Se comparte la postura de Alan Kay ya que los objetos pueden colaborar entre a través de los mensajes, lo que facilita la modularidad, el mantenimiento y la flexibilidad del sistema.

Los mensajes retrieve contents (recuperar contenido), collect (recolectar), asString (como secuencia) son la interpretación de lo que deseamos que el objeto realice en código.

Con base en el video Object Oriented Programming is not what I thought, la comunicación genera interacción entre los objetos, lo cual le permite al programador contar con los mecanismos necesarios para resolver problemas del mundo real a través de un paradigma que es moldeable, y que de alguna manera, genera relación con otros objetos.

Haciendo un comparativo con lo expuesto por Anjana Vakil en el video de los paradigmas, los equipos de cómputo pueden operar como objetos que tienen unas propiedades (similar a la POO), lo cual les permite ser interactuar, ser moldeables y escalables.

Con base en el video Object Oriented Programming is not what I thought la POO opera de similar manera a la definición de computación, toda vez que existe una representación de relaciones y comunicación de los objetos/computadores a través de protocolos de mensajería para conformar grandes redes.

This reminds me of a text I read for a Theory class last semester. It talks about how the personal is political. The two are interlocked and intersected. And as I mentioned in my earlier annotation, I do think education is very personal, which in turn makes it political

Looking at this sentence made me wonder if anything is politically neutral. When beliefs and politics have become so intertwined, there is no separation between politics and every other aspect of society. Being more open-minded and preferring change is linked with being liberal while being more comfortable with the status quo or disliking change is associated with being conservative. Also, the Department of Education is an extension of the government which is by definition, entangled with politics.

This should be y, not z

The text explains that writing a research paper is a skill that involves intelligently using quotations and paraphrases to support an argument. Research is not only about gathering information, but also using it effectively to make a point. Another key point is the difference between "rules" and "guidelines." Style guides like MLA or APA impose strict formatting rules, but also offer general writing tips that can vary based on suggestions from an instructor or classmates. This shows that writing a research paper requires both strict adherence to rules and clear, well-structured ideas.

modular visibility as a strategy of refusal and care is one of the best post-colonial strategies that has been offered so far in this course.

i love this type of mapping as a way to push back against GIS mapping

I wonder how this dual role as a grieving relative and researcher impacted the ethical frameworks guiding the memory project? Were there tensions between emotional proximity and scholarly detachment?

is this R9?

Read this paper to figure out why 95% cutoff is called "Principled"

This tool is good for taxonomic assignment of low abundance organisms from metagenome sequencing data. - low abundance makes it harder to assemble genomes, which is the standard way to assign taxonomy confidently?

Key innovation

key innovation in sylph is a statistical model based on zero-inflated Poisson statistics to debias containment ANI under low coverage

How does this differ from kraken2? - Kraken 2 uses short exact matches to a database (causes false positives) which are controlled by abundance cutoffs and confidence thresholds

Differ from sourmash? - k-mer sketching approaches have ANI estimation bias for low-abundance genomes due to incomplete read coverage - Will need arbitrary thresholds to work for low abundance micro-organisms

I guess universal marker gene methods use shortgun/metagenomic sequencing data so is not comparable to amplicon sequencing of 16S?

such methods =

Species-specific or universal marker gene methods (hereafter denoted as ‘marker gene methods’

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Reply to the reviewers

The authors first use a Bio-ID approach to search for interactors of the basket proteins TPR and NUP153, identifying proteins involved in various nuclear process, including many splicing components, and confirm some of these interactions using IP and PLA assays. PLA experiments further suggest that these interactions occur primary at or close to the nuclear periphery. Moreover, inhibiting splicing, but not transcription, reduced these interactions. The authors then investigated the role of NUP153 in loading of the splicing machinery and found a lower association of the NUP98/SF3A1 but not AQR interaction (measured through PLA). Furthermore, DamID experiments identified NUP153 bound genes proximal to LAD domains that are actively transcribed, contain overall longer introns with low GC-content, and fall within a group of genes located at the outermost shell of the nucleus (when compared to previously published LaminI ID /PGseq data). Lastly, they interrogate whether depletion of NUP153 results in a splicing defect for NUP153 bound genes.

The authors identify many proteins in their BioID interaction screen, however, only a single nucleoporin (Nup35, an inner ring protein). Previous BioID studies have identified NUP153 in BioID experiments including proteins of the Y-complex (PMID: 24927568 and others).

The BioID list summarises interactions of proteins present across five datasets and among two cell lines, HEK293T and Jurkat T cells. As the reviewer pointed out, these stringent criteria excluded proteins originally present in the BioID datasets. Indeed, the original datasets across the cell lines include a wide range of nucleoporins Nup37, Nup43, Nup53, Nup85, Nup107, Nup188, and Nup205. Apart from this, several other proteins were consistently found on the BioID of the basket nucleopore that had been previously found in the literature, namely transcription-related and export-related proteins, as the reviewer can depict on Fig 1 C.

To ensure that the BioID experiments indeed probe for interactions of NUP152 and TPR at the NPC, the authors should include control experiments that show that their NUP153 and TPR-BirA fusions primarily localize to the NPC. If a significant fraction is not NPC bound, this has to be taken into account when interpreting/discussing their data.

Before conducting the DamID experiment, we validated the peripheral localization of the constructs used here. We provide here the images showing the distribution of the two nucleoporins

Figure R1: Immunofluorescent images obtained for Nup153 and TPR in Jurkat cells prior to BioID experiment.

The authors describe DDX39b/UAP56 as an early splicing factor; DDX39b/UAP56 main role however seems to be in mRNA export and mRNP compaction. The authors might want to include this in the interpretation of their data.

The reviewer is right; the role of DDX39b/UAP56 goes beyond pre-mRNA splicing. This is indeed involved in posttranscriptional maturation and export from the nucleus to the cytoplasm of cellular RNAs. Originally identified as a helicase involved in pre-mRNA splicing, UAP56 has been shown to facilitate the formation of the A complex during spliceosome assembly. This is the reason why we included it in our study. Additionally, DDX39b/UAP56 has been found to be critical for interactions between components of the exon junction and transcription and export complexes to promote the loading of export receptors, while more recently has also been identified as a DNA:RNA helicase involved in the resolution of R-loops (PMID:32439635). At present, we indeed cannot distinguish between multiple functions of this protein at the NPC, and this is now acknowledged in our manuscript.

• Concerning PLA experiment controls, the authors perform TPR-PML as a negative control, however, no negative control for NUP153 is shown (Figure 2). Such a control should be added to allow evaluating the specificity of NUP153 PLA interactions, and/or discussed why this was not done. We would like to clarify better how we selected the antibodies for the control PLA experiments. In PLA, antibodies from different species have to be used. Nup153 is an anti-mouse antibody, and the control we used, PML, is also an anti-mouse; therefore, the two antibodies cannot be used in the PLA experiment. The controls used (mouse) were conjugated with rabbit antibodies, either TPR (PML) or AQR (B23). Both TPR:PML and AQR:B23 showed insignificant PLA signals. Therefore, we can confidently conclude that the PLA spots seen for the NPC/splicing proteins are of measurable quantities. Moreover, the conclusion that there’s a pool of splicing machinery associated with the NPC is sustained on several pieces of evidence accumulated through other experiments, not only PLA. We have supporting evidence from super-resolution microscopy, coIPs as well as the referred PLA.

Figure R2: Control PLA assay with anti-AQR (rabbit) and Nucleophosmin (B23)(mouse) antibodies.

• Quantification of the distance of PLA-NUP153/TPR interactions show interactions mainly close to the nuclear periphery. The imaging data shown in Figure 2b indeed shows that TPR/NUP153 interactions are exclusively at the nuclear periphery, whereas NUP153/splicing factor interactions are sometimes at the edge of the DAPI signal, but mostly somehow internalized (Figure 2B, S2b). Quantification (Figure 2d) shows these distributions to be very similar, likely due to the way the quantification was performed / the bin size of plotting the relative distance of a spot to the nuclear periphery was chosen. Looking at the scale bar/nuclear size and the position of the PLA spots for the NUP153/splicing factors, it appears that spots are often hundreds of nanometers away from the periphery. As the nuclear basket is thought to reach only about 100nm onto the nuclear interior, the conclusion by the authors that these interactions occur at the NPC would not be consistent with the data. The authors should better incorporate this in their interpretation of the data. Nup153/TPR are peripherally located at the most outer shells (0, 40% of signal and 1, 60% of signal). Consistently, based on figure 2d, Nup153:DDX39b is similarly distributed (0, 40% of signal and 1, 60% of signal) and the vast majority of the Nup153:AQR and Nup153:SF31A1 signal is also present in these two shells (20% and 40%). Although our super-resolution microscopy excludes the presence of internal Nup153 staining we cannot exclude that PLA potentially increases the signal of a possible internal Nup153/splicing interaction due to the rolling circle amplification reaction. However, as referred above this is not where primarily our interaction is occurring.

• The conclusion ' Nup153 aids the loading of splicing machinery' is not sufficiently supported by the data. The authors observed a reduction in PLA signal for the NUP98-AQR interaction, but not the NUP98-SF3A1 (Figure 3g). Their conclusion has to reflect this discrepancy in their data. Moreover, the studies focus is to determine the role of NUP153/TPR in recruiting the splicing machinery to the NPC. As in the experiments the authors interrogate the interaction of only NUP98, who has to a large extend splicing factor interactions within the nuclear interior and not at the periphery, the relevance of the experiments in Figure 3 towards the main focus of the paper is unclear. The reviewer is right to point out that the study focuses on determining the role of Nup153/TPR in recruiting the splicing machinery to the NPC. As TPR is docked to the NPC through Nup153 (PMID: 12802065; 39127037) we investigated Nup98 and performed internal controls to show that 1) Nup98 wasn’t disrupted by shNup153 (Fig below) and technically 2) Nup98 was used in the shNup153 studies because of the availability of a reliable mouse antibody that could be coupled with the various rabbit antibodies used previously for SF3A1, DDX39, XAB2, and AQR in PLA experiments' for which mouse counterparts do not exist and would therefore hinder the continuation of the study as explained above. For TPR only a reliable rabbit antibody exists that works in our hands and therefore we wouldn’t have been able to perform the PLA experiments shown here

Figure R3: Nup98 staining in wild type and shNup153 depleted Jurkat cells. In (a) co-immunofluorescence between AQR and Nup98 showing predominant positioning of Nup98 in the nuclear periphery (at the NPC). (b) Nup98 staining at the periphery persists also in shNup153 depleted cells, indicating that this Nup can be used as an NPC marker in Nup153 depleted conditions.

When investigating the effect of NUP153 depletion on splicing, the authors observe a splicing phenotype for multiple NUP153 genes (Figure 5). The authors however show only a single negative control gene (CBX5). It would significantly strengthen their argument if the authors would investigate splicing defects of periphery located noneNUP153 bound genes as well as for genes located in the nuclear interior to better understand whether this splicing phenotype is indeed specific for NUP153 genes (at the nuclear periphery/NPC).

We agree with the reviewer that expanding our observations to other peripherally located genes would be interesting; however, most of the other known peripheral genes are LAD-associated and mostly not expressed. While it was not our intention to claim that splicing at the periphery is specific only for Nup153-bound genes, we had obviously focused on Nup153-bound genes to understand the dynamics between Nup153/splicing machinery interactions. As stated above, other peripherally located genes within LADs are repressed.

It Is out of the scope of this study to understand other relevant splicing hubs, as the reviewer knows splicing can occur throughout the nucleus at different sites (outside speckles). However, we do understand the reviewer's point of view, and to include more controls as requested by this and other reviewers, we have designed primers for additional non-Nup153 bound genes, and these additional experiments will be included in the manuscript.

Figure R4: Preliminary data showing splicing of other Non-Nup153 Bound genes upon shNup153

The authors state in the text describing the SABER-FISH experiments in Figure 5f that 'were able to visualize the presence of a site of transcription where accumulation of these probes was close to the periphery for all except for GSTK1, which showed a wider nuclear distribution, similar to CBX5 control region not bound by Nup153'. However, their statement is not supported by the images shown in Fig 5f, which show TS in control cells in the nuclear interior. Also, a single cell but no quantification is shown. Moreover, what distance from the periphery is considered as close to the periphery is not defined (see also earlier comment on the question what should be considered a periphery and/or NPC association).

Measurement of the distance of FISH signals to the nuclear periphery for each probe (ie transript) performed in n>100 cells were represented graphically in Fig. 5f. We then compared total number of signals for each probe, obtained in shCtrl and shNup153 cells, and represented them graphically in Fig 5g; representative images shown in Fig 5g are those that were measured in Fig5g and represent the accumulation of the signal in cells upon shNup153 (not necessarily all at the nuclear periphery).

We hope that this clarifies better what is represented.

Limitation of the study does not discuss the limitations of the study but rather reads like the extension of the discussion. This section should be rewritten.

We will take into consideration this comment and will expand the section in the revised manuscript

Minor comments:

Western in Figure 3c does not represent well the quantification in 3e.

Figure S3 is mislabelled (pannel h is panel g).

__Reviewer #1 (Significance (Required)): __

Reviewer #1 (Significance (Required)):

The manuscript interrogates an important question related to the role of the NPC in gene regulation, in particular how interaction of genes/pre-mRNAs with the NPC might stimulate expression of specific genes/mRNAs. Stimulating splicing would be one way that could contribute to efficient gene expression, and this is the question the authors address in this manuscript. This study is therefore important and relevant to a wide audience. However, as outlined in the section above, the conclusions drawn by the authors do not always reflect the experimental data, and it is therefore unclear whether the overall conclusion as stated in the title of the manuscript is valid. Moreover, conceptually, if intron containing genes are transcribed at or near nuclear pores, and splicing often occurs co-transcriptionally, it is to be expected to find splicing components close to nuclear pores. While it is relevant to show that this actually happens, and this is, at least in part, done by the authors. However, the experiments presented do not show that the splicing machinery actually actively docks to the NPC and is not just passively recruited close to NPCs because nascent pre-mRNAs are spliced where they are transcribed (the authors state in their title that the NUP153 docks the splicing machinery at the NPC). Showing this require identifying direct interactions between spliceosome components with NUP153/nuclear basket components to stimulate splicing at the NPC. If this would indeed be the case, these findings would describe a novel mechanistic step to stimulate efficient splicing and subsequently export of a selected set of NPC-associated genes. This would open other questions such as how to achieve specificity for only some pre-mRNAs/introns. While addressing this question is likely beyond the scope of this manuscript, the question whether the process described here is an active or passive process should be incorporated in the interpretation of the data.

We are grateful to the reviewer for highlighting the importance and relevance of our work for a broad audience. We now provide additional experimental evidence that will hopefully aid in substantiating our overall conclusions, as suggested by the reviewer.

__ Reviewer 2:__

Summary:

In this manuscript, using a combination of proximity labelling, immunoprecipitations and imaging, the authors report a physical interaction between splicing factors (SFs) and the nuclear basket of nuclear pore complexes (i.e. NUP153). Using DamID, they further identify a set of NUP153-bound genes characterized by long, GC-poor introns. Finally, based on molecular analyses for a set of candidate loci, they report that inactivation of NUP153 triggers a (modest) reduction of intron splicing, which may specifically affect NUP153-bound genes.

Major comments:

• The BioID experiments (Fig. 1) lack proper controls. Proteins biotinylated by NUP-BirA fusions need to be compared with those modified upon expression of a control BirA protein, as has been done previously, especially when other NUPs were used as baits in BioID experiments (PMID: 24927568, to be cited). This control fusion should ideally be targeted to the same compartment (i.e. the nucleus or the nuclear side of the nuclear envelope). In our experimental setting, we have opted to use an unrelated protein tagged with BirA (Lck-BirA) rather than BirA-only control. The peripheral membrane proteins of the Src family kinase (SFK) Lck and its GPF tagged version (LckN18.GFP) localize predominantly at the plasma membrane (PMID: 29588370), whereas the GFP only (non-biothinylated) shows a broader nuclear distribution. All MS-detected proteins from the Lckn18-BirA and GFP negative control experiments were excluded. Moreover, we have analysed carefully the published data of nuclear transporter receptors binding to the NPC and the respective controls (BirA alone or the shuttling NLS_NES_Dendra with C or N terminal tags) (PMID: 29254951), and we did not find that any of these protein controls interact with the proteins of the splicing machinery.

• Here, the chosen controls are inappropriate as the authors are probing interactions between NPC proteins (NUP153/TPR) and proteins restricted to a different nuclear compartment (e.g., nucleophosmin in the nucleolus). We have used two different controls in our PLA experiments - initially, we used B23 for nucleolus stain and then PML protein, major component of PML NBs, as PML can be found scattered throughout the nucleus and sometimes even resides at the nuclear periphery. Both of these controls showed negligible amounts of PLA spots in all our experiments. We take the opportunity to clarify that in PLA, antibodies from different species have to be used. Nup153 is an anti-mouse antibody, and the control we used, PML is also an anti- mouse; therefore the two antibodies cannot be used in the PLA experiment. The controls used (mouse) were conjugated with rabbit antibodies, either TPR (PML) or AQR (B23). Both TPR:PML and AQR:B23 showed insignificant PLA signals. Therefore we can confidently conclude that the PLA spots seen for the NPC/splicing proteins are of measurable quantities.

• Would a control soluble, diffusible nucleoplasmic protein be detected in the vicinity of the NPC and sometimes colocalized with Nups? Precisely for this purpose we have used PML protein, that can be found both disperse in nucleoplasm as well as in PML NBs.

• In order to assess these possibilities, the authors should perform their immunoprecipitation on extracts treated with benzonase, thus abrogating DNA- and RNA-dependent interactions. We have performed this experiment assessing the binding of Nup153 with the components of the IBC and observed that Nup153 interaction with these splicing factors is DNA or RNA independent, with some factors being more affected than others (probably passively recruited by protein-protein interactions with their splicing counterparts).

__Figure R5: __RNA or DNA do not affect the interaction between Nup153 and splicing proteins. Lysates from HEK293T cells transfected with eGFP-Nup153 or eGFP were treated with RNase or DNase or left untreated prior to Co-IP for GFP-Nup153. The membrane was probed for GFP (confirmed successful transfection), AQR, SF3A1, XAB2, and DDX39B. The graph shows quantified bands of the bound fractions, normalized to the input and untreated control from one experiment.

NUP153 inactivation appears to have a modest effect on splicing (Fig. 5; S6), which is poorly characterized here. It is also unclear whether this effect is direct or caused by side consequences of the depletion of this nucleoporin (e.g., changes in nucleocytoplasmic exchanges or gene expression).

We have indeed asked if the presence of splicing components at the periphery could be a consequence of protein trafficking. To address whether nucleocytoplasmic exchange has a role in these associations, we have pharmacologically inhibited nuclear import by ivermectin (IVM). ​​IVM has been shown to block the importin-α/β-mediated nuclear import by directly interacting with karyopherin importin-α.(PMID: 30826604). HEK293T cells transfected with eGFPNup153 or eGFP alone were treated with IVM for 2hr (24hrs post transfection). As biochemical fractionation demonstrated (data not shown) there were slightly decreased protein levels of AQR, DDX39B and SF3A1 in the nuclear insoluble fraction. However, we barely observed any decrease in interactions between Nup153 and the splicing components we tested, indicating that the interaction with the spliceosomal components is not a consequence of nucleocytoplasmic exchange.

Figure R6: Association of splicing components with Nup153 is not only due to nuclear import. HEK293T cells transfected with eGFP-Nup153 or eGFP and treated with import inhibitor IVM or DMSO were analyzed with Co-IP for GFP-Nup153. The membrane was probed for GFP (confirmed successful transfection), AQR, SF3A1, XAB2 and DDX39 B. Quantified bands of the bound fractions were normalized to the input fraction and DMSO control and the results of two experiments were shown in the graph.

Related to the gene expression levels, we have not observed any significant changes in total expression levels of tested genes, probed with designed exonic primers (as indicated with blue arrows in Figure 5a). Additional control genes will be added as suggested by this and other reviewers.

• To confirm the specificity of the effects of NUP153 depletion on the splicing of NUP153-bound genes, the authors need to provide additional splicing measurements for several genes that are bound by NUP153 "in the nucleoplasm" (e.g. excluded from their analysis by the cutoff of proximity to LAD borders, line 192) and for other "non-NUP153" genes (beyond the unique control shown in Fig. S6a).

We acknowledge the comment of the reviewer that our work will benefit from additional controls. We are currently designing primers and probes to amplify additional regions, Nup153 bound and non-LAD proximal or non-Nup153 bound; Please also see the comment below.

- From the few examples provided, it is difficult to evaluate the type of splicing events affected by NUP153 inactivation. Are they uniquely intron retention events? The authors should analyze available RNA-seq data obtained from NUP153-depleted cells (PMID:32917881) to characterize the types of alternative splicing events that are impacted by NUP153.

In Aksenova et al, only 28 differentially expressed genes were detected during rapid degron Nup153 depletion (2h). With this small number of genes, it is highly unlikely we would be able to perform a statistically significant and detailed analysis. Importantly, the depletion was performed in colorectal adenocarcinoma cell line (DLD-1), whereas here we are reporting on T lymphocytes. Based on the analysis which we performed and explained below, there seem to be significant cell type specific differences in Nup153 associations, as already reported by others (PMID: 27807035; 32451376; 28919367).

Splicing dynamics and speckle localization propensity have been proposed to depend on the overall GC content and the overall average intron size by several studies (PMID: 39413186; 38720076; 35182478; 22832277; 35182477). Prompted by our observation that Nup153 genes have longer than average introns and lower than average GC content (Figure 4f and 4g), we analyzed the data from HeLa cells, where genes were classified into groups A, B and C based on their speckle localization and dynamics (PMID 39413186). We intersected our Nup153 genes with the list of ABC genes from HeLa cells and found that 43 out of our 461 protein-coding genes were represented among non-speckle enriched group C genes, with the lowest GC content and longest average intron length.

Figure R7: Nup153 genes comparison to the A_B_C genes from Wu J et al study. GC content and number of introns, used to classify the identified genes from HeLa cells are plotted on X and Y axis. A genes in Red, B in green or C in turquoise from HeLa cells were compared with Nup153 genes from Jurkat cells in the graph on the left. Nup153 genes are represented as triangles. A subgroup of Nup153 genes, classified as C group genes (long introns with high GC content and spliced away from speckles) are shown as turquoise triangles. Graph on the right shows total pool of Nup153 genes in violet (not identified in HeLa cells) and a subgroup of C Nup153 genes as turquoise. The list of Nup153 C genes is shown below.

query

entrezgene

name

symbol

ENSG00000168615

8754

ADAM metallopeptidase domain 9

ADAM9

ENSG00000139154

121536

AE binding protein 2

AEBP2

ENSG00000112249

10973

activating signal cointegrator 1 complex subunit 3

ASCC3

ENSG00000176788

10409

brain abundant membrane attached signal protein 1

BASP1

ENSG00000153956

781

calcium voltage-gated channel auxiliary subunit alpha2delta 1

CACNA2D1

ENSG00000153113

831

calpastatin

CAST

ENSG00000134371

79577

cell division cycle 73

CDC73

ENSG00000188517

84570

collagen type XXV alpha 1 chain

COL25A1

ENSG00000182158

64764

cAMP responsive element binding protein 3 like 2

CREB3L2

ENSG00000109861

1075

cathepsin C

CTSC

ENSG00000153904

23576

dimethylarginine dimethylaminohydrolase 1

DDAH1

ENSG00000139734

81624

diaphanous related formin 3

DIAPH3

ENSG00000102580

5611

DnaJ heat shock protein family (Hsp40) member C3

DNAJC3

ENSG00000173852

23333

dpy-19 like C-mannosyltransferase 1

DPY19L1

ENSG00000151914

667

dystonin

DST

ENSG00000165891

144455

E2F transcription factor 7

E2F7

ENSG00000138829

2201

fibrillin 2

FBN2

ENSG00000115414

2335

fibronectin 1

FN1

ENSG00000075420

64778

fibronectin type III domain containing 3B

FNDC3B

ENSG00000114861

27086

forkhead box P1

FOXP1

ENSG00000090615

2802

golgin A3

GOLGA3

ENSG00000196591

3066

histone deacetylase 2

HDAC2

ENSG00000071794

6596

helicase like transcription factor

HLTF

ENSG00000145012

4026

LIM domain containing preferred translocation partner in lipoma

LPP

ENSG00000065833

4199

malic enzyme 1

ME1

ENSG00000087053

8898

myotubularin related protein 2

MTMR2

ENSG00000145555

4651

myosin X

MYO10

ENSG00000061676

10787

NCK associated protein 1

NCKAP1

ENSG00000185630

5087

PBX homeobox 1

PBX1

ENSG00000113448

5144

phosphodiesterase 4D

PDE4D

ENSG00000163110

10611

PDZ and LIM domain 5

PDLIM5

ENSG00000070087

5217

profilin 2

PFN2

ENSG00000152952

5352

procollagen-lysine,2-oxoglutarate 5-dioxygenase 2

PLOD2

ENSG00000106772

158471

prune homolog 2 with BCH domain

PRUNE2

ENSG00000173482

5797

protein tyrosine phosphatase receptor type M

PTPRM

ENSG00000164292

22836

Rho related BTB domain containing 3

RHOBTB3

ENSG00000067900

6093

Rho associated coiled-coil containing protein kinase 1

ROCK1

ENSG00000112701

26054

SUMO specific peptidase 6

SENP6

ENSG00000154447

57630

SH3 domain containing ring finger 1

SH3RF1

ENSG00000187164

57698

shootin 1

SHTN1

ENSG00000198887

23137

structural maintenance of chromosomes 5

SMC5

ENSG00000116754

9295

serine and arginine rich splicing factor 11

SRSF11

ENSG00000152818

7402

utrophin

UTRN

Table R1: List of Nup153 genes that are characterized as C group of genes.

Only two Nup153 gene were found among A and B genes (Serine and arginine rich splicing factor 11 SRSF11 among A, and Phosphodiesterase 4D PDE4D among B genes). Despite the cell type specific expression and splicing patterns it is worth noting that we find Nup153 genes enriched among C group genes that are spliced out of speckles. We are currently probing the splicing of some of these genes, and these data will be added to the list of control genes.

Considering all these new observations related to the Nup153 splicing events and the general interest and relevance of our initial observations, a new dedicated study will have to be designed to tackle all these important questions that go beyond these current findings

Minor comments:

• Several studies have shown that the nuclear basket contributes to a splicing quality control process preventing the nuclear export of improperly spliced transcripts, both in yeast and mammalian cells (PMID:14718167, 19127978, 24452287, 25845599, 22253824, 22661231). These studies have to be mentioned and discussed here.

• Line 31: "movement of active genes towards the NPC would be favorable for their transcription and export ". Please rephrase: "...transcription and mRNA export".

• Line 163: "NUP153 plays a role in harboring splicing machinery". Please rephrase.

• Line 200-202: Fig. 4d and 4e (instead of S4d and S4e)

• Line 186 and beyond: All conclusions about NUP153-bound genes (e.g., "Majority of NUP153 bound genes are proximal to LADs and expressed") are not accurately phrased since the authors selected NUP153-bound genes with a cutoff of proximity to LAD borders. The conclusions are thus only valid for a subpopulation of NUP153-bound regions located in the vicinity of LADs.

• Line 292: "transport Nups less likely interact with splicing machinery". The term "transport Nup" is not correct. Does this mean "nuclear transport receptors"? Or "FG-Nups" (which interact with NTRs)?

All the comments will be addressed in the revised version of the manuscript

Reviewer #2 (Significance (Required):

It is increasingly recognized that NPCs are involved in a number of cellular processes beyond nucleo-cytoplasmic transport and, in particular, contribute to several genomic functions. In this context, the identification of a physical and functional interaction between NPCs and the splicing machinery could be of conceptual interest in the NPC field, and more generally, in cell and genome biology, although it needs to be (i) carefully controlled and validated in view of the strong limitations mentioned above, and (ii) discussed in line with the known links between the nuclear basket and splicing quality control (see minor comments). This coupling would be particularly relevant for genes that have been shown to be positioned at NPCs during transcriptional activation, in line with the "gene gating" model mentioned by the authors.

We greatly appreciate these insightful comments and suggestions, and the time and effort that this reviewer invested in critical reading of our manuscript. We will certainly take the points into account as we revise the manuscript. Specifically, we will carefully address the concerns related to the NPC-splicing interaction, ensuring that the experimental validation is robust and well-controlled, and we will further discuss the connection between the nuclear basket and splicing quality control in the context of our findings.

Once again, thank you for your thoughtful and constructive feedback.

Reviewer #3

The authors discovered that the splicing machinery and nuclear baskets are sometimes in close proximity using Nup153 as a representative for the nuclear basket. They characterize this interaction using several different methods and propose that NUP153 is required to assemble the splicing machinery on genes that are transcribed in the nuclear periphery, which would supporting the gene gating model.

The manuscript is well written and structured and the experiments are carefully conducted and analyzed.

We thank the reviewer for the appreciation of our work. We have addressed all the major points here and will amend the manuscript text according to the suggestions.

Reviewer #3 (Significance (Required)):

The impression that I get from this manuscript is that we are looking at rather rare events with a small effect size. A definitive proof that the splicing machinery really assembles in the vicinity of NPCs docked via NUP153 is lacking. To assist in the revision process I will raise some questions to discuss but also propose some additional experiments to substantiate the claims.

1. It is not clear what NUP153 really binds to and which domain is important. The experiments shown suggest proximity and indirect interactions (Co-IPs), but it is not clear whether NUP153 binds to DNA, RNA or a specific splicing factor. The bioID experiment might label splicing factors because they are cargos that pass through the pores during import, or it labels splicing factors that remain bound to spliced mRNPs during mRNP export. For example DDX39b, also called UAP56 is an important subunit of the TREX complex and involved the final packaging of mRNPs at the NPC. In my opinion, this protein is not a good choice. Also, the negative control that was used in the experiments is a potassium channel in the plasmamembrane, which can exclude that signal occurs by chance. But it would have been better to use a nuclear protein as control to exclude these possibilities.

In line with a rare event, the Co-IP signals are very weak and barely higher than the GFP control. They should be repeated in the presence of RNase to confirm that this interaction occurs on nascent RNA during splicing and not e.g. to recycle or reroute splicing factors or during import.

We acknowledge all the points, some of them already brought to our attention by other reviewers, that we tried to address throughout our response here, and we will also incorporate our answers in the revised manuscript. In particular, we have already provided some evidence related to the role of DNA, RNA, as shown above in Figure R5 (Response to R2). We have also addressed the effect of nuclear-cytosolic transport by using IVM and described these results in Figure R6, showing that splicing factors interact with Nup153 even when cytosolic transport is blocked with IVM. We have also commented on the use of controls and on the additional control analysis that we performed (also mentioned in more detail in response to R2)

Moreover, we are also further trying to understand the binding of Nup153 to the splicing components. Intron Binding Complex, recently shown to be crucial for the activation of the spliceosome due to the activity of its helicase AQR (PMID: 37165190) is one of the protein complexes that we found bound to the NPC basket. We are interested in different functions of this helicase and have created the previously described mutant that has been shown to be defective in splicing. We have probed the interaction of Nup153 to this mutant, which we also characterized for its splicing inefficiency, and observed that Nup153 interacts with the splicing competent AQR, whereas the interaction with the splicing mutant seems to be less efficientThis set of additional data strengthens the bulk of data present here, details of which remain still to be further elucidated in an additional follow-up study.

__Figure R8 __Lysates from HEK 293T cells, transiently transfected with eGFP-NUP153 and AQR-His-FLAG or AQRK829A-His-FLAG were probed in co-IP experiments. Western blot of the co-IP performed with Dynabeads for His-tagged proteins; input (IN), unbound (U) and bound (B) fractions are shown. Densitometric analysis of the co-IP where eGFP-NUP153 intensity was quantified, bound fractions were normalized to input samples, and the results are expressed relative to the AQR-His-FLAG control (n = 2). pENTER is a control empty plasmid.

I do not understand why a NUP would be required to recruit or tether splicing factors to peripheral genes. Usually, splicing factors hitchhike on transcribing polymerase II or they are delivered by nuclear speckles which could happen also at the periphery. The authors should co-stain with a nuclear speckle marker to exclude this possibility.

__Figure R9 __STED microscopy resolves the position of sc-35, marker of splicing speckles with respect to AQR. Jurkat T cells were stained with anti AQR AB (rabbit) and anti - sc35 antibody (mouse) to probe the positioning of splicing speckles with respect to the splicing helicase AQR.

This is a very interesting remark, which we have addressed through co-staining experiments. Since the Nup153 antibody we used is anti-mouse, like SC-35, we instead co-stained SC-35 with AQR, a representative splicing factor. Our results show that a substantial number of AQR spots are detected away from nuclear speckles and near the nuclear rim, suggesting that a subset of splicing factors localize independently of speckles. While we have not directly stained the nuclear periphery, this pattern is consistent with the idea that splicing factors can be recruited outside of speckle-mediated delivery.

To further confirm that NPC-associated splicing does not rely on nuclear speckles, we are open to performing additional co-staining between Nup153 and SON, another speckle marker in a followup study. Furthermore, emerging evidence supports off-speckle splicing, particularly for genes with long introns and low GC content (PMID: 39413186, 38720076, 35182478, 22832277, 35182477). Our additional analysis (Figure R7) demonstrates that genes associated with Nup153 share characteristics with known off-speckle spliced genes, suggesting that these genes might be processed outside of speckles due to their transcription and splicing kinetics

What would be the advantage to splice in the vicinity of the pore? Given that genes with long introns take a long time to be transcribed, splicing would block the pores for hours and would prevent other activities. It would also be possible that splicing does occur at the periphery but NUP153 picks the mRNPs up at a later stage.

We thank the reviewer for these insightful comments. We would like to add here that there are numerous pores, according to our own estimations around 800 pores in Jurkat cells, which implies that there is also huge heterogeneity of the NPC which is at present largely unexplored. In yeast, some pores are basketless and the assembly of the basket is transcription dependent (PMID: 36220102) - suggesting that there’s more than one pore population. Similarly, looking into the statuses of genes that have been associated with pore, both polycomb-repressed and transcriptionally active genes have been found at pores- again pointing to an heterogeneity. This is a very interesting (and large) question that we pose ourselves and to the NPC field but not something we can address straight away.

One major drawback of the story is that the authors use very long-term depletion of NUP153 via shRNAs that will definitely screw up the import of many nuclear proteins; and a splicing inhibitor that has broad effects on nuclear architecture. Degron lines of Nup153 exist and should be used to substantiate at least some of the conclusions. Alternatively, a NUP153 mutant without zinc finger or IDR could be used to prevent DNA binding or basket association.

The reviewer is right, we have for over 2 years tried tirelessly to use the degron Nup153 system established in DLD-1 cells by Dasso’s Lab. This has been unsuccessful. Jurkats are difficult to transfect and more sensitive than other cell lines and they died with our trials. We have therefore used the shNup153 which has been used in X and Y and shown to not interfere with nucleocytoplasmic trafficking.

However, we understand the reviewers point and since then have tried to use a Nup153 mutant construct, containaing Nup153 N-terminus with and without a zinc finger (McKay et al 2009.), kindly sent by the Ulman Lab. Unfortunately, in our hands, the construct has low levels of GFP:Nup153 expression, not comparable to the ones we used in our coIP experiments and that would make conclusions hard.

We are now planning the cloning of our GFP:Nup153 construct to produce such plasmids,

N-terminus with/without the Zinc finger and use it in coIP experiments to understand the importance of the Zn finger domain in the splicing interactions.

Specific comments: Abstract: suggesting that a fraction of splicing occurs at the NPC. speckle-distant splicing events, it should be nuclear speckles, term not explained Line 20: Super enhancers not introduced

Line 39: Splicing and the different spliceosomal subcomplexes needs more explanation and introduction to understand the selection of proteins that were used in the study. Line 65: Choice of controls. LckN18 The genes should be written once in full and their choice should be explained better.

Line 123: The authors state: 'However, we detected a lower number of interacting spots between Nup98/DDX39b than with its Nup153 counterpart; a similar trend is followed between Nup98 and SF3A1 (Fig. S2g), suggesting that the interaction between splicing proteins and Nup98 might be further apart within the NPC structure.

A more likely explanation is that both proteins are shed from the mRNP at the basket as they are not shuttling with the mRNA and should not enter the pore.

Line 131: Mention right at the beginning of the sentence which splicing proteins were imaged here.

Line 141: Nuclear speckles are still not properly introduced. Why is SF3A1 not expected to be in nuclear speckles? It Should be co-stained for nuclear speckle markers. Lane 149: PlaB drastically changes the nuclear architecture. The reduced interactions can have also different reasons. The authors should image the distribution of the investigated factors in the presence of PlaB. Again the Co-IPs should be performed in the presence of RNase to confirm that the observed interactions depend on RNA. Line 181: The requirement of Nup153 to tether the splicing machinery to the NPC is not convincing from the presented data. The knockdown is way too long and the changes are tiny. Wouldn't it be better to use a Nup153 mutant without Zinc knuckle or IDR to show that now the splicing factors interactions are lost? Alternatively degron lines should be used.

Line 186: I do not understand the logic why NUP153 needs to bind to chromatin to fullfil its function in splicing. It could also bind to RNA with its zinc knuckle or IDRs. The authors should perform iCLIP or RIP to exclude this possibility? I also do not understand the logic to look to look for proximity to repressive LADs as a criterion, while investigating a function of NUP153 in splicing which requests actively transcribing genes. This has to be better motivated. Excluding the nucleoplasmic pool of NUP153 removes important data points that might be functionally relevant. Line 187: The entire paragraph on Dam-ID and all subsequent genome-wide analyses is way too densely written and hard to understand for non-experts. Analysis tools or thresholds are rarely given and it is unclear how the different data sets have been made and by who, what they mean and how they have been integrated. Chromatin patterns and expression profiles are very unspecific terms. Many of the used terms have not been introduced properly. The metaplots show very small differences. In the end I am not sure what we have learned from all the data integration. Is NUP153 bound to DNA, to nucleosomes, to nascent RNA or to splicing factors?

Lane 232: Unclear what NUP153 introns are. Is the entire gene where NUP153 binds to considered or only the intron with a NUP153 peak.

Lane 242: Again, the shRNA knockdown performed in this manuscript is way to long to observe a direct effect on splicing at the pore, which occurs at the level of minutes. Degrons should be used here to confirm this observation.

Line 249: More negative controls are needed for genes not bound by NUP153 for the splicing analysis. RNA-Seq analyzed for intron retention could be helpful.

All the text and specific comments will be addressed as suggested by the reviewer.

The experimental part to be included in the revised manuscript is explained in more detail above.

Reviewer expertise (keywords): nuclear pore complexes, nuclear organization, gene expression, mRNA biology

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

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Referee #1

Evidence, reproducibility and clarity

The authors first use a Bio-ID approach to search for interactors of the basket proteins TPR and NUP153, identifying proteins involved in various nuclear process, including many splicing components, and confirm some of these interactions using IP and PLA assays. PLA experiments further suggest that these interactions occur primary at or close to the nuclear periphery. Moreover, inhibiting splicing, but not transcription, reduced these interactions. The authors then investigated the role of NUP153 in loading of the splicing machinery and found a lower association of the NUP98/SF3A1 but not AQR interaction (measured through PLA). Furthermore, DamID experiments identified NUP153 bound genes proximal to LAD domains that are actively transcribed, contain overall longer introns with low GC-content, and fall within a group of genes located at the outermost shell of the nucleus (when compared to previously published LaminI ID /PGseq data). Lastly, they interrogate whether depletion of NUP153 results in a splicing defect for NUP153 bound genes.

The authors identify many proteins in their BioID interaction screen, however, only a single nucleoporin (Nup35, an inner ring protein). Previous BioID studies have identified NUP153 in BioID experiments including proteins of the Y-complex (PMID: 24927568 and others). To ensure that the BioID experiments indeed probe for interactions of NUP152 and TPR at the NPC, the authors should include control experiments that show that their NUP153 and TPR-BirA fusions primarily localize to the NPC. If a significant fraction is not NPC bound, this has to be taken into account interpreting/discussing their data.<br /> The authors should be more precise when describing the role of the different splicing factor identified in the BioID screen and their function in specific steps of splicing, as this is important when claiming that they identify factors acting at all steps of splicing. For example, the authors describe DDX39b/UAP56 as an early splicing factor; DDX39b/UAP56 main role however seems to be in mRNA export and mRNP compaction. The authors might want to include this in the interpretation of their data.

Concerning PLA experiment controls, the authors perform TPR-PML as a negative control, however, no negative control for NUP153 is shown (Figure 2). Such a control should be added to allow evaluating the specificity of NUP153 PLA interactions, and/or discussed why this was not done.

Quantification of the distance of PLA-NUP153/TPR interactions show interactions mainly close to the nuclear periphery. The imaging data shown in Figure 2b indeed shows that TPR/NUP153 interactions are exclusively at the nuclear periphery, whereas NUP153/splicing factor interactions are sometimes at the edge of the DAPI signal, but mostly somehow internalized (Figure 2B, S2b). Quantification (Figure 2d) shows these distributions to be very similar, likely due to the way the quantification was performed / the bin size of plotting the relative distance of a spot to the nuclear periphery was chosen. Looking at the scale bar/nuclear size and the position of the PLA spots for the NUP153/splicing factors, it appears that spots are often hundreds of nanometers away from the periphery. As the nuclear basket is thought to reach only about 100nm onto the nuclear interior, the conclusion by the authors that these interactions occur at the NPC would not be consistent with the data. The authors should better incorporate this in their interpretation of the data. The conclusion ' Nup153 aids the loading of splicing machinery' is not sufficiently supported by the data. The authors observed a reduction in PLA signal for the NUP98-AQR interaction, but not the NUP98-SF3A1 (Figure 3g). Their conclusion has to reflect this discrepancy in their data. Moreover, the studies focus is to determine the role of NUP153/TPR in recruiting the splicing machinery to the NPC. As in the experiments the authors interrogate the interaction of only NUP98, who has to a large extend splicing factor interactions within the nuclear interior and not at the periphery, the relevance of the experiments in Figure 3 towards the main focus of the paper is unclear. When investigating the effect of NUP153 depletion on splicing, the authors observe a splicing phenotype for multiple NUP153 genes (Figure 5). The authors however show only a single negative control gene (CBX5). It would significantly strengthen their argument if the authors would investigate splicing defects of periphery located noneNUP153 bound genes as well as for genes located in the nuclear interior to better understand whether this splicing phenotype is indeed specific for NUP153 genes (at the nuclear periphery/NPC). The authors state in the text describing the SABER-FISH experiments in Figure 5f that 'were able to visualize the presence of a site of transcription where accumulation of these probes was close to the periphery for all except for GSTK1, which showed a wider nuclear distribution, similar to CBX5 control region not bound by Nup153'. However, their statement is not supported by the images shown in Fig 5f, which show TS in control cells in the nuclear interior. Also, a single cell but no quantification is shown. Moreover, what distance from the periphery is considered as close to the periphery is not defined (see also earlier comment on the question what should be considered a periphery and/or NPC association).

Limitation of the study does not discuss the limitations of the study but rather reads like the extension of the discussion. This section should be rewritten.

Minor comments:

Western in Figure 3c does not represent well the quantification in 3e.

Figure S3 is mislabelled (pannel h is panel g).

Significance

The manuscript interrogates an important question related to the role of the NPC in gene regulation, in particular how interaction of genes/pre-mRNAs with the NPC might stimulate expression of specific genes/mRNAs. Stimulating splicing would be one way that could contribute to efficient gene expression, and this is the question the authors address in this manuscript. This study is therefore important and relevant to a wide audience. However, as outlined in the section above, the conclusions drawn by the authors do not always reflect the experimental data, and it is therefore unclear whether the overall conclusion as stated in the title of the manuscript is valid. Moreover, conceptually, if intron containing genes are transcribed at or near nuclear pores, and splicing often occurs co-transcriptionally, it is to be expected to find splicing components close to nuclear pores. While it is relevant to show that this actually happens, and this is, at least in part, done by the authors. However, the experiments presented do not show that the splicing machinery actually actively docks to the NPC and is not just passively recruited close to NPCs because nascent pre-mRNAs are spliced where they are transcribed (the authors state in their title that the NUP153 docks the splicing machinery at the NPC). Showing this require identifying direct interactions between spliceosome components with NUP153/nuclear basket components to stimulate splicing at the NPC. If this would indeed be the case, these findings would describe a novel mechanistic step to stimulate efficient splicing and subsequently export of a selected set of NPC-associated genes. This would open other questions such as how to achieve specificity for only some pre-mRNAs/introns. While addressing this question is likely beyond the scope of this manuscript, the question whether the process described here is an active or passive process should be incorporated in the interpretation of the data.

Para poder distinguir letras de variables es que se usa el signo $ que las precede.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In their manuscript, Kong Fang et al describe a robust pipeline for the isolation of small extracellular vesicles through a combination of size exclusion chromatography and miniaturized density gradient separation. Subsequently, they prove that the method is reproducible and suitable for small-volume operations while at the same time not compromising the quality of vesicles.

Strengths:

The paper narrates a robust method for purifying high-quality sEVs from small amounts of blood plasma. They also demonstrate that through this approach, they can derive sEVs without compromising the protein composition, integrity of the vesicles, or contamination with other proteins or lipids.

Weaknesses:

The paper is a nice summary of how to enrich sEVs from blood samples. Although well performed and substantiated with data, the paper primarily deals with method development and optimisation.

We agree with the reviewer's assessment that this paper primarily focuses on the development and optimization of a method. Using this robust technique for isolating small extracellular vesicles (sEVs) from small blood volumes, our future research will investigate sEVs isolated from clinical samples, with a particular focus on their role in various diseases.

Reviewer #2 (Public Review):

Summary:

In this work, the authors manage to optimize a simple and rapid protocol using SEC followed by DGCU to isolate sEVs with adequate purity and yield from small volumes of plasma. Isolated fractions containing sEVs using SEC, DGCU, SEC-DGCU, and DGCU-SEC are compared in terms of their yield, purity surface protein profile, and RNA content. Although the combined use of these methodologies has already been evaluated in previous works, the authors manage to adapt them for the use of small volumes of plasma, which allows working in 1.5 mL tubes and reducing the centrifugation time to 2 hours.

The authors finally find that although both the SEC-DGCU and DGCU-SEC combinations achieve isolates with high purity, the SEC-DGCU combination results in higher yields.

This work provides an interesting tool for the rapid obtention of sEVs with sufficient yield and purity for detailed characterization which could be very useful in research and clinical therapy.

Strengths:

- The work is well-written and organized.

- The authors clearly state the problem they want to address, that is, optimizing a method that allows sEV to be isolated from small volumes of plasma.

- Although these methodologies have been tested in previous works, the authors manage to isolate sEVs of high purity and good performance through a simple and fast methodology.

- The characteristics of all isolated fractions are exhaustively analyzed through various state-of-the-art methodologies.

- They present a good interpretation of the results obtained through the methodologies used.

Weaknesses:

- Lack of references that support some of the results obtained.

- Although this work focuses on comparing different techniques and their combinations to find an optimal option, the authors do not use any statistical method that reliably shows the differences between these techniques, except when repeatability is measured.

We appreciate the reviewer's insightful comments and will incorporate the suggested missing references. We acknowledge that we did not perform statistical analyses when comparing the differences among the three methods. Nevertheless, the superiority of the SEC-DGUC method is evident from observations based on several independent characterization methods, including Cryo-EM, TEM, western blot, and total RNA quantification.

Firstly, repeated Cryo-EM observations consistently confirm that the SEC-alone method shows severe lipoprotein contamination while the SEC-DGUC method drastically reduces such lipoprotein contamination. In comparing the SEC-DGUC and DGUC-SEC methods, multiple independent characterization methods showed that the SEC-DGUC method yields significantly greater quantity of sEVs: 1) The western blot experiment showed much higher signal intensity for all four tested sEV markers (CD9, CD63, CD81, and TSG101), with estimated concentrations approximately 2.1, 2.1, 4.7, and 4.2 times higher than the DGUC-SEC method. 2) The total RNA analysis showed that SEC-DGUC-1 contained more than 4 times the total amount of RNA compared to DGUC-SEC-PF. 3) Establishing the normalization baseline, particle size distributions in SEC-DGUC-1 and DGUC-SEC-PF measured by TEM were found to be similar, suggesting comparable purity and distribution of the captured sEVs. For comparison purposes, within each independent characterization method, the same plasma source and total plasma volume were used, while across different methods, different plasma sources were used. These independent characterization methods have consistently demonstrated the superiority of the SEC-DGUC method over the DGUC-SEC or SEC-alone methods.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

In my opinion, this work is elegantly designed and supported by data, which would motivate more studies related to blood-derived microvesicles in the context of infectious and systemic diseases. Overall, the manuscript is well-written and explained in sufficient detail. I only have minor comments.

(1) Recruitment of volunteers for blood/plasma collection: there is a need for a statement that this was in accordance with ethical and biosafety regulations of the Institute/Clinic.

We added two sentences at the beginning of the Blood Collection section (under Materials and methods): “All procedures involving peripheral blood specimens were approved by the Singapore National Health Group Domain Specific Review Board (the central ethics committee) and were mutually recognized by the Nanyang Technological University Institutional Review Board (IRB#2018/00671). All blood specimens were de-identified prior to their use in the experiments.”

(2) Since this is a method development and validation article, it would be good to include an image of the iodixanol gradient with the high-density sEV zone, after centrifugation.

We have incorporated an image after centrifugation in Supplementary Figure 3.

(3) Although several sEV markers are shown in Figure 7A, flotillin is missing in this figure which was part of Figure 6B. Does flotillin show a different pattern? Flotillin is a DRM-associated marker, and hence may behave differently, would be interesting to add any insights.

We appreciate the reviewer’s careful observation. In Figure 6B, Flotillin was used to confirm the presence of sEVs in different density zones. However, for the purpose of comparing the yield between the SEC-DGUC and DGUC-SEC methods, as shown in Figure 7A, Flotillin was not included in the western blot analysis. No obvious pattern changes were observed in other sEV markers tested in both Figures 6B and 7A.   

(4) Methods section of LC/MS analysis- which protein database was used for protein identification?

We added the following sentence at the end of the LC/MS analysis section: “The protein database used for protein identification was Uniprot Human.”

Reviewer #2 (Recommendations For The Authors):

In line 43 some references are needed.

We added this reference: EL Andaloussi, S., Mäger, I., Breakefield, X. et al. Extracellular vesicles: biology and emerging therapeutic opportunities. Nat Rev Drug Discov 12, 347–357 (2013). https://doi.org/10.1038/nrd3978

In line 107, please avoid using short forms such as "it's".

We have revised that to “it is.”

In line 153: "...separates low-density particles from those of high density, but a considerable amount of..." the word "but" should not be in the sentence.

We have removed “but” in this sentence.

In line 181 the authors establish that "Notably, SEC-PF exhibited a high level of ApoB and low expression of sEV markers." Is there any explanation for this?

SEC-PF represents the eluate from the SEC step, collected before the DGUC step. This fraction contains a mixture of lipoproteins and sEVs. Due to the overwhelming abundance of lipoproteins compared to sEVs, the western blot predictably shows a high level of ApoB with minimal expression of sEV markers. This highlights that SEC alone effectively reduces plasma protein content but does not efficiently remove lipoproteins. Figure 6C further illustrates this point, as cryo-EM images of SEC-PF reveal the presence of sEVs, which are vastly outnumbered by lipoproteins.

In line 198, the sentence "Theoretically, the DGUC-SEC protocol should also effectively isolate sEVs from plasma" need to be supported by references.

See for instance:

- Holcar M, Ferdin J, Sitar S, Tušek-Žnidarič M, Dolžan V, Plemenitaš A, Žagar E, Lenassi M. 2020. Enrichment of plasma extracellular vesicles for reliable quantification of their size and concentration for biomarker discovery. Sci Rep 10:21346. doi:10.1038/s41598-020-78422-y.

