Escribir una coma en las primeras tres siguiente ecuaciones y punto al final de la cuarta.

quitar las comas en las fracciones y en el primer cero de la primera línea. Escribir una coma al final de cada desigualdad excepto en la última. En la última desigualdad escribir un punto al final.

quitar las comas en las fracciones y en el primer cero de la primera línea. Escribir una coma al final de cada desigualdad excepto en la última. En la última desigualdad escribir un punto al final.

Escribir un punto al final de la línea siguiente y una "y" entre la penúltima y la última ecuación.

Falta caso A_1= vacío y A_2={b, c}

Revisar que no haya un espacio entre B y el punto.

Creo que en lugar de {b,c} quisiste escribir {c,d} y en lugar de {a,b,c} quisiste escribir {b,c,d}. De todos modos revisa bien porque me parece que la unión numerable no queda con estos arreglos. Posiblemente funcione F={vacío, {a}, {b,c,d}, {a,b,c,d}}.

Para ampliar este enfoque, recomiendo la lectura de:

El sabio, el mercader y el guerrero: Del rechazo del trabajo al surgimiento del cognitariado (Franco Berardi 'Bifo', Acuarela & Antonio Machado Libros, 2007)

Reviewer #1 (Public Review):

The paper proposes a new source reconstruction method for electroencephalography (EEG) data and claims that it can provide far superior spatial resolution than existing approaches and also superior spatial resolution to fMRI. This primarily stems from abandoning the established quasi-static approximation to Maxwell's equations.

The proposed method brings together some very interesting ideas, and the potential impact is high. However, the work does not provide the evaluations expected when validating a new source reconstruction approach. I cannot judge the success or impact of the approach based on the current set of results. This is very important to rectify, especially given that the work is challenging some long-standing and fundamental assumptions made in the field.

I also find that the clarity of the description of the methods, and how they link to what is shown in the main results hard to follow.

I am insufficiently familiar with the intricacies of Maxwell's equations to assess the validity of the assumptions and the equations being used by WETCOW. The work therefore needs assessing by someone more versed in that area. That said, how do we know that the new terms in Maxwell's equations, i.e. the time-dependent terms that are normally missing from established quasi-static-based approaches, are large enough to need to be considered? Where is the evidence for this?

I have not come across EFD, and I am not sure many in the EEG field will have. To require the reader to appreciate the contributions of WETCOW only through the lens of the unfamiliar (and far from trivial) approach of EFD is frustrating. In particular, what impact do the assumptions of WETCOW make compared to the assumptions of EFD on the overall performance of SPECTRE?

The paper needs to provide results showing the improvements obtained when WETCOW or EFD are combined with more established and familiar approaches. For example, EFD can be replaced by a first-order vector autoregressive (VAR) model, i.e. y_t = A y_{t-1} + e_t (where y_t is [num_gridpoints x 1] and A is [num_gridpoints x num_gridpoints] of autoregressive parameters).

The authors' decision not to include any comparisons with established source reconstruction approaches does not make sense to me. They attempt to justify this by saying that the spatial resolution of LORETA would need to be very low compared to the resolution being used in SPECTRE, to avoid compute problems. But how does this stop them from using a spatial resolution typically used by the field that has no compute problems, and comparing with that? This would be very informative. There are also more computationally efficient methods than LORETA that are very popular, such as beamforming or minimum norm.

In short, something like the following methods needs to be compared:

(1) Full SPECTRE (EFD plus WETCOW)<br /> (2) WETCOW + VAR or standard ("simple regression") techniques<br /> (3) Beamformer/min norm plus EFD<br /> (4) Beamformer/min norm plus VAR or standard ("simple regression") techniques

This would also allow for more illuminating and quantitative comparisons of the real data. For example, a metric of similarity between EEG maps and fMRI can be computed to compare the performance of these methods. At the moment, the fMRI-EEG analysis amounts to just showing fairly similar maps.

There are no results provided on simulated data. Simulations are needed to provide quantitative comparisons of the different methods, to show face validity, and to demonstrate unequivocally the new information that SPECTRE can _potentially_ provide on real data compared to established methods. The paper ideally needs at least 3 types of simulations, where one thing is changed at a time, e.g.:

(1) Data simulated using WETCOW plus EFD assumptions<br /> (2) Data simulated using WETCOW plus e.g. VAR assumptions<br /> (3) Data simulated using standard lead fields (based on the quasi-static Maxwell solutions) plus e.g. VAR assumptions

These should be assessed with the multiple methods specified earlier. Crucially the assessment should be quantitative showing the ability to recover the ground truth over multiple realisations of realistic noise. This type of assessment of a new source reconstruction method is the expected standard.

Author response:

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

Public reviews: 

Reviewer #1 (Public Review): 

Summary: 

In this study, Masroor Ahmad Paddar and his/her colleagues explore the noncanonical roles of ATG5 and membrane atg8ylation in regulating retromer assembly and function. They begin by examining the interactomes of ATG5 and expand the scope of these effects to include homeostatic responses to membrane stress and damage. 

Strengths: 

This study provides novel insights into the noncanonical function of ATG8ylation in endosomal cargo sorting process. 

Weaknesses: 

The direct mechanism by which ATG8ylation regulates the retromer remains unsolved. 

We agree with the reviewer.  We do however show how at least one aspect of atg8ylation contributes to the proper retromer function, which occurs via lysosomal membrane maintenance and repair. Understanding the more direct effects on retromer will require a separate study. We now emphasize this in the revised manuscript (p. 18) and point out the limitations of the present work (p. 18): “One of the limitations of our study is that beyond effects of membrane atg8ylation on quality of lysosomal membrane and its homeostasis there could be more direct effects of membrane modification with mATG8s that still need to be understood”.

Reviewer #2 (Public Review): 

Summary:

Padder et al. demonstrate that ATG5 mediates lysosomal repair via the recruitment of the retromer components during LLOMe-induced lysosomal damage and that mAtg8-ylation contributes to retromer-dependent cargo sorting of GLUT1. Although previous studies have suggested that during glucose withdrawal, classical autophagy contributes to retromer-dependent GLUT1 surface trafficking via interactions between LC3A and TBC1D5, the experiments here demonstrate that during basal conditions or lysosomal damage, ATGs that are not involved in mATG8ylation, such as FIP200, are not functionally required for retromer-dependent sorting of GLUT1. Overall, these studies suggest a unique role for ATG5 in the control of retromer function, and that conjugation of ATG8 to single membranes (CASM) is a partial contributor to these phenotypes. 

Strengths: 

(1) Overall, these studies suggest a unique non-autophagic role for ATG5 in the control of retromer function. They also demonstrate that conjugation of ATG8 to single membranes (CASM) is a partial contributor to these phenotypes. Overall, these data point to a new role for ATG5 and CASM-dependent mATG8ylation in lysosomal membrane repair and trafficking. 

(2) Although the studies are overall supportive of the proposed model that the retromer is controlled by CASM-dependent mATG8-ylaytion, it is noteworthy that previous studies of GLUT1 trafficking during glucose withdrawal (Roy et al. Mol Cell, PMID: 28602638) were predominantly conducted in cells lacking ATG5 or ATG7, which would not be able to discriminate between a CASM-dependent vs. canonical autophagy-dependent pathway in the control of GLUT1 sorting. Is the lack of GLUT1 mis-sorting to lysosomes observed in FIP200 and ATG13KO cells also observed during glucose withdrawal? Notably, deficiencies in glycolysis and glucose-dependent growth have been reported in FIP200 deficient fibroblasts (Wei et al. G&D, PMID: 21764854) so there may be differences in regulation dependent on the stress imposed on a cell. 

We thank the reviewer for the overall assessment of the strengths of the study.  We have discussed in the manuscript the elegant study by Roy et al., PMID 28602683. To accommodate reviewer’s comment, we have additionally emphasized in the text that our study is focused on basal conditions and conditions that perturb endolysosomal compartments. We agree with the reviewer that under metabolic stress conditions (such as glucose limitation) more complex pathways may be engaged and have acknowledged that in the discussion. We have now included this in the limitations of the study (p. 18): “Another limitation of our study is that we have focused on basal conditions or conditions causing lysosomal damage, whereas metabolic stress including glucose excess or limitation with its multitude of metabolic effects have not been addressed”.

Weaknesses: 

(1) Additional controls are needed to clarify the role of CASM in the control of retromer function. Because the manuscript proposes both CASM-dependent and independent pathways in the ATG5 mediated regulation of the retromer, it is important to provide robust evidence that CASM is required for retromer-dependent GLUT1 sorting to the plasma membrane vs. lysosome. The experiments with monensin in Fig. 7C-E are consistent with but not unequivocally corroborative of a role for CASM. 

We fully agree with the reviewer. In fact, our data with bafilomycin A1 treatment causing GLUT1 miss-sorting show that it is the perturbance of lysosomes  and not CASM per se that leads to mis-sorting of GLUT1 (Fig. 7D,E). Note that it has been shown (PMIDs: 28296541, 25484071 and 37796195) that although bafilomycin A1 deacidifies lysosomes it does not induce but instead inhibits CASM. This is because bafilomycin A1 causes dissociation of V1 and V0 sectors of V-ATPase, unlike other CASM-inducing agents which promote V1 V0 association. Complementing this, our data with ATG2AB DKO and ESCRT VPS37A KO (Fig. 8A-F) indicate that the repair of lysosomes is important to keep the retromer machinery functional (as illustrated in Fig. 8G). This may be one of the effector mechanisms downstream of membrane atg8ylation in general and hence also downstream of CASM. We have revised Fig. 7 title to read “Lysosomal perturbations cause GLUT1 mis-sorting” and have explained these relationships in the text (p. 12-13): “Since bafilomycin A1 does not induce CASM but disturbs luminal pH, we conclude that it is the less acidic luminal pH of the endolysosomal organelles, and not CASM, that is sufficient to interfere with the proper sorting of GLUT1.”

Based on the results shown with ATG16KO in Fig 4A-D, rescue experiments of these 16KO cells with WT vs. C-terminal WD40 mutant versions of ATG16 will specifically assess the requirement for CASM and potentially provide more rigorous support for the conclusions drawn. 

We have carried out complementation with ATG16L1 WT and its E230 mutant (devoid of WD40 repeats but still capable of canonical autophagy) and placed these data in Fig. 7 (panels I and J) as recommended by the reviewer. This is now described on p. 13 (To additionally test this notion, we compared ATG16L1 full length (ATG16L1FL) and ATG16L1E230 (Rai et al., PMID 30403914) for complementation of the GLUT1 sorting defect in ATG16L1 KO cells (Fig. 7I,J). ATG16L1E230 [Rai, 2019, 30403914] lacks the key domain to carry out CASM via binding to VATPase 29,30 31-33 but retains capacity to carry out atg8ylation.  Both ATG16L1FL and ATG16L1E230 complemented mis-sorting of GLUT1 (Fig. 7I,J). Collectively, these data indicate that it is not absence of CASM/VAIL but absence of membrane atg8ylation in general that promotes GLUT1 mis-sorting.).

(2) Also, the role of TBC1D5 should be further clarified. In Fig S7, are there any changes in the interactions between TBC1D5 and VPS35 in response to LLOMe or other agents utilized to induce CASM? 

We thank the reviewer for pointing this out. We do have data with VPS35 in co-IPs shown in Fig. S7.  There is no change in the amounts of VPS35 or TBC1D5 in GFP-LC3A co-IPs. We now include in Fig. S7 (new panel D) a graph with quantification in the revised manuscript and emphasize this point (p. 12): “However, under CASM-inducing conditions, no changes were detected (Fig. S7B-D) in interactions between TBC1D5 and LC3A or in levels of VPS35 in LC3A co-IP, a proxy for LC3A-TBC1D5-VPS29/retromer association. This suggests that CASM-inducing treatments and additionally bafilomycin A1 do not affect the status of the TBC1D5-Rab7 system”.        

Does TBC1D5 loss-of-function modulate the numbers of GLUT1 and Gal3 puncta observed in ATG5 deficient cells in response to LLOMe? 

We agree that TBC1D5 is an interesting aspect. However, because TBC1D5 does not change its interactions in the experiments in our study, we consider this topic (i.e. whether TBC1D5 phenocopies VPS35 and ATG5 KOs in its effects on Gal3) to be beyond the scope of the present work. We underscore that LLOMe (lysosomal damage) mis-sorts GLUT1 even without any genetic intervention (e.g., in WT cells in the absence of ATG5 KO; Fig. 7). Thus, in our opinion the effects of TBC1D5 inactivation may be a moot point.  

(3) Finally, the studies here are motivated by experiments in Fig. S1 (as well as other studies from the Deretic and Stallings labs) suggesting unique autophagy-independent functions for ATG5 in myeloid cells and neutrophils in susceptibility to Mycobacterium tuberculosis infection. However, it is curious that no attempt is made to relate the mechanistic data regarding the retromer or GLUT1 receptor mis-sorting back to the infectious models. Do myeloid cells or neutrophils lacking ATG5 have deficiencies in glucose uptake or GLUT1 cell surface levels? 

Reviewer’s point is well taken. Glucose uptake, its metabolism, and diabetes underly resurgence in TB in certain populations and are important factors in a range of other diseases. This was alluded to in our discussion (lines 461-469). However, these are complex topics for future studies. We have now expanded this section of the discussion (p. 18): “In the context of tuberculosis, diabetes, which includes glucose dysregulation, is associated with increased incidence of active disease and adverse outcomes” (Dheda et al., ,PMID: 26377143; Dooley, et al., PMID:19926034).

Reviewer #3 (Public Review): 

In this manuscript, Padder et al. used APEX2 proximity labeling to find an interaction between ATG5 and the core components of the Retromer complex, VPS26, VPS29, and VPS35. Further studies revealed that ATG5 KO inhibited the trafficking of GLUT1 to the plasma membrane. They also found that other autophagy genes involved in membrane atg8ylation affected GLUT1 sorting. However, knocking out other essential autophagy genes such as ATG13 and FIP200 did not affect GLUT1 sorting. These findings suggest that ATG5 participates in the function of the Retromer in a noncanonical autophagy manner. Overall, the methods and techniques employed by the authors largely support their conclusions. These findings are intriguing and significant, enriching our understanding of the non-autophagic functions of autophagy proteins and the sorting of GLUT1.

Nevertheless, there are several issues that the authors need to address to further clarify their conclusions. 

(1) The authors confirmed the interaction between Atg5 and the Retromer complex through Co-IP experiments. Is the interaction between Atg5 and the Retromer direct? If it is direct, which Retromer complex protein regulates the interaction with Atg5? Additionally, does ATG5 K130R mutant enhance its interaction with the Retromer? 

AlphaFold modeling in the initial submission of our study to eLife (absent from the current version) suggested the possibility of a direct interaction between ATG5 and VPS35 with ATG12—ATG5 complex facing outwards, in which case K130R would not matter. However, mutational experiments in putative contact residues did not alter association in co-IPs. So either ATG5 interacts with other retromer subunits or more likely is in a larger protein complex containing retromer. It will take a separate study to dissect associations and find direct interaction partners. 

(2) To more directly elucidate how ATG5 regulates Retromer function by interacting with the Retromer and participates in the trafficking of GLUT1 to the plasma membrane, the authors should identify which region or crucial amino acid residues of ATG5 regulate its interaction with the Retromer. Additionally, they should test whether mutations in ATG5 that disrupt its interaction with the Retromer affect Retromer function (such as participating in the trafficking of GLUT1 to the plasma membrane) and whether they affect Atg8ylation. They also need to assess whether these mutations influence canonical autophagy and lysosomal sensitivity to damage. 

Please see the response to point 1.

Recommendations for the authors.

Reviewer #1 (Recommendations For The Authors): 

While most data are solid and convincing, the following questions need to be addressed before publication: 

Major Concerns: 

(1) Examining only one cargo (GLUT1) is insufficient to reflect the retromer's function comprehensively. At least two additional cargoes should be analyzed to observe the phenotypes more accurately. 

We agree that having another retromer cargo (in addition to GLUT1) would be of interest. We point out that our data also show mis-sorting of SNX27 to lysosomes (Fig. 3H, quantifications in Fig. 3I).  SNX27 in turn sorts nearly 80 ion channels, signaling receptors, and other nutrient transporters. Which of the 80 cargos to prioritize and check (the expectation is that all 80 might be missorted given that they need SNX27)?  We have instead tested MPR, a SNX27-independent cargo. We now include data on effects of ATG5 knockout on CI-MPR (Fig. S9A-F). This is described in the text (p. 14; “Effect of ATG5 knockout on MPR sorting

We tested whether ATG5 affects cation-independent mannose 6-phosphate receptor (CI-MPR). For this, we employed the previously developed methods (Fig. S9A) of monitoring retrograde trafficking of CI-MPR from the plasma membrane to the TGN 70,118-121. In the majority of such studies, CI-MPR antibody is allowed to bind to the extracellular domain of CI-MPR at the plasma membrane and its localization dynamics following endocytosis serves as a proxy for trafficking of CI-MPR. We used ATG5 KOs in HeLa and Huh7 cells and quantified by HCM retrograde trafficking to TGN of antibody-labeled CI-MPR at the cell surface, after being taken up by endocytosis and allowed to undergo intracellular sorting, followed by fixation and staining with TGN46 antibody. There was a minor but statistically significant reduction in CIMPR overlap with TGN46 in HeLaATG5-KO that was comparable to the reduction in HeLa cells when

VPS35 was depleted by CRISPR (HeLaVPS35-KO) (Fig. S9B,C). Morphologically, endocytosed Ab-CI-

MPR appeared dispersed in both HeLaATG5-KO and HeLaVPS35-KO cells relative to HeLaWT cells (Fig. S9D). Similar HCM results were obtained with Huh7 cells (WT vs. ATG5KO; Fig. S9E,F). We interpret these data as evidence of indirect action of ATG5 KO on CI-MPR sorting via membrane homeostasis, although we cannot exclude a direct sorting role via retromer. We favor the former interpretation based on the strength of the effect and the controversial nature of retromer engagement in sorting of CI-MPR (57,70,75,98,120).”)

(2) The evidence from Alphafold predictions is weak. The direct interaction of ATG5 with retromer subunits should be tested. 

Please see the above response to Reviewer 3.

In addition, does retromer also interact with ATG16L1 similarly to the phenomenon in VAIL? 

We fully agree with the reviewer that finding the direct interacting partners between retromer and membrane atg8ylation machinery is an important direction as in our opinion it would expand the repertoire of E3 ligases and its adaptors. However, given the complexity and variety of possibilities, we believe that this is a topic for a future study.  

(3) In Line 166, Figures 2C and 2D, the Gal3 phenotype does not seem to be well complemented by VPS35. 

We have adjusted the text to acknowledge incomplete complementation (p.7). 

(4) In Figures 3 and 4, the authors show that KO of membrane atg8ylation machineries and ATG8-Hexa KO affects the localization of retromer cargo GLUT1 and SNX27. However, the mechanism by which membrane ATG8ylation affects retromer remains unresolved.

Additionally, are other retromer subunits' locations are also affected, if so, how are they impacted? At least a speculative explanation should be provided. 

Following reviewers request, we now state on p. 19 that “one of the limitations of our study is that beyond effects of membrane atg8ylation on quality of lysosomal membrane and its homeostasis there could be more direct effects of membrane modification with mATG8s on retromer that still need to be understood”.

(5) In Figure 3, endogenous IP results are required to examine the interaction of ATG5 with retromer if suitable retromer antibodies for IP are available. 

Endogenous IPs are given in Fig. 1. We have modified text on p. 8 to clarify this.

(6) In Figure 4, ATG8 Hexa KO, and triple KO of LC3s or GABARAPs all increase the localization of GLUT1 on lysosomes. It seems redundant for ATG8 family proteins here.

Can any individual member of the ATG8 family rescue this phenotype? 

If the intent of such complementation analysis is to identify a specific mATG8 responsible for the observed effects, this is already pre-empted by the fact that TKOs also have a similar effect as HEXA mutants (i.e. loss of at least two of mATG8s is enough to cause the phenotype). We now discuss this in the text (p. 10): “Thus, at least two mATG8s, each one from two different mATG8 subclasses (LC3s and GABARAPs) or the entire membrane atg8ylation machinery was engaged in and required for proper GLUT-1 sorting”.  

(7) In Figure 5, knockdown of ATG5 in FIP200 KO cells inhibited GLUT1 sorting from endosomes, leading to its trafficking to lysosomes. However, it is known that very little remnant ATG5 in ATG5 KD cells is enough to support ATG8 lipidation. Therefore, it is essential to repeat this experiment using ATG5/FIP200 double KO or ATG5 KO combined with an autophagy inhibitor. 

We point out to this limitation in the text (p. 11): “….we knocked down ATG5 in FIP200 KO cells (Fig. S5D) and found that GLUT1 puncta and GLUT1+LAMP2+ profiles increased even in the FIP200 KO background with the effects nearing those of VPS35 knockout (Figs. 5D-F and S5C), with the difference between VPS35 KO and ATG5 KD attributable to any residual ATG5 levels in cells subjected to siRNA knockdowns”.

(8) In Figure 7, the authors show that the induction of CASM inhibited GLUT1 sorting from endosomes. However, ATG5 KO, which abolishes membrane ATG8ylation, also inhibits GLUT1 sorting. This seems paradoxical and requires a reasonable explanation or discussion. 

We understand reviewer’s comment. The answer to this paradox is that it is actually the lysosomal damage that causes GLUT1 mis-sorting and not CASM. Membrane atg8ylation, such as CASM and probably other processes given that involvement of both ATG2 and ESCRTs (Fig. 8) counteracts the damage and works in the direction of restoring/maintaining proper retromer-dependent sorting. This is now explained better in the text, and have revised the title of Fig. 7 to read “Lysosomal damage causes GLUT1 mis-sorting”. Our data with bafilomycin A1 show that it is the perturbance of lysosomes (not CASM per se) that leads to mis-sorting of GLUT1 (Fig. 7D,E), and our data with ATG2AB DKO and ESCRT (VPS37A) KO (Fig. 8A-F) indicate that repair of lysosomes is important to keep the retromer working machinery functional (as illustrated in Fig. 8G), which may be one of the effector mechanisms downstream of membrane atg8ylation  in general (and hence also of CASM).  

(9) The immuno-staining results for Figures 7F and 7G are lacking. 

We now provide the requested images.

(10) In Figure 8D, the quality of the image for VPS37 KO cells treated with LLOME is not sufficient to show increased colocalization between GLUT1 and LAMP2. 

We now provide a different example image. We note that these are epiflorescent HCM images  

Minor Concerns: 

(1) It would be better to distinguish the function of the membrane ATG8ylation machinery (i.e., ATG5) from the function of membrane ATG8ylation in the description. No ATG8ylation-deficient mutants were used in this study. 

We have used atg8ylation mutants (e.g. KOs in ATG3, ATG5, ATG7, and ATG16L1). We now emphasize this better in the text (p. 10). 

(2) In Figure 2D, a green box appears there by incident. 

This has been fixed.

(3) In Figure 3A, the conjugate for ATG5-ATG12 is absent in the gel for IB: ATG5.

The ATG5 antibody used in Fig. 3A recognizes primarily the conjugated form of ATG5. This is now clarified in the figure legend. 

(4) Figure 5G is missing in the manuscript. 

Fig 5G is now mentioned in the text. Thank you.

(5) The gRNA sequence information for FIP200 KO is missing in the Methods section. 

Reference(s) to the already published gRNA sequence are in the manuscript. 

(6) Suggest moving the last paragraph in Result section to Discussion section. 

We kept this single-paragraph section in Results as it contains actual data.

Reviewer #2 (Recommendations For The Authors): 

(1) It is unclear why the rescue of VPS35KO cells in Fig 1C-D is so modest. 

Complementation data depend on transfection efficiency and some variability is to be expected.

Reviewer #3 (Recommendations For The Authors): 

(1) Figures 2A, 2C, 2E, and 2G lack scale bars. Figure 2D has a small square above the y axis. 

Relative scale bars are now included. 

(2) Figures S3B, S3D, and S3F lack scale bars. 

Relative scale bars are now included.

Reviewer #3 (Public review):

The author presents a novel theory and computational model suggesting that grid cells do not encode space, but rather encode non-spatial attributes. Place cells in turn encode memories of where those specific attributes occurred. The theory accounts for many experimental results and generates useful predictions for future studies. The model's simplicity and potential explanatory power will interest others in the field. There are, however, a few weaknesses outlined below which undermine the theory.

Main criticisms:

(1) A crucial assumption of the model is that grid cells express grid-like firing patterns if and only if the content of experience is constant in space. It is difficult to imagine a real world example that satisfies this assumption. Odors and sounds are used as examples. While they are often more spatially diffuse than an object on the ground, odors and sounds have sources that are readily detectable and thus are not constant in space. Animals can easily navigate to a food source or to a vocalizing conspecific. This assumption is especially problematic because it predicts that all grid cells should become silent when their preferred non-spatial attribute (e.g. a specific odor) is missing. I'm not aware of any experimental data showing that grid cells become silent. On the contrary, grid cells are known to remain active across all contexts that have been tested, including across sleep/wake states. Unlike place cells, grid cells have never been shown to turn off. Since grid cells are active in all contexts, their preferred attribute must also be present in all contexts, and therefore they would not convey any information about the specific content of an experience. The author lists many attributes that could in theory be constant in a laboratory setting, but there is no data I'm aware of that shows this is true in practice. As it stands, this crucial assumption of the model remains mere speculation.

(2) The proposed novelty of this theory is that other models all assume that grid cells encode space. This is not quite true of models based on continuous attractor networks, the discussion of which is essentially absent. More specifically, attractor models focus on the importance of intrinsic dynamics within entorhinal cortex in generating the grid pattern. While this firing pattern is aligned to space during navigation and therefore can be used a representation of that space, the neural dynamics are preserved even during sleep. Similarly, it is because the grid pattern does not strictly encode physical space that grid-like signals are also observed in relation to other two-dimensional continuous variables.

(3) The use of border cells or boundary vector cells as the main (or only) source of spatial information in the hippocampus is not well supported by experimental data. Border cells in entorhinal cortex are not active in the center of an environment. Boundary-vector cells can fire farther away from the walls, but are not found in entorhinal cortex. They are located in the subiculum, a major output of the hippocampus. While the entorhinal-hippocampal circuit is a loop, the route from boundary-vector cells to place cells is much less clear than from grid cells. Moreover, both border cells and boundary-vector cells (which are conflated in this paper) comprise a small population of neurons compared to grid cells.

Minor comments:

(1) There is substantial theoretical and experimental work supporting the idea that grid cell modules instantiate continuous attractor networks, yet this class of models is largely ignored:

p. 7 "In contrast, most grid cell models (Bellmund et al., 2016; Bush et al., 2015; Castro & Aguiar, 2014; Hasselmo, 2009; Mhatre et al., 2012; Solstad et al., 2006; Sorscher et al., 2023; Stepanyuk, 2015; Widloski & Fiete, 2014) are domain specific models of spatial navigation"

The following references should be added:

McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I. & Moser, M.-B. Path integration and the neural basis of the 'cognitive map'. Nat. Rev. Neurosci. 7, 663-678 (2006).

Fuhs, M. C. & Touretzky, D. S. A spin glass model of path integration in rat medial entorhinal cortex. J. Neurosci. 26, 4266-4276 (2006).

Burak, Y. & Fiete, I. R. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput. Biol. 5, e1000291 (2009).

Guanella, A., Kiper, D. & Verschure, P. A model of grid cells based on a twisted torus topology. Int. J. Neural Syst. 17, 231-240 (2007).

Couey, J. J. et al. Recurrent inhibitory circuitry as a mechanism for grid formation. Nat. Neurosci. 16, 318-324 (2013).

(Note: the Bellmund et al. (2016) citation is likely a mistake and was intended to be Bellmund et al. (2018).)

(2) The author claims in two places that this model is the first to explain that grid cell population activity lies on a torus. While it may be the first explicit computational account of why grid cell activity is mapped onto a torus, these claims should be moderated for clarity, for example by adding "but see McNaughton et al. (2006) and others".

Box 1. Results Uniquely Explained by this Memory Model - the population code of grid cells lies on a torus

p.11 "In addition, this simplifying assumption allows the model to capture the finding that the population of grid cells lies on a torus (Gardner et al., 2022), although I note that the model was developed before this result was known."

(3) Lateral entorhinal cortex is largely ignored in this memory model. It should be considered that the predominance of spatial representations reported in the literature is due to historical reasons. Namely, the discovery of hippocampal place cells spurred interest in looking upstream for the source of spatial information, which was later abundantly clear in medial entorhinal cortex. Lateral entorhinal cortex is relatively understudied, but is known to encode odors, objects, and time in a way that medial entorhinal cortex does not. It is therefore confusing to assume that these attributes are instead encoded by grid cells.

Author response:

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

Public Reviews

Reviewer #1 (Public Review): 

(1) Although the theory is based on memory, it also is based on spatially-selective cells.

Not all cells in the hippocampus fulfill the criteria of place/HD/border/grid cells, and place a role in memory. E.g., Tonegawa, Buszaki labs' work does not focus on only those cells, and there are certainly a lot of non-pure spatial cells in monkeys (Martinez-Trujillo) and humans (iEEG). Does the author mainly focus on saying that "spatial cells" are memory, but do not account for non-spatial memory cells? This seems to be an incomplete account of memory - which is fine, but the way the model is set up suggests that *all* memory is, place (what/where), and non-spatial attributes ("grid") - but cells that don't fulfil these criteria in MTL (Diehl et al., 2017, Neuron; non-grid cells; Schaeffer et al., 2022, ICML; Luo et al., 2024, bioRxiv) certainly contribute to memory, and even navigation. This is also related to the question of whether these cell definitions matter at all (Luo et al., 2024). The authors note "However, this memory conjunction view of the MTL must be reconciled with the rodent electrophysiology finding that most cells in MTL appear to have receptive fields related to some aspect of spatial navigation (Boccara et al., 2010; Grieves & Jeffery, 2017). The paucity of non-spatial cells in MTL could be explained if grid cells have been mischaracterized as spatial." Is the author mainly talking about rodent work?

There is a new section in the introduction that deals with these issues, titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’ That section reads:

“Spatial navigation is inherently a memory problem – learning the spatial arrangement of a new enclosure requires memory for the conjunction of what and where. This has long been realized and in the introduction to ‘Hippocampus as a Cognitive Map’, O’Keefe and Nadel (1978) wrote “We shall argue that the hippocampus is the core of a neural memory system providing an objective spatial framework within which the items and events of an organism's experience are located and interrelated” (emphasis added). Furthermore, in the last chapter of their book, they extended cognitive map theory to human memory for non-spatial characteristics. However, in the decades since the development of cognitive map theory, the rodent spatial navigation and human memory literatures have progressed somewhat independently.

The ideas proposed in this model are an attempt to reunify these literatures by returning to the original claim that spatial navigation is inherently a memory problem. The goal of the current study is to explain the rodent spatial navigation literature using a memory model that has the potential to also explain the human memory literature. In contrast, most grid cell models (Bellmund et al., 2016; Bush et al., 2015; Castro & Aguiar, 2014; Hasselmo, 2009; Mhatre et al., 2012; Solstad et al., 2006; Sorscher et al., 2023; Stepanyuk, 2015; Widloski & Fiete, 2014) are domain specific models of spatial navigation and as such, they do not lend themselves to explanations of human memory. Thus, the reason to prefer this model is parsimony. Rather than needing to develop a theory of memory that is separate from a theory of spatial navigation, it might be possible to address both literatures with a unified account.

This study does not attempt to falsify other theories of grid cells. Instead, this model reaches a radically different interpretation regarding the function of grid cells; an interpretation that emerges from viewing spatial navigation as a memory problem. All other grid cell models assume that an entorhinal grid cell displaying a spatially arranged grid of firing fields serves the function of spatial coding (i.e., spatial grid cells exist to support a spatial metric). In contrast, the proposed memory model of grid cells assumes that the hexagonal tiling reflects the need to keep memories separate from each other to minimize confusion and confabulation – the grid pattern is the byproduct of pattern separation between memories rather than the basis of a spatial code. 

It is now understood that grid-like firing fields can occur for non-spatial twodimensional spaces. For instance, human entorhinal cortex exhibits grid-like responses to video morph trajectories in a two-dimensional bird neck-length versus bird leg-length space (Constantinescu et al., 2016). As a general theory of learning and memory, the proposed memory model of grid cells is easily extended to explain these results (e.g., relabeling the border cell inputs in the model as neck-length and leg-length inputs). However, there are other grid cell models that can explain both spatial grid cells as well as non-spatial grid-like responses (Mok & Love, 2019; Rodríguez-Domínguez & Caplan, 2019; Stachenfeld et al., 2017; Wei et al., 2015). Similar to this memory model of grid cells, these models are also positioned to explain both the rodent spatial navigation and human memory literatures. Nevertheless, there is a key difference between this model and other grid cell models that generalize to non-spatial representations. Specifically, these other models assume that grid cells exhibiting spatial receptive fields serve the function of identifying positions in the environment (i.e., their function is spatial). As such, these models do not explain why most of the input to rodent hippocampus appears to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). This memory model of grid cells provides an answer to the apparent paucity of nonspatial cell types in rodent MTL by proposing that grid cells with spatial receptive fields have been misclassified as spatial (they are what cells rather than where cells) and that place cells are fundamentally memory cells that conjoin what and where.”

(2) Related to the last point, how about non-grid multi-field mEC cells? In theory, these also should be the same; but the author only presents perfect-look grid cells. In empirical work, clearly, this is not the case, and many mEC cells are multi-field non-grid cells (Diehl et al., 2017). Does the model find these cells? Do they play a different role? As noted by the author "Because the non-spatial attributes are constant throughout the two-dimensional surface, this results in an array of discrete memory locations that are approximately hexagonal (as explained in the Model Methods, an "online" memory consolidation process employing pattern separation rapidly turns an approximately hexagonal array into one that is precisely hexagonal). " If they are indeed all precisely hexagonal, does that mean the model doesn't have non-grid spatial cells? 

Grid cells with irregular firing fields are now considered in the discussion with the following paragraphs

“According to this model, hexagonally arranged grid cells should be the exception rather than the rule when considering more naturalistic environments. In a more ecologically valid situation, such as with landmarks, varied sounds, food sources, threats, and interactions with conspecifics, there may still be remembered locations were events occurred or remembered properties can be found, but because the non-spatial properties are non-uniform in the environment, the arrangement of memory feedback will be irregular, reflecting the varied nature of the environment. This may explain the finding that even in a situation where there are regular hexagonal grid cells, there are often irregular non-grid cells that have a reliable multi-location firing field, but the arrangement of the firing fields is irregular (Diehl et al., 2017). For instance, even when navigating in an enclosure that has uniform properties as dictated by experimental procedures, they may be other properties that were not well-controlled (e.g., a view of exterior lighting in some locations but not others), and these uncontrolled properties may produce an irregular grid (i.e., because the uncontrolled properties are reliably associated with some locations but not others, hippocampal memory feedback triggers retrieval of those properties in the associations locations).

In this memory model, there are other situations in which an irregular but reliable multilocation grid may occur, even when everything is well controlled. In the reported simulations, when the hippocampal place cells were based on variation in X/Y (as defined by Border cells), nothing else changed as a function of location, and the model rapidly produced a precise hexagonal arrangement of hippocampal place cell memories. When head direction was included (i.e., real-world variation in X, Y, and head direction), the model still produced a hexagonal arrangement as per face-centered cubic packing of memories, but this precise arrangement was slower to emerge, with place cells continuing to shift their positions until the borders of the enclosure were sufficiently well learned from multiple viewpoints. If there is real-world variation in four or more dimensions, as is likely the case in a more ecologically valid situation, it will be even harder for place cell memories to settle on a precise regular lattice. Furthermore, in the case of four dimensions, mathematicians studying the “sphere packing problem” recently concluded that densest packing is irregular (Campos et al., 2023). This may explain why the multifield grid cells for freely flying bats have a systematic minimum distance between firing fields, but their arrangement is globally irregular (Ginosar et al., 2021). Assuming that the memories encoded by a bat include not just the three real-world dimensions of variation, but also head direction, the grid will likely be irregular even under optimal conditions of laboratory control.”

(3) Theoretical reasons for why the model is put together this way, and why grid cells must be coding a non-spatial attribute: Is this account more data-driven (fits the data so formulated this way), or is it theoretical - there is a reason why place, border, grid cells are formulated to be like this. For example, is it an efficient way to code these variables? It can be both, like how the BVC model makes theoretical sense that you can use boundaries to determine a specific location (and so place cell), but also works (creates realistic place cells). 

The motivation for this model is now articulated in the new section, quoted above, titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’ Regarding the assumption that border cells provide a spatial metric, this assumption is made for the same reasons as in the BVC model. Regarding this, the text said: “These assumptions regarding border cells are based on the boundary vector cell (BVC) model of Barry et al. (2006). As in the BVC model, combinations of border cells encode where each memory occurred in the realworld X/Y plane.”. A new sentence is added to model methods, stating: “This assumption is made because border cells provide an efficient representation of Euclidean space (e.g., if the animal knows how far it is from different walls of the enclosure, this already available information can be used to calculate location).”

But in this case, the purpose of grid cell coding a non-spatial attribute, and having some kind of system where it doesn't fire at all locations seems a little arbitrary. If it's not encoding a spatial attribute, it doesn't have to have a spatial field. For example, it could fire in the whole arena - which some cells do (and don't pass the criteria of spatial cells as they are not spatially "selective" to another location, related to above).  

Some cells have a constant high firing rate, but they are the exception rather than the rule. More typically, cells habituate in the presence of ongoing excitatory drive and by doing so become sensitive to fluctuations in excitatory drive. Habituation is advantageous both in terms of metabolic cost and in terms of function (i.e., sensitivity to change). This is now explained in the following paragraph:

“In theory, a cell representing a non-spatial attribute found at all locations of an enclosure (aka, a grid cell in the context of this model), could fire constantly within the enclosure. However, in practice, cells habituate and rapidly reduce their firing rate by an order of magnitude when their preferred stimulus is presented without cessation (Abbott et al., 1997; Tsodyks & Markram, 1997). After habituation, the firing rate of the cell fluctuates with minor variation in the strength of the excitatory drive. In other words, habituation allows the cell to become sensitive to changes in the excitatory drive (Huber & O’Reilly, 2003). Thus, if there is stronger top-down memory feedback in some locations as compared to others, the cell will fire at a higher rate in those remembered locations rather than in all locations even though the attribute is found at all locations. In brief when faced with constant excitatory drive, the cell accommodates, and becomes sensitive to change in the magnitude of the excitatory drive. In the model simulation, this dynamic adaptation is captured by supposing that cells fire 5% of the time on-average across the simulation, regardless of their excitatory inputs.”

(4) Why are grid cells given such a large role for encoding non-spatial attributes? If anything, shouldn't it be lateral EC or perirhinal cortex? Of course, they both could, but there is less reason to think this, at least for rodent mEC.  

This is a good point and the following paragraph has been added to the introduction to explain that lateral EC is likely part of the explanation. But even when including lateral EC, it still appears that most of the input to hippocampus is spatial.

“One possible answer to the apparent lack of non-spatial cells in MTL is to highlight the role of the lateral entorhinal cortex (LEC) as the source of non-spatial what information for memory encoding (Deshmukh & Knierim, 2011). LEC can be contrasted with mEC, which appears to only provide where information (Boccara et al., 2010a; Diehl et al., 2017). Although it is generally true that LEC is involved in non-spatial processing, there is evidence that LEC provides some forms of spatial information (Knierim et al., 2014). The kind of non-spatial information provided by LEC appears to be in relation to objects (Connor & Knierim, 2017; Wilson et al., 2013). However, in a typical rodent spatial navigation study there are no objects within the enclosure. Thus, although the distinction between mEC and LEC is likely part of the explanation, it is still the case that rodent entorhinal input to hippocampus appears to heavily favor spatial information.”

(5) Clarification: why do place cells and grid cells differ in terms of stability in the model? Place cells are not stable initially but grid cells come out immediately. They seem directly connected so a bit unclear why; especially if place cell feedback leads to grid cell fields. There is an explanation in the text - based on grid cells coding the on-average memories, but these should be based on place cell inputs as well. So how is it that place fields are unstable then grid fields do not move at all? I wonder if a set of images or videos (gifs) showing the differences in spatial learning would be nice and clarify this point.  

In this revision, I provide a new video focused on learning of place cell memories that include head direction. This second video is in relation to the results reported in Figure 9. The short answer is that the grid fields for the non-spatial cell are based on the average across several view-dependent memories (i.e., across several place cells that have head direction sensitivity) and the average is reliable even if the place cells are unstable. The text of this explanation now reads:

“Why was the grid immediately apparent for the non-spatial attribute cell whereas the grid took considerable prior experience for the head direction cells? The answer relates to memory consolidation and the shifting nature of the hippocampal place cells. Head direction cells only produced a reliable grid once the hippocampal place cells (aka, memory cells) assumed stable locations. During the first few sessions, the hippocampal place cells were shifting their positions owing to pattern separation and consolidation. But once the place cells stabilized, they provided reliable top-down memory feedback to the head direction cells in some places but not others, thus producing a reliable grid arrangement to the firing maps of the head direction cells. In other words, for the head direction cells, the grid only appeared once the place cells stabilized. This slow stabilization of place fields is a known property (Bostock et al., 1991; Frank et al., 2004).

In the simulation, the place cells did not stabilize until a sufficient number of place cells were created (Figure 9C). Specifically, these additional memories were located immediately outside the enclosure, around all borders (Figure 9D). These “outside the box” memories served to constrain the interior place cells, locking them in position despite ongoing consolidation. This dynamic can be seen in a movie showing a representative simulation. The movie shows the positions of the head direction sensitive place cells during initial learning, and then during additional sessions of prior experience as the movie speeds up (see link in Figure 9 capture).

Why did the non-spatial grid cell (k) produce a grid immediately, before the place cells stabilized? As discussed in relation to Figure 8, the non-spatial grid cell is the projection through the 3D volume of real-world coordinates that includes X, Y, and head direction. Each grid field of a non-spatial grid cell reflects feedback from several place cells that each have a different head direction sensitivity (see for instance the allocentric pairs of memories illustrated in Figure 8C and 8D). Thus, each grid field is the average across several memories that entail different viewpoints and this averaging across memories provides stability even if the individual memories are not yet stable. This average of unstable memories produces a blurry sort of grid pattern without any prior experience.

A final piece of the puzzle relies on the same mechanism that caused the grid pattern to align with the borders as reported in the results of Figures 6 and 7. Specifically, there are some “sticky” locations with ongoing consolidation because the connection weights are bounded. Because weights cannot go below their minimum or above their maximum, it is slightly more difficult for consolidation to push or pull connection weights over the peak value or under the minimum value of the tuning curve. Thus, the place cells tend to linger in locations that correspond to the peak or trough of a border cell. There are multiple peak and trough locations but for the parameter values in this simulation, the grid pattern seen in Figure 9C shows the set of peak/trough locations that satisfy the desired spacing between memories. Thus, the average across memories shows a reliable grid field at these locations even though the memories are unstable.”

(6) Other predictions. Clearly, the model makes many interesting (and quite specific!) predictions. But does it make some known simple predictions? 

• More place cells at rewarded (or more visited) locations. Some empirical researchers seem to think this is not as obvious as it seems (e.g., Duvellle et al., 2019; JoN; Nyberg et al., 2021, Neuron Review).  

• Grid cell field moves toward reward (Butler et al., 2019; Boccera et al., 2019).  

• Grid cells deform in trapezoid (Krupic et al., 2015) and change in environments like mazes (Derikman et al., 2014).  

Thank you for these suggestions and I have added the following paragraph to the discussion:

“In terms of the animal’s internal state, all locations in the enclosure may be viewed as equally aversive and unrewarding, which is a memorable characteristic of the enclosure. Reward, or lack thereof, is arguably one of the most important nonspatial characteristics and application of this model to reward might explain the existence of goal-related activity in place cells (Hok et al., 2007; although see Duvelle et al., 2019), reflecting the need to remember rewarding locations for goal directed behavior. Furthermore, if place cell memories for a rewarding location activate entorhinal grid cells, this may explain the finding that grid cells remap in an enclosure with a rewarded location such that firing fields are attracted to that location (Boccara et al., 2019; Butler et al., 2019). Studies that introduce reward into the enclosure are an important first step in terms of examining what happens to grid cells when the animal is placed in a more varied environment.”

