmembrane protein with no leader peptide. In fact, we have checked the role of p24 family proteins (homologous to TMED10) in UapA trafficking. The knock-out of key p24 proteins does not affect UapA sorting to the PM (please consider this as confidential unpublished results)

-Fig. 1C. Can these intracellular structures be characterized in more detail?

As explained briefly to the handling editor above, and following the reviewer’s suggestion, we performed new experiments to better characterize the identity of the cargo-labeled fluorescent puncta. To do so, we used co-expression of a standard ERES marker, Sec16, in cells expressing either UapA or SynA, tagged with different fluorescent tags. More specifically, we constructed and analyzed strains co-expressing UapA-GFP/Sec16-mCherry or GFP-SynA/mCherry-Sec16 in the sec31ts genetic background, which allows synchronization and better analysis of ER exit, as described in our text. The new findings appear as Figure 5C __in the revised manuscript. Notice that sec16-mCherry introduced in the native sec16 locus by standard knock-in reverse genetics of A. nidulans (see Materials and methods) does not affect Aspergillus growth or secretion. Experiments depicted in __5C show that both cargoes, UapA and SynA, co-localize significantly (PCC ≈ 0.6), with Sec16, suggesting that most of these puncta are indeed ERES structures. Given that the puncta marked with UapA or SynA are clearly distinct (see Figures 1C,2A, 3A, 5B), this new experiment strongly suggests that there are indeed two distinct ERES, one populated mostly by UapA and the other by SynA. Notice, as we already outline in our response to the editor above, a three-colored approach using Sec16-BFP (or Sec13-BFP) for showing directly the existence of these two populations of cargo-specific ERES in the same cell failed as the BFP signal was problematic for colocalization studies.

Where is the Golgi localized in A. nidulans, is it decentralized like in yeast?

Yes, as in S. cerevisiae, A. nidulans Golgi cisternae are individually scattered throughout the cytoplasm, also similarly to other filamentous fungi. Notice that in A. nidulans Golgi structures are moderately polarized (Pantazopoulou and Penalva 2009).

Is the UapA at the time points shown in Fig. 1C in some sub-PM structures? To me the distribution at or near the PM is more punctate than in the steady state image shown in 1B

The punctuate appearance of PM transporters at the periphery of fungal cells is a common theme when these do not reach high, steady-state, levels of accumulation. In fact, several transporters mark specific subdomains of the PM, more evident before achieving their steady-state levels. For example, in yeast several amino acid and nucleobase transporters mark punctuate structures that colocalize with eisosomes markers (caveolin-like PM subdomains), while the proton pump ATPase Pma1 marks distinct punctuate domains. Similarly, UapA and other solute transporters mark punctuate structures before reaching their state-state accumulation in the PM. Figure 1C shows the de novo synthesis of cargoes after 100 min of transcription, while Figure 1B depicts the steady-state localization of UapA and SynA after 4h. In the latter case, the PM is ‘saturated’ with UapA molecules and thus the fluorescent signal of distinct puncta ‘fuses’, creating continuous fluorescent labeling. Notice also that in several cases, in our work, we have also performed UapA transport assays, which provide a direct tool to test and confirm the presence of UapA in the PM (see Figures 4D or 6C).

-Fig. 3A. To me it looks like there is actually a lot of colocalization of UapA and SynA, especially at or near the PM, where there is quite some white, punctate staining. The green fluorescence is just much stronger, overlaying the violet. Can you show separate channels and explain?

We think the reviewer means Figure 2A, which compares UapA and SynA (Figure 3A compares UapA with Golgi markers). If so, we have quantitatively estimated and performed statistical analysis (PCC) which indicates that this, visually apparent colocalization, is not significant (right panel in Figure 2A). Notice also that we cannot totally exclude very minimal colocalization of UapA and SynA signals as both cargoes mark very proximal early secretory domains (i.e., ERES or ERGIC), especially in fungal cells. Anyhow, in the revised Figure 2 we also added a panel depicting separate channels, as the reviewer asks.

-Fig. 3: In my opinion the statement that UapA "is probably sorted from an early secretory compartment, ultimately bypassing the need for Golgi maturation" is too strong at that point. You say for both UapA and SynA you don’t get significant colocalization with early Golgi/ERGIC marker, then you cannot conclude that one takes the conventional route via early-late Golgi and the other does not. What you can say is that UapA is apparently not going through late Golgi.

The reviewer is in principle correct. However, significant colocalization with the late Golgi marker, as SynA shows, strongly suggests that this cargo has passed via the early Golgi compartment. The fact we failed to detect significant colocalization of any cargo tested with early Golgi/ERGIC markers (e.g., SedV) is very probably due to very rapid passage of cargoes from these compartments, which conventional widefield or confocal microscopy cannot detect. To achieve this, ultra-fast fluorescent microcopy, as Lattice Light Sheet Microscopy (LLSM), should be used. In fact, we are currently initiating these studies, which will appear in the near future elsewhere.

-Fig. 4C: UapA does not seem to accumulate in the ER in the Sec24 and 13 mutants but in punctate structures. This for me is unexpected, any explanations? Can you characterize that punctate staining?

This is an interesting observation. Notice that UapA is a large homodimeric protein (e.g., 28 transmembrane domains) that oligomerizes further upon translocation into the ER membrane. Repression of Sec24, and to a less extent of Sec13, leads to inability to exit the ER properly. Consequently, this will lead to UapA overaccumulation in the ER, which might in turn lead to ER stress and turnover, reflected in UapA aggregates. In line with this, we have previously shown that specific mutants of UapA unable to exit the ER are indeed degraded by selective autophagy (Evangelinos et al., 2016). In contrast to UapA, SynA partitions in the entire ER without forming aggregates when sec24 or sec13 are repressed. This might be due to the fact that is a single-pass, much smaller, membrane protein compared to UapA and one that is not known to form oligomers. Thus, its overaccumulation in the ER might not lead to aggregation, allowing it to diffuse laterally in the membrane of the ER. A note on this is included in the Figure legend of the revised manuscript.

-Fig. 6D: You state that BFA "has only a very modest effect on UapA translocation to the PM". To me the PM (or very near PM) staining of UapA looks very different in the PFA treated cells, more uneven/punctate. Is there an explanation for that?

Our explanation is the following. When BFA is added, conventional secretion is blocked and Golgi collapses. We believe that this might have a moderate indirect effect also on cargoes bypassing the late Golgi/TGN, as UapA (i.e., lower levels of UapA present in the PM). This is based on the fact that UapA, in addition to conventional cargoes, requires the Q-SNARE complex SsoA/Sec9 to translocate to the PM. SsoA, being a membrane protein cargo itself, also needs to traffic to the PM. Interestingly, we have previously obtained evidence suggesting that SsoA traffics to the PM by both conventional and a Golgi-bypass routes (Dimou et al 2020). Thus, UapA translocation to the PM might indeed be partially impeded or delayed due to repression of proteins, such as SsoA (and probably Sec9), needed for its final integration into the PM bilayer. Importantly, in line with an indirect effect of BFA on the levels of UapA localized in the PM, notice that, unlike SynA, UapA was never trapped in brefeldin bodies (i.e., Golgi aggregates).

Reviewer #1 (Significance):

One strength of the study is the use of a model organism, A. nidulans, not cell cultures. Also, the use of both reporters, UapA and SynA, in the same cell is an advantage over previous studies using different lines and different promotors. Limitation of the study might be that it remains unclear to what extend the basic mechanism (UapA and SynA are transported to PM in different carrier and via different routes) can be generalized to other polarized (apically?) membrane proteins versus non-polarized membrane proteins in A. nidulans and whether a similar mechanism exists in other organisms. Some of the basic findings of the study are not new but were published by the same group. However, as the authors point out, the current study uses improved assays and extends their previous studies, advancing our understanding of the mechanistics of transport in the conventional secretory pathway and novel alternative routes. The study will be of interest for basic researchers in the trafficking field. My own expertise is transport through the secretory pathway in mammalian cells, many years ago more post-Golgi, now mostly ER-Golgi and ER itself.

We thank the reviewer for his positive comments.

__Reviewer #2 __

__ __The idea that transmembrane proteins of the plasma membrane move from the ER to the Golgi and then to the cell surface is firmly entrenched, and the mechanisms and components of this secretory pathway have been extensively characterized. Secretory vesicles are often delivered from the Golgi to sites of polarized growth. This paper builds on previous work by the same group to provide evidence that in Aspergillus nidulans, some non-polarly localized plasma membrane proteins follow a very different pathway, which bypasses components of the conventional secretory machinery such as SNAREs that have been implicated in secretion as well as the exocyst. In particular, they systematically compare the trafficking of the SNARE SynA, which follows the conventional secretory pathway, with that of the purine transporter UapA, which apparently does not. The two proteins were co-expressed in the same cells using the same promoter. A variety of genetic and microscopy methods are used to support the conclusion that UapA reaches the plasma membrane by a route distinct from that followed by SynA.

In my view, the authors present a convincing case. The individual experimental results are sometimes ambiguous, but the combined results favor the conclusion that UapA follows a novel pathway to the plasma membrane. I have only a few relatively minor comments.

Thank you for your positive comments

1. In the Introduction and elsewhere: to my knowledge, there is no clear evidence that AP-1-containing clathrin-coated vesicles carry cargoes from the Golgi to the plasma membrane. On the contrary, as recently reported by Robinson (https://pubmed.ncbi.nlm.nih.gov/38578286/), AP-1-containing vesicles likely mediate retrograde traffic in the late secretory pathway.

Thank you for this comment and the relative reference. We are aware that AP-1 is likely to also mediate retrograde traffic in the late secretory pathway or/and intra-Golgi recycling, as also reported by the group of Benjamin Glick. Thus, in the revised version we added a short comment on this plus relative references. Along this line, our previous work has shown that transcriptional repression of AP-1 arrests the polar localization of several apical markers in A. nidulans and we reported that this might be due to an effect on both anterograde and retrograde trafficking. Please see “Secretory Vesicle Polar Sorting, Endosome Recycling and Cytoskeleton Organization Require the AP-1 Complex in Aspergillus nidulans”. Martzoukou O, Diallinas G, Amillis S. Genetics. 2018 Aug;209(4):1121-1138. Overall, the fact that AP-1 was found absolutely dispensable for UapA trafficking, further strengthens our conclusion that UapA bypasses the Golgi.

1. In Figure 2, is there any known significance to the presence of UapA in "cytoplasmic oscillating thread structures decorated by pearl-like foci as well as a very faint vesicular/tubular network"?

At present we cannot answer this question. In order to understand what these structures represent and answer what is their role, we will need to employ super-resolution and ultra-fast microscopy and additional markers, which we envision to do. We suspect that they might be tubular networks, but this extends beyond the present work.

1. SynA is related to S. cerevisiae Snc1/2, which are known to be present in late Golgi compartments due to repeated rounds of endocytosis to the Golgi and exocytosis to the plasma membrane. The SynA shown here to colocalize with PHosbp is probably present in a similar recycling loop rather than being en route to the plasma membrane for the first time. Therefore, the differential colocalization of UapA and SynA with PHosbp does not by itself provide "strong evidence that the two cargoes studied traffic via different routes" as stated in the text but might instead indicate that only SynA undergoes frequent endocytosis. The text should be amended accordingly.

The reviewer is in principle correct. However, given that colocalization of SynA and PHosbp occurred all over the cytoplasm of hyphae and not only at the apical region, and because we record colocalization of cargoes before their steady-state accumulation to the PM, thus at a stage where recycling must be minimal, the recorded colocalization should reflect anterograde transport rather than recycling. We added this reasoning it the revised text.

1. A missing piece of the story is a test of whether the puncta visualized for the two cargoes in Figure 5B are indeed distinct populations of COPII-containing ER exit sites. The relevant experiment would involve co-labeling of the cargoes together with a COPII marker. Three-color labeling would presumably be needed.

This point was also raised by reviewer 1 (and review 3) and thus performed new experiments to better characterize the identity of the cargo-labeled fluorescent puncta. To do so, we used co-expression of a standard ERES marker, Sec16, in cells expressing either UapA or SynA, tagged with different fluorescent tags. More specifically, we constructed and analyzed strains co-expressing UapA-GFP/Sec16-mCherry or GFP-SynA/Sec16-mCherry in the sec31ts genetic background, which allows synchronization and better analysis of ER exit, as described in our text. The new findings appear as Figure 5C __in the revised manuscript. Notice that sec16-mCherry introduced in the native sec16 locus by standard knock-in reverse genetics of A. nidulans (see Materials and methods) does not affect Aspergillus growth or secretion. Experiments depicted in __5C show that both cargoes, UapA and SynA, co-localize significantly (PCC ≈ 0.6), with Sec16, suggesting that most of these puncta are indeed ERES structures. Given that the puncta marked with UapA or SynA are clearly distinct (see Figures 1C,2A, 3A, 5B), this new experiment strongly suggests that there are indeed two distinct ERES, one populated mostly by UapA and the other by SynA. Notice, as we already outline in our response to the editor above, a three-colored approach using Sec16-BFP (or Sec13-BFP) for showing directly the existence of these two populations of cargo-specific ERES in the same cell failed as the BFP signal was problematic for colocalization studies.

Reviewer #2 (Significance):

This study provides compelling evidence that in the fungus Aspergillus nidulans, some transmembrane transporter proteins reach the plasma membrane by a pathway that bypasses much of the conventional machinery associated with the Golgi apparatus and secretory vesicles. Although previous publications pointed toward a similar conclusion, the present work tackles the problem in a more rigorous and systematic way. These findings are important for cell biologists who study membrane traffic, it remains to be determined how prevalent this type of non-canonical secretion might be in other organisms.

We thank the reviewer for his positive comments

Reviewer #3

The manuscript by Sagia et al compares the trafficking of a polarized (SynA) with a non-polarized (UapA) transmembrane protein. In agreement with previous work of the same lab, they find that UapA reaches the plasma membrane through a Golgi-bypass route, which they characterize to some extent. Overall, the data are of good quality and the story is interesting and timely. Understanding trafficking routes that bypass the Golgi is highly interesting. Nevertheless, there are several points of criticism that I have and below is a list where I combine major and minor points together:

Thank you for your positive comments

Major Comments:

1- Is it possible that the polarized phenotype of SynA is caused by selective removal, i.e. SynA is delivered to the entire plasma membrane, but endocytosed rapidly from all areas except the tip of the hyphae. This would also result in a polarized distribution.

This is in principle possible, but here this is not the case. SynA is polarized due to rapid local endocytosis and immediate recycling at the subapical region, known as the subapical collar. Please see:

Taheri-Talesh N, Horio T, Araujo-Bazán L, Dou X, Espeso EA, Peñalva MA, Osmani SA, Oakley BR. The tip growth apparatus of Aspergillus nidulans. Mol Biol Cell. 2008 Apr;19(4):1439-49. doi: 10.1091/mbc.e07-05-0464.

Hernández-González M, Bravo-Plaza I, Pinar M, de Los Ríos V, Arst HN Jr, Peñalva MA. Endocytic recycling via the TGN underlies the polarized hyphal mode of life. PLoS Genet. 2018;14(4):e1007291. Published 2018 Apr 2. doi:10.1371/journal.pgen.1007291

This applies to all apical markers; they remain polarized by continuous local recycling after the diffuse laterally to the subapical collar.

2- The authors describe the distribution of SynA and UapA in cells deficient of various COPII/ERES proteins. However, these data are not shown, and it is not clear how they were quantified. It would be important to add quantitative data here.

Quantitative data are included in Figure 4C, displaying the percentages of cells with UapA either retained in the ER or reaching the PM for each background deficient in a COPII protein. Repression of SarA and Sec31 resulted in UapA retention in the ER in all analyzed cells (100%). However, repression of Sec12, Sec24, or Sec13 had a differential effect across the cell population, with UapA reaching the PM in some cells, while remaining trapped in the ER in others. To quantify these data and determine which cargo localization pattern prevails, we measured the number of cells in each category and represented them as percentages. A similar approach was used to examine the role of Golgi proteins in the trafficking of UapA and SynA (Figure 6).

3- on page 8, the authors discuss the discrepancy regarding the role of Sec13. They offer as an explanation that the previous studies have been performed in strains that separately expressed the two cargoes. However, I am unable to see why and how this would be a valid explanation.

Given that Sec13 has a variable/partial effect on UapA, we have previously been biased towards images that showed an effect on localization, as expected, and considered that the lack of an effect might have been due to inefficient repression in a fraction of cells. In our new system, we were able to directly compare UapA to SynA and find out that while SynA was always affected under our conditions, the effect of UapA was still variable. Thus, the partial effect of Sec13 on UapA is physiologically valid and not a matter of insufficient repression in a fraction of cells. This shows the importance of our new improved system where we follow the synchronous expression of two cargoes in the same cells.

4- Why is the effect of Sec24 depletion so much stronger than of Sec12 depletion? Sec12 is the GEF for SarA, without which Sec24 should not be recruited to ERES. The explanation that low amounts of Sec12 are still present and sufficient to carry out the role of this protein. What is the evidence for that?

Sec24 is the principal receptor of cargoes responsible for their recruitment to ERES. Sec12 is the catalytic effector for SarA required for the initiation of COPII vesicle formation. The question of the reviewer is thus logical.

However, Sec12 is indeed present at extremely very low levels when expressed from its native promoter under the condition of our experiment (minimal media). This is supported by our recent proteomic analysis, performed under similar conditions, which failed to detect the Sec12 protein, unlike all other COPII components (see Dimou et al., 2021, doi; 10.3390/jof7070560), but also by cellular studies of the group of M.A. Peñalva, who failed to detect Sec12 tagged with GFP (Bravo-Plaza et al., 2019, doi: 10.1016/j.bbamcr.2019.118551). Additionally, in yeast, immune detection of Sec12 has been possible only in cells harboring sec12 on a multicopy plasmid, suggesting its low abundance in wild-type cells (Nakano et al., 1988, doi:10.1083/jcb.107.3.851).

Given that repression of sec12 transcription via the thiAp promoter still allows 68% of cells to secrete normally both SynA and UapA, while 32% of cells are blocked in the trafficking of both cargoes, suggests that in most cells either SarA can catalyze the exchange of GDP for GTP without Sec12, maybe through a cryptic guanine nucleotide exchange factor (GEF), or that very small amounts of Sec12 remaining after repression are sufficient for significant SarA activation. Whichever scenario is true, Sec12, similarly to SarA, is not critical for distinguishing Golgi-dependent from Golgi-independent routes, as both cargoes are affected similarly. In the revised text we added a not on this issue.

5- In Figure 5, it would help readers who are not so familiar with Aspergillus organelle morphology to explain the figure a bit better. This might appear trivial for experts, but anyone from outside this field is slightly lost.

In the revised manuscript we added a figure panel depicting a schematic representation of A. nidulans key secretory compartments.

6- The authors write that not seeing UapA in Golgi membranes is evidence that it does not pass through this organelle. However, when they write that SynA is never seen in cis-Golgi elements, they do not conclude that SynA bypasses the cis-Golgi.

The fact that SynA, unlike UapA, colocalized significantly with late-Golgi/TGN and follows conventional secretion in general, strongly suggests that SynA also passes from the early-Golgi. Cargo traffic through the Golgi is mediated by cisternal maturation, where an individual cisterna gradually changes its nature from an earlier to a later one, while the cargo remains inside. UapA, unlike SynA, never colocalized with any Golgi marker used and was not affected by BFA. We agree with the reviewer that we did not have direct proof for passage of UapA or SynA from the early-Golgi in the wt background, which allows for the alternative, but rather unlikely hypothesis, that none of the two cargos is sorted to the early Golgi and that SynA traffics directly to late-Golgi/TGN. Our inability to detect sorting of any cargo to the early-Golgi is seemingly due to ultra-fast passage of cargoes from very early secretory compartments, such as ERGIC/early-Golgi. In fact, we have obtained evidence of this using Lattice Light Sheet microscopy (results in progress, to appear elsewhere).

7- Figure 5C: the authors claim that the CopA and ArfA affects trafficking of UapA and SynA from ER to plasma membrane and assign CopA and ArfA as regulators for anterograde trafficking. I think this interpretation is not justified by the data. Depletion of CopA and ArfA will affect the Golgi apparatus in structure and function. The more straight-forward interpretation is that repression of the COPI machinery results in a defect in Golgi exit and therefore retention in pre-Golgi compartments (including the ER and maybe the ERGIC should it exist in Aspergillus). The same is true for BFA treatment where there are also negative effects on ER export, which are rather indirect consequences of alterations of Golgi function and integrity. Likewise, the interpretation of the papers by Weigel et al and Shomron et al is not correct. It is more likely that COPI is recruited to the growing ERES-derived tubule (or ERGIC) to recycle proteins back to the ER. This is not necessarily a proof that COPI regulates anterograde trafficking

This is a highly debatable issue which our work cannot address. However, we amended the text accordingly.

8- Figure 6: The images look like in Figure 5, yet here you don't call them ER-associated.

The two images are not alike. In Figure 5 upon activation of Sec31 (permissive temperature) we detect mostly punctual structures resembling ERES, whereas at the nonpermissive temperature we detect a membranous network typical of the ER. Upon repression of CopA we also detect punctual structures similar to ERES. In Figure 6, we mostly detect an effect on SynA. Repression of early secretory steps (SedV, GeaA) lead to collapse of SynA in the entire ER network. Repression at later stages of Golgi maturation and post-Golgi secretion (RabO, HypB, RabE, AP-1) lead to the appearance of punctual structures, most probably Golgi aggregates.

9- Figure 6D: How long was the BFA treatment. I am surprised that the pool of SynA preexisting at the plasma membrane seems to also be sensitive to BFA.