- Jia Y, Yu L, Ma T, Xu W, Qian H, Sun Y, Shi H. 2022. Small extracellular vesicles isolation and separation: Current techniques, pending questions and clinical applications. Theranostics 12:6548-6575. doi:10.7150/thno.74305

- Vergauwen G, Dhondt B, Van Deun J, De Smedt E, Berx G, Timmerman E, Gevaert K, Miinalainen I, Cocquyt V, Braems G, Van den Broecke R, Denys H, De Wever O, Hendrix A. 2017. Confounding factors of ultrafiltration and protein analysis in extracellular vesicle research. Sci Rep 7:2704. doi:10.1038/s41598-017-02599-y

We have added this reference: Holcar M, Ferdin J, Sitar S, Tušek-Žnidarič M, Dolžan V, Plemenitaš A, Žagar E, Lenassi M. 2020. Enrichment of plasma extracellular vesicles for reliable quantification of their size and concentration for biomarker discovery. Sci Rep 10:21346. https://doi.org/10.1038/s41598-020-78422-y.  

In line 309 the authors establish that "NTA measured size distributions displayed well-overlapped histograms of particles". It is possible for the authors to analyze this overlapping using some statistical test as a chi-squared test?

We have conducted a statistical analysis of the histogram similarities using the Jensen-Shannon Divergence (JSD) method. This is reflected in the manuscript under the results section, “Repeatability and reliability of the SEC-DGUC protocol”, where we state: “We then compared size distributions for each plasma fraction using Jensen-Shannon Divergence (JSD). The JSD values, which are well below 0.1 (Figure 10B), indicate a consistent population of isolated particles, as further supported by Supplementary Figure 8.” Additionally, we included JSD values in the legend of Figure 10B: “JSD values for SEC-DGUC-1 to 4 are 0.015, 0.006, 0.001, and 0.002, indicating strong similarities among the histograms.” These additions demonstrate the robustness and repeatability of the SEC-DGUC protocol.

In line 360, "lasts ~ 16 hours or more." This statement needs a reference that supports this time.

We have added this reference: Vergauwen, G. et al. Robust sequential biophysical fractionation of blood plasma to study variations in the biomolecular landscape of systemically circulating extracellular vesicles across clinical conditions. J Extracell Vesicles 10, e12122 (2021).

In line 399, the reference format is different from the previously used format.

This is corrected. We thank the reviewer for this careful examination.

Line 466: This sentence is not quite clear. It can be understood that for every 0.5 mL of plasma, 2 mL of particle fraction are obtained and that for 6 mL of plasma, this method will give a total volume of 24 mL. However, it is not clear what is meant by the fact that it has been concentrated to 6 mL. While one can assume that those final 6 mL concentrates come from the initial 24 mL, perhaps the way this sentence was worded was not appropriate. I would recommend rewriting it for a simpler interpretation of how this method was performed.

We have changed the sentence to: “For the DGUC experiment using the 12 ml tube, 24 ml of PFs were obtained from 6 ml of plasma and subsequently concentrated to 6 ml. The 6 ml of concentrated PFs were then transferred to a Beckman Coulter ultra-clear centrifuge tube (344059, Beckman Coulter, USA) for further processing.”

Line 519: The authors established a second dilution to avoid absorbance values above 1.2. Is there any justification for this value, taking into account that the Lambert-Beer law presents more precision in the absorbance range of 0.2 to 0.8?

We have added this reference: https://diagnostic.serumwerk.com/wp-content/uploads/2021/05/V05-Serumwerk.pdf

Line 519-520: "Also included were water and 0.25 M sucrose as blanks". Perhaps authors could consider rephrasing this sentence.

We have changed the sentence to: “The absorbance measurements were made against water and 0.25 M sucrose blanks.”

In line 520, the sentence must say "each sample was made by triplicate".

We have changed the sentence to: “Each sample was prepared by triplicate to reduce error.” We thank the reviewer for this suggestion.

Line 673: The phrase "0.1% formic acid in 100% ACN" would be better, in my opinion, if it said "0.1% formic acid in ACN".

Yes, these two expressions have the same meaning. However, to ensure clarity, we have updated the description to “0.1% formic acid in ACN.”. We thank reviewer for this suggestion.

Supplementary Figure 1: in the Figure caption there is an error in the numbering: at the end, where it is written (E), it should be (F). Please, correct this.

We have made the necessary correction and sincerely appreciate the reviewer’s attentiveness.

Supplementary Figure 5: Some sEVs are hard to visualize due to poor image resolution. Is there any possibility for the authors to enhance these images?

We thank the reviewer for this valuable comment. To improve the visual clarity of the images, we have opted to display four sub-figures instead of nine.

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Reply to the reviewers

Reply to the Reviewers

I would like to thank the reviewers for their comments and interest in the manuscript and the study.

Reviewer #1

1. I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

The directional positioning of CTCF-binding sites at chromatin interaction sites was analyzed by CRISPR experiment (Guo Y et al. Cell 2015). We found that the machine learning and statistical analysis showed the same directional bias of CTCF-binding motif sequence and RAD21-binding motif sequence at chromatin interaction sites as the experimental analysis of Guo Y et al. (lines 229-253, Figure 3b, c, d and Table 1). Since CTCF is involved in different biological functions (Braccioli L et al. Essays Biochem. 2019 ResearchGate webpage), the directional bias of binding sites may be reduced in all binding sites including those at chromatin interaction sites (lines 68-73). In our study, we investigated the DNA-binding sites of proteins using the ChIP-seq data of DNA-binding proteins and DNase-seq data. We also confirmed that the DNA-binding sites of SMC3 and RAD21, which tend to be found in chromatin loops with CTCF, also showed the same directional bias as CTCF by the computational analysis.

__2. Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. __

Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 435 and 829: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

3. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). We found that the DNA-binding sites of the insulator-associated DBPs were statistically overrepresented in the 5 kb boundary sites more than other DBPs (Fig. 4d). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality of insulator-associated DNA-binding sites is their overall tendency, and it may be difficult to notice the directionality from each binding site because the directionality may be weaker than that of CTCF, RAD21, and SMC3 as shown in Table 1 and Supplementary Table 2. We also observed the directional biases of CTCF, RAD21, and SMC3 by using Micro-C chromatin interaction data as we estimated, but the directionality was more apparent to distinguish the differences between the four directions of FR, RF, FF, and RR using CTCF-mediated ChIA-pet chromatin interaction data (lines 287 and 288).

 I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. *Cell* 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay, and include less long-range interactions due to distance bias. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study.

 I discussed other causes in lines 614-622: Another reason for the difference may be that boundary sites are more closely associated with topologically associated domains (TADs) of chromosome than are insulator sites. Boundary sites are regions identified based on the separation of numerous chromatin interactions. On the other hand, we found that the multiple DNA-binding sites of insulator-associated DNA-binding proteins were located close to each other at insulator sites and were associated with distinct nested and focal chromatin interactions, as reported by Micro-C assay. These interactions may be transient and relatively weak, such as tissue/cell type, conditional or lineage-specific interactions.

 Furthermore, I have added the statistical summary of the analysis in lines 372-395 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

4. The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 405 - 412: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value 5. Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

6. Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2's comments.

Reviewer #2

1. Introduction, line 95: CTCF appears two times, it seems redundant.

On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

2. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

 I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

3. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

4. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines". On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

5. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

6. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 501: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

 In Aljahani A et al. *Nature Communications* 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. *Nature Genetics* 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

I added the following sentence on lines 569-577: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.

 FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. *Molecular Cell* 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. *Nucleic acids research* 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 556: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

7. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

 The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. *Nature Genetics* 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. *Proc Natl Acad Sci USA* 2021 ; Ortabozkoyun H et al. *Nature genetics* 2022 ; Wang R et al. *Nature communications* 2022). I have added the following sentences on lines 571-575: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 582-584: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

 As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. *Nature* 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. *Nature Reviews Molecular Cell Biology* 2021). Regarding loop extrusion, the 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. *EMBO Journal* 2024). I have added the following sentences on lines 543-547: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 577-582: The 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

 Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. *Elife* 2024) (Ke W et al. *Elife* 2024) (Fujioka M et al. *PLoS Genetics* 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 559-567: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

8. Do the authors think that the identified DBPs could work in that way as well?

The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

 Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. *Nucleic Acids Research* 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. *Cell Reports* 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 554: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

9. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 584-590: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

10. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 539 - 543 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.

 To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the *Drosophila even skipped *locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. *PLoS Genetics* 2016).

Reviewer #3

Major Comments:

1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 257 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 20 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 929 - 931 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 974 - 976 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 356 - 360: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 593-597: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

Minor Comments:

1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., ____https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2____or ____https://pubmed.ncbi.nlm.nih.gov/37486787____/). The authors should discuss how that would impact their results.

The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, although the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

 As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

 Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 623-628: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

 Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

Reviewer #1 (Public review):

Summary:

This study presents findings on dual TCR regulatory T cells (Tregs) using previously published single-cell RNA and TCR sequencing datasets. The authors aimed to quantify dual TCR Tregs in different tissues and analyze their characteristics. Rather than perform the difficult experiments needed to ascertain the functional role of dual receptors, this study relies entirely on scRNA-VDJ-seq data published by two other groups. The findings primarily confirm prior work rather than provide new insights, and the methodology has significant weaknesses that limit the study's impact. We have concerns about the scientific integrity of this work.

Strengths:

(1) The use of single-cell RNA and TCR sequencing is appropriate for addressing potential relationships between gene expression and dual TCR.

(2) The data confirm the presence of dual TCR Tregs in various tissues, with proportions ranging from 10.1% to 21.4%, aligning with earlier observations in αβ T cells.

(3) Tissue-specific patterns of TCR gene usage are reported, which could be of interest to researchers studying T cell adaptation, although these were more rigorously analyzed in the original works.

Weaknesses

(1) Lack of Novelty: The primary findings do not substantially advance our understanding of dual TCR expression, as similar results have been reported previously in other contexts.

(2) Incomplete Evidence: The claims about tissue-specific differences lack sufficient controls (e.g., comparison with conventional T cells) and functional validation (e.g., cell surface expression of dual TCRs).

(3) Methodological Weaknesses: The diversity analysis does not account for sample size differences, and the clonal analysis conflates counts and clonotypes, leading to potential misinterpretation.

(4) Insufficient Transparency: The sequence analysis pipeline is inadequately described, and the study lacks reproducibility features such as shared code and data.

(5) Weak Gene Expression Analysis: No statistical validation is provided for differential gene expression, and the UMAP plots fail to reveal meaningful clustering patterns.

(6) A quick online search reveals that the same authors have repeated their approach of reanalysing other scientists' publicly available scRNA-VDJ-seq data in six other publications:

(1) Peng, Q., Xu, Y. & Yao, X. scRNA+ TCR-seq revealed dual TCR T cells antitumor response in the TME of NSCLC. J Immunother Cancer 12 (2024). https://doi.org:10.1136/jitc-2024-009376

(2) Wang, H., Li, J., Xu, Y. & Yao, X. scRNA + BCR-seq identifies proportions and characteristics of dual BCR B cells in the peritoneal cavity of mice and peripheral blood of healthy human donors across different ages. Immun Ageing 21, 90 (2024). https://doi.org:10.1186/s12979-024-00493-6

(3) Xu, Y. et al. scRNA+TCR-seq reveals the pivotal role of dual receptor T lymphocytes in the pathogenesis of Kawasaki disease and during IVIG treatment. Front Immunol 15, 1457687 (2024). https://doi.org:10.3389/fimmu.2024.1457687

(4) Yuanyuanxu, Qipeng, Qingqingma & Yao, X. scRNA + TCR-seq revealed the dual TCR pTh17 and Treg T cells involvement in autoimmune response in ankylosing spondylitis. Int Immunopharmacol 135, 112279 (2024). https://doi.org:10.1016/j.intimp.2024.112279

(5) Zhu, L. et al. scRNA-seq revealed the special TCR beta & alpha V(D)J allelic inclusion rearrangement and the high proportion dual (or more) TCR-expressing cells. Cell Death Dis 14, 487 (2023). https://doi.org:10.1038/s41419-023-06004-7

(6) Zhu, L., Peng, Q., Wu, Y. & Yao, X. scBCR-seq revealed a special and novel IG H&L V(D)J allelic inclusion rearrangement and the high proportion dual BCR expressing B cells. Cell Mol Life Sci 80, 319 (2023). https://doi.org:10.1007/s00018-023-04973-8

In other words, the approach used here seems to be focused on quick re-analyses of publicly available data without further validation and/or exploration

Appraisal of the Study's Aims and Conclusions:

The authors set out to analyze dual TCR Tregs across tissues, but the lack of robust controls, incomplete analyses, and insufficient novelty limit the study's ability to achieve its aims. The results confirm prior findings but do not provide compelling evidence to support the broader claims about the characteristics or significance of dual TCR Tregs.

Impact and Utility:

While the study provides a descriptive analysis of dual TCR Tregs, its limited novelty and methodological weaknesses reduce its likely impact on the field. The methods and data could have utility for researchers interested in tissue-specific TCR gene usage, but additional rigor is required to make the findings broadly applicable.

Reviewer #2 (Public review):

Summary:

In this paper, Andriani and colleagues are examining the potential role of Zn flux in sperm and its effect on Slo3 channels. This is an interesting question that is likely critical to how sperm function properly and Slo3 channels are a possible candidate for a downstream molecule that is impacted by Zn. In this paper, the authors use Zn imaging, sperm motility assays, and electrophysiology to show that Zn flux impacts sperm function. They then go on to look at the impact Zn has on Slo3 current and propose a binding site based on MD simulations. While the ideas are interesting, the experiments are not well described in many places making understanding the results very difficult. In addition, critical controls are missing throughout the paper.

Strengths:

The question of how Zn flux impacts membrane potential and sperm motility is an important one. Moreover, Slo3 presents an interesting candidate or the target of Zn regulation. The combination of methods used here also has the potential to uncover mechanisms of Zn regulation of Slo3.

Weaknesses:

Much of the paper lacks experimental description which makes interpretation quite difficult, or a detailed discussion is missing. Examples include:

(1) Figure 1, particularly the Zn imaging, is not sufficiently described. How is the fluorescence intensity measured? A representative ROI? The whole tail and head? Are the sperm immobile? If not, there is evidence that motion artifacts can significantly distort these sorts of measures from Calcium measurements in Cilia. Were there controls done? Is the small amount of Zn seen in the tail above the background?

(2) The second half of Figure 1 is also not well described. What is the extracellular solution in the recordings? When you apply the Zn ionophore, do you expect influx or efflux? I assume efflux is based on the conclusions but this should be discussed explicitly.

(3) Figure 2H labels the Y axis, "normalized current". Normalized to what? Why do neither of the curves end at 1? A better description of what this figure represents is needed.

(4) The alpha fold simulations are not well described. How many Zn binding sites were found? Are all of the histidine mutations in Figure 4 Supplement 1 the ones that were found?

(5) There is no discussion of physiological intracellular Zn concentration. How much Zn is inside the sperm? How much if likely Free vs buffered? Is 100uM a reasonable physiological concentration?

There are a number of areas where the interpretation is not well supported by the data including:

(6) You say in the Figure 4 supplement, that "we did not observe any significant decrease in the percentage of current inhibition." But that is a pretty misleading statement. There are large changes (increases) in the amount of zinc inhibition. These might be allosteric changes but I don't think you can safely eliminate these as relevant Zn binding sites. Also, some of these mutations appear to allow at least some unbinding of Zn.

(7) Following up on the above point, it seems unfair to conclude that the D162S, E169A, and E205 mutants are part of the inhibitory binding site for Zn when the mutation has no effect on inhibition and only an effect on the washout. The mutations on the intracellular side also had an impact on the washout so it seems equally likely that they are the critical residues based on your data.

(8) Nowhere in the paper do you make the specific link between Zn flux and membrane hyperpolariation via Slo3. You show that Zn flux changes the ability of the sperm to hyperpolarize and you show that Slo3 is inhibited by Zn but the connection between the two is not demonstrated. There appears to be a specific Slo3 blocker. If you use this in sperm, do you no longer see the Zn effect?

(9) In the second half of Figure 1, the authors suggest that there is "no hyperpolization in 100uM Zn. That is not really true. It is reduced but not absent.

(10) The claim that Lrcc52 with Slo3 shows a higher current inhibition at pH 7.5 than pH 8 is not well supported because there are only 3 replicates in the 7.5 case. In addition, the claim is made in the test that 100uM ZnCl2 "already inhibited mSlo3+Lrcc52 at pH7.5", contrasted with mSlo3 alone, is not tested statistically.

In a number of places, better controls are needed.

(11) How specific is this effect for Zn? Mg2+, for instance, is also a divalent cation that is in the hundreds of uM range inside the cell. Does it exert the same effect? Each ion certainly has unique preferred coordination geometries, does your predicted binding with MD show what you might expect for tetrahedral coordination with Zn? Did you test other divalent cations functionally or in silicon?

(12) For the VCF experiments, a significantly higher concentration of Zn was used (10mM). What is the reason for this? There is no discussion of how much a "puff" is. Assuming you are using the RNA injector it is probably on the order of 50nL or less. Assuming the volume of an oocyte is 1uL that would argue that the final concentration is 500uM or higher. But this is also complicated by potential local effects of high Zn at the injection site, artifacts of injecting that much metal, and the fact that a great deal of the Zn will likely be bound to other things inside the cell. Better controls are needed for this experiment.

A telenovila and a MILF??

Expertos en inteligencia artificial (IA) y reglamentación con extenso conocimiento y experiencia en la industria.

I would change it to: Hemos desarrollado cursos bajo demanda, virtuales y presenciales que profundizan

I would change to: Nuestros cursos a demanda, virtuales y presenciales, ofrecen a los participantes los conocimientos y las habilidades necesarias

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

The paper by Fournier et al. investigates the sensitivity of neural circuits to changes in intrinsic and synaptic conductances. The authors use models of the stomatogastric ganglion (STG) to compare how perturbations to intrinsic and synaptic parameters impact network robustness. Their main finding is that changes to intrinsic conductances tend to have a larger impact on network function than changes to synaptic conductances, suggesting that intrinsic parameters are more critical for maintaining circuit function.

The paper is well-written and the results are compelling, but I have several concerns that need to be addressed to strengthen the manuscript. Specifically, I have two main concerns:

(1) It is not clear from the paper what the mechanism is that leads to the importance of intrinsic parameters over synaptic parameters.

(2) It is not clear how general the result is, both within the framework of the STG network and its function, and across other functions and networks. This is crucial, as the title of the paper appears very general.

I believe these two elements are missing in the current manuscript, and addressing them would significantly strengthen the conclusions. Without a clear understanding of the mechanism, it is difficult to determine whether the results are merely anecdotal or if they depend on specific details such as how the network is trained, the particular function being studied, or the circuit itself. Additionally, understanding how general the findings are is vital, especially since the authors claim in the title that "Circuit function is more robust to changes in synaptic than intrinsic conductances," which suggests a broad applicability.

I do not wish to discourage the authors from their interesting result, but the more we understand the mechanism and the generality of the findings, the more insightful the result will be for the neuroscience community.

Major comments

(1) Mechanism

While the authors did a nice job of describing their results, they did not provide any mechanism for why synaptic parameters are more resilient to changes than intrinsic parameters. For example, from Figure 5, it seems that there is mainly a shift in the sensitivity curves. What is the source of this shift? Can something be changed in the network, the training, or the function to control it? This is just one possible way to investigate the mechanism, which is lacking in the paper.

(2) Generality of the results within the framework of the STG circuit

(a) The authors did show that their results extend to multiple networks with different parameters (the 100 networks). However, I am still concerned about the generality of the results with respect to the way the models were trained. Could it be that something in the training procedure makes the synaptic parameters more robust than intrinsic parameters? For example, the fact that duty cycle error is weighted as it is in the cost function (large beta) could potentially affect the parameters that are more important for yielding low error on the duty cycle.

(b) Related to (a), I can think of a training scheme that could potentially improve the resilience of the network to perturbations in the intrinsic parameters rather than the synaptic parameters. For example, in machine learning, methods like dropout can be used to make the network find solutions that are robust to changes in parameters. Thus, in principle, the results could change if the training procedure for fitting the models were different, or by using a different optimization algorithm. It would be helpful to at least mention this limitation in the discussion.

(3) Generality of the function

The authors test their hypothesis based on the specific function of the STG. It would be valuable to see if their results generalize to other functions as well. For example, the authors could generate non-oscillatory activity in the STG circuit, or choose a different, artificial function, maybe with different duty cycles or network cycles. It could be that this is beyond the scope of this paper, but it would be very interesting to characterize which functions are more resilient to changes in synapses, rather than intrinsic parameters. In other words, the authors might consider testing their hypothesis on at least another 'function' and also discussing the generality of their results to other functions in the discussion.

(4) Generality of the circuit

The authors have studied the STG for many years and are pioneers in their approach, demonstrating that there is redundancy even in this simple circuit. This approach is insightful, but it is important to show that similar conclusions also hold for more general network architectures, and if not, why. In other words, it is not clear if their claim generalizes to other network architectures, particularly larger networks. For example, one might expect that the number of parameters (synaptic vs intrinsic) might play a role in how resilient the function is with respect to changes in the two sets of parameters. In larger models, the number of synaptic parameters grows as the square of the number of neurons, while the number of intrinsic parameters increases only linearly with the number of neurons. Could that affect the authors' conclusions when we examine larger models?

In addition, how do the authors' conclusions depend on the "complexity" of the non-linear equations governing the intrinsic parameters? Would the same conclusions hold if the intrinsic parameters only consisted of fewer intrinsic parameters or simplified ion channels? All of these are interesting questions that the authors should at least address in the discussion.

We thank Reviewer #1 for their valuable input. We agree with the reviewer that generality of the results may have been overstated. To address this we changed the title of the manuscript to make it more specific to rhythmic circuits and we included a sentence to this effect in the discussion. 

(1) We were more interested in knowing which set of conductances is more robust in a population of models, rather than a mechanism. If such a mechanism exists it will be the subject of a different study.

(2) (a) It is impossible to explore the whole parameter space of these models. Our method to find circuits will leave subsets of circuits out of the study. Our sole goal in constructing the model database was that the activities were similar but the conductances were different.  (b) Of course one could devise a cost function targeting circuits that are more or less robust to changes in one parameter. Whether those exist is a different matter. This is not what we intended to do.

(3) For this we would need a different circuit that produces non-oscillatory activity. A normal pyloric rhythm circuit always produces oscillatory activity unless it is “crashed"either by temperature or perturbations, but even in this case because we don’t have a proper “control” activity (circuits crash in different ways) we would not be able to utilize the same approach.

We think it is a valuable idea to perform a similar study in another small circuit with nonoscillatory (or rhythmic) activities. 

(4) We did not explore the issue of how our results generalize to larger networks as it would be pure speculation. It could be potentially interesting to do a similar sensitivity analysis with a large network trained to perform a simple task. Our understanding is that many large trained networks are extremely sensitive to perturbations in synaptic weights, at the same time that the intrinsic properties of neurons in ANN are typically oversimplified and identical across units. 

Reviewer #2 (Public review):

Summary:

This manuscript presents an important exploration of how intrinsic and synaptic conductances affect the robustness of neural circuits. This is a well-deserved question, and overall, the manuscript is written well and has a logical progression.

The focus on intrinsic plasticity as a potentially overlooked factor in network dynamics is valuable. However, while the stomatogastric ganglion (STG) serves as a well-characterized and valuable model for studying network dynamics, its simplified structure and specific dynamics limit the generalizability of these findings to more complex systems, such as mammalian cortical microcircuits.

Strengths:

Clean and simple model. Simulations are carefully carried out and parameter space is searched exhaustively.

Weaknesses:

(1) Scope and Generalizability:

The study's emphasis on intrinsic conductance is timely, but with its minimalistic and unique dynamics, the STG model poses challenges when attempting to generalize findings to other neural systems. This raises questions regarding the applicability of the results to more complex circuits, especially those found in mammalian brains and those where the dynamics are not necessarily oscillating. This is even more so (as the authors mention) because synaptic conductances in this study are inhibitory, and changes to their synaptic conductances are limited (as the driving force for the current is relatively low).

(2) Challenges in Comparison:

A significant challenge in the study is the comparison method used to evaluate the robustness of intrinsic versus synaptic perturbations. Perturbations to intrinsic conductances often drastically affect individual neurons' dynamics, as seen in Figure 1, where such changes result in single spikes or even the absence of spikes instead of the expected bursting behavior. This affects the input to downstream neurons, leading to circuit breakdowns. For a fair comparison, it would be essential to constrain the intrinsic perturbations so that each neuron remains within a particular functional range (e.g., maintaining a set number of spikes). This could be done by setting minimal behavioral criteria for neurons and testing how different perturbation limits impact circuit function.

(3) Comparative Metrics for Perturbation:

Another notable issue lies in the evaluation metrics for intrinsic and synaptic perturbations. Synaptic perturbations are straightforward to quantify in terms of conductance, but intrinsic perturbations involve more complexity, as changes in maximal conductance result in variable, nonlinear effects depending on the gating states of ion channels. Furthermore, synaptic perturbations focus on individual conductances, while intrinsic perturbations involve multiple conductance changes simultaneously. To improve fairness in comparison, the authors could, for example, adjust the x-axis to reflect actual changes in conductance or scale the data post hoc based on the real impact of each perturbation on conductance. For example, in Figure 6, the scale of the panels of the intrinsic (e.g., g_na-bar) is x500 larger than the synaptic conductance (a row below), but the maximal conductance for sodium hits maybe for a brief moment during every spike and than most of the time it is close to null. Moreover, changing the sodium conductance over the range of 0-250 for such a nonlinear current is, in many ways, unthinkable, did you ever measure two neurons with such a difference in the sodium conductance? So, how can we tell that the ranges of the perturbations make a meaningful comparison?

We thank Reviewer #2 for their comments. We agree with both reviewers about scope and generalizability. We changed the title of the manuscript and included a sentence in the discussion to address this. 

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Line 63: Tau_b is tau in Fig 1B? What is the 'network period' tau_n? Both are defined in the methods, but it would be good to clarify here and also in the figure.

This was fixed. Tau_b is the  bursting period and we indicated it in the figure. Network period means the period of the network activity. This was rewritten.  

(2) Line 74: "maximal conductances g_i." What is i? I can imagine what you meant, but it would be good to clarify the notation.

There are multiple different currents. Letter ‘i' is an index over the different types. It now reads as follows,

"The activity of the network depends on the values of the maximal conductances g ̄ i, where i is an index corresponding to the different current types (Na,CaS,CaT,Kd,KCa,A,H,Leak IMI)"

(3) Line 78: "conductances are changed by a random amount." How much is the "random amount"? In percentages? 

We fixed this sentence. This is how it reads now, 

"The blue trace in Figure 1C corresponds to the activity of the same model when each  of the intrinsic conductances is changed by a random amount within a range between 0  (completely removing the conductance) and twice its starting value, 2×gi, or equivalently, an increment of 100%."

Similarly, in Line 87: "by a similar percent." Can you provide Figures 1E-F in percentages? Are the percentages the same?

The phrase "by a similar percent.” Is misleading and unimportant. Thank you, we removed it. 

(4) Line 113: Why did you add I_MI? Is it important for the results or for the conclusions?

I_MI was added because the current is known to be there and it is not more or less important for the results or conclusions than any other current. 

(5) Line 117: "We used a genetic algorithm to generate a database." Confusing. I guess you meant that you used genetic algorithms to optimize the cost function.

Thank you for this comment. We fixed this sentence, see below. 

“We used a genetic algorithm to optimize the cost function, and in this way generated a database of N = 100 models with different values of maximal conductances (Holland 88)."

(6) Line 136: "The models in the database were constrained to produce solutions whose features were similar to the experimental measurements." Why are there differences in the features? Is this an optimization issue? I thought you wanted to claim that there are degenerate solutions, that is, solutions where the parameters are different, but the output is identical. Please clarify.

The concept of degenerate solutions does not imply that the solutions are mathematically identical. In biology this means that they provide very similar functions, but do so with different underlying parameters (in this case, maximal conductances). The activity of the pyloric network is slightly different across animals, and it also changes over time within the same individual. Variation across models reflects individual variation in the biological circuit, and it is strength of our modeling approach. The function of the circuits are equally good because they produce biologically realistic patterns, although the details of the activity patterns show differences. 

(7) Line 139: "distributed (p > 0.05)." What test did you use? N? Similarly, at Lines 218, 241, 239, etc. Please be more rigorous when reporting statistical tests.

Thank you. We now specify the test we utilized every time we report a p value. 

(8) Line 143: "In this case, it is not possible to identify clusters, suggesting that there are no underlying relationships between the features in the model database." The 2D plot is misleading, as the features are in 11 dimensions. Claims should be about the 11D space, not projections onto 2D. In fact, I don't think you can rule out correlations between the features based on the 2D plots. For example, shouldn't there be correlations between the on and off phases and the burst durations?

Thank you. These sentences were confusing and were removed. We added the following sentence to the end of that paragraph.

"Because the feature vectors are similar, their t-SNE projections do not form groups or clusters."

(9) Related to this, I don't understand this sentence: "Even though the conductances are broadly distributed over many-fold ranges, the output of the circuits results in tight yet uncorrelated distributions.”

This sentence is confusing and was removed. 

(10) Line 158: Repetition of Line 152: Figure 3 shows the currentscapes of each cell in two model networks.

We removed the second instance of the repeated sentences. 

(11) Line 160: "yet the activity of the networks is similar." Well, they are similar, but not identical. I can also say that the current scapes are 'similar'. This should be better quantified and not left as a qualitative description.

While this is an interesting point it will not change the results and conclusions of the present study. The network models are different since the values of their maximal conductances are distributed over wide ranges.  

(12) Line 218: midpoint parameter? Is that b - the sharpness? Please be consistent. Regarding the mechanism (see above) - any ideas what leads to this shift in the sensitivity curves between the two types of parameters?

Yes, we made a mistake. ‘b’ is the midpoint parameter. This was fixed in the text, thank you.

(13) Figure 6 illustrates why synaptic parameters are more robust, but it is not quantified. Why not provide a quantitative measure for this claim? For example, calculate the colored area within the white square for each pair, for each cell, and for each model. Show that these measures can predict improved robustness for one model over another and for synaptic vs. intrinsic parameters.

The ratio of areas of the colored and non-colored regions in the whole hyperboxes (for intrinsic and synaptic conductances) is the number reported in the y-axis of the sensitivity curves when we include all conductances (and not just a pair). 

We computed the ratios of the colored/noncolored areas in all panels in figure 6 and now report these quantities as follows, 

"We computed the proportions of areas of the white boxes that correspond to pyloric activity. These values for the intrinsic conductances panels are PD = 0.58, LP = 0.50, PY = 0.49, and the proportions for the synaptic conductances panels are PDPY = 0.62, P DLP = 0.87, and LPPD = 0.94. The occupied areas for synaptic conductances are larger than in the intrinsic conductances panels, consistent with our finding that the circuits’ activities are more robust to changes in synaptic conductances versus changes in intrinsic conductances."

"As before, we computed the proportion of areas of pyloric activity within the white boxes: PD = 0.61, LP = 0.55, PY = 0.52, and the proportions for the synaptic conductances panels are PDPY = 0.88, PDLP = 0.87, and LPP D = 0.83. These results provide an intuition of the complexities of GP . Not only are these regions hard-to-impossible to characterize in one circuit, but they are also different across circuits.” 

(14) Does the sign of the synaptic weights affect the conclusions?

We did not explore this issue because all chemical synapses in this network are inhibitory.

(15) Line 492: typo: deltai.

We fixed this.

Reviewer #2 (Recommendations for the authors):

(1) Line 301 - you can also add Williams and Fletcher 2019 Neuron.

We added the reference. Thank you. 

(2) Line 316 - this is a strange comment as these exact regions that were shown intrinsic plasticity (e.g., Losonczy, Attila, Judit K. Makara, and Jeffrey C. Magee. "Compartmentalized dendritic plasticity and input feature storage in neurons." Nature 452.7186 (2008): 436-441).

We did not understand this comment. 

(3) I found only one citation for the work of Turrigiano, the most relevant of which is only mentioned in the Method section. This is odd, as her work directly relates how synaptic conductance perturbation results in changes in intrinsic conductance.

We included more references to the work of Turrigiano to provide more context. 

"Desai, Niraj S., Lana C. Rutherford, and Gina G. Turrigiano. "Plasticity in the intrinsic excitability of cortical pyramidal neurons." Nature neuroscience 2, no. 6 (1999): 515-520.” "Desai, Niraj S., Sacha B. Nelson, and Gina G. Turrigiano. "Activity-dependent regulation of excitability in rat visual cortical neurons." Neurocomputing 26 (1999): 101-106.”

(4) Line 329 - The list of citations is very limited regarding studies of ext/int balance which started really way before 2009. Please give some of the credit to the classics.

We included the following additional references.

Van Vreeswijk, Carl, and Haim Sompolinsky. "Chaos in neuronal networks with balanced excitatory and inhibitory activity." Science 274, no. 5293 (1996): 1724-1726.

Rubin, Ran, L. F. Abbott, and Haim Sompolinsky. "Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity." Proceedings of the National Academy of Sciences 114, no. 44 (2017): E9366-E9375.

Wang, Xiao-Jing. "Macroscopic gradients of synaptic excitation and inhibition in the neocortex." Nature reviews neuroscience 21, no. 3 (2020): 169-178.

Lo, Chung-Chuan, Cheng-Te Wang, and Xiao-Jing Wang. "Speed-accuracy tradeoff by a control signal with balanced excitation and inhibition." Journal of Neurophysiology 114, no. 1 (2015): 650-661.

(5) In Figure 1B, why does it say 'OFF' when the neuron is spiking?

The label indicates the interval of time elapsed between the first spike in the PD neuron (taken as a reference), and the last spike in the burst (PD off). 

Summary of changes to figures:

Figure 1:

Fixed labels indicating bursting period and burst duration.

Figure 5:

Added labels in panels C and D specifying the symbol corresponding to the sigmoidal parameter.

Additional changes

We changed the title of the manuscript as follows:

"Rhythmic circuit function is more robust to changes in  synaptic than intrinsic conductances." We included the following sentence at the end of the Discussion Section. 

"We believe our results will hold for other rhythmic circuits and will be relevant for similar studies in other circuits with more complex functions.”

We realized we made a mistake with the units for maximal conductances. They were incorrectly expressed in nS (nano Siemens) in the figure labels, and correctly expressed in micro Siemens in the methods section. This was fixed and now conductances are expressed in micro Siemens consistently in the manuscript.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review): 

Summary:

The authors examine the role of the medial prefrontal cortex (mPFC) in cognitive control, i.e. the ability to use task-relevant information and ignore irrelevant information, in the rat. According to the central-computation hypothesis, cognitive control in the brain is centralized in the mPFC and according to the local hypothesis, cognitive control is performed in task-related local neural circuits. Using the place avoidance task which involves cognitive control, it is predicted that if mPFC lesions affect learning, this would support the central computation hypothesis whereas no effect of lesions would rather support the local hypothesis. The authors thus examine the effect of mPFC lesions in learning and retention of the place avoidance task. They also look at functional interconnectivity within a large network of areas that could be activated during the task by using cytochrome oxidase, a metabolic marker. In addition, electrophysiological unit recordings of CA1 hippocampal cells are made in a subset of (lesioned or intact) animals to evaluate overdispersion, a firing property that reflects cognitive control in the hippocampus. The results indicate that mPFC lesions do not impair place avoidance learning and retention (though flexibility is altered during conflict training), do not affect cognitive control seen in hippocampal place cell activity (alternation of frame-specific firing), a measure of location-specific firing variability, in pretraining. It nevertheless has some effect on functional interconnections. The results overall support the local hypothesis. 

Strengths:

Straightforward hypothesis: clarification of the involvement of the mPFC in the brain is expected and achieved. Appropriate use of fully mastered methods (behavioral task, electrophysiological recordings, measure of metabolic marker cytochrome oxidase) and rigorous analysis of the data. The conclusion is strongly supported by the data. 

Weaknesses:

No notable weaknesses in the conception, making of the study, and data analysis. The introduction does not mention important aspects of the work, i.e. cytochrome oxidase measure and electrophysiological recordings. The study is actually richer than expected from the introduction. 

The revised Introduction now includes:

“We used cytochrome oxidase, a metabolic marker of baseline neuronal activity, to confirm the mPFC lesions were effective and that there are non-local network consequences despite the local lesion. We first evaluated cytochrome oxidase activity in regions known to be associated with performance in the active place avoidance task, or regions with known connectivity to the mPFC. We then evaluated covariance of activity amongst the regions in an effort to detect network consequences of the lesion.”

Reviewer #2 (Public review): 

Park et al. set out to test two competing hypotheses about the role of the medial prefrontal cortex (PFC) in cognitive control, the ability to use task-relevant cues and ignore taskirrelevant cues to guide behavior. The "central computation" hypothesis assumes that cognitive control relies on computations performed by the PFC, which then interacts with other brain regions to accomplish the task. Alternatively, the "local computation" hypothesis suggests that computations necessary for cognitive control are carried out by other brain regions that have been shown to be essential for cognitive control tasks, such as the dorsal hippocampus and the thalamus. If the central computation hypothesis is correct, PFC lesions should disrupt cognitive control. Alternatively, if the local computation hypothesis is correct, cognitive control would be spared after PFC lesions. The task used to assess cognitive control is the active place avoidance task in which rats must avoid a section of a rotating arena using the stationary room cues and ignoring the local olfactory cues on the rotating platform. Performance on this task has previously been shown to be disrupted by hippocampal lesions and hippocampal ensembles dynamically represent the room and arena depending on the animal's proximity to the shock zone. They found no group (lesion vs. sham) differences in the three behavioral parameters tested: distance traveled, latency to enter the shock zone, and number of shock zone entries for both the standard task and the "conflict" task in which the shock zone was rotated by 180 degrees. The only significant difference was the savings index; the lesion group entered the new shock zone more often than the sham group during the first 5 minutes of the second conflict session. This deficit was interpreted as a cognitive flexibility deficit rather than a cognitive control failure. Next, the authors compared cytochrome oxidase activity between sham and lesion groups in 14 brain regions and found that only the amygdala showed significant elevation in the lesion vs. sham group. Pairwise correlation analysis revealed a striking difference between groups, with many correlations between regions lost in the lesion group (between reuniens and hippocampus, reuniens and amygdala and a correlation between dorsal CA1 and central amygdala that appeared in the lesion group and were absent in the sham group. Finally, the authors assessed dorsal hippocampal representations of the spatial frame (arena vs. room) and found no differences between lesion and sham groups. The only difference in hippocampal activity was reduced overdispersion in the lesion group compared to the sham group on the pretraining session only and this difference disappeared after the task began. Collectively, the authors interpret their findings as supporting the local computation hypothesis; computations necessary for cognitive control occur in brain regions other than the PFC. 

Strengths:

(1) The data were collected in a rigorous way with experimental blinding and appropriate statistical analyses. 

(2) Multiple approaches were used to assess differences between lesion and sham groups, including behavior, metabolic activity in multiple brain regions, and hippocampal singleunit recording. 

Weaknesses:

(1) Only male rats were used with no justification provided for excluding females from the sample.

This is a weakness we acknowledge. The experiments were performed at a time when we did not have female rats in the lab.

(2) The conceptual framework used to interpret the findings was to present two competing hypotheses with mutually exclusive predictions about the impact of PFC lesions on cognitive control. The authors then use mainly null findings as evidence in support of the local computation hypothesis. They acknowledge that some people may question the notion that the active place avoidance task indeed requires cognitive control, but then call the argument "circular" because PFC has to be involved in cognitive control. This assertion does not address the possibility that the active place avoidance task simply does not require cognitive control. 

We beg to differ that the possibility was not addressed. Prior to making the assertion, the manuscript describes the evidence that the active place avoidance task requires cognitive control. The evidence is multifold, and includes task design, behavior, and electrophysiology; we argue that this is more evidence than has been provided for other tasks that are asserted to require cognitive control. Specifically line 417 states:

“We have previously demonstrated cognitive control in the active place avoidance task variant we used (Fig. 1) because the rats must ignore local rotating place cues to avoid the stationary shock zone. Even when the arena does not rotate, rats distinctly learn to avoid the location of shock according to distal visual room cues and local olfactory arena cues, such that the distinct place memories can be independently manipulated using probe trials [49, 50]. When the arena rotates as in the present studies, neural manipulations that impair the place avoidance are no longer impairing when the irrelevant arena cues are hidden by shallow water [14, 15, 51, 52]. Furthermore, persistent hippocampal neural circuit changes caused by active place avoidance training are not detected when shallow water hides the irrelevant arena cues to reduce the cognitive control demand [10, 31, 33]. While these findings unequivocally demonstrate the salience of relevant stationary room cues to use for avoiding shock and irrelevant arena cues to ignore during active place avoidance, the most compelling evidence of cognitive control comes from recording hippocampal ensemble discharge. Hippocampal ensemble discharge purposefully represents current position using stationary room information when the subject is close to the stationary shock zone and alternatively represents rotating arena information when the mouse is far from the stationary shock zone [Fig. 4; 10].”

Line 436, however, acknowledges a fact that will always be true: no matter what anyone opines - until there are universally agreed upon objective criteria, it is logically possible that active place avoidance does not require cognitive control. The revision states: Despite this evidence from task design, behavioral observations, and direct electrophysiological representational switching as required to directly demonstrate cognitive control, one might still argue that it is logically possible that the active place avoidance task does not require cognitive control and this is why the mPFC lesion did not impair place avoidance of the initial shock zone. We consider such reasoning to be unproductive because it presumes that only tasks that require an intact mPFC can be cognitive control tasks. We nonetheless acknowledge that for some, we have not provided sufficient evidence that the active place avoidance requires cognitive control.

“We assert the evidence is compelling, and together these findings require rejecting the central-computation hypothesis that the mPFC is essential for the neural computations that are necessary for all cognitive control tasks.”

(3) The authors did not link the CO activity with the behavioral parameters even though the CO imaging was done on a subset of the animals that ran the behavioral task nor did they make any attempt to interpret these findings in light of the two competing hypotheses posed in the introduction. Moreover, the discussion lacks any mechanistic interpretations of the findings. For example, there are no attempts to explain why amygdala activity and its correlation with dCA1 activity might be higher in the PFC lesioned group. 

The CO study was performed to assess the effects of the lesion, as stated on line 262 “Cytochrome oxidase (CO), a sensitive metabolic marker for neuronal function [27], was used to evaluate whether lesion effects were restricted to the mPFC.” Furthermore, as a matter of fact, line 411 states “Thus, CO imaging and electrophysiological evidence identify changes in the brain beyond the directly damaged mPFC area. In particular, the dorsal hippocampus loses the inhibitory input from mPFC [45, 46] and loses the metabolic correlation with the nucleus reuniens, which is thought to be a relay between the mPFC and the dorsal hippocampus [47, 48].”

These CO measures assess baseline metabolic function and so it would be inappropriate to correlate them with the measures of behavior. Because the lesion and control groups do not differ on most measures of behavior, a relationship to CO measures is not expected. Importantly, even if there were differences in correlations between CO activity and behavioral measures, what could they mean? The study was designed to distinguish between two hypotheses, not to determine what CO differences could mean for behavior. As such, it is not at all clear how metabolic consequences of the lesion relate to the two hypotheses being evaluated, and so we consider it inappropriate to speculate. We did examine, and now include, the correlation between lesion size and conflict behavior. The Fig. 1 legend states “Savings was not related to lesion size r = 0.009, p = 0.98. *p < 0.05.”

(4) Publishing null results is important to avoid wasting animals, time, and money. This study's results will have a significant impact on how the field views the role of the PFC in cognitive control. Whether or not some people reject the notion that the active place avoidance task measures cognitive control, the findings are solid and can serve as a starting point for generating hypotheses about how brain networks change when deprived of PFC input. 

We thank the reviewer for the acknowledgement.

Reviewer #3 (Public review): 

Summary:

This study by Park and colleagues investigated how the medial prefrontal cortex (mPFC) influences behavior and hippocampal place cell activity during a two-frame active place avoidance task in rats. Rats learned to avoid the location of mild shock within a rotating arena, with the shock zone being defined relative to distal cues in the room. Permanent chemical lesions of the mPFC did not impair the ability to avoid the shock zone by using distal cues and ignoring proximal cues in the arena. In parallel, hippocampal place cells alternated between two spatial tuning patterns, one anchored to the distal cues and the other to the proximal cues, and this alteration was not affected by the mPFC lesion. Based on these findings, the authors argue that the mPFC is not essential for differentiating between task-relevant and irrelevant information. 

Strengths:

This study was built on substantial work by the Fenton lab that validated their two-frame active place avoidance task and provided sound theoretical and analytical foundations. Additionally, the effectiveness of mPFC lesions was validated by several measures, enabling the authors to base their argument on the lack of lesion effects on behavior and place cell dynamics. 

Weaknesses:

The authors define cognitive control as "the ability to judiciously use task-relevant information while ignoring salient concurrent information that is currently irrelevant for the task." (Lines 77-78). This definition is much simpler than the one by Miller and Cohen: "the ability to orchestrate thought and action in accordance with internal goals (Ref. 1)" and by Robbins: "processes necessary for optimal scheduling of complex sequence of behaviour." (Dalley et al., 2004, PMID: 15555683). Differentiating between task-relevant and irrelevant information is required in various behavioral tasks, such as differential learning, reversal learning, and set-shifting tasks. Previous rodent behavioral studies have shown that the integrity of the mPFC is necessary for set-shifting but not for differential or reversal learning (e.g., Enomoto et al., 2011, PMID: 21146155; Cho et al., 2015, PMID: 25754826). In the present task design, the initial training is a form of differential learning between proximal and distal cues, and the conflict training is akin to reversal learning. Therefore, the lack of lesion effects is somewhat expected. It would be interesting to test whether mPFC lesions impair set-shifting in their paradigm (e.g., the shock zone initially defined by distal cues and later by proximal cues). If the mPFC lesions do not impair this ability and associated hippocampal place dynamics, it will provide strong support for the authors' local computation hypothesis.

Thank you for these comments. In addressing them we have provided a significant revision to the manuscript’s Introduction. While authors like those cited by the reviewer have defined cognitive control, those definitions are difficult to test rigorously, as it is almost a matter of opinion whether a subject is displaying “the ability to orchestrate thought and action in accordance with internal goals" or whether they are using "processes necessary for optimal scheduling of complex sequence of behaviour." What would such definitions of cognitive control predict about neuronal activity? We have deliberately used a simple, operational definition of cognitive control because it is physiologically testable. In the revision, starting at line 93, we have provided an excerpt from Miller and Cohen (2001) with discussion. The importance of that work is that it provides explicit neuronal criteria and a means to operationally define cognitive control. As stated on Line 118 “Accordingly, cognitive control would be at work when there is sustained neuronal network representations of task-relevant information that suppresses or gates representations of salient task-irrelevant information in accord with purposeful judicious behavior.”

We used a R+A- task variant in which there is a stationary room-frame shock zone and task irrelevant arena-frame information. A strict correspondence to shift-shifting task design cannot be accomplished with active place avoidance because an A+R- task that requires avoiding an arena-frame shock zone in the absence of a room-frame shock zone can be accomplished trivially if the subject chooses to not move when it is in a place with no shock. However, the R+A+ task variant is readily learned, in which there is both a room-frame and an arena-frame shock zone (see cited work below). This task variant requires the subject to judiciously shift between avoiding the room-frame shock zone using stationary room information and avoiding the arena-frame shock zone using rotating arena information. This R+A+ task variant might meet the reviewer’s criteria for cognitive control. We have recorded hippocampal and entorhinal ensemble activity during the R+A+ task variant and it is very similar to the activity during the R+A- task we used. Nonetheless, future work will investigate the efect of mPFC lesion on the R+A+ task variant.

Cited work:

Fenton AA, Wesierska M, Kaminsky Y, Bures J (1998), Both here and there: simultaneous expression of autonomous spatial memories in rats. Proc Natl Acad Sci U S A 95:11493-11498. Kelemen E, Fenton AA (2010), Dynamic grouping of hippocampal neural activity during cognitive control of two spatial frames. PLoS Biol 8:e1000403.

Burghardt NS, Park EH, Hen R, Fenton AA (2012), Adult-born hippocampal neurons promote cognitive flexibility in mice. Hippocampus 22:1795-1808.

Park EH, Keeley S, Savin C, Ranck JB, Jr., Fenton AA (2019), How the Internally Organized Direction Sense Is Used to Navigate. Neuron 101:1-9.

Recommendations for the authors:  

Reviewer #1 (Recommendations for the authors): 

(1) Incorporate the cytochrome oxidase and hippocampal recordings (rationale and hypothesis) in the introduction, explaining how these aspects are relevant to the general question. 

We have done this as requested. See lines 159-173 of the revised introduction.

(2) Figure 1C. On Day 4-5 (conflict training) in which the shock zone was relocated 180 deg from the initial location, the behavioral tracks did not show any presence of the rat in this sector (in particular for the lesion example). Figure 4 nevertheless indicates that entrances have been made (which was expected since rats have to know that the shock zone was relocated).

Thanks for pointing this out. The tracks are from the end of the sessions. The labels have been changed to specify which trials the tracks are from.

(3) Figure 1C. The caption is huge as it contains the statistical analyses details. I would prefer to have these details in the text and keep the caption at a "reasonable" length. At the end of the caption (l. 190-191), it would be less confusing the keep the numbering of the training days: replace D1T1 with D2T1 and D2T9 with D3T9).

The statistical details have been relocated to the main text and the numbering updated, as suggested, thank you.

(4) It was not inconsiderable to show that mPFC lesion had some effects in the present task if it were only to validate the effectiveness of the lesion. This brain area has been shown to be important for planning, cognitive flexibility, etc. Indeed the authors found that the saving index was greater in sham than in mPFC rats (overdispersion in hippocampal firing was also reduced in pretraining) and interpreted this result as impaired flexibility. Would an alternative explanation be a memory deficit? I nevertheless expected that impaired flexibility in mPFC rats would be expressed in conflict trials in the form of more entrances in the zone that was initially not associated with shock (at least in the first trials of Day 4). But it appears to not be the case.

A memory deficit is unlikely to explain the difference between the groups on the first trial of Day 5. Memory in the lesion rats was tested multiple times, specifically at the start of each trial (time to first entrance), including on the 24-h retention test, and no deficits were observed. Performance on Day 9 trial 1 is worse in the lesion group than in the controls, but it is not parsimonious to attribute this to a simple memory deficit since 24-h memory was good and similar between lesion and control rats on days 3 and 4, and memory on Day 5 was equally poor in both the lesion and control rats, as measured by time to first entrance.  

(5) Material and methods. The injected volume of ibotenic acid should be mentioned. 

The volume 0.2 µl was added. See line 531.

(6) The rationale for doing the conflict training session should be indicated somewhere. 

The rationale was provided. See lines 204-208.

Reviewer #2 (Recommendations for the authors): 

(1) Line 132: The text states that all sham rats improved and only 6/10 lesion rats improved is followed by a t-test, which tests the difference between means; it does not compare proportions. Also, what criterion was used to determine if an improvement was seen or not? 

The statistical comparison is provided (now lines 230: test of proportions z = 2.3, p = 0.03). Improvement was simply numerically fewer entrances.

(2) Line 138: This is a very long and confusing sentence. Consider revising for clarity. 

The sentence (now line 234) was revised.

(3) Figure 1B only includes data from 3 animals. Most published studies show the whole dataset by presenting the largest and smallest lesions. 

Supplemental Figure S2 was added with all the lesions depicted and quantified.

(4) Figure 1C suggestion to make the schematic shock zone line up with the shock zone shown for the tracking data. 

Graphically, it looks better as drawn as it uses to perspective to depict a three-dimensional structure.

(5) Methods: Clarify if the shock zone location was the same across all rats. 

Line 570 states that the shock zone was the same for all rats.

(6) Line 158: "Behavioral tracks" is not clear. Suggest more precise wording.

Reworded to “Tracked room-frame positions” (now line 249)

(7) Line 166: "effect of trial" - should this be the main effect of trial?; "interaction" - should this be "group x trial" interaction? 

Reworded (now line 181).

(8) Line 167: "or their interaction" is awkward in the context of the sentence. 

Reworded (now line 182).

(9) Line 182: Avoid talking about "trends" as if they are almost significant unless the authors suspect that they did not have sufficient statistical power to detect differences. In that case, a power analysis should be provided. 

Removed.

(10) Line 190: "left:...right..." is hard to follow, especially with acronyms like D1T1. Consider revising for clarity. 

