Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study by Wang et al. identifies a new type of deacetylase, CobQ, in Aeromonas hydrophila. Notably, the identification of this deacetylase reveals a lack of homology with eukaryotic counterparts, thus underscoring its unique evolutionary trajectory within the bacterial domain.

Strengths:

The manuscript convincingly illustrates CobQ's deacetylase activity through robust in vitro experiments, establishing its distinctiveness from known prokaryotic deacetylases. Additionally, the authors elucidate CobQ's potential cooperation with other deacetylases in vivo to regulate bacterial cellular processes. Furthermore, the study highlights CobQ's significance in the regulation of acetylation within prokaryotic cells.

Weaknesses:

While the manuscript is generally well-structured, some clarification and some minor corrections are needed.

Reviewer #2 (Public Review):

In recent years, lots of researchers have tried to explore the existence of new acetyltransferase and deacetylase by using specific antibody enrichment technologies and high-resolution mass spectrometry. This study adds to this effort. The authors studied a novel Zn2+- and NAD+-independent KDAC protein, AhCobQ, in Aeromonas hydrophila. They studied the biological function of AhCobQ by using a biochemistry method and used MS identification technology to confirm it. The results extend our understanding of the regulatory mechanism of bacterial lysine acetylation modifications. However, I find their conclusion to be a little speculative, and unfortunately, it also doesn't totally support the conclusion that the authors provided. In addition, regarding the figure arrangement, lots of the supplementary figures are not mentioned, and tables are not all placed in context.

Major concerns:

- In the opinion of this reviewer, is a little arbitrary to come to the title "Aeromonas hydrophila CobQ is a new type of NAD+- and Zn2+-independent protein lysine deacetylase in prokaryotes." This should be modified to delete the "in the prokaryotes", unless the authors get new or more evidence in the other prokaryotes for the existence of the AhCobQ.

Thanks for your suggestions. " in the prokaryotes " has been deleted in the revised manuscript.

- I was confused about the arrangement of the supplementary results. There are no citations for Figures S9-S19.

Thank you very much for your suggestion. We have made revisions and highlighted in the undated manuscript.

- No data are included for Tables S1-S6.;

Dear reviewer, sorry to confuse you. We have included the Supplementary Tables in the undated manuscript.

- The load control is not all integrated. All of the load controls with whole PAGE gel or whole membrane western blot results should be provided. Without these whole results, it is not convincing to come to the conclusion that the authors have.

Dear reviewer, thanks for your suggestion. We have meticulously incorporated the complete PVDF membranes from our Western blot experiments into Supplementary Material 1. Furthermore, we have included the Coomassie Blue R-350 staining outcomes of these PVDF membranes, post-Western blot detection, as a loading control in accordance with the protocol outlined in the reference by Charlotte et al. (Journal of Proteome Research, 2011, 10:1416–1419).

- The materials & methods section should be thoroughly reviewed. It is unclear to me what exactly the authors are describing in the method. All the experimental designs and protocols should be described in detail, including growth conditions, assay conditions, purification conditions, etc.

Dear reviewer, thanks for your valuable comments. We have carefully reviewed the entire manuscript and made revisions, highlighted in red.

- Relevant information should be included about the experiments performed in the figure legends, such as experimental conditions, replicates, etc. Often it is not clear what was done based on the figure legend description.

Thank you very much for your suggestion. We have made revisions and highlighted in red.

Reviewer #3 (Public Review):

Summary:

This study reports on a novel NAD+ and Zn2+-independent protein lysine deacetylase (KDAC) in Aeromonas hydrophila, termed AhCobQ (AHA_1389). This protein is annotated as a CobQ/CobB/MinD/ParA family protein and does not show similarity with known NAD+-dependent or Zn2+-dependent KDACs. The authors show that AhCobQ has NAD+ and Zn2+-independent deacetylase activity with acetylated BSA by western blot and MS analyses. They also provide evidence that the 195-245 aa region of AhCobQ is responsible for the deacetylase activity, which is conserved in some marine prokaryotes and has no similarity with eukaryotic proteins. They identified target proteins of AhCobQ deacetylase by proteomic analysis and verified the deacetylase activity using site-specific acetyllysine-incorporated target proteins. Finally, they show that AhCobQ activates isocitrate dehydrogenase by deacetylation at K388.

Strengths:

The finding of a new type of KDAC has a valuable impact on the field of protein acetylation. The characters (NAD+ and Zn2+-independent deacetylase activity in an unknown domain) shown in this study are very unexpected.

Weaknesses:

(1) As the characters of AhCobQ are very unexpected, to convince readers, MSMS data would be needed to exactly detect deacetylation at the target site in deacetylase activity assays. The authors show the MSMS data in assays with acetylated BSA, but other assays only rely on western blot.

(2) They prepared site-specific Kac proteins and used them in deacetylase activity assays. The incorporation of acetyllysine at the target site needs to be confirmed by MSMS and shown as supplementary data.

(3) The authors imply that the 195-245 aa region of AhCobQ may represent a new domain responsible for deacetylase activity. The feature of the region would be of interest but is not sufficiently described in Figure 5. The amino acid sequence alignments with representative proteins with conserved residues would be informative. It would be also informative if the modeled structure predicted by AlphaFold is shown and the structural similarity with known deacetylases is discussed.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The protein molecules of AhCobB and AhCobQ are greater than 45 kDa. But the gene sequences don't seem to match. Please explain.

We are sorry to confuse you. The vector used for the purification of CobB and CobQ in the manuscript is pET-32a, which carries the TrxA fusion protein and is approximately 20kDa in size. Therefore, the final molecular weight of recombinant AhCobB and AhCobQ is 48.3(28.3+ ~20kDa) and 49.8 (29.8+ ~20kDa), respectively.

(2) Figure 7: The gels look very smeary. Please explain.

Dear esteemed reviewer, in our study, we have meticulously crafted recombinant site-specific Kac proteins utilizing an innovative two-plasmid system, grounded on the seminal work published in Nature Chemical Biology (2017, 13(12): 1253-1260), which introduced the genetic encoding of Nᵋ-acetyllysine into recombinant proteins. However, we have encountered a prevalent challenge—the occurrence of protein truncation due to premature translation termination at the reassigned codon. This phenomenon not only diminishes protein yields, as highlighted in ChemBioChem (2017, 18(20): 1973-1983), but also plagues many recombinant proteins with a troublesome backdrop in Western Blot (WB) outcomes.

Despite our rigorous approach, involving at least two independent repetitions for WB analysis of site-specific Kac proteins, yielding consistent results, we acknowledge that the overall quality of these WB assays remains suboptimal. This variability is inherently tied to the intrinsic properties of the target proteins themselves. Illustratively, the WB outcomes for proteins such as ENO and ICD exhibit notable differences in quality across biological replicates, emphasizing the complexity and nuances involved in this process.

Thus, while our methodology remains robust and reproducible, we are mindful of the limitations imposed by the nature of the proteins under investigation and strive to continually refine our approaches to mitigate these challenges.

(3) To ensure that the phenotype shown in Figure 1 is not due to polar effects, results of supplementing complementary strains should be provided.

Thank you for your suggestion. We have constructed a complement strain and tested the bacterial migration ability. As shown in the Figure S1, the complement strain does not affect the physiological phenotype mentioned above.

(4) The caption to Figure 8 includes * and *** to indicate significance levels, but only *** appears in the picture.

Thank you for your suggestion. It has been modified and highlighted in red.

(5) Has the mechanistic role of lysine 388 in ICD been characterized?

Thank you for your invaluable professional insights. Indeed, the acetylation sites of ICD have been established to exert a significant influence on its enzymatic activity. Sumana Venkat et al., in their seminal work published in the Journal of Molecular Biology (2018, 430(13): 1901-1911), convincingly demonstrated that the acetylation of specific lysine residues—K100, K230, K55, and K350—in ICD proteins from E. coli serves as a negative regulatory mechanism for enzyme activity. Intriguingly, the functional implications of the Kac modification on K387 (corresponding to the K388 site in ICD from A. hydrophila ATCC 7966, as featured in this manuscript) remain an uncharted territory.

Our experimental endeavors have illuminated that the K388 site of ICD in A. hydrophila holds the potential to modulate enzymatic activity and is under the regulatory influence of AhCobQ.

(6) The format of the references is not uniform enough, for example, some journal names are abbreviated, and some are not, please check and correct.

Thank you for your suggestion. It has been modified and highlighted in red.

(7) Page 23, line 13, gene not expressed in italics, please correct.

Thank you for your suggestion. It has been modified and highlighted in red.

(8) Figure S8 does not appear to match the gene size.

We are sorry to confuse you. The vector used for the purification of recombinant protein in the manuscript is pET-32a, which carries the TrxA fusion protein and is approximately 20kDa in size. Therefore, the final molecular weight of recombinant protein is 25.5(5.5+ ~20kDa).

(9) The format of the two figures in Figure S10 is not uniform.

Thank you for your suggestion. It has been modified and highlighted in red.

Reviewer #2 (Recommendations For The Authors):

Minor concerns:

L147, L177 - Please arrange the results as they are shown in the content sequentially. For example, rename Figure S2 with Figure S1.

Thank you for your suggestion. It has been modified and highlighted in red.

L174 Figure 2D - There is no big change in the acetylation between the wild type and ahcobQ mutant from Figure 2D, but the ahcobB mutant is.

I am extremely grateful for your insightful comment. As clearly depicted in the right panel of Figure 2D, the overall Kac protein levels in both the ahcobQ and ahcobB knockout strains exhibit a marked elevation compared to the wild-type strain, despite equivalent loading of total cellular proteins (the left panel of Figure 2D). Notably, this increase is particularly pronounced among proteins with a molecular weight below 35 kDa. We wholeheartedly concur with your perspective that the deletion of ahcobB leads to a more substantial enhancement in Kac protein levels, suggesting CobB may play a pivotal role in regulating a broader spectrum of acetylated proteins or Kac sites. This hypothesis is further strengthened by subsequent mass spectrometry analyses, which lend additional credence to our shared understanding.

L174-187, L795 - Please show the whole membrane (or PAGE gel) of the loading control of CobB, and CobQ, except for the Kac-BSA.

Dear esteemed reviewer, we have thoroughly revised our submission to include the full western blot (WB) membrane for all figures and supplementary figures within the updated Supplementary Material 1. Additionally, we would like to clarify a few crucial points to ensure transparency and accuracy.

Firstly, in Figure 2D, we present WB results solely pertaining to whole-cell samples from cobB or cobQ mutant strains. Consequently, these findings do not directly correlate with recombinant CobB or CobQ proteins.

Secondly, the objective of Figure 2 is to validate the lysine deacetylase activity of AhCobQ protein through a qualitative, rather than quantitative, experimental approach. Hence, the crucial loading control lies in the amount of Kac-BSA, rather than CobB or CobQ. Prior to conducting the in vitro deacetylase assay, we ensured equal protein concentrations of purified CobB or CobQ using BCA assay, adhering to the protocol's specified deacetylase-to-Kac-BSA loading ratio of 1:5. However, this ratio renders the deacetylase (CobB or CobQ) undetectable on Coomassie Blue R-350-stained blots or WB membranes (as detailed in the whole WB membrane in Supplementary Material 1).

To reinforce our observations, we reiterated the analysis of protein samples by subjecting them once again to SDS-PAGE, maintaining the same loading quantity as utilized in the preceding western blotting experiment shown in Figure 2E. As Author response image 1 clearly illustrates, the CobB/CobQ bands are indeed discernible, albeit they exhibit significantly fainter intensities when compared to the Kac-BSA bands. Notably, upon reviewing the full strained PVDF membrane presented in Supplementary Material 1, we find that the CobB/CobQ bands are not readily visible. This observation can be attributed to the potential loss of proteins during the transfer process from SDS-PAGE to the PVDF membrane.

Author response image 1.

The SDS-PAGE gel displayed the loading amounts of Kac-BSA and CobB/CobQ.

Furthermore, recognizing the potential for confusion given the similar molecular weights of CobB (257aa) and CobQ (264aa, excluding fusion tags), we conducted a comparative analysis of deacetylase activity between His-tagged and GST-fused recombinant CobQ proteins. Encouragingly, both variants exhibited deacetylase activity (as presented in Figure S5 of the revised manuscript), thereby excluding any influence from nonspecific proteins that might have contaminated the purification process.

We hope these clarifications and additions to our submission address your concerns and enhance the overall quality of our work. Thank you for your valuable time and consideration.

- Could you provide the raw data of these anti-acetylation western blot results?