Regarding the changes in shape of the environment, this was discussed in the section of the paper that reads “As seen in Figure 12, because all but one of the place cells was exterior when the simulated animal was constrained to a narrow passage, the hippocampal place cell memories were no longer arranged in a hexagonal grid. This disruption of the grid array for narrow passages might explain the finding that the grid pattern (of grid cells) is disrupted in the thin corner of a trapezoid (Krupic et al., 2015) and disrupted when a previously open enclosure is converted to a hairpin maze by insertion of additional walls within the enclosure (Derdikman et al., 2009).” This particular section of the paper now appears in the Appendix and Figure 12 is now Appendix Figure 2.

Reviewer #2 (Public Review): 

The manuscript describes a new framework for thinking about the place and grid cell system in the hippocampus and entorhinal cortex in which these cells are fundamentally involved in supporting non-spatial information coding. If this framework were shown to be correct, it could have high impact because it would suggest a completely new way of thinking about the mammalian memory system in which this system is non-spatial. Although this idea is intriguing and thought-provoking, a very significant caveat is that the paper does not provide evidence that specifically supports its framework and rules out the alternate interpretations. Thus, although the work provides interesting new ideas, it leaves the reader with more questions than answers because it does not rule out any earlier ideas. 

Basically, the strongest claim in the paper, that grid cells are inherently non-spatial, cannot be specifically evaluated versus existing frameworks on the basis of the evidence that is shown here. If, for example, the author had provided behavioral experiments showing that human memory encoding/retrieval performance shifts in relation to the predictions of the model following changes in the environment, it would have been potentially exciting because it could potentially support the author's reconceptualization of this system. But in its current form, the paper merely shows that a new type of model is capable of explaining the existing findings. There is not adequate data or results to show that the new model is a significantly better fit to the data compared to earlier models, which limits the impact of the work. In fact, there are some key data points in which the earlier models seem to better fit the data.  

Overall, I would be more convinced that the findings from the paper are impactful if the author showed specific animal memory behavioral results that were only supported by their memory model but not by a purely spatial model. Perhaps the author could run new experiments to show that there are specific patterns of human or animal behavior that are only explained by their memory model and not by earlier models. But in its current form, I cannot rule out the existing frameworks and I believe some of the claims in this regard are overstated. 

As previously detailed in Box 1 and as explained in the text in several places, the model provides an explanation of several findings that remain unexplained by other theories (see “Results Uniquely Explained by the Memory Model”). But more generally this is a good point, and the initial draft failed to fully articulate why a researcher might choose this model to guide future empirical investigations. A new section in the introduction that deals with these issues, titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’ That section reads:

“Spatial navigation is inherently a memory problem – learning the spatial arrangement of a new enclosure requires memory for the conjunction of what and where. This has long been realized and in the introduction to ‘Hippocampus as a Cognitive Map’, O’Keefe and Nadel (1978) wrote “We shall argue that the hippocampus is the core of a neural memory system providing an objective spatial framework within which the items and events of an organism's experience are located and interrelated” (emphasis added). Furthermore, in the last chapter of their book, they extended cognitive map theory to human memory for non-spatial characteristics. However, in the decades since the development of cognitive map theory, the rodent spatial navigation and human memory literatures have progressed somewhat independently.

The ideas proposed in this model are an attempt to reunify these literatures by returning to the original claim that spatial navigation is inherently a memory problem. The goal of the current study is to explain the rodent spatial navigation literature using a memory model that has the potential to also explain the human memory literature. In contrast, most grid cell models (Bellmund et al., 2016; Bush et al., 2015; Castro & Aguiar, 2014; Hasselmo, 2009; Mhatre et al., 2012; Solstad et al., 2006; Sorscher et al., 2023; Stepanyuk, 2015; Widloski & Fiete, 2014) are domain specific models of spatial navigation and as such, they do not lend themselves to explanations of human memory. Thus, the reason to prefer this model is parsimony. Rather than needing to develop a theory of memory that is separate from a theory of spatial navigation, it might be possible to address both literatures with a unified account.

This study does not attempt to falsify other theories of grid cells. Instead, this model reaches a radically different interpretation regarding the function of grid cells; an interpretation that emerges from viewing spatial navigation as a memory problem. All other grid cell models assume that an entorhinal grid cell displaying a spatially arranged grid of firing fields serves the function of spatial coding (i.e., spatial grid cells exist to support a spatial metric). In contrast, the proposed memory model of grid cells assumes that the hexagonal tiling reflects the need to keep memories separate from each other to minimize confusion and confabulation – the grid pattern is the byproduct of pattern separation between memories rather than the basis of a spatial code. 

It is now understood that grid-like firing fields can occur for non-spatial twodimensional spaces. For instance, human entorhinal cortex exhibits grid-like responses to video morph trajectories in a two-dimensional bird neck-length versus bird leg-length space (Constantinescu et al., 2016). As a general theory of learning and memory, the proposed memory model of grid cells is easily extended to explain these results (e.g., relabeling the border cell inputs in the model as neck-length and leg-length inputs). However, there are other grid cell models that can explain both spatial grid cells as well as non-spatial grid-like responses (Mok & Love, 2019; Rodríguez-Domínguez & Caplan, 2019; Stachenfeld et al., 2017; Wei et al., 2015). Similar to this memory model of grid cells, these models are also positioned to explain both the rodent spatial navigation and human memory literatures. Nevertheless, there is a key difference between this model and other grid cell models that generalize to non-spatial representations. Specifically, these other models assume that grid cells exhibiting spatial receptive fields serve the function of identifying positions in the environment (i.e., their function is spatial). As such, these models do not explain why most of the input to rodent hippocampus appears to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). This memory model of grid cells provides an answer to the apparent paucity of nonspatial cell types in rodent MTL by proposing that grid cells with spatial receptive fields have been misclassified as spatial (they are what cells rather than where cells) and that place cells are fundamentally memory cells that conjoin what and where.”

- The paper does not fully take into account all the findings regarding grid cells, some of which very clearly show spatial processing in this system. For example, findings on grid-bydirection cells (e.g., Sargolini et al. 2006) would seem to suggest that the entorhinal grid system is very specifically spatial and related to path integration. Why would grid-bydirection cells be present and intertwined with grid cells in the author's memory-related reconceptualization? It seems to me that the existence of grid-by-direction cells is strong evidence that at least part of this network is specifically spatial.

Head by direction grid cells were a key part of the reported results. These grid cells naturally arise in the model as the animal forms memories (aka, hippocampal place cells) that conjoin location (as defined by border cells), head direction at the time of memory formation, and one or more non-spatial properties found at that location. In this revision, I have attempted to better explain how including head direction in hippocampal memories naturally gives rise to these cell types. The introduction to the head direction module simulations now reads:

“According to this memory model of spatial navigation, place cells are the conjunction of location, as defined by border cells, and one or more properties that are remembered to exist at that location. Such memories could, for instance, allow an animal to remember the location of a food cache (Payne et al., 2021). The next set of simulations investigates behavior of the model when one of the to-be-remembered properties is head direction at the time when the memory was formed (e.g., the direction of a pathway leading to a food cache). Indicating that head direction is an important part of place cell representations, early work on place cells in mazes found strong sensitivity to head direction, such that the place field is found in one direction of travel but not the other (McNaughton et al., 1983; Muller et al., 1994). Place cells can exhibit a less extreme version of head direction sensitivity in open field recordings (Rubin et al., 2014), but the nature of the sensitivity is more complicated, depending on location of the animal relative to the place field center (Jercog et al., 2019).

It is possible that some place cell memories do not receive head direction input, as was the case for the simulations reported in Figures 6/7 – in those simulations, place cells were entirely insensitive to head direction, owing to a lack of input from head direction cells. However, removal of head direction input to hippocampus affects place cell responses (Calton et al., 2003) and grid cell responses (Winter et al., 2015), suggesting that head direction is a key component of the circuit. Furthermore, if place cells represent episodic memories, it seems natural that they should include head direction (i.e., viewpoint at the time of memory formation).

In the simulations reported next, head direction is simply another property that is conjoined in a hippocampal place cell memory. In this case, a head direction cell should become a head direction conjunctive grid cell (i.e., a grid cell, but only when the animal is heading in a particular direction), owing to memory feedback from the hexagonal array of hippocampal place cell memories. When including head direction, the real-world dimensions of variation are across three dimensions (X, Y, and head direction) rather than two, and consolidation will cause the place cells to arrange in a three-dimensional volume. The simulation reported below demonstrates that this situation provides a “grid module”.”

- I am also concerned that the paper does not do enough to address findings regarding how the elliptical shape of grid fields shifts when boundaries of an environment compress in one direction or change shape/angles (Lever et al., & Krupic et al). Those studies show compression in grid fields based on boundary position, and I don't see how the authors' model would explain these findings.  

This finding was covered in the original submission: “For instance, perhaps one egocentric/allocentric pair of mEC grid modules is based on head direction (viewpoint) in remembered positions relative to the enclosure borders whereas a different egocentric/allocentric pair is based on head direction in remembered positions relative to landmarks exterior to the enclosure. This might explain why a deformation of the enclosure (moving in one of the walls to form a rectangle rather than a square) caused some of the grid modules but not others to undergo a deformation of the grid pattern in response to the deformation of the enclosure wall (see also Barry et al., 2007). More specifically, if there is one set of non-orthogonal dimensions for enclosure borders and the movement of one wall is too modest as to cause avoid global remapping, this would deform the grid modules based the enclosure border cells. At the same time, if other grid modules are based on exterior properties (e.g., perhaps border cells in relation to the experimental room rather than the enclosure), then those grid modules would be unperturbed by moving the enclosure wall.”

I apologize for being unclear in describing how the model might explain this result. The paragraph has been rewritten and now reads:

“Consider the possibility that one mEC grid modules is based on head direction (viewpoint) in remembered positions relative to the enclosure borders (e.g., learning the properties of the enclosure, such as the metal surface) while a different grid module is based on head direction in remembered positions relative to landmarks exterior to the enclosure (e.g., learning the properties of the experimental room, such as the sound of electronics that the animal is subject to at all locations). This might explain why a deformation of the enclosure (moving one of the walls to form a rectangle rather than a square) caused some of the grid modules but not others to undergo a deformation of the grid pattern in response to the deformation of the enclosure wall (see also Barry et al., 2007). More specifically, suppose that the movement of one wall is modest and after moving the wall, the animal views the enclosure as being the same enclosure, albeit slightly modified (e.g., when a home is partially renovated, it is still considered the same home). In this case, the set of non-orthogonal dimensions associated with enclosure borders would still be associated with the now-changed borders and any memories in reference to this border-determined space would adjust their positions accordingly in real-world coordinates (i.e., the place cells would subtly shift their positions owing to this deformation of the borders, producing a corresponding deformation of the grid). At the same time, there may be other sets of memories that are in relation to dimensions exterior to the enclosure. Because these exterior properties are unchanged, any place cells and grid cells associated with the exterior-oriented memories would be unchanged by moving the enclosure wall.”

- Are findings regarding speed modulation of grid cells problematic for the paper's memory results? 

- A further issue is that the paper does not seem to adequately address developmental findings related to the timecourses of the emergence of different cell types. In their simulation, researchers demonstrate the immediate emergence of grid fields in a novel environment, while noting that the stabilization of place cell positions takes time. However, these simulation findings contradict previous empirical developmental studies (Langston et al., 2010). Those studies showed that head direction cells show the earliest development of spatial response, followed by the appearance of place cells at a similar developmental stage. In contrast, grid cells emerge later in this developmental sequence. The gradual improvement in spatial stability in firing patterns likely plays a crucial role in the developmental trajectory of grid cells. Contrary to the model simulation, grid cells emerge later than place cells and head direction cells, yet they also hold significance in spatial mapping. 

- The model simulations suggest that certain grid patterns are acquired more gradually than others. For instance, egocentric grid cells require the stabilization of place cell memories amidst ongoing consolidation, while allocentric grid cells tend to reflect average place field positions. However, these findings seemingly conflict with empirical studies, particularly those on the conjunctive representation of distance and direction in the earliest grid cells. Previous studies show no significant differences were found in grid cells and grid cells with directional correlates across these age groups, relative to adults (Wills et al., 2012). This indicates that the combined representation of distance and direction in single mEC cells is present from the earliest ages at which grid cells emerge. 

These are good points and they have been addressed in a new section of the introduction titled ‘The Scope of the Proposed Model’. That section reads:

“The reported simulations explain why most mEC cell types in the rodent literature appear to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). Assuming that rodents can form non-spatial memories, rodent hippocampus must receive non-spatial input from entorhinal cortex. These simulations suggest that characterization of the rodent mEC cortex as primarily spatial might be incorrect if most grid cells (except perhaps head direction conjunctive grid cells) have been mischaracterized as spatial. Other literatures with other species find non-spatial representations in MTL (Gulli et al., 2020; Quiroga et al., 2005; Wixted et al., 2014) and non-spatial hippocampal memory encoding has been found in rodents (Liu et al., 2012; McEchron & Disterhoft, 1999). The proposed memory model is compatible with these results – the ideas contained in this model could be applied to nonspatial memory representations. However, surveys of cell types in rodent entorhinal cortex seem to indicate that most cells are spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). How can the rodent hippocampus encode nonspatial memories if most of its input is spatial? The goal of the reported simulations is to explain the apparent paucity of non-spatial cells in rodent entorhinal cortex by proposing that grid cells have been misclassified as spatial (see also Luo et al., 2024).

Given the simplicity of the proposed model, there are important findings that the model cannot address -- it is not that the model makes the wrong predictions but rather that it makes no predictions. The role of running speed (Kraus et al., 2015) is one such variable for which the model makes no predictions. Similarly, because the model is a rate-coded model rather than a model of oscillating spiking neurons, it makes no predictions regarding theta oscillations (Buzsáki & Moser, 2013). The model is an account of learning and memory for an adult animal, and it makes no predictions regarding the developmental (Langston et al., 2010; Muessig et al., 2015; Wills et al., 2012) or evolutionary (Rodrıguez et al., 2002) time course of different cell types. This model contains several purely spatial representations such as border cells, head direction cells, and head direction conjunctive grid cells and it may be that these purely spatial cell types emerged first, followed by the evolution and/or development of non-spatial cell types. However, this does not invalidate the model. Instead, this is a model for an adult animal that has both episodic memory capabilities and spatial navigation capabilities, irrespective of the order in which these capabilities emerged.

This model has the potential to explain context effects in memory (Godden & Baddeley, 1975; Gulli et al., 2020; Howard et al., 2005). According to this model, different grid cells represent different non-spatial characteristics and place cells represent the combination of these “context” factors and location. In the simulation, just one grid cell is simulated but the same results would emerge when simulating hundreds of different non-spatial inputs provided that all of the simulated non-spatial inputs exist throughout the recording session. However, there is evidence that hippocampus can explicitly represent the passage of time (Eichenbaum, 2014), and time is assuredly an important factor in defining episodic memory (Bright et al., 2020). Thus, although the current model addresses unique combinations of what and where, it is left to future work to incorporate representations of when in the memory model.”

Reviewer #3 (Public Review): 

A crucial assumption of the model is that the content of experience must be constant in space. It's difficult to imagine a real-world example that satisfies this assumption. Odors and sounds are used as examples. While they are often more spatially diffuse than an objects on the ground, odors and sounds have sources that are readily detectable. Animals can easily navigate to a food source or to a vocalizing conspecific. This assumption is especially problematic because it predicts that all grid cells should become silent when their preferred non-spatial attribute (e.g. a specific odor) is missing. I'm not aware of any experimental data showing that grid cells become silent. On the contrary, grid cells are known to remain active across all contexts that have been tested, including across sleep/wake states. Unlike place cells, grid cells do not seem to turn off. Since grid cells are active in all contexts, their preferred attribute must also be present in all contexts, and therefore they would not convey any information about the specific content of an experience.  

These are good points and in this revision I have attempted to explain that there is a great deal of contextual similarity across all recording sessions. One paragraph in the discussion now reads

“In a typical rodent spatial navigation study, the non-spatial attributes are wellcontrolled, existing at all locations regardless of the enclosure used during testing (hence, a grid cell in one enclosure will be a grid cell in a different enclosure). Because labs adopt standard procedures, the surfaces, odors (e.g., from cleaning), external lighting, time of day, human handler, electronic apparatus, hunger/thirst state, etc. might be the same for all recording sessions. Additionally, the animal is not allowed to interact with other animals during recording and this isolation may be an unusual and highly salient property of all recording sessions. Notably, the animal is always attached to wires during recording. The internal state of the animal (fear, aloneness, the noise of electronics, etc.) is likely similar across all recording situations and attributes of this internal state are likely represented in the hippocampus and entorhinal input to hippocampus. According to this model, hippocampal place cells are “marking” all locations in the enclosure as places where these things tend to happen.”

The proposed novelty of this theory is that other models all assume that grid cells encode space. This isn't quite true of models based on continuous attractor networks, the discussion of which is notably absent. More specifically, these models focus on the importance of intrinsic dynamics within the entorhinal cortex in generating the grid pattern. While this firing pattern is aligned to space during navigation and therefore can be used as a representation of that space, the neural dynamics are preserved even during sleep. Similarly, it is because the grid pattern does not strictly encode physical space that gridlike signals are also observed in relation to other two-dimensional continuous variables. 

These models were briefly discussed in the general discussion section and in this revision they are further discussed in the introduction in a new section, titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’ That section reads:

“Spatial navigation is inherently a memory problem – learning the spatial arrangement of a new enclosure requires memory for the conjunction of what and where. This has long been realized and in the introduction to ‘Hippocampus as a Cognitive Map’, O’Keefe and Nadel (1978) wrote “We shall argue that the hippocampus is the core of a neural memory system providing an objective spatial framework within which the items and events of an organism's experience are located and interrelated” (emphasis added). Furthermore, in the last chapter of their book, they extended cognitive map theory to human memory for non-spatial characteristics. However, in the decades since the development of cognitive map theory, the rodent spatial navigation and human memory literatures have progressed somewhat independently.

The ideas proposed in this model are an attempt to reunify these literatures by returning to the original claim that spatial navigation is inherently a memory problem. The goal of the current study is to explain the rodent spatial navigation literature using a memory model that has the potential to also explain the human memory literature. In contrast, most grid cell models (Bellmund et al., 2016; Bush et al., 2015; Castro & Aguiar, 2014; Hasselmo, 2009; Mhatre et al., 2012; Solstad et al., 2006; Sorscher et al., 2023; Stepanyuk, 2015; Widloski & Fiete, 2014) are domain specific models of spatial navigation and as such, they do not lend themselves to explanations of human memory. Thus, the reason to prefer this model is parsimony. Rather than needing to develop a theory of memory that is separate from a theory of spatial navigation, it might be possible to address both literatures with a unified account.

This study does not attempt to falsify other theories of grid cells. Instead, this model reaches a radically different interpretation regarding the function of grid cells; an interpretation that emerges from viewing spatial navigation as a memory problem. All other grid cell models assume that an entorhinal grid cell displaying a spatially arranged grid of firing fields serves the function of spatial coding (i.e., spatial grid cells exist to support a spatial metric). In contrast, the proposed memory model of grid cells assumes that the hexagonal tiling reflects the need to keep memories separate from each other to minimize confusion and confabulation – the grid pattern is the byproduct of pattern separation between memories rather than the basis of a spatial code. 

It is now understood that grid-like firing fields can occur for non-spatial two dimensional spaces. For instance, human entorhinal cortex exhibits grid-like responses to video morph trajectories in a two-dimensional bird neck-length versus bird leg-length space (Constantinescu et al., 2016). As a general theory of learning and memory, the proposed memory model of grid cells is easily extended to explain these results (e.g., relabeling the border cell inputs in the model as neck-length and leg-length inputs). However, there are other grid cell models that can explain both spatial grid cells as well as non-spatial grid-like responses (Mok & Love, 2019; Rodríguez-Domínguez & Caplan, 2019; Stachenfeld et al., 2017; Wei et al., 2015). Similar to this memory model of grid cells, these models are also positioned to explain both the rodent spatial navigation and human memory literatures. Nevertheless, there is a key difference between this model and other grid cell models that generalize to non-spatial representations. Specifically, these other models assume that grid cells exhibiting spatial receptive fields serve the function of identifying positions in the environment (i.e., their function is spatial). As such, these models do not explain why most of the input to rodent hippocampus appears to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). This memory model of grid cells provides an answer to the apparent paucity of nonspatial cell types in rodent MTL by proposing that grid cells with spatial receptive fields have been misclassified as spatial (they are what cells rather than where cells) and that place cells are fundamentally memory cells that conjoin what and where.”

The use of border cells or boundary vector cells as the main (or only) source of spatial information in the hippocampus is not well supported by experimental data. Border cells in the entorhinal cortex are not active in the center of an environment. Boundary-vector cells can fire farther away from the walls but are not found in the entorhinal cortex. They are located in the subiculum, a major output of the hippocampus. While the entorhinalhippocampal circuit is a loop, the route from boundary-vector cells to place cells is much less clear than from grid cells. Moreover, both border cells and boundary-vector cells (which are conflated in this paper) comprise a small population of neurons compared to grid cells.

AUTHOR RESPONSE: The model can be built without assuming between-border cells (early simulations with the model did not make this assumption). Regarding this issue, the text reads “Unlike the BVC model, the boundary cell representation is sparsely populated using a basis set of three cells for each of the three dimensions (i.e., 9 cells in total), such that for each of the three non-orthogonal orientations, one cell captures one border, another the opposite border, and the third cell captures positions between the opposing borders (Solstad et al., 2008). However, this is not a core assumption, and it is possible to configure the model with border cell configurations that contain two opponent border cells per dimension, without needing to assume that any cells prefer positions between the borders (with the current parameters, the model predicts there will be two border cells for each between-border cell). Similarly, it is possible to configure the model with more than 3 cells for each dimension (i.e., multiple cells representing positions between the borders).” The Solstad paper found a few cells that responded in positions between borders, but perhaps not as many as 1 out of 3 cells, such as this particular model simulation predicts. If the paucity of between-border cells is a crucial data point, the model can be reconfigured with opponent-border cells without any between border cells. The reason that 3 border cells were used rather than 2 opponent border cells was for simplicity. Because 3 head direction cells were used to capture the face-centered cubic packing of memories, the simulation also used 3 border cells per dimensions to allow a common linear sum metric when conjoining dimensions to form memories. If the border dimensions used 2 cells while head direction used 3 cells, a dimensional weighting scheme would be needed to allow this mixing of “apples and oranges” in terms of distances in the 3D space that includes head direction.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Specific questions/clarifications:  

(1) Assumption of population-based vs single unit link to biological cells: At the start, the author assumes that each unit here can be associated with a population: "the simulated activation values can be thought of as proportional to the average firing rate of an ensemble of neurons with similar inputs and outputs (O'Reilly & Munakata, 2000)." But is a 'grid cell' found here a single cell or an average of many cells? Does this mean the model assumes many cells that have different fields that are averaged, which become a grid-like unit in the model? But in biology, these are single cells? Or does it mean a grid response is an average of the place cell inputs? 

I apologize for being unclear about this. The grid cells in the model are equivalent to real single cells except that the simulation uses a ratecoded cell rather than a spiking cell. The averaging that was mentioned in the paper is across identically behaving spiking cells rather than across cells with different grid field arrangements. To better explain this, I have added the following text:

“For instance, consider a set of several thousand spiking grid cells that are identical in terms of their firing fields. At any moment, some of these identically-behaving cells will produce an action potential while others do not (i.e., the cells are not perfectly synchronized), but a snapshot of their behavior can be extracted by calculating average firing rate across the ensemble. The simulated cells in the model represent this average firing rate of identically-behaving ensembles of spiking neurons.” 

This is a mathematical short-cut to avoid simulating many spiking neurons. Because this model was compared to real spike rate maps, this real-valued average firing rate is down-sampled to produce spikes by finding the locations that produced the top 5% of real-valued activation values across the simulation.

(2) It is not clear to me why they are circular border cells/basis sets.  

In the initial submission, there was a brief paragraph describing this assumption. In this revision, that paragraph has been expanded and modified for greater clarity. It now reads:

“Because head direction is necessarily a circular dimension, it was assumed that all dimensions are circular (a circular dimension is approximately linear for nearby locations). This assumption of circular dimensions was made to keep the model relatively simple, making it easier to combine dimensions and allowing application of the same processes for all dimensions. For instance, the model requires a weight normalization process to ensure that the pattern of weights for each dimension corresponds to a possible input value along that dimension. However, the normalization for a linear dimension is necessarily different than for a circular dimension. Because the neural tuning functions were assumed to be sine waves, normalization requires that the sum of squared weights add up to a constant value. For a linear dimension, this sum of squares rule only applies to the subset of cells that are relevant to a particular value along the dimension whereas for a circular dimension, this sum of squares rule is over the entire set of cells that represent the dimension (i.e., weight normalization is easier to implement with circular dimensions). Although all dimensions were assumed to be circular for reasons of mathematical convenience and parsimony, circular dimensions may relate to the finding that human observers have difficultly re-orienting themselves in a room depending on the degree of rotational symmetry of the room (Kelly et al., 2008). In addition, this simplifying assumption allows the model to capture the finding that the population of grid cells lies on a torus (Gardner et al., 2022), although I note that the model was developed before this result was known.”

(3) Why is it 3 components? I realise that the number doesn't matter too much, but I believe more is better, so is it just for simplicity? 

In this revision, additional text has been added to explain this assumption: “To keep the model simple, the same number of cells was assumed for all dimensions and all dimensions were assumed to be circular (head direction is necessarily circular and because one dimension needed to be circular, all dimensions were assumed to be circular). Three cells per dimensions was chosen because this provides a sparse population code of each dimension, with few border cells responding between borders, with few border cells responding between borders, while allowing three separate phases of grid cells within a grid cell module (in the model, a grid cell module arises from combination of a third dimension, such as head direction, with the real-world X/Y dimensions defined by border cells).”

As a reminder, the text explaining the sparse coding of border cells reads: “However, this is not a core assumption, and it is possible to configure the model with border cell configurations that contain two opponent border cells per dimension, without needing to assume that any cells prefer positions between the borders (with the current parameters, the model predicts there will be two border cells for each between-border cell). Similarly, it is possible to configure the model with more than 3 cells for each dimension (i.e., multiple cells representing positions between the borders).”

The model can work with just two opponent cells or with more than three cells per basis set. In different simulations, I have explored these possibilities. Three was chosen because it is a convenient way to highlight the face-centered cubic packing of memories that tends to occur (FCP produces 3 alternating layers of hexagonally arranged firing fields). Thus, each of the three head direction cells captures a different layer of the FCP arrangement. A more realistic simulation might combine 6 different head direction cells tiling the head direction dimension with opponent border cells (just 2 cells for each border dimensions). Such a combination would produce responses at borders, but no responses between borders and, at the same time, the head direction cells would still reveal the FCP arrangement. However, it is not easy to find the right parameters for such a mix-and-match simulation in which different dimensions have different numbers of tuning functions (e.g., some dimensions having 2 cells while others have 3 or 6 and some dimensions being linear while others are circular). When all of the dimensions are of the same type, the simple sum that arises from multiplying the input by the weight values gives rise to Euclidean distance (see Figure 3B). With a mix-and-match model of different dimension-types, it should be possible to adjust the sum to nevertheless produce a monotonic function with Euclidean distance although I leave this to future work. To keep things simple, I assumed that all dimensions are of the same type (circular, with 3 cells per dimension).  

(4) Confusion due to the border cells/box was unclear to me. "If the period of the circular border cells was the same as the width of the box, then a memory pushed outside the box on one side would appear on the opposite side of the box, in which case the partial grid field on one side should match up with its remainder on the other side. This would entail complete confusion between opposite sides of the box, and the representation of the box would be a torus (donut-shaped) rather than a flat two-dimensional surface. To reduce confusion ..." Is this confusion of the model? Of the animal?  

This would be confusion of the animal (e.g., a memory field overlapping with one border would also appear at the opposite border in the corresponding location). At one point in model development, I made the assumption that one side of the box wraps to the other side, and I asked Trygve Solstad to run some analyses of real data to see if cells actually wrap around in this manner. He did not find any evidence of this, and so I decided to include outsidethe-box representational area which, as it turned out, allowed the model to capture other behaviors as detailed in the paper.

This section of the paper now reads:

“The cosine tuning curves of the simulated border cells represent distance from the border on both sides of the border (i.e., firing rate increases as the animal approaches the border from either the inside or the outside of the enclosure). Experimental procedures do not allow the animal to experience locations immediately outside the enclosure, but these locations remain an important part of the hypothetic representation, particularly when considering the modification of memories through consolidation (i.e., a memory created inside the enclosure might be moved to a location outside the enclosure). This symmetry about the border cell’s preferred location is needed to maintain an unbiased representation, with a constant sum of squares for the border cell inputs (see methods section). Rather than using linear dimensions, all dimensions were assumed to be circular to keep the model relatively simple. This assumption was made because head direction is necessarily a circular dimension and by having all dimensions be circular, it is easy to combine dimensions in a consistent manner to produce multidimensional hippocampal place cell memories. Thus, the border cells define a torus (or more accurately a three-torus) of possible locations. This provides a hypothetical space of locations that could be represented.

In light of the assumption to represent border cells with a circular dimension, when a memory is pushed outside the East wall of the enclosure, it would necessarily be moved to the West wall of the enclosure if the period of the circular dimension was equal to the width of the enclosure. If this were true, then the partial grid field on one side of the enclosure would match up with its remainder on the other side. Such a situation would cause the animal to become completely confused regarding opposite sides of the enclosure (a location on the West wall would be indistinguishable from the corresponding location on the East wall). To reduce confusion between opposite sides of the enclosure, the width of the enclosure in which the animal navigated (Figure 5) was assumed to be half as wide as the full period of the border cells. In other words, although the space of possible representations was a three-torus, it was assumed that the real-world twodimensional enclosure encompassed a section of the torus (e.g., a square piece of tape stuck onto the surface of a donut). The torus is better thought of as “playing field” in which different sizes and shapes of enclosure can be represented (i.e., different sizes and shapes of tape placed on the donut). Furthermore, this assumption provides representational space that is outside the box without such locations wrapping around to the opposite side of the box.”

(5) Figure 3 - This result seems to be related to whether you use Euclidean or city-block distance. If you use Euclidean distances in two dimensions wouldn't this work out fine?  

Euclidean distance was the metric used in the analysis of the two-dimensional simulation, but this did not work out. To make this clear, I have changed the label on the x-axes to read “Euclidean distance” for both the two- and three-dimensional simulations. The two-dimensional simulation produced city block behavior rather than Euclidean behavior because memory retrieval is the sum of the two dimensions, as is standard in neural networks, rather than the Euclidian distance formula, which would require that memory retrieval be the square root of the sum of squares of the two dimensions. One way to address this problem with the two-dimensional simulation would be to use a specific Euclidean-mimicking activation function rather than a simple sum of dimensions. The very first model I developed used such an activation function as applied to opponent border cells with just two dimensions (so 4 cells in total – left/right and top/down). This produced Euclidean behavior, but the activation function was implausible and did not generalize to simulations that also included head direction. In contrast, with three non-orthogonal dimensions, the simple sum of dimensions is approximately Euclidean.

(6) Final sentence of the Discussion: "However, unlike the present model, these models still assume that entorhinal grid cells represent space rather than a non-spatial attribute." I am not sure if the authors of the cited papers will agree with this. They consider the spatial cases, but most argue they can treat non-spatial features as well. What the author might mean is that they assume non-spatial features are in some metric space that, in a way, is spatial. However, I am not sure if the author would argue that non-spatial features cannot be encoded metrically (e.g., Euclidean distance based on the similarity of odours). 

In this section, when referring to “entorhinal grid cells” I was specifically referring to traditional grid cells in a rodent spatial navigation experiment. I did not mean to imply that these other theories cannot explain nonspatial grid fields, such as in the two-dimensional bird space grid cells found with humans. The way in which the proposed memory model and these other models differ is in terms of what they assume regarding the function of grid cells that exhibit spatial grid fields. In this revision, I have changed this text to read:

“These models can capture some of the grid cell results presented in the current simulations, including extension to non-spatial grid-like responses (e.g., grid field that cover a two-dimensional neck/leg length bird space). Furthermore, these models may be able to explain memory phenomena similar to the model proposed in this study. However, unlike the proposed model, these models assume that the function of entorhinal grid cells that exhibit spatial X/Y grid fields during navigation is to represent space. In contrast, the memory model proposed in this study assume that the function of spatial X/Y grid cells is to represent a non-spatial attribute; the only reason they exhibit a spatial X/Y grid is because memories of that non-spatial attribute are arranged in a hexagonal grid owing to the uncluttered/unvarying nature of the enclosure. Thus, these model do not explain why most of the input to rodent hippocampus appears to be spatial (Boccara et al., 2010b; Diehl et al., 2017; Grieves & Jeffery, 2017) whereas the proposed model can explain this situation as reflecting the miss-classification of grid cells with a spatial arrangement as providing spatial input to hippocampus.”

(7) It would be interesting to see videos/gifs of the model learning, and an idea of how many steps of trials it takes (is it capturing real-time rodent cell firing whilst foraging, or is it more abstracted, taking more trials). 

The short answer is “yes”, the model is capturing real-time rodent cell firing while foraging. This is particularly true when simulating place cell memories in the absence of head direction information, as was shown in a video provided in the initial submission in relation to Figure 4. In this revision, I have provided a second video of learning when simulating place cell memories that include head direction. This second video is in relation to the results reported in Figure 9. This shows that even when learning a three-dimensional real-world space (X, Y, and head direction), the model rapidly produces an on-average hexagonal arrangement of place cells memories owing to the slight tendency of the place cell memories to linger in some locations as compared to others during consolidation. More specifically, they are more likely to linger in the locations that are the intersections of the peaks and/or troughs of the border cells and it is this tendency that supports the immediate appearance of grid cells. However, because the place cell memories are still shifting, head direction conjunctive grid cells are slower to emerge (the head direction conjunctive grid cells require stabilization of the place cells). The video then speeds up the learning process to so how place cells eventually stabilize after sufficient learning of the borders of the enclosure from different head/view directions.

(8) One question is whether all the results have to be presented in the main text. It was difficult to see which key predictions fit the data and do so better than a spatial/navigation account. 

Thank you for this suggestion. To make the paper more readable and easier for different readers with different interests to choose different aspects of the results to read, the second half of the results have been put in an appendix. More specifically, the second half of the results concerned place cells rather than grid cells. Thus, in this revision, the main text concerns grid cell results and the appendix concerns place cell results.

Reviewer #3 (Recommendations For The Authors):  

The title could usefully be shortened to focus on the main argument that observed firing patterns could be consistent with mapping memories instead of space. It's a stretch to argue that memory is the primary role when no such data is presented (i.e., there is no comparison of competing models). 

This is a good point (I do not present evidence that conclusively indicates the function of MTL). This original title was chosen to make clear how this account is a radical departure from other accounts of grid cells. The revised title highlights that: 1) a memory model can also explain rodent single cell recording data during navigation; and 2) grid cell may not be non-spatial. The revised title is: “A Memory Model of Rodent Spatial Navigation: Place Cells are Memories Arranged in a Grid and Grid Cells are Non-spatial”

When arguing that the main role of the hippocampus is memory, I strongly suggest engaging with the work of people like Howard Eichenbaum who spent the better part of their career arguing the same (e.g. DOI:10.1152/jn.00005.2017.)  

Thank you for pointing out this important oversight. Early in introduction, I now write: “The proposal that hippocampus represents the multimodal conjunctions that define an episode is not new (Marr et al., 1991; Sutherland & Rudy, 1989) and neither is the proposal that hippocampal memory supports spatial/navigation ability (Eichenbaum, 2017). This view of the hippocampus is consistent with “feature in place” results (O’Keefe & Krupic, 2021) in which hippocampal cells respond to the conjunction of a non-spatial attribute affixed to a specific location, rather than responding more generically to any instance of a non-spatial attribute. In other words, the what/where conjunction is unique. Furthermore, the uniqueness of the what/where conjunction may be the fundamental building block of spatial memory and navigation. In reviewing the hippocampal literature, Howard Eichenbaum (2017) concludes that ‘the hippocampal system is not dedicated to spatial cognition and navigation, but organizes experiences in memory, for which spatial mapping and navigation are both a metaphor for and a prominent application of relational memory organization.’”

With a focus on episodic memory, there should be a mention of the temporal component of memory. While it may rightfully be beyond the scope of this model, it's confusing to omit time completely from the discussion. 

This issue and several others are now addressed in a new section in the introduction titled ‘The Scope of the Proposed Model’. That section reads:

“The reported simulations explain why most mEC cell types in the rodent literature appear to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). Assuming that rodents can form non-spatial memories, rodent hippocampus must receive non-spatial input from entorhinal cortex. These simulations suggest that characterization of the rodent mEC cortex as primarily spatial might be incorrect if most grid cells (except perhaps head direction conjunctive grid cells) have been mischaracterized as spatial. Other literatures with other species find non-spatial representations in MTL (Gulli et al., 2020; Quiroga et al., 2005; Wixted et al., 2014) and non-spatial hippocampal memory encoding has been found in rodents (Liu et al., 2012; McEchron & Disterhoft, 1999). The proposed memory model is compatible with these results – the ideas contained in this model could be applied to nonspatial memory representations. However, surveys of cell types in rodent entorhinal cortex seem to indicate that most cells are spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). How can the rodent hippocampus encode nonspatial memories if most of its input is spatial? The goal of the reported simulations is to explain the apparent paucity of non-spatial cells in rodent entorhinal cortex by proposing that grid cells have been misclassified as spatial (see also Luo et al., 2024).

Given the simplicity of the proposed model, there are important findings that the model cannot address -- it is not that the model makes the wrong predictions but rather that it makes no predictions. The role of running speed (Kraus et al., 2015) is one such variable for which the model makes no predictions. Similarly, because the model is a rate-coded model rather than a model of oscillating spiking neurons, it makes no predictions regarding theta oscillations (Buzsáki & Moser, 2013). The model is an account of learning and memory for an adult animal, and it makes no predictions regarding the developmental (Langston et al., 2010; Muessig et al., 2015; Wills et al., 2012) or evolutionary (Rodrıguez et al., 2002) time course of different cell types. This model contains several purely spatial representations such as border cells, head direction cells, and head direction conjunctive grid cells and it may be that these purely spatial cell types emerged first, followed by the evolution and/or development of non-spatial cell types. However, this does not invalidate the model. Instead, this is a model for an adult animal that has both episodic memory capabilities and spatial navigation capabilities, irrespective of the order in which these capabilities emerged.

This model has the potential to explain context effects in memory (Godden & Baddeley, 1975; Gulli et al., 2020; Howard et al., 2005). According to this model, different grid cells represent different non-spatial characteristics and place cells represent the combination of these “context” factors and location. In the simulation, just one grid cell is simulated but the same results would emerge when simulating hundreds of different non-spatial inputs provided that all of the simulated non-spatial inputs exist throughout the recording session. However, there is evidence that hippocampus can explicitly represent the passage of time (Eichenbaum, 2014), and time is assuredly an important factor in defining episodic memory (Bright et al., 2020). Thus, although the current model addresses unique combinations of what and where, it is left to future work to incorporate representations of when in the memory model.”

I recommend explaining the motivation of the theory in more detail in the introduction. It reads as "what if it's like this?" It would be helpful to instead highlight the limitations of current theories and argue why this theory is either a better fit for the data or is logically simpler. 

This issue and several others are now addressed in the new section in the introduction titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’, which I quoted above in response to the public reviews.

It's worth considering shortening the results section to include only those that most convincingly support the main claim. The manuscript is quite long and appears to lack focus at times. 

Thank you for this suggestion. To make the paper more readable and easier for different readers with different interests to choose different aspects of the results to read, the second half of the results have been put in an appendix. More specifically, the second half of the results concerned place cells rather than grid cells. Thus, in this revision, the main text concerns grid cell results and the appendix concerns place cell results.

The discussion of path dependence on the formation of the grid pattern is important but only briefly discussed. It may be useful to add simulations testing whether different paths (not random walks) produce distorted grid patterns. 

The short answer is that the path doesn’t affect things in general. The consolidation rule ensures equally spaced memories even if, for instance, one side of the enclosure is explored much more than the other side. As just one example, I have run simulations with a radial arm maze and even though the animal is constrained to only run on the maze arms. The memories still arrange hexagonally as memories become pushed outside the arms. Rather than adding additional simulations to study, I now briefly describe this in the model methods:

“Of note, the ability of the model to produce grid cell responses does not depend on this decision to simulate an animal taking a random walk – the same results emerge if the animal is more systematic in its path. All that matters for producing grid cell responses is that the animal visits all locations and that the animal takes on different head directions for the same location in the case of simulations that also include head direction as an input to hippocampal place cells.”

I struggle to understand in Figure 3 why retrieval strength ought to scale monotonically with Euclidean distance, and why that justifies a more complex model (three non-orthogonal dimensions). 

The introduction to this section now reads: “Animals can plan novel straight line paths to reach a known position and evidence suggests they do so by learning Euclidean representations of space (Cheng & Gallistel, 2014; Normand & Boesch, 2009; Wilkie, 1989). Thus, it was assumed that hippocampal place cells represent positions in Euclidean space (as opposed to non-Euclidean space, such a occurs with a city-block metric).”

p.17 "although the representational space is a torus (or more specifically a three-torus), it is assumed that the real-world two-dimensional surface is only a section of the torus (e.g., a square piece of tape stuck onto the surface of a donut)." I fail to understand how the realworld surface is only a part of the torus. In the existing theoretical and experimental work on toroidal topology of grid cell activity, the torus represents a very small fraction of the real world, and repeating activity on the toroidal manifold is a crucial feature of how it maps 2D space in a regular manner. Why then here do you want the torus to be larger than the realworld? 

This section has been rewritten to better explain these assumptions. The relevant paragraphs now read:

“The cosine tuning curves of the simulated border cells represent distance from the border on both sides of the border (i.e., firing rate increases as the animal approaches the border from either the inside or the outside of the enclosure). Experimental procedures do not allow the animal to experience locations immediately outside the enclosure, but these locations remain an important part of the hypothetic representation, particularly when considering the modification of memories through consolidation (i.e., a memory created inside the enclosure might be moved to a location outside the enclosure). This symmetry about the border cell’s preferred location is needed to maintain an unbiased representation, with a constant sum of squares for the border cell inputs (see methods section). Rather than using linear dimensions, all dimensions were assumed to be circular to keep the model relatively simple. This assumption was made because head direction is necessarily a circular dimension and by having all dimensions be circular, it is easy to combine dimensions in a consistent manner to produce multidimensional hippocampal place cell memories. Thus, the border cells define a torus (or more accurately a three-torus) of possible locations. This provides a hypothetical space of locations that could be represented.

In light of the assumption to represent border cells with a circular dimension, when a memory is pushed outside the East wall of the enclosure, it would necessarily be moved to the West wall of the enclosure if the period of the circular dimension was equal to the width of the enclosure. If this were true, then the partial grid field on one side of the enclosure would match up with its remainder on the other side. Such a situation would cause the animal to become completely confused regarding opposite sides of the enclosure (a location on the West wall would be indistinguishable from the corresponding location on the East wall). To reduce confusion between opposite sides of the enclosure, the width of the enclosure in which the animal navigated (Figure 5) was assumed to be half as wide as the full period of the border cells. In other words, although the space of possible representations was a three-torus, it was assumed that the real-world twodimensional enclosure encompassed a section of the torus (e.g., a square piece of tape stuck onto the surface of a donut). The torus is better thought of as “playing field” in which different sizes and shapes of enclosure can be represented (i.e., different sizes and shapes of tape placed on the donut). Furthermore, this assumption provides representational space that is outside the box without such locations wrapping around to the opposite side of the box.”

p.28 "More specifically, egocentric grid cells (e.g., head direction conjunctive grid cells) require stabilization of the place cell memories in the face of ongoing consolidation whereas allocentric grid cells reflect on-average place field positions." and p.32 "if place cells represent episodic memories, it seems natural that they should include head direction (an egocentric viewpoint)." But the head direction signal is not egocentric, it is allocentric. I'm unsure whether this is a typo or a potentially more serious conceptual misunderstanding. 

Any reference to egocentric has been removed in this revision. In the initial submission, when I used egocentric, I was referring to memories that depended on the head direction of the animal at the time of memory formation. I was using “egocentric” in relation to whether the memory was related to the animal’s personal bodily experience at the time of memory formation. But I concede that this is confusing since the ego/allo distinction is typically used to differentiate angular directions that are relative to the person (left/right) versus earth (East/West). Instead, throughout the manuscript I now refer to these as view-dependent memories since head direction would entail having a different view of the environment at the time of memory formation. I still refer to the stacking of multiple view-dependent memories on the same X/Y location as being the development of an allocentric representation however, since this can be thought of as one way to learn a cognitive map of the enclosure that is view independent.

p.37 "But if the border cells had changed their alignment with the new enclosure (e.g., if the E border dimension aligned with the North-South borders), then the place cells would have appeared to undergo global remapping as their positions rotated by 90 degrees and the grid pattern would have also rotated." But this would not be interpreted as global remapping by standard analyses of place and grid cell responses. A coherent rotation of firing patterns is not interpreted as remapping. 