Revised (now lines 246-248).

(11) Line 195: "effectiveness of the PFC to impair" is unnecessarily verbose. 

Reworded (now lines 255-257).

(12) Savings results: There is a lot of variability in the lesion group. It would be interesting to know if the extent of the lesion correlates with savings.

Savings was not related to lesion. See line 259.

(13) Line 300: The thalamic recording results are not reported in the results section (other than appearing in the table). Moreover, there is no detail about which thalamic nucleus these recordings are from.

Lines 411 and 614 provides these details.  

(14) Line 312: "no longer impair" contains a grammatical error. 

Corrected (now line 422)

(15) Line 325: "was not impairing" contains a grammatical error. 

Corrected (now line 437).

(16) Line 327: The sentence ending with "...opinion of others" seems unnecessarily confrontational. 

Previous reviewers at other journals have maintained this position, we therefore included such a strong statement in our initial submission. However, we now revised this statement to avoid appearing confrontational.

(17) Line 329: Sentence is awkward. Consider revising. 

Revised (now line 443).

(18) Line 384: The authors should disclose if there was an objective metric for determining the adequacy of the lesion. 

The lesion assessment and quantification is better explained in the Methods under “Cytochrome oxidase activity and Nissl staining,” (lines 708-714).

(19) Line 385: The authors should clarify how they got from 15 rats (Line 376) to 10. 

This information is provided in the methods.

(20) Line 390: It is not clear why skin irritation in the cage mate would prevent the rat from being tested. 

This has been explained in the Methods under “Behavioral analysis followed by cytochrome oxidase activity” (lines 515-518).

(21) Methods section: The authors should describe how the tracking data were acquired. Overhead camera? Tracker based on luminance or body position? What software program was used? What was the sampling rate? 

This is now better explained in the Methods under “Active place avoidance task) (lines 538551).

(22) Methods section: Include how fast the arena was rotating and other details about the task such as where rats were placed during the ITI. 

Better explained in the Methods under “Active place avoidance task”.

(23) Line 439: The recording system used (hardware & software) should be stated. 

This is now included in the Methods (line 538).

(24) Line 435: Though overdispersion calculation is described thoroughly, there is nothing in the paper that tells me what overdispersion means. 

What the measure means is now described in the Methods under “Electrophysiology data analysis” (lines 646-650).

(25) Line 561: The test used to assess effect sizes should be stated. 

Effect sizes corresponding to the statistical tests are provided.

Reviewer #3 (Recommendations for the authors): 

(1) At the end of the conflict training, rats with mPFC lesions learned to avoid the new shock zone (Figure 1F, Block 16), but their place cells did not show room-preferring activity near the shock zone (Figure 4B). This observation questions whether spatial frame-specific representation is relevant for active avoidance. Can the authors clarify this point?

This is a dynamic behavior and the hippocampal dynamics match, changing with a dynamic that is a few seconds, as we have shown in several published papers. The lack of a preference averaged over 20 minutes when the rats are avoiding both the current and former shock zones during the conflict session is pretty much what would be expected from such a coarse measurement. The important measure is the spatially-resolved measure of room versus arena preference. Figure 4B shows that in the lesion rats there is less of a frame preference during conflict, generally (consistent with poorer flexibility). However, Figure 4D quantifies the frame preference near and far from the shock zone and accordingly, there is no difference between the groups.

(2) Related to the point above, the author might consider including panels in Figures 4C and D to show the neural activity during the pretraining and conflict training retention period. I assume p(room) will be comparable between the Near and Far segment in both sessions, but the p(room) may be higher in the Conflict training session than the Pretraining session. This would show that the mPFC lesion impairs suppressing the place cell activity encoding the old shock location. 

Thanks for the suggestion. While we don’t think we can draw any strong conclusions from this analysis we are fine to show it. The issue is that during conflict, the rats have two perfectly reasonable representations of where there was shock, the initial location that was turned off to make the conflict, and the most recent conflict location of shock. Importantly, these recordings are during conflict retention after we turned off the shock for the retention recording (for the second time in the rat’s experience). Turning off the shock allows us to exactly match the physical conditions of pretraining, initial retention and conflict retention, which was the experimental design’s goal. However, the experiential history of the rats prior to initial retention and conflict retention cannot match, because during initial retention the rats had never experienced a changed shock zone whereas, by conflict retention, they had experienced multiple changes. Importantly, we have previously shown that mouse hippocampal ensembles represent both initial and conflict shock locations, as the animals consider their options during conflict trials (see Dvorak et al 2018, PLoS Biol 16:e2003354). Consequently, we cannot make any strong predictions about whether or not hippocampal activity during conflict retention should be room-frame preferring selectively in the vicinity of the current shock zone. As I am sure the reviewer appreciates from their own introspection, mental representations are mercifully not obliged to dictate behavior. In fact, that is what is interesting and controversial about cognitive control – it is a dynamic internal process and the innovation of our work lies in demonstrating that one cannot only rely on behavior to assess this process. Nonetheless, we did this analysis and now present it in the revised Fig. 4. During pretraining both lesion and sham groups express no particular spatially-modulated preference for either the room or the arena frame, as expected. During initial training both groups express a room-frame preference in the vicinity of the shock zone, as we initially reported. By inspection, during conflict, the sham rats express a preference for room-frame activity in the vicinity of the most recent shock zone location; this preference is weaker than what is expressed during initial retention. The lesion rats do not show this preference. These impressions are quantified in revised Fig. 4D; the comparisons within the conflict retention sessions did not reach statistical significance. We leave it to the reader to interpret what that means. Thanks for the nudge.

(3) The significant group difference in place cell overdispersion during the pretraining phase (Figure 3C) is interesting, but some readers would appreciate additional sentences on its functional implication. Does it mean the spatial tuning of place cells was disrupted by the mPFC lesion?

Only the reliability of spatial firing was altered, not the spatial tuning.

(4) Although the method section described how to calculate overdispersion and SFEP, some concise, intuitive descriptions of these measures in the result section would help readers understand these results.

Overdispersion is better explained. See lines 646-650.

(5) I recommend adding a figure of the task performance of the rats used in the electrophysiological recording experiment and a table summarizing the number of cells recorded per animal. 

We have included Table S2 with the cell counts and a summary of the performance for each of the rat in the electrophysiological recording experiment.

(6) Readers would appreciate additional information on task apparatus, such as the size, appearance, and rotating speed of the arena, as well as stationary cues available in the room. 

This is now provided in the Methods under “Active place avoidance task”.

(7) Lines 425-416: "On the fourth day of the behavioral training, the rats had a single trial with the shock on to test retention of the training." Shouldn't it be "shock off"? 

No the shock was on to prevent extinction learning and to increase the challenge for conflict learning.

Voici un sommaire de la discussion avec des indications temporelles basées sur l'ordre d'apparition dans le transcript :

• Introduction et présentation d'Albert Moukheiber (Docteur en neurosciences et psychologue clinicien). Il explique qu'il essaie d'allier la compréhension du fonctionnement du cerveau avec ses applications pratiques.
• Genèse du livre "Neuromania". L'idée initiale était de déconstruire des phrases courantes et simplistes entendues sur le cerveau et notre fonctionnement. Des exemples incluent l'idée que les gens n'aiment pas changer ou que la tristesse est due à un déséquilibre chimique. L'auteur cite Stanislas Lem sur le danger de la sursimplification.
• Pourquoi ces simplifications ont pris autant de place. Plusieurs raisons sont évoquées : le caractère récent des neurosciences, l'incertitude scientifique ouvrant la voie aux interprétations, et l'instrumentalisation des découvertes scientifiques (comme la radioactivité ou la physique quantique à des fins marketing).
• Remise en question du paradigme selon lequel "les gens n'aiment pas changer". Ce concept est davantage lié aux systèmes qu'aux individus. La résistance au changement est souvent utilisée pour justifier des passages en force. Le changement dépend du contexte et de ce qu'il implique.
• La possibilité de changer et la question de la patience. Changer de tempérament est différent de la résistance au changement organisationnel. Les traits de personnalité (stables) se distinguent des états (temporaires). Le changement prend du temps, ce qui peut être problématique pour quelqu'un d'impatient.
• Différences entre traits et états de personnalité. Des exemples sont donnés pour illustrer comment quelqu'un peut être colérique (trait) mais pas toujours en colère (état), et comment le contexte influence l'expression de ces traits.
• La stabilité des traits pour la prédictibilité et la coopération. Un changement trop rapide et constant rendrait la collaboration difficile. L'idée d'un "self" unique et constant est remise en question par la notion de "self switching" selon les contextes.
• Comment travailler sur l'impatience. Identifier les situations déclenchantes, les conditions propices et agir au niveau approprié. Accepter que le progrès se fait par étapes et prend du temps. La "cognition incarnée" souligne que nos réactions dépendent de notre corps et de notre environnement.
• Stratégies pour gérer l'impatience. "Think our way out of it" est possible dans certaines situations (exemple du métro en retard), mais pas dans d'autres. Distraction, relaxation corporelle et se souvenir des succès ("count the hits not the misses") sont des pistes. Il faut aussi accepter parfois de ressentir de la frustration.
• La dichotomie émotion/raison. L'auteur a toujours eu un problème avec la hiérarchisation de ces processus. Souvent, les pensées justifient les émotions. L'opposition frontale est rare.
• Distinction entre émotions (substrat biologique), ressenti phénoménologique (affects) et communication de l'émotion. Le langage français utilise souvent le même mot pour ces trois réalités, contrairement à l'anglais ("emotions" et "feelings"). Dans le cerveau, il y a des neurones, pas des émotions ou des pensées en soi.
• L'opposition se fait plutôt entre différentes émotions et pensées. L'exemple de la jalousie ou de l'énervement suivi d'un apaisement est donné. Penser en termes de diades ou de triades (incluant le ressenti corporel) est plus pertinent. Les personnes anosognosiques (ne reconnaissant pas leurs émotions) ne deviennent pas de meilleurs décideurs rationnels, car les émotions sont une forme de feedback.
• Rôle des émotions comme feedback pour adapter notre comportement. Le déficit émotionnel rend le comportement inopérant. Les bases biologiques des émotions sont dans le cerveau et le corps (cognition incarnée). L'opposition émotion/raison remonte à l'Antiquité (Platon).
• Les émotions peuvent être remplacées. Nous avons des émotions et des pensées automatiques, mais aussi des processus métacognitifs et métaémotionnels qui permettent de les modifier progressivement. La rumination (une cognition) nourrit l'émotion, montrant leur interdépendance.
• La porte d'entrée pour casser un cycle émotionnel/cognitif dépend des individus. Injonction émotionnelle, raisonnement, action corporelle, distraction attentionnelle ou acceptation peuvent être utilisés. Les émotions négatives sont importantes et adaptées dans certains contextes (deuil après un licenciement). Vouloir toujours aller bien est une forme de folie.
• L'idéologie de la maximisation et de l'efficience. Cette tendance, visible dans le minimalisme et la technologie, se reflète dans la volonté de maximiser les expériences positives et d'éliminer les émotions négatives. L'auteur ne cherche pas à se maximiser constamment dans tous les domaines.
• Apprendre plus vite est parfois une illusion. Il n'y a pas toujours de raccourcis, notamment pour l'apprentissage des langues. Des vendeurs profitent du désespoir pour vendre des méthodes inefficaces. L'immersion est souvent nécessaire.
• La quête d'amélioration et la comparaison aux autres. Admirer ceux qui apprennent vite ne doit pas nous faire oublier que les compétences varient. Il faut accepter les "règles du jeu" de notre esprit, comme on accepte les limites physiques de notre corps.
• Comparaison avec les capacités physiques. On n'aurait pas la même discussion sur la possibilité de sauter du troisième étage ou de soulever une voiture. L'amélioration du cerveau passe par son utilisation. La quête d'une amélioration constante peut être une source d'insatisfaction.
• La motivation et le changement constant. Ce qui motive l'auteur n'est pas une quête intense, mais plutôt l'intérêt. Le changement est inévitable ; la question est comment et à quelle vitesse.
• Philosophie de vie et éducation de sa fille. Ne pas se prendre au sérieux est une protection. Ne pas lui dire qu'elle est spéciale, mais normale.
• Remise en question du système scolaire. L'école n'est pas conçue pour le bien-être des enfants, mais pour permettre aux parents de travailler. L'heure de réveil est absurde. Malgré cela, sa fille ira à l'école comme les autres.
• Inculquer la tolérance à la frustration. La société actuelle cherche à éviter la frustration, ce qui pourrait être problématique. Le côté coercitif de l'école peut paradoxalement aider à développer cette tolérance.
• La période d'adaptation à l'école. L'objectif initial est la survie et l'adaptation. L'apprentissage viendra plus tard. L'auteur évite les pratiques éducatives qu'il juge néfastes. Il n'a pas lu de livres sur la parentalité pour éviter de se "mêler le cerveau".
• Le phénomène de l'éducation positive. L'enfant d'une amie, élevé sans se voir dire non, semble bien se développer, mais l'avenir reste incertain. Il faut éviter le piège de la cause unique : de nombreux facteurs influencent le développement d'un enfant (amis, professeurs, etc.). L'éducation positive reflète aussi le désir des adultes d'éviter la frustration.
• La notion de "bon niveau explicatif". Pour comprendre un phénomène, il faut l'observer au niveau pertinent (exemple de l'embouteillage, de la voiture, d'Alzheimer, de la dépression). Pour comprendre le développement d'un enfant, il faut considérer la famille, l'entourage et la société. Nous avons des pistes sur ce qu'il ne faut pas faire (manque d'affection) et quelques-unes sur ce qu'il faut faire, mais pas de feuille de route précise. La société actuelle produit de la frustration, d'où l'importance de la tolérance.
• L'approche translationnelle (aller-retour entre théorie et pratique). L'expérience clinique (thérapie) a montré que le déséquilibre chimique n'est pas le bon niveau explicatif pour la dépression, car des facteurs de vie concrets sont souvent en cause. La science a longtemps cherché à objectiver, mais la subjectivité humaine est essentielle.
• Le défi de développer une science de la subjectivité. La douleur est un exemple de phénomène subjectif difficile à objectiver. Les inégalités de traitement de la douleur entre hommes et femmes sont mentionnées. La recherche sur le cerveau se fait majoritairement en IRM, dans des conditions non naturelles. Il y a une surestimation de ce que l'on sait sur le cerveau ("Neuromania").
• Appel à refaire de la science avec émerveillement et imagination. La science actuelle est perçue comme trop aseptisée (blouses blanches et statistiques). L'imagination et l'émotion sont nécessaires pour faire avancer la science. La collaboration entre différentes expertises (philosophie, mathématiques, biologie) est cruciale.
• Paradoxe apparent entre la critique de la surresponsabilisation de l'individu et l'appel à une science de la subjectivité. Retour à la notion de niveau explicatif : selon le phénomène étudié, le niveau pertinent varie. S'intéresser à la subjectivité est important pour comprendre l'individu, mais pour certains phénomènes (comme un embouteillage ou une tendance sociétale), il faut considérer un niveau d'analyse plus large.
• ** L'exemple de la propriété émergente de l'eau (nécessité de six molécules pour "mouiller")**. Certains phénomènes n'apparaissent qu'à un certain niveau d'organisation. Pour comprendre le vote, regarder l'activité cérébrale individuelle est descriptif, pas explicatif.
• Importance de distinguer le bon niveau explicatif, la correspondance entre les niveaux et la différence entre description et explication (corrélation vs causalité). L'exemple de la corrélation entre le nombre de McDo et les cas de COVID est donné.
• Facteurs méta (sociétaux) sous-estimés. Organisation sociétale, rythme social, heures de travail, pression financière et matérielle. La perte des aides sociales et communautaires d'avant est évoquée. L'explosion des burnouts et des troubles anxieux ces dernières années est notable.
• Le biais des populations "WEIRD" (White, Industrialized, Educated, Rich, and Democratic). La majorité des études en psychologie et neurosciences sont réalisées sur ces populations, puis généralisées à toute l'humanité, ce qui est problématique car ces populations ne sont pas représentatives.
• Exemple de jeu économique (ultimatum game) montrant des différences culturelles dans les comportements. Les étudiants américains réagissent différemment des populations d'autres cultures face à des offres inéquitables.
• Remise en question de croyances psychologiques universelles basées sur des échantillons restreints. La culture a une influence profonde. L'exemple du faible taux de natalité en Corée, potentiellement lié au patriarcat, est donné.
• La complexité des problèmes et le rejet des solutions simplistes. L'auteur se sent dépassé par cette complexité. L'exemple de la dépression, parfois liée à l'environnement social et culturel, est mentionné.
• Nécessité d'une "thérapie de la société" plutôt que d'une sur-focalisation sur l'individu et le développement personnel. La volonté individuelle a ses limites face aux déterminants environnementaux et sociaux.
• Expérience de l'auteur face à des patients où il se sent impuissant à aider. Il en parle ouvertement avec le patient et l'oriente parfois vers d'autres thérapeutes plus adaptés. La psychologie clinique est encore une discipline jeune avec des limites. Il est important de reconnaître ces limites pour éviter de culpabiliser les patients.
• Reconnaissance des limites actuelles de la psychologie clinique. Le mythe d'une compréhension totale de la créativité, des émotions, etc., est dangereux. Il est crucial d'expliquer aux patients que leur souffrance n'est pas forcément de leur faute. La psychologie clinique n'est pas aussi mature que d'autres domaines de la santé.
• Le mensonge des cinq sens. Nous en avons neuf : la vue, l'ouïe, l'odorat, le goût, le toucher, la thermosception (chaleur/froid), la proprioception (position du corps), la nociception (douleur) et l'interception (organes internes). L'omission des quatre autres est un mystère.
• Le pseudo "sixième sens" (souvent associé à l'intuition). L'auteur pense que ceux qui ont popularisé cette idée ignoraient l'existence des quatre autres sens. Le sixième sens est interprété différemment par chacun.
• La fascination du réel déjà complexe. L'auteur ne comprend pas pourquoi on cherche à ajouter des artifices (comme des "auras") alors que le fonctionnement réel est déjà extraordinaire. La réalité est plus complexe à comprendre qu'à inventer. La facilité n'est pas nécessairement ce que les gens aiment, mais certains profitent de ce biais apparent.
• Les raisons du succès des solutions simplistes et erronées. Promesse de résultats rapides et faciles, détresse des personnes cherchant de l'aide, asymétrie argumentative (il est plus facile de convaincre de ne pas faire quelque chose en exagérant un risque que de convaincre de faire quelque chose en prouvant une sécurité à 100%). La loi du nombre explique le succès des démarcheurs téléphoniques.
• Les neuf sens et la misophonie (sensibilité excessive à certains sons). La question de savoir comment agir sur ces sens est posée. L'auteur partage son expérience de la misophonie et de la difficulté à la gérer.
• Le bon niveau explicatif pour la misophonie est l'interaction, pas seulement l'individu. Avant de chercher à changer, il faut évaluer l'impact du problème. Des solutions mécaniques (atténuation du son) peuvent être envisagées, mais sont contraignantes. Un travail sur la gestion émotionnelle peut aider. Parfois, l'acceptation est la meilleure voie.
• Fonctionnement "bottom-up" (des sens vers le cerveau) et découverte récente du fonctionnement "top-down" (du cerveau vers les sens). Notre cerveau "hallucine" activement le réel (processing prédictif).
• Le cerveau influence activement notre perception sensorielle. Les illusions d'optique illustrent ce phénomène. Il serait intéressant d'explorer si des processus "top-down" pourraient aider à moduler la sensibilité à certains sons comme dans la misophonie. Référence à Andy Clark et son livre "The Experience Machine" sur le cerveau prédictif.
• Expériences personnelles de l'auteur en matière de changement. Il a consciemment travaillé sur son anxiété, sa manière de parler et sa gestuelle. Il essaie d'avoir plus d'opinions et de ne pas être toujours indifférent aux choix collectifs. Le changement est constant.
• Recommandation d'une ressource marquante récente : la série "Severance" sur Apple TV+. Une série de science-fiction explorant la séparation entre vie professionnelle et personnelle au niveau cérébral.
• Le plus grand obstacle surmonté par l'auteur : son anxiété et son stress durant l'adolescence. L'acceptation de soi et le fait de moins se prendre au sérieux l'ont aidé.
• Personnes que l'auteur aimerait entendre au micro : Zoé Dubu (historienne des psychédéliques) ou Lucy Berkovitz (psychiatre travaillant sur la psilocybine).
• Définition de "ne pas prendre le pouvoir de sa vie" : la subir. Clarification importante : il ne s'agit pas de prôner la soumission, mais de souligner que vouloir tout contrôler et subir sont tous deux indésirables. Maximiser l'agentivité est essentiel, mais cela passe par un effort collectif et ne dépend pas uniquement de la volonté individuelle.
• Conclusion et remerciements. Invitation à soutenir les librairies indépendantes.

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Reply to the reviewers

__Reviewer #1 __

(Evidence, reproducibility and clarity (Required)):

The manuscript identified a novel role of Intraflagellar Transport Protein 20 (IFT20) in the function and homeostasis of lymphatic endothelial junctions. The authors showed that IFT20 regulates VE-cadherin localization at adherens junctions in lymphatic endothelial cells. The authors performed impressive in vivo work that shows the requirement for IFT20 for the homeostasis of intercellular junctions, lymphangiogenesis, and drainage function of lymphatic vessels. In contrast, the cell biology part of the paper was underwhelming and will need significant revisions to support the proposed model. In the result section, several conclusions have to be toned down to match the actual results. The study employs in vivo mouse models, immunofluorescence, biochemical assays, and loss-of-function experiments to support their conclusions.

Major comments - The authors present disrupted localization of VE-cadherin. Is this a mislocalization and/or protein stability issue in IFT20 KD cells? A western blot can help assess protein levels, and a phase-chase endocytosis assay of VE-cadherin can strengthen evidence. The authors did not confirm the permeability phenotype seen in vivo.

We thank the reviewer for this helpful suggestion.

Planned 1: Western blot to assess total VE-cadherin protein levels in IFT20 WT and KD cells.

Planned 2: Immunofluorescence staining for cell-surface VE-cadherin using permeabilized and non-permeabilized IFT20 WT and KD cells during VEGF-C stimulation and washout.

Together, these two experiments will assess VE-cadherin stability and more directly test the hypothesis that VE-cadherin does not recycle effectively back to the cell surface in the absence of IFT20.

• While the authors focused on IFT20 and rab5, we do not have a clear idea about the vesicular dynamics as well as the status of early, late, and recycling endosomes in IFT20 KD cells. Is IFT 20 localized to non-rab5+ endosomes, and if yes, what are the species? A more general endosomal profiling would help strengthen the authors' message. For example, in Fig. 4-5, the authors will have to stain for other early endosomal markers as well as late, and recycling endosomal markers in control and IFT20 KD cells.

Thank you for this helpful suggestion.

Planned 3: Immunofluorescence staining for EEA1 (early endosome), RAB7 (late endosome), RAB4 (fast recycling), RAB11 (recycling endosome) along with IFT20 to determine its localization pattern.

This experiment will determine the localization of IFT20 relative to various endosomal compartments.

• In fig. 6C, a majority of VE-cadherin is not associated with Rab5. Staining with additional endosomal markers might help identify other endosomal species colocalizing with VE-cadherin. It will be critical to add to Fig. 6c the intensity profiles depicting colocalizations. The authors can also live image a fluorescently (f)-tagged VE-cadherin (maybe with another f-tagged rab5) and assess their association dynamics in IFT20 KD cells (similar to fig6C).

Thank you for this helpful suggestion.

Planned 4: Immunofluorescence staining for EEA1 (early endosome), RAB7 (late endosome), RAB4 (fast recycling), RAB11 (recycling endosome) along with VE-cadherin in IFT20 WT and KD cells to determine its localization pattern.

Planned 5: Additional colocalization analysis such as adding intensity profiles and possibly proximity ligation assay.

Beyond the scope of this manuscript 1: While we agree that imaging the dynamics of FP-tagged VE-cadherin in live cells would provide more detail about its localization, we feel that this is beyond the scope of the current manuscript.

These experiments will determine the localization of VE-cadherin across various endosomal compartments and strengthen the current colocalization data.

• Primary cilia do seem to regulate vascular plexus in the mouse retina as well as endothelial permeability through mediating subcellular localization of junction proteins. The authors do not clearly exclude the ciliary function of IFT20 in mediating lymphatic endothelial cell-cell junctions. A rescue experiment can help settle this question by targeting IFT20 exclusively to cilia (or not) and assessing, for example, VE-cadherin localization. The following is optional: It is also unclear whether the described regulation is specific to IFT20 or can be phenocopied by the ablation of another IFT subunit and/or cilia ablation through the depletion of a non-IFT cilia assembly regulator.

Thank you for this helpful suggestion. We propose an alternative strategy.

Planned 6: To determine the role of ciliary vs. nonciliary functions, we will knockdown IFT74, an IFT protein in the same IFT complex B as IFT20 that is required for cilia assembly and function but is not known to participate in vesicular trafficking. We will assess VE-cadherin localization in IFT74 WT and KD cells by immunofluorescence.

Beyond the scope of the manuscript 2: We have not optimized reagents for targeting IFT20 to the cilium (e.g. ciliary targeting sequence) and believe that assessing the effects of a protein from the same IFT complex (IFT74) without known nonciliary functions will alleviate the reviewer’s concern.

• Figs. 7A and B do not seem very convincing. The control vs. IFT20 KD western blot levels look mostly similar between the two conditions. The result section does not translate the actual data in Fig. 7A and B. Additionally, there are no statistical comparisons between control and KD conditions in the graphs. Except for a potential pVEGFR-3 increase at 30 min VEGF-C in IFT20 KD cells, but after washout the level is similar to control. This figure does not support well the model presented in fig. 8. The conclusion in lines 456-459 has to be toned down.

Thank you for this helpful suggestion.

Planned 7: We will remove these western blot data with the exception of pVEGFR-3 and add phospho-tyrosine immunofluorescence. We will use immunofluorescence to quantify phosphorylated tyrosine levels and repeat western blots for pVEGFR-3 at different concentrations and time points of VEGF-C stimulation in IFT20 WT and KD cells. We will remove the other western blot data and revise the text accordingly. We will also attempt to pull down total VEGFR-3 and then blot for pVEGFR-3 to improve sensitivity of this assay.

These experiments will focus our analysis on the activation of VEGFR-3.

• The authors were not able to stain for pVEGFR3. It would still be helpful to see a colocalization between total VEGFR3, IFT20, and VE-cadherin in control cells and IFT20 KD cells (VEGFR3 and VE-cadherin).

Thank you for this helpful suggestion.

Planned 8: We will perform immunofluorescence for VEGFR-3, IFT20, and VECAD and assess their localization.

Minor comments - The control used in Figures 1 and 2 does not seem ideal. The proper control would be IFT20fl/fl cre neg. Is there a reason why the authors excluded a lox allele in control? Also, the authors have to provide the mice age used in these figures and when the Cre kicks in in the result section.

Thank you for this helpful suggestion.

Planned 9: We will clarify the use of control genotypes, and add mouse ages and Cre details to results/methods. This is a constitutive LYVE-1 Cre.

• Please describe the overall mouse phenotype(s) of the LYVE1 CRE-IFT20 flox.

Thank you for pointing out this oversight.

Planned 10: We will include a description of the overall phenotypes of LYVE1 Cre IFT20 KOs in the text. One notable phenotype is abdominal ascites.

• Line 109:'By expression, the authors probably mean immunostained.

Thank you for pointing out this oversight.

Planned 11: We will change to “immunostaining for”.

• Many graphs exhibit undefined Y-axis labels and units. Please clarify these as well as the way they were quantified. Include such information in figure legends and/or in the materials and methods section. The figures in question are fig1C, E and F, fig2E, fig3E, fig4b and D, fig6b and D, fig7B and C.

Thank you for this helpful suggestion.

Planned 12: We will clarify the quantification strategies and units in the text and figure legends and make sure the axes are clearly labeled.

• Line 295:'homeostasis"-the authors probably mean in a serum-rich condition.

Thank you for this helpful suggestion.

Planned 13: That is indeed what we meant. We will merge this sentence and the next sentence to be clearer.

• Fig4C specifically the lower two images on the right side: the images do not seem to represent the corresponding graphs.

Thank you for this helpful suggestion.

Planned 14: We will double check these images and adjust if necessary.

• Please add the statistical tests used to evaluate significance in all figure legends.

Thank you for pointing out this oversight.

Planned 15: We will be sure statistical tests are named in all figure legends.

Reviewer #1 (Significance (Required)):

This study provides novel insights into IFT20's role in VE-cadherin trafficking and endothelial junction stability, with its strongest aspect being the in vivo data in Figures 1 and 2, demonstrating lymphatic defects upon IFT20 loss. This represents a conceptual advance by extending IFT protein function beyond cilia (if one of the major comments is addressed) to vascular integrity. However, mechanistic depth is lacking, and ciliary role was not tested-additional rescue and colocalization experiments are needed to confirm the model. The study will interest vascular and lymphatic biologists, as well as cell biologists studying intracellular trafficking and cilia.

Expertise: cilia and mouse genetics

__Reviewer #2 __

(Evidence, reproducibility and clarity (Required)):

Paulson et al. use an in vivo model of IFT20 deletion (Lyve1-Cre) and primary lymphatic endothelial cell (LEC) cultures to investigate the role of IFT20 in controlling LEC-LEC junction dynamics. The key findings/suggestions include: i) Authors show alterations in the VE-cadherin (or ZO-1) staining at the LEC junctions upon IFT20 deletion or silencing. ii) They also show evidence of the IFT20 localization to RAB5 endosomes and alteration of RAB5 endosome dynamics upon IFT20 silencing.

In the current manuscript, some of the key data are not convincing. Further experimentation and analysis (also of the existing data) are needed to solidify the authors' statements as detailed below. I expect that the suggested experiments can be executed in 3-to-6 months and require, at least, antibodies, which have not been used in the current manuscript.

Major comments

1. The data information, presented in the figure legends, is difficult to understand. The authors should always indicate how many biological replicates and independent experiments the data is derived from. This holds also for the representative images. Now, it seems that some of the quantified data are derived from only 1 experiment (see, for example, rows 423-425: "Graphs show one representative biological replicate of two, each comprising two technical replicates with 100+ cells per condition"). The quantifications should be based on data from at least three independent experiments.

Often data points represent the field of views from a single sample, thus, biasing the statistical testing. The data points should represent biological replicates or independent experiments to allow the reader to make conclusions, about whether the findings are statistically significant and can be repeated.

Thank you for this helpful critique.

Planned 16: We will be sure to indicate biological and technical replicates and ensure that quantifications are representative of at least three independent experiments. We will also ensure that quantifications are statistically robust.

The Lyve1-Cre is not specific for lymphatic vasculature (for example https://www.jax.org/strain/012601# and Lee LK et al. 2020, Cell Reports), as also stated by the authors (row 112). However, this is not shown in the data and complicates the interpretation of the data. Here, authors can stain the IFT20 with their existing mouse IFT20-specific antibody to show the loss in the lymphatic and/or blood vasculature. If IFT20 is lost in both vasculature types, it is not possible to say "lymphatic specific" (for example, row 143) and draw conclusions that the observed phenotypes would be primary to IFT20 loss in the lymphatic vasculature.

Thank you for this helpful suggestion.

Planned 17: We will assess IFT20 KO in blood vasculature and tone down lymphatic-specific language in the text.

The authors write (rows 164-168) "Lymphatic vessels in the IFT20 KO or VE-cadherin KO embryonic dorsal skin exhibited increased and variable lumen size and excessive branching, suggesting that impaired lymphatic organization and function contributed to the fluid homeostasis defect. Here, immunofluorescence staining for LYVE-1 in the ear skin revealed similar patterning defects in adult IFT20 KO lymphatic vessels (Figure 2A), that have also been described in VE-cadherin KO mice (Hägerling et al., 2018)." However, based on Figure 2A, it is not obvious that there would be excessive lymphatic vessel branching, impaired organization or similarities to VE-cadherin deleted lymphatic vessels. To justify their statement, the authors should provide quantification of the branching (at least 3 mice/genotype).

Thank you for this helpful critique.

Planned 18: Based on the suggestion from Reviewer 3, we will remove these morphological and skin drainage data.

IFT20 deletion or silencing causes alterations in the cell junction pattern/VE-cadherin intensity. The authors' interpretation that IFT20 deletion/silencing would cause discontinuous or "button-like" junctions is not supported by the provided images (Figures 1E, 3F, 6A, 6C). Rather, it seems that the levels of VE-cadherin in vivo are decreased, whereas the "continuity" of the junction is not altered. In cell culture, IFT20 silencing seems to cause wider and, to some extent, overlapping VE-cadherin junctions and not "discontinuous". These junctions may represent a more immature state. The authors should change the nomenclature accordingly or provide additional data. Using the existing cell culture experiment images, it would be more appropriate to analyze the width of the VE-cadherin junctions, instead of the "granularity".

Thank you for this helpful suggestion.

To assess VE-cadherin levels in vitro, we will perform western blots as described in Planned 1 above.

Planned 19: We will measure widths of junctions from IFT20 KD and WT images and adjust the language in the text.

Paulson et al. show images of IFT20 and RAB5 double-stained samples. The co-localization seems to happen mostly at the weakly IFT20 positive puncta (Figure 3A-B). Authors should show the disappearance of the signal in the siIFT20 treated samples (in comparison to siControl samples) to highlight the specificity of the weak signal.

Thank you for this helpful suggestion.

Planned 20: We will add data showing the IFT20 KD more clearly at high magnification.

1. The Authors analyze the co-localization of VE-cadherin and RAB5 as co-localization area (Figure 6C-D). The images show that the co-localization is stated to happen at LEC periphery/junctions. LEC periphery is notoriously thin and microscope Z-resolution does not allow distinction of truly co-localizing or "on top of each other" signal. Based on row 607 co-localization would be expected to happen at least in EEA1+ vesicles, which are located perinuclearly (not at the junctions) in LECs (Korhonen et al. 2022, JCI). Authors could use EEA1, RAB5, and VE-cadherin triple staining for the quantification.

Thank you for this helpful suggestion.

Please see Planned 3 and Planned 4 above where we propose experiments to address this concern.

In the current experiments, authors cannot conclude whether the VE-cadherin signal is at the cell junction (non-internalized), in endosomes (internalized during the experiment), or newly produced VE-cadherin on its way to the plasma membrane. To allow conclusions about the internalized VE-cadherin, and its localization in RAB5 vesicles, authors should conduct, for example, a classical endocytosis assay: incubation of live cells with non-blocking anti-VE-cadherin antibody, followed by acid wash to remove the non-internalized antibody, fixation and staining for RAB5. Also, shorter VEGF-C treatment would allow conclusions about the VE-cadherin dynamics.

Thank you for this helpful suggestion.

In Planned 2 above, we will perform immunofluorescence staining for cell-surface VE-cadherin using permeabilized and non-permeabilized IFT20 WT and KD cells during VEGF-C stimulation at various timepoints and washout to address this concern.

siRNAs can have off-target effects and, thus, the use of at least two independent methods/oligos for silencing is needed. Paulson et al. use a pool of 4 oligos for silencing. They should rather test the efficacy of the single oligos and then use the two best oligos (1/sample) to show and quantify the same phenotype. This is needed at least for the key experiments shown in Figures 4C-D, Figure 6A-B (see also comment #3), Figure 7A-B

Thank you for this helpful suggestion. We chose these reagents based on pooled siRNAs at low concentration minimizing off-target effects while still achieving strong KD vs. single siRNAs at higher concentration. Please see this technical note for further information about minimizing off-target effects by the use of pooled siRNAs vs. single siRNAs: https://horizondiscovery.com/-/media/Files/Horizon/resources/Application-notes/off-target-tech-review-technote.pdf?sc_lang=en

1. “SMARTpool siRNA reagents pool four highly functional SMARTselection designed siRNAs targeting the same gene. Studies show that strong on-target gene knockdown can be achieved with minimal off-target effects if a pool consisting of highly functional multiple siRNA is subsituted for individual duplexes. This finding is in contrast to speculation that mixtrues of siRNAs can compound off-target effects. … [Their data show that] while individual duplexes delivered at 100 nM can induce varying numbers of off-targeted genes, transfection of the corresponding SMARTpool siRNA (100 nM total concentration) induces only a fraction of the total off-target profile.”
2. “Our scientists have identified a unique combination of [chemical] modifications that eliminate as much as 80% of off-target effects.”
3. “The ON-TARGETplus product line is comprised of four individual siRNAs, and SMARTpool reagents which are chemically modified and rationally designed to minimize off-target effects.”

   OPTIONAL: Paulson et al stated in the first article (2021, Front. Cell Dev. Biol.) that IFT20 deletion/silencing causes lymphatic endothelial phenotypes due to its role in primary cilia, whereas here the authors conclude that IFT20 controls VE-cadherin dynamics at the RAB5 vesicles. However, the current experiments cannot dissect the role of IFT20 in these two distinct locations. For this, authors could delete/silence another gene required for primary cilia or RAB5 endosomes and then analyze, which IFT20 phenotypes are recapitulated.

Thank you for this helpful suggestion. Please see Planned 6 above where we propose to determine the role of ciliary vs. nonciliary IFT functions by knocking down IFT74, an IFT protein in the same IFT complex B as IFT20 that is required for cilia assembly and function but is not known to participate in vesicular trafficking. We will assess VE-cadherin localization in IFT74 WT and KD cells by immunofluorescence.

The data shown in Figure 2 B-E (Lymphatic drainage) is not necessary for the current manuscript ("IFT20 regulates VE-cadherin traffic in LECs") and can be removed. As the authors state in the manuscript, the drainage phenotype may be due to lymphatic vessel valve defects (rows 584-585) rather than primary for LEC-LEC junction defects. The data does not justify the abstract sentence "and lymph transport is impaired by intracellular sequestration of VE-cadherin" (row 42).

Thank you for this helpful suggestion. Please see Planned 18 above, where we propose to remove these data.

Minor comments

1. For some of the images, the signal should be enhanced to allow visual inspection also in the paper version (Figures 5A-B and 6C, magenta).

Thank you for this helpful suggestion.

Planned 21: We will enhance the signal in the indicated figures.

Authors show representative Western Blots and quantification of several biological replicates/sample types to investigate signaling responses upon VEGF-C treatment of control and siIFT20 cells. The authors state that the P-levels of VEGFR3, ERK, VE-cadherin, and AKT have different dynamics in control and IFT20-silenced cells. To justify this conclusion, authors should test the statistical significance between the siControl and siIFT20 samples at each time point. The current quantification (Figure 7B) shows that there is, at least, a trend of increased p-VEGFR3, p-VE-cadherin, p-ERK, and p-AKT in IFT20 silenced cells. However, the representative Western Blot image does not display a clear difference (Figure 7A). Authors should include the original western blots, used for quantification, as supplements.

Thank you for this helpful suggestion. Please see Planned 7 above where we propose to remove these data with the exception of pVEGFR-3 and add corresponding immunofluorescence data. We will ensure blots are included as supplemental figures.

The authors use western blot quantification to show that the altered LEC junctions affect VEGFR3 signaling. They further hypothesize that the increased VEGFR3 signaling may be a consequence of VEGFR3 localization in endosomes. The authors did not detect any signal using the phospho-specific VEGFR3 antibody (rows 441-442). To analyze the location of VEGFR3 upon VEGF-C treatment in siControl and siIFT20 LECs, the authors should use anti-VEGFR3 (total) antibodies that have been shown to detect VEGFR3 in similar assays.

Thank you for this helpful suggestion.

Please see Planned 8 above where we will perform immunofluorescence for VEGFR-3, IFT20, and VECAD and assess their localization.

The normality of the data should be tested before the selection of the statistical test. If this has been done, please, indicate it in the materials and methods or re-run the statistical analysis, if some of the data is not normally distributed.

Thank you for this helpful suggestion.

Planned 22: We will double check the statistics and normality for all quantifications.

The authors should use arrows, arrowheads, etc. to highlight examples of relevant features in the images. For example, in Figure 3C, the increased stress fiber formation is not obvious to the reader.

Thank you for this helpful suggestion.

Planned 23: We will add arrows etc. where appropriate.

Reviewer #2 (Significance (Required)):

Lymphatics are essential for fluid, leukocyte, and lipid trafficking to lymph nodes and/or systemic circulation. Recent findings have promoted lymphatics as a potential target to control the level of adaptive immunity in inflammation-associated diseases, including tumorigenesis (for example Song et al 2020, Nature). Early work on lymphatic endothelium in vivo, highlighted the dynamics of lymphatic endothelial junction, which, reversibly, can alter between continuous and discontinuous ("button-like") states (Baluk 2007, Am. Jour. Pathol.; Yao 2012, Am J. Pathol.). These changes may have an effect on fluid drainage capacity, lymphatic vessel growth, and prevention of pathogen dissemination to the systemic circulation. Recently, lymphatic junctions have been shown to present hubs of VEGFR3 signaling, VEGFR3 and VE-cadherin dynamics, and leukocyte transmigration (Sung et al. 2022, Nat. Cardiovasc. Res.; Hagerling et al. 2018, EMBO J.; Liaqat et al. 2024, EMBO J.). Thus, the manuscript by Paulson et al. investigates a topical subject.

The authors suggest a role for IFT20 in the control of VE-cadherin dynamics. Based on my expertise in lymphatic endothelial biology, I envision that the manuscript can potentially increase knowledge on the regulators of the lymphatic endothelial junctions, which might have physiological, and in the long term, translational significance. However, in the current manuscript, the exact mechanisms of how IFT20 controls lymphatic endothelial junctions are left open. In addition to the lymphatic research field, the study is, potentially of interest to researchers working on blood vasculature or, even, epithelium, i.e. tissues where junctional dynamics play a major role in health and disease.

Furter controls, analysis, and experimentation are needed to warrant the authors' statements. In their future work, the authors should also consider means to rigorously dissect the IFT20 functions in primary cilia and endosomes.

__Reviewer #3 __

(Evidence, reproducibility and clarity (Required)):

In this manuscript, the group of Fink and coworkers investigates mechanistic aspects of the intraflagellar transport protein 20 (IFT20) function in lymphatic endothelial cells (LECs). In a previous study, this group had demonstrated the presence of primary cilia on LECs and shown that loss of IFT20 during development resulted in edema, lymphatic vessel dilation and altered branching. Lymphatic-specific deletion of IFT20 cell-autonomously exacerbated acute lymphangiogenesis after corneal suture. In this manuscript, Paulson et al. recapitulate the suture-induced hyper-lymphangiogenesis after lymphatic-specific IFT20 KO using a LYVE1-Cre delete strain and demonstrate a reduced, more discontinuous VE-Cadherin (VECad) staining in newly formed lymphatic vessels (LVs). Prompted by distended and hyperbranching dermal vessels, the performed functional tracer injection experiments and demonstrate increased lymphatic backflow and leakage into the interstitium. To gain further mechanistic insights the authors turned to reductionist cell culture models, starting with a mouse LEC line, in which IFT20 had been deleted using CRISPR/Cas9 resulting in loss of primary cilia, increased stress fibre formation and impaired junctional integrity. More importantly, similar effects were detected in human dermal (HD)LECs after IFT20 KD. Further IFT20 KD HDLECs showed accumulation of RAB5+ vesicles indicating defective endosome maturation. Indistinguishable formation of RAB5+ endosomes after VEGF-C stimulation in HDLECs and IFT20KD HDLECs indicated that endocytosis and formation of early endosomes occur independent if IFT20. Through starvation, stimulation and wash-out experiments the authors provide colocalization data suggesting that after VEGF-C stimulation IFT20 is recruited to endosomes where it contributes to VECad recycling. Finally, the authors addressed if the increase in RAB5+ endosomes following VEGF-C stimulation resulted in prolonged retention of signaling-active VEGFR-3 in endosomes. Western blotting for phosphorylation of VEGFR-3 and its downstream signaling components after activation of starved HDLECs or IFT20KD HDLECs and subsequent factor wash out provided evidence towards this model.

Subsequently open question and potential suggestions for improvement are listed: The authors describe a slight leakiness of the LYVE1-Cre deleter strain to result in massive hemangiogenesis (line112). How extensive is the resulting deletion in blood endothelial cells? What are the consequences for VECad distribution in BEC junctions i.e. for blood vessels and vascular permeability? Are the defects described specific for LECs or are the manifestation of generic defects in LECs?

Thank you for these helpful suggestions.

Please see Planned 17 above where we will assess IFT20 KO in blood vasculature and tone down lymphatic-specific language in the text.

Fig. 1 E, what is the distribution of LYVE1 in IFT20 KO LECs at higher magnification, is LYVE1 excluded from the VECad expression domain?

Thank you for this helpful suggestion.

Planned 24: We will review our corneal confocal data to address this question.

Fig.1 F, what does VECad-positive LV (%) area (line 154 - 155) refer to, given that all LECs are VECad+ but the junctional distribution of the protein is distinctly different?

Thank you for pointing out our need to clarify. This quantification measures the overlap of VE-cadherin with LYVE-1 as a way to measure the area covered by adherens junctions between lymphatic endothelial cells. Where junctions are punctate, they have smaller area vs. long continuous junctions.

Planned 25: We will update the text to clarify this measurement.

In the discussion, the authors speculate that the development of valves could be potentially impaired in IFT20 LEC KO mice. Ear skin would be an excellent tissue to stain the valves and analyse their structure in collecting LVs. Of particular interest in this context are Int a9, VECad, FOXC2 and PROX1 expression. The later two are required for valve formation and upregulated in valve forming areas in response to oscillatory shear stress (Sabine A et al. (2012) Dev Cell 22 (2):430-445. doi:10.1016/j.devcel.2011.12.020).

Thank you for this helpful suggestion. Based on the suggestion from Reviewer 2, we will remove the ear lymph drainage data and focus on the cell biology in this manuscript. Our current experiments focus more on lymphatic valve formation in this context and these data can be moved to a separate manuscript.

Planned 26: We will revise the text to remove speculation about valve development in this model and address this in a later manuscript.

Does IFT20 KO and loss of the primary cilium impair OSS sensing and result in a failure to express sufficient levels of PROX1 for valve formation (Fig. 7 C).

Thank you for this helpful comment. We will address the role of cilia in OSS sensing and valve formation in a forthcoming manuscript.

A larger area view including pre-collectors and collectors would be informative and reveal changes in the overall structure of the lymphatic vessel bed in absence of IFT20.

Based on the suggestion from Reviewer 2, we will remove these data.

Fig. 2 A, (line 187 - 190) please indicate the age of analysed animals.

Planned 27: We will add the ages of mice used.

With respect to Fig.1, LVs in the area are mainly capillaries, what is the distribution of VECad? Are the LVs comprised of oak-leave shaped LECs, higher magn. pictures would be required.

Thank you for this helpful suggestion.

Planned 28: We will include higher magnification images of capillaries.

Fig. 2 (C - E) Line 201 - 203 the description of retrograde flow using a clock terminology is unusual and not clear to the reader. Is this meant relative to the point of injection with 12 being at the top or relative to the injection axis (i.e. forward / backward direction)? It would seem that indication of the angle in combination with a sketch of the analysis would help the reader to interpret these data.

Thank you for this helpful critique. We will remove these data based on this suggestion and that of Reviewer 2.

The application of cell culture models is appropriate, however, the value of the mLEC model is questionable given that VECad is not detectable in these cells and PROX1 and VEGFR-3 staining are not shown. Therefore, the HDLEC model bears significantly more relevance. In Fig. 3D, were mLECS mitotically arrested during the 24hrs transwell migration, to exclude division and crowding effects during the observation time?

Thank you for this helpful critique.

Planned 29: We will clarify the methods for this experiment in the text.

Fig. 6 It is commendable that the authors report their lack of success to directly visualize VEGFR-3 endocytosis by IF and attempt a WB analysis instead. However given the spread of the results normalization to ß-actin as a loading control appears inappropriate. Phosphorylated forms of VEGFR-3 and VECad should be normalized to the expression of the total protein as measured with a non-phospospecific antibody, exactly the way done here for ERK1/2 and AKT. Generally, IP-WB experiments provide superior data in this type of setting.

Thank you for this helpful suggestion. Based on suggestions from the other reviewers, we will remove these WB data with the exception of pVEGFR-3 and add corresponding immunofluorescence. We will include additional time points and include blots used for quantifications as supplements.

Line 597 - 599: "VEGFR-3 signaling is required for the establishment of VE-cadherin button junctions as lymphatic collecting vessels mature but is not required for their maintenance (Jannaway et al., 2023)." Collecting LVs are characterized by zipper junctions, but not button junctions. Therefore, this sentence needs clarification.

Thank you for this helpful suggestion.

Planned 30: We will clarify this text.

Reviewer #3 (Significance (Required)):

The role of IFT20 in formation of the primary cilium and endocytic vesicle transport warrants its investigation in lymphatic endothelial cells. Therefore, this study addresses relevant questions and provides important first insights into the cell biological function of IFT20 in this cell type. IFT20 has so far not been implicated in endocytosis and recycling of VECad and VEGFR-3 and the model suggested by the authors is compelling and adds to the mechanistic understanding of previous studies on the role of VECad in LECs. In particular, it could be of relevance for the enigmatic formation of button junction in lymphatic capillaries and the mechano-response of LECS underlying valve formation. At this point, the picture obtained from the endocytosis assays is more conclusive compared to the analysis of the impact of IFT20 loss on button junction formation. Clearly the study is of interest for a general cell biological audience as well as vascular biologists.

• *

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Referee #1

Evidence, reproducibility and clarity

The manuscript identified a novel role of Intraflagellar Transport Protein 20 (IFT20) in the function and homeostasis of lymphatic endothelial junctions. The authors showed that IFT20 regulates VE-cadherin localization at adherens junctions in lymphatic endothelial cells. The authors performed impressive in vivo work that shows the requirement for IFT20 for the homeostasis of intercellular junctions, lymphangiogenesis, and drainage function of lymphatic vessels. In contrast, the cell biology part of the paper was underwhelming and will need significant revisions to support the proposed model. In the result section, several conclusions have to be toned down to match the actual results. The study employs in vivo mouse models, immunofluorescence, biochemical assays, and loss-of-function experiments to support their conclusions.

Major comments

• The authors present disrupted localization of VE-cadherin. Is this a mislocalization and/or protein stability issue in IFT20 KD cells? A western blot can help assess protein levels, and a phase-chase endocytosis assay of VE-cadherin can strengthen evidence. The authors did not confirm the permeability phenotype seen in vivo.
• While the authors focused on IFT20 and rab5, we do not have a clear idea about the vesicular dynamics as well as the status of early, late, and recycling endosomes in IFT20 KD cells. Is IFT 20 localized to non-rab5+ endosomes, and if yes, what are the species? A more general endosomal profiling would help strengthen the authors' message. For example, in Fig. 4-5, the authors will have to stain for other early endosomal markers as well as late, and recycling endosomal markers in control and IFT20 KD cells.
• In fig. 6C, a majority of VE-cadherin is not associated with Rab5. Staining with additional endosomal markers might help identify other endosomal species colocalizing with VE-cadherin. It will be critical to add to Fig. 6c the intensity profiles depicting colocalizations. The authors can also live image a fluorescently (f)-tagged VE-cadherin (maybe with another f-tagged rab5) and assess their association dynamics in IFT20 KD cells (similar to fig6C).
• Primary cilia do seem to regulate vascular plexus in the mouse retina as well as endothelial permeability through mediating subcellular localization of junction proteins. The authors do not clearly exclude the ciliary function of IFT20 in mediating lymphatic endothelial cell-cell junctions. A rescue experiment can help settle this question by targeting IFT20 exclusively to cilia (or not) and assessing, for example, VE-cadherin localization. The following is optional: It is also unclear whether the described regulation is specific to IFT20 or can be phenocopied by the ablation of another IFT subunit and/or cilia ablation through the depletion of a non-IFT cilia assembly regulator.
• Figs. 7A and B do not seem very convincing. The control vs. IFT20 KD western blot levels look mostly similar between the two conditions. The result section does not translate the actual data in Fig. 7A and B. Additionally, there are no statistical comparisons between control and KD conditions in the graphs. Except for a potential pVEGFR-3 increase at 30 min VEGF-C in IFT20 KD cells, but after washout the level is similar to control. This figure does not support well the model presented in fig. 8. The conclusion in lines 456-459 has to be toned down.
• The authors were not able to stain for pVEGFR3. It would still be helpful to see a colocalization between total VEGFR3, IFT20, and VE-cadherin in control cells and IFT20 KD cells (VEGFR3 and VE-cadherin).