Thank you very much for your suggestion. The raw results have been uploaded in the supplementary materials.

- According to the loading control, the protein quantity of BSA is very big, however, why is the acetylation of Kac-BSA relatively low? Is it consistent between the western blot and loading control?

Thank you very much for your suggestion, first of all, all the western blot and loading control in the manuscript are the same membrane, and the specific method is described in "Western blot". Therefore, there is no possibility that the western blot and loading control do not correspond. Secondly, not every site of BSA has acetylation modifications, and the amount of modifications at each site is also different, so there will be a large amount of protein but a small amount of acetylation.

Figure 2C - Could the Dot blot experiment be described in detail in the Methods part?

Thank you for your suggestion. It has been added and highlighted in red.

Figure 2C&2D - Please provide the anti-acetylation antibody information.

Thank you for your suggestion. It has been added and highlighted in red.

Figure 2E - It is confusing why the acetylation of Kac-BSA is higher than adding NAD+ with CobB? But only CobB can deacetylate the Kac-BSA without NAD+?

We are sorry to confuse you. The information in the figure is incorrect. For somehow, we provided the uncorrected version, and we have revised it in the undated manuscript.

Figure 2F - The control of this experiment should include the NAM, CobB, and NAM+CobB. Similar to 2E, it also should include NAD, CobB, and NAD+CobB, respectively. Same with 2H.

We are sorry to confuse you. The intent of Figure 2F is to further confirm that AhCobQ is different from AhCobQ and can remove the acetylation modification of BSA without relying on NAD+, so NAD+ was added to this group of experiments. We have revised the manuscript to add details about the experiments.

L178 Figure S1C - One question about the protein AhAcuC. From the PCR results, it is larger than ahcobB and ahcobQ, however, why is the protein AhAcuC smaller than them?

We are sorry to confuse you. The images in the original manuscript may have had some errors in protein size due to different PAGE gels. We have re-run the gels and replaced them in the manuscript in the Supplementary Figure S3 in revised manuscript.

- All the proteins are expressed and purified from E.coli BL21(DE3). How did you avoid the pollution of the deacetylase from the E.coli? There is no control over it in your experiment. Without this control, it is not easy to come to the conclusion that the deacetylation is from the AhCobQ but not from the pollution from the protein purification.

In response to your inquiry, we have conducted a meticulous comparative analysis of the deacetylase activity exhibited by both His-tagged and GST-fused recombinant AhCobQ proteins. Reassuringly, our findings reveal that both variants possess robust deacetylase activity, as clearly demonstrated in Figure S5 of the revised manuscript. Furthermore, to ensure the rigor of our experiments, we employed GST protein purified from E.coli strains as a negative control in Figure S8. The Western blot (WB) results conclusively demonstrate that GST protein alone lacks deacetylase activity, thereby reinforcing the authenticity of our findings and effectively mitigating any concerns regarding potential interference from nonspecific proteins during the purification process.

L190 - Could you provide the raw data for Table S1?

Thank you very much for your suggestion. The raw MS data were deposited in the public ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD038735 or IPX0005366000(iProx database). We also uploaded the analysis results in Table S1 and Supplementary material 2.

- I am not an expert on MS. I have one question about the MS results. Why there is no peak for the CobB or CobQ as they add to the reaction system?

Thank you for your insightful question. To clarify, the Kac peptides identified from Kac-BSA, as presented in Table S1, were meticulously selected for the purpose of enhancing their display and facilitating interpretation. The comprehensive raw mass spectrometry (MS) data, along with detailed analytical outcomes, have been diligently deposited within the ProteomeXchange Consortium, specifically through the PRIDE partner repository, under the dataset identifier PXD038735 or alternatively accessible via the iProx database under IPX0005366000. The analysis results also included in the Table S1 and Supplementary material 2.

Furthermore, it is crucial to note that in this study, we utilized Bovine serum albumin (BSA) as the foundational database for our MS searches. Consequently, the absence of CobB or CobQ proteins in our MS results stems from the inherent focus on BSA and the specific experimental design, which did not encompass the detection of these particular proteins.

We appreciate your attention to these details and hope this clarification addresses your query.

L189-L206 - Based on the results here, the function of CobB and CobQ overlaps on the same STDKac peptides.

Dear esteemed reviewer, our mass spectrometry (MS) analysis has revealed an intriguing finding: CobB and CobQ indeed function on the same STDKac peptide, suggesting a potential collaboration among distinct deacetylases in regulating protein function. This observation is further corroborated by our subsequent quantitative Kac proteomics results, which were obtained from three deacetylase mutants. These results underscore the possibility that CobB, CobQ, and AcuC possess both unique and overlapping protein substrates, reinforcing our hypothesis that multiple deacetylases work in concert to modulate protein activity.

- Do you assay the Km and Kcat about the CobQ by using Kac-BAS as the substrate by comparing with AhCobB?

Dear reviewer, thanks for your professional suggestion. In accordance with your guidance, we diligently attempted to analyze the Km or Kcat values of CobQ during its incubation with the substrate Kac-BSA using LC-MS/MS, repeating the process twice. However, to our disappointment, our current experimental platform has been unable to detect any discernible metabolites. We suspect that this may stem from operational proficiency challenges, as even our positive control experiment involving CobB incubation has failed to yield satisfactory results.

Given our uncertainty regarding the root cause of these issues, coupled with the suggestion from experts that the LC column might be a contributing factor except for skill, we have decided against repeating the experiments at this juncture. Nonetheless, we would like to assure you that we have rigorously validated the deacetylase activity of CobQ proteins through mass spectrometry, as detailed in our manuscript.

Furthermore, I am delighted to share that our preliminary findings have sparked interest among other research teams. In fact, one such group, upon reading our preprint, has independently tested the activity of CobQ and uncovered an additional intriguing function. We are actively exploring the possibility of collaborating with this team to delve deeper into the research and, hopefully, in the future, conduct a more refined analysis of the Km and Kcat of CobQ.

L214- Same question with Figures 2E-2H. Could you provide the whole page gel about the loading control? I want to know the quantity of the AhCobQ in this experiment except for the Kac-BSA. To tell the truth, the quantity of BSA is too much in the deacetylation reaction system to be able to tell its deacetylation activity in vitro.

Thank you very much for your suggestion. The raw data has been uploaded in the supplementary materials and the clarification is similar with above mentioned.

L217 - There might be a wrong citation of Figure S2 here.

Thank you for your suggestion. It has been corrected.

L244-250, Figure 6A - Are there 47, not 46 Kac proteins?

Thank you for your suggestion. It has been corrected.

- Are there nineteen, not nine increased Kac peptides common between the ΔahcobQ and ΔahacuC strains?

Thank you for your suggestion. It has been corrected.

- Are there ten, not six increased Kac peptides common between the ΔahcobQ and ΔahcobB strains?

Thank you for your suggestion. It has been corrected.

- Are there 69, not 65 increased Kac peptides common between the ΔahcobB and ΔahacuC strains?

Thank you for your suggestion. It has been corrected.

- Where is the raw data for Table S2?

Thank you very much for your suggestion. The raw data has been uploaded in the supplementary materials.

Figure 6B - Are there 52, not 51 Kac peptides?

Thank you for your suggestion. It has been corrected.

L272 - Why do you choose these 11 target proteins? There is no description of this background in the context.

We have opted to prioritize these proteins for subsequent validation, as their Kac levels exhibit a notable upregulation in the ΔahcobQ strain, potentially indicating their role as protein substrates for AhCobQ. We will incorporate this clarification into the revised manuscript to ensure clarity and comprehensiveness.

L277 - Figure S6 - Please show the whole PAGE gel about the loading control.

Dear esteemed reviewer, we sincerely apologize for any confusion our previous presentation may have caused. We would like to clarify that the bottom panel of Figure S6 depicts a Coomassie Blue R-350 stained whole PVDF membrane, rather than a PAGE gel, as may have been mistakenly inferred. To facilitate a comprehensive understanding, we have included the entire stained PVDF membranes in Supplementary Material 1.

As we have previously elaborated, the recombinant His-tagged or GST-fused AhCobQ proteins were not as discernible on the PVDF membrane due to a relatively lower loading amount compared to that of Kac-BSA.

-There might be a wrong citation in Figure S6. As you mentioned in the context, you expressed and purified 11 proteins and then tested their acetylation background.

Thank you for your suggestion. It has been corrected.

L280 - Figure S7 -The label of the Figure should be modified for the ATP.

Thank you for your suggestion. It has been modified.

- How did you do the experiment for 0h of ATP? There is no description of it in the Methods.

Thank you for your suggestion. It has been added.

- Please show the whole PAGE gel about the loading control.

Thank you very much for your suggestion. The whole PAGE gel has been uploaded in the supplementary materials.

L282 - Figure 7 - Please show the whole PAGE gel about the loading control.

Dear esteemed reviewer, we sincerely apologize for any confusion our previous presentation may have caused. We would like to clarify that the bottom panel of Figure S6 depicts a Coomassie Blue R-350 stained whole PVDF membrane, rather than a PAGE gel, as may have been mistakenly inferred. To facilitate a comprehensive understanding, we have included the entire stained PVDF membranes in Supplementary Material 1.

- Please adjust the font size of "A" and "B".

Thank you for your suggestion. It has been adjusted.

Figure 7A - The anti-acetylation Western blot here does not look good. All the western blots here should be re-done.

Dear reviewer, the recombinant site-specific Kac proteins were constructed by two-plasmid system based on genetically encoding Nᵋ-acetyllysine in recombinant proteins in this study (Nature chemical biology, 2017, 13(12): 1253-1260). However, a common problem experienced is protein truncation arising from translation termination at the reassigned codon, lowering protein yields (ChemBioChem, 2017, 18(20): 1973-1983), and leading to a dirty background of WB results in many recombinant proteins. Although we did perform at least two times independent repeats for site-specific Kac protein WB and got similar results, the WB quality of site-specific Kac proteins are general poor and that depend on the properties of target proteins. For example, the WB results of ENO and ICD can display considerable qualities in different biological repeats.

- Why did you choose the PAGE gel but not the anti-His Western blot as the loading control?

Thank you very much for your suggestion. Labeling antibodies is a very effective loading control. However, in order to ensure the accuracy of the data, both the experimental data and loading control in this manuscript are required to be reflected on the same membrane. If His tags are used, the membrane will be washed repeatedly for secondary color development. Based on the fact that acetylation modification is already difficult for color development, this will greatly affect the quality of the results presented. Meanwhile, while ensuring consistent protein levels, we believe that changes in acetylation modifications can also explain the issue. Therefore, you choose the PAGE gel but not the anti-His Western blot as the loading control.

L278 - Where are the results of the site-specific lysine acetylation of the target protein by using two-plasmid-based system of genetically encoded Nε-acetyllysine. Usually, there will be a shift when it is full acetylated by compared with the wild-type protein.

Sorry for the confusion caused. As the size of the acetyl group is only about 40.6Da, which is thousands of times smaller than the size of the protein, the changes in size of the protein before and after modification cannot be seen with the naked eye.

L287 - Where is Figure 7C?

We are sorry to confuse you. It has been corrected.

- Here the citation might be Figure 7A but not Figure 7B.

Thank you for your suggestion. It has been corrected.

L290 - It is difficult to read here, please rearrange this Figure S8. There is no useful label.

Thank you for your suggestion. It has been corrected.

- The citation of Figure S8 is wrong.

Thank you for your suggestion. It has been corrected.

- For Figure S8, please add the label on the figure. And add anti-GST western blot as well. Because the GST is about 26KD, why are the purified recombinant truncated proteins (GST-fusion) so small?

Sorry for the inconvenience caused. The truncated fragment used for recombinant purification in Figure S8 is very small, and when converted to protein, it is approximately between 1-5kDa. Therefore, the resulting protein is also very small.

- Why there are two Figure S8 in the supplemental materials?

We are sorry to confuse you. It has been corrected.

L293 - Where is Figure 7D?

We are sorry to confuse you. It has been corrected.

L297-313 - Please provide the MS result of the ICDK388?

Author response image 2.

The mass spectrum of Kac modification on ICD protein at K388 site.

Dear reviewer, we are pleased to present the mass spectrum data pertaining to the Kac modification at the K388 site of the ICD protein in Δ_ahcobQ_ strain in Figure2 in this responding letter. It is important to clarify that, while we have not directly validated the Kac status of site-specific lysine acetylation at the recombinant ICD K388 site through mass spectrometry (MS) in this particular study, we have strong reasons to believe in its specificity.

