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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

General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

• We thank the reviewers for their useful suggestions regarding how to improve our manuscript.
• Reviewer 3 declared that s/he did not find and evaluate the provided Supplementary Materials. As a result, many of her/his criticisms seem invalid: the requested data, validations etc. were already there in the Supplementary Figures and Tables.
• To avoid confusion, we renamed the transgene that is commonly used as a readout for STAT-activated transcription from 10xStat92E-GFP to 10xStat92E DNA binding site-GFP (please see comments by Reviewer 2 that show how easily one can think that Stat92E protein levels go up because of the misleading name of this transgene).
• One co-author, Martin Csordós was among the authors by mistake. Although first considered, his contribution was not included in either the original or the current manuscript version, so we removed his name from the revised version with his permission.
• We prefer to use colour coding for Sections 2., 3. and 4. in our responses to Reviewer comments rather than splitting the responses to queries in separate sections, because many of our answers contain a mixture of planned experiments (labeled as bold), already available data (labeled as underlined), and *explanations why we think that no additional analyses are necessary* (between asterisks). Data already provided in the original submission but missed by Reviewers has white background in our responses. Reviewer comments

Reviewer 1

Major comments:

R1/1. ”Figure 6E seems to indicate that a subset of Su(var)2-10/PIAS isoforms may bind to ATG8 (directly or indirectly). This leads to the straightforward prediction that this subset should be differentially affected by the selective autophagy at the center of the manuscript. That could be tested to strengthen that point. “

Response:

The Atg8a-binding subset of Su(var)2-10/PIAS isoforms could indeed be differentially affected by selective autophagy__. To test this, we will analyze in vivo Su(var)2-10 isoform abundance on western blots with an anti- Su(var)2-10 antibody in __Atg8aΔ12and ____Atg8aK48A/Y49A (Atg8aLDS) mutants.

Minor comments:

R1/2. “ in Fig S1B,C the colocalization between GFP reporters for STAT92E and AP-1 activity and glia marker does not seem convincing, indicating other cell types may be expressing them as well.”

*Response: *

*The overlap between glia labelling and STAT92E and AP-1 transcriptional readout reporter expression is indeed not complete. First of all, epithelial cells in the wing display both STAT92E and AP-1 activity even in uninjured conditions when glial expression of these reporters is not yet observed. Transcriptional reporter activity outside of the wing nerve was previously indicated in figures with arrowheads, now the epithelium is labeled and the regions containing nerve glia are outlined everywhere. *

The fiber-like reporter expression after injury in the wing nerve could correspond to either glia or axons1–3. Glia in the wing nerve have a filament-like appearance resembling axons in confocal images, even glial nuclei are flat/elongated1. Importantly, STAT92E enhancer-driven GFP also labels the nucleus in expressing cells, as opposed to glially driven mtdTomato that is membrane-tethered (and thus excluded from the nucleus: see Fig. S1B, C). Of note, TRE-GFP and Stat-GFP are not expressed in neurons because the cell bodies and nuclei of wing vein neurons are never GFP-positive, see Fig. 2C, Figs. S1, S4 in Neukomm et al.1 and Figure 1 for Reviewers. We also explain this better now in the revised manuscript (please see the legend of Fig. S1).

Nonetheless, we plan to analyze colocalization of mtdTomato-labeled neurons and TRE-GFP and Stat-GFP around the neuronal cell bodies to unequivocally show their different identities. Additionally, we will include transverse confocal sections of the genotypes in Fig. S1B, C that may better illustrate the colocalization.

Fig. 1 for Reviewers. Neuronal (nSyb+) and Stat92E-GFP+ cell morphology in the L1 vein at the anterior wing margin around the neuronal cell bodies which occupy a stereotypical position at the sensilla1. The location and shape of neuronal nuclei (left panel) are different from Stat-GFP+ cell nuclei (right panel, please see also Fig. S1B, C) based on the circumferential GFP signal. Therefore, cells expressing TRE-GFP and Stat-GFP in injured wing nerves are glia and not neurons.

R1/3. “p.7 Instead of "Su(var)2-10 is mainly nuclear due to its transcriptional repressor and chromatin organizer functions" It may be better to say" .. .consistent with its transcriptional repressor and chromatin organizer functions"”

Response:

We have modified the manuscript accordingly.

R1/4. “It is not clear whether the differences in Su(var)2-10/PIAS accumulation between Atg16 and Atg101 RNAi indicate functional differences of blocking autophagy at different stages or simply differences in RNAi efficiency (Atg16) versus the Atg101 mutant.”

Response:

We have added glial Atg1 (the catalytic subunit of the autophagy initiation complex that also includes Atg101) knockdown experiments that show the same lack of Su(var)2-10 accumulation in uninjured conditions as seen in the Atg101 null mutant (please see Fig. S6C). Please note that Atg16-Atg5-Atg12 dependent conjugation of LC3/Atg8a is involved in various vesicle trafficking pathways in addition to autophagy4–6, alterations of which may perturb baseline Su(var)2-10 levels in uninjured animals.

Significance:

R1/5. “STAT92E-dependent glial upregulation of vir-1, but not Draper, is shown, but consequences for glial functions in nerve injury are not tested.”

Response:

We will test antimicrobial peptide (AMP) expression in glia after nerve injury and whether this is affected by STAT92E and vir-1. Certain AMPs such as Attacin C are known to be regulated by both the Stat and NF-____κΒpathways7, and AMPs can be generally upregulated in response to brain injury8,9. This could serve pathogen clearance functions after defence lines such as the epithelium and blood-brain barrier are compromised. In addition, we will test the recruitment of glial processes into the antennal lobe after olfactory nerve injury in animals with glial STAT92E or vir-1 deficiency. Glial invasion is an adaptive response to axon injury and a first step towards debris clearance10.

R1/6. “experiments indicate a role for Su(var)2-10/PIAS SUMOylation activity in tis autophagic degradation, but it is not clear whether the critical substrata Su(var)2-10/PIAS itself or another protein.”

“binding of Su(var)2-10/PIAS to ATG8 is indicated, but no in vitro experiment performed to test whether this is direct and perhaps SUMOylation dependent.”

Response:

*We aimed to answer this question by using a point mutant form of Su(var)2-10: CTD2, which is unable to properly autoSUMOylate itself11, see Fig. 6D. CTD2 mutant Su(var)2-10 levels increased in S2 cells transfected with the mutant construct relative to the wild-type, similar to lysosome inhibition affecting the wild-type protein level but not the mutant variant. Importantly, wild-type Su(var)2-10 is present in CTD2 mutant Su(var)2-10-transfected cells, which can still SUMOylate other Su(var)2-10 targets. It is thus the intrinsic SUMOylation defect of the CTD2 mutant that results in its impaired degradation. It is firmly established that increased Su(var)2-10/PIAS levels repress STAT92E activity12, mammalian example: Liu et al., 199813, pointing to Su(var)2-10 as the critical substrate for autophagy during STAT92E derepression.*

We will further address this point and investigate if Su(var)2-10 directly binds to Atg8a by in vitro SUMOylation of GST-Su(var)2-10 and subsequent GST pulldown assay with HA-Atg8a. In vitro SUMOylation reaction with purified GST-Su(var)2-10 and negative controls are available via in-house collaboration11. We will incubate the resulting proteins and non-SUMOylated counterparts with in vitro transcribed /translated HA-Atg8a, and interactions will be tested by anti-HA western blotting with quantitative fluorescent LICOR Odyssey CLX detection.

Reviewer 2

Major comments:

R2/1. “The working hypothesis is that upon injury, Su(var)2-10 is degraded by autophagy and, as a consequence, Stat92E induces vir-1 expression.

Could the authors clarify why do Stat92E levels increase upon injury? Does Stat92E stability increase upon ATG mediated Su(var)2-10 degradation? Or does it expression/nuclear translocation change?“

Response:

We did not state that Stat92E levels increase during injury - we only used the 10xStat92E DNA binding site-GFP reporter (we have renamed it as such in our revised manuscript to avoid confusion) that is commonly referred to as 10xStat92E-GFP in the literature14, as a readout for Stat92E-dependent transcription.

To address these questions, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene (https://flybase.org/reports/FBti0147707.htm) to test if STAT92E stability/expression or translocation is altered during injury or upon disruption of selective autophagy. We have already tested this reporter and it is detected in the wing nerve nuclei after injury (Figure 2 for Reviewers, panel A).

As the Atg8aLDS mutation specifically impairs selective autophagy, we will use this mutant and wild-type controls to assess STAT92E::GFP::FLAG abundance on western blots from fly lysates with anti-GFP antibody. To assess STAT92E::GFP::FLAG nuclear translocation as well as stability/expression, we will use independently Atg8aLDS and Su(var)2-10 RNAi in glia to perturb STAT92E -dependent transactivation and visualize glia cell membrane by membrane-tethered tdTomato, glial nuclei by DAPI/anti-Repo and STAT92E with the STAT92E::GFP::FLAG fusion transgene in dissected brains. We can also evaluate STAT92E nuclear translocation with the same genotypes in the injured wing nerve glia. Of note, studies in mammals failed to identify an obvious effect of PIAS1 on STAT1 abundance13, please see Figure 2B from this paper as Figure 2 for Reviewers, panel B. Rather, PIAS family proteins bind tyrosine-phosporylated STAT dimers and impair their DNA binding thereby their transcriptional activation function15.

A.

Proc. Natl. Acad. Sci. USA Vol. 95, pp. 10626–10631

https://doi.org/10.1073/pnas.95.18.10626.

Fig. 2 for Reviewers.

1. Stat92E::GFP::FLAG expression and nuclear appearance in the wing nerve before and after injury
2. Increasing PIAS1 (Su(var)2-10 ortholog) levels does not affect STAT1 abundance in mammalian cells R2/2. “Also, since Su(var) levels increase upon ATG RNAi, independently of injury, do ATG levels increase upon injury? It does not seem to be the case from Fig 6D, but then, if the ATG levels do not increase, how to explain the injury mediated effects of Su(var)2-10? “

Response:

*We have not seen an effect of injury on the rate of autophagic degradation (flux) using the common flux reporter GFP-mCherry -Atg8a in glia after injury (shown in Fig. S2D – not 6D). Also, levels of the typical autophagic cargo p62/Ref(2)P and core autophagy proteins such as Atg12, Atg5, Atg16 do not change after nervous system injury16suggesting no change in general autophagic turnover. *

*An increase in general autophagy would be one option to promote degradation of a given cargo. Just as for the ubiquitin-proteasome system, in selective autophagy the labelling of the cargo/substrate for degradation is a regulated process. Dynamic ubiquitylation of a cargo often promotes its autophagic degradation17. We hypothesize that SUMO may fulfil a similar role in labelling cargo for elimination and this may be promoted by injury in the case of Su(var)2-10, which warrants future studies. *

R2/3. “Su(var)2-10 levels in control and injured wings are different between ATG18RNAi and ATG101 mutant (Fig 5). Could the authors explain the rational for using two ATG mutants? and the meaning of this difference? Also, why comparing data using the RNAi approach and a mutation?”

Response:

This issue was also raised in R1/4 and we refer the Reviewer/Editor to that section for our new Atg1 knockdown data and explanations.

*There is a consensus in the autophagy community that mutants for multiple Atg genes should always be used to ensure that it is indeed canonical autophagy that is affected (because Atg proteins can have non-autophagic roles, as is the case for Atg16 in regulation of phagosome maturation - LAP). *

R2/4. “Fig 6 What is the relevance of the Atg8, Sumo and Su(var)2-10 colocalization at puncta, since there is a lot of colocalization outside the puncta and also lots of Su(var)2-10 or Atg8 labeling that does not colocalize? “

Response:

*Su(var)2-10 orthologs PIAS1-4 localize to the nuclear matrix and certain foci in the chromatin and may play roles in heterochromatin formation, DNA repair, and repression of transposable elements in addition to transcriptional repression18–20. SUMO-modified proteins accumulate in response to PIAS activity in phase-separated foci also referred to as SUMO glue21. We show colocalization of Atg8a with similar Su(var)2-10 and SUMO double positive structures in foci. *

*We do not expect a full overlap between Su(var)2-10 and Atg8a labeling for a number of reasons. First, Su(var)2-10 has many different roles that may not be regulated by autophagy. Second, Atg8a+ autophagosomes in the cytoplasm deliver not only indidivual proteins such as Su(var)2-10 for degradation but also many other cellular components. Third, nuclear Atg8a is implicated in the removal of the Sequoia transcriptional repressor from autophagy genes that is unlikely to involve Su(var)2-1022. Now we include these points in the Discussion section.*

R2/5. “The statement made in the first sentence of the discussion is very strong: 'we have uncovered an activation mechanism for Stat92E', without sufficient supporting evidence.”

Response:

We have rephrased this section as follows:

Here we have uncovered the autophagy-dependent clearance of a direct repressor of the Stat92E transcription factor. This, synergistically with injury-induced Stat92E phosphorylation, may ensure proper Stat92E-dependent responses in glia after nerve injury to promote glial reactivity.

R2/6. “Could the authors validate (some) expression data by in situ hybridization experiments?”

Response:

*Our gene expression data were derived from wing nerve imaging or wing tissue. Unfortunately, in situ hybridization is not feasible in this organ because probes do not penetrate the thick chitin-based cuticule and wax cover of the wing (and the same is true for wing immunostaining).* We do provide independent evidence for vir-1 upregulation in the wing after injury via quantitative PCR (qPCR) in Fig. S5C. To corroborate reporter-based data, we will also analyze drpr in qPCR using wing material after injury at the same time points.

R2/7. “Could the authors validate the RNAi lines molecularly (or refer to published data on these lines?”

Response:

*Almost all RNAi lines have already been validated by qPCR, western blot, or immunostaining in Szabo et al., 202316 and other publications23–25. The only exception is Su(var)2-10JF03384 and we show that it is indistinguishable from the validated Su(var)2-10HMS00750 RNAi line (which causes 95% transcript reduction): it also strongly derepresses STAT activity. These reagents have also been widely used in the community (e.g. https://flybase.org/reports/FBal0242556.htm, https://flybase.org/reports/FBal0233496.htm).*

R2/8. „Clarifying the role of Su(var)2-10 on Stat92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect Stat92E accumulation, expression, translocation to the nucleus? Some of these experiments could be obtained in S2 cell transfection assays, if too complex in vivo.”

Response:

As explained in R2/1, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene to test if STAT92E stability/expression or translocation is altered upon disruption of selectiveautophagy (in Atg8aLDS mutant flies).

R2/9. „Also, what happens to the axons in the mutant conditions described in the manuscript? This would higher the impact of the work, but would require in vivo work with fly stocks containing several transgenes.”

Response:

We have already published in our previous paper, Szabo et al., 202316 that the mutants used in the current study display normal axon morphology__. There are only two mutants that we did not test in that paper: Atg8aLDS and our new Atg8anull and we will examine these remaining two during the revision, __but we already published in the above paper that axons appear normal in Atg8aΔ4, a widely used Atg8a mutant allele.

R2/10. „It has been published that Draper is involved in the response to injury in the adult wing nerve. See for example Neukomm et al (2014). The authors should discuss how this fits with their hypothesis and data. In this respect, Fig S4B, which should support the hypothesis, should be improved. It is rather hard to interpret it.”

Response:

Fig. S3 (draper protein trap-Gal4 driven GFP-RFP reporter expression) and S4B (intronic STAT92E binding site of the draper gene driven GFP-RFP reporter expression) show similar results: drpr is already expressed in wing nerve glia before injury, which is in line with Draper’s crucial role in the injury response because Draper-mediated glial signaling triggers glial reactivity. This has been added to the Discussion.

Minor comments:

R2/11. „Rubicon is also a negative regulator of autophagy (doi:10.1038/s41598-023-44203-6). in (Fig2 B, D) we have a higher GFP intensity in both uninjured and injured, and the difference between Injured/uninjured is less significant compared to control. It is possible that Rubicon KD causes more autophagy leading to a higher activation of Stat92E even in control. I wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”

Response:

Loss of Rubicon could indeed potentially remove more Su(var)2-10 via increased autophagy, leading to higher Stat92E activity. However, there is no statistically significant difference between injured and uninjured controls and injured and uninjured Rubicon knockdown, respectively, in Fig2 B, D (p=0.6975 and >0.9999 for each comparison). We are puzzled by the statement that the reviewer „wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”. We analyzed Rubicon as a factor critical for LAP and its deficiency does not prevent Stat transcriptional activity following injury unlike the loss of Atg8a, Atg16, Atg13 and Atg5. We will further support this result with a mutant of Atg16 with part of the WD40 domain deleted, because this region is critical for LAP but not for autophagy.16,26,27

R2/12. „The rationale for using both repoGal4 and repoGS is unclear. If, as mentioned, the goal is to avoid developmental defects, repoGS should be consistently used. Especially I don't understand how both were utilized to knock down the same genes, such as Atg16”

Response:

*We had to use repoGS (a drug-inducible Gal4 active in glia) because knocking down Su(var)2-10 with repoGal4 resulted in no viable adult progeny. Su(var)2-10 is an essential gene as opposed to most autophagy genes and its absence results in embryonic lethality24. Thus all Su(var)2-10 silencing experiments were done with repoGS. Similarly, Stat92E is involved in various developmental processes and its loss is embryonic lethal. repoGal4 was used for genes generally not having an adverse effect when absent during development16 in the first two figures. In Fig. 4D, we silenced Atg16 by repoGS because it is one of the controls for testing a genetic epistasis between Su(var)2-10 and Atg16. Please note that we see exactly the same phenotype in case of Atg16 knockdown when using either Gal4 version.* This has been explained in the revised methods section.

R2/13. „In the third paragraph of the introduction, I am confused whether Stat92E regulates drpr of the reverse”

Response:

Upon antennal injury, Drpr receptor binding to phagocytic cargo initiates a positive feedback loop in glial cells to promote its own transcription28. Drpr receptor in the plasma membrane regulates Stat92E and AP-1 activity via signal transduction. Stat92E and AP-1, in turn, increases drpr transcription10,28–30 that will result in more plasma membrane Drpr protein expression. We have explained this more clearly in the revised Introduction.

R2/14. „I cannot find the evidence for vir-1 being expressed in glia and target of Gcm in the refences that have been cited.”

Response:

We apologize for not explaining this better: vir-1 is called CG5453 in Freeman et al., 200331. It is listed in Table 1 as a Gcm target since there is no detectable CG5453 expression in a Gcm null mutant, please see below. We have updated the manuscript with this gene name.

.....

.....

Part of Table 1 from Freeman et al., 200331.

R2/15. „The presence of a Stat92E binding site on the vir-1 promoter has already bene described in the paper from Imler and collaborators, Nature immunology 2005. Actually, if this site is present in their transgenic line, it would help the authors strengthen the argument that Stat92E has a direct role on vir1 (for which they make a very strong statement in the discussion, with no direct evidence).”

Response:

*The evidence that Stat92E may have a direct role in vir-1 transcription in glia comes exactly from the same reporter transgene described by Imler and collaborators in the mentioned paper32. We received this transgenic line from the Imler group and monitored its expression after injury upon depletion of Stat92E (Fig. 3B). It thus contains the studied Stat binding site. This was referenced in the Methods and in all relevant sections of the main text, and we now explicitly state this in the revised text.*

R2/16. „In the Fig S2D, I do not see a lot of GFP+ (Glia) cells. I see more Atg8a in injured 3 dpi regardless of colocalization with glia”

Response:

Fig S2D uses one of the standard assays for autophagic turnover, which we now explain in more detail in the Results section. Basically, the dual tagged GFP::mCherry::Atg8a transgene is expressed in glia, and GFP is quenched in lysosomes after delivery by autophagy while mCherry remains fluorescent. So, in addition to double positive dots (autophagosomes), there are mCherry dots lacking GFP (autolysosomes) if autophagy is functional. All of these dots are in glia but the cell boudaries are not visible.

The images shown are single optical slices. The number of mCherry+ puncta are around 7-8 per field in both uninjured and injured (3 dpi) conditions, but puncta brightness is always variable. Since most mCherry+ puncta were rather bright in the original 3 dpi image, we changed it to a more representative image.

R2/17. „The quantification of the signals is made in a specific region of the wing, I guess throughout the nerve thickness. This could be represented more carefully in a schematic and It would also help defining colocalization in the first figure, by using a transverse section.”

Response:

The quantification method is described in Materials and Methods and we have added that quantification was done on single optical slices. The imaged region is depicted in Fig. S1A, where we indicated the rectangular region used in Fiji for image quantification. We will add transverse sections of wings as suggested.

R2/18. „A number of ATG genes are considered in the manuscript, but the rational for using them is not always clear. Showing a schematic would help clarify this. „

Response:

We have added a table showing the different steps of autophagy where the studied Atg genes/proteins function (now Supplementary Table 1). We also added whether the gene is considered specific for autophagy or can play a role in another process, e.g. LAP. We studied different autophagy genes in line with the assumption that disabling distinct autophagic complexes should produce the same phenotype if this process is indeed autophagy (and not LC3-associated phagocytosis for example).

R2/19. „Fig 7 is not cited and its legend is very short.”

Response:

We have now cited Fig 7 and expanded its legend.

R2/20. „Clarify the color coding in Fig S1E”

Response:

We added that red is injured, black is uninjured.

R2/21. „What is the tandem tagged autophagic fly reporter in fig S2D?”

Response:

This is one of the most common tools to study autophagy, please see the updated explanation above at your first question regarding Fig. S2D.

R2/22. „Add a schematic on the vir-1 isoforms.”

Response:

We have added a a schematic showing the vir-1 isoforms in Fig. S5B.

R2/23. „Fig S6B and Fig 5 relate on the levels of Su(var)2-10 upon Atg16 RNAi, but the scale is not the same, why?”

Response:

*The scales are different because these two images measure different things. Fig. 5 indeed displays quantification of Su(var)2-10 levels in brain glia. However, Fig S6B shows quantification of Stat92E-induced GFP reporter levels (as a proxy of Stat92E transcriptional activity) in the wing nerve upon Atg16 knockdown. *

Reviewer 3

R3/1. „The claim that the negative regulator of Stat92E signaling is removed by selective autophagy, involving selective autophagy receptors different from/in addition to Ref(2)P/p62 is not convincingly shown. This claim probably needs to be softened.”

Response:

*We have rephrased this sentence as follows: *

„These data suggest that selective autophagy is involved in Stat92E-dependent transcriptional activation in glia.”

R3/2. „The reporter that was used (10xSTAT92E-eGFP) is not a dynamic reporter of STAT92E activity. It accumulates in glia and is highly stable. The appropriate reporter to look at dynamic changes would be 10XSTAT92E-dGFP, which has a degradable (unstable) GFP that is required to see dynamic changes even in the CNS. All of the claims about STAT92E regulation use this reporter, so they are questionable.”

Response:

10XSTAT92E-dGFP featuring destabilized GFP could be a more appropriate tool for monitoring dynamic changes in transcription when short term- e.g. few hours - changes are investigated. However, we did not see any expression of 10XSTAT92E-dGFP (we tried 2 different transgenic insertions) in the wing nerve, please see Figure 3 for Reviewers. In the brain, dGFP expression with this reporter is also several times lower than stable GFP, please compare Fig. 4A and B in Doherty et al28.

The use of 10xSTAT92E-eGFP to follow dynamic expression changes is justified by many lines of evidence. First, there is no 10xSTAT92E-EGFP expression in uninjured wing nerves (Fig. S1D,E). Injury induces EGFP expression in the wing nerve with a sustained activation from 1 to 3 dpi (days post injury), and the EGFP expression returns to the baseline by 5 dpi (Fig. S1D, E). Second, the initial Stat-dependent upregulation of drpr and the 10XSTAT92E-dGFP signal in the brain both occur in the first 24 hours after injury and are sustained for 72 hours28 similar to our results with 10xSTAT92E-EGFP ((Fig. S1D,E). These results indicate that the dynamics of 10xSTAT92E-EGFP expression allows monitoring changes in Stat-dependent transcription occurring over days.

Figure 3 for Reviewers. Lack of 10XSTAT92E-dGFP signal in the wing nerve from two independent insertions of the same transgene at the indicated time points after wing injury.

R3/3. „The claim that glial drpr is not upregulated by wing injury and drpr accumulation is not apparently a prerequisite for efficient debris processing within the wing is weak. First, they did not stain for Draper using antibodies, rather they used expression constructs. Dee7 is a promoter that was found to be injury activated in the CNS (were they able to replicate that result? I did not receive the supplemental data), but it might not be the crucial regulator in the periphery. The MIMIC line that was converted is better, but might not represent the full spectrum of regulatory events at the draper locus. Finally, they never actually test for endogenous RNA changes, or use the antibody on westerns. Their lack of evidence is not as compelling as it could be.”

Response:

The__ original Supplemental Material already provides answers for this and subsequent questions of Reviewer 3__. We deposited the Supplemental Material to bioRxiv at the time of the first Review Commons submission and it was/is available at https://www.biorxiv.org/content/10.1101/2024.08.28.610109v2.supplementary-material.

Figs. S3 and S4 show in the wing and the brain (using two different drpr reporters for its transcriptional regulation) that drpr expression does not change much in the wing after nerve injury, as opposed to the brain.

*We did indeed replicate that dee7-Gal4 expression is induced in the brain after antennal injury using UAS- TransTimer (Fig. S4A). In contrast, wing cell nuclei already show expression of both fluorescent proteins in uninjured conditions, and RFP+ nucleus numbers do no change after wing injury (Fig. S4B, C). drpr-Gal4 was generated by conversion of a MiMIC gene trap element into a Gal4 that traps all transcripts. drprMI07659 is in an intron that is common in all drpr isoforms so it should capture the regulation of all transcript isoforms. *

We will further analyze drpr expression via independent methods during the revision: qPCR amplification of a common region of drpr transcripts, and western blot with anti-Drpr antibody to compare injured and uninjured wing material. Of note, we see no upregulation of drpr 2 days after wing injury in our (unpublished) RNAseq results either.

*Unfortunately, immunostaining of the adult wing is not feasible because antibodies do not penetrate the thick chitin-based cuticle and wax cover of the wing.*

R3/4. „The authors claim autophagy contributes to glial reactive states in part by acting on JAK-STAT pathway via regulation of Stat92E. They did not investigate other potential STAT92E targets. Does Atg16 knockdown alter STAT92E expression? Apparently Vir1 is still upregulated in the absence of Atg16 following injury, but they don’t show STAT92E changes.”

Response:

We did investigate other potential STAT92E targets besides vir-1. This is referred to in the text as „*immunity-related gene reporters” and it again can be found in the Supplemental Material (____Supplementary Table 2). None of these genes showed glia-specific upregulation following injury. *

We will investigate STAT92E expression with the STAT92E::GFP::FLAG protein-protein fusion transgene after disrupting autophagy as also suggested by Reviewer 2. Please see our detailed answer to the first comment of Reviewer 2.

*We do not agree with the comment that „Vir1 is still upregulated in the absence of Atg16 following injury” because Fig. 3F,G show that lack of Atg16 abolishes the upregulation of the vir-1 reporter: the change from uninjured to injured becomes statistically not significant and the mean GFP intensities are practically identical. *

R3/5. „The authors claim Su(var)2-10 is an autophagic cargo. They should better characterize Su(var)2-10 degradation and its regulation, and image quality needs to be improved (better images, merged examples, and clearer indication of what they are highlighting. There are many arrows in figures that I don't know what they are pointing to. Much of the labeling in Fig 1 (and others) looks like axons. Could TRE-GFP be turned on in neurons? How did they discriminate?”

Response:

As also explained to Reviewer 1’s last comment, we will carry out experiments to address whether SUMOylated Su(var)2-10 binds Atg8a, which can provide evidence for a direct SUMO-dependent autophagic elimination of Su(var)2-10. Please see our detailed response there.

We will further improve image quality for brain images and we already incorporated new images in Fig. S6. *Merged images were missing only in Fig 5, which we have included in the current version. Arrows and arrowheads were used as described in Figure legends, but instead of those, we now clearly label the epithelium and we outlined the region of wing nerve glia in all images. *

Please see our response to the first minor comment of Reviewer 1 regarding the expression of reporters in wing tissues.

R3/6. „The authors claim interaction of Su(var)2-10 with Atg8a in the nucleus and cytoplasm can trigger autophagic breakdown, involving Su(var)2-10 SUMOylation. The paper would benefit from showing direct SUMOylation of Su(var)2-10 after injury. Is there any way to examine this in vivo?”

Response:

We will test direct SUMOylation of Su(var)2-10 using a recently described method by Andreev et al., 202233. FLAG-GFP-Smt3 (SUMO)____ is expressed under SUMO transcriptional regulation and we will immunoprecipitate FLAG-GFP-SUMO and GFP alone as negative control with GFPTrap beads from lysates of heads subjected to traumatic brain injury that results in glial reactivity16____, and also from uninjured head lysates. We will use anti-____Su(var)2-10 ____western blotting to visualize SUMOylated Su(var)2-10 and whether its levels are modulated by brain injury.

R3/7. „The authors state in discussion "we find that draper is highly expressed in wing nerve glia already in uninjured conditions and it is not further induced by wing transection - indicating high phagocytic capacity in wing glia ... axon debris clearance takes substantially longer in the wing nerve than in antennal lobe glomeruli, thus draper levels may not readily predict actual phagocytic activity in glia". However, they never actually assess this in their experiments. All the conclusions about Draper are made from promoter fusions of integrated reporters, which are imperfect. This conclusion cannot be made.”

Response:

As described in our response to R3/3, we will further test drpr expression changes after wing injury using two independent methods: qPCR and western blot .

We deleted this part from the Discussion that were criticized by the reviewer because these are not important for the main message of our manuscript.

R3/8. „Both STAT92E and Jun are activated by a stress response. Could this be a stress response to disrupting autophagy that is somehow enhance by injury?”

Response:

*Stress responses are indeed relayed by AP-1 and Stat signaling, and impaired autophagy could be a source of stress. We would like to emphasize, though, that the main finding of our manuscript is that disrupting autophagy suppresses Stat-dependent transcription. Autophagy inhibition does not increase Stat signaling in uninjured wing nerves and while control flies upregulate Stat activity upon injury, autophagy-deficient animals fail to do so (Fig. 1). Thus, Stat signaling is not activated by loss of autophagy – it is activated by injury (that is the stress) and Stat activation requires autophagy in this setting.*

R3/9. „Minor:

I don't think that "glially" is a word.”

Response:

Online dictionaries such as Wiktionary list glially as a word, and many scientific articles use it: https://doi.org/10.1016/j.conb.2022.102653, https://doi.org/10.1016/j.yexcr.2013.08.016,https://doi.org/10.1016/j.jpain.2006.04.001*, to give some examples. *

We nonetheless refrain from using it in the updated text.

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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: The study is well-executed, and the claims are supported by appropriate experiments. As introduced by the authors in their introduction, ubiquitin-dependent endocytosis of AA transporters has been previously shown in S. cerevisiae and TXNIP has previously been identified as a regulator of glucose uptake by promoting endocytosis of GLUT1 and GLUT4. Here, the authors identify the molecular mechanism by which TXNIP promotes the endocytosis, and degradation of amino acid transporters (SLC7A5-SLC7A3) through its interaction with HECT-type ubiquitin ligases. This is an advance in the field and will be of interest for researchers in the fields of quiescence, metabolism and cell biology. Experiments are well designed and important controls have been performed. Overall, the claims and the conclusions are supported by the data.* *

Response: We thank the reviewer for the thorough evaluation of our manuscript and for the insightful, constructive comments. Reviewer 1 had five minor comments, and we have addressed them all.

Minor comment 1: The authors should indicate how often western blot experiments were repeated with similar results. Ideally band quantification (as in Fig. 2b) for the most relevant proteins should be provided for all shown Western blots.* *

Response: Each Western Blot (WB) experiment has been performed at least 3 times and each WB result for SLC7A5 is complemented by immunofluorescence and/or additionally by FACS analysis, across the manuscript.

In the partially revised version of the manuscript, we already__ incorporated WB quantifications of SLC7A5 protein levels__ for Figures 1c, f, h, Figure 3b, Figure 4b, and Figure 5a, c in Supplementary Figure 1b, Supplementary Figure 2c, f, Supplementary Figure 4a, e, and in Supplementary Figure 5a, c, respectively.

Minor comment 2: For confocal images no n number of experiments/analyzed cells is stated. Often only 2-3 cells are shown in these images. In some figures, conclusions from these confocal images are additionally supported by cell surface FACS.

Response: Each immunofluorescence experiment has been performed at least 3 times.

Minor ____comment 3: For panels with missing cell surface FACS quantifications, the authors should consider using the existing imaging data to perform quantifications of the membrane signal. In this way the reader can get the right impression of the reproducibility of the phenotype described.* *

Response: Each immunofluorescence experiment has been performed at least 3 times. In the partially revised version of the manuscript, line-scan quantification of immunofluorescence (IF) of SLC3A2 at the plasma membrane (PM) is now provided for immunofluorescence experiments in Figure 1e, g, Figure 3c, e in Supplementary Figure 2b, e, Supplementary Figure 4b, c, and for SLC2A1 in Supplementary Figure 3i, were FACS data was missing. In addition, WB experiments complement the results of each IF experiment.

Minor comment 4: I appreciate that the authors have also investigated SLC2A1 endocytosis in their experimental setup. Interestingly, they found that TXNIP mediated downregulation of SLC7A5-SLC3A2 was not linked to TXNIP mediated SLC2A1 endocytosis. Since the role of TXNIP in glucose metabolism has been studied in more detail in the past, it would be interesting if the authors could further comment on the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion.* *

Response: Thank you for bringing up this point. We now have added the following paragraph to the discussion to speculate about the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion:

‘Moreover, in RPE1 cells entering quiescence, GLUT1/4 was not downregulated. Hence, it seems that TXNIP can discriminate, in a context dependent manner, between targeting SLC7A5-SLC3A2 or GLUT1/4 for endocytosis. Since AKT mediated phosphorylation invariably appeared to inactivate TXNIP, and dephosphorylation re-activated it, additional mechanism must confer TXNIP selectivity towards SLC7A5-SLC3A2 or GLUT1/4. We consider it likely, that the exposure of sorting motifs in cytosolic tails of SLC7A5 or GLUT1/4 could regulate the binding of activated TXNIP and thus controls selective endocytosis to adapt nutrient uptake. The exposure of these sorting motifs could be dependent on the metabolic context / state of the cell. Indeed, yeast a-arrestins can detect n- or c-terminal acidic sorting motifs in amino acid transporters, respectively, that are alternatively exposed in response to amino acid excess or starvation (Ivashov et al., 2020a) (Guiney et al, 2016). Inspection of the SLC7A5 sequence indicates a possible n-terminal acidic sorting motif (17EEKEEAREK25). Two lysine residues (K19, K25) in this sequence have been found to be ubiquitinated in an earlier study upon protein kinase C (PKC) activation and mTORC1 inhibition (Barthelemy & Andre, 2019; Rosario et al, 2016).’

Minor ____comment 5: I would recommend a colour blind-friendly colour palette for the confocal images* *

Response: Thank you for pointing this out – we have changed the color palette accordingly.

Reviewer # 2: This study establishes TXNIP as a regulator of LAT1 endocytosis and metabolic homeostasis in quiescence. The integration of KO models and a TXNIP-deficient patient strengthens the findings, though clinical characterization remains underdeveloped relative to the mechanism reported, and biochemical interactions require endogenous validation. The work expands our understanding of TXNIP beyond association studies, positioning it as a key player in nutrient sensing and metabolic regulation. Addressing the concerns will enhance its relevance across fields - particularly metabolism, cell biology, and disease research. Overall, this is a very interesting study indeed. The use of TXNIP knockout models and a loss-of-function patient variant strengthens the conclusion that TXNIP is required for LAT1 degradation. The functional consequences of TXNIP deficiency (elevated intracellular aa, sustained mTORC1 activation, and accelerated quiescence exit) are well-supported by the data. The major concerns are as follows:

Response: We thank the reviewer for the thorough evaluation of our manuscript and for the insightful, constructive comments. Reviewer 2 had three major concerns and one minor comment.

Major concern 1. The identification of a biallelic TXNIP loss-of-function variant in a patient with metabolic disease and neurological dysfunction is highly significant. However, it is problematic that the manuscript effectively presents a case report but does not explicitly frame it as such, and the clinical details are very superficial (lack of pedigree, genetics, structured disease timeline, differential diagnosis, any histology/scans/photography and broader metabolic profiling - please see best practices for case reports). Although whole-exome sequencing identified the TXNIP variant, it remains unclear whether other genetic or metabolic contributors were systematically excluded. At first glance, the clinical discovery strengthens the physiological significance of the cell biology. However, a discrepancy remains between the clear neurological presentation of the patient (intellectual disability, autism and epilepsy) and the fibroblast-based TXNIP-LAT1 mechanism described in the study. Furthermore, the metabolic phenotype described in this manuscript is significantly more severe than that reported in a previous Swedish study of TXNIP deficiency in humans, where the clinical presentation was milder. This discrepancy suggests that different TXNIP mutations may lead to a spectrum of clinical outcomes, which is highly novel (i.e. metabolic and neurological in terms of loss of function, and carcinogenesis with respect to association studies, reviewed in PMID: 37794178). Of course, this could be influenced by mutation type, genetic background, compensatory mechanisms or environmental factors - it is noteworthy that the previous siblings had mitochondrial dysfunction, and this remains unknown in the present individual. Addressing this variability and discussing potential reasons for the pronounced phenotype observed in this patient would strengthen the manuscript overall. It is noteworthy that LAT1 is highly expressed in brain endothelial cells, which can also adopt a quiescent state (PMID: 33627876), and the authors should expand beyond the single sentence in their discussion. In the absence of the above details, the title and conclusions of Figure 3 and in the discussion greatly overstate causality, implying a direct relationship between TXNIP loss and metabolic dysfunction, despite data from only one patient. his may indeed be the case, but the claims should be carefully revised to reflect an association rather than definitive causation until additional patients are identified. Additionally, while it is assumed that the authors have obtained ethical approval and informed consent, this needs to be explicitly stated for transparency, with dedicated details in the methods sections. Addressing these issues will improve the rigor and mechanistic coherence of the study - otherwise it is quite disjointed.

Response: We have addressed many these valid concerns and provide a detailed description of the patient in the partially revised manuscript (please see below).

‘The patient is a boy, born in 2014 as the first child of healthy, consanguineous parents of Turkish origin. During pregnancy, the mother was diagnosed with polyhydramnios. At 38 + 6 weeks of gestation, the baby was in a breech position, leading to a cesarean section. At birth, he weighed 3880 g (P90), measured 55 cm in length, and had a head circumference of 38 cm.

On the seventh day of life, he exhibited floppiness, recurrent hypoglycaemia, and lactic acidosis, prompting his transfer from the birth hospital to a tertiary care centre. During the first three days there, his lowest recorded blood glucose level was 30 mg/dl, lactate levels were approximately 6.5 mmol/l, and pH was 7.11. Subsequently, he developed hypertriglyceridemia, with triglyceride levels reaching 364 mg/dl. Initially stable, he began experiencing elevated pCO2 levels (up to 70 mmHg due to bradypnea) and metabolic acidosis on day 10. A glucose infusion (10 mg/kg/min) stabilized his glucose and lactate concentrations, though lactate remained elevated at around 3-4 mmol/l. Regardless, his muscular hypotonia persisted. On day 12, a skin punch biopsy for a fibroblast culture was performed.

By day 20, glucose and lactate levels had stabilized with regular feeding, allowing his transfer back to a peripheral hospital. During infancy, his blood glucose concentrations were within standard range (Supplementary Table 1), but the boy experienced recurrent hypoglycaemia in response to metabolic stress, e.g., infections. He exhibited psychomotor developmental delays and, from 18 months of age, experienced increasing epileptic seizures (up to 3-4 per month), which were managed with levetiracetam, topiramate, and lamotrigine. Currently, he remains metabolically stable but presents with significant developmental delay, muscular hypotonia, and autistic features.

Whole-exome sequencing from peripheral blood of the patient detected a homozygous single nucleotide insertion c.642_643insT in exon 5 of 8 of the TXNIP gene. This variant was not recorded in the population genetic variant database gnomAD that lists TXNIP as likely haplosufficient (pLI = 0, LOEUF = 0,709: https://gnomad.broadinstitute.org accessed Sept. 10, 2024). No other (likely) pathogenic variant in any other gene, with known function in metabolism was identified as explanation of the clinical features in the child. Potential pathogenic variants in genes required for mitochondrial functions were also not detected, although they were initially expected to cause the phenotype of the boy.

The TXNIP variant c.642_643insT caused a frameshift and a premature stop codon after 59 AA (denoted p.Ile215TyrfsTer59), likely causing nonsense-mediated decay (NMD) or the synthesis of a severely truncated TXNIP protein (Figure 3a). Both parents are healthy heterozygous carriers for the TXNIP variant. Serendipitously, this TXNIP variant was similar to the gene-edited version in the RPE1 TXNIPKO cells (p.I215TfsX11).

The patient showed consistent metabolic alterations compatible with an AA transporter deficiency. Blood plasma concentrations of several large neutral amino acids (LNAAs, including L, I, V) were elevated throughout the years 2014 – 2022 (Supplementary Table 1). The increased molar ratio of the LNAAs (L, I, V) to aromatic AAs (F, Y), resulted in an elevated Fischer’s ratio (FR, 2014: FR = 4.46; 2016: FR = 5.38, 2018: FR = 5.90; 2021; FR= 6.98; 2022: FR = 4.23; FR reference range = 2.10 - 4). The methionine levels are not dramatically altered (Supplementary Table 1).’

We also provide the following ethical statement:

__‘Ethical statement __

All patients’ data were extracted from the medical routine records. Written informed consent for molecular genetic studies and publication of data was obtained from the legal guardians of the patient. This approach was approved by the ethics committee of the Medical University of Innsbruck (UN4501-MUI). The study was conducted in accordance with the principles of the Declaration of Helsinki.’

During the revision, we will additionally address how the other known TXNIP variant (TXNIP p.Gln58His; p.Gly59*; PMID: 30755400) affects nutrient transporter endocytosis. This TXNIP variant will be expressed in TXNIPKO RPE1 cells to analyze its effect on quiescence induced SLC7A5 downregulation. The results of this experiment will allow comparing directly the effect of both known TXNIP variants (p.Gln58His; p.Gly59* and p.Ile215TyrfsTer59) on SLC7A5 downregulation in an identical genetic background. In addition, we will compare how both TXNIP variants affect mitochondrial function (using Seahorse technology).

Major concern 2. The authors report that TXNIP interacts with HECT E3 ligases to regulate substrate degradation, yet this conclusion is drawn from overexpression-based immunoprecipitation studies, which do not confirm interaction under endogenous conditions. Without direct evidence of TXNIP-HECT E3 binding at native expression levels, this mechanistic link remains unresolved. Given that the authors have already generated antibody-validated TXNIP KO models, endogenous validation should be feasible if the interactions are not super-transient.

Response: While the manuscript was under review, we have improved the stringency of our TXNIP-HECT type ubiquitin ligase interaction experiments and developed additional biochemical experiments that strengthen our original conclusions. In the course of these experiments, we found that the interaction of TXNIP with NEDD4, WWP2 and HECW1/2 (but not with WWP1 or ITCH) were particularly dependent on the PPxY331 motif.

During the revision, we will conduct additional experiments to substantiate these findings and to narrow down the list possible ubiquitin ligases that are required for the downregulation of SLC7A5. In particular, we will test if endogenous TXNIP co-immunoprecipitates (in a PPxY motif dependent manner) NEDD4, HECW1/2 or another HECT type ubiquitin ligase.

Furthermore, we will include a newly developed ‘Bead-Immobilized Prey Assay (BIPA)’, were protein-protein interactions can be analyzed by microscopy in a fast in straight forward manner. In the BIPA, ALFA-TXNIP (or mutant variants) are first captured on ALFA-beads (Bead immobilized). These TXNIP beads are then incubated with cell lysates from HEK293 expressing GFP-tagged HECT type ubiquitin ligases (Prey). The binding of the GFP-tagged ubiquitin ligases to the TXNIP beads is analyzed by fluorescence microscopy and quantified (Figure 1b, a BIPA with YFP-NEDD4). This efficient assay will also be conducted with NEDD4, WWP1, WWP2, HECW2, and ITCH to analyze how they bind to TXNIP, TXNIP-PPxY331 and the PPxY double mutant.

Together we are confident that our experiments establish that TXNIP must interact with a specific subset of HECT type ubiquitin ligase (our prime candidate are NEDD4 and HECW1/2) to trigger SLC7A5-SLC3A2 ubiquitination, endocytosis and lysosomal degradation.

Major concern 3. What are the temporal dynamics of TXNIP-associated degradation, and is this process distinct from endosomal microautophagy (as reported in PMID: 30018090)? The authors present convincing, high-quality FACS-based data supporting TXNIP-mediated turnover. If this pathway is mechanistically separate from endosomal microautophagy, it suggests a hierarchy of degradation pathways leading to quiescence. Live cell imaging studies that define the temporal dynamics of this process using the tools the authors have created may reveal the relationship between these processes and refine the broader implications of TXNIP in homeostatic adaptation.

Response: Thank you for this interesting suggestion.

During the revision, we will first investigate a potential temporal correlation of endosomal micro-autophagy of p62/SQSTM1, NBR1, TAX1BP1, NDP52, and NCOA4 (PMID: 30018090) and the downregulation of SLC7A5 as cells enter quiescence. For these experiments, we will follow the turn-over of the above-mentioned autophagy adaptors and compare it to the turnover of SLC7A5, using either WB analysis, or microscopy or both.

Next, we will test if SLC7A5-SLC3A2 endocytosis and lysosomal degradation is required to initiate endosomal micro-autophagy of p62/SQSTM1, NBR1, TAX1BP1, NDP52, and NCOA4 in TXNIPKO cells.

Together, these experiments will address if the endosomal micro-autophagy and TXNIP mediated downregulation of SLC7A5 are mechanistically linked during entry into quiescence.

Minor comment 1. In the discussion, the authors might briefly speculate on the implications of any functional redundancy that might exist between other arrestins.

We will provide this information in the fully revised version of the manuscript.

In the years leading up to 1968, fights between students at the National Polytechnic Institute (IPN) and the National Autonomous University of Mexico (UNAM) were common, particularly due to their support for different American football teams.

In this context, in 1968 a huge student protest arose in Mexico City against the government of Gustavo Díaz Ordaz (Institutional Revolutionary Party), whose main axis was anti-authoritarianism, and whose central demands were compliance with the Constitution, the end of government repression, punishment of those responsible, compensation for the families of the dead and wounded, freedom for political prisoners and the demand for public dialogue.

In the midst of the Cold War, in Mexico the media was controlled by the government and a “discreet anti-communism” was perceived

Mexico in the 1950s created a state with great economic growth, especially giving a rise to children of lower social classes the opportunity to pursue higher education. This economic growth and its benefits within mexican society perpetuated the idea that mexico was a democracy

Voici un sommaire de la vidéo "La montagne en 2050, l’après ski ?" avec des estimations de timestamps basées sur le déroulement de la conférence :

• [0:00 - 0:10] Introduction par Hélène Billong Grandand (Les Shifters Lyon) : Présentation d'Hélène Billong Grandand et du groupe local des Shifters Lyon. Elle explique que les Shifters sont liés au Shift Project, un think tank travaillant sur la décarbonation de l'économie, présidé par Jean-Marc Jancovici. Les Shifters sont une association de bénévoles qui collaborent avec le Shift Project, informent, s'informent, débattent et sensibilisent sur la transition énergétique. La conférence du jour porte sur la montagne en 2050 et la question de l'après-ski.

• [0:10 - 0:20] Présentation des intervenants et du sujet : Hélène annonce les intervenants : Guillaume Desmurs sur l'histoire du développement économique de la montagne, Fiona Mille sur les enjeux de transition en montagne, et Guillaume Desru, maire de Bourg Saint-Maurice, avec un exemple concret. Le choix du sujet est motivé par l'absence de conférence sur ce thème à Lyon, la saison hivernale, et le travail du Shift Project sur la résilience des territoires de montagne.

• [0:20 - 0:25] Quelques chiffres clés sur la montagne (Shift Project) : Présentation de quelques chiffres issus du travail du Shift Project : 31% du stock national carbone de la forêt en zone montagne, 30% de l'offre d'hébergement touristique national en montagne, 75% des exploitations agricoles spécialisées dans l'élevage en montagne (dont 50% bovin). L'empreinte carbone du tourisme en montagne est majoritairement liée au transport des touristes.

• [0:25 - 0:30] Regards de pratiquants de la montagne (vidéos de témoignages de shifters) : Hélène introduit une série de témoignages vidéo de shifters lyonnais partageant leur expérience et leur pratique de la montagne.

• [0:30 - 0:35] Témoignage d'Éric (52 ans, Culoz) : Éric raconte ses différentes découvertes de la montagne : les grandes stations de ski en famille, la simplicité de la montagne en Pologne, et la montagne découverte avec ses enfants (bonhommes de neige, luge). Il apprécie désormais la montagne à travers les raquettes, le ski, la baignade dans les lacs, la randonnée et le vélo.

• [0:35 - 0:38] Témoignage de Louise (9 ans, Chambéry) : Louise décrit la montagne avec sa végétation, ses prairies et ses forêts. Elle apprécie les activités hivernales (ski alpin, ski de fond, luge, raquettes) et estivales (randonnée, course à pied, vélo) en famille et avec ses amis.

• [0:38 - 0:42] Témoignage d'Arnaud (37 ans, Chambéry) : Arnaud a découvert la montagne l'hiver en skiant en famille à La Plagne. Il a appris le ski avec l'ESF et le ski de fond à l'école. Il décrit la montagne hivernale comme hostile mais d'une grande beauté et un terrain de jeu naturel. Sa pratique a évolué vers le ski de randonnée, moins gourmand en infrastructures, tout en continuant le ski de piste et le ski de fond nocturne.

• [0:42 - 0:47] Témoignage de Frédéric (54 ans, Lyon) : Frédéric a découvert le ski à 6 ans en Haute-Maurienne. Il a continué à skier jeune adulte avec l'UCPA, découvrant différentes stations et le ski hors-piste (Grands Montets, Vallée Blanche). Habitant à Lyon, il pratique plus souvent la montagne, été comme hiver, et s'interroge sur l'impact de sa pratique sur l'environnement.

• [0:47 - 0:50] Transition vers l'intervention de Guillaume Desmurs : Hélène commente les témoignages et souligne que beaucoup de Lyonnais pratiquent la montagne sans y vivre. Elle évoque les problèmes de trafic pour se rendre en station le samedi et introduit Guillaume Desmurs, venu d'Annecy, pour parler de l'histoire des stations de ski.

• [0:50 - 1:25] Intervention de Guillaume Desmurs : Histoire du développement économique des stations de ski : Guillaume Desmurs rappelle la dépendance actuelle aux voitures pour monter en station. Il présente une version courte de sa conférence sur l'histoire des stations de ski françaises, soulignant que comprendre les décisions des années 60-70 est essentiel pour la transition. Il cite André Compagnonville sur la nécessité de penser le vrai plutôt que le plaisir. Il partage son expérience personnelle d'avoir grandi en station de ski. Il montre des magazines de ski des années 30-40, soulignant que le plaisir du ski n'a pas beaucoup changé. Avant-guerre, les villages de montagne s'équipaient organiquement avec des téléskis locaux. La première tentative de station de ski dédiée est Cestrière en Italie. Après la Seconde Guerre mondiale, Émile Allais et Laurent Chappis sont des figures clés. Allais invente le domaine skiable damé et les équipes de secouristes à Courchevel. Chappis, architecte urbaniste, dessine Courchevel en 1945, prototype de la station moderne avec un front de neige orienté sud. Chappis n'avait pas anticipé l'importance de la voiture. Il s'interrogeait sur la nature d'une station de sport d'hiver et la place de la population locale. L'aménagement des Trois Vallées a été imaginé par Chappis et Maurice Michot en camp de prisonniers.

• [1:25 - 1:45] L'essor des stations du plan neige (années 60-70) : Les années 60 voient des changements majeurs dans le matériel de ski (du cuir au plastique, skis sandwich). Les stations de ski françaises du plan neige (1964-1975) sont une création de la société du carbone. L'État soutient la construction de grandes stations pour fixer les populations et concurrencer l'Autriche et la Suisse ("la bataille du ski européen"). Les années 60 sont celles des Trente Glorieuses et de la société du carbone (voiture, avion, immobilier). L'immobilier est le cœur du modèle économique des stations. Le tourisme de masse concentre les flux de touristes dans le temps et l'espace. La Tarentaise concentre plus de la moitié du business du ski en France (Les Arcs, La Plagne, Val d'Isère, Les Trois Vallées). On compte 400 000 lits en Tarentaise, dont la moitié sont froids. La construction continue malgré tout. La brochure de vente de Val Thorens (ouverture en 1973) promettait des liaisons incroyables. Dès 1968, la vente d'immobilier est centrale.

• [1:45 - 1:55] La fin du plan neige et la loi montagne (1985) : La fin du plan neige est marquée par une crise économique et des événements dramatiques (avalanche à Val d'Isère en 1970). Le discours de Valéry Giscard d'Estaing en 1977 constate les excès du développement et marque un changement de direction. 350 000 lits touristiques ont été construits pendant le plan neige ; on en compte 2,7 millions aujourd'hui, dont la moitié sont froids. La loi montagne de 1985 devait donner la main aux maires sur l'immobilier avec les UTN, mais cela n'a pas limité la construction.

• [1:55 - 2:00] Tensions et perspectives actuelles : L'immobilier continue de faire vivre les stations, générant des tensions entre promoteurs et habitants. Manifestations contre la bétonisation (La Clusaz, 2022). Des maires comme celui de Villard-de-Lans perçoivent les remontées mécaniques comme vitales. Philippe Bourdeau appelle à optimiser l'existant et à travailler l'habitabilité plutôt que l'attractivité. Le pourcentage élevé de résidences secondaires (75 à 92%) rend l'habitation à l'année difficile.

• [2:00 - 2:05] Question sur la dépendance à la voiture et exemples : Question sur des exemples de stations non dépendantes de la voiture. Guillaume Desmurs cite Avoriaz (accès en voiture avec parkings souterrains), Zermatt (sans voiture), et Bourg Saint-Maurice (funiculaire).

• [2:05 - 2:10] Question sur l'importance de l'immobilier par rapport aux remontées mécaniques : Question sur la rentabilité de l'immobilier par rapport aux remontées mécaniques. Guillaume Desmurs cite un rapport de la Cour des Comptes de février 2024 qui rappelle le cercle vertueux (aujourd'hui vicieux) entre immobilier et remontées mécaniques. Alain Boulogne (Saint-Gervais) dit qu'on vit de l'immobilier, pas du tourisme. L'immobilier rapporte de l'argent aux promoteurs qui ne sont pas forcément intéressés par le développement du territoire.