Minor comments

• The control used in Figures 1 and 2 does not seem ideal. The proper control would be IFT20fl/fl cre neg. Is there a reason why the authors excluded a lox allele in control? Also, the authors have to provide the mice age used in these figures and when the Cre kicks in in the result section.
• Please describe the overall mouse phenotype(s) of the LYVE1 CRE-IFT20 flox.
• Line 109:'By expression, the authors probably mean immunostained.
• Many graphs exhibit undefined Y-axis labels and units. Please clarify these as well as the way they were quantified. Include such information in figure legends and/or in the materials and methods section. The figures in question are fig1C, E and F, fig2E, fig3E, fig4b and D, fig6b and D, fig7B and C.
• Line 295:'homeostasis"-the authors probably mean in a serum-rich condition.
• Fig4C specifically the lower two images on the right side: the images do not seem to represent the corresponding graphs.
• Please add the statistical tests used to evaluate significance in all figure legends.

Significance

This study provides novel insights into IFT20's role in VE-cadherin trafficking and endothelial junction stability, with its strongest aspect being the in vivo data in Figures 1 and 2, demonstrating lymphatic defects upon IFT20 loss. This represents a conceptual advance by extending IFT protein function beyond cilia (if one of the major comments is addressed) to vascular integrity. However, mechanistic depth is lacking, and ciliary role was not tested-additional rescue and colocalization experiments are needed to confirm the model. The study will interest vascular and lymphatic biologists, as well as cell biologists studying intracellular trafficking and cilia.

Expertise: cilia and mouse genetics

Se hace enfasis, junto con el parrafo anterior, que en general, las muertes fetales, primero se van a a dar mayormente en embarazos a termino y que la presencia de factores de riesgos, no aumentan mucho el riesgo, el unico que si, es una muerte fetal previa

Se hace una anotación sobre si el SARVS CoV 2 puede ser factor de riesgo o no sobre la muerte fetal, teniendo 2 casos, donde en uno si aumenta y en otro no

Reviewer #1 (Public review):

Summary:

Fluorescence imaging has become an increasingly popular technique for monitoring neuronal activity and neurotransmitter concentrations in the living brain. However, factors such as brain motion and changes in blood flow and oxygenation can introduce significant artifacts, particularly when activity-dependent signals are small. Yogesh et al. quantified these effects using GFP, an activity-independent marker, under two-photon and wide-field imaging conditions in awake behaving mice. They report significant GFP responses across various brain regions, layers, and behavioral contexts, with magnitudes comparable to those of commonly used activity sensors. These data highlight the need for robust control strategies and careful interpretation of fluorescence functional imaging data.

Strengths:

The effect of hemodynamic occlusion in two-photon imaging has been previously demonstrated in sparsely labeled neurons in V1 of anesthetized animals (see Shen and Kara et al., Nature Methods, 2012). The present study builds on these findings by imaging a substantially larger population of neurons in awake, behaving mice across multiple cortical regions, layers, and stimulus conditions. The experiments are extensive, the statistical analyses are rigorous, and the results convincingly demonstrate significant GFP responses that must be accounted for in functional imaging experiments.

In the revised version, the authors have provided further methodological details that were lacking in the previous version, expanded discussions regarding alternative explanations of these GFP responses as well as potential mitigation strategies. They also added a quantification of brain motion (Fig. S5) and the fraction of responsive neurons when conducting the same experiment using GCaMP6f (Fig. 3D-3F), among other additional information.

Weaknesses:

(1) The authors have now included a detailed methodology for blood vessel area quantification, where they detect blood vessels as dark holes in GFP images and measure vessel area by counting pixels below a given intensity threshold (line 437-443). However, this approach has a critical caveat: any unspecific decrease in image fluorescence will increase the number of pixels below the threshold, leading to an apparent increase in blood vessel area, even when the actual vessel size remains unchanged. As a result, this method inherently introduces a positive correlation between fluorescence decrease and vessel dilation, regardless of whether such a relationship truly exists.

To address this issue, I recommend labelling blood vessels with an independent marker, such as a red fluorescence dye injected into the bloodstream. This approach would allow vessel dilation to be assessed independently of GFP fluorescence -- dilation would cause opposite fluorescence changes in the green and red channels (i.e., a decrease in green due to hemodynamic occlusion and an increase in red due to the expanding vessel area). In my opinion, only when such ani-correlation is observed can one reliably infer a relationship between GFP signal changes and blood vessel dynamics.

Because this relationship is central to the author's conclusion regarding the nature of the observed GFP signals, including this experiment would greatly strengthen the paper's conclusion.

(2) Regarding mitigation strategy, the authors advocate repeating key functional imaging experiments using GFP, and state that their aim here is to provide a control for their 2012 study (Keller et al., Neuron). Given this goal, I find it important to discuss how these new findings impact the interpretation of their 2012 results, particularly given the large GFP responses observed.

For example, Keller et al. (2012) concluded that visuomotor mismatch strongly drives V1 activity (Fig. 3A in that study). However, in the present study, mismatch fails to produce any hemodynamic/GFP response (Fig. 3A, 3B, rightmost bar), and the corresponding calcium response is also the weakest among the three tested conditions (Fig. 3D). How do these findings affect their 2012 conclusions?

Similarly, the present study shows that GFP reveals twice as many responsive neurons as GCaMP during locomotion (Fig. 3A vs. Fig. 3D, "running"). Does this mean that their 2012 conclusions regarding locomotion-induced calcium activity need reconsideration? Given that more neurons responded with GFP than with GCaMP, the authors should clarify whether they still consider GCaMP a reliable tool for measuring brain activity during locomotion.

(3) More generally, the author should discuss how functional imaging data should be interpreted going forward, given the large GFP responses reported here. Even when key experiments are repeated using GFP, it is not entirely clear how one could reliably estimate underlying neuronal activity from the observed GFP and GCaMP responses.

For example, consider the results in Fig. 3A vs. 3D: how should one assess the relative strength of neuronal activity elicited by running, grating, or visuomotor mismatch? Does mismatch produce the strongest neuronal activity, since it is least affected by the hemodynamic/GFP confounds (Fig. 3A)? Or does mismatch actually produce the weakest neuronal activity, given that both its hemodynamic and calcium responses are the smallest?

In my opinion, such uncertainty makes it difficult to robustly interpret functional imaging results. Simply repeating experiments with GFP does not fully resolve this issue, as it does not provide a clear framework for quantifying the underlying neuronal activity. Does this suggest a need for a better mitigation strategy? What could these strategies be?

In my opinion, addressing these questions is critical not only for the authors' own work but also for the broader field to ensure a robust and reliable interpretation of functional imaging data.

(4) The authors now discuss various alternative sources of the observed GFP signals. However, I feel that they often appear to dismiss these possibilities too quickly, rather than appreciating their true potential impacts (see below).

For example, the authors argue that brain movement cannot explain their data, as movement should only result in a decrease in observed fluorescence. However, while this might hold for x-y motion, movement in the axial (z) direction can easily lead to both fluorescence increase and decrease. Neurons are not always precisely located at the focal plane -- some are slightly above or below. Axial movement in a given direction will bring some cells into focus while moving others out of focus, leading to fluorescence changes in both directions, exactly as observed in the data (see Fig. S2).

Furthermore, the authors state that they discard data with 'visible' z-motion. However, subtle axial movements that escape visual detection could still cause fluorescence fluctuations on the order of a few percent, comparable to the reported signal amplitudes.

Finally, the authors state that "brain movement kinematics are different in shape than the GFP responses we observe". However, this appears to contradict what they show in Fig. 2A. Specifically, the first example neuron exhibits fast GFP transients locked to running onset, with rapid kinematics closely matching the movement speed signals in Fig. S5A. These fast transients are incompatible with slower blood vessel area signals (Fig. 4), suggesting that alternative sources could contribute significantly.

In sum, the possibility that alternative signal sources could significantly contribute should be taken seriously and more thoroughly discussed.

(5) The authors added a quantification of brain movement (Fig. S5) and claim that they "only find detectable brain motion during locomotion onsets and not the other stimuli." However, Fig. S5 presents brain 'velocity' rather than 'displacement'. A constant (non-zero) velocity in Fig. S5 B-D indicates that the brain continues to move over time, potentially leading to significant displacement from its initial position across all conditions. While displacement in the x-y plane are corrected, similar displacement in the z direction likely occurs concurrently and cannot be easily accounted for. To assess this possibility, the authors should present absolute displacement relative to pre-stimulus frames, as displacement -- not velocity -- determines the size of movement-related fluorescence changes.

(6) In line 132-133, the authors draw an analogy between the effect of hemodynamic occlusion and liquid crystal display (LCD) function. However, there are fundamental differences between the two. LCDs modulate light transmission by rotating the polarization of light, which then passes through a crossed polarizer. In contrast, hemodynamic occlusion alters light transmission by changing the number and absorbance properties of hemoglobin. Additionally, LCDs do not involve 'emission' light - back-illumination travels through the liquid crystal layer only once, whereas hemodynamic occlusion affects both incoming excitation light and the emitted fluorescence. Given these fundamental differences, the LCD analogy may not be entirely appropriate.

Este acontecimiento es muy semejante al disturbio en los EEUU en el 6 de enero 2021, casi exactamente dos años antes. El disturbio en Brasil y el en los EEUU demostraron un rechazo por la gente de extrema derecho de los resultados de sus propios elecciones.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

Evading predation is of utmost importance for most animals and camouflage is one of the predominant mechanisms. Wu et al. set out to test the hypothesis of a unique camouflage system in leafhoppers. These animals coat themselves with brochosomes, which are spherical nanostructures that are produced in the Malpighian tubules and are distributed on the cuticle after eclosion. Based on previous findings on the reflectivity properties of brochosomes, the authors provide very good evidence that these nanostructures indeed reduce the reflectivity of the animals thereby reducing predation by jumping spiders. Further, they identify four proteins, which are essential for the proper development and function of brochosomes. In RNAi experiments, the regular brochosome structure is lost, the reflectivity reduced and the respective animals are prone to increased predation. Finally, the authors provide some phylogenetic sequence analyses and speculate about the evolution of these essential genes.

Strengths:

The study is very comprehensive including careful optical measurements, EM and TM analysis of the nanoparticles and their production line in the malphigian tubules, in vivo predation tests, and knock-down experiments to identify essential proteins. Indeed, the results are very convincingly in line with the starting hypothesis such that the study robustly assigns a new biological function to the brochosome coating system.

A key strength of the study is that the biological relevance of the brochosome coating is convincingly shown by an in vivo predation test using a known predator from the same habitat.

Another major step forward is an RNAi screen, which identified four proteins, which are essential for the brochosome structure (BSMs). After respective RNAi knock-downs, the brochosomes show curious malformations that are interesting in terms of the self-assembly of these nanostructures. The optical and in vivo predation tests provide excellent support for the model that the RNAi knock-down leads to a change of brochosomes structure, which reduces reflectivity, which in turn leads to a decrease of the antipredatory effect.

Thank you very much for your positive feedback and insightful comments on our manuscript. We are delighted that you acknowledge the efforts we have made in studying the components and functions of Brochosomal proteins. We have carefully considered your suggestions and have thoroughly revised the manuscript to address the shortcomings identified in our original submission. We hope that the revised version meets with your approval. Below, please find our detailed point-by-point responses.

Weaknesses:

The reduction of reflectivity by aberrant brochosomes or after ageing is only around 10%. This may seem little to have an effect in real life. On the other hand, the in vivo predation tests confirm an influence. Hence, this is not a real weakness of the study - just a note to reconsider the wording for describing the degree of reflectivity.

Thank you for your valuable suggestions. Based on your recommendations, we have revised the manuscript accordingly. Although the absolute reduction in light reflection due to Brochosomal coverage is approximately 10%, the relative decrease in light reflection on the leafhopper's surface is nearly 30%. Specifically, in the ultraviolet region, the reflection is reduced from about 30% to 20%, and in the visible light region, it is reduced from 20% to 10%. For detailed revisions, please refer to lines 151-156 of the revised manuscript.

The single gene knockdowns seemed to lead to a very low penetrance of malformed brochosomes (Figure Supplement 3). Judging from the overview slides, less than 1% of brochosomes may have been affected. A quantification of regular versus abnormal particles in both, wildtype and RNAi treatments would have helped to exclude that the shown aberrant brochosomes did not just reflect a putative level of "normal" background defects. Of note, the quadruple knock-down of all BSMs seemed to lead to a high penetrance (Figure 4), which was already reflected in the microtubule production line. While the data shown are convincing, a quantification might strengthen the argument.

While the RNAi effects seemed to be very specific to brochosomes and therefore very likely specific, an off-target control for RNAi was still missing. Finding the same/similar phenotype with a non-overlapping dsRNA fragment in one off-target experiment is usually considered required and sufficient. Further, the details of the targeted sequence will help future workers on the topic.

Thank you for your valuable suggestions. Based on your recommendations, we have synthesized dsRNA targeting two non-overlapping regions of the coding sequences for four Brochosomal structural protein genes. These dsRNAs were injected individually and in combination for each gene. Our RNAi experiments for each BSM gene demonstrated that both individual and combined injections significantly suppressed the expression of the target genes, with the combined injection yielding slightly better silencing efficiency. Statistical analysis of the SEM observations revealed that the combined injection of dsRNAs targeting two non-overlapping regions led to a 60-70% reduction in the surface area coverage of Brochosomes. Additionally, approximately 20% of the remaining Brochosomes exhibited significant morphological changes. For detailed revisions, please refer to lines 199-211 of the revised manuscript, as well as Figures 3A and 3C, and Supplementary Figures 4 and 5.

The main weakness in the current manuscript may be the phylogenetic analysis and the model of how the genes evolved. Several aspects were not clearly or consistently stated such that I felt unsure about what the authors actually think. For instance: Are all the 4 BSMs related to each other or only BSM2 and 3? If so, not only BSM2 and 3 would be called "paralogs" but also the other BSMs. If they were all related, then a phylogenetic tree including all BSMs should be shown to visualize the relatedness (including the putative ancestral gene if that is the model of the authors). Actually, I was not sure about how the authors think about the emergence of the BSMs. Are they real orphan genes (i.e. not present outside the respective clade) or was there an ancestral gene that was duplicated and diverged to form the BSMs? Where in the phylogeny does the first of the BSMs or ancestral proteins emerge (is the gene found in Clastoptera arizonana the most ancestral one?)? Maybe, the evolution of the BSMs would have to be discussed individually for each gene as they show somewhat different patterns of emergence and loss (BSM4 present in all species, the others with different degrees of phylogenetic restriction).

Thank you very much for your constructive feedback on our phylogenetic analysis and the modeling of gene evolution. We fully agree with your insights and acknowledge that the evolutionary analysis of BSM genes remains somewhat ambiguous. This ambiguity is primarily due to the limited research on the precise structural protein composition of Brochosomes. While proteomics studies have analyzed and discussed the structural proteins of Brochosomes, the accurate composition of these proteins is still poorly understood. In this study, we identified four BSM proteins, but given the intricate structure of Brochosomes as proteinaceous spheres, we believe there may be additional BSM genes that have not yet been identified. Moreover, despite the presence of over ten thousand species within the Cicadomorpha, only three species have genome sequences available, and fewer than a hundred species have transcriptome sequencing data. The scarcity of research on Brochosomes, as well as the limited availability of genomic and transcriptomic data, poses significant challenges for our phylogenetic analysis and understanding of BSM gene evolution.

Based on your suggestions, we have revised the manuscript accordingly. Specifically, we have updated Figure 5C by including ten additional species from Cereopoidea, Cicadoidea, and Fulgoroidea to better illustrate that BSM genes are true orphan genes. We have also added a phylogenetic tree of BSM genes within Cicadidae in Supplementary Figure 3. Additionally, we have expanded the discussion of BSM gene evolution in the manuscript (lines 503-556). For detailed revisions, please refer to Figure 5C, Supplementary Figure 3, and lines 507-585 of the revised manuscript.

Related to these questions I remained unsure about some details in Figure 5. On what kind of analysis is the phylogeny based? Why are some species not colored, although they are located on the same branch as colored ones? What is the measure for homology values - % identity/similarity? The homology labels for Nephotetix cincticeps and N. virescens seem to be flipped: the latter is displayed with 100% identity for all genes with all proteins while the former should actually show this. As a consequence of these uncertainties, I could not fully follow the respective discussion and model for gene evolution.

Thank you very much for your insightful comments and suggestions. We have carefully considered your feedback and have thoroughly revised our manuscript accordingly. Specifically, we have enhanced the description of the phylogenetic analysis process to provide greater clarity and transparency, with the detailed methods now included in lines 789-798. Regarding Figure 5C, we appreciate your attention to the coloring scheme. We would like to clarify that the family Cicadellidae comprises 25 subfamilies, many of which are represented by only one species in our figure. To ensure clarity and meaningful representation, we have chosen to color only those subfamilies with more than three species, thereby avoiding visual clutter and emphasizing the most relevant taxonomic groups. Additionally, we have corrected the inverted homology labels for Nephotetix cincticeps and Nephotetix virescens to ensure the accuracy and consistency of our data presentation.

Conclusion:

The authors successfully tested their hypothesis in a multidisciplinary approach and convincingly assigned a new biological function to the brochosomes system. The results fully support their claims - only the quantification of the penetrance in the RNAi experiments would be helpful to strengthen the point. The author's analysis of the evolution of BSM genes remained a bit vague and I remained unsure about their respective conclusions.

The work is a very interesting study case of the evolutionary emergence of a new system to evade predators. Based on this study, the function of the BSM genes could now be studied in other species to provide insights into putative ancestral functions. Further, studying the self-assembly of such highly regular complex nano-structures will be strongly fostered by the identification of the four key structural genes.

Reviewer #1 (Recommendations for the authors):

Main manuscript:

Please consider the annotated pdf with suggestions for wording and comments at the authors' discretion:

Thank you very much for your detailed suggestions and comments provided in the annotated PDF. We have carefully reviewed each of your points and have revised the manuscript accordingly. All changes have been highlighted in red text for your convenience. The revised manuscript with tracked changes is available for your review. We believe these revisions have improved the clarity and quality of our manuscript. Thank you again for your valuable feedback.

Supplementary Figure 2 C:

Y-axes:

- label: "surface coverage in %"

- there are different scale values for the different days (e.g. 80-105 for day 5 and 0-80 at day 25). As a comparison between days is interesting, it would help to have the same scale values for all. That would show the decrease more intuitively.

Thank you very much for your suggestion regarding the Y-axis in Supplementary Figure 2C. We agree that using a consistent scale across all time points is essential for clear and intuitive comparison. In the revised manuscript, we have standardized the Y-axis scale for Supplementary Figure 2C to a uniform range of 0-100% for all days. This change allows for a more straightforward visualization of the decreasing trend in surface coverage over time.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors investigate the optical properties of brochosomes produced by leafhoppers. They hypothesize that brochosomes reduce light reflection on the leafhopper's body surface, aiding in predator avoidance. Their hypothesis is supported by experiments involving jumping spiders. Additionally, the authors employ a variety of techniques including micro-UV-Vis spectroscopy, electron microscopy, transcriptome and proteome analysis, and bioassays. This study is highly interesting, and the experimental data is well-organized and logically presented.

Strengths:

The use of brochosomes as a camouflage coating has been hypothesized since 1936 (R.B. Swain, Entomol. News 47, 264-266, 1936) with evidence demonstrated by similar synthetic brochosome systems in a number of recent studies (S. Yang, et al. Nat. Commun. 8:1285, 2017; L. Wang, et al., PNAS. 121: e2312700121, 2024). However, direct biological evidence or relevant field studies have been lacking to directly support the hypothesis that brochosomes are used for camouflage. This work provides the first biological evidence demonstrating that natural brochosomes can be used as a camouflage coating to reduce the leafhoppers' observability of their predators. The design of the experiments is novel.

We are extremely grateful for your positive feedback and insightful comments on our manuscript. We are delighted that you have recognized the efforts we have put into our research on how brochosomes serve as a camouflage coating to reduce the detectability of leafhoppers to their predators. We have carefully considered your suggestions and have thoroughly revised the manuscript to address the shortcomings of the original version. We hope that the revised version meets with your approval. Below, please find our detailed point-by-point responses.

Weaknesses:

(1) The observation that brochosome coatings become sparse after 25 days in both male and female leafhoppers, resulting in increased predation by jumping spiders, is intriguing. However, since leafhoppers consistently secrete and groom brochosomes, it would be beneficial to explore why brochosomes become significantly less dense after 25 days.

Thank you very much for your valuable suggestions. We appreciate your interest in the reduction of brochosomal density on the surface of leafhoppers after 25 days.We believe that the primary reason for the decreased density of brochosomes on the leafhopper surface after 25 days is the reduced synthesis and secretion of brochosomes. The Malpighian tubules are the main sites for brochosome synthesis. As shown in Figure 2D and Supplementary Figure 1, the thick glandular segments of the Malpighian tubules in both male and female leafhoppers begin to atrophy 15 days after reaching adulthood. This indicates a gradual decline in brochosome synthesis and secretion after day 15 of adulthood. Following your suggestion, we have revised the discussion section of the manuscript to elaborate on this observation. The detailed changes can be found in lines 474-491 of the revised manuscript.

(2) The authors demonstrate that brochosome coatings reduce UV (specular) reflection compared to surfaces without brochosomes, which can be attributed to the rough geometry of brochosomes as discussed in the literature. However, it would be valuable to investigate whether the proteins forming the brochosomes are also UV absorbing.

Thank you very much for your valuable suggestions. Following your advice, we have successfully expressed four BSM genes in a prokaryotic system, purified the corresponding proteins, and applied them to quartz glass surfaces. We then measured the light reflectance of the quartz glass surfaces coated with these purified proteins. The results showed that the purified BSM proteins did not exhibit better antireflective properties compared to the control GST protein. For more details, please refer to Supplementary Figure 8 in the revised manuscript.  We believe that the excellent antireflective properties of brochosomes are fundamentally due to their unique geometric shapes. The hollow pores within the brochosomes, with diameters of approximately 100 nm, are significantly smaller than most wavelengths in the visible spectrum. When light passes through these tiny pores, diffraction occurs, while light passing through the ridges of the brochosomes causes scattering. The interference between the diffracted and scattered light from these pores and ridges results in the observed extinction characteristics of brochosomes. We have incorporated these insights into the discussion section of the revised manuscript (lines 416-425 and lines 432-442 of the revised manuscript).

(3) The experiments with jumping spiders show that brochosomes help leafhoppers avoid predators to some extent. It would be beneficial for the authors to elaborate on the exact mechanism behind this camouflage effect. Specifically, why does reduced UV reflection aid in predator avoidance? If predators are sensitive to UV light, how does the reduced UV reflectance specifically contribute to evasion?

Thank you very much for your valuable suggestions. Based on your advice, we have included a detailed discussion on how reducing ultraviolet (UV) reflection can help insects avoid predation. The revised content can be found in lines 445-460 of the revised manuscript.

“UV light serves as a crucial visual cue for various insect predators, enhancing foraging, navigation, mating behavior, and prey identification (Cronin & Bok, 2016; Morehouse et al., 2017; Silberglied, 1979). Predators such as birds, reptiles, and predatory arthropods often rely on UV vision to detect prey (Church et al., 1998; Li & Lim, 2005; Zou et al., 2011). However, UV reflectance from insect cuticles can disrupt camouflage, increasing the risk of detection and predation, as natural backgrounds like leaves, bark, and soil typically reflect minimal UV light (Endler, 1997; Li & Lim, 2005; Tovee, 1995). To mitigate this risk, insects often possess anti-reflective cuticular structures that reduce UV and broad-spectrum light reflectance. This strategy is widespread among insects, including cicadas, dragonflies, and butterflies, and has been shown to decrease predator detection rates (Hooper et al., 2006; Siddique et al., 2015; Zhang et al., 2006). For example, the compound eyes of moths feature hexagonal protuberances that reduce UV reflectance, aiding nocturnal concealment (Blagodatski et al., 2015; Stavenga et al., 2005). In butterflies, UV reflectance from eyespots on wings can attract predators, but reducing UV reflectance or eyespot size can lower predation risk and enhance camouflage (Chan et al., 2019; Lyytinen et al., 2004). Hence, the reflection of ultraviolet light from the insect cuticle surface increases the risk of predation by disrupting camouflage (Tovee, 1995)”

(4) An important reference regarding the moth-eye effect is missing. Please consider including the following paper: Clapham, P. B., and M. C. Hutley. "Reduction of lens reflection by the 'Moth Eye' principle." Nature 244: 281-282 (1973).

Thank you very much for pointing out the omission of the important reference on the “moth eye” effect. We sincerely apologize for the oversight. Based on your suggestion, we have now included the seminal paper by Clapham and Hutley (1973) in the revised manuscript. The reference has been added to both the Introduction and Discussion sections to provide a more comprehensive context for our discussion on anti-reflective structures in insects.

(5) The introduction should be revised to accurately reflect the related contributions in literature. Specifically, the novelty of this work lies in the demonstration of the camouflage effect of brochosomes using jumping spiders, which is verified for the first time in leafhoppers. However, the proposed use of brochosome powder for camouflage was first described by R.B. Swain (R.B. Swain, Notes on the oviposition and life history of the leafhopper Oncometopta undata Fabr. (Homoptera: Cicadellidae), Entomol. News. 47: 264-266 (1936)). Recently, the antireflective and potential camouflage functions of brochosomes were further studied by Yang et al. based on synthetic brochosomes and simulated vision techniques (S. Yang, et al. "Ultra-antireflective synthetic brochosomes." Nature Communications 8: 1285 (2017)). Later, Lei et al. demonstrated the antireflective properties of natural brochosomes in 2020 (C.-W. Lei, et al., "Leafhopper wing-inspired broadband omnidirectional antireflective embroidered ball-like structure arrays using a nonlithography-based methodology." Langmuir 36: 5296-5302 (2020)). Very recently, Wang et al. successfully fabricated synthetic brochosomes with precise geometry akin to those natural ones, and further elucidated the antireflective mechanisms based on the brochosome geometry and their role in reducing the observability of leafhoppers to their predators (L. Wang et al. "Geometric design of antireflective leafhopper brochosomes." Proceedings of the National Academy of Sciences 121: e2312700121 (2024)).

Thank you very much for your valuable suggestions regarding the revision of the introduction to accurately reflect the relevant contributions in the literature. Based on your feedback, we have thoroughly revised the introduction and added the suggested references to provide a comprehensive context for our study. The details of these revisions can be found in lines 84-94 of the revised manuscript.

Reviewer #3 (Recommendations for the authors):

(1) In Figure 2E, the data for Male-5d appears to be missing. Please verify and ensure all relevant data is included.

Thank you for pointing out the issue regarding the data presentation in Figure 2E.We apologize for any confusion caused by the overlapping data points and the less conspicuous color choice for Male-5d. We have carefully reviewed the data and confirmed that all relevant data points, including Male-5d, are indeed present in the dataset. In the revised manuscript, we have adjusted the color scheme for Male-5d and Female-5d in Figure 2E to ensure that both curves are clearly distinguishable, even in areas where they overlap. This adjustment should facilitate a more accurate and convenient observation of the data trends. We appreciate your attention to detail, and we believe these revisions have improved the clarity and readability of the figure.

(2) In Figure 6, please clarify the reflectance data in the inset. Clearly explain what the blue and light blue curves represent.

Thank you for your suggestion regarding Figure 6.We have revised the figure to improve clarity. The light blue curve now represents the reflectance measurements of leafhoppers with higher brochosome coverage, while the dark blue curve corresponds to those with lower coverage. These changes, along with updated labels in the figure legend, ensure that the data are clearly distinguishable and easy to interpret. We appreciate your feedback and believe these revisions have enhanced the overall clarity of the figure.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Weaknesses (clarifications needed):

(1) Experimental Design:

The study does not mention whether the authors examined sex differences or any measures of attractiveness or hierarchy among participants (e.g., students vs. teachers). Including these variables could provide a more nuanced understanding of group dynamics.

We are grateful to the reviewer for pointing out this valuable question. We have clarified that future studies should include sex differences or any measures of attractiveness or hierarchy among participants (e.g., students vs. teachers) (p. 27).

“Finally, future research should investigate additional variables, including sex differences and measures of attractiveness or hierarchy among participants, such as students versus teachers.”  p. 27

(2) fNIRS Data Acquisition:

The authors' approach to addressing individual differences in anatomy is lacking in detail. Understanding how they identified the optimal channels for synchrony between participants would be beneficial. Was this done by averaging to find the location with the highest coherence?

We apologize for missing some details here. We have included the following information in the fNIRS data acquisition and fNIRS data analyses to clarify the details (pp. 8 and 12).

We employed the one-sample t-test method to assess the GNS disparity between the baseline and task sessions, identifying particular channels of interest. This analysis did not ascertain the maximum coherence level, but rather pinpointed the channel exhibiting significant divergence between the two sessions, which we designated as pertinent to the group decision-making task. Furthermore, we selected the PFC and left TPJ as our reference brain regions, guided by existing literature.

“Two optode probe sets were used to cover each participant's prefrontal and left TPJ regions (Figure S1). The DLPFC plays a crucial role in group decision-making processes, with findings suggesting that individuals exhibiting reduced prefrontal activity were more prone to out-group exclusion and demonstrated stronger in-group preferences (Goupil et al., 2021; Jankovic, 2014; Yang et al., 2020). Similarly, the left TPJ has been previously reported to be associated with decision-making and information exchange (Freitas et al., 2019; Tindale et al., 2019).”  p. 8

“Time-averaged GNS (also averaged across channels in each group) was compared between the baseline session (i.e., the resting phase) and the task session (from reading information to making decisions) using a series of one-sample t-tests. Here, p-values were thresholded by controlling for FDR (p < 0.05; Benjamini & Hochberg, 1995). When determining the frequency band of interest, the time-averaged GNS was also averaged across channels. After that, we analyzed the time-averaged GNS of each channel. Then, channels showing significant GNS were regarded as regions of interest and included in subsequent analyses.” p. 12

(3) Behavioral Analysis:

For group identification, the analysis currently uses a dichotomous approach. Introducing a regression model to capture the degree of identification could offer more granular insights into how varying levels of group identification affect collective behavior and performance.

Thank you for your suggestion. As suggested, we have conducted the regression model to examine how varying levels of group identification affect collective performance, with the score of group identification being the independent variable and collective performance as the dependent variable (pp.9 and 15).

“Moreover, we employed a regression model to examine how varying levels of group identification affect collective performance, using group identification scores as the independent variable and collective performance as the dependent variable.”  p.9

“The results from the regression model highlighted a significant association between the degree of group identification and collective performance (β \= 0.45, t = 4.56, p \= 0.019).”  p.15

(4) Single Brain Activation Analysis:

The application of the General Linear Model (GLM) is unclear, particularly given the long block durations and absence of multiple trials. Further explanation is needed on how the GLM was implemented under these conditions.

Thank you for your suggestion, we have added more details in this section (p.11).

“In the GLM model analysis, HbO was the dependent variable, and the regression amount was set for different task stages (a. Reading information, b. Sharing private information, c. Discussing information, d. Decision). After that, we convolved the regression factor with the Hemodynamic Response Function (HRF) and obtained the brain activation β value of each participant in each channel at different task stages through regression analysis.’  p.11

(5) Within-group neural Synchrony (GNS) Calculation:

The method for calculating GNS could be improved by using mutual information instead of pairwise summation, as suggested by Xie et al. (2020) in their study on fMRI triadic hyperscanning. Additionally, the explanation of GNS calculation is inconsistent. At one point, it is mentioned that GNS was averaged across time and channels, while elsewhere, it is stated that channels with the highest GNS were selected. Clarification on this point is essential.

We appreciate the reviewer for highlighting this inquiry. We utilized a conventional GNS calculation approach, as detailed in Line 296 of the manuscript, where the GNS was determined in pairs after the WTC computation, and then averaged. Further details regarding the second question have been provided in the article (p.12).

(6) Placement of fNIRS Probes:

The probes were only placed in the frontal regions, despite literature suggesting that the superior temporal sulcus (STS) and temporoparietal junction (TPJ) regions are crucial for triadic team performance. A justification for this choice or inclusion of these regions in future studies would be beneficial.

The original manuscript clearly stated the use of two optode probe sets to encompass the prefrontal and left TPJ regions of each participant (see Figure S1, p. 8).

(7) Interpretation of fNIRS Data:

Given that fNIRS signals are slow, similar to BOLD signals in fMRI, the interpretation of Figure 6 raises concerns. It suggests that it takes several minutes (on the order of 4-5 minutes) for people to collaborate, which seems implausible. More context or re-evaluation of this interpretation is needed.

The question you have pointed out is very pertinent, and we have added more explanation for this result (pp. 25-26).

As previous studies have shown, the BOLD signal collected by fNIRS is slowly increasing compared to neuronal activity, which means that it has hysteresis (Turner et al., 1998). In social interactions such as group decision-making, the time of neural synchronization is delayed because people need to spend time increasing the number of dialogues to improve collaboration efficiency and form the same preference (Zhang et al., 2019). For example, the study of group consensus found that participants would show significant neural alignment after completing a period of dialogue (Sievers et al., 2024). In the task of cooperation, with the improvement of tacit understanding between two participants, the higher degree of neural synchronization (Cui et al., 2012). Therefore, the generation of neural synchronization depends on the interaction over a period of time. Therefore, we believe that the 4-5 minutes of collaboration time shown in Figure 6 may be related to establishing consensus and the same preference of team members, which is reflected in the dynamic time change of neural synchronization.

Moreover, previous studies on neural synchronization during social interaction and group decision-making revealed that substantial neural synchronization occurred around 50-55 seconds into a teaching task involving prior knowledge (Liu et al., 2019) and persisted approximately 6 minutes into the discussion period (Xie et al., 2023). These results collectively validate the suitability of utilizing fNIRS signal response time in our study (pp. 25-26).

“Our study also has demonstrated significant increases in single-brain activation, DLPFC-OFC functional connectivity, and GNS at 7, 12, and 17 minutes, respectively, following task initiation. The significant increase in these neural activities together constructs the two-in-one neural model that explains how group identification influences the collective performance we proposed. As previous studies have shown, the BOLD signal collected by fNIRS is slowly increasing compared to neuronal activity, which means that it has hysteresis (Turner et al., 1998). In social interactions such as group decision-making, the time of neural synchronization is delayed because people need to spend time increasing the number of dialogues to improve collaboration efficiency and form the same preference (Zhang et al., 2019). For example, participants would exhibit significant neural alignment, but only after they had completed a period of dialogue (Sievers et al., 2024). In the task of cooperation, with the improvement of cooperation efficiency between two participants, the higher degree of neural synchronization (Cui et al., 2012). Therefore, the generation of neural synchronization depends on the interaction over a period of time, which can affect the estimation of collaboration time. Prior research has shown that when the teaching task with prior knowledge began 50-55 seconds, significant neural synchronization could be generated between teacher and students, which meant that students and teacher achieved the same goal of learning knowledge (Liu et al., 2019). Moreover, a noteworthy increase in GNS was observed approximately 6 minutes into the group discussion period for better discussing and solving the problem (Xie et al., 2023). These findings are similar to ours. Therefore, the time points we found could reflect the dynamic time change of the neural process of team collaboration.’ pp.25-26

Reviewer #2 (Public review):

Weaknesses:

The authors need to clearly articulate their hypothesis regarding why neural synchronization occurs during social interaction. For example, in line 284, it is stated that "It is plausible that neural synchronization is closely associated with group identification and collective performance...", but this is far from self-evident. Neural synchronization can occur even when people are merely watching a movie (Hasson et al., 2004), and movie-watchers are not engaged in collective behavior. There is no direct link between the IBS and collective behavior. The authors should explain why they believe inter-brain synchronization occurs in interactive settings and why they think it is related to collective behavior/performance.

Thank you for bringing these points to our attention, we have clarified the relationship between neural synchronization and collective behavior in the Introduction section. (p.4). Moreover, in order to investigate whether neural synchronization stems from a common task or environment, we pseudo-randomized all pairs of subjects and created a null distribution consisting of 1,000 pseudo-groups, as described in Lines 311-315. This approach enabled us to eliminate neural synchronization resulting from factors other than social interaction, allowing us to identify neural patterns associated with collective performance (p.12).

“Moreover, Ni et al. (2024) indicated that neural synchronization was linked to the strength of social-emotional communication and connections between individuals. An increase in neural synchronization has also been shown to predict the coordination and cooperation abilities of group members (Lu et al., 2023). Therefore, we hypothesize that neural synchronization may be related to group performance.” p.4

“After that, the nonparametric permutation test was conducted on the observed interaction effects on GNS of the real group against the 1,000 permutation samples. By pseudo-randomizing the data of all participants, a null distribution of 1000 pseudo-groups was generated (e.g., time series from member 1 in group 1 were grouped with member 2 in group 2 & member 3 in group 3). The GNS of 1,000 reshuffled pseudo-groups was computed, and the GNS of the real groups was assessed by comparing it with the values generated by 1000 reshuffled pseudo-groups.” p.12

The authors state that "GNS in the OFC was a reliable neuromarker, indicating the influence of group identification on collective performance," but this claim is too strong. Please refer to Figure 4B. Do the authors really believe that collective performance can be predicted given the correlation with the large variance shown? There is a significant discrepancy between observing a correlation between two variables and asserting that one variable is a predictive biomarker for the other.

Thank you for your suggestion, we have revised the relevant statement (p.18).

“Through correlation and regression model analysis, we found that in group decision-making, the increase in group identity would affect group performance by improving GNS in the OFC brain region.”  p.18

Why are the individual answers being analyzed as collective performance (See, L-184)? Although these are performances that emerge after the group discussion, they seem to be individual performances rather than collective ones. Typically, wouldn't the result of a consensus be considered a collective performance? The authors should clarify why the individual's answer is being treated as the measure of collective performance.

We appreciate the insightful comment provided by the reviewer. The decision to utilize individual responses as a metric of overall performance is based on several key considerations. Previous studies on various hidden profile tasks have utilized averaged individual scores to represent collective performance (e.g., Stasser et al., 1995; Wittenbaum et al., 1996; Brockner et al., 2022). Secondly, while consensus outcomes are typically regarded as collective expressions, we argue that in the context of this study, individual responses are not independent entities but rather extensions of the group decision-making process. The collective deliberation process significantly influenced individual thinking and decision-making in this study. Through group discussions, members shared perspectives, adjusted their stances, and formulated their responses based on collective insights. The responses provided by participants in this study were molded by the dynamics of group conversations, serving as an indirect measure of group performance and potentially indicating the efficacy of collective deliberations.

Performing SPM-based mapping followed by conducting a t-test on the channels within statistically significant regions constitutes double dipping, which is not an acceptable method (Kriegeskorte et al., 2011). This issue is evident in, for example, Figures 3A and 4A.

Please refer to the following source: https://www.nature.com/articles/nn.2303

We have carefully reviewed the articles provided by the reviewer, and we acknowledge the concerns regarding selective analysis and double dipping in our statistical approach. To address this, we believe it is important to clarify this issue further in the Discussion section (pp.26-27).

Our study introduces a novel perspective while utilizing conventional fNIRS-based hyperscanning analyses (Liu et al., 2019; Pärnamets et al., 2020; Reinero et al., 2021; Számadó et al., 2021; Solansky, 2011), methods that are widely endorsed within the field. In our analysis, significant channels were first identified using a one-sample t-test, followed by additional analyses including ANOVA, independent samples t-tests, and other procedures. We would like to emphasize that the statistical assumptions underlying the one-sample t-test and paired-sample t-test in our study maintain a level of independence. Moreover, to further mitigate concerns about the potential for double dipping, we employed permutation testing to validate the robustness of our results and ensure that our findings are not influenced by biases inherent in the selection of significant regions.

We recognize the importance of rigorous statistical practices and are committed to upholding the highest standards of analysis. As such, we have revisited our methodology and included a more detailed explanation of the steps taken to avoid double dipping and ensure the integrity of our analyses in the revised manuscript.

“Although our study has found a new perspective, the analysis method still refers to and uses the traditional fNIR-based hyperscanning analyses (Liu et al., 2019; P¨arnamets et al., 2020; Reinero et al., 2021; Számadó et al., 2021; Solansky, 2011), which is generally accepted by the majority of fNIR-based hyperscanning researchers. For example, we would first identify significant channels through a one-sample t-test and then conduct further analyses, such as ANOVA or independent samples t-tests. Selective analysis is a powerful tool and is perfectly justified whenever the results are statistically independent of the selection criterion under the null hypothesis (Kriegeskorte et al., 2019). However, it may lead to double dipping and missing information. In this study, the absence of statistically significant TPJ activation in the analyzed data led to the TPJ being ignored. In the future, it should be made explicit in the analysis, and the reliability of the results should be ensured by appropriate statistical methods (e.g., cross-validation, independent data sets, or techniques to control for selective bias).” p.26-27

In several key analyses within this study (e.g., single-brain activation in the paragraph starting from L398, neural synchronization in the paragraph starting from L393), the TPJ is mentioned alongside the DLPFC. However, in subsequent detailed analyses, the TPJ is entirely ignored.

We thank the reviewer for your careful review and valuable comment. TPJ is referenced in certain analyses within this paper (as detailed in paragraphs L414 and L440); however, its role remains inadequately investigated and expounded upon in subsequent more intricate analyses. This is due to the absence of statistically significant TPJ activation in the analyzed data. As pointed out by the reviewer, limitations may exist in pursuing further analyses through ROIs, a point we also have addressed in the Discussion section (p.27).

The method for analyzing single-brain activation is unclear. Although it is mentioned that GLM (generalized linear model) was used, it is not specified what regressors were prepared, nor which regressor's β-values are reported as brain activity. Without this information, it is difficult to assess the validity of the reported results.

We have revised the relevant description to clarify the analyses of single-brain activation (p. 11)

While the model illustrated in Figure 7 seems to be interesting, for me, it seems not to be based on the results of this study. This is because the study did not investigate the causal relationships among the three metrics. I guess, Figure 5D might be intended to explain this, but the details of the analysis are not provided, making it unclear what is being presented.

We regret the confusion that has arisen. Firstly, as highlighted by the reviewer, the model depicted in Figure 7 is not directly derived from the causal analysis conducted in this study. Our investigation did not directly explore the causal relationships among the three indicators; instead, we constructed a model based on correlations and potential mechanisms. In the revised manuscript, we have explicitly stated that Figure 7 represents a descriptive model (p.22).

Regarding Figure 5D, the reviewer noted that while it may offer some explanatory value, it lacks the necessary analytical detail to elucidate the chart's significance clearly. We have clarified the details of the analysis in Figure 5 (pp.13-14). The model in Figure 5D suggested that the connection between the similarity in individual-collective performance and the correlation of brain activation, as well as whether the impact of each individual’s single-brain activation on the corresponding group’s GNS was regulated by their brain activation connectivity.

“Finally, we employed correlation and mediation analyses to assess if brain activation connectivity could explain the connection between individuals’ single-brain activation and the related group’s GNS. We examined the connection between the similarity in individual-collective performance and the correlation of brain activation, as well as whether the impact of each individual’s single-brain activation on the corresponding group’s GNS was regulated by their brain activation connectivity. We utilized the PROCESS tool in SPSS to investigate the proposed moderation effect. Specifically, we applied Model 1 with 5000 bootstrap resamples to examine the interaction between the independent variable (i.e., single-brain activation) and the moderator (i.e., brain activation connectivity) in predicting the dependent variable (i.e., GNS). It is noteworthy that prior to analysis, all variables in the moderation model were mean-centered to reduce multicollinearity and improve the interpretability of interaction terms.”  p.13-14

“Building on the above results, we have developed a two-in-one neural model that explains how group identification influences collective performance. This descriptive model aims to illustrate the potential interrelationships among these indicators and establish a conceptual framework to inspire forthcoming research endeavors.”  p.21

The details of the experiment are not described at all. While I can somewhat grasp what was done abstractly, the lack of specific information makes it impossible to replicate the study.

As suggested, we have clarified the details of the experiment in the manuscript.

(1) As stated in the public review, the details of the experiment are not described at all and while I can somewhat grasp what was done abstractly, the lack of specific information makes it impossible to replicate the study. In points a-e below, I list the aspects that I could not fully understand, but I am not asking for direct answers to these points. Instead, please provide a detailed description of the experiment so that it can be replicated.

Thank you for your suggestion; we have responded to each question sequentially and elaborated on the experiment specifics to ensure replicability.

(a) Please provide more detailed information about the Group Identification Task. How much did each participant speak (was there any asymmetry in the amount of speaking, and was there any possibility that the asymmetry influenced the identification rating)? Did the three participants interact in person, or online? Are they isolated from experimenters? How was the rating conducted, what I mean is that it's a PC-based rating?

We apologize for the lack of detail in our description of the procedures for the experiment.

For the first question, we draw upon previous studies concerning the manipulation of group identity while controlling the content of pre-task conversations. Specifically, the high-identity group engaged in self-introductions and identified similarities among the three members, whereas the low-identity group discussed topics related to the current semester's classes (Xie et al., 2023; Yang et al., 2020). Both discussions were conducted for the same duration of three minutes, ensuring that the number of exchanges between the two groups remained comparable. There was almost no asymmetry in the amount of speaking. We also conducted a manipulation check, which confirmed the effectiveness of our identity manipulation(pp.5-6).

Xie, E., Li, K., Gu, R., Zhang, D., & Li, X. (2023). Verbal information exchange enhances collective performance through increasing group identification. NeuroImage, 279, 120339.

Yang, J., Zhang, H., Ni, J., De Dreu, C. K., & Ma, Y. (2020). Within-group synchronization in the prefrontal cortex associates with intergroup conflict. Nature neuroscience, 23(6), 754-760.

“Both discussions were conducted for the same duration of three minutes, ensuring that the number of exchanges between the two groups remained comparable.”  p.5-6

For the second question，the three participants interacted offline in a face-to-face setting, while the experimenter remained outside the laboratory (p.6).

“The three participants conducted face-to-face offline interaction throughout the manipulation process.” p.6

For the third question, at the beginning of the experimental task, participants were isolated from the experimenters (p.6).

“In addition to explaining the next phase of the task and controlling the timer, experimenters would be isolated from participants.” p.6

For the last question, the rating of group identification was conducted through a questionnaire presented on participants’ phones (p.6).

“The questionnaire was presented on participants’ phones.” p.6

(b) The procedures of the Main Task are also unclear. For the Reading Information (5 min): How was the information presented? PC-based or paper-based? How were the participants seated? Did they read it independently?

We apologize for the missing details. We have included the following information in the article.

For the first and last question, each participant would get a piece of paper, which presents the common information and private information. They read independently. (p.6)

“Each participant would get a piece of paper, which presented the information. Participants could read independently.” p.6

About how the participants sat, the three participants sat around a table without partitions between each other. Only in the discussion stage, they could communicate face-to-face (p.6).

“They sat around a table without partitions between each other.” p.6

“In this process of discussion, the participants were able to communicate face-to-face and verbally.” p.6

(c) For Sharing Private Information: The authors stated they share text messages using Tencent Meeting. If so, how and with what devices? How was the information displayed on the screen? Were the participants even in the same room?

Thank you for your reminder. We have added more details now (p.6). Firstly, the experimenter sent the Tencent Meeting link to the participants. After the participants entered the meeting through their mobile phones, they could text the information they wanted to share in the chat box of the meeting. They were in the same room, with Tencent Meeting recording shared information, the participants could view them at any time.

“During the group sharing, participants entered Tencent Meeting via their mobile phones and were able to text their private information in the chat box to their group members for 5 minutes.” p.6

(d) For Discussing Information: It's a verbal interaction. How did they interact with others? What is the distance between them? I found a very small picture in Figure 8, but that is all information about experiment settings, that is provided by the authors.

We are sorry about the missing details. As we have explained in the article it’s a verbal communication, so participants could talk face to face in one room. We have included the following information in the article (p.6).

“Participants were sitting and communicating around a table. The distance between adjacent participants was about 15 cm, and the distance between face-to-face participants was about 40 cm. In this process of discussion, the participants were able to communicate face-to-face and verbally.” p.6

(e) For the Decision Process (5 min): How did they answer (What I mean is verbally, writing, or computer-based input), and how did the experimenters record these answers?

The questions were presented on paper, so the participants could write down their answers and experimenters could count the answers on paper. We have included the following information in the article(p.7).

“After discussion, all triads were given 5 minutes to answer the following questions (i) the probability of three suspects, 0%-100% for each suspect; (ii) the motivation and tool of crime; and (iii) deduced the entire process of crime. The three questions were presented on paper, allowing participants to write their answers directly on the same sheet. Subsequently, three independent raters used these paper questionnaires to record and calculate the scores for each group.” p.7

(2) I find the model presented in Figure 7 to be intriguing. Understanding why inter-brain synchronization occurs and how it is supported by specific single-brain activations or intra-brain functional connectivity is indeed a critical area for researchers conducting hyperscanning studies to explore. However, the content depicted in this model is not based on the results of this study. This is because the study did not investigate the causal relationships among the three metrics. I guess, Figure 5D might be intended to explain this, but the details of the analysis are not provided, making it unclear what is being presented. Please include a detailed explanation.

The specific answers are available on page 5 of our response letter.

(3) The analysis of single-brain activation analysis (and probably other analyses) focuses on the period from reading to making decisions (L237). Why was this entire interval chosen for analysis? Reading does not involve social interaction. As mentioned in a previous comment, the details of the tasks are unclear, so it's difficult to understand what was actually done in the reading period. Anyway, why were these different phases combined as the focus of analysis? Please clarify the reasoning behind this choice.

Thank you for your feedback. The decision to analyze the entire interval, spanning from reading to decision-making, was primarily made to grasp the continuum of information processing comprehensively. While reading itself lacks social interaction, it serves as the foundation for subsequent decision-making, during which participants' cognitive states and affective responses gradually evolve. Therefore, examining these two phases collectively enables a more thorough investigation into how information influences decision-making. Furthermore, considering the task details remain ambiguous, we aim to uncover the underlying cognitive and affective mechanisms through a holistic analysis.

(4) The method for analyzing single-brain activation is unclear. Please provide a detailed description of the analysis methods.

Thank you for your suggestion, we have added more details in the Method section (p.11).

“In the GLM model analysis, HbO was the dependent variable, and the regression amount was set to different task stages (a. Reading information, b. Sharing private information, c. Discussion information, d. Decision). After that, we convolved the regression factor with the Hemodynamic Response Function (HRF), and obtained the brain activation β value of each participant in each channel at different task stages through regression analysis.”  p.11

(5) In the periods of Reading Information and Sharing Private Information, there appears to be no social interaction between participants (Figure1D). However, Figure 6 shows an increase in brain activity correlation even during the first 10 minutes (it corresponds to the Reading and Sharing period). Why does inter-brain correlation (GNS, in this study) increase even though there is no interaction between participants? Please provide an explanation.

Sharing private information fosters interactive engagement, necessitating its exchange during Tencent Meetings to facilitate sharing. Previous research suggests that heightened correlations in brain activity can be attributed to (1) intrinsic cognitive processes, wherein participants display similar cognitive and emotional responses, fostering shared cognitive processing and brain activity synchronization despite limited external interaction; (2) emotional connections, as divulging private information elicits emotional responses that can be neurally correlated among individuals; and (3) environmental influences, where shared environments and contexts prompt neural interaction among participants even in the absence of direct social engagement. These factors collectively contribute to increased brain activity correlations without active interaction. Our primary focus, however, lies in the phase characterized by significant synchronized brain activity.

Minor Comments:

(6) Equation 1 Explanation: There is no explanation of Equation 1. It mentions Yi as the collective score, but what constitutes the collective score Yi is not defined in the manuscript. Additionally, while "i" is referred to as an item (in Line 196), the meaning of "item" is not clear. Therefore, the meaning of this equation is not understood.

We apologize for this confusion. We have added a description in the manuscript (p.9).

“In Eq.1, x is the individual score, y is the collective score (y is calculated from the three per capita scores), and i stands for the group number for the item. So, x_i means the individual score of participants in the _i group, and y_i means the collective score of the _i group. _d (x, y) r_epresents the distance from the individual to the collective score.”  p.9

(7) Equation 2 Explanation: There is no explanation for Equation 2. Please provide descriptions for all variables such as S, t, and w.