Cells were grown overnight under repressed conditions for both UapA and SynA. After 12-14h cells were shifted to derepressed conditions using fructose as carbon source. BFA was added after 90min of cargo derepression, while both cargoes were still in cytoplasmic structures so there was not preexisting SynA or UapA at the PM (see also Figure 1C). Subcellular localization of both cargoes was studied for 60min after BFA treatment.

10- This might be beyond the scope of this study, but as far as I know UapA is not N-glycosylated. Would the introduction of an N-glycosylation site shift it towards the Golgi-based route?

Thank you for this suggestion. We have performed this experiment, adding a glycosylation site on UapA, based on the glycosylation sites found in tis mammalians homologues. We did not detect any effect on UapA trafficking route or its activity. As the reviewer recognizes this goes beyond the scope of this study and thus, we did not include it the manuscript. Differential cargo glycosylation is however an important issue to be studied systemically in respect to different trafficking routes, and we envision to investigate it systematically.

Minor Comments

1- This might be just a personal preference, but I think that the term polar is misleading, because it implies something about the polarity of the amino acids. I think "polarized" might be the more common term. Anyway, this is just a minor point and just a suggestion from my side.

Amended in the revised text.

2- The paper by the Saraste lab should be mentioned and discussed (PMID: 16421253), which I think is very relevant to the current story.

We thank the reviewer for pointing out this important publication. In that case, the Rab1 GTPase defined a pathway connecting a pre-Golgi intermediate compartment with the PM in mammalians nerve cells. Thus, the Saraste lab publication is indeed along the lines of findings supporting that Golgi-independent unconventional cargo trafficking routes initiate at very early secretory compartments. Notice, however, that RabO, the A. nidulans homologue of Rab1, which in their case was essential for direct cargo sorting from the ERES/ERGIC to the PM, in or system, was dispensable for Golgi bypass. The Saraste lab article is now mentioned and discussed.

3- Having worked with ERES for over two decades, I find it strange to see it written ERes. I see no reason why ER exit sites in Aspergillus should be abbreviated differently from all other types of cells (yeast, drosophila, worms, mammals). I think that the entire acronym should be capitalized.

Amended in the text

4- When discussing the data about the partial effect of Sec13, it would be good to refer to a previous paper by the Stephens lab that showed that silencing Sec13/31 results in a defect in trafficking of collagen, but not of VSVG (PMID: 18713835).

We thank the reviewer for also pointing out the publication of the Stephens lab, now mentioned in the revised text. Noticeably, in that case silencing of both Sec13 and Sec31 has no effect on the trafficking of specific cargoes, whereas in our case Sec31 is still absolutely needed for both conventional and Golgi-independent secretion of SynA and UapA, respectively.

Reviewer #3 (Significance):

Overall, the data are of good quality and the story is interesting and timely. Understanding trafficking routes that bypass the Golgi is highly interesting. The main weakness is the lack of mechanistic understanding of the Golgi-bypass pathway. In addition, the study is limited to two proteins as representatives of polarized vs. non-polarized proteins. The main target audience for this paper are scientists working in the area of secretion and trafficking in the secretory pathway.

We thank the reviewer for his positive comments.

We are aware that the mechanistic details of Golgi bypass are missing and this is our next goal, dissecting those via various approaches genetic and biochemical approaches and employment of super resolution and ultra-fast microscopy.

__Reviewer #4 __

In this study, Sagia et al investigate the trafficking of different secretory cargo in Aspergillus nidulans under conditions that repress expression of transport factors or block stages in membrane trafficking. The primary approach is to conduct dual live-cell imaging of GFP-tagged UapA (plasma membrane localized purine transporter) and SynA (plasma membrane R-SNARE) after their simultaneous derepression to monitor trafficking routes. In germlings, both secretory proteins are detected in non-overlapping intracellular compartments and puncta after 60-90 min of derepression. After 4-6 hrs, SynA localizes to hyphal tips whereas UapA localizes to non-polar regions of the PM. Colocalization studies do not show UapA overlap with Golgi markers (SedV, PH-OSBP) during its biogenesis whereas SynA displays significant co-localization. Repression of COPII and COPI components generally block transport of both cargos to the PM and cause accumulation in ER compartments, although there are some differential effects on UapA and SynA localization. Finally, repression of other transport factors (ER-Golgi SNAREs, Golgi transport factors, and exocytic machinery) had differential effects on UapA and SynA localization over time with UapA reaching the plasma membrane in many instances and SynA accumulating in intracellular compartments.

Based on these observations, the authors conclude that UapA and SynA follow distinct trafficking routes to the plasma membrane where SynA uses a canonical SNARE-dependent secretory pathway route and UapA follows a non-canonical route that may bypass Golgi compartments. The study is extensive and supports the model that biogenesis of SynA and UapA follow distinct processes. However, there are some complexities that may limit interpretation. First, the cargo studied are targeted to the ER differently. UapA is a multispanning transmembrane protein that is likely dependent on the Sec61 translocon for co-translational membrane insertion and will involve ER chaperones and quality control machinery for its biogenesis. SynA will depend on the tail-anchored machinery (GET/TRC pathway) for insertion into the ER and is processed by cytosolic factors/chaperones. Therefore, the sites of ER insertion and the rates of biogenesis of these cargoes will be different. In addition, the repression of trafficking machinery used in this study appears to be variable and may exert partial blocks on intracellular transport stages. Regardless, the study clearly documents that SynA and UapA follow distinct biogenesis and transport processes when co-expressed in cells under experimentally controlled conditions.

Thank you for your positive comments.

To our knowledge there is no evidence suggesting that SynA translocates via a tail-anchored machinery (GET/TRC pathway) and not through the translocase. Despite this, we agree with the reviewer that translocation to the ER, as well as exit from it, might be cargo-dependent, especially when it concerns proteins with very different size, structures and oligomerization. Thus, the rate of biogenesis of UapA and SynA is probably quite different. However, this still does not dismiss our basic conclusion that the two cargoes follow distinct routes to traffic to the PM. The ‘problem’ of variable transcriptional repression of some trafficking-related proteins is solved by comparing the relative effect on the two cargoes in the same cells, and this is in fact the advantage of our new system. Importantly, notice that we took care to use conditions of repression where SynA trafficking by the conventional path was totally abolished and compared it to UapA.

1. It was not clear if the translation, ER insertion and folding of UapA and SynA are fully synchronous. Is it possible that the rate of UapA synthesis and transport to the plasma membrane is substantially faster than for SynA? The imposition of transport blocks could trap SynA and not UapA if this cargo was at later transport stages.

As already discussed above translation, ER insertion and folding of UapA and SynA might indeed by different. This might somehow affect the trafficking path followed, but this issue is beyond the scope of this work. Notice, however, that the transcription of both cargoes is kept fully repressed during establishment of repression of secretion. Only when repression and blocking of secretion is established (12-14 h germination), as verified by Western blot analysis, we derepress the transcription of UapA and SynA, expressed from the same promoter, and follow their dynamic subcellular localization. Hence, this system ensures that both cargoes start from the earliest transport stage, the ER, upon imposition of transport blocks.

1. In repressing transport factors (e.g., SarA, Sec12, Sec24, Sec13, SedV, RabE), it is clear that under thiamine repressing conditions these cells do not grow or have greatly reduced growth rates. However, it was not clear if proteins are depleted to the same extent in cells after repression for 12-14 hr or 16-22 hr. as mentioned in the methods. Indeed, in some cases depleted cells display different cargo localization patterns, for example 67% of cells show normal localization of UapA and SynA after sec12 repression and 33% show ER accumulation of both cargoes. There is differential localization of UapA and SynA in many cases where transport factors are repressed, but this could be due to partial inhibition and not complete blocks. It would be helpful to clearly indicate the time points and conditions in each of the figure legends as in points 3-5 below.

In the revised manuscript we did our best to clearly indicate the time points and conditions in each of the figure legends. Differential localization of UapA and SynA in many cases where trafficking factors are repressed is indeed an interesting outcome. Inefficient repression was dismissed based on the lack of colony growth (see relative growth tests of SarA, Sec24, Sec13, Sec31, SedV, GeaA, RabO, RabE, Ykt6, Sft1, SsoA and Sec9), but also by western blots (e.g., Sec24, Sec13, Sec31 or Sec9 shown in the present manuscript, or other trafficking proteins studied previously. Martzoukou et al., 2018; Dimou et al., 2020). Repression of Sec12 and HypB, and to lower degree AP-1, allowed formation of small and/or compact colonies, but even in these cases relative protein levels could not be detected in western blots, guaranteeing efficient repression.

1. In Fig 4A immunoblot, HA-tagged proteins are not detected after thiamine repression. Please state the time of thiamine repression used before protein extraction and blot. Is this for the same length of time as for cells shown in panel 4C? It would also be helpful to state the time of cargo derepression before capturing images in 4C. The methods section mentions 12-14 hr or 16-22 hr of growth, presumably with thiamine in the culture, and then 1-8 hr or 60 min to 4 hr of cargo derepression before imaging. Please specify.

The time of thiamine repression before protein extraction was 16-18h. The same repression time was used for experiments shown in Figures 4C and 6C (ER/COPII and Golgi/post-Golgi repression respectively). More specifically, for microscopy experiments cells were grown in the presence of glucose and thiamine for 12-14h (repressed UapA/SynA and thiAp expressed gene). After this time, cells were shifted to fructose and thiamine for 4h (derepression of UapA/SynA and repression of thiAp expressed gene). In both cases (protein extraction and microscopy experiments) the total time of thiamine repression was 16-18h.

1. For the thiA-copA and thiA-arfA repression experiments (Fig 5C), the methods section states that thiamine was not added ab initio in the culture, but after an 8 h time window without thiamine at the start of spore incubation. This is interpreted to mean that repression was for a shorter period to time than the 12-14 hr overnight growth. However, the figure legend states that De novo synthesis of cargos takes place after full repression of CopA and ArfA is achieved (>16 hr). Please clarify.

We think that the review was confused with repression of cargo synthesis (via alcAp+glucose) versus repression of trafficking proteins (via thiAp+thiamine). Please see Materials and methods. We clarify our protocol also here:

For the thiAp-copA and thiAp-arfA repression experiments addition of thiamine ab initio in the culture leads to total arrest of spore germination and germling formation. Thus, we added an 8-hour time window without thiamine to allow conidiospores to germinate until the stage of young germlings, under conditions where cargo expression via the alcAp was repressed by glucose. Subsequently, thiamine was added in the media (16-18 h) to repress CopA and ArfA, while cargo expression remained glucose-repressed. The transcriptional repression of the cargoes UapA and SynA was maintained for a longer period (24-26 h) compared to other repression experiments, but longer times of repression of cargoes do not make any difference, as full repression is achieved already at 12 h. De novo cargo trafficking was followed next day by eliciting depression, via a shift to fructose media, while still maintaining thiamine to repress CopA or ArfA.

1. In Fig 6D, BFA treatment is shown to trap SynA in Golgi aggregates while UapA still reaches the plasma membrane. Please state the time of BFA treatment before collecting these images. Do longer treatments with BFA before cargo derepression cause accumulation of UapA in intracellular compartments?

As mentioned above (response to Reviewer’s #3 comment 9) cells were grown overnight under repressed conditions for both UapA and SynA. After 12-14h cells were shifted to derepressed conditions using fructose as carbon source. BFA was added after 90min of cargo derepression, while both cargoes were still in cytoplasmic structures so there was not preexisting SynA or UapA at the PM (see also Figure 1C). We have not noticed any different effect on UapA trafficking after a max of 1h of BFA treatment.

1. A minor point, but on page 21 the methods state that "cells were shifted down to the permissive temperature (25 C), to restore the secretory block...". Suggest changing to "to reverse the secretory block..."

Modified accordingly

Reviewer #4 (Significance):

This manuscript nicely builds on a developing line of investigation in the Aspergillus nidulans model that specific plasma membrane proteins are efficiently delivered to the cell surface in a pathway that is distinct from the canonical secretory pathway. Previous work from this lab has suggested that a subpopulation of COPII carriers can bypass the Golgi for delivery of specific cargo to the plasma membrane. The current study uses dual expression of UapA-GFP and mCherry-SynA to provide further support for this model. Molecular definition of a direct ER to PM transport pathway for secretory cargo would be a significant advance to a broad audience. This study provides additional depth and support that such a pathway exists but does not define how COPII vesicles or related intermediates are transported to the PM.

Again, thank you for your positive comments.

is symbol is a griffin and he's with young grif now

atleast she hates incest (the bar is in hell)

lyanna probably had the same idea

NOOO FUCK

no :(

poor girls :(

wtf

you've lost man you've lost :(

thats kinda cute ig

real ones even if greatjon hates women

the dad that stepped up

thats insaneee

can never doubt that this man loves his siblings lmao

oh edric storm!

Imunidade cultural

anterioridade nonagesimal

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Reply to the reviewers

Rebuttal_ Preprint- #RC-2023-02144

First of all we would like to thank the three reviewers for their constructive and positive comments and suggestions, and the time spent in reviewing our manuscript. Their suggestions and comments had contributed to improve our manuscript. We feel the manuscript is much strengthened by this revision.

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

__Summary:____ __The manuscript by Dabsan et al builds on earlier work of the Igbaria lab, who showed that ER-luminal chaperones can be refluxed into the cytosol (ERCYS) during ER stress, which constitutes a pro-survival pathway potentially used by cancer cells. In the current work, they extent these observations and a role for DNAJB12&14 in ERCYS. The work is interesting and the topic is novel and of great relevance for the proteostasis community. I have a number of technical comments:

We thank the reviewer for his/her positive comments on our manuscript.

__Major and minor comments: __

1- In the description of Figure 2, statistics is only show to compare untreated condition with those treated with Tg or Tm, but no comparison between condition and different proteins. As such, the statement made by the authors "...DNAJB14-silenced cells were only affected in AGR2 but not in DNAJB11 or HYOU1 cytosolic accumulation" cannot be made.

Answer: We totally agree with the reviewer#1. The aim of this figure is to show that during ER stress, a subset of ER proteins are refluxed to the cytosol. This is happening in cells expressing DNAJB12 and DNAJB14. We are not comparing the identity of the expelled proteins between DNAJB12-KD cells and DNAJB14-KD cells, This is not the scoop of this paper as such the statement was removed.

2- Figure S2C: D11 seems to increase in the cytosolic fraction after Tm and Tg treatment. However, this is not reflected in the text. The membrane fraction also increases in the DKO. Is the increase of D11 in both cytosol and membrane and indication for a transcriptional induction of this protein by Tm/Tg? Again, the authors are not reflecting on this in their text.

Answer: We performed qPCR experiments in control, DNAJB12-KD, DNAJB14-KD and in the DNAJB12/DNAJB14 double knock down cells (in both A549 and PC3 cells) to follow the mRNA levels of DNAJB11. As shown in (Figure S2F-S2N), there is no increase in the mRNA levels of DNAJB11, AGR2 or HYOU1 in the different cells in normal (unstressed conditions). Upon ER stress with tunicamycin or thapsigargin there is a little increase in the mRNA levels of HYOU1 and AGR2 but not in DNAJB11 mRNA levels. On the other hand, we also performed western blot analysis and we did not detect any difference between the different knockdown cells when we analyzed the levels of DNAJB11 compared to GAPDH. Those data are now added as (Figure S2F-S2N).

We must note that although AGR2 and HYOU1 are induced at the mRNA as a result of ER stress, the data with the overexpression of DNAJB12 and DNAJB14 are important as control experiments because when DNAJB12 is overexpressed it doesn’t inducing the ER stress (Figure S3C-S3D). In those conditions there is an increase of the cytosolic accumulation of AGR2, HYOU1 and DNAJB11 despite that there was no induction of AGR2, HYOU1 or DNAJB11 (Figure 3C and Figure 3E, Figure S3, Figure 4, and Figure S4) . Those results argue against the idea that the reflux is a result of protein induction and an increase in the total proteins levels.

3- Figure 2D: Only p21 is quantified. phospho-p53 and p53 levels are not quantified.

Answer: We added the quantification of phospho-p53 and the p53 levels to (Figure 2E-G). Additional blots of the P21, phosphor-p53 and p53 now added to FigureS2O.

4- Figure 2D: There appears to be a labelling error

Answer: Yes, the labelling error was corrected.

5- Are there conditions where DNAJB12 would be higher?

Answer: In some cancer types there is a higher DNAJB12, DNAJB14 and SGTA expression levels that are associated with poor prognosis and reduced survival (New Figure S6E-M). The following were added to the manuscript: “Finally, we tested the effect of DNAJB12, DNAJB14, and SGTA expression levels on the survival of cancer patients. A high copy number of DNAJB12 is an unfavorable marker in colorectal cancer and in head and neck cancer because it is associated with poor prognosis in those patients (Figure S6E). A high copy number of DNAJB12, DNAJB14, and SGTA is associated with poor prognosis in many other cancer types, including colon adenocarcinoma (COAD), acute myeloid leukemia (LAML), adrenocortical carcinoma (ACC), mesothelioma (MESO), and Pheochromocytoma and paraganglioma (PCPG) (Figure S6F-M). In uveal melanoma (UVM), a high copy number of the three tested genes, DNAJB12, DNAJB14, and SGTA, are associated with poor prognosis and poor survival (Figure S6I, S6J, and S6M). The high copy number of DNAJB12, DNAJB14, and SGTA is also associated with poor prognosis in many other cancer types but with low significant scores. More data is needed to make significant differences (TCGA database). We suggest that the high expression of DNAJB12/14 and SGTA in those cancer types may account for the poor prognosis by inducing ERCYS and inhibiting pro-apoptotic signaling, increasing cancer cells' fitness.

6- What do the authors mean by "just by mass action"?

Answer: Mass action means increasing the amount of the protein (overexpression). We corrected this in the main text to overexpression.

7- Figure 3C: Should be labelled to indicate membrane and cytosolic fraction. The AGR2 blot in the left part is not publication quality and should be replaced.

Answer: We added the labelling to indicate cytosolic and membrane fractions to Figure 3C. We re-blotted the AGR2, new blot of AGR2 was added.

8- What could be the reason for the fact that DNAJB12 is necessary and sufficient for ERCYS, while DNAJB14 is only necessary?

Answer: Because of their very high homology, we speculate that the two proteins have partial redundancy. Partial because we believe that some of the roles of DNAJB12 cannot be carried by DNAJB14 in its absence. Although they are highly homologous, we expect that they probably have different affinities in recruiting other factors that are necessary for the reflux of proteins.

We further developed around this point in the discussion and the main text.

9- Figure 5A: Is the interaction between SGTA and JB12 UPR-independent?HCS70 seems to show only background binding. The interaction of JB12 with SGTA is not convincing. A better blot is needed.

Answer: In the conditions of Figure 5A, we did not observe any induction of the UPR (Figure S3C-D). Thus, we concluded that in those condition of overexpression, DNAJB12 interacts with SGTA in UPR independent manner.

We repeated this experiment another 3 times with very high number of cells (2X15cm2 culture dishes for each condition) and instead of coimmunoprecipitating with DNAJB12 antibodies we IP-ed with FLAG-beads, the results are very clear as shown in the new Figure 5A compared to Figure S5A.

10- Figure 5B: the expression of DNAJB14 was induced by Tg50, but not by Tg25 or Tm. However, the authors have not commented on this. This should be mentioned in the text and discussed.

Answer: In most of the experiments we did not see an increase in DNAJB14 upon ER stress except in this replicate. To be sure we looked at the DNAJB14 levels upon ER stress by protein and qPCR experiment as shown in new (in the Input of Figure 5 and Figure S5) and (Figure S5H-I). We also added new IP experiments in Figure 5 and Figure S5.

11- Figure 6A: Why is a double knockdown important at all? DNAJB14 does not seem to do much at all (neither in overexpression nor with single knockdown).

Answer: the data shows that DNAJB12 can compensate for the lack of DNAJB14 while DNAJB14 can only partially compensate for some of the DNAJB12 functions. DNAJB12 could have higher affinity to recruit other factor needed for the reflux process and thus the impact of DNAJB12 is higher. In summary, neither DNAJB12 or DNAJB14 is essential in the single knockdown which means that they compensate for each other. In the overexpression experiment, it is enough to have the endogenous DNAJB14 for the DNAJB12 activity. When DNAJB14 is overexpressed at very high levels, we believe that it binds to some factors that are needed for proper DNAJB12 activity (Figure 4 showing that the WT-DNAJB14 inhibits ER-stress induced ER protein reflux when overexpressed). We believe that DNAJB14 is important because only when we knock both DNAJB12 and DNAJB14 we see an effect on the ER-protein reflux. DNAJB14 is part of a complex of DNAJB12/HSC70 and DSGTA.

(DNAJB12 is sufficient while DNAJB14 is not- please refer to point #8 above).