• [2:10 - 2:12] Question sur les stations sans remontées mécaniques (ski de rando) : Question sur l'existence de stations axées sur le ski de randonnée (Montespluga en Italie, vallée en Autriche, station fermée au Canada).

• [2:12 - 2:15] Réponse : Fin d'un modèle économique carboné, pas du ski : Guillaume Desmurs explique que l'on sort d'un modèle économique carboné (voiture, parking, immobilier), pas du ski. Ces stations alternatives rapportent peu. L'été en station (VTT, randonnée) n'est pas rentable ; seul le ski alpin en hiver permet de rentabiliser les infrastructures.

• [2:15 - 2:20] Question sur les politiques pour réduire le nombre de résidences secondaires : Question sur les politiques pour réduire le nombre et l'impact des résidences secondaires (72% minimum, jusqu'à 95%). Guillaume Desmurs explique que c'est le cœur de l'économie. Il cite un rapport de l'Inspection des finances (2023) sur la raréfaction du logement principal dans les zones touristiques. D'autres pays (Australie, Nouvelle-Zélande, Autriche, Suisse) ont des lois limitant les résidences secondaires. En France, les outils actuels (taxe sur les résidences secondaires) sont marginaux. L'Inspection des finances rappelle qu'il est possible de limiter le droit de propriété pour l'intérêt général. Joël Giraud évoque des dispositifs de rénovation urbaine. Le fort taux de résidences secondaires entraîne une perte d'habitants et la fermeture de classes.

• [2:20 - 2:25] Transition vers l'intervention de Fiona Mille (Montagne Wilderness) : Guillaume Desmurs conclut en expliquant que Fiona Mille va prendre la parole et qu'il restera pour les questions à la fin.

• [2:25 - 2:30] Intervention de Fiona Mille : Enjeux de la transition en montagne : Fiona Mille se présente comme bénévole et présidente de Montagne Wilderness France. Elle explique que l'association (fondée en 1988) se concentre sur la cohabitation entre les montagnes sauvages (85% de la biodiversité terrestre) et la montagne à vivre. L'objectif est de trouver un équilibre entre protection et vie à l'année. Montagne Wilderness est une association nationale avec une équipe salariée à Grenoble et près de 2000 bénévoles. L'association travaille sur la défense des espaces naturels, encourage les pratiques respectueuses et amplifie la transition des territoires de montagne, vus comme un laboratoire.

• [2:30 - 2:35] Représentations de la montagne et questions sur la transition : Fiona interroge l'audience sur leurs représentations de la montagne (neige, air pur, beauté, etc.). Elle note que personne n'a encore mentionné le ski. Elle demande ensuite ce à quoi l'audience pense quand on parle de transition en montagne (transport, ski, fromage, gestion de l'eau, manque de neige, lits froids).

• [2:35 - 2:40] La transition souvent réduite à la question du ski : Fiona constate que médiatiquement, la transition en montagne est souvent réduite à la question de la transition du modèle économique du ski. Elle montre des exemples de titres d'articles de presse. Cela se comprend en raison du poids culturel et économique du ski.

• [2:40 - 2:45] Relativisation du poids économique du ski et vision anthropocène des stations : Fiona relativise le poids économique du ski en donnant l'exemple des Pyrénées où l'emploi touristique représente 10 à 13% des emplois, et le ski 10% de cet emploi touristique. L'économie de la montagne est plus diversifiée. Elle cite Diego Landivar et Emmanuel Bonet qui voient les stations comme des organisations typiques de l'anthropocène, basées sur une ressource naturelle (la neige) que l'on croyait renouvelable mais qui se raréfie.

• [2:45 - 2:50] La maladaptation des territoires de montagne : Fiona montre une photo peu engageante de Champrousse. Malgré les enjeux économiques, de nombreuses stations s'endettent pour faire perdurer le ski, ce qui constitue une grave maladaptation au changement climatique (retenues collinaires, canons à neige). Elle donne l'exemple de Font-Romeu où 80% du domaine est équipé en canons à neige dans un département en crise hydrique. Même Zermatt, souvent cité pour sa mobilité, utilise des pelleteuses sur glacier. Le modèle économique est à bout de souffle. La question n'est pas de savoir si on skiera demain, mais dans quelles conditions. Le retrait de Martin Fourcade de la présidence du comité d'organisation des JO est un signal fort. Le ski sur neige 100% artificielle ne fait plus rêver.

• [2:50 - 2:55] Nécessité d'une transition plus large que la seule question du ski : Fiona souligne la nécessité d'accélérer la transition des stations de haute et moyenne montagne, et de stopper la maladaptation. Cependant, il serait limitant de réduire la transition à la seule question du ski, car les défis sont plus vastes. Il faut repenser l'avenir des montagnes au-delà de l'après-ski.

• [2:55 - 3:05] Les défis du changement climatique en montagne : Les territoires de montagne sont des sentinelles du climat, avec des effets du dérèglement climatique deux fois plus rapides qu'en plaine. Les conséquences sont visibles : passage de la neige à la pluie, disparition des glaciers (80% pourraient disparaître d'ici 2100 à +4°C). Les écosystèmes montagnards sont en première ligne : forêts victimes d'incendies et de scolytes, pâturages asséchés, perte de biodiversité. Au-delà de l'attractivité, la question de l'habitabilité est cruciale.

• [3:05 - 3:15] Défis de la mobilité, du logement et de l'agriculture : La mobilité est un défi essentiel (touristique et quotidienne en montagne), surtout dans une société post-carbone. Vivre en montagne sans voiture est compliqué. Le logement est une question majeure, avec une forte proportion de résidences secondaires qui empêchent la vie à l'année. La rentabilité à court terme nuit à l'habitabilité. La question de l'agriculture et de la résilience alimentaire en montagne est peu traitée au niveau national.

• [3:15 - 3:20] Conclusion : Comment vivre demain en montagne ? Consultation nationale de Montagne Wilderness : Malgré les complexités, la montagne fait rêver. La question centrale est de savoir comment respecter et repenser la vie dans ces territoires. Montagne Wilderness a lancé une consultation nationale le 27 janvier pour recueillir des idées sur l'avenir des montagnes (près de 100 000 votes et 1000 propositions en 2 semaines). La montagne est un reflet des débats sociétaux. Des acteurs économiques, collectifs citoyens et élus s'engagent pour le changement. Il faut passer d'une consommation de la montagne à une relation plus profonde et respectueuse. La consultation est ouverte jusqu'au 30 mars. Il est important d'avoir ce débat avec ceux qui traversent ces territoires. Il faut redéfinir collectivement nos imaginaires de la montagne.

• [3:20 - 3:23] Question sur le développement industriel de la montagne (hydroélectricité) : Question sur le potentiel d'un développement industriel de la montagne, en prenant l'exemple de l'hydroélectricité dans la vallée de la Romanche.

• [3:23 - 3:28] Réponse : Diversification économique à l'échelle du territoire : Fiona répond que Montagne Wilderness a travaillé sur le sujet de l'industrie de demain en montagne. La diversification économique doit se penser à l'échelle du territoire, pas seulement des stations. Il faut investir dans l'agriculture, l'artisanat, l'industrie. Le débat n'est pas encore à ce niveau. Les maires de station sont face à une impasse s'ils cherchent la même rentabilité qu'avec le ski au sein de leur seule station. La coopération territoriale est nécessaire.

• [3:28 - 3:30] Question sur les "technosolutions" (couverture de glaciers, albédo) : Question sur le risque que les solutions techniques nous éloignent de solutions plus durables.

• [3:30 - 3:35] Réponse : Les glaciers comme signaux d'alerte, pas comme objets à "sauver" artificiellement : Fiona explique que ces "technosolutions" sont de faux problèmes. Une partie des glaciers va disparaître inéluctablement. L'enjeu est de voir les glaciers comme des signaux d'alerte pour réduire nos émissions de gaz à effet de serre. Elle mentionne un événement autour des glaciers en 2025. Il faut aussi questionner la pertinence de continuer à aménager des glaciers en voie de disparition.

• [3:35 - 3:38] Question sur la répartition des richesses en montagne : Question sur la répartition des richesses générées par le tourisme en montagne entre différents acteurs (grands hôteliers, saisonniers).

• [3:38 - 3:42] Réponse : Nécessité d'une coalition d'acteurs et d'une meilleure compréhension des économies locales : Fiona explique qu'une coalition d'acteurs est en cours de création pour travailler sur ce sujet essentiel. Il manque de données nationales précises sur ce qui fait vivre les territoires. Elle donne l'exemple d'une initiative dans le Vercors (The Camp de Loïs Habert) qui montre que l'économie de la montagne ne se limite pas au ski. Il faut accompagner ceux qui font déjà vivre la transition et soutenir les initiatives locales.

• [3:42 - 3:45] Introduction de Guillaume Desru (maire de Bourg Saint-Maurice) : Hélène introduit Guillaume Desru, maire de Bourg Saint-Maurice, en évoquant l'évolution de la perception des saisons par les jeunes et le changement des "stars" du collège (skieurs à skateurs). Elle pose la question de ce qu'est devenu Bourg Saint-Maurice depuis les années 90.

• [3:45 - 4:00] Intervention de Guillaume Desru : L'exemple de Bourg Saint-Maurice : Guillaume Desru se présente comme maire de Bourg Saint-Maurice depuis 2020. Il plaisante sur sa position de dernier intervenant et sur son profil atypique (prof de physique normand arrivé en 2000, cycliste). Il explique que sa liste a été montée sous forme d'apéro-débats pour provoquer le débat public. Il cite un proverbe savoyard : "C'est quand il fait beau qu'on refait le toit". Présentation du territoire de Bourg Saint-Maurice (180 km², 800-3800m d'altitude, trois massifs, quatre stations de ski internationales dont Les Arcs, 5ème station mondiale, 40 000 lits touristiques). Les Arcs ont été bien conçues dès les années 60 (station sans voiture imaginée par Charlotte Perriand). La montagne est en mouvement (funiculaire iconique). Face aux événements climatiques, naturels, sanitaires et géopolitiques récurrents, il faut être responsable et engagé. Il faut investir maintenant.

• [4:00 - 4:05] Mobilité à Bourg Saint-Maurice : La mobilité représente 60% de l'empreinte carbone. Un "choc de l'offre" a été mis en place pour favoriser le train et les transports en commun locaux (coût du réseau de navettes : 3 millions par an, +40% en 4 ans, prix divisé par 3, horaires et périodes étendus). Promotion de la station en lien avec le train. En 5 ans, un quart des visiteurs arrivent en train. Offres spéciales pour ceux venant en train.

• [4:05 - 4:10] Logement à Bourg Saint-Maurice : Moratoire sur les constructions touristiques neuves. La loi Climat Résilience et le Zéro Artificialisation Nette imposent des choix (logement pour habitants vs. résidence touristique). Exemple du quartier des Alpins : concertation avec les habitants pour privilégier le logement à l'année plutôt qu'un projet touristique initial. La commune est l'aménageur pour maîtriser le projet de A à Z. Création d'une SEM pour gérer le logement sans spéculation.

• [4:10 - 4:15] Plan Local d'Urbanisme (PLU) de Bourg Saint-Maurice : Le PLU reprend les orientations de la municipalité (préservation des terres agricoles, forestières et naturelles, optimisation de chaque mètre carré artificialisé). Il a été bien accueilli par la CDPENAF et les associations (Mountain Wilderness, FNSEA). Optimisation des lits touristiques aux Arcs.

• [4:15 - 4:20] Diversification économique et touristique : Diversification notamment l'été grâce au funiculaire, à la dimension de "petite ville" de Bourg Saint-Maurice (7200 habitants), à une culture d'événements bénéficiant aux habitants et aux visiteurs. Formation avec un campus connecté et accompagnement des entreprises. Clin d'œil aux Shifters et au rêve de voir Jean-Marc Jancovici à Bourg Saint-Maurice.

• [4:20 - 4:25] Environnement et préservation du vivant : Anecdote sur le glacier de l'Aiguille Rouge : face à sa fonte, le domaine skiable voulait le creuser pour continuer le ski. Après concertation et présentation des données scientifiques (perte de 6m de glace par an), un consensus s'est dégagé pour rendre cette zone à la montagne (zone de freeride) d'ici 1-2 ans.

• [4:25 - 4:30] Gestion de l'eau à Bourg Saint-Maurice : Pas ultra exemplaire, mais recherche d'un équilibre (pas plus de 50% d'enneigement de culture, autorisations respectées, efforts pour être vertueux). Tarification progressive de l'eau (les gros consommateurs paient plus cher).

• [4:30 - 4:35] Invitation à l'événement "La montagne en mouvement" (20-22 mars à Bourg Saint-Maurice et aux Arcs) : Invitation à venir découvrir les enjeux de la montagne, notamment les glaciers, et à participer à un événement positif sur l'avenir de la montagne.

• [4:35 - 4:40] Conclusion : Résultats et perspectives : Au niveau des mobilités, l'offre a explosé grâce à une volonté politique. L'occupation touristique augmente (+4% par an) sans construction de nouveaux lits depuis 2021, grâce à une meilleure optimisation. Arrêt de la promotion touristique hors Europe. Forte politique en faveur des habitants (80% du budget d'investissement 2025 dédié aux transitions). Appel à l'engagement citoyen pour les élections municipales de 2026.

• [4:40 - 4:45] Question sur l'organisation des JO 2030 : Question sur l'opinion concernant l'organisation des JO 2030. Fiona exprime un dilemme entre l'opportunité de raconter une autre histoire de la montagne et les signaux négatifs depuis le retrait de Martin Fourcade.

• [4:45 - 4:50] Intervention de Guillaume Desmurs sur les JO 2030 : Guillaume Desmurs a écrit un livre ("La Montagne en 2030 : Enquête sur les Jeux Olympiques et Paralympiques") sur la candidature des JO 2030. Il critique l'absence de dossier de candidature publique et analyse les promesses (JO durables, économes, réutilisation des infrastructures) qu'il juge peu crédibles. Il remet en question l'argument que les JO seraient au service de la transition des territoires de montagne, soulignant l'absence de projet de territoire existant.

• [4:50 - 4:53] Opinion de Fiona Mille sur les JO 2030 : Fiona estime que les JO d'hiver incarnent un frein à la transition en véhiculant un imaginaire dépassé autour des sports d'hiver. Elle a également écrit un livre ("Réinventons la montagne. Alpes 2030 : un autre imaginaire est possible") avec trois scénarios pour 2030, dont un avec les JO entraînant une fuite en avant. Si les JO sont le seul cap, cela risque de décevoir.

• [4:53 - 4:57] Parallèle avec les JO de Denver 1976 et enjeux immobiliers : Guillaume Desmurs établit un parallèle entre la candidature des JO 2030 et celle avortée de Denver en 1976 (opposition citoyenne, objectif de promotion des stations et de développement immobilier). Il souligne que les JO 2030 visent à maintenir le modèle économique en place et à continuer la construction immobilière. Le pôle glace des JO 2030 est prévu à Nice.

• [4:57 - 5:00] Question sur l'influence de Bourg Saint-Maurice comme zone de transit : Question sur l'influence de Bourg Saint-Maurice comme zone de transit vers d'autres stations de Haute-Tarentaise.

• [5:00 - 5:05] Réponse de Guillaume Desru : Plan de mobilité en Haute-Tarentaise : Guillaume Desru reconnaît le traumatisme de la réputation de Bourg Saint-Maurice comme ville de transit. Un plan de mobilité est en cours de mise en place en Haute-Tarentaise avec renforcement des navettes pour favoriser l'accès en train. Les travaux du pôle d'échange multimodal sont prévus au printemps. Bourg Saint-Maurice a réussi à rassurer ses voisins et travaille en coopération sur la mobilité.

• [5:05 - 5:10] Question sur l'impact climatique et la fermeture potentielle de stations : Question sur les prévisions d'impact climatique à horizon 2030-2050 et le nombre de stations potentiellement amenées à fermer.

• [5:10 - 5:15] Réponse de Guillaume Desru : Préparation au choc et projections scientifiques : La température à Bourg Saint-Maurice a augmenté de +2.7°C en 50 ans. Préparation du territoire pour amortir le choc, car un hiver sur deux pourrait être très compliqué dès 2040. Seules trois stations (Montgenèvre, Bourg Saint-Maurice/Les Arcs, Tignes) se préparent réellement. Une étude de 2023 prévoit que 98% des stations fermeraient sans neige artificielle dans un monde à +4°C, et 60% à +2°C.

• [5:15 - 5:20] Fiona Mille : Enneigement artificiel et remise en question du modèle : La question est de savoir ce que l'on souhaite autour de l'enneigement artificiel. La transition est écologique, sociale, sociétale et doit questionner notre rapport à la montagne. Un modèle économique totalement hors sol a-t-il encore du sens ?. Une étude d'Atout France montre que les jeunes sont moins intéressés par le ski sur neige artificielle. Le CIO exige 100% de neige artificielle pour l'équité des épreuves des JO. Le ski devient hors sol. Il faut un débat de société.

• [5:20 - 5:22] Guillaume Desmurs : Localisation du pôle glace des JO 2030 : Le pôle glace des JO 2030 sera situé à Nice, dans la plaine du Var.

• [5:22 - 5:28] Question sur la persistance de l'achat immobilier en montagne et les possibilités d'interdiction pour les communes : Question sur les raisons pour lesquelles l'achat immobilier en montagne continue malgré les risques, et sur les possibilités pour les communes d'interdire ce type de transactions.

• [5:28 - 5:33] Réponse de Guillaume Desmurs : Investissement, spéculation et manque d'outils réglementaires : Les stations de ski sont devenues des lieux d'investissement pour les banques. Les acheteurs pourraient perdre de l'argent à long terme. Les JO garantissent une certaine pérennité jusqu'en 2030. Le droit à la propriété en France empêche une interdiction totale des résidences secondaires comme en Suisse (Lex Weber). Des outils comme les Baux Emphytéotiques et les SEM logement existent, mais sont insuffisants. La montagne et la mer sont des lieux d'investissement bancables avec des durées d'amortissement de 20-25 ans. Les assurances pourraient changer leur approche face aux risques.

• [5:33 - 5:40] Question sur les alternatives au ski pour faire vivre la montagne : Question sur les alternatives au ski pour l'économie de la montagne. Guillaume Desru : On skiera différemment, quand il y aura de la neige. La montagne offre d'autres activités (promenade, forêt, contemplation, ressourcement, autres sports). Travailler l'imaginaire des vacances à la montagne même sans ski quotidien. L'exemple de l'année Covid où les gens se promenaient et faisaient de la luge.

• [5:40 - 5:45] Fiona Mille : Le besoin de montagne est évident, repenser l'accueil et les expériences : Le besoin de montagne est évident (60% des Français y sont allés récemment). La question est de repenser l'accueil pour des expériences riches et pleines de sens. Éviter une vision caricaturale (ski vs. sans ski). Imaginer des stations ouvertes toute l'année, axées sur la découverte de la montagne, le tourisme social, l'éducation à l'environnement. Passer d'une consommation à un rapport sensible à la montagne.

• **[5:45 - 5:50] Guillaume Desmurs : Sortir du "tout ski" et du "tout

Briefing Document : La Montagne en 2050, l’Après-Ski ? Date : 8 février 2024

Source : Excerpts de la conférence "La montagne en 2050, l’après ski ?" organisée par le groupe local des Shifters Lyon.

Intervenants principaux cités :

• Hélène Billong Grandand (Les Shifters Lyon)
• Guillaume Desmurs
• Fiona Mille (Montagne Wilderness)
• Guillaume Desru (Maire de Bourg-Saint-Maurice)
• Jean-Marc Jancovici (The Shift Project)
• Émile Allais
• Laurent Chappis
• Philippe Bourdeau

Thèmes Principaux et Idées Clés :

Cette conférence organisée par les Shifters de Lyon a exploré l'avenir de la montagne à l'horizon 2050, en se posant la question cruciale de "l'après-ski".

L'événement a combiné une introduction sur les activités du Shift Project et des Shifters, des témoignages de pratiquants de la montagne, et des présentations d'experts et d'acteurs locaux sur l'histoire du développement économique de la montagne, les enjeux de transition, et des exemples concrets comme la politique menée à Bourg-Saint-Maurice.

1. Introduction : Le Shift Project et les Shifters

Hélène Billong Grandand a introduit la conférence en présentant les Shifters, une association sœur du Shift Project, think tank présidé par Jean-Marc Jancovici, qui travaille sur la décarbonation de l'économie.

Elle a souligné la rigueur scientifique du Shift Project et sa mission d'éclairer et d'influencer les débats sur la transition énergétique.

Les Shifters, composés de bénévoles, ont pour missions de collaborer avec le Shift Project, d'informer et de débattre sur la transition énergétique, et de sensibiliser divers acteurs.

La conférence s'inscrivait dans cet objectif d'information et de débat sur le thème de la montagne en 2050.

Elle a mentionné que le Shift Project a travaillé sur la résilience des territoires, avec une partie spécifique dédiée à la montagne, dont les rapports sont accessibles en ligne.

Des chiffres clés issus de ces travaux ont été présentés pour illustrer les enjeux :

• Carbone : 31% du stock national de carbone forestier est en zone de montagne.
• Tourisme : 30% de l'offre d'hébergement touristique national est en montagne.
• Agriculture : 75% des exploitations agricoles spécialisées dans l'élevage sont en montagne (50% en élevage bovin).
• Empreinte Carbone du Tourisme : Majoritairement liée au transport des touristes.

2. Regards de Pratiquants de la Montagne

La conférence a diffusé des témoignages de quatre shifters aux profils variés (âge, lieu de résidence, pratiques) partageant leur relation à la montagne et son évolution :

• Éric (52 ans, Ain) : Évolution de sa pratique, des grandes stations axées sur les remontées mécaniques à la découverte des plaisirs simples comme la promenade et les activités douces (raquettes, ski de randonnée, baignade, vélo). Il souligne la découverte de la montagne avec ses enfants autour de jeux dans la neige.

• Louise (9 ans, Chambéry) : Description sensorielle de la montagne et de ses activités préférées (raquettes, ski alpin, ski de fond, luge, randonnée, VTT).

• Arnaud (37 ans, Chambéry) : Découverte de la montagne via le ski alpin en famille, évolution vers le ski de randonnée moins gourmand en infrastructure, et la pratique du ski de fond nocturne. Il a appris à respecter la force de la nature. "J'ai découvert un monde hostile, froid et dangereux où la nature est clairement plus puissante que l'homme, mais d'une très grande beauté et surtout j'ai pu trouver un terrain de jeu naturel où glisser, sauter et repousser mes limites."

• Frédéric (54 ans, Lyon) : Découverte de la montagne par le ski en famille, puis via l'UCPA avec la pratique du hors-piste (Grands Montets, Vallée Blanche). Il s'interroge sur l'impact de sa pratique face au réchauffement climatique et sur la manière de préserver la montagne. "Aujourd'hui avec le réchauffement climatique je m'interroge sur l'impact de ma pratique sur l'écosystème et l'environnement de la montagne comment faire au mieux pour que la montagne puisse nous émerveiller encore longtemps."

• Ces témoignages ont mis en lumière la diversité des pratiques et des liens émotionnels avec la montagne, ainsi que les préoccupations croissantes face aux changements environnementaux.

3. Histoire du Développement Économique de la Montagne (Guillaume Desmurs)

Guillaume Desmurs a retracé l'histoire du développement des stations de ski en France, soulignant les décisions prises dans les années 60-70 qui déterminent la situation actuelle et les blocages à la transition. Il a rappelé que le plaisir du ski est constant, mais le contexte a évolué.

• Avant-Guerre : Développement organique par les locaux avec des téléskis bricolés, ski de randonnée principal.

• Après-Guerre : Courchevel comme prototype de la station moderne, impulsée par le département en 1945. Rôle clé d'Émile Allais (invention du domaine skiable damé, des secours sur pistes) et de l'architecte urbaniste Laurent Chappis (conception du front de neige). Chappis n'avait pas anticipé l'ampleur de l'usage de la voiture. "Jamais j'aurais imaginé qu'il y aurait autant de voitures qu'on aurait besoin de faire autant de parking."

• Les Années 60 et le Plan Neige (1964-1975) : Période déterminante avec l'intervention de l'État pour créer des fleurons touristiques concurrentiels à l'Autriche et à la Suisse ("la bataille du ski européen"). Développement de la société du carbone (voiture, avion, immobilier). L'immobilier devient le cœur du modèle économique des stations. Tourisme de masse concentré dans le temps et l'espace. La Tarentaise concentre plus de la moitié du business du ski en France. "Les stations de ski elles vivent pas du tourisme, elles vivent de l'immobilier."

• Fin du Plan Neige (Années 70) : Crise économique, prise de conscience des excès (trop de résidences secondaires, impact sur les populations locales et l'environnement). Discours de Valéry Giscard d'Estaing en 1977 marquant cette fin.

• Post Plan Neige (Années 80 à Aujourd'hui) : Vote de la loi Montagne (1985) censée donner la main aux maires sur l'immobilier, mais la construction continue (de 350 000 lits en 1977 à 2,7 millions aujourd'hui, dont la moitié sont froids). Forte proportion de résidences secondaires (75 à 92%). Des tensions apparaissent entre les promoteurs et les habitants qui souhaitent un arrêt de la bétonisation. "Aujourd'hui il n'est pas possible d'habiter à l'année, de moins en moins possible d'habiter à l'année..."

• Nécessité de l'Habitabilité : Il faut cesser de travailler l'attractivité et se concentrer sur l'habitabilité des territoires de montagne (bien vivre, travailler, grandir, s'éduquer, se cultiver). "Il est temps aujourd'hui de travailler l'habitabilité, de cesser de travailler l'attractivité des territoires."

• Il a été souligné le manque d'exemples de stations en France ne dépendant pas de la voiture, contrairement à des cas comme Zermatt (Suisse) ou Avoriaz (où l'accès se fait en voiture malgré tout). Bourg-Saint-Maurice, avec son funiculaire, est un cas particulier mais minoritaire en termes de flux touristiques.

4. Les Enjeux de Transition en Montagne (Fiona Mille)

Fiona Mille a élargi le débat au-delà de la seule question du ski, en soulignant la richesse et la pluralité de la montagne (sports, grands espaces sauvages, biodiversité, vie à l'année, pastoralisme, sylviculture).

Elle a regretté que la transition en montagne soit souvent réduite médiatiquement à la question de la neige et de l'avenir des stations de ski.

• Le Ski : Un Poids Culturel et Économique, Mais Pas Tout : Si le ski a une forte importance culturelle et économique (notamment en Tarentaise), l'économie de la montagne est plus diversifiée (artisanat, agriculture, services).

• Les Stations : Miroirs de l'Anthropocène : Basées sur une ressource naturelle (la neige) que l'on croyait renouvelable et qui s'amenuise. Les efforts pour faire perdurer le ski (enneigement artificiel, retenues collinaires) peuvent être des maladaptations coûteuses et peu attrayantes. "Est-ce que la station des amoureux à Champrousse elle fait rêver aujourd'hui ? Je pense pas."

• Des Défis Multiples :Climat : Effets du dérèglement climatique deux fois plus rapides en montagne (fonte des neiges et des glaciers - 80% des glaciers mondiaux pourraient disparaître d'ici 2100 avec +4°C -, impact sur les écosystèmes, multiplication des événements extrêmes comme les éboulements et les crues). "Les écosystèmes montagnards ils sont aussi en première ligne des effets du dérèglement climatique."

• Mobilité : Défi majeur de la mobilité touristique (forte émettrice de GES) et de la mobilité quotidienne en montagne dans une société post-carbone. "Comment on va en station en mobilité douce demain ? C'est aussi comment on se déplace au quotidien en mobilité douce ? C'est très compliqué aujourd'hui de vivre en montagne sans voiture."

• Logement : Question cruciale de l'habitabilité face à la prolifération des résidences secondaires. "Comment on vit une bonne vie à l'année en montagne ? C'est déjà pouvoir pouvoir se loger."

• Agriculture : Résilience alimentaire en montagne, impact du dérèglement climatique sur les pâturages.

• Nécessité d'un Autre Regard : Passer d'une logique d'attractivité à une logique d'habitabilité et de respect des limites planétaires. "Comment on protège et on fait vivre les montagnes demain ?"

• Consultation Nationale "Montagne 2030" (Montagne Wilderness) : Lancée pour recueillir les idées sur l'avenir de la montagne. Un fort engouement témoigne de l'intérêt du sujet. Il est crucial de redéfinir collectivement nos imaginaires de la montagne.

5. Exemple de Bourg-Saint-Maurice (Guillaume Desru)

Guillaume Desru, maire de Bourg-Saint-Maurice, a présenté la politique proactive menée par sa commune face aux enjeux de la transition, sous le mot d'ordre "C'est quand il fait beau qu'on refait le toit."

• **Territoire et Contexte : Vaste commune avec une forte diversité (urbain, station internationale des Arcs sans voiture grâce à Charlotte Perriand, villages, patrimoine militaire). 5ème station de ski du monde.

• Confrontation aux Risques : Multiples risques (climatiques, environnementaux, sanitaires, géopolitiques) rendent la transition nécessaire.

• Actions Concrètes :Mobilité : "Choc de l'offre" sur les transports en commun (prix divisé par 3, horaires et périodes étendues), promotion du train (1/4 des visiteurs arrivent en train), investissement important dans le pôle d'échange multimodal. "Venez en train !"

• Logement : Moratoire sur les constructions touristiques neuves. Priorité à l'habitat permanent (exemple de la reconversion du quartier des Alpins). Création d'une SEM pour maîtriser le logement et éviter la spéculation.

• Urbanisme : PLU préservant les terres agricoles, les espaces forestiers et naturels, optimisant chaque mètre carré artificialisé.

• Diversification Économique et Touristique : Développement du tourisme estival (funiculaire ouvert l'été), culture et événements pour les habitants et les visiteurs, formation (campus connecté), accompagnement des entreprises.

• Environnement et Préservation du Vivant : Remise en question de l'aménagement du glacier de l'Aiguille Rouge (laisser place à une zone de freeride). Gestion de l'eau (pas plus de 50% d'enneigement de culture, tarification progressive de l'eau).

• Bilan Provisoire : Augmentation de la fréquentation touristique sans construction de nouveaux lits, forte politique en faveur des habitants, investissements importants dans les transitions. "On n'a pas construit un lit depuis 2021, on fait +4% par an en terme de fréquentation."

• Appel à l'Engagement : Incitation à s'engager dans la vie politique locale.**

6. Débat et Perspectives d'Avenir

Le débat a permis d'approfondir plusieurs points :

• Jeux Olympiques d'Hiver 2030 : Vus comme un frein à la transition par Fiona Mille et Guillaume Desmurs (maintien du modèle économique, accent sur l'immobilier, projet non transparent). La démission de Martin Fourcade est un signal fort. L'histoire des JO de Denver 1976 a été évoquée comme parallèle.

• Résidences Secondaires : Nécessité de réguler leur nombre et leur impact, mais manque d'outils juridiques en France (contrairement à la Suisse ou l'Autriche). Le cœur du modèle économique actuel rend difficile un changement radical.

• Alternatives au "Tout Ski" : Importance de travailler l'imaginaire de la montagne à toutes les saisons, valoriser d'autres activités (randonnée, contemplation, culture), et diversifier l'économie locale (artisanat, agriculture, services, industrie). L'année du COVID a montré une fréquentation possible sans ski alpin.

• Exemples de Transition : Difficulté de trouver des exemples parfaits de transition réussie en raison des choix de développement différents selon les pays. Mention des démarches intéressantes de Métabief, Bourg-Saint-Maurice/Les Arcs et Tignes. Importance de considérer les petites initiatives locales.

• Impact Climatique : Les projections indiquent une fermeture potentielle de nombreuses stations sans enneigement artificiel. Préparer les territoires au choc est crucial.

• Rôle des Médias : Évolution positive du traitement des sujets montagnes au niveau national, avec une meilleure prise en compte des enjeux de transition.

Conclusion :

La conférence a mis en évidence la complexité des enjeux auxquels la montagne est confrontée à l'horizon 2050.

Si le modèle économique basé sur le ski intensif et l'immobilier est clairement remis en question par les changements climatiques et les aspirations des habitants, des alternatives émergent et une prise de conscience s'opère.

La transition nécessitera une vision globale, une action politique volontariste, une diversification économique, et un changement de regard sur la montagne, passant d'une logique de consommation à une relation plus respectueuse et durable.

L'engagement citoyen et la participation aux débats locaux sont essentiels pour construire cet avenir.

A través de las preguntas, no afirmaciones, podemos convocar la negociación y sugerencias sobre como mejorar tu curso.

La retroalimentación es un punto esencial en el aprendizaje de los estudiantes ya que al recibirla permite concientizar sobre sus errores y/o potencialidades, para mejorar, cambiar o proyectar. Esta puede ser a través de sus profesores, tutores o pares.

Actualmente la tecnología es muy importante dentro de la educación ya que permite crear nuevas metodologías, y con ayuda de sus herramientas y aplicaciones para que se de un proceso de aprendizaje más efectivo e interactivo, saliendo de lo tradicional. Sin embargo la tecnología es un complemento para la educación, no sólo puede ser basada en ella, debe existir un equilibrio.

Perfil de egreso

La educación en línea, en especial apoyado del modelo tecnopedagógico debe tener un proceso de diseño, el cuál permite la correcta formulación de un programa, curso o taller, complementando la tecnología y la pedagogía que garantice el aprendizaje de los estudiantes de acuerdo a sus necesidades, tiempos y espacios, sin presionarse. Por lo tanto la educación en línea permite esta flexibilidad, adaptación y evaluación de contenidos.

Es importante buscar nuevas formas de interacción (didáctica) entre profesor-estudiantes y estudiante-estudiante que les permita aprender de forma significativa.

La educación en línea abarca una planeación, un diseño, una metodología y formas de evaluación más específicas de acuerdo a la modalidad y requerimientos de los estudiantes, mientras que en el confinamiento tuvo que haber una adaptación a los contenidos presenciales para presentarlos en línea, sin embargo no tan específicos como se da en la verdadera Educación en línea.

I agree with the idea that tension builds gradually and peaks at the climax before resolving, as this structure keeps audiences engaged and invested in the narrative. I find this framework useful because it provides a clear roadmap for crafting compelling stories, and it reinforces my understanding of how conflict and resolution drive a plot forward. While this model is widely applicable, some stories may deviate from it by having multiple climaxes or structures, which can create different emotional impacts.

Seeing the graph reminded me of how any essays, movies, or stories go. Every stage of the story is extremely important as the beginning sets the audience up with the information that they should worry about. Then by emphasizing the role of tension throughout the story and during the conflict, it makes the story stand out much more as authors are able to effectively connect classic storytelling principles to real-world marketing challenges. The conclusion also plays an important role as it's a way for the user to be satisfied with the outcome or be left with a thought-provoking question that lingers in their mind.

There are pictures that go along with each category. This helps the consumer understand what each section is for. In the surfing section there is a picture of someone surfing. This helps people with reading disabilities understand the webpage better.

Le sommaire minuté que vous demandez ne peut pas être fourni précisément car la transcription de la vidéo YouTube ne contient pas d'indications de temps spécifiques. Cependant, je peux vous offrir un résumé des points principaux abordés dans la vidéo :

• La crédibilité des médias américains a considérablement diminué depuis les années 1940-1960. Aujourd'hui, elle est au bas de la liste de confiance, au même niveau que le Congrès. L'animateur se demande si ce manque de confiance et ce reportage partisan sont propres aux États-Unis.
• Un réalignement politique majeur a eu lieu aux États-Unis après le mouvement des droits civiques, entraînant une cohérence idéologique partisane accrue. Les Américains évaluent souvent les politiques à travers leur prisme partisan.
• À cette époque, l'information télévisée était principalement limitée à trois chaînes principales (CBS, ABC, NBC). Le gouvernement américain réglementait leur contenu, notamment par la doctrine de l'équité (Fairness Doctrine) de 1949, qui obligeait à présenter des points de vue équilibrés et accordait un droit de réponse.
• L'essor de la télévision par câble et la multiplication des chaînes ont théoriquement rendu la doctrine de l'équité obsolète, et elle a été supprimée en 1987.
• Aujourd'hui, l'accès à l'information est plus large, mais elle est souvent présentée de manière biaisée. Les chaînes d'information câblées sont des entreprises qui fonctionnent grâce aux audiences et à la publicité, brouillant la frontière entre information et divertissement.
• Bien que peu fiables, Fox News et CNN sont en tête des sources d'information utilisées par les Américains. La tonalité divisive de l'information câblée est devenue son attrait.
• La confiance dans les médias a chuté de manière significative entre 1973 et 2024, avec un écart considérable entre les Républicains (faible confiance) et les Démocrates (confiance plus élevée). Il y a peu de chevauchement dans les sources d'information auxquelles les deux partis font confiance, à l'exception notable de The Weather Channel.
• Bien que l'Europe ne soit pas exempte de fausses informations, celles-ci sont principalement concentrées sur les médias sociaux. L'Union européenne préfère encourager la pluralité d'opinions plutôt que de simplement interdire la désinformation.
• En ce qui concerne les mass médias (télévision et journaux), il existe un plus grand degré de balance et de neutralité dans les nations d'Europe occidentale. Cela est en partie dû au rôle plus important des médias d'information publics.
• Dans plusieurs pays d'Europe occidentale, la principale source d'information est une organisation publique (BBC, SVT, ARD) plutôt qu'une privée, contrairement aux États-Unis (NPR et PBS sont moins bien classés). Ces chaînes publiques sont souvent financées par des redevances obligatoires et ont le devoir de fournir un contenu éducatif et objectif, en maintenant un équilibre politique (comme l'ARD et la ZDF en Allemagne).
• Si une chaîne comme Fox News tentait de s'implanter en Allemagne, elle pourrait avoir des problèmes légaux et perdre sa licence de diffusion pour manque d'équilibre. Elle serait considérée comme un réseau de propagande plutôt qu'une chaîne d'information.
• Bien qu'il existe des publications partisanes en Europe, elles ne sont pas financées par des institutions publiques, et les Européens, ayant un niveau d'éducation aux médias plus élevé, sont moins susceptibles de confondre une chambre d'écho politique avec une information objective.
• La confiance dans les médias varie en Europe, étant plus élevée dans les pays nordiques, baltes et occidentaux, et plus faible dans les pays du sud et de l'est. Cependant, les experts et le public s'accordent généralement sur la bonne réputation et l'objectivité des médias européens.
• Les médias d'information publics en Europe occidentale sont indépendants, malgré leur financement public. Les comparer à des médias comme Russia Today serait une grossière erreur de qualification.
• La montée des partis nationalistes et populistes s'accompagne d'une suspicion croissante à l'égard des diffuseurs publics, accusés de promouvoir une vision de gauche. Même aux États-Unis, PBS a été critiqué pour son orientation prétendument "woke".
• La distinction entre le biais et l'équilibre est une métrique en constante évolution. Un rapport de Harvard a montré que la couverture du président Trump par l'ARD en Allemagne était majoritairement négative.
• Les journalistes ne sont pas des arbitres neutres ; leurs choix de sujets et leur langage sont subjectifs. La confiance dans les médias est liée à l'idéologie politique et à la satisfaction à l'égard du gouvernement en place.
• Les États-Unis sont l'un des rares pays où les partisans du parti au pouvoir sont moins satisfaits de leurs médias que les non-partisans.
• La presse américaine est majoritairement composée de personnes issues de milieux libéraux. Même les organisations médiatiques visant l'objectivité peuvent être influencées par les croyances et les connaissances de leur personnel.
• Il y a peu de preuves d'une conspiration des médias pour nuire à une administration politique. Parfois, les efforts des médias libéraux pour éviter le biais ont involontairement contribué à la montée des partis de droite. L'exemple de la couverture de l'affaire des emails d'Hillary Clinton par rapport aux scandales de la campagne Trump est cité.
• Le manque de neutralité des médias n'est pas nouveau ; l'administration Nixon avait déjà des préoccupations à ce sujet.
• Une certaine isolation intellectuelle des États-Unis, comparée aux liens historiques et culturels étroits des pays européens, pourrait expliquer une différence dans la perception et la consommation de l'information. Les médias européens, étant plus éducatifs et objectifs, contribuent à un sentiment public plus unifié sur les questions cruciales.
• Le biais médiatique n'est pas seulement une question de gauche contre droite, mais aussi une question de la manière dont on apprend et comprend le discours critique. La recherche de "clics" et l'accent mis sur les conflits et les personnalités peuvent conduire à des bulles de confirmation et à une fragmentation de l'opinion publique.

La vidéo se termine en invitant les spectateurs à partager leur opinion sur le biais de leurs sources d'information et leur démarche pour rechercher des points de vue opposés afin de se former une opinion éclairée.

docker e2b sandbox

Estas referencias no son actuales.

Very interesting

Redes de significados, emociones y experiencias previas del lector, que construyen una realidad mental que va más allá de la mera visualización.

Sin embrago, hay que considerar que la exposición a múltiples fuentes de información audiovisual inhibe la lectura y/o cantidad de lo leido.

Hay que poner atención a que no haya saturación de informración y eso pueda llegar a abrumar al lector

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Reply to the reviewers

Response to reviewers

We sincerely thank all reviewers for taking the time to review our manuscript and for providing insightful comments and suggestions. Your feedback has been invaluable in improving the quality and clarity of our work.

Reviewer #1

Evidence, reproducibility and clarity

This manuscript by Peterl and colleagues seeks to understand the long-standing observation that influenza A virus generally exhibits a filamentous phenotype in vivo which is lost upon serial passaging in vitro or in embryonated chicken eggs. In addressing this question, the authors perform a detailed quantitative comparison of how filamentous and spherical strains of influenza spread in cell culture in the presence or absence of perturbations including neutralizing antibodies, mucin, and disruption of cell-cell junctions.

The manuscript reports several observations that will be of interest to researchers in the area of influenza virus morphology and spread. Using a combination of imaging modalities, the authors convincingly demonstrate that spherical strains of influenza virus produce larger plaques than filamentous strains that are isogenic except for mutations in M1. The authors show that this is at least partly attributable to differences in entry kinetics. The authors also recapitulate a prior finding that filamentous viruses are more resistant to neutralizing antibodies than spherical ones. In most cases, the authors' claims are supported by the data presented. A few partial exceptions are noted below.

The paper would be strengthened by a clearer description of some of the experimental approaches which lack important details in some instances. The scope of the paper is also limited somewhat by the use of immortalized cell lines that lack physiological features of the airway epithelium. Although this limitation is understandable from a technical standpoint, a discussion of these limitations should be included. Specific comments are listed below.

Major Points

In Figure 4, it is not stated at what time the cell density is measured in panel B, and how this might change across the time points sampled in panel C. This would make the experiment difficult to reproduce. This could be a very important consideration if the cells reach confluency soon after the infection is initiated, since the plaque sizes seem statistically similar out to 24hpi in 4B.

Thank you for your comment on cell densities in Figure 4 B. We agree that the quantification of cell confluency across the time points is crucial in this context. Furthermore, we recognize that counting the number of nuclei within a well is not the most accurate method for comparing the two cell lines. We now provide measurements of relative cell density based on plasma membrane staining for uninfected MDCK-WT and MDCK-α-Catenin-KO cells at 24h and 48h for three biological replicates (Figure 4 A and B). These data show that MDCK-α-Catenin-KO have lower confluency (area=229.69 µm2) at 48 h compared to MDCK-WT cells (area=361.24 µm2). While the confluency of MDCK-WT cells was > 95% at both time points, MDCK-a-Catenin-KO cells did not reach 70% confluency, which reflects the lack of adherens junctions in these cells.

In Figure 4F, it appears that plaque sizes for M1Ud are less affected by mucin than M1WSN plaques at all concentrations tested. However, the authors conclude that "mucin did not show any IAV morphology-dependent inhibitory effect as indicated by the slopes of linear fits of the plaque diameters" (Line 265). I understand that the authors are looking for dose-dependent effects, but it is not clear to me why an analysis based on the slope is preferable, especially when the response to mucins may not be linear. How does the availability of IAV receptors in the porcine gastric mucin used here compare to human airway mucins? Finally, the authors should clarify the number of replicates for this experiment.

Thank you for pointing out that the data representation of IAV WSN and WSN-M1Udorn plaque growth in the presence of mucin (Figure 4 C) lacked clarity. We agree and removed the regression fitting and, instead, show all individual plaque sizes (Extended Figure 4 B). We now provide relative reduction of plaque sizes compared between WSN and WSN-M1Udorn plaques at each mucin concentration using 3 or 4 independent experiments (Figure 4 E). This did not reveal that there was a significant reduction in plaque size change between WSN and WSN-M1Udorn in the absence or presence of mucin. We changed our conclusion: "mucin did not show an IAV morphology-dependent inhibitory effect as indicated by the relative plaque size decrease of WSN-M1Udorn compared to WSN across the mucin concentrations" (Line 278).

We have included information on the mucus composition and receptor availability in the discussion: "Notably, we used porcine gastric mucin, which might differ structurally and in the sialic acid linkage types compared to human mucins (Nordman et al., 2002, doi: 10.1042/bj3640191; Zhang et al., 2021, doi: 10.1007/s10719-021-10014-y). However, both in the porcine stomach and human airway, MUC5AC molecules are the predominant gel-forming mucins." (Graigner et al., 2006, 10.1007/s11095-006-0255-0) (Line 436).

One key difference between the cells used here and the airway epithelium is the presence of multiciliated cells that could alter viral transport in ways that depend on morphology and may be difficult to predict. I appreciate that this concept is outside the scope of the current work, but it is an important point that warrants mention.

We have now included fluorescence microscopy data using anti-MUC5AC antibody to assess mucin production in Calu-3 cells. Importantly, we could demonstrate that Calu-3 cells used in our study express mucins (Figure 4 D). We acknowledge that the absence of multiciliated cells is a limitation and plan to address this in future studies by using air-liquid interface cultures and by incorporating primary human bronchial cells. We established a transwell Calu-3 cell culture under air-liquid interface (ALI) conditions, which allowed for cell polarization. The apical surface of Calu-3 cells grown in an ALI culture contains more mucin than in liquid-covered unpolarized cultures. We plan to adapt and further develop a correlative imaging workflow to be able to assess spread in transwells in a separate study, as this is technically more challenging. We have included this in the discussion (Line 440-444).

Minor Points

It is somewhat unclear what is being captured in the data in Figure 5D-I. I assume that the cell surfaces that are imaged here are from infected cells within the plaque. If this is the case, it is difficult to tell whether the particles that are being quantified are incoming viruses or viruses that are currently budding. MEDI8852 is a stalk-binding antibody which would not be expected to inhibit viral attachment. This is unlikely to change the interpretation since the data shows differences between spherical and filamentous strains. However, a clearer description of this data would be helpful.

We appreciate your constructive feedback. Figure 5 captures the effect of HA-stalk-binding MEDI8852 antibodies on IAV spread and morphology. While this antibody does not prevent receptor binding, it blocks membrane fusion and exerts pressure on the viruses, which, based on our hypothesis, can be overcome by increasing the number of HA on the surface of filamentous viruses. This is now also confirmed in Figure 5B showing that entry of spherical viruses is more sensitive to MEDI8852 than entry of filamentous viruses above concentration of 5 nM.

SEM images of IAV plaques in MDCK cells in the presence of 1 nM MEDI8852 antibody show that viral morphology is not altered by antibody pressure. We agree that this method provides information on IAV morphology but does not allow us to distinguish between incoming or budding viruses. However, virus entry is fast, and IAV release from plasma membrane is slow as obvious from transmission electron microscopy studies showing large quantities of budding virions connected to plasma membrane by budding neck (example: DOI: 10.1099/vir.0.036715-0). Hence, it can be assumed that the majority of viruses captured by SEM on the cell surface are budding viruses. We have included this in the discussion (Line 409-414).

Nevertheless, to further address this limitation, we now provide a more robust analysis of IAV particle numbers and morphologies from supernatants of serial passaging in MDCK cells under MEDI8852 antibody pressure, using cryo-EM (Fig. 5 D, E). In accordance with the SEM data, we did not observe morphological changes of IAV in the presence of the antibody.

For experiments in Calu-3 cells, is trypsin added to the culture media following infection? If not, what percentage of HA is proteolytically cleaved? I would expect these cells to express activating proteases, but if activation is less efficient, this could favor the filamentous strain (as discussed in ref 49).

Thank you for this comment. Yes, trypsin was added to the medium of Calu-3 cells during infection. We included this in the methods section.

The schematic in Figure 4D illustrates mucins as tethered to the cell surface. This does not reflect the experiments in Figure 4E and F, where secreted mucins are added to the overlay media.

We agree, and we removed the schematic representation of mucins in Figure 4D, instead we show data on mucin production in Calu-3 cells (Figure 4 D).

There are a few small typos. Line 61: "to results in" and Line 111: "neutralizing antibodies against hemagglutinin are more effectively blocking virions with spherical morphology."

We corrected the typo in line 61 and changed the phrasing of lines 111-112 for more clarity.

Significance

A strength of this manuscript is the quantitative rigor of the approaches used, which reveal interesting differences in the spread of filamentous and spherical influenza. These differences are compelling, but are limited somewhat in their significance by the difficulty of evaluating whether or not some of the observations would be preserved in differentiated airway epithelial cells. The authors do not over-generalize their conclusions, but more detailed discussion of these potential limitations is warranted.

As mentioned above, we agree that a differentiated airway is important; however, assessing determining factors responsible for inhibition might be difficult due to the high complexity of the culture composed of different cells. The presented methods allow quantitatively assessing individual factors, which provides benefits. Hence, both approaches are valid and important.

Reviewer #2

Evidence, reproducibility and clarity

Summary: This manuscript by Peteryl and colleagues explores the question of why some influenza viruses (typically those that have been recently isolated from animals, though also the Udorn strain) produce filamentous particles, while influenza viruses that have been adapted to eggs or cell culture form spherical particles. This is a long standing question in the influenza field, and the authors have used a nice set of new tools and approaches to shed light on this question. They created mScarlet labelled viruses that produce spherical (WSN) or predominantly filamentous (WSN with an M segment from Udorn) virions, but share the same glycoproteins. While this approach is not novel (the fact that the segment 7 of Udorn drives a filamentous phenotype has been previously demonstrated), the authors used these viruses in an elegant series of experiments to look at the rate of cell to cell spread within a plaque to show that the spherical viruses spread more quickly. The authors then explored the effect of cell density, inhibitors designed to inhibit different routes of viral entry, and the presence of neutralizing antibody. The experiments are thoughtfully designed, and the electron microscopy in particular is beautifully done. In general, the conclusions are supported by the data, though the specific claim that filamentous viruses have an advantage in viral entry in the presence of neutralizing antibody would be significantly strengthened by performing the specific entry assay the authors employ earlier in the manuscript.

Major comments: The key conclusions are largely convincing, though the authors should perform the entry assays they employ in figure 3 (measuring the kinetics of entry and the efficiency of entry) to determine whether the delay in cell to cell spread they observe for spherical viruses in the presence of neutralizing antibody is due specifically to the effect on entry. I also am concerned about the method used to determine that the antibody treatment in Fig 5D-H results in a difference in the number of virions produced. While I appreciate that SEM is time consuming and difficult to quantify, counting the number of virions seen in a single field of view from 7 or 12 cells does not provide a robust foundation to support the central claim of the paper, that the difference in speed of filamentous and spherical viral spread is due to a difference in their ability to support viral entry in the presence of neutralizing antibody . If the authors wish to count virions produced by the WSN/WSN M-Udorn viruses in the presence/absence of neutralizing antibody it would be sensible to perform a synchronized high MOI infection and measure infectious titer by plaque assay (as this would be able to quickly and easily measure millions of virions produced by hundreds of thousands of cells).

Thank you very much for the suggestion to perform an entry assay in the presence of a neutralizing antibody to determine whether the antibody acts at the level of viral entry. We now provide data on the entry efficiency of WSN and WSN-M1Udorn in the presence of increasing MEDI8852 concentrations (Figure 5 B). The results show that entry of the WSN spherical viruses are more affected by MEDI8852 at 5 nM and 10 nM, compared to WSN-M1Udorn, suggesting that the reduced plaque growth presented in Figure 5 C reflects an inhibition of IAV entry.

We agree that the quantification of virions at the surface of 7-12 cells in SEM images is not a robust method. Therefore, we removed the quantification as it is technically very time-consuming to obtain a large enough dataset or to perform statical power analysis on how many cells would need to be screened. We additionally performed a serial passaging experiment of WSN and WSN-M1Udorn under antibody pressure, providing a more robust analysis of IAV particle numbers and morphologies from supernatants using cryo-EM (Fig. 5 D, E). By quantifying the length/diameter ratio of at least 80 virions per condition, we observed that both IAV morphologies remained stable in the presence of the antibody after five passages.

The two entry assays could be done in parallel, and I anticipate them to take ~3 days per replicate (a day to seed, a day to infect/add NH4Cl at the indicated time points and fix, a day to image and analyze data). Similarly, infected cells at high MOI in the presence/absence of nAb, collecting viral supernatants, and tittering by plaque assay should take ~one week. The reagents to perform these experiments are already in hand, and as the costs will be limited to standard tissue culture reagents, using a microscopy set up the authors already possess. The experiments throughout the paper are well described, with appropriate methodological detail and statistical analysis.

Minor comments: • Viruses without the mScarlet spread faster, the WSN-Udorn has more viruses with mScarlet than the WSN does so how do we know that some of the difference isn't down to that?

Thank you for this important question. It is correct that viruses without mScarlet spread faster. We used WSN mScarlet viruses for CLSEM and live cell imaging of Calu-3 cells. To ensure that the observed differences in viral spread kinetics were not attributable to the presence or absence of mScarlet but to viral morphology, we conducted additional immunofluorescence staining for viral nucleoprotein (NP) or matrix protein 2 (M2) (Extended Figure 1 H-I). This allowed us to account for all viral plaques, including those that were not mScarlet-positive. This way we obtained data for our experiments with MDCK-α-Catenin-KO cells, mucin, zanamivir and MEDI8852 (Figure 4 and 5).

• While Calu3 cells are reported to make mucus the authors should verify the expression of relevant mucus proteins in their hands, and this phenotype can be variable depending on culture conditions.

Thank you for highlighting this important point. We verified the expression of MUC5AC in Calu-3 cells grown on cover slips and observed MUC5AC expression in distinct puncta (Figure 5 D).

• In 5F and I does 'mock' mean no antibody or no virus?

We apologize for the imprecise nomenclature in Figure 5 F and changed the Figure description.

• The authors should either include data to support the claim in line 410: "Our data provide further evidence that IAV filamentous morphology is lost to accelerate cell-to-cell spread by faster entry kinetics and to achieve higher entry efficiency" or reword this sentence, since at present this manuscript does not include experiments demonstrating the loss of filamentous morphology in tissue culture of the WSN-M1 Udorn virus.