We have clearly stated the meaning of s, t, and w in the first edition of the manuscript article (p.12).

As shown in L291-293: Here, t denotes the time, s denotes the wavelet scale, 〈⋅〉 represents a smoothing operation in time, and W is the continuous wavelet transform (Grinsted, Moore, & Jevrejeva, 2004).

(8) Acronyms: Please define all acronyms upon their first appearance (e.g., CFI, TLI, RMSEA in L380).

We apologize for these mistakes, and we have added full explanations for abbreviations upon their first use (p.16).

“The mediation model demonstrated a satisfactory fit (CFI = 0.93, TLI = 0.93, RMSEA = 0.04) (CFI-Comparative Fit Index; TLI-Tucker-Lewis index; RMSEA-Root-Mean-Square Error of Approximation), suggesting that the perceived group identification of each individual affected the alterations in single-brain activations in the DLPFC, consequently leading to variations in their performance (β_a = 0.16, t = 2.20, p = 0.030; β_b = 0.26, t = 3.56, p < 0.001; β_c = 0.18, t = 2.34, p = 0.020) (Figure 3C).”  p.16

(9) Hyperscanning fMRI Studies: Since there are hyperscanning fMRI studies analyzing communication among three people (e.g., Xie et al., 2020, PNAS), it would be beneficial to cite this research. pnas.org/doi/pdf/10.1073/pnas.1917407117.

As suggested, we have cited this paper. (p.4)

(10) Line 272; Line 275: Should these references be to Benjamini & Hochberg (1995)?

As suggested, we have revised our citation.

(11) Research Objectives: The authors' aim seems to be understanding the relationship between Group Identification Level (High or Low), collective performance, and inter-brain synchronization (GNS). If so, shouldn't the results shown in Figure 6 illustrate how these differ between High and Low groups?

We are grateful to the reviewer for your insightful comment. This study aimed to investigate the impact of group identity levels on collective performance and interbrain synchronization. Our analysis primarily focused on inter-group disparities to elucidate the potential influence of varying levels of group identification on collective behavior and neural synchrony, as highlighted by the reviewer. It is important to note that the relationship between group identification levels and collective performance, as well as neural synchronization, may represent a continuous or correlational process, rather than a binary comparison between two distinct groups. Notably, we treated group identification as a continuous variable and, consequently, Figure 6 was designed to illustrate trends in the association between group identification levels and both collective performance and neural synchronization, without conducting significance tests between groups. We are confident that the depiction in Figure 6 effectively captures the evolving dynamics between group identification levels and both collective performance and neural synchronization.

(12) Figure 6 Star-Marker: What is the star marker shown in Figure 6? Please provide an explanation.

We apologize for this confusion. We have added this explanation to the article. (p.21)

“The red star sign indicates that at this time point, the neural signal began to increase significantly.” p.21

(13) Pearson's Correlation: Use "Pearson's correlation" instead of "Pearson correlation."

Thanks for your comments, we've changed Pearson correlation to Pearson's Correlation for a total of 10 places in the original text (pp. 9,11,13, 15,16, 19,23).

“Moreover, the Pearson’s correlation was used to examine the relationship between group identification_2 and collective performance.” p.9

“Subsequently, we used Pearson’s correlation analyses to investigate the relationship between single-brain activation and individual performance.” p.11

“Second, the Pearson’s correlation between GNS and collective performance was performed.” p.13

“Following that, we analyzed Pearson’s correlations between the original HbO data in the region related to individual and collective performance, denoted as brain activation connectivity (Lu et al., 2010).” p.13

“Subsequently, the Pearson’s correlation between the quality of information exchange and collective performance was assessed.” p.15

“Furthermore, the results of the Pearson’s correlation indicated that groups with higher group identification were more likely to exhibit better collective performance (r \= 0.38, p \= 0.003) (Figure 2B).” p.15

“The Pearson’s correlation and its associated analyses were based on the data from group identification_2. *p < 0.05.” p.16

“We first extracted the HbO brain activities related to individual performance (e.g., DLPFC, CH4) and collective performance (e.g., OFC, CH21) of each group member and conducted a Pearson’s correlation between the two.” p.19

“Subsequently, Pearson’s correlation was used to test whether individual differences in the similarity in individual-collective performance were reflected by DLPFC-OFC connectivity.” p.19

“Pearson’s correlation showed that the higher quality of information exchange, the better collective performance (r \= 0.36, p \= 0.007) (Figure 8C).” p.23

(14) MNI Coordinates: The MNI coordinates for each channel are listed in the supporting information. How were these coordinates measured? Were they consistent for all participants? Was MRI conducted for each participant to obtain these coordinates?

Thank you for your reminder, we have included the necessary instructions in the revised version. First, we need to clarify that we referred to previous literature to determine the placement of the optical probe plates. Following the completion of data collection, we utilized the Vpen positioning system to accurately locate the detection light poles, ultimately obtaining the MNI positioning coordinates. These coordinates were basically consistent for each participant. (p.8)

“For each participant, one 3 × 5 optode probe set (8 emitters and 7 detectors forming 22 measurement points with 3 cm optode separation, see Table S1 for detailed MNI coordinates) was placed over the prefrontal cortex (reference optode is placed at Fpz, following the international 10-20 system for positioning). The other 2 × 4 probe set (4 emitters and 4 detectors forming 10 measurement points with 3 cm optode separation, see Table S2 for detailed MNI coordinates) was placed over the left TPJ (reference optode is placed at T3, following the international 10-20 system for positioning). The probe sets were examined and adjusted to ensure consistency of the positions across the participants. After the completion of data collection, we utilized the Vpen positioning system to accurately locate the detection light poles, ultimately obtaining the MNI positioning coordinates.”  p.8

ho diria en paraules: we want to study the probability of the outcome (P(Y = 1) = p)

ho ficaria en la següent diapo. També afegiria en algun punt la correlation matrix que em sembla una funció molt útil per a reportar la correlació (https://www.sthda.com/sthda/RDoc/figure/statistics/correlation-matrix-graph-1.png)

no es ben bé això perquè també hi ha el coeficient de l'error quan X = 0. Ficaria Y expected value

Aquí podries posar e= \hat{y} - y

Aquí els hi pots dir que és com si NOMÉS poguéssim explicar Y amb la mitjana de Y.

Aquí és important que diguis que no hi ha jerarquia entre les variables. X sobre Y ó Y sobre X ho llegirem igual.

I també està pensada per raons linials i que si no ho són la rho no és interpretable (feu el gràfic). Els podries mostrar el quartet de Anscombe's

uhm... "indicates whether there is statistically significant evidence that X is associated with Y"

2 columnas aquí también para que no tengas múltiples instrucciones en la misma línea y el theme_bw() en último para seguir el orden de ideas que explicó Pau

para que se vea mejor usaría 2 columnas. Una con el código y otra con el plot

No m'acaba de convencer això d'establish:

To quantify the relationship between a dependent variable Y and a predictor variables X

Millor amb un exemple concret.... What is the effect of sugary drinks (X) on diabetes risk (Y)?

Estimate a clinical prediction model to predict the risk of diabetes (Y) given a subject's demographic and clinical characteristics (X_i)

Reviewer #1 (Public review):

Summary:

In this meticulously conducted study, the authors show that Drosophila epidermal cells can modulate escape responses to noxious mechanical stimuli. First, they show that activation of epidermal cells evokes many types of behaviors including escape responses. Subsequently, they demonstrate that most somatosensory neurons are activated by activation of epidermal cells, and that this activation has a prolonged effect on escape behavior. In vivo analyses indicate that epidermal cells are mechanosensitive and require stored-operated calcium channel Orai. Altogether, the authors conclude that epidermal cells are essential for nociceptive sensitivity and sensitization, serving as primary sensory noxious stimuli.

Strengths:

The manuscript is clearly written. The experiments are logical and complementary. They support the authors' main claim that epidermal cells are mechanosensitive and that epidermal mechanically evoked calcium responses require the stored-operated calcium channel Orai. Epidermal cells activate nociceptive sensory neurons as well as other somatosensory neurons in Drosophila larvae, and thereby prolong escape rolling evoked by mechanical noxious stimulation.

Weaknesses:

In several places the text is unclear. For example, core details are missing in the protocols, including the level of LED intensity used, which are necessary for other researchers to reproduce the experiments. Secondly, the rationales are missing for some experiments (for experiments X, Y, and Z). It would be helpful to clarify for your readers why the experiments (for example Figure 3S2) were performed. Finally, for most experiments, the epidermal cells are activated for 60 s, which is long when considering that nocifensive rolling occurs on a timescale of milliseconds. It would be informative to know the shortest duration of epidermal cell activation that is sufficient for observing the behavioral phenotype (prolongation of escape behavior) and activation of sensory neurons.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary.

In this meticulously conducted study, the authors show that Drosophila epidermal cells can modulate escape responses to noxious mechanical stimuli. First, they show that activation of epidermal cells evokes many types of behaviors including escape responses. Subsequently, they demonstrate that most somatosensory neurons are activated by activation of epidermal cells, and that this activation has a prolonged effect on escape behavior. In vivo analyses indicate that epidermal cells are mechanosensitive and require stored-operated calcium channel Orai. Altogether, the authors conclude that epidermal cells are essential for nociceptive sensitivity and sensitization, serving as primary sensory noxious stimuli.

Strengths.

The manuscript is clearly written. The experiments are logical and complementary. They support the authors' main claim that epidermal cells are mechanosensitive and that epidermal mechanically evoked calcium responses require the stored-operated calcium channel Orai. Epidermal cells activate nociceptive sensory neurons as well as other somatosensory neurons in Drosophila larvae, and thereby prolong escape rolling evoked by mechanical noxious stimulation.

Weaknesses.

Core details are missing in the protocols, including the level of LED intensity used, which are necessary for other researchers to reproduce the experiments. For most experiments, the epidermal cells are activated for 60 s, which is long when considering that nocifensive rolling occurs on a timescale of milliseconds. It would be informative to know the shortest duration of epidermal cell activation that is sufficient for observing the behavioral phenotype (prolongation of escape behavior) and activation of sensory neurons.

(1) We agree with the reviewer that the LED intensity is an important detail of the experimental paradigm. We updated the methods to include intensity measurements for the stimuli used throughout the manuscript.

(2) The Reviewer asks about the shortest duration of epidermal cell activation sufficient for observing the behavior phenotype. We note in the manuscript that behavioral responses to optogenetic epidermal stimulation are apparent within 2 seconds of stimulus (see Figure 2F); this is consistent with our calcium imaging data in which C4da response reaches its maximum within 2-3 sec of stimulation.

Reviewer #1 (Recommendations):

(1) The epidermal cells in this study are activated for 60 s. In the real world, the nociceptive stimulation (a poke, such as penetration by the ovipositor of a parasitic wasp) that evokes escape rolling is short. Does optogenetic activation of 1 s or less still evoke rolling? For example, it is unclear in Figure 4K how long the epidermal cells need to be activated before the poke stimulus prolongs rolling. Is it possible to test behavior and GCaMP activity in sensory neurons when epidermal cells are briefly (1 second) activated?

As described above, behavioral responses to optogenetic epidermal stimulation are apparent within 2 seconds of stimulus (see Figure 2F); this is consistent with our calcium imaging data in which C4da response reaches its maximum within 2-3 sec of stimulation. The kinetics are consistent with a role for epidermal cells in modulating neuronal responses to nocifensive stimuli, and similar to the response kinetics observed in mammalian epidermal cells that modulate neuronal touch and pain responses  (Maksimovic et al., 2014; Woo et al., 2014; Mikesell et al., 2022).

(2) The protocol for optogenetic screening states that the authors used a 488-nm LED. Why was a 488-nm LED used instead of the 610-nm LED for Chrimson activation? No information (except figure 4K) about the light intensity is provided in the figure legend or the protocol section. Please state the LED intensity used for all optogenetic experiments (GCaMP imaging, behavioral experiments, etc.).

We used 488 nm light for the initial screen for technical reasons. The screen was conducted by students at the MBL Neurobiology course (hence the affiliation; student authors are included in the manuscript), and the only LED available to us at that time delivered insufficient illumination at longer wavelenths to be useful. We chose to include the student’s data because (1) we found that the 488 nm light alone did not induce rolling in our setup, (2) we repeated and extended the studies with the epidermal drivers using a higher resolution imaging platform and longer wavelength stimulation (all studies other than Fig. 1), and (3) we observed qualitatively similar results when we repeated stimulation with all drivers using 561 nm light.

We agree that the LED intensity is an important detail of the experimental paradigm. We updated the methods to include intensity measurements for the stimuli used throughout the manuscript. We also include the intensities here:

- 30 μW/mm^2 for calcium imaging experiments Fig 3B-E, Fig 4A, Fig 3S1A-D, Fig 4S1A

- 300 μW/mm^2 for behavior studies in Fig 2B-E, Fig 1S6, Fig 2S1, Fig 3E-F, Fig 3S2A-C

- 25 μW/mm^2 for behavior studies in Fig 4E-J

- 1.16 μW/mm^2 for behavior studies in Fig 4K

(3) Lines 150 - 152: Although the authors refer to "a stereotyped behavior sequence" in Fig 2D, there are no data supporting this claim in Fig 2. Rather, the data appear to represent proportions of different types of behavior at each time point, rather than behavior sequences. If the authors wish to claim that the data show stereotyped behavior sequences, they should analyze the data using a different method (e.g., Markov models).

We agree that in the absence of additional analysis we should avoid commenting on stereotypy of behavior sequences; we therefore adjusted the text to reflect the tendency of nociceptive behaviors to precede non-nociceptive behaviors. The raster plots shown in Supplemental Fig. 2A illustrate this point: in larvae exhibiting nociceptive behaviors, these behaviors appear first, followed by backing and frequently freezing. As one quantitative readout of this sequence we show that the latency of rolling (nociceptive) is shorter compared with backing or freezing (non-nociceptive) (Fig. 2F, Fig. S2G).

(4) Figure 3A-E: a cursory glance at the data suggests that the most responsive sensory neurons are C1da, with all sensory neurons activated. However, at the behavioral level, only some sensory neurons are activated. If all sensory were activated by Chrimson, what behavioral phenotypes would the authors expect to see? Would it be the same as epidermal activation?

The Reviewer raises an interesting question, but we intentionally avoid comparing the response properties among sensory neurons because of differences in driver strength. Likewise, extrapolating “activation” at the behavioral level is exceedingly difficult if/when multiple sensory neurons are simultaneously activated. In response to the Reviewer’s specific question, when all da neurons are activated simultaneously, larvae largely exhibited hunching rather than rolling (Hwang et al., 2007). We find that epidermal stimulation rarely elicits hunching; instead, epidermal stimulation generally triggers nocifensive behaviors followed by non-nocifensive behaviors such as backing and freezing, suggesting an order or priority in neurons activated by epidermal cells (or different response times). Defining the mechanisms by which epidermal cells communicate with different types of sensory neurons is therefore a top priority for future studies.

(5) Figure 3S2; The behavior phenotypes between Fig. 3E, F and Fig 3S2 seems a slightly different. I suggest adding some comments in different behavior phenotype depending on the different GAL4. Specifically, is there increased freezing in some genotypes (e.g., ppk-LexA or NompC-lexA)? Can you show this without TNT data? Is this a background effect or specific GAL4 phenotype?

We currently do not have the driver-only control for this experiment, but our effector-only control experiment (see Fig. 3S2A) suggests that larvae carrying the AOP-TNT insertion exhibit enhanced nociceptive behavioral responses. This point is addressed in our manuscript by the following (copied from the figure legend):

“We note that although baseline rolling probability is elevated in all genetic backgrounds containing the AOP-LexA-TnT insertion, silencing C4da and C3da neurons significantly attenuates responses to epidermal stimulation.”

(6) Calcium-free solution is used in Figure 3. Why do the authors still observe calcium influx? Does this mean that internal calcium stores are released? If so, does the calcium influx represent an action potential? How do the authors focus their LED stimulation to activate epidermal cells and avoid activation of the imaging laser?

The specimens were imaged in calcium-free solution to minimize movement artifacts. However, the CNS is wrapped by glial cells and over short timescales such as those used for the imaging we speculate that extracellular calcium persists in the CNS.

(7) It is unclear when animals begin to crawl after the epidermal cells are mechanically stimulated. How do the authors distinguish between peristaltic crawling and a poke by Orai receptors? Although the in vitro experiments beautifully show radial tensions, it is unclear to what extent A-P axis tension (peristaltic crawling) and radial tension (poke) differ. It might be helpful to explain in the discussion section how epidermal cells are selectively activated.

The Reviewer raises an interesting question about the types and thresholds of forces required to elicit epidermal responses. We cannot eliminate the possibility that peristaltic crawling (or crawling through a 3D substrate) stimulates epidermal cells to a certain degree. Indeed, our results demonstrate a dose-dependent response of Drosophila epidermal cells and human keratinocytes to radial stretch. However, we do not have any information about selectivity in response to different stimuli, though we agree that this is an intriguing avenue for future studies. For example, we don't know whether stretch-responsive cells are more or less responsive to poke. But, a salient feature of our studies is the recruitment of greater numbers of responders with increasing stimulus intensity, therefore we added the following statement to the discussion to clarify our model:

“Finally, we find that epidermal cells exhibit a dose-dependent response to radial stretch; we therefore anticipate that the output of epidermal cells is likewise dependent on the stimulus intensity.  Hence, rather than a fixed threshold beyond which epidermal cells are selectively activated, we hypothesize that increasing stimulus intensities drive increasing signal outputs to neurons.”

(8) Some Protocols are missing. For example, in Figure 4, many stimulus combinations were used to test behavior. How were stimuli of different modalities applied to the animals? Further details need to be provided in the protocols.

We thank the Reviewer for identifying this oversight. The methods section of our original submission detailed most of the stimulus combinations but omitted the opto + mechano combination (4F). We updated our methods to correct these omissions.

(9) It might be helpful if the authors could provide a sample video for each behavior to clarify how they were each defined.

Our manuscript includes a table with a detailed description of the behaviors (Table S2), and we added two annotated videos that show representative behavioral responses to optogenetic nociceptor or epidermis stimulation.

(10) A supplementary summary table of genotypes might be helpful for the reader.

Experimental genotypes are provided in the figure legends, and a detailed list of all alleles used in the study as well as their source is provided in supplemental table S1.

Reviewer #2 (Public Review):

Summary.

The authors provide compelling evidence that stimulation of epidermal cells in Drosophila larvae results in the stimulation of sensory neurons that evoke a variety of behavioral responses. Further, the authors demonstrate that epidermal cells are inherently mechanoresponsive and implicate a role for store-operated calcium entry (mediated by Stim and Orai) in the communication to sensory neurons.

Strengths.

The study represents a significant advance in our understanding of mechanosensation. Multiple strengths are noted. First, the genetic analyses presented in the paper are thorough with appropriate consideration to potential confounds. Second, behavioral studies are complemented by sophisticated optogenetics and imaging studies. Third, identification of roles for store-operated calcium entry is intriguing. Lastly, conservation of these pathways in vertebrates raise the possibility that the described axis is also functional in vertebrates.

Weaknesses.

The study has a few conceptual weaknesses that are arguably minor. The involvement of store-operated calcium entry implicates ER calcium store release. Whether mechanical stimulation evokes ER calcium release in epidermal cells and how this might come about (e.g., which ER calcium channels, roles for calcium-induced calcium release etc.) remains unaddressed. On a related note, the kinetics of store-operated calcium entry is very distinct from that required for SV release. The link between SOC and epidermal cells-neuron transmission is not reconciled. Finally, it is not clear how optogenetic stimulation of epidermal cells results in the activation of SOC.

(1) The involvement of store-operated calcium entry implicates ER calcium store release. Whether mechanical stimulation evokes ER calcium release in epidermal cells and how this might come about (e.g., which ER calcium channels, roles for calcium-induced calcium release etc.) remains unaddressed.

Our studies suggest that mechanically evoked responses in epidermal cells involve both ER calcium release and store-operated calcium entry. Notably, we show that depletion of ER calcium stores before mechanical stimulation, by treating with thapsigargin, reduces (but does not eliminate) mechanically evoked calcium responses in fly epidermal cells (Fig. 6C-6F). Likewise, fly epidermal cells and human keratinocytes both exhibit mechanically evoked calcium responses in the absence of extracellular calcium (10mM EGTA to chelate all free calcium ions). These data support a model whereby mechanical stimuli trigger calcium release from ER stores and influx. Indeed, several cell types have been shown to display mechanically evoked release of calcium from stores. For example, mechanical stimulation of enteroendocrine cells of the gut epithelium results in both calcium release from ER stores and calcium influx across the plasma membrane (Knutson et al., 2023). Similar to our findings, Knutson et al found that depleting stores decreased mechanically evoked calcium signals by over 70% in these gut epithelial stores. In our revised manuscript we have more clearly emphasized these points.

We agree with the reviewer that deciphering the mechanisms by which mechanical stimuli promote ER calcium release and subsequent store-operated calcium entry is an exciting topic to explore. One potential mechanism is the activation of a mechanosensitive receptor that promotes calcium release from the ER via calcium-induced calcium release or IP3 production, as has been proposed for enteroendocrine cells. A recent paper demonstrated that the ER itself is mechanosensitive and that mechanical stimuli promotes calcium release via the opening of calcium-permeable ion channels in the ER membrane (Song et al., 2024). Determining the relative contributions of store-operated calcium entry and ER calcium release and deciphering their underlying mechanisms will require a thorough investigation of ER calcium channels and receptors, thus we believe this would be beyond the scope of the present manuscript and merits publication on its own. However, we now include this in our discussion as an exciting new direction we aim to pursue.

(2) The kinetics of store-operated calcium entry is very distinct from that required for SV release. The link between SOC and epidermal cells-neuron transmission is not reconciled.

The Reviewer raises an interesting point regarding the mode of epidermal cell-neuronal communication. We demonstrated a requirement for dynamin-dependent vesicle release from epidermal cells in mechanical sensitization. However, the nature of the vesicular pool, the mode and kinetics of release, and the type of neuromodulator released remain to be characterized. Hence, it’s not clear that kinetics of synaptic vesicle release is an appropriate comparison. Our studies do demonstrate that behavioral responses to optogenetic epidermal stimulation are relatively slow – on the order of seconds – which is not incompatible with the kinetics of store-operated calcium entry. Furthermore, the primary functional output we define for epidermal mechanosensory responses, mechanical nociceptive sensitization, is apparent 10 sec following the stimulus and persists for minutes in our behavior assays. Consistent with this model, studies of the mammalian touch dome have shown that touch-sensitive Merkel cells secrete neurotransmitters to modulate neurons and promote sustained action potential firing on a similar timescale. Likewise, mechanically evoked ER calcium-release promotes sustained secretion of serotonin from enterochromaffin cells.

(3) It is not clear how optogenetic stimulation of epidermal cells results in the activation of SOC.

We appreciate the opportunity to clarify our results. We demonstrate that optogenetic epidermal stimulation elicits behavioral responses in larvae and calcium responses in somatosensory neurons, but we do not claim that optogenetic epidermal stimulation elicits SOC. Our optogenetic studies demonstrate the capacity for epidermal stimulation to modulate somatosensory function, but we characterize contributions of SOC only to mechanical stimuli which are more physiologically relevant. However, it is worth noting that CsChrimson is a calcium-permeable channel, suggesting that an increase in intracellular calcium may trigger epidermal-evoked neuronal responses and behaviors during optogenetic stimulation.

References

Hwang, RY, Zhong, L, Xu, Y, Johnson, T, Zhang, F, Deisseroth, K, and Tracey, WD (2007). Nociceptive neurons protect Drosophila larvae from parasitoid wasps. Curr Biol 17, 2105–2116.

Knutson, KR, Whiteman, ST, Alcaino, C, Mercado-Perez, A, Finholm, I, Serlin, HK, Bellampalli, SS, Linden, DR, Farrugia, G, and Beyder, A (2023). Intestinal enteroendocrine cells rely on ryanodine and IP3 calcium store receptors for mechanotransduction. J Physiol 601, 287–305.

Maksimovic, S, Nakatani, M, Baba, Y, Nelson, AM, Marshall, KL, Wellnitz, SA, Firozi, P, Woo, S-H, Ranade, S, Patapoutian, A, et al. (2014). Epidermal Merkel cells are mechanosensory cells that tune mammalian touch receptors. Nature 509, 617–621.

Mikesell, AR, Isaeva, O, Moehring, F, Sadler, KE, Menzel, AD, and Stucky, CL (2022). Keratinocyte PIEZO1 modulates cutaneous mechanosensation. Elife 11, e65987.

Song, Y, Zhao, Z, Xu, L, Huang, P, Gao, J, Li, J, Wang, X, Zhou, Y, Wang, J, Zhao, W, et al. (2024). Using an ER-specific optogenetic mechanostimulator to understand the mechanosensitivity of the endoplasmic reticulum. Dev Cell 59, 1396-1409.e5.

Woo, S-H, Ranade, S, Weyer, AD, Dubin, AE, Baba, Y, Qiu, Z, Petrus, M, Miyamoto, T, Reddy, K, Lumpkin, EA, et al. (2014). Piezo2 is required for Merkel-cell mechanotransduction. Nature 509, 622–626.

quizás quitaría esto ya que te dará más espacio en la diapo y ellos ya lo saben hacer

Pasa algo con el formato del código

Reviewer #3 (Public review):

Summary:

The authors record from the ACC during a task in which animals must switch contexts to avoid shock as instructed by a cue. As expected, they find neurons that encode context, with some encoding of actions prior to the context, and encoding of neurons post-action. The primary novelty of the task seems to be dynamically encoding action-outcome in a discrimination-avoidance domain, while this is traditionally done using operant methods. While I'm not sure that this task is all that novel, I can't recall this being applied to the frontal cortex before, and this extends the well-known action/context/post-context encoding of ACC to the discrimination-avoidance domain.

While the analysis is well done, there are several points that I believe should be elaborated upon. First, I had questions about several details (see point 3 below). Second, I wonder why the authors downplayed the clear action coding of ACC ensembles. Third, I wonder if the purported 'novelty' of the task (which I'm not sure of) and pseudo-debate on ACC's role undermines the real novelty - action/context/outcome encoding of ACC in discrimination-avoidance and early learning.

Strengths:

Recording frontal cortical ensembles during this task is particularly novel, and the analyses are sophisticated. The task has the potential to generate elegant comparisons of action and outcome, and the analyses are sophisticated.

Weaknesses:

I had some questions that might help me understand this work better.

(1) I wonder if the field would agree that there is a true 'debate' and 'controversy' about the ACC and conflict monitoring, or if this is a pseudodebate (Line 34). They cite 2 very old papers to support this point. I might reframe this in terms of the frontal cortex studying action-outcome associations in discrimination-avoidance, as the bulk of evidence in rodents comes from overtrained operant behavior, and in humans comes from high-level tasks, and humans are unlikely to get aversive stimuli such as shocks.

(2) Does the purported novelty of the task undermine the argument? While I don't have an exhaustive knowledge of this behavior, the novelty involves applying this ACC. There are many paradigms where a shock triggers some action that could be antecedents to this task.

(3) The lack of details was confusing to me:

a) How many total mice? Are the same mice in all analyses? Are the same neurons? Which training day? Is it 4 mice in Figure 3? Five mice in line 382? An accounting of mice should be in the methods. All data points and figures should have the number of neurons and mice clearly indicated, along with a table. Without these details, it is challenging to interpret the findings.

b) How many neurons are from which stage of training? In some figures, I see 325, in some ~350, and in S5/S2B, 370. The number of neurons should be clearly indicated in each figure, and perhaps a table.

c) Were the tetrodes driven deeper each day? The depth should be used as a regressor in all analyses?

d) Was is really ACC (Figure 2A)? Some shanks are in M2? All electrodes from all mice need to be plotted as a main figure with the drive length indicated.

e) It's not clear which sessions and how many go into which analysis

f) How many correct and incorrect trials (<7?) are there per session?

g) Why 'up to 10 shocks' on line 358? What amplitudes were tried? What does scrambled mean?

(4) Why do the authors downplay pre-action encoding? It is clearly evident in the PETHs, and the classifiers are above chance. It's not surprising that post-shuttle classification is so high because the behavior has occurred. This is most evident in Figure S2B, which likely should be a main figure.

(5) The statistics seem inappropriate. A linear mixed effects model accounting for between-mouse variance seems most appropriate. Statistical power or effect size is needed to interpret these results. This is important in analyses like Figure 7C or 6B.

(6) Better behavioral details might help readers understand the task. These can be pulled from Figures S2 and S5. This is particularly important in a 'novel' task.

(7) Can the authors put post-action encoding on the same classification accuracy axes as Figure 6B? It'd be useful to compare.

(8) What limitations are there? I can think of several - number of animals, lack of causal manipulations, ACC in rodents and humans.

Minor:

(1) Each PCA analysis needs a scree plot to understand the variance explained.

(2) Figure 4C - y and x-axes have the same label?

(3) What bin size do the authors use for machine learning (Not clear from line 416)?

(4) Why not just use PCA instead of 'dimension reduction' (of which there are many?)

(5) Would a video enhance understanding of the behavior?

Je me demande s'il ne serait pas plus efficace, sur le plan de la narration de placer ce segment plus tôt dans le texte, au sein d'une introduction méthodologique plus soutenue par exemple, pour présenter "ce qu'il y a avant le 19e siècle".

Ce passage illustre à merveille et de manière excessivement pertinente l'objectif principal du texte.

Contra tesis del progreso

Lo social se explica por lo social

yod

Unidad básica de la actividad refleja integrada, que consta de un órgano sensorial, una neurona aferente, una o más sinapsis dentro de una estación de integración central, una neurona eferente y un efector.

• dehumanising Qasim - wild beast

DOI: 10.7554/eLife.36095

Resource: RRID:BDSC_63509

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_63509

What is this?

This figure is misleading. The Equivalence Point is where the moles of acid and conjugate base are equal. The Midpoint to ionization is NOT where the moles are equal. The midpoint is where the pKa is determined... by sliding left from the midpoint dot to the Y axis and reading the pH. The pKa = pH at the Midpoint (which is found by taking the 1/2 Equivalence Point (or half the distance required (on the X axis) for the ionization) and moving up to the graphed line). Presumably the next diagram also has the same error.

INteresting

It is without a doubt that food plays an important role in the countless cultures that are present within our plan. This also rings true for European cultures such as the French culture and way of life that is depicted within this story created by Rbalais. They key feature I would like within this excerpt is the use of the word "celestial" to describe the quintessential items needed for a French breakfast. "Celestial" being a word used to relate to things in the sky which in turn connotes to something being positive and close to heaven. This shows that they treat their food with a level of sanctity and that that aspect of their lives is very important to this culture.

Another large signifier of food's importance in a culture that is displayed in this story is that the major events and conflicts that occurs in this story is related to sustenance. In the academic article titled "Food as a cultural symbol: The case study of François Rabelais’s Gargantua and Pantagruel and its Ukrainian translations" by Taras Shmiher and Yuliia Naniak, they state that it is of interest that the such a key conflict involving the bakers of Lerne and their opposers occur at the result of arguments regarding food (129). Following this logic we can also make connections to how much food is emphasized in the story when major things are occurring. For example: the descriptions of Gargantua's vast meals as a baby, the offer to allow Gargantua's horse being allowed to graze in their lands as being a symbolic gesture of welcome, and the shenanigans with the monks in Gargantua's salad are all examples of how food and consumption plays an important role in the story.

Works Cited: Shmiher, T., & Naniak, Y. (2023). Food as a cultural symbol: The case study of François Rabelais’s Gargantua and Pantagruel and its Ukrainian translations. Romanica Cracoviensia, 23(2), 127-134. https://doi.org/10.4467/20843917RC.23.013.18399

Briefing Doc : Faut-il en finir avec la démocratie participative ?

Introduction

Ce document présente une synthèse des principaux thèmes et idées ressortant de la discussion organisée par l'UNADEL (Union Nationale des Acteurs du Développement Local) autour de la question :

"Faut-il en finir avec la démocratie participative ?".

L'événement a réuni des personnalités aux expériences et points de vue divers, notamment * Nicolas Rio (politiste et co-auteur du livre éponyme), * Laurence Bart (maître de conférences et administratrice à l'UNADEL), et * Claire Touri (présidente du Mouvement Associatif).

La discussion a abordé les constats critiques de la démocratie participative actuelle, les attentes citoyennes révélées par les écoutes territoriales, et le rôle potentiel du mouvement associatif, tout en explorant des pistes pour revitaliser le fonctionnement démocratique.

Thème 1 : Critique de la démocratie participative actuelle

Nicolas Rio, s'appuyant sur son livre, a exposé une critique fondamentale de la manière dont la démocratie participative est souvent déployée aujourd'hui.

Son argument central est qu'elle tend à focaliser les efforts sur l'expression des citoyens, partant du principe que le problème réside dans un manque de voix citoyenne.

Or, selon lui, le problème majeur se situe davantage du côté de la "surdité des institutions" :

"...le problème c'est pas que les citoyens ne s'exprimeraient pas euh mais davantage que les pouvoirs publics dans leur diversité ne sont pas en capacité euh de d'entendre euh ce que les citoyens formulent..."

Rio souligne que la multiplication des dispositifs participatifs (conseils de quartier, conventions citoyennes, consultations en ligne) contraste avec un "constat d'impuissance" remontant du terrain, tant de la part des professionnels que des citoyens impliqués.

Il remet en question la "fausse équivalence entre participation et démocratie", arguant que plus de participation ne signifie pas nécessairement plus de démocratie, et inversement.

Un autre point critique majeur concerne l'égalité démocratique. Rio met en lumière le fait que les dispositifs participatifs ont tendance à attirer les mêmes profils de participants (diplômés, âgés, déjà engagés), renforçant ainsi les inégalités de représentation et laissant de côté les "inaudibles" :

"...globalement c'est euh souvent les mêmes profils qu'on retrouve dans la plupart des dispositifs participatifs euh les variables les plus discriminantes étant le niveau de diplôme et l'âge..."

Il soutient que ces dispositifs peuvent même "décupler ces inégalités de représentation" en donnant davantage la parole à ceux qui l'ont déjà, au détriment de ceux qui sont en retrait de la vie démocratique.

Enfin, Rio questionne la capacité transformatrice réelle de ces dispositifs, les considérant comme dépendants de l'institution initiatrice et soumis à un "biais de confirmation", où seuls les avis conformes aux orientations initiales ont tendance à être retenus.

Il alerte sur le risque que l'engouement pour la démocratie participative masque la "fragilisation de nos contrepouvoirs".

Thème 2 : Attentes citoyennes et la "démocratie du faire"

Laurence Bart a partagé les enseignements tirés des écoutes territoriales menées par l'UNADEL.

Ces écoutes révèlent un "formidable fourmillement d'initiative" et d'engagements citoyens diversifiés et souvent informels, participant parfois à l'attractivité des territoires.

Cependant, elles mettent également en évidence des "effets d'usure" liés aux difficultés rencontrées, un manque de connexion entre les initiatives, et une "difficulté à faire projet commun" avec les institutions.

Bart observe une transformation des modes d'organisation avec une "prime à l'informel" et des engagements à géométrie variable, une "quête de sens" individuelle et collective, une action tournée vers les dimensions de "l'habité" et les droits fondamentaux, une recherche de "reconnexion au territoire" sans enfermement, et une logique d'"expérience d'action pragmatique" qu'elle nomme "démocratie du faire".

Cette "démocratie du faire" est confrontée à trois défis majeurs :

• Le défi du "faire savoir" et de la reconnaissance de l'expertise citoyenne et de sa légitimité.
• Le défi de la "coopération" et de l'élargissement des cercles d'engagement.
• Le défi de la "coconstruction d'un projet politique" autour des communs et de l'habitabilité.

Thème 3 : Rôle et attentes du mouvement associatif

Claire Touri a apporté la perspective du mouvement associatif.

Elle reconnaît les tensions démocratiques actuelles (distance avec les représentants, défiance envers les institutions) tout en soulignant une forte mobilisation citoyenne (20 millions de bénévoles).

Pour elle, la démocratie participative ne doit pas se limiter à de la consultation mais doit mettre les citoyens en situation de "construire".

Elle estime qu'il n'est pas nécessaire de recourir à la démocratie participative "tout le temps et à tous les étages".

L'expérience de la Convention Citoyenne sur la Fin de Vie est éclairante.

Touri souligne que le recours à une telle instance se justifie sur des sujets complexes où les espaces de représentation traditionnels peinent à trouver un consensus.

La convention devient alors un "nouveau maillon dans la construction de la décision".

Cependant, elle pointe un problème majeur : le manque de pouvoir des assemblées citoyennes et des corps intermédiaires pour garantir que leurs travaux soient pris en compte, dépendant souvent de la "bonne volonté de quelques personnes".

Touri insiste sur la nécessité de "recréer des espaces de politisation" au sens d'espaces où se construit une "conflictualité positive".

Elle s'inquiète d'une "dépolitisation" alimentée par des dispositifs d'engagement parfois superficiels. Pour elle, les corps intermédiaires ont un rôle crucial à jouer dans cette repolitisation.

Elle conteste l'idée que la seule légitimité soit celle de l'élection, plaidant pour la reconnaissance de différentes sources d'intelligence et de légitimité dans la construction de la décision.

Pistes et Préconisations

Plusieurs pistes pour revitaliser la démocratie ont été évoquées :

• Renforcer la capacité d'écoute des institutions plutôt que de se concentrer uniquement sur l'expression citoyenne (Nicolas Rio).
• Redonner de la substance à la délibération politique au sein des institutions représentatives (Nicolas Rio).
• S'inspirer du rôle du Défenseur des Droits pour prendre en compte les sentiments d'injustice et les traduire en actions collectives (Nicolas Rio).
• Viser un objectif redistributif dans les dispositifs participatifs pour faire entendre la voix des "inaudibles" (Nicolas Rio).
• Travailler sur le "faire savoir" et la reconnaissance de l'expertise citoyenne (Laurence Bart).
• Développer la coopération et l'élargissement des cercles d'engagement (Laurence Bart).
• Coconstruire un projet politique autour des communs et de l'habitabilité (Laurence Bart).
• Revaloriser le rôle des corps intermédiaires comme espaces de démocratie du quotidien et de politisation (Claire Touri).
• Admettre différentes sources de légitimité au-delà de l'élection (Claire Touri).
• Envisager des assemblées politiques avec une part de citoyens tirés au sort pour une meilleure représentativité sociologique (Nicolas Rio).
• Former les élus à l'écoute et redéfinir les cadres participatifs avec des objectifs clairs (résultats du sondage).
• Réviser l'écosystème de la démocratie représentative, notamment au niveau local (intervention d'un élu).
• Réactiver le développement local comme espace de "politique du faire" (Jean-Louis Pinot).
• Réintroduire la "joie" dans la démocratie (citation d'une participante).
• Gérer la controverse comme moteur de progrès (conclusion).

Conclusion

La discussion a mis en lumière une insatisfaction partagée quant au fonctionnement actuel de la démocratie, y compris dans ses formes participatives.

Si la démocratie participative suscite un engouement et peut apporter des éclairages précieux, elle n'est pas exempte de critiques concernant son efficacité, sa représentativité et son impact réel sur les décisions publiques.

Les intervenants ont souligné la nécessité de ne pas opposer démocratie représentative et participative, mais plutôt de les envisager comme complémentaires, en insistant sur la nécessité de renforcer la première et de repenser la seconde pour qu'elle contribue véritablement à une démocratie plus inclusive, à l'écoute et capable de répondre aux défis contemporains.

La "conflictualité positive" et la reconnaissance de multiples légitimités apparaissent comme des éléments clés pour une revitalisation démocratique réussie.

Briefing Doc : L'avenir des associations face à la raréfaction des subventions publiques

Thème central : La raréfaction des subventions publiques et privées pour les associations en France représente un risque majeur pour la démocratie, la cohésion sociale et la capacité de la société civile à répondre aux besoins et à innover.

Sources :

• Excerpts from "Jeudi du Développement Local "un jour les associations sans subventions publiques : un risque ? "" : Transcription d'une table ronde introduite par Claude Grivel (président de Lunadel) et
• animée par Carole et Anne Carton (Lunadel), avec les interventions de Cécile Malot (Fondation de France) et Dominique Joseph (membre du CESE et co-rédactrice du rapport "Financement des associations : une urgence démocratique").

Points clés et Idées Principales :

1. Introduction et Contexte (Claude Grivel & Carole) :

Les "Jeudis du Développement Local" sont un rendez-vous régulier depuis 2020, témoignant de l'engagement continu autour des enjeux du développement local et de la vie associative. La situation des associations ne s'est pas améliorée depuis le rapport du CESE de mai 2024, suscitant des inquiétudes quant à leur gestion et leur avenir. L'absence d'associations aurait un coût social considérable, difficile à évaluer précisément, soulignant leur rôle essentiel dans la société française. Le développement local repose sur la capacité à rassembler divers acteurs et habitants autour d'un territoire et d'un désir commun de "faire société", rôle dans lequel les associations sont fondamentales. La matinée est structurée autour des regards croisés de différents intervenants concernés par la vie associative.

2. Le Rôle et la Fragilisation des Associations (Cécile Malot) :

• Rôle essentiel : Les associations sont créatrices d'emploi (11% des salariés du secteur privé), offrent un sens à la vie des bénévoles et salariés, assurent la prise en charge de l'intérêt général en complément de l'action publique, sont des lieux d'agilité, d'expérimentation et de créativité, et constituent un gage de cohésion et de lien social. "s'il y avait pas d'association je crois que la société irait très mal".
• Fragilisation :Baisse des financements publics : Que ce soit de l'État ou des collectivités territoriales, cette baisse ne pourra pas être compensée par la philanthropie. "la Fondation de France ne peut pas faire de fausses promesses de ce point de vue nous n'aurons pas les moyens de compenser la baisse des financement publics et ce n'est pas notre rôle."
• Mise en concurrence par les appels à projets : Tant publics que privés, les appels à projets peuvent être vertueux lorsqu'ils soutiennent des initiatives libres des associations. Cependant, lorsqu'ils prennent la forme de commandes, ils entravent l'expression citoyenne, baissent la qualité des projets et peuvent nourrir un sentiment de rejet et de ressentiment.
• Injonction à l'hybridation des ressources : La vente de biens et services, bien que pouvant apporter des financements, peut fragiliser la capacité des associations à recevoir des dons et certaines activités à caractère économique ne sont pas éligibles au mécénat.

3. La Nouvelle Stratégie de la Fondation de France (Cécile Malot) :

• Constat : Le fonctionnement basé à 95% sur les appels à projets avait des limites (machine à dire non, temps passé pour les associations et les équipes, enfermement des projets dans des "tuyauteries").
• Virage stratégique :Développement de collectifs d'action : Neuf collectifs thématiques réunissant des acteurs philanthropiques, des personnalités qualifiées et des personnes concernées pour établir des stratégies de financement communes. "ensemble acteurs philanthropiques personnes qualifiées personnes concernées nous allons établir une stratégie de financement sur le thème qui est le nôtre."
• Réduction de la part des appels à projets : Objectif de maximum 50%, impliquant le développement d'autres modes d'action.
• Développement du "repérage direct" : Présence des équipes dans les territoires pour des diagnostics sensibles, soutien à des structures relais (comme les écoutes territoriales de Lunadel), dialogue organique avec le secteur associatif pour nourrir la stratégie.
• Soutien structurel pluriannuel : Moins de projets mais des subventions plus conséquentes et dans la durée, orientées vers la structure plutôt que vers une action spécifique.
• Logique de changement systémique : Agir à la racine des problèmes, pas uniquement sur les symptômes.
• Défis de la nouvelle approche : Question de l'équité, de l'ouverture, de la manière de ne pas être dans un entre-soi et de financer toujours les mêmes, conciliation entre approche systémique et soutien aux besoins urgents.

4. L'Urgence Démocratique et le Rôle des Associations (Dominique Joseph) :

• Une France sans associations : Toujours imaginable, mais avec des conséquences désastreuses sur les liens sociaux, la confiance citoyenne (les associations étant un rempart face à la crise de confiance), la réponse aux besoins (au-delà des services, les besoins de lien), et la démocratie (lieux d'apprentissage du débat et de la décision collective). "une France sans association c'est une France dont les liens entre les citoyens et citoyennes seraient complètement brisés".
• Lien entre fin des subventions et risque démocratique : Le développement des appels à projets a contribué à un changement de paradigme où les associations sont analysées à l'aune de ce qu'elles font plutôt que de ce qu'elles sont, menaçant leur éthique et leur capacité d'initiative citoyenne. Le manque de financement peut entraîner la disparition d'associations essentielles au lien social et à la réponse aux besoins, notamment dans les "diagonales du vide".
• Éléments marquants du rapport CESE :La professionnalisation/gestionnarisation et la fatigue exprimée par les bénévoles et salariés.
• Le nombre important de réponses à l'enquête (6500), soulignant l'ampleur du problème.
• La prise de conscience au sein du CESE de l'urgence démocratique.
• Réactions et impact du rapport :Sentiment de reconnaissance et de prise en compte des problématiques par les associations.
• Possibilité de "coalition" et de mobilisation des acteurs associatifs.
• Intérêt et demandes de présentation du rapport par des collectivités territoriales et des parlementaires.
• Nécessité d'une prise de conscience des décideurs sur la transformation des modalités de financement au-delà des enveloppes globales.

5. Perspectives et Mobilisation (Jean-Baptiste Jobard, Collectif des Associations Citoyennes) :

• Grille de lecture du CAC : Le rapport CESE est lu à travers la grille "Quel monde associatif demain ?" (scénario d'affaiblissement vs. renforcement).
• Scénario de l'affaiblissement (4+1 mots clés) : Marchandisation, Instrumentalisation, Managérialisation, Répression, Désadministration (conséquences des réformes de l'administration).
• Antidotes : Démarcheisation, Coconstruction, Démocratisation interne, Droit, Alliances.
• Appropriation du rapport CESE : Importance de préconisations comme la remise en question du CER (Contrat d'Engagement Républicain) et la transformation de la déductibilité des dons en crédit d'impôts.
• Mot clé : Subvention. Vue comme l'autre nom de l'initiative citoyenne organisée, l'enjeu est la sécurisation et la démocratisation de la subvention (collectivisation de la délibération sur la répartition des fonds).
• Convergence avec la philanthropie : Le rapport permet de mieux asseoir l'action associative dans une perspective "polyesque" (économie solidaire au-delà du marché).
• Mobilisation "Vers des soulèvements associatifs" : Lancement le 1er juillet pour informer, agir et faire entendre la voix des associations comme porteuses de solutions face aux défis sociétaux (et non seulement défendre leur propre financement). Actions symboliques autour de la devise républicaine. Articulation avec les forums associatifs de septembre et une campagne nationale de grande ampleur à venir.

Conclusion Générale (Claude Grivel) :

• Nécessité de passer d'une logique de défiance à une logique de confiance envers les associations.
• Importance de la contractualisation, de la coopération et d'une meilleure connaissance mutuelle entre les sphères publique, privée et associative.
• Le monde associatif est une source essentielle de créativité et d'innovation au service du développement local et de la réponse aux besoins.
• Consolider la démocratie passe par la valorisation de la liberté associative et de la capacité à "faire société" ensemble.
• Le monde associatif est un rempart essentiel pour la défense de la démocratie et des valeurs républicaines.
• Prochain rendez-vous : Jeudi du Développement Local, 24 avril, 10h, sur le thème des 32 heures et de l'engagement.

événement en ligne, un "Jeudi du Développement Local" organisé par le réseau associatif Unadel.

La discussion porte sur le risque de la disparition des subventions publiques pour les associations, un sujet introduit par le président de L'unadel qui souligne l'importance cruciale des associations pour la société et la démocratie.

L'événement structure son propos autour de trois intervenants : la Fondation de France qui présente une évolution de sa stratégie de financement face à la raréfaction des fonds publics, une membre du CESE qui a co-rédigé un rapport alarmant sur le financement des associations et son impact démocratique, et le Collectif des Associations Citoyennes qui propose une analyse des menaces pesant sur le secteur associatif et des pistes de mobilisation.

L'objectif de cette rencontre est donc d'analyser la situation actuelle, d'échanger sur les défis et les solutions, et de souligner l'urgence démocratique qu'implique la fragilisation du monde associatif.

Chronologie des événements principaux abordés dans les sources :

• Avant 2020 : Les "Jeudis du Développement Local" n'existent pas encore sous cette forme, mais il existe un besoin de rencontres et d'échanges au sein du réseau l'Union nationale des acteurs du développement local (Lunadel) et des réseaux régionaux.
• Depuis 2020 (Début de la COVID) : Face au confinement, Lunadel et les réseaux régionaux, dont "Grand Est citoyen territoire Grand Est" dirigé par Claire, mettent en place des rencontres régulières en ligne initialement appelées "Jeudis du confinement", puis "Jeudis du déconfinement".
• Période ultérieure (Post-confinement) : Les rencontres régulières se pérennisent et prennent le nom de "Jeudis du Développement Local", se tenant mensuellement.
• Il y a un mois avant mars 2025 (Février 2025) : Le "Jeudi du Développement Local" initialement programmé est reporté au mois de mars en raison d'indisponibilités de plusieurs intervenants.
• Mai 2024 : Un rapport sur la situation des associations et le renforcement de leurs financements est présenté au Conseil Économique, Social et Environnemental (CESE). Dominique Joseph est co-rapportrice de ce rapport. Ce rapport met en évidence l'urgence démocratique liée au financement des associations.
• Il y a environ 2 ans avant mars 2025 (Début 2023) : La Fondation de France entreprend un virage stratégique important dans ses modes de soutien au secteur associatif, passant d'une prédominance des appels à projets vers un soutien plus structurel, pluriannuel et basé sur un repérage direct et des coopérations au sein de "collectifs d'action".
• Octobre 2020 : L'Observatoire des libertés associatives, créé par le Collectif des Associations Citoyennes (CAC), publie son premier rapport, objectivant le phénomène de répression de l'action associative.
• 2021 : Le Collectif des Associations Citoyennes (CAC) publie le livre "Quel monde associatif demain ?", présentant deux scénarios : l'affaiblissement et le renforcement du monde associatif.
• Début février 2025 : Face à l'adoption du budget et à la prise de conscience de ses conséquences potentiellement désastreuses pour le monde associatif et les collectivités territoriales, une réflexion et une mobilisation intersectorielle s'amorcent au sein du CAC, aboutissant au projet de "soulèvements associatifs".
• Mois prochain par rapport à mars 2025 (Avril 2025) : L'Observatoire de la marchandisation du CAC prévoit de sortir son deuxième rapport, axé sur les pistes concrètes de démarcheisation. Le Réseau National des Maisons des Associations (RNMA) et Exopé doivent dévoiler les résultats d'une étude flash sur la situation des associations.
• 1er juillet 2025 (Prévu) : Lancement officiel des "soulèvements associatifs" par le collectif d'associations, sous la forme d'actions symboliques et d'une prise de parole pour souligner le rôle essentiel des associations.
• Septembre 2025 (Prévu) : Les forums associatifs pourraient servir de relais et de développement à la mobilisation initiée en juillet.
• Automne 2025 et années suivantes (Prévu) : Le mouvement associatif envisage une campagne massive et de grande ampleur sur les enjeux du secteur associatif.
• 24 avril 2025 (Prévu) : Prochain "Jeudi du Développement Local" organisé par Lunadel, abordant la question des 32 heures et de l'engagement.