**Referees cross-commenting**

I agree with the comments raised by reviewer 1 about the manuscript. I also agree with the points written in this consultation session. In my opinion, the comments of reviewer 2 are phrased in a harsh tone and thus the reviewer reaches the conclusion that there are "serious" problems with this manuscript. However, I think that the authors could address many of the points of this reviewer in a matter of 3 months easily. For instance, it is easy to control for the expression levels of exogenous wild type and mutant D12 and compare it to the endogenous one (point 3). This is a very good point of this reviewer and I agree with this experiment. Likewise, it is easy to provide data about the levels of AGR2 to address the concern whether its synthesis is affected by D12 and D14 overexpression. Again, an excellent suggestion, but no reason for rejecting the story. As for not citing the literature, I think this can also easily be addressed and I am sure that this is just an oversight and no ill intention by the authors. __Overall, I am unable to see why the reviewer reaches such a negative verdict about this work. With proper revisions that might take 3 months, I think the points of all reviewers can be addressed. __

Reviewer #1 (Significance (Required)):

Significance: The strength of the work is that it provides further mechanistic insight into a novel cellular phenomenon (ERCYS). The functions for DNAJB12&14 are unprecedented and therefore of great interest for the proteostasis community. Potentially, the work is also of interest for cancer researchers, who might capitalize of the ERCYS to establish DNAJB12/14 as novel therapeutic targets. The major weaknesses are as follows: (i) the work is limited to a single cell line. To better probe the cancer relevance, the work should have used at least a panel of cell lines from one (or more) cancer entity. Ideally even data from patient derived samples would have been nice. Having said this, I also appreciate that the work is primarily in the field of cell biology and the cancer-centric work could be done by others. Certainly, the current work could inspire cancer specialists to explore the relevance of ERCYS. (ii) No physiological or pathological condition is shown where DNAJB12 is induced or depleted.

Answer: We previously showed that ERCYS is conserved in many different cell lines including A549, MCF7, GL-261, U87, HEK293T, MRC5 and others and is also conserved in murine models of GBM (GL-261 and U87 derived tumors) and human patients with GBM (Sicari et al. 2021). Here, we tested the reflux process and the IP experiments in many different cell lines including A549, MCF-7, PC3 and Trex-293 cells. We also added new fractionation experiment in DNAJB12 and DNAJB14 -depleted MCF-7, PC3 and A549 cells. We added all those data to the revised version.

We also added survival curves from the TCGA database showing that high copy number of DNAB12, DNAJB14 and SGTA are associated with poor prognosis compared to conditions where DNAJB12, DNAJB14, and SGTA are at low copy number (Figure S6E-M). Finally, we included immunofluorescent experiment to show that the interaction between the refluxed AGR2 and the cytosolic SGTA occurs in tumors collected from patients with colorectal cancer patients (Figure S5F-G) compared to non-cancerous tissue.

This study is highly significant and is relevant not only to cancer but for other pathways that may behave in similar manner. For instance, DNAJB12 and DNAJB14 are part of the mechanism that is used by non-envelope viruses to escape the ER to the cytosol. Thus, the role of those DNAJB proteins seems to be mainly in the reflux of functional (not misfolded) proteins from the ER to the cytosol. We reported earlier that the UDP-Glucose-Glucosyl Transferase 1 (UGGT1) is also expelled during ER stress. UGGT1 is important because it is redeploy to the cytosol during enterovirus A71 (EA71) infection to help viral RNA synthesis (Huang et al, 2017). This redeployment of EAA71 is similar to what happens during the reflux process because on one hand, UGGT1 exit the ER by an ER stress mediated process (Sicari et al. 2021) and it is also a functional in the cytosol as a proteins which help viral RNA synthesis ((Huang et al, 2017). All those data showing that there is more of DNAJB12, DNAJB14, DNAJC14, DNAJC30 and DNAJC18 that still needs to be explored in addition to what is published. Thus, we suggest that viruses hijacked this evolutionary conserved machinery and succeeded to use it in order to escape the ER to the cytosol in a manner that depends on all the component needed for ER protein reflux.

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

The authors present a study in which they ascribe a role for a complex containing DNAJB12/14-Hsc70-SGTA in facilitating reflux of a AGR2 from the ER to cytosol during ER-stress. This function is proposed to inhibit wt-P53 during ER-stress.

Concerns: 1. The way the manuscript is written gives the impression that this is the first study about mammalian homologs of yeast HLJ1, while there are instead multiple published papers on mammalian orthologs of HLJ1. Section 1 and Figure 1 of the results section is redundant with a collection of previously published manuscripts and reviews. The lack of proper citation and discussion of previous literature prevents the reader from evaluating the results presented here, compared to those in the literature.

Answer: We highly appreciate the reviewer’s comments. This paper is not to show that DNAJB12 and DNAJB14 are the orthologues of HLJ-1 but rather to show that DNAJB12 and DNAJB14 are part of a mechanism that we recently discovered and called ERCYS that cause proteins to be refluxed out of the ER. A mechanism that is regulated in by HLJ-1 in yeast. ERCYS is an adaptive and pro-survival mechanism that results in increased chemoresistance and survival in cancer cells. The papers that reviewer #2 refer to are the ones that report DNAJB12 can replace some of the ER-Associated Degradation (ERAD) functions of HLJ-1 in degradation of membranal proteins such as CFTR. These two mechanism are totally different and the role of the yeast HLJ-1 in degradation of CFTR is not needed for ERCYS. This is because we previously showed that the role of the yeast HLJ-1 and probably its orthologues in ERCYS is independent of their activity in ERAD(Igbaria et al. 2019). Surprisingly, the role of HLJ-1 in refluxing the ER proteins is not only independent of the reported ERAD-functions of HLJ1 and the mammalian DNAJBs but rather proceeds more rigorously when the ERAD is crippled (Igbaria et al. 2019). This role of DNAJBs is unique in cancer cells and is responsible in regulating the activity of p53 during the treatment of DNA damage agents.

In our current manuscript we show by similarity, functionality, and topological orientation, that DNAJB12 and DNJB14 may be part of a well conserved mechanism to reflux proteins from the ER to the cytosol. A mechanism that is independent of DNAJB12/14’s reported activity in ERAD(Grove et al. 2011; Yamamoto et al. 2010; Youker et al. 2004). In addition, DNAJB12 and DNAJB14 facilitate the escape of non-envelope viruses from the ER to the cytosol in similar way to the reflux process(Goodwin et al. 2011; Igbaria et al. 2019; Sicari et al. 2021). All those data show that HLJ-1 reported function may be only the beginning of our understanding on the role that those orthologues carry and that are different from what is known about their ERAD function.

Action: We added the references to the main text and discussed the differences between the reported DNAJB12 and HLJ-1 functions to the function of DNAJB12, DNAJB14 and the other DNAJ proteins in the reflux process. We also developed around this in the discussion.

The conditions used to study DNAJB12 and DNAJ14 function in AGR2 reflux from the ER do not appear to be of physiological relevance. As seen below they involve two transfections and treatment with two cytotoxic drugs over a period of 42 hours. The assay for ERCY is accumulation of lumenal ER proteins in a cytosolic fraction. Yet, there is no data or controls that describe the path taken by AGR2 from the ER to cytosol. It seems like pleotropic damage to the ER due the experimental conditions and accompanying cell death could account for the reported results?

Transfection of cells with siRNA for DNAJB12 or DNAJB14 with a subsequent 24-hour growth period.

Transfection of cells with a p53-lucifease reporter.

Treatment of cells with etoposide for 2-hours to inhibit DNA synthesis and induce p53. D. Treatment of cells for 16 hours with tunicamycin to inhibit addition of N-linked glycans to secretory proteins and cause ER-stress.

Subcellular fractionation to determine the localization of AGR2, DNAJB11, and HYOU1

KD of DNAJB12 or DNAJB14 have modest if any impact on AGR2 accumulation in the cytosol. There is an effect of the double KD of DNAJB12 or DNAJB14 on AGR2 accumulation in the cytosol. Yet there are no western blots showing AGR2 levels in the different cells, so it is possible that AGR2 is not synthesized in cells lacking DNAJB12 and DNAKB14. The lack of controls showing the impact of single and double KD or DNAJB12 and DNAJB14 on cell viability and ER-homeostasis make it difficult to interpret the result presented. How many control versus siRNA KD cells survive the protocol used in these assays?

Answer: Despite the long protocol we see differences between the control cells and the DNAJB-silenced cells in terms of the quantity of the refluxed proteins to the cytosol. The luciferase construct was used to assess the activity of p53 so the step of the second transfection was used only in experiments were we assayed the p53-luciferase activity. The rest of the experiments especially those where we tested the levels of p53 and P21 levels, were performed with one transfection. Moreover, all the experiments with the subcellular protein fractionation were performed after one transfection without the second transfection of the p53-Luciferase reporter. Finally, the protocol of the subcellular protein fractionation requires first to trypsinize the cells to lift them up from the plates, at the time of the experiment the cells were almost at 70-80% confluency and in the right morphology under the microscope.

Here, we performed XTT assay and Caspase-3 assay to asses cell death at the end of the experiment and before the fractionation assay. We did not observe any differences at this stage between the different cell lines (Figure-RV1 for reviewers Only). This can be explained by the fact that we use low concentrations of Tm and Tg for short time of 16 hour after the pulse of etoposide.

Finally, the claim that and ER-membrane damage result in a mix between the ER and cytosolic components is not true for the following reasons: (1) In case of mixing we would expect that GAPDH levels in the membrane fraction will be increased and that we do not see, and (2) we used our previously described transmembrane-eroGFP (TM-eroGFP) that harbors a transmembrane domain and is attached to the ER membrane facing the ER lumen. The TM-eroGFP was found to be oxidized in all conditions tested. Those data argue against a rupture of the ER membrane which can results in a mix of the highly reducing cytosolic environment with the highly oxidizing ER environment by the passage of the tripeptide GSH from the cytosol to the ER. All those data argue against (1) cell death, and (2) rupture of the ER membrane. Figure RV1 Reviewers Only.

Moreover, as it is shown in Figure S2, AGR2 is found in the membrane fraction in all the four different knock downs, thus it is synthesized in all of them. Moreover, we assayed the mRNA levels of AGR2 in all the knockdowns and we so that they are at the same levels in all the 4 different conditions and still AGR2 mRNA levels increase upon ER stress in all of the 4 knockdown cells in different backgrounds (Figure S2F-N).

In Figure 3 the authors overexpress WT-D12 and H139Q-D12 and examine induction of the p53-reporter. There are no western blots showing the expression levels of WT-D12 and H139Q-D12 relative to endogenous DNAJB12. HLJ1 stands for high-copy lethal DnaJ1 as overexpression of HLJ1 kills yeast. The authors present no controls showing that WT-D12 and H139-D12 are not expressed at toxic levels, so the data presented is difficult to evaluate.

Answer: The expression levels of the overexpression of DNAJB12 and DNAJB14 were present in the initial submission of the manuscript as Figure S3A and S3B. The data showing the relationship between the expression degree and the viability were also included in the initial submission as Figure S3C (Now S3H).

There is no mechanistic data used to help explain the putative role DNAJB12 and DNAJB14 in ERCY? In Figure 4, why does H139Q JB12 prevent accumulation of AGR2 in the cytosol? There are no westerns showing the level to which DNAJB12 and DNAJB14 are overexpressed.

Answer: The data showing the levels of DNAJB12 compared to the endogenous were present in the initial submission as Figure S3A and S3B.

We suggest a mechanism by which DNAJB12 and DNAJB14 interact (Figure 5 and Figure S5) and oligomerize to expel those proteins in similar way to expelling non-envelope viruses to the cytosol. Thus, when expressing the mutant DNAJB12 H139Q may indicate that the J-domain dead-mutant can still be part of the complex but affects the J-domain activity in this oligomer and thus inhibit ER-protein reflux. In other words, we showed that the H139Q exhibits a dominant negative effect when overexpressed. Moreover, here we added another IP experiment in the D12/D14-DKD cells to show that in the absence of DNAJB12 and DNAJB14, SGTA cannot bind the ER-lumenal proteins because they are not refluxed (Figure 5 and Figure S5). Those data indicate that in order for SGTA bind the refluxed proteins they have to go through the DNAJB12 and DNAJB14 and their absence this interaction does not occur. This explanation was also present in the discussion of the initial submission.

Mechanistically, we show that AGR2 interacts with DNAJB12/14 which are necessary for its reflux. This mechanism involves the functionality of cytosolic HSP70 chaperones and their cochaperones (SGTA) proteins that are recruited by DNAJB12 and 14. This mechanism is conserved from yeast to mammals. Moreover, by using the alpha-fold prediction tools, we found that AGR2 is predicted to interact with SGTA in the cytosol by the interaction between the cysteines of SGTA and AGR2 in a redox-dependent manner.

**Referees cross-commenting**

__ __ I appreciate the comments of the other reviewers. I agree that the authors could revise the manuscript. Yet, based on my concerns about the physiological significance of the process under study and lack of scholarship in the original draft, I would not agree to review a revised version of the paper.

Answer: Regards the physiological relevance, we showed in our previous study (Sicari et al. 2021) how relevant is ERCYS in human patients of GBM and murine model of GBM. ERCYS is conserved from yeast to human and is constitutively active in GL-261 GBM model, U87 GBM model and human patients with GBM (Sicari et al. 2021). Here, extended that to other tumors and showed that DNAJB12, DNAJB14 and SGTA high levels are associated with poor prognosis in many cancer types (Figure S6). We also show some data from to show the relevance and added data showing the interaction of SGTA with AGR2 in CRC samples obtained from human patients compared to healthy tissue (Figure S5). This study is highly significant and is relevant not only to cancer but for other pathways that may behave in similar manner. For instance, DNAJB12 and DNAJB14 are part of the mechanism that is used by non-envelope viruses to escape the ER to the cytosol. Thus, the role of those DNAJB proteins seems to be mainly in the reflux of functional (not misfolded) proteins from the ER to the cytosol. We reported earlier that the UDP-Glucose-Glucosyl Transferase 1 (UGGT1) is also expelled during ER stress. UGGT1 is important because it is redeploy to the cytosol during enterovirus A71 (EA71) infection to help viral RNA synthesis (Huang et al, 2017). This redeployment of EAA71 is similar to what happens during the reflux process because on one hand, UGGT1 exit the ER by an ER stress mediated process (Sicari et al. 2021) and it is also a functional in the cytosol as a proteins which help viral RNA synthesis ((Huang et al, 2017). All those data showing that there is more of DNAJB12, DNAJB14, DNAJC14, DNAJC30 and DNAJC18 that still needs to be explored in addition to what is published. We suggest that viruses hijacked this evolutionary conserved machinery and succeeded to use it in order to escape.

We appreciate the time spent to review our paper and we are sorry that the reviewer reached such verdict that is also not understood by the other reviewers. Most of the points raised by reviewer 2 were already addressed and explained in the initial submission, anyways we appreciate the time and the comments of reviewer #2 on our manuscript.

Reviewer #2 (Significance (Required)):

Overall, there are serious concerns about the writing of this paper as it gives the impression that it is the first study on higher eukaryotic and mammalian homologs of yeast HLJ1. The reader is not given the ability to compare the presented data to related published work. There are also serious concerns about the quality of the data presented and the physiological significance of the process under study. In its present form, this work does not appear suitable for publication.

Answer: Again we thank reviewer #2 for giving us the opportunity to explain how significant is this manuscript especially for people who are less expert in this field. The significance of this paper (1) showing a the unique role of DNAJB12 and DNAJB14 in the molecular mechanism of the reflux process in mammalian cells (not their role in ERAD), (2) showing the implication of other cytosolic chaperones in the process including HSC70 and SGTA (3), our alpha-fold prediction show that this process may be redox dependent that implicate the cysteines of SGTA in extracting the ER proteins, (4) overexpression of the WT DNAJB12 is sufficient to drive this process, (5) mutation in the HPD motif prevent the reflux process probably by preventing the binding to the cytosolic chaperones, and (6) we need both DNAJB12 and DNAJB14 in order to make the interaction between the refluxed ER-proteins and the cytosolic chaperones occur.

In Summary, this study is highly significant in terms of physiology, we previously reported that ERCYS is conserved in mammalian cells and is constitutively active in human and murine tumors (Sicari et al. 2021). Moreover, DNAJB12 and DNAJB14 are part of the mechanism that is used by non-envelope viruses to escape the ER to the cytosol in a mechanism that is similar to reflux process (Goodwin et al. 2011; Goodwin et al. 2014). Thus, the role of those DNAJB proteins seems to be mainly in the reflux of functional proteins from the ER to the cytosol, viruses used this evolutionary conserved machinery and succeeded to use in order to escape. This paper does not deal with the functional orthologues of the HLJ-1 in ERAD but rather suggesting a mechanism by which soluble proteins exit the ER to the cytosol.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)):____ __

Summary: Reflux of ER based proteins to the cytosol during ER stress inhibits wt-p53. This is a pro-survival mechanism during ER stress, but as ER stress is high in many cancers, it also promotes survival of cancer cells. Using A549 cells, Dabsan et al. demonstrate that this mechanism is conserved from yeast to mammalian cells, and identify DNAJB12 and DNAJB14 as putative mammalian orthologues of yeast HLJ1.

This paper shows that DNAJB12 and 14 are likely orthologues of HLJ1 based on their sequences, and their behaviour. The paper develops the pathway of ER-stress > protein reflux > cytosolic interactions > inhibition of p53. The authors demonstrate this nicely using knock downs of DNAJB12 and/or 14 that partially blocks protein reflux and p53 inhibition. Overexpression of WT DNAJB12, but not the J-domain inactive mutant, blocks etoposide-induced p53 activation (this is not replicated with DNAJB14) and ER-resident protein reflux. The authors then show that DNAJB12/14 interact with refluxed ER-resident proteins and cytosolic SGTA, which importantly, they show interacts with the ER-resident proteins AGR2, PRDX4 and DNAJB11. Finally, the authors show that inducing ER stress in cancer cell lines can increase proliferation (lost by etoposide treatment), and that this is partially dependent on DNAJB12/14.

This is a very interesting paper that describes a nice mechanism linking ER-stress to inhibition of p53 and thus survival in the face of ER-stress, which is a double edged sword regarding normal v cancerous cells. The data is normally good, but the conclusions drawn oversimplify the data that can be quite complex. The paper opens a lot of questions that the authors may want to develop in more detail (non-experimentally) to work on these areas in the future, or alternatively to develop experimentally and develop the observations further. There are only a few experimental comments that I make that I think should be done to publish this paper, to increase robustness of the work already here, the rest are optional for developing the paper further.

We thank the reviewer for his/her positive comments His/her comments contributed to make our manuscript stronger.

__Major comments:____ __

1. Number of experimental repeats must be mentioned in the figure legends. Figures and annotations need to be aligned properly

__Answer____: __All experiments were repeated at least 3 times. We added the number of repeats on each figure in the figures legends

Results section 2:

No intro to the proteins you've looked at for relocalization. Would be useful to have some info on why you chose AGR2. Apart from them being ER-localized, do they all share another common characteristic? Does ability to inhibit p53 vary in potency?

Answer: We previously showed that AGR2 is refluxed from the ER to the cytosol to bind and inhibit wt-p53 (Sicari et al. 2021). Here, we used AGR2 because, (1) we know that AGR2 is refluxed from the ER to the cytosol, and (2) we know which novel functions it gains in the cytosol so we are able to measure and provide a physiological significance of those novel functions when the levels of DNAJB12 and DNAJB14 are altered. Moreover, we used DNAJB11 (41 kDa) and HYOU1 (150 kDa) proteins to show that alteration in DNAJB12 or DNAJB14 prevent the reflux small, medium and large sized proteins. We added a sentence in the discussion stating that DNAJB12/14 are responsible for the reflux of ER-resident proteins independently of their size. We also added in the result section that we are looking at proteins of different sizes and activities.

What are the roles of DNAJB12/14 if overexpression can induce reflux? Does it allow increased binding of an already cytosolic protein, causing an overall increase in an interaction that then causes inhibition of p53? What are your suggested mechanisms?

Answer: Previously it was reported that over-expression of DNAJB12 and DNAJB14 tend to form membranous structures within cell nuclei, which was designate as DJANGOS for DNAJ-associated nuclear globular structures(Goodwin et al. 2014). Because those structures which contain both DNAJB12 and DNAJB14 also form on the ER membrane (Goodwin et al. 2014), we speculate that during stress DNAJB12/14 overexpression may facilitate ERCYS. Interestingly, those structures contain Hsc70 and markers of the ER lumen, the nuclear and ER and nuclear membranes (Goodwin et al. 2014).

The discussion was edited accordingly to further strengthen and clarify this point

Fig3: A+B show overexpression of individual DNAJs but not combined. As you go on to discuss the effect of the combination on AGR2 reflux, it would be useful to include this experimentally here.

Answer: This is a great idea, we tried to do it for long time. Unfortunately when we used cells overexpress DNAJB12 under the doxycycline promoter and transfect with DNAJB14 plasmid expressing DNAJB14 under the CMV promoter, most of the cells float within 24 hours compared to cells transfected with the empty vector alone or with DNAJB14-H136Q. We also did overexpression of DNAJB14 in cells with DNAJB12 conditional expression and also were lethal in Trex293T cells and A549-cells.

Fig 3C: Subfractionation of cells shows AGR2 in the cytosol of A549 cells. The quality of the data is good but the bands are very high on the blot. For publication is it possible to show this band more centralized so that we are sure that we are not missing bands cut off in the empty and H139Q lanes?

Also, you have some nice immunofluorescence in the 2021 EMBO reports paper, is it possible to show this by IF too? It is not essential for the story, but it would enrich the figure and support the biochemistry nicely. Also it is notable that the membrane fraction of the refluxed proteins doesn't appear to have a decrease in parallel (especially for AGR2). Is this because the % of the refluxed protein is very small? Is there a transcriptional increase of any of them (the treatments are 12+24 h so it would be enough time)? This could be a nice opportunity to discuss the amount of protein that is refluxed, whether this response is a huge emptying of the ER or more like a gentle release, and also the potency of the gain of function and effect on p53 vs the amount of protein refluxed. This latter part isn't essential but it would be a nice element to expand upon.

Answer: We re-blotted the AGR2 again, new blot of AGR2 was added. More blots also are added in Figure S2, the text is edited accordingly.

In new Figure S5 we added immunofluorescence experiment from tumors and non-tumors tissues obtained from Colorectal cancer (CRC) patients showing that the interaction between SGTA and the refluxed AGR2 also occurs in more physiological settings. It is also to emphasize that the suggested mechanism that implicates SGTA is also valid in CRC tumors.