Firstly, our confidence stems from the well-established and rigorously validated two-plasmid system methodology for site-directed acetylation modification. This approach has been successfully employed in modifying diverse and specific sites across various proteins, as evidenced by the pioneering work of David et al. in Nature Chemical Biology (2017, 13(12), 1253-1260).

Secondly, we have taken meticulous measures to ensure the accuracy and reliability of our findings. This includes double-checking our PCR primers and DNA sequencing for the genetic code expansion technology employed. Furthermore, we have included control experiments utilizing proteins that were not subjected to site-directed acetylation (ICD), as detailed in Figure 8A in revised manuscript, thereby providing an additional layer of validation and reinforcing the robustness of our results.

We believe that these two lines of evidence, combined with our rigorous experimental design and execution, provide a solid foundation for our conclusion regarding the specific acetylation of the K388 site in ICD.

- Please provide the whole PAGE gel of loading control. Or other anti-His results?

Dear esteemed reviewer, we sincerely apologize for any confusion our previous presentation may have caused. We would like to clarify that the bottom panel of Figure S6 depicts a Coomassie Blue R-350 stained whole PVDF membrane, rather than a PAGE gel, as may have been mistakenly inferred. To facilitate a comprehensive understanding, we have included the entire stained PVDF membranes in Supplementary Material 1.

- Do you have site-specific antibody of ICDK388? It should be better to identify the ICDK388 with site-specific anti-acetylation antibody.

Thank you for your insightful suggestion. We fully concur that a site-specific antibody targeting ICDK388 would be an optimal tool to elucidate the impact of CobQ on the acetylation status (Kac) of this protein. Unfortunately, we are currently without such an antibody due to the intricate and time-consuming process of its production, which also requires rigorous validation to ensure specificity. Furthermore, the cost associated with its development is considerable.

To address this limitation, in the present manuscript, we have innovatively employed a two-plasmid system for site-directed acetylation modification of ICDK388. This method, which has been extensively validated and utilized in modifying diverse specific sites (David et al., Nature Chemical Biology, 2017, 13(12), 1253-1260), allowed us to precisely manipulate the acetylation status of our target protein. Additionally, we incorporated control experiments using proteins that were not subjected to site-directed acetylation, as depicted in Figure 8A in revised manuscript, thereby reinforcing the robustness and reliability of our findings.

- Please give some background information about K388 site of ICD in the context.

Thank you for your suggestion. It has been added.

L484 - Could you provide the reference for this assay method "Protein deacetylation assay in vitro"?

Thank you for your suggestion. The work published in science 327, 1004 (2010) and Nat. Protoc.5, 1583-1595.

L490 - There is no detailed information about the growh condition for the quantitative acetylome analysis. Without these information, the proportion of the Kac peptides doesn't make any sense.

Thank you for your suggestion. It has been added.

L531 - Insert one line before the paragraph of Western blot.

Thank you for your suggestion. It has been inserted.

Reviewer #3 (Recommendations For The Authors):

Tables S1 and S2 are missing. I could not fully understand the manuscript without them.

We are sorry to confuse you.The data has been uploaded in the supplementary materials.

Line 130. The gene IDs of AhCobB and AhAcuC should be presented.

Thank you for your suggestion. It has been presented.

Line 285. What is different between ArcA and ArcA-2? Please clarify.

Thank you for your suggestion. ArcA is aerobic respiration control protein ArcA, gene name AHA_3026 (https://www.uniprot.org/uniprotkb/A0KMM9/entry). ArcA-2 is arginine deiminase, which gene name is AHA_4093  (https://www.uniprot.org/uniprotkb/A0KQG6/entry). Therefore, they are different proteins according to Uniport annotation.

Line 303. 8further, a bug?

We are sorry to confuse you. It has been corrected.

Line 412-416. The related papers on ICD acetylation in E. coli should be cited.

We are sorry to confuse you. It has been added.

Line 478. Not in vivo but in vitro?

Sorry to confuse you. It should be in vitro. We have revised in the updated manuscript.

Figure 3C and 3D. The image resolution is bad. The figures should be improved so that readers to know easily that Kac is exactly incorporated at the target site.

Thank you for your suggestion. It has been corrected.

Figure 4B. The amino acid residues of the whole AhCobB should be 1-264 aa.

Thank you for your suggestion. It has been corrected.

Figure 8. It would be better to use the same colors between panels C and D. It should be shown the significance between ICD-Kac388 and ICD-Kac388+AhCobB to support the authors' conclusion that AhCobQ activates ICD by deacetylation at K388.

Thank you for your suggestion. It has been adjusted.

eLife assessment

In this valuable study, the authors studied a novel Zn2+- and NAD+-independent KDAC protein, AhCobQ, in Aeromonas hydrophila, which lacks homology with eukaryotic counterparts, thus underscoring its unique evolutionary trajectory within the bacterial domain. They attempt to demonstrate deacetylase activity, however, assays to detect this are still incomplete and require further refinement. The work will be of interest to microbiologists studying metabolism and post-translational modifications.

Reviewer #1 (Public review):

Summary:

This study by Wang et al. identifies a new type of deacetylase, CobQ, in Aeromonas hydrophila. Notably, the identification of this deacetylase reveals a lack of homology with eukaryotic counterparts, thus underscoring its unique evolutionary trajectory within the bacterial domain.

Strengths:

The manuscript convincingly illustrates CobQ's deacetylase activity through robust in vitro experiments, establishing its distinctiveness from known prokaryotic deacetylases. Additionally, the authors elucidate CobQ's potential cooperation with other deacetylases in vivo to regulate bacterial cellular processes. Furthermore, the study highlights CobQ's significance in the regulation of acetylation within prokaryotic cells.

Weaknesses:

The problem I raised has been well resolved. I have no further questions.

Reviewer #2 (Public review):

In recent years, lots of researchers tried to explore the existence of new acetyltransferase and deacetylase by using specific antibody enrichment technologies and high resolution mass spectrometry. Here is an example for this effort. Yuqian Wang et al. studied a novel Zn2+- and NAD+-independent KDAC protein, AhCobQ, in Aeromonas hydrophila. They studied the biological function of AhCobQ by using biochemistry method and MS identification technology to confirm it. These results extended our understanding of the regulatory mechanism of bacterial lysine acetylation modifications. However, I find this conclusion is a little speculative, and unfortunately, it also doesn't totally support the conclusion as the authors provided.

Reviewer #3 (Public review):

Summary:

This study reports on a novel NAD+ and Zn2+-independent protein lysine deacetylase (KDAC) in Aeromonas hydrophila, termed as AhCobQ (AHA_1389). This protein is annotated as a CobQ/CobB/MinD/ParA family protein and does not show similarity with known NAD+-dependent or Zn2+-dependent KDACs. The authors showed that AhCobQ has NAD+ and Zn2+-independent deacetylase activity with acetylated BSA by western blot and MS analyses. They also provided evidence that the 195-245 aa region of AhCobQ is responsible for the deacetylase activity, which is conserved in some marine prokaryotes and has no similarity with eukaryotic proteins. They identified target proteins of AhCobQ deacetylase by proteomic analysis and verified the deacetylase activity using site-specific Kac proteins. Finally, they showed that AhCobQ activates isocitrate dehydrogenase by deacetylation at K388.

Strengths:

The finding of a new type of KDAC has a valuable impact on the field of protein acetylation. The characters (NAD+ and Zn2+-independent deacetylase activity in an unknown domain) shown in this study are very unexpected.

Weaknesses:

(1) The characters (NAD+ and Zn2+-independent deacetylase activity in an unknown domain) shown in this study are very unexpected. To convince readers, MSMS data must be necessary to accurately detect (de)acetylation at the target site in the deacetylase activity assay. The authors showed the MSMS data in assays with acetylated BSA, but other assays only rely on western blot.

(2) They prepared site-specific Kac proteins and used them in deacetylase activity assays. Incorporation of acetyllysine at the target site should be confirmed by MSMS and shown as supplementary data.

(3) The authors imply that the 195-245 aa region of AhCobQ may represent a new domain responsible for deacetylase activity. The feature of the region would be of interest but is not sufficiently described in Figure 5. The amino acid sequence alignments with representative proteins with conserved residues would be informative. It would be also informative if the modeled structure predicted by AlphaFold is shown and the structural similarity with known deacetylases is discussed.

eLife assessment

This valuable study has characterized the unique expression of Schlemm's canal endothelial cells (SECs) using FACS-sorted specific cell bulk RNA-Seq and scRNA-/snRNA-Seq of mouse SECs. The compelling study identified novel biomarkers for SECs and molecular markers for two inner wall SEC states and outwall SECs in mouse eyes. Significant gene networks and pathways were elucidated for their potential contribution to glaucoma pathogenesis, providing targets for further research in relation to glaucoma.

Reviewer #1 (Public review):<br /> <br /> Summary:

Balasubramanian et al. characterized the cell types comprising mouse Schlemm's canal (SC) using bulk and single cell RNA sequencing (scRNA-seq). The results identify expression patterns the delineate the SC inner and outer wall cells and two inner wall 'states'. Further analysis demonstrates expression patterns of glaucoma associated genes and receptor ligand pairs between SEC's and neighboring trabecular meshwork.

Strengths:

While mouse SC has been profiled in previous scRNA-seq studies (van Zyl et al 2020, Thomson et al 2021), these data provide higher resolution of SC cell types, particularly endothelial cell (SEC) populations. SC is an important regulator of anterior chamber outflow and has important consequences for glaucoma.

Comments on the latest version:

The authors have addressed my primary concerns with the first version of the manuscript. This study represents a valuable resource in the molecular characterization of mouse Schlemm's canal cell types.

Reviewer #2 (Public review):

Summary:

This revised article has characterized the mouse Schlemm's canal expression profile using a comprehensive approach based on sorted SEC, LEC, and BEC total RNA-Seq, scRNA-Seq, and snRNA-Seq to enrich the selection of SECs. The revised study has successfully profiled genome-wide gene expression using sorted SECs, demonstrating that SECs have a closer similarity to LECs than BECs. The combined scRNA- and snRNA-Seq data with deep coverage of gene expression led to the successful identification of many novel biomarkers for inner wall SECs, outer wall SECs, collector channel ECs, and pericytes. In addition, the study also identified two novel states of inner wall SECs separated by new markers. The study provides significant novel information about the biology and expression profile of SECs in the inner and outer walls. It is of great significance to have this novel, convincing, and comprehensive study led by leading researchers published in this journal. The revision has improved the clarity and significance of the study with more details.

Strengths:

This is a comprehensive study using various data to support the expression characterization of mouse SECs. First, the study profiled genome-wide expression using sorted SECs, LECs, and BECs from the same tissue/organ to identify the similarities and differences among the three types of cells. Second, snRNA-Seq was applied to enrich the number of SECs from mouse ocular tissues significantly. Increased sampling of SECs and other cells led to more comprehensive coverage and characterization of cells, including pericytes. Third, the combined scRNA- and snRNA-Seq data analyses increase the power to further characterize the subtle differences within SECs, leading to identifying the expression markers of Inner and Outer wall SECs, collector channel ECs, and distal region cells. Fourth, the identified unique markers were validated for RNA and protein expression in mouse ocular tissues. Fifth, the study explored how the IOP- and glaucoma-associated genes are expressed in the ScRNA- and snRNA-Seq data, providing potential connections of these GWAS genes with IOP and glaucoma. Sixth, the initial pathway and network analyses generated exciting hypotheses that could be tested in other independent studies.

Weaknesses:

The authors have addressed most of the previous comments by adding more details about the protocol and additional discussions. Several comments requiring additional experimental data have been addressed as future directions, such as protein validation, RNA expression validation in human samples, and GWAS-identified IOP genes.

Comments on the latest version:

The authors have addressed previous comments responsively. The authors have suggested several experiments to be completed in the future since these could be time-consuming with human samples. The revised article is with better clarity and clearer significance. No additional comments.

Author response:

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

Reviewer #1 (Public Review): 

Summary: 

Balasubramanian et al. characterized the cell types comprising mouse Schlemm's canal (SC) using bulk and single-cell RNA sequencing (scRNA-seq). The results identify expression patterns that delineate the SC inner and outer wall cells and two inner wall 'states'. Further analysis demonstrates expression patterns of glaucoma-associated genes and receptor-ligand pairs between SEC's and neighboring trabecular meshwork. 

Strengths: 

While mouse SC has been profiled in previous scRNA-seq studies (van Zyl et al 2020, Thomson et al 2021), these data provide higher resolution of SC cell types, particularly endothelial cell (SEC) populations. SC is an important regulator of anterior chamber outflow and has important consequences for glaucoma. 