Cast of personalités et Bios Succinctes :

• Claude Grivel : Président de Lunadel (l'Union nationale des acteurs du développement local). Il introduit le "Jeudi du Développement Local", soulignant l'importance des associations pour la société et la fragilité de leur situation financière suite au rapport du CESE de mai 2024.
• Claire : Représentante des réseaux régionaux et dirigeante du réseau "Grand Est citoyen territoire Grand Est". Elle co-organise les "Jeudis du Développement Local" et anime techniquement la rencontre.
• Carole : Anime la rencontre du "Jeudi du Développement Local", notamment en introduisant les intervenants et en posant des questions.
• Anne Carton : Membre de Lunadel, elle participe à l'animation technique de la rencontre, notamment en recueillant et restituant les contributions du chat.
• Cécile Malot : Responsable "Grande Cause Territoire" à la Fondation de France. Elle intervient sur la baisse des financements publics et privés pour les associations, le rôle de la philanthropie, les limites de la compensation, et la nouvelle stratégie de la Fondation de France axée sur le repérage direct, le soutien structurel et les collectifs d'action.
• Jean-Baptiste Jobard : Coordinateur du Collectif des Associations Citoyennes (CAC). Il réagit au rapport du CESE à travers la grille de lecture des scénarios d'affaiblissement et de renforcement du monde associatif issus du livre "Quel monde associatif demain ?". Il évoque également la mobilisation en préparation des "soulèvements associatifs" à partir du 1er juillet.
• Dominique Joseph : Membre du Conseil Économique, Social et Environnemental (CESE) et co-rapportrice du rapport de mai 2024 sur le financement des associations ("Financement des associations : une urgence démocratique"). Elle partage ses réflexions sur l'importance des associations pour la démocratie, les impacts de la baisse des subventions et les réactions suite à la publication du rapport.
• Martin Beaubel : Mentionné comme ayant co-rédigé avec Dominique Joseph le rapport du CESE. Il a également récemment intervenu à la Fondation de France.
• Mariec Martel : Mentionnée pour un avis antérieur du CESE qui abordait notamment la question de la formation à la vie associative.
• Laurent Fres : Mentionné pour son rapport sur la coconstruction de l'action publique, travail en lien avec le CAC.
• Jean-Louis Laville : Mentionné comme poursuivant le travail sur la coconstruction de l'action publique avec le CAC et d'autres réseaux.
• Carl Polyani : Sociologue et économiste dont la perspective sur les différentes formes d'économie (marchande, domestique, réciprocitaire, redistribution) est évoquée par Jean-Baptiste Jobard pour contextualiser le rôle de l'économie associative.
• Opal : Organisation ayant réalisé des études monographiques sur la dimension réciprocitaire de l'économie des associations.

Voici un sommaire de la vidéo avec des indications temporelles approximatives basées sur le déroulement de la conversation :

• Introduction (environ 0:00 - 0:02) :

Claude Grivel, président de Lunadel, introduit la matinée "Jeudi du Développement Local" co-organisée avec les réseaux régionaux.

Il rappelle que ces rencontres mensuelles existent depuis le début de la COVID.

Il souligne l'importance des associations et les inquiétudes concernant leur gestion dans le contexte actuel suite à un rapport présenté au CE en mai 2024.

Il insiste sur le rôle essentiel des associations dans le développement local, le rassemblement des acteurs et la cohésion sociale.

Il annonce l'intervention de trois personnes : * Cécile Malot de la Fondation de France, * Jean-Baptiste Jobard et * Dominique Joseph, co-rapportrice du rapport au CE.

• Présentation de Cécile Malot (environ 0:02 - 0:23) :

Carole introduit Cécile Malot, responsable grande cause territoire à la Fondation de France. Cécile Malot évoque la baisse des financements publics et privés comme une urgence démocratique.

Elle rappelle l'importance des associations en termes d'emploi, de sens pour les bénévoles et salariés, de prise en charge de l'intérêt général, d'agilité, d'expérimentation et de cohésion sociale.

Elle souligne la fragilisation des associations par la baisse des financements publics, que la philanthropie ne pourra pas compenser, la mise en concurrence via les appels à projets et l'injonction à l'hybridation des ressources.

Elle nuance l'appel à projet, distinguant l'invitation à proposer des projets librement conçus de la commande publique, qu'elle juge dangereuse pour la démocratie.

Elle explique le virage stratégique de la Fondation de France, passant d'un financement majoritairement par appels à projets à une approche de coopération via des collectifs d'action.

Elle détaille la composition et les thèmes de ces collectifs. Elle précise les nouvelles modalités d'intervention : repérage direct, soutien structurel pluriannuel et logique de changement systémique.

Elle aborde les questions d'équité et d'ouverture soulevées par ce changement. Elle confirme l'augmentation des sollicitations à la Fondation de France.

• Questions et réponses avec Cécile Malot (environ 0:23 - 0:30) :

Anne relaie les questions du chat concernant la difficulté des petites associations face aux appels à projets, les autres acteurs philanthropiques participant aux collectifs d'action et la forme concrète des éventuels appels à projets.

Cécile Malot précise que les premiers partenaires des collectifs sont les fondations abritées par la Fondation de France et d'autres fonds et fondations rencontrés.

Concernant les appels à projets, elle indique qu'ils devraient prendre la forme d'un appel à initiatives dans le cadre du repérage direct. Elle évoque également la fatigue des élus locaux face aux appels à projets.

Elle précise que les coopérations et repérages se font à toutes les échelles territoriales.

Elle explique que la période de confinement n'est pas à l'origine du changement stratégique de la fondation mais l'a accéléré. Claude Grivel souligne l'expérience territoriale de Cécile Malot comme ayant influencé cette approche.

• Introduction et présentation de Dominique Joseph (environ 0:30 - 0:52) :

Claude Grivel accueille Dominique Joseph, membre du CE et co-rédactrice du rapport "Financement des associations : une urgence démocratique".

Il l'invite à aborder l'imaginabilité d'une France sans association, le lien entre la fin des subventions publiques et le risque démocratique, ce qui l'a le plus marquée lors de la rédaction du rapport et les réactions à sa publication.

Dominique Joseph estime qu'une France sans association est imaginable mais avec des conséquences majeures, notamment la rupture des liens sociaux et une perte de confiance.

Elle souligne la confiance accordée aux associations, au même titre que les élus locaux, face à la crise de confiance démocratique.

Elle insiste sur la diversité des associations et leur rôle essentiel dans le lien social et la réponse aux besoins. Elle met en avant le rôle des associations comme lieu d'apprentissage de la démocratie et de la vie en société.

Elle évoque l'étude empirique sur le lien entre absence d'associations et montée des votes extrêmes.

Elle rappelle que le rapport au CE met en avant le passage d'une analyse des associations sur ce qu'elles sont plutôt que sur ce qu'elles font, comme une urgence démocratique.

Ce qui l'a marquée est le parallèle entre le financement des associations et celui des mutuelles, ainsi que les difficultés et défis rencontrés par les associations de toutes tailles.

Le nombre de réponses à l'enquête du CE (6500) a été un élément marquant. Les réactions à la publication du rapport ont montré une reconnaissance des problématiques vécues par les associations.

Le rapport a permis de souligner la nécessité de coalition et de dialogue entre les associations et avec les décideurs. Des collectivités territoriales ont réagi en demandant la présentation de l'avis.

• Réactions et questions pour Dominique Joseph (environ 0:52 - 1:00) :

Cécile Malot exprime son accord avec les propos et souligne l'importance du regroupement des associations. Jean-Baptiste Jobard remercie pour l'espoir que représente ce rapport et son analyse de la situation des associations, y compris la dimension européenne. Anne relaie les réactions du chat, notamment le téléchargement du rapport, le témoignage du Conseil Départemental de Haute-Garonne, la suggestion d'un soulèvement formel du monde associatif et la proposition de formations à la vie associative dès l'école. Dominique Joseph réagit à la question de la formation, soulignant que l'engagement se fait naturellement dès le plus jeune âge mais qu'il y a un point d'interrogation dans la phase de travail.

Elle évoque la mise en concurrence des associations et la nécessité de distinguer les associations du secteur de l'ESS.

Elle aborde le cadre européen et la possibilité de subrogation pour les collectivités.

Elle commente la suggestion d'une journée sans association et mentionne les préconisations législatives du rapport.

Claude Grivel ajoute son point de vue sur la formation et le rôle des familles dans l'engagement, ainsi que la désignation des associations comme premières victimes des restrictions budgétaires.

• Introduction et présentation de Jean-Baptiste Jobard (environ 1:00 - 1:16) :

Carole introduit Jean-Baptiste Jobard du Collectif des associations citoyennes. Jean-Baptiste Jobard situe sa lecture du rapport du CE à partir du travail collectif "Quel monde associatif demain" (2021).

Il présente les deux scénarios du livre : l'affaiblissement et le renforcement. Il précise son point de vue à travers son rôle au CAC (Collectif des Associations Citoyennes) et ses travaux sur l'histoire des libertés associatives.

Il détaille le scénario de l'affaiblissement avec les mots clés : marchandisation, instrumentalisation (et la révolution invisible du financement public), managérialisation et répression.

Il évoque le néologisme de désadministration et ses conséquences pour le monde associatif. Pour chaque composante de l'affaiblissement, il propose une "antidote" : démarcheisation, coconstruction, renforcement démocratique interne et le droit.

Il cite des préconisations du rapport du CE, notamment sur le CER et la transformation de la déductibilité des dons en crédit d'impôts. Il résume le rapport avec le mot "subvention", envisagée comme l'autre nom de l'initiative citoyenne organisée, et la nécessité de sa sécurisation et démocratisation.

Il cite des exemples de collectivités pratiquant une délibération collective pour la répartition des fonds. Il établit un lien avec la perspective polanyienne d'une économie plurielle et le rôle de l'économie associative et solidaire. Il aborde la question de la défense de la civilisation incluant la solidarité démocratique.

• Réactions et discussion avec Jean-Baptiste Jobard (environ 1:16 - 1:23) :

Carole note l'efficacité de la présentation et le rôle des collectivités mises en avant. Dominique Joseph, avant de quitter la réunion, remercie Jean-Baptiste et souligne la convergence des analyses sur la nécessité d'une autre économie et d'un choix de société.

Elle mentionne son prochain rendez-vous avec des sénateurs sensibilisés à l'avis du CE. Carole relaie l'absence de nouvelles questions dans le chat et invite à consulter les ressources partagées par Jean-Baptiste.

Cécile Malot partage sa réflexion sur la manière concrète d'agir pour des acteurs comme la Fondation de France, notamment dans les territoires en perte d'associations et de services publics. Jean-Baptiste Jobard prend la parole pour évoquer le soulèvement associatif en préparation depuis février 2025, suite à l'adoption du budget.

Il explique les trois axes de cette mobilisation : informer, agir pour se faire entendre (avec une action symbolique le 1er juillet autour de la devise républicaine) et mettre en avant le rôle des associations comme porteuses de solutions.

Il mentionne les prochaines étapes de la mobilisation. Anne confirme l'absence d'autres questions et la suggestion de partager le rapport du CE aux élus.

• Conclusion (environ 1:23 - 1:27) :

Claire amorce la conclusion en invitant les participants à partager un mot clé de ce qu'ils retiennent. De nombreux mots sont partagés dans le chat.

Claude Grivel prend la parole pour sa conclusion, soulignant l'envie de continuer l'échange.

Il reprend l'idée d'économie réciprocitaire et le lien entre la défense des services publics et du secteur associatif.

Il insiste sur la nécessité d'une prise de conscience de la société entière quant à l'importance des associations.

Il met en avant quatre mots clés commençant par "C" : contractualisation (avec un besoin de sérénité), confiance (à reconstruire), créativité (indispensable) et consolidation (de la démocratie).

Il rappelle le rôle essentiel des associations dans la défense de la démocratie. Il remercie les intervenants, les participants et l'équipe d'animation.

Il annonce le prochain "Jeudi du Développement Local" le 24 avril sur le thème des 32 heures et de l'engagement. Claire remercie et clôture la matinée.

DOI: 10.1007/s00335-024-10102-y

Resource: Rat Resource and Research Center (RRID:SCR_002044)

Curator: @bandrow

SciCrunch record: RRID:SCR_002044

What is this?

Présentation de Didier Fassin intitulée "Re-Imagining Punishment (8)" tiré de la transcription vidéo, avec des estimations de timestamps** basées sur la structure du texte :

• Introduction (environ 0:00 - 1:30) :

Didier Fassin explique que cette rencontre est une exception à la règle du Collège de France d'utiliser le français, car elle permet de découvrir des pratiques et réflexions internationales sur la punition.

Il souligne que cet événement est accessible en ligne, notamment sur YouTube, permettant ainsi à un public non francophone de suivre ces réflexions.

• Premier point : Nous sommes toujours dans un moment punitif (environ 1:30 - 3:00) :

Malgré une diminution des taux d'incarcération dans de nombreux pays européens et aux États-Unis, Fassin rappelle que ces taux restent beaucoup plus élevés qu'ils ne l'étaient dans les années 1960 ou 1970. Nous sommes donc toujours dans un moment que l'on peut qualifier de punitif.

• Deuxième point : Absence de corrélation entre crime et punition (environ 3:00 - 5:30) :

Il n'existe pas de corrélation statistique nécessaire entre le crime et la punition, et l'on peut même douter d'un lien analytique nécessaire. L'exemple de la Finlande, qui a divisé par trois son taux d'incarcération sans augmentation de la criminalité, et celui de l'Angleterre, qui a vu sa population carcérale augmenter proportionnellement à la criminalité, illustrent ce point. Cette observation remet en question le sens commun et invite à une réflexion sur le lien entre le type de crime et le type de punition.

• Troisième point : Complexité des facteurs expliquant l'évolution de la population carcérale (environ 5:30 - 7:30) :

Les raisons de l'évolution des populations carcérales, y compris les diminutions observées, sont multiples et complexes, et peuvent ne pas être complètement comprises. La présentation a permis une compréhension plus riche de cette évolution, notamment en ce qui concerne le modèle nordique, caractérisé par un faible taux d'incarcération mais un nombre élevé d'entrées en prison, ce qui soulève des questions sur la désocialisation. Le simple fait d'entrer en prison, même pour une courte durée, a des conséquences importantes.

• Quatrième point : Question de la visibilité et de la visibilisation de l'incarcération (environ 7:30 - 10:00) :

La conversation se concentre souvent sur la prison liée à l'activité criminelle, mais il existe d'autres formes d'enfermement, notamment celle des migrants sans papiers, qui sont parfois incarcérés illégalement. Il est important de considérer également la "punition par ricochet" qui affecte les familles et les communautés des personnes incarcérées. La décision d'un juge d'emprisonner une personne a des répercussions sur tout son entourage.

• Cinquième point : Distribution, inégalité et "punissabilité" (environ 10:00 - 12:30) :

La question de savoir qui est puni implique de comprendre ce qui est puni et qui est considéré comme "punissable". Ce concept de "punissabilité" est lié à la position sociale, raciale et de genre des individus. La punition est un élément crucial dans la reproduction des inégalités.

• Sixième et dernier point : Susciter l'imagination et réflexion sur l'abolition de la prison (environ 12:30 - fin) :

L'analyse des politiques et tendances en Europe et les exemples inspirants d'histoire, d'ethnographie et d'études communautaires ouvrent des perspectives.

Fassin conclut en citant une réflexion de Claude Lévi-Strauss dans Tristes Tropiques qui compare nos pratiques de justice et de prison à l'anthropophagie, et souligne l'horreur qu'inspirerait à des sociétés dites primitives l'isolement des individus dangereux hors du corps social.

Lévi-Strauss notait que ces sociétés privilégiaient la réparation des liens sociaux par un système de dettes et de dons. Fassin invite à imaginer la critique que Lévi-Strauss aurait pu faire de notre système carcéral actuel, considérant l'augmentation considérable du nombre de détenus depuis la publication de son ouvrage en 1955.

Voici un sommaire de la vidéo "Réenchanter les maths à l’école (2) - Agir pour l'éducation (2024-2025)" avec une indication de la progression des sujets abordés, faute de timestamps précis dans le transcript :

• Introduction au programme "Réenchanter les maths" : Présentation de la deuxième séance du programme initié par Stéphane Mallat, Stanislas Dehaene et d'autres collègues, visant à redonner de l'attrait pour les mathématiques en les abordant par de multiples approches. L'objectif n'est pas de rendre les maths magiques, mais de montrer qu'elles méritent d'être apprises par différentes voies, en s'appuyant sur la pédagogie et l'innovation. Malgré une brillante école mathématique française, la réussite en mathématiques au collège et au lycée (et même au primaire) n'est pas optimale.

• Présentation des intervenants : Jean-Michel Blanquer introduit Stéphane Mallat, titulaire de la chaire Science des données au Collège de France, membre de l'Académie des Sciences et directeur d'équipe à l'École normale supérieure, soulignant son engagement à rendre la recherche accessible, notamment pour transformer l'éducation. Miguel Toket, inspecteur d'académie-inspecteur pédagogique régionale de mathématiques (académie de Lille), ayant enseigné au collège et à l'université et collaboré sur le rapport Villani-Torossian, est également présenté pour son travail sur le co-pilotage des labos maths et le déploiement de stratégies académiques concernant l'intelligence artificielle. Hakim Vienet, cofondateur et directeur de Matth a data, interviendra pour les questions et le débat.

• Le titre de la conférence : "Enseigner les mathématiques au lycée avec les challenges d'intelligence artificielle" est mis en avant comme un sujet d'actualité, allant de l'intuition à l'abstraction.

• Crise de l'enseignement des mathématiques en France : Stéphane Mallat aborde la crise, soulignant l'anxiété vis-à-vis des mathématiques (enquête PISA, notamment chez les filles et les élèves défavorisés) et le manque de motivation suite à la possibilité de choisir de ne plus faire de mathématiques. Une des explications est un enseignement trop formel, comparable à enseigner la musique uniquement par le solfège. L'intuition est fondamentale et se construit par l'expérience, avec un lien essentiel entre les mathématiques, la physique et le monde réel. L'abstraction doit avoir du sens, construit par le lien entre l'expérimentation et l'abstraction. L'expérience personnelle de Stéphane Mallat avec les mathématiques modernes et la découverte d'une approche plus expérimentale aux États-Unis sont mentionnées.

• Nécessité de passer à l'échelle pour introduire plus d'expérimentation : Malgré les recherches didactiques, peu d'initiatives ont réussi à généraliser l'introduction de plus d'expérimentation et de manipulation en mathématiques au collège et au lycée.

• L'approche par problème (Problem Based Learning - PBL) : Cette approche, initiée dans les années 70 (Canada, études médicales), met l'accent sur la construction du sens à travers la résolution de problèmes ouverts et liés au monde, favorisant la collaboration. Bien que présentant des avantages (meilleure compréhension, motivation, travail en équipe), elle nécessite beaucoup de temps et un changement de rôle pour les professeurs, ce qui a limité son passage à l'échelle.

• L'intelligence artificielle comme une réserve de problèmes : Les challenges d'intelligence artificielle offrent une nouvelle source de problèmes pour s'engager dans une pédagogie par problème, avec un potentiel de passage à l'échelle. L'intrusion de l'IA à l'école par les élèves (utilisation de modèles de langage) impose aux enseignants de réfléchir à son intégration pour l'apprentissage des fondamentaux.

• Utiliser l'IA comme un tuteur personnalisé : Bien que potentiellement intéressant, l'enseignement nécessite la présence de l'enseignant comme médiateur. Il est crucial que les professeurs comprennent la nature de l'IA pour en faire un objet de connaissance et non une "boîte noire". L'IA est une opportunité pour les mathématiques car elle repose sur de belles mathématiques.

• L'approche du programme matadata : Partir de problèmes du monde réel permettant un passage rapide aux mathématiques, contrairement à d'autres disciplines comme la physique qui nécessitent un enseignement préalable. Presque toutes les mathématiques du lycée peuvent être abordées par cette approche, centrée sur l'expérimentation numérique et la créativité des élèves.

L'aspect ludique (amélioration des scores, travail en groupe) est également important. Le modèle mathématique de l'apprentissage en IA offre un miroir pour l'enseignement, où l'erreur est fondamentale. L'enseignement est explicite et guidé, basé sur un co-développement avec l'Éducation nationale. Matadata apporte un savoir-faire en mathématiques liées au numérique et à l'IA, tandis que l'aspect pédagogique en classe est apporté par la collaboration avec les professeurs. L'enjeu est un passage à l'échelle nationale.

• Qu'est-ce qu'un challenge d'intelligence artificielle ? : Définition générale : à partir de données (D), répondre à une question (R) en développant un algorithme (séquence d'instructions). Exemples de challenges : reconnaissance de chiffres manuscrits, diagnostic médical à partir d'un électrocardiogramme fœtal, attribution d'auteurs de textes (Molière/Corneille), reconnaissance de chants de baleines. Chaque challenge implique des données et le développement d'un algorithme pour trouver une estimation de la réponse.

• Exemple détaillé : reconnaissance d'images de chiffres manuscrits : Les images sont des tableaux de nombres (niveaux de gris). L'enjeu est de reconnaître le chiffre (ex: 2 ou 7). Tous les challenges suivent le même cadre mathématique, centré sur l'apprentissage en IA. À partir des données, on calcule des caractéristiques pour donner une réponse avec un classificateur.

Les algorithmes dépendent de paramètres appris au fur et à mesure lors d'une phase d'apprentissage (minimisation des erreurs sur une base de données d'entraînement). L'objectif est la généralisation : l'algorithme doit bien répondre sur des données qu'il n'a jamais vues.

• Couverture des mathématiques du lycée à travers ce cadre :

  • Statistiques : Avec une seule caractéristique (ex: somme des pixels), on peut aborder les écarts, l'échantillonnage, etc. La notion d'histogramme et le meilleur seuil (intersection des histogrammes) permettent de couvrir l'étendue, la variance, les quartiles.

  • Géométrie : Avec deux caractéristiques (ex: valeur moyenne de l'image dans les parties supérieure et inférieure), l'image est représentée comme un point dans un plan. Un classificateur devient une droite à ajuster (paramètres : coefficient directeur et ordonnée à l'origine), permettant de couvrir la géométrie du plan (droites, vecteurs, produit scalaire). Trois caractéristiques permettent d'aborder la géométrie dans l'espace.

  • Analyse de fonctions : La minimisation de l'erreur (fonction dépendant d'un paramètre) amène naturellement à la notion de dérivée (incrément local, pente) pour trouver le minimum (descente de dérivée, tableau de variation), couvrant l'analyse enseignée au lycée. Les notions de convexité et de suites convergentes sont également accessibles.

  • Probabilités : La généralisation repose sur la loi des grands nombres, permettant d'aborder les probabilités, les probabilités conditionnelles, etc..

  • Informatique et Intelligence Artificielle : Les réseaux de neurones permettent un enseignement de l'informatique et de l'IA.

• Cadre d'enseignement explicite proposé : Établi avec Hakim Vienet et Michel Toket, il comprend trois phases:

  • Poser le cadre : Formalisation mathématique du problème d'apprentissage et de l'IA, rappel des notions essentielles (stats, géométrie, analyse).

  • Manipulation : Expérimentation sur ordinateur pour résoudre le challenge avec les outils mathématiques et développer des solutions créatives améliorant le score.

  • Retour sur les maths : Interprétation des résultats, approfondissement des mathématiques à travers des exercices et des démonstrations.

• Contraintes et adaptations pour l'enseignement au lycée : Respecter le temps imparti, s'assurer que cette approche s'intègre au programme (décliné suivant ce type de problèmes). Les contenus sont modifiables par les professeurs et créés en co-développement avec l'Éducation nationale (académie de Lille).

• Exemple d'expérimentation numérique : Structure en phases : découverte des données avec un premier algorithme, puis modification pour améliorer les performances. Exemple en géométrie : manipulation de deux caractéristiques, passage à la représentation dans le plan, manipulation de la droite de séparation (visualisation du lien entre paramètres et performance). L'accent est mis sur les mathématiques, sans programmation lourde. La phase finale vise à comprendre les bonnes caractéristiques (modélisation mathématique).

• Lien entre les expérimentations et le programme de mathématiques : Le retour sur les maths permet de comprendre les résultats des expérimentations (ex: seuil optimal, lien moyenne/variance/histogramme) et de faire le lien avec les exercices classiques du cours.

• Lien entre mathématiques et informatique : Souvent un parent pauvre de l'enseignement en France. L'objectif est de dépasser les tensions et de permettre aux élèves de découvrir les réseaux de neurones en comprenant les mathématiques sous-jacentes et en programmant un peu (plutôt pour les classes de SNT ou NSI).

• Présentation du challenge IA lié au Sommet IA : Accessible aux lycéens de toutes les filières via un site web. Parcours guidé pour comprendre les fondamentaux de l'IA, nécessitant les bases de Python de seconde. L'enjeu est la reconnaissance de tous les chiffres manuscrits.

• Explication simplifiée d'un neurone et d'un réseau de neurones (perceptron) : Un neurone prend plusieurs caractéristiques en entrée, effectue une moyenne pondérée (avec des poids à apprendre) plus une constante (biais), et produit un vote. Un réseau est une organisation de plusieurs neurones en couches, où chaque neurone vote pour une réponse possible. La réponse finale est l'index correspondant au vote le plus grand. L'apprentissage se fait à partir des erreurs, ajustant les poids (algorithme de Rosenblatt, 1957). Le challenge propose aux participants de trouver leurs propres caractéristiques pour minimiser l'erreur.

• Objectifs du programme et évaluation : Améliorer la motivation et la compréhension des maths au lycée, évalué avec le programme ID (évaluations randomisées). Encourager l'orientation vers des études scientifiques (suivi des parcours des élèves). Démystifier l'IA en montrant que derrière il y a des mathématiques et de l'informatique, remettant les mathématiques au centre.

• Évolution du projet : Travail initial en 2024 avec les académies de Lille et de Paris, et quelques expérimentations à New York (culture différente mais mêmes difficultés). Formation de 40 professeurs testée dans plus de 10 établissements (environ 300 élèves, quatre chapitres). Objectif pour l'année prochaine : plus de 100 professeurs et plus de 2000 élèves. L'enjeu majeur est le passage à l'échelle (nécessité de contenus simples à prendre en main, formations courtes, adaptation à la diversité des élèves).

• Formation des professeurs et modèle envisagé pour le passage à l'échelle : Formation initiale en présentiel, mais nécessité de passer à un modèle plus large (formation par vidéo avec mentorat, modèle proposé par Mathieu Nebra d'Open Classroom).

• Motivation des professeurs à s'engager : Confrontation inévitable à l'IA en classe, intérêt pour les belles mathématiques sous-jacentes (lien avec leurs études supérieures), plaisir de découvrir que les maths qu'ils enseignent sont connectées à l'IA. Optimisme quant au passage à l'échelle malgré les obstacles.

• Remerciements à l'équipe de matadata : Mathieu Nebra, Hakim Vienet (responsable pédagogique), Delphine Grison, l'équipe informatique, Louis Capietto et Andrada Chitan (développement de contenus et co-développement avec l'Éducation nationale). Transition vers l'intervention de Miguel Toket.

• Point de vue institutionnel de Miguel Toket (Inspecteur d'académie) : Approfondissement du propos en tant que mathématicien et perspective institutionnelle. Le programme rencontre les enjeux et problématiques du ministère, notamment le cheminement vers l'abstraction (approche "manipuler, représenter, abstraire" plus difficile au lycée). Matadata aborde ce point de vue avec une approche manipulatoire favorisant le franchissement des ruptures conceptuelles entre le collège et le lycée, y compris pour le formalisme des examens. Permet une compréhension en profondeur des concepts, contrairement à une compréhension de surface souvent rencontrée (ex: fractions au collège, fonctions et dérivées au lycée). Engagement des élèves observé en classe, développement de l'intuition. Relie les mathématiques aux évolutions technologiques (IA), répondant à la question "à quoi ça sert les maths ?". Redonne du lustre à la discipline.

• Exemples marquants pour l'institution :

  • Statistiques : L'approche avec un seuil simple permettant 30% d'erreur pour la reconnaissance de chiffres est extraordinaire comparée aux exercices traditionnels peu motivants.
  • Analyse (descente de dérivée) : L'algorithme enseigné en spécialité terminale est le même que l'algorithme de descente de gradient utilisé dans les grands modèles de langage (LLM), ce qui est inédit et constitue un levier de développement professionnel pour les enseignants.

• Impact sur les enseignants et les pratiques pédagogiques : Retours très positifs des formations (bouffée d'air), redonne du sens et de la puissance à une matière parfois décriée. Développement professionnel et compréhension du fonctionnement de l'IA. Plaisir de l'expérimentation en classe et impact positif sur les élèves. Changement de posture et d'approche pédagogique vers une approche expérimentale (complexe à mettre en place habituellement) facilitée par les blocs petits et clé en main de matadata.

• Réhabilitation de la place de l'erreur dans l'enseignement : L'approche progressive avec l'amélioration des scores permet de considérer l'erreur comme un jalon d'apprentissage. Lien avec le plaisir et l'intuition (compétences psychosociales). Explicitation de l'enseignement avec un contrat didactique interne favorisant l'apprentissage par l'erreur et l'échange entre élèves.

• Enjeu d'éducation à l'IA : Préparation aux métiers d'aujourd'hui et de demain, réponse à une problématique sociétale (sommet mondial de l'IA). Développement d'une culture de l'IA chez les élèves, même ceux qui abandonnent les maths en fin de seconde. Projet s'inscrivant dans une dynamique nationale (cadre d'usage de l'IA) et académique (stratégie autour de l'IA dans l'académie de Lille, s'appuyant sur les cadres de compétences de l'UNESCO).

• Dynamique de co-développement : Collaboration entre l'équipe matadata, l'inspection académique et les enseignants (logique de co-construction et de c-éveloppement). Triple regard (pédagogique, didactique et institutionnel) pour construire des ressources adaptées. Importance de l'itération et de l'équilibre entre les différentes parties prenantes. Sélection d'enseignants avec des profils variés pour tester et adapter les ressources. Volonté de mutualiser les expertises et d'assurer la pérennisation des usages.

• Perspectives du passage à l'échelle : Accompagnement des enseignants et des inspecteurs (formations développées, développement pluriannuel, acculturation des inspecteurs). Structuration du déploiement avec le choix de professeurs formateurs locaux. Articulation avec les politiques nationales et perspectives académiques (égalité filles-garçons, orientation, réduction des inégalités).

• Modalités envisagées pour le passage à l'échelle : Pensé par paliers (15, puis 50 professeurs, puis académie autonome). Accompagnement personnalisé et suivi des équipes par matadata. Principe de l'Académie autonome permettant un passage complet à l'échelle avec des ajustements continus grâce au retour d'expérience et aux tests de nouveaux défis. Logique de réseau croissant facilitant le passage à l'échelle. Ressources robustes et éprouvées dès les premières phases. Potentiel d'atteindre un grand nombre d'élèves rapidement. Création à l'intérieur des classes avec des retours de professeurs aux profils variés, rendant les ressources adaptées à différents contextes d'enseignement (mathématiques, numérique, IA).

sommaire de la conférence avec des indications temporelles approximatives basées sur le déroulement du discours :

•Début (environ 0:00 - 0:03) : Introduction par l'animateur du Collège de France pour la première conférence de la série "Agir pour l'éducation 2024-2025" consacrée à réenchanter les maths à l'école.

Il remercie la Fondation du Collège de France et présente le cycle de six conférences avec les noms des intervenants et leurs sujets respectifs.

L'objectif est de faire un panorama des recherches et de dégager des idées pour améliorer l'enseignement des mathématiques.

L'animateur présente ensuite la professeure Elisabeth Spelke de Harvard, une figure majeure en psychologie du développement, à l'origine du concept de "core knowledge" (noyau de connaissance) montrant que les jeunes enfants possèdent déjà des connaissances abstraites sur le monde, y compris mathématiques.

Il mentionne également le livre récent de Liz Spelke, "What Infants Know". Il établit un lien avec les travaux de Jacques Meller en France sur la cognition des bébés.

•Introduction de la recherche d'Elisabeth Spelke (environ 0:03 - 0:07) :

Elisabeth Spelke exprime sa joie d'être invitée et son admiration pour la capacité d'apprentissage des enfants dès la naissance.

Elle explique que sa recherche porte sur des expériences avec les tout-petits et dans les écoles.

Elle se concentre sur un projet d'enseignement des maths en Inde et donne trois raisons pour ce choix : la richesse culturelle et linguistique de l'Inde, la présence de grandes ONG éducatives comme J-PAL et Pratham, et les défis posés par les résultats des sondages sur la maîtrise des mathématiques par les enfants indiens.

Ces sondages montrent qu'un faible pourcentage d'enfants maîtrise les concepts de mathématiques enseignés l'année précédente. La question est de savoir pourquoi et comment aider ces enfants.

•Bases des sciences cognitives du projet (environ 0:07 - 0:13) :

Elisabeth Spelke explique que le projet est fondé sur des capacités cognitives présentes dès la naissance, révélées par les sciences cognitives du développement, notamment les travaux de Jacques Meller. Ces systèmes cognitifs se concentrent sur des concepts abstraits et universels.

Elle illustre ces systèmes avec l'exemple du nombre, en mentionnant des expériences à Paris montrant une sensibilité aux quantités numériques chez les nouveau-nés et les nourrissons.

Elle distingue deux systèmes de nombres (approximatif et exact pour les petits nombres) ainsi que des systèmes de géométrie, qui soutiennent l'apprentissage des maths. Un cinquième élément crucial est l'acquisition du langage et des symboles.

L'apprentissage du langage commence très tôt et permet la transmission de perspectives distinctes.

Une expérience est décrite pour illustrer comment le langage aide les enfants à comprendre le point de vue des autres. L'éducation, notamment en maths, consiste à présenter de nouvelles perspectives.

•Jeux mathématiques et expériences en Inde (environ 0:13 - 0:18) :

L'hypothèse est qu'en présentant des activités ludiques qui suscitent les concepts intuitifs mathématiques et en les associant au langage et aux symboles, on peut faciliter l'apprentissage. Des jeux de maths ont été créés et testés à Boston puis en Inde avec des résultats surprenants de similarité dans l'engagement des enfants.

Une première expérience avec 1500 enfants dans des écoles maternelles a utilisé des jeux de nombres et de géométrie basés sur le "core knowledge", comparés à un programme normal et à des jeux sur des concepts sociaux.

Les résultats montrent un effet positif sur les intuitions mathématiques, mais limité pour les compétences symboliques à long terme.

Une deuxième expérience en maternelle a comparé des jeux purement intuitifs, purement symboliques et mixtes. Les jeux mixtes (alternant intuitif et symbolique) ont montré une synergie en renforçant à la fois les intuitions et la maîtrise des symboles.

Une troisième expérience a été menée dans des classes de CP et CE1 à Delhi avec des jeux mixtes adaptés pour des grands groupes et des enseignants peu formés. Des modifications ont été apportées aux jeux et à la procédure pour faciliter l'engagement des enfants et le suivi par les enseignants.

•Résultats et perspectives futures (environ 0:18 - fin) : Les résultats de l'expérience en CP et CE1 montrent un effet positif des jeux mathématiques, notamment pour les élèves de CE1.

Ces jeux sont en cours d'intégration dans les programmes de l'école primaire dans plusieurs états en Inde, posant des défis d'adaptation, de portabilité et de motivation des enfants.

Des améliorations suggérées par les enseignants, comme l'utilisation de pailles pour la géométrie et la création de jeux de navigation réels, sont mentionnées.

La question de l'efficacité des jeux reste ouverte, mais plusieurs hypothèses sont avancées : ils s'appuient sur des connaissances de base universelles, les affiches donnent de l'autonomie aux enfants, le jeu en groupe favorise la coopération et l'enseignement mutuel, et leur caractère ludique et social crée de bonnes conditions d'apprentissage.

Elisabeth Spelke conclut en soulignant l'importance du sentiment d'appartenance et de confiance pour l'apprentissage.

sur les grpahique choisir 100 comme étant la référence sur l'axe des Y : couts [CHF/an] 100 = 34731 CHF/an sur le graphique laisser les valeurs de coût total,

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

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility, and clarity)

The manuscript by Song et al presents evidence to show that the predicted cysteine protease type 6 secretion system (T6SS) effector Cpe1 inhibits target cell growth by cleaving type II DNA Topoisomerases GyrB and ParE. The authors determined the structure of the protein complex formed by Cpe1 and its immunity protein Cpi1, which allowed them to reveal the mechanism of inhibition. Moreover, the authors identified type II DNA topoisomerases GyrB and ParE as the targets of Cpe1. Overall, the major conclusions were well supported by experimental data of high quality. The findings have expanded our appreciation of the mechanism utilized by T6SS effectors to inhibit target cell growth.

We thank the reviewer for their positive remarks and valuable suggestions to improve this manuscript.

Major comments

To better establish that GyrB and ParE are the sole targets of Cpe1, the authors should express the GG mutant in target cells and determine whether these cells become resistant to Cpe1-mediated killing (inhibition). They can also determine whether co-expression of the cleavage resistant mutants suppresses the toxicity of Cpe1.

We appreciate the reviewer’s suggestion to investigate additional substrates of Cpe1 beyond GyrB and ParE, which may not have been fully captured in our crosslinking-mass spectrometry experiments due to technical limitations or low protein abundance. To address this topic, we generated target cells heterologously expressing cleavage-resistant GyrB and ParE variants (GyrBΔG102 and ParEΔG98) that are not susceptible to Cpe1, as described in our original manuscript (Figures 3h, i). We performed both Cpe1 expression assay and competition assay to assess if expression of the cleavage-resistant variants suppresses Cpe1 toxicity (Author Response Figures 1a, b). However, we did not observe a substantial protective effect. While this outcome could suggest that GyrB and ParE are not the sole targets of Cpe1, alternative explanations are also plausible. In the Cpe1 expression assay, high levels of Cpe1 could still act on endogenous wild-type GyrB and ParE, and although we attempted to increase variant expression, precise quantification remains challenging. In the competition assay, highly active Cpe1 may have continued to target wild-type substrates throughout the experiment, potentially masking any protective effect. Additionally, reduced activity of the mutant proteins could contribute to the observed results. Finally, deletion of the global repressor H-NS in the Cpe1-producing E. coli strain may have induced other interbacterial competition mechanisms1, leading to growth inhibition independently of Cpe1. Addressing these questions comprehensively would require a more systematic investigation under a wider range of conditions. We consider this an important avenue for future studies.

Results in Figure 7 clearly show that Cpi1 is capable of displacing ParE from Cpe1 due to higher affinity. Yet, the "competitive inhibition model" described in the last result section does not completely match what is really happening in Cpe1-mediated interbacterial competition. If Cpi1 is in the target cell, it would more likely engage the incoming Cpe1 before it can interact with ParE or GyrB, so competition does not occur in this scenario. Similarly, in the predatory cells expressing Cpe1 and Cpi1, these two proteins will form a stably protein complex, and no competition with the target will occur. The authors should reconsider their model.

We thank the reviewer for their comments and appreciate the opportunity to clarify this point. First, we believe the reviewer is referring to Figure 5 rather than Figure 7. In our model, the primary role of immunity proteins in interbacterial competition is to neutralize cognate toxins and prevent self- or kin-intoxication. These immunity proteins exhibit high specificity and strong binding affinity toward their associated toxins, ensuring effective protection2. In predatory cells, immunity proteins are typically co-expressed with their corresponding toxins, likely enabling immediate suppression upon translation. During kin competition, immunity proteins can protect cells even after foreign toxins engage their substrates.

Our results demonstrate that Cpi1 binds Cpe1 with higher affinity than its substrates and can displace them from pre-formed Cpe1-substrate complexes (Figures 5b-f). This aligns with the established function of immunity proteins in interbacterial competition and provides a mechanistic basis for how they confer protection, even when toxins have initially engaged their targets2. We acknowledge the reviewer’s point that in both scenarios—whether in the recipient cell or the toxin-producing cell—Cpe1 may first encounter Cpi1. However, our model underscores that Cpi1 not only binds at the substrate site but also exhibits superior affinity for Cpe1, ensuring robust protection against Cpe1-mediated toxicity.

Minor comments

"Intoxication" was used throughout the text numerous times to describe the activity of Cpe1. Looking in the Marriam-Webster dictionary, "Intoxication" means "a condition of being drunk". This word should be replaced with "toxicity" or some other terms in this line.

We thank the reviewer for this comment. We acknowledge that the term "intoxication" is commonly associated with alcohol consumption, yet the Merriam-Webster dictionary also defines it as "an abnormal state that is essentially a poisoning" (https://www.merriam-webster.com/dictionary/intoxication). This definition aligns with its well-established usage in the field of interbacterial competition to describe the effects of interbacterial toxins during antagonism3-5, which we have adopted in our manuscript. However, we appreciate the reviewer’s concern and remain open to revising the terminology if deemed necessary for clarity.

Lines 46-48, references on contact-dependent killings by these systems mentioned should cited. Ref. 9 cited does NOT cover the information at all.

We thank the reviewer for this comment. We have revised the citation and now reference studies that specifically describe contact-dependent killing systems in the relevant sentences (Lines 45–____50)

"characterizations" should be "characterization".

We have now modified the sentence as requested (Line 69)

Line 229 "Cpe1-Bpa monomers" should be " apo Cpe1-Bpa". The results cannot distinguish whether these bands are monomers or multimers.

We appreciate the reviewer’s careful assessment of our manuscript. The results in Line 233 (Figure 3c) show the enrichment of His-tagged proteins, including crosslinked complexes and overproduced Cpe1-Bpa. Based on the molecular weight marker, the Cpe1-Bpa bands appear between 10–15 kDa, consistent with the molecular weight of Cpe1 monomers (Figure 3a). Therefore, we have labeled this band as “Cpe1-Bpa monomers” and maintained this terminology throughout the text. This designation aligns with previous studies utilizing site-specific crosslinking via Bpa incorporation6,7

Line 283, was the mutation deletion? Substitution was used I think.

We thank the reviewer for highlighting this point. The GyrB and ParE mutants used to confirm the cleavage sites were deletion mutants, with a single glycine removed from the predicted double-glycine motifs. We have now revised the text for clarity (Lines 285–290)

Lines 439-444 the discussion should be extended to include other bacterial toxins that target type II DNA topoisomerases (e.g. PMID: 26299961 and PMID: 26814232).

We appreciate the reviewer’s suggestion. The studies referenced (PMID: 26299961 and PMID: 26814232) describe FicT toxin with adenylyl transferase activity that target and post-translationally modify GyrB and ParE at their ATPase domains, highlighting a potential hotspot for topoisomerase inhibition. We have now incorporated an additional paragraph in the Discussion section to describe these findings (Lines 424–439).

Reviewer #1 (Significance)

The authors determined the structure of the protein complex formed by Cpe1 and its immunity protein Cpi1, which allowed them to reveal the mechanism of inhibition. Moreover, the authors identified type II DNA topoisomerases GyrB and ParE as the targets of Cpe1. Overall, the major conclusions were well supported by experimental data of high quality. The findings have expanded our appreciation of the mechanism utilized by T6SS effectors to inhibit target cell growth.

We sincerely thank the reviewer for their positive comments and for the suggestions to improve our manuscript.

Reviewer #2 (Evidence, reproducibility, and clarity)

The manuscript, titled "An Interbacterial Cysteine Protease Toxin Inhibits Cell Growth by Targeting Type II DNA Topoisomerases GyrB and ParE", describes how an effector family was identified and characterized as a papain-like cysteine protease (PLCP) that negatively impacts bacterial growth in the absence of its co-encoded immunity protein. This thorough report includes (1) bioinformatic analysis of prevalence, finding this PLCP effector encoded in many gram-negative bacteria, (2) confirming conservation of catalytic active site via structural (crystallographic) analysis, as well as visualizing contacts with the immunity protein, (3) validation of results using growth studies combined with mutagenesis, (4) using a cell-based cross-linking method to pull out potential targets, which were subsequently identified via mass spectrometry, (5) validation of these results using in vitro protease assays with purified (potential) substrates, including verification of the motif recognized on the substrate(s), and cell-based phenotype analyses, and finally, (6) demonstrating competition between immunity protein and ParE substrate using an in vitro pull-down approach. Overall, this is a strong body of work with compelling conclusions that are well supported by multiple experimental approaches.

We appreciate the reviewer for their positive comments regarding our original submission.

Major comments

The claims made based on the presented results are well supported, including that this PLCP effector toxin is widespread, is neutralized in a competitive mechanism by its immunity partner, and that it effectively cleaves both GyrB and ParE (subunits of bacterial type II topoisomerases) at a conserved motif, resulting in suppression of bacterial cell growth via mis-regulating chromosome segregation. No additional experiments are needed to further validate these results, and the authors are commended on the cell-based and in vitro studies to deduce very specific mechanisms and structural details.

We appreciate the reviewer’s positive feedback.

Minor comments

While the writing and data presentation are extremely clear, in general I recommend the authors indicate the level(s) of replication for experiments. Figure legends generally note that mean values with standard deviations are shown, but I did not find where the number of replicates (and independent versus technical) were listed.

We appreciate the reviewer’s suggestion. We have now revised the manuscript to specify the levels of replication (independent vs. technical) for each experiment in the figure legends, particularly in Figures 2 and 3.

The figures are very clear, but in many instances the addition of PLCP toxin is indicated as "before" and "after"; while a modest change, I recommend altering this to some type of "-" and "+" type nomenclature rather than a time-based notation (especially as presumably both samples were treated identically, just with or without protease).

We thank the reviewer for this helpful comment. In Figures 3 and Supplementary Figures 5, 9, we used "before" and "after" to indicate the time points for in vitro cleavage assays verifying Cpe1 cleavage. To minimize variations between reactions, the catalytic mutant Cpe1tox (Cpe1toxC362A) was used as a comparison rather than a reaction without Cpe1tox. In these assays, duplicate reaction mixtures were prepared: one was denatured immediately after preparation ("before" reaction) to serve as a baseline, while the other was incubated to allow enzymatic activity ("after" reaction). This labeling clarifies the comparison between initial and processed samples. We believe this approach clearly distinguishes the effects of Cpe1 activity and provides a reliable basis for assessing proteolysis in our assays.

I also suggest quantifying the intensities of the gel images presented in Figure 5c, d (for example, Cpe1 intensity as a ratio to that of the ParE ATPase domain), to make the interpretation even more evident.

We thank the reviewer for the valuable suggestion to quantify the signal intensities of the gel images presented in Figures 5c, d. We have now included the quantification results in Supplementary Figures 9e, f and have updated the respective text in the manuscript (Lines 826-828 and 1066-1087).

Crystallographic structure: the PDB report notes some higher-than-expected RZR (RSRZ) scores; I interpret this to mean that there was strain around the catalytic site of one of the two toxins in the asymmetric unit, or that this copy was less well ordered. The RZR outliers likely arise from non-optimal weighting for geometric restraints. While no figures of electron density are presented, these modest outliers are not expected to alter the conclusions reached in the current work. One point of interest that is not addressed, however, is if any variance between the two complexes in the asymmetric unit are noted? A passage compares the current toxins to others in the larger subfamily and notes a rotation of a side chain is needed to superpose (Line 159). Can the authors please clarify around which bond this rotation is needed, and if both copies in the asymmetric unit are in the same orientation at this site?

We appreciate the reviewer’s insightful comments.

1. We have provided the electron density map for the RSR-Z outlier residues along with the model (Author response Figure 2a). These outlier residues are located at the loop regions of a molecule within the asymmetric unit in the crystal (Chain B). As a result, the electron density for their side chains appears to be noisier compared to residues in the well-folded regions, leading to higher RSR-Z scores. Notably, when we superimposed the models of two complexes within the asymmetric unit, the calculated RMSD value was 0.402 Å (Author response Figure 2b), indicating that the two models are structurally very similar and that these residues are properly assigned. Therefore, the RSR-Z outliers do not significantly impact the overall structure.
2. Here, we provide a zoomed-in view of Figure 2d, highlighting the superimposed crystal structures of Cpe1 and the closely related PLCPs, ComA and LahT (Author response Figure 2c). As shown, the side chain of the catalytic cysteine residue in ComA adopts a different orientation, positioning it slightly farther from the homologous residues in Cpe1 and LahT. However, since the backbone and catalytic pockets remain structurally intact, we believe that this deviation arises due to results from crystal packing effects rather than an inherent functional distinction. We have now modified the main text (Lines 159-166) to clarify this and prevent any potential misinterpretation.

Reviewer #2 (Significance)

Bacteria encode numerous effectors to successfully compete in natural environments or to mediate virulence; these effectors are typically associated with type VI secretion system machinery or referred to as contact dependent inhibition systems. The current work has identified a sub-family of papain-like cysteine protease effectors that are unique by targeting type II topoisomerases. Among the actionable findings is the identification of both the specific site of interaction with the topo substrates, as well as the specific motif recognized for cleavage. This should enable the field to move forward probing for this activity with other toxins and substrates. The insights provided by the competitive neutralization mechanism also stand out as an important contribution that can be more broadly applied. Within the literature, few effector targets are identified, making the current study stand out as impactful by the well-executed experiments that directly support the conclusions.

While the current study has strong elements of novelty and is complete, it also nicely sets up future studies for remaining open questions. For example, does the nucleotide-bound status of the ATPase domain, or other catalytic intermediate, impact the susceptibility of topoisomerases to cleavage? Is this identified motif found in other ATPase domains? Is the negative supercoiling activity unique to gyrase also impacted, or is the phenotypic mechanism of cell toxicity reliant only on chromosome segregation? What types of kinetic parameters do this class of toxins demonstrate, and does sequence variability alter this? These ideas are a testament to the intriguing study as presented, capturing the readers' curiosity for additional details that are clearly beyond the scope of the current work.

I anticipate this work will be of interest to the broad field of microbiologists that study interbacterial communication as well as pathogenic mechanisms. While the research is largely fundamental in nature, it is wide in scope with applications to many gram-negative bacteria that inhabit a myriad of niches. The work will also be of interest to specialists in topoisomerases, as the list of toxins that target these essential enzymes is growing and the therapeutic utility of topoisomerase inhibition remains vital. My interest lies in the latter, in toxin-mediated inhibition of topoisomerase enzymes as a means to alter bacterial cell growth. While I have strong expertise in structural biology, I am lacking in expertise for mass spectrometry. I note this because this method was used for the identification of the target substrate.

We appreciate the reviewer’s insightful discussion and interest in our study. We agree that further investigations are crucial to address the open questions posed, and we have initiated work on some of these avenues.

For example, considering Cpe1's specificity for the ATPase domain of GyrB and ParE, we have begun examining whether Cpe1 targets other ATPase domains by searching for the consensus sequence or double glycine motifs in the sequences of ATPase domains beyond GyrB and ParE. Among the 42 E. coli ATPase domains identified by the PEC database8, we found several with double glycine residues. However, none contained the exact LHAGGKF consensus sequence identified in GyrB and ParE, which are targeted by Cpe1 (Author Response Figure 3). These findings suggest that Cpe1 is less likely to target other ATPase domains. Nonetheless, due to Cpe1’s potential tolerance of certain variations within the consensus sequence, we cannot draw a definitive conclusion without further investigation into the cleavage sites.

Another critical open question is the impact of Cpe1-mediated cleavage on the function of GyrB and ParE. To address this topic, we have begun investigating if Cpe1 cleavage affects the ATPase activity of these proteins. As expected, our biochemical analysis has demonstrated a significant decrease in ATP hydrolysis in the presence of active Cpe1tox, but not in the presence of the catalytic mutant Cpe1toxC362A (Author response Figures 4a, b). These results confirm that the ATP-dependent activities of both GyrB and ParE are disrupted following Cpe1 cleavage9. Previous work on FicT toxin that inhibits GyrB and ParE ATPase activity through post-translational modification found that ATP-dependent activities such as DNA supercoiling, relaxation, and decatenation were inhibited10,11. Interestingly, GyrB’s relaxation of negative supercoiled DNA, which does not require ATP, was also affected to some extent. This outcome raises the question as to whether Cpe1-cleaved GyrB results in similar downstream defects. Investigating this possibility would provide valuable insights into Cpe1’s mode of action, although we feel doing so is beyond the scope of the current study. Consequently, we view this as an important area for future research.

Finally, regarding the potential applications of Cpe1, we are interested in further investigating its enzymatic specificity and properties. In this study, we analyzed the binding kinetics between Cpe1 and its substrate (Figure 5f) and currently we are endeavoring to characterize the kinetics of Cpe1-mediated proteolysis. To better probe hydrolytic dynamics, we plan to utilize a substrate with a reporting group (such as a chromogenic or fluorogenic leaving group) to monitor cleavage over time. We could achieve this by designing a recombinant substrate based on our knowledge of Cpe1’s native substrates (GyrB and ParE) and the target sequence (“LHAGGKF”). Alternatively, a secondary reaction leading to colorimetric changes could be employed for detection. We consider this an exciting research direction and an important next step for this study.