This sentence now reads: “But if the border cells had changed their alignment with the new enclosure (e.g., if the E border dimension aligned with the North-South borders), then the place cells would remain in their same positions relative to the now-rotated borders (i.e., no remapping relative to the enclosure) and the corresponding grid cells would also retain their same alignment relative to the enclosure.”

p.37 "this is more accurately described as partial remapping (nearly all place fields were unaffected)." If nearly all place fields were unaffected, this should be interpreted as a stable map. Partial remapping is a mix of stability, rate remapping, and global remapping within a population of place cells. 

This sentence has been removed.

p.40 "The dependence of grid cell responses on memory may help explain why grid cells have been found for bats crawling on a two-dimensional surface (Yartsev et al., 2011), but three-dimensional grid cells have never been observed for flying bats." This is not true. Ginosar et al. (2021) observed 3D grid cells in flying bats.  

Thank you for highlighting this issue. In the initial submission I was using “grid cell” to mean a cell that produced a precise hexagonal grid, which is not the case for the 3D grid cells in bats. In this revision, I now discuss grid cell that produce irregular grid fields, writing:

“According to this model, hexagonally arranged grid cells should be the exception rather than the rule when considering more naturalistic environments. In a more ecologically valid situation, such as with landmarks, varied sounds, food sources, threats, and interactions with conspecifics, there may still be remembered locations were events occurred or remembered properties can be found, but because the non-spatial properties are non-uniform in the environment, the arrangement of memory feedback will be irregular, reflecting the varied nature of the environment. This may explain the finding that even in a situation where there are regular hexagonal grid cells, there are often irregular non-grid cells that have a reliable multi-location firing field, but the arrangement of the firing fields is irregular (Diehl et al., 2017). For instance, even when navigating in an enclosure that has uniform properties as dictated by experimental procedures, they may be other properties that were not well-controlled (e.g., a view of exterior lighting in some locations but not others), and these uncontrolled properties may produce an irregular grid (i.e., because the uncontrolled properties are reliably associated with some locations but not others, hippocampal memory feedback triggers retrieval of those properties in the associations locations).

In this memory model, there are other situations in which an irregular but reliable multi-location grid may occur, even when everything is well controlled. In the reported simulations, when the hippocampal place cells were based on variation in X/Y (as defined by Border cells), nothing else changed as a function of location, and the model rapidly produced a precise hexagonal arrangement of hippocampal place cell memories. When head direction was included (i.e., real-world variation in X, Y, and head direction), the model still produced a hexagonal arrangement as per face centered cubic packing of memories, but this precise arrangement was slower to emerge, with place cells continuing to shift their positions until the borders of the enclosure were sufficiently well learned from multiple viewpoints. If there is realworld variation in four or more dimensions, as is likely the case in a more ecologically valid situation, it will be even harder for place cell memories to settle on a precise regular lattice. Furthermore, in the case of four dimensions, mathematicians studying the “sphere packing problem” recently concluded that densest packing is irregular (Campos et al., 2023). This may explain why the multifield grid cells for freely flying bats have a systematic minimum distance between firing fields, but their arrangement is globally irregular (Ginosar et al., 2021). Assuming that the memories encoded by a bat include not just the three realworld dimensions of variation, but also head direction, the grid will likely be irregular even under optimal conditions of laboratory control.”

Multiple typos are found on page 25, end of paragraph 3: "More specifically, if there is one set of non-orthogonal dimensions for enclosure borders and the movement of one wall is too modest as to cause avoid global remapping, this would deform the grid modules based the enclosure border cells."

As detailed above in the response the public reviews, this paragraph has been rewritten.

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

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Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 231, in init self.links = row['document']['link'] TypeError: string indices must be integers

DOI: 10.1038/s41467-023-44343-3

Resource: RRID:BDSC_38937

Curator: @mzhang007

SciCrunch record: RRID:BDSC_38937

What is this?

DOI: 10.34133/research.0548

Resource: (Cell Signaling Technology Cat# 24307, RRID:AB_2651140)

Curator: @scibot

SciCrunch record: RRID:AB_2651140

What is this?

DOI: 10.3389/fimmu.2024.1466870

Resource: (Dr. Julio Coll; el Instituto Nacional de Investigaciones y Experiencias Agronómicas y Forestales Cat# 2C9, RRID:AB_2716276)

Curator: @scibot

SciCrunch record: RRID:AB_2716276

What is this?

DOI: 10.1002/jmv.70088

Resource: (ATCC Cat# HTB-33, RRID:CVCL_1401)

Curator: @scibot

SciCrunch record: RRID:CVCL_1401

What is this?

Sommaire minuté des temps forts du webinaire

0:00 - 5:00 Introduction et présentation du Crips Ile-de-France

• Juliette Descroix et Jen Cuba se présentent et expliquent leur rôle au sein du Crips Ile-de-France.
• Le Crips, une association d'intérêt public, lutte contre le VIH et s'engage dans la prévention et la promotion de la santé, notamment pour les publics scolaires et les personnes en situation de vulnérabilité.
• L'accent est mis sur l'éducation à la sexualité, la prévention des consommations de drogues, les écrans, le bien-être et la santé mentale.

5:00 - 15:00 Importance de l'interactivité et des compétences psychosociales

• L'éducation complète à la sexualité vise à développer des compétences, des attitudes et des valeurs pour que chacun puisse décider et bien vivre sa sexualité.
• Une anecdote sur une expérience menée aux États-Unis en 1943 met en lumière l'efficacité de l'interaction et des discussions pour l'adoption de comportements souhaités. Les groupes de discussion se sont avérés dix fois plus efficaces que les conférences.
• Les formats interactifs, centrés sur les situations réelles des participants, sont plus efficaces que la simple transmission d'informations.

15:00 - 25:00 Les différentes postures professionnelles en animation

• Trois postures sont présentées : la facilitation, la formation et l'animation, illustrées par l'image d'un puzzle.
• L'importance de passer d'une posture à l'autre pour une animation plus efficace est soulignée.
• Des exemples de questions illustrant chaque posture sont donnés.

25:00 - 35:00 Analyse de campagnes de prévention et l'importance d'une approche positive

• Deux jeux d'affiches, l'un sur le VIH et l'autre sur le tabac, sont analysés pour illustrer différentes approches de prévention.
• Les affiches basées sur la peur sont comparées à celles qui encouragent et donnent du pouvoir aux individus.
• L'approche positive, axée sur l'empowerment, est mise en avant comme étant plus efficace.
• L'approche par la peur peut stigmatiser et éloigner des soins.

35:00 - 50:00 Définition et importance des compétences psychosociales

• Définition des compétences psychosociales : un ensemble de capacités cognitives, émotionnelles et sociales pour l'autonomisation et le bien-être.
• Leur importance pour l'adoption de comportements favorables à la santé et le développement personnel est soulignée.
• Elles sont divisées en trois catégories : cognitives, sociales et émotionnelles. Des exemples concrets sont donnés.
• Travailler sur ces compétences nécessite des interventions multiples et répétées dans le temps.

50:00 - 1:00:00 L'inclusion, un concept clé de l'approche du Crips

• L'inclusion et l'inclusivité, nées des luttes des personnes handicapées, féministes et séropositives, visent à une ouverture et une bienveillance active.
• Il est crucial d'avoir conscience de ses propres représentations et de les questionner pour une approche plus inclusive.
• Des exemples concrets, comme l'utilisation du pronom approprié, sont donnés pour illustrer l'inclusion en pratique.
• Des pistes concrètes pour une approche inclusive sont proposées, comme la connaissance des publics, l'analyse de son espace de travail et l'adaptation des projets.

1:00:00 - 1:10:00 Les thématiques abordées en éducation à la sexualité

• L'éducation à la sexualité couvre des thématiques biologiques, juridiques et sociales.
• Le Crips met l'accent sur la promotion de la santé, notamment le consentement, les relations interpersonnelles et les discriminations.
• L'importance du débat et de l'échange sur des sujets comme le consentement est mise en avant.

1:10:00 - 1:20:00 Guides et ressources pour construire une intervention

• Les principes directeurs internationaux de l'UNESCO sur l'éducation à la sexualité proposent des thématiques adaptées à l'âge.
• Le projet de programme de l'Éducation nationale sur l'éducation à la sexualité propose une trame pour les interventions en milieu scolaire.
• L'importance de s'appuyer sur ces recommandations pour la légitimité et la structuration des interventions est soulignée.

1:20:00 - 1:35:00 Posture professionnelle, valeurs et techniques d'animation

• Les valeurs clés pour l'animation : l'empathie, la confiance, l'authenticité et la suspension du jugement.
• L'importance d'incarner ces valeurs pour créer un climat de confiance et de respect est mise en avant.
• Techniques d'animation : questions ouvertes, écoute active, reformulation et importance de la réflexion plutôt que de la persuasion.
• Le guide "Parole de Pro" du Crips propose des conseils pour bien choisir ses mots et éviter les malentendus.

1:35:00 - 1:50:00 Méthodologie et déroulé d'une animation

• La courbe de l'attention et de la participation du groupe illustre le rythme d'une animation.
• Différentes phases sont proposées pour structurer l'animation : attirer l'attention, susciter l'enthousiasme, développer le cœur du sujet, retranscrire et conclure.

1:50:00 - 2:00:00 Le cadre de l'animation et sa co-construction avec le groupe

• Le cadre, co-construit avec le groupe, vise à protéger l'intimité, suspendre le jugement et respecter la parole de chacun.
• L'importance d'impliquer tous les participants, y compris les adultes présents, dans le respect du cadre est soulignée.
• L'adaptation du cadre en fonction du groupe et de la thématique est encouragée.

2:00:00 - Fin Conclusion et présentation des ressources du Crips

• La posture et la préparation sont les clés d'une animation réussie.
• Présentation de l'Animatek, des formations, des animations et des ressources en ligne du Crips.

Questions/Réponses

• Gestion des informations fausses ou inappropriées : relancer la question au groupe, questionner la source de l'information, se référer au cadre juridique si nécessaire.
• Importance de ne pas stigmatiser les opinions, même si elles sont différentes.
• La mixité en animation est privilégiée, sauf cas particuliers et en fonction de l'âge.
• Pas de diplôme spécifique requis pour animer des séances d'éducation à la sexualité, mais l'importance de la formation et de la connaissance de ses limites est mise en avant.
• La confidentialité est nuancée : il ne s'agit pas de tout garder secret, mais de respecter l'intimité et l'anonymat des personnes.
• En cas de problématiques particulières, comme le harcèlement, adapter le cadre et être attentif aux tensions au sein du groupe.
• Proposer des modes de participation variés pour permettre à chacun de s'exprimer.

limita el agujero ciatico mayor y menor

en mi opinión no todas son en ambientes controlados, pero además eso no compromete la calidad de los datos.

qué entiendes tú por global? sugiero quitarlo, global se entiende de todo el planeta y no lo es, solo es de estados unidos

está mal citado el primer apellido no es Lorentzen sino Chatellier

According to all known laws of aviation,

there is no way a bee should be able to fly.

Its wings are too small to get its fat little body off the ground.

The bee, of course, flies anyway

because bees don't care what humans think is impossible.

Yellow, black. Yellow, black. Yellow, black. Yellow, black.

Ooh, black and yellow! Let's shake it up a little.

Barry! Breakfast is ready!

Ooming!

Hang on a second.

Hello?

Barry?

Adam?

Oan you believe this is happening?

I can't. I'll pick you up.

Looking sharp.

Use the stairs. Your father paid good money for those.

Sorry. I'm excited.

Here's the graduate. We're very proud of you, son.

A perfect report card, all B's.

Very proud.

Ma! I got a thing going here.

You got lint on your fuzz.

Ow! That's me!

Wave to us! We'll be in row 118,000.

Bye!

Barry, I told you, stop flying in the house!

Hey, Adam.

Hey, Barry.

Is that fuzz gel?

A little. Special day, graduation.

Never thought I'd make it.

Three days grade school, three days high school.

Those were awkward.

Three days college. I'm glad I took a day and hitchhiked around the hive.

You did come back different.

Hi, Barry.

Artie, growing a mustache? Looks good.

Hear about Frankie?

Yeah.

You going to the funeral?

No, I'm not going.

Everybody knows, sting someone, you die.

Don't waste it on a squirrel. Such a hothead.

I guess he could have just gotten out of the way.

I love this incorporating an amusement park into our day.

That's why we don't need vacations.

Boy, quite a bit of pomp… under the circumstances.

Well, Adam, today we are men.

We are!

Bee-men.

Amen!

Hallelujah!

Students, faculty, distinguished bees,

please welcome Dean Buzzwell.

Welcome, New Hive Oity graduating class of…

…9:15.

That concludes our ceremonies.

And begins your career at Honex Industries!

Will we pick ourjob today?

I heard it's just orientation.

Heads up! Here we go.

Keep your hands and antennas inside the tram at all times.

Wonder what it'll be like? A little scary. Welcome to Honex, a division of Honesco

and a part of the Hexagon Group.

This is it!

Wow.

Wow.

We know that you, as a bee, have worked your whole life

to get to the point where you can work for your whole life.

Honey begins when our valiant Pollen Jocks bring the nectar to the hive.

Our top-secret formula

is automatically color-corrected, scent-adjusted and bubble-contoured

into this soothing sweet syrup

with its distinctive golden glow you know as…

Honey!

That girl was hot.

She's my cousin!

She is?

Yes, we're all cousins.

Right. You're right.

At Honex, we constantly strive

to improve every aspect of bee existence.

These bees are stress-testing a new helmet technology.

What do you think he makes? Not enough. Here we have our latest advancement, the Krelman.

What does that do? Oatches that little strand of honey that hangs after you pour it. Saves us millions.

Oan anyone work on the Krelman?

Of course. Most bee jobs are small ones. But bees know

that every small job, if it's done well, means a lot.

But choose carefully

because you'll stay in the job you pick for the rest of your life.

The same job the rest of your life? I didn't know that.

What's the difference?

You'll be happy to know that bees, as a species, haven't had one day off

in 27 million years.

So you'll just work us to death?

We'll sure try.

Wow! That blew my mind!

"What's the difference?" How can you say that?

One job forever? That's an insane choice to have to make.

I'm relieved. Now we only have to make one decision in life.

But, Adam, how could they never have told us that?

Why would you question anything? We're bees.

We're the most perfectly functioning society on Earth.

You ever think maybe things work a little too well here?

Like what? Give me one example.

I don't know. But you know what I'm talking about.

Please clear the gate. Royal Nectar Force on approach.

Wait a second. Oheck it out.

Hey, those are Pollen Jocks! Wow. I've never seen them this close.

They know what it's like outside the hive.

Yeah, but some don't come back.

Hey, Jocks! Hi, Jocks! You guys did great!

You're monsters! You're sky freaks! I love it! I love it!

I wonder where they were. I don't know. Their day's not planned.

Outside the hive, flying who knows where, doing who knows what.

You can'tjust decide to be a Pollen Jock. You have to be bred for that.

Right.

Look. That's more pollen than you and I will see in a lifetime.

It's just a status symbol. Bees make too much of it.

Perhaps. Unless you're wearing it and the ladies see you wearing it.

Those ladies? Aren't they our cousins too?

Distant. Distant.

Look at these two.

Oouple of Hive Harrys. Let's have fun with them. It must be dangerous being a Pollen Jock.

Yeah. Once a bear pinned me against a mushroom!

He had a paw on my throat, and with the other, he was slapping me!

Oh, my! I never thought I'd knock him out. What were you doing during this?

Trying to alert the authorities.

I can autograph that.

A little gusty out there today, wasn't it, comrades?

Yeah. Gusty.

We're hitting a sunflower patch six miles from here tomorrow.

Six miles, huh? Barry! A puddle jump for us, but maybe you're not up for it.

Maybe I am. You are not! We're going 0900 at J-Gate.

What do you think, buzzy-boy? Are you bee enough?

I might be. It all depends on what 0900 means.

Hey, Honex!

Dad, you surprised me.

You decide what you're interested in?

Well, there's a lot of choices. But you only get one. Do you ever get bored doing the same job every day?

Son, let me tell you about stirring.

You grab that stick, and you just move it around, and you stir it around.

You get yourself into a rhythm. It's a beautiful thing.

You know, Dad, the more I think about it,

maybe the honey field just isn't right for me.

You were thinking of what, making balloon animals?

That's a bad job for a guy with a stinger.

Janet, your son's not sure he wants to go into honey!

Barry, you are so funny sometimes. I'm not trying to be funny. You're not funny! You're going into honey. Our son, the stirrer!

You're gonna be a stirrer? No one's listening to me! Wait till you see the sticks I have.

I could say anything right now. I'm gonna get an ant tattoo!

Let's open some honey and celebrate!

Maybe I'll pierce my thorax. Shave my antennae.

Shack up with a grasshopper. Get a gold tooth and call everybody "dawg"!

I'm so proud.

We're starting work today! Today's the day. Oome on! All the good jobs will be gone.

Yeah, right.

Pollen counting, stunt bee, pouring, stirrer, front desk, hair removal…

Is it still available? Hang on. Two left! One of them's yours! Oongratulations! Step to the side.

What'd you get? Picking crud out. Stellar! Wow!

Oouple of newbies?

Yes, sir! Our first day! We are ready!

Make your choice.

You want to go first? No, you go. Oh, my. What's available?

Restroom attendant's open, not for the reason you think.

Any chance of getting the Krelman? Sure, you're on. I'm sorry, the Krelman just closed out.

Wax monkey's always open.

The Krelman opened up again.

What happened?

A bee died. Makes an opening. See? He's dead. Another dead one.

Deady. Deadified. Two more dead.

Dead from the neck up. Dead from the neck down. That's life!

Oh, this is so hard!

Heating, cooling, stunt bee, pourer, stirrer,

humming, inspector number seven, lint coordinator, stripe supervisor,

mite wrangler. Barry, what do you think I should… Barry?

Barry!

All right, we've got the sunflower patch in quadrant nine…

What happened to you? Where are you?

I'm going out.

Out? Out where?

Out there.

Oh, no!

I have to, before I go to work for the rest of my life.

You're gonna die! You're crazy! Hello?

Another call coming in.

If anyone's feeling brave, there's a Korean deli on 83rd

that gets their roses today.

Hey, guys.

Look at that. Isn't that the kid we saw yesterday? Hold it, son, flight deck's restricted.

It's OK, Lou. We're gonna take him up.

Really? Feeling lucky, are you?

Sign here, here. Just initial that.

Thank you. OK. You got a rain advisory today,

and as you all know, bees cannot fly in rain.

So be careful. As always, watch your brooms,

hockey sticks, dogs, birds, bears and bats.

Also, I got a couple of reports of root beer being poured on us.

Murphy's in a home because of it, babbling like a cicada!

That's awful. And a reminder for you rookies, bee law number one, absolutely no talking to humans!

All right, launch positions!

Buzz, buzz, buzz, buzz! Buzz, buzz, buzz, buzz! Buzz, buzz, buzz, buzz!

Black and yellow!

Hello!

You ready for this, hot shot?

Yeah. Yeah, bring it on.

Wind, check.

Antennae, check.

Nectar pack, check.

Wings, check.

Stinger, check.

Scared out of my shorts, check.

OK, ladies,

let's move it out!

Pound those petunias, you striped stem-suckers!

All of you, drain those flowers!

Wow! I'm out!

I can't believe I'm out!

So blue.

I feel so fast and free!

Box kite!

Wow!

Flowers!

This is Blue Leader. We have roses visual.

Bring it around 30 degrees and hold.

Roses!

30 degrees, roger. Bringing it around.

Stand to the side, kid. It's got a bit of a kick.

That is one nectar collector!

Ever see pollination up close? No, sir. I pick up some pollen here, sprinkle it over here. Maybe a dash over there,

a pinch on that one. See that? It's a little bit of magic.

That's amazing. Why do we do that?

That's pollen power. More pollen, more flowers, more nectar, more honey for us.

Oool.

I'm picking up a lot of bright yellow. Oould be daisies. Don't we need those?

Oopy that visual.

Wait. One of these flowers seems to be on the move.

Say again? You're reporting a moving flower?

Affirmative.

That was on the line!

This is the coolest. What is it?

I don't know, but I'm loving this color.

It smells good. Not like a flower, but I like it.

Yeah, fuzzy.

Ohemical-y.

Oareful, guys. It's a little grabby.

My sweet lord of bees!

Oandy-brain, get off there!

Problem!

Guys! This could be bad. Affirmative.

Very close.

Gonna hurt.

Mama's little boy.

You are way out of position, rookie!

Ooming in at you like a missile!

Help me!

I don't think these are flowers.

Should we tell him? I think he knows. What is this?!

Match point!

You can start packing up, honey, because you're about to eat it!

Yowser!

Gross.

There's a bee in the car!

Do something!

I'm driving!

Hi, bee.

He's back here!

He's going to sting me!

Nobody move. If you don't move, he won't sting you. Freeze!

He blinked!

Spray him, Granny!

What are you doing?!

Wow… the tension level out here is unbelievable.

I gotta get home.

Oan't fly in rain.

Oan't fly in rain.

Oan't fly in rain.

Mayday! Mayday! Bee going down!

Ken, could you close the window please?

Ken, could you close the window please?

Oheck out my new resume. I made it into a fold-out brochure.

You see? Folds out.

Oh, no. More humans. I don't need this.

What was that?

Maybe this time. This time. This time. This time! This time! This…

Drapes!

That is diabolical.

It's fantastic. It's got all my special skills, even my top-ten favorite movies.

What's number one? Star Wars?

Nah, I don't go for that…

…kind of stuff.

No wonder we shouldn't talk to them. They're out of their minds.

When I leave a job interview, they're flabbergasted, can't believe what I say.

There's the sun. Maybe that's a way out.

I don't remember the sun having a big 75 on it.

I predicted global warming.

I could feel it getting hotter. At first I thought it was just me.

Wait! Stop! Bee!

Stand back. These are winter boots.

Wait!

Don't kill him!

You know I'm allergic to them! This thing could kill me!

Why does his life have less value than yours?

Why does his life have any less value than mine? Is that your statement?

I'm just saying all life has value. You don't know what he's capable of feeling.

My brochure!

There you go, little guy.

I'm not scared of him. It's an allergic thing.

Put that on your resume brochure.

My whole face could puff up.

Make it one of your special skills.

Knocking someone out is also a special skill.

Right. Bye, Vanessa. Thanks.

Vanessa, next week? Yogurt night?

Sure, Ken. You know, whatever.

You could put carob chips on there.

Bye.

Supposed to be less calories.

Bye.

I gotta say something.

She saved my life. I gotta say something.

All right, here it goes.

Nah.

What would I say?

I could really get in trouble.

It's a bee law. You're not supposed to talk to a human.

I can't believe I'm doing this.

I've got to.

Oh, I can't do it. Oome on!

No. Yes. No.

Do it. I can't.

How should I start it? "You like jazz?" No, that's no good.

Here she comes! Speak, you fool!

Hi!

I'm sorry.

You're talking. Yes, I know. You're talking!

I'm so sorry.

No, it's OK. It's fine. I know I'm dreaming.

But I don't recall going to bed.

Well, I'm sure this is very disconcerting.

This is a bit of a surprise to me. I mean, you're a bee!

I am. And I'm not supposed to be doing this,

but they were all trying to kill me.

And if it wasn't for you…

I had to thank you. It's just how I was raised.

That was a little weird.

I'm talking with a bee. Yeah. I'm talking to a bee. And the bee is talking to me!

I just want to say I'm grateful. I'll leave now.

Wait! How did you learn to do that? What? The talking thing.

Same way you did, I guess. "Mama, Dada, honey." You pick it up.

That's very funny. Yeah. Bees are funny. If we didn't laugh, we'd cry with what we have to deal with.

Anyway…

Oan I…

…get you something?

Like what? I don't know. I mean… I don't know. Ooffee?

I don't want to put you out.

It's no trouble. It takes two minutes.

It's just coffee.

I hate to impose.

Don't be ridiculous!

Actually, I would love a cup.

Hey, you want rum cake?

I shouldn't.

Have some.

No, I can't.

Oome on!

I'm trying to lose a couple micrograms.

Where? These stripes don't help. You look great!

I don't know if you know anything about fashion.

Are you all right?

No.

He's making the tie in the cab as they're flying up Madison.

He finally gets there.

He runs up the steps into the church. The wedding is on.

And he says, "Watermelon? I thought you said Guatemalan.

Why would I marry a watermelon?"

Is that a bee joke?

That's the kind of stuff we do.

Yeah, different.

So, what are you gonna do, Barry?

About work? I don't know.

I want to do my part for the hive, but I can't do it the way they want.

I know how you feel.

You do? Sure. My parents wanted me to be a lawyer or a doctor, but I wanted to be a florist.

Really? My only interest is flowers. Our new queen was just elected with that same campaign slogan.

Anyway, if you look…

There's my hive right there. See it?

You're in Sheep Meadow!

Yes! I'm right off the Turtle Pond!

No way! I know that area. I lost a toe ring there once.

Why do girls put rings on their toes?

Why not?

It's like putting a hat on your knee.

Maybe I'll try that.

You all right, ma'am?

Oh, yeah. Fine.

Just having two cups of coffee!

Anyway, this has been great. Thanks for the coffee.

Yeah, it's no trouble.

Sorry I couldn't finish it. If I did, I'd be up the rest of my life.

Are you…?

Oan I take a piece of this with me?

Sure! Here, have a crumb.

Thanks! Yeah. All right. Well, then… I guess I'll see you around.

Or not.

OK, Barry.

And thank you so much again… for before.

Oh, that? That was nothing.

Well, not nothing, but… Anyway…

This can't possibly work.

He's all set to go. We may as well try it.

OK, Dave, pull the chute.

Sounds amazing. It was amazing! It was the scariest, happiest moment of my life.

Humans! I can't believe you were with humans!

Giant, scary humans! What were they like?

Huge and crazy. They talk crazy.

They eat crazy giant things. They drive crazy.

Do they try and kill you, like on TV?

Some of them. But some of them don't.

How'd you get back?

Poodle.

You did it, and I'm glad. You saw whatever you wanted to see.

You had your "experience." Now you can pick out yourjob and be normal.

Well… Well? Well, I met someone.

You did? Was she Bee-ish?

A wasp?! Your parents will kill you!

No, no, no, not a wasp.

Spider?

I'm not attracted to spiders.

I know it's the hottest thing, with the eight legs and all.

I can't get by that face.

So who is she?

She's… human.

No, no. That's a bee law. You wouldn't break a bee law.

Her name's Vanessa. Oh, boy. She's so nice. And she's a florist!

Oh, no! You're dating a human florist!

We're not dating.

You're flying outside the hive, talking to humans that attack our homes

with power washers and M-80s! One-eighth a stick of dynamite!

She saved my life! And she understands me.

This is over!

Eat this.

This is not over! What was that?

They call it a crumb. It was so stingin' stripey! And that's not what they eat. That's what falls off what they eat!

You know what a Oinnabon is? No. It's bread and cinnamon and frosting. They heat it up…

Sit down!

…really hot!

Listen to me! We are not them! We're us. There's us and there's them!

Yes, but who can deny the heart that is yearning?

There's no yearning. Stop yearning. Listen to me!

You have got to start thinking bee, my friend. Thinking bee!

Thinking bee. Thinking bee. Thinking bee! Thinking bee! Thinking bee! Thinking bee!

There he is. He's in the pool.

You know what your problem is, Barry?

I gotta start thinking bee?

How much longer will this go on?

It's been three days! Why aren't you working?

I've got a lot of big life decisions to think about.

What life? You have no life! You have no job. You're barely a bee!

Would it kill you to make a little honey?

Barry, come out. Your father's talking to you.

Martin, would you talk to him?

Barry, I'm talking to you!

You coming?

Got everything?

All set!

Go ahead. I'll catch up.

Don't be too long.

Watch this!

Vanessa!

We're still here. I told you not to yell at him. He doesn't respond to yelling!

Then why yell at me? Because you don't listen! I'm not listening to this.

Sorry, I've gotta go.

Where are you going? I'm meeting a friend. A girl? Is this why you can't decide?

Bye.

I just hope she's Bee-ish.

They have a huge parade of flowers every year in Pasadena?

To be in the Tournament of Roses, that's every florist's dream!

Up on a float, surrounded by flowers, crowds cheering.

A tournament. Do the roses compete in athletic events?

No. All right, I've got one. How come you don't fly everywhere?

It's exhausting. Why don't you run everywhere? It's faster.

Yeah, OK, I see, I see. All right, your turn.

TiVo. You can just freeze live TV? That's insane!

You don't have that?

We have Hivo, but it's a disease. It's a horrible, horrible disease.

Oh, my.

Dumb bees!

You must want to sting all those jerks.

We try not to sting. It's usually fatal for us.

So you have to watch your temper.

Very carefully. You kick a wall, take a walk,

write an angry letter and throw it out. Work through it like any emotion:

Anger, jealousy, lust.

Oh, my goodness! Are you OK?

Yeah.

What is wrong with you?! It's a bug. He's not bothering anybody. Get out of here, you creep!

What was that? A Pic 'N' Save circular?

Yeah, it was. How did you know?

It felt like about 10 pages. Seventy-five is pretty much our limit.

You've really got that down to a science.

I lost a cousin to Italian Vogue. I'll bet. What in the name of Mighty Hercules is this?

How did this get here? Oute Bee, Golden Blossom,

Ray Liotta Private Select?

Is he that actor?

I never heard of him.

Why is this here?

For people. We eat it.

You don't have enough food of your own?

Well, yes.

How do you get it?

Bees make it.

I know who makes it!

And it's hard to make it!

There's heating, cooling, stirring. You need a whole Krelman thing!

It's organic. It's our-ganic! It's just honey, Barry.

Just what?!

Bees don't know about this! This is stealing! A lot of stealing!

You've taken our homes, schools, hospitals! This is all we have!

And it's on sale?! I'm getting to the bottom of this.

I'm getting to the bottom of all of this!

Hey, Hector.

You almost done? Almost. He is here. I sense it.

Well, I guess I'll go home now

and just leave this nice honey out, with no one around.

You're busted, box boy!

I knew I heard something. So you can talk!

I can talk. And now you'll start talking!

Where you getting the sweet stuff? Who's your supplier?

I don't understand. I thought we were friends.

The last thing we want to do is upset bees!

You're too late! It's ours now!

You, sir, have crossed the wrong sword!

You, sir, will be lunch for my iguana, Ignacio!

Where is the honey coming from?

Tell me where!

Honey Farms! It comes from Honey Farms!

Orazy person!

What horrible thing has happened here?

These faces, they never knew what hit them. And now

they're on the road to nowhere!

Just keep still.

What? You're not dead?

Do I look dead? They will wipe anything that moves. Where you headed?

To Honey Farms. I am onto something huge here.

I'm going to Alaska. Moose blood, crazy stuff. Blows your head off!

I'm going to Tacoma.

And you? He really is dead. All right.

Uh-oh!

What is that?!

Oh, no!

A wiper! Triple blade!

Triple blade?

Jump on! It's your only chance, bee!

Why does everything have to be so doggone clean?!

How much do you people need to see?!

Open your eyes! Stick your head out the window!

From NPR News in Washington, I'm Oarl Kasell.

But don't kill no more bugs!

Bee!

Moose blood guy!!

You hear something?

Like what?

Like tiny screaming.

Turn off the radio.

Whassup, bee boy?

Hey, Blood.

Just a row of honey jars, as far as the eye could see.

Wow!

I assume wherever this truck goes is where they're getting it.

I mean, that honey's ours.

Bees hang tight. We're all jammed in. It's a close community.

Not us, man. We on our own. Every mosquito on his own.

What if you get in trouble? You a mosquito, you in trouble. Nobody likes us. They just smack. See a mosquito, smack, smack!

At least you're out in the world. You must meet girls.

Mosquito girls try to trade up, get with a moth, dragonfly.

Mosquito girl don't want no mosquito.

You got to be kidding me!

Mooseblood's about to leave the building! So long, bee!

Hey, guys! Mooseblood! I knew I'd catch y'all down here. Did you bring your crazy straw?

We throw it in jars, slap a label on it, and it's pretty much pure profit.

What is this place?

A bee's got a brain the size of a pinhead.

They are pinheads!

Pinhead.

Oheck out the new smoker. Oh, sweet. That's the one you want. The Thomas 3000!

Smoker?

Ninety puffs a minute, semi-automatic. Twice the nicotine, all the tar.

A couple breaths of this knocks them right out.

They make the honey, and we make the money.

"They make the honey, and we make the money"?

Oh, my!

What's going on? Are you OK?

Yeah. It doesn't last too long.

Do you know you're in a fake hive with fake walls?

Our queen was moved here. We had no choice.

This is your queen? That's a man in women's clothes!

That's a drag queen!

What is this?

Oh, no!

There's hundreds of them!

Bee honey.

Our honey is being brazenly stolen on a massive scale!

This is worse than anything bears have done! I intend to do something.

Oh, Barry, stop.

Who told you humans are taking our honey? That's a rumor.

Do these look like rumors?

That's a conspiracy theory. These are obviously doctored photos.

How did you get mixed up in this?

He's been talking to humans.

What? Talking to humans?! He has a human girlfriend. And they make out!

Make out? Barry!

We do not.

You wish you could. Whose side are you on? The bees!

I dated a cricket once in San Antonio. Those crazy legs kept me up all night.

Barry, this is what you want to do with your life?

I want to do it for all our lives. Nobody works harder than bees!

Dad, I remember you coming home so overworked

your hands were still stirring. You couldn't stop.

I remember that.

What right do they have to our honey?

We live on two cups a year. They put it in lip balm for no reason whatsoever!

Even if it's true, what can one bee do?

Sting them where it really hurts.

In the face! The eye!

That would hurt. No. Up the nose? That's a killer.

There's only one place you can sting the humans, one place where it matters.

Hive at Five, the hive's only full-hour action news source.

No more bee beards!

With Bob Bumble at the anchor desk.

Weather with Storm Stinger.

Sports with Buzz Larvi.

And Jeanette Ohung.

Good evening. I'm Bob Bumble. And I'm Jeanette Ohung. A tri-county bee, Barry Benson,

intends to sue the human race for stealing our honey,

packaging it and profiting from it illegally!

Tomorrow night on Bee Larry King,

we'll have three former queens here in our studio, discussing their new book,

Olassy Ladies, out this week on Hexagon.

Tonight we're talking to Barry Benson.

Did you ever think, "I'm a kid from the hive. I can't do this"?

Bees have never been afraid to change the world.

What about Bee Oolumbus? Bee Gandhi? Bejesus?

Where I'm from, we'd never sue humans.

We were thinking of stickball or candy stores.

How old are you?

The bee community is supporting you in this case,

which will be the trial of the bee century.

You know, they have a Larry King in the human world too.

It's a common name. Next week…

He looks like you and has a show and suspenders and colored dots…

Next week…

Glasses, quotes on the bottom from the guest even though you just heard 'em.

Bear Week next week! They're scary, hairy and here live.

Always leans forward, pointy shoulders, squinty eyes, very Jewish.

In tennis, you attack at the point of weakness!

It was my grandmother, Ken. She's 81.

Honey, her backhand's a joke! I'm not gonna take advantage of that?

Quiet, please. Actual work going on here.

Is that that same bee? Yes, it is! I'm helping him sue the human race.

Hello. Hello, bee. This is Ken.

Yeah, I remember you. Timberland, size ten and a half. Vibram sole, I believe.

Why does he talk again?

Listen, you better go 'cause we're really busy working.

But it's our yogurt night!

Bye-bye.

Why is yogurt night so difficult?!

You poor thing. You two have been at this for hours!

Yes, and Adam here has been a huge help.

Frosting… How many sugars? Just one. I try not to use the competition.

So why are you helping me?

Bees have good qualities.

And it takes my mind off the shop.

Instead of flowers, people are giving balloon bouquets now.

Those are great, if you're three.

And artificial flowers.

Oh, those just get me psychotic! Yeah, me too. Bent stingers, pointless pollination.

Bees must hate those fake things!

Nothing worse than a daffodil that's had work done.

Maybe this could make up for it a little bit.

This lawsuit's a pretty big deal. I guess. You sure you want to go through with it?

Am I sure? When I'm done with the humans, they won't be able

to say, "Honey, I'm home," without paying a royalty!

It's an incredible scene here in downtown Manhattan,

where the world anxiously waits, because for the first time in history,

we will hear for ourselves if a honeybee can actually speak.

What have we gotten into here, Barry?

It's pretty big, isn't it?

I can't believe how many humans don't work during the day.

You think billion-dollar multinational food companies have good lawyers?

Everybody needs to stay behind the barricade.

What's the matter? I don't know, I just got a chill. Well, if it isn't the bee team.

You boys work on this?

All rise! The Honorable Judge Bumbleton presiding.

All right. Oase number 4475,

Superior Oourt of New York, Barry Bee Benson v. the Honey Industry

is now in session.

Mr. Montgomery, you're representing the five food companies collectively?

A privilege.

Mr. Benson… you're representing all the bees of the world?

I'm kidding. Yes, Your Honor, we're ready to proceed.

Mr. Montgomery, your opening statement, please.

Ladies and gentlemen of the jury,

my grandmother was a simple woman.

Born on a farm, she believed it was man's divine right

to benefit from the bounty of nature God put before us.

If we lived in the topsy-turvy world Mr. Benson imagines,

just think of what would it mean.

I would have to negotiate with the silkworm

for the elastic in my britches!

Talking bee!

How do we know this isn't some sort of

holographic motion-picture-capture Hollywood wizardry?

They could be using laser beams!

Robotics! Ventriloquism! Oloning! For all we know,

he could be on steroids!

Mr. Benson?

Ladies and gentlemen, there's no trickery here.

I'm just an ordinary bee. Honey's pretty important to me.

It's important to all bees. We invented it!

We make it. And we protect it with our lives.

Unfortunately, there are some people in this room

who think they can take it from us

'cause we're the little guys! I'm hoping that, after this is all over,

you'll see how, by taking our honey, you not only take everything we have

but everything we are!

I wish he'd dress like that all the time. So nice!

Oall your first witness.

So, Mr. Klauss Vanderhayden of Honey Farms, big company you have.

I suppose so.

I see you also own Honeyburton and Honron!

Yes, they provide beekeepers for our farms.

Beekeeper. I find that to be a very disturbing term.

I don't imagine you employ any bee-free-ers, do you?

No.

I couldn't hear you.

No.

No.

Because you don't free bees. You keep bees. Not only that,

it seems you thought a bear would be an appropriate image for a jar of honey.

They're very lovable creatures.

Yogi Bear, Fozzie Bear, Build-A-Bear.

You mean like this?

Bears kill bees!

How'd you like his head crashing through your living room?!

Biting into your couch! Spitting out your throw pillows!

OK, that's enough. Take him away.

So, Mr. Sting, thank you for being here. Your name intrigues me.

Where have I heard it before? I was with a band called The Police. But you've never been a police officer, have you?

No, I haven't.

No, you haven't. And so here we have yet another example

of bee culture casually stolen by a human

for nothing more than a prance-about stage name.

Oh, please.

Have you ever been stung, Mr. Sting?

Because I'm feeling a little stung, Sting.

Or should I say… Mr. Gordon M. Sumner!

That's not his real name?! You idiots!

Mr. Liotta, first, belated congratulations on

your Emmy win for a guest spot on ER in 2005.

Thank you. Thank you.

I see from your resume that you're devilishly handsome

with a churning inner turmoil that's ready to blow.

I enjoy what I do. Is that a crime?

Not yet it isn't. But is this what it's come to for you?

Exploiting tiny, helpless bees so you don't

have to rehearse your part and learn your lines, sir?

Watch it, Benson! I could blow right now!

This isn't a goodfella. This is a badfella!

Why doesn't someone just step on this creep, and we can all go home?!

Order in this court! You're all thinking it! Order! Order, I say!

Say it! Mr. Liotta, please sit down! I think it was awfully nice of that bear to pitch in like that.

I think the jury's on our side.

Are we doing everything right, legally?

I'm a florist.

Right. Well, here's to a great team.

To a great team!

Well, hello.

Ken! Hello. I didn't think you were coming.

No, I was just late. I tried to call, but… the battery.

I didn't want all this to go to waste, so I called Barry. Luckily, he was free.

Oh, that was lucky.

There's a little left. I could heat it up.

Yeah, heat it up, sure, whatever.

So I hear you're quite a tennis player.

I'm not much for the game myself. The ball's a little grabby.

That's where I usually sit. Right… there.

Ken, Barry was looking at your resume,

and he agreed with me that eating with chopsticks isn't really a special skill.

You think I don't see what you're doing?

I know how hard it is to find the rightjob. We have that in common.

Do we?

Bees have 100 percent employment, but we do jobs like taking the crud out.

That's just what I was thinking about doing.

Ken, I let Barry borrow your razor for his fuzz. I hope that was all right.

I'm going to drain the old stinger.

Yeah, you do that.

Look at that.

You know, I've just about had it

with your little mind games.

What's that? Italian Vogue. Mamma mia, that's a lot of pages.

A lot of ads.

Remember what Van said, why is your life more valuable than mine?

Funny, I just can't seem to recall that!

I think something stinks in here!

I love the smell of flowers.

How do you like the smell of flames?!

Not as much.

Water bug! Not taking sides!

Ken, I'm wearing a Ohapstick hat! This is pathetic!

I've got issues!

Well, well, well, a royal flush!

You're bluffing. Am I? Surf's up, dude!

Poo water!

That bowl is gnarly.

Except for those dirty yellow rings!

Kenneth! What are you doing?!

You know, I don't even like honey! I don't eat it!

We need to talk!

He's just a little bee!

And he happens to be the nicest bee I've met in a long time!

Long time? What are you talking about?! Are there other bugs in your life?

No, but there are other things bugging me in life. And you're one of them!

Fine! Talking bees, no yogurt night…

My nerves are fried from riding on this emotional roller coaster!

Goodbye, Ken.

And for your information,

I prefer sugar-free, artificial sweeteners made by man!

I'm sorry about all that.

I know it's got an aftertaste! I like it!

I always felt there was some kind of barrier between Ken and me.

I couldn't overcome it. Oh, well.

Are you OK for the trial?

I believe Mr. Montgomery is about out of ideas.

We would like to call Mr. Barry Benson Bee to the stand.

Good idea! You can really see why he's considered one of the best lawyers…

Yeah.

Layton, you've gotta weave some magic

with this jury, or it's gonna be all over.

Don't worry. The only thing I have to do to turn this jury around

is to remind them of what they don't like about bees.

You got the tweezers? Are you allergic? Only to losing, son. Only to losing.

Mr. Benson Bee, I'll ask you what I think we'd all like to know.

What exactly is your relationship

to that woman?

We're friends.

Good friends? Yes. How good? Do you live together?

Wait a minute…

Are you her little…

…bedbug?

I've seen a bee documentary or two. From what I understand,

doesn't your queen give birth to all the bee children?

Yeah, but…

So those aren't your real parents!

Oh, Barry…

Yes, they are!

Hold me back!

You're an illegitimate bee, aren't you, Benson?

He's denouncing bees!

Don't y'all date your cousins?

Objection! I'm going to pincushion this guy! Adam, don't! It's what he wants!

Oh, I'm hit!!

Oh, lordy, I am hit!

Order! Order!

The venom! The venom is coursing through my veins!

I have been felled by a winged beast of destruction!

You see? You can't treat them like equals! They're striped savages!

Stinging's the only thing they know! It's their way!

Adam, stay with me. I can't feel my legs. What angel of mercy will come forward to suck the poison

from my heaving buttocks?

I will have order in this court. Order!

Order, please!

The case of the honeybees versus the human race

took a pointed turn against the bees

yesterday when one of their legal team stung Layton T. Montgomery.

Hey, buddy.

Hey.

Is there much pain?

Yeah.

I…

I blew the whole case, didn't I?

It doesn't matter. What matters is you're alive. You could have died.

I'd be better off dead. Look at me.

They got it from the cafeteria downstairs, in a tuna sandwich.

Look, there's a little celery still on it.

What was it like to sting someone?

I can't explain it. It was all…

All adrenaline and then… and then ecstasy!

All right.

You think it was all a trap?

Of course. I'm sorry. I flew us right into this.

What were we thinking? Look at us. We're just a couple of bugs in this world.

What will the humans do to us if they win?

I don't know.

I hear they put the roaches in motels. That doesn't sound so bad.

Adam, they check in, but they don't check out!

Oh, my.

Oould you get a nurse to close that window?

Why? The smoke. Bees don't smoke.