We performed qPCR experiments in control, DNAJB12-KD, DNAJB14-KD and in the DNAJB12/DNAJB14 double knock down cells (in both A549 and PC3 cells) to follow the mRNA levels of DNAJB11. As shown in the Figure S2F-N, there is no increase in the mRNA levels of DNAJB11, AGR2 or HYOU1 in the different cells in normal (unstressed conditions). Upon ER stress with tunicamycin or thapsigargin there is a little increase in the mRNA levels of HYOU1 and AGR2 but in DNAJB11 mRNA levels. On the other hand, we also performed western blot analysis and we did not detect any difference between the different knockdown cells when we analyzed the levels of DNAJB11 compared to GAPDH. Those data are now added to Figure S2F-N. We must note that in AGR2 and HYOU1 are induced at the mRNA as a result of ER stress. The data with the overexpression of DNAJB12 and DNAJB14 are important control experiment where we show a reflux when DNAJB12 is overexpressed without inducing the ER stress (Figure 3, Figure 4, and Figure S3). In those conditions no induction of AGR2, HYOU1 or DNAJB11 were observed. Those results argue against the reflux as a result of protein induction and the increase in the proteins levels.

The overall protein levels in steady state are function of how much proteins are made, degraded and probably secreted outside the cell. We do see in Figure S2 under ER stress there are some differences in the levels of the mRNA, moreover, from our work in yeast we showed that the expelled proteins have very long half-life in the cytosol (Igbaria et al. 2019). Because it is difficult to assay how many of the mRNA is translated and how much of it is stable/degraded and the stability of the cytosolic fraction vs the ER, it is hard to interpret on the stability and the levels of the proteins.

Those data are now added to the manuscript, the text is edited accordingly.

You still mention DNAJB12 and 14 as orthologues, even though DNAJB14 has no effect on p53 activity when overexpressed. Do you think that this piece of data diminishes this statement?

Answer: The fact that DNAJB12 and DNAJB14 are highly homologous and that only the double knockdown has a great effect on the reflux process may indicate that they are redundant. Moreover, because only DNAJB12 is sufficient may indicate that some of DNAJB12 function cannot be carried by DNAJB14. In one hand they share common activities as shown in the double knock down and on the other hand DNAJB12 has a unique function that may not be compensated by DNAJB14 when overexpressed.

__ __ Fig 3D/F: Overexpression of DNAJB14 induces reflux of DNAJB11 at 24h, what does this suggest? Does this indicate having the same role as DNAJB12 but less potently? What's your hypothesis?

Answer: ERCYS is new and interesting phenomenon and the redistribution of proteins to the cytosol has been documented lately by many groups. Despite that we still do not know what is the specificity of DNAJB12 and DNAJB14 to the refluxed proteins. DNAJB11 is glycosylated protein and now we are testing whether other glycosylated proteins prefer the DNAJB14 pathway or not. This data is beyond the scope of this paper

"This suggests that the two proteins may have different functions when overexpressed, despite their overlapping and redundant functions" What does it suggest about their dependence on each other? If overexpression of WT DNAJB12 inhibits Tg induced reflux, is it also blocking the ability of DNAJB14 to permit flux?

Answer: We hypothesize that it is all about the stichometry and the ratios between proteins. When we overexpress DNAJB14 (the one that is not sufficient to cause reflux it may hijack common components and factor by non-specifically binding to them. Those factors may be needed for DNAJB12 to function properly (Like the dominant negative effect of the DNAJB12-HPD mutant for instance). On the other hand, DNAJB12 may have higher affinity for some cytosolic partner and thus can do the job when overexpressed. Here, we deal with the DNAJB12/DNAJB14 as essential components of the reflux process, yet we need to identify the interactome of each of the proteins during stress and the role of the other DNAJ proteins that also share some of the topological and structural similarity to DNAJB12, DNAJB14 and HLJ-1 (DNAJC30, DNAJC14, and DNAJC18). We edited the text accordingly and integrated this in the discussion.

__ __ Fig 4: PDI shown in blots but not commented on in text. Then included in the schematics. Please comment in the text.

Answer: We commented PDI in the text.

Fig 4F: Although the quantifications of the blots look fine, the blot shown does not convincingly demonstrate this data for AGR2. The other proteins look fine, but again it could be useful to see the individual means for each experiment, or the full gels for all replicates in a supplementary figure.

Answer: the other two repeats are in Figure S4

__ __Results section 3

Fig 5A, As there is obviously a difference between DNAJB12/14 it would be useful to do the pulldown with DNAJB14 too. Re. HSC70 binding to DNAJB12 and 14, the abstract states that DNAJB12/14 bind HSC70 and SGTA through their cytosolic J domains. Fig 5 shows pulldowns of DNAJB12 with an increased binding of SGTA in FLAG-DNAJB12 induced conditions, but the HSC70 band does not seem to be enriched in any of the conditions, including after DNAJB12 induction. This doesn't support the statement that DNAJB12 binds HSC70. In fact, in the absence of a good negative control, this would suggest that the HSC70 band seen is not specific. There is also no data to show that DNAJB14 binds HSC70. I recommend including a negative condition (ie beads only) and the data for DNAJB14 pulldown.

Answer: In Figure 5A we used the Flp-In T-REx-293 cells as it is easier to control and to tune up and down the expression levels of DNAJB12 and DNAJB14. According to new Figure S5A, DNAJB12 binds at the basal levels to HSC70 all the time. It was also surprising for us not to see the differences in the overexpression and we relate that to the fact that all the HSC70 are saturated with DNAJB12. In order to better assay that we repeated the IP in Figure 5A but instead of the IP with DNAJB12, we IP-ed with FLAG antibodies to selectively IP the transfected DNAJB12. As shown in the new Fig 5A, the increase of DNAJB12-FLAG is accompanied with an increase in the binding of HSC70.

We further tested the interaction between DNAJB12, DNAJB14 and HSC70 during ER stress in cancer cells. In those cells we found that DNAJB12 and DNAJB14 bind to HSC70 and they recruit SGTA upon stress. We also tested the binding between DNAJB12 and DNAJB14, in unstressed conditions, there was a basal binding between both, this interaction was stronger during ER stress. Those data are now added to Figure 5 and Figure S5 and the discussion was edited accordingly.

The binding of DNAJB12 to SGTA under stress conditions in Fig5B looks much more convincing than SGTA to DNAJB12 in Fig 5A. Bands in all blots need to be quantified from 3 independent experiments, and repeated if not already n=3. If this is solely a technical difference, please explain in the text.

The conclusions drawn from this interaction data are important and shold be elaborated upon to support th claims made in the paper. The authors may also chose to expand the pulldowns to demonstrate their claims made on olidomerisation of DNAJB12 and 14 here. It is also clear that the interaction data of the SGTA with ER-resident proteins AGR2, PRDX4 and DNAJB11 is strong. The authors may want to draw on this in their hypotheses of the mechanism. I would imagine a complex such as DNAJB14/DNAJB12 - SGTA - AGR2/PRDX4/DNAJB11 would be logical. Have any experiments been performed to prove if complexes like this would form?

Answer: In Figure 5A we used the Flp-In T-REx-293 cells as it is easier to control and to tune up and down the expression levels of DNAJB12 and DNAJB14. T-REx-293 are highly sensitive to ER stress, they do not die (as we did not observe apoptosis markers to be elevated) but they float and can regrow after the stress is gone. In Figure 5B we are using ER stress without the need to express DNAJB12 in A549 cell line. In order to further verify those data, we repeated the IP in another cell line as well to confirm the data in 5B. We also repeated the IP in 5A with anti-FLAG antibody to improve the IP and to specifically map he interaction with the overexpressed FLAG-DNAJB12 (discussed above). All experiments were done in triplicates and added to Figure 5 and Figure S5.

We agree with the reviewer on the complex between the refluxed proteins and SGTA. We believed that SGTA may form a complex with other refluxed ER-proteins but we were unable to see an interaction between AGR2-DNAJB11 in the cytosolic fraction or between AGR2-PRDX4 in the conditions tested in the cytosolic fraction. We could not do this in the whole cell lysate because those proteins bind each other in the ER. Finally, our structural prediction using Alpha-fold suggests that the interaction between SGTA and the refluxed AGR2 (and probably others) is redox depending and that it requires disulfide bridge between cysteine 81 on AGR2 and cysteine 153 on SGTA. Thus, we hypothesize that SGTA binds one refluxed protein at the time.

We repeated the figure with improvement: (1) using more cells in order to increase the amount of IP-ed proteins and to overcome the problem of the faint bands, (2) performing the IP with the FLAG antibodies instead of the DNAJB12 endogenous antibodies.

Fig 5B: It is clear that DNAJB12 interacts with SGTA. The authors state that DNAJB14 also interacts with SGTA under normal and stress conditions, but the band in 25/50 Tg is very feint. Why would there be stronger binding at the 2 extremes than during low stress induction? In the input, there is a much higher expression of DNAJB14 in 50 Tg. What does this say about the interaction? Is there an effect of ER stress on DNAJB14 expression? A negative control should be included to show any background binding, such as a "beads only" control

__Answer: __DNAJB14 does not change with ER stress as shown in the Ips (Input) and in the qPCR experiment in Figure S5I. We added beads only control, we also added new Ips to assess the binding between DNAJB14 and DNAJB12, and between DNAJB14-SGTA. All the new Ips and controls now added as Figure 5 and Figure S5.

Fig 5C data is sound, although a negative control should be included.

Answer: Negative control was added in Figure S5.

__Results section 4____ __

Fig 6A-B: Given that there is the complexity of overexpression v KD of DNAJB12 v 14 causing similar effects on p53 actvity (Fig 2 v 3), it would be interesting to see whether the effect of overexpression mirrors the results in Fig 6A. Is it known what SGTA overexpression does (optional)?

Answer: In the overexpression system, cells overexpressing DNAJB12 start to die between 24-48 hours as shown in Figure S3C. Thus, it is difficult to assay the proliferation of these cells in those conditions. On the other hand, overexpression of Myc-tagged SGTA in A549 cells, MCF7 or T-ReX293 did not show any reflux of ER-proteins to the cytosol and it didn’t show any significant changes in the proliferation index (Figure Reviewers only RV2).

Fig 6D: resolution very low

Answer: Figure 6D was changed

__ __ Fig 6C-D: There is an interesting difference though between the proposed cytosolic actions of the refluxed proteins. You show that AGR2, PRDX4 and DNAJB11 all bind to SGTA in stress conditions, but in the schematics you show: DNAJB11 binding to HSC70 through SGTA (not shown in the paper), then also PDIA1, PDIA3 binding to SGTA and AGR2 binding to SGTA. What role does SGTA have in these varied reactions? Sometimes it is depicted as an intermediate, sometimes a lone binder, what is its role as a binder? It should be clarified which interactions are demonstrated in the paper (or before) and which are hypothesized in a graphical way (eg. for hypotheses dotted outlines or no solid fill etc). The schematics also suggest that DNAJB14 binding to HSC70 and SGTA is inducible in stress conditions, as is PDIA3, which is not shown in the paper. Discussion "In cancer cells, DNAJB12 and DNAJB14 oligomerize and recruit cytosolic chaperones and cochaperones (HSC70 and SGTA) to reflux AGR2 and other ER-resident proteins and to inhibit wt-p53 and probably different proapoptotic signaling pathways (Figure 5, and Figure 6C-6D)." You havent shown oligomerisation between DNAJB12/14. Modify the text to make it clear that it is a hypothesis.

Answer: We removed “oligomerize” from the text and added that it as a hypothesis. Figure (C-D) also were changed to be compatible with the text.

Minor comments:

__ __ It would be useful to have page or line numbers to help with document navigation, please include them. Typos and inconsistency in how some proteins are named throughout the manuscript

Answer: Page numbers and line numbers are added. Typos are corrected

Title: Include reference to reflux. Suggest: "chaperone complexes (?proteins) reflux from the ER to cytosol..." I presume it would be more likely that the proteins go separately rather than in complex. Do you have any ideas on the size range of proteins that can undergo this process?

Answer: this is true, proteins may cross the ER membrane separately and then be in a complex with cytosolic chaperones. The title is changed accordingly. As discussed earlier, the protein we chose were of different sizes to show that they are refluxed independently of their size. Moreover, our previous work showed that the proteins that were refluxed are of different sizes. Most importantly UGGT1 (around 180 Kda) which is reported to deploy to the cytosol upon viral infection (Huang et al. 2017; Sicari et al. 2020). In this study we used AGR2 (around 19 Kda) and HYOU1 (150Kda).

ERCY in abstract, ERCYS in intro. There are typos throughout, could be a formatting problem, please check

Answer: Checked and corrected

What about the selection of refluxed proteins? Is this only a certain category of proteins? Could it be anything? Have you looked at other cargo / ER resident proteins?

__ ____Answer: __in our previous study by (Sicari, Pineau et al. 2020) we looked at many other proteins especially glycoproteins from the ER. In (Sicari, Pineau et al. 2020) we used mass spectrometry in order to identify new refluxed proteins and we found 26 new glycoprotein that are refluxed from cells treated with ER stressor and from human tissues obtained from GBM patients (Sicari, Pineau et al. 2020).

We previously showed that AGR2 is refluxed from the ER to the cytosol to bind and inhibit p53 (Sicari, Pineau et al. 2020). Here, we selected AGR2 because we know that (1) it is refluxed, and (2) we know which novel functions it acquires in the cytosol so we are able to measure and provide a physiological significance of those novel functions when the levels of DNAJB12 and DNAJB14 are altered. Moreover, we selected DNAJB11 (41 kDa) and HYOU1 (150 kDa) proteins to show that alteration in DNAJB12 or DNAJB14 prevent the reflux small, medium and large protein (independently of their size). We also showed earlier by mass spectrometry analysis that the refluxed proteins range from small to very large proteins such as UGGT1, thus we believe that soluble ER-proteins can be substrates of ERCYS independently of their size. In the discussion, we added a note that the reflux by the cytosolic and ER chaperones operates on different proteins independently of their size.

"Their role in ERCYS and cells' fate determination depends..." Suggest change to "Their role in ERCYS and determination of cell fate..."

Answer: changed and corrected

I think that the final sentence of the intro could be made stronger and more concise. There's a repeat of ER and cytosol. Instead could you comment on the reflux permitting new interactions between proteins otherwise spatially separated, then the effect on wt-p53 etc.

Answer: The sentence was rephrased as suggested to “ In this study, we found that HLJ1 is conserved through evolution and that mammalian cells have five putative functionality orthologs of the yeast HLJ1. Those five DNAJ- proteins (DNAJB12, DNAJB14, DNAJC14, DNAJC18, and DNAJC30) reside within the ER membrane with a J-domain facing the cytosol (Piette et al. 2021; Malinverni et al. 2023). Among those, we found that DNAJB12 and DNAJB14, which are strongly related to the yeast HLJ1 (Grove et al. 2011; Yamamoto et al. 2010), are essential and sufficient for determining cells' fate during ER stress by regulating ERCYS. Their role in ERCYS and determining cells' fate depends on their HPD motif in the J-domain. Downregulation of DNAJB12 and DNAJB14 increases cell toxicity and wt-p53 activity during etoposide treatment. Mechanistically, DNAJB12 and DNAJB14 interact and recruit cytosolic chaperones (HSC70/SGTA) to promote ERCYS. This later interaction is conserved in human tumors including colorectal cancer.

In summary, we propose a novel mechanism by which ER-soluble proteins are refluxed from the ER to the cytosol, permitting new inhibitory interactions between spatially separated proteins. This mechanism depends on cytosolic and ER chaperones and cochaperones, namely DNAJB12, DNAJB14, SGTA, and HSC70. As a result, the refluxed proteins gain new functions to inhibit the activity of wt-p53 in cancer cells. “

__Figure legends: __

In some cases the authors state the number of replicates, but this should be stated for all experiments. If experiments don't already include 3 independent repeats, this should be done. Check text for typos, correct letter capitalisation, spaces and random bold text (some of this could be from incompatability when saving as PDF)

Answer: all experiments were repeated at least three times. The number of repeats is now indicated in the figure legends of each experiment. Typos and capitalization is corrected as well.

Fig2E: scrambled not scramble siRNA

Answer: corrected

Fig 3: "to expel" is a term not used in the rest of the paper for reflux. Useful to remain consistent with terminology where possible

Answer: Rephrased and corrected

Results section 1:

"Protein alignment of the yeast HLJ1p showed high amino acids similarity to the mammalian..."

Answer: Rephrased to “ Comparing the amino acid sequences revealed significant similarity between the yeast protein HLJ1p and the mammalian proteins DNAJB12 and DNAJB14”

__ __ Fig 1C: state in legend which organism this is from (presumably human)

Answer: in Figure 1C legends it is stated that: “ the HPD motif within the J-domain is conserved in HLJ-1 and its putative human orthologs DNAJB12, DNAJB14, DNAJC14, DNAJC18, and DNAJC30.”

Results Section 2

"Test the two strongest hits DNAJB12/14" Add reference to previous paper showing this

Answer: the references were added.

__ __ "In the WT and J-protein-silenced A549 cells, there were no differences in the cytosolic enrichment of the three ER resident proteins AGR2, DNAJB11, and HYOU1 in normal and unstressed conditions (Figure 2A-C and Figure S2C)." I think that this is an oversimplification, and in your following discussion, you show this it's more subtle than this.

Answer: We expanded on this both in the discussion and the results section.

__ __ The text here isn't so clear: normal and unstressed conditions? Do you mean stressed? Please be careful in your phrases: "DNAJB12-silenced cells are slightly affected in AGR2 and DNAJB11 cytosolic accumulation but not HYOU1." This is the wrong way around. DNAJB12 silencing effects AGR2, not that AGR2 effects the cells (which is how you have written it). This also occurs agan in the next para:

Answer: Normal cells are non-cancer cells. Unstressed conditions= without ER stress. The sentence was rephrased to: In the absence of ER stress, the cytosolic levels of the three ER-resident proteins (AGR2, DNAJB11, and HYOU1) were similar in wild-type and J-protein-silenced A549 cells.

"During stress, DNAJB12/DNAJB14 double knockdown was highly affected in the cytosolic..." I think you mean it highly affected the cytosolic accumulation, not that it was affected by the cytosolic accumulation. Please change in the text

Answer: the sentence is now rephrased to” During stress, double knockdown of DNAJB12 and DNAJB14 highly affected the cytosolic accumulation of all three tested proteins”

__ __ "DNAJB12 and DNAJB14 are strong hits of the yeast HLJ1" Not clear, I presume you mean they are likely orthologues? Top candidates for being closest orthologues?

Answer: this is correct, the sentence is rephrased and corrected

__ __ Fig 2D: typos in WB labelling? I think Tm should be - - +, not - + +as it is now (if it's not a typo, you need more controls, eto alone.

Answer: the labeling is now corrected

Fig 2D-E-F typos for DKD? D12/D12 or D12/14?

Answer: This is correct, thank you for pointing this out. The labeling in corrected

__ __ "We assayed the phosphorylation state of wt- p53 and p21 protein expression levels (a downstream target of p53 signaling) during etoposide treatment." What are the results of this? Explain what Fig 2D-E shows, then build on this with the +Tm results. Results should be explained didactically to be clear.

Answer: The paragraph was edited and we explained the results: In these conditions, we saw an increase in the phosphorylation of wt-p53 in the control cells and in cells knocked-down with DNAJB12, DNAJB14 or both. This phosphorylation increased the protein levels of p21 as well (Figure 2D-G). Tm addition to cells treated with etoposide resulted in a reduction in wt-p53 phosphorylation, and as a consequence, the p21 protein levels were also decreased (Figure 2D-G and Figure S2O). Cells lacking DNAJB12 or DNAJB14 have partial protection in wt-p53 phosphorylation and p21 protein levels. Silencing both proteins in A549 and MCF7 cells rescued wt-p53 phosphorylation and p21 levels (Figure 2D-G and Figure S2D). Moreover, similar results were obtained when we assayed the transcriptional activity of wt-p53 in cells transfected with a luciferase reporter under the p53-DNA binding site (Figure 2H). These data confirm that DNAJB12 and DNAJB14 are involved in ER protein reflux and the inhibition of wt-p53 activity during ER stress.

"(Figure 2D- E). Cells lacking DNAJB12 and or DNAJB14 have partial protection in wt-p53 phosphorylation and p21 protein levels."

Answer: This sentence is now removed

You comment on p53 phosphorylation, but you haven't quantified this. This should be done, normalized to p53 levels, if you want to draw these conclusions, especially as total p53 varies between condition. Does Eto increase p53 txn? Does Tm alone increase p53 activity/phospho-p53? These are shown in the Sicari EMBO reports paper in 2021, you should briefly reference those.

Answer: The blots are now quantified and new blot is added to Figure S2D. The Paragraph was edited and referenced to our previous paper (Sicari et al. 2021). “We then wanted to examine whether the gain of function of AGR2 and the inhibition of wt-p53 depends on the activity of DNAJB12 and DNJAB14. We assayed the phosphorylation state of wt-p53 and p21 protein expression levels (a downstream target of wt-p53 signaling) during etoposide treatment. In these conditions, there was an increase in the phosphorylation of wt-p53 in the control cells and in cells knocked down with DNAJB12, DNAJB14, or both. This phosphorylation also increases protein levels of p21 (Figure 2D-G and Figure S2O). Tm addition to cells treated with etoposide resulted in a reduction in wt-p53 phosphorylation, and as a consequence, the p21 protein levels were also decreased (Figure 2D-G and Figure S2O). Silencing DNAJB12 and DNAJB14 in A549 and MCF-7 cells rescued wt-p53 phosphorylation and p21 levels (Figure 2D-G and Figure S2O). Moreover, similar results were obtained when we assayed the transcriptional activity of wt-p53 in cells transfected with a luciferase reporter under the p53-DNA binding site (Figure 2H). In the latter experiment, etoposide treatment increased the luciferase activity in all the cells tested. Adding ER stress to those cells decreased the luciferase activity except in cells silenced with DNAJB12 and DNAJB14.