Overall, we are grateful for the reviewer’s recognition of the novelty and importance of our work in advancing the understanding of interbacterial toxins and their inhibitory effects on topoisomerases. We plan to further investigate the consequences of Cpe1 cleavage on GyrB and ParE and to explore Cpe1 kinetics and its mechanistic actions in more detail. This will not only deepen our understanding of bacterial toxin-mediated inhibition but may also provide critical insights into strategies for targeting type II DNA topoisomerases. The reviewer’s insightful feedback has proven invaluable in shaping our ongoing and future research directions.

Reviewer #3 (Evidence, reproducibility, and clarity)

Bacterial warfare in microbial communities has become illuminated by recent discoveries on molecular weapons that allow contact-dependent injection of bacterial toxins between competitors. Among the best characterized systems are the type VI secretion system (T6SS) or the contact-dependent inhibition (CDI) system (i.e. some of the T5SSs). These systems are delivering a plethora of toxins with various biochemical activities and a broad range of targets. In recent years many such toxins have been characterized and their relevance in pointing at appropriate drug targets is increasing.

In this study the authors built on a previously published association of a family of proteins, papain-like cysteine proteases (PLCPs), with their delivery by T6SS or CDI into target bacterial cells. Whereas this observation is not particularly novel, the findings that this set of proteins, that the authors called now Cpe1, can specifically target bacterial proteins such as ParE and GyrB, so that it affects chromosome partitioning and cell division, is groundbreaking. The authors are clearly demonstrating that Cpe1 cleaves their target proteins at double glycine recognition site which is in line with previous characterization of such proteases when fused to a particular category of ABC transporters. Even more remarkably they can show using biochemical approaches that Cpi1 is a cognate immunity for CpeI, preventing its activity, not by interfering with the catalytic site, but instead with the substrate binding site. The mechanism of competitive inhibition between immunity and substrate is also substantiated by biochemical data.

We sincerely appreciate the reviewer’s interest in and support of our study.

Major comments

• This is a very well conducted study which combines bacterial genetics and phenotypes with excellent biochemical evidence.

We thank the reviewer for their positive comments.

• There are 8 targets identified for Cpe1 and yet only two are cleaved by the enzyme. It is intriguing that FtsZ is one identified target by the pull down but not confirmed for cleavage. The authors rules this as false positive but the cell division defect associated with Cpe1 activity would be consistent here. Are there any double glycine in FtsZ that could be identified as cleavage site? Is it possible that slightly different incubation conditions may promote degradation of FtsZ?

We appreciate the reviewer’s thoughtful comment regarding FtsZ as a potential substrate of Cpe1. This was indeed an intriguing possibility, especially given the cell division defects observed following Cpe1 intoxication. Early on in the project, we also identified FtsZ as a Cpe1 interactor in our proteomic crosslinking assays, which further fueled the hypothesis that FtsZ might be a target.

To explore this possibility, first we examined the FtsZ protein sequence for potential Cpe1 cleavage sites and identified several double glycine motifs (Author response Figure 5a). However, none of these motifs matched the consensus sequence identified in GyrB and ParE, which is LHAGGKF, a sequence that we have shown to be critical for Cpe1 cleavage activity. In an effort to better understand if FtsZ could still be cleaved by Cpe1, we conducted additional cleavage assays under various conditions (Author response Figure 5b). We tested different incubation temperatures, including increasing the temperature to 37 °C, and extended the reaction time to overnight. However, we did not observe any cleavage of FtsZ under these conditions. Given that FtsZ undergoes significant conformational changes upon binding to GTP12, we also considered the possibility that the GTP-bound form of FtsZ might be cleaved by Cpe1. However, even under those conditions, no significant cleavage of FtsZ was detected (Author response Figure 5b). Based on these results, we do not have any evidence to support that FtsZ is a target of Cpe1. The observed cell division defects are more likely a secondary effect resulting from the cleavage of GyrB and ParE, direct targets of Cpe1 that are crucial for chromosome segregation.

• Could it be structurally predicted whether the GG of ParE or GyrB is fitted into the catalytic site of Cpe1.

We appreciate the reviewer’s insightful question regarding the structural prediction of the GG motif of ParE and GyrB fitting into the catalytic site of Cpe1. To address this possibility, we used Alphafold 3 to predict the interaction structure between Cpe1 and its substrates13. The resulting model of Cpe1 interacting with the ATPase domain of GyrB (GyrBATPase) is shown in Supplementary Figure 9c. As illustrated, the loop of the GyrB ATPase domain containing the consensus targeting sequence (“LHAGGKF”) fits into the catalytic site of Cpe1, with the GG motif positioned closest to the catalytic cysteine residue, which likely facilitates hydrolysis. We also attempted to model the interaction between Cpe1 and the ATPase domain of ParE. However, confidence for this model was lower (ipTM = 0.74, pTM = 0.71), possibly due to Alphafold’s preference for certain protein configurations. To gain a more accurate understanding of how Cpe1 binds and recognizes its substrates, we are currently working on co-crystallizing Cpe1tox with GyrB and ParE. This long-term project aims to provide precise structural insights into the Cpe1-substrate interaction and further elucidate the mechanism of cleavage.

Minor comments

• The authors described a family of proteases, PLPCs, and characterized one here called Cpe1. Not clear whether this is a generic name or one specific protein from one particular bacterial species. Indeed, it is unclear from which bacterial strain the Cpe1 protein studied here originates.

We thank the reviewer for this comment and apologize for the lack of clarity. To provide better context, we have now revised the manuscript (Lines 136-137 and 141-145) to clearly state that the Cpe1 protein characterized in this study originates from E. coli strain ATCC 11775.

• It may be worth to emphasize that the Cpe1 domain is found in all possible configurations as T6SS cargo and that is to be linked to VgrG, PAAR or Rhs.

Thank you for this suggestion. We have revised the manuscript accordingly to emphasize this point (Lines 106-109).

• Line 49 the authors could indicate that the Esx system is also known as type VII secretion system (T7SS).

Thank you for this suggestion. We have revised the manuscript accordingly (Line 48-50).

• Line 113 it may be better to use Proteobacteria instead of Pseudomonadota

We have revised the manuscript (Lines 114-115) as suggested by the reviewer. It is important to note that following the recent decision by the International Committee on Systematics of Prokaryotes (ICSP) to amend the International Code of Nomenclature of Prokaryotes (ICNP) and formally recognize "phylum" under official nomenclature rules14,15, the taxonomy database used in our analysis has adopted the updated nomenclature. To ensure consistency, we followed this updated nomenclature throughout the original manuscript.

Reviewer #3 (Significance)

This is an excellent piece of work. The characterization of Cpe1 might look poorly novel at the start when compared to previous studies. Yet the findings go crescendo by characterizing original mechanisms of action of the cognate immunity, and by identifying the molecular target of Cpe1. This is providing real conceptual advance in the T6SS field and not just reporting yet another T6SS toxin.

As a T6SS expert I genuinely feel that these findings are groundbreaking and could be targeted to broad audience since the possible implications of these observations for future antimicrobial drugs discovery or therapeutic approaches is highly relevant.

We sincerely appreciate the reviewer’s positive remarks and support of our study.

References

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2. Hersch, S.J., Manera, K., and Dong, T.G. (2020). Defending against the Type Six Secretion System: beyond Immunity Genes. Cell Rep 33, 108259. 10.1016/j.celrep.2020.108259.
3. Russell, A.B., Singh, P., Brittnacher, M., Bui, N.K., Hood, R.D., Carl, M.A., Agnello, D.M., Schwarz, S., Goodlett, D.R., Vollmer, W., and Mougous, J.D. (2012). A widespread bacterial type VI secretion effector superfamily identified using a heuristic approach. Cell Host Microbe 11, 538-549. 10.1016/j.chom.2012.04.007.
4. Jana, B., Fridman, C.M., Bosis, E., and Salomon, D. (2019). A modular effector with a DNase domain and a marker for T6SS substrates. Nat Commun 10, 3595. 10.1038/s41467-019-11546-6.
5. Halvorsen, T.M., Schroeder, K.A., Jones, A.M., Hammarlof, D., Low, D.A., Koskiniemi, S., and Hayes, C.S. (2024). Contact-dependent growth inhibition (CDI) systems deploy a large family of polymorphic ionophoric toxins for inter-bacterial competition. PLoS Genet 20, e1011494. 10.1371/journal.pgen.1011494.
6. Nguyen, T.T., Sabat, G., and Sussman, M.R. (2018). In vivo cross-linking supports a head-to-tail mechanism for regulation of the plant plasma membrane P-type H(+)-ATPase. J Biol Chem 293, 17095-17106. 10.1074/jbc.RA118.003528.
7. Liu, Y., Yu, J., Wang, M., Zeng, Q., Fu, X., and Chang, Z. (2021). A high-throughput genetically directed protein crosslinking analysis reveals the physiological relevance of the ATP synthase 'inserted' state. FEBS J 288, 2989-3009. 10.1111/febs.15616.
8. Yamazaki, Y., Niki, H., and Kato, J. (2008). Profiling of Escherichia coli Chromosome database. Methods Mol Biol 416, 385-389. 10.1007/978-1-59745-321-9_26.
9. Reece, R.J., and Maxwell, A. (1991). DNA gyrase: structure and function. Crit Rev Biochem Mol Biol 26, 335-375. 10.3109/10409239109114072.
10. Harms, A., Stanger, F.V., Scheu, P.D., de Jong, I.G., Goepfert, A., Glatter, T., Gerdes, K., Schirmer, T., and Dehio, C. (2015). Adenylylation of Gyrase and Topo IV by FicT Toxins Disrupts Bacterial DNA Topology. Cell Rep 12, 1497-1507. 10.1016/j.celrep.2015.07.056.
11. Lu, C., Nakayasu, E.S., Zhang, L.Q., and Luo, Z.Q. (2016). Identification of Fic-1 as an enzyme that inhibits bacterial DNA replication by AMPylating GyrB, promoting filament formation. Sci Signal 9, ra11. 10.1126/scisignal.aad0446.
12. Matsui, T., Han, X., Yu, J., Yao, M., and Tanaka, I. (2014). Structural change in FtsZ Induced by intermolecular interactions between bound GTP and the T7 loop. J Biol Chem 289, 3501-3509. 10.1074/jbc.M113.514901.
13. Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A.J., Bambrick, J., et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493-500. 10.1038/s41586-024-07487-w.
14. Oren, A., Arahal, D.R., Rossello-Mora, R., Sutcliffe, I.C., and Moore, E.R.B. (2021). Emendation of Rules 5b, 8, 15 and 22 of the International Code of Nomenclature of Prokaryotes to include the rank of phylum. Int J Syst Evol Microbiol 71. 10.1099/ijsem.0.004851.
15. Oren, A., and Garrity, G.M. (2021). Valid publication of the names of forty-two phyla of prokaryotes. Int J Syst Evol Microbiol 71. 10.1099/ijsem.0.005056.

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Reply to the reviewers

Reply to the Reviewers

We thank the reviewers for their evaluation of our previous submission and have responded to each point in detail below. Overall, we have revised the manuscript with the addition of several new data and corresponding figure panels that strengthen our previous conclusions and add new insights allowing us to extend the conclusions of the study. Important additions include new data showing the impact of loss of CLU on adapting to additional stressors during metabolic transitions that supports a mechanistic understanding of our omics results; by poly(dT) FISH we show that fly Clu granules indeed contain mRNAs; FRAP microscopy analysis supports that Clu1 granules have dynamic content similar to other LLPS membraneless organelles; and we have re-analysed our data to demonstrate more clearly the impact of Clu1 on translation efficiency and also the relative binding of mRNAs during translation. In addition, we provide some extra control analyses for completeness.

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

Summary:

In this manuscript the authors study the Clustered mitochondrial proteins Clu of Drosophila melanogaster and Clu1 of Saccharomyces cerevisiae, two homologues of the mammalian protein CLUH. They show in compelling microscopy analysis that both proteins form granules. This was the case for flies fed on yeast paste after starvation and in yeast in post-diauxic phase, in respiratory media or during mitochondrial stress. They show that these granules are found in proximity to mitochondria and that they behave like liquid-liquid-phase separated condensates. They show by co-staining for P-bodies and stress granules that Clu1-granules are distinct from these RNA granules. Furthermore, they found that the formation required active translation. In the second part, they show that Clu1 interacts with ribosomal and mitochondrial proteins by BioID. The deletion of Clu1 leads to slightly impaired growth on media containing Ethanol as a carbon source. They find that nascent polypeptides of some mitochondrial precursor proteins are decreased in the deletion of Clu1 and conclude that Clu1 regulates translation of these proteins. Using RNA immunoprecipitation of Clu1-GFP in presence of cycloheximid, EDTA and puromycin. The mRNAs of nuclear-encoded mitochondrial proteins found to be interacting with Clu1 were purified in conditions when the ribosomes are intact and the RNAs showed no interaction when ribosomes were disassembled. They show in sucrose gradients that Clu1 co-migrates with polysomes independent of its distribution state or carbon source. However, when cells are grown in conditions of granule formation, then polysomes and Clu1 run less deeply into the gradient. Form these data, the authors conclude that Clu/Clu1 regulates the translation of nuclear-encoded mitochondrial proteins.

Major comments:

-The authors state that Clu1 is regulating translation during metabolic shifts. However, it is not clear what the real impact on mitochondrial function is. They show that there is a minor growth defect on ethanol media when CLU1 is deleted. However, if Clu1 is necessary mainly for adaptation, the phenotype will be strongest observed in conditions where cells switch carbon sources. Growth curves would be suitable in which the lag-phase of yeast cells precultured either in glucose or glycerol switched to media of different carbon sources (glucose to glycerol or glycerol to glucose) are measured. One would expect that the deletion mutant shows a longer lag-phase compared to the wild type when shifted from glucose to glycerol media.

We agree that this is an important question, and, duly, we previously attempted to address this exactly as the reviewer described. Surprisingly, we were not able to observe any substantial differences in the duration of the lag phase between the wild-type and CLU1 knockout strains under these conditions. However, we did note that CLU1 knockout cells consistently reached stationary phase with a lower optical density when switched to ethanol media, consistent with these cells having a different metabolic efficiency during growth on ethanol media.

To further explore the role of Clu1, we noted that several of the Clu1 mRNA interactors were mitochondrial heat shock proteins (HSPs), which are crucial for mitochondrial protein folding and import during the transition from fermentation to respiration. Hence, we hypothesised that the absence of Clu1 might lead to increased sensitivity to heat shock during the metabolic shift.

To test this, we subjected both wild-type and CLU1 knockout cells to heat shock under three different conditions: (1) during growth on glucose-containing media (fermentation), (2) after shifting cells to media containing ethanol during the lag phase, when cells are adapting to respiration, and (3) after cells had fully adapted to ethanol and resumed growth. Interestingly, CLU1 knockout cells were more sensitive to heat shock selectively during the adaptation to respiration, which involves the translation of an extensive number of mitochondrial proteins. We think that the small difference in translation of mitochondrial HSPs becomes evident only upon additional heat shock, likely due to a deficient mitochondrial protein folding and import. These findings support our hypothesis that Clu1 is essential for optimal mitochondrial function during metabolic shifts.

These results have been added to the manuscript and shown in Fig. S6 and described on page 9.

-In line with this, how different is the mitochondrial proteome of the WT and the mutant? Do hits of the BioID, RIP and Punch-P experiments change at steady state or during metabolic shifts? Either proteomics of isolated mitochondria or western blots of whole cells or isolated mitochondria of WT and the deletion mutant grown in conditions of Clu1-granule formation or no granules for the hits could answer this question.

We also considered this question during the course of the work. However, in exploratory analyses we saw no obvious differences in overall mitochondrial proteomics at steady-state which is what prompted us to look at more subtle effects on translation. Considering this further, changes in steady-state levels can be complex to interpret as they represent the combined effects of protein production and degradation. Small changes arising from altered production could be masked by compensatory changes in turnover rate. In light of this, we believe that the translational regulation differences identified in our study remain central to understanding the role of Clu1, and any downstream proteomic changes would not alter our primary conclusions.

-The authors analyze RNAs bound in polysomes to assess translation efficiency. Translation efficiency is usually calculated by the fraction of RNA bound by ribosomes to the total RNA amount of an RNA species. Thus, doing RT-qPCR from whole cells would be necessary to assess if the occupancy of ribosomes on the transcripts is due to changes in RNA abundance or other regulatory pathways and would help to further assess what causes the observed changes.

Thanks for this recommendation. To address this and expand our analysis to other proteins differentially translated in clu1Δ cells, we measured the mRNA steady-state levels by performing RNAseq on WT and clu1Δ strains grown under the same conditions as used for Punch-P. We then calculated the translation efficiency by dividing the nascent protein levels (Punch-P) by steady-state mRNA levels (RNAseq), as previously described for Punch-P data (PMID: 26824027). The translation efficiency for the majority of proteins with reduced translation in the clu1Δ cells by Punch-P analysis was lower. Similarly, the majority of proteins with increased translation had higher translation efficiency.

The mRNA quantification in polysomes we originally presented in the manuscript, further showed that the decrease in translation efficiency is not caused by a simple decrease of mRNA engaged in translation and that Clu1 is regulating protein translation at the ribosome level. In contrast, for higher translated proteins, we detected an increase in mRNAs engaged in polysomes, likely underlying the increased translation. These results further support our conclusions regarding the regulatory effects of Clu1 on translation.

These results have been added to the manuscript and shown in Fig. 7E and described on page 9.

OPTIONAL:

-The authors show a co-localization of Clu/Clu1 with mitochondrial fission factors and conclude that the granules appear likely near fission sites. Indeed, CLUH has been implied in the past to play a role in mitochondrial fission (Yang, H., Sibilla, C., Liu, R. et al. Clueless/CLUH regulates mitochondrial fission by promoting recruitment of Drp1 to mitochondria. Nat Commun 13, 1582 (2022). https://doi.org/10.1038/s41467-022-29071-4). Thus, are fission sites required for Clu-granule localizations? What is the role of the mitochondrial network integrity for the granule distribution? Expressing Clu-GFP/Clu1-GFP in cells depleted for the fission factors would provide information on that.

Thanks for this suggestion. We agree that it would be interesting to know whether Clu1 granules still appear when mitochondrial fission is blocked. We tried to address this question but encountered some technical limitations. First, overexpression of Clu1-GFP via a plasmid did not replicate the endogenous Clu1 behaviour, making it necessary to delete the fission factors in the Clu1-GFP background. While crossing the Clu1-GFP strain with already available knockout strains would be straightforward, we would need access to a tetrad dissecting microscope, which unfortunately was not available to us. We also attempted PCR-based gene deletion but the sequence homology between the GFP-tagging cassette and the deletion cassettes made this very challenging. Given these limitations, and as the lab's yeast expert had already left, we were not able to pursue this experiment further and have removed these observations from our manuscript. We hope that future studies will explore this question in more detail.

-The author assess convincingly that Clu1 interacts with ribosomes and runs with polysomal fractions. However, how it actually regulates translation is not clear. To answer this question, selective ribosomal profiling would be necessary. The authors have established conditions which would be suitable for the experiment. They could use crosslinking and sucrose cushions to IP ribosomes with Clu1-GFP bound to be used for ribosomal profiling. However, this experiment is quite time-intensive (3-4 months) and expensive, thus, an optional suggestion.

We thank the reviewer for this suggestion. We agree that ribosome profiling could provide novel insights into the function of Clu1/Clu. While we recognise the potential of this approach, as the reviewer points out, this experiment would indeed be time- and resource-intensive. Based on our initial tests, where we included cross-linked samples (UV and formaldehyde) we anticipate that it could even take longer than the estimated 3-4 months, as the IP using cross-linked lysates was not as successful as the IP using non-cross-linked samples: we were not able to immunoprepitate Clu1 so efficiently likely to the epitope being poorly exposed to the antibody. Although we have optimised working conditions for co-immunoprecipitating Clu1 with ribosomes, performing ribosome profiling using our setup within the timeframe and resources of this study is unfortunately not currently feasible.

Minor comments:

Fig1: B, C, please add scale bars into the zoom ins.

These have been added.

Fig 2 would profit from inlets of zoom ins to visualize the distribution better.

These have been added.

Fig.3: Panel C does not really add much information. I would rather remove it or put it into supplements and therefore show a zoom of Panel E with a line plot showing the rings. It is not clear from the represented images where the rings are formed.

We think some confusion has arisen from the text description. It seems that the reviewer was under the impression that Fig. 3C and 3E were intended to be showing the Clu1 rings around the mitochondria, but this was shown only in Fig. S3A. We have re-written these sentences for better clarity. To be clear, Fig. 3C is a 3D rendering of the left-hand cell in 3B (3D is a line plot of part of the right-hand cell) and 3E is a different experiment showing the formation of Clu1 granules under a different respiratory stress (galactose plus CCCP). We have also added a line plot showing Clu1-GFP and mito-mCherry fluorescence intensity to highlight the Clu1 rings around the mitochondria in Fig. S3A.

Fig.3 panel F: Max projections are not appropriate to show colocalization as they can lead to false-positive overlaps. Just remove the max projections.

We tried a number of different approaches to improve this analysis but, ultimately, we were not able to generate sufficiently robust data to be convincing so we decided to remove this from the manuscript. The coincidence of Clu1 granules with mitochondrial fission factors was an adjunct observation and not a major part of the story and has been discussed by others relating to fly Clu (PMID: 35332133), so removal from the current manuscript does not impact the key conclusions of the study.

References 21 and 22 are the same.

Thanks. This has been fixed.

Reviewer #1 (Significance (Required)):

This manuscript shows in a convincing way that Clu and Clu1 form RNA granules and that Clu1 interacts with ribosomes. It is written in a clear way and the figures support the conclusions drawn in the text. The finding that Clu/Clu1 is important for metabolic adaptation has not been shown in fly or yeast to my knowledge. It is in line with findings for the mammalian homologue CLUH. Thus, the findings are supported by earlier work. This study is of value for a broader audience of the basic research field, especially of the mitochondrial and RNA granule field, as it supports the idea of post-transcriptional regulation of nuclear-encoded mitochondrial protein gene expression for dynamic adaptation of mitochondrial function. The conditions when Clu granules form is studied in detail, followed up by identification of target RNAs and interaction partners. Though the interaction of Clu1 with ribosomes is shown in a compelling way, a detailed mechanism of the function of Clu/Clu1 is missing and would require more experiments. Thus, even though a detailed mechanism is missing, the study does expand on our understanding of Clu/Clu1 in regulating mitochondrial biogenesis and is therefore of high interest of the mitochondrial field.

Expertise: mitochondria, yeast, RNA granules, mitochondrial biogenesis, next-generation sequencing, fluorescence microscopy

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

Summary:

In this manuscript the authors use D. melanogaster and S. cerevisiae to study the role of CLUH in the translation of nuclear-encoded mitochondrial proteins. During conditions requiring aerobic respiration, CLUH forms RNA-dependent granules that localise in the proximity to mitochondria. Furthermore, the authors demonstrate that CLUH interacts with translating ribosomes to facilitate the translation of specific target mRNAs. For this, the authors use a combination of GFP-tagged CLUH models. BioID, polysome translating proteomics, RNA-IP. The authors' main conclusions are that (i) CLUH forms dynamic, membrane-less, RNA-dependent granules under conditions that demand aerobic respiration, (ii) CLUH interacts with specific mRNAs encoding metabolic factors, and (iii) CLUH interacts with the translating ribosome. The manuscript is well written and the conclusions stand in proportion to the experimental output and the results. The main concern is with regards to lack of advancement in relationship to published data.

We appreciate the reviewer's feedback and specific comments which we respond to individually below. However, we would like to first address the point regarding "lack of advancement" and the use of the "CLUH" terminology which the reviewer uses throughout their critique. We would like to reiterate, as the reviewer states, our work focussed exclusively on yeast Clu1 and Drosophila Clu. None of our data relates to mammalian CLUH. While these proteins share substantial sequence homology, it is imprudent and scientifically unsound to assume cross-species equivalence without directly testing. Indeed, one of the central aims of our study was to characterise the molecular function of yeast Clu1, which remains almost entirely unstudied.

We acknowledge that some of the observations contained within our study have been described by others and we have appropriately noted and cited these in context. Nevertheless, (a) independent replication is always valuable but easily criticised as lacking novelty, and (b) the majority of the work was analysing the molecular dynamics and function of yeast Clu1 which is almost completely unstudied and may help provide hypotheses for others to test for conservation in mammalian CLUH. Hence, we consider that summarising the work as 'lacking advancement' is misplaced.

Comments:

To this reviewer it is not clear how CLUH can regulate the translation of specific mRNAs while being bound to ribosomes, regardless of being in a diffuse or granular state. The authors suggest that under metabolically active conditions, CLUH might aggregate translating ribosomes, forming the granular structures. How CLUH though can both be bound to translating ribosomes and recruit specific mRNAs at the same time is not explained.

It was indeed surprising to us that the data indicate that Clu1 can bind both mRNAs and ribosomes to affect translation, and we share the reviewer's curiosity about the precise mechanism of how this occurs. While we have provided novel insights into this situation, dissecting the precise molecular mechanisms is beyond the scope of the current study.

The authors might want to discuss how changes in metabolic demands signal the aggregation of CLUH, and how CLUH can recognise its target mRNAs.

We appreciate the reviewer's point here but as this would be pure speculation we have made only brief comments on this at the end of the Discussion.

What was the rationale to perform the RIP or the PUNCH-P experiments only under non-challenged conditions, but not under conditions demanding aerobic respiration?

We appreciate the reviewer's question. In fact, the Punch-P analysis was carried out on cells that had been transferred to ethanol to induce respiration. This was stated in the Methods, but we appreciate that this may have been missed so we have now clarified this in the main text (p9).

Regarding the RIP, our initial tests showed that mRNAs encoding proteins found to interact with Clu1 by BioID were interacting with Clu1 in both fermenting and respiring conditions. Due to this consistency, it did not seem necessary to perform the RIP experiments under both metabolic conditions, so we chose to conduct the experiment under the simpler growth condition.

If CLUH is ubiquitously bound to ribosomes, has CLUH been seen in any structural representation of the cytosolic ribosome?

This is a good question, and we wondered the same. To our knowledge, Clu1/Clu/CLUH has not been observed in any structural studies of the ribosome, and no formal structure of any Clu family proteins has been resolved.

Nevertheless, we would like to clarify that we do not think, or suggest in the manuscript, that Clu/Clu1 is ubiquitously bound to ribosomes. First, current evidence supports that Clu/Clu1 only regulates a specific subset of mRNAs. Second, our work, particularly the sucrose gradient experiments, shows that Clu1 interacts transiently with ribosomes, as cross-linking was required to capture the full extent of this interaction. This transient and selective interaction of Clu/Clu1 with the ribosome, together with the fact that transient interactors are often lost during ribosome purification, makes Clu/Clu1 detection in structural studies unlikely. Due to the transient interaction and dynamic localisation of Clu/Clu1, capturing Clu/Clu1 in ribosomal structures will require significant work in the future.

Reviewer #2 (Significance (Required)):

CLUH has been studied in various publications, showing data very similar to that presented in this manuscirpt. However, the authors provide a comprehensive analysis on both yeast and fly CLUH. The strength of the manuscript is the combination of several elegant methods and genetically modified model systems in two species to elucidate the role of CLUH during the translation of specific mRNA. In my view through, the advancement of understanding the function of CLUH is limited.

Although the authors work in yeast and DM, the results seem applicable to other species, including humans, and thus, the presented results will be of interest in a range of researchers working in the field of metabolic regulation and gene expression.

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

Summary: This study from Miller-Fleming et al. employs yeast and Drosophila as model systems to explore the function of the RNA-binding protein Clu1, which is involved in mitochondrial biogenesis. The first part of the manuscript characterizes so called "Clu1 granules", and their dependance from metabolic transitions. In particular, using yeast, they find a relocalisation of Clu1 upon starvation and several mitochondrial stress conditions. These granules are not stress granules, and are dissolved by RNAse and puromycin treatment. The second part of the study aims to understand the molecular function of the protein and its link to translation. The results confirm an evolutionary conserved role of Clu1 in binding mRNAs for mitochondrial proteins and in interacting with mitochondrial proteins, ribosomal components and polysomes. In addition, the authors claim that binding of Clu1 to RNA is enhanced when mRNAs are trapped in polysomes by treatment with cycloheximide (CHX), leading to the proposal that Clu1 binds mRNAs during active translation.

Major comments:

-The claim of Clu1 granule localization next to mitochondria (Figure 3) would be more convincing if any of the experiment would be quantified. Especially in the case of panel 3G in Drosophila egg chambers where there are a lot of mitochondria, one wonders whether the closeness to mitochondria is just random. Furthermore, mdv1-signal does not look very convincing, being blurry and not dotty as expected. Thus, the conclusion that Clu1 granules partially colocalization with site of fission appears premature.

The claim that Clu/Clu1 granules are often found in close proximity to mitochondria was inferred from observations from multiple analyses from yeast (we looked at hundreds of cells in several different conditions) and flies, where it had already been demonstrated (Cox and Spradling, 2009). We agree that observations of the fly egg chambers are challenging due to the very high density of mitochondria (and other cellular components - see the new analysis of poly(A) mRNAs) in these highly active cells. These considerations motivated us to take the CLEM approach (in addition to investigating the membraneless nature), to gain a much higher resolution view of the localisation of the granules. This analysis unequivocally showed that the Clu granules were exactly juxtaposed to several mitochondria. It is noteworthy that even in the TEM images shown, there is ample cytoplasm in which the Clu granule could be located if the association with mitochondria was coincidental and all granules had mitochondria in close proximity.

Regarding the possible coincidence of Clu1 with mitochondrial fission factors, as mentioned above for Reviewer 1, we tried a number of different approaches to improve this analysis but, ultimately, we were not able to generate sufficiently robust data to be convincing so have decided to remove this from the manuscript. Since this was an adjunct observation and not a major part of the story and has been discussed by others relating to fly Clu (PMID: 35332133), removal from the current manuscript does not impact the key conclusions of the study.

Based on the ability of 1,6-hexanediol to dissolve the granules (Figure 4), the authors conclude that: "Clu1 foci have membraneless nature". As they correctly state in the discussion, treatment with 1,6-hexanediol can have other effects. I suggest to be more cautious with the conclusions or add additional experiments. Are the granules dynamics if using FRAP? Do they fuse?

The inference that the Clu1 granules are membraneless organelles was not solely based on the observation that they disassemble upon 1,6-hexanediol treatment but was made in conjunction with the CLEM analysis that showed unambiguously that Clu granules are not associated with any detectable membrane, which is strong evidence that these granules are membraneless in nature. Indeed, as the reviewer mentioned, we are cautious in concluding they have been formed by liquid-liquid phase separation (LLPS) and we do acknowledge that 1,6-hexanediol can have other effects in cells. Nevertheless, following the reviewer's suggestion we have analysed Clu1 granule dynamics using FRAP, even though we are aware that FRAP is also not a definitive proof that a structure is formed by LLPS. The FRAP analysis, shown in new Figure 4C, D, revealed approximately 50% recovery over 10 min imaging timeframe. As discussed on page 13, this indicates a dynamic nature of these granules, but this dynamism can vary widely between different types of granules and even different proteins within the same granule. Further work is warranted to fully investigate the dynamic nature of Clu/Clu1 granule components.

The experiment in which the granules are dissolved by treatment with RNAse is very interesting. However, per se this does not directly demonstrate that the granules contain mRNA. To state this the author should perform FISH experiments for example using a probe to detect poly-A.

We thank the reviewer for this suggestion. We have performed poly(dT) FISH in egg chambers. Initial analysis showed that the fluorescence was diffuse and widely distributed, as expected for these highly active cells, but with no specific accumulation in Clu granules. Interestingly, we observed that treatment with RNase A, which we initially used to demonstrate probe specificity, revealed an enrichment of poly(A) RNAs in Clu granules. So, while treating the live egg chambers with RNase revealed that granules depend on RNA for their stability, treating fixed egg chambers revealed more directly the presence of RNAs in granules.

These results have been added to the manuscript and shown in Fig. 5 and described on page 7.

The authors show that puromycin prevents the granule formation before insulin addition in the fly. Are these results (upon RNAse treatment and puromycin treatment) recapitulated in the yeast system? The authors conclude that Clu1 formation depends on mRNAs being engaged in translation, but never show that the granules are site of active translation. More experiments in this direction (for example using puro-PLA of specific mRNAs) are missing and would clearly improve the manuscript.

Thanks for this very interesting consideration. We agree that we have not formally shown that the Clu1 granules are sites of active translation. A major limitation to addressing this is that puromycin is not able to penetrate the yeast cell wall, so cannot be used for analysis of intact cells as would be needed in this case. We agree that this would be a welcome addition but is beyond the scope of the current study.

The interactome of Clu1-neighbouring proteins (Figure 6) is interesting and a valuable addition to data in other organisms. I am wondering why the authors have not used as a control a cytosolic BirA-GFP, which would have been the right control for this experiment, especially since GFP tends to form aggregates.

We thank the reviewer for this comment. With hindsight, we agree that a cytosolic BirA-GFP would have been a better control. However, we are confident in our results for the following reasons:

1. The levels of GFP obtained from Clu1-GFP expression are low, and under these conditions, we observed no evidence of GFP aggregation. Even in experiments where GFP is overexpressed from a high-copy 2µ plasmid under a strong promoter, we do not detect aggregation. Aggregation is not a concern in our experimental setup.
2. Our conclusions are not solely based on the interactome analysis (BioID) but are supported by complementary findings. Specifically, several proteins identified in the proximity to Clu1 in the BioID analysis showed reduced translation in Clu1 knockout cells, and their corresponding mRNAs were found to interact with Clu1 during translation. These complementary results from independent techniques provide strong evidence for Clu1's role and validate the findings of the interactome analysis. Given this robust and complementary dataset, having BirA as a control strain was sufficient to validate our conclusions.

Figure 7B: The log 2 FC for the changed proteins are in many cases small, implying that the difference in translation for these proteins is not so large. For this reason, it is relevant to know how was the statistical significance calculated for these MS measurements. In the supplementary Tables and in Fig 7B, a p value is indicated and it is not clear if this is a simple p value or an adjusted p value (FDR or q value). If not shown, I recommend showing the adjusted p value, so that one can have an idea of the solidity of the data and the claim. Again, this is an important piece of evidence, since the authors base on this experiment the conclusion that Clu1 controls translation of these mRNAs.

Thanks for this comment. We have now included the q-value in the supplementary table.

Minor comments:

-Figure 1: The change in Clu1 localisation in post-diauxic phase or upon changing of the medium is evident from the images shown. However, it seems that the experiment has been performed only once (the same for Figure 2). Is this the case? An important information would be to show the expression levels of Clu1-GFP in the different conditions. Does recruitment of CLU1 to granules associate to increased expression levels?

The experiments shown in figures 1 and 2 were performed independently at least three times, as stated in the figure legends. The numbers shown are indicative values from one of the replicate experiments. This has now been added to the figure legends.

We agree that providing the information regarding the expression levels of Clu1-GFP is important to address whether the recruitment of Clu1 to granules is associated with changes in its abundance. To this end, we have performed an additional experiment to quantify Clu1-GFP levels under the conditions where Clu1 is diffuse (log growth phase in glucose-containing media) and when Clu1 is in granules (sodium azide treatment).

These results have been added to the manuscript and shown in Fig. S2 and described on page 4.

Figure 2 A-B. The authors claim that the only stressor capable of inducing Clu1 granules formation alone is inhibition of complex IV activity via sodium azide treatment. Other mitochondrial stresses like CCCP treatment or OA treatment are efficient only when combined to starvation. It should be mentioned that sodium azide treatment is not only capable of inhibiting complex IV but has also uncoupling function.

Thanks for this comment. We have now mentioned this (p4).

Figure 2 D-E: investigation of colocalization with Bre5 would help to understand how similar the yeast Clu1 granules are compared to the mammalian CLUH granules (Pla-Martin et al., 2020).

This is an interesting suggestion and one that we also considered, but with limited time and resources we were not able to pursue this line of inquiry as well.

Figure 8. This figure summarizes one of the most novel pieces of data about Clu1, the interaction with mRNAs via the ribosome. The way how panel A-C are represented is however a bit misleading. The Y axis in Figure B and C has the same amplitude as the one in A. Therefore, potential differences in Clu1-RNA pull-down in presence of EDTA or puromycin cannot be assessed. It is true that in presence of CHX there is much more pulled down RNA, but one cannot judge from these panels if there is any difference between Clu1 targets and controls also in the other conditions. The graphs should be modified and statistics added.

We appreciate the reviewer's feedback regarding the presentation of the RIP-qPCR data in Fig. 8. Based on the comments, we have revised how the results are represented, improved the normalisation of the data and added statistical analysis.

First, it is worth clarifying that the presentation of the original charts was done specifically to highlight the huge differences between RNA-pulldown in CHX versus disrupted ribosomes. It is also important to note that these RIP experiments were performed simultaneously under identical experimental conditions, so any differences lie in the treatments applied. To improve cross-comparison between treatments we have now incorporated an additional normalisation step. We normalised the enrichment levels of each mRNA tested against the non-specific binding observed with the negative control housekeeping genes (UBC6 and TAF10). This ensures that differences in bead loss or other technical variations are accounted for.

We now show the comparison of the six positive hits and two negative controls normalised as described above, on the same scale (Fig. 8A). We now also present the relative effects of the three conditions (CHX, EDTA, and puromycin) within the same graph for each mRNA tested (Fig. 8B). This format enables direct comparison of Clu1 target mRNA enrichment and two negative controls across treatments, which is the relevant comparison for testing the hypothesis of ribosome-dependent interactions. We have adjusted the Y-axis scaling for each mRNA, as requested by the reviewer, and added statistical comparisons. For clarity, the data shown in Fig. 8A are also represented in the panels of Fig. 8B (CHX). We have amended the text appropriately and hope that these changes improve the comparisons between treatments and more readily demonstrate that Clu1 target enrichment is lost upon ribosome disassembly, either by EDTA or by puromycin.

In addition, RNAse treatment in panel L does not seem to have really worked.

These samples were cross-linked prior to treatment to preserve the transient interaction of Clu1 with the ribosome, hence, the normal dramatic effect of RNase to collapse the polysomes is much less pronounced. Nevertheless, the purpose of this experiment was to monitor whether Clu1 co-migrated with ribosomes, which it does.

The authors should cite Vornlocher et al. (PMID: 10358023), who were the first to implicate Clu1 (Tif31) with translation.

Thank you for this prompt. We have now added a comment on this in the Discussion (page 13).

References 21 and 22 are the same.

Thanks. This has been fixed.

Reviewer #3 (Significance (Required)):

The data reported in this manuscript are valuable, because they confirm an evolutionary conserved role of Clu1 in binding mRNAs for mitochondrial proteins and regulating their translation. It is also interesting that in yeast, similar to Drosophila and mammalian cells, Clu1 can form granular structures upon metabolic rewiring. A limitation of the study is that direct experiments to support the claim that Clu1 concentrates ribosomes engaged in translation are not provided. Furthermore, it is not clear what is the functional role of the Clu1 granules, since the proximity interactome and the binding of Clu1 to the polysomes is not affected by treatments that dissolve or stimulate granule formation.

The study is of interest to a general cell biology audience.

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Referee #3

Evidence, reproducibility and clarity

This study from Miller-Fleming et al. employs yeast and Drosophila as model systems to explore the function of the RNA-binding protein Clu1, which is involved in mitochondrial biogenesis. The first part of the manuscript characterizes so called "Clu1 granules", and their dependance from metabolic transitions. In particular, using yeast, they find a relocalisation of Clu1 upon starvation and several mitochondrial stress conditions. These granules are not stress granules, and are dissolved by RNAse and puromycin treatment. The second part of the study aims to understand the molecular function of the protein and its link to translation. The results confirm an evolutionary conserved role of Clu1 in binding mRNAs for mitochondrial proteins and in interacting with mitochondrial proteins, ribosomal components and polysomes. In addition, the authors claim that binding of Clu1 to RNA is enhanced when mRNAs are trapped in polysomes by treatment with cycloheximide (CHX), leading to the proposal that Clu1 binds mRNAs during active translation.

Major comments:

• The claim of Clu1 granule localization next to mitochondria (Figure 3) would be more convincing if any of the experiment would be quantified. Especially in the case of panel 3G in Drosophila egg chambers where there are a lot of mitochondria, one wonders whether the closeness to mitochondria is just random. Furthermore, mdv1-signal does not look very convincing, being blurry and not dotty as expected. Thus, the conclusion that Clu1 granules partially colocalization with site of fission appears premature.
• Based on the ability of 1,6-hexanediol to dissolve the granules (Figure 4), the authors conclude that: "Clu1 foci have membraneless nature". As they correctly state in the discussion, treatment with 1,6-hexanediol can have other effects. I suggest to be more cautious with the conclusions or add additional experiments. Are the granules dynamics if using FRAP? Do they fuse?
• The experiment in which the granules are dissolved by treatment with RNAse is very interesting. However, per se this does not directly demonstrate that the granules contain mRNA. To state this the author should perform FISH experiments for example using a probe to detect poly-A.
• The authors show that puromycin prevents the granule formation before insulin addition in the fly. Are these results (upon RNAse treatment and puromycin treatment) recapitulated in the yeast system? The authors conclude that Clu1 formation depends on mRNAs being engaged in translation, but never show that the granules are site of active translation. More experiments in this direction (for example using puro-PLA of specific mRNAs) are missing and would clearly improve the manuscript.
• The interactome of Clu1-neighbouring proteins (Figure 6) is interesting and a valuable addition to data in other organisms. I am wondering why the authors have not used as a control a cytosolic BirA-GFP, which would have been the right control for this experiment, especially since GFP tends to form aggregates.
• Figure 7B: The log 2 FC for the changed proteins are in many cases small, implying that the difference in translation for these proteins is not so large. For this reason, it is relevant to know how was the statistical significance calculated for these MS measurements. In the supplementary Tables and in Fig 7B, a p value is indicated and it is not clear if this is a simple p value or an adjusted p value (FDR or q value). If not shown, I recommend showing the adjusted p value, so that one can have an idea of the solidity of the data and the claim. Again, this is an important piece of evidence, since the authors base on this experiment the conclusion that Clu1 controls translation of these mRNAs.

Minor comments:

• Figure 1: The change in Clu1 localisation in post-diauxic phase or upon changing of the medium is evident from the images shown. However, it seems that the experiment has been performed only once (the same for Figure 2). Is this the case? An important information would be to show the expression levels of Clu1-GFP in the different conditions. Does recruitment of CLU1 to granules associate to increased expression levels?
• Figure 2 A-B. The authors claim that the only stressor capable of inducing Clu1 granules formation alone is inhibition of complex IV activity via sodium azide treatment. Other mitochondrial stresses like CCCP treatment or OA treatment are efficient only when combined to starvation. It should be mentioned that sodium azide treatment is not only capable of inhibiting complex IV but has also uncoupling function.
• Figure 2 D-E: investigation of colocalization with Bre5 would help to understand how similar the yeast Clu1 granules are compared to the mammalian CLUH granules (Pla-Martin et al., 2020).
• Figure 8. This figure summarizes one of the most novel pieces of data about Clu1, the interaction with mRNAs via the ribosome. The way how panel A-C are represented is however a bit misleading. The Y axis in Figure B and C has the same amplitude as the one in A. Therefore, potential differences in Clu1-RNA pull-down in presence of EDTA or puromycin cannot be assessed. It is true that in presence of CHX there is much more pulled down RNA, but one cannot judge from these panels if there is any difference between Clu1 targets and controls also in the other conditions. The graphs should be modified and statistics added. In addition, RNAse treatment in panel L does not seem to have really worked.
• The authors should cite Vornlocher et al.. ( PMID: 10358023), who were the first to implicate Clu1 (Tif31) with translation.
• References 21 and 22 are the same.

Significance

The data reported in this manuscript are valuable, because they confirm an evolutionary conserved role of Clu1 in binding mRNAs for mitochondrial proteins and regulating their translation. It is also interesting that in yeast, similar to Drosophila and mammalian cells, Clu1 can form granular structures upon metabolic rewiring. A limitation of the study is that direct experiments to support the claim that Clu1 concentrates ribosomes engaged in translation are not provided. Furthermore, it is not clear what is the functional role of the Clu1 granules, since the proximity interactome and the binding of Clu1 to the polysomes is not affected by treatments that dissolve or stimulate granule formation. The study is of interest to a general cell biology audience.

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Reply to the reviewers

1. General Statements

We thank the editor for handling our manuscript and the reviewers for their constructive critiques. We are deeply convinced that the reviewers’ suggestions have substantially raised the quality and possible impact of our manuscript. We also like to thank the reviewers for their judgements that the subject of our manuscript is biologically and clinically significant and of high importance, and that our manuscript might help to increase focus and visibility for affected individuals.

New text passages in the manuscript are colored in red. Below is a point-by-point response to the reviewers’ comments.

2. Point-by-point description of the revisions

Response to reviewer 1 comments

Major comments

Point 1-1

The authors performed qRT-PCR validation for markers of differentiation and hypoxia, with a major absence of VEGF and HIF1a. The paper would be strengthened by mention of these factors, especially by qRT-PCR or Western blot.

We thank the reviewer for the suggestion to include the bona fide hypoxia markers Vegfa and Hif1-alpha. We followed the suggestion and performed qRT-PCR on Vegfa transcripts at each tested condition (Figs. 1A,2A,3A,4A,5A,5D,5I,5N). As Hif1α is rather regulated on protein than on transcript level, we followed the advice to perform Western blots. We analyzed Hif1α protein levels on proliferating cells and quantified by normalization to actin (Figs. 1B,C and 5 B,C).

Point 1-2

Please provide justification of selection 0.5% as their hypoxic condition or perhaps repeat experiments in a less extreme environment to see if their conclusions still hold true.

We admit that our approach to use 0.5% hypoxia was a drastic challenge for the cells. It should be noted, however, that physiologic oxygen levels during pregnancy at times drop to lower than 1% (Hansen et al, 2020; Ng et al, 2017). In the first place, we had used oxygen levels lower than this, because we had wanted to ensure that we can detect responses by bulk RNA-seq with a limited number of samples. As we had many conditions to compare, we did not want to use more than 3-4 samples per condition. The fact that the cells showed normal proliferation underscores the fact that 0.5% O2 per se was not so low that it would be overly stressful to the cells.

Nevertheless, we are very grateful to the reviewer for the suggestion to include a milder hypoxic condition. We chose 2% O2, because this equals the physiological oxygen concentration shortly before the onset of cranial neural crest cell (CNCC) differentiation. We could recapitulate the phenomenon of impaired differentiation to chondrocytes, osteoblasts and smooth muscle cells at these mild hypoxic conditions, as shown by qRT-PCR and immunofluorescence of typical markers (Figs. 5D-R). Moreover, the differentiation-specific induction of the two central hypoxia-attenuated risk genes associated with orofacial clefts that we had identified by our bioinformatic analyses at 0.5% O2 (Boc and Cdo1), was still observable at 2% O2 (Figs. S6C,D). Interestingly, in some rare cases, the attenuation of induction was lost or not as drastic as in 0.5% O2.

We are convinced that the experiments at 2% O2 strongly increased the relevance of our manuscript, because we thus detected that oxygen levels prevailing shortly before the onset of CNCC differentiation still can influence their differentiation. This leads to the conclusion that only slight decreases of intra-uterine oxygen levels indeed might interfere with correct differentiation of CNCC.

Point 1-3

Standard immunohistochemistry or histology of differentiated cells would strengthen the authors' claims of reduced differentiation under hypoxic conditions, e.g., Alcian blue, alk-phos or Alizarin red, and smooth muscle actin or other indicator.

We are grateful to the reviewer for the suggestion to include stainings of cells, as these stainings visualized the drastic effects of hypoxia on the cells. We performed immunofluorescent stainings against at least one marker protein for each differentiation paradigm. At 0.5% O2, each protein signals were nearly completely absent and cell morphology was disrupted (Figs. 2E,F, 3E, 4E). At 2% O2, we detected some more protein deposition than at 0.5%. Importantly, cells had retained their normal shape at mild hypoxia (Figs. 5H,M,R, S5A).

Point 1-4

The authors identify a few genes that appear down-regulated in all three differentiation conditions. If it is within the scope of the study, it would strengthen the claim of these genes' function to show the effect of knock-down or knock-out for validation.

We thank the reviewer for the suggestion of gene knock-down or knock-out in order to prove functional relevance of our findings. As this would have been too much effort and beyond the scope of our study, we rather followed the suggestion of reviewer 2 (cf. points 2-6, and 2-8) that headed to the same direction: we mined publicly available sequence data on orofacial development for gene expression or marks of active enhancers. We found robust expression of the two central hypoxia-attenuated OFC risk genes Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells with the help of a single cell RNA-seq dataset (Figs. 7C-E, S6B).

Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are grateful for the suggestion to circumvent gene knockouts by reviewer 2, as we think these data strongly emphasized the importance of our findings.

Point 1-5

Another major critique lies in the initial claim that proliferation of O9-1 cells is not significantly impacted by hypoxia. In figures 1E-H, photograms of the cells cultured 24 -72 hours and quantifications of live vs dead cells are shown as evidence for this argument. However, the increased density of cells in normoxic conditions may be a confounding variable in this assay. It would be interesting for the researchers to assess the percent of dead vs alive cells between normoxic and hypoxic conditions when the plates reach equivalent densities.

We apologize for the use of image sections from photographs with different cell densities. Of course, as demonstrated by our quantification, cell densities between 0.5% and 21% O2 in total were equal (cf. Figs. 1D,E). We therefore replaced the formerly used sections with new image sections with equal cell numbers.

We thank the reviewer for the suggestion to examine if cell numbers influence cell death rates. We followed this advice by several approaches: first, we seeded cells at different densities, incubated them for 72 h (the same time span where a minimal difference had been detected) and performed live/dead stainings (Fig. S1B). The seeding density did not affect percentages of dead cells and the values were in the same range as in our initial experiment (Fig. 1J). Moreover, we performed TUNEL stainings of apoptotic cells at different time points to have an additional readout of cell death (Figs. 1K,L). As expected, the percentages of TUNEL-positive cells were identical between hypoxic and normoxic cells at all analyzed time points.

We therefore concluded that hypoxia does not influence the rate of cell death of proliferating CNCC and accordingly specified our wording in the results section.

Point 1-6

At end of Fig 1 section authors attempt to tie phenotypes observed in a cell line in vitro to the complex biological processes. They are not comparable and in vivo models would be better suited for these types of comparisons.

We apologize for the overconfident wording in our manuscript. Of course, our in vitro experiments cannot fully simulate the complex developmental processes taking place in vivo. We therefore changed the text to a more careful formulation. Moreover, we kept the wording in the discussion section that we cannot exclude that in the in vivo situation proliferation of CNCC is also affected by low oxygen levels because nutrients might not be available in such excess as they are in cell culture.

Point 1-7

Fig 2: if qRT-PCR did not show statistically different results between experimental and control groups why move on to bulk RNA seq?

We apologize that the sentence about statistical significance was misleading. What we wanted to express is that there was only a little difference (if any at all) between differentiated cells at 0.5% O2 and proliferating cells at 0.5% O2 or 21% O2. For the sake of clarity and readability, we deleted this misleading sentence.

Point 1-8

Fig 5: hypoxia this intense is going to affect broad range of biological processes and genes. Finding a few genes that are affected in extreme hypoxia that are also risk genes is highly unlikely. How can the authors be assured that these overlaps are actually significant and not just by chance?

We thank the reviewer for the suggestion to test for statistical significance. We tested significance of the overlap of respective gene sets (nsOFC vs. hyp-a; OFC vs. hyp-a) by Fisher’s exact test. We included Venn diagrams depicting the overlap and present the exact p-values (Figs. S5C,D). In each case where overlap of genes occurred, p-values indicated significance.

Point 1-9

Would appreciate discussion on how examination of neural crest is relevant for OFC, as most animal models of OFC demonstrate the pathogenesis in embryonic epithelium or periderm, not in the neural crest. Defects in neural crest are associated with other congenital craniofacial anomalies such as craniosynostosis or complex (Tessier) clefts, not the typical orofacial cleft. Please revise rationale of study, interpretation of data and Discussion to specifically state how neural crest cells are involved in the pathogenesis of orofacial cleft.