Thank you, we agree and modified the sentence.

Significance

The data and conclusions presented in this manuscript are exciting and novel, and should be of high interest to virologists and cell biologists. The work builds on (and appropriately references) prior work in the field of influenza particle shape by the Lamb, Barclay, Garcia-Sastre, Vahey, Fletcher and Ivanovic groups. It provides new information and techniques to show that spherical virions spread faster than filamentous virions within plaques, and this advantage is not negated by cell density, the presence of mucus, or different entry inhibitors but is significantly reduced in the presence of neutralizing antibodies. It also includes other useful observations to the field (the fact that infected Calu3 cells migrate to the center of infected plaques, the fact that the entry kinetics and success rate of filaments is lower compared to spheres). Expertise: virology, influenza, virion morphology, cell biology

__Reviewer #3 __

Evidence, reproducibility and clarity:

The manuscript by Peterl et al. deals with the still interesting question of why influenza A viruses are filamentous in natural isolates but adopt a spherical phenotype in cell culture. The authors generated recombinant IAV reporter viruses that display identical antigenic (HA and NA) surfaces but differ in their morphology due to expression of an M1 protein that confers a spherical or filamentous phenotype. The data show that spherical viruses exhibit increased entry kinetics and spread faster in cell culture compared to filamentous viruses and that this is also the case in the presence of mucins and at a low cell density. Interestingly, the authors found that spherical viruses are more efficiently blocked by neutralizing HA antibodies than filamentous viruses, providing an interesting advantage for the filamentous phenotype of natural IAV isolates due to antibody pressure. The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear. The fact that a current preprint also describes that neutralizing antibodies drives filamentous virus formation (as mentioned by the authors in the discussion) does not diminish the message and quality of this work. There were a few minor open questions that came to mind that could be included in the discussion: The authors found that the filamentous morphology was stable throughout multiple rounds of infection during plaque formation. Is this still the case even with multiple passages (e.g 10x) in cell culture or does the number of spherical particles increase at some point?

Thank you for your positive feedback and this suggestion. We performed serial passaging of WSN and WSN-M1Udorn in MDCK cells in the presence of 1 nM MEDI8852 antibody and harvested supernatants from passage 1 and 5. Supernatants were plunge-frozen, and virion counts and morphologies were determined by cryo-electron microscopy. Data from at least 80 analyzed virions per condition showed that the overall number of spherical and filamentous virions was reduced after passage 5 under antibody pressure (Fig 5 D). However, both morphologies remained stable throughout five passages in the presence of MEDI8852 (Fig. 5 E). We did not observe an increase in spherical particles after five passages.

The filamentous virus spreads slower in cell culture. Does NA play a role here? NA is probably distributed differently on the surface of filamentous viruses (at the tips) than on spherical viruses?

Thank you for this comment. As correctly pointed out, NA is enriched on one side/tip of filamentous (Calder et al., 2010, doi:10.1073/pnas.1002123107) or spherical IAV as now highlighted in Figure 1 D and E (white arrowheads). This asymmetric NA distribution and the HA-NA balance have been reported to be crucial for the release of newly formed virions and their spread through the mucus layer in the airway epithelium (De Vries et al., 2019, doi: 10.1016/j.tim.2019.08.010). Additionally, we compared the role of NA in the spread of spherical and filamentous IAV by performing fluorescent plaque assays in the presence of Zanamivir, a potent NA inhibitor. Analysis of plaque growth in the presence of increasing Zanamivir concentrations showed that the spread of both IAV morphologies was inhibited to a comparable extent (Figure 4 F and extended Figure 4 C). This result suggests that the inhibition of NA enzymatic activity does not influence the IAV morphology-dependent spread. We have included this information in the results (Line 281-285) and discussion (Line 465-468).

Reviewer #3 (Significance (Required)):

The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear.

Similar to $$y\left(t\right)=y\left(0\right)+v_y\left(0\right)t+\tfrac{1}{2}gt^2$$

This is basically a "sideways" gravitational system, \(U_1\left(x\right)=\kappa\,x\) as compared to regular gravitational potential energy, \(U\left(y\right)=\gamma\,y\).

$$\kappa=-E/x_0$$ and $$\gamma=-mg$$

If horizontal gravity DID exist, it would be something like \(U_1\)

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Reply to the reviewers

1. General Statements

We thank the reviewers for their thorough evaluation of this manuscript. We are pleased that overall, they found our work and results valuable for the scientific community. Based on their feedback, we performed additional experiments and made several changes to strengthen the manuscript and expand the target audience.

*All three reviewers pointed out that the manuscript lacked demonstration of OneSABER method applicability across sample types (i.e., its claimed versatility) and other whole-mount systems beyond the Macrostomum lignano flatworm. *

We now include an additional results section with accompanying figures (Figs. 6 and 7) that demonstrate the application of OneSABER in whole-mount samples of another flatworm, the planarian Schmidtea mediterranea (Fig. 6), which is much larger than M. lignano, and in formalin-fixed paraffin-embedded (FFPE) mouse small intestine tissue sections (Fig. 7). We believe that these additional experiments on different sample types demonstrate the versatility of the OneSABER approach.

Please note that two more authors, Jan Freark de Boer and Folkert Kuipers, have been added for their contribution to mouse FFPE sections.

Furthermore, two reviewers asked for an additional main figure with a comparison of the signal strengths between the different OneSABER methods.

We have addressed this comment by including an additional results section and its adjacent figure (Fig. 5), where we provide a comparison of fluorescent signals from the same probes and gene but different OneSABER development methods.

Additionally, to implement the revisions, we modified Fig. 1 and Supplementary Fig. 6 and broadened Supplementary Tables S1-S2, S4-S6.

2. Point-by-point description of the revisions

Reviewer #1

1) “Fig.1 seems to suggest that the protocol for in vitro swapping of 3' concatemers happens in two consecutive PCR steps. I recommend indicating in the figure that the switching can be conducted in a single in vitro reaction.”

We have changed Fig. 1 to make this clearer.

2) “Is it possible to multiplex the switching in one single reaction? For example, perform p27 to p28 and p29 to p30 simultaneously? This will be crucial for the split-probe methodology.”

We did not test it. This should be possible if there is no overlap between the 3’ initiator sequences. However, it seems counterproductive as the elongation efficiencies of switching reactions from the 3’ initiator sequences to another concatemer may vary (Supplementary Fig. S6). Running independent extension/switch reactions and performing equimolar mixing of purified extended probes could be a better solution.

3) “Did the authors encounter any switching hairpins sequence that does not work? If not, can they postulate, what are the requirements for the design of switching sequences.”

The design criteria followed the requirements postulated in the original SABER article and its Supplementary Materials (Kishi et al 2019). All switching hairpins we tested in the pairs of the 3 used 3’ initiator sequences (p27, p28 and p30) worked, but elongation efficiencies varied (see an example in Supplementary Fig. S6).

4) “Is there cross hybridization between the switched and original hairpins? For example, can the authors show that the signals from p27 and p30 do not overlaps?”

The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smedwi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

5) “Can the authors quantify results from the direct, AP, TSA, and HCR? What do you mean by 'narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible'?”

*“I agree with reviewer #2 regarding the lack of comparison to standard SABER.” *

A comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

We have rephrased the sentence for clarity. From “As a result, despite higher intracellular resolution, some narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible for the human eye after SABER HCR (Figs. 3, 4).” to “As a result, despite higher intracellular resolution, some fine anatomical structures like neural chords (syt11) or muscles (tnnt2) are less resolved by widefield fluorescence microscopy after SABER HCR FISH compared to SABER TSA FISH”

Reviewer #2

1) “This work is building on standard SABER (a set of PER-extended primary probes that serve as landing pads for secondary fluorescently-labeled readout oligos) and pSABER (the readout oligo carries HRP instead of a dye for downstream TSA). The novelty of the work presented here is introducing additional variations of signal amplification, i.e. by using an hapten-labeled oligo to recruit a tertiary readout probe (antibodies conjugated with HRP or AP) or using SABER in combination with HCR. Since SABER can be seen as the underlying platform and pSABER was (arguably) also already introduced as a new platform by Attar et al. 2023, it seems difficult to introduce OneSABER as yet another new platform, of which standard SABER and pSABER are a part of. The reviewer encourages the authors to overthink the conceptual introduction, which in view of its certainly distinct novel features might allow a clearer distinction to previous work.”

We agree with the reviewer’s comments. We have added additional information in the Introduction section to clarify the novelty and key distinct features of OneSABER that justify its separation from other SABER protocols.

2) “Although the authors take care in tributing prior work, some of the studies are only mentioned in the results section, one of such cases is pSABER by Attar et al. 2023. The close relation between pSABER and SABER TSA (HRP on readout oligo vs. hapten on readout oligo + HRP-conjugated antibody) needs to be better positioned in the introduction, clearly framing earlier work, inspirations drawn etc.. This is in line with my previous point.”

The pSABER preprint article by Attar et al. 2023 (now published in a peer-reviewed journal as Attar et al. 2025) is now mentioned in the Introduction, and its inspirational impact on our research is clearly stated.

3) “Fig. 1 lists the individual modules of the OneSABER platform: i) standard SABER, ii) AP SABER, iii) SABER TSA, iv) pSABER (TSA FISH) (would recommend leaving it with original name when introducing it and include additional explanation in parentheses) and iv) SABER HCR. The main figures feature only AP SABER, SABER TSA and SABER HCR, for standard SABER and pSABER one must look up the SI. Since the authors describe the limited performance of standard SABER for one of their targets of interest (syt11) and since they have tested this target for all five conditions, it would be valuable to include a comparative view of all five platform modules in a single figure for syt11 or even also piwi, which also seems to have been tested for all five. Comparing the signal strength would be useful for the community, at least of each SABER variation compared to standard SABER.”

We agree with the reviewer’s comments. Except for pSABER, a comparison of fluorescence signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5. To make the comparison as objective as possible, all FISH developments were re-done using available “far red” fluorophores, except for pSABER. Unfortunately, our directly labeled HRP oligonucleotides for pSABER lost their activity after a year of storage at +4oC. These conjugated oligonucleotides are very expensive and, given their limited shelf life, we cannot justify ordering a new batch for this experiment. Therefore, we only have the data for pSABER syt11 with FITC green tyramide, which is not comparable to “far red” fluorophore signals. This issue has also been discussed in the main text.

In addition, we have modified Fig. 1, as suggested.

4) “The description of how the authors designed their probes is very detailed and they also provide a nice step-by-step protocol for their individual commands using Oligominer and BLAT software. This reviewer is wondering how the authors chose their PER sequences that they appended to their mined set of homologous in situ hybridization probes (p27,p28,p30). This is a general problem of multiplexed ISH approaches with single-stranded overhang, could the author's comment on potential self-interaction of the appended sequence with the homologous part, which might limit the PER efficiency, or elaborate on their choice?”

As being ourselves novice to SABER when we started our work, we based our selection of the p27, p28, and p30 PER sequences on their multiple co-occurrences in previous publications (Amamoto et al. 2019, doi: 10.7554/eLife.51452; Saka et al. 2019, doi: 10.1038/s41587-019-0207-y; Wang et al. 2020, doi: 10.1016/j.omtm.2020.10.003; Salinas-Saavedra et al. 2023, doi: 10.1016/j.celrep.2023.112687; and Attar et al. 2023, doi: 10.1101/2023.01.30.526264). We did not consider the potential interference between PER concatemers and homologous primary probe-binding sequences. However, as all PER concatemers were specifically designed to lack G nucleotides to keep them from self-annealing (Kishi et al. 2019, doi: 10.1038/s41592-019-0404-0), we assumed that it would also reduce potential annealing to the homologous part of the probe.

5) “Fig.1 and l. 125 describe straightforward in vitro switching of the concatemer sequence for an existing set of primary probes as a central feature of the OneSABER platform. However, the authors to my knowledge do not show such experiments themselves and only cite the original SABER paper by Kishi et al. 2019. This reviewer would be grateful to be pointed toward where in Kishi et al. 2019 this was demonstrated, however in view of this central part of the swopping scheme in the OneSABER platform an experiment showing this swopping is missing.”

In the article by Kishi et al. 2019, concatemer switching/swapping is termed as “primer remapping”. We found this term confusing because it does not describe the essence of the reaction. The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smewi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

6) “the description of Table S6 could use additional information in the legend such that the reader does not have to scroll down to Section S1 to retrieve the information (PER reaction, gel conditions, ladder is dsDNA, what are the individual bands)”

Probably, the reviewer meant Fig. S6. We now wrote a more detailed caption for the figure and extended it with a second panel (B) to illustrate the results of 3’ concatemer swapping.

7) “the manuscript features an extensive set of resources in main body, supplementary materials and protocols. It is important and usually not merited sufficiently making the effort to compare orthogonal approaches for a given aim. This reviewer particularly appreciates the detailed strengths & weaknesses discussion in Table S6.”

We thank the reviewer for the appreciation of our work.

8) “Minor comments:

-Definitions should be consistent, in Fig. 1 all approaches are defined with FISH added, but this definition is not followed consistently in the main text.”

These definitions are now made consistent throughout the text.

9) “Optional:

-The authors describe several newly developed optimization steps during sample preparation for M. lignano ISH experiments compared to established ones. If the data exists, they include a supplementary figure showing improvements of optimized protocol steps”

As almost every step and the buffer recipes were different from the original ISH protocol by Pfister et al. (2007) because of the use of liquid-exchange columns, different probes, and development chemistry, we believe that a comparison would be excessive. We think that the key difference points are already substantially highlighted in the results section.

Reviewer #3

1) “Despite including a whole figure (Figure 1) featuring the operation scheme of the OneSABER platform, the figure as well as the associated text fall short with respect to clearly stating the advantage of the different aspects of the platform. Consider a clearer and more thorough explanation of the different aspects of the platfrom.”

Details on the advantages and disadvantages of using different OneSABER methods in terms of their experimental application and cost efficiency are described in Supplementary Tables S4-S6 of the submitted manuscript. However, we agree that the description in Fig. 1 was too concise and also did not refer to these tables. We have expanded the description in Fig. 1.

2) “Related to the first comment: A more detailed description of the similarities and/or differences of this platform relative to similar applications such as the study by Hall et al, 2024”

The mere point of mentioning the preprint of Hall et al. 2024 (now peer-reviewed, https://doi.org/10.1016/j.celrep.2024.114892) was to acknowledge that in M. lignano the HCR technology has been previously applied (although only once), while all other previously published works on M. lignano utilized canonical antisense RNA probes colorimetric in situ hybridization. We have extensively mentioned the HCR approach and its working principles throughout the submitted manuscript.

3) “The authors describe the probes used as short, synthetic DNA probes targeting short RNA transcripts. Are these probes Oligopaints (Beliveau et al, 2015)? Why is that not more clearly stated in the text?”

Oligopaints use oligo libraries as a renewable source of FISH probes, and these libraries are amplified with fluorophore-conjugated PCR primers. We used synthetic DNA probes directly. In this sense, our probe sets are not oligopaints. However, we used the OligoMiner pipeline of Oligopaints for the design of the probes, and thus used the same tiling strategy as oligopaints. We believe that this has been explained in the manuscript. Please refer to comment 4 of Reviewer 2.

4) “Line 105, p5: The authors state that the number of probes depends on the target RNA length and its expression strength. This data should be in the main text and described in detail since it is a major aspect of the platform design.”

We believe that this statement is common sense, as one cannot design more than 5x 30-50 bp probes for 200 nt transcripts, while for a 2000 bp mRNA, the theoretical limit is ~50 probes. Similarly, weakly expressed genes (regardless of their length) would require either more probes to reach the detection threshold or stronger amplification through choice of concatemer length and/or signal developing techniques. We have rephrased this sentence in the main text to reflect this.

5) “Figure 2 showcases one of the most compelling data supporting the versatility of the platform. Can the signals in each panel be quantified and compared to 1. Published Ab staining? Is there a clear correlation in the intensity of the signals? 2. Between Vector Blue and NBT? 3. Chemical staining and FISH signals?”

Since M. lignano is a relatively new model, there are no published antibody stainings for M. lignano genes used in this study. Furthermore, colorimetric precipitate methods are not quantitative but rather qualitative, because their signal strength is proportional to both the target RNA level and the development time; thus, signals from weakly expressed transcripts can be “boosted” simply by longer development. Therefore, a correct quantitative comparison with colorimetric methods, as requested by the reviewer, was not possible. However, with some corrections on fluorophore differences and animal-to-animal variability, it is possible to roughly compare peak saturation intensities for FISH methods if the experiments are designed for this aim. We performed these experiments, and a comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

Minor comments:

6) “The whole mount images and signals are often diffuse, can they be visualized using a DIC where the morphology of the organism is clearer?”

We are unsure which images appear to be diffused to the reviewer. The other reviewers have not pointed out similar issues. Perhaps the question resolves once full-resolution uncompressed images are uploaded.

7) “In order to support the claim that this is a universal approach for whole-mount staining, can the authors show an example of applicability to C. elegans?”

This is now addressed. We included two additional results sections with two accompanying figures (Figs. 6 and 7) that demonstrate OneSABER’s application in whole-mount samples of a much larger than M. lignano model flatworm, the planarian Schmidtea mediterranea (Fig. 6), as well as in formalin-fixed paraffin-embedded (FFPE) small intestine tissue sections of a mouse model (Fig. 7).

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors used a subset of a very large, previously generated 16S dataset to:<br /> (1) Assess age-associated features; and (2) develop a fecal microbiome clock, based on an extensive longitudinal sampling of wild baboons for which near-exact chronological age is known. They further seek to understand deviation from age-expected patterns and uncover if and why some individuals have an older or younger microbiome than expected, and the health and longevity implications of such variation. Overall, the authors compellingly achieved their goals of discovering age-associated microbiome features and developing a fecal microbiome clock. They also showed clear and exciting evidence for sex and rank-associated variation in the pace of gut microbiome aging and impacts of seasonality on microbiome age in females. These data add to a growing understanding of modifiers of the pace of age in primates, and links among different biological indicators of age, with implications for understanding and contextualizing human variation. However, in the current version, there are gaps in the analyses with respect to the social environment, and in comparisons with other biological indicators of age. Despite this, I anticipate this work will be impactful, generate new areas of inquiry, and fuel additional comparative studies.

Thank you for the supportive comments and constructive reviews.

Strengths:

The major strengths of the paper are the size and sampling depth of the study population, including the ability to characterize the social and physical environments, and the application of recent and exciting methods to characterize the microbiome clock. An additional strength was the ability of the authors to compare and contrast the relative age-predictive power of the fecal microbiome clock to other biological methods of age estimation available for the study population (dental wear, blood cell parameters, methylation data). Furthermore, the writing and support materials are clear, informative and visually appealing.

Weaknesses:

It seems clear that more could be done in the area of drawing comparisons among the microbiome clock and other metrics of biological age, given the extensive data available for the study population. It was confusing to see this goal (i.e. "(i) to test whether microbiome age is correlated with other hallmarks of biological age in this population"), listed as a future direction, when the authors began this process here and have the data to do more; it would add to the impact of the paper to see this more extensively developed.

Comparing the microbiome clock to other metrics of biological age in our population is a high priority (these other metrics of biological age are in Table S5 and include epigenetic age measured in blood, the non-invasive physiology and behavior clock (NPB clock), dentine exposure, body mass index, and blood cell counts (Galbany et al. 2011; Altmann et al. 2010; Jayashankar et al. 2003; Weibel et al. 2024; Anderson et al. 2021)). However, we have opted to test these relationships in a separate manuscript. We made this decision because of the complexity of the analytical task: these metrics were not necessarily collected on the same subjects, and when they were, each metric was often measured at a different age for a given animal. Further, two of the metrics (microbiome clock and NPB clock) are measured longitudinally within subjects but on different time scales (the NPB clock is measured annually while microbiome age is measured in individual samples). The other metrics are cross-sectional. Testing the correlations between them will require exploration of how subject inclusion and time scale affect the relationships between metrics.

We now explain the complexity of this analysis in the discussion in lines 447-450. In addition, we have added the NPB clock (Weibel et al. 2024) to the text in lines 260-262 and to Table S5.

An additional weakness of the current set of analyses is that the authors did not explore the impact of current social network connectedness on microbiome parameters, despite the landmark finding from members of this authorship studying the same population that "Social networks predict gut microbiome composition in wild baboons" published here in eLife some years ago. While a mother's social connectedness is included as a parameter of early life adversity, overall the authors focus strongly on social dominance rank, without discussion of that parameter's impact on social network size or directly assessing it.

Thank you for raising this important point, which was not well explained in our manuscript. We find that the signatures of social group membership and social network proximity are only detectable our population for samples collected close in time. All of the samples analyzed in  Tung et al. 2015 (“Social networks predict gut microbiome composition in wild baboons”) were collected within six weeks of each other. By contrast, the data set analyzed here spans 14 years, with very few samples from close social partners collected close in time. Hence, the effects of social group membership and social proximity are weak or undetectable. We described these findings in Grieneisen et al. 2021 and Bjork et al. 2022, and we now explain this logic on line 530, which states, “We did not model individual social network position because prior analyses of this data set find no evidence that close social partners have more similar gut microbiomes, probably because we lack samples from close social partners sampled close in time (Grieneisen et al. 2021; Björk et al. 2022).”

We do find small effects of social group membership, which is included as a random effect in our models of how each microbiome feature is associated with host age (line 529) and our models predicting microbiome Dage (line 606; Table S6).

Reviewer #2 (Public review):

Summary:

Dasari et al present an interesting study investigating the use of 'microbiota age' as an alternative to other measures of 'biological age'. The study provides several curious insights into biological aging. Although 'microbiota age' holds potential as a proxy of biological age, it comes with limitations considering the gut microbial community can be influenced by various non-age related factors, and various age-related stressors may not manifest in changes in the gut microbiota. The work would benefit from a more comprehensive discussion, that includes the limitations of the study and what these mean to the interpretation of the results.

We agree and have text to the discussion that expands on the limitations of this study and what those limitations mean for the interpretation of the results. For instance, lines 395-400 read, “Despite the relative accuracy of the baboon microbiome clock compared to similar clocks in humans, our clock has several limitations. First, the clock’s ability to predict  individual age is lower than for age clocks based on patterns of DNA methylation—both for humans and baboons (Horvath 2013; Marioni et al. 2015; Chen et al. 2016; Binder et al. 2018; Anderson et al. 2021). One reason for this difference may be that gut microbiomes can be influenced by several non-age-related factors, including social group membership, seasonal changes in resource use, and fluctuations in microbial communities in the environment”

In addition, lines 405-411 now reads, “Third, the relationships between potential socio-environmental drivers of biological aging and the resulting biological age predictions were inconsistent. For instance, some sources of early life adversity were linked to old-for-age gut microbiomes (e.g., males born into large social groups), while others were linked to young-for-age microbiomes (e.g., males who experienced maternal social isolation or early life drought), or were unrelated to gut microbiome age (e.g., males who experienced maternal loss; any source of early life adversity in females).”

Strengths:

The dataset this study is based on is impressive, and can reveal various insights into biological ageing and beyond. The analysis implemented is extensive and high-level.

Weaknesses:

The key weakness is the use of microbiota age instead of e.g., DNA-methylation-based epigenetic age as a proxy of biological ageing, for reasons stated in the summary. DNA methylation levels can be measured from faecal samples, and as such epigenetic clocks too can be non-invasive. I will provide authors a list of minor edits to improve the read, to provide more details on Methods, and to make sure study limitations are discussed comprehensively.

Thank you for this point. In response, we have deleted the text from the discussion that stated that non-invasive sampling is an advantage of microbiome clocks. In addition, we now propose a non-invasive epigenetic clock from fecal samples as an important future direction for our population (see line 450).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Abstract - The opening 2 sentences are not especially original or reflective of the potential value/ premise of the study. Members of this team have themselves measured variation in biological age in many different ways, and the implication that measuring a microbiome clock is easy or straightforward is not compelling. This paper is very interesting and provides unique insight, but I think overall there is a missed opportunity in the abstract to emphasize this, given the innovative science presented here. Furthermore, the last 2 sentences of the abstract are especially interesting - but missing a final statement on the broader significance of research outside of baboons.

We appreciate these comments and have revised the Abstract accordingly. The introductory sentences now read, “Mammalian gut microbiomes are highly dynamic communities that shape and are shaped by host aging, including age-related changes to host immunity, metabolism, and behavior. As such, gut microbial composition may provide valuable information on host biological age.” (lines 31-34). The last two sentences of the abstract now read, “Hence, in our host population, gut microbiome age largely reflects current, as opposed to past, social and environmental conditions, and does not predict the pace of host development or host mortality risk. We add to a growing understanding of how age is reflected in different host phenotypes and what forces modify biological age in primates.” (lines 40-43).

If possible, it would be highly useful to present some comments on concordance in patterns at different levels. Are all ASVs assessed at both the family and genus levels? Do they follow similar patterns when assessed at different levels? What can we learn about the system by looking at different levels of taxonomic assignment?

The section on relationships between host age and individual microbiome features is already lengthy, so we have not added an analysis of concordance between different taxonomic levels. However, we added a justification for why we tested for age signatures in different levels of taxa to line 171, which reads, “We tested these different taxonomic levels in order to learn whether the degree to which coarse and fine-grained designations categories were associated with host age.”

To calculate the delta age - please clarify if this was done at the level of years, as suggested in Figure 3C, or at the level of months or portion months, etc?

Delta age is measured in years. This is now clarified in lines 294, 295, and 578.

Spelling mistake in table S12, cell B4 (Octovber)

Thank you. This typo has been corrected.

Given the start intro with vertebrates, the second paragraph needs some tweaking to be appropriate. Perhaps, "At least among mammals, one valuable marker of biological aging may lie in the composition and dynamics of the mammalian gut microbiome (7-10)." Or simply remove "mammalian".

We have updated this sentence based on your suggestions in line 54. It reads, “In mammals, one valuable marker of biological aging may lie in the composition and dynamics of the gut microbiome (Claesson et al. 2012; Heintz and Mair 2014; O’Toole and Jeffery 2015; Sadoughi et al. 2022).”

A rewrite at the end of the introduction is needed to avoid the almost direct repetition in lines 115-118 and 129-131 (including lit cited). One potentially effective way to approach this is to keep the predictions in the earlier paragraph and then more clearly center the approach and the overarching results statement in the latter paragraph. (I.e., "we find that season and social rank have stronger effects on microbiome age than early life events. Further, microbiome age does not predict host development or mortality.").

Thank you for pointing this out. We have re-organized the predictions in the introduction based on your suggestion. The alternative “recency effects” model now appears in the paragraph that starts in line 110. The final paragraph then centers on the overall approach and the results statement (lines 128-140)

Be clear in each case where taxon-level trends are discussed if it's at Family, Genus, or other level. It's there most, but not all, of the time.

We have gone through the text and clarified what taxa or microbiome feature was the subject of our analyses in any places where this was not clear.

In the legend for Figure 2, add clarification for how values to right versus left of the centered value should be interpreted with respect to age (e.g. "values to x of the center are more abundant in older individuals").

We now clarify in Figure 2C and 2D that “Positive values are more abundant in older hosts”.

Figure 3 - Are Panels A, B, and C all needed - can the value for all individuals not also be overlaid in the panel showing sex differences and the same point showing individuals with "old" and "young" microbiomes be added in the same plot if it was slightly larger?

We agree and have simplified Figure 3. We reduced the number of panels from three to two, and we added the information about how to calculate delta age to Panel A. We also moved the equation from the top of Panel C to the bottom right of Panel A.

Reviewer #2 (Recommendations for the authors):

Dasari et al present an interesting study investigating the use of 'microbiota age' as an alternative to other measures of 'biological age'. The study provides several curious insights which in principle warrant publication. However, I do think the manuscript should be carefully revised. Below I list some minor revisions that should be implemented. Importantly, the authors should discuss in the Discussion the pros and cons of using 'microbiota age' as a proxy of 'biological age'. Further, the authors should provide more information on Methods, to make sure the study can be replicated.

Thank you for these important points. Based on your comments and those of the first reviewer, we have expanded our discussion of the limitations of using microbiota age as a proxy for biological age (see edits to the paragraph starting in line 395).

We have also expanded our methods around sample collection, DNA extraction, and sequencing to describe our sampling methods, strategies to mitigate and address possible contamination, and batch effects. See lines 483-490 and our citations to the original papers where these methods are described in detail.

(1) Lines 85-99: I think this paragraph could be revisited to make the assumptions clearer. For instance, the last sentence is currently a little confusing: are authors expecting males to exhibit old-for-age microbiomes already during the juvenile period?

This prediction has been clarified. Line 96 now reads, “Hence, we predicted that adult male baboons would exhibit gut microbiomes that are old-for-age, compared to adult females (by contrast, we expected no sex effects on microbiome age in juvenile baboons).”

(2) Lines 118-121: Could the authors discuss this assumption in relation to what has been observed e.g., in humans in terms of delays in gut microbiome development? Delayed/accelerated gut microbiome development has been studied before, so this assumption would be stronger if related to what we know from previous studies.

This comment refers to the sentence which originally stated, “However, we also expected that some sources of early life adversity might be linked to young-for-age gut microbiota. For instance, maternal social isolation might delay gut microbiome development due to less frequent microbial exposures from conspecifics.” We have slightly expanded the text here (line 117) to explain our logic. We now include citations for our predictions. We did not include a detailed discussion of prior literature on microbiome development in the interest of keeping the same level of detail across all sections on our predictions.

(3) As the authors discuss, various adversities can lead to old-for-age but also young-for-age microbiome composition. This should be discussed in the limitations.

We agree. This is now discussed in the sentence starting at line 371, which reads, “…deviations from microbiome age predictions are explained by socio-environmental conditions experienced by individual hosts, especially recent conditions, although the effect sizes are small and are not always directionally consistent.” In addition, the text starting at line 405 now reads, “Third, the relationships between potential socio-environmental drivers of biological aging and the resulting biological age predictions were inconsistent. For instance, some sources of early life adversity were linked to old-for-age gut microbiomes (e.g., males born into large social groups), while others were linked to young-for-age microbiomes (e.g., males who experienced maternal social isolation or early life drought), or were unrelated to gut microbiome age (e.g., males who experienced maternal loss; any source of early life adversity in females).”

(4) In various places, e.g., lines 129-131, it is a little unclear at what chronological age authors are expecting microbiota to appear young/old-for-age.

This sentence was removed while responding to the comments from the first reviewer.

(5) Lines 132-133: this statement could be backed by stating that this is because the gut microbiota can change rapidly e.g., when diet changes (or whatever the authors think could be behind this).

We have added an expository sentence at line 123, including new citations. This sentence reads, “Indeed, gut microbiomes are highly dynamic and can change rapidly in response to host diet or other aspects of host physiology, behavior, or environments”.

We now cite:

· Hicks, A.L., et al. (2018). Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nature Communications 9, 1786.

· Kolodny, O., et al. (2019). Coordinated change at the colony level in fruit bat fur microbiomes through time. Nature Ecology & Evolution 3, 116-124.

· Risely, A., et al. (2021) Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats. Nat Commun 12, 6017.

(6) Lines 135-137: current or past season and social rank? This paragraph introduces the idea that it could be past rather than current socio-environmental factors that might predict microbiota age, so the authors should clarify this sentence.

We have clarified the information in this sentence. line 135 now reads, “In general, our results support the idea that a baboon’s current socio-environmental conditions, especially their current social rank and the season of sampling, have stronger effects on microbiome age than early life events—many of which occurred many years prior to sampling.”

(7) Lines 136-137: this sentence could include some kind of a conclusion of this finding. What might this mean?

We have added a sentence at line 138, which speculates that, “…the dynamism of the gut microbiome may often overwhelm and erase early life effects on gut microbiome age.”

(8) Use 'microbiota' or 'microbiome' across the manuscript; currently, the terms are used interchangeably. I don't have a strong opinion on this, although typically 'microbiota' is used when data comes from 16S rRNA.

We have updated the text to replace any instance of “microbiota” with “microbiome”. We use the term microbiome in the sense of this definition from the National Human Genome Research Institute, which defines a microbiome as “the community of microorganisms (such as fungi, bacteria and viruses) that exists in a particular environment”.

(9) Figure 1 legend: make sure to unify formatting; e.g., present sample sizes as N= or n=, rather than both, and either include or do not include commas in 4-digit values (sample sizes).

We have checked the formatting related to sample sizes and the use of commas in 4-digits in the main text and supplement. The formats are now consistent.

(10) Line 166: relative abundances surely?

Following Gloor et al. (2017), our analyses use centered log-ratio (CLR) transformations of read counts, which is the recommended approach for compositional data such as 16S rRNA amplicon read counts. CLR transformations are scale-invariant, so the same ratio is obtained in a sample with few read versus many reads. We now cite Gloor et al. (2017) at line 169 and in the methods in line 517, which reads “centered log ratio (CLR) transformed abundances (i.e., read counts) of each microbial phyla (n=30), family (n=290), genus (n=747), and amplicon sequence variance (ASV) detected in >25% of samples (n=358). CLR transformations are a recommended approach for addressing the compositional nature of 16S rRNA amplicon read count data (Gloor et al. 2017).”  

(11) Lines 167-172: were technical factors, e.g., read depth or sequencing batch, included as random effects?

Thank you for catching this oversight in the text. We did model sequencing depth and batch effects. The sentence starting at line 173 now reads, “For each of these 1,440 features, we tested its association with host age by running linear mixed effects models that included linear and quadratic effects of host age and four other fixed effects: sequencing depth, the season of sample collection (wet or dry), the average maximum temperature for the month prior to sample collection, and the total rainfall in the month prior to sample collection (Grieneisen et al. 2021; Björk et al. 2022; Tung et al. 2015). Baboon identity, social group membership, hydrological year of sampling, and sequencing plate (as a batch effect) were modeled as random effects.”

(12) Lines 175-180: When discussing how these alpha diversity results relate to previous findings, the authors should be clear about whether they talk about weighted or non-weighted measures of alpha diversity. - also maybe this should be included in the discussion rather than the results? Please consider this when revisiting the manuscript (see how it reads after edits).

Richness is the only unweighted metric, which we now clarify in line 181. We opted to retain the interpretation in the text in its original location to maintain the emphasis in the discussion on the microbiome clock results.

(13) Table S1 is very hard to interpret in the provided PDF format as columns are not presented side-by-side. It is currently hard to check model output for e.g., specific families. This needs to be revisited.

We agree. We believe that eLife’s submission portal automatically generates a PDF for any supplementary item. However, we also include the supplementary tables as an Excel workbook which has the columns presented side-by-side.

(14) Line 184: taxa meaning what? Unclear what authors refer to with this sentence, taxa across taxonomic levels, or ASVs, or what does the 51.6% refer to?

We have edited line 191 to clarify that this sentence refers to taxa at all taxonomic levels (phyla to ASVs).

(15) Line 191: a punctuation mark missing after ref (81).

We have added the missing period at the end of this sentence.

(16) Lines 189-197: this should go into the discussion in my opinion.

We have opted to retain this interpretation, now at line 183.

(17) Lines 215-219: Not sure what this means; do the authors mean features were not restricted to age-associated taxa, ie also e.g., diversity and other taxa-independent patterns were included? If so, the rest of the highlighted lines should be revisited to make this clear, currently to me it is very unclear what 'These could include features that are not strongly age-correlated in isolation' means. Currently, that sounds like some features included were only age-associated in combination with other features, but unclear how this relates to taxa-dependency/taxa-independency.

We agree this was not clear. We have revised line 224 to read, “We included all 9,575 microbiome features in our age predictions, as opposed to just those that were statistically significantly associated with age because removing these non-significant features could exclude features that contribute to age prediction via interactions with other taxa.”

(18) Line 403-407: There is now a paper showing epigenetic clocks can be built with faecal samples, so this argument is not valid. Please revisit in light of this publication: https://onlinelibrary.wiley.com/doi/epdf/10.1111/mec.17330

Thank you for bringing this paper to our attention. We deleted the text that describes epigenetic clocks as invasive, and we now cite this paper in line 450, which reads, “We also hope to measure epigenetic age in fecal samples, leveraging methods developed in Hanski et al. 2024.”

(19) Line 427: a punctuation mark/semicolon missing before However.

We have corrected this typo.

(20) Lines 419-428: I don't quite understand this speculation. Why would the priority of access to food lead to an old-looking gut microbiome? This paragraph needs stronger arguments, currently unclear and also not super convincing.

We agree this was confusing. We have revised this text to clarify the explanation. The text starting at line 424 now reads, “This outcome points towards a shared driver of high social status in shaping gut microbiome age in both males and females. While it is difficult to identify a plausible shared driver, one benefit shared by both high-ranking males and females is priority of access to food. This access may result in fewer foraging disruptions and a higher quality, more stable diet. At the same time, prior research in Amboseli suggests that as animals age, their diets become more canalized and less variable (Grieneisen et al. 2021). Hence aging and priority of access to food might both be associated with dietary stability and old-for-age microbiomes. However, this explanation is speculative and more work is needed to understand the relationship between rank and microbiome age.”

(21) Line 434: remove 'be'.

We have corrected this typo.

(22) Line 478: add information on how samples were collected; e.g., were samples collected from the ground? How was cross-contamination with soil microbiota minimised? Were samples taken from the inner part of depositions? These factors can influence microbiota samples quite drastically so detailed info is needed. Also what does homogenisation mean in this context? How soon were samples freeze-dried after sample collection?

We have expanded our methods with respect to sample collection. This text starts in line 483 and reads, “Samples were collected from the ground within 15 minutes of defecation. For each sample, approximately 20 g of feces was collected into a paper cup, homogenized by stirring with a wooden tongue depressor, and a 5 g aliquot of the homogenized sample was transferred to a tube containing 95% ethanol. While a small amount of soil was typically present on the outside of the fecal sample, mammalian feces contains 1000 times the number of microbial cells in a typical soil sample (Sender, Fuchs, and Milo 2016; Raynaud and Nunan 2014), which overwhelms the signal of soil bacteria in our analyses (Grieneisen et al. 2021). Samples were transported from the field in Amboseli to a lab in Nairobi, freeze-dried, and then sifted to remove plant matter prior to long term storage at -80°C.”

(23) Line 480 onwards: were negative controls included in extraction batches? Were samples randomised into extraction batches?

Yes, we included extraction blanks. These are now described in lines 495-500. This text reads, “We included one extraction blank per batch, which had significantly lower DNA concentrations than sample wells (t-test; t=-50, p < 2.2x10-16; Grieneisen et al. 2021). We also included technical replicates, which were the same fecal sample sequenced across multiple extraction and library preparation batches. Technical replicates from different batches clustered with each other rather than with their batch, indicating that true biological differences between samples are larger than batch effects.”

(24) Were extraction, library prep, and sequencing negative controls included? Is data available?

We included extraction blanks (described above) and technical replicates, which were the same sample sequenced across multiple extraction and library preparation batches. Technical replicates from different batches clustered with each other rather than with their batch, indicating that true biological differences between samples are larger than batch effects.

We have updated the data availability statement to read, “All data for these analyses are available on Dryad at https://doi.org/10.5061/dryad.b2rbnzspv. The 16S rRNA gene sequencing data are deposited on EBI-ENA (project ERP119849) and Qiita (study 12949). Code is available at the following GitHub repository: https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge”.

(25) Line 562: how were corrected microbiome delta ages calculated? Currently, the authors state x, y and z factors were corrected for, but it is unclear how this was done.

The paragraph starting at line 577 describes how microbiome delta age was calculated. We have made only a few changes to this text because we were not sure which aspects of these methods confused the reviewer. However, briefly, we calculated sample-specific microbiome Dage in years as the difference between a sample’s microbial age estimate, age_m from the microbiome clock, and the host’s chronological age in years at the time of sample collection, age_c. Higher microbiome Dages indicate old-for-age microbiomes, as age_m > age_c, and lower values (which are often negative) indicate a young-for-age microbiome, where age_c > age_m (see Figure 3).

(26) Line 579: typo 'as'.

We have corrected this typo.

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DOI: 10.1093/hmg/ddy146

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DOI: 10.1093/hmg/ddy146

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What is this?

DOI: 10.1093/hmg/ddy146

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/hmg/ddy146

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/hmg/ddy146

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/hmg/ddy146

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/hmg/ddy146

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/hmg/ddy146

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

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No se evaluó debido a que se utilizó el procesamiento automático y la decisión fue meramente humana. ¿Pero no es el procesamiento de datos en cierto nivel interpretativo y ello un sesgo para la decisión humana?

En la UE ya es un derecho el obtener intervención humana e parte de la decisión, expresar su punto de vista y cuestionar la decisión.

Una comparación histórico-sistemática no tiene mucho sustento a mi criterio frente a una revolución tecnológica, donde, las capacidades de un algoritmo frente a casos particulares aun se encuentran en tela de juicio y no se equiparan a una labor profesional humana.

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

In this manuscript, the authors highlight the importance of the Golgi apparatus during SARS-CoV-2 infection. Specifically, using different compounds able to alter Golgi structure and function, the authors show a strong reduction in SARS-CoV-2 infection rate. In particular it is interesting to observe that treatments of 24 hrs with BFA strongly impair viral infection, highlithing the importance of Golgi function for this virus. Albeit the time of treatment is different. this observation is in contrast with previous studies on related coronaviruses (Ghosh et al., 2020) that did not observe any effect upon treatment with BFA. This might imply that SARS-CoV-2 relies more on conventional trafficking pathways respect to other coronaviruses which, under certain conditions, favour different trafficking routes.

We thank the reviewer for the positive comments. Indeed, our results with BFA treatment for 24 hours are inconsistent with previous studies based on the prototype coronavirus MHV (Ghosh et al., 2020). To validate this observation, we have now performed new experiments with BFA treatment for 4, 6, and 8 hours, matching the time points used in the previous study (Ghosh et al, 2020). Our new results show that BFA treatment at these early time points significantly inhibits SARS-CoV-2 assembly and secretion, as measured by immunoblotting and TCID50 assays, without reducing intracellular viral RNA levels, which serve as a marker of genome replication. This implies that Golgi function and an intact ER-to-Golgi trafficking route are required for SARS-CoV-2 assembly and secretion. These new results are now presented as new Fig. 2C-H.

The authors additionally observed that viral infection increases TGN46 levels while decreasing GRASP55 levels. To dissect the role of TGN46 and GRASPR55, the authors performed several infection studies in cells in which the levels of the two proteins were modulated either by overexpression (GRASP55) and/or siRNA-mediated knock-down (GRASP55 and TGN46). Those approaches suggest that GRASPR55 overexpression, a protein essential for Golgi stack formation, decelerates viral trafficking and inhibits viral assembly while its depletion reverses the effects. On the other hand, TGN46 knock-down impairs viral trafficking but not assembly. Overall the study clearly shows the importance of the Golgi during SARS-CoV-2 and also shows that modulation of those two factors affect viral infection.

We appreciate the reviewer's accurate summary of our work and positive comments.

However the claims that specifically the trafficking (TGN46) and trafficking and assembly (GRASP55) are not fully substantiated. Regarding GRASP55, the authors state that viral infection decreases GRASPR55 levels and this results in Golgi fragmentation. However GRASPR55 levels decrease is shown at 24 hrs post infection while Golgi fragmentation occurs as early as 5 hrs. Thus there might be no direct casual effect between the two effects.

We agree with the reviewer that GRASP55 downregulation is unlikely to be the only reason for Golgi fragmentation in the infected cells. In our results, 5- or 8-hour post infection caused only mild Golgi fragmentation (Fig. S6D), while 24 hours post infection led to severe Golgi fragmentation. On the other hand, GRASP55 is likely to play a relevant role as SARS-CoV-2 induced Golgi fragmentation can be partially rescued by exogenous GRASP55 expression (Fig S6C). We have modified the text in lines 303-305 accordingly to acknowledge the possibility that other factors also contribute to Golgi fragmentation in infected cells.

Additionally, the authors show that overexpression of GRASP55 rescue Golgi fragmentation, as observed by imaging, however is not clear if only infected cells where quantified and if they had the same level of infection.

Yes, only infected cells with either GFP or GRASP55-GFP expression were quantified. The viral infection rate was significantly lower in GRASP55-GFP expressing cells compared to GFP expressing cells (Fig 5A-B).

The authors exclude and effect on entry based on experiment on Spike expressing pseudovirus in 293-ACE2, however they also clearly observe reduction of ACE2 on the membrane of GRASPR55 expressing cells (Fig S6B). Thus how can they explain this discrepancy and how ca defect in entry can be fully marked out in these cell lines?

We thank the reviewer for pointing this out. This discrepancy is likely due to the different systems used in the two experiments.

In the pseudovirus entry assay, ACE2 was exogenously expressed in 293T cells and GRASP55 expression did not show any effect on the viral entry efficiency. In contrast, Huh7-ACE2 cells were selected for a high surface expression of ACE2. While GRASP55 expression reduces surface ACE2 levels as shown in our cell surface biotinylation assay, we believe that the surface ACE2 levels in GRASP55-expressing cells remain sufficient to support viral entry. To further investigate whether GRASP55 expression affects viral entry using authentic SARS-CoV-2, we performed RT-qPCR analysis of intracellular RNA level of the spike, N, and RdRp in both GFP and GRASP55-GFP expressing cells 4 hours post infection (new Fig 5D). Our results show that GRASP55 expression does not affect SARS-CoV-2 entry efficiency, even though it reduces ACE2 surface expression levels.

It is not clear to which process the authors refer to when they write about "viral trafficking". Is it virion trafficking or viral proteins trafficking? The two process are linked but are not the same. This oversemplification can be misleading. For instance the authors show that overexpression of GRASP55 decreases Spike protein on the plasma membrane and its depletion increases S protein incorporation into psudoviruses. However it was shown that in infected cells S protein is mainly retained at the ERGIC by M and E (Boson et al., 2021) where viral assembly occurs. Thus an increase in S trafficking on the PM does not correlate with an increase in virion trafficking,

We agree with the reviewer that our use of the term "viral trafficking" is imprecise and we have changed this throughout the manuscript to be more specific. S trafficking to the PM may not necessarily be equal to an increase in virion trafficking and thus have rephrased these terms in our writing accordingly.

We acknowledge that our cell surface biotinylation assay results only demonstrate that GRASP55 overexpression slows down spike protein trafficking to the PM. We have accordingly also examined viral protein and infectious particle secretion into the culture medium as a more direct readout of virion trafficking (new Fig 2E, 2H, 6K, and 7P).

Finally, we have removed all of the data describing spike incorporation into pseudoviruses as we acknowledge that plasma membrane assembly of lentiviruses is not a good model for SARS-CoV-2 assembly.

...and ultimately, the data provided do not fully support the authors claim on a modulation of "virion trafficking" in response to GRASP or TGN46 changes, since no experiments clearly show a change in virions secretion.

In response to the above comment, we provide the following clarification: Our Western blotting, TCID50 assay, and plaque assay results collectively demonstrate that SARS-CoV-2 virion secretion is reduced in GRASP55 expressing cells (new Fig 5E-M) and in TGN46-depleted cells (new Fig 7F-H, 7L-N). Conversely, viral assembly and secretion appear to be increased in GRASP55-depleted cells (new Fig 6A, 6E-I) at 24 hpi. Furthermore, within a single viral secretion cycle (10 hpi), GRASP55 depletion increased viral secretion (new Fig 6K), while TGN46 depletion reduced viral secretion (new Fig 7P). These findings strongly support the conclusion that GRASP55 and TGN46 modulate viral secretion.

Importantly, the authors do not rule out potential effects of their perturbations on genome replication. The only experiment that they perform in this direction is presented in Fig. S7B, where the authors show similar percentage of infected cells at early stage upon silecing of GRASPR55. The experiment suggests that productive entry is similar in these conditions, but quantification of intracellular viral genome could exclude a change in viral replication. If no changes in viral replication are observed, the authors could verify an increase in particles secretion by collecting supernatants from the early time points and performing plaque assays and quantification of viral genomes by qRT-PCR, to prove that modulation of GRASPR55 indeed promote SARS-CoV-2 trafficking.

We thank the reviewer for the excellent suggestions. In response, we performed RT-qPCR analysis in GRASP55-expressing and TGN46-depleted cells at 4 hpi to compare the viral genome replication process. Additionally, we performed western blotting analysis and released viral titer assay of the culture media from both GRASP55-depleted and TGN46-depleted cells at 10 hpi to investigate virion release. Our new results show that GRASP55 depletion increases viral secretion (new Fig. 6K), while TGN46 depletion reduces viral secretion (new Fig. 7P). Furthermore, GRASP55 expression and TGN46 depletion do not perturb viral genome replication (new Fig. 5D and new Fig. 7R).

Finally, whenever reduction of viral infection is observed upon cell partubation, a robust analysis of cell viability should be presented to exclude pleiotropic effects. Expecially in presence of multiple pertubation that might affect cell metabolism. The authors should carefully control cell viability and growth in response to depletion of TGN46 and GRASP55.

We thank the reviewer for the excellent suggestions, which were also pointed out by reviewer #3. To address this, we performed the LDH cytotoxicity assay under SARS-CoV-2 infection conditions with TGN46 depletion and GRASP55 depletion/expression (new Fig. 5C, 6L, 7Q). Our new results show that no significant cell death was induced by TGN46 depletion, GRASP55 depletion/expression, or other perturbations.

Minor: show data on viability of the drug and add the relative section in Material and Methods.

We performed LDH assays of SARS-CoV-2 infected Huh7-ACE2 cells treated with 9 small molecules, and LDH release levels were similar across all treatments (new Fig. S3C). Additionally, a CellTiter Glo viability assay of 293T-ACE2 cells did not show any significant effect of cell viability with small molecule treatment (new Fig S3F). Detailed descriptions of these assays have been included in the Material and Methods section.

Figure 3A: should read spike and not nucleocapsid eported for SARS-CoV-2

Fig. 3A labeling is correct - cells were labeled with antibodies for GRASP65 (rabbit) and for nucleocapsid (mouse).

Lack of inhibition with camostat correlates with lack of TMPRSS2 in the Huh7. The sentence seems to be too general while in this case the effect is clearly cell specific. Similarly, the importance of the lysosome in viral entry is restricted to cells lacking TMPRSS2 and cannot be generalized since CQ, for example, does not work in Calu-3 cells that express TMPRSS2 cells.

We agree with the reviewer and have added one sentence: The relative smaller effect of camostat mesylate observed here, compared to previous studies (Hoffmann et al, 2021), might be due to the use of different cell lines across studies in lines 182-184. We also discussed the discrepancy of CQ treatment between our Huh7-ACE2 cells and Calu-3 cells (Hoffmann et al, 2020) in lines 466-473.

Typo: Fig S3B - Y axis should reat viral not vrial

Thank you - we have corrected this.

S3C: concentrations of the compound used in the assay should be reported. Was a viability assay performed also in the 293T-ACE2 cell line?

We thank the reviewer for the suggestion. We have added the concentration information to the legend in Fig. S3E "Cell entry assay of 293T or 293T-ACE2 cells by SARS-CoV-2 Spike pseudotyped lentivirus for 24h in the presence of indicated molecules at the same concentrations as in Fig. 2A." Additionally, we performed a CellTiter Glo assay to assess the viability of 293T-ACE2 cells treated with the 9 molecules. The results demonstrate that treatment with these 9 molecules does not alter cell viability (Fig. S3F).

Significance

Overall, the major strenght of the manuscript is that it has clarified the importance of the Golgi during SARS-CoV-2 infection. The drugs screening demonstrate that for SARS-CoV-2 the conventional secretion seems to have major role respect to other secretory routes observed for other coronaviruses. Also it is clear that the two factors identified by the authors have a role in viral infection, however the major limitation is that the authors failed to clearly highlight which step/s of the viral life cycle are modulated upon GRASP55 and TGN46 perturbatio. Expecially the claims on "trafficking" is not fully substantiated, since the only experiment in this direction is the transport of Spike protein on the plasma membrane upon GRASPR55 overexpression. It is risky to conclude that the trafficking of a single protein reflect the intracellular trafficking of the virions.

Several of the finding presented in the first part of the manuscript have been already previously reported (for example the fragmentation of the Golgi upon SARS-CoV-2 infection), however the role of GRASP55 and TGN46 in SARS-CoV-2 infection has been reported here for the first time. This manuscript can be of interest for a broad audience considering the topic (cell biology, host-pathogen interactions and molecular virology)

My expertise reside in the field of molecular virology, expecially in the contest of the mechanisms of viral replication and host-pathogen interactions.

We thank the reviewer for the overall positive comments and excellent suggestions. We hope that our new results have convincingly demonstrated that viral trafficking is regulated by GRASP55 and TGN46.

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

Summary: In this study, Zhang and colleagues address the impact on SARS-CoV-2 infection on the morphology of the Golgi apparatus and convincingly demonstrate a fragmentation of this organelle in infected cells. Conversely, they show that the modulation of TGN46 or GRASP55 expressions, two components of this organelle impact SARS_CoV-2 replication. By monitoring the relative levels of viral Spike and nucleocapsid in the cell supernatants, they conclude that GRASP55 regulates particle assembly and trafficking while TGN46 controls only secretion. The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were robustly quantified. I believe that this study is potentially interesting and relevant for the SARS-CoV-2 community since providing an extensive characterization of the interplay between SARS-CoV-2 and the Golgi apparatus.

We thank the reviewer for the positive comments.

However, as described below, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery used for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study, especially without clear molecular mechanisms about the interplay between SARS-CoV-2 proteins and TNG46/GRASP55.

We rephrased some sentences following the reviewer's suggestions. Although it was believed that SARS-CoV-2 is assembled at the ERGIC, there has been significant controversy surrounding the virion secretion pathway. Our results strongly support that SARS-CoV-2 virions traffic through the Golgi apparatus and that an intact ER-to-Golgi trafficking pathway is essential for SARS-CoV-2 assembly and secretion. Manipulation of two Golgi-resident proteins, GRASP55 and TGN46, significantly regulates SARS-CoV-2 secretion. Interestingly, GRASP55 regulates both assembly and secretion of SARS-CoV-2, while TGN46 exclusively modulates viral secretion. This is consistent with their subcellular localization, as GRASP55 is localized to the medial/trans Golgi, whereas TGN46 is localized to the TGN. We hope that our new experimental results (Figs. 2C-H, 5C-D, 6J-L, and 7O-R) have addressed all concerns from the reviewer. Identification of downstream protein targets involved in TGN46/GRASP55-mediated modulation of SARS-CoV-2 trafficking will be the focus of our future studies.