These data confirm that DNAJB12 and DNAJB14 are involved in the reflux of ER proteins in general and AGR2 in particular. Inhibition of DNAJB12 and DNAJB14 prevented the inhibitory interaction between AGR2 and wt-p53 and thus rescued wt-p53 phosphorylation and its transcriptional activity as a consequence. “

Fig3A: overexpression of DNAJB12 decreases Eto induced p53 but not at steady state. Is this because at steady state the activity is already basal? Or is there another reason?

Answer: yes, at steady state the activity is already basal

Switch Figs S3D and S3C as they are not referred to in order. Also Fig S3C: vary colour (or add pattern) on bars more between conditions

Answer: The Figures now are called by their order in the new version. Colors are now added to Figure S3C.

Need to define HLJ1 at first mention

Answer: defined as” HLJ1 - High copy Lethal J-protein -an ER-resident tail-anchored HSP40 cochaperone.

Results section 3

HSC70 cochaperone (SGTA) defined twice

Answer: the second one was removed

"These data are important because SGTA and the ER-resident proteins (PRDX4, AGR2, and DNAJB11) are known to be expressed in different compartments, and the interaction occurs only when those ER-resident proteins localize to the cytosol." Is there a reference for this?

Answer: Peroxireoxin 4 is the only peroxerodin that is expressed in the ER. AGR2 and DNAJB11 are also ER luminal proteins that are known to be solely expressed in the ER. SGTA is part of the cytosolic quality control system and is expressed in the cytosol. The references are added in the main text.

Results section 4

"by almost two folds"

Answer: corrected

Fig 6A: It seems strange that the difference between purple and blue bars in scrambled, and D14-KD are very significant but D12-KD is only significant. Why is this? The error bars don't look that different. It would be interesting to see the individual means for each different replicate.

Answer: Thank you for pointing this, the two asterixis were aligned in the middle as one during figure alignments. In D14 the purple one has a lower error bar thus this changes the significance when compared to the blue while in D12-KD, the error bars in the eto treatment and the eto-Tm both are slightly higher. Graphs of the three different replicates are now added in Figure S6. Each one of the three biological replicates was repeated in three different technical repeats (averaged in the graphs).

Figures: Fig 6A: Scale bars not well placed. Annotation on final set should be D12/D14 DKD?

Answer: both were Corrected

__Discussion __47. The authors mention that they want to use DNAJB12/4-HSC70/SGTA axis to impair cancer cell fitness: What effect would this have though in a non cancer model? Would this be a viable approach Although it is obviously early days, which approach would the authors see as potentially favorable?

Answer: In our previous study we used an approach to target AGR2 in the cytosol because the reflux of AGR2 occurs only in cancer cells and not in normal cells. In that study we targeted AGR2 with scFv that targets AGR2 and is expressed in the cytosol, in this case it will target AGR2 in the cytosol which only occurs in cancer. Here, we suggest to target the interaction between the refluxed proteins and their new partners in the cytosol or to target the mechanism that causes their reflx to the cytosol by inhibiting for instance the interaction between SGTA and DNAJB proteins.

---

__ __ Second para: Should be "Here we present evidences"

Answer: we replaced with “Here we present evidences”

"DNAJB12 overexpression was also sufficient to promote ERCYS by refluxing AGR2 and inhibit wt-p53 signaling in cells treated with etoposide" Suggest:

Answer: DNAJB12 overexpression is also sufficient to promote ERCYS by refluxing AGR2 and inhibit wt-p53 signaling in cancer cells treated with etoposide (Figure 3). This suggests that it is enough to increase the levels of DNAJB12 without inducing the unfolded protein response in order to activate ERCYS. Moreover, the downregulation of DNAJB12 and DNAJB14 rescued the inhibition of wt-p53 during ER stress (Figure 2). Thus, wt-p53 inhibition is independent of the UPR activation but depends on the inhibitory interaction of AGR2 with wt-p53 in the cytosol.

.

DNAJB12 overexpression was also sufficient to promote ERCYS by increasing reflux of AGR2 and inhibition of wt-p53 signaling in cells treated with etoposide

Answer: This sentence is repeated twice and was removed

"Moreover, DNAJB12 was sufficient to promote this phenomenon and cause ER protein reflux by mass action without causing ER stress (Figure 3, Figure 4, and Figure S3)." You dont look at induction of ER stress here, please change the text or explain in more depth with refs if suitable

Answer: In the initial submission and in the revised version we assayed the activation of the UPR by looking at the levels of spliced Xbp1 and Bip in the different conditions when DNAJB12 and DNAJB14 are overexpressed (Figure S3C and S3D). Our data show that although DNAJB12 overexpression induces ERCYS, there was no UPR activation.

The mention of viruses is sparse in this paper. If it is a main theory, put it more centrally to the concept, and explain in more detail. As it is, its appearance in the final sentence is out of context.

Answer: DNAJB12 and DNAJB14 were reported to facilitate the escape of non-envelope viruses from the endoplasmic reticulum to the cytosol. The mechanism of non-envelope penetration is highly similar to the reflux of proteins from the ER to the cytosol. Interestingly, this mechanism takes place when the DNAJB12 and DNAJB14 form a complex with chaperones from both the ER and the cytosol including HSC70, SGTA and BiP (Walczak et al. 2014; Goodwin et al. 2011; Goodwin et al. 2014)..

Moreover, the UGGT1 that was independently found in our previous mass spectrometry analysis of the digitonin fraction obtained from HEK293T cells treated with the ER stressor thapsigargin and from isolated human GBM tumors (Sicari et al. 2020), is known to deploy to the cytosol upon viral infection (Huang et al. 2017; Sicari et al. 2020). We therefore hypothesized that the same machinary that is known to allow viruses to escape the ER to penetrate the cytosol may play an important role in the reflux of ER proteins to the cytosol.

Because ER protein reflux and the penetration of viruses from the ER to the cytosol behave similarly, we speculate that viruses hijacked an evolutionary conserved machinery -ER protein reflux- to penetrate to the cytosol. This is key because it was also reported that during the process of nonenveloped viruses penetration, large, intact and glycosylated viral particles are able to penetrate the ER membrane on their way to the cytosol (Inoue and Tsai 2011).

Action: we developed the discussion around this point and clarified it better because we believe it central to show that viruses hijacked this conserved mechanism.

**Referees cross-commenting**

I agree with the comments from Reviewer 1.

Reviewer 2 also is correct in many ways, but I think that they have somewhat overlooked the relevance of the ER-stress element and treatments. The authors do need to reference past papers more to give a full story, as this includes the groups own papers, I don't think that it is an ethical problem but rather an oversight in the writing. Regarding reviewer 2's concerns about overexpression levels and cell death, the authors do use an inducible cell line and show the levels of DNAJB12 induced (could CRISPR also be considered?). This could be used to further address reviewer 2's concerns. It would also be useful to see data on cell death in the conditions used in the paper. Re concerns about ER integrity, this could be addressed by using IF (or EM) to show a secondary ER marker that remains ER-localised, and this would also be of interest regarding my comment on which categories of proteins can undergo reflux. If everything is relocalised, then reviewer 2's point would be validated.

Reviewer #3 (Significance (Required)):

Significance

General assessment: This paper robustly shows that the yeast system of ER to cytosol reflux of ER-resident proteins is conserved in mammalian cells, and it describes clearly the link between ER stress, protein reflux and inhibition of p53 in mammalian cells. The authors have the tools to delve deeper into this mechanism and robustly explore this pathway, however the mechanistic elements - where not instantly clear from the results - have been over interpreted somewhat The results have been oversimplified in their explanations and some points and complexities of the study need to be addressed further to make the most of them - these are often some of the more interesting concepts of the paper, for example the differences in DNAJB12/14 and how the proteins orchestrate in the cytosol to play their cytosol-specific effects. I think that many points can be addressed in the text, by the authors being clear and concise with their reporting, while other experiments would turn this paper from an observational one, into a very interesting mechanistic one.

Advance: This paper is based on previous nice papers from the group. It is a nice progressions from yeast, to basic mechanism, to physiological model. But as mentioned, without a strong mechanistic improvement, the paper would remain observatory.

Audience: This paper is interesting to cell biologists (homeostasis, quality control and trafficking) as well as cancer cell biologists (fitness of cancer cells and homeostasis) and it is a very interesting demonstration of a process that is a double edged sword, depending on the environment of the cells.

My expertise: cell biology, trafficking, ER homeostasis

Answer: We would like to thank the reviewer for his/her positive feedback on our manuscript. All the comments of the three reviewers are now addressed and the manuscript has been strengthen. We put more emphasis on the mechanistic aspect with more Ips and knockdowns. We also added data to show that it is physiologically relevant. We hope that after that the revised version addressed all the concerns raised by the reviewers.

Goodwin, E. C., A. Lipovsky, T. Inoue, T. G. Magaldi, A. P. Edwards, K. E. Van Goor, A. W. Paton, J. C. Paton, W. J. Atwood, B. Tsai, and D. DiMaio. 2011. 'BiP and multiple DNAJ molecular chaperones in the endoplasmic reticulum are required for efficient simian virus 40 infection', MBio, 2: e00101-11.

Goodwin, E. C., N. Motamedi, A. Lipovsky, R. Fernandez-Busnadiego, and D. DiMaio. 2014. 'Expression of DNAJB12 or DNAJB14 causes coordinate invasion of the nucleus by membranes associated with a novel nuclear pore structure', PLoS One, 9: e94322.

Grove, D. E., C. Y. Fan, H. Y. Ren, and D. M. Cyr. 2011. 'The endoplasmic reticulum-associated Hsp40 DNAJB12 and Hsc70 cooperate to facilitate RMA1 E3-dependent degradation of nascent CFTRDeltaF508', Mol Biol Cell, 22: 301-14.

Huang, P. N., J. R. Jheng, J. J. Arnold, J. R. Wang, C. E. Cameron, and S. R. Shih. 2017. 'UGGT1 enhances enterovirus 71 pathogenicity by promoting viral RNA synthesis and viral replication', PLoS Pathog, 13: e1006375.

Igbaria, A., P. I. Merksamer, A. Trusina, F. Tilahun, J. R. Johnson, O. Brandman, N. J. Krogan, J. S. Weissman, and F. R. Papa. 2019. 'Chaperone-mediated reflux of secretory proteins to the cytosol during endoplasmic reticulum stress', Proc Natl Acad Sci U S A, 116: 11291-98.

Inoue, T., and B. Tsai. 2011. 'A large and intact viral particle penetrates the endoplasmic reticulum membrane to reach the cytosol', PLoS Pathog, 7: e1002037.

Malinverni, D., S. Zamuner, M. E. Rebeaud, A. Barducci, N. B. Nillegoda, and P. De Los Rios. 2023. 'Data-driven large-scale genomic analysis reveals an intricate phylogenetic and functional landscape in J-domain proteins', Proc Natl Acad Sci U S A, 120: e2218217120.

Piette, B. L., N. Alerasool, Z. Y. Lin, J. Lacoste, M. H. Y. Lam, W. W. Qian, S. Tran, B. Larsen, E. Campos, J. Peng, A. C. Gingras, and M. Taipale. 2021. 'Comprehensive interactome profiling of the human Hsp70 network highlights functional differentiation of J domains', Mol Cell, 81: 2549-65 e8.

Sicari, D., F. G. Centonze, R. Pineau, P. J. Le Reste, L. Negroni, S. Chat, M. A. Mohtar, D. Thomas, R. Gillet, T. Hupp, E. Chevet, and A. Igbaria. 2021. 'Reflux of Endoplasmic Reticulum proteins to the cytosol inactivates tumor suppressors', EMBO Rep: e51412.

Sicari, Daria, Raphael Pineau, Pierre-Jean Le Reste, Luc Negroni, Sophie Chat, Aiman Mohtar, Daniel Thomas, Reynald Gillet, Ted Hupp, Eric Chevet, and Aeid Igbaria. 2020. 'Reflux of Endoplasmic Reticulum proteins to the cytosol yields inactivation of tumor suppressors', bioRxiv.

Walczak, C. P., M. S. Ravindran, T. Inoue, and B. Tsai. 2014. 'A cytosolic chaperone complexes with dynamic membrane J-proteins and mobilizes a nonenveloped virus out of the endoplasmic reticulum', PLoS Pathog, 10: e1004007.

Yamamoto, Y. H., T. Kimura, S. Momohara, M. Takeuchi, T. Tani, Y. Kimata, H. Kadokura, and K. Kohno. 2010. 'A novel ER J-protein DNAJB12 accelerates ER-associated degradation of membrane proteins including CFTR', Cell Struct Funct, 35: 107-16.

Youker, R. T., P. Walsh, T. Beilharz, T. Lithgow, and J. L. Brodsky. 2004. 'Distinct roles for the Hsp40 and Hsp90 molecular chaperones during cystic fibrosis transmembrane conductance regulator degradation in yeast', Mol Biol Cell, 15: 4787-97.

In this highlighted passage, Shamhat persuades Enkidu to meet Gilgamesh by describing the grandeur of Uruk and the exceptional qualities of its king. She highlights Uruk's vibrant culture and Gilgamesh’s strength, wisdom, and divine favor, challenging Enkidu to see him as a worthy figure. This passage is pivotal as it marks Enkidu's transition from the wilderness to civilization, introduces Gilgamesh’s character, and foreshadows their destined bond, emphasizing the epic's themes of civilization versus wilderness.

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Manuscript number: RC-2023-02306

Corresponding author(s): John, Yates

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1. General Statements [optional]

We greatly appreciate the reviewers taking time from their busy scientific careers to evaluate our manuscript. We were elated to read all the positive comments, such as “the conclusions are well-supported and convincing”, “should contribute to a more nuanced understanding of SCZ pathogenesis”; “The potential implications for drug development underscore the broader significance of the study in advancing our knowledge of neurobiology and its relevance to neurological disorders like schizophrenia”, and “The study is informative, and has great potential to enrich the specific literature of this field”. We also found the constructive criticism very helpful for improving our manuscript. We performed additional experiments and bioinformatic analyses, as requested. We modified the manuscript to answer the reviewers’ questions. Due to its complexity, it is difficult to describe the different and sometimes conflicting hypotheses of SCZ pathogenesis in a single manuscript. This complexity is reflected in the conflicting requests from the reviewers. One reviewer requested we investigate and highlight the role of non-neuronal cells in SCZ while another reviewer suggested we did not focus enough on synaptic proteins. We believe we have achieved a balance to represent the intricacy of SCZ biology and the different opinions of the reviewers.

Thanks again.

2. 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)):

Summary: Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). In this manuscript, McClatchy and colleagues used a conventional approach combining immunoprecipitation (IP) of endogenous target proteins (baits) followed by liquid chromatography mass spectrometry (MS) analysis of the co-immunoprecipitating proteins to map protein-protein interaction (PPI). This interaction network is centered around baits that had been annotated as susceptibility factors for schizophrenia (SCZ). A variety of previous studies have identified thousands of such SCZ susceptibility factors. Mostly based on the availability of antibodies, 8 bait proteins were selected in this study. The authors reasoned that immunoprecipitating endogenous proteins from tissues using specific antibodies was a more accurate view of physiological conditions than epitope tagging followed by affinity purification (AP) from cells in culture. The model system from which proteins were extracted was the hippocampus dissected from mice that had been treated or not by phencyclidine (PCP), a drug that has been shown to induce SCZ symptoms in humans and animals. By comparing the proteins identified and quantified from the PCP-treated samples against control IPs and/or saline-injected mouse controls, a large number of PPI were deemed statistically significant. Most of these potential interactors were not present in PPI databases (BioGRID), most likely because such databases are populated with large-scale APMS datasets from cell cultures, with very few studies using brain tissue. Strikingly, many of the co-immunoprecipitated proteins were also known as SCZ susceptibility factors, which lend weight to the hypothesis that these factors form a large protein interaction network, localized at the synapses.

Major comments: - Are the key conclusions convincing? Overall, the conclusions drawn from the experimental design, data analysis, and corroboration with existing literature are well-supported and convincing. When selecting the SCZ susceptibility factors, the authors clearly state their goal, the databases used for gene selection, and the rationale for choosing proteins with synaptic localization. The inclusion of evidence from genetic studies and previous publications strengthens the credibility of the selected genes. The methodology used to establish the novel SCZ PPI network is mostly well-described (see minor comments below). The use of an 15N internal standard also adds rigor to the quantitation of PPI. The GO enrichment analysis provides valuable insights into the biological functions and cellular components associated with the SCZ PPI network. The annotation of identified proteins using the SynGo synaptic database and the distribution of annotated synaptic proteins among different baits further support the biological relevance of this PPI network. The cross-referencing of the PPI network with published genetic studies on SCZ susceptibility genes adds robustness to the findings. Specifically, the observation that 68% of protein interactors have evidence of being potential SCZ risk factors is a strong corroboration of the prevailing hypothesis in the field. Finally, the significant changes induced by PCP that were identified for all baits except Syt1, along with the comparison of altered proteins with SAINT-identified PPI, add depth to the understanding of PCP modulation.

- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No, but note that APMS/IPMS has been around for more than a decade (Introduction page 3).

We agree and did not mean to imply that IP-MS is new technology. We tried to convey that IP-MS is not new technology, but the number of IP-MS studies employed to study the PPI of endogenous proteins in brain tissue is a small percentage of all the published PPI MS studies.

We added the following to the Conclusions to clarify this point: “Although IP-LC-MS technology has been employed for more than a decade, quantitation of proteins using this strategy in mammalian tissue is scarce in the literature.””

- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. One piece of data that is missing are Western blots using the 8 selected antibodies against the proteins extracted from their experimental samples to validate the antibodies recognize 1 protein of the expected size from these tissue extracts.

We took your suggestion and performed immunoblots with our 8 IP antibodies using the starting material (i.e. rat brain hippocampus). All antibodies recognized a single band of the approximate molecular weight of the target except for the Gsk3b, which produced a doublet instead of a single band. This image is similar to what has been observed with the phosphorylation of Gsk3b(Krishnankutty, Kimura et al. 2017, Vainio, Taponen et al. 2021). To provide evidence that the additional band observed for Gsk3b is the phosphorylated target protein, we searched our Gsk3b IP dataset for a differential phosphorylation (i.e. 79.9663) on S,T, or Y. Even though we did not perform phosphorylation enrichment, we identified S389 as abundantly phosphorylated in all Sal and PCP samples consistent with our immunoblot. Images of these immunoblots are now Supplementary Figure 1.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. Running SDS-PAGE and Western blotting should be straightforward and cheap.

- Are the data and the methods presented in such a way that they can be reproduced? Yes

- Are the experiments adequately replicated and statistical analysis adequate? Yes

Minor comments: - Specific experimental issues that are easily addressable. The rationale for the short duration between PCP injection and animal sacrifice is only explained in the discussion section (page 17). The fact that this short treatment of less than 30 min should prevent any change in transcription or translation should be introduced earlier (in the experimental procedures).

We agree this is an important aspect of the study and that it suggests that the effect of PCP is independent of changes in transcription and translation as stated in the Discussion.

We added the following to the Introduction:

“PCP was administered for less than 30min., which precluded any changes in transcription or translation and allowed us to focus on PPI.*” *

Note that the duration is written as 26 min on page 4 and 25 min on page 9. Please reconcile these numbers*. *

We have corrected this typo. It was 26min.<br /> Is there any biological significance for this SCZ study that the mice were maintained on a reverse day-night cycle?

Rats are nocturnal animals, i.e. active at night and sleep during the day. In this study, rats were housed on a reverse day-night cycle so that assessment of the response to PCP could be evaluated during their active phase. This is not specific SCZ research and is the routine protocol for behavioral testing in the Powell laboratory. It is not clear from reading Experimental Procedures/Bioinformatic Analysis section (page 6) if normalized N14/N15 protein ratios measured in the bait-IPs and control-IPs were used for the SAINT analysis? Or did the authors used label-free quantitation with spectral counts?

We apologize for not making the methods clearer. In the results, it is stated that the N14 identifications are used in the SAINT analysis, and we state in the Discussion that SAINT uses spectral counts. We modified the Experimental Procedures/Bioinformatic Analysis section (page 6) to state: The input for SAINT was only the 14N identifications.

*- Are prior studies referenced appropriately? Yes

• Are the text and figures clear and accurate? *Fig1C: The workflow is a little too simple, the authors might want to add more details.

We revised Fig1C with more details as suggested.

FigS1C: Please add x-axis title (spectral counts) directly to the figure.

“Spectral counts” was added to the x-axis. FigS1C is now FigS2C ,with the addition of the immunoblots you suggested. Fig2B-D: The color scale bar should have number values to denote lower and upper limits in % (as opposed to "lowest" and "highest"). Numerical values were added to replace the upper and lower limits. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? No * *

Reviewer #1 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. In this study, the authors have drastically expanded the protein interaction landscape around 8 known SCZ susceptibility factors by using a conventional IPMS approach. Performing the IPs on protein extracted from hippocampus dissected from mice treated with phencyclidine to model SCZ increases the biological significance of such lists of proteins. Furthermore, the co-immunoprecipitation of many other SCZ susceptibility factors along with the 8 selected baits supports the hypothesis that these proteins of varied functions are part of large interaction networks. Overall, the integration of experimental data with in silico networks, along with the quantification of PPI changes in response to PCP, should contribute to a more nuanced understanding of SCZ pathogenesis. The potential implications for drug development underscore the broader significance of the study in advancing our knowledge of neurobiology and its relevance to neurological disorders like schizophrenia.