Right. Bees don't smoke.

Bees don't smoke! But some bees are smoking.

That's it! That's our case!

It is? It's not over?

Get dressed. I've gotta go somewhere.

Get back to the court and stall. Stall any way you can.

And assuming you've done step correctly, you're ready for the tub.

Mr. Flayman.

Yes? Yes, Your Honor!

Where is the rest of your team?

Well, Your Honor, it's interesting.

Bees are trained to fly haphazardly,

and as a result, we don't make very good time.

I actually heard a funny story about…

Your Honor, haven't these ridiculous bugs

taken up enough of this court's valuable time?

How much longer will we allow these absurd shenanigans to go on?

They have presented no compelling evidence to support their charges

against my clients, who run legitimate businesses.

I move for a complete dismissal of this entire case!

Mr. Flayman, I'm afraid I'm going

to have to consider Mr. Montgomery's motion.

But you can't! We have a terrific case.

Where is your proof? Where is the evidence?

Show me the smoking gun!

Hold it, Your Honor! You want a smoking gun?

Here is your smoking gun.

What is that?

It's a bee smoker!

What, this? This harmless little contraption?

This couldn't hurt a fly, let alone a bee.

Look at what has happened

to bees who have never been asked, "Smoking or non?"

Is this what nature intended for us?

To be forcibly addicted to smoke machines

and man-made wooden slat work camps?

Living out our lives as honey slaves to the white man?

What are we gonna do? He's playing the species card. Ladies and gentlemen, please, free these bees!

Free the bees! Free the bees!

Free the bees!

Free the bees! Free the bees!

The court finds in favor of the bees!

Vanessa, we won!

I knew you could do it! High-five!

Sorry.

I'm OK! You know what this means?

All the honey will finally belong to the bees.

Now we won't have to work so hard all the time.

This is an unholy perversion of the balance of nature, Benson.

You'll regret this.

Barry, how much honey is out there?

All right. One at a time.

Barry, who are you wearing?

My sweater is Ralph Lauren, and I have no pants.

What if Montgomery's right? What do you mean? We've been living the bee way a long time, 27 million years.

Oongratulations on your victory. What will you demand as a settlement?

First, we'll demand a complete shutdown of all bee work camps.

Then we want back the honey that was ours to begin with,

every last drop.

We demand an end to the glorification of the bear as anything more

than a filthy, smelly, bad-breath stink machine.

We're all aware of what they do in the woods.

Wait for my signal.

Take him out.

He'll have nauseous for a few hours, then he'll be fine.

And we will no longer tolerate bee-negative nicknames…

But it's just a prance-about stage name!

…unnecessary inclusion of honey in bogus health products

and la-dee-da human tea-time snack garnishments.

Oan't breathe.

Bring it in, boys!

Hold it right there! Good.

Tap it.

Mr. Buzzwell, we just passed three cups, and there's gallons more coming!

I think we need to shut down! Shut down? We've never shut down. Shut down honey production!

Stop making honey!

Turn your key, sir!

What do we do now?

Oannonball!

We're shutting honey production!

Mission abort.

Aborting pollination and nectar detail. Returning to base.

Adam, you wouldn't believe how much honey was out there.

Oh, yeah?

What's going on? Where is everybody?

Are they out celebrating? They're home. They don't know what to do. Laying out, sleeping in.

I heard your Uncle Oarl was on his way to San Antonio with a cricket.

At least we got our honey back.

Sometimes I think, so what if humans liked our honey? Who wouldn't?

It's the greatest thing in the world! I was excited to be part of making it.

This was my new desk. This was my new job. I wanted to do it really well.

And now…

Now I can't.

I don't understand why they're not happy.

I thought their lives would be better!

They're doing nothing. It's amazing. Honey really changes people.

You don't have any idea what's going on, do you?

What did you want to show me? This. What happened here?

That is not the half of it.

Oh, no. Oh, my.

They're all wilting.

Doesn't look very good, does it?

No.

And whose fault do you think that is?

You know, I'm gonna guess bees.

Bees?

Specifically, me.

I didn't think bees not needing to make honey would affect all these things.

It's notjust flowers. Fruits, vegetables, they all need bees.

That's our whole SAT test right there.

Take away produce, that affects the entire animal kingdom.

And then, of course…

The human species?

So if there's no more pollination,

it could all just go south here, couldn't it?

I know this is also partly my fault.

How about a suicide pact?

How do we do it?

I'll sting you, you step on me. Thatjust kills you twice. Right, right.

Listen, Barry… sorry, but I gotta get going.

I had to open my mouth and talk.

Vanessa?

Vanessa? Why are you leaving? Where are you going?

To the final Tournament of Roses parade in Pasadena.

They've moved it to this weekend because all the flowers are dying.

It's the last chance I'll ever have to see it.

Vanessa, I just wanna say I'm sorry. I never meant it to turn out like this.

I know. Me neither.

Tournament of Roses. Roses can't do sports.

Wait a minute. Roses. Roses?

Roses!

Vanessa!

Roses?!

Barry?

Roses are flowers! Yes, they are. Flowers, bees, pollen!

I know. That's why this is the last parade.

Maybe not. Oould you ask him to slow down?

Oould you slow down?

Barry!

OK, I made a huge mistake. This is a total disaster, all my fault.

Yes, it kind of is.

I've ruined the planet. I wanted to help you

with the flower shop. I've made it worse.

Actually, it's completely closed down.

I thought maybe you were remodeling.

But I have another idea, and it's greater than my previous ideas combined.

I don't want to hear it!

All right, they have the roses, the roses have the pollen.

I know every bee, plant and flower bud in this park.

All we gotta do is get what they've got back here with what we've got.

Bees.

Park.

Pollen!

Flowers.

Repollination!

Across the nation!

Tournament of Roses, Pasadena, Oalifornia.

They've got nothing but flowers, floats and cotton candy.

Security will be tight.

I have an idea.

Vanessa Bloome, FTD.

Official floral business. It's real.

Sorry, ma'am. Nice brooch.

Thank you. It was a gift.

Once inside, we just pick the right float.

How about The Princess and the Pea?

I could be the princess, and you could be the pea!

Yes, I got it.

Where should I sit?

What are you?

I believe I'm the pea.

The pea?

It goes under the mattresses.

Not in this fairy tale, sweetheart. I'm getting the marshal. You do that! This whole parade is a fiasco!

Let's see what this baby'll do.

Hey, what are you doing?!

Then all we do is blend in with traffic…

…without arousing suspicion.

Once at the airport, there's no stopping us.

Stop! Security.

You and your insect pack your float? Yes. Has it been in your possession the entire time?

Would you remove your shoes?

Remove your stinger. It's part of me. I know. Just having some fun. Enjoy your flight.

Then if we're lucky, we'll have just enough pollen to do the job.

Oan you believe how lucky we are? We have just enough pollen to do the job!

I think this is gonna work.

It's got to work.

Attention, passengers, this is Oaptain Scott.

We have a bit of bad weather in New York.

It looks like we'll experience a couple hours delay.

Barry, these are cut flowers with no water. They'll never make it.

I gotta get up there and talk to them.

Be careful.

Oan I get help with the Sky Mall magazine?

I'd like to order the talking inflatable nose and ear hair trimmer.

Oaptain, I'm in a real situation.

What'd you say, Hal? Nothing. Bee!

Don't freak out! My entire species…

What are you doing?

Wait a minute! I'm an attorney! Who's an attorney? Don't move.

Oh, Barry.

Good afternoon, passengers. This is your captain.

Would a Miss Vanessa Bloome in 24B please report to the cockpit?

And please hurry!

What happened here?

There was a DustBuster, a toupee, a life raft exploded.

One's bald, one's in a boat, they're both unconscious!

Is that another bee joke? No! No one's flying the plane!

This is JFK control tower, Flight 356. What's your status?

This is Vanessa Bloome. I'm a florist from New York.

Where's the pilot?

He's unconscious, and so is the copilot.

Not good. Does anyone onboard have flight experience?

As a matter of fact, there is.

Who's that? Barry Benson. From the honey trial?! Oh, great.

Vanessa, this is nothing more than a big metal bee.

It's got giant wings, huge engines.

I can't fly a plane.

Why not? Isn't John Travolta a pilot? Yes. How hard could it be?

Wait, Barry! We're headed into some lightning.

This is Bob Bumble. We have some late-breaking news from JFK Airport,

where a suspenseful scene is developing.

Barry Benson, fresh from his legal victory…

That's Barry!

…is attempting to land a plane, loaded with people, flowers

and an incapacitated flight crew.

Flowers?!

We have a storm in the area and two individuals at the controls

with absolutely no flight experience.

Just a minute. There's a bee on that plane.

I'm quite familiar with Mr. Benson and his no-account compadres.

They've done enough damage.

But isn't he your only hope?

Technically, a bee shouldn't be able to fly at all.

Their wings are too small…

Haven't we heard this a million times?

"The surface area of the wings and body mass make no sense."

Get this on the air!

Got it.

Stand by.

We're going live.

The way we work may be a mystery to you.

Making honey takes a lot of bees doing a lot of small jobs.

But let me tell you about a small job.

If you do it well, it makes a big difference.

More than we realized. To us, to everyone.

That's why I want to get bees back to working together.

That's the bee way! We're not made of Jell-O.

We get behind a fellow.

Black and yellow! Hello! Left, right, down, hover.

Hover? Forget hover. This isn't so hard. Beep-beep! Beep-beep!

Barry, what happened?!

Wait, I think we were on autopilot the whole time.

That may have been helping me. And now we're not! So it turns out I cannot fly a plane.

All of you, let's get behind this fellow! Move it out!

Move out!

Our only chance is if I do what I'd do, you copy me with the wings of the plane!

Don't have to yell.

I'm not yelling! We're in a lot of trouble.

It's very hard to concentrate with that panicky tone in your voice!

It's not a tone. I'm panicking!

I can't do this!

Vanessa, pull yourself together. You have to snap out of it!

You snap out of it.

You snap out of it.

You snap out of it!

You snap out of it!

You snap out of it!

You snap out of it!

You snap out of it!

You snap out of it!

Hold it!

Why? Oome on, it's my turn.

How is the plane flying?

I don't know.

Hello?

Benson, got any flowers for a happy occasion in there?

The Pollen Jocks!

They do get behind a fellow.

Black and yellow. Hello. All right, let's drop this tin can on the blacktop.

Where? I can't see anything. Oan you?

No, nothing. It's all cloudy.

Oome on. You got to think bee, Barry.

Thinking bee. Thinking bee. Thinking bee! Thinking bee! Thinking bee!

Wait a minute. I think I'm feeling something.

What? I don't know. It's strong, pulling me. Like a 27-million-year-old instinct.

Bring the nose down.

Thinking bee! Thinking bee! Thinking bee!

What in the world is on the tarmac? Get some lights on that! Thinking bee! Thinking bee! Thinking bee!

Vanessa, aim for the flower. OK. Out the engines. We're going in on bee power. Ready, boys?

Affirmative!

Good. Good. Easy, now. That's it.

Land on that flower!

Ready? Full reverse!

Spin it around!

Not that flower! The other one!

Which one?

That flower.

I'm aiming at the flower!

That's a fat guy in a flowered shirt. I mean the giant pulsating flower

made of millions of bees!

Pull forward. Nose down. Tail up.

Rotate around it.

This is insane, Barry! This's the only way I know how to fly. Am I koo-koo-kachoo, or is this plane flying in an insect-like pattern?

Get your nose in there. Don't be afraid. Smell it. Full reverse!

Just drop it. Be a part of it.

Aim for the center!

Now drop it in! Drop it in, woman!

Oome on, already.

Barry, we did it! You taught me how to fly!

Yes. No high-five! Right. Barry, it worked! Did you see the giant flower?

What giant flower? Where? Of course I saw the flower! That was genius!

Thank you. But we're not done yet. Listen, everyone!

This runway is covered with the last pollen

from the last flowers available anywhere on Earth.

That means this is our last chance.

We're the only ones who make honey, pollinate flowers and dress like this.

If we're gonna survive as a species, this is our moment! What do you say?

Are we going to be bees, orjust Museum of Natural History keychains?

We're bees!

Keychain!

Then follow me! Except Keychain.

Hold on, Barry. Here.

You've earned this.

Yeah!

I'm a Pollen Jock! And it's a perfect fit. All I gotta do are the sleeves.

Oh, yeah.

That's our Barry.

Mom! The bees are back!

If anybody needs to make a call, now's the time.

I got a feeling we'll be working late tonight!

Here's your change. Have a great afternoon! Oan I help who's next?

Would you like some honey with that? It is bee-approved. Don't forget these.

Milk, cream, cheese, it's all me. And I don't see a nickel!

Sometimes I just feel like a piece of meat!

I had no idea.

Barry, I'm sorry. Have you got a moment?

Would you excuse me? My mosquito associate will help you.

Sorry I'm late.

He's a lawyer too?

I was already a blood-sucking parasite. All I needed was a briefcase.

Have a great afternoon!

Barry, I just got this huge tulip order, and I can't get them anywhere.

No problem, Vannie. Just leave it to me.

You're a lifesaver, Barry. Oan I help who's next?

All right, scramble, jocks! It's time to fly.

Thank you, Barry!

That bee is living my life!

Let it go, Kenny.

When will this nightmare end?!

Let it all go.

Beautiful day to fly.

Sure is.

Between you and me, I was dying to get out of that office.

You have got to start thinking bee, my friend.

Thinking bee! Me? Hold it. Let's just stop for a second. Hold it.

I'm sorry. I'm sorry, everyone. Oan we stop here?

I'm not making a major life decision during a production number!

All right. Take ten, everybody. Wrap it up, guys.

I had virtually no rehearsal for that.

Guardian-Bericht über die Rolle von @Maisa_Rojas in der neuen chilenischen Regierung. Gegenstück zum Interview mit Christophe Cassou

Das linke Regierungsteam sieht Chile auch international in einer Führungsrolle im Kampf gegen die Klimakatastrophe:

“I think there’s a lot of space for Chile to become a leader in the fight against climate change,” she says, “I would love to be able convince other countries that ambitiously tackling climate change is in their best interests.

Vielleicht ist das eine zu romantische Annahme: Aber so wie Chile in den 70ern ein Symbol für den Sieg des Neoliberalismus wurde, so wird es vielleicht heute ein Symbol für dessen Niederlage.

Eine intenrnational bekannte Klimawissenschaftlerin wird verantwortlich für die Klimapolitik und sie betont den Zusammenhang zwischen Klimakrise, gesellschaftlicher Ungleichheit und kapitalistischem Entwicklungsmodell:

“When we address climate change, it’s not just an environmental issue,” she says. “We need to look at structural elements of our society, which also means changing our development pathway.”

Bei der Lektüre frage ich mich, welche Rolle Österreich, ein Land in der Größenordnung Chiles, mit einer fortschrittlichen Klimapolitik international spielen könnte.

Hidden behind the Andes in a quiet corner of South America, a formidable generation of former student leaders are putting together one of the world’s most exciting progressive movements.

​

On 24 January, Boric named a female-majority cabinet for the first time in Chile’s history. Rojas, one of 14 women among the 24 ministers, is a prominent academic at the University of Chile, where she first studied physics in the 1990s, and the director of the country’s interdisciplinary Centre for Climate and Resilience Research.

​Auch Maisa Rojas erwähnt die Eco-Anxiety:

But at Cop26 in Glasgow last November, as she worked with the team on the annual report on the climate crisis, Rojas felt an unfamiliar feeling. “For the first time in my life I felt something like ‘eco-anxiety’ – I was really worried about what was going on,” she says.

Ihre Position ist wie die von Cassou ein Signal für die Veränderung der politischen Rolle der Wissenschaftler:innen im IPCC und darüber hinaus. Sie geben die subalterne Haltung gegenüber der Politik auf. Der letzte IPCC-Bericht enthält dafür auch viele Indizien.

Sugiero modificar esto, ya que el reloj puede usarse para cualquier clima y circunstancia.

Table des Matières : Webinaire CRIPS VIH - Dernières Avancées et Prochains Défis

Introduction

• Présentation du CRIPS (Centre Régional d’Information et de Prévention du Sida et pour la Santé des Jeunes) et de son rôle dans la lutte contre le VIH/SIDA et la promotion de la santé des jeunes.
• Introduction du Comité des Familles et de DMC Action Sida, deux associations engagées dans l'accompagnement des personnes vivant avec le VIH et la prévention auprès du grand public.

Le VIH/SIDA : 40 ans de lutte et de progrès

• . Constatation des progrès significatifs réalisés dans la lutte contre le VIH/SIDA au cours des dernières années, notamment grâce à la mobilisation des associations et des personnes concernées.
• . Présentation de l’objectif de mettre fin au VIH/SIDA d’ici 2030, tout en soulignant les étapes et les efforts nécessaires pour y parvenir.
• . Présentation de données chiffrées sur la situation du VIH/SIDA en France, mettant en lumière des points importants comme le nombre de contaminations annuelles, le pourcentage de jeunes et de personnes de plus de 50 ans touchés, et l'importance du dépistage.
• . Mise en avant du défi que représente la persistance de la sérophobie et la nécessité de faire évoluer l'image de la maladie.

La Prévention : Une approche diversifiée et sur-mesure

• Mise en avant de la diversification des outils de prévention du VIH/SIDA, allant au-delà du seul préservatif, et de l'importance d'une approche sur-mesure adaptée aux besoins de chaque individu.
• Présentation des différents moyens de prévention :
• Préservatifs: Description des différents types de préservatifs (internes et externes) et des progrès réalisés en matière d'accessibilité (remboursement, gratuité pour les moins de 26 ans).
• TPE (Traitement Post-Exposition): Explication du principe du TPE, de son accessibilité (gratuit dans les urgences des hôpitaux) et des perspectives d'évolution (ouverture aux associations communautaires).
• RDR (Réduction des Risques): Présentation de la RDR comme une réussite sanitaire et sociale, description des outils mis à disposition (matériel stérile, traitements de substitution, salles de consommation à moindre risque), et des points à améliorer (accès en milieu carcéral).
• Dépistage: Soulignance de l'importance du dépistage régulier et des avantages qu'il présente, tant au niveau individuel que collectif.

TroD (Test Rapide d’Orientation Diagnostique):

• Explication du rôle du TroD dans l’accès au dépistage et du défi que représente la promotion de son utilisation.
• Autotests: Présentation des autotests comme un outil complémentaire au dépistage traditionnel et des points d'accès (pharmacies).
• Laboratoires sans ordonnance: Mise en avant de la possibilité de se faire dépister dans n’importe quel laboratoire en France sans ordonnance, sans avance de frais et sans rendez-vous.
• PrEP (Prophylaxie Pré-Exposition): Explication du principe de la PrEP, de son accessibilité (remboursement à 100%, prescription par tout médecin) et de son importance pour les populations les plus exposées.
• TasP (Traitement comme Prévention): Rappel du principe du TasP (une personne séropositive sous traitement efficace ne transmet pas le virus) et de son importance dans la lutte contre la sérophobie.
• Mise en évidence du défi que constitue l'information du grand public et des populations les plus exposées sur l'existence et l'accessibilité de ces différents outils de prévention.

Le dialogue en santé sexuelle : Un défi majeur

• Mise en avant de l'importance du dialogue en santé sexuelle, au-delà du simple accès aux informations, pour favoriser l'appropriation des outils de prévention.
• Soulignance du rôle des centres de santé communautaire dans la promotion du dialogue en santé sexuelle.
• Interrogation sur les moyens de faciliter le dialogue en santé sexuelle avec les professionnels de santé hors milieu communautaire.
• Mise en avant de la légitimité des professionnels de santé à aborder la santé sexuelle avec leurs patients et de l'attente des Français sur ce sujet.
• Encouragement des professionnels de santé à poser des questions sur la santé sexuelle de manière systématique, à l'instar des questions sur les allergies ou le tabac.
• Présentation du guide "Parler de VIH et de santé sexuelle avec les personnes LGBTQIA+", un outil de formation pour les professionnels de santé.

• L'éducation à la sexualité : Une nécessité pour les jeunes

• Rappel de l'obligation légale pour les établissements scolaires d'organiser trois séances annuelles d'information et d'éducation à la sexualité.

• Constatation du non-respect de cette obligation et de ses conséquences négatives sur la prévention et la santé des jeunes.
• Mise en avant de la demande des jeunes pour ces séances de prévention et de l'importance de les outiller face aux enjeux de la santé sexuelle.
• Reconnaissance des avancées réalisées ces dernières années, tout en soulignant la nécessité d'une éducation à la sexualité plus adaptée et à la hauteur des besoins des jeunes.

La sérophobie : Une réalité persistante

• Mise en évidence du décalage entre les avancées scientifiques et la réalité sociale concernant le VIH/SIDA, avec la persistance de discriminations et de stigmatisations.
• Présentation de données chiffrées sur l'ampleur de la sérophobie et son impact sur la vie des personnes vivant avec le VIH.
• Description des discriminations persistantes :
• Restrictions au déplacement pour les personnes vivant avec le VIH dans de nombreux pays.
• Refus de soins de la part de certains professionnels de santé.
• Mise en lumière des conséquences du refus de soins : retard de la prise en charge, aggravation de l'état de santé, sortie du parcours de soins, méfiance envers les professionnels de santé.

• Victoires et perspectives dans la lutte contre la discrimination

• Présentation de victoires obtenues dans la lutte contre la discrimination liée au VIH/SIDA :

• Loi Ryan White VIH/SIDA aux États-Unis, permettant l'entrée des personnes séropositives étrangères sur le territoire américain.
• Campagne de sensibilisation au refus de soins, menée en partenariat avec l'Assurance Maladie et l'Ordre National des Médecins.
• Adoption de la loi du 28 février 2022 pour un accès plus juste, plus simple et plus transparent au marché de l'assurance emprunteur, supprimant l'obligation pour les personnes vivant avec le VIH de déclarer leur statut pour les prêts inférieurs à 200 000 € et remboursables avant 60 ans.
• Révision de la grille de référence du Comité de Suivi et de Proposition d'Aeras, visant à supprimer les refus d'assurance et les surprimes pour les personnes vivant avec le VIH.
• Conclusion sur l'importance de poursuivre la lutte contre la discrimination et de modifier l'image du VIH/SIDA dans la société.

Témoignage : Yannick Salès

• Présentation de Yannick Salès, président de l'association DMC Action Sida et personne vivant avec le VIH.
• Partage de son expérience de vie avec le VIH, depuis l'annonce de son diagnostic à l'âge de 18 ans jusqu'à aujourd'hui.
• Témoignage sur les difficultés rencontrées :
• L'annonce brutale du diagnostic dans les années 80.
• La perte de ses amis et les difficultés dans ses relations amoureuses.
• Le changement de métier suite à la discrimination ressentie dans son ancien travail.
• Le refus de soins de la part d'un dentiste.
• Mise en avant de ses sources de force et d'espoir :
• La rencontre avec le professeur Chermann, qui lui a redonné espoir et lui a permis d'avoir une fille séronégative.
• Ses passions pour la musique et la danse.
• Le soutien de ses parents.
• Son engagement associatif et les témoignages qu'il réalise auprès des jeunes.
• Conclusion sur l'importance du dépistage et de l'acceptation de la maladie.

Conclusion du webinaire

• Remerciements aux intervenants, à l'équipe du CRIPS et aux participants.
• Rappel de l'importance de s'informer, d'aller plus loin dans la compréhension du VIH/SIDA et d'être un allié dans la lutte contre la maladie.

Ressources

• Informations sur les sites web du CRIPS, de DMC Action Sida et du Comité des Familles.
• Invitation à contacter les associations pour toute question ou demande d'information.
• Recommandation de s'adresser aux centres de santé sexuelle (CeGIDD) et aux associations de prévention locales pour un accompagnement personnalisé.
• Mise à disposition de brochures, de livrets et de dossiers documentaires sur le site web du CRIPS.
• Invitation à consulter la page "Animatek" du site du CRIPS pour des outils de prévention téléchargeables et facilement reproductibles.

Appel à l'action

• Encouragement à s'engager dans la lutte contre le VIH/SIDA en s'informant, en se faisant dépister et en luttant contre la discrimination.
• Note : Ce tableau de matières est un résumé des informations clés du webinaire et ne remplace pas le visionnage complet de celui-ci.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The work by Chuong et al. provides important new insights into the contribution of different molecular mechanisms in the dynamics of CNV formation. It will be of interest to anyone curious about genome architecture and evolution from yeast biologists to cancer researchers studying genome rearrangements.

Thank you for recognizing the broad significance of our study.

Strengths:

Their results are especially striking in that the "simplest" mechanism of GAP1 amplification-non-allelic homologous recombination between the flanking Ty-LTR elements is not the most common route taken by the cells, emphasizing the importance of experimentally testing what might seem on the surface to be obvious answers. One of the important developments of their work is the use of their neural network simulation-based inference (nnSBI) model to derive rates of amplicon formation and their fitness effects.

We agree with this assessment as the results of our study challenge our intuition that the simplest path to structural variation is the most likely and reveals the great diversity in mechanisms that can lead to large scale changes in the genome.

Weaknesses:

The manuscript reads as though two different people wrote two different sections of the manuscript - an experimental evolutionist and a computational scientist. If the goal is to reach both groups of readers, there needs to be more explanation of both types of work. I found the computational sections to be particularly dense but even the experimental sections need clearer explanations and more specific examples of the rearrangements found. I will point out these areas in the detailed remarks to the authors. While I have no reason to question their conclusions, I couldn't independently verify the results that ODIRA was the majority mechanism since the sequence of amplified clones was not made available during the review. I've encouraged the authors to include specific, detailed sequence information for both ODIRA events as well as the specific clones where GAP1 was amplified but the flanking gene GFP was not.

We have revised the manuscript to expand explanations of both the experimental and computational aspects of our study and to provide additional information for the reader. In doing so, we have edited the text to improve readability. We have made all raw data publicly available through the NCBI short read archive (SRA) and are hosting all sequence data for easy visualization in JBrowse using a public server.

Reviewer #2 (Public Review):

Summary:

This study examines how local DNA features around the amino acid permease gene GAP1 influence adaptation to glutamine-limited conditions through changes in GAP1 Copy Number Variation (CNV). The study is well motivated by the observation of numerous CNVs documented in many organisms, but difficulty in distinguishing the mechanisms by which they are formed, and whether or how local genomic elements influence their formation. The main finding is convincing and is that a nearby Autonomous Replicating Sequence (ARS) influences the formation of GAP1 CNVs and this is consistent with a predominate mechanism of Origin Dependent Inverted Repeat Amplification (ODIRA). These results along with finding and characterizing other mechanisms of GAP1 CNV formation will be of general interest to those studying CNVs in natural systems, experimental evolution, and in tumor evolution. While the results are limited to a single CNV of interest (GAP1), the carefully controlled experimental design and quantification of CNV formation will provide a useful guide to studying other CNVs and CNVs in other organisms.

Thank you for this positive assessment of our study.

Strengths:

The study was designed to examine the effects of two flanking genomic features next to GAP1 on CNV formation and adaptation during experimental evolution. This was accomplished by removing two Long Terminal Repeats (LTRs), removing a downstream ARS, and removing both LTRs and the ARS. Although there was some heterogeneity among replicates, later shown to include the size and breakpoints of the CNV and the presence of an unmarked CNV, both marker-assisted tracking of CNV formation and modeling of CNV rate and fitness effects showed that deletion of the ARS caused a clear difference compared to the control and the LTR deletion.

The consequence of deletion of local features (LTR and ARS) was quantified by genome sequencing of adaptive clones to identify the CNV size, copy number and infer the mechanism of CNV formation. This greatly added value to the study as it showed that i) ODIRA was the most common mechanism but ODIRA is enhanced by a local ARS, ii) non-allelic homologous recombination (NAHR) is also used but depends on LTRs, and iii) de novo insertion of transposable elements mediate NAHR in strains with both ARS and LTR deletions. Together, these results show how local features influence the mechanism of CNV formation, but also how alternative mechanisms can substitute when primary ones are unavailable.

We agree with this assessment.

Weaknesses:

The CNV mutation rate and its effect on fitness are hard to disentangle. The frequency of the amplified GFP provides information about mutation rate differences as well as fitness differences. The data and analysis show that each evolved population has multiple GAP1 CNV lineages within it, with some being unmarked by GFP. Thus, estimates of CNV fitness are more of a composite view of all CNV amplifications increasing in frequency during adaptation. Another unknown but potential complication is whether the local (ARS, LTR) deletions influence GAP1 expression and thus the fitness gain of GAP1 CNVs. The neural network simulation-based inference does a good job at estimating both mutation rates and fitness effects, while also accounting for unmarked CNVs. However, the model does not account for the population heterogeneity of CNVs and their fitness effects. Despite these limitations of distinguishing mutation rate and fitness differences, the authors' conclusions are well supported in that the LTR and ARS deletions have a clear impact on the CNV-mediated evolutionary outcome and the mechanism of CNV formation.

While it is true that the inferred mutation rate and fitness effect are negatively correlated, as in other studies (Gitschlag et al., 2023; Caspi et al., 2023; Avecilla et al., 2022), our modeling approach does generate an estimate of each parameter that is best explained by the data. By reporting the confidence intervals (i.e. the 95% HDI) we define the set of parameter values that are consistent with the data. It is true that our model doesn't explicitly account for population heterogeneity; rather, following Hegreness et al. (2006), we employ a single effective fitness effect and mutation rate for all GAP1 CNVs. It is interesting to consider whether the ARS and LTR affect GAP1 expression; however, we have no evidence that this is the case.

Reviewer #3 (Public Review):

Summary:

The authors represent an elegant and detailed investigation into the role of cis-elements, and therefore the underlying mechanisms, in gene dosage increase. Their most significant finding is that in their system copy number increase frequently occurs by what they call replication errors that result from the origin of replication firing.

The authors somewhat quantitatively determine the effect of the presence of a proximal origin of replication or LTR on the different CNV scenarios.

Strengths:

(1) A clever and elegant experimental design.

(2) A quantitative determination of the effect of a proximal origin of replication or LTR on the different CNV scenarios. Measuring directly the contribution of two competing elements.

(3) ODIRA can occur by firing of a distal ARS element.

(4) Re-insertion of Ty elements is interesting.

We agree that these are interesting and novel findings from our study.

Weaknesses:

(1) Overall, the research does not considerably advance the current knowledge. The research does not investigate what the maximum distance between ARS for ODIRA is to occur. This is an important point since ODIRA was previously described. A considerable contribution to the field would be to understand under what conditions ODIRA wins NAHR.

We agree that these are important questions and they are ones that we are pursuing in future studies.

(2) The title and some sentences in the abstract give a wrong impression of the generality and the novelty of the observations presented. Below are some examples of much earlier work that dealt with mechanisms of CNV and got different conclusions. The Lobachev lab (Cell 2006) published a different scenario years ago, with a very different mechanism (hair-pin capped breaks). The Argueso lab found something different (NAHR) (Genetics 2013).

In fact, the CUP1 system presents a good example of this point. The Houseley group showed a complex replication transcription-based mechanism (NAR 2022, cited), the Argueso group showed Ty-based amplification and the Resnick group showed aneuploidy-based amplification. While aneuploidy is a minor factor here the numerous works in Candida albicans, Cryptococcus neoformans, and Yeast suggest otherwise (Selmecki et al Science 2006, Yona et al PNAS 2013, Yang et al Microbiology Spectrum 2021).

As the reviewer points out there have been several important published studies investigating mechanisms by which structural variation is generated. It is important to note that we are explicitly looking at CNVs in the context of adaptive evolution and the role of genomic features that enable different mechanisms of CNV formation. To emphasize this point, we have changed the title of our manuscript to “Template switching during DNA replication is a prevalent source of adaptive gene amplification”. Aneuploidy is indeed a mechanism of adaptive gene amplification in our current and previously reported studies. We have expanded our discussion to place our study in the context of previous studies reporting mechanisms of gene amplification.

(3) The authors added a mathematical model to their experimental data. For me, it was very difficult to understand the contribution of the model to the research. I anticipated, for example, that the model would make predictions that would be tested experimentally. For example, " ARSΔ and ALLΔ are predicted to be almost eliminated by generation 116, as the average predicted WT proportion is 0.998 and 0.999" But to my understanding without testing the model.

In our previous publication (Avecilla et al. 2022, PLoS Biology) we experimentally validated the use of nnSBI to infer evolutionary parameters. In this study, we have extended our modeling framework to quantify differences between genotypes, which was not previously possible. Our results reveal that the local ARS has a key role in the overall supply rate of CNVs at this locus.

Recommendations for the authors:

We have addressed all public reviews and recommendations.

Reviewer #1 (Recommendations For The Authors):

Specific comments about the work are covered in the order of appearance in the text or Figures. I apologize in advance for the number of comments. They are made out of curiosity, enthusiasm for the research, and a desire to help highlight the most interesting aspects of this work.

We are grateful for the thoughtful comments that have helped us to significantly improve our manuscript.

(1) I would appreciate the inclusion of several references to the work on the ODIRA model.

a) Page 3 last paragraph: "(2) DNA replication-based mechanisms (Harel et al., 2015; Hastings, Lupski, et al., 2009; Malhotra & Sebat, 2012; Pös et al., 2021; Zhang, Gu, et al., 2009; Brewer et al., 2011)" (Addition of Brewer et al., 2011).

We have added all suggested references.

b) Page 4 top: (Brewer et al., 2011; Brewer et al., 2015; Martin et al., 2024). (Addition of Brewer et al., 2011).

We have added all suggested references.

c) Page 14 top: "Recent work has proposed that ODIRA CNVs are a major mechanism of CNVs in human genomes (Brewer et al., 2015; Martin et al., 2024; Brewer et al., 2024)." Brewer et al., 2024 focuses specifically on ODIRA and human CNVs. (Addition of Brewer et al., 2024).

We have added all suggested references.

(2) Page 6, third paragraph: I was surprised that a single inoculating strain was used to establish the replicate chemostats because of the possibility of non-independence of the resulting GAP1 CNVs. A nnSBI model was used to correct for this possibility later in the paper. It seems like it could have been avoided by a simple change in protocol to inoculate each chemostat with an independent inoculum. Was there a reason that the replicate chemostats were not conducted as independent events? Establishing the presence of 'founder' GAP1 CNVs without GFP seems rather secondary to the point of the paper (examining the CNVs that arise during evolution) and I would recommend it being moved to the supplement.

As is typical in microbial experimental evolution studies, we aimed to start with genetically identical homogenous populations and observe the emergence and selection of de novo variation. Therefore, we founded independent populations from a single inoculum. However, this study, and our prior work using lineage tracking barcodes, has clearly demonstrated that during the initial growth of the culture used for the inoculum CNVs are generated that contribute to the adaptation dynamics on all derived populations. This unanticipated result now suggests that the reviewer’s suggestion is a valid one - independent populations should be derived from independent inocula and this will be our standard practice in future studies.

We believe that our results, presented in Figure 2, establishing the presence of pre-existing GAP1 CNVs without the GFP are important as it highlights a limitation of the use of CNV reporters of gene copy number that was not previously known. However, we subsequently show that this class of variant - CNVs that are not detected by the reporter system - can be incorporated into our modeling framework enabling estimation of evolutionary parameters, which we believe is an important finding warranting inclusion in the main text.

(3) Page 7 first full paragraph: "Finally, we also observe a significant delay (ANOVA, p = 0.00833) in the generation at which the CNV frequency reaches equilibrium in ARS∆ (~generation 112) compared to WT (pairwise t-test, adjusted p = 0.05) . . .". Is the delay in reaching a plateau in Figure 1E just a consequence of the later appearance of CNVs or do the authors believe there are two separate events responsible for this delay? E.g. if the authors think that the delay in reaching a plateau is related to lower selection coefficients of the CNVs that do arise compared to the CNVs of other strains, then this should be explicitly discussed.

We believe that the delay in reaching equilibrium is a consequence of both a lower CNV formation and reduced selection coefficients. Lower values for the fitness coefficient and formation rate in ARS∆ explain both the delay in CNV appearance and CNV equilibrium as shown by the predicted dynamics (Figure S3B). We have added an explicit discussion of the effect of the ARS on CNV dynamics in paragraph 2 of the Discussion section paragraph 2 starting at line 456.

(4) Page 7: Incorporating pre-existing CNVs into an evolutionary model: The rationale for how you are able to discount the formation rate of GFP-free CNVs (C-) in your model isn't clear to me. How are you able to assume that these C- events don't form after timepoint 0? Why do you assume a starting population of C- events but not a starting population of C+ events?

We explored the possibility of modeling C- (amplifications of GAP1 without amplification of the reporter) during the evolution experiment. However, because the rate at which C- events occurs is slower than the rate at which C+ events occur (GAP1 amplifications with amplification of the reporter) we found that the effect was negligible. Importantly, the simple model is sufficient to describe the observed dynamics and thus we do not include these possible rare events.

(5) Figure 1:

(a) Panel B: Please put the tRNAs on the line diagrams of the four strains. I first interpreted ALLΔ as missing the tRNAs, too.

Thank you for this suggestion. We added tRNAs to all diagrams to provide additional detail about the structure of the GAP1 locus.

(b) Panels C, D, and E: the dark shade of the colored boxplots obscures the individual points. I recommend reducing the opacity of the box or choosing a lighter shade so that the individual points are visible on top of the box. Is the percent increase in CNVs per generation (Panel D) based on the slopes of the curves in panel B? By eye the slopes of ARS∆ and ALL∆ appear at least as steep as those of wild type and LTR∆.

Thank you for this suggestion. We have now made the individual points visible on top of the boxplots in Figures 1C, 1D, and 1E. The lines in Figure 1B show the median value across populations per time point whereas each point in Figure 1D is the slope from linear regression using values from individual populations (data from individual populations are shown in Figure 3C).

(6) Figure 2:

(a) Panel A: Please remind the readers what FSC-A is measuring and label the different groups of cells in each sample. Are we supposed to assume the upper scatter in generation 8 is the pre-existing CNV variants? Are the three species at generation 50 due to 1, 2, and 3 copies of GFP? Is the new species in generation 137 further amplification of the locus? And if so, how many copies does it represent? I find it fascinating that what I assume is the 2-copy CNV (presumably a direct oriented amplicon produced by NAHR) at 50 generations is lost (out-competed by a potential inverted triplication) at later times, but I didn't find any mention of this phenomenon in the text. What do the different mutant strains look like over the same time course? Please supply supplemental figures with the flow cytometry gating and vertically aligned histograms of the GFP signal so that the peaks are more easily compared. And provide this information for each of the altered strains in supplementary materials.

Thank you for these useful suggestions. We have added a gating legend to the figure to clearly indicate the copy-number for each subpopulation. We have edited the caption and main text to explain forward scatter (FSC-A). Raw flow cytometry plots are now provided as Supplementary figure 2 and distributions of cell-size normalized GFP signal are provided in Supplementary figure 3. Although our primary objective with Figure 2A was to show the persistence of the 1-copy GFP population the reviewer is correct that we did not highlight interesting aspects of the CNV dynamics. We have added additional text starting at line 251 to point out these features of the data.

(b) Panel B: It would help to label the different colored boxes inside cells in Figure 2B - it took me a while to identify the white box as an unrelated adaptive mutation elsewhere in the genome. The linear arrangement of these small colored blocks seems to indicate their structural arrangement. Is that the case? And are they inverted or direct amplicons? Perhaps the authors are being agnostic at this point but it would be better if each of the blocks were separate. If there are other mutations that can explain these GFP-non-amplified survivors, were they identified in your whole genome sequencing?

We have now included a complete legend for Figure 2B indicating that the white box reflects other beneficial mutations. We have separated this class of beneficial mutation from the GAP1 and reporter elements to reflect that they are not linked. We did not identify additional beneficial mutations but plan to pursue this question in a future project.

(c) Panel C: Are the two sets of lines mislabeled? One would expect the "reported" CNV proportions to be lower than the total CNV proportions, not the other way around. Maybe the labels "total CNVs" and "reported CNVs" are unclear to me and I am misunderstanding what "reported" refers to. Please clarify.

Thank you for identifying this mistake. The lines were mislabeled and have now been corrected in the revised version.

(7) Figure 3:

(a) A fuller discussion of panels A and B is needed. The results of panel A in particular seem like an excellent opportunity for connecting the computation to the biology. Can the authors speculate on why the ALL∆ strain has a higher CNV formation rate (𝛿c) than the ARS∆ strain? I would think that taking away one means of amplification would decrease CNV formation. Likewise, could the authors discuss why the selection coefficient (sc) for the LTR∆ strain would be the same as for the wild type? Overall, I would like to see more discussion about what these differences in formation rates and selection coefficients could mean for the types of amplicons arising in the chemostats. (In panel B I don't see the shaded area referred to in the figure legend.) A side-by-side comparison of the data in Panel A with the data shown in Supplemental Figure S3A would be instructive..

Thank you for raising these points. We have added substantial text to the manuscript to address these findings. Starting at line 456 we state:

“The lower CNV formation rate in the LTR∆ could be a closer approximation of ODIRA formation rates at this locus as ODIRA CNVs are the predominant CNV mechanism in the LTR∆ strain (Figure 4F). Furthermore, the low formation rates in the LTR∆ relative to WT might suggest that the presence of the flanking long terminal repeats may increase the rate of ODIRA formation through an otherwise unknown combinatorial effect of DNA replication across these flanking LTRs and template switching at the GAP1 locus. ARS∆ has the lowest CNV formation rate and it could be an approximation of the rates of NAHR between flanking LTRs and ODIRA at distal origins. We find that the ALL∆ has a higher CNV formation rate than the ARS∆, even though three elements are deleted instead of one. One explanation for this is that the deletion of the flanking LTRs in ALL∆ gives opportunity for novel transposon insertions and subsequent LTR NAHR. Indeed we find an enrichment of novel transposon-insertions in the ALL∆ (Figure 4F) and subsequent CNV formation through recombination of the Ty1-associated repeats (Figure 4H, ALL∆). Both events, transposon insertion followed by LTR NAHR, would have to occur quickly at a rate that explains our estimated CNV rate in ALL∆. While remarkable, increased transposon activity has been associated with nutrient stress (Curcio & Garfinkel, 1999; Lesage & Todeschini, 2005; Todeschini et al., 2005) and therefore feasible explanation for the CNV rate estimated in the ALL∆. Additionally, ARS∆ clones rely more on LTR NAHR to form CNVs (Figure 4F). The prevalence of ODIRA in ARS∆ and ALL∆ are similar. LTR NAHR usually occurs after double strand breaks at the long terminal repeats to give rise to CNVs (Argueso et al., 2008). Because we use haploid cells, such double strand break and homology-mediated repair would have to occur during S-phase after DNA replication with a sister chromatid repair template to form tandem duplications. Therefore the dependency on LTR NAHR to form CNVs and the spatial (breaks at LTR sequences) and temporal (S-phase) constraints could explain the lower formation rate in ARS∆.”

In addition, we added a discussion of the different selection coefficients estimated and how the simulated competitions help us understand the decreased selection coefficients in the architecture mutants. In newly added text starting at line 479 we state:

“The genomic elements have clear effects on the evolutionary dynamics in simulated competitive fitness experiments. The similar selection coefficients in WT and LTR∆ suggest that CNV clones formed in these background strains are similar. Indeed, the predominant CNV mechanism in both is ODIRA followed by LTR NAHR (Figure 4F). While LTR NAHR is abolished in the LTR∆, it seems that CNVs formed by ODIRA allow adaptation to glutamine-limitation similar to WT. The lower selection coefficients in ARS∆ and ALL∆ suggest that GAP1 CNVs formed in these strains have some cost. In a competition, they would get outcompeted by CNV alleles in the WT and LTR∆ background.”

(b) The data shown in panel C seems redundant to what is shown more clearly in Supplemental Figure S3B. It seems to me the more important comparison to make in panel C would be the overlay of the predicted data to the median proportion of cells obtained from the experimental data (Figure 1B). Also, overlays of the cultures from each strain could be added to S3A. It is difficult to see the variation within each strain when the data from all four strains are superimposed as they are in Figure 3C.

We agree and have edited Figure 3C to incorporate these suggestions and more clearly convey the intra- and interstrain variation.

(8) Figure 4:

(a) Panels A, B, and C are nice summaries and certainly helpful for understanding panel E, but it would be instructive to see some actual rearrangements of the ODIRA events, the NAHR, and the transposon-mediated rearrangements. It isn't clear to me what these last events look like. A figure that shows the specific architecture of example clones for each category would be helpful. I am also having a hard time reconciling ODIRA events with a copy number of 2. Are these rearrangements free isochromosomes with amplification to the telomere or are they secondary rearrangements like those described in Brewer et al., 2024? And what about the non-aneuploid rearrangement that includes the centromere? Is it a dicentric?