Major comments: -All the assays have been performed in liver-derived Huh7 cells (overexpressing SARS-CoV-2 receptor) ACE2 (for infection) or kidney 293 cells (for pseudotyped HIV entry assays). However, no conclusion was validated in lung-derived cells (like A549-ACE2, Calu-3 or primary cells), which would be important since the respiratory tract is the main target of SARS-CoV-2

In our study, Huh7-ACE2 cells are sorted for the high expression of endogenous ACE2 protein, and we did not overexpress ACE2 protein. Also, the liver has been reported to be a site of SARS-CoV-2 infection in humans (Barnes, 2022). We did use A549 and Calu-3 cells in pilot experiments; A549 cells displayed infection rates that were too low for our purposes, and Calu-3 cells showed both low infection rates and relatively disorganized Golgi in the absence of viral infection. We were able to add new IF results from Calu-3 cells. Consistent with our findings in Huh7-ACE2 cells, SARS-CoV-2 infection disrupts Golgi structure and alters protein levels of TGN46 and GRASP55 in Calu3 cells (new Fig. S5R-W). We also confirmed GRASP55 downregulation and TGN46 upregulation in VeroE6 cells (Fig S5K-N).

-Fig2: The impact of the drugs on replication was assessed by measuring the % of infected cells. At 24 hpi, I am unsure about what this value is supposed to measure (the whole life cyle, intracellular replication or spread?), especially since it is not indicated when the drugs were added to the cells. Was it during, before or after the infection? This information should be provided.

Fig. 2 refers to infection, not replication. We agree that infection encompasses multiple steps of the viral cycle. In our experiments, cells were treated with the drugs immediately before viral infection. We have added the information into the Fig. 2 legend.

If the "Golgi" drugs impact egress only (as inferred by the genetic modulation phenotypes), I would expect that at this early time point, the % of infection would not drastically change (as well as intracellular RNA) but that the extracellular infectious titers would decrease. Plaque assays (or TCID50 assays) and RT-qPCR on intracellular viral RNA should be conducted to better understand the impact of drug treatments.

This is a great suggestion! As the reviewer expected, our new BFA time-point assay shows that at early time points, the intracellular RNA levels for S, N and RdRp are not reduced. However, the extracellular N protein (measured by WB) and virial titer (measured by TCID50 assay), which serve as readouts for virion secretion, are significantly decreased (new Fig. 2C-H).

On page 10, it is said that the virus makes three cycles of replication within 24 hours following infection. On what data is this based? This seems a lot. If this is true (and shown in Huh7-ACE2 cells), does the assay of figure 2 measure spread in general? More importantly, despite mentioned, the cell viability data are not provided. It is important to show them to ensure that these concentrations of drugs are not toxic at the tested concentrations.

It has been reported that a single cycle of SARS-CoV-2 infection is approximately 8 hours (Eymieux et al, 2021). Therefore, Fig. 2 represents a multicycle infection, reflecting a composite measure of viral infection and spread. Under the microscope, we did not observe dramatic cell death at the tested concentration. To further assess cytotoxicity, we performed a cell toxicity assay for the 9 small molecules that inhibit viral infection of Huh7-ACE2 cells. The results show that no or minor cell death was observed with all these compounds (Fig. S3C).

-I appreciated the extensive confocal microscopy analysis performed by the authors, which seems of high quality and overall, very convincing. They clearly show that SARS-CoV-2 infection induces the fragmentation of the Golgi apparatus although it was reported by others before as mentioned by the authors.

We thank the reviewer for the positive comments. We agree that Golgi fragmentation was observed during SARS-CoV-2 infection, as we mentioned. However, our study provides a comprehensive and systematic analysis of the entire host cell endomembrane system in the response to viral infection.

However, it was hard for me to make the functional link between these data and those related to GRASP55 and TGN46 overexpression/knockdown. First, the authors should assess the morphology of the Golgi apparatus in Huh7-ACE2 when GRASP55 is knocked down/out or when TGN46 is overexpressed. Second, in these 2 conditions that favor replication, it should be assessed whether this correlates with Golgi fragmentation. Even if this was probably shown before, it is relevant to show that these genetic modulations induce Golgi reshaping in this particular cell type by confocal microscopy (and ideally electron microscopy).

Thank you for the suggestion. We performed IF analysis to assess Golgi morphology in Huh7-ACE2 cells under conditions of GRASP55 knockdown or TGN46 overexpression. Our results show that GRASP55 depletion disrupts Golgi structure (Fig. S7D), whereas TGN46 expression does not significantly alter the Golgi morphology (Fig. S8D).

-The fact that GRASP55-GFP expression decreases in 293T the cell surface levels of ACE2, the receptor of Spike (Fig S6), raises concern that the effect of GRASP55 is not specific to the virus and suggests that the whole secretory pathway is altered, while an impairment of virus entry should be expected in this cell line. Is there a similar trend in Huh7-ACE2?

Reviewer 1 raised a similar question regarding viral entry efficiency. Fig. S6B, performed in Huh7-ACE2 cells, shows that GRASP55-GFP expression also decreases ACE2 surface level in these cells. To further assess whether GRASP55 expression affects viral entry, we performed RT-qPCR analysis of viral RNA at early time points of infection. We found that authentic SARS-CoV-2 entry efficiency was not altered by GRASP55 expression (new Fig. 5D). Although GRASP55 overexpression does alter the secretory pathway, we want to point out that SARS-CoV-2 infection downregulates endogenous GRASP55 expression. We have used GRASP55 overexpression as a probe to assess the effects of GRASP55 on the secretory pathway and on SARS-CoV-2 virion trafficking, but this does not actually reflect what is observed in SARS-CoV-2 infection.

In addition to addressing the functionality of the secretory machinery in Huh7-ACE2, it would be relevant to repeat the cell surface labelling in the context of pseudotyped virus production with other viral envelopes such as VSV G protein or HIV gp41/gp120. If the phenotype is specific to Spike trafficking, the cell surface abundance of these alternative viral proteins should not be impacted by GRASP55 overexpression. Otherwise, this would indicate a general effect of on the secretory pathway. Besides, since HIV Gag is directed directly to the plasma membrane during particle assembly without entering the secretory pathway, I am not convinced that upstream alteration on nucleocapsid assembly at the ERGIC should be excluded. Indeed, changes on the S/N ratios are generally mild and I feel that this cannot explain the phenotypes in the extracellular infectious titers.

We have removed the original figure because we acknowledge that HIV Gag is directed directly to the plasma membrane, which is different from the trafficking of SARS-CoV-2 spike protein. We appreciate the reviewer's recognition of the difference in extracellular infectious titers between GFP and G55-GFP expressing cells. We hypothesize that GRASP55 expression not only reduces the number of spikes on each virion but also inhibits the secretion of SARS-CoV-2, resulting in a significantly lower extracellular infectious titer. We agree that it would be interesting to test whether GRASP55 expression affects viral production with other viral envelopes. However, this is beyond the scope of the current study and represents a promising direction for future research.

More generally, the comparison between trafficking and assembly should be better assessed and not simply based on extracellular N and S levels. It was hard to see the differences between the two in terms of phenotypes. The authors should at least measure the intracellular infectivity upon TGN46 and GRASP55 knock/down and overexpression as well as intracellular vRNA abundance as a readout of RNA replication (which is anticipated to remain unchanged).

We thank the reviewer for the valuable suggestions. We performed RT-qPCR analysis of Spike, N, and RdRp at early time points of infection. The new results show that neither GRASP55 expression (new Fig. 5D) nor TGN46 depletion (new Fig. 7R) affects viral RNA abundance at an early infection timepoint (4 hpi). Also, we found that GRASP55 depletion increased intracellular infectivity (new Fig. 6J) while TGN46 depletion did not affect intracellular infectivity (new Fig. 7O), suggesting that GRASP55 modulates viral assembly but TGN46 does not.

-Finally, mechanistic insight about the viral determinants regulating the morphology of the Golgi would significantly strengthen the study.

Fig S6 shows that S expression decreases ACE2 surface levels? If so, could some S mutants be tested? Does it correlate with Golgi fragmentation? Do other viral structural proteins contribute to Golgi morphological alterations?

We thank the reviewer for the suggestions. These are indeed interesting experiments, but we believe that investigating viral determinants of Golgi fragmentation should be pursued by future studies.

In the same line of idea, how GRASP55 and TGN46 regulate replication. The link with Golgi morphology is unclear. Are these proteins hijacked by SARS-COV-2?

Our new data in this revised manuscript more clearly define the stages in the viral infection cycle that are modulated by GRASP55 and TGN46. New Fig. 5D and Fig. 7R show that neither GRASP55 nor TGN46 affects viral entry or early viral replication. However, GRASP55 perturbation modulates viral assembly and secretion, while TGN46 perturbation affects virion secretion but not assembly. Fig. S6C shows that GRASP55 overexpression in the presence of the virus partially rescues Golgi fragmentation. The mechanisms by which GRASP55 and TGN46 are hijacked by SARS-CoV-2 will be explored in the future studies.

Page 13 mentions some relevant mutants that could be assessed in this context and provide mechanistic insights.

It would be interesting to investigate the effects of GRASP55 mutants or specific domains on SARS-CoV-2 trafficking, which we plan to explore in future studies.

Minor comments: -The signal of calreticulin in Fig. S1 is too low to appreciate it distribution.

We have increased the intensity of calreticulin staining for both uninfected and infected cells in parallel in Fig. S1. Thank you.

-Fig 4K, Q: The differences in LC3 forms levels are not convincing. These results do not allow to draw any conclusion about autophagy, especially considering that this was done at steady-state and that the autophagic flux was not measured. Indeed, a bafilomycin A treatment control would be required to measure the real induction of autophagosomes. Lysosomal degradation inhibition allows the detection of LC3 accumulation.

We agree that additional experiments are needed to demonstrate autophagic flux alteration by SARS-CoV-2. We observed an increase in LC3II/LC3I ratio in infected cells at steady state and did not explore this further, since this is not our main focus of this study. Therefore, we have removed the LC3 blots and quantification from Figs. 4 and S5.

-In the GRASP55 overexpression and TGN46 knockdown studies, associated cell viability should be measured to control that that these genetic manipulations do not induce any cytotoxicity which may impact viral replication.

We appreciate the reviewer's suggestions. We performed the LDH cytotoxicity assay under SARS-CoV-2 infection with TGN46 depletion or GRASP55 expression. Our new results show that TGN46 depletion or GRASP55 depletion/expression did not induce significant cell death (Figs. 5C, 6L, and 7Q).

-The authors should test the impact of GRASP55 and GRASP65 knock-out on SARS-CoV-2 replication

Investigating the genetic GRASP55 knockout effect on SARS-CoV-2 replication would be valuable. However, ACE2 protein expression in our Huh7-ACE2 cells decreases with cell passages, making knockout construction on this background impractical due to low ACE2 levels and poor viral infection rates. We believe that both our GRASP55 overexpression and depletion assays sufficiently support its role in SARS-CoV-2 trafficking. Future studies will explore GRASP55 knockout in different cell lines.

-The authors should provide more details about the USA-WA1/2020 isolate in the Methods section. Is it related to the "Wuhan" strain or the variant which spread globally in early 2020 (with D614G mutation in Spike).

USA-WA1/2020 was isolated from an oropharyngeal swab from a patient who returned from China and developed COVID-19 on January 19, 2020, in Washington, USA. It is related to the "Wuhan" strain but does not have D614G mutation in spike. Additional details have been added to the Methods section.

-Fig 8: The combined modulation of GRASP55 and TGN46 expressions does not really seem additive to me since a 70% decrease of either protein modulation is observed while the combined condition brings this value to 75% in TCID50 assays. This does not bring much insight to the study in my opinion. I would suggest that the authors consider removing this figure.

We agree with the reviewer's recommendation and have removed Fig. 8.

Reviewer #2 (Significance (Required)):

General assessment and advance: The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were quantified extensively. However, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study. In addition, the antiviral activity of several tested drugs was also reported elsewhere. A clear mechanism of how SARS-CoV-2 induces a fragmentation of the Golgi would strengthen the study. In the same line of idea, it is unclear how TGN46 and GRASP55 regulate the late steps of the life cycle. The link between SARS-CoV-2-induced Golgi fragmentation and TGN46/GRASP55 is unclear. In my opinion, the data did not allow to clearly discriminate between virion assembly and egress. I was not convinced that it was not simply due to a general disruption of the secretory pathway (as attested by ACE2 down regulation upon GRASP55 overexpression).

Targeted audience: This study will be of high interest for molecular virologists (not only working on SARS-CoV-2) but could be very well fit into the scope of molecular/cell biology-focused generalist journals

Reviewer expertise: Molecular virology, virus-host interactions (especially involving membranous organelles), SARS-CoV-2, RNA viruses

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

Summary:

Zhang et al. demonstrated in this study that the Golgi apparatus and many other organelles are disturbed by SARS-CoV-2 infection. They focused on the Golgi apparatus and especially on TGN46 and GRASP55 which are both affected differently in their level of expression by the SARS-CoV-2 infection. TGN46 is overexpressed while GRASP55 is decreased in expression. Through different methods overexpression or depletion, the authors nicely demonstrated that modulation of both proteins either increased or decreased particles production. They demonstrated that in absence of GRASP55, SARS-CoV-2 release is increased in the medium. On the contrary, depletion of TGN46 decreases the secretion of SARS-CoV-2 particles.

We thank the reviewer for the accurate summary of our work.

Major comments:

Figure 1: The authors demonstrated that SARS-CoV-2 expression affected the morphology of multiple organelles. Although the results are clear, my concern was that the MOI=1 was really high which indeed would affect the whole cell. To have a less drastic effect on the cell, I would suggest realizing the visualization of some organelles (Golgi, EEA1, Rab7 for example) at a lower MOI=0.1. In addition, it would be nice to verify with a live-dead assay with the MOI=1 if after 24h the cells are still alive, which will confirm that these disturbances are not caused by cells in process of dying.

We thank the reviewer for the excellent suggestions. Investigating how SARS-CoV-2 reshapes subcellular organelles at low MOI (e.g., 0.1) and at different time points would be interesting but is beyond the scope of our study. However, we have performed LDH assay at MOI=1, 2 and 3 for 24 hours to assess cell death. Our results show that LDH release was similar across these conditions (Fig. S5R). We also performed RT-qPCR analysis of Spike, N, and RdRp at early time points of infection. The new results show that neither GRASP55 expression (new Fig. 5D) nor TGN46 expression (Fig. 7R) affects viral RNA abundance at an early infection timepoint (4 hpi).

Figure 2: The results indicated in that panel are really nice. However, the addition of a virus with drugs could increase the proportion of cell death. For the Figure 2C, I propose that the author use a LDH assay to prove that the decrease in infection is not caused by cell death. In addition, a RT-qPCR would be more appropriate to indicate the infection rate and support the microscopy data.

We thank the reviewer for the positive feedback and suggestions. As recommended, we performed an LDH assay to assess cytotoxicity under 9 small molecules treatment of infected cells. Additionally, we performed RT-qPCR analysis for the BFA time-point treatment assay. No significant cell death was observed under these conditions (new Figs. 2D, and S3C).

Figure 3: The authors should have been consistent and add spike instead of nucleocapsid for GalT. According to the figures, Spike seemed to co-localize more with GM130 than Golgin 245. Data analysis of colocalization between Spike and GM130 should be performed to complete the observation. Are no colocalizations of Spike observed with the other Golgi markers?

We agree with the reviewer that it was ideal if spike and GalT were co-stained. Unfortunately, both our spike antibody and GalT antibody are from rabbit, so co-staining could not be done as GM130/spike. We performed colocalization analysis between Spike and GM130, and the results show that GRASP55 expression did enhance Spike and GM130 colocalization to some extent (new Fig. S6E-F). We only co-stained spike with GM130 and Golgin-245 due to the antibody availability.

Figure 4K: While all the experiments were performed at MOI=1, why is the authors using MOI=2 for the immunoblots. Did they have a different result in protein expression for MOI=1 in HuH cells? if so they should show a blot indicating this result.

We did not perform WB to assess protein expression at MOI=1, but our cell toxicity assay showed that there is no significant difference between MOI=2 and MOI=1.

Figure 5: Viral infection should be indicated using RT-qPCR data analysis to support the microscopy observations.

We performed RT-qPCR analysis (new Figs. 2F, 5D, and 7R) and found that BFA treatment did not reduce viral RNA levels at all three time points. Also, GRASP55 expression and TGN46 depletion did not inhibit viral genome RNA levels within one viral infection cycle. Additionally, our new TCID50 assay results support our microscope observation (new Fig. 7O-P). Thanks for the suggestion.

Figure 6: The authors should look at the trafficking of ACE2 and TfR in case of GRASP55 depletion like they did in case of GRASP55 overexpression. It could demonstrate if the virus is using trafficking pathways that are common to the one used by some host receptors to reach the plasma membrane.

Thanks for the excellent suggestion. We performed cell surface biotinylation assay of control and GRASP55-depleted cells. We found that ACE2 and TfR receptor displayed a similar reduction on the cell surface (Fig. S7C), consistent with previous findings that GRASP55 depletion induced Golgi fragmentation and accelerated global conventional protein secretion.

Figure 7: Viral infection assay should also be performed by RT-qPCR. Figure 7H: The immunoblots conditions were performed at MOI=3 this time. The authors should indicate why they did not keep the same MOI conditions. In that case, they should use an intracellular marker for their medium experiment to prove that they isolated proteins that are secreted and not simply released from dead cells. I will also suggest to show LDH assay at MOI=2 and 3 to monitor cell death. Is the Golgi fragmented when GRASP 55 is overexpressed in presence of the virus? Microscopy observations should be performed to reply to this question as it will support their model. The authors suggest that GRASP55 overexpression decreases spike incorporation inside the virion. Can they observe if Spike still colocalizes with GM130 when GRASP55 is overexpressed?

We showed that TGN46 depletion inhibits viral infection by both IF and WB. We further confirmed this through TCID50 assay for both cells and media (new Fig. 7O-P), strengthening our hypothesis.

As we described above, we performed morphological analysis at MOI=1 so that we could observe a significant number of infected cells but minimize cell toxicity. We performed immunoblotting (in Fig. 7H) at MOI=3 to get a good viral infection rate.

As suggested, we also performed LDH assay at MOI=2 and 3 to monitor cell death (new Fig. S2O). Fig. S6C shows that GRASP55 overexpression in the presence of the virus partially rescues Golgi fragmentation. GRASP55 expression did also enhance Spike and GM130 colocalization to some extent (new Fig. S6E-F).

Minor comments:

Figure 1P in the text: Considering that Rab7 up-regulation is equal to "growth of late endosome" is an overstatement. Rab7 is cytosolic at its inactive state and at the endosome at its active state. The authors would have to prove this statement by monitoring an increased quantity of Rab7 at the endosomes which is not enough by just monitoring protein intensity by microscopy. As Rab7 is also localized in lysosomes, and the authors used Lamp2 as a lysosomal marker, it is strange that the area of these structures is not increased. The authors should replace the term "growth" by "an increase in the area of their vesicles".

We did observe less but larger LAMP2 puncta in the infected cells. We agree with the reviewer and rephrased "growth" by an increase in the area of their vesicles". Thank you for the excellent suggestions.

Figure 1Q-T: The observations described in the text did not match the quantification, the area of lysosomes is not significantly different from the non-infected conditions.

In Fig. 1Q-T, we did observe fewer but larger LAMP2 puncta in the infected cells, which was consistent with our quantification, i.e., fewer puncta (Fig. 1R), but each punctum was larger (Fig. 1S), and total area was similar.

Figure 8: In the text, it is mentioned that there is "a dramatic reduction of spike and N in the lysate in GRASP55-expressing and TGN46 depleted cells". However, the quantification indicated that the decrease in N and S content is non-significant. Can the authors precise what was the sample of comparison in the text (siControl versus siTGN46 or siTGN46+GFP versus siTGN46+GFP-GRASP55)?

The decrease in N and S content is significant with the lysate sample comparison (siControl versus siTGN46; siControl+GFP versus siTGN46+GFP; siTGN46+GFP versus siTGN46+GFP-GRASP55). We have now removed this Figure following Reviewer #2's suggestion, since the results are consistent with single protein manipulation and more experiments are needed to confirm whether there is an additive effect.

**Referee cross-commenting**

I agree with most of the concerns of the other reviewers. I do also consider that they should have done their study on cells expressing naturally ACE2. However, at this stage, it will be a lot of work to perform all of their study in a more relevant cell type. The authors should repeat some of their key experiments in lung-derived cell types, to determine if GRASP55 and TGN46 have the same effect on SARS-CoV-2 virion secretion/production.

We thank the reviewer for the suggestions and understanding. As we mentioned before, our study utilizes Huh7-ACE2 cells, which are sorted for the high expression of endogenous ACE2 protein, without ACE2 overexpression. Actually, we also tested A549 and Calu-3 cells. While A549 cells displayed very low infection rate, Calu-3 cells displayed disorganized Golgi without viral infection. However, we did perform immunofluorescence assays in Calu-3 cells. Consistent with our findings in Huh7-ACE2 cells, SARS-CoV-2 infection disrupts Golgi structure and alters protein levels of TGN46 and GRASP55 in Calu3 cells (new Fig. S5R-W). Also, others have reported that liver can be a target for SARS-CoV-2 infection in humans. Furthermore, we confirmed GRASP55 downregulation and TGN46 upregulation in VeroE6 cells (Fig. S6K-N).

Reviewer #3 (Significance (Required)):

The study identified two Golgi proteins (TGN46 and GRASP55) that are involved in modulating the release of SARS-CoV-2 particles from the cells. As these proteins are also acting on general secretion of host proteins to the plasma membrane, the effect on SARS-CoV-2 release could just be indirect. However, it does not change the informative points of the study raised by Zhang et al. It highlights really well how the host trafficking pathway could be diverted for the purpose of the virus, which is to produce particles to maintain its survival.

Strengths: The authors performed a precise and well quantified study. Observing how SARS-CoV-2 impacts host organelles morphology and uses host trafficking proteins to produce particles, brings more clarity on some unclear parts of the life cycle of the virus. In addition, it exposes new targets for therapeutic studies.

We thank the reviewer for the positive comments.

Weakness: The paper is mostly based on microscopy analysis and need some other methods to support their data. The paper lacks some molecular mechanisms explaining the clear role of GRASP55 and TGN46 in particle production or assembly.

In the revised version, we incorporated RT-qPCR assay, cell cytotoxicity assay, and BFA time-point treatment assay. Notably, we added intracellular and extracellular viral titer assays to more precisely distinguish between effects on virion assembly and virion secretion. We also confirmed the key observation that SARS-CoV-2 infection modulates GRASP55 and TGN46 expression in the Calu-3 lung cell line. Additionally, our early time-point results clearly support the role of GRASP55 and TGN46 in viral trafficking.

• Audience: The paper will be interesting for basic research for a virology and cell biology audience.
• Field of expertise with a few keywords: Virology and host cell trafficking.

References

Barnes E (2022) Infection of liver hepatocytes with SARS-CoV-2. Nat Metab 4: 301-302

Bekier ME, 2nd, Wang L, Li J, Huang H, Tang D, Zhang X, Wang Y (2017) Knockout of the Golgi stacking proteins GRASP55 and GRASP65 impairs Golgi structure and function. Mol Biol Cell 28: 2833-2842

Eymieux S, Rouille Y, Terrier O, Seron K, Blanchard E, Rosa-Calatrava M, Dubuisson J, Belouzard S, Roingeard P (2021) Ultrastructural modifications induced by SARS-CoV-2 in Vero cells: a kinetic analysis of viral factory formation, viral particle morphogenesis and virion release. Cell Mol Life Sci 78: 3565-3576

Ghosh S, Dellibovi-Ragheb TA, Kerviel A, Pak E, Qiu Q, Fisher M, Takvorian PM, Bleck C, Hsu VW, Fehr AR et al (2020) beta-Coronaviruses Use Lysosomes for Egress Instead of the Biosynthetic Secretory Pathway. Cell 183: 1520-1535 e1514

Hoffmann M, Hofmann-Winkler H, Smith JC, Kruger N, Arora P, Sorensen LK, Sogaard OS, Hasselstrom JB, Winkler M, Hempel T et al (2021) Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity. EBioMedicine 65: 103255

Hoffmann M, Mosbauer K, Hofmann-Winkler H, Kaul A, Kleine-Weber H, Kruger N, Gassen NC, Muller MA, Drosten C, Pohlmann S (2020) Chloroquine does not inhibit infection of human lung cells with SARS-CoV-2. Nature 585: 588-590

Xiang Y, Wang Y (2010) GRASP55 and GRASP65 play complementary and essential roles in Golgi cisternal stacking. J Cell Biol 188: 237-251

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, the authors highlight the importance of the Golgi apparatus during SARS-CoV-2 infection. Specifically, using different compounds able to alter Golgi structure and function, the authors show a strong reduction in SARS-CoV-2 infection rate. In particular it is interesting to observe that treatments of 24 hrs with BFA strongly impair viral infection, highlithing the importance of Golgi function for this virus. Albeit the time of treatment is different. this observation is in contrast with previous studies on related coronaviruses (Ghosh et al., 2020) that did not observe any effect upon treatment with BFA. This might imply that SARS-CoV-2 relies more on conventional trafficking pathways respect to other coronaviruses which, under certain conditions, favour different trafficking routes. The authors additionally observed that viral infection increases TGN46 levels while decreasing GRASP55 levels. To dissect the role of TGN46 and GRASPR55, the authors performed several infection studies in cells in which the levels of the two proteins were modulated either by overexpression (GRASP55) and/or siRNA-mediated knock-down (GRASP55 and TGN46). Those approaches suggest that GRASPR55 overexpression, a protein essential for Golgi stack formation, decelerates viral trafficking and inhibits viral assembly while its depletion reverses the effects. On the other hand, TGN46 knock-down impairs viral trafficking but not assembly.

Overall the study clearly shows the importance of the Golgi during SARS-CoV-2 and also shows that modulation of those two factors affect viral infection. However the claims that specifically the trafficking (TGN46) and trafficking and assembly (GRASP55) are not fully substantiated.

Regarding GRASP55, the authors state that viral infection decreases GRASPR55 levels and this results in Golgi fragmentation. However GRASPR55 levels decrease is shown at 24 hrs post infection while Golgi fragmentation occurs as early as 5 hrs. Thus there might be no direct casual effect between the two effects. Additionally, the authors show that overexpression of GRASP55 rescue Golgi fragmentation, as observed by imaging, however is not clear if only infected cells where quantified and if they had the same level of infection.

The authors exclude and effect on entry based on experiment on Spike expressing pseudovirus in 293-ACE2, however they also clearly observe reduction of ACE2 on the membrane of GRASPR55 expressing cells (Fig S6B). Thus how can they explain this discrepancy and how ca defect in entry can be fully marked out in these cell lines? It is not clear to which process the authors refer to when they write about "viral trafficking". Is it virion trafficking or viral proteins trafficking? The two process are linked but are not the same. This oversemplification can be misleading. For instance the authors show that overexpression of GRASP55 decreases Spike protein on the plasma membrane and its depletion increases S protein incorporation into psudoviruses. However it was shown that in infected cells S protein is mainly retained at the ERGIC by M and E (Boson et al., 2021) where viral assembly occurs. Thus an increase in S trafficking on the PM does not correlate with an increase in virion trafficking, and ultimately, the data provided do not fully support the authors claim on a modulation of "virion trafficking" in response to GRASP or TGN46 changes, since no experiments clearly show a change in virions secretion. Importantly, the authors do not rule out potential effects of their perturbations on genome replication. The only experiment that they perform in this direction is presented in figure S7B, where the authors show similar percentage of infected cells at early stage upon silecing of GRASPR55. The experiment suggests that productive entry is similar in these conditions, but quantification of intracellular viral genome could exclude a change in viral replication. If no changes in viral replication are observed, the authors could verify an increase in particles secretion by collecting supernatants from the early time points and performing plaque assays and quantification of viral genomes by qRT-PCR, to prove that modulation of GRASPR55 indeed promote SARS-CoV-2 trafficking.

Finally, whenever reduction of viral infection is observed upon cell partubation, a robust analysis of cell viability should be presented to exclude pleiotropic effects. Expecially in presence of multiple pertubation that might affect cell metabolism. The authors should carefully control cell viability and growth in response to depletion of TGN46 and GRASP55.

Minor:

show data on viability of the drug and add the relative section in Material and Methods

Figure 3A: should read spike and not nucleocapsid eported for SARS-CoV-2 Lack of inhibition with camostat correlates with lack of TMPRSS2 in the Huh7. The sentence seems to be too general while in this case the effect is clearly cell specific. Similarly, the importance of the lysosome in viral entry is restricted to cells lacking TMPRSS2 and cannot be generalized since CQ, for example, does not work in Calu-3 cells that express TMPRSS2 cells. Typo: Fig S3B - Y axis should reat viral not vrial S3C: concentrations of the compound used in the assay should be reported. Was a viability assay performed also in the 293T-ACE2 cell line?

Significance

Overall, the major strenght of the manuscript is that it has clarified the importance of the Golgi during SARS-CoV-2 infection. The drugs screening demonstrate that for SARS-CoV-2 the conventional secretion seems to have major role respect to other secretory routes observed for other coronaviruses. Also it is clear that the two factors identified by the authors have a role in viral infection, however the major limitation is that the authors failed to clearly highlight which step/s of the viral life cycle are modulated upon GRASP55 and TGN46 perturbatio. Expecially the claims on "trafficking" is not fully substantiated, since the only experiment in this direction is the transport of Spike protein on the plasma membrane upon GRASPR55 overexpression. It is risky to conclude that the trafficking of a single protein reflect the intracellular trafficking of the virions.

Several of the finding presented in the first part of the manuscript have been already previously reported (for example the fragmentation of the Golgi upon SARS-CoV-2 infection), however the role of GRASP55 and TGN46 in SARS-CoV-2 infection has been reported here for the first time. This manuscript can be of interest for a broad audience considering the topic (cell biology, host-pathogen interactions and molecular virology)

My expertise reside in the field of molecular virology, expecially in the contest of the mechanisms of viral replication and host-pathogen interactions.

Considero que moco va enlistado esta parte, el diseño tiene un poco de todo lo que lo rodea, como su objetivo, contexto de las personas que realizaran el diseño y quien lo disfrutara

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this article, Nedbalova et al. investigate the biochemical pathway that acts in circulating immune cells to generate adenosine, a systemic signal that directs nutrients toward the immune response, and S-adenosylmethionine (SAM), a methyl donor for lipid, DNA, RNA, and protein synthetic reactions. They find that SAM is largely generated through the uptake of extracellular methionine, but that recycling of adenosine to form ATP contributes a small but important quantity of SAM in immune cells during the immune response. The authors propose that adenosine serves as a sensor of cell activity and nutrient supply, with adenosine secretion dominating in response to increased cellular activity. Their findings of impaired immune action but rescued larval developmental delay when the enzyme Ahcy is knocked down in hemocytes are interpreted as due to effects on methylation processes in hemocytes and reduced production of adenosine to regulate systemic metabolism and development, respectively. Overall this is a strong paper that uses sophisticated metabolic techniques to map the biochemical regulation of an important systemic mediator, highlighting the importance of maintaining appropriate metabolite levels in driving immune cell biology.

Strengths:

The authors deploy metabolic tracing - no easy feat in Drosophila hemocytes - to assess flux into pools of the SAM cycle. This is complemented by mass spectrometry analysis of total levels of SAM cycle metabolites to provide a clear picture of this metabolic pathway in resting and activated immune cells.

The experiments show that the recycling of adenosine to ATP, and ultimately SAM, contributes meaningfully to the ability of immune cells to control infection with wasp eggs.

This is a well-written paper, with very nice figures showing metabolic pathways under investigation. In particular, the italicized annotations, for example, "must be kept low", in Figure 1 illustrate a key point in metabolism - that cells must control levels of various intermediates to keep metabolic pathways moving in a beneficial direction.

Experiments are conducted and controlled well, reagents are tested, and findings are robust and support most of the authors' claims.

Weaknesses:

The authors posit that adenosine acts as a sensor of cellular activity, with increased release indicating active cellular metabolism and insufficient nutrient supply. It is unclear how generalizable they think this may be across different cell types or organs.

In the final part of the Discussion, we elaborate slightly more on a possible generalization of our results, while being aware of the limited space in this experimental paper and therefore intend to address this in more detail and comprehensively in a subsequent perspective article.

The authors extrapolate the findings in Figure 3 of decreased extracellular adenosine in ex vivo cultures of hemocytes with knockdown of Ahcy (panel B) to the in vivo findings of a rescue of larval developmental delay in wasp egg-infected larvae with hemocyte-specific Ahcy RNAi (panel C). This conclusion (discussed in lines 545-547) should be somewhat tempered, as a number of additional metabolic abnormalities characterize Ahcy-knockdown hemocytes, and the in vivo situation may not mimic the ex vivo situation. If adenosine (or inosine) measurements were possible in hemolymph, this would help bolster this idea. However, adenosine at least has a very short half-life.

We agree with the reviewer, and in the 4th paragraph of the Discussion we now discuss more extensively the limitations of our study in relation to ex vivo adenosine measurements and the importance of the SAM pathway on adenosine production.

Reviewer #2 (Public review):

Summary:

In this work, the authors wish to explore the metabolic support mechanisms enabling lamellocyte encapsulation, a critical antiparasitic immune response of insects. They show that S-adenosylmethionine metabolism is specifically important in this process through a combination of measurements of metabolite levels and genetic manipulations of this metabolic process.

Strengths:

The metabolite measurements and the functional analyses are generally very strong and clearly show that the metabolic process under study is important in lamellocyte immune function.

Weaknesses:

The gene expression data are a potential weakness. Not enough is explained about how the RNAseq experiments in Figures 2 and 4 were done, and the representation of the data is unclear.

The RNAseq data have already been described in detail in our previous paper (doi.org/10.1371/journal.pbio.3002299), but we agree with the reviewer that we should describe the necessary details again here. The replicate numbers for RNAseq data were added to figure legends, the TPM values for the selected genes shown in figures are in S1_Data and new S4_Data file with complete RNAseq data (TPM and DESeq2) was added to this revised version.

The paper would also be strengthened by the inclusion of some measure of encapsulation effectiveness: the authors show that manipulation of the S-adenosylmethionine pathway in lamellocytes affects the ability of the host to survive infection, but they do not show direct effects on the ability of the host to encapsulate wasp eggs.

The reviewer is correct that wasp egg encapsulation and host survival may be different (the host can encapsulate and kill the wasp egg and still not survive) and we should also include encapsulation efficiency. This is now added to Figure 3D, which shows that encapsulation efficiency is reduced upon Ahcy-RNAi, which is consistent with the reduced number of lamellocytes.

Reviewer #3 (Public review):

Summary:

The authors of this study provide evidence that Drosophila immune cells show upregulated SAM transmethylation pathway and adenosine recycling upon wasp infection. Blocking this pathway compromises the lamellocyte formation, developmental delay, and host survival, suggesting its physiological relevance.

Strengths:

Snapshot quantification of the metabolite pool does not provide evidence that the metabolic pathway is active or not. The authors use an ex vivo isotope labelling to precisely monitor the SAM and adenosine metabolism. During infection, the methionine metabolism and adenosine recycling are upregulated, which is necessary to support the immune reaction. By combining the genetic experiment, they successfully show that the pathway is activated in immune cells.

Weaknesses:

The authors knocked down Ahcy to prove the importance of SAM methylation pathway. However, Ahcy-RNAi produces a massive accumulation of SAH, in addition to blocking adenosine production. To further validate the phenotypic causality, it is necessary to manipulate other enzymes in the pathway, such as Sam-S, Cbs, SamDC, etc.

We are aware of this weakness and have addressed it in a much more detailed discussion of the limitations of our study in the 6th paragraph of the Discussion.

The authors do not demonstrate how infection stimulates the metabolic pathway given the gene expression of metabolic enzymes is not upregulated by infection stimulus.

Although the goal of this work was to test by 13C tracing whether the SAM pathway activity is upregulated, not to analyze how its activity is regulated, we certainly agree with the reviewer that an explanation of possible regulation, especially in the context of the enzyme expressions we show, should be included in our work. Therefore, we have supplemented the data with methyltransferase expressions (Figure 2-figure supplement 3. And S3_Data) and better describe the changes in expression of some SAM pathway genes, which also support stimulation of this pathway by changes in expression. The enzymes of the SAM transmethylation pathway are highly expressed in hemocytes, and it is known that the activity of this pathway is primarily regulated by (1) increased methionine supply to the cell and (2) the actual utilization of SAM by methyltransferases. Therefore, a possible increase in SAM transmethylation pathway in our work can be suggested (1) by increased expression of 4 transporters capable of transporting methionine, (2) by decreased expression of AhcyL2 (dominant-negative regulator of Ahcy) and (3) by increased expression of 43 out of 200 methyltransferases. This was now added to the first section of Results.

Recommendations for the authors:

Reviewing Editor Comments:

In the discussion with the reviewers, two points were underlined as very important:

(1) Knocking down Ahyc and other enzymes in the SAM methylation pathway may give very distinct phenotypes. Generalising the importance of "SAM methyaltion" only by Ahcy-RNAi is a bit cautious. The authors should be aware of this issue and probably mention it in the Discussion part.

We are aware of this weakness and have addressed it in a much more detailed discussion of the limitations of our study in the 6th paragraph of the Discussion.

(2) Sample sizes should be indicated in the Figure Legends. Replicate numbers on the RNAseq are important - were these expression levels/changes seen more than once?

Sample sizes are shown as scatter plots with individual values wherever possible and all graphs are supplemented with S1_Data table with raw data. The RNAseq data have already been described in detail in our previous paper (doi.org/10.1371/journal.pbio.3002299), but we agree with the reviewers that we should describe the necessary details again here. The replicate numbers for RNAseq data were added to figure legends, the TPM values for the selected genes shown in figures are in S1_Data and new S4_Data file with complete RNAseq data (TPM and DESeq2) was added to this revised version.

Reviewer #1 (Recommendations for the authors):

Major points:

(1) Please provide sample sizes in the legends rather than in a supplementary table.

Sample sizes are shown either as scatter plots with individual values or added to figure legends now.

(2) More details in the methods section are needed:

For hemocyte counting, are sessile and circulating hemocytes measured?

We counted circulating hemocytes (upon infection, most sessile hemocytes are released into the circulation). While for metabolomics all hemocyte types were included, for hemocyte counting we were mainly interested in lamellocytes. Therefore, we counted them 20 hours after infection, when most of the lamellocytes from the first wave are fully differentiated but still mostly in circulation, as they are just starting to adhere to the wasp egg. This was added to the Methods section.

How were levels of methionine and adenosine used in ex vivo cultures selected? This is alluded to in lines 158-159, but no references are provided.

The concentrations are based on measurements of actual hemolymph concentrations in wild-type larvae in the case of methionine, and in the case of adenosine, we used a slightly higher concentration than measured in the adgf-a mutant to have a sufficiently high concentration to allow adenosine to flow into the hemocytes. This is now added to the Methods section.

Minor points:

Response to all minor points:  Thank you, errors has now been fixed.

(1) Line 186 - spell out MTA - 5-methylthioadenosine.

(2) Lines 196-212 (and elsewhere) - spelling out cystathione rather than using the abbreviation CTH is recommended because the gene cystathione gamma-lyase (Cth) is also discussed in this paragraph. Using the full name of the metabolite will reduce confusion.

We rather used cystathionine γ-lyase as a full name since it is used only three times while CTH many more times, including figures.

(3) Figure 2 - supplement 2: please include scale bars.

(4) Line 303 - spelling error: "trabsmethylation" should be "transmethylation".

(5) Line 373 - spelling error: "higer" should be "higher".

Reviewer #2 (Recommendations for the authors):

For the RNAseq data, it's unclear whether the gene expression data in Figures 2 and 4 include biological replicates, so it's unclear how much weight we should place on them.

The replicate numbers for RNAseq data were added to figure legends, the TPM values for the selected genes shown in figures are in S1_Data and new S4_Data file with complete RNAseq data (TPM and DESeq2) was added to this revised version.

The representation of these data is also a weakness: Figure 2 shows measurements of transcripts per million, but we don't know what would be high or low expression on this scale.

We have added the actual TPM values for each cell in the RNAseq heatmaps in Figure 2, Figure 2-figure supplement 3, and Figure 4 to make them more readable. Although it is debatable what is high or low expression, to at least have something for comparison, we have added the following information to the figure legends that only 20% of the genes in the presented RNAseq data show expression higher than 15 TPM.

Figure 4 is intended to show expression changes with treatment, but expression changes should be shown on a log scale (so that increases and decreases in expression are shown symmetrically) and should be normalized to some standard level (such as uninfected lamellocytes).

The bars in Figure 4C,D show the fold change (this is now stated in the y-axis legend) compared to 0 h (=uninfected) Adk3 samples - the reason for this visualization is that we wanted to show (1) the differences in levels between Adk3 and Adk2 and in levels between Ak1 and Ak2, respectively, and at the same time (2) the differences between uninfected and infected Adk3 and Ak1. In our opinion, these fold change differences are also much more visible in normal rather than log scale.

Reviewer #3 (Recommendations for the authors):

(1) It might be interesting to test how general this finding would be. How about Bacterial or fungal infection? The authors may also try genetic activation of immune pathways, e.g. Toll, Imd, JAK/STAT.

Although we would also like to support our results in different systems, we believe that our results are already strong enough to propose the final hypothesis and publish it as soon as possible so that it can be tested by other researchers in different systems and contexts than the Drosophila immune response.

(2) How does the metabolic pathway get activated? Enzyme activity? Transporters? Please test or at least discuss the possible mechanism.

The response is already provided above in the Reviewer #3 (Public review) section.

(3) The authors might test overexpression or genetic activation of the SAM transmethylation pathway.

Although we agree that this would potentially strengthen our study, it may not be easy to increase the activity of the SAM transmethylation pathway - simply overexpressing the enzymes may not be enough, the regulation is primarily through the utilization of SAM by methyltransferases and there are hundreds of them and they affect numerous processes. 

(4) Supplementation of adenosine to the Ahcy-RNAi larvae would also support their conclusion.

Again, this is not an easy experiment, dietary supplementation would not work, direct injection of adenosine into the hemolymph would not last long enough, adenosine would be quickly removed.

(5) It is interesting to test genetically the requirement of some transporters, especially for gb, which is upregulated upon infection.

Although this would be an interesting experiment, it is beyond the scope of this study; we did not aim to study the role of the SAM transmethylation pathway itself or its regulation, only its overall activity and its role in adenosine production.

Una diapo de cierre en plan para que es useful el dplyr y el tidyr? para no acabar así con esto

en todas estas diapos, puede estar siempre los datos y el código en el mismo sitio (derecho, izquierdo) y sin animaciones? para mi ahora mismo es un poco mareo

creo que esta diapo la pondría antes del código. Porque es como qué pasará. Para que entiendan qué hace el left_join y luego lo hagan

porque cambia de sitio el código y los datos y porque el código viene del lado???

No podemos hacer un ejemplo normal? Con un fichero que les subamos nosotros y tengan que leer? Lo digo porque es lo que harán en su vida real.

Reviewer #2 (Public review):

Summary:

The present study by Le Gac et al. investigates the vasoconstriction of cerebral arteries during neurovascular coupling. It proposes that pyramidal neurons firing at high frequency lead to prostaglandin E2 (PGE2) release and activation of arteriolar EP1 and EP3 receptors, causing smooth muscle cell contraction. The authors further claim that interneurons and astrocytes also contribute to the vasoconstriction via neuropeptide Y (NPY) and 20-hydroxyeicosatetraenoic acid (20-HETE) release, respectively. The study mainly uses brain slices and pharmacological tools in combination with Emx1-Cre;Ai32 transgenic mice expressing the H134R variant of channelrhodopsin-2 (ChR2) in the cortical glutamatergic neurons for precise photoactivation. Stimulation with 470 nm light using 10-second trains of 5-ms pulses at frequencies from 1-20 Hz revealed small constrictions at 10 Hz and robust constrictions at 20 Hz, which were abolished by TTX and partially inhibited by a cocktail of glutamate receptor antagonists. Inhibition of cyclooxygenase-1 (COX-1) or -2 (COX-2) by indomethacin blocked the constriction both ex vivo (slices) and in vivo (pial artery), and inhibition of EP1 and EP3 showed the same effect ex vivo. Single-cell RT-PCR from patched neurons confirmed the presence of the PGE2 synthesis pathway. While the data are convincing, the overall experimental setting presents some limitations. How is the activation protocol comparable to physiological firing frequency? The delay (minutes) between the stimulation and the constriction appears contradictory to the proposed pathway, which would be expected to occur rapidly. The experiments are conducted in the absence of vascular "tone," which further questions the significance of the findings. Some of the targets investigated are expressed by multiple cell types, which makes the interpretation difficult; for example, cyclooxygenases are also expressed by endothelial cells. Finally, how is the complete inhibition of the constriction by the NPY Y1 receptor antagonist BIBP3226 consistent with a direct effect of PGE2 and 20-HETE in arterioles? Overall, the manuscript is well-written with clear data, but the interpretation and physiological relevance have some limitations. However, vasoconstriction is a rather understudied phenomenon in neurovascular coupling, and the present findings may be of significance in the context of pathological brain hypoperfusion.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary of what the authors were trying to achieve:

In this manuscript, the authors investigated the role of β-CTF on synaptic function and memory. They report that β-CTF can trigger the loss of synapses in neurons that were transiently transfected in cultured hippocampal slices and that this synapse loss occurs independently of Aβ. They confirmed previous research (Kim et al, Molecular Psychiatry, 2016) that β-CTF-induced cellular toxicity occurs through a mechanism involving a hexapeptide domain (YENPTY) in β-CTF that induces endosomal dysfunction. Although the current study also explores the role of β-CTF in synaptic and memory function in the brain using mice chronically expressing β-CTF, the studies are inconclusive because potential effects of Aβ generated by γ-secretase cleavage of β-CTF were not considered. Based on their findings, the authors suggest developing therapies to treat Alzheimer's disease by targeting β-CTF, but did not address the lack of clinical improvement in trials of several different BACE1 inhibitors, which target β-CTF by preventing its formation.

We would like to thank the reviewer for his/her suggestions. We have addressed the specific comments in following sections.

Major strengths and weaknesses of the methods and results:

The conclusions of the in vitro experiments using cultured hippocampal slices were well supported by the data, but aspects of the in vivo experiments and proteomic studies need additional clarification.

(1) In contrast to the in vitro experiments in which a γ-secretase inhibitor was used to exclude possible effects of Aβ, this possibility was not examined in in-vivo experiments assessing synapse loss and function (Figure 3) and cognitive function (Figure 4). The absence of plaque formation (Figure 4B) is not sufficient to exclude the possibility that Aβ is involved. The potential involvement of Aβ is an important consideration given the 4-month duration of protein expression in the in vivo studies.

We appreciate the reviewer for raising this question. While our current data did not exclude the potential involvement of Aβ-induced toxicity in the synaptic and cognitive dysfunction observed in mice overexpressing β-CTF, addressing this directly remains challenging. Treatment with γ-secretase inhibitors could potentially shed light on this issue. However, treatments with γ-secretase inhibitors are known to lead to brain dysfunction by itself likely due to its blockade of the γ-cleavage of other essential molecules, such as Notch[1, 2]. Therefore, this approach is unlikely to provide a clear answer, which prevents us from pursuing it further experimentally in vivo. We hope the reviewer understands this limitation. We have included additional discussion (page 14 of the revised manuscript) to highlight this question.

(2) The possibility that the results of the proteomic studies conducted in primary cultured hippocampal neurons depend in part on Aβ was also not taken into consideration.

We thank the reviewer for raising this question. In the revised manuscript, we examined the protein levels of synaptic proteins after treatment with γ-secretase inhibitors and found that the levels of certain synaptic proteins were further reduced in neurons expressing β-CTF (Supplementary figure 5A-B). These results do not support Aβ as a major contributor of the proteomic changes induced by β-CTF.

Likely impact of the work on the field, and the utility of the methods and data to the community:

The authors' use of sparse expression to examine the role of β-CTF on spine loss could be a useful general tool for examining synapses in brain tissue.

We thank the reviewer for these comments.

Additional context that might help readers interpret or understand the significance of the work:

The discovery of BACE1 stimulated an international effort to develop BACE1 inhibitors to treat Alzheimer's disease. BACE1 inhibitors block the formation of β-CTF which, in turn, prevents the formation of Aβ and other fragments. Unfortunately, BACE1 inhibitors not only did not improve cognition in patients with Alzheimer's disease, they appeared to worsen it, suggesting that producing β-CTF actually facilitates learning and memory. Therefore, it seems unlikely that the disruptive effects of β-CTF on endosomes plays a significant role in human disease. Insights from the authors that shed further light on this issue would be welcome.

Response: We would like to express our gratitude to the reviewer for raising this question. It remains puzzling why BACE1 inhibition has failed to yield benefits in AD patients, while amyloid clearance via Aβ antibodies are able to slow down disease progression. One possible explanation is that pharmacological inhibition of BACE1 may not be as effective as its genetic removal. Indeed, genetic depletion of BACE1 leads to the clearance of existing amyloid plaques[3], whereas its pharmacological inhibition prevents the formation of new plaques but does not deplete the existing ones[4]. We think the negative results of BACE1 inhibitors in clinical trials may not be sufficient to rule out the potential contribution of β-CTF to AD pathogenesis. Given that cognitive function continues to deteriorate rapidly in plaque-free patients after 1.5 years of treatment with Aβ antibodies in phase three clinical studies[5], it is important to consider the potential role of other Aβ-related fragments in AD pathogenesis, such as β-CTF. We included further discussion in the revised manuscript (page 15 of the revised manuscript) to discusss this question.

Reviewer #2 (Public Review):

Summary:

In this study, the authors investigate the potential role of other cleavage products of amyloid precursor protein (APP) in neurodegeneration. They combine in vitro and in vivo experiments, revealing that β-CTF, a product cleaved by BACE1, promotes synaptic loss independently of Aβ. Furthermore, they suggest that β-CTF may interact with Rab5, leading to endosomal dysfunction and contributing to the loss of synaptic proteins.

We would like to thank the reviewer for his/her suggestions. We have addressed the specific comments in following sections.

Weaknesses:

Most experiments were conducted in vitro using overexpressed β-CTF. Additionally, the study does not elucidate the mechanisms by which β-CTF disrupts endosomal function and induces synaptic degeneration.

We would like to thank the reviewer for this comment. While a significant portion of our experiments were conducted in vitro, the main findings were also confirmed in vivo (Figure 3 and 4). Repeating all the experiments in vivo would be challenging and may not be possible because of technical difficulties. Regarding the use of overexpressed β-CTF, we acknowledge that this represents a common limitation in neurodegenerative disease studies. These diseases progress slowly over decades in patients. To model this progression in cell or mouse models within a time frame feasible for research, overexpression of certain proteins is often inevitable. Since β-CTF levels are elevated in AD patients[6], its overexpression is not a irrelevant approach to investigate its potential effects.

We did not further investigate the mechanisms by which β-CTF disrupted endosomal function because our preliminary results align with previous findings that could explain its mechanism. Kim et al. demonstrated that β-CTF recruits APPL1 (a Rab5 effector) via the YENPTY motif to Rab5 endosomes, where it stabilizes active GTP-Rab5, leading to pathologically accelerated endocytosis, endosome swelling and selectively impaired transport of Rab5 endosomes[6]. However, this paper did not show whether this Rab5 overactivation-induced endosomal dysfunction leads to any damages in synapses. In our study, we observed that co-expression of Rab5_S34N with β-CTF effectively mitigated β-CTF-induced spine loss in hippocampal slice cultures (Figures 6L-M), indicating that Rab5 overactivation-induced endosomal dysfunction contributed to β-CTF-induced spine loss. We included further discussion in the revised manuscript to clarify this (page 15 of the revised manuscript).

Reviewer #3 (Public Review):

Summary:

Most previous studies have focused on the contributions of Abeta and amyloid plaques in the neuronal degeneration associated with Alzheimer's disease, especially in the context of impaired synaptic transmission and plasticity which underlies the impaired cognitive functions, a hallmark in AD. But processes independent of Abeta and plaques are much less explored, and to some extent, the contributions of these processes are less well understood. Luo et all addressed this important question with an array of approaches, and their findings generally support the contribution of beta-CTF-dependent but non-Abeta-dependent process to the impaired synaptic properties in the neurons. Interestingly, the above process appears to operate in a cell-autonomous manner. This cell-autonomous effect of beta-CTF as reported here may facilitate our understanding of some potentially important cellular processes related to neurodegeneration. Although these findings are valuable, it is key to understand the probability of this process occurring in a more natural condition, such as when this process occurs in many neurons at the same time. This will put the authors' findings into a context for a better understanding of their contribution to either physiological or pathological processes, such as Alzheimer's. The experiments and results using the cell system are quite solid, but the in vivo results are incomplete and hence less convincing (see below). The mechanistic analysis is interesting but primitive and does not add much more weight to the significance. Hence, further efforts from the authors are required to clarify and solidify their results, in order to provide a complete picture and support for the authors' conclusions.

We would like to thank the reviewer for the suggestions. We have addressed the specific comments in following sections.

Strengths:

(1) The authors have addressed an interesting and potentially important question

(2) The analysis using the cell system is solid and provides strong support for the authors' major conclusions. This analysis has used various technical approaches to support the authors' conclusions from different aspects and most of these results are consistent with each other.

We would like to thank the reviewer for these comments.

Weaknesses:

(1) The relevance of the authors' major findings to the pathology, especially the Abeta-dependent processes is less clear, and hence the importance of these findings may be limited.

We would like to thank the reviewer for this question. Phase 3 clinical trial data from Aβ antibodies show that cognitive function continues to decline rapidly, even in plaque-free patients, after 1.5 years of treatment[5]. This suggests that plaque-independent mechanisms may drive AD progression. Therefore, it is crucial to consider the potential contributions of other Aβ species or related fragments, such as alternative forms of Aβ and β-CTF. While it is early to predict how much β-CTF contributes to AD progression, it is notable that β-CTF induced synaptic deficits in mice, which recapitulates a key pathological feature of AD. Ultimately, the contribution of β-CTF in AD pathogenesis can only be tested through clinical studies in the future.

(2) In vivo analysis is incomplete, with certain caveats in the experimental procedures and some of the results need to be further explored to confirm the findings.

We would like to thank the reviewer for this suggestion. We have corrected these caveats in the revised manuscript.

(3) The mechanistic analysis is rather primitive and does not add further significance.

We would like to thank the reviewer for this comment. We did not delve further into the underlying mechanisms because our analysis indicates that Rab5 overactivation-induced endosomal dysfunction underlies β-CTF-induced synaptic dysfunction, which is consistent with another study and has been addressed in our study[6]. We hope the reviewer could understand that our focus in this paper is on how β-CTF triggers synaptic deficits, which is why we did not investigate the mechanisms of β-CTF-induced endosomal dysfunction further.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Suggestions for improved or additional experiments, data, or analyses:

(1) In Figures 4H, 4J, 4K and Supplemental Figures 3C, 3E, and 3G, it was unclear whether a repeated measures 2-way ANOVA, rather than a 2-way ANOVA, followed by appropriate post-hoc analyses was used to strengthen the conclusion that there were significant effects in the behavioral tests.