• Place the work in the context of the existing literature (provide references, where appropriate). Overall, this study contributes to the existing literature by providing experimental data on in vivo PPI networks related to SCZ risk factors. Not only do the authors validate 124 known interactions but also they identify many novel PPI, due to a gap in the existing literature regarding the comprehensive mapping of PPI directly from tissue extracts, especially brain tissue. The authors advocate for more IPMS studies in mammalian tissues to generate robust tissue-specific in silico networks, which agrees with the growing understanding of the importance of tissue-specific networks for identifying disease mechanisms and potential drug targets. Furthermore, the SCZ PPI network reported here is enriched in proteins previously associated with SCZ, which aligns with the existing literature emphasizing the involvement of certain proteins and pathways in the pathogenesis of SCZ [References: 78-85]. The authors also investigate the response of the SCZ network to PCP treatment, hence providing insights into the potential effects of post-translational modifications, protein trafficking, and PPI alterations in a model of schizophrenia, which adds to existing knowledge about the impact of PCP on the molecular processes associated with SCZ [References: 88, 89, 92].

• State what audience might be interested in and influenced by the reported findings. Overall, the findings reported in this manuscript have implications for both basic research in molecular biology and potential translational applications in the development of targeted therapies for neurological disorders, particularly schizophrenia. The study delves into in vivo protein-protein interaction (PPI) networks related to genes implicated in schizophrenia (SCZ) risk factors. Researchers in neuroscience, molecular biology, and psychiatry would find the information valuable for understanding the molecular basis of SCZ. The study highlights the potential for identifying disease "hubs" that could be drug targets. Pharmacologists and drug developers interested in targeting protein complexes for drug development, especially in the context of neurological disorders, may find the study relevant.

• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Technical Expertise | biochemistry, liquid chromatography mass spectrometry, proteomics, computational biology, protein engineering, protein interaction networks, post-translational modifications, protein crosslinking, proximity labeling, limited proteolysis, thermal shift assay, label-free and isotope-labeled quantitation. Biological Applications | human transcriptional complexes, apicomplexan parasites, viruses, nuclear envelope, ubiquitin ligases, non-model organisms.

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

Summary: McClatchy, Powell and Yates aimed at identifying a protein interactome associated to schizophrenia. For that, they treated rats (N14 and N15) with PCP, which disturbs gutamatergic transmission, as a model for the disease and co-immunoprecipitated hippocampi proteins, which were further analyzed by standard LC-MS.

The study is new, considering not much has been done in this direction in the field of schizophrenia. This justifies its publication. On the other hand, a major flaw of the is the lack of information on the level of interaction of the so called protein interactome. Meaning, we cannot distinguish, as the study was performed, which proteins are directly interacting with the targets of interest from proteins which are interacting with targets´ interactors. The different shells of interaction are crucial information in protein interactomics.

Major: most of I am pointing below must be at least discussed or better presented in the paper, as It may not be solvable considering how the study has been conducted.

1) The study fails in defining the level of interaction of the protein interactome with the considered targets. This has been shortly mentioned in the discussion, but must be more explicit to readers, for instance, in the abstract, introduction and in the methods sections. We agree this is crucial information that is absent from our dataset. As we explained in the Discussion, we cannot distinguish between PPI that are direct interactors with the target protein and PPI that reside in a multi-protein complex that includes the protein (i.e. indirect). This is an inherent problem with any IP-MS study. We amended the Introduction to highlight the ambiguity of the interaction data produced by the IP-MS approach, as you suggested.

Text added to the Introduction:

“Regardless of whether Ab or tagged proteins are employed to identify PPI from a biological sample, it cannot be determined if the identified interactor binds directly to the target protein or reside in a complex of proteins that includes the target protein (i.e. indirect).”

Since this important information is routinely missing from IP-MS studies, we decided to try to determine the level of interaction by using the artificial intelligence algorithm AlphaFold3(AF3). We believe it is not yet optimized for PPI, but AF3 is a big leap forward in the field of structural biology. For example, we observed AF3 did not predict high confident structures for our large membrane target proteins and was unable to validate known direct PPI of these targets. In addition, analyzing data with AF3 is currently not automated or streamlined so with ~1600 PPI identified in our dataset, we chose to look at one target protein, Ppp1ca. AF3 identified many known direct binding proteins in our Ppp1ca PPI dataset, which gives high confidence to the novel PPI predicted to be direct interactors. The AF3 data is encompassed in an additional Figure 6.

The following was added to the Results Section:

“A disadvantage of IP-MS studies is that it cannot distinguish between a PPI that binds directly to the target protein, and a PPI in which the interactor and target protein reside the same multiprotein complex (i.e. indirect). We sought to predict which PPI may be directly interacting with its target protein by using the artificial intelligence algorithm AlphaFold3(AF3) (Abramson, Adler et al. 2024). First, we analyzed the predicted AF3 structure of the targets using the pTM score and the fraction of each structure calculated to be disordered (Figure 6A and Supplementary Table7). Our reasoning was that if targets have a poorly resolved structures, it will be difficult to screen them for direct PPI. A pTM score >0.5 suggests that the structure may be correct (the highest confidence score is 1). Undefined or disordered regions hinder the accuracy of the prediction. All targets possessed a pTM score > 0.5 except Syt1. The disordered fraction negatively correlated with the pTM score, as expected. Gsk3b, Ppp1ca, and Map2k1 had the highest pTM scores and were also the smallest of our target proteins (Figure 6B). Ppp1ca had the most confident structure (i.e. pTM 0.9) and the smallest disordered fraction (i.e. 0.07). Next, we determined the AF3 prediction of previously reported direct interactions of the targets. We used the iPTM score to determine interaction confidence. An iPTM score >0.8 is considered a highly confident direct interaction, whereas 0.8. These eight PPI have all previously been reported to form a direct interaction with Ppp1ca, except Phactr3 (Zhang, Zhang et al. 1998, Terrak, Kerff et al. 2004, Hurley, Yang et al. 2007, Marsh, Dancheck et al. 2010, Ragusa, Dancheck et al. 2010, Ferrar, Chamousset et al. 2012, Choy, Srivastava et al. 2024, Xu, Sadleir et al. 2024)*. Phactr3 is structurally similar to, but less studied than, the reported direct interactor Phactr1. These interactors are all inhibitors of PP1 except Ppp1r9b which targets Ppp1ca to specific subcellular compartments. Nine PPI were assigned a score The following has been added to the Discussion:

“Our SCZ PPI network consists of two types of PPI: direct physical interactions and “co-complex” or indirect interactions. Typically, the nature of the interaction can be distinguished in IP-MS studies. We decided to employ the new AF3 algorithm to screen the PPI of Ppp1ca to provide evidence for direct interactors. We chose to examine the PPI assigned to Ppp1ca, because its structure was the most confident among our target proteins and AF3 correctly predicted a known direct interactor with high confidence. Ppp1ca is a catalytic subunit of the phosphatase PP1, which is required to associate with regulatory subunits to create holoenzymes (Li, Wilmanns et al. 2013). Eighteen PPI were predicted to be directly interacting with Ppp1ca using a 0.6 or higher iPTM filter. This filter may be too conservative and generate false negatives, because another study employed a 0.3 filter followed by additional interrogation to screen for direct PPI (Weeratunga, Gormal et al. 2024). Forty-four percent of these predictions were confirmed by previous publications. Most of the validated direct interactions are inhibitors of the phosphatase, but one, Ppp1r9b (aka spinophilin), is known to target Ppp1ca to dendrite spines to enhance its activity to specific substrates (Allen, Ouimet et al. 1997, Salek, Claeboe et al. 2023). This high correlation with the literature provides substantial confidence in the novel PPI predicted to be direct Ppp1ca interactors. The AF3 screen predicted that NDRG2 directly interacts with Ppp1ca. This protein is known to regulate many phosphorylation dependent signaling pathways by directly interacting with other phosphatases including Pp1ma and PP2A (Feng, Zhou et al. 2022, Lee, Lim et al. 2022). Actin binding protein Capza1 was also predicted to directly interact with Ppp1ca and Ppp1ca interacts with actin and its binding proteins to maintain optimal localization for efficient activity to specific substrates (Foley, Ward et al. 2023). Hsp1e is a heat shock protein predicted to directly interact with Ppp1ca. Although there is no direct connection to Ppp1ca, other heat shock proteins have been reported to regulate Ppp1ca (Mivechi, Trainor et al. 1993, Flores-Delgado, Liu et al. 2007, Qian, Vafiadaki et al. 2011). We also observed that many of these direct PPI were altered with PCP treatment. One direct interactor, Ppp1r1b (aka DARPP-32), is phosphorylated at Thr34 by PKA in the brain upon PCP treatment. This phosphorylation event converts Ppp1rb to a potent inhibitor of Ppp1ca(Svenningsson, Tzavara et al. 2003). Importantly, manipulation of Thr34 attenuated the behavioral effects of PCP. Consistent with this report, Ppp1r1b-Ppp1ca interaction was only observed with PCP in our study. Further investigation is needed to determine if our novel direct interactors regulate the PCP phenotype. We conclude that AF3 can provide important structural insights into the nature of PPI obtained from large scale IP-MS studies.”

2) Considering the protein extraction protocol, it is fair to mention that only the most soluble proteins are being considered here. I am bringing this up since the importance of membrane receptors is clear in the studied context. This is an interesting point. It has been predicted that transmembrane proteins constitute 25-30% of the proteome(Dobson, Remenyi et al. 2015). Thus, we would predict our dataset will have more soluble proteins than membrane proteins. Half of our target proteins were transmembrane proteins, so in designing the protocol for this study we ensured that these membrane proteins could be significantly enriched compared to the control IPs (Supplementary Figure 2C). In addition, compared to soluble proteins, membrane proteins are notoriously difficult to identify by bottom-up proteomics (Savas, Stein et al. 2011). We decided to investigate how many of our protein interactors were transmembrane proteins. Using Uniprot, 199 (20%) of our protein interactors were determined to have a transmembrane domain. Therefore, this data does not support the statement that only the most soluble proteins are being considered in our study. We added this percentage of transmembrane proteins in our network to the text of the Results section.

3) It is not clear from the methods description if antibodies from all 8 targets were all together in one Co-IP or have been incubated separately in 8 different hippocampi samples. It seems the first, given how results have been presented. If so, this maximizes the major issue raised above (in 1). We apologize for not clearly describing our experimental design. All the targets were immunoprecipitated separately and analyzed separately on the mass spectrometer. With all the biological replicates and two conditions (i.e. Saline and PCP), we performed 48 individual, separate IPs. There were an additional 48 individual, separate IPs run in parallel that were the control IPs.

We modified the schematic of our experimental design in Figure 1C to clarify that the 8 targets IPs were analyzed separately. In addition, we modified the Results to read:

“In total, 96 (48 bait and 48 control) IPs were performed, and each was analyzed separately by LC-MS analysis.”

4) Definitely, results here are not representing a "SCZ PPI network". PCP-treated animals, as any other animal model, are rather limited models to schizophrenia. As a complex multifactorial disease, synaptic deficits, which is the focus of this study, can no longer be considered "the pivot" of the disease. Synaptic dysfunction is only one among many other factors associated to schizophrenia.

We do agree that synaptic dysfunction is only one factor associated with SCZ and we will discuss this more in our response to your next comment.

We understand the limitations of PCP as an animal model of SCZ. It is quite difficult to model a specific human complex multifactorial neurological disease in rodents and we would contend that there is no single universal SCZ model that everyone agrees with. We addressed this by adding the following to the Introduction:

“Since many SCZ symptoms are uniquely human, this is no single animal model that truly replicates all the complex human SCZ phenotypes(Winship, Dursun et al. 2019). In this respect, all SCZ animal models can be considered limited.* “ *

We respectfully disagree, however, with the term SCZ PPI network. This study is focused on SCZ by choosing proteins implicated in SCZ, quantitating how the PPI changes in a SCZ model, and discussing how our findings are relevant to SCZ pathogenesis. So, it seems logical to call our dataset a SCZ PPI network. We do concede that without further experimentation we do not know if these PPI play a causal role in SCZ. Furthermore, our novel PPI may involve biological pathways unrelated to SCZ and that have relevance to other biological conditions.

We added the following statement to the Discussion to address this comment:

“Even though our network was constructed in the context of SCZ, our dataset has relevance to other neurological diseases where our targets have been implicated in the pathogenesis.”

5) Authors should look for protein interactions that might be happening also in glial cells. They are not the majority in hippocampus, but are present in the type of tissue analyzed here. Thus, some of the interactions observed might be more abundantly present in those cells. Maybe enriching using bioinformatics tools the PPI network to different cell types.

As mentioned above, we agree that synaptic dysfunction is just one of the hypotheses of SCZ pathogenesis and emerging evidence suggests that dysfunction in astrocytes and microglia are factors. Since these non-neuronal cells can regulate synapses, these hypotheses are not mutually exclusively and suggests that at the cellular level SCZ etiology involves multiple cell types.

We addressed your query by comparing our PPI network to an RNA-seq analysis of different cell types in the rodent brain(Zhang, Chen et al. 2014). First, we analyzed our target proteins, and found that they were expressed in all cell types to varying degrees except Syngap which was not in the RNA-seq database. This data is now represented in Figure 3E. We then determined the RNA abundance distribution of all the protein interactors, which is represented in Figure 3D as a heatmap. From a bird’s eye view, it suggests that some PPI exist in non-neuronal cells. Next, we determine how many of our protein interactors were enriched in one cell type, which is shown in Figure 3F. We defined an enriched protein as having >50% of the RNA signal in one cell type. We identified 175 proteins that were enriched in one cell type compared to the entire RNA-seq dataset which had 4008 enriched proteins. In the entire RNA-seq dataset, 24% of the enriched proteins were in neurons whereas 47% of our protein interactors were enriched in neurons. This is consistent with the enrichment of synaptic proteins in our network. There was also an increased percentage of astrocytes (19%) and oligodendrocytes (6%) in our network compared to the entire database (i.e. astrocytes-11% and oligodendrocytes-4%). In other cell types, such as microglia, there was less protein enrichment in our network compared to the database. We have amended this cell type analysis to our manuscript and concluded that a portion of our PPI network may occur in non-neuronal cells. We also created a supplementary table of our network with its associated RNA-seq data.

Text added to the Results:

“Non-synaptic proteins represented 59% of our network suggesting that some PPI may occur in non-neuronal cells. To investigate this possibility, we annotated our network with a transcriptome rodent brain database of eight cell types(Zhang, Chen et al. 2014). All the targets were detected in all cell types but there was obvious enrichment in specific cell types for some targets (Figure 3E). Syngap1 was not in the database. We also observed a large variation of cellular distributions for the interactors (Figure 3D). Next, we sought to determine how many interactors are enriched in a particular cell type by defining cell enrichment as a protein having >50% RNA signal in one cell type. We identified 175 protein interactors enriched in one cell type, whereas the entire database had 4008 proteins enriched (Figure 3F). Consistent with our synaptic enrichment, 47% of the enriched protein interactors were in neurons whereas only 24% of the enriched protein in the entire database were in neurons. We also observed an increase in protein interactors enriched in astrocytes compared to the database. Overall, this analysis provides evidence that our identified PPI may occur in non-neuronal cells.”

Text added to the Discussion:

“The exact etiology of SCZ, however, remains unclear and synaptic dysfunction is only one hypothesis (Misir and Akay 2023). There is evidence for the involvement of non-neuronal cell types, including endothelial cells, astrocytes, and microglia(Tarasov, Svistunov et al. 2019, Rodrigues-Neves, Ambrosio et al. 2022, Stanca, Rossetti et al. 2024). Although we observed an enrichment of synaptic proteins in our SCZ network, we provided evidence that a portion of our network may occur in non-neuronal cells. Since non-neuronal cells can regulate synapses(Vilalta and Brown 2018, Bauminger and Gaisler-Salomon 2022), synaptic dysfunction and perturbations in non-neuron cells in SCZ etiology are not mutually exclusive. Our data corresponds with emerging evidence that pathogenesis is multifaceted, involving dysfunction in multiple cell types. “

Minor: 1) in the abstract, it is not clear if 90% of the PPI are novel to brain tissue in general or specifically schizophrenia. We apologize for the confusing sentence. 90% are novel meaning the PPI have not been reported in any study. We changed the abstract to read:

“Over 90% of the PPI have not been previously reported.”

2) authors refer to LC-MS-based proteomics as "MS" all across the text. Who am I to say this to Yates et al, but I think it is rather simplified use "Mass Spectrometry Analysis", when this is a typical LC-MS type of analysis We agree with you. We have replaced MS analysis with LC-MS analysis in the manuscript.

3) Several references used to construct the hypothesis of the paper are rather outdated: several from 10-15 years ago. It would be interesting to provide to the reader up to date references, given the rapid pace science has been progressing. We agree many of the references are 10-15 years old. Many of the hypotheses and biological mechanisms we discussed can be supported by too many studies to cite them all, due to space. If we could, we would. We also agree that there are many more recent studies that have confirmed and added more details to the original discovery or hypothesis cited. We cite the first study to support our conclusions because it deserves the most credit.

4) "UniProt rat database". Please, state the version and if reviewed or unreviewed.

This information was added to the Methods section. UniProt reviewed rat database with isoforms 03-25-2014.

Reviewer #2 (Significance (Required)):

The study is informative, and has great potential to enrich the specific literature of this field. But should tone down some arguments, given the experimental limitations of the PPI network (as described above) and should state PCP-treated rats as a limited model to schizophrenia.

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

Summary

It is now widely accepted that schizophrenia is polygenic disorder in which a large fraction of the genetic risk is in variants affecting the expression of synaptic proteins. Moreover, it is known that these synaptic proteins are found in multiprotein complexes and that many proteins encoded by schizophrenia risk genes interact directly or indirectly in these complexes. It is also known that some drugs including phencyclidine, which binds to NMDA receptors and to Dopamine D2 receptors (not mentioned by the authors) can induce schizophreniform psychosis. The authors have set out to advance on this position by performing proteomic mass spectrometry studies on proteins identified as encoded by schizophrenia risk genes. They target 8 proteins for immunoprecipitation from rat brain and identify coisolated proteins and perform various network analyses. In the most interesting part of the paper they ask if PCP-treatment altered protein interactions and report various changes.

Major comments:

1. Choice of target proteins. It was not until the first paragraph of the results section that the authors first name the 8 synaptic proteins that have chosen to study. This information should be in the abstract.

This information was added to the abstract as requested.

The authors then use figure 1A and 1B as evidence that these 8 "baits" are schizophrenia-relevant proteins. Figure 1A does not provide any evidence at all and Figure 1B is about as weak a line of evidence imaginable - a histogram of the number of papers that have the search term "schizophrenia" and the protein name. I tried this search for Grin2B and almost immediately found papers that reported no association between Grin2B and schizophrenia (e.g. PMID: 33237434). Figure 1B should be scrapped.

The purpose of Figure 1A was not to demonstrate that there is evidence that our proteins are involved in SCZ. The purpose of this figure is to show that these proteins are diverse in function and structure (blue = membrane proteins; yellow = soluble proteins), and that there are published studies reporting physical and functional interactions between these 8 proteins. This suggests that a more extensive network may exist.

We agree that Figure 1B does not specifically describe how each protein is related to SCZ but demonstrates how many papers investigating their connection to SCZ have been published. We understand how by itself, this can be considered weak. We still think it is important to show that multiple laboratories have published papers connecting these proteins to SCZ. Instead of scrapping this figure, we have moved it to the Supplementary Figure 2A.

We read PMID: 33237434 and interpret their findings quite differently than you. This report examined whether one single nucleotide mutation (SNV) in Grin2b is associated with the cognitive dysfunction in SCZ but did not examine if this mutation is associated with the other major SCZ phenotypes (i.e. psychotic and emotional). Specifically, the study selected 117 “patients in whom cognitive dysfunctions are present despite effective antipsychotic treatment of other schizophrenia symptoms.” The study concluded that Grin2B SNV was not associated with this subset of patients but concluded that they need to search for other NMDAR variants and study their association with SCZ. We would argue that the only reason this group performed these experiments was the well-known association between Grin2b and SCZ. Many studies have found SNVs in Grin2B that are associated with SCZ, but there are conflicting reports. It is unclear if the discrepancies are connected to different cohorts, complexity of SCZ phenotype, or small sample sizes. Regardless of Grin2B mutations significantly associated with SCZ, there are several lines of evidence that Grin2B is involved in SCZ. Most importantly, Grin2b is a component of the NMDAR, which is a key player to the SCZ hypo-glutamate hypothesis and the receptor that binds PCP. By immunoprecipitating Grin2b, we are analyzing the PPI network of NMDAR, which is arguably the most studied complex in SCZ research.

The remaining part of paragraph 1 of the results does not provide an adequate, let alone systematic, justification for the use of the 8 baits. It would be appropriate to construct a table with the 8 proteins and cite relevant papers and identify the basis for why they are implicated in schizophrenia (is it a direct mutation or some other evidence?). What makes these 8 proteins better than many others that are cited as synaptic schizophrenia relevant proteins?