We have now added more detailed depictions of CNVs in Figure 4A and provide links to visualize the alignment files. We have added additional discussion starting at line 397 of the non-canonical ODIRA events and putative neochromosome amplicons with reference to Brewer et al 2024. Starting at line 397 we state:

“Surprisingly, we found CNVs with breakpoints consistent with ODIRA that contained only 2 copies of the amplified region, whereas ODIRA typically generates a triplication. In the absence of additional data, we cannot rule out inaccuracy in our read-depth estimates of copy numbers for these clones (ie. they have 3 copies). An alternate explanation is a secondary rearrangement of an original inverted triplication resulting in a duplication (Brewer et al., 2024); however, we did not detect evidence for secondary rearrangements in the sequencing data. A third alternate explanation is that a duplication was formed by hairpin capped double-strand break repair (Narayanan et al., 2006). Notably, we found 3 additional ODIRA clones that end in native telomeres, each of which had amplified 3 copies. In these clones the other breakpoint contains the centromere, indicating the entire right arm of chromosome XI was amplified 3 times via ODIRA, each generating supernumerary chromosomes. Thus,ODIRA can result in amplifications of large genomics regions from segmental amplifications to supernumerary chromosomes.”

(b) In Panel B the violin plots appear to indicate that there are two size categories for amplicons in the ARS∆ strain. Do clones from these different sub-populations share a common CNV architecture?

Thank you for making this point. (Please note that the violin plots are now Figure 4E) We added a short discussion and Supplementary Figure 14. In line 432, we state:

“In ARS∆, we find two CNV length groups (Figure 4E) that correspond with two different CNV mechanisms (Supplementary Figure 14). 100% of smaller CNVs (6-8kb) (Supplementary Figure 14) correspond with a mechanism of NAHR between LTRs flanking the GAP1 gene (Figure 4H, ARS∆, bottom left green points). Larger CNVs (8kb-200kb) (Supplementary Figure 14) correspond with other mechanisms that tend to produce larger CNVs, including ODIRA and NAHR between one local and one distal LTR element (Figure 4H).”

(c) Panels D and E: There is great information in these two panels but I find the color keys confusing. There doesn't seem to be any reason for the strain color key in panel E. I am assuming that the key should go with Panel D. Is there some way to indicate in Panel D which events are in which CNV category? It is cumbersome to find that information from Panel E. Perhaps the color-coding from Panel E could be applied to the row labels in Panel D. Being able to link amplicon to the mechanism of CNV formation is especially important for seeing which ODIRA events contain an origin.

Thank you for this suggestions. We now indicate the mechanism of CNV formation using a consistent color coding in panels G and H (previously panels D and E).

(d) Panel E: I don't understand the two axes in Panel E. If both axes are log scales, why is the origin 0 for the X-axis and 1 for the Y-axis? And why are the focal amplicons (most of which are recombination events between the two LTRs) scattered in both X and Y coordinates? Shouldn't they form a single point? The same for the recombinants with distal LTRs. Also, orange and red (ODIRA and complex CNVs, respectively) are very hard to distinguish. All of these data need to be presented in a spreadsheet identifying each clone's strain ID, chemostat number, GAP1 and GFP copy numbers, sequence across the junction, and their coordinates. The SRA project (PRJNA1016460) for the sequence data was not found in SRA. Will this data be available to easily look at read depth across chromosome XI for all of the sequenced strains - perhaps as .bam files?

Thank you for calling these issues with data visualization to our attention. Indeed, the focal amplifications do form around a single point. We originally had jittered the data to show each individual focal amplification but agree that this is confusing. We now overlay the individual points and have altered opacity to enable visualization of individual values. The suggested table of clone data is provided in Supplementary File 2 and the SRA project is now publicly available. Moreover, we are providing all alignment (.bam) files, split, and discordant read depth profiles for each CNV strain and their corresponding ancestor aligned to our custom reference genomes in a public jbrowse server at:

https://jbrowse.bio.nyu.edu/gresham/?data=data/ee_gap1_arch_muts for WT strains, https://jbrowse.bio.nyu.edu/gresham/LTRKO_clones for LTR∆ strains, https://jbrowse.bio.nyu.edu/gresham/ARSKO_clones for ARS∆ strains, https://jbrowse.bio.nyu.edu/gresham/ALLKO_clones for ALL∆ strains.

(e) Supplementary Table 1 and Supplementary Figure S2: Please indicate which rearrangements (of the 8 reported in Figure S2A) were identified in each of the clones described in the table. If each of the 8 amplicons is identified by a letter, then this information could be added as a column in the table. I am assuming that each of the eight rearrangements was found in more than one chemostat. Showing these data is crucial for establishing the possibility that they were preexisting at the time of chemostat inoculation. The other possibility is that the clones with amplified GAP1 but a single copy of GFP could have been created by a secondary rearrangement in the outgrowth of the clones that originally had amplified both genes to the same extent. What is the structure of these amplicons? Is there a common junction between GAP1 and GFP? I couldn't find these data in the paper. A suggestion for Supplemental Figure S2A - include a zoomed-in inset for the GAP1 GFP region for each of the 8 read-depth plots. It is hard to see the exact location of GFP and GAP1 across all 8 tracks without getting out a ruler. Were these sequences aligned to your custom reference genome or the reference genome without GFP? If they were aligned to the custom reference that includes the GFP reporter, the reader could visually confirm the absence of GFP amplification.

Thank you for these suggestions. We edited Supplementary Table 1 and Supplementary Figure 1A as requested. We now provide the precise CNV breakpoints in the GFP-GAP1 region (supplemental figure 1B) displaying both genome read depth and split read depth tracks. These sequences were aligned to the custom reference containing the GFP reporter, which is now clearer in the figure and caption text in line 1226.

The clones in this figure were sampled from the five different chemostats and we have clarified this in the edited table and text at line 210. We did not detect the same CNV allele in different chemostats and therefore we do not have evidence to support GAP1 amplification without the GFP reporter pre-existing at time of inoculation. We are not able to definitively distinguish whether the amplicons were pre-existing at the time of inoculation or occurred after as we do not have barcoded lineages. We isolated clones carrying this class of amplification from the 1-GFP-copy subfraction late in the experimental evolution (generation 165-182). Given that the alleles appear to differ between populations we think the most parsimonious explanation is that these amplifications occurred after chemostat inoculation but early in the evolution experiment. We explicitly state this in the text starting in line 219.

(9) Page 8-9: I am sorry to say that I can't evaluate the "HDI of posterior distributions". It is out of my competency range. So I am not sure what this analysis is adding to the paper. The same goes for the rest of the supplementary figures.

HDI is a measure of certainty in an estimate, similar to confidence interval. We state this in the text in line 276. With the editing of the text we hope the modeling and its supplementary figures are more clear now.

(10) Page 9 top: Deletion of the ARS appears to lower the fitness of the amplified GAP1 variants. Can the authors speculate on why the ARS deletion would reduce fitness? Did they consult published replication profiles to determine the size of the origin-free gap that could result from the deletion of this mid-S phase origin? Could it explain the delay in the appearance of GAP1 amplicons in the ARS-deletion strains and be responsible for their reduced selection coefficients? Did you examine the growth properties of the starting strain or any of the amplified GAP1 derivatives? Perhaps this consideration could contribute to the discussion. Could there be a bit fuller discussion on the interaction between CNV length differences as shown in Figure 4A and differences in selection coefficient as determined by the nnSBI?

Thank you for raising this point. We have now added text to our discussion of the reduced fitness in ARS∆ in relation to DNA replication starting on line 359:

“ARS1116 is a major origin (McGuffee et al., 2013) and ODIRA CNVs found around this origin corroborate its activity. GAP1 is highly transcribed in glutamine-limited chemostats (Airoldi et al., 2016). Head-on transcription-replication collisions at this locus may be contributing to the higher CNV formation rate in wild type and LTR∆. Elimination of the local ARS could result in less transcription-replication collisions and the slower CNV formation rates estimated. Once formed they get outcompeted by faster-forming CNVs and thus in theory are less fit than CNVs in other strain backgrounds. These simulated competitions further suggest that the ARS is a more important contributor to adaptive evolution mediated by GAP1 CNVs.”

We examined replication profiles in McGuffee et al. Mol Cell. 2013 but could not determine the size of the origin-free gap. ARS1116 and its neighboring ARSs, ARS1118 downstream and ARS1115 upstream are efficient firing origins (Supplement 1 of McGuffee et al. 2013) and therefore the gap is likely to be minimal. The dynamics of the distal firing ARS elements involved in creating ODIRA CNVs might explain the reduced fitness, but further experiments would be required to address this. Regarding growth properties, the growth rate at steady-state in the chemostat is the same as the dilution rate regardless of strain background. Because we had the same dilution rate for each chemostat, the ARS∆ populations would have the same replication rate as the other three strains even if there may be replication rate differences in bulk culture growth. Finally, we found no significant interaction between CNV length and selection coefficients and we state this in line 359.

(11) Page 10: WT competition simulations: It may help to explicitly state that the competition modeling approach was experimentally validated in Avecilla 2022 as opposed to just citing the paper. I found the results much more convincing after reading Avecilla 2022, but I imagine many readers may skip that.

We added a sentence to state that the nnSBI method was experimentally validated in Avecilla et 2022 at line 249.

Reviewer #2 (Recommendations For The Authors):

(1) Figure 2: says reported CNV proportions (dashed). This may be a typo since I think the GFP reported should be solid, not dashed. Also, (C) isn't bold.

Thank you for identifying these mistakes. We have corrected the figure’s caption in line 1157.

(2) "compared to 898/345 clones" Does this refer to transposition/clone? Seems more natural to compare clones with transpositions to a total number of clones. This could be clarified.

We rephrased the sentence (lines 519-520) to clarify that in their study Hays et al. 2023 found 898 novel Ty insertions across 345 nitrogen-evolved clones. As a result of this high rate of transposition, some clones are expected to have multiple Ty insertions.

(3) The methods state that Kan replaces the Nat cassette that was used to make the deletions. It should be made more clear whether Kan is present and where Kan is with respect to GFP and GAP1.

Thank you for pointing this out. To clarify we added the following sentence to the methods starting in line 567:

“The CNV reporter is 3.1 kb and located 1117 nucleotides upstream of the GAP1 coding sequence. It consists of, in the following order, an ACT1 promoter, mCitrine (GFP) coding sequence, ADH1 terminator, and kanamycin cassette under control of a TEF promoter and terminator.”

Additionally in line 571 we clarify the drug resistance of the genomic architecture ∆ strains that are kanamycin(+) and nourseothricin(-).

Reviewer #3 (Recommendations For The Authors):

(1) The major advancement of the manuscript is stated in the title "DNA replication errors are a major source of adaptive gene amplification" First, in my humble opinion the term replication errors is not quite right; the term template switching is more accurate. In that regard, recently a paper was published just on this topic (Martin et al Plos Genetics, 2024).

We have changed the title to “Template-switching during DNA replication is a prevalent source of adaptive gene amplification”. We cite Martin et al Plos Genetics 2024 throughout the main text in lines 93, 126, 159, 502, 555.

(2) I find the statement "We find that 49% of all GAP1 CNVs are mediated by the DNA replication-based mechanism Origin Dependent Inverted Repeat Amplification (ODIRA) regardless of background strain." Somewhat misleading, there were considerable differences between the strains. If I am not mistaken the range was 20-80%.

Thank you for pointing this out. Indeed, the range was 26-80% across the four strains. We updated this sentence in the abstract at line 40, and in the main text at line 141 to clearly state the range.

(3) In their attempt to fill the gap of knowledge regarding the fitness effect of the adaptive CNV the authors use a mathematical model. As an experimental biologist, I found the description lacking. It is hard for me to evaluate the contribution of the model to understanding the results and I think the authors could improve this part.

We have edited the text regarding the modeling and associated results and hope that it is now more clear. The mathematical model describes the experiment in a simplified manner. We use it to predict the outcomes of additional experiments without additional experimental work. For example, we used it to simulate a competition between two strains, predict the total proportion of GAP1 CNVs, and predict the relative genetic diversity.

(4) Experiments the authors may want to consider to increase the novelty of their work:

a) Place the GAP1 gene right in the middle of the two most distant ARS elements and test the mechanism of CNV.

Thank you for this proposed experiment. It is beyond the scope of this paper and will be pursued in future studies.

b) The finding of de-novo Ty element insertion is interesting. What happens if the overdose strain of Jef Boeke is used (Retrotransposon overdose and genome integrity, PNAS 2009) or in contrast, a reverse transcriptase deficient strain?

We agree. Our study has revealed a critical role for novel Ty insertion in mediating CNVs. The suggested experiments as well as using strains that lack Ty sequences will be very interesting to explore in followup studies.

c) The genomic analyses were based on single colony isolates. To my understanding, the CNV events are identified at least partly by split reads. Therefore, each event may have a "signature" that is unique and can be concluded from single reads and not necessarily from the assembled genome. If true, a distinction between the scenarios could be achieved if bulk cultures are sequenced with enough depth. Thus, a truly dynamic and quantitative determination of the different events, rate of appearance, and disappearance can be made.

Thank you for this suggestion, which is a good idea but not currently feasible for several reasons. First, although split reads are a powerful way to detect CNV breakpoints, we have found that even at high coverage (21-153X, median 78.5X), in clonal samples that are rare with only 3-30 split reads (median 14) detected. These observations are from a total of 23 breakpoints across 16 sequenced clones. Thus, when sequencing heterogeneous cultures, in which different CNVs only comprise a fraction of the population, our ability to detect single CNV alleles by split reads and quantify their frequency is limited. Given our observations, with a median of 14 split reads when sequencing to 78.5X genome-wide read coverage it is possible we may be able to detect an individual CNV allele once it makes up (14/78.5) 17% of the population. However, our previous study has shown that there are tens to hundreds of unique CNV alleles initially and thus this would only be feasible at very late timepoints. Second, recurrent CNVs may occur independently at the same exact location, such as LTR NAHR. Thus, unique signatures may not be obtained even if they are independent events. Third, it would be not appropriate to pursue this analysis with our current dataset, as we lack lineage tracking barcodes to validate the results.

Author response:

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

Reviewer #1 (Public Review):

The authors sometimes seem to equivocate on to what extent they view their model as a neural (as opposed to merely behavioral) description. For example, they introduce their paper by citing work that views heterogeneity in strategy as the result of "relatively independent, separable circuits that are conceptualized as supporting distinct strategies, each potentially competing for control." The HMM, of course, also relates to internal states of the animal. Therefore, the reader might come away with the impression that the MoA-HMM is literally trying to model dynamic, competing controllers in the brain (e.g. basal ganglia vs. frontal cortex), as opposed to giving a descriptive account of their emergent behavior. If the former is really the intended interpretation, the authors should say more about how they think the weighting/arbitration mechanism between alternative strategies is implemented, and how it can be modulated over time. If not, they should make this clearer.

The MoA-HMM is meant to be descriptive in identifying behaviorally distinct strategies. Our intention in connecting it with a “mixture-of-strategies” view of the brain is that the results of the MoA-HMM could be indicative of an underlying arbitration process, but not modeling that process per se, that can be used to test neural hypotheses driven by this idea. We’ve added additional clarification in the discussion to highlight this point.

Explicitly, we added the following sentence in the discussion: “For example, while the MoA-HMM itself is a descriptive model of behavior and is not explicitly modeling an underlying arbitration of controllers in the brain, the resulting behavioral states may be indicative of underlying neural processes and help identify times when different neural controllers are prevailing”

Second, while the authors demonstrate that model recovery recapitulates the weight dynamics and action values (Fig. 3), the actual parameters that are recovered are less precise (Fig. 3 Supplement 1). The authors should comment on how this might affect their later inferences from behavioral data. Furthermore, it would be better to quantify using the R^2 score between simulated and recovered, rather than the Pearson correlation (r), which doesn't enforce unity slope and zero intercept (i.e. the line that is plotted), and so will tend to exaggerate the strength of parameter recovery.

In the methods section, we noted that the interaction between parameters can cause the recovery of randomly drawn parameter sets to fail, as seen in Figure 3 Supplement 1. This is because there are parameter regimes (specifically when a softmax temperature is near zero) which causes choices to be random, and therefore other parameters no longer matter. To address this, we included a second supplemental figure, Figure 3 Supplement 2, where we recovered model parameters from data simulated solely from models inferred from the behavioral data. Recovery of these models is much more precise, which credits our later inferences from the behavioral data.

To make this point clearer, we changed the reference to Figure 3 Supplements 1 & 2 to: “(Figure 3 – figure supplement 1 for recovery of randomized parameters with noted limitations, and figure supplement 2 for recovery of models fit to real data)” We additionally added the following to the Figure 3 Supplement 1 caption: “Due to the interaction between different model parameters (e.g. a small 𝛽 weight will affect the recoverability of the agent’s learning rate 𝛼), a number of “failures” can be seen.”

Furthermore, we added an R^2 score that enforces unity slope and zero intercept alongside the Pearson correlation coefficient for more comprehensive metrics of recovery. The R^2 scores are plotted on both Figure 3 Supplements 1 & 2 as “R2”, and the following text was added in both captions: “"r" is the Pearson's correlation coefficient between the simulated and recovered parameters, and "R2" is the coefficient of determination, R2, calculating how well the simulated parameters predict the recovered parameters.”

Finally, the authors are very aware of the difficulties associated with long-timescale (minutes) correlations with neural activity, including both satiety and electrode drift, so they do attempt to control for this using a third-order polynomial as a time regressor as well as interaction terms (Fig. 7 Supplement 1). However, on net there does not appear to be any significant difference between the permutation-corrected CPDs computed for states 2 and 3 across all neurons (Fig. 7D). This stands in contrast to the claim that "the modulation of the reward effect can also be seen between states 2 and 3 - state 2, on average, sees a higher modulation to reward that lasts significantly longer than modulation in state 3," which might be true for the neuron in Fig. 7C, but is never quantified. Thus, while I am convinced state modulation exists for model-based (MBr) outcome value (Fig. 7A-B), I'm not convinced that these more gradual shifts can be isolated by the MoA-HMM model, which is important to keep in mind for anyone looking to apply this model to their own data.

We agree with the reviewers that our initial test of CPD significance was not sufficient to support the claims we made about state differences, especially for Figure 7D. To address this, we updated the significance test and indicators in Figure 7B,D to instead signify when there is a significant difference between state CPDs. This updated test supports a small, but significant difference in early post-outcome reward modulation between states 2 and 3.

We clarified and updated the significance test in the methods with the following text:

“A CPD (for a particular predictor in a particular state in a particular time bin) was considered significant if that CPD computed using the true dataset was greater than 95% of corresponding CPDs (same predictor, same state, same time bin) computed using these permuted sessions. For display, we subtract the average permuted session CPD from the true CPD in order to allow meaningful comparison to 0.

To test whether neural coding of a particular predictor in a particular time bin significantly differed according to HMM state, we used a similar test. For each CPD that was significant according to the above test, we computed the difference between that CPD and the CPD for the same predictor and time bin in the other HMM states. We compare this difference to the corresponding differences in the circularly permuted sessions (same predictor, time bin, and pair of HMM states). We consider this difference to be significant if the difference in the true dataset is greater than 95% of the CPD differences computed from the permuted sessions.”

We updated the significance indicators above the panels in Figure 7B,D (colored points) to refer to significant differences between states, with additional text to the left of each row of points to specify the tested state and which states it is significantly greater than. We updated the figure caption for both B and D to reflect these changes.

We also changed text in the results to focus on significant differences between states. Specifically, we replaced the sentence “Looking at the CPD of expected outcome value split by state (Figure 7B) reveals that the trend from the example neuron is consistent across the population of OFC units, where state 2 shows the greatest CPD.” with the sentence “Looking at the CPD of expected outcome value split by state (Figure 7B) reveals that the trend from the example neuron is consistent across the population of OFC units, where state 2 has a significantly greater CPD than states 1 and 3.”

We also replaced the sentence “Suggestively, the modulation of the reward effect can also be seen between states 2 and 3 – state 2, on average, sees a higher modulation to reward that lasts significantly longer than modulation in state 3.” with the sentence “Additionally, the modulation of the reward effect can also be seen between states 2 and 3 — immediately after outcome, we see a small but significantly higher modulation to reward during state 2 than during state 3.”

Reviewer #2 (Public Review):

There were a lot of typos and some figures were mis-referenced in the text and figure legends.

We apologize for the numerous typos and errors in the text and are grateful for the assistance in identifying many of them. We have taken another thorough pass through the manuscript to address those identified by the reviewer as well as fix additional errors. To reduce redundancy, we’ll address all typoand error-related suggestions from both reviewers here.

● We fixed all Figure 1 references. We additionally reversed the introduction order of the agents in Figure 1 and in the results section “Reinforcement learning in the rat two-step task”, where we introduce both model-free agents before both model-based agents. This is to make the model-based choice agent description (which references the model-free choice agent in the statement “That is, like MFc, this agent tends to repeat or switch choices regardless of reward”) come after introducing the model-free choice agent.

● We fixed all Figure 4 references.

● We fixed all Figure 6 references and fixed the panel references in the figure caption to match the figure labeling: Starting with panel B, the reference to (i) was removed, and the reference to (ii) was updated to C. The previous reference to C was updated to D.

● All line-numbered suggestions were addressed.

● The text “(move to supplement?)” was removed from the methods heading, and the mistaken reference to Q_MBr was fixed.

● We removed all “SR” acronyms from the statistics as it was an artifact from an earlier draft.

● We homogenized notation in Figure 2, replacing all “c” variable references with “y”, as well as homogenized notation of β

● We replaced many uses of the word “action” with the word “choice” for consistency throughout the manuscript.

● We addressed many additional minor errors

Reviewer #1 (Recommendations For The Authors):

(1) Could the authors comment on why the cross-validated accuracy continues to increase, albeit non-significantly, after four states, as opposed to decreasing (as I would naively expect would be the result due to overfitting)?

Due to the large amounts of trials and sessions obtained from each rat (often >100 sessions with >200 trials per session) and the limited number of training iterations (capped at 300 iterations), it is not guaranteed that the cross-validated accuracy would decrease over the range of states we included in Figure 4, especially given that the number of total parameters in the largest model shown (7-states, 95 parameters) is greatly less than the number of observations. Since we’re mainly interested in using this tool to identify interpretable, consistent structure across animals, we did not focus on interpreting the regime of larger models.

(2) It seems like the model was refit multiple times with different priors ("Estimation of Population Prior"), each derived from the previous step of fitting. I'm not very familiar with fitting these kinds of models. Is this standard practice? It gives off the feeling of double-dipping. It would be helpful if the authors could cite some relevant literature here or further justify their choices.

We adopted a “one-step” hierarchical approach, where we estimate the population prior a single time on (nearly) unconstrained model fits, and use it for a second, final round of model fits which were used for analysis. Since the prior is only estimated once, in practice there isn’t risk of converging on an overly constrained prior. This is a somewhat simplified approach motivated by analogy to the first step of EM fit in a hierarchical model, in which population- and subject-level parameters are iteratively re-estimated in terms of one another until convergence (Huys et al., 2012; Daw 2010). We have clarified this approach in the methods with citations by adding the following paragraph:

“Hierarchical modeling gives a better estimate of how model parameters can vary within a population by additionally inferring the population distribution over which individuals are likely drawn (Daw, 2011). This type of modeling, however, is notoriously difficult in HMMs; therefore, as a compromise, we adopt a “one-step” hierarchical model, where we estimate population parameters from “unconstrained” fits on the data, which are then used as a prior to regularize the final model fits. This approach is motivated by analogy to the first step of EM fit in a hierarchical model, in which population- and subject-level parameters are iteratively re-estimated in terms of one another until convergence (Daw, 2011; Huys et al., 2012). It is important to emphasize, since we aren’t inferring the population distributions directly, that we only estimate the population prior a single time on the “unconstrained” fits as follows.”

Reviewer #2 (Recommendations For The Authors):

Figure 3a.iii: Did the model capture the transition probabilities correctly as well?

We have updated Figure 3E to include additional panels (iii) and (iv) to show the recovered initial state probabilities and transition matrix.

For Figure 6, panel B makes it look like there is a larger influence of state on ITI rate after omission, in both the top and bottom plots. However, the violin plots in panel C show a different pattern, where state has a greater effect on ITIs following rewarded trials. Is it that the example in panel B is not representative of the population, or am I misinterpreting?

We thank the reviewer for catching this issue, as the colors were erroneously flipped in panel C. We have fixed this figure by ensuring that the colors appropriately matched the trial type (reward or omission). Additionally, we updated the colors in B and C that correspond to reward (previously gray, now blue) and omission (previously gold, now red) trials to match the color scheme used in Figure 1. We also inverted the corresponding line styles (reward changed to solid, omission changed to dashed) to match the convention used in Figure 7. To differentiate from the reward/omission color changed, we additionally changed the colors in Figure 6D and Figure 7 Supplement 1, where the color for “time” was changed from blue to gray, and the color for “state” was changed from red to gold.

For figure 4B right, I am confused. The legend says that this is the change in model performance relative to a model with one fewer state. But the y-axis says it's the change from the single-state model. Please clarify.

The plot is showing the increase in performance from the single-state model, while the significance tests were done between consecutive numbered states. We updated the significance indicators on the plot to more clearly identify that adjacent models are being compared (with the exception of the 2-state model, which is being compared to 0). We updated the Figure 4B caption text for the left panel to state: “Change in normalized, cross-validated likelihood when adding additional hidden states into the MoA-HMM, relative to the single-state model. Significant changes are computed with respect to models with one fewer states (e.g. 2-state vs 1-state, 3-state vs 2-state)”

Esta razón ecológica y su oposición a la razón instrumental se correlaciona con la distinción que hace Heidegger de la techné griega, que es un acto de poiesis entendida como "traer-ahí-delante"; por contra, la técnica moderna sería una "estructura de emplazamiento" [Gestell] que "trata a todo como un stock de existencias, como recursos para ser explotados" (Yuk Hui en el Prefacio de Fragmentar el Futuro)

Author response:

Public Review:

In this work, the authors develop a new computational tool, DeepTX, for studying transcriptional bursting through the analysis of single-cell RNA sequencing (scRNA-seq) data using deep learning techniques. This tool aims to describe and predict the transcriptional bursting mechanism, including key model parameters and the steady-state distribution associated with the predicted parameters. By leveraging scRNA-seq data, DeepTX provides high-resolution transcriptional information at the single-cell level, despite the presence of noise that can cause gene expression variation. The authors apply DeepTX to DNA damage experiments, revealing distinct cellular responses based on transcriptional burst kinetics. Specifically, IdU treatment in mouse stem cells increases burst size, promoting differentiation, while 5FU affects burst frequency in human cancer cells, leading to apoptosis or, depending on the dose, to survival and potential drug resistance. These findings underscore the fundamental role of transcriptional burst regulation in cellular responses to DNA damage, including cell differentiation, apoptosis, and survival. Although the insights provided by this tool are mostly well supported by the authors' methods, certain aspects would benefit from further clarification.

The strengths of this paper lie in its methodological advancements and potential broad applicability. By employing the DeepTXSolver neural network, the authors efficiently approximate stationary distributions of mRNA count through a mixture of negative binomial distributions, establishing a simple yet accurate mapping between the kinetic parameters of the mechanistic model and the resulting steady-state distributions. This innovative use of neural networks allows for efficient inference of kinetic parameters with DeepTXInferrer, reducing computational costs significantly for complex, multi-gene models. The approach advances parameter estimation for high-dimensional datasets, leveraging the power of deep learning to overcome the computational expense typically associated with stochastic mechanistic models. Beyond its current application to DNA damage responses, the tool can be adapted to explore transcriptional changes due to various biological factors, making it valuable to the systems biology, bioinformatics, and mechanistic modelling communities. Additionally, this work contributes to the integration of mechanistic modelling and -omics data, a vital area in achieving deeper insights into biological systems at the cellular and molecular levels.

We thank the reviewers for their positive opinion on our manuscript. As reflected in our detailed responses to the reviewers’ comments, we will make significant changes to address their concerns comprehensively.

This work also presents some weaknesses, particularly concerning specific technical aspects. The tool was validated using synthetic data, and while it can predict parameters and steady-state distributions that explain gene expression behaviour across many genes, it requires substantial data for training. The authors account for measurement noise in the parameter inference process, which is commendable, yet they do not specify the exact number of samples required to achieve reliable predictions. Moreover, the tool has limitations arising from assumptions made in its design, such as assuming that gene expression counts for the same cell type follow a consistent distribution. This assumption may not hold in cases where RNA measurement timing introduces variability in expression profiles.

Thank you for your detailed and constructive feedback on our work. We will address the key concerns raised from the following points:

(1) Clarification on the required sample size: We tested the robustness of our inference method on simulated datasets by varying the number of single-cell samples. Our results indicated that the predictions of burst kinetics parameters become accurate when the number of cells reaches 500 (Supplementary Figure S3d, e). This sample size is smaller than the data typically obtained with current single-cell RNA sequencing (scRNA-seq) technologies, such as 10x Genomics and Smart-seq3 (Zheng GX et al., 2017; Hagemann-Jensen M et al., 2020). Therefore, we believed that our algorithm is well-suited for inferring burst kinetics from existing scRNA-seq datasets, where the sample size is sufficient for reliable predictions. We will clarify this point in the main text to make it easier for readers to use the tool.

(2) Assumption-related limitations: One of the fundamental assumptions in our study is that the expression counts of each gene are independently and identically distributed (i.i.d.) among cells, which is a commonly adopted assumption in many related works (Larsson AJM et al., 2019; Ochiai H et al., 2020; Luo S et al., 2023). However, we acknowledged the limitations of this assumption. The expression counts of the same gene in each cell may follow distinct distributions even from the same cell type, and dependencies between genes could exist in realistic biological processes. We recognized this and will deeply discuss these limitations from assumptions and prospect as an important direction for future research.

The authors present a deep learning pipeline to predict the steady-state distribution, model parameters, and statistical measures solely from scRNA-seq data. Results across three datasets appear robust, indicating that the tool successfully identifies genes associated with expression variability and generates consistent distributions based on its parameters. However, it remains unclear whether these results are sufficient to fully characterize the transcriptional bursting parameter space. The parameters identified by the tool pertain only to the steady-state distribution of the observed data, without ensuring that this distribution specifically originates from transcriptional bursting dynamics.

We appreciate your insightful comments and the opportunity to clarify our study’s contributions and limitations. Although we agree that assessing whether the results from these three realistic datasets can represent the characterize transcriptional burst parameter space is challenging, as it depends on data property and conditions in biology, we firmly believe that DeepTX has the capacity to characterize the full parameter space. This believes stems from the extensive parameters and samples we input during model training and inference across a sufficiently large parameter range (Method 1.3). Furthermore, the training of the model is both flexible and scalable, allowing for the expansion of the transcriptional burst parameter space as needed. We will clarify this in the text to enable readers to use DeepTX more flexibly.

On the other hand, we agree that parameter identification is based on the steady-state distribution of the observed data (static data), which loses information about the fine dynamic process of the burst kinetics. In principle, tracking the gene expression of living cells can provide the most complete information about real-time transcriptional dynamics across various timescales (Rodriguez J et al., 2019). However, it is typically limited to only a small number of genes and cells, which could not investigate general principles of transcriptional burst kinetics on a genome-wide scale. Therefore, leveraging the both steady-state distribution of scRNA-seq data and mathematical dynamic modelling to infer genome-wide transcriptional bursting dynamics represents a critical and emerging frontier in this field. For example, the statistical inference framework based on the Markovian telegraph model, as demonstrated in (Larsson AJM et al., 2019), offers a valuable paradigm for understanding underlying transcriptional bursting mechanisms. Building on this, our study considered a more generalized non-Mordovian model that better captures transcriptional kinetics by employing deep learning method under conditions such as DNA damage. This provided a powerful framework for comparative analyses of how DNA damage induces alterations in transcriptional bursting kinetics across the genome. We will highlight the limitations of current inference using steady-state distributions in the text and look ahead to future research directions for inference using time series data across the genome.

A primary concern with the TXmodel is its reliance on four independent parameters to describe gene state-switching dynamics. Although this general model can capture specific cases, such as the refractory and telegraph models, accurately estimating the parameters of the refractory model using only steady-state distributions and typical cell counts proves challenging in the absence of time-dependent data.

We thank you for highlighting this critical concern regarding the TXmodel's reliance on four independent parameters to describe gene state-switching dynamics. We acknowledge that estimating the parameters of the TXmodel using only steady-state distributions and typical single-cell RNA sequencing (scRNA-seq) data poses significant challenges, particularly in the absence of time-resolved measurements.

As described in the response of last point, while time-resolved data can provide richer information than static scRNA-seq data, it is currently limited to a small number of genes and cells, whereas static scRNA-seq data typically capture genome-wide expression. Our framework leverages deep learning methods to link mechanistic models with static scRNA-seq data, enabling the inference of genome-wide dynamic behaviors of genes. This provides a potential pathway for comparative analyses of transcriptional bursting kinetics across the entire genome.

Nonetheless, the refractory model and telegraphic model are important models for studying transcription bursts. We will discuss and compare them in terms of the accuracy of inferred parameters. Certainly, we agree that inferring the molecular mechanisms underlying transcriptional burst kinetics using time-resolved data remains a critical future direction. We will include a brief discussion on the role and importance of time-resolved data in addressing these challenges in the discussion section of the revised manuscript.

The claim that the GO analysis pertains specifically to DNA damage response signal transduction and cell cycle G2/M phase transition is not fully accurate. In reality, the GO analysis yielded stronger p-values for pathways related to the mitotic cell cycle checkpoint signalling. As presented, the GO analysis serves more as a preliminary starting point for further bioinformatics investigation that could substantiate these conclusions. Additionally, while GSEA analysis was performed following the GO analysis, the involvement of the cardiac muscle cell differentiation pathway remains unclear, as it was not among the GO terms identified in the initial GO analysis.

We thank the reviewer for this valuable feedback and for pointing out the need for clarification regarding the GO and GSEA analyses. We agree that the connection between the cardiac muscle cell differentiation pathway identified in the GSEA analysis and the GO terms from the initial analysis requires further clarification. This discrepancy arises because GSEA examines broader sets of pathways and may capture biological processes not highlighted by GO analysis due to differences in the statistical methods and pathway definitions used. We will revise the manuscript to address this point, explicitly discussing the distinct yet complementary nature of GO and GSEA analyses and providing a clearer interpretation of the results.

As the advancement is primarily methodological, it lacks a comprehensive comparison with traditional methods that serve similar functions. Consequently, the overall evaluation of the method, including aspects such as inference accuracy, computational efficiency, and memory cost, remains unclear. The paper would benefit from being contextualised alongside other computational tools aimed at integrating mechanistic modelling with single-cell RNA sequencing data. Additional context regarding the advantages of deep learning methods, the challenges of analysing large, high-dimensional datasets, and the complexities of parameter estimation for intricate models would strengthen the work.

We greatly appreciate your insightful feedback, which highlights important considerations for evaluating and contextualizing our methodological advancements. Below, we emphasize our advantages from both the modeling perspective and the inference perspective compared with previous model. As our work is rooted in a model-based approach to describe the transcriptional bursting process underlying gene expression, the classic telegraph model (Markovian) and non-Markovian models which are commonly employed are suitable for this purpose:

Classic telegraph model: The classic telegraph model allows for the derivation of approximate analytical solutions through numerical integration, enabling efficient parameter point estimation via maximum likelihood methods, e.g., as explored in (Larsson AJM et al., 2019). Although exact analytical solutions for the telegraph model are not available, certain moments of its distribution can be explicitly derived. This allows for an alternative approach to parameter inference using moment-based estimation methods, e.g., as explored in (Ochiai H et al., 2020). However, it is important to note that higher-order sample moments can be unstable, potentially leading to significant estimation bias.

Non-Markovian Models: For non-Markovian models, analytical or approximate analytical solutions remain elusive. Previous work has employed pseudo-likelihood approaches, leveraging statistical properties of the model’s solutions to estimate parameters, e.g., as explored in (Luo S et al., 2023). However, the method may suffer from low inference efficiency.

In our current work, we leverage deep learning to estimate parameters of TXmodel, which is non-Markovian model. First, we represent the model's solution as a mixture of negative binomial distributions, which is obtained by the deep learning method. Second, through integration with the deep learning architecture, the model parameters can be optimized using automatic differentiation, significantly improving inference efficiency. Furthermore, by employing a Bayesian framework, our method provides posterior distributions for the estimated dynamic parameters, offering a comprehensive characterization of uncertainty. Compared to traditional methods such as moment-based estimation or pseudo-likelihood approaches, we believe our approach not only achieves higher inference efficiency but also delivers posterior distributions for kinetics parameters, enhancing the interpretability and robustness of the results. We will present and emphasize the computational efficiency and memory cost of our methods the revised version.

Reference

Zheng, G.X., Terry, J.M., Belgrader, P., Ryvkin, P., Bent, Z.W., Wilson, R., Ziraldo, S.B., Wheeler, T.D., McDermott, G.P., Zhu, J., Gregory, M.T., Shuga, J., Montesclaros, L., Underwood, J.G., Masquelier, D.A., Nishimura, S.Y., Schnall-Levin, M., Wyatt, P.W., Hindson, C.M., Bharadwaj, R., Wong, A., Ness, K.D., Beppu, L.W., Deeg, H.J., McFarland, C., Loeb, K.R., Valente, W.J., Ericson, N.G., Stevens, E.A., Radich, J.P., Mikkelsen, T.S., Hindson, B.J., Bielas, J.H. 2017. Massively parallel digital transcriptional profiling of single cells. Nature Communications 8: 14049. DOI: https://dx.doi.org/10.1038/ncomms14049, PMID: 28091601

Hagemann-Jensen, M., Ziegenhain, C., Chen, P., Ramsköld, D., Hendriks, G.J., Larsson, A.J.M., Faridani, O.R., Sandberg, R. 2020. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat Biotechnol 38: 708-714. DOI: https://dx.doi.org/10.1038/s41587-020-0497-0, PMID: 32518404

Larsson, A.J.M., Johnsson, P., Hagemann-Jensen, M., Hartmanis, L., Faridani, O.R., Reinius, B., Segerstolpe, A., Rivera, C.M., Ren, B., Sandberg, R. 2019. Genomic encoding of transcriptional burst kinetics. Nature 565: 251-254. DOI: https://dx.doi.org/10.1038/s41586-018-0836-1, PMID: 30602787

Ochiai, H., Hayashi, T., Umeda, M., Yoshimura, M., Harada, A., Shimizu, Y., Nakano, K., Saitoh, N., Liu, Z., Yamamoto, T., Okamura, T., Ohkawa, Y., Kimura, H., Nikaido, I. 2020. Genome-wide kinetic properties of transcriptional bursting in mouse embryonic stem cells. Science Adavances 6: eaaz6699. DOI: https://dx.doi.org/10.1126/sciadv.aaz6699, PMID: 32596448

Luo, S., Wang, Z., Zhang, Z., Zhou, T., Zhang, J. 2023. Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics. Nucleic Acids Research 51: 68-83. DOI: https://dx.doi.org/10.1093/nar/gkac1204, PMID: 36583343

Rodriguez, J., Ren, G., Day, C.R., Zhao, K., Chow, C.C., Larson, D.R. 2019. Intrinsic dynamics of a human gene reveal the basis of expression heterogeneity. Cell 176: 213-226.e218. DOI: https://dx.doi.org/10.1016/j.cell.2018.11.026, PMID: 30554876

Luo, S., Zhang, Z., Wang, Z., Yang, X., Chen, X., Zhou, T., Zhang, J. 2023. Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model. Royal Society Open Science 10: 221057. DOI: https://dx.doi.org/10.1098/rsos.221057, PMID: 37035293

DOI: 10.1016/j.brainres.2018.03.037

Resource: RRID:BDSC_6925

Curator: @dawnn.marie

SciCrunch record: RRID:BDSC_6925

What is this?

muy importante. Motivo de conversión. Herida ontológica

Author response:

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

Reviewer #1 (Recommendations For The Authors):

(1) Gap of knowledge:

From the introduction, I got the impression that the manuscript tries to answer the question of whether homeostatic structural plasticity is functionally redundant to synaptic scaling. However, the importance of this question needs to be worked out better. Also, I think it is hard to tackle this question with the shown experiments as one would have to block all other redundant mechanisms and see whether HSP functionally replaces them.

We appreciate the reviewer’s valuable feedback regarding the relationship between homeostatic structural plasticity (HSP) and synaptic scaling. The main objective of our study is indeed to investigate whether structural plasticity is homeostatically regulated, and if so, whether it acts as a redundant or heterogeneous mechanism in relation to synaptic scaling, which is widely recognized as a primary homeostatic process.

In our revised introduction, we have clarified this central question and its significance. Specifically, we explored why experimentally observed changes in spine density, a measure of structural plasticity, do not exhibit the same homeostatic characteristics as changes in spine head size, which reflects synaptic scaling, particularly under conditions of activity blockade.

We hypothesized two key points:

(1) Structural plasticity may not follow a monotonically activity-dependent rule as strictly as synaptic scaling.

(2) The observed changes in spine density may be influenced by the simultaneous modulation of spine size, suggesting that structural plasticity and synaptic scaling interact within the same biological system.

Both hypotheses were tested through a combination of experimental observations and systematic computer simulations. Our conclusions demonstrate that spine-number-based structural plasticity follows a biphasic activity-dependent rule. While it largely overlaps with synaptic scaling under typical conditions, it exhibits heterogeneity under extreme conditions, such as activity silencing. Furthermore, our simulations revealed that both mechanisms can compete and complement each other within neural networks.

We believe that these results offer a nuanced understanding of the interaction between structural plasticity and synaptic scaling, highlighting their redundancy under most conditions but also their heterogeneity under specific circumstances. Blocking all other redundant mechanisms, as suggested, would provide a more reductionist view, which may not capture the complexity and interplay of these processes in a physiological setting. Our approach reflects this complexity, providing insight into how these mechanisms operate together in a naturalistic context.

We have revised the introduction to better convey these points and emphasize the significance of this question for understanding the dynamics of homeostatic regulation in neural networks.

Similarly, the simulations do not really tackle redundancy as, e.g. network growth cannot be achieved by scaling alone.

We appreciate the reviewer’s comment regarding synaptic scaling's limitations in achieving network growth. We would like to clarify that we did not intend to suggest that structural plasticity and synaptic scaling are fully redundant. In fact, it is well established in the literature that structural plasticity plays a dominant role during development, particularly in network growth, which synaptic scaling alone cannot achieve.

The primary objective of our study was to investigate the interaction between structural plasticity and synaptic scaling under conditions of activity perturbation, rather than during network growth or development. To avoid any confusion regarding developmental processes, we chose to grow the network using only structural plasticity in our simulations. Synaptic scaling was then introduced (or not) during the phase of activity deprivation to specifically examine its role in regulating homeostasis under these conditions.

We have revised the corresponding sections of the manuscript to clarify this distinction, and we have ensured that the simulations reflect our focus on activity perturbation rather than network development. This distinction should help readers avoid conflating developmental processes with the specific goals of our study.

Instead, the section on "Integral feedback mechanisms" (L112-129) contains a much better description of the actual goals of the paper than is given in the introduction. Moreover, this section does not seem to include any new results (at least the Ca-dependent structural plasticity and synaptic scaling rules seem to be very common for me). I, therefore, suggest fusing this paragraph in the introduction to obtain a clearer and better understandable gap of knowledge, which is addressed by the paper.

We agree that the "Integral feedback control" section provides key information relevant to both the Introduction and Methodology. It outlines the theoretical framework and serves as a basis for the experimental design.

To better reflect this, we have revised the Introduction to include the gap in knowledge. However, we opted to retain the section in the Results, slightly modified, to set the context for the first experiment.

Along this line, as it seems a central point of the manuscript to distinguish the controller dependencies on Calcium, the different dependencies (working models) should be described in more detail. Also, the description of the inconsistencies of the previous results on HSP can be moved from the discussion (l419-l441) to the introduction.

We have revised the manuscript to place less emphasis on the controller models while retaining the core principles of control theory. The description of the HSP model has been moved to the Introduction, as suggested, while the detailed history remains in the Discussion to maintain the manuscript's consistency.

Systematic text revision: Regarding comment (1), we thank the reviewer for suggesting the text reorganization. We have adjusted several parts in the introduction, M&M section, and results section to increase clarity.

(2) Pharmacological Choice:

It should be discussed why NBQX is used to induce the homeostatic effect instead of TTX. As there are studies showing that it might block homeostatic rewiring (doi.org/10.1073/pnas.0501881102) as well as synaptic scaling (10.1523/JNEUROSCI.3753-08.2009), it seems unclear whether the observed effects are actually corresponding to those in other publications.