We appreciate the reviewer for raising this point and apologize for the lack of clear description in the manuscript. In those figures mentioned above, we use a repeated measures 2-way ANOVA to analyze the data by Graphpad Prism. In Figure 4H, fear conditioning tests were conducted. The same cohort of mice were used in the baseline, contextual and cued tests. Firstly, baseline freezing was tested; then these mice underwent tone and foot shock training, followed by contextual test and cued test. So, a repeated measures 2-way ANOVA is more appropriate for the experiment.

In water T maze tests (Figure 4J and K), the same cohort of mice were trained and tested each day. So, it’s also appropriate to use a repeated measures 2-way ANOVA.

In Supplementary figure 3C, 3E and 3G, OFT was conducted. In this experiment, the locomotion of the same cohort of mice were recorded. Also, it’s appropriate to use a repeated measures 2-way ANOVA.

Clearer description for these experiments has been provided in the revised manuscript.

(2) Including gender analyses would be helpful.

The mice we used in this study were all males.

Minor corrections to text and figures:

(1) Quantitative analyses in Figures 5A-C, 5H, 6G, 6H, and Supplementary Figures 4 and 5C would be helpful.

We have provided quantitative analysis of these results (Figure 5D, 5J, 6K, Supplementary figure 4D, 5F) mentioned above in the revised manuscript.

(2) Percent correct (%) in Figures 4J and 4K should be labeled as 0, 50, and 100 instead of 0.0, 0.5, and 1.0.

We would like to thank the reviewer for pointing out this. We have made corrections in the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

In the study conducted by Luo et al, it was observed that the fragment of amyloid precursor protein (APP) cleaved by beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), known as β-CTF, plays a crucial role in synaptic damage. The study found increasing expression of β-CTF in neurons could induce synapse loss both in vitro and in vivo, independent of Aβ. Mechanistically, they explored how β-CTF could interfere with the endosome system by interacting with RAB5. While this study is intriguing, there are several points that warrant further investigation:

(1) The study involved overexpressing β-CTF in neurons. It would be valuable to know if the levels of β-CTF are similarly increased in Alzheimer's disease (AD) patients or AD mouse models.

We would like to thank the reviewer for the suggestion. It’s reported β-CTF levels were significantly elevated in the AD cerebral cortex[6]. Most AD mouse models are human APP transgenic mouse models with elevated β-CTF levels[7].

(2) The study noted that β-CTF in neurons is a membranal fragment, but the overexpressed β-CTF was not located in the membrane. It is important to ascertain whether the membranal β-CTF and cytoplasmic β-CTF lead to synapse loss in a similar manner.

We apologize for not clearly explaining the localization of β-CTF in the original manuscript. β-CTF is produced from APP through β-cleavage, a process that occurs in organelles such as endo-lysosomes[8]. The overexpressed β-CTF is also primarily localized in the endo-lysosomal systems (Figure 5C and Supplementary figure 4C), similar to those generated by APP cleavage.

(3) The study found a significant decrease in GluA1, a subunit of AMPA receptors, due to β-CTF. It would be beneficial to investigate whether there are systematic alterations in NMDA receptors, including GluN2A and GluN2B.

We would like to express our gratitude to the reviewer for bringing up this question. The protein levels of GluN2A and GluN2B are also reduced in neurons expressing β-CTF (Figure 6E-F)

(4) The study showed a significant decrease in the frequency of miniature excitatory postsynaptic currents (mEPSC), indicating disrupted presynaptic vesicle neurotransmitter release. It would be pertinent to test whether the expression level of the presynaptic SNARE complex, which is required for vesicle release, is altered by β-CTF.

We would like to express our gratitude to the reviewer for bringing up this question. The protein level of the presynaptic SNARE complex, such as VAMP2, is also reduced in neurons expressing β-CTF (Figure 6E, G).

(5) Since AMPA receptors are glutamate receptors, it is important to determine whether the ability of glutamate release is altered by β-CTF. In vivo studies using a glutamate sensor should be conducted to examine glutamate release.

We would like to express our gratitude to the reviewer for this suggestion. It will be interesting to use glutamate sensors to assess the ability of glutamate release in the future.

(6) The quality of immunostaining associated with Figures 4B and 4C was noted to be suboptimal.

We apologize for the suboptimal quality of these images. The immunostaining in Figures 4B and 4C were captured using the stitching function of a confocal microscope to display larger areas, including the entire hemisphere and hippocampus. We have reprocessed the images to obtain higher-quality versions.

(7) It would be insightful to investigate whether treatment with a BACE1 inhibitor in the study could reverse synaptic deficits mediated by β-CTF.

We would like to thank the reviewer for this sggestion. In Figure 1I-M, we constructed an APP mutant (APP_MV), which cannot be cleaved by BACE1 to produce β-CTF and Aβ but has no impact on β’-cleavage. When co-expressed with BACE1, APP_MV failed to induce spine loss, supporting the effect of β-CTF. We think these results domonstrate that β-CTF underlies the synaptic deficits. It would be interesting to test the effects of BACE1 inhibition in the future.

(8) Considering the potential implications for therapeutics, it is worth exploring whether extremely low levels of β-CTF have beneficial effects in regulating synaptic function or promoting synaptogenesis at a physiological level.

We would like to thank the reviewer for raising this question. We found that when the plasmid amount was reduced to 1/8 of the original dose, β-CTF no longer induced a decrease in dendritic spine density (Supplementary figure 2E-F). It’s reported APP-Swedish mutation in familial AD increased synapse numbers and synaptic transmission, whereas inhibition of BACE1 lowered synapse numbers, suppressed synaptic transmission in wild type neurons, suggesting that at physiological level, β-CTF might be synaptogenic[9].

(9) The molecular mechanism through which β-CTF interferes with Rab5 function should be elucidated.

We would like to thank the reviewer for raising this question. Kim et al have elucidated the mechanism through which β-CTF interferes with Rab5 function. β-CTF recruited APPL1 (a Rab5 effector) via YENPTY motif to Rab5 endosomes, where it stabilizes active GTP-Rab5, leading to pathologically accelerated endocytosis, endosome swelling and selectively impaired transport of Rab5 endosomes[6]. We have included additional discussion for this question in the revised manuscript (page 15 of the revised manuscript).

(10) The study could compare the role of β-CTF and Aβ in neurodegeneration in AD mouse models.

We would like to thank the reviewer for raising this point. While it is easier to dissect the role of Aβ and β-CTF in vitro, some of the critical tools are not applicabe in vivo, such as γ-secretase inhibitors, which lead to severe side effects because of their inhibition on other γ substrates[1, 2]. Therefore it will be difficult to deomonstrate their different roles in vivo. There are studies showing that β-CTF accumulation precedes Aβ deposition in model mice and mediates Aβ independent intracellular pathologies[10, 11], consistent with our results.

(11) Based on the findings, it would be valuable to discuss possible explanations for the failure of most BACE1 inhibitors in recent clinical trials for humans.

Response: We would like to express our gratitude to the reviewer for raising this recommendation. It is a big puzzle why BACE1 inhibition failed to provide beneficial effects in AD patients whereas clearance of amyloid by Aβ antibodies could slow down the AD progress. One potential answer is that pharmacological inhibition of BACE1 might be not as effective as its genetic removal. Indeed, genetic depletion of BACE1 leads to clearance of existing amyloid plaques[3], whereas pharmacological inhibition of BACE1 could not stop growth of existing plaques, although it prevents formation of new plaques[4]. The negative result of BACE1 inhibitors might not be sufficient to exclude the possibility that β-CTF could also contribute to the AD pathogenesis. We have included additional discussion for this question in the revised manuscript (page 15 of the revised manuscript).

Reviewer #3 (Recommendations For The Authors):

Major:

(1) The cell experiments were performed at DIV 9, do the authors know whether at this age, the neurons are still developing and spine density has not reached a pleated yet? If so, the observed effect may reflect the impact on development and/or maturation, rather than on the mature neurons. The authors should be more specific about this issue.

We would like to thank the reviewer for pointing out this question. These slice cultures were made from 1-week-old rats. DIV 9 is about two weeks old. These neurons are still developing and spine density has not reached a plateau yet[12]. In addition, we also investigated the effects of β-CTF on the synapses of mature neurons in two-month-old mice (Figure 3). So we think the observed effect reflects the impact on both immature and mature neurons.

(2) mEPSCs shown in Figure 3D were of small amplitudes, perhaps also indicating that these synapses are not yet mature.

In Figure 3D, the mEPSC results were obtained from pyramidal neurons in the CA1 region of two-month-old mice. At the age of two months, neurotransmitter levels and synaptic density have reached adult levels[13].

(3) There was no data on the spine density or mEPSCs in the mice OE b-CTF, hence it is unclear whether a primary impact of this manipulation (b-CTF effect) on the synaptic transmission still occurs in vivo.

In Figure 3, we examined the density of dendritic spines and mEPSCs from CA1 pyramidal neurons infected with lentivirus expressing β-CTF in mice and showed that those neurons expressing additional amount of β-CTF exhibited lower spine density and less mEPSCs, supporting that β-CTF also damaged synaptic transmission in vivo.

(4) OE of b-CTF should lead to the production of Abeta, although this may not lead to the formation of significant plaques. How do the authors know whether their findings on behavioral and cognitive impairments were not largely mediated by Abeta, which has been widely reported by previous studies?

We would like to thank the reviewer for pointing out this question. Indeed, our in vivo data could not exclude the potential involvement of Aβ in the pathology, despite the absence of amyloid plaque formation. It will be difficult to demonstrate this question in vivo because of the severe side effects from γ inhibition.

(5) Figure 4H, the freezing level in the cued fear conditioning was very high, likely saturated; this may mask a potential reduction in the b-CTF OE mice (there is a hint for that in the results). The authors should repeat the experiments using less strong footshock strength (hence resulting in less freezing, <70%).

We would like to express our gratitude to the reviewer for bringing up this question. The contextual fear conditioning test assesses hippocampal function, while the cued fear conditioning test assesses amygdala function. We hope the reviewer understands that our primary goal is to assess hippocampus-related functions in this experiment and we did see a significant difference between GFP and β-CTF groups. Therefore, we think the intensity of footshock we used was suitable to serve the primary purpose of this experiment.

(6) Why was the deficit in the Morris water maze in the b-CTF OE mice only significant in the training phase?

We would like to thank the reviewer for rasing this question and apologize for not describing the test clearly. This is a water T maze test, not Morris water maze test.

To make the behavioral paradigm of the water T maze test easier to understand, we have provided a more detailed description of the methods in the new version of the manuscript.

The acquisition phase of the Water T Maze (WTM) evaluates spatial learning and memory, where mice use spatial cues in the environment to navigate to a hidden platform and escape from water, while the reversal learning measures cognitive flexibility in which mice must learn a new location of the hidden platform[14]. In reversal learning task (Figure 4J-K), the learning curves of the two groups of mice did not show any significant differences, indicating that the expression of β-CTF only damages spatial learning and memory but not cognitive flexibility. This is consistent with a previous report using APP/PS1 mice[15].

(7) Will the altered Rab5 in the b-CTF OE condition also affect the level of other proteins?

We would like to express our gratitude to the reviewer for raising this interesting question.  Expression of Rab5_S34N in β-CTF-expressing neurons did not alter the levels of synapse-related proteins that were reduced in these neurons (Supplementary figure 5G-H), suggesting Rab5 overactivation did not contribute to these protein expression changes induced by β-CTF.

(8) How do the authors reconcile their findings with the well-established findings that Abeta affects synaptic transmission and spine density? Do they think these two processes may occur simultaneously in the neurons, or, one process may dominate in the other?

APP, Aβ, and presenilins have been extensively studied in mouse models, providing convincing evidence that high Aβ concentrations are toxic to synapses[16]. Moreover, addition of Aβ to murine cultured neurons or brain slices is toxic to synapses[17]. However, Aβ-induced synaptotoxicity was not observed in our study. A major difference between our study and others is that our study used a isolated expression system that apply Aβ only to individual neurons surrounded by neurons without excessive amount of Aβ, whereas the rest studies generally apply Aβ to all the neurons. Therefore, we predict that Aβ does not lead to synaptic deficits from individual neurons in cell autonomous manners, whereas β-CTF does. Aβ and β-CTF represent two parallel pathways of action. Additional discussion for this question has been included in the revised manuscript (page 14 of the revised manuscript).

Minor:

Fig 2F-G, "prevent" rather than "reverse"?

We would like to thank the reviewer for pointing this out. We have made corrections in the revised manuscript.

Reference:

(1) GüNER G, LICHTENTHALER S F. The substrate repertoire of γ-secretase/presenilin [J]. Seminars in cell & developmental biology, 2020, 105: 27-42.

(2) DOODY R S, RAMAN R, FARLOW M, et al. A phase 3 trial of semagacestat for treatment of Alzheimer's disease [J]. The New England journal of medicine, 2013, 369(4): 341-50.

(3) HU X, DAS B, HOU H, et al. BACE1 deletion in the adult mouse reverses preformed amyloid deposition and improves cognitive functions [J]. The Journal of experimental medicine, 2018, 215(3): 927-40.

(4) PETERS F, SALIHOGLU H, RODRIGUES E, et al. BACE1 inhibition more effectively suppresses initiation than progression of β-amyloid pathology [J]. Acta neuropathologica, 2018, 135(5): 695-710.

(5) SIMS J R, ZIMMER J A, EVANS C D, et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial [J]. Jama, 2023, 330(6): 512-27.

(6) KIM S, SATO Y, MOHAN P S, et al. Evidence that the rab5 effector APPL1 mediates APP-βCTF-induced dysfunction of endosomes in Down syndrome and Alzheimer's disease [J]. Molecular psychiatry, 2016, 21(5): 707-16.

(7) MONDRAGóN-RODRíGUEZ S, GU N, MANSEAU F, et al. Alzheimer's Transgenic Model Is Characterized by Very Early Brain Network Alterations and β-CTF Fragment Accumulation: Reversal by β-Secretase Inhibition [J]. Frontiers in cellular neuroscience, 2018, 12: 121.

(8) ZHANG X, SONG W. The role of APP and BACE1 trafficking in APP processing and amyloid-β generation [J]. Alzheimer's research & therapy, 2013, 5(5): 46.

(9) ZHOU B, LU J G, SIDDU A, et al. Synaptogenic effect of APP-Swedish mutation in familial Alzheimer's disease [J]. Science translational medicine, 2022, 14(667): eabn9380.

(10) LAURITZEN I, PARDOSSI-PIQUARD R, BAUER C, et al. The β-secretase-derived C-terminal fragment of βAPP, C99, but not Aβ, is a key contributor to early intraneuronal lesions in triple-transgenic mouse hippocampus [J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 2012, 32(46): 16243-1655a.

(11) KAUR G, PAWLIK M, GANDY S E, et al. Lysosomal dysfunction in the brain of a mouse model with intraneuronal accumulation of carboxyl terminal fragments of the amyloid precursor protein [J]. Molecular psychiatry, 2017, 22(7): 981-9.

(12) HARRIS K M, JENSEN F E, TSAO B. Three-dimensional structure of dendritic spines and synapses in rat hippocampus (CA1) at postnatal day 15 and adult ages: implications for the maturation of synaptic physiology and long-term potentiation [J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 1992, 12(7): 2685-705.

(13) SEMPLE B D, BLOMGREN K, GIMLIN K, et al. Brain development in rodents and humans: Identifying benchmarks of maturation and vulnerability to injury across species [J]. Progress in neurobiology, 2013, 106-107: 1-16.

(14) GUARIGLIA S R, CHADMAN K K. Water T-maze: a useful assay for determination of repetitive behaviors in mice [J]. Journal of neuroscience methods, 2013, 220(1): 24-9.

(15) ZOU C, MIFFLIN L, HU Z, et al. Reduction of mNAT1/hNAT2 Contributes to Cerebral Endothelial Necroptosis and Aβ Accumulation in Alzheimer's Disease [J]. Cell reports, 2020, 33(10): 108447.

(16) CHAPMAN P F, WHITE G L, JONES M W, et al. Impaired synaptic plasticity and learning in aged amyloid precursor protein transgenic mice [J]. Nature neuroscience, 1999, 2(3): 271-6.

(17) WANG Z, JACKSON R J, HONG W, et al. Human Brain-Derived Aβ Oligomers Bind to Synapses and Disrupt Synaptic Activity in a Manner That Requires APP [J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 2017, 37(49): 11947-66.

això està malament (trampós...)

DOI: 10.3390/cells13110944

Resource: (Alomone Labs Cat# APC-030, RRID:AB_2313726)

Curator: @scibot

SciCrunch record: RRID:AB_2313726

What is this?

Voici un résumé minuté des idées principales du podcast, en mettant en évidence les points clés en gras :

• Introduction (0:00-1:20) : Le podcast explore les complexités des relations fraternelles, en se demandant pourquoi les disputes entre frères et sœurs sont si douloureuses et difficiles à aborder. L'animateur mentionne des difficultés personnelles à se disputer avec ses sœurs et soulève des questions sur la purge des vieilles querelles et l'injonction familiale à bien s'entendre.

• Manque de recherche sur les frateries (1:20-2:20) : Il y a étonnamment peu de recherches en sciences humaines sur les relations fraternelles, contrairement à la richesse des histoires et des mythes qui les mettent en scène. La psychologue clinicienne Stéphanie AX est présentée comme une spécialiste des frateries, ayant constaté l'impact des conflits fraternels non résolus sur la vie adulte.

• Injustice et blessures non traitées (2:20-3:20) : Les sentiments d'injustice vécus pendant l'enfance, tels que le favoritisme parental, ne disparaissent pas et peuvent affecter la vie adulte. Stéphanie AX propose une thérapie spécifique pour les frateries adultes en souffrance, soulignant l'importance de soigner ces relations.

• Impact sur d'autres relations (3:20-5:30) : Les problèmes non résolus avec les frères et sœurs peuvent contaminer d'autres relations, notamment le couple. La relation de fraterie est horizontale, sans hiérarchie, ce qui est similaire à la relation conjugale. Les rivalités non dites dans l'enfance peuvent resurgir dans le couple, par exemple en matière d'investissement parental.

• Cas clinique et rivalités (5:30-6:50) : Un cas clinique illustre comment une rivalité infantile avec une sœur brillante a conduit une femme à envisager la séparation de son couple en raison de la proximité de son mari avec une collègue. L'expérience de la fraterie peut aider à dézoomer sur les problèmes actuels.

• Témoignages d'enfance (6:50-8:00) : Des témoignages d'enfants révèlent la violence et le côté terre à terre des disputes fraternelles, avec des exemples de farces et de trahisons. L'animateur s'inquiète de l'impact de ces conflits sur les relations futures de ses filles.

• Rivalité comme apprentissage (8:00-9:30) : La rivalité fraternelle n'est pas nécessairement négative ; elle fait partie de l'apprentissage social et permet de développer des stratégies de défense et de mesure à l'autre. Il est important de ne pas gommer les conflits, car ils aident à se préparer à la vie adulte.

• Conséquences de l'évitement des conflits (9:30-10:00) : Étouffer les conflits peut mener à la rupture, car les enfants intériorisent l'idée que le conflit est dangereux. Il est essentiel d'apprendre à gérer la colère et à ne pas craindre la rupture.

• Souvenirs d'enfance et injustices (10:00-11:30) : Des souvenirs d'enfance, même anciens, peuvent rester vifs et influencer le comportement parental. Les parents peuvent créer des injustices en voulant l'égalité à tout prix, par exemple en offrant les mêmes cadeaux à tous les enfants lors des anniversaires.

• Rivalité constante et besoin d'espace (11:30-12:30) : Une rivalité constante, surtout entre enfants proches en âge et de même sexe, peut être néfaste. Les parents doivent offrir à chaque enfant un espace où il peut se reposer de la compétition et investir son propre territoire.

• Différence d'âge et complicité tardive (12:30-14:30) : Une grande différence d'âge peut mener à l'indifférence pendant l'enfance, mais aussi à une complicité tardive. L'histoire de deux sœurs illustre comment un voyage en Espagne a permis de créer un lien de confiance et de révéler des secrets. Le coming out de l'une des sœurs a été maladroitement révélé aux parents par l'autre, mais cela a finalement permis de briser le silence.

• Thérapie et justice familiale (14:30-15:30) : Stéphanie AX utilise un cadre thérapeutique rigoureux dans ses consultations, s'appuyant sur les travaux du psychiatre Ivan bojorminagi. Ce dernier était sensible à la question de la justice au sein de la famille.

• Conclusion (15:30-16:00) : L'animateur encourage à appeler son frère ou sa sœur et rappelle le slogan du podcast : "Vivons heureux avant la fin du monde".

Author response:

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

General Response to Public Reviews

We thank the three reviewers for their positive evaluation of our work, which presents the first molecular characterization of type-II NB lineages in an insect outside the fly Drosophila. They seem convinced of our finding of an additional type-II NB and increased proliferation during embryogenesis in the red flour beetle. The reviewers expressed hesitations on our interpretation that the observed quantitative differences of embryonic lineages can directly be linked to the embryonic development of the central complex in Tribolium. While we still believe that a connection of both observations is a valid and likely hypothesis, we acknowledge that due the lack of functional experiments and lineage tracing a causal link has not directly been shown. We have therefore changed the manuscript to an even more careful wording that on one hand describes the correlation between increased embryonic proliferation with the earlier development of the Cx but on the other hand also stresses the need for additional functional and lineage tracing experiments to test this hypothesis. We have also strengthened the discussion on alternative explanations of the increased lineage size and emphasize the less disputed elements like presence and conservation of type-II NB lineages. 

While our manuscript could in conclusion not directly show that the reason of the heterochronic shift lies in the progenitor behaviour, we still provide a first approach to answering the question of the developmental basis of this shift and testable hypotheses directly emerge from our work. We agree with reviewer#1 that functional work is best suited to test our hypothesis and we are planning to do so. However, we believe that the presented work is already rich in novel data and significantly advances our understanding on the conservation and divergence of type-II NBs in insects. We would also like to stress that most transgenic tools for which genome-wide collections exist for Drosophila have to be created for Tribolium and doing so can be quite time consuming. Conducting RNAi experiments is certainly possible in Tribolium but observing phenotypes in this defined cellular context will need laborious optimization. We have for example tried knocking down Tc-fez/erm but could not see any embryonic phenotype which might be due to an escaper effect in which only mildly affected or wild type-like embryos survive while the others die in early embryogenesis. Due to pleiotropic functions of the involved genes a cell-specific knockdown might be necessary and we are working towards establishing a system to do that in the red flour beetle. For the stated reasons, we see our work as an important basis to inspire future functional studies that build up on the framework that we introduced. 

In response to these common points, we have made the following changes to the manuscript

-        The title has been changed from ‘being associated’ to ‘correlate’

-        The conclusions part of the abstract has been changed

-        We deleted the statement ‘…thus providing the material for the early central complex formation…’

-        Rephrased to saying that the two observations just correlate

-        The part of the discussion ‘Divergent timing of type-II NB activity and heterochronic development of the central complex’ has been extensively rewritten and now discusses several alternative explanations that were suggested by the reviewers. It also stresses the need for further functional work and lineage tracing (line 859-862 (608-611)).

In addition, we have made numerous changes to the manuscript to account for more specific comments of the reviewers and to the recommendations for the authors.

Our responses to the individual comments can be found in the following. 

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

Insects inhabit diverse environments and have neuroanatomical structures appropriate to each habitat. Although the molecular mechanism of insect neural development has been mainly studied in Drosophila, the beetle, Tribolium castaneum has been introduced as another model to understand the differences and similarities in the process of insect neural development. In this manuscript, the authors focused on the origin of the central complex. In Drosophila, type II neuroblasts have been known as the origin of the central complex. Then, the authors tried to identify those cells in the beetle brain. They established a Tribolium fez enhancer trap line to visualize putative type II neuroblasts and successfully identified 9 of those cells. In addition, they also examined expression patterns of several genes that are known to be expressed in the type II neuroblasts or their lineage in Drosophila. They concluded that the putative type II neuroblasts they identified were type II neuroblasts because those cells showed characteristics of type II neuroblasts in terms of genetic codes, cell diameter, and cell lineage. 

Strengths: 

The authors established a useful enhancer trap line to visualize type II neuroblasts in Tribolium embryos. Using this tool, they have identified that there are 9 type II neuroblasts in the brain hemisphere during embryonic development. Since the enhancer trap line also visualized the lineage of those cells, the authors found that the lineage size of the type II neuroblasts in the beetle is larger than that in the fly. They also showed that several genetic markers are also expressed in the type II neuroblasts and their lineages as observed in Drosophila. 

Weaknesses: 

I recommend the authors reconstruct the manuscript because several parts of the present version are not logical. For example, the author should first examine the expression of dpn, a well-known marker of neuroblast. Without examining the expression of at least one neuroblast marker, no one can say confidently that it is a neuroblast. The purpose of this study is to understand what makes neuroanatomical differences between insects which is appropriate to their habitats. To obtain clues to the question, I think, functional analyses are necessary as well as descriptive analyses. 

The expression of an exclusive type-II neuroblast marker would indeed have been the most convincing evidence. However, asense is absent from type-II NBs and deadpan is not specific enough as it is expressed in many other cells of the developing protocerebrum. The gene pointed, although also expressed elsewhere, emerged as the the most specific marker. Therefore, we start with pointed and fez/erm to describe the first appearance and developmental progression of the cells and then add further evidence that these cells are indeed type-II neuroblasts. Further evidence is provided in the following chapters.  We have discussed the need for functional work in the general response. 

Reviewer #2 (Public Review): 

The authors address the question of differences in the development of the central complex (Cx), a brain structure mainly controlling spatial orientation and locomotion in insects, which can be traced back to the neuroblast lineages that produce the Cx structure. The lineages are called type-II neuroblast (NB) lineages and are assumed to be conserved in insects. While Tribolium castaneum produces a functional larval Cx that only consists of one part of the adult Cx structure, the fan-shaped body, in Drosophila melanogaster a non-functional neuropile primordium is formed by neurons produced by the embryonic type-II NBs which then enter a dormant state and continue development in late larval and pupal stages. 

The authors present a meticulous study demonstrating that type-II neuroblast (NB) lineages are indeed present in the developing brain of Tribolium castaneum. In contrast to type-I NB lineages, type-II NBs produce additional intermediate progenitors. The authors generate a fluorescent enhancer trap line called fez/earmuff which prominently labels the mushroom bodies but also the intermediate progenitors (INPs) of the type-II NB lineages. This is convincingly demonstrated by high-resolution images that show cellular staining next to large pointed labelled cells, a marker for type-II NBs in Drosophila melanogaster. Using these and other markers (e.g. deadpan, asense), the authors show that the cell type composition and embryonic development of the type-II NB lineages are similar to their counterparts in Drosophila melanogaster. Furthermore, the expression of the Drosophila type-II NB lineage markers six3 and six4 in subsets of the Tribolium type-II NB lineages (anterior 1-4 and 1-6 type-II NB lineages) and the expression of the Cx marker skh in the distal part of most of the lineages provide further evidence that the identified NB lineages are equivalent to the Drosophila lineages that establish the central complex. However, in contrast to Drosophila, there are 9 instead of 8 embryonic type-II NB lineages per brain hemisphere and the lineages contain more progenitor cells compared to the Drosophila lineages. The authors argue that the higher number of dividing progenitor cells supports the earlier development of a functional Cx in Tribolium. 

While the manuscript clearly shows that type-II NB lineages similar to Drosophila exist in Tribolium, it does not considerably advance our understanding of the heterochronic development of the Cx in these insects. First of all, the contribution of these lineages to a functional larval Cx is not clear. For example, how do the described type-II NB lineages relate to the DM1-4 lineages that produce the columnar neurons of the Cx? What is the evidence that the embryonically produced type-II NB lineage neurons contribute to a functional larval Cx? The formation of functional circuits could rely on larval neurons (like in Drosophila) which would make a comparison of embryonic lineages less informative with respect to understanding the underlying variations of the developmental processes. Furthermore, the higher number of progenitors (and consequently neurons) in Tribolium could simply reflect the demand for a higher number of cells required to build the fan-shaped body compared to Drosophila. In addition, the larger lineages in Tribolium, including the higher number of INPs could be due to a greater number of NBs within the individual clusters, rather than a higher rate of proliferation of individual neuroblasts, as suggested. What is the evidence that there is only one NB per cluster? The presented schemes (Fig. 7/12) and description of the marker gene expression and classification of progenitor cells are inconsistent but indicate that NBs and immature INPs cannot be consistently distinguished. 

We thank this reviewer for pointing out the inconsistency in our classification of cells within the lineages as one central part of our manuscript. These were due to a confusion in the used terms (young vs. immature). We have corrected this mistake and have changed the naming of the INP subtypes to immature-I and immature-II. We are confident that based on the analysed markers, type-II NBs and immature INPs can actually be distinguished with confidence.

We agree that a functional link of increased proliferation to heterochronic CX development is not shown although we consider it to be likely. As stated in the general response we have changed the manuscript to saying that the two observations (higher number of progenitors and larger lineages/more INPs) correlate but that a causal link can only be hypothesized for the time being. At the same time, we have strengthened the discussion on alternative explanations.

We would like to remain with our statement of an increased number of embryonic progeny of Tribolium type-II NBs. We counted the total number of progenitor cells emerging from the anterior median cluster and divided this by the number of type II NBs in that cluster. Hence, the shown increased number of cells represents an average per NB but is not influenced by the increased number of NBs. On the same line, we have never seen indication for the presence of additional NBs within any cluster while one type-II NB is what we regularly found. Hence, we are confident that we know the number of respective NBs. The fact that the fly data included also neurons and was counted at a later stage indicates that the observed differences are actually minimum estimates.

We have discussed that based on the position and comparison to the grasshopper we believe that Tribolium type-II NB 1-4 contribute to the x, y, z and w tracts. To confirm this, lineage tracing experiments would be necessary, for which tools remain to be developed. 

We agree that the role of larvally born neurons and the fate of Tribolium neuroblasts through the transition from embryo to larva and pupa need to be further studied.

Available data suggests that the adult fan shaped body in Tribolium does not hugely differ in size from the Drosophila counterpart, although no data in terms of cell number is available. In the larva, however, no fan shaped body or protocerebral bridge can be distinguished in flies while in beetle larvae, these structures are clearly developed. Hence, we think that it is more likely that differences observed in the embryo reflect differences in the larval central complex. We discuss the need for further investigation of larval stages.

The main difference between Tribolium and Drosophila Cx development with regards to the larval functionality might be that Drosophila type-II NB lineage-derived neurons undergo quiescence at the end of embryogenesis so that the development of the Cx is halted, while a developmental arrest does not occur in Tribolium. However, this needs to be confirmed (as the authors rightly observe). 

Indeed, there is evidence that cells contributing to the CX go into quiescence in flies – hence, this certainly is one of the mechanisms. However, based on our data we would suggest that in addition, the balance of embryonic versus larval proliferation of type-II lineages is different between the two insects: The increased embryonic proliferation and development leads to a functional larval CX in beetles while in flies, postembryonic proliferation may be increased in order to catch up.

Reviewer #3 (Public Review):

Summary: 

In this paper, Rethemeier et al capitalize on their previous observation that the beetle central complex develops heterochronically compared to the fly and try to identify the developmental origin of this difference. For this reason, they use a fez enhancer trap line that they generated to study the neuronal stem cells (INPs) that give rise to the central complex. Using this line and staining against Drosophila type-II neuroblast markers, they elegantly dissect the number of developmental progression of the beetle type II neuroblasts. They show that the NBs, INPs, and GMCs have a conserved marker progression by comparing to Drosophila marker genes, although the expression of some of the lineage markers (otd, six3, and six4) is slightly different. Finally, they show that the beetle type II neuroblast lineages are likely longer than the equivalent ones in Drosophila and argue that this might be the underlying reason for the observed heterochrony. 

Strengths: 

- A very interesting study system that compares a conserved structure that, however, develops in a heterochronic manner. 

- Identification of a conserved molecular signature of type-II neuroblasts between beetles and flies. At the same time, identification of transcription factors expression differences in the neuroblasts, as well as identification of an extra neuroblast. 

- Nice detailed experiments to describe the expression of conserved and divergent marker genes, including some lineaging looking into the co-expression of progenitor (fez) and neuronal (skh) markers. 

Weaknesses: 

- Comparing between different species is difficult as one doesn't know what the equivalent developmental stages are. How do the authors know when to compare the sizes of the lineages between Drosophila and Tribolium? Moreover, the fact that the authors recover more INPs and GMCs could also mean that the progenitors divide more slowly and, therefore, there is an accumulation of progenitors who have not undergone their programmed number of divisions. 

We understand the difficulty of comparing stages between species, but we feel that our analysis is on the save side. At stages comparable with respect to overall embryonic development (retracting or retracted germband), the fly numbers are clearly smaller. To account for potential heterochronic shifts in NB activity, we have selected the stages to compare based on the criteria given: In Drosophila the number of INPs goes down after stage 16, meaning that they reach a peak at the selected stages. In Tribolium the chosen stages also reflect the phase when lineage size is larger than in all previous stages. Therefore, we believe that the conclusion that Tribolium has larger lineages and more INPs is well founded. Lineage size in Tribolium might further increase just before hatching (stage 15) but we were for technical reasons not able to look at this. As lineage size goes down in the last stage of Drosophila embryogenesis the number of INPs goes down and type-II NB enter quiescence, we think it is highly unlikely that the ratio between Tribolium and Drosophila INPs reverses at this stage, but a study of the behaviour of type-II NB in Tribolium and whether there is a stage of quiescence is still needed.

- The main conclusion that the earlier central complex development in beetles is due to the enhanced activity of the neuroblasts is very handwavy and is not the only possible conclusion from their data. 

As discussed in the general response we have made several changes to the manuscript to account for this criticism and discuss alternative explanations for the observations.

- The argument for conserved patterns of gene expression between Tribolium and Drosophila type-II NBs, INPs, and GMCs is a bit circular, as the authors use Drosophila markers to identify the Tribolium cells. 

We tested the hypothesis that in Tribolium there are type-II NBs with a molecular signature similar to flies. Our results are in line with that hypothesis. If pointed had not clearly marked cells with NB-morphology or fez/erm had not marked dividing cells adjacent to these NBs, we would have concluded that no such cells/lineages exist in the Tribolium embryo, or that central complex producing lineages exist but express different markers. Therefore, we regard this a valid scientific approach and hence find this argument not problematic.  

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions: Based on the above, I believe that the authors, despite advancing significantly, fall short of identifying the reasons for the divergent timing of central complex development between beetle and fly. 

We agree that based on the available data, we cannot firmly make that link and we have changed the text accordingly.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

In addition to these descriptive analyses, functional analyses can be included. RNAi is highly effective in this beetle. 

We agree that functional analyses of some of the studied genes and possible effects of gene knockdowns on the studied cell lineages and on central complex development could be highly informative. However, when studying specific cell types or organs these experiments are less straight forward than it may seem as knockdowns often lead to pleiotropic effects, sterility or lethality. All the genes involved are expressed in additional cells and may have essential functions there. Given the systemic RNAi of Tribolium, it is challenging to unequivocally assign phenotypes to one of the cell groups. Overcoming these challenges is often possible but needs extensive optimization. Our study, though descriptive is already rich in data and is the first description of NB-II lineages in Tribolium central complex development. We see it as a basis for future studies on central complex development that will include functional experiments.

(1) Introduction 

For these reasons the beetle... 

Could you explain the differences in the habitats between Tribolium and Drosophila? or What is the biggest difference between these two species at the ecological aspect? 

We have added a short characterisation of the main differences.

The insect central complex is an anterior... 

The author should explain why they focus on the structure. 

Added

It is however not known how these temporal... 

If the authors want to get the answer to the question, they need to conduct functional analyses. 

While we agree with the importance of functional work (see above) we believe that detailed descriptions under the inclusion of molecular markers as presented here is very informative by itself for understanding developmental processes and sets the foundation for the analysis of mutant/RNAi- phenotypes in future studies.

CX - Central complex? 

We have opted to not use this abbreviation anymore for clarity.

“because intermediate cycling progenitors have also been...” 

Is the sentence correct? 

We have included ‘INPs’ in the sentence to make clear what the comparison refers to and added a comma

“However, molecular characterization of such lineage in another...” 

The authors should explain why molecular characterization is necessary. 

We have done so

(2) Results 

a) Figure 8. Could you delineate the skh/eGFP expression region? 

We have added brackets to figure 1 panel A to indicate the extent of skh and other gene expressions within the lineages.

b) This section should be reorganized for better logical flow. 

There certainly are different ways to organize this part and we have considered different structures of the results part. We eventually subjectively concluded that the chosen one is the best fit for our data (also see comment below on dpn-expression).

c) For the tables. The authors should mention what statistical analysis they have conducted. 

The tables themselves are just listing the raw numbers. They are the basis for the graph in figure 9. Statistical tests (t-test) are mentioned in the legend of that figure and now also in the Methods sections.

“We also found that the large Tc-pnt...” 

The authors could examine the mitotic index using an anti-pH3 antibody. 

We have used the anti-pH3 antibody to detect mitoses (figure 3C, table 1 and 3) but as data on mitoses based on this antibody is only a snapshot it would require a lot of image data to reliably determine an index in this specific cells. While mitotic activity over time possibly combined with live imaging might be very interesting in this system also with regards to the timing of development, for this basic study we are satisfied with the statement that the type-II NB are indeed dividing at these stages.

“Based on their position by the end of embryogenesis...” 

How can the authors conclude that they are neuroblasts without examining the expression of NB markers? 

Type-II NB do not express asense as the key marker for type I neuroblasts. To corroborate our argument that the cells are neuroblasts we have used several criteria:

- We have used the same markers that are used in Drosophila to label type-II NBs (pnt, dpn, six4). We are not aware of any other marker that would be more specific.

- We have shown that these cells are larger and have larger nuclei than neighbouring cells and they are dividing

- We have shown that these cells through their INP lineages give rise to central complex neuropile

We believe that these features taken together leave little doubt that the described cells are indeed neuroblasts. 

“We found that the cells they had assigned as...” 

How did the authors distinguish that they are really neuroblasts? 

We see the difficulty that we first describe the position and development of these cells (e.g. fig 3) and then add further evidence (cell size, additional marker dpn) that these are neuroblasts (also see above). However, without previous knowledge on position (and on pnt expression as the most specific marker) the type-II NB could not have been distinguished from other NBs based on cell size or expression of other markers.

“Conserved patterns of gene expression...” 

This must be the first (especially dpn). 

Dpn is not specific to type-II NB because it is also expressed in type-1 NBs, mature INPs and possibly other neural cells. It is therefore impossible to identify type-II NBs based on this gene alone. We therefore first used the most specific marker, pnt, in addition to adjacent fez expression to identify candidates for type-II lineages. Then we mapped expression of further genes on these lineages to support the interpretation (and show homology to the Drosophila lineages). Although of course the structure of a paper does not necessarily have to reflect the sequence in which experiments were done we would find putting dpn expression first misleading as it would not be clear why exactly a certain part of the expression should belong to type-II NB. Also, our pnt-fez expression data shows the position of the NB-II in the context of the whole head lobe whereas the other gene expressions are higher magnifications focussing on details. We therefore believe that the structure we chose best fits our data and the other reviewers seemed to find it acceptable as well.  

“As type-II NBs contribute to central...” 

Before the sentence, the author could explain differences in the central complex structure between Tribolium and Drosophila in terms of cell number and tissue size. 

We have added references on the comparisons of tissue sizes, but unfortunately there is no Tribolium data that can be directly compared to available Drosophila resources in terms of cell number.  

“We conclude that the embryonic development of...” 

How did the authors conclude? They must explain their logic. 

Actually, before this sentence, I only found the description of the comparison between Tribolium NBs and Drosophila once. 

We agree that this conclusion is not fully evident from the presented data. We have therefore changed this part to stating that there is a correlation with the earlier central complex development described in Tribolium. See also response to the general reviewer comments.

“Hence, we wondered...” 

The authors need to do a functional assessment of the genes they mentioned. 

We agree that the goals originally stated at the beginning of this paragraph can only be achieved with functional experiments. We have therefore rephrased this part.

(3) Discussion

“A beetle enhancer trap line...” 

This part should be moved elsewhere (it does not seem to be a discussion) 

In accordance with this comment and reviewer#2’s similar comment we have removed this section. We have added a statement on the importance of testing the expression of an enhancer trap line to the results part and an added the use of CRISPR-Cas9 for line generation to the introduction. 

“We have identified a total...” 

The authors emphasized that they discovered 9 type II NBs. The authors should clarify how important this it

We have added some discussion on the importance of this finding.

Dpn is a neural marker - Is this correct? 

According to Bier et al 1992 (now added as reference) dpn is a pan-neural marker. Reviewer#2 also recommended calling dpn a neural marker.

“Previous work described a heterochronic...” - reference? 

Reference have been added

“By contrast, we show that Tribolium...” 

What about the number of neurons in the central complex in Tribolium and Drosophila? 

Does the lineage size of type II NBs reflect the number? 

Unfortunately, we do not have numbers for that.  

Reviewer #2 (Recommendations For The Authors): 

I recommend using page and line numbers to make reviewing and revising less timeconsuming. 

We apologize for this oversight. We include a line numbering system into our resubmission.

(1) Abstract 

"These neural stem cells are believed to be conserved among insects, but their molecular characteristics and their role in brain development in other insect neurogenetics models, such as the beetle Tribolium castaneum have so far not been studied." 

I recommend explaining the importance of studying Tribolium with regard to the evolution of brain centres rather than just stating that data are lacking. 

We have now emphasized the importance of Tribolium as model for the evolution of brain centres.

"Intriguingly, we found 9 type-II neuroblast lineages in the Tribolium embryo while Drosophila produces only 8 per brain hemisphere." 

It should be made clear that the 9 lineages also refer to brain hemispheres. 

We have added this information

(2) Introduction 

I would remove the first paragraph of the introduction; the use of Tribolium as model representative for insects is too general. The authors should focus on the specific question, i.e. the introduction should start with paragraph 2. 

While we can relate to the preference for short and concise writing, we feel that giving some background on Tribolium might be important as we expect that many of our readers might be primarily Drosophila researchers. Keeping this paragraph also seems in line with a recommendation of reviewer#1 to add some additional information on Tribolium ecology.  

"Several NBs of the anterior-most part of the neuroectoderm contribute to the CX and compared…”

The abbreviation has not been introduced. 

For clarity we have now opted to not use this abbreviation but to always spell out central complex.

"Several NBs of the anterior-most part of the neuroectoderm contribute to the CX and compared to the ventral ganglia produced by the trunk segments, it is of distinctively greater complexity..." 

Puzzling statement. Why would you compare a brain center with ventral ganglia? I recommend removing this. 

We have changed this statement to just emphasizing the complexity of the brain structure.

"The dramatically increased number of neural cells that are produced by individual type-II lineages, and the fact that one lineage can produce different types of neurons..."  In my opinion, this statement is too vague and unprofessional in style. Instead of "dramatically increased" use numbers. 

We have removed ‘dramatically increased’ and now give a numeric example.

"The dramatically increased number of neural cells that are produced by individual type-II lineages, and the fact that one lineage can produce different types of neurons, leads to the generation of increased neural complexity within the anterior insect brain when compared to the ventral nerve cord.." 

I assume that this statement relates to the comparison of type I and II nb lineages. However, type I NB lineages also produce different types of neurons due to GMC temporal identity, and neuronal hemi-lineage identity. 

We have rephrased and tried to make clear that the second part of the statement is not specific to type-II NB only. In line with the comment above we have also removed the reference to the ventral nerve cord.

"In addition, in Drosophila brain tumours have been induced from type-II NBs lineages [34], opening up the possibility of modelling tumorigenesis in an invertebrate brain, thus making these lineages one of the most intriguing stem cell models in invertebrates [35,36]." 

This statement is misplaced here; it should be mentioned at the start (if at all). 

We have moved this statement up.

"However, molecular characterisation of such lineages in another insect but the fly and a thorough comparison of type-II NBs lineages and their sub-cell-types between fly and beetle are still lacking" 

The background information should include what is known about type-II NB lineages in Tribolium, including marker gene expression, e.g. Farnworth et al. 

We refer to He et al 2019, Farnworth et al 2020 and Garcia-Perez 2021. All these publications speculate about a contribution of type-II NBs to Tribolium central complex development but do not show evidence of it. As we emphasize throughout the manuscript, the present work is the first description of type-II NB in Tribolium. 

"The ETS-transcription factor pointed (pnt) marks type-II NBs [40,41], which do not express the type-I NB marker asense (ase) but the pro-neural gene deadpan (dpn)"  Deadpan is considered a pan-neural gene. To avoid confusion, I would remove "proneural" throughout.

We have done so throughout the manuscript.

"We further found that, like the type-II NBs itself, the youngest Tc-pnt-positive but fezmm-eGFP-negative INPs neither express Tc-ase (Fig. 5D, pink arrowheads)."  What is the evidence that these are the youngest pnt positive cells? Position? This needs to be explained. 

We have clarified that ‘youngest pnt-positive cells’ refers to the position of these cells close to the type-II NB.

"Therefore these neural markers can be used for a classification of type II NBs (Tc-pnt+, Tcase-), young INPs (Tc-pnt+, Tc-fez/erm-, Tc-ase-), immature INPs (Tc-pnt+, Tcfez/erm+, Tcase+), mature INPs (Tc-dpn+, Tc-ase+, Tc-fez/erm+, Tc-pros+), and GMCs (Tc-ase+, Tcfez/ erm+, Tc-pros+, Tc-dpn). This classification is summarized in Fig. 7 A-B." 

This is not the best classification and not in line with the schemes in Figure 7 - the young INPs are also immature. What is the difference? It needs to be explained what "mature" means (dividing?). 

Thank you for pointing this out. We have corrected the error in this part that confused the two original groups (young and immature). To take the immaturity of both types of INPs into account we have then also changed our naming of INP subtypes into immature-I and immature-II and throughout the manuscript). Figure 7 and figure 12 were also changed accordingly. While our classification if primarily based on gene expression the available data indicates that both types of immature INPs are not dividing, whereas mature INPs are. We have added a statement on that to this part.

"In beetles a single-unit functional central complex develops during embryogenesis while in flies the structure is postembryonic." 

This statement is vague - the authors need to explain what is meant by "single-unit". The phrase "The structure is postembryonic" also needs more explanation. The Drosophila CX neuroblasts lineages originate in the embryo and the neurons form a commissural tract that becomes incorporated into the fan-shaped body of the Cx. 

We have explained single-unit central complex and have improved our summary of known differences in central complex development between fly and beetle.

"To assess the size of the embryonic type-II NBs lineages in beetles we counted the Tc- fez/erm positive (fez-mm-eGFP) cells (INPs and GMCs) associated with a Tc-pntexpressing type-II NBs of the anterior medial group (type-II NBs lineages 1-7).  It is not clear what is meant by "with a Tc-pnt-expressing type-II NBs". Is this a typo?" 

We have removed this bit.

(3) Discussion 

I would remove the first paragraph "A beetle enhancer trap lines reflects Tc-fez/earmuff expression". This is a repetition of the methods rather than a discussion. 

This part has been removed also in line with reviewer#1’s comment.

(4) Figures 

Figure 2 

To which developing structure do the strongly labelled areas in Figure 2D correspond? 

We believe that these areas from the protocerebrum including central complex, mushroom bodies and optic lobe. We have added this to the text and to the figure legend.

Figure 7 

What do A and B represent? Different stages? 

A and B show the same lineage but map the expression of different additional markers for clarity. We have added an explanation of this. 

The classification contradicts the description in the section "Conserved patterns of gene expression mark Tribolium type-II NBs, different stages of INPs and GMCs" (last sentence) where young INPs are first in the sequence and described as pnt+, erm-, ase- and immature INPs as pnt+ erm+ and ase+. 

We have corrected this mistake and changed the names of the subtypes into immatureI and immature-II (see above).

"We conclude that the evolutionary ancient six3 territory gives rise to the neuropile of the z, y, x and w tracts." 

Please clarify if six3 is also expressed in the corresponding grasshopper NB lineages or if your conclusion is based on the comparison of Drosophila and Tribolium and you assume that this is the ancestral condition. 

Six3 expression has not been studied in grasshoppers. Owing to the highly conserved nature of an anterior median six3 domain in arthropods and bilaterian animals in general, we would expect it to be expressed anterior-medially in grasshoppers as well. In Drosophila the gene is expressed in the anterior-medial embryonic region where the type-II NBs are expected to develop, but to our knowledge it has not been specifically studied which type-II NB lineages are located within this domain. We have clarified in our text that we do not claim that the origin of anterior-medial type-II NB 1-4 and the X,Y, Z and W lineages from the six3 territory is highly conserved but only the territory itself. As far as we know our work is the first to analyse the relationship of type-II lineages and the conserved head patterning genes six3 and otd. We have added some clarification of this into this part of the discussion.

(5) Methods 

The methods section should include the methods for cell counting, as well as cell and nuclei size measurements including statistics (e.g. how many embryos, how many NB lineages). The comparison of the Tribolium NB lineage cell numbers to published Drosophila data should include a brief description of the method used in Drosophila (in addition to the method used here in Tribolium) so that the reader can understand how the data compare. 

We have added a separate section on this to the Methods part which also includes the criteria used in Drosophila. We have also included some more information to the results part on the inclusion of neurons in the Drosophila counts that may only be partially included in our numbers. This does however not change the results in terms of larger numbers of progenitor cells in Tribolium.

(6) Typos and minor errors 

Abstract 

“However, little is known on the developmental processes that create this diversity” 

Change to ... little is known about

Changed.

NBs lineages 

Change to NB lineages throughout. 

We have used text search to find and replace all position where this was used erroneously,

Results 

"Schematic drawing of expression different markers in type-II NB lineages.." 

Schematic drawing of expression of different markers 

Corrected

Discussion 

"However, the type-II NB 7, which is we assigned to the anterior medial group but which..." 

.... which we assigned.... 

corrected

"......might be the one that does not have a homologue in the fly embryo The identification of more..."  Full stop missing. 

Added.

"Adult like x, y, and w tracts as well as protocerebral bridge are...." 

Change to "The adult like x, y, and w tracts as well as the protocerebral bridge are.... 

This part has been removed with the rewriting of this paragraph.  

Reviewer #3 (Recommendations For The Authors): 

(1) Suggestions for improved or additional experiments, data, or analyses: 

a) The analysis of nuclear size is wrong. The authors compare the largest cell of a cluster of cells with a number of random cells from the same brain. It is obvious that the largest cell of a cluster will be larger than the average cell of the same brain. A better control would be to compare the largest cell of the pnt+ cluster with the largest cell of a random sample of cells, although this also comes with biases. Personally, I have no doubt that the authors are looking at neuroblasts, based on the markers they are using, so I would recommend completely eliminating Figure 4.

We agree that we produced a somewhat biased and expected result when we select the largest cell of a cluster for size comparison. However, we found it important to show based on a larger sample that these cells are also statistically larger than the average cell of a brain, which we think our assessment shows. We do not claim that type-II NBs are the largest cells of a brain, or that they are larger than type-I NBs, therefore in a random sample there might be cells that are equally big (see also distribution of the control sample shown in figure 4, and we have added a note on this to the text). We are happy to hear that this reviewer has no doubts we are looking at neural stem cells. However, reviewer#1 did express some hesitations and therefore we think it is important to keep the information on cell size as part of our argument that we are indeed looking at type-II NBs (gene expression, cell size, dividing, part of a neural lineage).

b) The comparison of NB, INP, and GMC numbers between Drosophila and Trbolium (section "The Tribolium embryonic lineages of type-II NBs are larger and contain more mature INPs than those of Drosophila") compares an experiment that the authors did with published data. I would suggest that the authors repeat the Drosophila stainings and compare themselves to avoid cases of batch effects, inconsistent counting, etc.

None of the authors is a Drosophila expert or has any experience at working with this model and reassessing the lineage size would require a number of combinatorial staining. Therefore, we feel that using the published data produced by experts and which also includes repeat experiments is for us the more reliable approach.

c) In Figure 10, there are some otd+ GFP+ cells laterally. What are these? 

We believe that these cells contribute to the eye anlagen. We have added this information to the legend.

(2) Minor corrections to the text and figures: 

a) There are some typos in the text: e.g. "pattering" in the abstract. 

We have carefully checked the text for typos and hope that we have found everything.

b) The referencing of figures in the text is inconsistent (eg "Figure 5 panel A" vs "Figure 5D" on page 12). 

We have checked throughout the manuscript and made sure to always refer to a panel correctly.

c) In Figure 3C, the white staining (anti-PH3) is not indicated in the Figure. 

The label has been added in the figure.

d) Moreover, in Figure 3, green is not very visible in the images. 

We have improved the colour intensity where possible.

e) In the figures, it might be better to outline the cells with color-coded dashed circles instead of using arrows. 

We think that this would obscure some details of the stainings and create a rather artificial representation. We also feel that doing this consistently in all our images is an amount of work not justified by the degree of expected improvement to the figures

NOTE: We are submitting a revised version of the supplementary material which only contains two minor changes: a headline was added to Table S4 (Antibodies and staining reagents) and a typo was corrected in line one of table S5 (TC to Tc).

Author response:

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

Reviewer 1:

Point 1 of public reviews and point 2 of recommendations to authors. 

Temporal ambiguity in credit assignment: While the current design provides clear task conditions, future studies could explore more ambiguous scenarios to further reflect real-world complexity…. The role of ambiguity is very important for the credit assignment process. However, in the current task design, the instruction of the task design almost eliminates the ambiguity of which the trial's choice should be assigned credit to. The authors claim the realworld complexity of credit assignment in this task design. However, the real-world complexity of this type of temporal credit assignment involves this type of temporal ambiguity of responsibility as causal events. I am curious about the consequence of increasing the complexity of the credit assignment process, which is closer to the complexity in the real world.

We agree that the structure of causal relationships can be more ambiguous in real-world contexts. However, we also believe that there are multiple ways in which a task might approach “real-world complexity”. One way is by increasing the ambiguity in the relationships between choices and outcomes (as done by Jocham et al., 2016). Another is by adding interim decisions that must be completed between viewing the outcome of a first choice, which mimics task structures such as the cooking tasks described in the introduction. In such tasks, the temporal structure of the actions maybe irrelevant, but the relationship between choice identities and the actions is critical to be effective in the task (e.g., it doesn’t matter whether I add spice before or after the salt, all I need to know that adding spice will result in spicy soup).  While ambiguity about either form of causal relation is clearly an important part of real-world complexity, and would make credit assignment harder, our study focuses on how links between outcomes and specific past choice identities are created at the neural level when they are known to be causal. 

We consequently felt it necessary to resolve temporal ambiguity for participants. Instructing participants on the structure of the task allowed us to make assumptions about how credit assignment for choice identities should proceed (assign credit to the choice made N trials back) and allowed us make positive predictions about the content of representations in OFC when viewing an outcome. This gave the highest power to detect multivariate information about the causal choice and the highest interpretability of such findings. 