We apologize for not clearly and thoroughly describing the reasons for choosing our baits. As stated in the first paragraph of the Results, we chose the proteins that had evidence of being a SCZ risk factor in SCZ databases that included a plethora of human genomic studies. This criterion by itself results in ~5000 genes. To further narrow our candidates, we chose targets that were synaptic and were observed to have phosphorylation changes in response to PCP in an SCZ animal model. Since protein-protein interactions (PPI) are often dependent on phosphorylation, we believe this is an important criterion for quantitation of PPI in response to PCP. These requirements still resulted in a list of hundreds of proteins. So, what makes these better than any other SCZ relevant protein? As stated in the manuscript, the major limiting criterion was identifying commercial antibodies that can efficiently immunoprecipitate their target in brain tissue. Since there are many reports associating our targets with SCZ, we directed the reader to SCZ databases that compile large genomic association studies. We understand, however, the request for more specific information regarding the biological connection between these proteins and SCZ. We took your suggestion and constructed a table with our 8 targets, and it is now Figure 1A. In this table, we selected references to indicate if the target has reported changes in expression and/or activity in SCZ samples (i.e. human and animal model) or genetic association with SCZ in human studies.

The methods of protein extraction are particularly concerning. The postsynaptic density of excitatory synapses (which contains several of the target proteins in this study) has been notoriously difficult to solubilise unless one uses high pH (9) and harsh detergent extraction (1% deoxycholate). The authors use pH 7 and weak detergent conditions, which are likely to be inefficient for solubilising at least several of the target proteins. Nowhere do the authors report how much of the total of their target protein is being solubilised. Indeed, there are no figures showing biochemical conditions at all. What if only a small percentage of the target protein is being immunoprecipitated - what does this mean for the interaction data? How do we know if the fraction being immunoprecipitated is from the synapse? (why did they not use synaptosomes).

How do we know if the fraction being immunoprecipitated is from the synapse? (why did they not use synaptosomes). The absence of this kind of data undermines the reader's confidence in the findings.

We apologize for not clearly explaining our experimental design We were not interested in identifying the PPI of the PSD. All these proteins have been localized to the synapse, but they are also localized to other neuronal compartments and non-neuronal cell types. Synaptic dysfunction is one hypothesis of SCZ pathogenesis, but there is evidence of other cell types, including astrocytes, microglia, and oligodendrocytes(Kerns, Vong et al. 2010, Ma, Abazyan et al. 2013, Goudriaan, de Leeuw et al. 2014, Park, Noh et al. 2020). For these reasons, we chose an unbiased approach to identifying PPI.

The Results have been amended to read: “All the targets are localized to the synapse, but also localized to non-synaptic compartments and expressed in non-neuronal cells. Thus, since there is also evidence for non-synaptic perturbations contributing to SCZ pathogenesis, we chose to perform an unbiased analysis in unfractionated brain tissue (Tarasov, Svistunov et al. 2019, Rodrigues-Neves, Ambrosio et al. 2022, Stanca, Rossetti et al. 2024). “

Why do we choose a specific solubilization strategy? Harsh detergents can disrupt PPI and prevent efficient enrichment of the target by disrupting the target-antibody interaction(Pankow, Bamberger et al. 2015). To identify protein interactions, mild detergent conditions are typically employed in PPI studies. We used a combination of “weak” detergents (i.e. 0.5% NP-40, 0.5% Triton, and 0.01% Deoxycholate) to help prevent non-specific PPI, but still allowing efficient enrichment of the target proteins. We do agree that with our conditions the targets were not completely solubilized. It is a balancing act to find the correct conditions for IP-MS analysis. Since we are unable to immunoprecipitate all the target protein, we did not identify all the PPI for each target, and we did not make this claim. Importantly, we did identify known interactions for all our targets. Our mild detergent protocol is similar to other PPI studies and our results validates results reported in previous studies. It is more important to significantly enrich the target protein over control than to achieve complete solubilization (Supplementary Figure 2D). This allows us to use control IPs to successfully employ the SAINT algorithm to determine which proteins are confident PPI using a 5% FDR.

How do we know protein are being immunoprecipitated from the synapse? As we show in Figures 2B and 3A, multiple proteins are annotated to the synapse with different databases, Gene ontology and SynGO. Well-known synaptic PPI were also observed, such as Grin2B-Dlg4(i.e. PSD-95), providing further evidence for proteins being immunoprecipitated for the synapses. Besides validating over a hundred published PPI interactions, we also identified many reciprocal interactions between the target datasets demonstrating the reproducibility of our protocol. Thus, we respectfully disagree with you and assert that our PPI network is very confident.

The immunoprecipitation protocol is unusual in that the homogenates were incubated overnight (twice), which is a very long period compared to most published protocols. This is a concern because spurious protein interactions could form during this long incubation.

There are many different immunoprecipitation protocols in the literature. The IP conditions depend upon the target protein and the antibody employed. Specifically, the abundance of the target and the affinity of the antibody to the target will dictate the IP conditions. We routinely perform overnight incubation for our IP-MS studies(Pankow, Bamberger et al. 2016, McClatchy, Yu et al. 2018). In our experience with brain tissue, this results in the highest enrichment of the target protein and the best reproducibility between biological replicates compared to IP protocols with shorter incubation times. Many other laboratories use overnight incubations(Lin and Lai 2017, Iqbal, Akins et al. 2018, Lagundzin, Krieger et al. 2022), so we do not consider our protocol unusual. We do find that IPs with tagged proteins in cell culture are more amenable to short incubation times. We have no evidence that overnight incubation causes spurious protein interactions nor could find any in the literature. Non-specific interactions are a concern with IP-MS experiments regardless of the incubation time. We took multiple steps to reduce the non-specific PPI from affecting our dataset. The first overnight incubation was incubating the brain lysate with agarose beads linked to IgGs to preclear the lysate from “sticky” non-specific interactors binding to IgGs and the beads. In addition, control IPs with IgG crosslinked to beads were incubated with brain lysate in parallel to each target IP. We computationally compared the non-specific control IPs with the target IPs using the SAINT algorithm to generate a confident list of PPI with a stringent 5% FDR. Therefore, our pipeline is specifically designed to prevent spurious PPI.

In the section "Biological interpretation of scz PPI network". Surprisingly the authors found that synaptic proteins that are exclusively postsynaptic (Grin2B, SynGAP) or exclusively presynaptic (Syt1) show very high percentages of their interacting proteins are from the synaptic compartments where the target protein is not expressed. The authors offer no explanation for this paradox. One explanation for this could be that spurious PPIs have formed in the protein extraction/immunoprecipitation protocol. These findings need validation by biochemical fractionation of synapses into pre and post synaptic fractions and immunohistochemistry to demonstrate the subsynaptic localisation of the proteins. Grin2b is traditionally described as exclusively post-synaptic, but there is evidence for other localizations, including presynaptic(Berretta and Jones 1996, Sjostrom, Turrigiano et al. 2003, Bouvier, Larsen et al. 2018) and expression in astrocytes(Serrano, Robitaille et al. 2008, Lee, Ting et al. 2010, Lalo, Koh et al. 2021, Kim, Choi et al. 2024). Syngap has been localized to non-synaptic sites and glia expression in addition to its heavily studied role at the post synapse(Moon, Sakagami et al. 2008, Araki, Zeng et al. 2015, Birtele, Del Dosso et al. 2023). Syt1 is commonly used as a presynaptic marker, but along with other proteins previously reported to be exclusively presynaptic (such as SNAP-25), it has been localized to the postsynapse (Selak, Paternain et al. 2009, Tomasoni, Repetto et al. 2013, Hussain, Egbenya et al. 2017, Madrigal, Portales et al. 2019, Sumi and Harada 2023). Similarly, SynGo database assigns both post-synaptic and pre-synaptic localizations to Grin2b as stated in the manuscript. Thus, our data is not paradoxical, but supports the emerging evidence against the canonical exclusivity of the pre- and post-synaptic compartments. Determining subsynaptic localization of a protein is a huge undertaking and requires expertise we do not possess. This is why we relied on synaptic databases and the literature for our interpretation of our data, as other publications have done.

We added the following to the Discussion to address this issue:

“Using the SynGo database, 418 proteins (i.e. 41% of our network) were identified as synaptic proteins consistent with the targets having a synaptic localization. Defining the synaptic proteome is inherently difficult because the synapse is an “open organelle”, and many synaptic proteins also have non-synaptic localizations and are expressed in non-neuronal cells. We further attempted to define our synaptic PPI by differentiating between pre- and post- synaptic compartments via SynGo. Half of our targets were annotated to both compartments and all targets had PPI that were annotated to both. This data supports the emerging evidence against the canonical localization exclusivity of the pre and post synapse(Bouvier, Larsen et al. 2018, Madrigal, Portales et al. 2019).”

My concerns about spurious interactions are raised again because the authors say that 92% of their interactions are novel (I note that they authors have not compared their interaction data of the NMDA receptor with published datasets from Dr Seth Grant's laboratory). BioGrid itself is good but not enough for comparison, maybe at this point it worth taking String, which accumulates several sources of PPIs, just select the direct PPIs.

Since the MS-IP experiments in our study have never been performed before, we are not surprised by the extent of novel data we produced. As described above, we took many steps to prevent spurious PPI from entering our final dataset, including the use of detergents, preclearing and stringent bioinformatic filtering. Our entire dataset is very large, so the 8% of PPI that we replicated from other studies represents 124 interactions. We believe this to be an impressive number which correlates to the confidence of our data. Providing more confidence, we identified many reciprocal PPI where shared protein interactors between target proteins were identified in both target protein datasets.

    The PPI described for our targets in BioGrid encompassed 713 publications.  Two of the BioGrid datasets that were compared to our Grin2b PPI data were from the laboratory of Seth Grant.  Arbuckle et al (2010) is a low-throughout paper that describes a Grin2b and DLG4 PPI (that we also identified) and Husi et al (__2000__) is a seminal paper using high-throughput LC-MS to identify PPI in the PSD of mouse brain.  There were many differences between Husi et al and our pipeline.  Husi et al employed the C-terminal Grin2b peptide to pull down interactors from the PSD fraction whereas we employed Grin2b antibody to enrich Grin2b and its interactors from unfractionated brain tissue.  Despite these differences, our studies found 8 proteins in common.

We took your suggestion and compared our data to String which includes direct PPI and functional PPI. Our input was the high confidence PPI identified by SAINT with 5% FDR as with the BioGrid comparison. The PPI network for each target protein had a more significant enrichment (p We think the problem you suggest with SynGO is more of an inherent problem with characterizing the synaptic proteome. The synaptic proteome is difficult to define since it is an “open organelle” with proteins transporting in and out. In addition, most synaptic proteins, such as mitochondrial and translational proteins, also have non-synaptic localizations. It is not possible to isolate a contaminant-free “pure” synaptic preparation by biochemical fractionation. Recently, SynGO was used in a meta-analysis of previously published PSD datasets(Kaizuka, Hirouchi et al. 2024). Kaizuka et al. found 123 proteins identified in 20 PSD datasets. SynGo annotated proteins with post-synaptic localization from this list. To a lesser extent they also identified presynaptic localizations, but it is unclear if the presynaptic proteins are novel localizations. Kaizuka et al. continued the investigation and identified a novel PSD protein, thus demonstrating that our knowledge of pre- and post- synaptic proteomes is incomplete.

Minor comments

1. A number of papers have reported protein interactions of native NMDA receptor complexes and their associated proteins isolated from rodent brain and are neither referenced in this paper. It would be relevant to compare these published datasets with the Grin2B IP datasets.

We employed BioGrid as a reference of reported PPI for each of our target proteins. For Grin2B, the PPI came from 142 different publications. For eight target proteins, we decided *BioGrid * was the best resource for determining the novelty of our PPI because it is routinely used for large-scale unbiased PPI analysis. To determine the novelty of our network, we compared our PPI network to 713 publications via BioGrid. We are unsure whether the papers you are referring to are included in the BioGrid database. To make it easier for readers with similar queries, we added an additional supplementary table (TableS4) including all the publications (i.e. PMID numbers) included in BioGrid comparison for each target protein.

We amended the Results with the following sentence, so the readers realized the extensiveness of the Biogrid comparison analysis:

“There were 713 publications in BioGrid that describe at least one interaction with one of our targets (Supplementary Table4).”

The use of the term "bait" in purification experiments typically refers to a protein and not an antibody. I suggest removing the word bait to avoid ambiguity and simply use the word target. We took your suggestion and used “target” instead of “bait” to avoid ambiguity.

26 mins of treatment gives completely different set of PPIs between PCP and saline which is very interesting, so both networks should be included in Supplementary. Also, it would be useful to have a list of modulated (phosphorylated in their case, but also ubiquitinated etc) proteins, which is not presented. Table S1 lists the PPI for each target, and we designated whether the interactors were for Sal, PCP, or both. Phosphorylated and ubiquitinated proteins are very hard to reproducibly identify without an additional enrichment step. Since we did not perform this enrichment step, we did not search for these modifications and do not have any modified proteins to report.

As they say their final network is composed of "direct physical and "co-complex" interactors and they cannot distinguish between them. This is particularly bad for the postsynapse, where all the PSD components can be co-IP-ed in different combinations. It can explain the Figure 5C, where most of the proteins have FDR = 1, which means they do not reproduce. Figure 5C represents the intersection of 15N quantification and SAINT analysis. The x-axis is the FDR reported for SAINT analysis, and the y-axis is the significant proteins from the N15 analysis. This figure demonstrates that some proteins that were significantly different with PCP via N15 quantification also were annotated as PPI by SAINT (i.e. 5%. As stated in the Discussion, we concluded that the SAINT analysis and N15 quantitation are complementary in identifying PPI and that the quantification of a biological perturbation may aid the identification of PPI. Figure 5C is not related to whether our PPI are direct physical or "co-complex" interactors. Distinguishing between direct physical and co-complex interactors is an inherent problem for all IP studies. Since another reviewer also highlighted this deficit in our manuscript, we decided to analyze our PPI dataset with the artificial intelligence algorithm AlphaFold 3(AF3). The AF3 data is encompassed in Figure 6.

The following AF3 data was added to the Results Section:

“A disadvantage of IP-MS studies is that it cannot distinguish between a PPI that binds directly to the target protein, and a PPI in which the interactor and target protein reside in the same multiprotein complex (i.e. indirect). We sought to predict which PPI may be directly interacting with its target protein by using the artificial intelligence algorithm AlphaFold3(AF3) (Abramson, Adler et al. 2024). First, we analyzed the predicted AF3 structure of the targets using the pTM score, and determined the fraction of each structure that was calculated to be disordered (Figure 6A and Supplementary Table7). Our reasoning was that if our targets have a poorly resolved structures then it will be difficult to screen for direct PPI. A pTM score >0.5 suggests that the structure may be correct, with the highest confidence equaling 1. Undefined or disordered regions hinder the accuracy of the prediction, and all our targets possessed a pTM score > 0.5 except Syt1. The fraction of disordered negatively correlated with the pTM score, as expected. Gsk3b, Ppp1ca, and Map2k1 were the target proteins with the highest pTM scores and were also the smallest of our targets (Figure 6B). Ppp1ca had the most confident structure (i.e. pTM 0.9) and the least fraction disordered (i.e. 0.07). Next, we determined the AF3 prediction of previously reported direct interactions of the targets. We used the iPTM score to determine an interaction confidence. An iPTM score >0.8 is a highly confident direct interaction, whereas 0.8. These eight PPI have all previously been reported to form a direct interaction with Ppp1ca, except Phactr3 (Zhang, Zhang et al. 1998, Terrak, Kerff et al. 2004, Hurley, Yang et al. 2007, Marsh, Dancheck et al. 2010, Ragusa, Dancheck et al. 2010, Ferrar, Chamousset et al. 2012, Choy, Srivastava et al. 2024, Xu, Sadleir et al. 2024)*. Phactr3 is structurally similar to, but less studied than, the reported direct interactor, Phactr1. These interactors are all inhibitors of PP1 except for Ppp1r9b which targets Ppp1ca to specific subcellular compartments. Nine PPI were assigned a score The following AF3 interpretation was added to the Discussion:

“Our SCZ PPI network consists of two types of PPI: direct physical interactions and “co-complex” or indirect interactions. Typically, the nature of the interaction cannot be distinguished in IP-MS studies. We decided to employ the new AF3 algorithm to screen the PPI of Ppp1ca to provide evidence for direct interactors. We chose to examine the PPI assigned to Ppp1ca, because its structure was the most confident among our target proteins and AF3 correctly predicted a known direct interactor with high confidence. Ppp1ca is a catalytic subunit of the phosphatase PP1, which is required to associate with regulatory subunits to create holoenzymes (Li, Wilmanns et al. 2013). Eighteen PPI were predicted to be directly interacting with Ppp1ca using a 0.6 or higher iPTM filter. This filter may be too conservative and may generate false negatives, because another study employed a 0.3 filter followed by additional interrogation to screen for direct PPI (Weeratunga, Gormal et al. 2024). Forty-four percent of these predictions were confirmed by previous publications. Most of these validated direct interactions are inhibitors of the phosphatase, but one, Ppp1r9b (aka spinophilin), is known to target Ppp1ca to dendritic spines (Allen, Ouimet et al. 1997, Salek, Claeboe et al. 2023). This high correlation with the literature provides substantial confidence to the novel PPI predicted to be direct Ppp1ca interactors. The AF3 screen predicted that NDRG2 directly interacts with Ppp1ca. This protein is known to regulate many phosphorylation dependent signaling pathways by directly interacting with other phosphatases including Pp1ma and PP2A (Feng, Zhou et al. 2022, Lee, Lim et al. 2022). Actin binding protein Capza1 was also predicted to directly interact with Ppp1ca and Ppp1ca interacts with actin and its binding proteins to maintain optimal localization for efficient activity to specific substrates (Foley, Ward et al. 2023). Hsp1e is a heat shock protein predicted to directly interact with Ppp1ca. Although there is no direct connection to Ppp1ca, other heat shock proteins have been reported to regulate Ppp1ca (Mivechi, Trainor et al. 1993, Flores-Delgado, Liu et al. 2007, Qian, Vafiadaki et al. 2011). We also observed that many of the direct PPI were altered with PCP treatment. One direct interactor, Ppp1r1b (aka DARPP-32), is phosphorylated at Thr34 by PKA in the brain upon PCP treatment. This phosphorylation event converts Ppp1rb to a potent inhibitor of Ppp1ca(Svenningsson, Tzavara et al. 2003). Importantly, the manipulation of Thr34 attenuated the behavioral effects of PCP. Consistent with this report, Ppp1r1b-Ppp1ca interaction was only observed with PCP in our study. Further investigation is needed to determine if our novel direct interactors regulate the PCP phenotype. We conclude that AF3 can provide important structural insights into the nature of PPI obtained from large scale IP-MS studies.”

The way PPI data is reported can be improved so that I does not have to be extracted from Table 1 and 2. It would be good if they provide just two columns PPI list, with names or IDs, plus PSP/saline/both conditions in third column, for ease of comparison with other sources and building the graph. They can add it as another spreadsheet to Table 2. We generated this table (TableS2) as you requested.

Is Figure 2 built for Sal or PCP conditions? as they have only 23% interactions in common (Figure 4A) the Figure 2 should be pretty different for two conditions. Are the 1007 interactors combined from SAL and PCP?

Figure 2 contains ALL the unique PPI for each target regardless of Sal or PCP conditions. The 1007 protein interactors shown in Figure 2Awhere Sal and PCP were combined to generate a non-redundant list of proteins for each target.

We amended the Results to make this clearer:

“When the PCP and SAL datasets were combined, there were 1007 unique proteins.”

This sentence was added to Figure 2A:

“For this comparison, Sal and PCP PPI were combined into a unique PPI list for each target.”

Figure 1F is mentioned but no figure is shown. We apologize for this oversight, and we have corrected the manuscript. 8. Overall the paper could be edited and made more concise, especially the introduction and discussion. We extensively edited the manuscript to be more concise.

Reviewer #3 (Significance (Required)):

General assessment

Proteomic mass spectrometry of immunoprecipitated complexes from synapses has been extensively studied since Husi et al (2000) first study of NMDA receptor and AMPA receptor complexes. Since then, a wide variety of methods have been employed to purify synaptic protein complexes including peptide affinity, tandem-affinity purification of endogenous proteins tagged with FLAG and Histine-affinity tags amongst other methods. Purification of protein complexes and the postsynaptic density from the postsynaptic terminal of mammalian excitatory synapses have been crucial for establishing that schizophrenia is a polygenic disorder affecting synapses (e.g. Fernandez et al, 2009; Kirov et al, 2012; Purcell et al, 2014, Fromer et al, 2014 etc). Network analyses of the postsynaptic proteome have described networks of schizophrenia interacting proteins (e.g. Pocklington et al, 2006; Fernandez et al, 2009) and other neuropsychiatric disorders.

Hundreds of synaptic protein complexes have been identified (Frank et al, 2016), but very few have been characterised using proteomic mass spectrometry. This paper has chosen 8 protein targets for such analysis and identified many proteins that a putative interactors of the target protein. At this level the current manuscript does not represent a conceptual advance and the value of the data lies in its utility as a resource that may be used in future studies.

The findings from the 8 target proteins from normal adult rat brain were used for a secondary study that describes the effects that PCP has on the interaction networks. Interestingly, this work shows that 26 minutes of drug treatment leads to considerable changes in the interactomes of the target proteins. These descriptive data could be used in future studies to understand the cell biological mechanisms that mediate these rapid changes in the proteome. PCP and drugs that interact with NMDA receptors are known to induce changes in synaptic proteome phosphorylation including modifications in protein-protein interaction sites, which may explain the PCP effects.

The study would benefit from validation of experimental protocols for solubilisation and immunoprecipitation and validation of described interactions using orthogonal biochemical or localisation experiments.

Audience Specialists in synapse proteins and mechanisms of schizophrenia.