We apologize for not pointing out enough the role of epithelial cells in the emergence of orofacial clefts. We revised our introduction, results and discussion sections in this regard and emphasized the role of epithelial cells. Importantly, we addressed the possible influence of the results gained in CNCC on epithelial cells by analyzing scRNA-seq data with the algorithm CellChat, as suggested by reviewer 2 (cf. point 2-8). We detected several cell communication pathways from CNCC to epithelial cells which contain components that are misexpressed upon hypoxia in our dataset (Figs. 7F-I). Therefore, during hypoxia, these pathways might influence epithelial cells and therefore indirectly cause orofacial clefts. We outlined this possible interplay in the discussion and briefly mentioned it in the abstract.

We have not discussed more strongly the role of CNCC in the emergence of OFC in the revised manuscript, because we did not want to put even more emphasis on this matter. Numerous studies have proven the contribution of cranial neural crest tissue to the emergence of orofacial clefts. This fact is also pointed out in several review articles about orofacial clefts. In most cases, this knowledge was achieved by mouse models, because tissue-specific conditional knockouts are feasible (in contrast to genetic studies on patients), usually via deletion with the Wnt1-Cre driver. Funato et al. give an excellent (but quite old) overview of mouse models in which the neural crest-specific knockout of a gene leads to emergence of OFC and lists 17 genes for which this is the case (Funato et al, 2015). Moreover, several recent studies also report on the emergence of orofacial clefts upon neural crest-specific deletion (Forman et al, 2024; Li et al, 2025). These include genes responsible for DNA methylation (Ulschmid et al, 2024), and a study on subunits of chromatin remodeling complexes that are necessary for correct transcription of their target genes, which was conducted by our group (Gehlen-Breitbach et al, 2023).

Minor comments

__Point 1-10 __

The author should replace "Final proof" in the introduction with "further evidence supporting."

We apologize for the incorrect wording. Of course, it is highly questionable if there is such a thing as final proof in life sciences. We re-phrased the text according to the reviewer’s suggestion.

Point 1-11

Authors are inconsistent when referring to Figures- sometimes they capitalize (i.e. 1J) and other times they leave lower case (i.e. 1i). Needs to be consistent throughout. Figures are not numbered.

We apologize for the inconsistency. We corrected the references to figures. Moreover, we apologize for the missing figure numbers. We also corrected this and included figure numbers.

Point 1-12

In figures authors would sometimes list 21% O2 first then 0.5% O2 or vice versa. (i.e. Fig on page 21 panels I, J, K). Needs to be consistent.

We again apologize for being inconsistent. We corrected the inconsistency in Fig. 1D. Now, 21% O2 is presented before/above 0.5% O2.

Point 1-13

Figures on pages 28, 29, 30 panel J and page 31 panel F: there is no legend on what the scale/measurement is for the difference in expression level other than it ranges from -1 to +3.

We thank the reviewer for the hint. We are aware that from the heatmaps we used one cannot infer relative expression rates of different genes or similar. If we would have considered expression strength of single genes, many of the gene-specific differing expression rates under the different conditions would have been hard to detect, as presentation would have been dominated by the differences in expression rates between genes. We therefore plotted gene-wise scaled expression.

We included an explanation of the procedure in the materials and methods section.

Point 1-14

Will the authors please comment on the one normoxic sample in Figure 1I that did not cluster with the others? Did this meet the standards to merit exclusion as an outlier?

We regret that the default scale of our plot of the principal component analysis is a bit misleading. This is the case because x-axis accounts for 80.3% of variance and y-axis only accounts for 6.1%. Therefore, the sample that might seem as an outlier actually met our standards. Nevertheless, we decided to keep the default scaling as is, in order not to embellish the graph (Fig. 1M).

Point 1-15

The authors refer to DEG as deregulated genes; while not strictly incorrect, the more standard usage is "differentially expressed genes." Please address.

We apologize for the incorrect explanation of the acronym. Of course, this was corrected in the revised manuscript.

Significance

This work on neural crest cells and hypoxia are biologically and clinically significant.

We are deeply grateful to the reviewer for considering our manuscript significant for both biologists and clinicians. We are convinced that the additional data we gathered in the course of the revision has significantly increased the importance of our work. Therefore, we once again express our gratitude to the reviewer for the valuable suggestions.

Response to reviewer 2 comments

Major comments

Point 2-1

The conclusions drawn from the experimental data are carefully formulated for the most part. One of the main concerns is that the cells were subjected to extreme hypoxic conditions, while it may be more biologically relevant to include a condition representing more mild hypoxia (e.g. 10%).

Please refer to the response to point 1-2.

Point 2-2

One of the opening claims regarding severe hypoxia only mildly affecting cell proliferation is not shown clearly, since no mitotic markers have been analyzed (i.e. KI67 or PCNA staining or a simple EdU incorporation assay). Thus, the claim that they assessed cell proliferation is not very convincing, even though cell death was analyzed.

We appreciate the reviewer’s suggestion to include a more thorough analysis of proliferation rates. We followed the advice and performed immunofluorescent stainings against Ki67 (accounting for cells in proliferative state) and phospho-histone H3 (accounting for cells undergoing mitosis). We performed this assay at different time points of culture in order to address the question if cell density might influence proliferation rates (Figs. 1F-H). Neither for Ki67 nor for pHH3 a difference was detected between 21% and 0.5% O2.

We are convinced that these analyses strengthened our initial findings and provide strong evidence that hypoxia does not influence proliferation rates of CNCC.

Point 2-3

Additionally, cellular morphology of the cells could be assessed (brightfield images), since previous studies observed that hypoxia can be an inducive factor in cranial neural crest and driving EMT (Scully et al. 2016; Barriga et al. 2013).

We thank the reviewer’s hint and followed the advice. We analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. S1C-F). As outlined in the results section, we did not detect a difference in these parameters between 21% and 0.5% O2.

We included the second reference mentioned by the reviewer (Barriga et al, 2013) additionally to Scully et al. 2016 that had already been cited.

Point 2-4

Furthermore, in the RNA seq analysis of chondrogenic fate biased cells the authors draw a conclusion based on the proximity of the samples on the PCA plot, which is not very convincing. More careful analysis of the bulk RNA seq data sets they have generated for key marker genes will be more convincing (for example, a heatmap with selected genes would be a helpful representation).

We apologize for the rash and inaccurate conclusion based on proximity on PCA plots. We are grateful to the reviewer for the suggestion to include heatmaps with selected marker genes. Following this advice, we generated heatmaps on our bulk RNA-seq data with the GO terms specific for each differentiation paradigm (Figs. S2F, S3F, S4F).

We are convinced that these maps are perfect additions to the heatmaps of the 200 top differentially-expressed genes that already had been included in the manuscript (Figs. 2K, 3J, 4J) and helped to strengthen our findings. For chondrocytes and smooth muscle cells, the new, GO-specific heatmaps perfectly recapitulated the phenomenon of hypoxia-attenuated induction. Interestingly, for osteoblasts, about half of the induced genes were hypoxia-attenuated, while the other half was induced stronger than under normoxia. This pointed to gene-specific mechanisms of hypoxia-dependent attenuation of transcription. Moreover, it shed light on a hypoxia-evoked complete dysregulation of transcriptional induction in osteoblasts, as nearly none of the genes was induced similar to normoxia.

__ __

Point 2-5

As mentioned above, a straight-forward and not time consuming experiment (given that it was assessed for a maximum of 72 hrs) would be to repeat the culture of NCCs and stain for mitotic markers, and quantify the number of positively stained cells over total cell numbers. Furthermore, it is not that demanding to add an experimental condition of less severe hypoxia in this assay.

We thank the reviewer for the suggestion and followed the advice (cf. point 2-2). The conducted experiments straightened our results, because the initially detected slight tendency to lower cell numbers at 0.5% O2 could thus be falsified: We did not detect any difference for Ki67 and pHH3 between 0.5% and 21% O2 at any analyzed time point (Figs. 1F-H). Moreover, percentages of dead or apoptotic cells at 0.5% O2 did not vary from 21% (Figs. 1I-L, S1B). As we could not detect any difference in proliferation between 21% and 0.5% O2, we skipped the analysis of proliferating cells at 2% O2.

Point 2-6

Without underestimating how time consuming this would be, a major lack of experimental validation of the key genes they identify as important across all conditions may be the limitation of the study (this would be the difference between correlation and a probable underlying mechanism). This can be circumvented by more extensive reference to in situ data sets from mouse or existing data sets of single cell and spatial transcriptomics. A suggested targeted knock-down (for example with siRNA, shRNA or CRISPR) to validate a few of the key genes revealed as important could take a few months, with an estimated cost up to 5,000 euros per targeted gene and replicate.

We thank the reviewer for the notion that targeted knockdowns are beyond the scope of our manuscript. We are deeply grateful for the reviewer’s constructive criticism and for the suggestion to analyze publicly available data sets in order to gather data depicting in vivo relevance of our identified central hypoxia-attenuated OFC risk genes Boc, Cdo1 and Actg2 (cf. point 1-4). We detected robust expression of Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells by reanalysis of a scRNA-seq dataset (Figs. 7C-E, S6B). This data comprised scRNA-seq of mouse embryonic maxillary prominence at stages E11.5 and E14.5 (Sun et al, 2023).

Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are deeply grateful for the suggestion, as we think these data strongly emphasize the importance of our findings.

Point 2-7

On methods, replicates and statistics: The experimental methods and approach are described efficiently and seem reproducible. All biological and technical replicates are of a minimum of N=3 from independent experiments and statistical tests have been run in all cases.

We thank the reviewer for the appreciation of our methodology, descriptions and statistical analyses.

Minor points

Point 2-8

One of the key implications of NCCs in palate formation is interaction with orofacial epithelial cells, which the authors also mention. It may be interesting to check if any signaling pathways involved in this crosstalk are affected under hypoxic conditions in their existing data sets of bulk RNA SEQ. This can be done by using available algorithms such as CellChat (Jin et al. 2021; Jin, Plikus, and Nie 2023), which has been reported to work also in bulk RNA seq data analysis (according to GitHub). The authors could mine the literature for existing RNA sequencing data that include osteoblasts, chondrocytes and epithelial cells (Ozekin, O'Rourke, and Bates 2023; Piña et al. 2023).

We are very grateful to the reviewer for this suggestion. Moreover, we like to thank the reviewer for mentioning exemplary references. We followed the advice by the methodology lined out in results and materials and methods sections: we applied the CellChat algorithm on a scRNA-seq dataset (Pina et al, 2023; Sun et al., 2023) to identify pathways containing components that are hypoxia-attenuated (and associated with a risk for OFC) in our bulk RNA-seq dataset (Figs. 7F-I). We did not use the datasets the reviewer had suggested, because the data were not available for us or the file format was not well-suited for the analysis with CellChat. Importantly, the dataset from Sun et al. has the following advantages over the suggested references: the complete maxillary prominence was used (instead of palatal shelves only), and different time points were included. Thus, we were able to follow the expression of genes of interest at different developmental stages before the onset of differentiation and after (Figs. 7C-E and S6B). By our approach, we identified several OFC-related pathways that contain hypoxia-attenuated components such as BMP and FGF signaling and deposition of collagen and fibronectin (Figs. 7F-I). Importantly, the named pathways (and others) send outgoing communication patterns to epithelial cells. Therefore, hypoxia-attenuated gene induction in CNCC could influence epithelial cells via these pathways.

We believe that the use of the CellChat algorithm has brought a deeper understanding of how hypoxia can have indirect consequences on the important topic of epithelial cells and thus could also evoke OFC. We therefore once again like to express our gratitude to the reviewer.

Point 2-9

Additionally, another process that may be affected is EMT (epithelial-to-mesenchymal-transition) and is possible to assess by re-analysis of bulk RNA-seq data while focusing on key genes implicated in this process (i.e. E-cadherin, vimentin, EpCAM, Snail, Twist, PRRX1).

We thank the reviewer for the advice. We followed the advice and analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. S1C-F) (cf. point 2-3). As we did not detect any differences between 21% and 0.5% O2, and because the cells we used for our analyses represent mesenchymal cells, i.e. cells that had already undergone EMT, we did not re-analyze our dataset with the focus on EMT.

Point 2-10

Lastly, when the authors report on the significantly up- or down-regulated genes, it may be interesting to categorize them by ligands, receptors, intracellular molecules and transcription factors (and use separate plots to visualize them). While a big focus of the manuscript are down-regulated genes, less emphasis was given in upregulated genes (other than the response to hypoxia gene module).

We thank the reviewer for the advice. Following this advice, we categorized genes according to Panther protein classes "intercellular signal molecule" (PC00207), "transmembrane signal receptor" (PC00197) and "gene-specific transcriptional regulator" (PC00264) and depicted the results with violin plots (Fig. S5B). We could not analyze intracellular molecules, because this protein class does not exist in the Panther database. We had not focused on the genes with stronger induction in hypoxic condition, because the number of genes was low in each differentiation paradigm (7 in chondrocytes, less than 30 in osteoblasts, none in smooth muscle cells) and the transcriptional changes were mostly not as drastic as for the attenuated genes. In order to achieve a broader overview of deregulated processes, we now included GO term analyses of genes downregulated during the differentiation regimes both at 21% and 0.5% O2 (Figs. S2D,E, S3D,E, S4D,E).

Point 2-11

The authors are referencing extensively and accurately existing studies in the field and the manuscript is exceptionally well-written, with only a few points of limited clarity or increased complexity. Such an example is when the authors refer to OFC risk genes, because it is not clearly stated how the referenced studies reached their conclusions (for example, are they mouse studies, do they involve mutants, are any of these studies based on GWAS on human cohorts). This matter would significantly improve the flow of the text and highlight the importance of the study and their findings.

We would like to thank the reviewer very much for the appreciation of our scientific writing. We apologize for not explaining exactly how our OFC risk gene lists had been curated. We included this information for both non-syndromic and other OFC risk genes at the respective sites in the results section. Moreover, we included the Human Phenotype Ontology terms that had been used in the search in the materials and methods section.

We thank the reviewer for this suggestion, as we agree that this information significantly highlights the importance of our findings.

Point 2-12

The figures could be redesigned to be more intuitive to interpret. For example, using violin plots and heatmaps, as discussed, and including references or re-analysis/re-use of existing spatial transcriptomics and in situs for marker genes.

In all cases where there is a comparison of gene expression levels, violin plots would be a better representation of up- and down-regulated genes (i.e. selected genes from Fig1K, comparison of gene expression between normoxic and hypoxic NCCs, Fig 2G when analyzing chondrogenesis and the respective analysis for osteoblasts and smooth muscle cells, as well as when comparing the three fate-biasing conditions to identify common genes that are misregulated).

We thank the reviewer for the advice and for the appreciation of the usage of heatmaps (Figs. 2K, 3J, 4J, 6F). Unfortunately, as the number of biological replicates is only three to four, the visualization of gene expression data from our bulk RNA-seq data with violin plots was not intuitive. We therefore retained the heatmaps rather than choosing bar graphs, because they are much clearer when presenting expression data of several to many genes. We included violin plots whenever possible due to high numbers of data points (Figs. S1C, S1D, S1E, S1F, S5B). Moreover, we added additional heatmaps to depict transcriptional changes of genes associated with GO terms with the various differentiation regimes (Figs. S2F, S3F, S4F). Unfortunately, we did not detect the three central hypoxia-attenuated genes in spatial transcriptomics data on craniofacial development. But we used scRNA-seq data of different stages of orofacial mouse tissue where we could identify expression of Boc and Cdo1 (cf. points 1-4 and 2-6). These data helped, together with other in vivo data to gain evidence for the in vivo function of Boc and Cdo1 during CNCC differentiation and helped to dismiss Actg2 as another central player.

Significance

Several pieces of evidence have pointed to hypoxia as an environmental factor contributing to congenital orofacial clefts, ranging from studies in mouse to observations in human. The authors are doing an excellent job in putting this information together and the question they are trying to answer is of high importance, given the prevalence of such congenital syndromes.

We are deeply grateful to the reviewer for the appreciation of our work and for classifying our research topic as highly important.

In terms of the methods and model employed, there are some limitations, related to the choice of a mouse cell line over one from human, the severe hypoxia induced (over a more mild), and the conditions of directed differentiation not allowing for simultaneous examination of more complex lineage transitions. The methods as a whole are not that up-to-date, given the single cell and multiplexed transcriptomic advances the last couple of decades, advanced bioinformatics that could be used in combination with in vitro lineage tracing methods.

We thank the reviewer for the honest evaluation of our methods, especially for the constructive suggestions that were given to address our hypotheses with more up-to-date methods and at milder hypoxic conditions. As outlined above, we followed the advice and re-analyzed existing scRNA-seq datasets (cf. points 2-6 and 2-8) and checked our central hypotheses at milder hypoxic conditions (cf. response to point 1-3).

We are deeply convinced that both significantly increased the biological relevance of our results, because we thus (1) gathered evidence for the in vivo function of Boc and Cdo1 and (2) were able to show that the phenomenon of hypoxia-attenuated gene induction still holds true at biologically relevant hypoxic conditions.

The audience this work will reach are neural crest experts, developmental biologists, and potentially clinical doctors. The general public outreach of such a paper is also diverse, as more focus and visibility is required for the individuals affected by those syndromes and their families.

We thank the reviewer for the judgement that our manuscript will not only reach neural crest experts, but also developmental biologists in general and potentially also clinicians. We are very much pleased that the reviewer shares our opinion that affected individuals should be more in the focus of public attention. We like to express our gratitude for the judgement that our manuscript might help to increase focus and visibility for them.

References

Barriga EH, Maxwell PH, Reyes AE, Mayor R (2013) The hypoxia factor Hif-1α controls neural crest chemotaxis and epithelial to mesenchymal transition. The Journal of cell biology 201: 759-776, 10.1083/jcb.201212100.

Forman TE, Sajek MP, Larson ED, Mukherjee N, Fantauzzo KA (2024) PDGFRα signaling regulates Srsf3 transcript binding to affect PI3K signaling and endosomal trafficking. Elife 13, 10.7554/eLife.98531.

Funato N, Nakamura M, Yanagisawa H (2015) Molecular basis of cleft palates in mice. World journal of biological chemistry 6: 121-138, 10.4331/wjbc.v6.i3.121.

Gehlen-Breitbach S, Schmid T, Fröb F, Rodrian G, Weider M, Wegner M, Gölz L (2023) The Tip60/Ep400 chromatin remodeling complex impacts basic cellular functions in cranial neural crest-derived tissue during early orofacial development. International Journal of Oral Science 15: 16, 10.1038/s41368-023-00222-7.

Hansen JM, Jones DP, Harris C (2020) The Redox Theory of Development. Antioxid Redox Signal 32: 715-740, 10.1089/ars.2019.7976.

Li D, Tian Y, Vona B, Yu X, Lin J, Ma L, Lou S, Li X, Zhu G, Wang Y et al (2025) A TAF11 variant contributes to non-syndromic cleft lip only through modulating neural crest cell migration. Hum Mol Genet 34: 392-401, 10.1093/hmg/ddae188.

Ng KYB, Mingels R, Morgan H, Macklon N, Cheong Y (2017) In vivo oxygen, temperature and pH dynamics in the female reproductive tract and their importance in human conception: a systematic review. Human Reproduction Update 24: 15-34, 10.1093/humupd/dmx028.

Pina JO, Raju R, Roth DM, Winchester EW, Chattaraj P, Kidwai F, Faucz FR, Iben J, Mitra A, Campbell K et al (2023) Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis. Nature communications 14: 5687, 10.1038/s41467-023-41349-9.

Sun J, Lin Y, Ha N, Zhang J, Wang W, Wang X, Bian Q (2023) Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence. J Genet Genomics 50: 676-687, 10.1016/j.jgg.2023.02.008.

Ulschmid CM, Sun MR, Jabbarpour CR, Steward AC, Rivera-González KS, Cao J, Martin AA, Barnes M, Wicklund L, Madrid A et al (2024) Disruption of DNA methylation-mediated cranial neural crest proliferation and differentiation causes orofacial clefts in mice. Proc Natl Acad Sci U S A 121: e2317668121, 10.1073/pnas.2317668121.

Author response:

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

We are disappointed that the reviewers do not acknowledge that our data constitute a major step forward for the field. We will prepare a revised version that takes care of the remaining small issues concerning the technical descriptions and a detailed response to the current round of comments. We will also add a summary of the major new findings of our study.

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

We appreciate the time of the reviewers and their detailed comments, which have helped to improve the manuscript.

Our study presents the largest systematic dataset so far on the evolution of sex-biased gene expression in animals. It is also the first that explores the patterns of individual variation in sex-biased gene expression and the SBI is an entirely new procedure to directly visulize these variance patterns in an intuitive way.

Also, we should like to point out that our study contradicts recent conclusions that had suggested that a substantial set of sex-biased genes has conserved functions between humans and mice and that mice can therefore be informative for gender-specific medicine studies. Our data suggest that only a very small set of genes are conserved in their sex-biased expression between mice and humans in more than one organ.

In the revised version we have made the following major updates:

- added a rate comparison of gene regulation turnover between sex-biased and non-sex-biased genes

- added additional statistics to the variance comparisons and selection tests

- added a regulatory module analysis that shows that much of the gene turnover happens within modules

- added a mosaic pattern analysis that shows the individual complexity of sex-biased patterns

- extended introduction and discussion

Reviewer #1 (Public Review):<br /> The authors describe a comprehensive analysis of sex-biased expression across multiple tissues and species of mouse. Their results are broadly consistent with previous work, and their methods are robust, as the large volume of work in this area has converged toward a standardized approach.

I have a few quibbles with the findings, and the main novelty here is the rapid evolution of sex-biased expression over shorter evolutionary intervals than previously documented, although this is not statistically supported. The other main findings, detailed below, are somewhat overstated.

(1) In the introduction, the authors conflate gametic sex, which is indeed largely binary (with small sperm, large eggs, no intermediate gametic form, and no overlap in size) with somatic sexual dimorphism, which can be bimodal (though sometimes is even more complicated), with a large variance in either sex and generally with a great deal of overlap between males and females. A good appraisal of this distinction is at . This distinction in gene expression has been recognized for at least 20 years, with observations that sex-biased expression in the soma is far less than in the gonad.

For example, the authors frame their work with the following statement:

"The different organs show a large individual variation in sex-biased gene expression, making it impossible to classify individuals in simple binary terms. Hence, the seemingly strong conservation of binary sex-states does not find an equivalent underpinning when one looks at the gene-expression makeup of the sexes"

The authors use this conflation to set up a straw man argument, perhaps in part due to recent political discussions on this topic. They seem to be implying one of two things. a) That previous studies of sex-biased expression of the soma claim a binary classification. I know of no such claim, and many have clearly shown quite the opposite, particularly studies of intra-sexual variation, which are common - see https://doi.org/10.1093/molbev/msx293, https://doi.org/10.1371/journal.pgen.1003697, https://doi.org/10.1111/mec.14408, https://doi.org/10.1111/mec.13919, https://doi.org/10.1111/j.1558-5646.2010.01106.x for just a few examples. Or b) They are the first to observe this non-binary pattern for the soma, but again, many have observed this. For example, many have noted that reproductive or gonad transcriptome data cluster first by sex, but somatic tissue clusters first by species or tissue, then by sex (https://doi.org/10.1073/pnas.1501339112, https://doi.org/10.7554/eLife.67485)

Figure 4 illustrates the conceptual difference between bimodal and binary sexual conceptions. This figure makes it clear that males and females have different means, but in all cases the distributions are bimodal.

I would suggest that the authors heavily revise the paper with this more nuanced understanding of the literature and sex differences in their paper, and place their findings in the context of previous work.

We are sorry that our introduction seems to have been too short to make our points sufficiently clear. Of course, overlapping somatic variation has been shown for morphological characters, but we were aiming to assess this at the sex-biased transcriptome level. Previous studies looking at sex-biased genes were usually limited by the techniques that were available at their times, resulting in a focus on gonads in most studies and almost all have too few individuals included to study within-group variation. We detail this below for the papers that are mentioned by the referee. In view of this, we cite them now as examples for the prevalent focus on gonadal comparisons in most studies. Only Scharmann et al. 2021 on plant leaf dimorphism is indeed relevant for our study with respect to its general findings and we make now extensive reference to it. In addition, we have generally modified the introduction and substantially extended the discussion to make our points clear.

Snell-Rood 2010: the paper focuses on sex-specific morphological structures in beetles. It samples six somatic tissues for four individuals each of each class. Analysis is done via microarray hybridizations. While categorial differences were traced, variability between individuals was not discussed. By today´s standards, microarrays have anyway too much technical variability to even consider such a discussion.

Pointer et al. 2013: this paper studies three sexual phenotypes in a bird species, females, dominant males and subordinate males. Tissues include telencephalon, spleen and left gonad. The focus of the analysis is on the gonads, since only few sex-biased genes were found in spleen and brain (according to suppl. Table S1, 0 for the spleen and 2 for the brain). No inferences could be made on somatic variation.

Harrison 2015: this paper focuses on gonads plus spleen in six bird species with between 2-6 individuals for each sex collected. In the spleen, only one female biased gene and no male biased gene was detected. Hence, the data do not allow to infer patterns of somatic variation.

Dean et al. 2016: this paper compares four categories of fish caught around nests, with four to seven individuals per category. Only gonads were analyzed, hence no inferences could be made about somatic variability between individuals.

Cardoso et al. 2017: this paper test categories of fish with alternative reproductive tactics based on brain transcriptomes. While it uses 9-10 individuals per category, it uses pools for sequencing with two pools per category. This does not allow to make any inference on individual variation.

Todd et al 2017: this paper focuses on three categories of a fish species, females and dominant and sneaker males. It uses brain and gonads as samples with five individuals each for each category. For the brain, more different genes were found between the two types of males, rather than between females and males (3 and 9 respectively). The paper focuses on individual gene descriptions and does not mention somatic variation.

Scharmann 2021: the paper focuses on 10 species of plants with sexually dimorphic leafs. 5-6 individuals were sampled per sex. The major finding is that sex-biased gene expression does not correlate with the degree of sexual dimorphism of the leafes. The study shows also a fast evolution of sex-biased expression and states that signatures of adaptive evolution are weak. But it does not discuss variance patterns within populations.

(2) The authors also claim that "sexual conflict is one of the major drivers of evolutionary divergence already at the early species divergence level." However, making the connection between sex-biased genes and sexual conflict remains fraught. Although it is tempting to use sex-biased gene expression (or any form of phenotypic dimorphism) as an indicator of sexual conflict, resolved or not, as many have pointed out, one needs measures of sex-specific selection, ideally fitness, to make this case (https://doi.org/10.1086/595841, 10.1101/cshperspect.a017632). In many cases, sexual dimorphism can arise in one sex only without conflict (e.g. 10.1098/rspb.2010.2220). As such, sex-biased genes alone are not sufficient to discriminate between ongoing and resolved conflict.

We imply sexual conflict as a driver of genomic divergence patterns in a similar way as it has been done by many authors before (e.g. Mank 2017a, Price et al. 2023, Tosto et al. 2023). While we fully appreciate the point of the referee, we do not really see where we deviate from the standard wording that is used in the context of genomic data. In such data, it is of course usually assumed that they represent solved conflicts (Figure 1D in Cox and Calsbeek) where selection differentials would not be measurable anyway. (Please note also that the phylogenetic approach used in Oliver and Monteiro 2010 becomes rather problematic in view of introgressive hybridization patterns in butterflies), We have extended the discussion to address this.

(3) To make the case that sex-biased genes are under selection, the authors report alpha values in Figure 3B. Alpha value comparisons like this over large numbers of genes often have high variance. Are any of the values for male- female- and un-biased genes significantly different from one another? This is needed to make the claim of positive selection.

Sorry, we had accidentally not included the statistics in the final version of the figure. We have added this now in the supplementary table but have also generally changed the statistical approach and the design of the figure.

Reviewer #2 (Public Review):

The manuscript by Xie and colleagues presents transcriptomic experiments that measure gene expression in eight different tissues taken from adult female and male mice from four species. These data are used to make inferences regarding the evolution of sex-biased gene expression across these taxa. The experimental methods and data analysis are appropriate; however, most of the conclusions drawn in the manuscript have either been previously reported in the literature or are not fully supported by the data.

We are not aware of any study that has analyzed somatic sex-biased expression in such a large and taxonomically well resolved closely related taxa of animals. Only the study by Scharman et al. 2021 on plant leaves comes close to it, but even this did not specifically analyze the intragroup variation aspects. Of course, some of our results confirm previous conclusions, but we should still like to point out that they go far beyond them.

There are two ways the manuscript could be modified to better strengthen the conclusions.

First, some of the observed differences in gene expression have very little to no effect on other phenotypes, and are not relevant to medicine or fitness. Selectively neutral gene expression differences have been inferred in previous studies, and consistent with that work, sex-biased and between-species expression differences in this study may also be enriched for selectively neutral expression differences. This idea is supported by the analysis of expression variance, which indicates that genes that show sex-biased expression also tend to show more inter-individual variation. This perspective is also supported by the MK analysis of molecular evolution, which suggests that positive selection is more prevalent among genes that are sex-biased in both mus and dom, and genes that switch sex-biased expression are under less selection at the level of both protein-coding sequence and gene expression.

We have now revisited these points by additional statistical analysis of the variance patterns and an extended discussion under the heading "Neutral or adaptive?". 

As an aside, I was confused by (line 176): "implying that the enhanced positive selection pressure is triggered by their status of being sex-biased in either taxon." - don't the MK values suggest an excess of positive selection on genes that are sex-biased in both taxa?

There are different sets of genes that are sex-biased in these two taxa - hence this observation is actually a strong argument for selection on these genes. We have changed the correspondiung text to make this clearer.

Without an estimate of the proportion of differentially expressed genes that might be relevant for broader physiological or organismal phenotypes, it is difficult to assess the accuracy and relevance of the manuscript's conclusions. One (crude) approach would be to analyze subsets of genes stratified by the magnitude of expression differences; while there is a weak relationship between expression differences and fitness effects, on average large gene expression differences are more likely to affect additional phenotypes than small expression differences.

We agree that it remains a challenge to show functional effects for the sex-biased genes. The argument that they should have a function is laid out above (and stated in many reviews on the topic). To use the expression level as a proxy of function does not seem justified, given the current literature. For example, genes that are highly conected in modules are not necessrily highly expressed (e.g. transcription factors). Also, genes may be highly expressed in a rare cell type of an organ and have an important funtion there, but this would not show up across the RNA of the whole organ. The most direct functional relationship between sex-biased expression and phenotype comes from the human data in Naqvi et al. 2019 - which we had cited.

Another perspective would be to compare the within-species variance to the between-species variance to identify genes with an excess of the latter relative to the former (similar logic to an MK test of amino acid substitutions).

Such an analysis was actually our intial motivation for this study. However, the new (and surprising!) result is that the status of being sex-biased shows such a high turnover that not many genes are left per organ where one could even try to make such a test. However, we have extended the variance analysis with reciprocal gene sets (as we had done it for the MK test) and extended the discussion on the topic, including citation of our prior work on these questions.

Second, the analysis could be more informative if it distinguished between genes that are expressed across multiple tissues in both sexes that may show greater expression in one sex than the other, versus genes with specialized function expressed solely in (usually) reproductive tissues of one sex (e.g. ovary-specific genes). One approach to quantify this distinction would be metrics like those used defined by [Yanai I, et al. 2005. Genome-wide midrange transcription profiles reveal expression-level relationships in human tissue specification. Bioinformatics 21:650-659.] These approaches can be used to separate out groups of genes by the extent to which they are expressed in both sexes versus genes that are primarily expressed in sex-specific tissue such as testes or ovaries. This more fine-grained analysis would also potentially inform the section describing the evolution/conservation of sex-biased expression: I expect there must be genes with conserved expression specifically in ovaries or testes (these are ancient animal structures!) but these may have been excluded by the requirement that genes be sex-biased and expressed in at least two organs.

Given that our study focuses on somatic sex-biased genes, we refrain from a comparative analysis of genes that are only expressed in the sex-organs in this paper. With respect to sharing of sex-biased gene expresssion between the somatic tissues, we show in Figure 8 that there are only very few of them (8 female-biased and 3 male-biased). A separate statistical treatment is not possible for this small set of genes.

There are at least three examples of statements in the discussion that at the moment misinterpret the experimental results.

The discussion frames the results in the context of sexual selection and sexually antagonistic selection, but these concepts are not synonymous. Sexual selection can shape phenotypes that are specific to one sex, causing no antagonism; and fitness differences between males and females resulting from sexually antagonistic variation in somatic phenotypes may not be acted on by sexual selection. Furthermore, the conditions promoting and consequence of both kinds of selection can be different, so they should be treated separately for the purposes of this discussion.

We cannot make such a distinction for gene expression patterns - and we are not aware that this was done before in the literature (except gene expression was directly linked to a morphological structure). We have updated this discussion accordingly.

The discussion claims that "Our data show that sex-biased gene expression evolves extremely fast" but a comparison or expectation for the rate of evolution is not provided. Many other studies have used comparative transcriptomics to estimate rates of gene expression evolution between species, including mice; are the results here substantially and significantly different from those previous studies? Furthermore, the experimental design does not distinguish between those gene expression phenotypes that are fixed between species as compared to those that are polymorphic within one or more species which prevents straightforward interpretation of differences in gene expression as interspecific differences.

Our statement was in relation to the comparison between somatic and gondadal gene turnover, as well as the comparison to humans. We have now included an additional analysis for a direct comparison with non-sex-biased genes in the same populations (Figure 2B). Note that gene expression variances cannot get fixed anyway, they can only become different in average and magnitude.

The conclusion that "Our results show that most of the genetic underpinnings of sex differences show no long-term evolutionary stability, which is in strong contrast to the perceived evolutionary stability of two sexes" - seems beyond the scope of this study. This manuscript does not address the genetic underpinnings of sex differences (this would involve eQTL or the like), rather it looks at sex differences in gene expression phenotypes.

This comes back to the points discussed above about the validity to infer function from sex-biased expression. We have updated the text to clarify this.

Simply addressing the question of phenotypic evolutionary stability would be more informative if genes expressed specifically in reproductive tissues were separated from somatic sex-biased genes to determine if they show similar patterns of expression evolution.

Our study is generally focused on somatic gene expression. The comparison with reproductive tissues serves merely as a reference. Since they are of course very different tissues, they should not be compared with each other in the same way. We have now specifically addressed this point in the discussion.

Reviewer #3 (Public Review):

This manuscript reports some interesting and important patterns. The results on sex-bias in different tissues and across four taxa would benefit from alternative (or additional) presentation styles. In my view, the most important results are with respect to alpha (fraction of beneficial amino acid changes) in relation to sex-bias (though the authors have made this as a somewhat minor point in this version).

The part that the authors emphasize I don't find very interesting (i.e., the sexes have overlapping expression profiles in many nongonadal tissues), nor do I believe they have the appropriate data necessary to convincingly demonstrate this (which would require multiple measures from the same individual).

This is the first study that reports such overlaps and we show that this is not always the case (e.g. liver and kidney data in mice). We are not aware of any preditions of how such patterns would look like and how they would evolve - why should such a new finding not be interesting? Concerning the appropriateness of the data we do not agree with the point the referee makes - see response below.

This study reports several interesting patterns with respect to sex differences in gene expression across organs of four mice taxa. An alternative presentation of the data would yield a clearer and more convincing case that the patterns the authors claim are legitimate.

I recommend that the authors clarify what qualifies as "sex-bias".

This is defined by the statistical criteria that we have applied, following the general standard of papers on this topic.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) "However, already Darwin has pointed out that the phenotypes of the sexes should evolve fast". I think the authors mean that Darwin was quick to point out that sex-specific phenotypes evolve quickly".

We have modified this text part.

(2) Non-gonadal is more often referred to as somatic. I would encourage the authors to use this more common term for accessibility.

We have adopted this term

(3) Figure 5 is interesting, however, it is difficult to know whether the decreased bimodality in humans compared to mice is biological or technical due to the differences in the underlying data. For example, the mouse samples tightly controlled age and environmental conditions within each species. It is not possible to do that with human samples, and there are very good reasons to think that these factors will affect variance in both sexes.

Yes, this is certainly true and we know this also from other comparative data between mice and humans. Still, this is human reality vs mouse artificialness. We pick this now up in the discussion.

(4) Line 273. The large numbers of cells needed for single-cell analysis require that most studies pool multiple samples, however these pools are helpful in themselves. This approach was used by https://doi.org/10.1093/evlett/qrad013 to quantify the degree of sex-bias within cell types across multiple tissues and to compare how bulk and single-cell sex-bias measures compare. Sex-bias in some somatic cell types was very high, even when bulk sex-bias in those tissues was not. This suggests that the bulk data the authors use in this study may in fact obscure the pattern of sex-bias.

Yes, we agree, and this is exactly how we did the analysis and interpretation, based on the cited paper.

(5)- Line 379 "Total RNAs were" should be "Total RNA was"

Corrected

References cited in this review and which should be included in the manuscript :

Sam L Sharpe, Andrew P Anderson, Idelle Cooper, Timothy Y James, Alexandra E Kralick, Hans Lindahl, Sara E Lipshutz, J F McLaughlin, Banu Subramaniam, Alicia Roth Weigel, A Kelsey Lewis, Sex and Biology: Broader Impacts Beyond the Binary, Integrative, and Comparative Biology, Volume 63, Issue 4, October 2023, Pages 960-967.

Included

Masculinization of Gene Expression Is Associated with Exaggeration of Male Sexual Dimorphism Pointer MA, Harrison PW, Wright AE, Mank JE (2013) Masculinization of Gene Expression Is Associated with Exaggeration of Male Sexual Dimorphism. PLOS Genetics 9(8): e1003697.

Included

Erica V Todd, Hui Liu, Melissa S Lamm, Jodi T Thomas, Kim Rutherford, Kelly C Thompson, John R Godwin, Neil J Gemmell, Female Mimicry by Sneaker Males Has a Transcriptomic Signature in Both the Brain and the Gonad in a Sex-Changing Fish, Molecular Biology and Evolution, Volume 35, Issue 1, January 2018, Pages 225-241.

Included

Cardoso SD, Gonçalves D, Goesmann A, Canário AVM, Oliveira RF. Temporal variation in brain transcriptome is associated with the expression of female mimicry as a sequential male alternative reproductive tactic in fish. Mol Ecol. 2018; 27: 789-803.

Included

Dean, R., Wright, A.E., Marsh-Rollo, S.E., Nugent, B.M., Alonzo, S.H. and Mank, J.E. (2017), Sperm competition shapes gene expression and sequence evolution in the ocellated wrasse. Mol Ecol, 26: 505-518.

Included

Emilie C. Snell‐Rood, Amy Cash, Mira V. Han, Teiya Kijimoto, Justen Andrews, Armin P. Moczek, DEVELOPMENTAL DECOUPLING OF ALTERNATIVE PHENOTYPES: INSIGHTS FROM THE TRANSCRIPTOMES OF HORN‐POLYPHENIC BEETLES, Evolution, Volume 65, Issue 1, 1 January 2011.

Not included, since its technical approach is not really comparable

Harrison PW, Wright AE, Zimmer F, Dean R, Montgomery SH, Pointer MA, Mank JE (2015) Sexual selection drives evolution and rapid turnover of male gene expression. Proceedings of the National Academy of Sciences, USA 112: 4393-4398.

Included

Mathias Scharmann, Anthony G Rebelo, John R Pannell (2021) High rates of evolution preceded shifts to sex-biased gene expression in Leucadendron, the most sexually dimorphic angiosperms eLife 10:e67485.

Included

Sexually Antagonistic Selection, Sexual Dimorphism, and the Resolution of Intralocus Sexual Conflict. Robert M. Cox and Ryan Calsbeek , The American Naturalist 2009 173:2, 176-187.

Included

Ingleby FC, Flis I, Morrow EH. Sex-biased gene expression and sexual conflict throughout development. Cold Spring Harb Perspect Biol. 2014 Nov 6;7(1):a017632.

Included

Oliver JC, Monteiro A 2011. On the origins of sexual dimorphism in butterflies. Proc Biol Sci 278: 1981-1988.

Included

Iulia Darolti, Judith E Mank, Sex-biased gene expression at single-cell resolution: cause and consequence of sexual dimorphism, Evolution Letters, Volume 7, Issue 3, June 2023, Pages 148-156.

Included

Reviewer #2 (Recommendations For The Authors):

I am concerned the smoothed density plots in Figure 4 may be providing a misleading sense of the distributions since each distribution is inferred from only 9 values. A boxplot might better represent the data to the reader.

Boxplots with 9 values are much more difficult to interpret for a reader, this is the very reason why one tends to smoothen them. In this way, they also become similar to the standard plots that are used for showing morphological variation between the sexes. Note that the original data are availble for the individual values, if these are of special interest in some cases. In addition, our new “mosaic” analysis (Figure 6) provides another presentation for readers.

Line 235: "the overall numbers are lower" I assume this is the number of genes included in the analyses, but this should be explicitly stated.

Clarified in the text

The analysis of gene expression from different brain regions in control individuals from the Alzheimer's study (line 273) suffers from low power and it is not clear to me how much taking samples from different brain regions eliminates the issue of different cell types within a sample (the stated motivation for this analysis). While I support publishing negative results, this section does not feel like it adds much to the manuscript and could be cut in my opinion.

This is actually a study on single cell types, differentiating each of them. We are sorry that the text was apparently unclear about this. Given that there are studies that show the importance of looking at single cell data, we still think that is a suitable analysis. We have updated the text to make it clearer.

It might be useful to separate out X-linked genes from autosomal genes to see if they show consistent patterns with regard to sex-bias.

We have added this information in suppl. Table S2 and include some description in the text.

Reviewer #3 (Recommendations For The Authors):

Comments follow the order of the Results section:

(1) The latter half of this line in the Methods is too vague to be helpful: "We have explored a range of cutoffs and found that a sex-bias ratio of 1.25-fold difference of MEDIAN expression values combined with a Wilcoxon rank sum test and Benjamini-Hochberg FDR correction (using FDR <0.1 as cutoff) (Benjamini & Hochberg, 1995) yields the best compromise between sensitivity and specificity". What precisely is meant by "the best compromise between sensitivity and specificity"?

We explain now that this was based on pre-tests with comparing randomized with actual data. However, we agree that this is in the end a subjective decision, but there is no single standard used in the literature, especially when somatic organs are included. We consider our criteria as rather stringent.

(2) The 1.25 number for sex bias is, ultimately, an arbitrary cut-off. It is common in this literature to choose some arbitrary level and, in this sense, the authors are following common practice. The choice of 1.25 should be stated in the main text as it is a lower (but not reasonable) value than has been used in many other papers.

It is not only the cutoff, but also the Wilcoxon test and FDR correction that defines the threshold. See also comment above.

(3) In truth, dimorphism is continuous rather than discrete (i.e, greater or less than 1.25 fold different). Thus, where possible it would be useful to present results in a fashion that allows readers to see the continuous range of ratios rather than having to worry about whether the patterns are due to the rather arbitrary choices of how genes were binned into sex-bias categories.

It is necessary to work with cutoffs in such cases - and this is the usual practice for any such paper. But we provide now in Figure 1 Figure supplement 1 plots with the female/male ratio distributions.

a) Number of genes that are female- / male-biased. I would like to be able to see a version of Figure 1 showing the full distribution of TPM ratios rather than bar graphs of the numbers of (arbitrarily defined) female- and male-biased genes. This will be, of course, a larger figure (a full distribution rather than 2 bars for each species for each organ) and so could be relegated to Supplementary Material (assuming the message of that figure is the same as the current Figure 1).

This is a very unusual request, given that no other paper has done this either. It would indeed result in a non-managable figure size, or many separate figures that would be difficult to scrutinize. Note that there would be one plot of two (female and male) TPM distributions for each sex-biased gene in each organ and each taxon, leading to hundreds of thousands of plots. We think that by providing the general distributions as plots (see above), and the original data as supplements is sufficient.

b) Turnover of genes with sex bias. This important issue is addressed in Figure 2. First, it is not precisely clear what "percentages of sums of shared genes for any pairwise comparison" in Figure 2 legend means and no further detail is given in the Methods; this must be made clearer or the info in Figure 2 is meaningless. Regardless, this approach again relies heavily on the arbitrary criterion of defining sex-bias. Thus, I would like to see correlation plots of the log(TPM ratio) between taxa as done in the classic multispecies fly paper of Zhang et al. 2007. In Figure 2 it is quite clear that male-biased genes evolve with respect to sex bias more rapidly than female-biased genes.

We have provided a better explanation of this analysis. Note that the Zhang et al. 2007 paper was not focussing on somatic expression and covers a much broader evolutionary spectrum. Hence, the results are not comparable. Also, we doubt that it would be so helpful to generate a huge figure with all these plots.

(4) Is there a simpler explanation for the results in the "Variance patterns" section? The total variance for any variable can be decomposed into the variance within and among "groups". If we use "sex" as the group, then there are genes - labelled sex-biased genes - that were identified as such, in essence, because they have high among-group variance. Given that we then know a priori at the start of this section of sex-biased genes have high among-group variance, is it at all surprising that they have higher total variance than the unbiased genes (which we know a priori have low among-group variance)? Perhaps I misunderstood the point of this section. Maybe it would be more meaningful to examine the WITHIN-SEX variance (averaged across the two sexes) instead.

We did calculate IQR/median (“normalized variance”) with the nine mice for each gene and each sex in each organ, hence sex is not a variance factor in this calculation. The algorithm steps are outlined in suppl. Table S17. We have now also added a variance calculation for reciprocal gene sets and added an extended discussion of these results.

(5) Analysis of alpha for sex-biased genes. This was the most interesting part of this manuscript to me.

(a) More information about what SNVs were used is required.

i. Were only sites where SPR was fixed used? (If not, how was polarization done?)

ii. Were sites only considered diverged if they were fixed for different bases in DOM and MUS? (If not, what was the criteria?)

iii. Using, say, DOM as the focal species, a site must be polymorphic in DOM. But did its status (polymorphic/fixed) in MUS matter?

We have added a more detailed description on this in the Methods section. For the direct answers of the three questions: (i) yes; (ii) yes; (iii) no, considering that DOM and MUS are two subspecies of Mus musculus separating recently, a variant might occur before separating and there might be gene flow between them.

(b) A particularly interesting part of the analysis is the investigation of alpha for genes that are NOT sex-biased in one taxa but are sex-biased in the other. At the moment (as I understand it), alpha is only calculated for these genes in the taxa where they are NOT sex-biased (and this alpha value can be compared to the alpha of sex-biased genes and of unbiased genes in that taxa). I would like to see both sets of genes (set 1: those sex-biased in MUS and not in DOM; set 2: those sex-biased DOM and not in MUS) analyzed in each of the 2 species, with results presented in a 2x2 table.

By definition of these categories, these genes are sex-biased in the respective other taxon, hence the values are already in the table. They are named as “reciprocal”.

(c) No confidence intervals are given for the alpha values, despite the legend of Figure 3 referring to them.

These were accidentally omitted - we now included the full table in suppl. Table S6; Figure 3 was modified to show violin plots of the bootstrap distributions

The author's creation and use of a "sex-bias index" (SBI). My greatest skepticism of this manuscript is with respect to the value of their manufactured index, SBI. Of course, it is possible to create such an index but does this literature really need this index or does this just add to the "clutter" in the literature for this field? Is it helping to illuminate important patterns? This index is presumably some attempt to quantify how "male-like" or "female-like" overall expression is for a given individual (for a given organ). It is calculated as SBI = (MEDIAN of all female-biased tpm) - (MEDIAN of all male-biased tpm).

(6) A main result that comes from this is that the sexes tend to overlap for these values for most nongonad tissues but are clearly distinct for gonadal tissues. I do not think this result would come as a surprise to almost anyone and I'm far from convinced that this metric is a good way to quantify that point. Let's consider testes vs. ovaries. Compared to non-gonadal tissues, I am reasonably certain that not only are there many more genes that are classified as "sex-biased" in gonads but also the magnitude of sex-bias among these genes is typically much greater than it is for the so-called sex-biased genes in nongonadal tissue (density plots requested in #3a would make this clear). In other words, males and females are, on average, very different with respect to expression in gonads so even allowing for variation within each sex will still result in a clear separation of all individuals of the two sexes. In contrast, males and females are, on average, much less different in, say, heart so when we consider the variation within each sex, there is overlap. One could imagine a variety of different metrics which could be used to make this point. The merits of "SBI" are unclear. It is a novel metric and its properties are poorly understood. (A simple alternative would be looking at individual scores along the axis separating mean/median males and females; almost certainly, for gonads, this would be very similar to PC scores for PC1.)

As throughout the text, we use gonadal comparisons only as general reference, not as the main result. The main result that we are stressing is the fast turnover of these patterns, including from binary to overlapping for kidney and liver in mouse. We consider this as a new finding. If it comes "not to a surprise to anyone", isn´t it great that one does not have to guess anymore but has finally real data on this?

We have now also added a mosaic analysis to show that the SBI can be used as summary measure in different presentations.

The use of a single PC axis is no good alternative, since it throws away the information from the other axis.

We have now included an explicit discussion on the usefulness of the SBI.

(7) For simplicity, let's assume all males are identical and all females are identical. Let's imagine that heart and kidney have the exact same set of sex-biased genes. There are 20 female-biased genes; they all happen to be identical in expression level (within tissue) and look like this:

Female TPM Male TPM TPM ratio (F:M)

Heart 4 2 2

Kidney 40 20 2

And there are 20 male-biased genes that look like this:

Female TPM Male TPM TPM ratio (F:M)

Heart 1 3 1/3

Kidney 10 30 1/3

Most people would describe these two tissues as equally sex-biased.

However, the SBIs would be:

Female SBI Male SBI Sex difference (F - M)

Heart 4-1 = 3 2 - 3 = -1 4

Kidney 40-10 =30 20-30 = -10 40

Is it a desirable property that by this metric these two tissues have wildly different SBI values for each sex as well as for the difference between sexes? (At the very least, shouldn't you make readers aware of these strange properties of SBI so they can decide how much value they put into them?)

Actually, in this example the simple ratio between the expression levels has a strange property, since it does not reflect a much higher expression of the relevant genes in the kidney. The SBI is actually more suitable for making such cases clear. Of course, this is under the assumption that expression level has a meaning for the phenotype, but this is the general assumption for all RNA-Seq experiment comparisons.

(8) With respect to Figure 4, why do females often have mean SBI values close to zero or even negative (e.g., kidney, mammary glands)? Is this simply because the female-biased genes tend to have lower TPM than the male-biased genes? It seems that the value zero for this metric is really not very biologically meaningful because this metric is a difference of two things that are not necessarily expected to be equal.

This is the extra information about the expression levels that is gained via the SBI values (see comment above). However, we noticed that people can get confused about this. We have now added a re-scaling step to focus completely on the variance information in these plots.

(9) Interpreting variances. A substantial fraction of the latter half of the manuscript focuses on interpreting variances among individual samples. This is problematic because there is no replication within individuals (i.e.., "repeatability"), thus it is impossible to infer the extent of observed variance among individuals of a given group (e.g., among females) is due to true biological differences among individuals or is simply due to noise (i.e., "measurement error" in the broad sense). Is the larger variance for mammary glands than liver or gonads just due to measurement error? What is the evidence?

This point was of course a major issue during the times where microarrays were used for transcriptome studies. However, the first systematic RNA-Seq studies showed already that the technical replicability is so high, that technical replicates are not required. In fact, practically all RNA-Seq studies are done without technical replicates for this reason.

(10) Because I have little confidence in the SBI metric (#7-8) and in interpreting within sex variances (#9), I found little value in the human results and how SBI distributions (and degree of overlap between sexes) compare between humans and mice.

We disagree - the current published status is that there are thousands of sex-biased gene in humans and this has implications for gender-specific medicine (Oliva et al. 2020). Our results show a much more nuanced picture in this respect.

(11) I found even less value in the single-cell data. It too suffers from the issues above. Further, as the authors more or less state, the data are too limited to say much of value here. It is impossible to tell to what extent the results are simply due to data limitations.

We have pointed out that it is still valuable to have them. They are good enough to exclude the possibility that only a small set of cells drives the overall pattern across an organ. We have further clarified this in the text.

(12) The code for data analysis should be posted on GitHub or some other repository.

The code for the sex-biased gene detection and analysis has been posted on GitHub (see Code availability in the manuscript).

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