In contrast, if we had not resolved this ambiguity, it would be difficult to tell if incorrect decoding from the classifier resulted from noise in the neural signal, or if on that trial participants were assigning credit to non-causal choices that they erroneously believed to have caused the outcome due to the perceived temporal structure. We believe this would have ultimately decreased our power to determine whether representations of the causal choice were present at the time of outcome because we would have to make assumptions about what counts as a “true” causal representation. 

We have commented on this in the discussions (p.13): 

“While our study was designed to focus on the complexity of assigning credit in tasks with different known causal structures, another important component of real-world credit assignment is temporal ambiguity. To isolate the mechanisms which create associations between specific choices and specific outcomes, we instructed participants on the causal structure of each task, removing temporal ambiguity about the causal choice.  However, our results are largely congruent with previously reported results in tasks that dissolved the typical experimental trial structure, producing temporal ambiguity, and which observed more pronounced spreading of effect, in addition to appropriate credit assignment (Jocham et al, 2016).  Namely, this study found that activation in the lOFC increased only when participants received rewards contingent on a previous action, an effect that was more pronounced in subjects whose behavior reflected more accurate credit assignment. This suggests a shared lOFC mechanism for credit assignment in different types of complex environments. Whether these mechanisms extend to situations where the temporal causal structure is completely unknown remains an important question.”

Point 2 of public reviews and point 1 of recommendations to authors

Role of task structure understanding: The difference in task comprehension between human subjects in this study and animal subjects in previous studies offers an interesting point of comparison…. The credit assignment involves the resolution of the ambiguity in which the causal responsibility of an outcome event is assigned to one of the preceding events. In the original study of Walton and his colleagues, the monkey subjects could not be instructed on the task structure defining the causal relationships of the events. Then, the authors of the original study observed the spreading of the credit assignments to the "irrelevant" events, which did not occur in the same trial of the outcome event but to the events (choices) in neighbouring trials. This aberrant pattern of the credit assignment can be due to the malfunctions of the credit assignment per se or the general confusion of the task structure on the part of the monkey subjects. In the current study design, the subjects are humans and they are not confused about the task structure. Consistently, it is well known that human subjects rarely show the same patterns of the "spreading of credit assignment". So the implicit mechanism of the credit assignment process involves the understanding of the task structure. In the current study, there are clearly demarked task conditions that almost resolve the ambiguity inherent in the credit assignment process. Yet, the focus of the current analysis stops short of elucidating the role of understanding the task structure. It would be great if the authors could comment on the general difference in the process between the conditions, whether it is behavioral or neural.

We would like to thank the reviewer for making this important point. We believe that understanding the structure of the credit-assignment problem above is quite important, at least for the type of credit assignment described here. That is, because participants know that the outcome viewed is caused by the choice they made, 0 or 1 trials into the past, they can flexibly link choice identities to the newly observed outcomes as the probabilities change. Note, however, that this is already very challenging in the 1-back condition because participants need to track the two independently changing probabilities. We believe this is critical to address the questions we aimed to answer with this experiment, as described above. 

We agree that this might be quite different from previous studies done with non-human primates, which also included many more training trials and lesions to the lOFC. Both of these aspects could manifest as difference in task performance and processing at behavioural and neural levels, respectively. Consistent with this possibility, in our task, we found no differences in credit spreading between conditions, suggesting that humans were quite precise in both, despite causal relationships being harder to track in the “indirect transition condition”. This lack of credit spreading could be because humans better understood the task-structure compared to macaques or be due to differences in functioning of the OFC and other regions. Because all participants were trained to understand, and were cued with explicit knowledge of, the task structure, it is difficult to isolate its role as we would need another condition in which they were not instructed about the task structure. This would also be an interesting study, and we leave it to future research to parse the contributions of task-structure ambiguity to credit assignment. 

Point 3 of public reviews. 

The authors used a sophisticated method of multivariate pattern analysis to find the neural correlate of the pending representation of the previous choice, which will be used for the credit assignment process in the later trials. The authors tend to use expressions that these representations are maintained throughout this intervening period. However, the analysis period is specifically at the feedback period, which is irrelevant to the credit assignment of the immediately preceding choice. This task period can interfere with the ongoing credit assignment process. Thus, rather than the passive process of maintaining the information of the previous choice, the activity of this specific period can mean the active process of protecting the information from interfering and irrelevant information. It would be great if the authors could comment on this important interpretational issue.

We agree that lFPC is likely actively protecting the pending choice representation from interference with the most recent choice for future credit assignment. This interpretation is largely congruent with the idea of “prospective memory” (e.g., Burgess, Gonen-Yaacovi, Volle, 2011), in which the lFPC can be thought of as protecting information that will be needed in the future but is not currently needed for ongoing behavior. That said, from our study alone it is difficult to make claims about whether the information maintained in frontal pole is actively protecting this information because of potentially interfering processes. Our “indirect transition condition” only contains trials where there is incoming, potentially interfering information about new outcomes, but no trials that might avoid interference (e.g., an interim choice made but there is nothing to be learned from it). We comment on this important future direction on page 14:  

“One interpretation of these results is that the lFPC actively protects information about causal choices when potentially interfering information must be processed. Future studies will be needed to determine if the lFPC’s contributions are specific to these instances of potential interference, and whether this is a passive or active process”

Point 3 of recommendation to authors 

A slightly minor, but still important issue is the interpretation of the role of lOFC. The authors compared the observed patterns of the credit assignment to the ideal patterns of credit assignment. Then, the similarity between these two matrices is used to find the associated brain region. In the assumption that lOFC is involved in the optimal credit assignment, the result seems reasonable. But as mentioned above, the current design involves the heavy role of understanding the task structure, it is debatable whether the lOFC is just involved in the credit assignment process or a more general role of representing the task structure.

We agree that this is an important distinction to make, and it is very likely that multiple regions of the OFC carry information about the task structure, and the extent to which participants understood this structure may be reflected in behavioral estimates of credit assignment or the overall patterns of the matrices (though all participants verbalized the correct structure prior to the task). However, we believe that in our task the lOFC is specifically involved in credit-assignment because of the content of the information we decoded. We demonstrated that the lOFC and HPC carry information about the causal choice during the outcome. These results cannot be explained by differences in understanding of the task structure because that understanding would have been consistent across trials where participants choose either shape identity. Thus, a classifier could not use this to separate these types of trials and would reflect chance decoding.   

One interpretation of the lOFC’s role in credit assignment is that it is particularly important when a model of the task structure has to be used to assign credit appropriately. Here, we show lOFC the reinstates specific causal representations precisely at the time credit needs to be assigned, which are appropriate to participants’ knowledge of the task structure.  These representations may exist alongside representations of the task structure, in the lOFC and other regions of the brain (Park et al., 2020; Boorman et al., 2021; Seo and Lee, 2010; Schuck et al., 2016). We have added the following sentences to clarify our perspective on this point in the discussion (p. 13):

“Our results from the “indirect transition” condition show that these patterns are not merely representations of the most recent choice but are representations of the causal choice given the current task structure, and may exist alongside representations of the task structure, in the lOFC and elsewhere (Boorman et al., 2021; Park et al., 2020; Schuck et al., 2016; Seo & Lee, 2010).”

Point 4 of public reviews and point 4 of recommendation to authors

Broader neural involvement: While the focus on specific regions of interest (ROIs) provided clear results, future studies could benefit from a whole-brain analysis approach to provide a more comprehensive understanding of the neural networks involved in credit assignment… Also, given the ROI constraint of the analysis, the other neural structure may be involved in representing the task structure but not detected in the current analysis

Given our strong a priori hypotheses about regions of interest (ROIs) in this study, we focused on these specific areas. This choice was based on theoretical and empirical grounds that guided our investigation. However, we thank the reviewer for pointing this out and agree that there could be other unexplored areas that are critical to credit-assignment which we did not examine. 

We conducted the same searchlight decoding procedure on a whole brain map and corrected for multiple comparisons using TFCE. We found no significant regions of the brain in the “direct transition condition” but did find other significant regions in our information connectivity analysis of the “indirect transition condition”. In addition to replicating the effects in lOFC and HPC, we also found a region of mOFC which showed a strong correlation with pending choice in lFPC. It’s difficult to say whether this region is involved in credit assignment per se, because we did not see this region in the “direct transition condition” and so we cannot say that it is consistently related to this process. However, the mOFC is thought to be critical to representing the current task state (Schuck et al., 2016), and the task structure (Park et al., 2020). In our task, it could be a critical region for communicating how to assign credit given the more complex task structure of the “indirect transition condition” but more evidence would be needed to support this interpretation. 

For now, we have added the results of this whole brain analysis to a new supplementary figure S7 (page 41), and all unthresholded maps have been deposited in a Neurovault repository, which is linked in the paper, for interested readers to assess.  

Minor points:

There are some missing and confusing details in the Figure reference in the main text. For example, references to Figure 3 are almost missing in the section "Pending item representations in FPl during indirect transitions predict credit assignment in lOFC". For readability, the authors should improve this point in this section and other sections.

Thank you to the reviewer for pointing this out. We have now added references to Figure 3 on page 8:

“Our analysis revealed a cluster of voxels specifically within the right lFPC ([x,y,z] = [28, 54, 8], t(19) = 3.74, pTFCE <0.05 ROI-corrected; left hemisphere all pTFCE > 0.1, Fig. 3A)”

And on page 10: 

Specifically, we found significant correlations in decoding distance between lFPC and bilateral lOFC ([x,y,z] = [-32,24, -22], t(19) = 3.81, [x,y,z] = [20, 38, -14], t(19) = 3.87, pTFCE <0.05 ROI corrected]) and bilateral HC ([x,y,z] = [-28, -10, -24], t(19) = 3.41, [x,y,z] = [22, -10, -24], t(19) = 4.21, pTFCE <0.05 ROI corrected]), Fig. 3C).

Task instructions for the two conditions (direct and indirect) play important roles in the study. If possible, please include the following parts in the figures and descriptions in the introduction and/or results sections.

We have now included a short description of the condition instructions beginning on page 5: 

“Participants were instructed about which condition they were in with a screen displaying “Your latest choice” in the direct transition condition, and “Your previous choice” in the indirect condition.”

And have modified Figure 1 to include the instructions in the title of each condition. We thought this to be the most parsimonious solution so that the choice options in the examples were not occluded. 

The subject sample size might be slightly too small in the current standards. Please give some justifications.

We originally selected the sample size for this study to be commensurate with previous studies that looked for similar behavioral and neural effects (see Boorman et al., 2016; Howard et al., 2015; Jocham et al., 2016). This has been mentioned in the “methods” section on page 24.  

However, to be thorough, we performed a power analysis of this sample size using simulations based on an independently collected, unpublished data set. In this data set, 28 participants competed an associative learning task similar to the task in the current manuscript. We trained a classifier to decode causal choice option at the time of feedback, using the same searchlight and cross-validation procedures described in the current manuscript, for the same lateral OFC ROI. We calculated power for various sample sizes by drawing N participants with replacement 1000 times, for values of N ranging from 15 to 25. After sampling the participants, we tested for significant decoding for the causal choice within the subset of data, using smallvolume TFCE correction to correct for multiple comparisons. Finally, we calculated the proportion of these samples that were significant at a level of pTFCE <.05.  

The results of this procedure show that an N of 20 would result in 84.2% power, which is slightly above the typically acceptable level of 80%. We have added the following sentences to the methods section on page 25: 

“Using an independent, unpublished data set, we conducted a power analysis for the desire neural effect in lOFC. We found that this number of participants had 84% power to detect this effect (Fig. S8).” 

We also added the following figure to the supplemental figures page (42):

Reviewer 2:

I have several concerns regarding the causality analyses in this study. While Multivariate analyses of information connectivity between regions are interesting and appear rigorous, they make some assumptions about the nature of the input data. It is unclear if fMRI with its poor temporal resolution (in addition to possible region-specific heterogeneity in the readouts), can be coupled with these casual analysis methods to meaningfully study dynamics on a decision task where temporal dynamics is a core component (i.e., delay). It would be helpful to include more information/justification on the methods for inferring relationships across regions from fMRI data. Along this line, discussing the reported findings in light of these limitations would be essential.

We agree that fMRI is limited for capturing fast neural dynamics, and that it can be difficult to separate events that occur within a few seconds. However, we designed the information connectivity analysis to maximally separate the events in question – the representations of the causal choice being held in a pending state, and the representation of the causal choice during credit assignment. These events were separated by at least 10 seconds and by 15 seconds on average, which is commensurate with recommended intervals for disentangling information in such analysis (Mumford et al., 2012, 2014, also see van Loon et al., 2018, eLife; as example of fluctuations in decodability over time). This feature of our task design may not have been clear because information connectivity analyses are typically performed in the same task period. We clarify this point on page 32:

“Note that the decoding fidelity metric at each time point represents the decodability of the same choice at different phases of the task. These phases were separated by at least 10 seconds and 15 seconds on average, which can be sufficient for disentangling unique activity (Mumford et al., 2012, 2014).”

However, we agree with the reviewer that the limitations of fMRI make it difficult to precisely determine how roles of the OFC and lFPC might change over time, and whether other regions may contribute to information transfer at times scales which cannot be detected by fMRI. Further, we do not wish to imply causality between lFPC and lOFC (something we believe we do not claim in the paper), only that information strength in lFPC predicts subsequent strength of the same information in the OFC and HC. We have clarified this limitation on page 14:

“Although we show evidence that lFPC is involved in maintaining specific content about causal choices during interim choices, the limited temporal resolution of fMRI makes it difficult to tell if other regions may be supporting the learning processes at timescales not detectable in the BOLD response. Thus, it is possible that the network of regions supporting credit assignment in complex tasks may be much larger. Our results provide a critical first stem in discerning the nature of interactions between cognitive subsystems that make different contributions to the learning process in these complex tasks.”

Reviewer 3:  

Point 1 of public reviews:

They do find (not surprisingly) that the one-back task is harder. It would be good to ensure that the reason that they had more trouble detecting direct HC & lOFC effects on the harder task was not because the task is harder and thus that there are more learning failures on the harder oneback task. (I suspect their explanation that it is mediated by FPl is likely to be correct. But it would be nice to do some subsampling of the zero-back task [matched to the success rate of the one-back task] to ensure that they still see the direct HC and lOFC there).

We would like to thank the reviewer for this comment and agree that the “indirect transition condition” is more difficult than the direct transition condition. However, in this task it is difficult to have an explicit measure of learning failures per se because the “correctness” of a choice is to some extent subjective (i.e., based on the gift card preference and the computational model). We could infer when learning failures occur through the computational model by looking at trials in which participants made choices that the model would consider improbable, (i.e., non-reward maximizing) while accounting for outcome preference. However, there are also a myriad of other possible explanations for these choices, such as exploratory/confirmatory strategies, lapses in attention etc. Thus, we could not guarantee that the two conditions would be uniquely matched in difficulty with specific regard to learning even if we subsampled these trials. We feel it would be better left to future experiments which can specifically compare learning failures to tackle this issue. We have now addressed this point when discussing the model on page 31:  

“Note that learning failures are not trivial to identify in our paradigm and model, because every choice is based on a participant’s preference between gift card outcomes, and the ability of the computational model to accurately estimate participants’ beliefs in the stimulus-outcome transition probabilities.”

Point 2 of public reviews:

The evidence that they present in the main text (Figure 3) that the HC and lOFC are mediated by FPl is a correlation. I found the evidence presented in Supplemental Figure 7 to be much more convincing. As I understand it, what they are showing in SF7 is that when FPl decodes the cue, then (and only then) HC and lOFC decode the cue. If my understanding is correct, then this is a much cleaner explanation for what is going on than the secondary correlation analysis. If my understanding here is incorrect, then they should provide a better explanation of what is going on so as to not confuse the reader.

SF7 (now Figures 3C and 3D) does show that positive decoding in the HC and lOFC are more likely to occur when there is positive decoding in lFPC. However, the analysis shown in these figures are only meant to be control analysis to further characterise what is being captured, but not necessarily implied, by the information connectivity analysis. For example, in principle the classifier might never correctly decode a choice label in the lOFC or HC while still getting closer to the hyperplane when the lFPC patterns are correctly decoded. This would lead to a positive correlation, but a difficult to interpret result since patterns in lOFC and HPC are incorrect. Figure SF7A (now Fig. 3C) shows that this is not the case. Lateral OFC and HC have higher than chance positive decoding when lFPC has positive decoding. Figure SF7B (now Fig. 3D) shows that we can decode that information even if a new hyperplane is constructed. However, both cases have less information about the relationship between these regions because they do not include the trials where lOFC/HC and lFPC classifiers were incorrect at the same time. The correlation in Figure 3B includes these failures, giving a more wholistic picture of the data. We therefore try to concisely clarify this point on page 10:

“These signed distances allow us to relate both success in decoding information, as well as failures, between regions.”

And here on page 10: 

“Subsequent analyses confirmed that this effect was due to these regions showing a significant increase in positive (correct) decoding in trials where pending information could be positively (correctly) decoded in lFPC, and not simply due to a reduction in incorrect information fidelity (see Fig. 3C & 3D).”

And have integrated these figures on page 9:

Point 3 of public reviews:

I like the idea of "credit spreading" across trials (Figure 1E). I think that credit spreading in each direction (into the past [lower left] and into the future [upper right]) is not equivalent. This can be seen in Figure 1D, where the two tasks show credit spreading differently. I think a lot more could be studied here. Does credit spreading in each of these directions decode in interesting ways in different places in the brain?

We agree that this an interesting question because each component of the off diagonal (upper and lower triangles) may reflect qualitatively different processes of credit spreading. However, we believe this analysis is difficult to carry out with the current dataset for two reasons. First, we designed this study to ask specifically about the information represented in key credit assignment regions during precise credit assignment, meaning we did not optimize the task to induce credit spreading at any point. Indeed, our efforts to train participants on the task were to ensure they would correctly assign credit as much as possible. Figure 1F shows that the regression coefficients representing credit spreading in each condition are near zero (in the negative direction), with little individual differences compared to the credit assignment coefficients. Thus, any analysis aiming to test for credit spreading would unfortunately be poorly powered. Studies such as Jocham et al. (2016), with more variability in causal structures, or studies with ambiguity about the causal structure by dissolving the typical trial structure would be better suited to address this interesting question. The second reason why such an analysis would be challenging is that due to our design, it is difficult to intuitively determine what kind of information should be coded by neural regions when credit spreads to the upper diagonal, since these cells reflect current outcomes that are being linked to future choices. 

Replace all the FPl with LFPC (lateral frontal polar cortex)

We have no replace “FPl” with “LFPC” throughout the text and figures

Reviewer #3 (Public review):

Summary:

Ruan and colleagues consider a branching process model (in their terminology the "Haldane model") and the most basic Wright-Fisher model. They convincingly show that offspring distributions are usually non-Poissonian (as opposed to what's assumed in the Wright-Fisher model), and can depend on short-term ecological dynamics (e.g., variance in offspring number may be smaller during exponential growth). The authors discuss branching processes and the Wright-Fisher model in the context of 3 "paradoxes" --- 1) how Ne depends on N might depend on population dynamics; 2) how Ne is different on the X chromosome, the Y chromosome, and the autosomes, and these differences do match the expectations base on simple counts of the number of chromosomes in the populations; 3) how genetic drift interacts with selection. The authors provide some theoretical explanations for the role of variance in the offspring distribution in each of these three paradoxes. They also perform some experiments to directly measure the variance in offspring number, as well as perform some analyses of published data.

Strengths:

- The theoretical results are well-described and easy to follow.<br /> - The analyses of different variances in offspring number (both experimentally and analyzing public data) are convincing that non-Poissonian offspring distributions are the norm.<br /> - The point that this variance can change as the population size (or population dynamics) change is also very interesting and important to keep in mind.<br /> - I enjoyed the Density-Dependent Haldane model. It was a nice example of the decoupling of census size and effective size.<br /> - Equation (10) is a nice result

Comments on revisions:

I appreciate the effort that the authors have put into the revision, but I still find the framing to be a bit confusing -- these apparent paradoxes only appear in the most basic version of Wright-Fisher models, and so framing the paper as the solution to these paradoxes overlooks much previous work. Saying that existing work discussing exactly these phenomena is "beyond the scope of this study", without citing or interacting in any way with that work is unscholarly. I agree with the authors that the apparent paradoxes that they consider and interesting, and by thinking about branching processes, the apparent paradoxes appear to be less paradoxical, but without contextualizing this work in the substantial Wright-Fisher literature (e.g., Cannings Exchangeable Models and the work of Möhle) it misrepresents the state of the field and the contributions of this paper.

Reviewer #2 (Public review):

Summary:

The authors used a large panel of hepatocellular carcinoma patient-derived xenograft models to test the hypothesis that the developmental dependence of the liver on Jagged1-Notch2 signaling is retained in at least a subset of hepatocellular carcinomas. This led to the identification of two models that were extraordinarily sensitive to well-characterized, specific inhibitory antibodies against Jagged1 or Notch2. Based on additional analyses in these in vivo models, the authors provide compelling evidence that the response is due to the inhibition of human Notch2 and human Jagged1 on tumor cells and that this inhibition leads to a change in gene expression from a progenitor-like state to a hepatocyte-like state accompanied by cell cycle arrest. This change in cell state is associated with up-regulation of HNF4a and CEBPB and increased accessibility of predicted HNF4a and CEBPB genomic binding sites, accompanied by loss of accessibility to sequences predicted to bind TFs linked to multipotent liver progenitors. The authors put forth a plausible model in which inhibition of Notch2 downregulates transcriptional repressors of the Hairy/Enhancer of Split family, leading to increased expression of CEBPB and changes in gene expression that drive hepatocyte differentiation.

Strengths:

The strengths of the paper include the breadth of the preclinical screen in PDX models (which may be of an unprecedented size as preclinical trials go), the high quality of the well-characterized antibodies used as therapeutics and as biological perturbagens, the quality of the data and data analysis, and the authors balanced discussion of the strengths and weaknesses of their findings.

Weaknesses:

The principal weakness is the inability to clearly demonstrate the "translatability" of the PDX findings to primary human hepatocellular carcinoma.

Additional Comments:

Hepatocellular carcinoma is increasing in frequency and is difficult to treat; cure is only possible through early diagnosis and surgery, often in the form of liver transplantation. It is also a common cancer, and so even if only 5% of tumors (a value based on the frequency of super-responders in this preclinical trial) fall into the Jagged1-Notch2 group defined by Seidel et al., the development of an effective therapy for this subgroup would be a very important advance. The chief limitation of their work is that it stops short of identifying primary human hepatocellular carcinomas that correspond to the super-responder PDX models. It can be hoped that their intriguing observations will spur work aimed at filling this gap

There are several other loose ends. An unusual feature of this model is that both Jagged 1 and Notch2 are expressed in the same cells, and even in the same individual cells. In developmental systems, the expression of ligands and receptors in the same cell generally produces receptor inhibition rather than activation, a phenomenon described as cis inhibition. Their super-responder tumor models appear to break this rule, and how and why this is so remains to be understood. A follow-up question is what explains the observed heterogeneity in tumor cells, both at the level of Notch2 activation and scRNAseq clustering, and whether these different cell states are static or interchangeable.

Another unanswered issue pertains to the nature of the tumor response to Notch signaling blockade, which appears to be mainly cell cycle arrest. There are a number of human tumors with cell autonomous Notch signaling due to gain of function Notch receptor mutations that also respond to Notch blockade with cell cycle arrest, such as T cell acute lymphoblastic leukemia (T-ALL). In general, clinical trials of pan-Notch inhibitors such as gamma-secretase inhibitors have been disappointing in such tumors, perhaps reflecting a limitation of treatments with significant toxicity that do not kill tumor cells directly. It could be argued that this limitation will be mitigated by the apparently excellent safety profile of Notch2 blocking antibody, which perhaps could be administered for a sustained period, akin to the use of tyrosine kinase inhibitors in chronic myeloid leukemia---but this remains to be determined.

A minor comment is reserved for the statement in the discussion that "In chronic myelomonocytic leukemia, which results from an inactivating mutation in the y-secretase complex component nicastrin, Notch signaling has a tumor suppressive function, that is mediated through direct repression of CEBPA and PU.1 by HES1 (Klinakis et al., 2011)". Thousands of cases of CMML and related myeloid tumors have been subjected to whole exome and even whole genome sequencing without the identification of Notch signaling pathway mutations. Thus, an important tumor suppressive role for Notch-mediated through HES1 in myeloid tumors is not proven.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The significance of Notch in liver cancer has been inconsistently described to date. The authors conduct a PDX screen using JAG1 ab and identify 2 sensitive tumor models. Further characterization with bulk RNA seq, scRNA seq, and ATAC seq of these tumors was performed.

Strengths:

The reliance on an extensive panel of PDXs makes this study more definitive than prior studies.

Gene expression analyses seem robust.

Identification of a JAG1-dependent signature associated with hepatocyte differentiation is interesting.

Weaknesses:

The introduction is rather lengthy and not entirely accurate. HCC is a single cancer type/histology. There may be variants of histology (allusion to "mixed-lineage" is inaccurate as combined HCC-CCa are not conventionally considered HCC and are not treated as HCC in clinical practice as they are even excluded from HCC trials), but any cancer type can have differences in differentiation. Just state there are multiple molecular subtypes of this disease.

We will shorten the Introduction, in part by eliminating the discussion of histological variation in HCC and focusing on the molecular classifications.

There is minimal data on the PDXs, despite this being highlighted throughout the text. Clinical and possibly some molecular characterization of these cancers should be provided. It is also odd that the authors include only 35 HCC and then a varied sort of cancer histologies, which is peculiar given their prior statements regarding the heterogeneity of HCC.

We agree that clinical and molecular characterizations of the PDX models would be helpful and will follow up with the relevant contract research organization to determine what characterization is available.

Regarding the liver cancer PDX panel, we suggest that a major strength of the manuscript is the large number of HCC models that were tested (the reviewer also notes the importance of the “extensive” panel); thus, we are a bit confused by the reference to “only 35 HCC”.  To clarify the choice of models in the PDX screen, it may help to put the screen in historical perspective as the project unfolded.  In retrospect, our preliminary efficacy studies using only two HCC models were fortunate to identify the highly sensitive model, LIV78.  To go beyond the simple diagnostic hypothesis that focused on Jag1, Notch2 and Hes1 expression, we took an unbiased approach to discover features linked to Notch dependence.  This approach meant running an efficacy screen in all liver cancer models that were up and running at our chosen research organization, without biased selection criteria.  That set of models is what is represented in the “pre-clinical screen” in Fig. 1B

"super-responder" is not a meaningful term, I would eliminate this use as it has no clinical or scientific convention that I am aware of.

We were aware of the interchangeable terms of “exceptional-“ or “super-responder” and prefer to leave this language in the text.  Some references are as follows: 

● Prasad et al., Characteristics of exceptional or super responders to cancer drugs. Mayo Clinic Proceedings, 2015. 

● NCI Press Release 2020:  https://www.cancer.gov/news-events/press-releases/2020/cancer-exceptional-responders-study-genetic-alterations-may-contribute

● NIH Info:  https://www.nih.gov/news-events/nih-research-matters/understanding-exceptional-responders-cancer-treatment

● “What is a Super Responder?  Bradley Jones, Cancer Today, June 26, 2020.

● “What is a Super Responder?”  AACR.  https://www.aacr.org/patients-caregivers/progress-against-cancer/what-is-a-super-responder/

The "expansion" of the PDX screen is poorly described. Why weren't these PDXs included in the first screen? This is quite odd as the responses in the initial screen were underwhelming. What was the denominator number of all PDXs that were assessed for JAG1 and NOTCH2 expression? This is important as it clarifies how relevant JAG1 inhibition would be to an unselected HCC population.

We will revise the writing here to clarify as requested.  For now, we can hopefully clarify by building on the historical context described above.  As the reviewer notes and as we describe in the text, the in vivo screen revealed only a modest JAG1 dependence.  The screen also highlighted that LIV78 was exceptional, and we wanted to understand why.  Hypothesizing that the expression of progenitor markers in LIV78 were important for understanding its JAG1 dependence, we identified four additional models at other contract research organizations.  It is this set of four that comprises the “expansion” cohort.

Was there some kind of determination of the optimal dose or dose dependency for the JAG1 ab? The original description of the JAG1 ab was in mouse lungs, not malignant or liver cells. In addition, supplementary Figure 2D is missing. There needs to be data provided on the specificity of the human-specific JAG1 ab and the anti-NOTCH2 ab. I'm not familiar with these ab, and if they are not publicly accessible reagents, more transparency on this is needed. In addition, given the reliance of the entire paper on these antibodies, I would recommend orthogonal approaches (either chemical or genetic) to confirm the sensitivity and insensitivity of select PDXs to Notch inhibition.

First, we note that the anti-human/mouse Jagged1 and Notch2 blocking antibodies used in our study have been extensively characterized as potent and selective and have been widely used outside of our group by the Notch research community (for the human/mouse cross-reactive antibodies, see Wu et al., Nature, 2010 for anti-NOTCH2 and Lafkas et al., Nature 2015 for anti-JAG1). As noted, the antibodies have been used in studies of normal mouse lungs (Lafkas et al.).  Please note that the characterization also includes mouse models of primary liver cancer that formed the foundation for the current work (please refer to Huntzicker et al, 2015).

While we show dose responses in Figures 1A and 1D, we have not optimized dosing, for example by determining the minimal drug exposures needed for pharmacodynamic changes (pathway inhibition) and efficacy.  For the purposes of this study, we erred on the side of dosing at high concentrations to minimize the risk of false negative responses.

Regarding the specificity of the human-specific anti-JAG1 antibody, which is revealed here for the first time, we apologize that we incorrectly provided a text reference to Supplementary Figure 2D instead of Supplementary Figure 1D.  We will revise accordingly.  Fig. 1D shows results from a reporter assay demonstrating that the antibody blocks signaling induced by human but not mouse JAG1.

We appreciate the value of orthogonal methods in establishing the credibility of a novel finding.  We note that genetic approaches are technically highly challenging in PDX models.  Chemically, we could have tested y-secretase inhibitors (GSIs). Our position is that such inhibitors are poor substitutes for the selective antibodies that we employed, at least for addressing the questions that are relevant in this study.   Although commonly used to perturb Notch signaling, GSIs target numerous proteins and signaling cascades independent of Notch.  Moreover, their use in vivo leads to intestinal and other toxicities, limiting exposure. 

scRNA-seq data seems to add little to the paper and there is no follow-up of the findings. Are the low-expressing JAG1 cells eventually enriched in treated tumors contributing to disease recurrence?

We respectfully disagree with this sentiment. The single-cell RNA sequencing dataset revealed the enrichment of hepatocyte-like tumor cells following Notch inhibition. Importantly, this dataset also allowed us to identify transcription factor activities regulating different cell states, which we could not have done otherwise. This understanding in turn was fundamental to develop our hypothesis that Notch inhibition, through derepressing CEBPA expression, allows chromatin engagement of HNF4A and CEPBA and thereby promotes a hepatocyte differentiation program that is not compatible with tumor maintenance.  

The discussion should be tempered. The finding of only 2 PDXs that are sensitive out of 45+ tumors treated or selected for indicates that JAG1/NOTCH2 inhibition is likely only effective in rare HCC.

We agree that strong responses to Notch inhibition in the PDX models are rare (~5%) and state as much in both the Results and Discussion sections. We maintain that it is important to put this PDX response frequency into a larger context.  First, establishing PDX models---human tumor samples that grow on the flanks of immunocompromised mice---represents a strong selective pressure.  In other words, we don’t know precisely how the frequency of responses in this selected set of PDX models may compare to the frequency that would be observed in human patient populations. Second, the magnitude of the response points to important and hitherto unappreciated biology, with blocking JAG1 or NOTCH2 reproducibly inducing regressions in the most sensitive models.  Our hope is that the field can build from this study to generate diagnostic tools that identify sensitive patient tumors, define the true frequency of this patient group within the larger HCC population (even though likely rare), and direct the relevant Notch-based therapeutics to these patients.  Within this context, and while noting the rarity of PDX responses, we hope that we have not overstated the case.

Reviewer #2 (Public review):

Summary:

The authors used a large panel of hepatocellular carcinoma patient-derived xenograft models to test the hypothesis that the developmental dependence of the liver on Jagged1-Notch2 signaling is retained in at least a subset of hepatocellular carcinomas. This led to the identification of two models that were extraordinarily sensitive to well-characterized, specific inhibitory antibodies against Jagged1 or Notch2. Based on additional analyses in these in vivo models, the authors provide compelling evidence that the response is due to the inhibition of human Notch2 and human Jagged1 on tumor cells and that this inhibition leads to a change in gene expression from a progenitor-like state to a hepatocyte-like state accompanied by cell cycle arrest. This change in cell state is associated with up-regulation of HNF4a and CEBPB and increased accessibility of predicted HNF4a and CEBPB genomic binding sites, accompanied by loss of accessibility to sequences predicted to bind TFs linked to multipotent liver progenitors. The authors put forth a plausible model in which inhibition of Notch2 downregulates transcriptional repressors of the Hairy/Enhancer of Split family, leading to increased expression of CEBPB and changes in gene expression that drive hepatocyte differentiation.

Strengths:

The strengths of the paper include the breadth of the preclinical screen in PDX models (which may be of an unprecedented size as preclinical trials go), the high quality of the well-characterized antibodies used as therapeutics and as biological perturbagens, the quality of the data and data analysis, and the authors balanced discussion of the strengths and weaknesses of their findings.

Weaknesses:

The principal weakness is the inability to clearly demonstrate the "translatability" of the PDX findings to primary human hepatocellular carcinoma.

We agree that translatability has not been fully addressed.  As noted in our response to Reviewer 1, our hope is that the field can build from this study to generate diagnostic tools that identify sensitive patient tumors, define the true frequency of this patient group within the larger HCC population, and direct the relevant Notch-based therapeutics to these patients.  We remain encouraged by the strength of the response in the sensitive models.

Additional Comments:

Hepatocellular carcinoma is increasing in frequency and is difficult to treat; cure is only possible through early diagnosis and surgery, often in the form of liver transplantation. It is also a common cancer, and so even if only 5% of tumors (a value based on the frequency of super-responders in this preclinical trial) fall into the Jagged1-Notch2 group defined by Seidel et al., the development of an effective therapy for this subgroup would be a very important advance. The chief limitation of their work is that it stops short of identifying primary human hepatocellular carcinomas that correspond to the super-responder PDX models. It can be hoped that their intriguing observations will spur work aimed at filling this gap.

There are several other loose ends. An unusual feature of this model is that both Jagged 1 and Notch2 are expressed in the same cells, and even in the same individual cells. In developmental systems, the expression of ligands and receptors in the same cell generally produces receptor inhibition rather than activation, a phenomenon described as cis inhibition. Their super-responder tumor models appear to break this rule, and how and why this is so remains to be understood. A follow-up question is what explains the observed heterogeneity in tumor cells, both at the level of Notch2 activation and scRNAseq clustering, and whether these different cell states are static or interchangeable.

We enthusiastically agree that these are fascinating questions, worthy of further study.  As noted, the majority of tumor cells express both ligand and receptor and seem to be “on” for Notch signaling.  We have not been able to determine whether the signal is induced in a cell autonomous or non-autonomous manner (or both).  As the reviewer notes, the HCC features we observe are inconsistent with the dogma that has arisen from studies on Notch signaling in developmental contexts.

We do not yet have the experimental data to fully address the second question of what causes the heterogeneity of Notch2 activation and scRNAseq clustering.  We speculate that the cell states may be dynamic, which would be consistent with the changes in cell populations observed after antibody treatment.

Another unanswered issue pertains to the nature of the tumor response to Notch signaling blockade, which appears to be mainly cell cycle arrest. There are a number of human tumors with cell autonomous Notch signaling due to gain of function Notch receptor mutations that also respond to Notch blockade with cell cycle arrest, such as T cell acute lymphoblastic leukemia (T-ALL). In general, clinical trials of pan-Notch inhibitors such as gamma-secretase inhibitors have been disappointing in such tumors, perhaps reflecting a limitation of treatments with significant toxicity that do not kill tumor cells directly. It could be argued that this limitation will be mitigated by the apparently excellent safety profile of Notch2 blocking antibody, which perhaps could be administered for a sustained period, akin to the use of tyrosine kinase inhibitors in chronic myeloid leukemia---but this remains to be determined.

We agree that a full understanding of the tumor response warrants further investigation.  Like the reviewer, we speculate that the improved safety profile of selective antibodies relative to pan-Notch inhibitors may enable greater and sustained therapeutic coverage of Notch inhibition than has been feasible in T-ALL trials.  Given that in the sensitive PDX models we observe rapid tumor regressions, not just stasis, it would seem to follow that the mechanism underpinning the tumor response involves more than just cell cycle blockade.  Whether tumor shrinkage reflects additional cell death mechanisms or simply tumor cell turnover after cell cycle arrest remains to be determined. 

A minor comment is reserved for the statement in the discussion that "In chronic myelomonocytic leukemia, which results from an inactivating mutation in the y-secretase complex component nicastrin, Notch signaling has a tumor suppressive function, that is mediated through direct repression of CEBPA and PU.1 by HES1 (Klinakis et al., 2011)". Thousands of cases of CMML and related myeloid tumors have been subjected to whole exome and even whole genome sequencing without the identification of Notch signaling pathway mutations. Thus, an important tumor suppressive role for Notch-mediated through HES1 in myeloid tumors is not proven.

We agree that our sentence about Notch and CMML does not fit well with the prevalent paradigm established by genome wide sequencing and other methods.  We will edit this paragraph accordingly, focusing on Hes1 negative regulation of CEBPA in myeloid fate control and how that shapes our thinking on molecular mechanisms in the Notch-dependent HCCs.

Reviewer #3 (Public review):

Summary:

Notch is active in HCC, but generally not mutated. The authors use a JAG1-selective blocking antibody in a large panel of liver cancer patient-derived xenograft models. They find JAG-dependent HCCs, and these are aggressive and proliferative. Notch inhibition induces cycle arrest and promotes hepatocyte differentiation, through upregulation of CEBPA expression and activation of existing HNF4A, mimicking normal developmental programs.

The authors use aJ1.b70, a potent and selective therapeutic antibody that inhibits JAG1 against PDX models. They tested over 40 PDX models and found a handful of super-responders to single-agent inhibition. In LIV78 and Li1035 cancer cells, NOTCH2 was expressed and required, in contrast to NOTCH1. RNA-seq showed that the responsive HCCs resembled the S2 transcriptional class of HCCs, which were enriched for Notch-dependent models. They conclude that these dependent tumors have transcriptomes that resemble a hybrid progenitor cell expressing FGF9 and GAS7. Inhibition was able to induce hepatocyte differentiation away from a NOTCH-driven progenitor program. scRNA-seq analysis showed a large population of NOTCH-JAG expressing cells but also showed that there are cells that did not. Not surprisingly, NOTCH2 inhibition leads to increased CEBPA and HNF4A transcriptional activity, which are standard TFs in hepatocytes.

Strengths:

The paper provides useful information about the frequency of HCCs and CCA that respond to NOTCH inhibition and could allow us to anticipate the super-responder rate if these antibodies were actually used in the clinic. The inhibitor tools are highly specific, and provide useful information about NOTCH activities in liver cancers. The large number of PDXs and the careful transcriptomic analyses were positives about the study.

Weaknesses:

The paper is mostly descriptive.

Briefing Document : Examen des biais institutionnels et de leur impact sur la prestation des services publics Source : Excerpts from "Comprendre les biais institutionnels et leur impact sur la prestation des services publics" (Youth Job Accelerator - Yojoa, basé sur diverses recherches dont Mugglin et al., 2022).

Date : 18 juin 2024

Introduction :

• Ce document présente une synthèse des principaux thèmes et idées clés issus de l'article "Comprendre les biais institutionnels et leur impact sur la prestation des services publics".

Ce travail explore la nature des biais institutionnels, leurs différences avec les biais individuels et leurs conséquences significatives sur la qualité et l'équité de la prestation des services publics, en particulier dans les secteurs de la santé et des services d'urgence.

L'article s'appuie sur une revue de la littérature internationale et met en lumière les résultats d'études menées en Suisse, notamment celles de Mugglin et al. (2022) sur le racisme structurel.

Thèmes Principaux et Idées Clés :

1. Définition et Prévalence des Biais :

• Biais Individuel : Préjugé en faveur ou contre une chose, une personne ou un groupe par rapport à un autre, souvent considéré comme injuste ou déraisonnable dans les processus décisionnels.

"Le biais est défini comme un préjugé en faveur ou contre une chose, une personne ou un groupe par rapport à un autre, ce qui est généralement considéré comme injuste ou déraisonnable, en particulier dans les processus décisionnels (Sparkman-Key, 2020)".

• Universalité des Biais : Les biais affectent tout le monde, influencés par des facteurs historiques, la socialisation, l'exposition aux médias et les expériences personnelles (genre, opinions politiques, classe sociale, âge, handicap, religion, sexualité, race, ethnicité, langue et nationalité).

• Biais Institutionnel (ou Institutionnalisé) : Discriminations systémiques intégrées dans les structures et les pratiques des institutions, désavantageant certains groupes.

Ce sont des "coutumes et des pratiques établies qui reflètent et produisent systématiquement des inégalités basées sur les groupes (Dovidio, 2013)".

• Distinction Individuel vs. Institutionnel : Le biais individuel se situe au niveau des préjugés personnels, tandis que le biais institutionnel opère au niveau des organisations et des systèmes.

Ils peuvent se renforcer mutuellement. "Dans les cas où les individus peuvent ne pas avoir de préjugés ou de comportements stéréotypés, les espaces dans lesquels ils se trouvent peuvent manifester un biais systémique contre les groupes sociaux marginalisés."

• Origines du Biais Institutionnel : Il ne résulte pas toujours d'une discrimination délibérée, mais souvent de l'adhésion involontaire de la majorité aux normes sociales existantes et de l'application (consciente ou inconsciente) des biais par la direction dans les politiques et pratiques (recrutement, promotion, services, etc.).

2. Impacts du Biais Institutionnel sur la Prestation des Services :

Système de Santé :

• Préjugés Inconscients des Professionnels : De nombreux professionnels de la santé présentent des préjugés inconscients à l'égard de certains groupes de patients.

• Racisme dans les Interactions : Le biais racial affecte l'octroi de traitement et de diagnostic, entraînant un manque de confiance et un retard dans la recherche de soins pour les minorités racialisées.

"Selon l'étude, les minorités racialisées reçoivent des soins inadéquats dans les interactions de soins de santé, ce qui entraîne un manque de confiance et un retard dans la recherche de soins."

• Stéréotypes Négatifs : Les patients appartenant à des minorités raciales sont parfois perçus comme difficiles.

• Biais en Faveur du Groupe Majoritaire : Influence négative sur les décisions médicales.

• Réticence à Discuter du Racisme : Tendance à considérer le service de santé comme impartial et à éviter les discussions sur le racisme au travail.

• Expériences de Racisme en Suisse : Des soignants racisés en Suisse rapportent que le racisme structurel passe souvent inaperçu, avec des minimisations ou des évitements de la discussion par les patients et parfois les collègues.

La discrimination va du rejet aux doutes sur les compétences et aux commentaires blessants. "Schwarz (2019) souligne que 'ce qui rend ces expériences particulièrement inquiétantes, c'est leur caractère récurrent'."

• Race comme Déterminant de la Santé : La race, en tant que construction sociale, influence les inégalités dans la prestation des soins.

• Trois Facteurs Alimentant les Inégalités : attitudes et biais implicites des prestataires, stéréotypes de maladies et nomenclature clinique, et algorithmes cliniques, outils et directives de traitement.

• Stéréotypes de Maladies et Nomenclature Clinique : Association erronée de certaines maladies à des origines raciales spécifiques (exemples de la drépanocytose et de la maladie de Tay-Sachs). Nécessité de passer des stéréotypes raciaux aux facteurs de risque réels.

• Algorithmes Cliniques et Technologies : Biais et performances sous-optimales de certaines technologies médicales en raison d'une prise en compte insuffisante de la diversité des patients (biais de sélection, décisions inéquitables, racisme systémique).

• Impact de la Pigmentation de la Peau : Les oxymètres de pouls sont moins précis chez les individus à la pigmentation de peau plus foncée, pouvant entraîner des décisions cliniques incorrectes. Des disparités similaires existent pour certains dispositifs ophtalmologiques.

• Biais dans le Triage d'Urgence : Une étude révèle que le sexe et l'origine ethnique peuvent influencer la décision de priorisation. Les cas masculins sont plus souvent considérés comme des urgences vitales que les cas féminins, et les patients noirs simulés reçoivent une priorité inférieure par rapport aux autres groupes ethniques.

• Syndrome Méditerranéen : Biais implicite où les professionnels de la santé ont la fausse idée que les personnes d'origine maghrébine, africaine et d’Europe de l'Est sont moins sensibles à la douleur ou exagèrent leur douleur, menant à de moins bons résultats de santé.

• Pseudo-diagnostics Non Divulgués : En Suisse, des professionnels de la santé ont posé des pseudo-diagnostics sans les communiquer à des patients racisés en raison de difficultés de communication.

• Croyance en une Tolérance à la Douleur Plus Élevée : Persistance de cette croyance chez le personnel médical suisse concernant les individus noirs, affectant la proposition d'anesthésie péridurale aux femmes noires en travail.

• Disparités Ethniques en Santé Maternelle : Au Royaume-Uni et en Irlande, les femmes noires ont un risque significativement plus élevé de mortalité maternelle et post-partum, avec des témoignages de douleur ignorée et de stéréotypes. "Il y a un stéréotype selon lequel les femmes noires ne ressentent pas la douleur et sont assez agressives et bruyantes, très fortes, donc nous sommes capables de supporter plus de douleur."

Organisations des Services d'Urgence (Police, Pompiers, Services Médicaux d'Urgence) :

• Sous-représentation des Personnes de Couleur : Présence de biais pouvant conduire à cette sous-représentation dans le personnel de sécurité.

• Biais Raciaux dans la Vérification des Antécédents : Utilisation de facteurs sociaux (pauvreté) et de jugement humain (biais d'affinité, de confirmation, statistique) pouvant défavoriser les personnes de couleur dans l'obtention d'habilitations de sécurité.

• Sous-représentation chez les Pompiers aux États-Unis :

Les personnes blanches et non hispaniques représentent une majorité écrasante.

Les causes potentielles incluent des biais socio-économiques liés à l'éducation primaire et secondaire (ségrégation raciale dans les écoles publiques) et aux effets de la criminalité et de la pauvreté (faible qualité de vie et d'éducation, problèmes de santé mentale).

3. Perspectives de la Recherche de Mugglin et al. (2022) en Suisse :

• Racisme Structurel Avéré : L'étude a révélé des indications claires de discrimination institutionnelle et structurelle dans divers domaines en Suisse.

• Domaine du Travail :Moins de chances pour les personnes hautement qualifiées issues de milieux migrants d'accéder à des postes de direction dans le travail social, même avec une éducation et une naturalisation suisses.

• Discrimination variable selon l'origine, avec moins de discrimination pour les personnes venant de France, d'Allemagne ou du Portugal comparées à celles perçues comme venant des Balkans ou d'Afrique.

• Écarts salariaux, taux de chômage et types d'emplois disproportionnés pour les personnes originaires des Balkans ou d'Afrique.

• Marché du Travail (Étude de Hangartner et al., 2021) : Les personnes portant des noms de famille étrangers reçoivent moins de clics sur les plateformes de recrutement en ligne, avec des écarts plus importants pour les personnes d'Europe de l'Est, des pays de l'ex-Yougoslavie et d'Afrique subsaharienne.

• Police : Le profilage racial est une pratique institutionnelle. Les hommes noirs et les personnes d'origine asiatique sont particulièrement concernés. Des témoignages d'hommes d'Afrique de l'Ouest en Suisse rapportent des contrôles arbitraires, un manque d'explication, un sentiment d'impuissance et une limitation de la liberté de mouvement.

4. Rôle de la Culture Institutionnelle :

• Une culture institutionnelle progressiste favorise l'égalité et la productivité.

• L'intégration de la diversité et de l'inclusion accélère les performances.

• Une étude sur les pompiers monégasques montre une tension entre les préjugés individuels et la culture officielle de service public égalitaire.

L'engagement envers le service public peut atténuer les préjugés personnels et inciter à agir de manière impartiale.

"Même lorsque les pompier·e·s individuel·le·s avaient des préjugés inconciliables avec l'engagement de l'institution envers un service égalitaire, l'étude a montré leur capacité à contenir leurs préjugés et à agir de manière impartiale dans leurs responsabilités envers le public."

• Des politiques et pratiques institutionnelles solides, valorisant le service et l'égalitarisme, sont essentielles.

5. Stratégies pour Lutter Contre les Biais Institutionnels :

• Promouvoir une Culture Progressive Axée sur le Service : Les institutions et les agences gouvernementales devraient activement développer une telle culture.

• Mise en Œuvre de Politiques et Pratiques Inclusives et Diversifiées : L'inclusion à tous les niveaux et dans les processus décisionnels est cruciale.

• Identification et Correction Rapide des Biais : La direction et les employés doivent être vigilants et réactifs.

• Examen Critique Régulier des Politiques, Lois et Pratiques : Pour identifier et démanteler les biais.

• Encourager des Canaux de Communication Efficaces : Entre les employés et la direction pour traiter les préoccupations liées aux biais. L'utilisation de technologies d'IA pour la communication dans le secteur de la santé peut aider à réduire les biais liés aux barrières linguistiques.

• Éducation Continue sur l'Inclusion, la Diversité et les Biais : Sensibiliser les travailleurs du secteur public à leurs propres biais personnels et institutionnels.

• Promouvoir la Sensibilisation du Public aux Biais : Habiliter les individus à identifier et accepter leurs biais pour pouvoir les corriger.

• Formation aux Compétences de Gestion des Biais et Sensibilisation aux Effets Néfastes : Particulièrement dans le système de santé.

• Programmes d'Action Positive : Pour accroître la représentation des groupes défavorisés dans divers secteurs de leadership.

Conclusion :

Le biais institutionnel est un problème sociétal omniprésent, profondément ancré dans les structures systémiques et allant au-delà du biais individuel.

Il entraîne des disparités significatives dans l'emploi, l'éducation, les soins de santé et l'application de la loi.

Pour le combattre efficacement, une approche multifacette est nécessaire, incluant des réformes politiques, une sensibilisation accrue et un changement de culture institutionnelle.

Les institutions doivent également tenir compte des contextes culturels lors de l'élaboration de stratégies contre les biais. Seuls des efforts concertés permettront de construire une société plus équitable garantissant un accès égal aux services publics pour tous.

DOI: 10.1016/j.xcrm.2025.101986

Resource: (Cell Signaling Technology Cat# 2748, RRID:AB_823640)

Curator: @scibot

SciCrunch record: RRID:AB_823640

What is this?

Me parece un gran texto. Dos preguntas:

1. Pareces postular una esencialidad pura, un estadio abandonado, una humanidad cuyo contenido no provendría de algo externo. Si es así, ¿qué compone esa humanidad?

2. Todo el texto parece debordiano, sobre todo el énfasis en la forma-mercancía de lo que antes tenía forma-humano. Y la noción de visibilidad, punto de partida de cualquier intervención hoy, parece el espectáculo debordiano en esteorides. Desaparecer parece imposible, por lo que estoy de acuerdo: es la misión.

Reviewer #3 (Public review):

Summary:

Using a mouse model of Staphylococcus aureus gut colonization Lejeune et al demonstrate that the microbiome, immune system, and sex are important contributing factors for whether this important human pathogen persists in the gut. The work begins by describing differential gut clearance of S. aureus in female B6 mice bred at NYU compared to those from Jackson Laboratories (JAX). NYU female mice cleared S. aureus from the gut but NYU male mice and mice of both sexes from JAX exhibited persistent gut colonization. Further experimentation demonstrated that differences between staphylococcal gut clearance in NYU and JAX female mice were attributed to the microbiome. However, NYU male and female mice harbor similar microbiomes, supporting the conclusion that the microbiome cannot account for the observed sex-dependent clearance of S. aureus gut colonization. To identify factors responsible for female clearance of S. aureus, the authors performed RNAseq on intestinal epithelia cells and cells enriched within the lamina propria. This analysis revealed sex-dependent transcriptional responses in both tissues. Genes associated with immune cell function and migration were distinctly expressed between the sexes. To determine which immune cell types contribute to S. aureus clearance Lejeune et al employed genetic and antibody-mediated immune cell depletion. This experiment demonstrated that CD4+ IL17+ cells and neutrophils promote elimination of S. aureus from the gut. Subsequent experiments, including the use of the 'four core genotype model' were conducted to discern between the roles of sex chromosomes and sex hormones. This work demonstrated that sex-chromosome linked genes are not responsible for clearance, increasing the likelihood that hormones play a dominant role in controlling S. aureus gut colonization.

Strengths:

A strength of the work is the rigorous experimental design. Appropriate controls were executed and, in most cases, multiple approaches were conducted to strengthen the authors' conclusions. The conclusions are supported by the data.<br /> The following suggestions are offered to improve an already strong piece of scholarship.

Weaknesses:

The correlation between female sex hormones and elimination of S. aureus from the gut could be further validated by quantifying sex hormones produced in the four core genotype mice in response to colonization. Additionally, and this may not be feasible, but according to the proposed model administering female sex hormones to male mice should decrease colonization. Finally, knowing whether the quantity of IL-17a CD4+ cells change in the OVX mice has the potential to discern whether the abundance/migration of the cells or their activation is promoted by female sex hormones.

In the Discussion the authors highlight previous work establishing a link between immune cells and sex hormone receptors, but whether the estrogen (and progesterone) receptor is differentially expressed in response to S. aureus colonization could be assessed in the RNAseq dataset. Differential expression of known X and Y chromosome linked genes were discussed but specific sex hormones or sex hormone receptors, like the estrogen receptor were not. This potential result could be highlighted.

Comments on revisions:

The authors have adequately addressed my comments. I have only one minor adjustment: the Esr1 mice should be included the Materials and Methods.

Author response:

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

Reviewer #1 (Public review):

Summary:

The manuscript by Cao et al. examines an important but understudied question of how chronic exposure to heat drives changes in affective and social behaviors. It has long been known that temperature can be a potent driver of behaviors and can lead to anxiety and aggression. However, the neural circuitry that mediates these changes is not known. Cao et al. take on this question by integrating optical tools of systems neuroscience to record and manipulate bulk activity in neural circuits, in combination with a creative battery of behavior assays. They demonstrate that chronic daily exposure to heat leads to changes in anxiety, locomotion, social approach, and aggression. They identify a circuit from the preoptic area (POA) to the posterior paraventricular thalamus (pPVT) in mediating these behavior changes. The POA-PVT circuit increases activity during heat exposure. Further, manipulation of this circuit can drive affective and social behavioral phenotypes even in the absence of heat exposure. Moreover, silencing this circuit during heat exposure prevents the development of negative phenotypes. Overall the manuscript makes an important contribution to the understudied area of how ambient temperature shapes motivated behaviors.