We thank the reviewers for their thorough reading of our manuscript and their insightful comments.

Weaknesses: 

(1) Since SC has previously been characterized in mouse, human, and other species by scRNA-seq in other studies, this study would benefit from more direct comparisons to published datasets. For example, Table 4 could be expanded to list the SC cell numbers profiled in each study. Expression patterns highlighted in this study could be independently verified by plotting in publicly available mouse SC datasets. Further, a comparison to human expression patterns would assess whether type-specific expression patterns are conserved. Alternatively, an integrated analysis could be performed. Indeed, the authors mention that an integrated analysis was attempted but the data is not shown. It is unclear if this was because of a lack of agreement between datasets or other reasons.

Table 4 now includes an expanded list of SC cell numbers in each study. We profiled the expression of Npnt, Selp, and Ccl21a in the Thomson et al., 2021 dataset and have included the concurring results in Figure S5. We were unable to do a similar profile using the Van Zyl., 2020 dataset due to small SC numbers. As previously mentioned, differences such as read depth, strain of animals used (including pigmented vs albino), method of cell isolation (including drug exposure), and number of cells profiled raise a significant impediment to integration with previously published datasets. A comparison to human atlas is a focus of future work.

(2) Figure 1 presents bulk RNA seq results comparing SEC, BEC, and LEC expression patterns. These populations were isolated using cell surface markers and enrichment by FACS. Since each EC population is derived from the same sample, the accuracy of this data hinges on the purity of enrichment. However, a reference is not given for this method and it is not clear how purity was validated. The authors later note that marker Emcn, which was used to identify BECs, is also expressed in SECs and LECs at lower levels. It should be demonstrated that these populations are clearly separated by flow cytometry. 

We have added the following clarifying text to the methods section: Forward and side scatter gates were first used to eliminate events with low scatter which include debris, cell fragments and pyknotic cells. Then propidium iodide positive dead cells were gated out. Further gating on the viable cells was applied such that distinct population of cells were isolated a) SECs: GFP+Lvye1-, b) LECs: GFP+ Lyve1+, c) GFP- BECs: Endomucin+.

We show here a representative of the flow sort showing the clear distinction in SEC and LEC cell isolation.

Author response image 1.

Flow sorted SEC and LEC. We obtained two distinct populations; 1. SEC cells (GPF+LYVE1--blue) 2. LEC (GPF+LYVE1+- red). Note eFluor 660 emission was collected using the Alexa647 (A647) setting of the flow cytometer. Additionally, SEC marker expression from bulk RNA-seq aligns with signature gene expression from SECs in single cell RNA-seq (Figure S3).

(3) Bulk RNA-seq analysis infers similarity from the number of DEGs between samples, however, this is not a robust indicator. A correlation analysis should be run to verify conclusions. 

We have provided a heatmap with hierarchical clustering based on Euclidean distance of the EC subtypes (Figure 1B) analyzed by bulk RNA seq in addition to number of DE genes between subtypes.

(4) Figures 2-4 present three different datasets targeting the same tissue: 1) C57bl/6j scRNA-seq, 2) C57bl/6j snRNA-seq, 3) 129/sj scRNA-seq. Integrated analysis comparing datasets #1 to #2 and #3 is also presented. Integration methods are not described beyond 'normalization for cell numbers'. It is unclear if additional alignment methods were used. Integration across each of these datasets needs careful consideration, especially since different filtering methods were used (e.g. <20% mito in scRNA-seq and <5% in snRNA-seq). Improper integration could affect the ability to cluster or exaggerate differences between cell/types and states. It would be useful to demonstrate the contribution of different samples and datasets to each cell type/state to verify that these are not driven by batch effects, mouse strain, or collection platform. 

We agree that integration should be performed with careful consideration to confounding factors. We demonstrate the contribution of different samples and datasets to show how our datasets integrated well (we had added panels to Figure 3C and 4C) and that cell types/states contribution was uniformly distributed across methods (C57BL/6J single cell and single nuc) and backgrounds (C57BL/6J and 129/Sj) were not a result of integration.

(5) IW1 and IW2 are not well separated, and it is unclear if these represent truly different cell states. Figure 5b shows the staining of CCL21A and describes expression in the 'posterior portion' but in the image there are no DAPI+ nuclei in the anterior portion, suggesting the sampling in this section is different from Figure 5a. This would be improved by co-staining NPNT and CCL21A to demonstrate specificity. 

Since both our antibodies are derived from the same species (goat), a co-labeling wasn’t possible. To be prudent, we used adjacent sections, flat-mounts, and RNAscope and provided further evidence of the anterior/posterior “bias” in supplemental figures.

(6) The substructures observed within clusters in sc/snRNA-seq data suggest that overall profiling may still not be comprehensive. This should be noted in the discussion. 

We agree and have added this note in the discussion: “With greater sampling and deeper transcriptomic depth, it is likely that additional SEC cell states/types will be identified.”

Reviewer #2 (Public Review):

Summary: 

This article has characterized the mouse Schlemm's canal expression profile using a comprehensive approach based on sorted SEC, LEC, and BEC total RNA-Seq, scRNA-Seq, and snRNA-Seq to enrich the selection of SECs. The study has successfully profiled genome-wide gene expression using sorted SECs, demonstrating that SECs have a closer similarity to LECs than BECs. The combined scRNA- and snRNA-Seq data with deep coverage of gene expression led to the successful identification of many novel biomarkers for inner wall SECs, outer wall SECs, collector channel ECs, and pericytes. In addition, the study also identified two novel states of inner wall SECs separated by new markers. The study provides significant novel information about the biology and expression profile of SECs in the inner and outer walls. It is of great significance to have this novel, convincing, and comprehensive study led by leading researchers published in this journal. 

Strengths: 

This is a comprehensive study using various data to support the expression characterization of mouse SECs. First, the study profiled genome-wide expression using sorted SECs, LECs, and BECs from the same tissue/organ to identify the similarities and differences among the three types of cells. Second, snRNA-Seq was applied to enrich the number of SECs from mouse ocular tissues significantly. Increased sampling of SECs and other cells led to more comprehensive coverage and characterization of cells, including pericytes. Third, the combined scRNA- and snRNA-Seq data analyses increase the power to further characterize the subtle differences within SECs, leading to identifying the expression markers of Inner and Outer wall SECs, collector channel ECs, and distal region cells. Fourth, the identified unique markers were validated for RNA and protein expression in mouse ocular tissues. Fifth, the study explored how the IOP- and glaucoma-associated genes are expressed in the ScRNA- and snRNA-Seq data, providing potential connections of these GWAS genes with IOP and glaucoma. Sixth, the initial pathway and network analyses generated exciting hypotheses that could be tested in other independent studies. 

We thank the reviewer for their comments on the strengths of this study.

Weaknesses: 

A few minor weaknesses have been noted. First, since snRNA-Seq and scRNA-Seq generated different coverage of expressed genes in the cells, how did the combined analyses balance the un-equal sequencing coverage and missing data points in the snRNA-Seq data? Second, the RNA/protein validation of selected SEC molecular markers was done using mouse anterior segment tissues. It would be more helpful to examine whether these molecular markers for SECs could work well in human SECs. Third, the effort to characterize the GWAS-identified IOP- and glaucoma-associated genes is exciting but with limited new information. Additional work could be performed to prioritize these genes.

Integration of sc-Seq and sn-Seq data: We have addressed a similar integration question from reviewer 1 and have now included a plot showing the distribution of cells upon integration. Integration methods are not perfect and generally result in some loss of data especially when datasets of un-equal sequencing coverage are integrated. However, we did not observe any obvious differences between the original (un-integrated) and integrated datasets. We also noted that cell types/states contribution was similarly distributed across methods (C57BL/6J single cell and single nuc) and backgrounds (C57BL/6J and 129/Sj) and clustering were not a result of batch-effects.

We agree about the human relevance of SEC markers, and this will be a focus of future work.

Another focus of our future work is to understand how GWAS identified IOP and glaucoma genes change in disease states.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Minor: 

(1) Figure 5- DAPI should be listed in the legend. 

(2) Figure 5- It would be helpful to label IW1 and IW2 regions in the UMAPs. 

We have incorporated the suggestions in Figure 5 and legend.

Reviewer #2 (Recommendations For The Authors): 

(1) The study has validated RNA/protein expression of the selected biomarkers for IW/OW SECs in mouse eyes. It would be more helpful to confirm that these newly identified molecular biomarkers for SECs could apply to human eyes. This could be examined through available human scRNA-/snRNA-Seq data or targeted RNA and protein staining experiments. The additional validation in human SECs would make the current discovery more convincing. 

We agree with the importance of validation in human samples, and is the scope of future work.

(2) The combination of scRNA-Seq and snRNA-Seq from three batches of experiments increased the statistical power to identify subtypes of SECs. It would be helpful to include more details on how the qc, missing data, and normalization across different batches were dealt with. 

We have incorporated more details in the methods section of the paper.

(3) The authors explored the underlying molecular connection between the newly identified IOP/glaucoma-associated genes using the newly generated SEC-targeted scRNA/snRNA-Seq data. Many of these associated genes were present in the same SEC cells. It would be interesting to see how many of these genes' expression levels are correlated with each other via a network. These potential correlation networks across SECs could lead to identifying novel upstream regulators or network hubs, which could target many IOP-associated genes for future studies. 

We agree with the importance of a correlation network analysis, but this is a focus of future work, especially in normal and disease states.

eLife assessment

This important study introduces and evaluates the efficacy of a novel form of non-invasive brain stimulation in humans: kilohertz transcranial magnetic perturbation (kTMP). The evidence provided for the ability of kTMP to increase cortical excitability with minimal sensation is compelling, with two separate replication experiments. Although exploratory in nature, this work represents new avenues for non-invasive brain stimulation research that has potential long-term appeal for both clinical and research applications. This paper will be of significant interest to neuroscientists interested in brain stimulation.

Reviewer #1 (Public review):

Summary:

This paper reports the first results on the effects of a novel waveform for weak transcranial magnetic stimulation, which is refered to as "perturbation" (kTMP). The waveform is sinusoidal at kHz frequency with subthreshold intensities of 2V/m, instead of the suprathreshold pulses used in conventional TMS (~100V/m). The effect reported here concerns motor-evoked potentials (MEPs) elicited on the hand with single-pulse TMS. These MEPs are considered a marker of "corotico-spinal excitability". The manuscripts report that kTMP at 3.5kHz enhances MEPs with a medium effect size, with independent replication of this finding on 3 separate cohorts of subjects (N=16, 15, 16). This result is important for the field of non-invasive brain stimulation. The evidence in support of this claim is compelling. Despite the replications, this remains an exploratory study that will require replication with adequately powered planned comparisons.

Strengths:

• This is a novel modality for non-invasive brain stimulation.<br /> • Knowing the history in this field, this is likely to lead to a large number of follow-up studies in basic and clinical research.<br /> • The modality causes practically no sensation, which makes it perfectly suitable for control conditions. Indeed, the study itself used a persuasive double-blinding procedure.<br /> • The replication of the main result in two subsequent experiments is very compelling.<br /> • The effect size of Cohen's d=0.5 is very promising.<br /> • It is nice the E-fields were measured on a phantom, in addition to modeling.

Weakness:

• Statistical analysis combining Experiments 1, 2, 3 after inspecting the data is inappropriate.<br /> • Post-hoc definition of outliers that were removed is unfortunate.<br /> • While sensation has been documented, blinding was not directly assessed.<br /> • Despite the replications, this remains an exploratory study as it lacks power analysis and planned comparisons.

Other comments from an earlier review were adequately addressed.

Reviewer #2 (Public review):

Summary:

kTMP is a novel method of stimulating the brain using electromagnetic fields. It has potential benefits over existing technology because it is a safe and easy technology. It explores a range of brain frequencies that has not been explored in depth before (2-5kHz) and thus offers new opportunities.

Strengths:

This work relied on standard methods and was carefully and conservatively performed.

Weaknesses:

There were few weaknesses. The sham condition was prepared as well as could be done, but sham is always challenging in a treatment with sound and sensation, and with knowledgeable operators. New technology, also, is very exciting to subjects and it is difficult to achieve a natural experiment. These difficulties are related to the technology, however, and not to the execution of these experiments..

Author response:

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

Response to Public Comments

(1) BioRxiv version history.