The rationale for using NBQX in our experiments, rather than TTX, is detailed in the public response. We selected NBQX based on specific experimental motivations relevant to our study’s objectives, while acknowledging the potential differences in effects compared to other studies.

Local text revision: We added one paragraph in the discussion section to explain the idea better.

(3) Model-Experiment Connection:

The paper combines simulations with experimental work, which is very good. However, in my opinion, the only connection between the two parts is that the experiments suggest a non-monotonic dependency between firing rate and synapse density (i.e. the biphasic dependency). The rest of the experimental results seem to be neglected in the modeling part. It is not even shown that the model reproduces the experiments. Instead, the model is tested in different situations and paradigms (blocking AMPARs in the whole culture vs network growth or silencing a sub-population). I think it would make the paper stronger and more consequential when a reproduction of the experiment by the model is demonstrated (with analogue analyses).

The experimental results serve three main purposes. First, as the reviewer noted, the spine analysis was conducted to inform the biphasic rule. Second, spine size analysis was performed to replicate published findings and confirm our modeling results, showing that activity deprivation leads to fewer synapses with larger sizes or higher weights. Third, the correlation analysis of spine density and size across dendritic segments suggested a hybrid combination of two types of plasticity across different neurons.

While we addressed these aspects in the Results and Discussion sections, the collective presentation in Fig. 2 may have caused some confusion. To improve clarity, we have now split the experimental results, presenting them alongside the relevant modeling data in Fig. 2, Fig. 8, and Fig. 9.

Also, there are a few more mismatches between the experiment and the model that you will want to discuss:

• The size-dependent homeostatic effect (l154ff, Fig2F) is not reflected by the used scaling model.

We revised Fig 8 and the corresponding text to explain how the scaling model reflects such an effect.

• The model assumes reduced Ca levels. Yet, the experimental protocol blocks AMPARs, which are to my knowledge not the primary source of Ca influx, but rather the NMDARs.

The model is based on neural activity, with calcium concentration serving as an internal integral signal of the firing rate, allowing for integral control. While calcium plays a critical role in homeostasis, we caution against drawing a strict correspondence between the model's calcium dynamics and the experimental protocol, as calcium can be sourced from multiple pathways in neurons beyond AMPARs, such as NMDARs, voltage gated calcium channels, and intracellular stores. Also, our recent work demonstrated that under baseline conditions, the majority of AMPARs are not Ca2+ permeable, i.e., GluA2-lacking (Kleidonas et al., 2023)

Improving the calcium dynamics, including secondary calcium release and calcium stores, is part of our future plan to refine the HSP model and address experimental findings that are not fully explained by the current model.

• The model further assumes silencing by input removal, whereas the recurrent connections stay intact. Wouldn't this rather correspond to a deafferentation experiment, where connections to another brain area are cut?

Thank you for pointing at this. The modeling section was not intended to directly replicate the tissue culture experiments but rather to provide insights into a broader range of scenarios, including pharmacological treatments, deafferentation, lesions, and even monocular deprivation.

Systematic text revision: Regarding comment (3), the goal of our modeling work was more than reproducing. To better serve the purposes of experimental results used in the present study, to inform, confirm, and inspire, we have systematically adjusted the layout of experimental and modeling results to link them better.

(4) Is the recurrent component too weak?

Your results show that HSP does not restore activity after silencing (deafferentation), whereas you discuss that earlier models did achieve this by active neighbors in a spatially organized network. However, the silenced neurons in your simulations also receive inputs through the "recurrent" connections from their neighbors (at least shortly after silencing). Therefore, given the recurrent input is strong enough, they should be able to recover in a similar way as the spatially organized ones. As a consequence, I obtained the impression that, in your model networks, activity is strongly driven by external stimulation and less by recurrent connections. I understand that this is important to achieve silencing through removing the Poisson stimulation. Yet, this fact may be responsible for the failure to restore activity such that presented effects are only applicable for networks that are strongly driven by external inputs, but not for strongly recurrent networks, which would severely limit the generality of the results. As a consequence, the paper would benefit from a systematic analysis of the trade-off between recurrent strength and input strength. Maybe, different constant negative currents could be injected in all neurons, such that HSP creates more recurrent synapses in the network.

We appreciate this insight. However, increasing recurrent input strength is beyond the scope of the current study, as it would fundamentally alter the predefined network dynamics of the Brunel network used. As noted in the manuscript, complete isolation or cell death is not always the outcome after input deprivation, lesion, or stroke, which cannot be fully explained by the Gaussian HSP rule alone. Butz and colleagues offered a solution using growth rules that maximized recurrent input, and we recognize the importance of their work.

That said, we approached the issue from a different angle, emphasizing the role of synaptic scaling in recurrence rather than relying solely on recurrent input strength. In biological networks, external inputs may vary, recurrency can be weak or strong, and synaptic scaling can dominate. Our model offers a complementary hypothesis, suggesting that these factors, in combination, contribute to the diverse and sometimes contradictory results found in the literature, rather than posing a strict constraint on network topology.

Local text revision: We emphasized these points in the Discussion section again.

(5) Missing conclusions / experimental predictions

As already described, the modelling work is not reproducing the presented or previous experimental data. Hence, the goal of modelling should be to derive a more general understanding and make experimental predictions. Yet, the conclusions in the discussion stay superficial and vague and there are no specific experimental predictions derived from the model results.

For example, the authors report that the recovery of activity in silenced cultures is observed in a previously spatially structured model but not in theirs -- at least with slow or no scaling. Yet it is left to the reader to think about whether the current model is an improvement to the previous one, how they could be experimentally distinguished, or to which experimental findings they relate or compare, which I would expect at this point. I would advise reworking the discussion and thoroughly working out which new insights the modelling part of the study has generated (not to be confused with the assumptions of the model aka the biphasic plasticity rule) and relating them to experimental pre- and postdiction.

We recognize the reviewer’s concern, which is closely related to comment (4). We have addressed these points by reorganizing the text to better clarify the purpose of our experimental work and its connection to the modeling results.

Specifically, we have reworked the discussion to highlight the new insights gained from the modeling, and how these can inform experimental predictions and interpretations. This includes distinguishing our model from previous ones and providing clearer connections to experimental findings.

Systematic text revision: Most of the comments on combining experiments and modeling results and on developing the story based on our expectations raised here are sincere and may also reflect the expectations and concerns of a broader readership, so we have accordingly adjusted the text in the Results and Discussion sections to make our points clear.

Suggestions for minor changes:

Fig 1I: Please check the graph and make it more self-explaining. For example, mark the "setpoint" activity (in my opinion, both curves should be at baseline there. In that case, however, I do not see the biphasic behavior anymore). Maybe the table and the graph can be aligned along the activity axis? Also: synaptic inhibition should be increased and not decreased, right?

Local text and figure revision: I guess the reviewer meant for Fig. 2I? We have improved the visualization to avoid confusion.

L74-81: I would reverse the order of associative and homeostatic plasticity in this paragraph.

Local text and figure revision: We have fine-tuned the order in the first and second paragraphs to match the readers' expectations.

L74-75: Provide references for such theories.

Local text and figure revision: fixed.

L84-86: Please provide a reference for the claim that negative feedback, redundancy, and heterogeneity contribute to robustness.

Local text and figure revision: fixed.

L 95-97: I think the heterogeneity aspect needs to be worked out a bit better. Do you mean that the described mechanisms contribute to firing rate homeostasis in a different mixture for each neuron (as shown assumed in the last figure)?

Local text and figure revision: The term heterogeneity is used in the manuscript for two major different settings: (1) heterogeneity in terms of control theory and (2) different combinations of HSP and SS rules. We have named the second condition as diversity to avoid confusion.

L 132: The question of linearity has not been posed so far. Also, I think "monotonous" would be a much better term than linear (as a test for linearity would require more than 2 datapoints).

Local text and figure revision: We agreed linear is not a good term. We replaced it with ‘monotonic’ throughout the manuscript.

Fig2 Bii: The data for 50um is clearly not Gaussian.

We did not imply that the 50 µM condition is Gaussian. Instead, we noted that the non-linearity observed in both the 200 nM and 50 µM data suggests a non-monotonic growth rule rather than a linear one. We applied the Gaussian rule because it has been extensively studied in previous simulations, allowing us to benchmark our findings against those results.

Fig2 D, E inset: The point at time 0 does not convey any information and could be left out.

The time zero data is included to demonstrate that the three groups have a similar baseline, ensuring that any observed differences are due to the treatment and not pre-existing biases in the grouping.

L 178: As the Gaussian rule drops below zero above the upper set-point again, it is rather tri-phasic than bi-phasic.

We intended to convey that inhibition results in either spine growth or deletion, reflecting a bi-phasic response rather than a true tri-phasic one.

Fig 6A: You may want to mark the eta variables in the curves.

Local text and figure revision: fixed.

Fig 6E: The curve of the S population extending to the next panel looks a bit messy.

We retained the curve extension to visually convey the impression of excessive network activity.

L272: It needs to be better described/motivated how protocol 1 and 2 are supposed to study the role of recurrent connection as well as what kind of biological situation this may be.

Local text and figure revision: The corresponding text has been adjusted to avoid confusion.

L 272: It is not clear how faster simulation leads to less recurrent connectivity, when the stimulation protocol and the rates stay the same and the algorithm compensates for the timestep properly. Maybe you rather want to say that you silence 10x longer and stimulate 10x longer?

Local text revision: The corresponding text has been adjusted to avoid confusion.

L. 302: "reactivate"?

Local text revision: fixed.

L 322f: I would suggest showing the connectivity matrix for a time-point with restored activity as well.

Local text and figure revision: fixed.

Fig 8A: The use of the morphological reconstructions is a bit misleading as the model uses point neuron.

Local text revision: Now after reorganization, it is in Fig.9. We kept the reconstruction figure for motivational purposes, suggesting how to understand the meaning of the combinations in more biologically realistic scenarios. The corresponding text has been adjusted to avoid confusion.

Fig 8E-F: the y axis should be in the same orientation as in panel D.

Local text and figure revision: Good idea and fixed in the new Fig. 9.

Fig. 8F: The results here look a little bit random. Maybe more runs with the same parameters would smooth out the contours or reveal a phase transition.

Local text and figure revision: Thank you for the suggestion. We conducted an additional ten random trials to average the traces and heatmaps, improving the clarity of the results now presented in Fig. 9.

L411: Note that there are earlier HSP models by Damasch and van Ooyen & van Pelt, that might be worth discussing here.

Local text revision: fixed.

L416 "beyond synaptic scaling" reference needed.

Local text revision: fixed.

L419: The biphasic rule was suggested by Butz already.

Local text revision: We adjusted the text to emphasize our contribution in suggesting/confirming the biphasic rule based on direct experimental observations.

L 419-44: Most of this is actually state-of-the art and may be better placed in the introduction to justify the use of NBQX as a competititve blocker.

Local text revision: We adjusted the text in the introduction and Discussion sections to cover the raised points.

L487: In my opinion, although scaling adapts the weights quickly, the information about deviating firing rate is still stored in the calcium signal such that it will also give rise to structural changes (although they may be small when the rate is low). Thus, I think that fast scaling does not abolish structural changes.

Local text revision: We adjusted the text to account for other factors that could lead to the same or opposite conclusions.

L502f: Sentence unclear. Do you mean Ca is an integrated (low-pass filtered) version of the firing rate?

Yes.

L504: What is the cumulative temporal effect of error in estimating firing rates?

We were referring to the potential instability in numeric simulations if the firing rate is not tracked by an integral signal (calcium concentration) but is instead estimated through average spike counts over time. In our model, calcium serves as a proxy for the firing rate to guide homeostatic structural plasticity. The intake and decay constants are set to minimize the accumulation of errors over time, making long-term error accumulation unlikely. In any case, this is not intended to be a precise measure of the firing rate but rather a smooth guide for homeostatic control.

Local text revision: We rewrote the section so as not to cause extra concerns.

L505: Which two rules are meant here? Ca- and firing rate based or HSP and scaling?

Local text revision: The two rules are the HSP rule and the HSS rule. We have adjusted the text to improve clarity.

L505ff: I did not really understand the control theoretic view here and Supp Fig 5 is not self-explaining enough to help. In my view, scaling is a proportional controller for the calcium level (the setpoint is defined for calcium and not firing rate). Also, all of the HSP rules do neither contain an integral nor a differential of the error and are thus nonlinear but proportional controllers in first approximation. If this part is supposed to stay in the manuscript, the supporting information should contain a more detailed mathematical explanation. Relevant previous work on homeostatic control by synaptic scaling and homeostatic rewiring, e.g. doi: 10.23919/ECC54610.2021.9655157 should be discussed

Local text revision: We have updated the last paragraph to increase clarity. The HSP and HSS rules are proportional and integral for neural activity, as neural firing rate homeostasis is the meaningful goal. However, it is also correct that the integral component is gone if we view calcium concentration as the goal or setpoint. This paper is discussed and cited in a paragraph above this one.

Reviewer #2 (Recommendations For The Authors):

I have some additional suggestions and questions for the authors, which I am presenting following the order of the figures.

Fig 1A: I'm a little bit puzzled by the timescales between Hebbian and homeostatic plasticity; a wealth of data suggests that Hebbian plasticity acts on a faster timescale than homeostatic plasticity, while Aii-Aiii implies the opposite. In lesion-induced degeneration, for instance, which is mentioned later by the authors, spine loss has been suggested to be Hebbian (LTD) while the subsequent recovery is homeostatic. Additionally, it will not be clear to the reader if the same stimulus could induce Hebbian and homeostatic plasticity, or why; the rest of the manuscript seems to imply that any stimulus could and would trigger homeostatic plasticity, which is not the case. Finally, there should be a mention somewhere that Hebbian structural plasticity also exists.

Local text and figure revision: We thank the reviewer for pointing out the time scale issue, which was not explicitly considered here and is now updated.

Fig. 2Bii: There is no significant difference at 200nm NBQX for sEPSC amplitude, contrary to what is stated in the text (line 136). Which one is it?

Local text revision: We thank the reviewer for pointing out the mistake. We have inspected the original statistical file and corrected the text.

Fig. 2F: The description of Fig. 2F in the text confused me for the longest time. I am still unsure why 200nm NBQX is described as leading to a general size increase when it follows the control line so closely, crosses 0 at the same point, and is even below the control line for the largest spine sizes. Similarly, 50um NBQX neatly overlaps with the control condition except for the smallest and largest spines, so the "shrinkage of middle-sized spines" doesn't seem different from the control condition. I also couldn't find any data supporting the statement that 50um NBQX increased only the size of "a small subset of large spines". Maybe the authors could clarify this section? I would also suggest adding statistics between the treatments at each spine size bin to support the claims, as they are central to the rest of the paper.

Importantly, there is no description of the normalization nor the quantification of the difference between days in the methods; I am assuming post-pre for the difference and (post-pre)/pre for the normalization, but this should be much more detailed in the methodology. I was happy to see the baseline raw spine sizes in Supplementary Fig. 1, and would also suggest adding the raw spine sizes after treatment for comparison.

Local text and figure revision: We have adjusted the text and figure to improve clarity.

Fig. 2G/S2A: a scale for the label sizes would be helpful. I would also like to have the same correlation for 50um NBQX treatment and the control condition (at least in the supplementary figures).

Local text and figure revision: We have adjusted the text and figure to improve clarity.

Fig. 2I: I might be missing something, but why is the activity line flat when there are changes in spine density and size?

Local text and figure revision: We have adjusted the text and figure to improve clarity.

Fig. 3C-D: they are referenced in the text as Fig. 1C-D (lines 188-194).

Local text revision: fixed.

Fig. 5: it is interesting that the biphasic model captures both spine loss and recovery, fitting well with lesion-induced degeneration and recovery. Does this mean that the model captures other types of plasticity, or does it suggest to the authors that both steps are homeostatic?

Indeed, the biphasic HSP rule captures two types of activity dependence. The pioneering work by Gallinaro and Rotter (2018) also demonstrated that the HSP rule, even in its monotonic/linear form, exhibits associative properties, which are typically associated with Hebbian plasticity.

Fig. 6A: This figure requires a more detailed legend - what are the various insets? Does the top right graph only have one curve because they are overlapping and the growth rules are the same for axons and dendrites?

Local text revision: fixed.

Fig. 6E: There is usually an overshoot when a stimulus is removed, in this case at the end of the silencing period (as shown in Fig. 1Aiii). Is there a reason why this is not recapitulated here? It shouldn't be as extreme as in the right panel so there should be no degeneration.

We agree that removing the stimulus would typically trigger an opposite homeostatic process. However, in this protocol, we aimed to emphasize the role of recurrency by presenting extreme cases to illustrate potential scenarios for the readers.

Local text revision: We revised this paragraph to walk the readers through the rationale better.

Fig. 6: the authors mention distance-dependent connectivity (line 268), but I couldn't find any data related to that statement. I was particularly curious about that aspect, so I would like to know what this statement is based on, especially as they touch again on the role of morphology in Fig. 8, and distance-dependent connectivity is more prominent in the discussion. On a similar note, would the authors have data from other layers of CA1 that would show similar or other rules? Please note that I am not asking to include these data in the present paper - I am just curious if these data exist (or if the experiments are considered).

Such an extensive dataset is included and thoroughly investigated in another study that has just been published in Lenz et al., 2023. We updated the reference in the revised text.

Fig. 7E top: the scalebar is missing.

Local text revision: fixed.

Fig. 8A: do the colors have meaning? If yes, please state them. Also indicate that the left two neurons are pyramidal cells from CA1 and the right neurons are granule cells from the dentate gyrus.

Local text revision: fixed.

Line 302: "reactive" should be "reactivate".

Local text revision: fixed.

supprimer

pas de colonne dans le tableau - problème d'affichage, il devrait il y avoir une ligne verticale de séparation

supprimer

retirer

Author response:

Reviewer #1 (Public review):

The significance of the target molecule and mechanisms may help in understanding the molecular mechanisms of metformin.

We greatly appreciate the reviewer’s insightful comment regarding the significance of the target molecule and its mechanisms in understanding the molecular actions of metformin. ATP5I is responsible for the dimerization of the F₁F₀-ATPase(1-3). Hence, we propose conducting BN-PAGE followed by a western blot using the β-subunit of the F1 domain of F1F0-ATP synthase to investigate whether metformin affects its dimerization. This will provide a more direct evidence of the on target action of metformin on ATP5I. Due to the high abundance of F₁F₀-ATP synthase in cells and the slow ability of metformin to enter mitochondria, we plan to perform long-term treatments (3 and 6 days) with high concentrations of metformin (10 mM) to enhance the likelihood of detecting subtle yet biologically relevant shifts in the monomer and dimer populations. Prolonged exposure is expected to reveal the cumulative effects of metformin on F₁F₀-ATP synthase dimers/monomers ratio. We do not expect that metformin will totally mimic the cumulative effect of the dimerization as in ATP5I KO cells but we think it will be important to report to what extent this ratio is affected.

Reviewer #2 (Public review):

(1) The interpretation of the cellular co-localization of the biotin-biguanide conjugate with TOMM20 (Figure 1-D) as mitochondrial "accumulation" of the conjugate is overstated because it cannot exclude binding of the conjugate to the mitochondrial membrane. It would have been more convincing if additional incubations with the biotin-biguanide conjugate in combination with metformin had shown that metformin is competitive with the biotin-conjugate.

We appreciate the reviewer’s insightful comment and agree that the resolution provided by fluorescence microscopy makes it challenging to pinpoint the specific mitochondrial compartment where the biotin-biguanide conjugate localizes, even with additional markers such as TOMM20 antibodies for the inner mitochondrial membrane. While it remains a possibility that the conjugate binds to the mitochondrial surface, another plausible explanation is that the biotin moiety may facilitate entry into mitochondria through a biotin-specific transporter, adding further mechanistic intricacies. Furthermore, while a competition assay with metformin might help investigate interactions with mitochondrial targets and transporters (OCT family), it would not compete for biotin-mediated transport. Thus, while we acknowledge the reviewer’s suggestion, we believe such an experiment may not provide conclusive evidence regarding the conjugate’s mitochondrial localization or mechanism of entry. Instead, we will revise the manuscript to more accurately describe the findings as "mitochondrial association" rather than "mitochondrial accumulation," ensuring that our interpretation remains consistent with the resolution and limitations of the data presented.

(2) The manuscript reports the identification of 69 proteins by mass spectrometry of the pull-down assay of which 31 proteins were eluted by metformin. However, no Mass Spectrometry data is presented of the peptides identified. The methodology does not state the minimum number of peptides (1, 2?) that were used for the identification of the 31/69 proteins.

Concerning the mass spectrometry results, our intention was to provide a comprehensive table summarizing these findings in a separate data sheet, as part of the data availability section. To address the reviewer’s comment and ensure full transparency, we will include this table as supplementary material in the revised manuscript. Additionally, we will update the methodology section to explicitly state these criteria and ensure clarity regarding the identification process.

(3) The validation of ATP5I was based on the use of recombinant protein (which was 90% pure) for the SPR and the use of a single antibody to ATP5I. The validity of the immunoblotting rests on the assumption that there is no "non-specific" immunoactivity in the relevant mol wt range. Information on the validation of the antibody would be helpful.

Regarding the recombinant protein used for SPR, its purity was evaluated using a Coomassie-stained gel. For the antibody used in immunoblotting, its specificity was validated through knockout cell lines, ensuring minimal concerns about non-specific immunoactivity within the relevant molecular weight range. Unfortunately, the KO data comes in the paper after the first immunoblots are presented. In the revised manuscript, we will clearly outline these validation steps in the methods section and additional manufacturer documentation for the antibody we used.

(4) Knock-out of ATP5I markedly compromised the NAD/NADH ratio (Fig.3A) and cell proliferation (Figure 3D). These effects may be associated with decreased mitochondrial membrane potential which could explain the low efficacy of metformin (and most of the data in Figures 3-5). This possibility should be discussed. Effects of [metformin] on the NAD/NADH ratio in control cells and ATP5I-KO would have been helpful because the metformin data on cell growth is normalized as fold change relative to control, whereas the NAD/NADH ratio would represent a direct absolute measurement enabling comparison of the absolute effect in control cells with ATP5I KO.

The mitochondrial membrane potential depends on a functional electron transport chain which drives proton pumping from the matrix to the intermembrane space. Metformin can decrease the mitochondrial membrane potential and this usually explained as a consequence of complex I inhibition(4). It has been published the metformin requires this membrane potential to accumulate in mitochondria so the actions of metformin are self-limiting due to this requirement. The reviewer is right that ATP5I KO cells could be resistant to metformin because they may have a lower membrane potential. We do not believe this to be the case because the response to phenformin, another biguanide that can enter mitochondria through the membrane without the need of the OCT transporters(5), is also affected in ATP5IKO cells. Of note, compensatory mechanisms such as enhanced glycolysis, as observed in ATP5I-KO cells (elevated ECAR and increased sensitivity to 2-D-deoxyglucose), and the ATPase activity of F₁F₀-ATP synthase could potentially help maintain membrane potential suggesting that this might not be an issue in the ATP5I KO cells. We will discuss these possibilities in the revised manuscript.

Nevertheless, to experimentally address this point, we propose measuring mitochondrial membrane potential using tetramethylrhodamine methyl ester (TMRE) and ATP levels using luciferase-based assays (CellTiter-Glo) in ATP5I-KO cells.

Regarding the NAD+/NADH in both control and KO cells may not be very helpful because this ratio can be corrected by LDH which is induced as part of the glycolytic adaptation that occurs after inhibition of respiration. Since our KO cells have been propagated already for several passages, the extent of this adaptation is likely different from metformin-treated cells. As we mentioned in answering Reviewer 1, we will provide a more direct measurement of metformin acting on ATP5I: the levels of F1F0-ATPase dimers and monomers.

(5) Figure-6 CRISPR/Cas9 KO at 16mM metformin in comparison with 70nM rotenone and 2 micromolar oligomycin (in serum-containing medium). The rationale for the use of such a high concentration of metformin has not been explained. In liver cells metformin concentrations above 1mM cause severe ATP depletion, whereas therapeutic (micromolar) concentrations have minimal effects on cellular ATP status. The 16mM concentration is ~2 orders of magnitude higher than therapeutic concentrations and likely linked to compromised energy status. The stronger inhibition of cell proliferation by 16mM metformin compared with rotenone or oligomycin raises the issue of whether the changes in gene expression may be linked to the greater inhibition of mitochondrial metabolism. Validation of the cellular ATP status and NAD/NADH with metformin as compared with the two inhibitors could help the interpretation of this data.

To address the reviewer’s final comment, we would like to clarify the rationale behind our experimental approach. NALM-6 cells are very glycolytic, have low respiration rates, and weak dependence on ATP5I (DepMap score: -0.47)(6). The concentration of 16 mM metformin was chosen based on the IC50 for this cell line. This approach aligns with our focus on the anticancer mechanism of action rather than the antidiabetic effects of metformin. Both ATP status and NAD+/NADH ratios will depend on the extent of the compensatory glycolysis. On the other hand, our genetic screening evaluates cell proliferation as an integration of all metabolic activities required for the process. This unbiased screening revealed a common pathway affected by metformin and oligomycin different that the pathway affected by rotenone, which is consistent with the finding that metformin acts of the F₁F₀ATPase.

Reviewer #3 (Public review):

(1) Most of the data are based on measurements of the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) measured by the Seahorse analyser in control and ATP5l KO cells. However, these measurements are conducted by a single injection of a biguanide, followed over time and presented as fold change. By doing so, the individual information on the effect of metformin and derivate on control and KO cells are lost. In addition, the usual measurement of OCR is coupled with certain inhibitors and uncouplers, such as oligomycin, FCCP, and Antimycin A/rotenone, to understand the contribution of individual complexes to respiration. Since biguanides and ATP5l KO affect protein levels of components of complex I and IV, it would be informative to measure their individual contributions/effects in the Seahorse. To further strengthen the data, it would be helpful to obtain measurements of actual ATP levels in these cells, as this would explain the activation of AMPK.

We appreciate the reviewer’s observations regarding the Seahorse measurements and acknowledge the potential limitations of presenting the data as fold change. Due to experimental challenges in maintaining KP-4 and ATP5I-KO cells with sufficient nutrients, caused by their rapid glucose uptake and subsequent lactate production, it was more practical to present the Seahorse results in this format. Using inhibitors at each time point during the Seahorse experiment was not feasible, as the delay between inhibitor injections and the corresponding changes in oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) would introduce variability and complicate the interpretation of dynamic responses. Nevertheless, we recognize the importance of understanding the contributions of specific respiratory complexes to OCR and ECAR. To address this, we will include a representative figure showcasing a typical Seahorse analysis, highlighting ATP turnover and proton leak after oligomycin addition, maximal respiration with FCCP, and disruption with rotenone and antimycin A. While these experiments are inherently complex due to the metabolic demands of ATP5I-KO cells, this approach will provide a clearer breakdown of mitochondrial activity. Furthermore, as mentioned in our response to Reviewer 2, we will measure ATP levels using a luciferase-based assay (CellTiter-Glo) in both control and ATP5I-KO cells to better explain AMPK activation. This will provide additional context to strengthen the interpretation of mitochondrial function and metabolic compensation mechanisms in these cells.

(2) The authors report on alterations in mitochondrial morphology upon ATP5l KO, which is measured by subjective quantifications of filamentous versus puncta structures. Fiji offers great tools to quantify the mitochondrial network unbiasedly and with more accuracy using deconvolution and skeletonization of the mitochondria, providing the opportunity to measure length, shape, and number quantitatively. This will help to understand better, whether mitochondria are really fragmented upon ATP5l KO and rescued by its re-introduction.

Concerning the analysis of mitochondrial morphology, we acknowledge the potential benefits of using Fiji and additional plugins such as MiNA for more accurate and unbiased quantification. Indeed, this approach could provide stronger evidence for mitochondrial fragmentation upon ATP5I-KO and its potential rescue by ATP5I reintroduction. We will consider integrating this methodology into our analysis to enhance the precision and robustness of our findings.

(3) Finally, the authors report in the last part of the paper a genetic CRISPR/Cas9 KO screen in NALM-6 cells cultured with high amounts of metformin to identify potential new mediators of metformin action. It is difficult to connect that to the rest of the paper because a) different concentrations of metformin are used and b) the metabolic effects on energy consumption are not defined. They argue about the molecular function of the obtained hits based on literature and on a comparison of the pattern of genetic alterations based on treatments with known inhibitors such as oligomycin and rotenone. However, a direct connection is not provided, thus the interpretation at the end of the results that "the OMA1-DEL1-HRI pathway mediates the antiproliferative activity of both biguanides and the F1ATPase inhibitor oligomycin" while increasing glycolysis, needs to be toned down. This is an interesting observation, but no causality is provided. In general, this part stands alone and needs to be better connected to the rest of the paper.

NALM-6 are very glycolytic, have low respiration rates, and weak dependence on ATP5I(6), forcing us to use higher concentrations of metformin to inhibit their growth. Recent results show that metformin targets PEN2 in the cytosol to increase AMPK activity, controlling both the glucose lowering and the life span extension abilities of metformin 7. This work raises the question whether the antiproliferative and anticancer effects of metformin are due to a mitochondrial activity or are controlled by this new pathway of AMPK activation. Hence, the genetic screening was performed to unbiasedly find how metformin works. The results provide compelling evidence for mitochondria and in particular the ATP synthase as potential targets of metformin and a foundation for future studies. We will revise the text and abstract to better reflect the exploratory nature of this finding and ensure clarity.

(1) Paumard, P. et al. Two ATP synthases can be linked through subunits i in the inner mitochondrial membrane of Saccharomyces cerevisiae. Biochemistry 41, 10390-10396 (2002). https://doi.org/10.1021/bi025923g

(2) Paumard, P. et al. The ATP synthase is involved in generating mitochondrial cristae morphology. EMBO J 21, 221-230 (2002). https://doi.org/10.1093/emboj/21.3.221

(3) Habersetzer, J. et al. ATP synthase oligomerization: from the enzyme models to the mitochondrial morphology. Int J Biochem Cell Biol 45, 99-105 (2013). https://doi.org/10.1016/j.biocel.2012.05.017

(4) Xian, H. et al. Metformin inhibition of mitochondrial ATP and DNA synthesis abrogates NLRP3 inflammasome activation and pulmonary inflammation. Immunity 54, 1463-1477 e1411 (2021). https://doi.org/10.1016/j.immuni.2021.05.004

(5) Hawley, S. A. et al. Use of cells expressing gamma subunit variants to identify diverse mechanisms of AMPK activation. Cell metabolism 11, 554-565 (2010). https://doi.org/10.1016/j.cmet.2010.04.001

(6) Hlozkova, K. et al. Metabolic profile of leukemia cells influences treatment efficacy of L-asparaginase. BMC Cancer 20, 526 (2020). https://doi.org/10.1186/s12885-020-07020-y

(7) Ma, T. et al. Low-dose metformin targets the lysosomal AMPK pathway through PEN2. Nature 603, 159-165 (2022). https://doi.org/10.1038/s41586-022-04431-8

Reviewer #2 (Public review):

The present study by Le Gac et al. investigates the vasoconstriction of cerebral arteries during neurovascular coupling. It proposes that pyramidal neurons firing at high frequency lead to prostaglandin E2 (PGE2) release and activation of arteriolar EP1 and EP3 receptors, causing smooth muscle cell contraction. The authors further claim that interneurons and astrocytes also contribute to vasoconstriction via neuropeptide Y (NPY) and 20-hydroxyeicosatetraenoic acid (20-HETE) release, respectively. The study mainly uses brain slices and pharmacological tools in combination with Emx1-Cre; Ai32 transgenic mice expressing the H134R variant of channelrhodopsin-2 (ChR2) in the cortical glutamatergic neurons for precise photoactivation. Stimulation with 470 nm light using 10-second trains of 5-ms pulses at frequencies from 1-20 Hz revealed small constrictions at 10 Hz and robust constrictions at 20 Hz, which were abolished by TTX and partially inhibited by a cocktail of glutamate receptor antagonists. Inhibition of cyclooxygenase-1 (COX-1) or -2 (COX-2) by indomethacin blocked the constriction both ex vivo (slices) and in vivo (pial artery), and inhibition of EP1 and EP3 showed the same effect ex vivo. Single-cell RT-PCR from patched neurons confirmed the presence of the PGE2 synthesis pathway.

While the data are convincing, the overall experimental setting presents some limitations. How is the activation protocol comparable to physiological firing frequency? The delay (minutes) between the stimulation and the constriction appears contradictory to the proposed pathway, which would be expected to occur rapidly. The experiments are conducted in the absence of vascular "tone," which further questions the significance of the findings. Some of the targets investigated are expressed by multiple cell types, which makes the interpretation difficult; for example, cyclooxygenases are also expressed by endothelial cells. Finally, how is the complete inhibition of the constriction by the NPY Y1 receptor antagonist BIBP3226 consistent with a direct effect of PGE2 and 20-HETE in arterioles? Overall, the manuscript is well-written with clear data, but the interpretation and physiological relevance have some limitations. However, vasoconstriction is a rather understudied phenomenon in neurovascular coupling, and the present findings may be of significance in the context of pathological brain hypoperfusion.

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Sommaire de la transcription avec timestamps

Introduction et principe du conseil d'élèves

• 0:00 - 0:30 : Introduction de l'épisode et présentation de Clémence Édou, professeure des écoles en CM2.
• 0:30 - 1:00 : Explication du principe du conseil d'élèves : les enfants déposent des cartes dans une boîte tout au long de la semaine pour exprimer problèmes, propositions, questions, compliments ou excuses. Le président du conseil, qui change chaque semaine, anime la séance.
• 1:00 - 2:06 : Exemple concret de problème soulevé lors d'un conseil d'élèves (Inaya qui parle trop) et gestion de la situation par les élèves.
• 2:06 - 2:15 : Clémence Édou explique qu'il faut distinguer les problèmes récurrents des problèmes ponctuels qui nécessitent une intervention immédiate.
• 2:15 - 2:34 : Description des différents temps du conseil : questions, propositions et votes, compliments.

Bénéfices du conseil d'élèves pour les élèves

• 2:34 - 3:17 : Clémence Édou souligne l'importance de finir le conseil sur une note positive avec les compliments.
• 3:17 - 4:31 : Le conseil d'élèves s'inscrit dans le cadre de l'enseignement moral et civique. Il permet de travailler de nombreuses compétences : identifier et nommer ses émotions, se sentir membre d'une collectivité, comprendre l'importance des règles, assumer ses responsabilités, différencier intérêt personnel et intérêt collectif, développer l'écoute, s'estimer soi-même et avoir confiance en soi.

Exemples de propositions et vote

• 4:31 - 5:12 : Exemple de proposition faite par une élève (Louise) pour ajouter des détails à une frise chronologique et vote des élèves pour son adoption.

Gestion des conflits et anecdotes

• 5:12 - 6:40 : Clémence Édou évoque des situations où il y a eu beaucoup de problèmes à gérer. Elle raconte une anecdote illustrant comment le conseil a permis de comprendre un élève au comportement agressif.

Fin du conseil et échanges de compliments

• 6:40 - 8:10 : Exemples de compliments échangés entre élèves et compliment de la maîtresse à la classe.

Partage de pratiques et importance du conseil

• 8:10 - 8:20 : Clémence Édou parle de l'importance du partage de pratiques entre enseignants et des différentes manières d'animer un conseil d'élèves.
• 8:20 - 8:24 : Conclusion : Clémence Édou met en avant les effets positifs du conseil d'élèves sur le climat scolaire et le recul que les enfants apprennent à prendre.

Fin de l'épisode

• 8:24 - 8:28: Fin de l'épisode et invitation à laisser un avis.

DOI: 10.1128/spectrum.00914-24

Resource: (KCB Cat# KCB 82002YJ, RRID:CVCL_Z230)

Curator: @evieth

SciCrunch record: RRID:CVCL_Z230

What is this?

DOI: 10.1158/2767-9764.CRC-24-0316

Resource: Cancer Dependency Map Portal (RRID:SCR_017655)

Curator: @scibot

SciCrunch record: RRID:SCR_017655

What is this?

DOI: 10.1016/j.isci.2024.111246

Resource: (Abcam Cat# ab18207, RRID:AB_444319)

Curator: @scibot

SciCrunch record: RRID:AB_444319

What is this?

Reviewer #1 (Public review):

Summary:

The manuscript by Bindu et al. created an AAV-based tool (GEARAOCS) to perform in vivo genome editing of mouse astrocytes. The authors engineered a versatile AAV vector that allows for gene deletion through NHNJ, site-specific knock-in by HDR, and gene trap. By utilizing this tool, the authors deleted Sparcl1 virally in subsets of astrocytes and showed that thalamocortical synapses in cortical layer IV are indeed reduced during a critical period of ocular dominance plasticity and in adulthood, whereas there is no change in excitatory synapse number in cortical layer II/III. Furthermore, the authors made a VAMP2 gene-trap AAV vector and showed that astrocyte-derived VAMP2 is required for the maintenance of both excitatory and inhibitory synapses.

Strengths:

This AAV-based tool is versatile for astrocytic gene manipulation in vivo. The work is innovative and exciting, given the paucity of tools available to probe astrocytes in vivo.

Weaknesses:

Several important considerations need to be made for the validation and usage of this tool, including:

Major points:

(1) Efficiency and specificity of spCas9-sgRNA mediated gene knockout in astrocytes. In Figure 3, the authors utilized Sparcl1 gene deletion as the proof-of-principle experiment. The readout for Sparcl1 KO efficiency is solely the immunoreactivity using an antibody raised against Sparcl1. As the method is based on NHEJ, the indels can be diverse and can occur in one allele or two. For the tool and proof-of-principle experiment, it will be important to know the percentage of editing near the PAM site, as well as the actual sequences of indels. This can be done by single-cell PCR of edited astrocytes, similar to the published work (Ye... Chen, Nature Biotechnology 2019).

(2) Along the same line, the authors showed that GEARBOCS TagIn of Sparcl1 resulted in 12.49% efficiency based on the immunohistochemistry of mCherry tag. It is understandable that the knock-in efficiency is much reduced as compared to gene knockout. However, it remains unclear if those 12.49% knock-in cells represent sequence-correct ones, as spCas9-mediated HDR is also an error-prone process, and it may accidentally alter nucleotides near the PAM site without causing the frameshift. The author will need to consider the related evidence or make comments in the discussion.

(3) What are the efficiencies of Sparcl1 GEARBOCS GeneTrap (Figure 3V) and Vamp2 GeneTrap and HA TagIn (Figure 5)?

Minor points:

(1) Figure 3H-J. The authors only showed the representative images of Sparcl1 KO. Please consider including the control (without gRNA), given that there are still many Sparcl1+ signals in Figure 3I (likely because of its expression in other cell types?).

(2) In figure 3Q-T, it appears that some Cas9-EGFP+ astrocytes (Q) do not express Sparcl1 (R). Is Sparcl1 expressed in subsets of astrocytes? Does Cas9-EGFP or Sparcl1-TagIn alter Sparcl1 endogenous expression?

(3) On Page 8, for the explanation of the design of the GEARBOCS construct, the authors have made a self-citation (#43). That was a BioRxiv paper that is being reviewed currently.

(4) For Figures 4 and 6, the graphs seem to be made in R with the x-axis labeled as "Condition". The y-axis labels are too small to read properly, especially in print. It would be better to make the graphs clearer like Figure 2 and Figure 3.

(5) On Page 13, "Figures 3V-Y" were referred to. However, there are no Figures 3W, X, and Y.

(6) There are a few typos in the manuscript, including line 900 "immunofluorescence microscopy images of a Cas9-EGFP-positive astrocytes (green)".

Produce Actnomicosis, una infección bacteriana crónica que se caracteriza por la formación de abscesos en la cara y el cuello, aunque puede afectar otras partes del cuerpo

13b formülü var. stress coeff ile ilgili

One example could be solve exponentials matrix of exact or holomorphs or armonic diferential equations?

Reviewer #3 (Public review):

Summary:

The authors used the model organism Drosophila melanogaster to show that the neurotrophin Toll-6 and its ligands, DNT-2 and kek-6, play a role in maintaining the number of dopaminergic neurons and modulating their synaptic connectivity. This supports previous findings on the structural plasticity of dopaminergic neurons and suggests a molecular mechanism underlying this plasticity.

Strengths:

The experiments are overall very well designed and conclusive. Methods are in general state-of-the-art, the sample sizes are sufficient, the statistical analyses are sound, and all necessary controls are at place. The data interpretation is straight forwards, and the relevant literature is taken into consideration. Overall, the manuscript is solid and presents novel, interesting and important findings.

Weaknesses:

There are three technical weaknesses that could perhaps be improved.

First, the model of reciprocal, inhibitory feedback loops (figure 2F) is speculative. On the one hand, glutamate can act in flies as excitatory or inhibitory transmitter (line 157!), and either situation can be the case here. On the other hand, it is not clear how an increase or decrease in cAMP level translates into transmitter release. One can only conclude that two type of neurons potentially influence each other.

Second, the quantification of bouton volumes (no y-axis label in Figure 5 C and D!) and dendrite complexity are not convincingly laid out. Here, the reader expects fine-grained anatomical characterizations of the structures under investigation, and a method to precisely quantify the lengths and branching patterns of individual dendritic arborizations as well as the volume of individual axonal boutons.

Third, figure 1C shows two neurons with the goal of demonstrating between-neuron variability. It is not convincingly demonstrated that the two neurons are actually of the very same type of neuron in different flies, or two completely different neurons.

Review of the revised manuscript:

The authors have addressed some points of concern raised by the reviewers. I would like to emphasize that I find the overall research study highly interesting and important.

Author response:

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

Reviewer #1 (Public review):

Summary:

Sun et al. are interested in how experience can shape the brain and specifically investigate the plasticity of the Toll-6 receptor-expressing dopaminergic neurons (DANs). To learn more about the role of Toll-6 in the DANs, the authors examine the expression of the Toll-6 receptor ligand, DNT-2. They show that DNT-2 expressing cells connect with DANs and that loss of function of DNT-2 in these cells reduces the number of PAM DANs, while overexpression causes alterations in dendrite complexity. Finally, the authors show that alterations in the levels of DNT-2 and Toll-6 can impact DAN-driven behaviors such as climbing, arena locomotion, and learning and long-term memory.

Strengths:

The authors methodically test which neurotransmitters are expressed by the 4 prominent DNT-2 expressing neurons and show that they are glutamatergic. They also use Trans-Tango and Bac-TRACE to examine the connectivity of the DNT-2 neurons to the dopaminergic circuit and show that DNT-2 neurons receive dopaminergic inputs and output to a variety of neurons including MB Kenyon cells, DAL neurons, and possibly DANS.

We are very pleased that Reviewer 1 found our connectivity analysis a strength.

Weaknesses:

(1) To identify the DNT-2 neurons, the authors use CRISPR to generate a new DN2-GAL4.

They note that they identified at least 12 DNT-2 plus neurons. In Supplementary Figure 1A, the DNT-2-GAL4 driver was used to express a UAS-histoneYFP nuclear marker. From these figures, it looks like DNT-2-GAL4 is labeling more than 12 neurons. Is there glial expression?

Indeed, we claimed that DNT-2 is expressed in at least 12 neurons (see line 141, page 6 of original manuscript), which means more than 12 could be found. The membrane tethered reporters we used – UAS-FlyBow1.1, UASmcD8-RFP, UAS-MCFO, as well as UAS-DenMark:UASsyd-1GFP – gave a consistent and reproducible pattern. However, with DNT-2GAL4>UAS-Histone-YFP more nuclei were detected that were not revealed by the other reporters. We have found also with other GAL4 lines that the patterns produced by different reporters can vary. This could be due to the signal strength (eg His-YFP is very strong) and perdurance of the reporter (e.g. the turnover of His-YFP may be slower than that of the other fusion proteins).

We did not test for glial expression, as it was not directly related to the question addressed in this work.

(2) In Figure 2C the authors show that DNT-2 upregulation leads to an increase in TH levels using q-RT-PCR from whole heads. However, in Figure 3H they also show that DNT-2 overexpression also causes an increase in the number of TH neurons. It is unclear whether TH RNA increases due to expression/cell or the number of TH neurons in the head.

Figure 3H shows that over-expression of DNT-2 FL increased the number of Dcp1+ apoptotic cells in the brain, but not significantly (p=0.0939). The ability of full-length neurotrophins to induce apoptosis and cleaved neurotrophins promote cell survival is well documented in mammals. We had previously shown that DNT-2 is naturally cleaved, and that over-expression of DNT-2 does not induce apoptosis in the various contexts tested before (McIlroy et al 2013 Nature Neuroscience; Foldi et al 2017 J Cell Biol; Ulian-Benitez et al 2017 PLoS Genetics). Similarly, throughout this work we did not find DNT-2FL to induce apoptosis.