Strengths:

The use of state-of-the-art systems neuroscience tools (in vivo optogenetics and fiber photometry, slice electrophysiology), chronic temperature-controlled experiments, and a rigorous battery of behavioral assays to determine affective phenotypes. The optogenetic gain of function of affective phenotypes in the absence of heat, and loss of function in the presence of heat are very convincing manipulation data. Overall a significant contribution to the circuit-level instantiation of temperature-induced changes in motivated behavior, and creative experiments.

Weaknesses：

(1) There is no quantification of cFos/rabies overlap shown in Figure 2, and no report of whether the POA-PVT circuit has a higher percentage of Fos+ cells than the general POA population. Similarly, there is no quantification of cFos in POA recipient PVT cells for Figure 2 Supplement 2.

Thanks for the comment. The quantification results of c-Fos signal have been provided in the main text and figures.  

(2) The authors do not address whether stimulation of POA-PVT also increases core body temperature in Figure 3 or its relevant supplements. This seems like an important phenotype to make note of and could be addressed with a thermal camera or telemetry.

Thanks for raising this point. We did indeed monitor the core body temperature during stimulation of POA-PVT pathway, but we did not observe any significant changes. We have included this finding in the revised manuscript.

(3) In Figure 3G: is Day 1 vs Day 22 "pre-heat" significant? The statistics are not shown, but this would be the most conclusive comparison to show that POA-PVT cells develop persistent activity after chronic heat exposure, which is one of the main claims the authors make in the text. This analysis is necessary in order to make the claim of persistent circuit activity after chronic heat exposure.

Figure 3G does compare the Day 1 preheat to Day22 preheat, and the difference was significant. The wording has been corrected to avoid confusion. Also, we have modified Figure 3D to 3H in our revised manuscript to improve the clarity of these plots.

(4) In Figure 4, the control virus (AAV1-EYFP) is a different serotype and reporter than the ChR2 virus (AAV9-ChR2-mCherry). This discrepancy could lead to somewhat different baseline behaviors.

Thanks for bringing out this issue. We acknowledge that using AA1-EGFP (a different serotype and reporter compared to the AAV9-ChR2-mCherry) as our control virus is not ideal. But based on our own prior experiments, we observed no significant differences in baseline behaviors between animals injected with AAV1 and AAV9 EYFP as well as control mice without virus injection. Therefore, we believe that the baseline behaviors of the animals were unaffected.

(5) In Figure 5G, N for the photometry data: the authors assess the maximum z-score as a measure of the strength of calcium response, however the area under the curve (AUC) is a more robust and useful readout than the maximum z score for this. Maximum z-score can simply identify brief peaks in amplitude, but the overall area under the curve seems quite similar, especially for Figure 5N.

Thanks for the comment. We agree with the reviewer that the area under the curve (AUC) is an alternative readout for measurement of the strength of calcium response. However, the reason why we chose the maximum z-score is based on the observation that we found POA recipient pPVT neurons after chronic heat treatment exhibited a higher calcium peak corresponding to certain behavioral performances when compared to pre-heat conditions. We thus applied the maximum z-score as a representative way to describe the neuronal activity changes of mice during certain behaviors before and after chronic heat treatment. The other consideration is that we want to reflect that POA recipient pPVT neurons become more sensitive and easier to be activated after chronic heat exposure under the same stressful situations compared to control mice. The maximum z score represented by peak in combination with particular behavioral performances is considered more suitable to highlight our findings in this study.

(6) For Fig 5V: the authors run the statistics on behavior bouts pooled from many animals, but it is better to do this analysis as an animal average, not by compiling bouts. Compiling bouts over-inflates the power and can yield significant p values that would not exist if the analysis were carried out with each animal as an n of 1.

Thanks for the comment and suggestion. We had tried both methods and the statistical results were similar. As suggested, we have updated Fig 5V, as well as Fig. 5H and 5O by comparing animal average in our revised manuscript.

(7) In general this is an excellent analysis of circuit function but leaves out the question of whether there may be other inputs to pPVT that also mediate the same behavioral effect. Future experiments that use activity-dependent Fos-TRAP labeling in combination with rabies can identify other inputs to heat-sensitive pPVT cells, which may have convergent or divergent functions compared to the POA inputs.

Thanks for the valuable suggestion, which would enhance the conclusion. We will consider adopting this approach in future investigations into this question.

Reviewer #2 (Public review):

Summary

The study by Cao et al. highlights an interesting and important aspect of heat- and thermal biology: the effect of repetitive, long-term heat exposure and its impact on brain function.

Even though peripheral, sensory temperature sensors and afferent neuronal pathways conveying acute temperature information to the CNS have been well established, it is largely unknown how persistent, long-term temperature stimuli interact with and shape CNS function, and how these thermally-induced CNS alterations modulate efferent pathways to change physiology and behavior. This study is therefore not only novel but, given global warming, also timely.

The authors provide compelling evidence that neurons of the paraventricular thalamus change plastically over three weeks of episodic heat stimulation and they convincingly show that these changes affect behavioral outputs such as social interactions, and anxiety-related behaviors.

Strengths

(1) It is impressive that the assessed behaviors can be (i) recruited by optogenetic fiber activation and (ii) inhibited by optogenetic fiber inhibition when mice are exposed to heat. Technically, when/how long is the fiber inhibition performed? It says in the text "3 min on and 3 min off". Is this only during the 20-minute heat stimulation or also at other times?

Thanks for pointing out the need for clarification. Our optogenetic inhibition had been conducted for 21 days during the heat exposure period (90 mins) for each mouse. And to avoid the light-induced heating effect, we applied the cyclical mode of 3 minutes’ light on and 3 minutes’ light off only during the process of heat exposure but not other time. The detailed description has been supplemented in the Method part of our revised manuscript.

(2) It is interesting that the frequency of activity in pPVT neurons, as assessed by fiber photometry, stays increased after long-term heat exposure (day 22) when mice are back at normal room temperature. This appears similar to a previous study that found long-term heat exposure to transform POA neurons plastically to become tonically active (https://www.biorxiv.org/content/10.1101/2024.08.06.606929v1). Interestingly, the POA neurons that become tonically active by persistent heat exposure described in the above study are largely excitatory, and thus these could drive the activity of the pPVT neurons analyzed in this study.

Thanks for pointing out this study that suggests similar plasticity of POA neurons under long-term heat exposure serving a different purpose. We have included this information in our discussion as well.  

(3) How can it be reconciled that the majority of the inputs from the POA are found to be largely inhibitory (Fig. 2H)? Is it possible that this result stems from the fact that non-selective POA-to-pPVT projections are labelled by the approach used in this study and not only those pathways activated by heat? These points would be nice to discuss.

Thanks for raising these important questions. Although it is not our primary focus, we are aware of the substantial inhibitory inputs from POA to pPVT which suggests an important function. However, we do not think that this pathway, which would exert an opposite effect on POA-recipient pPVT neurons compared to the excitatory input, contributes to the long-term effect of chronic heat exposure. This is due to the increased, rather than decreased, excitability of the neurons. There is a possibility that this inhibitory input serves as a short-term inhibitory control for other purpose. Further work is needed to fully address this question.

(4) It is very interesting that no LTP can be induced after chronic heat exposure (Figures K-M); the authors suggest that "the pathway in these mice were already saturated" (line 375). Could this hypothesis be tested in slices by employing a protocol to extinguish pre-existing (chronic heat exposure-induced) LTP? This would provide further strength to the findings/suggestion that an important synaptic plasticity mechanism is at play that conveys behavioral changes upon chronic heat stimulation.

We agree with the reviewer that the results of the suggested experiment would further strengthen our hypothesis. We will try to confirm this in future studies.

(5) It is interesting that long-term heat does not increase parameters associated with depression (Figure 1N-Q), how is it with acute heat stress, are those depression parameters increased acutely? It would be interesting to learn if "depression indicators" increase acutely but then adapt (as a consequence of heat acclimation) or if they are not changed at all and are also low during acute heat exposure.

Based on our observations, we did not find increased depression parameters after acute heat stress in our experiments (data not shown), which was consistent with other two previous studies (Beas et al., 2018; Zhang et al., 2021). It appears that acute heat stress is more associated with anxiety-like behavior and may not be sufficient to induce depression-like phenotypes in rodents, aligning with our observation during experiments.

Beas BS, Wright BJ, Skirzewski M, Leng Y, Hyun JH, Koita O, Ringelberg N, Kwon HB, Buonanno A, Penzo MA (2018) The locus coeruleus drives disinhibition in the midline thalamus via a dopaminergic mechanism Nat Neurosci 21:963-973.

Zhang GW, Shen L, Tao C, Jung AH, Peng B, Li Z, Zhang LI, Whit Tao HZ (2021) Medial preoptic area antagonistically mediates stress-induced anxiety and parental behavior Nat Neurosci 24:516-528.

Weaknesses/suggestions for improvement.

(1) The introduction and general tenet of the study is, to us, a bit too one-sided/biased: generally, repetitive heat exposure --heat acclimation-- paradigms are known to not only be detrimental to animals and humans but also convey beneficial effects in allowing the animals and humans to gain heat tolerance (by strengthening the cardiovascular system, reducing energy metabolism and weight, etc.).

Thanks for the suggestion. We have modified the introduction in our revised manuscript to make it more balanced.

(2) The point is well taken that these authors here want to correlate their model (90 minutes of heat exposure per day) to heat waves. Nevertheless, and to more fully appreciate the entire biology of repetitive/chronic/persistent heat exposure (heat acclimation), it would be helpful to the general readership if the authors would also include these other aspects in their introduction (and/or discussion) and compare their 90-minute heat exposure paradigm to other heat acclimation paradigms. For example, many past studies (using mice or rats)m have used more subtle temperatures but permanently (and not only for 90 minutes) stimulated them over several days and weeks (for example see PMID: 35413138). This can have several beneficial effects related to cardiovascular fitness, energy metabolism, and other aspects. In this regard: 38{degree sign}C used in this study is a very high temperature for mice, in particular when they are placed there without acclimating slowly to this temperature but are directly placed there from normal ambient temperatures (22{degree sign}C-24{degree sign}C) which is cold/coolish for mice. Since the accuracy of temperature measurement is given as +/- 2{degree sign}C, it could also be 40{degree sign}C -- this temperature, 40{degree sign}C, non-heat acclimated C57bl/6 mice will not survive for long.

The authors could consider discussing that this very strong, short episodic heat-stress model used here in this study may emphasize detrimental effects of heat, while more subtle long-term persistent exposure may be able to make animals adapt to heat, become more tolerant, and perhaps even prevent the detrimental cognitive effects observed in this study (which would be interesting to assess in a follow-up study).

Thanks for pointing out the important aspect regarding the different heat exposure paradigms and their potential impacts. We have incorporated these points into both the Introduction and Discussion sections of the revised manuscript.

(3) Line 140: It would help to be clear in the text that the behaviors are measured 1 day after the acute heat exposure - this is mentioned in the legend to the figure, but we believe it is important to stress this point also in the text. Similarly, this is also relevant for chronic heat stimulation: it needs to be made very clear that the behavior is measured 1 day after the last heat stimulus. If the behaviors had been measured during the heat stimulus, the results would likely be very different.

Thanks for the suggestion, and we have clarified the procedure in the revised manuscript.

(4) Figure 2 D and Figure 2- Figure Supplement 1: since there is quite some baseline cFos activity in the pPVT region we believe it is important to include some control (room temperature) mice with anterograde labelling; in our view, it is difficult/not possible to conclude, based on Fig 2 supplement 2C, that nearly 100% of the cfos positive cells are contacted by POA fibre terminals (line 168). By eye there are several green cells that don't have any red label on (or next to) them; additionally, even if there is a little bit of red signal next to a green cell: this is not definitive proof that this is a synaptic contact. It is therefore advisable to revisit the quantification and also revisit the interpretation/wording about synaptic contacts.

In relation to the above: Figure 2h suggests that all neurons are connected (the majority receiving inhibitory inputs), is this really the case, is there not a single neuron out of the 63 recorded pPVT neurons that does not receive direct synaptic input from the POA?

Thanks for the comments. For Figure 2-figure supplement 1, the baseline c-Fos activity in pPVT were indeed measured from mouse under room temperature. Observed activity may be attributed to the diverse functions that the pPVT is responsible for. Compared to the heat-exposed group, we observed significant increases in c-Fos signals, suggesting the effect of heat exposure.

For Figure 2-figure supplement 2, through targeted injection of AAV1-Cre into the POA, we achieved selective expression of Cre-dependent ChR2-mCherry in pPVT neurons receiving POA inputs. Following heat exposure, we observed substantial colocalization between heat-induced c-Fos expression (green signal) and ChR2-mCherry-labeled neurons (red signal) in the pPVT. This extensive overlap indicates that POA-recipient pPVT neurons are predominantly heat-responsive and likely mediate the behavioral alterations induced by chronic heat exposure. We have validated these signals and included updated quantification in our revised manuscript.

For Fig 2H, we specifically patched those neurons that were surrounded by red fluorescence under the microscope, ensuring that the patched neurons had a high likelihood of being innervated from POA. This is why all 63 recorded pPVT neurons were found to receive direct synaptic input from the POA.

(5) It would be nice to characterize the POA population that connects to the pPVT, it is possible/likely that not only warm-responsive POA neurons connect to that region but also others. The current POA-to-pPVT optogenetic fibre stimulations (Figure 4) are not selective for preoptic warm responsive neurons; since the POA subserves many different functions, this optogenetic strategy will likely activate other pathways. The referees acknowledge that molecular analysis of the POA population would be a major undertaking. Instead, this could be acknowledged in the discussion, for example in a section like "limitation of this study".

Thanks for the suggestion. We have supplemented this part in our revised manuscript.

(6) Figure 3a the strategy to express Gcamp in a Cre-dependent manner: it seems that the Gcamp8f signal would be polluted by EGFP (coming from the Cre virus injected into the POA): The excitation peak for both is close to 490nm and emission spectra/peaks of GCaMP8f (510-520 nm) and EGFP (507-510 nm) are also highly overlapping. We presume that the high background (EGFP) fluorescence signal would preclude sensitive calcium detection via Gcamp8f, how did the authors tackle this problem?

Thank you for pointing out this issue. We acknowledge that we included AAV1-EGFP when recording the GCaMP8F signal to assist in the post-verification of the accuracy of the injection site. But we also collected recording data from mice with AAV1-Cre without EGFP injected into POA and Cre-dependent GCaMP8F in pPVT, albert in a smaller number. We did not observe any obvious differences in the change in calcium signal between these two virus strategies, suggesting that the sensitivity of the GCaMP signals was not significantly affected by the increased baseline fluorescence due to EGFP.

(7) How did the authors perform the social interaction test (Figures 1F, G)? Was the intruder mouse male or female? If it was a male mouse would the interaction with the female mouse be a form of mating behavior? If so, the interpretation of the results (Figures 1F, G) could be "episodic heat exposure over the course of 3 weeks reduces mating behavior".

Thanks for the comment. For this female encounter test, we strictly followed the protocol by Ago Y, et al., (2015). During this test, both the strange male and female mice were placed into a wired cup (which is made up of mental wire entanglement and the size for each hole is 0.5 cm [L] x 0.5 cm [W]), which successfully prevented large body contact and the mating behavior but only innate sex-motivated moving around the cup. We have supplemented the details in the method part of our revised manuscript.

Ago Y, Hasebe S, Nishiyama S, Oka S, Onaka Y, Hashimoto H, Takuma K, Matsuda T (2015) The Female Encounter Test: A Novel Method for Evaluating Reward-Seeking Behavior or Motivation in Mice Int J Neuropsychopharmacol 18: pyv062.

Reviewer #3 (Public review):

In this study, Cao et al. explore the neural mechanisms by which chronic heat exposure induces negative valence and hyperarousal in mice, focusing on the role of the posterior paraventricular nucleus (pPVT) neurons that receive projections from the preoptic area (POA). The authors show that chronic heat exposure leads to heightened activity of the POA projection-receiving pPVT neurons, potentially contributing to behavioral changes such as increased anxiety level and reduced sociability, along with heightened startle responses. In addition, using electrophysiological methods, the authors suggest that increased membrane excitability of pPVT neurons may underlie these behavioral changes. The use of a variety of behavioral assays enhances the robustness of their claim. Moreover, while previous research on thermoregulation has predominantly focused on physiological responses to thermal stress, this study adds a unique and valuable perspective by exploring how thermal stress impacts affective states and behaviors, thereby broadening the field of thermoregulation. However, a few points warrant further consideration to enhance the clarity and impact of the findings.

(1) The authors claim that behavior changes induced by chronic heat exposure are mediated by the POA-pPVT circuit. However, it remains unclear whether these changes are unique to heat exposure or if this circuit represents a more general response to chronic stress. It would be valuable to include control experiments with other forms of chronic stress, such as chronic pain, social defeat, or restraint stress, to determine if the observed changes in the POA-pPVT circuit are indeed specific to thermal stress or indicative of a more universal stress response mechanism.

We also share similar considerations as the reviewer and indeed have conducted experiments to explore this possibility. Our findings suggest that the POA-pPVT pathway may also mediate behavioral changes induced by other chronic stress, e.g. chronic restraint stress. Nevertheless, given the well-known prominent role of POA neurons in heat perception, we do believe that the POA-pPVT has a specialized role in mediating chronic heat induced changes. The role of this pathway in other stress-related responses will need a more comprehensive study in the future.

(2) The authors use the term "negative emotion and hyperarousal" to interpret behavioral changes induced by chronic heat (consistently throughout the manuscript, including the title and lines 33-34). However, the term "emotion" is broad and inherently difficult to quantify, as it encompasses various factors, including both valence and arousal (Tye, 2018; Barrett, L. F. 1999; Schachter, S. 1962). Therefore, the reviewer suggests the authors use a more precise term to describe these behaviors, such as valence. Additionally, in lines 117 and 137-139, replacing "emotion" with "stress responses," a term that aligns more closely with the physiological observations, would provide greater specificity and clarity in interpreting the findings.

Thanks for the suggestion. We have modified the description of “emotion” to “emotional valence” in various places throughout the revised manuscript.

(3) Related to the role of POA input to pPVT,

a) The authors showed increased activity in pPVT neurons that receive projections from the POA (Figure 3), and these neurons are necessary for heat-induced behavioral changes (Figures 4N-W). However, is the POA input to the pPVT circuit truly critical? Since recipient pPVT neurons can receive inputs from various brain regions, the reviewer suggests that experiments directly inhibiting the POA-to-pPVT projection itself are needed to confirm the role of POA input. Alternatively, the authors could show that the increased activity of pPVT neurons due to chronic heat exposure is not observed when the POA is blocked. If these experiments are not feasible, the reviewer suggests that the authors consider toning down the emphasis on the role of the POA throughout the manuscript and discuss this as a limitation.<br /> b) In the electrophysiology experiments shown in Figures 6A-I, the authors conducted in vitro slice recordings on pPVT neurons. However, the interpretation of these results (e.g., "The increase in presynaptic excitability of the POA to pPVT excitatory pathway suggested plastic changes induced by the chronic heat treatment.", lines 349-350) appears to be an overclaim. It is difficult to conclude that the increased excitability of pPVT neurons due to heat exposure is specifically caused by inputs from the POA. To clarify this, the reviewer suggests the authors conduct experiments targeting recipient neurons in the pPVT, with anterograde labeling from the POA to validate the source of excitatory inputs.

For point (a), we acknowledge that pPVT neurons receiving POA inputs may also receive projections from other brain regions. While these additional inputs warrant investigation, they fall beyond the scope of our current study and represent promising directions for future research. Notably, compared to other well-characterized regions such as the amygdala and ventral hippocampus, the pPVT receives particularly robust projections from hypothalamic nuclei (Beas et al., 2018). Our optogenetic inhibition of POA-recipient pPVT neurons during chronic heat exposure effectively prevented the influence of POA excitatory projections on pPVT neurons. Furthermore, selective optogenetic activation of POA excitatory terminals within the pPVT was sufficient to induce similar behavioral abnormalities in mice, strongly supporting the causal role of POA inputs in mediating chronic heat exposure-induced behavioral alterations.

Beas BS, Wright BJ, Skirzewski M, Leng Y, Hyun JH, Koita O, Ringelberg N, Kwon HB, Buonanno A, Penzo MA (2018) The locus coeruleus drives disinhibition in the midline thalamus via a dopaminergic mechanism Nat Neurosci 21:963-973.

Regarding point (b), we acknowledge certain limitations in our in vitro patch-clamp recordings when attributing increased pPVT neuronal excitability to enhanced presynaptic POA inputs. Nevertheless, our brain slice recordings clearly demonstrated heightened excitability of pPVT neurons following chronic heat exposure. This finding was further corroborated by our in vivo fiber photometry recordings specifically targeting POA-recipient pPVT neurons, which confirmed that the increased pPVT neuronal activity was indeed modulated by POA inputs. The causal relationship was strengthened by our observation that optogenetic activation of POA excitatory terminals within the pPVT reproduced behavioral abnormalities similar to those observed in chronic heat-exposed mice. Additionally, our inability to induce circuit-specific LTP in the POA-pPVT pathway suggests that these synapses were already potentiated and saturated, reflecting enhanced excitatory inputs from the POA to pPVT. Collectively, these findings support our conclusion that increased excitatory projections from the POA to pPVT likely represent a key mechanism underlying chronic heat exposure-induced behavioral alterations in mice.

(4) The authors focus on the excitatory connection between the POA and pPVT (e.g., "Together, our results indicate that most of the pPVT-projecting POA neurons responded to heat treatment, which would then recruit their downstream neurons in the pPVT by exerting a net excitatory influence.", lines 169-171). However, are the POA neurons projecting to the pPVT indeed excitatory? This is surprising, considering i) the electrophysiological data shown in Figures 2E-K that inhibitory current was recorded in 52.4% of pPVT neurons by stimulation of POA terminal, and ii) POA projection neurons involved in modulating thermoregulatory responses to other brain regions are primarily GABAergic (Tan et al., 2016; Morrison and Nakamura, 2019). The reviewer suggests showing whether the heat-responsive POA neurons projecting to the pPVT are indeed excitatory (This could be achieved by retrogradely labeling POA neurons that project to the pPVT and conducting fluorescence in situ hybridization (FISH) assays against Slc32a1, Slc17a6, and Fos to label neurons activated by warmth). Alternatively, demonstrate, at least, that pPVT-projecting POA neurons are a distinct population from the GABAergic POA neurons that project to thermoregulatory regions such as DMH or rRPa. This would clarify how the POA-pPVT circuit integrates with the previously established thermoregulatory pathways.

Thanks for the comment and suggestion. We acknowledge that there are both excitatory and inhibitory projections from POA to pPVT. Although it is not our primary focus, we are aware of the substantial inhibitory inputs from POA to pPVT which suggests an important function. However, we do not think that this pathway, which would exert an opposite effect on POA-recipient pPVT neurons compared to the excitatory input, contributes to the long-term effect of chronic heat exposure. This is due to the increased, rather than decreased, excitability of the neurons. There is a possibility that this inhibitory input serves as a short-term inhibitory control for other purpose. Further work is needed to fully address this question.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I have a number of suggested minor edits that would improve the readability and interpretation of figures for the reader. In many figures, there are places where it is unclear what is being tested, and making minor changes would make the manuscript flow more easily for the reader:

(1) The authors could add additional details about the behavior paradigms in the Figures, especially Figure 1. How long was the chronic heat exposure for? At what temperature? What is the length of time between the end of heat exposure and the start of behaviors? What was the schedule of testing for EPM and social behaviors? Was it all on the same day or on different days? These details will make it easier for the reader to understand the behavior tests.

We have revised our experimental scheme, especially Figure 1, and added more detailed descriptions in the method section. The modifications have also been applied to the other figures.

(2) In Figures 1J and 1K, it is a bit unclear what is being shown in the right panel, since there are no axes or labels to interpret what is being plotted.

We have added body kinetics (purple dot) in the left panel of Figure 1J and 1K to align with the right panels, and we have updated our descriptions in the figure legend.

(3) In general, Figure 1 would benefit from more headers/labels or schematics to demonstrate what is being tested (for example, it's unclear that forced swim, tail suspension, open field, aggression, sucrose preference, or acoustic startle are being studied unless the reader looks at the figure legend in depth. Simple schematics or titles for each panel would help.

We have added the abbreviated titles for each panel of Figure 1 to help readers to better understand what was being tested.

(4) Figure 2A would benefit from edits to the schematic so that it is clear that heat exposure is being done before the animal is sacrificed and cFos is stained.

We have revised the text to clarify that heat exposure occurred before the animal was sacrificed and c-Fos was stained.

(5) Figure 2D: would help if the quantification of overlap of cFos and rabies was shown in the figure in addition to reporting it in the text (84%).

We have added quantification in Figure 2D.

(6) The supplemental data in Figure 2 - Supplemental Figure 1 showing increased Fos in PVT and POA after heat exposure would actually help if it was in main Figure 2 so that the reader can more clearly see the rationale for choosing the POA-PVT circuit. But this is a matter of preference and up to the author where they want to show this data.

Thanks for the suggestion. But considering the layout and space, we will prefer to retain this part in Figure 2-supplemental figure 1.

(7) Figure 3 would benefit from a behavior schematic illustrating the time course of the experiment and what the heat exposure protocol is for each day (how many minutes heat 'on' vs 'off', the temperature of heat, etc). Also, what is different about day 22 that makes it chronic heat vs day 21? Currently, it is a bit hard to understand the protocol.

We have added the temperature and time of chronic heat exposure in the schematic of Figure 3. The “day 22” represented the time point after chronic heat exposure. And we measured the calcium activity of POA recipient pPVT neurons on day 22 to compare with day 1 to demonstrate that the activity changes of POA recipient pPVT neurons after chronic heat exposure.

(8) Figure 3D, it is unclear what the difference is between the Day 1 data on the left and Day 1 data on the right. Same with Figure 3H, unclear what the difference is between the left and the right.

The left panel and right panel reflect different parameters: frequency /min (left) and amplitude (△F/F) for Figure 3D-3H. By doing this, we want to reflect the dynamic activity changes of POA recipient pPVT neurons throughout chronic heat exposure process. Now, all figures in panel 3D to 3H have been revised to make them clearer in meaning.

(9) Figure 4A would benefit from schematics showing the stimulation protocol for chronic optogenetics (how many days? Frequency? Duration of time? Etc)

We have added detailed schematics in our Figure 4A.

Reviewer #2 (Recommendations for the authors)

(1) It is interesting that social behavior appears to be reduced upon long-term heat exposure but not after acute heat exposure. Interaction of animals, such as huddling, can be used by animals as a form of behavioral thermoregulation in cold environments and heat may drive animals apart to allow for better heat dissipation. The social interaction measured here is not huddling (because, I assume, the animals are separated by a divider?) but is this form of behavior measured here related to huddling/"social thermoregulation"? This could be discussed.

Our behavioral tests were performed at room temperature. Even though huddling is a type of social behavior, based on our observation, the tested mouse was actively revolving around the mental cap, suggesting this type of behavior is not related to huddling/social thermoregulation type of social behavior.

(2) Line 113: The statement "Chronic treatment did not change body temperature" should be clarified/rephrased because 90 minutes of 38 degrees centigrade exposure to heat will increase the body temperature of mice. It would be helpful if the authors made clear that they measure body temperature before the heat stimulus (and not during the heat stimulus), which is now only obvious if one digs into the methods section.

We have revised the text and clarified that body temperature was measured before the heat stimulus in the revised manuscript.

(3) Figure 1J and K: for the non-experts, these graphs are difficult to interpret, some more explanation is needed (what exactly is measured ?). We believe that the term "arousal" may not be justified in this context because the authors have not measured sleep patterns (EEG and EMG) to show that the mice arouse from a sleep (or sleep-like) stage; the authors may consider changing the terminology, e.g. something along the lines of "agitation" or "activity".

We have further elaborated the meaning of Figure 1J and K in our revised manuscript. The acoustic startle response is a well-recognized behavioral parameter reflecting arousal levels in rodent model. The more agitation in response to stimulus, the higher the arousal levels in mice. We have used the term “agitation” to describe mice’s performance in the acoustic startle response test.

Reviewer #3 (Recommendations for the authors):

(1) The authors suggest in the introduction of the manuscript that the HPA axis and other multifaceted factors may influence emotional changes caused by heat stress (lines 63-78). However, there are no experiments or discussions on how the POA-pPVT circuit interacts with these factors. In line with the study's proposed direction in the introduction section, it would be valuable to explore, or at least discuss, whether and how the POA-pPVT circuit interacts with the HPA axis or other neural circuits known to regulate emotional and stress responses. Alternatively, the reviewer suggests revising the content of the introduction to align with the focus of the study.

Although POA is known to possibly interact with the HPA axis via its connection with the paraventricular nucleus of the hypothalamus, there is hardly any evidence for the pPVT. Thus, we prefer not to speculate this question, which remains open, in our current manuscript.

(2) In Figure 5, the authors report that pPVT neurons that receive projections from the POA exhibited increased responses to stressful situations following chronic heat exposure. However, considering the long pre- and post-recording time gap of approximately three weeks, the additional expression of GCaMP protein over time could potentially account for the increased signal. Therefore, the reviewer recommends including a control group without heat exposure to rule out this possibility.

We have included Figure 3-figure supplement 1 in our manuscript to exclude the effect of expression of GCaMP protein over time on the recording of calcium signal.

(3) Related to Figure 2, a) Please include quantification data of the overlap between retrogradely labeled and c-Fos-expressing POA neurons, which can be presented as a bar graph in Figure 2. This would be beneficial for readers to estimate how many warm-activated POA neurons connected to the pPVT are actively engaged under these conditions.

In the revised manuscript, we have included the quantification analysis in Figure 2.

b) The images in Figure 2 - Figure Supplement 1 seem to degrade in quality when magnified, making it difficult to discern finer details. Higher-resolution images would greatly improve the clarity and help in accurately visualizing the c-Fos expression patterns in the POA and pPVT regions.

We have changed our images of Figure 2-figure supplement 1 to higher-resolution in the revised manuscript.

c) The c-Fos images in Figure 2D and Figure 2 - Figure Supplement 2C appear unusual in that the c-Fos signal seems to fill the entire cell, whereas c-Fos protein is localized to the nucleus. Could the authors clarify whether this image accurately represents c-Fos staining or if there might be an issue with the staining or imaging process?

We are confident that the green signals in both Figure 2D and Figure 2-figure supplement 2C, which did not occupy the whole cell body, have already accurately reflected the c-Fos and that they were nucleus staining. We have updated the amplified picture in Figure 2D.

d) In Supplemental Figure 2B, the square marking the region of interest should be clearly explained in the figure legend to ensure that readers can fully understand the context and focus of the image.

We have further modified our figure legend in Figure 2-figure supplement 1 in our revised manuscript.

Author response:

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

Public Reviews:

Reviewer #1(Public review):

Strengths:

Utilization of both human placental samples and multiple mouse models to explore the mechanisms linking inflammatory macrophages and T cells to preeclampsia (PE).<br /> Incorporation of advanced techniques such as CyTOF, scRNA-seq, bulk RNA-seq, and flow cytometry.

Identification of specific immune cell populations and their roles in PE, including the IGF1-IGF1R ligand-receptor pair in macrophage-mediated Th17 cell differentiation.<br /> Demonstration of the adverse effects of pro-inflammatory macrophages and T cells on pregnancy outcomes through transfer experiments.

Weaknesses:

Comment 1. Inconsistent use of uterine and placental cells, which are distinct tissues with different macrophage populations, potentially confounding results.

Response1: We thank the reviewers' comments. We have done the green fluorescent protein (GFP) pregnant mice-related animal experiment, which was not shown in this manuscript. The wild-type (WT) female mice were mated with either transgenic male mice, genetically modified to express GFP, or with WT male mice, in order to generate either GFP-expressing pups (GFP-pups) or their genetically unmodified counterparts (WT-pups), respectively. Mice were euthanized on day 18.5 of gestation, and the uteri of the pregnant females and the placentas of the offspring were analyzed using flow cytometry. The majority of macrophages in the uterus and placenta are of maternal origin, which was defined by GFP negative. In contrast, fetal-derived macrophages, distinguished by their expression of GFP, represent a mere fraction of the total macrophage population. We have added the GFP pregnant mice-related data in uterine and placental cells (Line204-212).

Comment 2. Missing observational data for the initial experiment transferring RUPP-derived macrophages to normal pregnant mice.

Response 2: We thank the reviewers' comments. We have added the observational data (Figure 4-figure supplement 1D, 1E） and a corresponding description of the data (Line 198-203).

Comment 3. Unclear mechanisms of anti-macrophage compounds and their effects on placental/fetal macrophages.

Response 3: We thank the reviewers' comments. PLX3397, the inhibitor of CSF1R, which is needed for macrophage development (Nature. 2023, PMID: 36890231; Cell Mol Immunol. 2022, PMID: 36220994), we have stated that on Line 227-230. However, PLX3397 is a small molecule compound that possesses the potential to cross the placental barrier and affect fetal macrophages. We have discussed the impact of this factor on the experiment in the Discussion section (Line457-459).

Comment 4. Difficulty in distinguishing donor cells from recipient cells in murine single-cell data complicates interpretation.

Response 4: We thank the reviewers' comments. Upon analysis, we observed a notable elevation in the frequency of total macrophages within the CD45⁺ cell population. Then we subsequently performed macrophage clustering and uncovered a marked increase in the frequency of Cluster 0, implying a potential correlation between Cluster 0 and donor-derived cells. RNA sequencing revealed that the F480⁺CD206^- pro-inflammatory donor macrophages exhibited a Folr2⁺Ccl7⁺Ccl8⁺C1qa⁺C1qb⁺C1qc⁺ phenotype, which is consistent with the phenotype of cluster 0 in macrophages observed in single-cell RNA sequencing (Figure 4D and Figure 5E). Therefore, we believe that the donor cells should be cluster 0 in macrophages.

Comment 5. Limitation of using the LPS model in the final experiments, as it more closely resembles systemic inflammation seen in endotoxemia rather than the specific pathology of PE.

Response 5: We thank the reviewers' comments. Firstly, our other animal experiments in this manuscript used the Reduction in Uterine Perfusion Pressure (RUPP) mouse model to simulate the pathology of PE. However, the RUPP model requires ligation of the uterine arteries in pregnant mice on day 12.5 of gestation, which hinders T cells returning from the tail vein from reaching the maternal-fetal interface. In addition, this experiment aims to prove that CD4⁺ T cells are differentiated into memory-like Th17 cells through IGF-1R receptor signaling to affect pregnancy by clearing CD4⁺ T cells in vivo with an anti-CD4 antibody followed by injecting IGF-1R inhibitor-treated CD4⁺ T cells. And we proved that injection of RUPP-derived memory-like CD4⁺ T cells into pregnant mice induces PE-like symptoms (Figure 6F-6H). In summary, the application of the LPS model in the final experiments does not affect the conclusions.

Reviewer #2 (Public review):

Strengths:

(1) This study combines human and mouse analyses and allows for some amount of mechanistic insight into the role of pro-inflammatory and anti-inflammatory macrophages in the pathogenesis of pre-eclampsia (PE), and their interaction with Th17 cells.

(2) Importantly, they do this using matched cohorts across normal pregnancy and common PE comorbidities like gestation diabetes (GDM).

(3) The authors have developed clear translational opportunities from these "big data" studies by moving to pursue potential IGF1-based interventions.

Weaknesses:

(1) Clearly the authors generated vast amounts of multi-omic data using CyTOF and single-cell RNA-seq (scRNA-seq), but their central message becomes muddled very quickly. The reader has to do a lot of work to follow the authors' multiple lines of inquiry rather than smoothly following along with their unified rationale. The title description tells fairly little about the substance of the study. The manuscript is very challenging to follow. The paper would benefit from substantial reorganizations and editing for grammatical and spelling errors. For example, RUPP is introduced in Figure 4 but in the text not defined or even talked about what it is until Figure 6. (The figure comparing pro- and anti-inflammatory macrophages does not add much to the manuscript as this is an expected finding).

Response 1: We thank the reviewers' comments. According to the reviewer's suggestion, we have made the necessary revisions. Firstly, the title of the article has been modified to be more specific. We also introduce the RUPP mouse model when interpreted Figure 4-figure supplement 1. Thirdly, We have moved the images of Figure 7 to the Figure 6-figure supplement 2 make them easier to follow. Finally, we diligently corrected the grammatical and spelling errors in the article. As for the figure comparing pro- and anti-inflammatory macrophages, the Editor requested a more comprehensive description of the macrophage phenotype during the initial submission. As a result, we conducted the transcriptome RNA-seq of both uterine-derived pro-inflammatory and anti-inflammatory macrophages and conducted a detailed analysis of macrophages in scRNA-seq.

Comment 2. The methods lack critical detail about how human placenta samples were processed. The maternal-fetal interface is a highly heterogeneous tissue environment and care must be taken to ensure proper focus on maternal or fetal cells of origin. Lacking this detail in the present manuscript, there are many unanswered questions about the nature of the immune cells analyzed. It is impossible to figure out which part of the placental unit is analyzed for the human or mouse data. Is this the decidua, the placental villi, or the fetal membranes? This is of key importance to the central findings of the manuscript as the immune makeup of these compartments is very different. Or is this analyzed as the entirety of the placenta, which would be a mix of these compartments and significantly less exciting?

Response 2: We thank the reviewers' comments. Placental villi rather than fetal membranes and decidua were used for CyToF in this study. This detail about how human placenta samples were processed have been added to the Materials and Methods section (Line564-576).

Comment 3. Similarly, methods lack any detail about the analysis of the CyTOF and scRNAseq data, much more detail needs to be added here. How were these clustered, what was the QC for scRNAseq data, etc? The two small paragraphs lack any detail.

Response 3: We thank the reviewers' comments. The details about the analysis of the CyTOF (Line577-586) and scRNAseq (Line600-615) data have been added in the Materials and Methods section.

Comment 4. There is also insufficient detail presented about the quantities or proportions of various cell populations. For example, gdT cells represent very small proportions of the CyTOF plots shown in Figures 1B, 1C, & 1E, yet in Figures 2I, 2K, & 2K there are many gdT cells shown in subcluster analysis without a description of how many cells are actually represented, and where they came from. How were biological replicates normalized for fair statistical comparison between groups?

Response 4: We thank the reviewers' comments. In our study, approximately 8×10^⁵ cells were collected per group for analysis using CyTOF. Of these, about 10% (8×10^⁴ cells per group) were utilized to generate Figure 1B. As depicted in Figure 1B, gdT cells constitute roughly 1% of each group, with specific percentages as follows: NP group (1.23%), PE group (0.97%), GDM group (0.94%), and GDM&PE group (1.26%), which equates to approximately 800 cells per group. For the subsequent gdT cell analysis presented in Figure 2I, we employed data from all cells within each group to construct the tSNE maps, comprising approximately 8000 cells per group. Consequently, it may initially appear that the number of gdT cells is significantly higher than what is shown in Figure 1B. To clarify this, we have included pertinent explanations in the figure legend. Given the relatively low proportions of gdT cells, we did not pursue further investigations of these cells in subsequent experiments. Following your suggestion, we have relocated this result to the supplementary materials, where it is now presented as Figure 2-figure supplement 1D-E.

The number of biological replicates (samples) is consistent with Figure 1, and this information has been added to the figure legend.

Comment 5. The figures themselves are very tricky to follow. The clusters are numbered rather than identified by what the authors think they are, the numbers are so small, that they are challenging to read. The paper would be significantly improved if the clusters were clearly labeled and identified. All the heatmaps and the abundance of clusters should be in separate supplementary figures.

Response 5: We thank the reviewers' comments. Based on your suggestions, we have labeled and defined the Clusters (Figure 2A, 2F, Figure 3A, Figure 5C and Figure 6A). Additionally, we have moved most of the heatmaps to the supplementary materials.

Comment 6. The authors should take additional care when constructing figures that their biological replicates (and all replicates) are accurately represented. Figure 2H-2K shows N=10 data points for the normal pregnant (NP) samples when clearly their Table 1 and test denote they only studied N=9 normal subjects.

Response 6: We thank the reviewers' careful checking. During our verification, we found that one sample in the NP group had pregnancy complications other than PE and GDM. The data in Figure 2H-2K was not updated in a timely manner. We have promptly updated this data and reanalyze it.

Comment 7. There is little to no evaluation of regulatory T cells (Tregs) which are well known to undergird maternal tolerance of the fetus, and which are well known to have overlapping developmental trajectory with RORgt+ Th17 cells. We recommend the authors evaluate whether the loss of Treg function, quantity, or quality leaves CD4+ effector T cells more unrestrained in their effect on PE phenotypes. References should include, accordingly: PMCID: PMC6448013 / DOI: 10.3389/fimmu.2019.00478; PMC4700932 / DOI: 10.1126/science.aaa9420.

Response 7: We thank the reviewers' comments. We have done the Treg-related animal experiment, which was not shown in this manuscript. We have added the Treg-related data in Figure 6F-6H. The injection of CD4⁺CD44⁺ T cells derived from RUPP mouse, characterized by a reduced frequency of Tregs, could induce PE-like symptoms in pregnant mice (Line297-304). Additionally, we have added a necessary discussion about Tregs and cited the literature you mentioned (Line433-439).

Comment 8. In discussing gMDSCs in Figure 3, the authors have missed key opportunities to evaluate bona fide Neutrophils. We recommend they conduct FACS or CyTOF staining including CD66b if they have additional tissues or cells available. Please refer to this helpful review article that highlights key points of distinguishing human MDSC from neutrophils: https://doi.org/10.1038/s41577-024-01062-0. This will both help the evaluation of potentially regulatory myeloid cells that may suppress effector T cells as well as aid in understanding at the end of the study if IL-17 produced by CD4+ Th17 cells might recruit neutrophils to the placenta and cause ROS immunopathology and fetal resorption.

Response 8: We thank the reviewers' comments. Although we do not have additional tissues or cells available to conduct FACS or CyTOF staining, including for CD66b, we have utilized CD15 and CD66b antibodies for immunofluorescence stain of placental tissue, and our findings revealed a pronounced increase in the proportion of neutrophils among PE patients, fostering the hypothesis that IL-17A produced by Th17 cells might orchestrate the migration of neutrophils towards the placental milieu (Figure 6-figure supplement 2F; Line 325-328). We have cited these references and discussed them in the Discussion section (Line 459-465).

Comment 9. Depletion of macrophages using several different methodologies (PLX3397, or clodronate liposomes) should be accompanied by supplementary data showing the efficiency of depletion, especially within tissue compartments of interest (uterine horns, placenta). The clodronate piece is not at all discussed in the main text. Both should be addressed in much more detail.

Response 9: We thank the reviewers' comments. We already have the additional data on the efficiency of macrophage depletion involving PLX3397 and clodronate liposomes, which were not present in this manuscript, and we'll add it to the Figure 4-figure supplement 2A,2B. The clodronate piece is mentioned in the main text (Line236-239), but only briefly described, because the results using clodronate we obtained were similar to those using PLX3397.

Comment 10. There are many heatmaps and tSNE / UMAP plots with unhelpful labels and no statistical tests applied. Many of these plots (e.g. Figure 7) could be moved to supplemental figures or pared down and combined with existing main figures to help the authors streamline and unify their message.

Response 10: We thank the reviewers' comments. We have moved the images of Figure 7 to the Figure 6-figure supplement 2. We also have moved most of the heatmaps to the supplementary materials.

Comment 11. There are claims that this study fills a gap that "only one report has provided an overall analysis of immune cells in the human placental villi in the presence and absence of spontaneous labor at term by scRNA-seq (Miller 2022)" (lines 362-364), yet this study itself does not exhaustively study all immune cell subsets...that's a monumental task, even with the two multi-omic methods used in this paper. There are several other datasets that have performed similar analyses and should be referenced.

Response 11: We thank the reviewers' comments. We have search for more literature and reference additional studies that have conducted similar analyses (Line382-393).

Comment 12. Inappropriate statistical tests are used in many of the analyses. Figures 1-2 use the Shapiro-Wilk test, which is a test of "goodness of fit", to compare unpaired groups. A Kruskal-Wallis or other nonparametric t-test is much more appropriate. In other instances, there is no mention of statistical tests (Figures 6-7) at all. Appropriate tests should be added throughout.

Response 12: We thank the reviewers' comments. As stated in the Statistical Analysis section (lines 672-676), the Kruskal-Wallis test was used to compare the results of experiments with multiple groups. Comparisons between the two groups in Figures 5 were conducted using Student's t-test. The aforementioned statistical methods have been included in the figure legends.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Overall, the study has several strengths, including the use of human samples and animal models, as well as the incorporation of multiple cutting-edge techniques. However, there are some significant issues with the murine model experiments that need to be addressed:

Comment 1. The authors are not consistent in their use of or focus on uterine and placental cells. These are distinct tissues, and numerous prior reports have indicated differences in the macrophage populations of these tissues, due in part to the predominantly maternal origin of macrophages in the uterus and the largely fetal origin of those in the placenta. The rationale for switching between uterine and placental cells in different experiments is not clear, and the inclusion of cells from both (such as in the bulk RNAseq experiments) could be potentially confounding.

Response 1: We thank the reviewers' comments. We have done the green fluorescent protein (GFP) pregnant mice-related animal experiment, which was not shown in this manuscript. The wild-type (WT) female mice were mated with either transgenic male mice, genetically modified to express GFP, or with WT male mice, in order to generate either GFP-expressing pups (GFP-pups) or their genetically unmodified counterparts (WT-pups), respectively. Mice were euthanized on day 18.5 of gestation, and the uteri of the pregnant females and the placentas of the offspring were analyzed using flow cytometry. The majority of macrophages in the uterus and placenta are of maternal origin, which was defined by GFP negative. In contrast, fetal-derived macrophages, distinguished by their expression of GFP, represent a mere fraction of the total macrophage population, signifying their inconsequential or restricted presence amidst the broader cellular landscape. We have added the GPF pregnant mice-related data in Figure 4-figure supplement 1D-1E to explain the different macrophage populations in the uterine and placental cells.

Comment 2. The observational data for the initial experiment transferring RUPP-derived macrophages to normal pregnant mice (without any other manipulations) seems to be missing. They do not seem to be presented in Figure 4 where they are expected based on the results text.

Response 2: We thank the reviewers' comments. We thank the reviewers' comments. We have added the observational data (Figure 4-figure supplement 1D, 1E） and a corresponding description of the data (Line 198-203).

Comment 3. The action of the anti-macrophage compounds is not well explained, nor are their mechanisms validated as affecting or not affecting the placental/fetal macrophage populations. It is important to clarify whether the macrophages are depleted or merely inhibited by these treatments, and it is absolutely critical to determine whether these treatments are affecting placental/fetal macrophage populations (the latter indicative of placental transfer), given the focus on placental macrophages.

Response 3: We thank the reviewers' comments. PLX3397, the inhibitor of CSF1R, which is needed for macrophage development (Nature. 2023, PMID: 36890231; Cell Mol Immunol. 2022, PMID: 36220994), we have stated that on Line227-230. However, PLX3397 is a small molecule compound that possesses the potential to cross the placental barrier and affect fetal macrophages. We will discuss the impact of this factor on the experiment in the Discussion section (Line457-459).

Comment 4. The interpretation of the murine single-cell data is hampered by the lack of means for distinguishing donor cells from recipient cells, which is important when seeking to identify the influence of the donor cells.

Response 4: We thank the reviewers' comments. Upon analysis, we observed a notable elevation in the frequency of total macrophages within the CD45⁺ cell population. Then we subsequently per formed macrophage clustering and uncovered a marked increase in the frequency of Cluster 0, implying a potential correlation between Cluster 0 and donor-derived cells. RNA sequencing revealed that the F480⁺CD206^- pro-inflammatory donor macrophages exhibited a Folr2⁺Ccl7⁺Ccl8⁺C1qa⁺C1qb⁺C1qc⁺ phenotype, which is consistent with the phenotype of cluster 0 in macrophages observed in single-cell RNA sequencing (Figure 4D and Figure 5E). Therefore, the donor cells should be in cluster 0 in macrophages.

Comment 5. The switch to the LPS model in the final experiments is a limitation, as this model more closely resembles the systemic inflammation seen in endotoxemia rather than the specific pathology of preeclampsia (PE). While this is not an exhaustive list, the number of weaknesses in the experimental design makes it difficult to evaluate the findings comprehensively.

Response 5: We thank the reviewers' comments. Firstly, our other animal experiments in this manuscript used the RUPP mouse model to simulate the pathology of PE. However, the RUPP model requires ligation of the uterine arteries in pregnant mice on day 12.5 of gestation, which hinders T cells returning from the tail vein from reaching the maternal-fetal interface. In addition, this experiment aims to prove that CD4⁺ T cells are differentiated into memory-like Th17 cells through IGF-1R receptor signaling to affect pregnancy by clearing CD4⁺ T cells in vivo with an anti-CD4 antibody followed by injecting IGF-1R inhibitor-treated CD4⁺ T cells. We proved that injection of RUPP-derived memory-like CD4⁺ T cells into pregnant rats induces PE-like symptoms (Figure 6F-6H). In summary, applying the LPS model in the final experiments does not affect the conclusions.

Minor comments:

Comment 1. Introduction, Lines 67-74: The phrasing here is unclear as to the roles that each mentioned immune cell subset is playing in preeclampsia. Given the statement "Elevated levels of maternal inflammation...", does this imply that the numbers of all mentioned immune cell subsets are increased in the maternal circulation? If not, please consider rewording this.

Response 1: We thank the reviewers' comments. We have revised the manuscript as follows: Currently, the pivotal mechanism underpinning the pathogenesis of preeclampsia is widely acknowledged to involve an increased frequency of pro-inflammatory M1-like maternal macrophages, along with an elevation in Granulocytes capable of superoxide generation, CD56⁺ CD94⁺ natural killer (NK) cells, CD19⁺CD5⁺ B1 lymphocytes, and activated γδ T cells. Conversely, this pathological process is accompanied by a notable decrease in the frequency of anti-inflammatory M2-like macrophages and NKp46⁺ NK cells (Line67-77).

Comment 2. Introduction, Lines 67-80: Is the involvement of the described immune cell subsets largely ubiquitous to preeclampsia? Recent multi-omic studies suggest that preeclampsia is a heterogeneous condition with different subsets, some more biased towards systemic immune activation than others. Thus, it is important to clarify whether the involvement of specific immune subsets is generally observed or more specific.

Response 2: We thank the reviewers' comments. We have added a new paragraph as follows: Moreover, as PE can be subdivided into early- and late-onset PE diagnosed before 34 weeks or from 34 weeks of gestation, respectively. Research has revealed that among the myriad of cellular alterations in PE, pro-inflammatory M1-like macrophages and intrauterine B1 cells display an augmented presence at the maternal-fetal interface of both early-onset and late-onset PE patients. Decidual natural killer (dNK) cells and neutrophils emerge as paramount contributors, playing a more crucial role in the pathogenesis of early-onset PE than late-onset PE (Front Immunol. 2020. PMID: 33013837) (Line83-89).

Comment 3. Introduction, Lines 81-86: The point of this short paragraph is not clear; the authors mention two very specific cellular interactions without explaining why.

Response 3: In the previous paragraph, we uncovered a heightened inflammatory response among multiple immune cells in patients with PE, yet the intricate interplay between these individual immune cells has been seldom elucidated in the context of PE patient. This is precisely why we delve into the realm of specific immune cellular interactions in relation to other pregnancy complications in this paragraph (Line91-98).

Comment 4. Methods: What placental tissues (e.g., villous tree, chorionic plate, extraplacental membranes) were included for CyTOF analysis? Was any decidual tissue (e.g., basal plate) included? Please clarify.

Response 4: Placental villi rather than chorionic plate and extraplacental membranes were used for CyToF in this study. The relevant content has been incorporated into the "Materials and Methods" section (Line564-576).

Comment 5. Results, Table 1: The authors should clarify that all PE samples were not full term (i.e., were less than 37 weeks of gestation), which is to be expected. In addition, were the PE cases all late-onset PE?

Response 5: All PE samples enumerated in Table 1 demonstrate a late-onset preeclampsia, with placental specimens being procured from patients more than 35 weeks of gestation and less than the 38 weeks of pregnancy. The relevant content has been incorporated into the "Materials and Methods" section (Line574-576).

Comment 6. Results, Figure 1: Are the authors considering the identified Macrophage cluster as being largely fetal (e.g., Hofbauer cells)? This also depends on whether any decidual tissue was included in the placental samples for CyTOF.

Response 6: Firstly, the specimens subjected to CyToF analysis were devoid of decidual tissue and exclusively comprised placental villi. Secondly, the Macrophage cluster in Figure 1 undeniably encompasses Hofbauer cells, and we considering fetal-derived macrophages likely constituting the substantial proportion of the cellular population. However, a limitation of the CyToF technique lies in its inability to discern between maternal and fetal origins of these cells, thereby precluding a definitive distinction.

Comment 7. Results, Figure 2C: Did the authors validate other T-cell subset markers (e.g., Th1, Th2, Th9, etc.)?

Response 7: In this study, we did not validate additional T-cell subset markers presented in Figure 2C, recognizing the potential for deeper insights. As we embark on our subsequent research endeavors, we aim to meticulously explore and characterize the intricate changes in diverse T-cell populations at the maternal-fetal interface, with a particular focus on preeclampsia patients, thereby advancing our understanding of this complex condition.

Comment 8. Results, Figure 2D: Where were the detected memory-like T cells located in the placenta? Did they cluster in certain areas or were they widely distributed?

Response 8: Upon a thorough re-evaluation of the immunofluorescence images specific to the placenta, we observed a notable preponderance of memory-like T cells residing within the placental sinusoids (Line135-139).

Comment 9. Results, Figure 2E: I would suggest separating the two plots so that the Y-axis can be expanded for TIM3, as it is impossible to view the medians currently.

Response 9: We thank the reviewers' comments. We have made the adjustment to Figure 2E according to the reviewers' suggestions.

Comment 10. Results, Lines 138-140: Do the authors consider that the altered T-cells are largely resident cells of the placenta or newly invading/recruited cells? The clarification of distribution within the placental tissues as mentioned above would help answer this.

Response 10: Our analysis revealed the presence of memory-like T cells within the placental sinusoids, as evident from the immunofluorescence examination of placental tissues. Consequently, these T cells may represent recently recruited cellular entities, traversing the placental vasculature and integrating into this unique maternal-fetal microenvironment (Line135-139).

Comment 11. Results, Figure 3C: Has a reduction of gMDSCs (or MDSCs in general) been previously reported in PE?