Reviewer 1 correctly noted that we have posted different versions of the paper on bioRxiv and that there were significant changes between the initial version and the one posted as part of the eLife preprint process. Here we provide a summary of that history.

We initially posted a bioRxiv preprint in November, 2021 (Version I) that included the results of two experiments. In Experiment 1, we compared conditions in which the stimulation frequency was at 2 kHz, 3.5 kHz, or 5.0 kHz. In Experiment 2, we replicated the 3.5 kHz condition of Experiment 1 and included two amplitude-modulated (AM) conditions, with a 3.5 kHz carrier signal modulated at 20 Hz or 140 Hz. Relative to the sham stimulation, non-modulated kTMP at 2 kHz and 3.5 kHz resulted in an increase in cortical excitability in Experiment 1. This effect was replicated in Experiment 2.

In the original posting, we reported that there was an additional boost in excitability in the 20 Hz AM condition above that of the non-modulated condition. However, in re-examining the results, we recognized that the 20 Hz AM condition included an outlier that was pulling the group mean higher. We should have caught this outlier in the initial submission given that the resultant percent change for this individual is 3 standard deviations above the mean. Given the skew in the distribution, we also performed a log transform on the MEPs (which improves the normality and homoscedasticity of MEP distributions) and repeated the analysis. However, even here the participant’s results remained well outside the distribution. As such, we removed this participant and repeated all analyses. In this new analysis, there was no longer a significant difference between the 20 Hz AM and non-modulated conditions in Experiment 2. Indeed, all three true stimulation conditions (non-modulated, AM 20 Hz, AM 140 Hz) produced a similar boost in cortical excitability compared to sham. Thus, the results of Experiment 2 are consistent with those of Experiment 1, showing, in three new conditions, the efficacy of kHz stimulation on cortical excitability. But the results fail to provide evidence of an additional boost from amplitude modulation. 

We posted a second bioRxiv preprint in May, 2023 (Version 2) with the corrected results for Experiment 2, along with changes throughout the manuscript given the new analyses.

Given the null results for the AM conditions, we decided to run a third experiment prior to submitting the work for publication. Here we used an alternative form of amplitude modulation (see Kasten et. al., NeuroImage 2018). In brief, we again observed a boost in cortical excitability in from non-modulated kTMP at 3.5 kHz, but no additional effect of amplitude modulation.  This work is included in the third bioRrxiv preprint (Version 3), the paper that was submitted and reviewed at eLife.

(2) Statistical analysis.

Reviewer 1 raised a concern with the statistical analyses performed on aggregate data across experiments.  We recognize that this is atypical and was certainly not part of an a priori plan. Here we describe our goal with the analyses and the thought process that led us to combine the data across the experiments.

Our overarching aim is to examine the effect of corticospinal excitability of different kTMP waveforms (carrier frequency and amplitude modulated frequency) matched at the same estimated cortical E-field (2 V/m). Our core comparison was of the active conditions relative to a sham condition (E-field = 0.01 V/m). We included the non-modulated 3.5 kHz condition in Experiments 2 and 3 to provide a baseline from which we could assess whether amplitude modulation produced a measurable difference from that observed with non-modulated stimulation. Thus, this non-modulated condition as well as the sham condition was repeated in all three experiments. This provided an opportunity to examine the effect of kTMP with a relatively large sample, as well as assess how well the effects replicate, and resulted in the strategy we have taken in reporting the results. 

As a first step, we present the data from the 3.5 kHz non-modulated and sham conditions (including the individual participant data) for all three experiments in   4. We used a linear mixed effect model to examine if there was an effect of Experiment (Exps 1, 2, 3) and observed no significant difference within each condition. Given this, we opted to pool the data for the sham and 3.5 kHz non-modulated conditions across the three experiments. Once data were pooled, we examined the effect of the carrier frequency and amplitude modulated frequency of the kTMP waveform. 

(3) Carry-over effects

As suggested by Reviewer 1, we will examine in the revision if there is a carry-over effect across sessions (for the most part, 2-day intervals between sessions). For this, we will compare MEP amplitude in baseline blocks (pre-kTMP) across the four experimental sessions.

Reviewer 1 also commented that mixing the single- and paired-pulse protocols might have impacted the results. While our a priori focus was on the single-pulse results, we wanted to include multiple probes given the novelty of our stimulation method. Mixing single- and different paired-pulse protocols has been relatively common in the non-invasive brain stimulation literature (e.g., Nitsche 2005, Huang et al, 2005, López-Alonso 2014, Batsikadze et al 2013) and we are unaware of any reports suggested that mixed designs (single and paired) distort the picture compared to pure designs (single only).

(4) Sensation and Blinding

Reviewer 2 bought up concerns about the sham condition and blinding of kTMP stimulation. We do think that kTMP is nearly ideal for blinding. The amplifier does emit an audible tone (at least for individuals with normal hearing) when set to an intensity to produce a 2 V/m E-field. For this reason, the participants and the experimenter wore ear plugs. Moreover, we played a 3.5 kHz tone in all conditions, including the sham condition, which effectively masked the amplifier sound. We measured the participant’s subjective rating of annoyance, pain, and muscle twitches after each kTMP session (active and sham). Using a linear mixed effect model, we found no difference between active and sham for each of these ratings suggesting that sensation was similar for active and sham (Fig 8). This matches our experience that kHz stimulation in the range used here has no perceptible sensation induced by the coil. To blind the experimenters (and participants) we used a coding system in which the experimenter typed in a number that had been randomly paired to a stimulation condition that varied across participants in a manner unknown to the experimenter.

Reviewer 1 asked why we did not explicitly ask participants if they thought they were in an active or sham condition. This would certainly be a useful question. However, we did not want to alert them of the presence of a sham condition, preferring to simply describe the study as one testing a new method of non-invasive brain stimulation. Thus, we opted to focus on their subjective ratings of annoyance, pain, and finger twitches after kTMP stimulation for each experimental session.

Response to Recommendations for the Authors

Reviewer #1: 

Reviewer # 1 in the public review noted the possibility of carry-over effects and suggested that we compare the amplitude of the MEPS in the pre blocks across the four sessions.

Although we did not anticipate carry-over effects lasting 2 or more days, we have now conducted an analysis in which we use a linear mixed effect model with a fixed factor of Session and a random factor of Participant. The results show that there is not an effect of session [χ2(3) = 4.51, p \= 0.211].

Author response table 1.

Detailed comments and some suggestions to maybe improve the writing and figures: 

Abstract: 

BioRxiv Version 1: "We replicated this effect in Experiment 2 and found that amplitude-modulation at 20 Hz produced an additional boost in cortical excitability. " 

BioRxiv Version 2, 3 and current manuscript: "Although amplitude-modulated kTMP increased MEP amplitude compared to sham, no enhancement was found compared to non-modulated kTMP." 

I am a little concerned about this history because the conclusions seem to have changed. It looks like the new data has a larger number of subjects, which could explain the divergence. Although it is generally not good practice to analyze the data at interim time points, without accounting for alpha spending. It appears that data analysis methods may have also changed, as some of the extreme points in version 1 seem to be no longer in the new manuscript (Figure 4 Sham Experiment 1). 

In the public review above we explain in detail the different versions of the bioRxiv preprint and how the results changed from the first version to the current manuscript.

Introduction: <br /> "Second, the E-fields for the two methods exist in orthogonal subspaces" Can you explain what this means? 

Thank you for this suggestion, we have updated the paper (pg. 4, line 78-81) by adding two sentences to explain what we mean by orthogonal subspaces and describe the consequences of this with respect to the E-fields resulting from tES and TMS. Specifically, we now comment that even if the E-fields of tES and TMS are similar in focality, they may target different populations of neurons.  

"In addition, the kTMP waveform can be amplitude modulated to potentially mimic E-fields at frequencies matching endogenous neural rhythms [15]." That may be so, but reference [15] makes the exact opposite point, namely, that kHz stimulation has little effect on neuronal firing until you get to very strong fields. The paper that makes that claim is by Nir Grossman, but in my view, it is flawed as responses are most likely due to peripheral nerve (axon) stimulation there given the excessive currents used in that study. The reference to Wang and Peterchev [17] is in agreement with that by showing that you need 2 orders of magnitude stronger fields to activate neurons. 

The reviewers are correct that that Ref 15 (Esmaeilpour et al, 2021), as well as Wang et al, 2023 use much higher E-fields than we target in our present study. However, our point here is that, while we cannot use our approach to apply E-fields at endogenous frequencies, we can do amplitude modulation of the kHz carrier frequency at these lower frequencies. We cited Esmaeilpour et al., (2021) because they show that high frequency stimulation with amplitude-modulated waveforms resulted in dynamic modulation at the “beating” frequency. Given we are well in subthreshold space in this paper, and well below the E-field levels in Esmaeilpour et al (2021), the open question is whether amplitude modulation at this level will be able to perturb neural activity (e.g., increase power of endogenous oscillations at the targeted frequency). 

To address this concern, we modified the sentence (pg.6, lines 120-121) to now read "In addition, the kTMP waveform can be amplitude modulated at frequencies matching endogenous neural rhythms." In this way, we are describing a general property of kTMP (as well as other methods that can use high frequency signals).

I am not aware of any in-vitro study showing the effects of kHz stimulation at 2V/m. The review paper by Neudorfer et al is very good. But if I got it correctly in a quick read it is not clear that there is experimental evidence for subthreshold effects. They do talk about facilitation, but the two experimental papers cited there on the auditory nerve don't quantify field magnitudes. I would really love it if you could point me to a relevant empirical study showing the effects of kHz stimulation at 2 V/m. 

Perhaps all this is a moot point as you are interested in lasting (plastic) effects on MEP. For this, you cite one study with 11 subjects showing the effects of kHz tACS on MEPs [20]. I guess that is a start. The reference [21] is only a safety study, so it is probably not a good reference for that. Reference [22] also seems out of place as it is a modeling study. The effects on depression of low-intensity magnetic stimulation in references [23-26] are intriguing. 

We agree with the reviewer that Ref 20 (now Ref 18: Chaieb, Antal & Paulus; 2011) is the most relevant one to cite here since it provides empirical evidence for changes in neural excitability from kHz stimulation, and in fact, serves as the model for the current study. We have retained Refs 23-26 (now Ref 19-22: Rohan et al., 2014; Carlezon et al., 2005; Rohan et al., 2004 & Dublin et al., 2019) since they also do show kHz effects on mood and removed Refs 21 (Chaieb et al., 2014) and 22 (Wang et al., 2018) for the reasons cited by the Reviewer.

Figure 1: "The gray dashed function depicts the dependence of scalp stimulation threshold upon frequency [14]." It's hard to tell from that reference what the exact shape is, but the frequency dependence is likely steeper than what is shown here, i.e. 2 mA at 10 Hz can be really quite unpleasant. 

We have removed the gray dashed line given that this might be taken to suggest a discrete transition. We now just have a graded transition to reflect that the tolerance of tES is subjective. We start the shading at 2 mA for the lowest frequencies given that there is general agreement that 2 mA is well-tolerated and decrease the shading intensity as frequency increases. The general aim of the figure is not to make strong claims about the threshold of scalp discomfort for tES, but to show that kTMP can target much higher cortical E-fields within the tolerable range.

Methods: <br /> Procedures: <br /> It does not seem like double-blinding has been directly assessed. 

We did not assess double blinding by directly assessing whether the participant was in a sham or active condition. We did not want to alert the participants of the presence of a sham condition after the first session of the 4-session study, preferring to simply describe the study as a test of a new method of non-invasive brain stimulation. For this reason, we opted to focus on their subjective ratings of annoyance, pain, and finger twitches after kTMP stimulation for each experimental session. These ratings did not differ between active and sham kTMP, which suggests kTMP has good potential for double blinding.

MEP data analysis: Taking the mean of log power is unusual, but I suppose the reference provided gives a good justification. Does this explain the deviation from the biorxiv v1 results? 

We opted to perform a logarithmic transformation of MEP amplitudes to improve the normality and homoscedasticity of the MEP distribution. We cite three papers (Refs 50-52: Peterchev et al., 2013, Nielsen 1996a, & Nielsen 1996b) that have applied a similar approach in handling MEP data. We had not done the transformation in the first bioRxiv but opted to do so in the eLife submission based on further review of the literature. We note that the two analyses produce similar statistical outcomes once we removed the outlier discussed in the Public Review.

"Interactions were tested by comparing a model in which the fixed effects were restricted to be additive against a second model that could have multiplicative and additive effects." Not sure what this means. Why not run a full model with interactions included and read off the stats from that single model for the various factors? Should one not avoid running multiple models as one would have to correct p-values for multiple comparisons for every new test? 