Instead, in Figure 3G we show that over-expression of DNT-2FL causes a statistically significant increase in the number of TH+ cells. This is an important finding that supports the plastic regulation of PAM cell number. We thank the Reviewer for highlighting this point, as we had forgotten to add the significance star in the graph. In this context, we cannot rule out the possibility that the increase in TH mRNA observed when we over-express DNT-2FL could not be due to an increase in cell number instead. Unfortunately, it is not possible for us to separate these two processes at this time. Either way, the result would still be the same: an increase in dopamine production when DNT-2 levels rise.

We have now edited the abstract lines 38-39 adding that “By contrast, over-expressed DNT-2 increased DAN cell number,…”, within the main text in Results page 10 lines 259-265 and in the Discussion section page 15 lines 391, 393-396.

(3) DNT-2 is also known as Spz5 and has been shown to activate Toll-6 receptors in glia (McLaughlin et al., 2019), resulting in the phagocytosis of apoptotic neurons. In addition, the knockdown of DNT-2/Spz5 throughout development causes an increase in apoptotic debris in the brain, which can lead to neurodegeneration. Indeed Figure 3H shows that an adult specific knockdown of DNT-2 using DNT2-GAL4 causes an increase in Dcp1 signal in many neurons and not just TH neurons.

Indeed, we did find Dcp1+ TH-negative cells too (although not widely throughout the brain), although this is not shown in the images of Figure 3H where we showed only TH+ Dcp+ cells.

That is not surprising, as DNT-2 neurons have large arborisations that can reach a wide range of targets; DNT-2 is secreted, and could reach beyond its immediate targets; Toll-6 is expressed in a vast number of cells in the brain; DNT-2 can bind promiscuously at least also Toll-7 and other Keks, which are also expressed in the adult brain (Foldi et al 2017 J Cell Biology; Ulian-Benitez et al 2017 PLoS Genetics; Li et al 2020 eLife). Together with the findings by McLaughlin et al 2019, our findings further support the notion that DNT-2 is a neuroprotective factor in the adult brain. It will be interesting to find out what other neuron types DNT-2 maintains.

We have made some edits on these points in page 10 lines 259-265.

We would like to thank Reviewer 1 for their positive comments on our work and their interesting and valuable feedback.

Reviewer #2 (Public review):

This paper examines how structural plasticity in neural circuits, particularly in dopaminergic systems, is regulated by Drosophila neurotrophin-2 (DNT-2) and its receptors, Toll-6 and Kek-6. The authors show that these molecules are critical for modulating circuit structure and dopaminergic neuron survival, synaptogenesis, and connectivity. They show that loss of DNT-2 or Toll-6 function leads to loss of dopaminergic neurons, dendritic arborization, and synaptic impairment, whereas overexpression of DNT-2 increases dendritic complexity and synaptogenesis. In addition, DNT-2 and Toll-6 modulate dopamine-dependent behaviors, including locomotion and long-term memory, suggesting a link between DNT-2 signaling, structural plasticity, and behavior.

A major strength of this study is the impressive cellular resolution achieved. By focusing on specific dopaminergic neurons, such as the PAM and PPL1 clusters, and using a range of molecular markers, the authors were able to clearly visualize intricate details of synapse formation, dendritic complexity, and axonal targeting within defined circuits. Given the critical role of dopaminergic pathways in learning and memory, this approach provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. However, despite the promise in the abstract and introduction of the paper, the study falls short of establishing a direct causal link between neurotrophin signaling and experience-induced plasticity.

Simply put, this study does not provide strong evidence that experience-induced structural plasticity requires DNT-2 signaling. To support this idea, it would be necessary to observe experience-induced structural changes and demonstrate that downregulation of DNT-2 signaling prevents these changes. The closest attempt to address this in this study was the artificial activation of DNT-2 neurons using TrpA1, which resulted in overgrowth of axonal arbors and an increase in synaptic sites in both DNT-2 and PAM neurons. However, this activation method is quite artificial, and the authors did not test whether the observed structural changes were dependent on DNT-2 signaling. Although they also showed that overexpression of DNT-2FL in DNT-2 neurons promotes synaptogenesis, this phenotype was not fully consistent with the TrpA1 activation results (Figures 5C and D).

In conclusion, this study demonstrates that DNT-2 and its receptors play a role in regulating the structure of dopaminergic circuits in the adult fly brain. However, it does not provide convincing evidence for a causal link between DNT-2 signaling and experience-dependent structural plasticity within these circuits.

We would like to thank Reviewer 2 for their very positive assessment of our approach to investigate structural circuit plasticity. We are delighted that this Reviewer found our cellular resolution impressive. We are also very pleased that Reviewer 2 found that our work demonstrates that DNT-2 and its receptors regulate the structure of dopaminergic circuits in the adult fly brain. This is already a very important finding that contributes to demonstrating that, rather than being hardwired, the adult fly brain is plastic, like the mammalian brain. Furthermore, it is remarkable that this involves a neurotrophin functioning via Toll and kinase-less Trks, opening an opportunity to explore whether such a mechanism could also operate in the human brain.

We are very pleased that this Reviewer acknowledges that this work provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. We provide a molecular mechanism and proof of principle, and we demonstrate a direct link between the function of DNT-2 and its receptors in circuit plasticity. We also showed a link of DNT-2 to neuronal activity, as neuronal activity increased the production of DNT-2GFP, induced the cleavage of DNT-2 and a feedback loop between DNT-2 and dopamine, and both neuronal activity and increased DNT-2 levels promoted synaptogenesis.

As the Reviewer acknowledges this approach provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. Finding out the direct link in response to lived experience is a big task, beyond the scope of this manuscript, and we will be testing this with future projects. Nevertheless, it is important to place our findings within this context together with the link to mammalian neurotrophins (as explained in the discussion), as it is here where the findings have deep and impactful implications.

To accommodate the criticism of this Reviewer, we have now toned down our narrative. This does not diminish the importance of the findings, it makes the argument more stringent. Please see edits in: Abstract page 2 lines 42-44; and Discussion page 22 line 586 – which were the only points were a direct claim had been made.

We would like to thank Reviewer 2 for the positive and thoughtful evaluation of our work, and for their feedback.

Reviewer #3 (Public review):

Summary:

The authors used the model organism Drosophila melanogaster to show that the neurotrophin Toll-6 and its ligands, DNT-2 and kek-6, play a role in maintaining the number of dopaminergic neurons and modulating their synaptic connectivity. This supports previous findings on the structural plasticity of dopaminergic neurons and suggests a molecular mechanism underlying this plasticity.

Strengths:

The experiments are overall very well designed and conclusive. Methods are in general state-of-the-art, the sample sizes are sufficient, the statistical analyses are sound, and all necessary controls are in place. The data interpretation is straightforward, and the relevant literature is taken into consideration. Overall, the manuscript is solid and presents novel, interesting, and important findings.

We are delighted that Reviewer 3 found our work solid, novel, interesting and with important findings. We are also very pleased that this Reviewer found that all necessary controls have been carried out.

Weaknesses:

There are three technical weaknesses that could perhaps be improved.

First, the model of reciprocal, inhibitory feedback loops (Figure 2F) is speculative. On the one hand, glutamate can act in flies as an excitatory or inhibitory transmitter (line 157), and either situation can be the case here. On the other hand, it is not clear how an increase or decrease in cAMP level translates into transmitter release. One can only conclude that two types of neurons potentially influence each other.

Thank you for pointing out that glutamate can be inhibitory. In response, we have removed the word ‘excitatory’ from the only point it had been used in the text: page 7 line 167.

In mammals, the neurotrophin BDNF has an important function in glutamatergic synapses, thus we were intrigued by a potential evolutionary conservation. Our evidence that DNT-2A neurons could be excitatory is indirect, yet supportive: exciting DNT-2 neurons with optogenetics resulted in an increase in GCaMP in PAMs (data not shown); over-expression of DNT-2 in DNT-2 neurons increased TH mRNA levels; optogenetic activation of DNT-2 neurons results in the Dop2R-dependent downregulation of cAMP levels in DNT-2 neurons. Dop2R signals in response to dopamine, which would be released only if dopaminergic neurons had been excited. Accordingly, glutamate released from DNT-2 neurons would have been rather unlikely to inhibit DANs.

cAMP is a second messenger that enables the activation of PKA. PKA phosphorylates many target proteins, amongst which are various channels. This includes the voltage gated calcium channels located at the synapse, whose phosphorylation increases their opening probability. Other targets regulate synaptic vesicle release. Thus, a rise in cAMP could facilitate neurotransmitter release, and a downregulation would have the opposite effect. Other targets of PKA include CREB, leading to changes in gene expression. Conceivably, a decrease in PKA activity could result in the downregulation of DNT-2 expression in DNT-2 neurons. This negative feedback loop would restore the homeostatic relationship between DNT-2 and dopamine levels.

We agree with this Reviewer that whereas our qRT-PCR data show that over-expression of DNT-2 increases TH mRNA levels, this does not demonstrate that originates from PAM neurons. Similarly, although our EPAC data imply that dopamine must be released from DANs and received by DNT-2 neurons to explain those data, the evidence did not include direct visualisation of dopamine release in response to DNT-2 neuron activation. To accommodate these criticisms, we have edited the summary Figure 2E adding question marks to indicate inference points and page 9 line 221.

Our data indeed demonstrate that DNT-2 and PAM neurons influence each other, not potentially, but really. We have provided data that: DNT-2 and PAMs are connected through circuitry; that the DNT-2 receptors Toll-6 and kek-6 are expressed in DANs, including in PAMs; that alterations in the levels of DNT-2 (both loss and gain of function) and loss of function for the DNT-2 receptors Toll-6 and Kek-6 alter PAM cell number, alter PAM dendritic complexity and alter synaptogenesis in PAMs; alterations in the levels of DNT-2, Toll-6 and kek-6 in adult flies alters dopamine dependent behaviours of climbing, locomotion in an arena and learning and long-term memory. These data firmly demonstrate that the two neuron types DNT-2 and PAMs influence each other.

We have also shown that over-expression of DNT-2 in DNT-2 neurons increases TH mRNA levels, whereas activation of DNT-2 neurons decreases cAMP levels in DNT-2 neurons in a dopamine/Dop2R-dependent manner. These data show a functional interaction between DNT-2 and PAM neurons.

Second, the quantification of bouton volumes (no y-axis label in Figure 5 C and D!) and dendrite complexity are not convincingly laid out. Here, the reader expects fine-grained anatomical characterizations of the structures under investigation, and a method to precisely quantify the lengths and branching patterns of individual dendritic arborizations as well as the volume of individual axonal boutons.

Figure 5C, D do contain Y-axis labels, all our graphs in main manuscript and in supplementary files contain Y-axis labels.

In fact, we did use a method to precisely quantify the lengths and branching patterns of individual dendritic arborisations, volume of individual boutons and bouton counting. These analyses were carried out using Imaris software. For dendritic branching patterns, the “Filament Autodetect” function was used. Here, dendrites were analysed by tracing semi-automatically each dendrite branch (ie manual correction of segmentation errors) to reconstruct the segmented dendrite in volume. From this segmented dendrite, Imaris provides measurements of total dendrite volume, number and length of dendrite branches, terminal points, etc. For bouton size and number, we used the Imaris “Spot” function. Here, a threshold is set to exclude small dots (eg of background) that do not correspond to synapses/boutons. All samples and genotypes are treated with the same threshold, thus the analysis is objective and large sample sizes can be analysed effectively. We had already provided a description of the use of Imaris in the methods section.

We have now exapanded the protocol on how we use Imaris to analyse dendrites and synapses, in: Materials and Methods section, page 28 lines 756-768 and page 29 lines 778-799.

Third, Figure 1C shows two neurons with the goal of demonstrating between-neuron variability. It is not convincingly demonstrated that the two neurons are actually of the very same type of neuron in different flies or two completely different neurons.

We thank Reviewer 3 for raising this interesting point. It is not possible to prove which of the four DNT-2A neurons per hemibrain, which we visualised with DNT-2>MCFO, were the same neurons in every individual brain we looked at. This is because in every brain we have looked at, the soma of the neurons were not located in exactly the same location. Furthermore, the arborisation patterns are also different and unique, for each individual brain. Thus, there is natural variability in the position of the soma and in the arborisation patterns. Such variability presumably results from the combination of developmental and activity-dependent plasticity. Importantly, for every staining we carried out using DNT-2GAL4 and various membrane reporters and MCFO clones, we never found two identical DNT-2 neuron profiles.

To increase the evidence in support of this point, we have now expanded Figure 1, adding one more image of DNT-2>FlyBow (Figure 1A) and two more images of DNT-2>MCFO (Figure 1D). In total, seven images in Figure 1 and two further images in Figure 5A demonstrate the variability of DNT-2 neurons.

We would like to thank Reviewer 3 for the very positive evaluation of our work and the interesting and valuable feedback.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

In the fly list, several fly lines are missing references and sources. 

Apologies for this over-sight, this has now been corrected.

We thank Reviewer 1 for their effort and time to scrutinise our work, and for their very positive and helpful feedback.

Reviewer #2 (Recommendations for the authors):

(1) Here I provide some more specific comments that I hope will help the authors further improve the study.

(2) L148: "single neuron clones revealed variability in the DNT-2A". How do the authors know that they are labeling the same subtype of DNT-2A neurons? 

There are four anterior DNT-2A cells per hemibrain, that project from the SOG area to the SMP. It is not possible to verify that every time we look at exactly the same neuron, because the exact position of the somas and the arborisation patterns vary from brain to brain. We know this from two sources of data: (1) when using DNT-2GAL4 to visualise the expression of membrane reporters (e.g. UAS-FlyBow, UAS-mCD8-GFP, UAS-CD8-RFP) no brain ever showed a pattern identical to that of another brain, neither in the exact position of the somas nor in the exact arborisation patterns. (2) When we generated DNT-2>MCFO clones to visualise 1-2 cells at a time, no single neuron or 2-neuron clones ever showed an identical pattern. The most parsimonious interpretation is that the exact location of the somas and the exact arborisation patterns vary across individual flies. Developmental variability in neuronal patterns has also been reporter by Linneweber et al (2020) Science.

To make our evidence more compelling, and in response to this Reviewer’s query, we have now added further images. Please find in revised Figure 1 A,B three examples of three different brains expressing DNT-2>FlyBow1.1. In Figure 1D, two more examples (altogether 4) of DNT-2>MCFO clones. Here it is clear to see that no neuron shape is identical to that of others, demonstrating variability in individual fly brains. We now show four images in Figure 1 and two more in Figure 5A that demonstrate the variability of DNT-2A neurons.

(3) Figure 1E: Are all DNT-2A neurons positive for vGlut and Dop2R? This figure shows only two DNT-2A neurons. 

Yes, all four DNT-2A neurons per hemibrain are vGlut positive and we have now added more images to Supplementary Figure S1A (right), also showing that presynaptic DNT-2A endings at SMP also coincide with a vGlut+ domain (Figure S1A left).

Yes, all all four DNT-2A neurons per hemibrain are Dop2R positive and we have now added more images to Supplementary Figure S1B.

(4) L156: Glutamate is generally considered to be inhibitory in the adult fly brain. More evidence is needed before the authors can claim that "DNT-2A neurons are excitatory glutamatergic neurons". 

Thank you for pointing this out. Although our data do not conclusively demonstrate it, they are consistent with DNT-2A neurons being excitatory. BDNF is most commonly released from glutamatergic neurons in mammals, its release is activity-dependent and leads to formation and stabilisation of synapses.  The phenotypes we have observed are consistent with this and reveal functional evolutionarily conservation: (1) exciting DNT-2 neurons with TrpA1 results in increased production and cleavage of DNT-2GFP and de novo synaptogenesis; (2) over-expression of DNT-2 in the adult induces de novo synaptogenesis; (3) down-regulation or loss of DNT-2 and its receptors Toll-6 and Kek-6 impair synaptogenesis. Furthermore, we show that DNT-2 dependent synaptogenesis is between DNT-2 and dopaminergic neurons, which are involved in the control of locomotion, reward learning and long-term memory, and dopamine itself is required for such behaviour. Consistently with this we found that: (1) over-expression of DNT-2 increases TH mRNA levels, which would lead to the up-regulation of dopamine production; (2) exciting DNT-2 neurons increases locomotion speed in an arena; (3) knock-down of DNT-2 and its receptors decreases locomotion, whereas over-expression of DNT-2 increases locomotion; (4) over-expression of DNT-2 increases learning and long-term memory. Finally, in a previous version in bioRxiv, we also showed using optogenetics and calcium imaging that exciting DNT-2 neurons induced GCaMP signalling in their output PAM neurons, and in this version we show that exciting DNT-2 neurons regulates cAMP in DNT-2 neurons via dopamine-release dependent feedback. Altogether, the most parsimonious interpretation of these data is that vGlut+ DNT-2 neurons are excitatory.

In any case, to address this reviewer’s point, we have now removed the word ‘excitatory’ from page 7 line 167.

(5) Figure 1H, I: A more detailed description of the Toll-6 and Kek-6 expressing neurons will be helpful. Are they expressed in specific types of PAM and PPL1 DANs? The legend in Figure S2 mentions labeling in γ2α′1 zones, but it seems to be more than that.

This information had been already provided, presumable this Reviewer overlooked this. This was already described in great detail by comparing our microscopy data with the single cell RNA-seq data available through Fly Cell Atlas (https://flycellatlas.org) and Scope (https://scope.aertslab.org/#/b77838f4-af3c-4c37-8dd9-cf7a41e4b034/*/welcome).

Please see our previously submitted Table S1 “Expression of Tolls, keks and Toll downstream adaptors in cells related to DNT-2A neurons”.

(6) Figure S3 should be controls for Figure 2A. It is incorrectly labeled as controls for Figure 3A. 

Thank you for pointing out this typo, this has now been corrected.

(7) L197: The authors state, "This showed that DNT-2 could stimulate dopamine production in neighboring DANs". However, the results do not fully support this conclusion because the experiments measure overall TH levels in the brain, not specifically in neighboring DANs. The observed effect could be indirect via other neurons. 

Indeed, we have now edited the text to: “This showed that DNT-2 could stimulate dopamine production”: page 8 line 208.

(8) Figure 3: If Toll-6 is expressed in specific subtypes of PAM DANs, are they the dying cells when Toll-6 was knocked down? I think the paper will be significantly improved if the authors provide a more in-depth analysis of the phenotype. Also, permissive temperature controls are missing for the experiments in (E)-(H). Permissive controls are essential to confirm that the observed effects are due to adult-specific RNAi knockdown.

Current tools do not enable us to visualise Toll-6+ neurons at the same time as manipulating DNT-2 neurons and at the same time as monitoring Dcp1. Stainings with Dcp1 in the adult brain are not trivial. Thus, we cannot guarantee this. However, Toll-6 is the preferential receptor for DNT-2, and given that apoptosis increases when we knock-down DNT-2, the most parsimonious interpretation is that the dying cells bear the DNT-2 receptor Toll-6. Even if DNT-2 can promiscuously bind other Toll receptors, the simplest way to interpret these data remains that DNT-2 promotes cell survival by signalling via its receptors, as no other possible route is known to date. This would be consistent with all other data in this figure.

We thank this Reviewer for the feedback on the controls. Unfortunately, these are not trivial experiments, they require considerable time, effort, dedication and skill. This manuscript has already taken 5 years of daily hard work. We no longer have the staff (ie the first author left the lab) nor resources to dedicate to address this point.

(9) Figure 4B: This phenotype in DNT-2 mutants is very striking. Did the neurons still survive and did their axonal innervation in the lobes remain intact?

Homozygous DNT-2 mutants are viable and have impair climbing, as we had already shown in Figure 7C.

(10) L261: The authors mention that "PAM-β2β′2 neurons express Toll-6 (Table S1)". However, I cannot find this information in Table S1. 

Unfortunately, I cannot identify the source of that statement at present and the first authors has left the lab. In any case, although the fact that knocking down Toll-6 in these neurons causes a phenotype means they must, it does not directly prove it. We have now corrected this to: “PAM-b2b'2 neuron dendrites overlap axonal DNT2 projections”, page 11 line 280.

(11) Figure 4C, D: What about their synaptogenesis? Do they agree with the result in Figure 4B? 

This was not tested at the time. Unfortunately, these are not trivial experiments and require considerable time, effort, dedication and skill. Addressing this point experimentally is not possible for us at this point. In any case, given the evidence we already provide, it is highly unlikely they would alter the interpretation of our findings and the value of the discoveries already provided.

(12) L270: The authors state: "To ask whether DNT-2 might affect axonal terminals, we tested PPL1 axons." However, it is unclear why the focus was shifted to PPL1 neurons when similar analyses could have been performed on PAM DANs for consistency. In addition, it would be beneficial to assess dendritic arbor complexity and synaptogenesis in PPL1-γ1-pedc neurons to provide a more comprehensive comparison between PPL1 and PAM DANs. Performing parallel analyses on both neuron types would strengthen the study by providing insight into the generality and specificity of DNT-2 in different dopaminergic circuits. 

The question we addressed with Figure 4 was whether the DNT-2 and its receptors could modify axons, dendrites and synapses, ie all features of neuronal plasticity. The reason we used PPL1-g1-pedc to analyse axonal terminals was because of their morphology, which offered a clearer opportunity to visualise axonal endings than PAMs did. An exhaustive analysis of PPL1-g1-pedc is beyond the scope of this work and not the central focus.

(13) Figure 4G lacks a permissive temperature control, which is essential to confirm that the observed effects are due to adult-specific RNAi knockdown. 

We thank this Reviewer for this feedback, which we will bear in mind for future projects.

(14) Figure 5A requires quantification and statistical comparison.

We thank this Reviewer for this feedback. We did consider this, but the data are too variable to quantify and we decided it was best to present it simply as an observation, interesting nonetheless. This is consistent as well with the data in Figure 1, which we have now expanded with this revision, which show the natural variability in DNT-2 neurons.

(15) Figure 5B: Many green signals in the control image are not labeled as PSDs, raising concerns about the accuracy of the image analysis methods used for synapse identification. While I trust that the authors have validated their analysis approach, it would strengthen the study if they provided a clearer description or evidence of the validation process. 

This was done using the Imaris “Spot function”, in volume. A threshold is set to exclude spots due to GFP background and select only synaptic spots. The selection of spots and quantification are done automatically by Imaris. All spots below the threshold are excluded, regardless of genotype and experimental conditions, rendering the analysis objective. We have now provided a detailed description of the protocol in the Materials and Methods section: page 29 lines 778-799.

(16) Figure 5C lacks genotype controls (i.e., DNT2-GAL4-only and UAS-TrpA1-only). These controls are essential because elevated temperatures alone, without activation of DNT2 neurons, could potentially increase Syt-GCaMP production, leading to an increase in the number of Syt+ synapses. Including these controls would help ensure that the observed effects are truly due to the activation of DNT2 neurons and not temperature-related artifacts. 

We thank this Reviewer for this feedback, which we will bear in mind for future projects.

(17) L314-316: The authors state, "Here, the coincidence of... revealed that newly formed synapses were stable." I think this statement needs to be toned down because there is no evidence that these pre- and post-synaptic sites are functionally connected. 

The Reviewer is correct that our data did not visualise together, in the same preparation and specimen, both pre- and post-synaptic sites. Still, given that PAMs have already been proved by others to be required for locomotion, learning and long-term memory, our data strongly suggest that synapses between them at the SMP are functionally connected.

Nevertheless, as we do not provide direct cellular evidence, we have now edited the text to tone down this claim: “Here, the coincidence of increased pre-synaptic Syt-GFP from PAMs and post-synaptic Homer-GFP from DNT-2 neurons at SMP suggests that newly formed synapses could be stable”, page 13 line 351.

(18) Figure 5D lacks permissive temperature controls. Also, the DNT-2FL overexpression phenotypes are different from the TpA1 activation phenotypes. The authors may want to discuss this discrepancy. 

Regarding the controls, these are not appropriate for this data set. These data were all taken at a constant temperature of 25°C, there were no shifts, and therefore do not require a permissive temperature control. We thank this Reviewer for drawing our attention to the fact that we made a mistake drawing the diagram, which we have now corrected in Figure 5D.

Regarding the discrepancy, this had already been discussed in the Discussion section of the previously submitted version, page 19 Line 509-526. Presumably this Reviewer missed this before.

(19) Figure 6A, B lack permissive temperature controls. These controls are important if the authors want to claim that the behavioral defects are due to adult-specific manipulations. In addition, there is no statistical difference between the PAM-GAL4 control and the RNAi knockdown group. The authors should be careful when stating that climbing was reduced in the RNAi knockdown flies (L341-342). 

We thank this Reviewer for this feedback, which we will bear in mind for future projects.

Point taken, but climbing of the tubGAL80ts, PAM>Toll-6RNAi flies was significantly different from that of the UAS-Toll-6RNAi/+ control.

(20) Figure 6C: It seems that the DAN-GAL4 only control (the second group) also rescued the climbing defect. The authors may want to clarify this point. 

The phenotype for this genotype was very variable, but certainly very distinct from that of flies over-expressing Toll-6[CY].

We thank Reviewer 2 for their very thorough analysis of our paper that has helped improve the work.

Reviewer #3 (Recommendations for the authors): 

Overall, the manuscript reports highly interesting and mostly very convincing experiments. 

We are very grateful to this Reviewer for their very positive evaluation of our work.

Based on my comments under the heading "public review", I would like to suggest three possible improvements. 

First, the quantification of structural plasticity at the sub-cellular level should be explained in more detail and potentially improved. For example, 3D reconstructions of individual neurons and quantification of the structure of boutons and dendrites could be undertaken. At present, it is not clear how bouton volumes are actually recorded accurately. 

Thank you for the feedback. The analyses of dendrites and synapses were carried out in 3D-volumes using Imaris “Filament” module and “Spot function”, respectively. Dendrites are analysed semi-automatically, ie correcting potential branching errors of Imaris, and synapses are counted automatically, after setting appropriate thresholds. Details have now been expanded in the Materials and Sections section: page 28 lines 756-768 and page 29 lines 780-799.

We would also like to thank Imaris for enabling and facilitating our remote working using their software during the Covid-19 pandemic, post-pandemic lockdowns and lab restrictions that spanned for over a year.

Second, the variability between DNT-2A-positive neurons with increasing sample size compared to a control (DNT-2A-negative neurons) should be demonstrated. Figure 2C does currently not present convincing evidence of increased structural variability. 

It is unclear what data the Reviewer refers to. Figure 2C shows qRT-PCR data, and it does not show structural variability, which instead is shown with microscopy. If it is the BacTrace data in Figure 2B, the controls had been provided and the data were unambiguous. If Reviewer means Figure 1C, it is unclear why DNT-2GAL4-negative flies are needed when the aim was to visualise normal (not genetically manipulated) DNT-2 neurons. Thus, unfortunately we do not understand what the point is here.

The observation that DNT-2 neurons are very variable, naturally, is highly interesting, and presumably this is what drew the attention of Reviewer 3. We agree that showing further data in support of this is interesting and valuable. Thus, in response to this Reviewer’s comment we have now increased the number of images that demonstrate variability of DNT-2 neurons:

(1) We have added an extra image, altogether providing three images in new Figure 1A showing three different individual brains stained with DNT-2GAL4>UAS-FlyBow1.1. These show common morphology and features, but different location of the somas and distinct detailed arborisation patterns. Two more images using DNT-2GAL4 are provided in Figure 5A.

(2) We have now added two further MCFO images, altogether showing four examples where the somas are not always in the same location and the axons arborise consistently at the SMP, but the detailed projections are not identical: new Figure 1D.

These data compellingly show natural variability in DNT-2 neuron morphology.

Third, I propose to simplify the feedback model (Figure 2F) to be less speculative. 

Indeed, some details in Figure 2F are speculative as we did not measure real dopamine levels. Accordingly, we have now edited this diagram, adding question marks to indicate speculative inference, to distinguish from the arrows that are grounded on the data we provide.

Accordingly, we have also edited the text in:

- page 9, lines 221: “Altogether, this shows that DNT-2 up-regulated TH levels (Figure 2E), and presumably via dopamine release, this inhibited cAMP in DNT-2A neurons (Figure 2F)”.

- page 20, lines 515: “Importantly, we showed that activating DNT-2 neurons increased the levels and cleavage of DNT-2, up-regulated DNT-2 increased TH expression, and this initial amplification resulted in the inhibition of cAMP signalling via the dopamine receptor Dop2R in DNT-2 neurons.”

As minor points: 

(1) Appetitive olfactory learning is based on Tempel et al., (1983); Proc Natl Acad Sci U S A. 1983 Mar;80(5):1482-6. doi: 10.1073/pnas.80.5.1482. This paper should perhaps be cited. 

Thank you for bringing this to our attention, we have now added this reference to page 14 line 394.

(2) Line 34: I would add ..."ligand for Toll-6 AND KEK-6,". 

Indeed, thank you, now corrected.

(3) Line 39: DNT-2-POSITIVE NEURONS. 

Now corrected, thank you.

(4) The levels of TH mRNA were quantified. Why not TH or dopamine directly using antibodies, ELISA, or HPLC? After all, later it is explicitly written that DNT modulates dopamine levels (line 481)! 

We thank this Reviewer for this suggestion. We did try with HPLC once, but the results were inconclusive and optimising this would have required unaffordable effort by us and our collaborators. Part of this work spanned over the pandemic and subsequent lockdowns and lab restrictions to 30% then 50% lab capacity that continued for one year, making experimental work extremely challenging. Although we were unable to carry out all the ideal experiments, the DNT-2-dependent increase in TH mRNA coupled with the EPAC-Dop2R data provided solid evidence of a DNT-2-dopamine link.

(5) Line 271: The PPL1-g1-pedc neuron has mainly (but not excusively) a function in short-term memory! 

They do, but others have also shown that PPL1-g1-pedc neurons have a gating function in long-term memory (Placais et al 2012; Placais et al 2017; Huang et al 2024) and are required for long-term memory (Adel and Griffith 2020; Boto et al 2020).

(6) Line 401: Reward learning requires PAM neurons. PPL1 neurons are required for aversive learning. 

Indeed, PPL1 neurons are required for aversive learning, but they also have a gating function in long-term memory common for both reward and aversive learning (Adel and Griffith, 2020 Neurosci Bull; Placais et al, 2012 Nature Neuroscience; Placais et al 2017 Nature Communications; Huang et al 2024 Nature).

Overall, the manuscript presents extremely interesting, novel results, and I congratulate the authors on their findings. 

We would like to thank this Reviewer for taking the time to scrutinise our work, their helpful feedback that has helped us improve the work and for their interest and positive and kind works.

La Santé de l'Enfant et de l'Adolescent en Île-de-France : Priorités et Actions

Les sources fournies mettent en lumière l'importance accordée à la santé de l'enfant et de l'adolescent dans le Schéma Régional de Santé (SRS) d'Île-de-France.

Le document souligne les multiples signaux d'alerte concernant la santé des jeunes et l'incidence de ces enjeux sur le long terme, tant sur le plan sociétal qu'individuel.

Des Besoins Spécifiques et des Inégalités Marquées

L'enfance et l'adolescence sont des périodes cruciales pour le développement physique et mental, et le SRS met l'accent sur la nécessité de garantir un accès équitable à une offre de services de prévention, de dépistage, d'éducation à la santé et de soins.

Plusieurs défis sont mis en avant :

• Augmentation des maladies chroniques chez les enfants franciliens (maladies respiratoires, asthme, diabète de type 1, obésité, drépanocytose).
• Disparités territoriales en matière de santé infantile, avec des indicateurs moins favorables que les moyennes nationales, notamment en ce qui concerne la mortalité infantile.
• Vulnérabilité accrue de certains groupes d'enfants et d'adolescents, notamment ceux en situation de handicap, en danger ou placés sous protection de l'enfance.
• Tensions sur les ressources humaines en pédiatrie, notamment au sein de la Protection Maternelle et Infantile (PMI), qui constitue pourtant un interlocuteur crucial pour de nombreux jeunes enfants.

Des Actions Multisectorielles et une Approche Intégrée

Le SRS propose une série d'actions concrètes pour répondre à ces enjeux, en s'appuyant sur une approche multisectorielle et intégrée :

Renforcer le pilotage de la politique régionale de santé de l'enfant et de l'adolescent:

• Mise en place de gouvernances régionales et départementales dédiées à la santé de l'enfant et de l'adolescent, associant l'ensemble des acteurs concernés.
• Renforcement des dispositifs de prévention individuelle organisés, tels que la PMI et la santé scolaire.

Structurer une offre accessible et adaptée aux besoins des enfants et adolescents:

• Développement d'un "panier de services de prévention" accessible à tous les enfants, y compris ceux en situation de handicap.
• Structuration des ressources pour le dépistage, le diagnostic et la prise en charge des troubles du neuro-développement et des pathologies infantiles.
• Amélioration de la prise en charge des enfants en danger ou protégés.
• Développement de stratégies renforcées de promotion de la santé et d'accès aux soins pour les jeunes enfants en situation de grande pauvreté.
• Amélioration de la formation et de l'attractivité des métiers de la pédiatrie et des spécialités connexes.

Agir sur les déterminants de la santé des enfants et adolescents:

• Promotion des compétences psychosociales des parents et des enfants, notamment par le biais de programmes spécifiques.
• Prise en compte des déterminants sociaux, tels que la précarité, le logement et l'environnement.
• Renforcement des partenariats avec les acteurs de la protection de l'enfance et de la petite enfance.
• Développement d'actions collectives de promotion de la santé et de prévention.
• Sensibilisation à l'hygiène bucco-dentaire en milieu scolaire et dans les structures de suivi médical des jeunes enfants.

Prendre en compte les spécificités des adolescents et des jeunes adultes:

• Mise en place de programmes de renforcement des compétences psychosociales adaptés aux adolescents, en tenant compte de leur situation et de leur environnement.
• Réduction des inégalités de santé entre jeunes, en renforçant leur pouvoir d'agir et en promouvant des facteurs positifs pour la santé.
• Développement de programmes de prévention ciblés sur les principaux facteurs de risque pour la santé des adolescents et jeunes adultes (couverture vaccinale, santé mentale, nutrition, activité physique).
• Élaboration de feuilles de route spécifiques pour certains groupes de jeunes, tels que les étudiants et les mineurs non accompagnés.

Des Objectifs Ambitieux et Mesurables

Le SRS fixe des objectifs stratégiques et opérationnels à 5 ans, assortis d'indicateurs de suivi, pour mesurer l'impact des actions menées.

L'objectif est de donner un cap clair et de permettre une déclinaison territoriale en fonction des problématiques locales.

Conclusion

Le SRS témoigne d'une volonté forte d'améliorer la santé des enfants et adolescents en Île-de-France.

La mise en œuvre des actions prévues, en collaboration avec les partenaires locaux, est essentielle pour garantir un accès équitable à des soins de qualité et pour répondre aux besoins spécifiques de chaque territoire.

Note : Ce document de briefing se base exclusivement sur les informations contenues dans les sources fournies.

Situation et Perspectives des Yvelines : Santé et Au-delà

Le document "Schéma régional de santé" (SRS) offre un aperçu complet de la situation sanitaire en Île-de-France, y compris les Yvelines.

Bien que le document ne se concentre pas exclusivement sur les Yvelines, il fournit des informations cruciales sur les défis et les opportunités qui se présentent au département.

Points importants concernant les Yvelines:

• Santé mentale: Le SRS met l'accent sur l'amélioration de la santé mentale des Franciliens, notamment des jeunes. Des actions spécifiques pour les Yvelines incluent le développement de l'offre d'hospitalisation partielle en psychiatrie périnatale, en particulier dans le sud du département.
• Soins de proximité: Le SRS souligne la nécessité d'une meilleure répartition de l'offre de soins dans certains territoires, notamment le sud des Yvelines. Cela concerne particulièrement les SMR gériatriques et polyvalents.
• Urgences pédiatriques: Les urgences pédiatriques des Yvelines ont connu des difficultés durant l'hiver 2022-2023, nécessitant un accès régulé et des délestages. Le SRS met en avant la pression sur ces services comme cause de fuite des praticiens.
• PDSES: Le schéma de Permanence des Soins en Établissements de Santé (PDSES) existant est reconduit dans le SRS, en attendant une révision basée sur de nouvelles évaluations.

Perspectives et actions clés:

• Plan Inclus'IF 2030: Ce plan de développement massif de l'offre médico-sociale pour les personnes handicapées aura un impact significatif sur les Yvelines. Il vise à créer des solutions adaptées, souples et évolutives, en tenant compte des besoins spécifiques de chaque territoire.
• Renforcement des équipes mobiles: Le SRS prévoit une amélioration de la couverture en équipes mobiles médico-sociales, notamment en Seine-et-Marne et dans les Yvelines. L'objectif est de mailler plus finement les départements et d'assurer un meilleur accompagnement des populations en difficulté.
• Jeux Olympiques et Paralympiques 2024: Les JOP 2024 offrent une opportunité unique de développer des actions de prévention et de promotion de la santé dans les Yvelines. Le SRS encourage la capitalisation sur la dynamique olympique pour lutter contre la sédentarité et améliorer la santé environnementale.

Enjeux transversaux:

• Ressources humaines: Le SRS identifie les tensions en ressources humaines comme un défi majeur pour le système de santé francilien, y compris dans les Yvelines. Des actions ciblées sont nécessaires pour attirer et fidéliser les professionnels de santé.
• Adaptation au changement climatique: Le SRS met en avant la nécessité d'adapter le système de santé au changement climatique. Des diagnostics de vulnérabilité des infrastructures et des mesures préventives face aux événements climatiques extrêmes sont nécessaires.
• Cybersécurité: Le renforcement de la cybersécurité est un enjeu crucial pour tous les acteurs du système de santé, y compris dans les Yvelines.

Le SRS préconise la mise en place de plans ORSAN Cyber et le soutien à la reconstruction des systèmes d'information en cas d'attaque.

Conclusion:

Le SRS fournit un cadre stratégique pour l'amélioration de la santé et du bien-être dans les Yvelines.

La mise en œuvre des actions prévues, en collaboration avec les partenaires locaux, est essentielle pour répondre aux besoins spécifiques du département et garantir un accès équitable à des soins de qualité.

Let's make it a page note

Lorem Ipsum es simplemente el texto de relleno de las imprentas y archivos de texto. Lorem Ipsum ha sido el texto de relleno estándar de las industrias desde el año 1500, cuando un impresor (N. del T. persona que se dedica a la imprenta) desconocido usó una galería de textos y los mezcló de tal manera que logró hacer un libro de textos especimen. No sólo sobrevivió 500 años, sino que tambien ingresó como texto de relleno en documentos electrónicos, quedando esencialmente igual al original. Fue popularizado en los 60s con la creación de las hojas "Letraset", las cuales contenian pasajes de Lorem Ipsum, y más recientemente con software de autoedición, como por ejemplo Aldus PageMaker, el cual incluye versiones de Lorem Ipsum.

Author response:

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

Reviewer #1:

Reviewer #1 was very appreciative of our results and commented “This is a novel result in ferredoxin and a significant contribution to the field”. We are very honored and pleased.

Reviewer #2:

(1) Changing the nomenclature of the models investigated to include the oxidation state being discussed. As they are now (CM, CMNA, etc), multiple re-reads were required to ascertain which redox state was being discussed for a particular model in a given section of the text. Appending "Ox" or "Red" for oxidized or reduced would be sufficient. 

As you indicated there are several nomenclatures to distinguish the model systems in the text. On the other hand, the main issue discussed in the text is the ionization potential (IP), which is calculated by the difference in energies between oxidized and reduced states for each model. In other words, a discussion of the IP value on each model includes both the “Ox” and “Red” energies. In order to clarify the relationship between the nomenclature of models and redox states, we added sentences below.

“Note that the IP value is obtained for each model by calculating both the Ox and Red state energies of the model.” (lines 195-196).

On the other hand, we must specify the charge state when the geometry optimization is performed for CM and CMH models. Therefore, we revised the sentence as follows.

“The decrease in |IP| value indicates that the relative stability of the Red state is suppressed compared with the CMH but is significantly larger than the CM, suggesting the importance of the protonation of Asp64 (Fig. S2B). 

To consider the effect of the structural change caused by the redox on the IP, geometrical optimization of the 4Fe-4S core was performed for the CM (Red) and CMH (Red) models using the same level of theory to the single-point calculations. The optimized Cartesian coordinates are summarized in Table S3. As illustrated in Fig. S2A, the IP values of CM and CMH change from –3.27 to –2.38 eV (|DIP| = 0.89 eV), and from –1.06 to –0.19 eV (|DIP| = 0.87 eV), respectively, before and after the geometrical optimization.” (lines 224-232)

(2) In addition to the very thorough DFT investigation of the different spin and charge combinations, did the authors try a broken-symmetry calculation to obtain the ground state description of the FeS cluster? Given the ubiquity of this approach in other FeS cluster studies, it was surprising that this approach was not taken here. Granted, the DFT investigation of each possible combination is sufficiently thorough and need not be redone. 

Thank you for your comments. A term “spin-unrestricted method”, which is used in the manuscript in the text is synonym of “broken-symmetry method”. In order to emphasize this, we revised the manuscript as follows. 

“All calculations were performed by using the spin-unrestricted (broken-symmetry) hybrid DFT method with the B3LYP functional set. As the basis set, 6-31G* and 6-31+G* were used for [Fe, C, N, O, H] and [S] atoms, respectively, for the IP calculations.” (Line 451)

(3) Line 161 "an" to "a" 

We corrected the mistake. Thank you so much. (Line 161)

(4) Figure 4A seems a bit odd. Why do the traces eclipse the y-axis? And the traces between 330 and 370 nm are much noisier and appear thicker than the rest of the plot. Is this an issue with the monochromator grating used in wavelength selection? Reducing the thickness of the individual traces may help the data presentation in this figure. Also, the arrows on the plot have an opaque white background. Can this be removed so that the arrows do not eclipse the traces in the plot? 

The spectrum in the Fig.4A seemed to be odd. The spectral figure has been revised to improve its appearance. (We have also corrected E53A in Figure 5B.) This reviewer also pointed out that “the traces between 330 and 370 nm are much noisier”. We are struggling with the noise caused by the grating (or the motor malfunction) of the monochromator as you pointed out. Once the monochromator is repaired and a smooth spectrum is obtained, we will upload further revisions.

(5) Figure S9 is a very nice schematic illustrating the general findings of the study. Can this be moved to the main text?