Expertise

The reviewers' expertise is in molecular biology of synapses including synapse proteomics, protein interaction and network analysis, and genetics of schizophrenia and other brain disorders.

Abramson, J., J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A. J. Ballard, J. Bambrick, S. W. Bodenstein, D. A. Evans, C. C. Hung, M. O'Neill, D. Reiman, K. Tunyasuvunakool, Z. Wu, A. Zemgulyte, E. Arvaniti, C. Beattie, O. Bertolli, A. Bridgland, A. Cherepanov, M. Congreve, A. I. Cowen-Rivers, A. Cowie, M. Figurnov, F. B. Fuchs, H. Gladman, R. Jain, Y. A. Khan, C. M. R. Low, K. Perlin, A. Potapenko, P. Savy, S. Singh, A. Stecula, A. Thillaisundaram, C. Tong, S. Yakneen, E. D. Zhong, M. Zielinski, A. Zidek, V. Bapst, P. Kohli, M. Jaderberg, D. Hassabis and J. M. Jumper (2024). "Accurate structure prediction of biomolecular interactions with AlphaFold 3." Nature 630(8016): 493-500.

Allen, P. B., C. C. Ouimet and P. Greengard (1997). "Spinophilin, a novel protein phosphatase 1 binding protein localized to dendritic spines." Proc Natl Acad Sci U S A 94(18): 9956-9961.

Anschuetz, A., K. Schwab, C. R. Harrington, C. M. Wischik and G. Riedel (2024). "A Meta-Analysis on Presynaptic Changes in Alzheimer's Disease." J Alzheimers Dis 97(1): 145-162.

Araki, Y., M. Zeng, M. Zhang and R. L. Huganir (2015). "Rapid dispersion of SynGAP from synaptic spines triggers AMPA receptor insertion and spine enlargement during LTP." Neuron 85(1): 173-189.

Bauminger, H. and I. Gaisler-Salomon (2022). "Beyond NMDA Receptors: Homeostasis at the Glutamate Tripartite Synapse and Its Contributions to Cognitive Dysfunction in Schizophrenia." Int J Mol Sci 23(15).

Berretta, N. and R. S. Jones (1996). "Tonic facilitation of glutamate release by presynaptic N-methyl-D-aspartate autoreceptors in the entorhinal cortex." Neuroscience 75(2): 339-344.

Birtele, M., A. Del Dosso, T. Xu, T. Nguyen, B. Wilkinson, N. Hosseini, S. Nguyen, J. P. Urenda, G. Knight, C. Rojas, I. Flores, A. Atamian, R. Moore, R. Sharma, P. Pirrotte, R. S. Ashton, E. J. Huang, G. Rumbaugh, M. P. Coba and G. Quadrato (2023). "Non-synaptic function of the autism spectrum disorder-associated gene SYNGAP1 in cortical neurogenesis." Nat Neurosci 26(12): 2090-2103.

Bouvier, G., R. S. Larsen, A. Rodriguez-Moreno, O. Paulsen and P. J. Sjostrom (2018). "Towards resolving the presynaptic NMDA receptor debate." Curr Opin Neurobiol 51: 1-7.

Choy, M. S., G. Srivastava, L. C. Robinson, K. Tatchell, R. Page and W. Peti (2024). "The SDS22:PP1:I3 complex: SDS22 binding to PP1 loosens the active site metal to prime metal exchange." J Biol Chem 300(1): 105515.

Dobson, L., I. Remenyi and G. E. Tusnady (2015). "The human transmembrane proteome." Biol Direct 10: 31.

Feng, D., J. Zhou, H. Liu, X. Wu, F. Li, J. Zhao, Y. Zhang, L. Wang, M. Chao, Q. Wang, H. Qin, S. Ge, Q. Liu, J. Zhang and Y. Qu (2022). "Astrocytic NDRG2-PPM1A interaction exacerbates blood-brain barrier disruption after subarachnoid hemorrhage." Sci Adv 8(39): eabq2423.

Ferrar, T., D. Chamousset, V. De Wever, M. Nimick, J. Andersen, L. Trinkle-Mulcahy and G. B. Moorhead (2012). "Taperin (c9orf75), a mutated gene in nonsyndromic deafness, encodes a vertebrate specific, nuclear localized protein phosphatase one alpha (PP1alpha) docking protein." Biol Open 1(2): 128-139.

Flores-Delgado, G., C. W. Liu, R. Sposto and N. Berndt (2007). "A limited screen for protein interactions reveals new roles for protein phosphatase 1 in cell cycle control and apoptosis." J Proteome Res 6(3): 1165-1175.

Foley, K., N. Ward, H. Hou, A. Mayer, C. McKee and H. Xia (2023). "Regulation of PP1 interaction with I-2, neurabin, and F-actin." Mol Cell Neurosci 124: 103796.

Goudriaan, A., C. de Leeuw, S. Ripke, C. M. Hultman, P. Sklar, P. F. Sullivan, A. B. Smit, D. Posthuma and M. H. Verheijen (2014). "Specific glial functions contribute to schizophrenia susceptibility." Schizophr Bull 40(4): 925-935.

Hemmings, H. C., Jr., P. Greengard, H. Y. Tung and P. Cohen (1984). "DARPP-32, a dopamine-regulated neuronal phosphoprotein, is a potent inhibitor of protein phosphatase-1." Nature 310(5977): 503-505.

Hurley, T. D., J. Yang, L. Zhang, K. D. Goodwin, Q. Zou, M. Cortese, A. K. Dunker and A. A. DePaoli-Roach (2007). "Structural basis for regulation of protein phosphatase 1 by inhibitor-2." J Biol Chem 282(39): 28874-28883.

Hussain, S., D. L. Egbenya, Y. C. Lai, Z. J. Dosa, J. B. Sorensen, A. E. Anderson and S. Davanger (2017). "The calcium sensor synaptotagmin 1 is expressed and regulated in hippocampal postsynaptic spines." Hippocampus 27(11): 1168-1177.

Iqbal, H., D. R. Akins and M. R. Kenedy (2018). "Co-immunoprecipitation for Identifying Protein-Protein Interactions in Borrelia burgdorferi." Methods Mol Biol 1690: 47-55.

Kaizuka, T., T. Hirouchi, T. Saneyoshi, T. Shirafuji, M. O. Collins, S. G. N. Grant, Y. Hayashi and T. Takumi (2024). "FAM81A is a postsynaptic protein that regulates the condensation of postsynaptic proteins via liquid-liquid phase separation." PLoS Biol 22(3): e3002006.

Kaizuka, T., T. Suzuki, N. Kishi, K. Tamada, M. W. Kilimann, T. Ueyama, M. Watanabe, T. Shimogori, H. Okano, N. Dohmae and T. Takumi (2024). "Remodeling of the postsynaptic proteome in male mice and marmosets during synapse development." Nat Commun 15(1): 2496.

Kerns, D., G. S. Vong, K. Barley, S. Dracheva, P. Katsel, P. Casaccia, V. Haroutunian and W. Byne (2010). "Gene expression abnormalities and oligodendrocyte deficits in the internal capsule in schizophrenia." Schizophr Res 120(1-3): 150-158.

Kim, H., S. Choi, E. Lee, W. Koh and C. J. Lee (2024). "Tonic NMDAR Currents in the Brain: Regulation and Cognitive Functions." Biol Psychiatry.

Koopmans, F., P. van Nierop, M. Andres-Alonso, A. Byrnes, T. Cijsouw, M. P. Coba, L. N. Cornelisse, R. J. Farrell, H. L. Goldschmidt, D. P. Howrigan, N. K. Hussain, C. Imig, A. P. H. de Jong, H. Jung, M. Kohansalnodehi, B. Kramarz, N. Lipstein, R. C. Lovering, H. MacGillavry, V. Mariano, H. Mi, M. Ninov, D. Osumi-Sutherland, R. Pielot, K. H. Smalla, H. Tang, K. Tashman, R. F. G. Toonen, C. Verpelli, R. Reig-Viader, K. Watanabe, J. van Weering, T. Achsel, G. Ashrafi, N. Asi, T. C. Brown, P. De Camilli, M. Feuermann, R. E. Foulger, P. Gaudet, A. Joglekar, A. Kanellopoulos, R. Malenka, R. A. Nicoll, C. Pulido, J. de Juan-Sanz, M. Sheng, T. C. Sudhof, H. U. Tilgner, C. Bagni, A. Bayes, T. Biederer, N. Brose, J. J. E. Chua, D. C. Dieterich, E. D. Gundelfinger, C. Hoogenraad, R. L. Huganir, R. Jahn, P. S. Kaeser, E. Kim, M. R. Kreutz, P. S. McPherson, B. M. Neale, V. O'Connor, D. Posthuma, T. A. Ryan, C. Sala, G. Feng, S. E. Hyman, P. D. Thomas, A. B. Smit and M. Verhage (2019). "SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse." Neuron 103(2): 217-234 e214.

Krishnankutty, A., T. Kimura, T. Saito, K. Aoyagi, A. Asada, S. I. Takahashi, K. Ando, M. Ohara-Imaizumi, K. Ishiguro and S. I. Hisanaga (2017). "In vivo regulation of glycogen synthase kinase 3beta activity in neurons and brains." Sci Rep 7(1): 8602.

Lagundzin, D., K. L. Krieger, H. C. Law and N. T. Woods (2022). "An optimized co-immunoprecipitation protocol for the analysis of endogenous protein-protein interactions in cell lines using mass spectrometry." STAR Protoc 3(1): 101234.

Lalo, U., W. Koh, C. J. Lee and Y. Pankratov (2021). "The tripartite glutamatergic synapse." Neuropharmacology 199: 108758.

Lee, B. H., F. Schwager, P. Meraldi and M. Gotta (2018). "p37/UBXN2B regulates spindle orientation by limiting cortical NuMA recruitment via PP1/Repo-Man." J Cell Biol 217(2): 483-493.

Lee, K. W., S. Lim and K. D. Kim (2022). "The Function of N-Myc Downstream-Regulated Gene 2 (NDRG2) as a Negative Regulator in Tumor Cell Metastasis." Int J Mol Sci 23(16).

Lee, M. C., K. K. Ting, S. Adams, B. J. Brew, R. Chung and G. J. Guillemin (2010). "Characterisation of the expression of NMDA receptors in human astrocytes." PLoS One 5(11): e14123.

Li, X., M. Wilmanns, J. Thornton and M. Kohn (2013). "Elucidating human phosphatase-substrate networks." Sci Signal 6(275): rs10.

Lin, J. S. and E. M. Lai (2017). "Protein-Protein Interactions: Co-Immunoprecipitation." Methods Mol Biol 1615: 211-219.

Ma, T. M., S. Abazyan, B. Abazyan, J. Nomura, C. Yang, S. Seshadri, A. Sawa, S. H. Snyder and M. V. Pletnikov (2013). "Pathogenic disruption of DISC1-serine racemase binding elicits schizophrenia-like behavior via D-serine depletion." Mol Psychiatry 18(5): 557-567.

Madrigal, M. P., A. Portales, M. P. SanJuan and S. Jurado (2019). "Postsynaptic SNARE Proteins: Role in Synaptic Transmission and Plasticity." Neuroscience 420: 12-21.

Marsh, J. A., B. Dancheck, M. J. Ragusa, M. Allaire, J. D. Forman-Kay and W. Peti (2010). "Structural diversity in free and bound states of intrinsically disordered protein phosphatase 1 regulators." Structure 18(9): 1094-1103.

McClatchy, D. B., N. K. Yu, S. Martinez-Bartolome, R. Patel, A. R. Pelletier, M. Lavalle-Adam, S. B. Powell, M. Roberto and J. R. Yates (2018). "Structural Analysis of Hippocampal Kinase Signal Transduction." ACS Chem Neurosci 9(12): 3072-3085.

Misir, E. and G. G. Akay (2023). "Synaptic dysfunction in schizophrenia." Synapse 77(5): e22276.

Mivechi, N. F., L. D. Trainor and G. M. Hahn (1993). "Purified mammalian HSP-70 KDA activates phosphoprotein phosphatases in vitro." Biochem Biophys Res Commun 192(2): 954-963.

Moon, I. S., H. Sakagami, J. Nakayama and T. Suzuki (2008). "Differential distribution of synGAP alpha1 and synGAP beta isoforms in rat neurons." Brain Res 1241: 62-75.

Pankow, S., C. Bamberger, D. Calzolari, A. Bamberger and J. R. Yates, 3rd (2016). "Deep interactome profiling of membrane proteins by co-interacting protein identification technology." Nat Protoc 11(12): 2515-2528.

Pankow, S., C. Bamberger, D. Calzolari, S. Martinez-Bartolome, M. Lavallee-Adam, W. E. Balch and J. R. Yates, 3rd (2015). "∆F508 CFTR interactome remodelling promotes rescue of cystic fibrosis." Nature 528(7583): 510-516.

Park, G. H., H. Noh, Z. Shao, P. Ni, Y. Qin, D. Liu, C. P. Beaudreault, J. S. Park, C. P. Abani, J. M. Park, D. T. Le, S. Z. Gonzalez, Y. Guan, B. M. Cohen, D. L. McPhie, J. T. Coyle, T. A. Lanz, H. S. Xi, C. Yin, W. Huang, H. Y. Kim and S. Chung (2020). "Activated microglia cause metabolic disruptions in developmental cortical interneurons that persist in interneurons from individuals with schizophrenia." Nat Neurosci 23(11): 1352-1364.

Partiot, E., A. Hirschler, S. Colomb, W. Lutz, T. Claeys, F. Delalande, M. S. Deffieu, Y. Bare, J. R. E. Roels, B. Gorda, J. Bons, D. Callon, L. Andreoletti, M. Labrousse, F. M. J. Jacobs, V. Rigau, B. Charlot, L. Martens, C. Carapito, G. Ganesh and R. Gaudin (2024). "Brain exposure to SARS-CoV-2 virions perturbs synaptic homeostasis." Nat Microbiol.

Qian, J., E. Vafiadaki, S. M. Florea, V. P. Singh, W. Song, C. K. Lam, Y. Wang, Q. Yuan, T. J. Pritchard, W. Cai, K. Haghighi, P. Rodriguez, H. S. Wang, D. Sanoudou, G. C. Fan and E. G. Kranias (2011). "Small heat shock protein 20 interacts with protein phosphatase-1 and enhances sarcoplasmic reticulum calcium cycling." Circ Res 108(12): 1429-1438.

Ragusa, M. J., B. Dancheck, D. A. Critton, A. C. Nairn, R. Page and W. Peti (2010). "Spinophilin directs protein phosphatase 1 specificity by blocking substrate binding sites." Nat Struct Mol Biol 17(4): 459-464.

Rodrigues-Neves, A. C., A. F. Ambrosio and C. A. Gomes (2022). "Microglia sequelae: brain signature of innate immunity in schizophrenia." Transl Psychiatry 12(1): 493.

Salek, A. B., E. T. Claeboe, R. Bansal, N. F. Berbari and A. J. Baucum, 2nd (2023). "Spinophilin-dependent regulation of GluN2B-containing NMDAR-dependent calcium influx, GluN2B surface expression, and cleaved caspase expression." Synapse 77(3): e22264.

Savas, J. N., B. D. Stein, C. C. Wu and J. R. Yates, 3rd (2011). "Mass spectrometry accelerates membrane protein analysis." Trends Biochem Sci 36(7): 388-396.

Selak, S., A. V. Paternain, M. I. Aller, E. Pico, R. Rivera and J. Lerma (2009). "A role for SNAP25 in internalization of kainate receptors and synaptic plasticity." Neuron 63(3): 357-371.

Serrano, A., R. Robitaille and J. C. Lacaille (2008). "Differential NMDA-dependent activation of glial cells in mouse hippocampus." Glia 56(15): 1648-1663.

Sjostrom, P. J., G. G. Turrigiano and S. B. Nelson (2003). "Neocortical LTD via coincident activation of presynaptic NMDA and cannabinoid receptors." Neuron 39(4): 641-654.

Stanca, S., M. Rossetti, L. Bokulic Panichi and P. Bongioanni (2024). "The Cellular Dysfunction of the Brain-Blood Barrier from Endothelial Cells to Astrocytes: The Pathway towards Neurotransmitter Impairment in Schizophrenia." Int J Mol Sci 25(2).

Sumi, T. and K. Harada (2023). "Muscarinic acetylcholine receptor-dependent and NMDA receptor-dependent LTP and LTD share the common AMPAR trafficking pathway." iScience 26(3): 106133.

Svenningsson, P., E. T. Tzavara, R. Carruthers, I. Rachleff, S. Wattler, M. Nehls, D. L. McKinzie, A. A. Fienberg, G. G. Nomikos and P. Greengard (2003). "Diverse psychotomimetics act through a common signaling pathway." Science 302(5649): 1412-1415.

Tarasov, V. V., A. A. Svistunov, V. N. Chubarev, S. S. Sologova, P. Mukhortova, D. Levushkin, S. G. Somasundaram, C. E. Kirkland, S. O. Bachurin and G. Aliev (2019). "Alterations of Astrocytes in the Context of Schizophrenic Dementia." Front Pharmacol 10: 1612.

Terrak, M., F. Kerff, K. Langsetmo, T. Tao and R. Dominguez (2004). "Structural basis of protein phosphatase 1 regulation." Nature 429(6993): 780-784.

Tokizane, K., C. S. Brace and S. I. Imai (2024). "DMH(Ppp1r17) neurons regulate aging and lifespan in mice through hypothalamic-adipose inter-tissue communication." Cell Metab 36(2): 377-392 e311.

Tomasoni, R., D. Repetto, R. Morini, C. Elia, F. Gardoni, M. Di Luca, E. Turco, P. Defilippi and M. Matteoli (2013). "SNAP-25 regulates spine formation through postsynaptic binding to p140Cap." Nat Commun 4: 2136.

Vainio, L., S. Taponen, S. M. Kinnunen, E. Halmetoja, Z. Szabo, T. Alakoski, J. Ulvila, J. Junttila, P. Lakkisto, J. Magga and R. Kerkela (2021). "GSK3beta Serine 389 Phosphorylation Modulates Cardiomyocyte Hypertrophy and Ischemic Injury." Int J Mol Sci 22(24).

van Oostrum, M., T. M. Blok, S. L. Giandomenico, S. Tom Dieck, G. Tushev, N. Furst, J. D. Langer and E. M. Schuman (2023). "The proteomic landscape of synaptic diversity across brain regions and cell types." Cell 186(24): 5411-5427 e5423.

Vilalta, A. and G. C. Brown (2018). "Neurophagy, the phagocytosis of live neurons and synapses by glia, contributes to brain development and disease." FEBS J 285(19): 3566-3575.

Weeratunga, S., R. S. Gormal, M. Liu, D. Eldershaw, E. K. Livingstone, A. Malapaka, T. P. Wallis, A. T. Bademosi, A. Jiang, M. D. Healy, F. A. Meunier and B. M. Collins (2024). "Interrogation and validation of the interactome of neuronal Munc18-interacting Mint proteins with AlphaFold2." J Biol Chem 300(1): 105541.

Winship, I. R., S. M. Dursun, G. B. Baker, P. A. Balista, L. Kandratavicius, J. P. Maia-de-Oliveira, J. Hallak and J. G. Howland (2019). "An Overview of Animal Models Related to Schizophrenia." Can J Psychiatry 64(1): 5-17.

Xu, Z., L. Sadleir, H. Goel, X. Jiao, Y. Niu, Z. Zhou, G. de Valles-Ibanez, G. Poke, M. Hildebrand, N. Lieffering, J. Qin and Z. Yang (2024). "Genotype and phenotype correlation of PHACTR1-related neurological disorders." J Med Genet 61(6): 536-542.

Zhang, J., L. Zhang, S. Zhao and E. Y. Lee (1998). "Identification and characterization of the human HCG V gene product as a novel inhibitor of protein phosphatase-1." Biochemistry 37(47): 16728-16734.

Zhang, Y., K. Chen, S. A. Sloan, M. L. Bennett, A. R. Scholze, S. O'Keeffe, H. P. Phatnani, P. Guarnieri, C. Caneda, N. Ruderisch, S. Deng, S. A. Liddelow, C. Zhang, R. Daneman, T. Maniatis, B. A. Barres and J. Q. Wu (2014). "An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex." J Neurosci 34(36): 11929-11947.

Esto es algo que no sabia y tiene sentido ya que, yo cuando estoy leyendo un texto muy largo, tiendo a regresar rápidamente a la oración anterior antes de seguir con la siguiente.

Es cierto, leer un texto que tiene mucha redundacia o utiliza mucho una misma palabra la que podemos omitir de diversas formas lo hace más dificil de leer, por lo que debemos omitir esas palabras extras cuando se pueda

Debemos tener cuidado con el largo de nuestras frases si queremos que el lector nos comprenda correctamente, como en los anuncios que suelen ser muy cortos y concisos para que el lector o el receptor comprenda y se quede con él el mensaje

Muchos de los errores que cometo al escribir suelen ser por la estructura de las oraciones, comienzo pensando en un enunciado y cuando lo escribo se me olvida o se mezcla con otro que estaba pensando. Para escribir se debe tener claro el enunciado que se plasmará antes de comenzarlo a escribir

Condimento que se añade a un guiso, compuesto por diversos ingredientes fritos en aceite, especialmente cebolla o ajo entre otros. Esta palabra esta usada para describir algo que no es alimento

Comparto esta idea con el escrito, las subordinadas suelen quedar mejor al final, ordenadas de más cortas a más largas o según su significado. Esto facilita la comprensión y la fluidez del texto, al presentar primero la idea principal y luego los detalles adicionales.

De esta frase podemos inferir que alude al primer sospechoso, incluso si no se indica explícitamente.

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