We used the lme4 package in R to fit our linear mixed effect models (Ref 54: Bates, Mächler, Bolker & Walker, 2015). In this package they intentionally leave out p-values for individual models or factors because they note there is a lack of convergence in the field about how to calculate parameter estimates in complex situations for linear mixed effect models (e.g., unbalanced designs). They suggest model comparison using the likelihood-ratio test to obtain and report p-values, which is what we report in the current manuscript.

We revised the text in the section Linear Mixed Effects Models to state that likelihood ratio tests were used to obtain p-values to remove any confusion.

Procedures: <br /> kTPM: Nice that fields were measured. Would be nice to see the data that established the empirical constant k. 

We have expanded our discussion of how we established k in the Methods section. We first derived k using the equation E0 \= kfcI based on previously published reports of the current (I) and frequency (fc) of the MagVenture Cool-B65 coil (now Refs 29-30: Deng, Lisanby & Peterchev, 2013; Drakaki, Mathiesen, Siebner, Madsen & Thielscher, 2022). We then verified this value using the triangular E-field probe to within 5% error.

Figure 3, spectrum. The placement of the fm label on the left panel is confusing. It suggests that fm was at the edge of the spectrum shown, which would not be the best way to show that there is nothing there - obviously, there isn't, but the figure could be more didactic. 

Thanks for pointing this out. We modified the figure, moving the ‘fm’ label to the center of the first panel. This change makes it clear that there is no peak at the amplitude modulated frequency.

"a trio of TMS assays of cortical excitability" Can you clarify what this means? 

Sorry for the confusion. The trio of TMS assays refers to the single pulse and two paired-pulse protocols (SICI - ICF). We edited the Procedure section to clarify this (pg 9, line 195-197).

Figure 2A: it would be nice to indicate which TMS blocks were single pulse and which were the two paired-pulse protocols. It is hard to keep track of it all for the three different experiments. 

We have now clarified in the text (see above) that all three probes were used in each block for Experiments 1 and 2, and only the single-pulse probe in Experiment 3. We have modified the legend for Figure 2 to also provide this information.

Results: <br /> "Based on these results, we combined the data across the three experiments for these two conditions in subsequent analyses." This strikes me as inappropriate. Should not a single model have been used with a fixed effect of experiment and fixed effect of stimulation condition? 

We recognize that pooling data across experiments may be atypical. Indeed, our initial plan was to simply analyze each experiment on its own (completely within-subject analysis). However, after completing the three experiments, we realized that since the sham and non-modulated 3.5 kHz conditions were included in each experiment, we had an opportunity to examine the effect of kTMP in a relatively large N study (for NIBS research). Before pooling the data, we wanted to make sure that the factor of experiment did not impact the results and our analysis showed there was no effect of experiment. Note that we did not include the factor of stimulation condition in this model because we did not want to do multiple comparisons of the same contrast (3.5 kHz compared to sham). By pooling the data before analysis of the stimulation conditions we could then focus on our two key independent variables: 1) kTMP carrier frequency and 2) kTMP amplitude modulated frequency, doing fewer significance tests to minimize multiple comparisons. The linear mixed effect (LME) model allows us to include a random effect of participant. In this way, we account for the fact that some comparisons are within subjects and some comparisons are between subjects.

The reviewer is correct that after pooling the data, we could have continued to include the factor of experiment in the LME models. This factor could still account for variance even though it was not significant in the initial test. Given this, we have now reanalyzed the data including the fixed factor of experiment in all the comparisons that contain data from multiple experiments. This has led us to modify the text in the Methods section under Linear Mixed Effects Models and in the Results section under Repeated kTMP Conditions (3.5 kHz and Sham) across Experiments. In addition, the results of the LME models have been updated throughout the Results section. We note that the pattern of results was unchanged with this modification of our analyses.

"Pairwise comparisons of each active condition to sham showed that an increase was observed following both 2 kHz ..." I suppose this is all for Experiment 1? It is a little confusing to go back and forth between combining experiments and then separate analyses per experiment without some guiding text, aside from being a bit messy from the statistical point of view. 

We did not go back to performing separate analyses of the experiments after pooling the data. Once we ran the test to justify pooling the data, subsequent tests were done with the pooled data to evaluate the effects of carrier frequency and amplitude modulation.

Figure 5 is confusing because the horizontal lines with ** on top seem to refer to the same set of sham subjects, but the subjects of Experiments 2 and 3 are different from Experiment 1, so in these pairwise comparisons there is a mix of between-subject and within subject-comparison going on here. Did I get that right? 

Yes – that is correct. As noted above we pooled the data after showing that there was no effect of experiment. Thus, the data for the sham and 3.5 kHz non-modulated conditions are from three different experiments. There was some overlap of subjects in Experiments 1 and Experiment 2 (Experiment 3 was all new participants).  We used a linear mixed effect model so that we could account for this mixed design. Participant was always included as a random factor, which allows us to account for the fact that some comparisons are within, and some are between. Based on a previous comment, we now include Experiment as a fixed factor (see above) which provides a way to evaluate variance across the different experiments.

"We next compared sham vs. active non-modulated kTMP and found that active kTMP produced a significant increase in corticospinal excitability [χ2(1) = 23.46 p < 0.001" Is this for the 3.5Hz condition? 

No, that is for an omnibus comparison of non-modulated kTMP (including 2 kHz, 3.5 kHz and 5 kHz conditions) vs. sham. We have edited the paper to include the three conditions that are included as the active non-modulated kTMP conditions for clarity (pg. 22, line 463). Having observed a significant omnibus result, we continued with paired comparisons: “Pairwise comparisons of each active condition to sham showed that an increase was observed following both 2 kHz [χ2(1) = 6.90, p = 0.009; d = 0.49] and 3.5 kHz kTMP [χ2(1) = 37.75, p < 0.001; d = 0.70; Fig 5: Non-Modulated conditions]. The 5 kHz condition failed to reach significance [χ2(1) = 1.43, p = 0.232; d = 0.21].”

Paired-Pulse Assays: There are a number of results here without pointing to a figure, and at one point there is a reference to Figure 6, which may be in error. It would help to point the reader to some visual corresponding the the stats. 

Thank you. This was an error on line 542. It should have read Figure 7. We have added two other pointers to Figure 7 where we discuss the absence of an effect of kTMP on SICI.

Reviewer #2 (Recommendations For The Authors):

I would recommend a couple of changes to the background.

"Orthogonal subspaces" line 78. This is a fairly formal term that has little relevance here, although the difference between scalar and vector potential-based fields is interesting to think about. If it stays, it should be mathematically supported, but it's easily rewritten to deliver the gist of it. 

We have updated the paper by adding text that we hope will clarify what we mean by orthogonal subspaces (pg. 4, line 78-81). We note that we developed the math behind this statement in a previous paper (Ref # 10: Sheltraw et al., 2021). We have changed the location of the citation so that it directly follows these sentences and will provide a pointer to readers interested in the physics and math concerning orthogonal subspaces. 

The statement that the scalp e-field for TES is greater than the e-field for TMS for similar cortical fields needs a little more clarification, since historically they have operated orders of magnitude apart, and it is easy to misread and trip over this statement (although it is factually true). Presenting a couple of numbers at cortical and scalp positions would help illustrate the point. That you are not considering applying TES at traditional TMS levels but rather TMS at TES values is what is initially easy to miss. 

We appreciate the feedback and have updated this section to provide the reader with a better intuition of this point. We now specify that the scalp to cortical E-field ratio is approximately 18 times larger for tES compared to TMS and cite our previous paper which has much more detail about how this was calculated.

A note that the figures show scalp sensation around 1.0 V/m while the text states 0.5; cortical depths are an important thing for the reader to keep in mind. 

This comment, when considered in tandem with one of the comments of Reviewer 1 led us to revise Figure 1. We removed the dashed gray line which might be taken to suggest a strict cutoff in terms of tolerability (which we did not intend). We now use shading that fades away to make the point of continuity. We have extended this down to a cortical E-field of 0.5 V/m to correspond with the text.  

This is a nicely done and carefully reported experiment and I look forward to seeing more. 

Thank you for your kind note!

Author response:

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

Public reviews:

Reviewer #1:

This work by Leclercq and colleagues performed metabolomics on biospecimens collected from 96 patients diagnosed with several types of alcohol use disorders (AUD). The authors discovered strong alterations in circulating glycerophospholipids, bile acids, and some gut microbe-derived metabolites in AUD patients compared to controls. An exciting part of this work is that metabolomics was also performed in frontal cortex of post-mortem brains and cerebrospinal fluid of heavy alcohol users, and some of the same metabolites were seen to be altered in the central nervous system. This is an important study that will form the basis for hypothesis generation around diet-microbe-host interactions in alcohol use disorder. The work is done in a highly rigorous manner, and the rigorously collected human samples are a clear strength of this work. Overall, many new insights may be gained by this work, and it is poised to have a high impact on the field.

Strengths:

(1) The rigorously collected patient-derived samples.

(2) There is high rigor in the metabolomics investigation.

(3) Statistical analyses are well-described and strong.

(4) An evident strength is the careful control of taking blood samples at the same time of the day to avoid alterations in meal- and circadian-related fluctuations in metabolites.

Weaknesses:

(1) Some validation in animal models of ethanol exposure compared to pair-fed controls would help strengthen causal relationships between metabolites and alterations in the CNS.

(2) The classification of "heavy alcohol users" based on autopsy reports may not be that accurate.

(3) The fact that most people with alcohol use disorder choose to drink over eating food, there needs to be some more discussion around how dietary intake (secondary to heavy drinking) most likely has a significant impact on the metabolome.<br />

We thank this reviewer for his/her encouraging comments and for highlighting the fact that this study is important in the field to generate hypotheses around diet-microbe-host interactions in alcohol use disorder.

Concerning weakness #1: Regarding the validation in animal models of ethanol exposure, we were very careful in our discussion to avoid pretending that the study allowed to test causality of the factors. This was certainly not the objective of the present study. The testing of causality would indeed probably necessitate animal models but these models could only test the effects of one single metabolite at a time and could not at the same time capture the complexity of the changes occurring in AUD patients. The testing of metabolites would be a totally different topic. Hence, we do not feel comfortable in conducting rodent experiments for several reasons. First, AUD is a very complex pathology with physiological and psychological/psychiatric alterations that are obviously difficult to reproduce in animal models. Secondly, as mentioned by the reviewer, AUD pathology spontaneously leads to nutritional deficits, including significant reductions in carbohydrates, lipids, proteins and fiber intakes. We have recently published a paper in which we carefully conducted detailed dietary anamneses and described the changes in food habits in AUD patients (Amadieu et al., 2021). As explained below, some blood metabolites that are significantly correlated with depression, anxiety and craving belong to the xanthine family and are namely theobromine, theophylline, and paraxanthine, which derived from metabolism of coffee, tea or chocolate (which are not part of the normal diet of mice or rats).Therefore, conducting an experiment in animal model of ethanol exposure compared to pair-fed controls will omit the important impact of nutrition in blood metabolomics and consequently won’t mimic the human AUD pathology. In addition, if we take into consideration the European Directive 2010/63/EU (on the protection of animals used for scientific purposes) which aims at Reducing (Refining, Replacing) the number of animals used in experiment, it is extremely difficult to justify, at the ethical point of view, the need to reproduce human results in an animal model that won’t be able to mimic the nutritional, physiological and psychological alterations of alcohol use disorder.

Concerning weakness #2: The classification of subjects to the group who have a history of heavy alcohol use was not solely on autopsy record, but was also based on medical history i.e. diagnosis of alcohol-related diseases: ICD-10 codes F10.X, G31.2, G62.1, G72.1, I42.6, K70.0-K70.4, K70.9, and K86.0, or signs of heavy alcohol use in the clinical or laboratory findings, e.g., increased levels of gamma-glutamyl transferase, mean corpuscular volume, carbohydrate-deficient transferrin, as stated in the methods section of the manuscript. In Finland, the medical records from the whole life of the subjects are available. We consider that getting diagnosis of alcohol-related disease is clear sign of history of heavy alcohol use.

Concerning weakness#3:  As explained above, we do agree with the reviewer that AUD is not only “drinking alcohol” but is also associated with reduction in food intake that obviously influenced the metabolomics data presented in this current study.  We have therefore added some data, which have not been published before, in the results section that refer to key nutrients modified by alcohol intake and we refer to those data and their link with metabolomics in the discussion section:

Results section page 8, Line 153-155. This sentence has been added:

“The changes in metabolites belonging to the xanthine family during alcohol withdrawal could be explained by the changes in dietary intake of coffee, tea and chocolate (see Fig S5).”