Thank you for your helpful comment. Accordingly, the Fig.9S and its legend are moved to the main text. (Lines 675-680)

pas sûr: est-ce qu'on peut séparer écriture et support? Un support est support seulement parce qu'il y a écriture... et vice versa

Sommaire de la table ronde du congrès FCPE avec timestamps

Introduction et contexte (00:00:04 - 00:02:58)

• Introduction à la table ronde, avec un format axé sur les expériences locales et les partenariats (00:00:04 - 00:02:30)
• Retour sur les thèmes des congrès FCPE des 10 dernières années (00:02:30 - 00:02:53)
• Thème du congrès actuel : "Valoriser les différences pour les inégalités" (00:02:54 - 00:02:58)

Présentation des intervenants (00:02:59 - 00:03:45)

• Introduction des trois intervenants : Jean-Christophe Olivier (Réseau Canopé), Charlotte Brun (Mairie de Lille) et Charles Prevot (Association AERGES) (00:02:59 - 00:03:40)

Intervention de Jean-Christophe Olivier (Réseau Canopé) : Accompagnement numérique des familles (00:03:46 - 00:18:33)

• Présentation du Réseau Canopé et de son rôle dans l'accompagnement numérique des familles (00:03:46 - 00:08:01)
• Exemples d'actions menées dans les lycées, notamment l'accompagnement en présentiel lors de réunions de parents d'élèves (00:08:02 - 00:09:52)
• Importance de former des "relais" pour accompagner les parents, y compris des parents eux-mêmes (00:09:53 - 00:11:22)
• Partenariat avec la FCPE de Picardie pendant trois ans, avec des formations et un accompagnement des parents sur le numérique éducatif (00:11:22 - 00:13:05)
• Bilan quantitatif de l'accompagnement : 2500 personnes touchées sur les deux dernières années (00:13:05 - 00:13:26)
• Retours positifs des parents, qui découvrent les outils numériques à leur disposition (00:13:27 - 00:14:56)
• Importance de l'accompagnement numérique pour les élèves, notamment pour la révision et l'accès aux cours (00:14:57 - 00:15:48)
• Projets futurs : formations en ligne et en présentiel sur la coéducation et l'accompagnement numérique (00:15:49 - 00:17:32)
• Conclusion et présentation des fiches pratiques sur la cybersécurité et l'aide numérique (00:17:33 - 00:18:33)

Intervention de Charlotte Brun (Mairie de Lille) : Gratuité des fournitures scolaires (00:18:34 - 00:28:58)

• Introduction sur l'importance de l'école publique et gratuite pour tous les enfants (00:18:34 - 00:20:21)
• Remerciements à la FCPE de Lille pour leur alerte sur les inégalités liées aux fournitures scolaires (00:20:21 - 00:21:19)
• Explication du processus mis en place par la ville de Lille pour la gratuité des fournitures scolaires, en collaboration avec l'éducation nationale et les parents d'élèves (00:21:19 - 00:22:37)
• Conditions pour la réussite du dispositif : suppression totale des listes de fournitures et achat de manuels scolaires par les écoles (00:22:38 - 00:24:56)
• Duplicabilité du processus et partage d'un mémo avec d'autres villes (00:24:56 - 00:25:42)
• Impact positif sur les familles et les enfants : appréciation, diminution des tensions en classe et affirmation du principe d'une école républicaine gratuite (00:25:43 - 00:27:47)
• Regret sur le manque de mobilisation de l'État sur cet enjeu (00:27:48 - 00:28:18)
• Conclusion et encouragement à la mutualisation de cette bonne pratique (00:28:19 - 00:28:58)

Intervention de Charles Prevot (Association AERGES) : Sensibilisation à la diversité des genres et des sexualités (00:29:00 - 00:40:38)

• Présentation de l'association AERGES et de ses missions de sensibilisation à la diversité des orientations sexuelles et des identités de genre (00:29:00 - 00:30:22)
• Agrément de l'Éducation nationale et participation à l'observatoire sur les LGBTphobies de l'académie de Lille (00:30:23 - 00:30:38)
• Bilan des actions menées en 2022-2023 : sensibilisation de 360 élèves et formation de 138 adultes du personnel scolaire (00:30:38 - 00:31:04)
• Objectif de former l'ensemble du personnel scolaire à l'accompagnement des élèves LGBTQ+ (00:31:04 - 00:31:38)
• Projets de l'association :
  • Interventions scolaires en classe entière sur les orientations sexuelles et les identités de genre (00:31:38 - 00:32:10)
  • Formation du personnel scolaire à l'accompagnement des élèves LGBTQ+ (00:32:10 - 00:32:33)
  • Intervention en cas d'urgence, notamment pour des coming out de personnes transgenres (00:32:34 - 00:32:59)
  • Interventions dans les UPA (Unités Pédagogiques pour Allophones) pour sensibiliser les élèves primo-arrivants à la diversité des identités (00:32:59 - 00:33:46)
  • Projet "Injonctions à la masculinité" pour sensibiliser les classes de lycée professionnel majoritairement masculines (00:33:46 - 00:35:37)
  • Projet "Alliances" pour former des élèves à devenir des témoins et des relais dans la lutte contre les LGBTphobies au sein des établissements (00:35:37 - 00:36:25)
• Axes de développement : salariat, intervention dans le secteur médical et social, consolidation des projets avec l'Éducation nationale et recrutement de bénévoles (00:36:27 - 00:38:13)
• Exemple d'événement organisé avec la mairie de Lille pour la journée de lutte contre l'homophobie et la transphobie : scène ouverte féministe, karaoké et drag show (00:38:13 - 00:38:58)
• Présentation de l'activité "La journée de Mona" pour sensibiliser les élèves aux difficultés rencontrées par les personnes transgenres (00:38:58 - 00:40:38)

Conclusion (00:40:39)

• Remerciements à Charles Prevot pour son intervention.

Conclusion générale

La table ronde du congrès FCPE a permis de mettre en lumière des initiatives locales et des partenariats visant à "Valoriser les différences pour les inégalités" dans le domaine de l'éducation. Les interventions ont abordé des thématiques variées telles que l'accompagnement numérique des familles, la gratuité des fournitures scolaires et la sensibilisation à la diversité des genres et des sexualités. La table ronde a également permis de souligner l'importance de la mobilisation des parents d'élèves et des collectivités locales, ainsi que le besoin d'un engagement plus important de l'État sur ces enjeux.

Eliza Lucas’s letters provide a rare glimpse into the role of women in managing southern plantations during the 18th century. In a region where agriculture was the backbone of the economy, managing estates was crucial to maintaining wealth and influence, and Lucas’s ability to do so speaks to her remarkable business acumen. At the time, South Carolina was one of the leading colonies in indigo production, which became a lucrative export to Britain. The plantation economy was heavily dependent on enslaved labor, and Lucas’s success in managing the plantation was intertwined with the exploitation of enslaved African Americans. The letters also show the intricate balance of power, where women like Eliza Lucas had influence in business while still operating within a patriarchal system. Her ability to take on such a significant role reflects the social and economic changes that allowed some women more control over wealth and property.

Thể hiện sự ngây thơ và trong sáng của zeze khi không biệt rằng bài hát mình vừa hát là bậy bạ và sẽ bị chừng phạt. Zeze thực sự không biết ý nghĩa của bài hát và chỉ học theo người hát rong.

"Một xu năm viên bi", "Một xu mười thẻ bài" cho thấy tính tháo vát và khả năng tự lập của cậu bé Zezé.

Trong chương này, Zeze thường hay để ý đến việc ông Bồ có vẻ rất béo, nhưng đối với cậu ta, đây là một điều tốt. Đây là vì một người béo sẽ là một người giàu có, không thiếu đồ ăn, giống như những ông vua trong những lá bài, và đối với Zeze đây là một đặc điểm độc đáo mà cậu ấy thích hay kính nể của ông Bồ

Trong sự ngây thơ của mình, Zeze không hiểu được những lời bài hát mình đang hát, nhưng cậu bé chỉ để ý rằng dường như cậu đang giúp cha mình vui hơn. Điều này cho thấy lòng yêu thương vẫn còn ngây thơ của cậu bé.

Sau khi Zezé nghịch ngợm tới sứt cả chân khi không để ý mà dẵm vào một mảnh sành, cậu đã cho thấy sự tinh quái của mình khi thuyết phục được Gloria. Qua đây, ta cũng có thể thấy rằng Gloria là một người nhân hậu, yêu thương gia đình. Nét tích cách ấy được cô thể hiện xuyên suốt từ đầu truyện đến giờ, nhưng lần này, tác giả cho chúng ta thấy Gloria thật cả tin và hiền lành. Từng cử chỉ lo lắng cô dành cho cậu em hiếu kì của mình đã nói lên tất cả.

Cô Cecilia là nhân vật tạo ra tương phản đạo đức so với những thành viên gia đình, tuy trớ trêu ở chỗ cậu bé nói thật về cả "biết nhiều câu chửi tục hơn tất cả các bạn khác trong lớp" và "không đứa trẻ nào tinh quái như tôi".

"Ở trường, tôi là một thiên thần" ngụ ý/tương phản sắc nét với việc ở nhà cậu bé hai bị gọi là một con tiểu quỷ.

Bị các bạn trêu chọc trong một cách "khủng khiếp" --> Thể sự thô lỗ của những đứa bé bằng tuổi Zeze, cũng thể hiện các bạn học Zeze còn chưa non nớt.

Zeze cảm giác nghĩ đó là cho đùa "khủng khiếp" --> Thể hiện sự quan trọng của ý kiến của mọi người

A refugee, Ahmad al-Hallabi, claims to have witnessed the horrors committed under al Assad's government. Now, he hopes for peace.

Other people want to help from the countries they escaped to, and believe it is better if doctors and engineers go back to help Syria instead.

Cách thiết kế meta-prompt: - Thứ tự của các chỉ dẫn trước đó: so sánh các cài đặt sau với nhau: + Từ thấp nhất đến cao nhất (mặc định) + Từ cao nhất đến thấp nhất + Ngẫu nhiên Hình 7 cho thấy cài đặt mặc định có kết quả tốt hơn và hội tụ nhanh hơn. Gỉa thiết được đưa ra cho hiện tượng này là do đầu ra của optimizer LLM bị ảnh hưởng bởi các chỉ dẫn trước đó mà ở gần meta-prompt hơn. Điều này phù hợp với thiên lệch ở gần (recency bias) cho rằng LLM có xu hướng tạo ra các token tương đồng với phần cuối của prompt.

• Tác động của điểm chỉ dẫn: Đối với việc thể hiện điểm accuracy, 3 cài đặt được so sánh:
• Làm tròn điểm thành số tự nhiên
• Gom nhóm các điểm thành 20 nhóm.

• Không ghi điểm, chỉ ghi các chỉ dẫn theo thứ tự giảm dần của điểm. Hình 7c và 7d cho thấy các điểm accuracy hỗ trợ optimizer LLM trong việc hiểu về chất lượng của các chỉ dẫn trước, từ đó LLM có thể đưa ra các chỉ dẫn tốt hơn, tương đồng với các chỉ dẫn tốt nhất

• Tác động của các ví dụ: 3 lựa chọn được so sánh:

• Ghi 3 ví dụ từ bái toán
• Ghi 10 ví dụ từ bài toán
• Không có Hình 7e và 7F cho thấy việc thể hiện các ví dụ trong meta-prompt là vô cùng quan trọng, bởi nó cung cấp thông tin về bài toán và giúp optimizer LLM có thể tạo các chỉ dẫn tốt hơn. Tuy nhiên, việc có nhiều hơn các ví dụ không phải lúc nào cũng dẫn đến việc cải thiện kết quả. Hơn nữa, việc có nhiều ví dụ còn dẫn đến việc meta-prompt bị dài hơn với phần ví dụ dài hơn các phần khác, dẫn đến việc LLM dễ bị xao nhãng mà không chú ý các phần quan trọng khác.

Bảng 4 tổng hợp các chỉ dẫn tốt nhất trên bài toán GSM8K với các scorer và optimizer LLM khác nhau. Nhận xét: - Các phong cách tạo chỉ dẫn của các LLM có sự khác nhau lớn: PaLM 2-L IT và text-bison thường ngắn gọn, còn GPT thì dài và nhiều chi tiết. - Mặc dù một số chỉ dẫn tốt nhất chứ cụm từ "step-by-step", tất cả các cụm khác đều đạt kết quả tương tự hoặc cao hơn với các cụm có ý nghĩa khác.

Hạn chế: Cần lưu ý rằng OPRO được thiết kế không phải để đạt kết quả cao hơn so với các thuật toán tối ưu SOTA hay các thuật toán chuyên biệt cho các bài toán tối ưu kinh điển như TSP. Thay vào đó, mục tiêu của OPRO là chứng minh LLM có thể tối ưu hóa nhiều bài toán khác nhau chỉ thông qua việc prompting và đạt được kết quả tối ưu toàn cục ở các bài toán có quy mô nhỏ. Đánh giá kết quả cũng cho thấy một số hạn chế của OPRO trong việc tối ưu toán học. Cụ thể, giới hạn độ dài của cửa số ngữ cảnh LLM khiến việc mô tả các bài toán tối ưu có quy mô lớn bằng ngôn ngữ tự nhiên trở nên khó khăn (ví dụ: hồi quy tuyến tính trên nhiều chiều và TSP với nhiều node). Ngoài ra, bối cảnh tối ưu của một số bài toán cũng không ổn định để LLM có thể đưa ra một hướng giải quyết hội tụ. khiến cho việc tối ưu bị chững lại

Cài đặt của bài toán: - Lấy mẫu ngẫu nhiên n điểm với 2 giá trị tọa độ x và y đều nằm trong khoảng [-100, 100]. - Gurobi solver được sử dụng để tạo giải pháp ground-truth. Điểm đánh giá được sử dụng cho các giải pháp được sinh ra là khoảng cách tối ưu (optimality gap). Trong đó, khoảng cách tối ưu được định nghĩa là hiệu giữa giải pháp được tạo sinh và giải pháp ground-truth sau đó chia cho giá trị của giải pháp ground-truth.

Cài đặt của bài toán Linear Regression: - Biến phụ thuộc X và biến độc lập y đều là các giá trị 1 chiều, sao cho có 2 biến 1 chiều là w và b được tối ưu. - Các giá trị ground-truth với 1 cặp giá trị w_true và b_true sẽ được lấy mẫu với với số lượng 50. Công thức y = w_true*X + b_true + \(ε\), trong đó X nằm trong khoảng 1 đến 50 và ε là nhiều Gaussian

info para ensayo

heeft te maken met behavioral managent theory

proceso + concreto de cierre

galería internacional permite sostener gustos mas alejados del status quo

Love this part!

Establecer el derecho a la información en los sistemas de IA y mejorar la transparencia algorítmica

Habilitar y realizar evaluaciones obligatorias del impacto sobre los derechos humanos

Desarrollar medidas de rendición de cuentas para los sistemas y procesos algorítmicos del sector público

Promover la participación pública y comunitaria efectiva

Invertir en el desarrollo de capacidades entre los grupos marginados

Legislar los derechos de participación pública ex ante

Proteger los datos colectivos y los derechos de la IA

Involucrar a los grupos marginados en funciones técnicas y no técnicas en todo el ecosistema de IA

Invertir en el desarrollo de capacidades para la inclusión institucional

Permiso para el tratamiento de categorías especiales de datos

Financiar la investigación y el diseño de tecnologías transformadoras en la innovación de la IA

Categorías:

1. Diseño inclusivo e innovación democrática

2. Participación significativa en la gobernanza de la IA

3. Transparencia y rendición de cuentas para la prevención de daños

4. Acceso efectivo a la justicia

Varias de las características propuestas por Tsing (Citation2009) resuenan con el capitalismo de la cadena de suministro de la IA en la actualidad. Sin embargo, la principal diferencia es que las empresas capitalistas dentro de la economía política de la IA están acumulando más capital en 2024 que Wal-Mart o Nike en 2009. Por lo tanto, es relevante comprender mejor la línea de producción capitalista de la IA.

El capitalismo de la cadena de suministro ofrece un marco teórico para tener en cuenta la línea de producción de la IA y su infraestructura, desde las minas hasta los desechos electrónicos, y dar cuenta de las asimetrías geográficas y las luchas ambientales que podrían no ser evidentes.

Un examen más detallado del capitalismo de la cadena de suministro de la IA revela daños ambientales como las luchas por el agua, que no se han considerado anteriormente en la literatura sobre daños y resistencia algorítmica.

Este artículo muestra que las luchas controvertidas surgen al investigar puntos específicos en las cadenas de suministro de la IA.

El caso de Maconí muestra que, si bien la industria de los centros de datos está explotando los recursos hídricos para enfriar su infraestructura digital, las personas en áreas rurales e indígenas no tienen acceso al agua. Juan, el hombre de mediana edad citado al comienzo de este artículo, vive actualmente en Maconí y todavía camina al menos ocho horas para recolectar agua para su vida diaria.

Este artículo es un llamado a investigar el impacto infraestructural de la IA desde una perspectiva crítica y ambiental y abrir una crítica hacia la creciente industria de la IA para que podamos construir un mundo de muchos mundos donde quepamos todos (Blaser, Citation2018).

La inteligencia artificial (IA) está entretejida en una cadena de suministro de capital, materias primas y trabajo humano que ha sido descuidada en los debates críticos.

Dado el auge actual de la IA generativa, que se estima que impulsará la extracción de recursos naturales como minerales, combustibles fósiles o agua, es vital investigar toda su línea de producción desde una perspectiva infraestructural crítica.

Basándose en el capitalismo de la cadena de suministro, un concepto acuñado por Anna L. Tsing en 2009, este artículo contribuye a los estudios críticos de la IA al investigar la estructura de las cadenas de suministro de IA, teniendo en cuenta la industria minera, electrónica, digital y de desechos electrónicos.

Este artículo ilustra cómo el capitalismo de la cadena de suministro de la IA está precipitando asimetrías geográficas conectadas con luchas controvertidas en México al centrarse en un elemento clave de estas cadenas: los centros de datos.

En tiempos de emergencia climática, este artículo llama a reconsiderar los daños y la resistencia algorítmica mediante la investigación de toda la línea de producción capitalista de la industria de la IA desde una perspectiva crítica y ambiental.

Instrumentos de educación y sensibilización pública

Instrumentos de participación pública (procedimientos)

Instrumentos económicos

Instrumentos de políticas legislativas y normativas

Instrumentos auto-normativos

Puntos de acción

Emplear métodos participativos e inclusivos para desarrollar mecanismos de seguimiento y evaluación ex ante y ex post para evaluar y mitigar el sesgo en los sistemas y procesos de IA.

Realizar auditorías de los sistemas de IA para detectar y abordar la discriminación directa e indirecta que afecta especialmente a las comunidades marginadas.

Asegurarse de que los sistemas de IA se sometan a pruebas rigurosas para detectar sesgos y que se los controle de forma continua y sistemática para detectar resultados discriminatorios.

Puntos de acción

Desarrollar protocolos claros que aseguren que las decisiones e intervenciones cumplan con los criterios de legitimidad, necesidad y proporcionalidad, a fin de garantizar la protección de los derechos y libertades de terceros cuando pueda haber conflictos de intereses entre las partes interesadas (lo que puede dar lugar a impactos desproporcionados en un grupo o grupos en comparación con otro grupo o grupos de personas).

Garantizar que existan marcos jurídicos suficientes (tanto nacionales como internacionales) para proteger y brindar reparación a las personas afectadas por la IA.

Permitir que las personas afectadas por sistemas de IA busquen soluciones sin temor a represalias.

Desarrollar estrategias para garantizar que el público en general sea consciente de los riesgos y oportunidades relacionados con la IA, y de los mecanismos disponibles para identificar cuándo el uso o la aplicación de la IA puede haber resultado en una violación de sus derechos.

Puntos de acción

Implementar medidas de codiseño que involucren a las comunidades marginadas y otros stakeholders relevantes a lo largo del ciclo de vida de la IA.

Involucrar a las comunidades marginadas en la identificación de problemas, la formulación de problemas, el diseño y la toma de decisiones en materia de gobernanza de la IA.

Integrar perspectivas diversas en el diseño y desarrollo de la IA para evitar sesgos sistémicos y resultados discriminatorios.

Asegúrese de que los procesos de información y consulta sean accesibles e inclusivos para los grupos marginados. Crear y distribuir materiales de fácil lectura con antelación para garantizar que las personas sin educación formal o conocimientos de inteligencia artificial puedan comprender los temas clave. Evitar la jerga técnica cuando sea suficiente con un lenguaje sencillo.

Incorporar principios de diseño inclusivos y transformadores y metodologías participativas en las estrategias nacionales o regionales de IA para garantizar que los sistemas de IA respeten la autonomía, la dignidad y los valores culturales de los grupos marginados.

Aplicar políticas de protección, incluidos mecanismos para denunciar casos de discriminación, situaciones de violencia y otras situaciones que dificulten la participación de las mujeres y otros grupos marginados en el lugar de trabajo en igualdad de condiciones con los demás. Garantizar el acceso a estos mecanismos para permitir la denuncia y la reparación de agravios.

Puntos de acción

Asignar un presupuesto para los costos de participación. Incluir líneas presupuestarias específicas para cubrir los costos de participación de representantes de grupos marginados, como compensación por su tiempo y experiencia, y adaptaciones razonables para garantizar la accesibilidad (por ejemplo, intérpretes de lengua de señas, intérpretes guía para personas sordociegas, intérpretes de lenguas indígenas).

Al facilitar la participación, considera las dinámicas de poder específicas del contexto, incluyendo género, expresión de género, edad, raza, clase, cultura, identidad, habilidades y lenguaje, para garantizar una inclusión efectiva. Además, crea incentivos que motiven a los actores privados a involucrarse activamente en el diseño, desarrollo y gobernanza de sistemas de IA inclusivos.

Puntos de acción

**Mapear políticas y marcos normativos internacionales relevantes sobre derechos humanos, así como compromisos regionales o nacionales aplicables. **

**Mapear y evaluar si los tratados internacionales y leyes nacionales existentes son suficientes para proteger, promover y garantizar los derechos involucrados, revisando análisis legales disponibles para determinar la solidez de dichas protecciones. **

Considerar las características sociales, económicas, demográficas, políticas, históricas y culturales que puedan influir en el proceso, identificando las principales causas de exclusión de grupos marginados y cómo la IA puede amplificarlas.

Resultados esperados con metas claras

Puntos de acción

Incluir a grupos marginados, alineándolos con recomendaciones de políticas transformadoras de igualdad de género y diversidad.

Traducir estos objetivos en planes de acción detallados que incorporen responsabilidades, indicadores de monitoreo, presupuestos y directrices, asegurando la evaluación de políticas institucionales para identificar mejoras.

Definir hitos y métricas claras para medir el progreso, considerando la sostenibilidad y relevancia de las soluciones para prevenir y abordar daños relacionados con la IA.

Identificar las principales causas de exclusión de grupos marginados y cómo la IA puede amplificarlas, analizando el contexto social, económico, político, histórico y cultural, así como los marcos legales y normativos relevantes para proteger los derechos en riesgo.

Comité intersectorial de stakeholders múltiples.

Puntos de acción

Mapear a los stakeholders clave e invitar representantes, especialmente de grupos marginados, para compartir sus perspectivas.

Incluir académicos, ONG y comunidades técnicas, evaluando su interés en la igualdad sustantiva y la capacidad de los grupos marginados para influir en las decisiones.

Considerar sus necesidades, compensarlos por su tiempo, garantizar accesibilidad y fomentar condiciones que fortalezcan su agencia.

Establecer un comité multiactorial intersectorial que asegure diversidad en la planificación y decisiones, comprendiendo desequilibrios de poder y necesidades de acción.

Identificar grupos posiblemente marginalizados por la IA.

Plan de acción

Mapear grupos marginados y subrepresentados, instituciones responsables de derechos humanos, igualdad de género y diversidad, organizaciones representativas de grupos marginados y defensoras de derechos humanos para promover un enfoque inclusivo y equitativo en la gobernanza de sistemas de IA.

Garantizar inclusión y equidad. Identificar actores relevantes (academia, ONG y comunidades técnicas) evaluando su interés en la igualdad sustantiva.

Analizar la capacidad de grupos marginados para influir en las decisiones, considerando sus necesidades y contexto.

Garantizar compensación y accesibilidad para participantes.

Fomentar la agencia de estos grupos.

Establecer un comité multiactoral intersectorial para integrar perspectivas diversas.

I would like to see figures depicting the performance (error) of these different methods for each parameter estimate, but plotted as a function of tree size (i.e. a head map of error as a function of parameter value and tree size) - I can't help but wonder if part of the pattern of increasing error rates as a function of increasing diversification parameter is simply a result of there being greater variability in simulated tree shape/size at these larger parameter values.

Additionally, I think it could be quite informative to plot a heatmap of error with speciation and extinction rates as the x and y axes - I suspect this would highlight a clear, predictable pattern, particularly with increasing error rates being characteristic of parameter combinations where both speciation and extinction rates are high, leading to high species turnover and thus greater "volatility" of diversification outcomes.

Author response:

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

Reviewer #1:

- Summary: 

Recordings were made from the dentate nucleus of two monkeys during a decision-making task. Correlates of stimulus position and stimulus information were found to varying degrees in the neuronal activities. 

We agree with this summary.

- Strengths: 

A difficult decision-making task was examined in two monkeys.

We agree with this statement.

- Weaknesses: 

One of the monkeys did not fully learn the task. The manuscript lacked a coherent hypothesis to be tested, and no attempt was made to consider the possibility that this part of the brain may have little to do with the task that was being studied. 

We understand the reviewers concern. It is correct that one of the monkeys (Mi) did not perform at a high level, but it should be noted that both monkeys learned significantly above chance level. Therefore, we would argue that both monkeys in fact did learn the task but Mi’s performance was suboptimal. This difference in the performance levels gave us a rare opportunity to dive deeper into the reasons why some animals perform better than the others and we show that Mi (the lower performing monkey) paid more attention to the outcome of the previous trial – this is evident from our behavioural and decoding models.

We tested the overall hypothesis that neurons of the nucleus dentate can dynamically modulate their activity during a visual attention task, comprising not only sensorimotor but also cognitive attentional components. Many neurons in the dentate are multimodal (Figure 3C-D) which was something that was theorized. One of the specific hypotheses that we tested is that the dentate cells can be direction-selective for both the sensorimotor and cognitive component. Given that many of the recorded cells showed direction-selectivity in their firing rate modulation for gap directions and/or stimulus directions, we provide strong evidence that this hypothesis is correct. We have now spelled out this hypothesis more explicitly in the introduction of the revised version. We now also explain better why we tested this specific hypothesis. Indeed, earlier studies in primates such as those by Herzfeld and colleagues (2018, Nat. Neuro.) and van Es and colleagues (2019, Current Biol) have indicated that direction-selectivity of cerebellar activity may occur in various sensorimotor domains.

We also appreciate the comment of this Reviewer that in our original submission we did not show our attempt to consider the possibility that this part of the brain may have little to do with the task that was being studied. We in fact did consider this possibility in that we successfully injected 3 ml of muscimol (5 μg/ml, Sigma Aldrich) into the dentate nucleus in vivo in one of the monkeys (Mo). This application resulted in a reduction of more than 10% in correct responses of the covert attention task after 45 minutes, whereas the performance remained the same following saline injections. Unfortunately, due to the timing of the experiments and Covid19-related laboratory restrictions we were unable to perform these experiments in the other monkey or repeat them in Mo. We aim to replicate this in future experiments and publish it when we have full datasets of at least two monkeys available. For this paper we have prioritized our tracing experiments, highlighting the connections of the dentate nucleus with attention related areas in brainstem and cortex in both monkeys, following perfusion.

- Perhaps the large differences in performance between the two subjects can be used as a way to interpret the neural data's relationship to behavior, as it provided a source of variance. This is what we would hypothesize if we believed that this area of the brain is playing a significant role in the task. If one animal learns much more poorly, and this region of the brain is important for that behavior, then shouldn't there be clear, interpretable differences in the neural data? 

We thank the Reviewer for this comment. We have added a new Supplementary Figure 2, in which we present the data for both monkeys separately in the revised manuscript. Comparing the two datasets however, we see more commonalities related to the significant learning in both monkeys than differences that might be related to their different levels of learning. We have therefore decided to show the different datasets transparently in the new Supplementary Figure 2, but to stay on the conservative side in our interpretations.

- How should we look for these differences? A number of recent papers in mice have uncovered a large body of data showing that during the deliberation period, when the animal is interpreting a sensory stimulus (often using the whisker system), there is ramping activity in a principal component space among neurons that contribute to the decision. This ramping activity is present (in the PCA space) in the motor areas of the cortex, as well as in the medial and lateral cerebellar nuclei. Perhaps a similar computational approach would benefit the current manuscript. 

We also appreciate this point. We have done the principal component analysis accordingly, and we indeed do find the ramping activity in several components of the dentate activity of both monkeys (Mi and Mo). We have now added a new Supplementary Figure 3 with the first three components of both correct and incorrect trials for Mi and Mo, highlighting their potential contribution.

- What is the hypothesis that is being tested? That is, what do you think might be the function of this region of the cerebellum in this task? It seems to me that we are not entirely in the dark, as previous literature on mice decision-making tasks has produced a reasonable framework: the deliberation period coincides with ramping activity in many regions of the frontal lobe and the cerebellum. Indeed, the ramp in the cerebellum appears to be a necessary condition for the ramp to be present in the frontal lobe. Thus, we should see such ramping activity in this task in the dentate. When the monkey makes the wrong choice, the ramp should predict it. If you don't see the ramping activity, then it is possible that the hypothesis is wrong, or that you are not recording from the right place. 

It is indeed one of our specific hypotheses that the dentate cells can be direction-selective for the preparing cognitive component and/or sensorimotor response. We provide evidence that this hypothesis may be correct when we analyze the regular time response curves (see Figure 2 and the new Supplementary Figure 2 where the data of both monkeys are now presented separately). Moreover, we have now verified this by analysing the ramping curves of PCA space (new Supplementary Figure 3) and firing frequency of DN neurons that modulated upon presentation of the C-stimulus (new Supplementary Figure 4). These figures and findings are now referred to in the main text.

- As this is a difficult task that depends on the ability of the animals to understand the meaning of the cues, it is quite concerning that one of the monkeys performed poorly, particularly in the early sessions. Notably, the disparity between the two subjects is rather large: one monkey at the start of the recordings achieved a performance that was much better than the second monkey did at the end of the recording sessions. You highlighted the differences in performance in Figure 1D and mentioned that you started recording once the animals reached 60% performance. However, this did not make sense to me as the performance of Mi even after the final day of recording did not reach the performance of Mo on the first day of recording. Thus, in contrast to Mo, Mi appeared to be not ready for the task when the recording began.

We understand this point. However, please note that the learning performance of the monkeys concerned retraining sessions after they had had several weeks of vacation. So, even though it is correct that one of the two monkeys had a very good consolidation and started already at a relatively high level on the first retraining session, the other one also started and ended at a level above chance level (the y-axis starts at 0.5). We now highlight this point better in the Results section.

- One objective of having two monkeys is to illustrate that what is true in one animal is also true in the other. In some figures, you show that the neural data are significantly different, while in others you combine them into one. Thus, are you confident that the neural data across the animals should be combined, as you have done in Figure 2? Perhaps you can use the large differences in performance as a source of variance to find meaning in the neural data. 

This is a valid question; as highlighted above, we have now addressed this point in the new Supplementary Figure 2, where the data for both monkeys are presented separately. Given the sample sizes and level of variances, it is in general difficult to draw conclusions about the potential differences and contributions, but the data are sufficiently transparent to observe common trends. With regard to linking differences in the neural data to the differences in performance level, please also consider Figure 4, the new Supplementary Figure 3 (with the ramping PCA component) and new Supplementary Figure 4 (with the additional analysis of the ramping activity of DN neurons that modulated upon presentation of the C-stimulus), which suggests that the ramping stage of Mo starts before that of Mi. This difference highlights the possibility that injecting accelerations of the simple spike modulations of Purkinje cells in the cerebellar hemispheres into the complex of cerebellar nuclei may be instrumental in improving the performance of responses to covert attention, akin to what has been shown for the impact of Purkinje cells of the vestibulocerebellum on eye movement responses to vestibular stimulation (De Zeeuw et al. 1995, J Neurophysiol). This possibility is now also raised in the Discussion.

- How do we know that these neurons, or even this region of the brain, contribute to this task? When a new task is introduced, the contributions of the region of the brain that is being studied are usually established via some form of manipulation. This question is particularly relevant here because the two subjects differed markedly in their performance, yet in Figure 3 you find that a similar percentage of neurons are responding to the various elements of the task.

We appreciate this question. As highlighted above, we are refraining from showing our muscimol manipulation (3 ml of 5 μg/ml muscimol, Sigma Aldrich), as it only concerns 1 successful dataset and 1 control experiment. We hope to replicate this reversible lesion experiment in the future and publish it when we have full new datasets of at least two monkeys available. As explained above, for this paper we have sacrificed both monkeys following a timed perfusion, so as to have similar survival times for the transport of the neuro-anatomical tracer involved.  

- Behavior in both animals was better when the gap direction was up/down vs. left/right. Is this difference in behavior encoded during the time that the animal is making a decision? Are the dentate neurons better at differentiating the direction of the cue when the gap direction is up/right vs. left/right? 

These data have now been included in the new Supplementary Figure 2; we did not observe any significant differences in this respect.

Reviewer #2:

- The authors trained monkeys to discriminate peripheral visual cues and associate them with planning future saccades of an indicated direction. At the same time, the authors recorded single-unit neural activity in the cerebellar dentate nucleus. They demonstrated that substantial fractions of DN cells exhibited sustained modulation of spike rates spanning task epochs and carrying information about stimulus, response, and trial outcome. Finally, tracer injections demonstrated this region of the DN projects to a large number of targets including several known to interconnect the visual attention network. The data compellingly demonstrate the authors' central claims, and the analyses are well-suited to support the conclusions. Importantly, the study demonstrates that DN cells convey many motor and nonmotor variables related to task execution, event sequencing, visual attention, and arguably decision-making/working memory. 

We thank the Reviewer for this positive and constructive feedback.

- The study is solid and I do not have major concerns, but only points for possible improvement. 

We thank the Reviewer for this positive feedback.

- A key feature of this data is the extended changes/ramps in DN output across epochs (Figure 2). Crudely, this presents a challenge for the view that DN output mainly drives motor effectors, as the saccade itself lasts only a tiny fraction of the overall task. Some discussion of this dichotomy in thinking about the function(s) of the cerebellum, vis a vis the multifarious DN targets the authors demonstrate here, etc., would be helpful. 

We agree with the Reviewer and we have expanded our Discussion on this point, also now highlighting the outcome of the new PCA analysis recommended by Reviewer 1 (see the new Supplementary figure Figure 3).

- A high-level suggestion on the data: the presentation of the data focuses (sensibly) on the representation of the stimulus and response epochs (Figures 2-3). Yet, the authors then show that from decoding, it is, in fact, a trial outcome that is best represented in the population (Figure 4). While there is nothing 'wrong' with this, it reads slightly incongruously, and the reader does a bit of a "double take" back to the previous figures to see if they missed examples of the trial-outcome signals, but the previous presentations only show correct trials. Consider adding somewhere in the first 3 main figures some neural data showing comparisons with incorrect trials. This way, the reader develops prior expectations for the outcome decoding result and frame of reference for interpreting it. On a related note, the text contains an earlier introduction of this issue (p24 last sentence) and p25 paragraph 1 cites Figure 3D and 3E for signals "related to the absence of reward" - but the caption says this includes only correct trials? 

We thank the Reviewer for bringing up these points. We have addressed the textual suggestions. Moreover, we have done the PCA analysis suggested by Reviewer 1 for both the correct and incorrect trials (see Supplementary material).

- P29: The discrepancy in retrograde labeling between monkeys (2 orders of magnitude): I realize the authors can't really do anything about this, but the difference is large enough to warrant concerns in the interpretation (how did the tracer spread over the drastically larger area? Isotropically? Could it cross more "hard boundaries" and incorporate qualitatively different inputs/outputs?). A small discussion of possible caveats in interpreting the outcomes would be helpful. 

We fully agree with this comment. As highlighted in the text, in both monkeys we first identified the optimal points for injection in the dentate nucleus electrophysiologically and we used the same pump with the same settings to carry out the injections, but even so the differences are substantial. We suspect that the larger injection might have been caused by an air bubble trapped in the syringe or a deviation in the stock solution, but we can never be sure of that. We have added a potential explanation for the caveat that might have played a role.

- And a list of quick points: 

We have addressed all points listed below; we want to thank the Reviewer for bringing them up.

P3 paragraph 2 needs comma "in daily life,". 

P4 paragraph 2 "C-gap" terminology not previously defined. 

P4 paragraph 2 "animals employed different behavioral strategies". Grammatically, you should probably say "each animal employed a different behavioral strategy," but also scientifically the paragraph doesn't connect this claim to anything about the DN (whereas, e.g., the abstract does make this connection clear). 

P5 paragraph 1 "theca" should be "the". 

P6 paragraph 1 problem with ignashenkova citation insert. 

P10 paragraph 1 I think the spike rate "difference between highest and lowest" is not exactly the same as "variance," you might want to change the terminology. 

P10 paragraph 1 should probably say "To determine if a cell preferentially modulated". 

P10 paragraph 1 last sentence the last clause could be clearer. 

P17 paragraph 2 should be something like "as well as those by Carpenter and..."? 

P20 caption: consider "...directionality in the task: only one C-stim...". 

P20 caption: consider "to the left and right in the [L/R] task...to the top/bottom in the [U/D] task". 

Fig1E and S1 - is there a physical meaning of the "weight" unit, and if none, can this be transformed into a more meaningful unit? 

P21 paragraph 1 consider "activity was recorded for 304 DN neurons...". 

P21 paragraph 1 "correlations with the temporal windows" it's not clear how activity can "correlate" with a time window, consider rephrasing (activity levels changed during these time epochs, depending on stimulus identity). 

P21 paragraph 1 should be "by comparing the number of spikes in a bin...". 

P22 paragraph 2 "when we aligned the neurons to the time of maximum change" needs clarification. The maximum change of what? And per neuron? Across the population? 

P22 paragraph 2 "than that of the facilitating" should be "than did the facilitating units". 

P24 paragraph 1 needs a comma and rewording "Within each direction, trials are sorted by the time of saccade onset". 

P24 paragraph 1 should probably say "Same as in G, but for suppressed cells". 

P24 paragraph 2 should say "more than one task event" not "events". 

P24 paragraph 2 needs a comma "To fully characterize the neural responses, we fitted". 

P25 paragraph 1 should probably say "we sampled from similar populations of DN". 

P34 paragraph 3 consider rephrasing the sentence that contains both "dissociation" and "dissociate". 

P37 last line: consider "coordination of cerebellum and cerebral cortex *in* higher order mental..."? 

P38 paragraph 1 citation needed for "kinematics of goal-directed hand actions of others"? 

P38 paragraph 1 commas probably not needed "map visual input, from high-level visual regions, onto..." 

References

- Herzfeld D.J., Kojima Y, Soetedjo R, Shadmehr R (2018) Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum. Nat Neurosci 21:736–743.

- van Es, D.M., van der Zwaag W., and Knapen T. (2019) Topographic Maps of Visual Space in the Human Cerebellum. Current Biol Volume 29, Issue 10p1689-1694.e3May 20.

- De Zeeuw CI, Wylie DR, Stahl JS, Simpson JI. (1995) Phase relations of Purkinje cells in the rabbit flocculus during compensatory eye movements. J Neurophysiol. Nov;74(5):2051-64. doi: 10.1152/jn.1995.74.5.2051.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The pituitary gonadotropins, FSH and LH, are critical regulators of reproduction. In mammals, synthesis and secretion of FSH and LH by gonadotrope cells are controlled by the hypothalamic peptide, GnRH. As FSH and LH are made in the same cells in mammals, variation in the nature of GnRH secretion is thought to contribute to the differential regulation of the two hormones. In contrast, in fish, FSH and LH are produced in distinct gonadotrope populations and may be less (or differently) dependent on GnRH than in mammals. In the present manuscript, the authors endeavored to determine whether FSH may be independently controlled by a distinct peptide, cholecystokinin (CCK), in zebrafish.

Strengths:

The authors demonstrated that the CCK receptor is enriched in FSH-producing relative to LH-producing gonadotropes, and that genetic deletion of the receptor leads to dramatic decreases in gonadotropin production and gonadal development in zebrafish. Also, using innovative in vivo and ex vivo calcium imaging approaches, they show that LH- and FSH-producing gonadotropes preferentially respond to GnRH and CCK, respectively. Exogenous CCK also preferentially stimulated FSH secretion ex vivo and in vivo.

Weaknesses:

The concept that there may be a distinct FSH-releasing hormone (FSHRH) has been debated for decades. As the authors suggest that CCK is the long-sought FSHRH (at least in fish), they must provide data that convincingly leads to such a conclusion. In my estimation, they have not yet met this burden. In particular, they show that CCK is sufficient to activate FSH-producing cells, but have not yet demonstrated its necessity. Their one attempt to do so was using fish in which they inactivated the CCK receptor using CRISPR-Cas9. While this manipulation led to a reduction in FSH, LH was affected to a similar extent. As a result, they have not shown that CCK is a selective regulator of FSH.

Our conclusion regarding the necessity of CCK signaling for FSH secretion is based on the following evidence:

(1) CCK-like receptors are expressed in the pituitary gland predominantly on FSH cells.

(2) Application of CCK to pituitaries elicits FSH cell activation and to a much lesser degree activation of LH cells.  (calcium imaging assays)

(3) Application of CCK to pituitaries and by injections in-vivo significantly increased only FSH release.

(4) Mutating the FSH-specific CCK receptor in a different species of fish (medaka) also causes a complete shutdown of FSH production and phenocopies a fsh-mutant phenotype (Uehara, Nishiike et al. 2023).

Taken together, we believe that this data strongly supports the conclusion that CCK is necessary for FSH production and release from the fish pituitary. Admittedly, the overlapping effects of CCK on both FSH and LH cells in zebrafish (evident in both our calcium imaging experiments and especially in the KO phenotype) complicates the interpretation of the phenotype. We speculate that the effect of CCK on LH cells in zebrafish can be caused either by paracrine signaling within the gland or by the effects of CCK on GnRH neurons that were shown to express CCK receptors .

In the current version, we emphasize that CCK also induces LH secretion. Although it does not affect LH to the same extent as FSH, an overlap does exist. This is mentioned in the abstract and discussion.

Moreover, they do not yet demonstrate that the effects observed reflect the loss of the receptor's function in gonadotropes, as opposed to other cell types.

Although there is evidence for the expression of CCK receptor in other tissues, we do show a direct decrease of FSH and LH expression in the gonadotrophs of the pituitary of the mutant fish; taken together with its significant expression in FSH cells compared to the rest of the cells of the pituitary in the cell specific transcriptomic, it is the most reasonable explanation for the mutant phenotype.

Unfortunately, unlike in mice, technologies for conditional knockout of genes in specific cell types are not yet available for our model and cell types. Additional tissue distribution of the three receptors types of CCK was added in supplementary figure 1, from this tissue distribution it can be appreciated how in the pituitary only CCKBRA (our identified CCK receptor) is expressed, while in other tissues it is either not expressed or expressed with the additional CCK receptors that can compensate its activity.

It also is not clear whether the phenotypes of the fish reflect perturbations in pituitary development vs. a loss of CCK receptor function in the pituitary later in life. Ideally, the authors would attempt to block CCK signaling in adult fish that develop normally. For example, if CCK receptor antagonists are available, they could be used to treat fish and see whether and how this affects FSH vs. LH secretion.

While the observed gonadal phenotype of the KO (sex inversed fish) should have a developmental origin since it requires a long time to manifest, the effect of the KO on FSH and LH cells is probably more acute. Unfortunately a specific antagonist that affect only CCKRBA and not the other CCK receptors wasn’t identified yet.

In the Discussion, the authors suggest that CCK, as a satiety factor, may provide a link between metabolism and reproduction. This is an interesting idea, but it is not supported by the data presented. That is, none of the results shown link metabolic state to CCK regulation of FSH and fertility. Absent such data, the lengthy Discussion of the link is speculative and not fully merited.

In the revised manuscript, we provided data to link cck with metabolic status in supplementary figure 1 and modified the discussion to tone down the link between metabolic status to and reproductive state.

Also in the Discussion, the authors argue that "CCK directly controls FSH cells by innervating the pituitary gland and binding to specific receptors that are particularly abundant in FSH gonadotrophs." However, their imaging does not demonstrate innervation of FSH cells by CCK terminals (e.g., at the EM level).

Innervation of the fish pituitary does not imply a synaptic-like connection between axon terminals and endocrine cells. In fact, such connections are extremely rare, and their functionality is unclear. Instead, the mode of regulation between hypothalamic terminals and endocrine cells in the fish pituitary is more similar to "volume transmission" in the CNS, i.e. peptides are released into the tissue and carried to their endocrine cell targets by the circulation or via diffusion. A short explanation was added in lines 395-398 in the discussion

Moreover, they have not demonstrated the binding of CCK to these cells. Indeed, no CCK receptor protein data are shown.

Our revised manuscript  includes detailed experiments showing the activation of the receptor by its homologous ligand, supplementary Figure 1 includes a transactivation  assay of CCK to its receptor and the effect of the different mutants on the activation of the receptor. Unfortunately, no antibody is available against this fish specific receptor (one of the caveats of working with fish models); therefore, we cannot present receptor protein data.

The calcium responses of FSH cells to exogenous CCK certainly suggest the presence of functional CCK receptors therein; but, the nature of the preparations (with all pituitary cell types present) does not demonstrate that CCK is acting directly in these cells.

We agree with the reviewer that there are some disadvantages in choosing to work with a whole-tissue preparation. However, we believe that the advantages of working in a more physiological context far outweigh the drawbacks as it reflects the natural dynamics more precisely. Since our transcriptome data, as well as our ISH staining, show that the CCK receptor is exclusively expressed in FSH cells, it is improbable that the observed calcium response is mediated via a different pituitary cell type.

Indeed, the asynchrony in responses of individual FSH cells to CCK (Figure 4) suggests that not all cells may be activated in the same way. Contrast the response of LH cells to GnRH, where the onset of calcium signaling is similar across cells (Figure 3).

The difference between the synchronization levels of LH and FSH cells activity stems from the gap-junction mediated coupling between LH cells that does not exist between FSH cells(Golan, Martin et al. 2016). Therefore, the onset of calcium response in FSH cells is dependent on the irregular diffusion rate of the peptide within the preparation, whereas the tight homotypic coupling between LH cells generates a strong and synchronized calcium rise that propagates quickly throughout the entire population

The differences in connectivity between LH and FSH cells is mentioned in lines 194-195

Finally, as the authors note in the Discussion, the data presented do not enable them to conclude that the endogenous CCK regulating FSH (assuming it does) is from the brain as opposed to other sources (e.g., the gut).