Response 11: Myeloid-derived suppressor cells (MDSCs) constitute a diverse population of myeloid-derived cells that exhibit immunosuppressive functions under various conditions. Previous reports have documented a decrease in the levels of gMDSCs from peripheral blood or umbilical cord blood among patients with preeclampsia (Am J Reprod Immunol. 2020, PMID: 32418253; J Reprod Immunol. 2018, PMID: 29763854; Biol Reprod. 2023, PMID: 36504233). Nevertheless, there was no documented reports thus far on the alterations and specific characteristics in gMDSCs within the placenta of PE patients.

Comment 12. Results, Figure 3D-E: It is not clear what new information is added by the correlations, as the increase of both cluster 23 in CD11b+ cells and cluster 8 in CD4+ T cells in PE cases was already apparent. Are these simply to confirm what was shown from the quantification data?

Response 12: Despite the evident increase in both cluster 23 within CD11b⁺ cells and cluster 8 within CD4⁺ T cells in PE cases, the existence of a potential correlation between these two clusters remains elusive. To gain insight into this question, we conducted a Pearson correlation analysis, which is presented in Figure 3D-E, revealing a positive correlation between the two clusters.

Comment 13. Results, Figure 4A: Please clarify in the results text that the RNA-seq of macrophages from RUPP mice was performed prior to their injection into normal pregnant mice.

Response 13: We thank the reviewers' comments. We have updated Figure 4A according to the reviewers' suggestions.

Comment 14. Results / Methods, Figure 4: For the transfer of macrophages from RUPP mice into normal mice, why were the uterine tissues included to isolate cells? The uterine macrophages will be almost completely maternal, as opposed to the largely fetal placental macrophages, and despite the sorting for specific markers these are likely distinct subsets that have been combined for injection. This could potentially impact the differential gene expression analysis and should be accounted for. In addition, did murine placental samples include decidua? This should be clarified.

Response 14: We thank the reviewers' comments. For our experimental design involving human samples, we meticulously selected placental tissue as the primary focus. Initially, we aimed for uniformity by contemplating the utilization of mouse placenta. However, a pivotal revelation emerged from the GFP pregnant mice-related data in Figure 4-figure supplement 1D,1E: the uterus and placenta of mice are predominantly populated by maternal macrophages, with fetal macrophages virtually absent, marking a notable divergence from the human scenario. Furthermore, the uterine milieu exhibits a macrophage concentration exceeding 20% of total cellular composition, whereas in the placenta, this proportion dwindles to less than 5%, underscoring a distinct distribution pattern. Given these discrepancies and considerations, we incorporated mouse uterine tissues into our protocol to isolate cells, ensuring a more comprehensive and informative exploration that acknowledges the inherent differences between human and mouse placental biology.

Comment 15. Results, Lines 186-187: I think the figure citation should be Figure 4D here.

Response 15: We thank the reviewers' careful checking. We have revised and updated Figure 4 accordingly.

Comment 16. Results, Figure 4: Where are the results of the injection of anti-inflammatory and pro-inflammatory macrophages into normal mice? This experiment is mentioned in Figure 4A, but the only results shown in Figure 4 are with the PLX3397 depletion.

Response 16: The aim of this experiment in figure 4 is to conclusively ascertain the influence of pro-inflammatory and anti-inflammatory macrophages on the other immune cells within the maternal-fetal interface, as well as their implications for pregnancy outcomes. To achieve this, we employed a strategic approach involving the administration of PLX3397, a compound capable of eliminating the preexisting macrophages in mice. Subsequently, anti-inflam or pro-inflam macrophages were injected to these mice, thereby eliminating the confounding influence of the native macrophage population. This methodology allows for a more discernible observation of the specific effects these two types of macrophages exert on the immune landscape at the maternal-fetal interface and their ultimate impact on pregnancy outcomes.

Comment 17. Results, Lines 189-190: Does PLX3397 inhibit macrophage development/signaling/etc. or result in macrophage depletion? This is an important distinction. If depletion is induced, does this affect placental/fetal macrophages or just maternal macrophages?

Response 17: We thank the reviewers' comments. We have updated the additional data on the efficiency of macrophage depletion involving PLX3397 in Figure 4-figure supplement 2A. PLX3397 is a small molecule compound that possesses the potential to cross the placental barrier and affect fetal macrophages. We have discussed the impact of this factor on the experiment in the Discussion section (Line457-459).

Comment 18. Results, Lines 197-198: Similarly, does clodronate liposome administration affect only maternal macrophages, or also placental/fetal macrophages?

Response 18: We thank the reviewers' comments. We have updated the additional data on the efficiency of macrophage depletion involving Clodronate Liposomes in Figure 4-figure supplement 2B. Clodronate Liposomes, which are intricate vesicles encapsulating diverse substances, while only small molecule compounds possess the potential to cross the placental barrier. Consequently, we hold the view that the influence of these liposomes is likely confined to the maternal macrophages (Artif Cells Nanomed Biotechnol. 2023. PMID: 37594208).  

Comment 19. Results, Line 206: A minor point, but consider continuing to refer to the preeclampsia model mice as RUPP mice rather than PE mice.

Response 19: We thank the reviewers' comments. We have revised and updated this section accordingly.

Comment 20. Results / Methods, Figure 5: For these experiments, why did the authors focus on the mouse uterus?

Response 20: We have previously addressed this query in our Response 14. We incorporated mouse uterine tissues for cell isolation due to the profound differences in placental biology between humans and mice.

Comment 21. Results, Figure 5: Did the authors have a means of distinguishing the transferred donor cells from the recipient cells for their single-cell analysis? If the goal is to separate the effects of the macrophage transfer on other uterine immune cells, then it would be important to identify and separate the donor cells.

Response 21: We thank the reviewers' comments. Upon analysis, we observed a notable elevation in the frequency of total macrophages within the CD45⁺ cell population. Then we subsequently performed macrophage clustering and uncovered a marked increase in the frequency of Cluster 0, implying a potential correlation between Cluster 0 and donor-derived cells. RNA sequencing revealed that the F480⁺CD206^- pro-inflammatory donor macrophages exhibited a Folr2⁺Ccl7⁺Ccl8⁺C1qa⁺C1qb⁺C1qc⁺ phenotype, which is consistent with the phenotype of cluster 0 in macrophages observed in single-cell RNA sequencing (Figure 4D and Figure 5E). Therefore, the donor cells should be in cluster 0 in macrophages.

Comment 22. Results, Lines 247-248: While the authors have prudently noted that the observed T-cell phenotypes are merely suggestive of immunosuppression, any claims regarding changes in the immunosuppressive function after macrophage transfer would require functional studies of the T cells.

Response 22: We thank the reviewers' comments. Upon revisiting and meticulously reviewing the pertinent literature, we have refined our terminology, transitioning from 'immunosuppression' to 'immunomodulation', thereby enhancing the accuracy and precision of our Results (Line285-287).

Comment 23. Results, Figure 6G: The observation of worsened outcomes and PE-like symptoms after T-cell transfer is interesting, but other models of PE induced by the administration of Th1-like cells have already been reported. Are the authors' findings consistent with these reports? These findings are strengthened by the evaluation of second-pregnancy outcomes following the transfer of T cells in the first pregnancy.

Response 23: We thank the reviewers' comments. As we verified in Figure 6F-6H, the injection of CD4⁺CD44⁺ T cells derived from RUPP mouse, characterized by a reduced frequency of Tregs and an increased frequency of Th17 cells, could induce PE-like symptoms in pregnant mice. In line with other studies, which have implicated Th1-like cells in the manifestation of PE-like symptoms, we posit a novel hypothesis: beyond Th1 cells, Th17 cells also have the potential to induce PE-like symptoms.

Comment 24. Results, Lines 327-337: The disease model implied by the authors here is not clear. Given that the authors' human findings are in the placental macrophages, are the authors proposing that placental macrophages are induced to an M1 phenotype by placenta-derived EVs? Please elaborate on and clarify the proposed model.

Response 24 In the article authored by our team, titled "Trophoblast-Derived Extracellular Vesicles Promote Preeclampsia by Regulating Macrophage Polarization" published in Hypertension (Hypertension. 2022, PMID: 35993233), we employed trophoblast-derived extracellular vesicles isolated from PE patients as a means to induce an M1-like macrophage phenotype in macrophages from human peripheral blood in vitro. Consequently, in the present study, we have directly leveraged this established methodology to induce pro-inflammatory macrophages.

Comment 25. Results / Methods, Figure 8E-H: What is the reasoning for switching to an LPS model in this experiment? LPS is less specific to PE than the RUPP model.

Response 25: We thank the reviewers' comments. Firstly, our other animal experiments in this manuscript used the RUPP mouse model to simulate the pathology of PE. However, the RUPP model requires ligation of the uterine arteries in pregnant mice on day 12.5 of gestation, which hinders T cells returning from the tail vein from reaching the maternal-fetal interface. In addition, this experiment aims to prove that CD4⁺ T cells are differentiated into memory-like Th17 cells through IGF-1R receptor signaling to affect pregnancy by clearing CD4⁺ T cells in vivo with an anti-CD4 antibody followed by injecting IGF-1R inhibitor-treated CD4⁺ T cells. And we proved that injection of RUPP-derived memory-like CD4⁺ T cells into pregnant mice induces PE-like symptoms (Figure 6). In summary, the application of the LPS model in the final experiments does not affect the conclusions.

Comment 26. Discussion: What do the authors consider to be the origins of the inflammatory cells associated with PE onset? Are these maternal cells invading the placental tissues, or are these placental resident (likely fetal) cells?

Response 26: We thank the reviewers' comments. Numerous reports have consistently observed the presence of inflammatory cells and factors in the maternal peripheral blood and placenta tissues of PE patients, fostering the prevailing notion that the progression of PE is intricately linked to the maternal immune system's inflammatory response towards the fetus. Nevertheless, intriguing findings from single-cell RNA sequencing, analyzed through bioinformatic methods, have challenged this perspective (Elife. 2019. PMID: 31829938；Proc Natl Acad Sci U S A. 2017.PMID: 28830992). These studies reveal that the placenta harbors not just immune cells of maternal origin but also those of fetal origin, raising questions about whether these are maternal cells infiltrating placental tissues or resident (possibly fetal) placental cells. Further investigation is imperative to elucidate this complex interplay.

Comment 27. Discussion: Given the observed lack of changes in the GDM or GDM+PE groups, do the authors consider that GDM represents a distinct pathology that can lead to secondary PE, and thus is different from primary PE without GDM?

Response 27: It's possible. Though previous studies reported GDM is associated with aberrant maternal immune cell adaption the findings remained controversial. It seems that GDM does not induce significant alterations in placental immune cell profile in our study, which made us pay more attention to the immune mechanism in PE. However, it is confusing for the reasons why individuals with GDM&PE were protected from the immune alterations at the maternal fetal interface. Limited placental samples in the GDM&PE group can partly explain it, for it is hard to collect clean samples excluding confounding factors. A study reported that macrophages in human placenta maintained anti-inflammatory properties despite GDM (Front Immunol, 2017, PMID: 28824621).Barke et al. also found that more CD163⁺ cells were observed in GDM placentas compared to normal controls (PLoS One, 2014, PMID: 24983948). Thus, GDM is likely to have a protective property in the placental immune environment when the individuals are complicated with PE.

Reviewer #2 (Recommendations for the authors):

Comment 1. IF images need to be quantified.

Response 1: We thank the reviewers' comments. We have quantified and calculated the fluorescence intensity and added it in Figure 2D.

Comment 2. Cluster 12 in Figure 3 is labeled as granulocytes but listed under macrophages.

Response 2: We thank the reviewers' careful checking. We have revised and updated Figure 3A.

Comment 3. Figure 4 labels in the text and figure do not match, no 4G in the figure.

Response 3: We thank the reviewers' careful checking. The figure labels of Figure 4 have been revised and updated.

Author response:

Reviewer #1 (Public review):

Summary:

This study identified three independent components of glucose dynamics-"value," "variability," and "autocorrelation", and reported important findings indicating that they play an important role in predicting coronary plaque vulnerability. Although the generalizability of the results needs further investigation due to the limited sample size and validation cohort limitations, this study makes several notable contributions: validation of autocorrelation as a new clinical indicator, theoretical support through mathematical modeling, and development of a web application for practical implementation. These contributions are likely to attract broad interest from researchers in both diabetology and cardiology and may suggest the potential for a new approach to glucose monitoring that goes beyond conventional glycemic control indicators in clinical practice.

Strengths:

The most notable strength of this study is the identification of three independent elements in glycemic dynamics: value, variability, and autocorrelation. In particular, the metric of autocorrelation, which has not been captured by conventional glycemic control indices, may bring a new perspective for understanding glycemic dynamics. In terms of methodological aspects, the study uses an analytical approach combining various statistical methods such as factor analysis, LASSO, and PLS regression, and enhances the reliability of results through theoretical validation using mathematical models and validation in other cohorts. In addition, the practical aspect of the research results, such as the development of a Web application, is also an important contribution to clinical implementation.

We appreciate reviewer #1 for the positive assessment and for the valuable and constructive comments on our manuscript.

Weaknesses:

The most significant weakness of this study is the relatively small sample size of 53 study subjects. This sample size limitation leads to a lack of statistical power, especially in subgroup analyses, and to limitations in the assessment of rare events.

We appreciate the reviewer’s concern regarding the sample size. We acknowledge that a larger sample size would increase statistical power, especially for subgroup analyses and the assessment of rare events.

We would like to clarify several points regarding the statistical power and validation of our findings. Our sample size determination followed established methodological frameworks, including the guidelines outlined by Muyembe Asenahabi, Bostely, and Peters Anselemo Ikoha. “Scientific research sample size determination.” (2023). These guidelines balance the risks of inadequate sample size with the challenges of unnecessarily large samples. For our primary analysis examining the correlation between CGM-derived measures and %NC, power calculations (a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4) indicated that a minimum of 47 participants was required. Our sample size of 53 exceeded this threshold and allowed us to detect statistically significant correlations, as described in the Methods section. Moreover, to provide transparency about the precision of our estimates, we have included confidence intervals for all coefficients.

Furthermore, our sample size aligns with previous studies investigating the associations between glucose profiles and clinical parameters, including Torimoto, Keiichi, et al. “Relationship between fluctuations in glucose levels measured by continuous glucose monitoring and vascular endothelial dysfunction in type 2 diabetes mellitus.” Cardiovascular Diabetology 12 (2013): 1-7. (n=57), Hall, Heather, et al. “Glucotypes reveal new patterns of glucose dysregulation.” PLoS biology 16.7 (2018): e2005143. (n=57), and Metwally, Ahmed A., et al. “Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning.” Nature Biomedical Engineering (2024): 1-18. (n=32).

Furthermore, the primary objective of our study was not to assess rare events, but rather to demonstrate that glucose dynamics can be decomposed into three main factors - mean, variance and autocorrelation - whereas traditional measures have primarily captured mean and variance without adequately reflecting autocorrelation. We believe that our current sample size effectively addresses this objective.

Regarding the classification of glucose dynamics components, we have conducted additional validation across diverse populations including 64 Japanese, 53 American, and 100 Chinese individuals. These validation efforts have consistently supported our identification of three independent glucose dynamics components.

However, we acknowledge the importance of further validation on a larger scale. To address this, we conducted a large follow-up study of over 8,000 individuals (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

To address the sample size considerations, we will add the following sentences in the Discussion section:

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed to improve the predictive utility and generalizability of our findings.

We appreciate the reviewer’s feedback and believe that these clarifications will strengthen the manuscript.

In terms of validation, several challenges exist, including geographical and ethnic biases in the validation cohorts, lack of long-term follow-up data, and insufficient validation across different clinical settings. In terms of data representativeness, limiting factors include the inclusion of only subjects with well-controlled serum cholesterol and blood pressure and the use of only short-term measurement data.

We appreciate the reviewer’s comment regarding the challenges associated with validation. In terms of geographic and ethnic diversity, our study includes validation cohorts from diverse populations, including 64 Japanese, 53 American and 100 Chinese individuals. These cohorts include a wide range of metabolic states, from healthy individuals to those with diabetes, ensuring validation across different clinical conditions. In addition, we recognize the limited availability of publicly available datasets with sufficient sample sizes for factor decomposition that include both healthy individuals and those with type 2 diabetes (Zhao, Qinpei, et al. “Chinese diabetes datasets for data-driven machine learning.” Scientific Data 10.1 (2023): 35.). The main publicly available datasets with relevant clinical characteristics have already been analyzed in this study using unbiased approaches.

However, we fully agree with the reviewer that expanding the geographic and ethnic scope, including long-term follow-up data, and validation in different clinical settings would further strengthen the robustness and generalizability of our findings. To address this, we conducted a large follow-up study of over 8,000 individuals with two years of follow-up (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

Regarding the validation considerations, we will add the following sentences to the Discussion section:

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed to improve the predictive utility and generalizability of our findings.

Although our LASSO and factor analysis indicated that CGM-derived measures were strong predictors of %NC, this does not mean that other clinical parameters, such as lipids and blood pressure, are irrelevant in T2DM complications. Our study specifically focused on characterizing glucose dynamics, and we analyzed individuals with well-controlled serum cholesterol and blood pressure to reduce confounding effects. While we anticipate that inclusion of a more diverse population would not alter our primary findings regarding glucose dynamics, it is likely that a broader data set would reveal additional predictive contributions from lipid and blood pressure parameters.

In terms of elucidation of physical mechanisms, the study is not sufficient to elucidate the mechanisms linking autocorrelation and clinical outcomes or to verify them at the cellular or molecular level.

We appreciate the reviewer’s point regarding the need for further elucidation of the physical mechanisms linking glucose autocorrelation to clinical outcomes. We fully agree with the reviewer that the detailed molecular and cellular mechanisms underlying this relationship are not yet fully understood, as noted in our Discussion section.

However, we would like to emphasize the theoretical basis that supports the clinical relevance of autocorrelation. Our results show that glucose profiles with identical mean and variability can exhibit different autocorrelation patterns, highlighting that conventional measures such as mean or variance alone may not fully capture inter-individual metabolic differences. Incorporating autocorrelation analysis provides a more comprehensive characterization of metabolic states. Consequently, incorporating autocorrelation measures alongside traditional diabetes diagnostic criteria - such as fasting glucose, HbA1c and PG120, which primarily reflect only the “mean” component - can improve predictive accuracy for various clinical outcomes. While further research at the cellular and molecular level is needed to fully validate these findings, it is important to note that the primary goal of this study was to analyze the characteristics of glucose dynamics and gain new insights into metabolism, rather than to perform molecular biology experiments.

Furthermore, our previous research has shown that glucose autocorrelation reflects changes in insulin clearance (Sugimoto, Hikaru, et al. “Improved Detection of Decreased Glucose Handling Capacities via Novel Continuous Glucose Monitoring-Derived Indices: AC_Mean and AC_Var.” medRxiv (2023): 2023-09.). The relationship between insulin clearance and cardiovascular disease has been well documented (Randrianarisoa, Elko, et al. “Reduced insulin clearance is linked to subclinical atherosclerosis in individuals at risk for type 2 diabetes mellitus.” Scientific reports 10.1 (2020): 22453.), and the mechanisms described in this prior work may potentially explain the association between glucose autocorrelation and clinical outcomes observed in the present study.

Rather than a limitation, we view these currently unexplored associations as an opportunity for further research. The identification of autocorrelation as a key glycemic feature introduces a new dimension to metabolic regulation that could serve as the basis for future investigations exploring the molecular mechanisms underlying these patterns.

While we agree that further research at the cellular and molecular level is needed to fully validate these findings, we believe that our study provides a strong theoretical framework to support the clinical utility of autocorrelation analysis in glucose monitoring, and that this could serve as the basis for future investigations exploring the molecular mechanisms underlying these autocorrelation patterns, which adds to the broad interest of this study. Regarding the physical mechanisms linking autocorrelation and clinical outcomes, we will add the following sentences in the Discussion section:

This study also provided evidence that autocorrelation can vary independently from the mean and variance components using simulated data. In addition, simulated glucose dynamics indicated that even individuals with high AC_Var did not necessarily have high maximum and minimum blood glucose levels. This study also indicated that these three components qualitatively corresponded to the four distinct glucose patterns observed after glucose administration, which were identified in a previous study (Hulman et al., 2018). Thus, the inclusion of autocorrelation in addition to mean and variance may improve the characterization of inter-individual differences in glucose regulation and improve the predictive accuracy of various clinical outcomes.

Despite increasing evidence linking glycemic variability to oxidative stress and endothelial dysfunction in T2DM complications (Ceriello et al., 2008; Monnier et al., 2008), the biological mechanisms underlying the independent predictive value of autocorrelation remain to be elucidated. Our previous work has shown that glucose autocorrelation is influenced by insulin clearance (Sugimoto et al., 2023), a process known to be associated with cardiovascular disease risk (Randrianarisoa et al., 2020). Therefore, the molecular pathways linking glucose autocorrelation to cardiovascular disease may share common mechanisms with those linking insulin clearance to cardiovascular disease. Although previous studies have primarily focused on investigating the molecular mechanisms associated with mean glucose levels and glycemic variability, our findings open new avenues for exploring the molecular basis of glucose autocorrelation, potentially revealing novel therapeutic targets for preventing diabetic complications.

Reviewer #2 (Public review):

Sugimoto et al. explore the relationship between glucose dynamics - specifically value, variability, and autocorrelation - and coronary plaque vulnerability in patients with varying glucose tolerance levels. The study identifies three independent predictive factors for %NC and emphasizes the use of continuous glucose monitoring (CGM)-derived indices for coronary artery disease (CAD) risk assessment. By employing robust statistical methods and validating findings across datasets from Japan, America, and China, the authors highlight the limitations of conventional markers while proposing CGM as a novel approach for risk prediction. The study has the potential to reshape CAD risk assessment by emphasizing CGM-derived indices, aligning well with personalized medicine trends.

Strengths:

(1) The introduction of autocorrelation as a predictive factor for plaque vulnerability adds a novel dimension to glucose dynamic analysis.

(2) Inclusion of datasets from diverse regions enhances generalizability.

(3) The use of a well-characterized cohort with controlled cholesterol and blood pressure levels strengthens the findings.

(4) The focus on CGM-derived indices aligns with personalized medicine trends, showcasing the potential for CAD risk stratification.

We appreciate reviewer #2 for the positive assessment and for the valuable and constructive comments on our manuscript.

Weaknesses:

(1) The link between autocorrelation and plaque vulnerability remains speculative without a proposed biological explanation.

We appreciate the reviewer’s point about the need for a clearer biological explanation linking glucose autocorrelation to plaque vulnerability. We fully agree with the reviewer that the detailed biological mechanisms underlying this relationship are not yet fully understood, as noted in our Discussion section.

However, we would like to emphasize the theoretical basis that supports the clinical relevance of autocorrelation. Our results show that glucose profiles with identical mean and variability can exhibit different autocorrelation patterns, highlighting that conventional measures such as mean or variance alone may not fully capture inter-individual metabolic differences. Incorporating autocorrelation analysis provides a more comprehensive characterization of metabolic states. Consequently, incorporating autocorrelation measures alongside traditional diabetes diagnostic criteria - such as fasting glucose, HbA1c and PG120, which primarily reflect only the “mean” component - can improve predictive accuracy for various clinical outcomes.

Furthermore, our previous research has shown that glucose autocorrelation reflects changes in insulin clearance (Sugimoto, Hikaru, et al. “Improved Detection of Decreased Glucose Handling Capacities via Novel Continuous Glucose Monitoring-Derived Indices: AC_Mean and AC_Var.” medRxiv (2023): 2023-09.). The relationship between insulin clearance and cardiovascular disease has been well documented (Randrianarisoa, Elko, et al. “Reduced insulin clearance is linked to subclinical atherosclerosis in individuals at risk for type 2 diabetes mellitus.” Scientific reports 10.1 (2020): 22453.), and the mechanisms described in this prior work may potentially explain the association between glucose autocorrelation and clinical outcomes observed in the present study.

Rather than a limitation, we view these currently unexplored associations as an opportunity for further research. The identification of autocorrelation as a key glycemic feature introduces a new dimension to metabolic regulation that could serve as the basis for future investigations exploring the molecular mechanisms underlying these patterns.

While we agree that further research at the cellular and molecular level is needed to fully validate these findings, we believe that our study provides a strong theoretical framework to support the clinical utility of autocorrelation analysis in glucose monitoring, and that this could serve as the basis for future investigations exploring the molecular mechanisms underlying these autocorrelation patterns, which adds to the broad interest of this study. Regarding the physical mechanisms linking autocorrelation and clinical outcomes, we will add the following sentences in the Discussion section:

This study also provided evidence that autocorrelation can vary independently from the mean and variance components using simulated data. In addition, simulated glucose dynamics indicated that even individuals with high AC_Var did not necessarily have high maximum and minimum blood glucose levels. This study also indicated that these three components qualitatively corresponded to the four distinct glucose patterns observed after glucose administration, which were identified in a previous study (Hulman et al., 2018). Thus, the inclusion of autocorrelation in addition to mean and variance may improve the characterization of inter-individual differences in glucose regulation and improve the predictive accuracy of various clinical outcomes.

Despite increasing evidence linking glycemic variability to oxidative stress and endothelial dysfunction in T2DM complications (Ceriello et al., 2008; Monnier et al., 2008), the biological mechanisms underlying the independent predictive value of autocorrelation remain to be elucidated. Our previous work has shown that glucose autocorrelation is influenced by insulin clearance (Sugimoto et al., 2023), a process known to be associated with cardiovascular disease risk (Randrianarisoa et al., 2020). Therefore, the molecular pathways linking glucose autocorrelation to cardiovascular disease may share common mechanisms with those linking insulin clearance to cardiovascular disease. Although previous studies have primarily focused on investigating the molecular mechanisms associated with mean glucose levels and glycemic variability, our findings open new avenues for exploring the molecular basis of glucose autocorrelation, potentially revealing novel therapeutic targets for preventing diabetic complications.

(2) The relatively small sample size (n=270) limits statistical power, especially when stratified by glucose tolerance levels.

We appreciate the reviewer’s concern regarding sample size and its potential impact on statistical power, especially when stratified by glucose tolerance level. We fully agree that a larger sample size would increase statistical power, especially for subgroup analyses.

We would like to clarify several points regarding the statistical power and validation of our findings. Our sample size determination followed established methodological frameworks, including the guidelines outlined by Muyembe Asenahabi, Bostely, and Peters Anselemo Ikoha. “Scientific research sample size determination.” (2023). These guidelines balance the risks of inadequate sample size with the challenges of unnecessarily large samples. For our primary analysis examining the correlation between CGM-derived measures and %NC, power calculations (a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4) indicated that a minimum of 47 participants was required. Our sample size of 53 exceeded this threshold and allowed us to detect statistically significant correlations, as described in the Methods section. Moreover, to provide transparency about the precision of our estimates, we have included confidence intervals for all coefficients.

Furthermore, our sample size aligns with previous studies investigating the associations between glucose profiles and clinical parameters, including Torimoto, Keiichi, et al. “Relationship between fluctuations in glucose levels measured by continuous glucose monitoring and vascular endothelial dysfunction in type 2 diabetes mellitus.” Cardiovascular Diabetology 12 (2013): 1-7. (n=57), Hall, Heather, et al. “Glucotypes reveal new patterns of glucose dysregulation.” PLoS biology 16.7 (2018): e2005143. (n=57), and Metwally, Ahmed A., et al. “Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning.” Nature Biomedical Engineering (2024): 1-18. (n=32).

Regarding the classification of glucose dynamics components, we have conducted additional validation across diverse populations including 64 Japanese, 53 American, and 100 Chinese individuals. These validation efforts have consistently supported our identification of three independent glucose dynamics components.

However, we acknowledge the importance of further validation on a larger scale. To address this, we conducted a large follow-up study of over 8,000 individuals with two years of follow-up (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

To address the sample size considerations, we will add the following sentences in the Discussion section:

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed to improve the predictive utility and generalizability of our findings.

(3) Strict participant selection criteria may reduce applicability to broader populations.

We appreciate the reviewer’s comment regarding the potential impact of strict participant selection criteria on the broader applicability of our findings. We acknowledge that extending validation to more diverse populations would improve the generalizability of our findings.

Our study includes validation cohorts from diverse populations, including 64 Japanese, 53 American and 100 Chinese individuals. These cohorts include a wide range of metabolic states, from healthy individuals to those with diabetes, ensuring validation across different clinical conditions. However, we acknowledge that further validation in additional populations and clinical settings would strengthen our conclusions. To address this, we conducted a large follow-up study of over 8,000 individuals (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

We will add the following text to the Discussion section to address these considerations:

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed to improve the predictive utility and generalizability of our findings.

Although our LASSO and factor analysis indicated that CGM-derived measures were strong predictors of %NC, this does not mean that other clinical parameters, such as lipids and blood pressure, are irrelevant in T2DM complications. Our study specifically focused on characterizing glucose dynamics, and we analyzed individuals with well-controlled serum cholesterol and blood pressure to reduce confounding effects. While we anticipate that inclusion of a more diverse population would not alter our primary findings regarding glucose dynamics, it is likely that a broader data set would reveal additional predictive contributions from lipid and blood pressure parameters.

(4) CGM-derived indices like AC_Var and ADRR may be too complex for routine clinical use without simplified models or guidelines.

We appreciate the reviewer’s concern about the complexity of CGM-derived indices such as AC_Var and ADRR for routine clinical use. We acknowledge that for these indices to be of practical use, they must be both interpretable and easily accessible to healthcare providers.

To address this concern, we have developed an easy-to-use web application that automatically calculates these measures, including AC_Var, mean glucose levels, and glucose variability. This tool eliminates the need for manual calculations, making these indices more practical for clinical implementation.

Regarding interpretability, we acknowledge that establishing specific clinical guidelines would enhance the practical utility of these measures. For example, defining a cut-off value for AC_Var above which the risk of diabetes complications increases significantly would provide clearer clinical guidance. However, given our current sample size limitations and our predefined objective of investigating correlations among indices, we have taken a conservative approach by focusing on the correlation between AC_Var and %NC rather than establishing definitive cutoffs. This approach intentionally avoids problematic statistical practices like p-hacking. It is not realistic to expect a single study to accomplish everything from proposing a new concept to conducting large-scale clinical trials to establishing clinical guidelines. Establishing clinical guidelines typically requires the accumulation of multiple studies over many years. Recognizing this reality, we have been careful in our manuscript to make modest claims about the discovery of new “correlations” rather than exaggerated claims about immediate routine clinical use.

To address this limitation, we conducted a large follow-up study of over 8,000 individuals in the next study (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which proposed clinically relevant cutoffs and reference ranges for AC_Var and other CGM-derived indices. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, by integrating automated calculation tools with clear clinical thresholds, we expect to make these measures more accessible for clinical use.

We will add the following text to the Discussion section to address these considerations:

While CGM-derived indices such as AC_Var and ADRR hold promise for CAD risk assessment, their complexity may present challenges for routine clinical implementation. To improve usability, we have developed a web-based calculator that automates these calculations. However, the definition of clinically relevant thresholds and reference ranges requires further validation in larger cohorts.

(5) The study does not compare CGM-derived indices to existing advanced CAD risk models, limiting the ability to assess their true predictive superiority.

We appreciate the reviewer’s comment regarding the comparison of CGM-derived indices with existing CAD risk models. Given that our study population consisted of individuals with well-controlled total cholesterol and blood pressure levels, a direct comparison with the Framingham Risk Score for Hard Coronary Heart Disease (Wilson, Peter WF, et al. “Prediction of coronary heart disease using risk factor categories.” Circulation 97.18 (1998): 1837-1847.) may introduce inherent bias, as these factors are key components of the score.

Nevertheless, to further assess the predictive value of the CGM-derived indices, we performed additional analyses using linear regression to predict %NC. Using the Framingham Risk Score, we obtained an R² of 0.04 and an Akaike Information Criterion (AIC) of 330. In contrast, our proposed model incorporating the three glycemic parameters - CGM_Mean, CGM_Std, and AC_Var - achieved a significantly improved R² of 0.36 and a lower AIC of 321, indicating superior predictive accuracy.

We will add the following text to the Result section:

The regression model including CGM_Mean, CGM_Std and AC_Var to predict %NC achieved an R² of 0.36 and an Akaike Information Criterion (AIC) of 321. Each of these indices showed statistically significant independent positive correlations with %NC. In contrast, the model using conventional glycemic markers (FBG, HbA1c, and PG120) yielded an R² of only 0.05 and an AIC of 340. Similarly, the model using the Framingham Risk Score for Hard Coronary Heart Disease (Wilson et al., 1998) showed limited predictive value, with an R² of 0.04 and an AIC of 330.

(6) Varying CGM sampling intervals (5-minute vs. 15-minute) were not thoroughly analyzed for impact on results.

We appreciate the reviewer’s comment regarding the potential impact of different CGM sampling intervals on our results. To assess the robustness of our findings across different sampling frequencies, we performed a down sampling analysis by converting our 5-minute interval data to 15-minute intervals. The AC_Var value calculated from 15-minute intervals was significantly correlated with that calculated from 5-minute intervals (R = 0.99, 95% CI: 0.97-1.00). Furthermore, the regression model using CGM_Mean, CGM_Std, and AC_Var from 15-minute intervals to predict %NC achieved an R² of 0.36 and an AIC of 321, identical to the model using 5-minute intervals. These results indicate that our results are robust to variations in CGM sampling frequency.

We will add this analysis to the Result section:

The AC_Var value calculated from 15-minute intervals was significantly correlated with that calculated from 5-minute intervals (R = 0.99, 95% CI: 0.97-1.00). Consequently, the regression model including CGM_Mean, CGM_Std and AC_Var from 15-minute intervals to predict %NC achieved an R² of 0.36 and an AIC of 321.

Reviewer #3 (Public review):

Summary:

This is a retrospective analysis of 53 individuals over 26 features (12 clinical phenotypes, 12 CGM features, and 2 autocorrelation features) to examine which features were most informative in predicting percent necrotic core (%NC) as a parameter for coronary plaque vulnerability. Multiple regression analysis demonstrated a better ability to predict %NC from 3 selected CGM-derived features than 3 selected clinical phenotypes. LASSO regularization and partial least squares (PLS) with VIP scores were used to identify 4 CGM features that most contribute to the precision of %NC. Using factor analysis they identify 3 components that have CGM-related features: value (relating to the value of blood glucose), variability (relating to glucose variability), and autocorrelation (composed of the two autocorrelation features). These three groupings appeared in the 3 validation cohorts and when performing hierarchical clustering. To demonstrate how these three features change, a simulation was created to allow the user to examine these features under different conditions.

We appreciate reviewer #3 for the valuable and constructive comments on our manuscript.

Review:

The goal of this study was to identify CGM features that relate to %NC. Through multiple feature selection methods, they arrive at 3 components: value, variability, and autocorrelation. While the feature list is highly correlated, the authors take steps to ensure feature selection is robust. There is a lack of clarity of what each component (value, variability, and autocorrelation) includes as while similar CGM indices fall within each component, there appear to be some indices that appear as relevant to value in one dataset and to variability in the validation.

We appreciate the reviewer’s comment regarding the classification of CGM-derived measures into the three components: value, variability, and autocorrelation. As the reviewer correctly points out, some measures may load differently between the value and variability components in different datasets. However, we believe that this variability reflects the inherent mathematical properties of these measures rather than a limitation of our study.

For example, the HBGI clusters differently across datasets due to its dependence on the number of glucose readings above a threshold. In populations where mean glucose levels are predominantly below this threshold, the HBGI is more sensitive to glucose variability (Fig. S7A). Conversely, in populations with a wider range of mean glucose levels, HBGI correlates more strongly with mean glucose levels (Fig. 3A). This context-dependent behavior is expected given the mathematical properties of these measures and does not indicate an inconsistency in our classification approach.

Importantly, our main findings remain robust: CGM-derived measures systematically fall into three components-value, variability, and autocorrelation. Traditional CGM-derived measures primarily reflect either value or variability, and this categorization is consistently observed across datasets. While specific indices such as HBGI may shift classification depending on population characteristics, the overall structure of CGM data remains stable.

To address these considerations, we will add the following text to the Discussion section:

Some indices, such as HBGI, showed variation in classification across datasets, with some populations showing higher factor loadings in the “value” component and others in the “variability” component. This variation occurs because HBGI calculations depend on the number of glucose readings above a threshold. In populations where mean glucose levels are predominantly below this threshold, the HBGI is more sensitive to glucose variability (Fig. S7A). Conversely, in populations with a wider range of mean glucose levels, the HBGI correlates more strongly with mean glucose levels (Fig. 3A). Despite these differences, our validation analyses confirm that CGM-derived indices consistently cluster into three components: value, variability, and autocorrelation.

We are sceptical about statements of significance without documentation of p-values.

We appreciate the reviewer’s concern regarding statistical significance and the documentation of p values.

First, given the multiple comparisons in our study, we used q values rather than p values, as shown in Figure S1. Q values provide a more rigorous statistical framework for controlling the false discovery rate in multiple testing scenarios, thereby reducing the likelihood of false positives.

Second, our statistical reporting follows established guidelines, including those of the New England Journal of Medicine (Harrington, David, et al. “New guidelines for statistical reporting in the journal.” New England Journal of Medicine 381.3 (2019): 285-286.), which recommend that “reporting of exploratory end points should be limited to point estimates of effects with 95% confidence intervals” and that “replace p values with estimates of effects or association and 95% confidence intervals”. According to these guidelines, p values should not be reported in this type of study. We determined significance based on whether these 95% confidence intervals excluded zero - a statistical method for determining whether an association is significantly different from zero (Tan, Sze Huey, and Say Beng Tan. "The correct interpretation of confidence intervals." Proceedings of Singapore Healthcare 19.3 (2010): 276-278.).

For the sake of transparency, we provide p values for readers who may be interested, although we emphasize that they should not be the basis for interpretation, as discussed in the referenced guidelines. Specifically, in Figure 1, the p values for CGM_Mean, CGM_Std, and AC_Var were 0.02, 0.02, and <0.01, respectively, while those for FBG, HbA1c, and PG120 were 0.83, 0.91, and 0.25, respectively. In Figure 3C, the p values for factors 1–5 were 0.03, 0.03, 0.03, 0.24, and 0.87, respectively, and in Figure S10B, the p values for factors 1–3 were <0.01, <0.01, and 0.20, respectively.

We appreciate the opportunity to clarify our statistical methodology and are happy to provide additional details if needed.

While hesitations remain, the ability of these authors to find groupings of these many CGM metrics in relation to %NC is of interest. The believability of the associations is impeded by an obtuse presentation of the results with core data (i.e. correlation plots between CGM metrics and %NC) buried in the supplement while main figures contain plots of numerical estimates from models which would be more usefully presented in supplementary tables.

We appreciate the reviewer’s comment regarding the presentation of our results and recognize the importance of ensuring clarity and accessibility of the core data.

The central finding of our study is twofold: first, that the numerous CGM-derived measures can be systematically classified into three distinct components-mean, variance, and autocorrelation-and second, that each of these components is independently associated with %NC. This insight cannot be derived simply from examining scatter plots of individual correlations, which are provided in the Supplementary Figures. Instead, it emerges from our statistical analyses in the main figures, including multiple regression models that reveal the independent contributions of these components to %NC.

However, we acknowledge the reviewer’s concern regarding the accessibility of key data. To improve clarity, we will move several scatter plots from the Supplementary Figures to the main figures to allow readers to more directly visualize the relationships between CGM-derived measures and %NC. We believe this revision will improve the transparency and readability of our results while maintaining the rigor of our analytical approach.

Given the small sample size in the primary analysis, there is a lot of modeling done with parameters estimated where simpler measures would serve and be more convincing as they require less data manipulation. A major example of this is that the pairwise correlation/covariance between CGM_mean, CGM_std, and AC_var is not shown and would be much more compelling in the claim that these are independent factors.

We appreciate the reviewer’s feedback on our statistical analysis and data presentation. The correlations between CGM_Mean, CGM_Std, and AC_Var are documented in Figure S1B. However, to improve accessibility and clarity, we will move these correlation analyses to the main figures. Regarding our modeling approach, we chose LASSO and PLS methods because they are well-established techniques that are particularly suited to scenarios with many input variables and a relatively small sample size. These methods have been extensively validated in the literature as robust approaches for variable selection under such conditions (Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J R Stat Soc 58:267–288. Wold S, Sjöström M, Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics Intellig Lab Syst 58:109–130. Pei X, Qi D, Liu J, Si H, Huang S, Zou S, Lu D, Li Z. 2023. Screening marker genes of type 2 diabetes mellitus in mouse lacrimal gland by LASSO regression. Sci Rep 13:6862. Wang C, Kong H, Guan Y, Yang J, Gu J, Yang S, Xu G. 2005. Plasma phospholipid metabolic profiling and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis. Anal Chem 77:4108–4116.).

Lack of methodological detail is another challenge. For example, the time period of CGM metrics or CGM placement in the primary study in relation to the IVUS-derived measurements of coronary plaques is unclear. Are they temporally distant or proximal/ concurrent with the PCI?

We appreciate the reviewer’s important question regarding the temporal relationship between CGM measurements and IVUS-derived plaque assessments. As described in our previous work (Otowa‐Suematsu, Natsu, et al. “Comparison of the relationship between multiple parameters of glycemic variability and coronary plaque vulnerability assessed by virtual histology–intravascular ultrasound.” Journal of Diabetes Investigation 9.3 (2018): 610-615.), all individuals underwent continuous glucose monitoring for at least three consecutive days within the seven-day period prior to the PCI procedure. To improve clarity for readers, we will include this methodological detail in the revised manuscript.

A patient undergoing PCI for coronary intervention would be expected to have physiological and iatrogenic glycemic disturbances that do not reflect their baseline state. This is not considered or discussed.

We appreciate the reviewer’s concern regarding potential glycemic disturbances associated with PCI. As described in our previous work (Otowa‐Suematsu, Natsu, et al. “Comparison of the relationship between multiple parameters of glycemic variability and coronary plaque vulnerability assessed by virtual histology–intravascular ultrasound.” Journal of Diabetes Investigation 9.3 (2018): 610-615.), all CGM measurements were performed before the PCI procedure. This temporal separation ensures that the glycemic patterns analyzed in our study reflect the baseline metabolic state of the patients, rather than any physiological or iatrogenic effects of PCI. To avoid any misunderstanding, we will clarify this temporal relationship in the revised manuscript.

The attempts at validation in external cohorts, Japanese, American, and Chinese are very poorly detailed. We could only find even an attempt to examine cardiovascular parameters in the Chinese data set but the outcome variables are unspecified with regard to what macrovascular events are included, their temporal relation to the CGM metrics, etc. Notably macrovascular event diagnoses are very different from the coronary plaque necrosis quantification. This could be a source of strength in the findings if carefully investigated and detailed but due to the lack of detail seems like an apples-to-oranges comparison.

We appreciate the reviewer’s comment regarding the validation cohorts and the need for greater clarity, particularly in the Chinese dataset. We acknowledge that our initial description lacked sufficient methodological detail, and we will expand the Methods section to provide a more comprehensive explanation.

For the Chinese dataset, the data collection protocol was previously documented (Zhao, Qinpei, et al. “Chinese diabetes datasets for data-driven machine learning.” Scientific Data 10.1 (2023): 35.). Briefly, trained research staff used standardized questionnaires to collect demographic and clinical information, including diabetes diagnosis, treatment history, comorbidities, and medication use. Physical examinations included anthropometric measurements, and body mass index was calculated using standard protocols. CGM monitoring was performed using the FreeStyle Libre H device (Abbott Diabetes Care, UK), which records interstitial glucose levels at 15-minute intervals for up to 14 days. Laboratory measurements, including metabolic panels, lipid profiles, and renal function tests, were obtained within six months of CGM placement. While previous studies have linked necrotic core to macrovascular events (Xie, Yong, et al. “Clinical outcome of nonculprit plaque ruptures in patients with acute coronary syndrome in the PROSPECT study.” JACC: Cardiovascular Imaging 7.4 (2014): 397-405.), we acknowledge the limitations of the cardiovascular outcomes in the Chinese data set. These outcomes were extracted from medical records rather than standardized diagnostic procedures or imaging studies. To address these concerns, we will expand the Discussion section to clarify the differences in outcome definitions and methodological approaches between the data sets.

Finally, the simulations at the end are not relevant to the main claims of the paper and we would recommend removing them for the coherence of this manuscript.

We appreciate the reviewer’s feedback regarding the relevance of the simulation component of our manuscript. The primary contribution of our study goes beyond demonstrating correlations between CGM-derived measures and %NC; it highlights three fundamental components of glycemic patterns-mean, variability, and autocorrelation-and their independent relationships with coronary plaque characteristics.

The simulations are included to illustrate how glycemic patterns with identical means and variability can have different autocorrelation structures. Because temporal autocorrelation can be conceptually difficult to interpret, these visualizations were intended to provide intuitive examples for the readers.

However, we recognize the reviewer’s concern about the coherence of the manuscript. In response, we will streamline the simulation section by removing technical simulations that do not directly support our primary conclusions, while retaining only those that enhance understanding of the three glycemic components.

DOI: 10.1016/j.isci.2025.112138

Resource: (Cell Signaling Technology Cat# 9721, RRID:AB_330951)

Curator: @scibot

SciCrunch record: RRID:AB_330951

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Document de Synthèse: Importation et Exportation de Données (CSV et Excel) dans Tabletop Creator

Introduction

Ce document résume les principaux thèmes et informations clés concernant l'importation et l'exportation de données en utilisant les formats CSV et Excel dans Tabletop Creator. Il met en évidence les options de configuration, de filtrage, de formatage des données et de cartographie (mapping), ainsi que les aspects à considérer pour assurer une importation/exportation réussie.

1. Localisation des Options d'Import/Export

CSV: Les options d'importation et d'exportation CSV se trouvent dans la zone "Set/Item Properties", accessible via le menu "More Options". Excel: Les options d'importation et d'exportation Excel se trouvent dans l'écran "Components". 2. Exportation de Données

2.1. Options de Contenu des Données

Les deux formats offrent des options similaires pour sélectionner les propriétés à inclure dans l'export:

Quantité de l'élément ("Item amount"). Valeur principale du panneau ("panel main value"). Visibilité du panneau ("panel visible property"). Couleur du panneau ("panel color"). Couleur de fond/remplissage du panneau ("panel background / fill color"). Propriétés avancées du panneau ("panel advanced properties"). 2.2. Options de Filtrage des Données

Les deux formats permettent de filtrer les données exportées :

Inclusion des valeurs du blueprint ("Include blueprint values"). Inclusion des valeurs par défaut du blueprint ("Include blueprint default property values"). Inclusion des panneaux non éditables du blueprint ("Include non-editable blueprint panels"). Inclusion des détails non référencés par le blueprint ("Include details not referenced by the blueprint"). Suppression des propriétés de détail de colonne inutilisées ("Skip unused column detail properties"), pour réduire la redondance. 2.3. Options de Formatage des Données

Les options de formatage des données comprennent :

Inclusion des noms de détails dans la première ligne (en-tête) : "Include detail names in the first row" Tri des colonnes par nom de détail : "Sort columns by detail name" Forcer le panneau principal à être trié comme première colonne : "Force main panel to be sorted as the first column" (Excel seulement) Formatage des cellules NULL avec une couleur gris clair : "Format NULL cells with light gray color" 3. Importation de Données

3.1. Configuration Générale

Les deux formats nécessitent une configuration avant l'importation :

Indiquer si la première ligne contient les noms de colonnes ("First row contains the column names"). Cette information est essentielle pour le fonctionnement de la fonctionnalité d'auto-mapping. Choisir si l'on doit supprimer tous les éléments existants avant l'importation (mode remplacement) : "Delete all Items before importing (replace mode)". Définir si les éléments existants doivent être mis à jour en utilisant la valeur du panneau principal du blueprint comme identifiant d'élément : "Update existing items in the set using the blueprint main panel as item ID". Spécifier si toutes les valeurs des propriétés doivent être écrasées, y compris les valeurs NULL du fichier (CSV ou Excel) : "Override all property values, including null values from the CSV" / "Override all property values including null values from the Excel". 3.2. Auto-Mapping

Tabletop Creator offre une fonctionnalité d'auto-mapping qui tente de faire correspondre automatiquement les colonnes du fichier importé aux données du Set. Pour que l'auto-mapping fonctionne correctement :

CSV: Les noms des colonnes doivent suivre un certain format ( [Item Amount], detail_name, detail_name:property_name). Excel: Les feuilles Excel doivent être nommées selon le format set_name@blueprint_name, et les colonnes doivent respecter les mêmes formats que pour le CSV. Il est possible de réinitialiser l'auto-mapping si nécessaire.

3.3. Data Mapping Manuel

L'utilisateur a la possibilité d'ajuster manuellement les correspondances pour chaque colonne, en spécifiant le panneau cible et la propriété concernée. Des icônes d'avertissement ⚠️ signalent les erreurs potentielles.

Exemples d'avertissements courants :

CSV:Nombre irrégulier de colonnes. Colonnes non mappées. Noms de détails inexistants (ils seront créés). Propriétés non utilisées (elles seront importées). Mappage dupliqué. Excel:Feuille Excel non mappée à aucun set. Nom de Set inexistant (sera créé). Les mêmes erreurs que CSV. 4. Format de Données Compris

CSV: "Comma-Separated Values". Chaque ligne représente un enregistrement, et les valeurs sont séparées par des virgules. Les caractères spéciaux doivent être entourés de guillemets doubles. Excel: Format XLSX (OOXML). Les formats plus anciens comme XLS ne sont pas pris en charge. An important consideration is that when a cell in Excel contains no data (an empty string), it is treated as NULL. If you need to override a property in your item with an empty string as its value, the cell must contain the Unicode character U+200B (Zero width space). Conclusion

L'importation et l'exportation de données via CSV et Excel dans Tabletop Creator offrent une grande flexibilité. Il est crucial de comprendre les options de configuration, de filtrage et de mapping pour garantir l'intégrité des données. La résolution des avertissements avant l'importation est fortement conseillée.

synthèse basé sur les sources que vous avez fournies, rédigé en français.

Document de Synthèse: Importation et Exportation de Données (CSV et Excel) dans Tabletop Creator

Introduction

Ce document résume les principaux thèmes et informations clés concernant l'importation et l'exportation de données en utilisant les formats CSV et Excel dans Tabletop Creator. Il met en évidence les options de configuration, de filtrage, de formatage des données et de cartographie (mapping), ainsi que les aspects à considérer pour assurer une importation/exportation réussie.

1. Localisation des Options d'Import/Export

CSV: Les options d'importation et d'exportation CSV se trouvent dans la zone "Set/Item Properties", accessible via le menu "More Options". Excel: Les options d'importation et d'exportation Excel se trouvent dans l'écran "Components". 2. Exportation de Données

2.1. Options de Contenu des Données

Les deux formats offrent des options similaires pour sélectionner les propriétés à inclure dans l'export:

Quantité de l'élément ("Item amount"). Valeur principale du panneau ("panel main value"). Visibilité du panneau ("panel visible property"). Couleur du panneau ("panel color"). Couleur de fond/remplissage du panneau ("panel background / fill color"). Propriétés avancées du panneau ("panel advanced properties"). 2.2. Options de Filtrage des Données

Les deux formats permettent de filtrer les données exportées :

Inclusion des valeurs du blueprint ("Include blueprint values"). Inclusion des valeurs par défaut du blueprint ("Include blueprint default property values"). Inclusion des panneaux non éditables du blueprint ("Include non-editable blueprint panels"). Inclusion des détails non référencés par le blueprint ("Include details not referenced by the blueprint"). Suppression des propriétés de détail de colonne inutilisées ("Skip unused column detail properties"), pour réduire la redondance. 2.3. Options de Formatage des Données

Les options de formatage des données comprennent :

Inclusion des noms de détails dans la première ligne (en-tête) : "Include detail names in the first row" Tri des colonnes par nom de détail : "Sort columns by detail name" Forcer le panneau principal à être trié comme première colonne : "Force main panel to be sorted as the first column" (Excel seulement) Formatage des cellules NULL avec une couleur gris clair : "Format NULL cells with light gray color" 3. Importation de Données

3.1. Configuration Générale

Les deux formats nécessitent une configuration avant l'importation :

Indiquer si la première ligne contient les noms de colonnes ("First row contains the column names"). Cette information est essentielle pour le fonctionnement de la fonctionnalité d'auto-mapping. Choisir si l'on doit supprimer tous les éléments existants avant l'importation (mode remplacement) : "Delete all Items before importing (replace mode)". Définir si les éléments existants doivent être mis à jour en utilisant la valeur du panneau principal du blueprint comme identifiant d'élément : "Update existing items in the set using the blueprint main panel as item ID". Spécifier si toutes les valeurs des propriétés doivent être écrasées, y compris les valeurs NULL du fichier (CSV ou Excel) : "Override all property values, including null values from the CSV" / "Override all property values including null values from the Excel". 3.2. Auto-Mapping

Tabletop Creator offre une fonctionnalité d'auto-mapping qui tente de faire correspondre automatiquement les colonnes du fichier importé aux données du Set. Pour que l'auto-mapping fonctionne correctement :

CSV: Les noms des colonnes doivent suivre un certain format ( [Item Amount], detail_name, detail_name:property_name). Excel: Les feuilles Excel doivent être nommées selon le format set_name@blueprint_name, et les colonnes doivent respecter les mêmes formats que pour le CSV. Il est possible de réinitialiser l'auto-mapping si nécessaire.

3.3. Data Mapping Manuel

L'utilisateur a la possibilité d'ajuster manuellement les correspondances pour chaque colonne, en spécifiant le panneau cible et la propriété concernée. Des icônes d'avertissement ⚠️ signalent les erreurs potentielles.

Exemples d'avertissements courants :

CSV:Nombre irrégulier de colonnes. Colonnes non mappées. Noms de détails inexistants (ils seront créés). Propriétés non utilisées (elles seront importées). Mappage dupliqué. Excel:Feuille Excel non mappée à aucun set. Nom de Set inexistant (sera créé). Les mêmes erreurs que CSV. 4. Format de Données Compris

CSV: "Comma-Separated Values". Chaque ligne représente un enregistrement, et les valeurs sont séparées par des virgules. Les caractères spéciaux doivent être entourés de guillemets doubles. Excel: Format XLSX (OOXML). Les formats plus anciens comme XLS ne sont pas pris en charge. An important consideration is that when a cell in Excel contains no data (an empty string), it is treated as NULL. If you need to override a property in your item with an empty string as its value, the cell must contain the Unicode character U+200B (Zero width space). Conclusion

L'importation et l'exportation de données via CSV et Excel dans Tabletop Creator offrent une grande flexibilité. Il est crucial de comprendre les options de configuration, de filtrage et de mapping pour garantir l'intégrité des données. La résolution des avertissements avant l'importation est fortement conseillée.