Discussion section: Page 11, Line 235-240.

“Interestingly, the caffeine metabolites belonging to the xanthine family such as paraxanthine, theophylline and theobromine that were decreased at baseline in AUD patients compared to controls, increased significantly during alcohol withdrawal to reach the levels of healthy controls. Changes in dietary intake of coffee, tea and chocolate during alcohol withdrawal could explain these results”.

In the conclusion, Page 16, Line 354-356, we clearly stated that: “LC-MS metabolomics plasma analysis allowed for the identification of metabolites that were clearly linked to alcohol consumption, and reflected changes in metabolism, alterations of nutritional status, and gut microbial dysbiosis associated with alcohol intake”

Reference:

Amadieu C, Leclercq S, Coste V, Thijssen V, Neyrinck AM, Bindels LB, Cani PD, Piessevaux H, Stärkel P, Timary P de, Delzenne NM. 2021. Dietary fiber deficiency as a component of malnutrition associated with psychological alterations in alcohol use disorder. Clinical Nutrition 40:2673–2682. doi:10.1016/j.clnu.2021.03.029

Leclercq S, Cani PD, Neyrinck AM, Stärkel P, Jamar F, Mikolajczak M, Delzenne NM, de Timary P. 2012. Role of intestinal permeability and inflammation in the biological and behavioral control of alcohol-dependent subjects. Brain Behav Immun 26:911–918. doi:10.1016/j.bbi.2012.04.001

Leclercq S, De Saeger C, Delzenne N, de Timary P, Stärkel P. 2014a. Role of inflammatory pathways, blood mononuclear cells, and gut-derived bacterial products in alcohol dependence. Biol Psychiatry 76:725–733. doi:10.1016/j.biopsych.2014.02.003

Leclercq S, Matamoros S, Cani PD, Neyrinck AM, Jamar F, Stärkel P, Windey K, Tremaroli V, Bäckhed F, Verbeke K, de Timary P, Delzenne NM. 2014b. Intestinal permeability, gut-bacterial dysbiosis, and behavioral markers of alcohol-dependence severity. Proc Natl Acad Sci U S A 111:E4485–E4493. doi:10.1073/pnas.1415174111

Voutilainen T, Kärkkäinen O. 2019. Changes in the Human Metabolome Associated With Alcohol Use: A Review. Alcohol and Alcoholism 54:225–234. doi:10.1093/alcalc/agz030

Public Reviewer #2:

The authors carried out the current studies with the justification that the biochemical mechanisms that lead to alcohol addiction are incompletely understood. The topic and question addressed here are impactful and indeed deserve further research. To this end, a metabolomics approach toward investigating the metabolic effects of alcohol use disorder and the effect of alcohol withdrawal in AUD subjects is valuable. However, it is primarily descriptive in nature, and these data alone do not meet the stated goal of investigating biochemical mechanisms of alcohol addiction. The current work's most significant limitation is the cross-sectional study design, though inadequate description and citation of the underlying methodological approaches also hampers interest. Most of the data are cross-sectional in the study design, i.e., alcohol use disorder vs controls. However, it is well established that there is a high degree of interpersonal variation with metabolism, and further, there is somewhat high intra-personal variation in metabolism over time. This means that the relatively small cohort of subjects is unlikely to reflect the broader condition of interest (AUD/withdrawal). The authors report a comparison of a later time-point after alcohol withdrawal (T2) vs. the AUD condition. However, without replicative time points from the control subjects it is difficult to assess how much of these changes are due to withdrawal vs the intra-personal variation described above.

We agree with the reviewer. Our goal was not to investigate the biochemical mechanisms of AUD but rather to investigate how metabolomics could contribute to the psychological alterations of AUD. The goals of the study are defined at the end of the introduction (Page 4 – Lines 80-91), as follows:

“The aims of this study are multiple. First, we investigated the impact of severe AUD on the blood metabolome by non-targeted LC-MS metabolomics analysis. Second, we investigated the impact of a short-term alcohol abstinence on the blood metabolome followed by assessing the correlations between the blood metabolome and psychological symptoms developed in AUD patients. Last, we hypothesized that metabolites significantly correlated with depression, anxiety or alcohol craving could potentially have neuroactive properties, and therefore the presence of those neuroactive metabolites was confirmed in the central nervous system using post-mortem analysis of frontal cortex and cerebrospinal fluid of persons with a history of heavy alcohol use. Our data bring new insights on xenobiotics- or microbial-derived neuroactive metabolites, which can represent an interesting strategy to prevent or treat psychiatric disorders such as AUD”.

Due to the fact that the method section describing the study design is located at the end of the manuscript, we have decided to clarify the methodological approach in the first paragraph of the result section in order to show that in fact, we have performed a longitudinal study (which includes the same group of AUD, tested at two time points – at the beginning and at the end of alcohol withdrawal). This is stated as follows:

Results section, Page 6, Line 97-99: “All patients were hospitalized for a 3-week detoxification program, and tested at two timepoints: T1 which represents the first day of alcohol withdrawal, and T2 which represents the last day of the detoxification program”.

We propose to add a figure with a schematic representation of the protocol. We let the editor deciding whether this figure can be added (as supplemental material).

Author response image 1.

Schematic representation of the protocol

We agree with the reviewer that the correlational analysis (between blood metabolites and psychological symptoms) is conducted at one time point (T1) only, which has probably led to the confusion between cross-sectional and longitudinal study. In fact we had a strong motivation to provide correlations at T1, instead of T2. T1, which is at the admission time, is really the moment where we can take into account variability of the psychological scores. Indeed, after 3 weeks of abstinence (T2), the levels of depression, anxiety and alcohol craving decreased significantly ( as shown in other studies from our group (Leclercq et al., 2014b, 2014a, 2012)) and remained pretty low in AUD patients, with a much lower inter-individual variability which makes the correlations less consistent.

We agree with the reviewer that there is a high intra and inter-personal variability in the metabolomics data, that could be due to the differences in previous meals intakes within and between subjects. While AUD subjects have been tested twice (at the beginning and at the end of a 3-week detoxification program), the control subjects have only been tested once. Consequently, we did not take into account the intra-personal variability in the control group. The metabolomics changes observed in AUD patients between T1 and T2 are therefore due to alcohol withdrawal but also to intra-personal variability. This is a limitation of the study that we have now added in the discussion section, Page 16, Lines 354-357  as follows:

“The selection of the control group is always challenging in alcohol research. Here, the healthy subjects were matched for sex, age and BMI but not for smoking status or nutritional intake. Alcohol addiction is a major cause of malnutrition in developed countries and tobacco smoking is more prevalent in alcohol users compared to healthy subjects. These two main confounding factors, although being an integral part of the alcoholic pathology, are known to influence the blood metabolome. Furthermore, another limitation is that the control group was tested only once, while the AUD patients were tested twice (T1 and T2). This means that we do not take into consideration the intra-personal variability of the metabolomics data when interpreting the results of alcohol withdrawal effects”.

The limitation concerning the small sample size is already mentioned in the discussion section, as follows:

“Large studies are usually required in metabolomics to observe small and medium size changes. Here, we included only 96 AUD patients, but they were all well characterized and received standardized therapies (for instance, vitB supplementation) during alcohol withdrawal”.

Overall, there is not enough experimental context to interpret these findings into a biological understanding. For example, while several metabolites are linked with AUD and associated with microbiome or host metabolism based on existing literature, it's unclear from the current study what function these changes have concerning AUD, if any. The authors also argue that alcohol withdrawal shifts the AUD plasma metabolic fingerprint towards healthy controls (line 153). However, this is hard to assess based on the plots provided since the change in the direction of the orange data subset is considers AUD T2 vs T1. In contrast, AUD T2 vs Control would represent the claimed shift. To support these claims, the authors would better support their argument by showing this comparison as well as showing all experimental groups (including control subjects) in their multi-dimensional model (e.g., PCA).

We thank the reviewer for these comments. It is true in this type of discovery-based approach the causality cannot be interpreted nor do we claim so. The aim was to characterize the metabolic alterations in this population, response to withdrawal period and suggest potential candidate metabolites linked to psychological symptoms. Rigorous pre-clinical assays and validation trials in humans are required to prove the causality, if any, of the discussed metabolites.

The original claim on line 153 was poorly constructed and the Figure 2c is meant to visualize the influence of withdrawal on selected metabolites and also show the effect of chronic alcohol intake on the selected metabolites at baseline. The description of the Figure 2c has been modified in result section from line 156 onwards: “Overall, Fig. 2c demonstrates that a number of identified metabolites altered in sAUD patients relative to control are affected by alcohol withdrawal. Apart from 4-pyridoxic acid, cotinine, and heme metabolites bilirubin and biliverdin, the shifts observed in the selected metabolites are generally in the opposite direction as compared to the baseline.”

The authors attempt to extend the significance of their findings by assessing post-mortem brain tissues from AUD subjects; however, the finding that many of the metabolites changed in T2/T1 are also present in AUD brain tissues is interesting; however, not strongly supporting of the authors' claims that these metabolites are markers of AUD (line 173). Concerning the plasma cohort itself, it is unclear how the authors assessed for compliance with alcohol withdrawal or whether the subjects' blood-alcohol levels were independently verified.

We did not claim that the metabolites significantly correlated with the psychological symptoms - and present in central nervous system (frontal cortex or CSF) -  are “markers of AUD”. Line 173 did not refer to this idea, and the terms “markers of AUD” do not appear in the whole manuscript.

Regarding the compliance with alcohol cessation, we did not assess the ethanol blood level. The patients are hospitalized for a 3-week detoxification program, they are not allowed to drink alcohol and are under strict control of the nurses and medical staff of the unit. Consuming alcoholic beverage within the hospitalization unit is a reason for exclusion. However, we carefully monitored the liver function during alcohol withdrawal. For the reviewers’ information, we have added here below, the evolution of liver enzymes (ALT, AST, gGT) during the 3-week detoxification program as indirect markers of alcohol abstinence.

Author response image 2.

Data are described as median ± SEM. AST, Aspartate transaminase; ALT, Alanine transaminase; gGT: gamma glutamyltranspeptidase. ** p<0.01 vs T1, *** p<0.001 vs T1

 

The second area of concern is the need for more description of the analytical methodology, the lack of metabolite identification validation evidence, and related statistical questions. The authors cite reference #59 regarding the general methodology. However, this reference from their group is a tutorial/review/protocol-focused resource paper, and it is needs to be clarified how specific critical steps were actually applied to the current plasma study samples given the range of descriptions provided in the citations. The authors report a variety of interesting metabolites, including their primary fragment intensities, which are appreciated (Supplementary Table 3), but no MS2 matching scores are provided for level 2 or 3 hits. Further, level 1 hits under their definition are validated by an in-house standard, but no supporting data are provided besides this categorization. Finally, a common risk in such descriptive studies is finding spurious associations, especially considering many factors described in the current work. These include AUD, depression, anxiety, craving, withdrawal, etc. The authors describe the use of BH correction for multiple-hypothesis testing. However, this approach only accounts for the many possible metabolite association tests within each comparison (such as metabolites vs depression). It does not account for the multi-variate comparisons to the many behavior/clinical factors described above. The authors should employ one of several common strategies, such as linear mixed effects models, for these types of multi-variate assessments.

The methodological details related to the sample processing, data acquisition, data pre-processing and metabolite identification have been provided in the supplementary materials and described below. Supplementary table 3 has been amended with characteristic MS2 fragments for both positive and negative ionization modes if data was available. Additionally, all annotations against the in-house library additions have been rechecked, identification levels corrected and EICs for all level 1 identifications are provided in the supplementary material.

As described in the statistical analysis methods, BH correction was employed in the group-wise comparisons to shortlist the altered features for identification. Manual curating was then applied for the significant features and annotated metabolites subjected to correlation analysis. In this discovery-based approach the aim was to discover potential candidates linked with psychological symptoms for subsequent work to evaluate causality. Hence, the application of multi-variate analysis assessing biomarker candidates is not in the scope of this study.

“LC-MS analysis. Plasma sample preparation and LC-MS measurement followed the parameters previously detailed in Klåvus et al (57).  Samples were randomized and thawed on ice before processing. 100 µl of plasma was added to 400 µl of LC-MS grade acetonitrile, mixed by pipetting four time, followed by centrifugation in 700 g for 5 minutes at 4 °C. A quality control sample was prepared by pooling 10 µl of each sample together. Extraction blanks having only cold acetonitrile and devoid of sample were prepared following the same procedure as sample extracts. LC-MS grade acetonitrile, methanol, water, formic acid and ammonium formate (Riedel-de Haën™, Honeywell, Seelze, Germany) were used to prepare mobile phase eluents in reverse phase (Zorbax Eclipse XDBC18, 2.1 × 100 mm, 1.8 μm, Agilent Technologies, Palo Alto, CA, USA) and hydrophilic interaction (Acquity UPLC® BEH Amide 1.7 μm, 2.1 × 100 mm, Waters Corporation, Milford, MA, USA) liquid chromatography separation. In reverse phase separation, the samples were analyzed by Vanquish Flex UHPLC system (Thermo Scientific, Bremen, Germany) coupled to high-resolution mass spectrometry (Q Exactive Focus, Thermo Scientific, Bremen, Germany) in both positive and negative polarity mass range from 120 to 1200, target AGC 1e6 and resolution 70,000 in full scan mode. Data dependent MS/MS data was acquired for both modes with target AGC 8e3 and resolution 17,500, precursor isolation window was 1.5 amu, normalized collision energies were set at 20, 30 and 40 eV and dynamic exclusion at 10.0 seconds. In hydrophobic interaction separation, the samples were analyzed by a 1290 LC system coupled to a 6540 UHD accurate mass Q-ToF spectrometer (Agilent Technologies, Waldbronn, Karlsruhe, Germany) using electrospray ionization (ESI, Jet Stream) in both positive and negative polarity with mass range from 50 to 1600 and scan rate of 1.67 Hz in full scan mode. Source settings were as in the protocol. Data dependent MS/MS data was acquired separately using 10, 20 and 40 eV collision energy in subsequent runs. Scan rate was set at 3.31 Hz, precursor isolation width of 1.3 amu and target counts/spectrum of 20,000, maximum of 4 precursor pre-cycle, precursor exclusion after 2 spectra and release after 15.0 seconds. Detectors were calibrated prior sequence and continuous mass axis calibration was performed throughout runs by monitoring reference ions from infusion solution for operating at high accuracy of < 2 ppm. Quality control samples were injected in the beginning of the analysis to equilibrate the system and after every 12 samples for quality assurance and drift correction in all modes. All data were acquired in centroid mode by either MassHunter Acquisition B.05.01 (Agilent Technologies) or in profile mode by Xcalibur 4.1 (Thermo Fisher Scientific) softwares.

Metabolomics analysis of TSDS frontal cortex and CSF samples using the same 1290 LC system coupled with a 6540 UHD accurate mass Q-ToF spectrometer has been previously accomplished by Karkkainen et al (10).

Peak picking and data processing. Raw instrumental data (*raw and *.d files) were converted to ABF format using Reifycs Abf Converter (https://www.reifycs.com/AbfConverter). MS-DIAL (Version 4.70) was employed for automated peak picking and alignment with the parameters according to Klåvus et al., 2020 (57) separately for each analytical mode. For the 6540 Q-ToF mass data minimum peak height was set at 8,000 and for the Q Exactive Focus mass data minimum peak height was set at 850,000. Commonly, m/z values up to 1600 and all retention times were considered, for aligning the peaks across samples retention time tolerance was 0.2 min and MS1 tolerance 0.015 Da and the “gap filling by compulsion” was selected. Alignment results across all modes and sample types as peak areas were exported into Microsoft Excel sheets to be used for further data pre-processing.

Pre-processing including drift correction and quality assessment was done using the notame package v.0.2.1 R software version 4.0.3 separately for each mode. Features present in less than 80% of the samples within all groups and with detection rate in less than 70% of the QC samples were flagged. All features were subjected to drift correction where the features were log-transformed and a regularized cubic spline regression line was fitted for each feature against the quality control samples. After drift correction, QC samples were removed and missing values in the non-flagged features were imputed using random forest imputation. Finally, the preprocessed data from each analytical mode was merged into a single data matrix.

Molecular feature characteristics (exact mass, retention time and MS/MS spectra) were compared against in-house standard library, publicly available databases such as METLIN, HMDB and LIPIDMAPS and published literature. Annotation of metabolites and the level of identification was based on the recommendations given by the Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI) (59): 1 = identified based on a reference standard, 2 = putatively annotated based on physicochemical properties or similarity with public spectral libraries, 3 = putatively annotated to a chemical class and 4 = unknown.”

Reference 59: Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007;3:211–221.

Recommendations for the authors:

Reviewer #1:

(1) There should be more discussion comparing and contrasting the differences between the 2 cohorts (ALCOHOLBIS versus GUT2BRAIN), instead of stressing the similarities.

As indicated in the results section, we have verified that the ALCOHOLBIS cohort and GUT2BRAIN cohort are similar in term of age, gender, smoking habits, drinking habits and severity of psychological symptoms. Those similar features are important to allow the combination of the metabolomics data from the two cohorts, which subsequently allows to have a bigger sample size (n = 96) and more statistical power.

(2) The identification of 97 heavy alcohol users based on hospital codes at autopsy may not be the most rigorous way to define those with AUD. More information is needed on how these 97 were classified as heavy alcohol users.

The classification of subjects to the group who have a history of heavy alcohol use was not based solely on the autopsy records. The classification was also based on medical history, which in Finland is available from the whole life of the subjects, and including diagnoses and laboratory finding. The subjects needed to have a diagnosis of alcohol-related disease, as stated in the methods section of the manuscript. However, since some of the used diagnoses are related to organ damage related to heavy alcohol use, we do not claim that these subjects would all have alcohol dependence. But history of heavy use of alcohol is needed to get organ damage associated with alcohol use. Therefore, we consider that diagnosis of alcohol-related disease is a clear sign of a history of heavy alcohol use.

(3) The fact that the control group mainly died of cardiovascular disease confounds the interpretations around alcohol impact metabolite levels. How much of the metabolomics differences are related to hyperlipidemia or other CVD risk factors in the controls?

There are no healthy controls in post-mortem studies, since all subjects need to die from something to be included to the cohort. The challenge in studying AUD is that they die relatively young. The only other group of individuals who die outside of hospital at the relatively same age as subjects with AUD are those with CVD. Post-mortem autopsies are done in Finland to all who die outside of hospital, and these are the main source of samples for post-mortem sample cohorts. Therefore, there is no other control group to compare AUD subject to in these types of studies.

As for the altered metabolites in the post-mortem sample, the phospholipids observed could be associated with CVD. However, alterations in phospholipids are also commonly associated with alcohol use and AUD (for a review see (Voutilainen and Kärkkäinen, 2019)) and this effect is also seen in the results from the clinical cohorts in this study (Figure 1). Therefore, it cannot be said that these phospholipids finding would be due to selection of the control group.

(4) When examining metabolomics alterations, it is extremely important to understand what people are eating (i.e., providing a substrate). A major confounding issue here is that heavy alcohol users typically choose drinking over eating food. How much of the observed alterations in the plasma metabolome is due to the decreased food intake? Some validation in animal models of ethanol exposure compared to pair-fed controls would help strengthen causal relationships between metabolites and alterations in the circulation and CNS.

Regarding the validation in animal models of ethanol exposure, we were very careful in our discussion to avoid pretending that the study allowed to test causality of the factors. This was certainly not the objective of the present study. The testing of causality would indeed probably necessitate animal models but these models could only test the effects of one single metabolite at a time and could not at the same time capture the complexity of the changes occurring in AUD patients. The testing of metabolites would be a totally different topic. Hence, we do not feel comfortable in conducting rodent experiments for several reasons. First, AUD is a very complex pathology with physiological and psychological/psychiatric alterations that are obviously difficult to reproduce in animal models. Secondly, as mentioned by the reviewer, AUD pathology spontaneously leads to nutritional deficits, including significant reductions in carbohydrates, lipids, proteins and fiber intakes. We have recently published a paper in which we carefully conducted detailed dietary anamneses and described the changes in food habits in AUD patients (Amadieu et al., 2021). As explained below, some blood metabolites that are significantly correlated with depression, anxiety and craving belong to the xanthine family and are namely theobromine, theophylline, and paraxanthine, which derived from metabolism of coffee, tea or chocolate (which are not part of the normal diet of mice or rats).Therefore, conducting an experiment in animal model of ethanol exposure compared to pair-fed controls will omit the important impact of nutrition in blood metabolomics and consequently won’t mimic the human AUD pathology. In addition, if we take into consideration the European Directive 2010/63/EU (on the protection of animals used for scientific purposes) which aims at Reducing (Refining, Replacing) the number of animals used in experiment, it is extremely difficult to justify, at the ethical point of view, the need to reproduce human results in an animal model that won’t be able to mimic the nutritional, physiological and psychological alterations of alcohol use disorder.

As explained above, we do agree with the reviewer that AUD is not only “drinking alcohol” but is also associated with reduction in food intake that obviously influenced the metabolomics data presented in this current study.  We have therefore added some data, which have not been published in the previous version of the manuscript, in the results section that refer to key nutrients modified by alcohol intake and we refer to those data and their link with metabolomics in the discussion section:

Results section page 8, Line 153-155. This sentence has been added:

“The changes in metabolites belonging to the xanthine family during alcohol withdrawal could be explained by the changes in dietary intake of coffee, tea and chocolate (see Fig S5).”

Discussion section: Page 11, Line 234-238.

“Interestingly, the caffeine metabolites belonging to the xanthine family such as paraxanthine, theophylline and theobromine that were decreased at baseline in AUD patients compared to controls, increased significantly during alcohol withdrawal to reach the levels of healthy controls. Changes in dietary intake of coffee, tea and chocolate during alcohol withdrawal could explain these results”.

In the conclusion, Page 16, Line 360-32, we clearly stated that: “LC-MS metabolomics plasma analysis allowed for the identification of metabolites that were clearly linked to alcohol consumption, and reflected changes in metabolism, alterations of nutritional status, and gut microbial dysbiosis associated with alcohol intake”

Reference:

Amadieu C, Leclercq S, Coste V, Thijssen V, Neyrinck AM, Bindels LB, Cani PD, Piessevaux H, Stärkel P, Timary P de, Delzenne NM. 2021. Dietary fiber deficiency as a component of malnutrition associated with psychological alterations in alcohol use disorder. Clinical Nutrition 40:2673–2682. doi:10.1016/j.clnu.2021.03.029

Leclercq S, Cani PD, Neyrinck AM, Stärkel P, Jamar F, Mikolajczak M, Delzenne NM, de Timary P. 2012. Role of intestinal permeability and inflammation in the biological and behavioral control of alcohol-dependent subjects. Brain Behav Immun 26:911–918. doi:10.1016/j.bbi.2012.04.001

Leclercq S, De Saeger C, Delzenne N, de Timary P, Stärkel P. 2014a. Role of inflammatory pathways, blood mononuclear cells, and gut-derived bacterial products in alcohol dependence. Biol Psychiatry 76:725–733. doi:10.1016/j.biopsych.2014.02.003

Leclercq S, Matamoros S, Cani PD, Neyrinck AM, Jamar F, Stärkel P, Windey K, Tremaroli V, Bäckhed F, Verbeke K, de Timary P, Delzenne NM. 2014b. Intestinal permeability, gut-bacterial dysbiosis, and behavioral markers of alcohol-dependence severity. Proc Natl Acad Sci U S A 111:E4485–E4493. doi:10.1073/pnas.1415174111

Voutilainen T, Kärkkäinen O. 2019. Changes in the Human Metabolome Associated With Alcohol Use: A Review. Alcohol and Alcoholism 54:225–234. doi:10.1093/alcalc/agz030

Reviewer #2:

(1) More methodological information about the laboratory processing of samples, instrumentation, and data analysis needs to be provided. Reference 59 needs to be more specific and include important methodological details for this project. Please provide an actual methods section for the mass-spectrometry-based metabolomics.

The reviewer is correct that the methods should be described in detail but due to word limits, the description was moved to a supplementary file. Methodological details are provided in the answer to the final comment in the public reviews section and we kindly refer to that for the methodological details. Reference 57 (Klåvus et al) is a method paper and covers the whole untargeted metabolomics pipeline that is used in our work.

(2) The VIP figures, e.g., Figure 1b and Figure 2b are not very informative and would be better represented in a supplementary table

VIP scores for all annotated metabolites are provided in the supplementary table 3 along with peak data and other values derived from statistical tests. Furthermore, we have removed the VIP value in figures 1 and 2 and we have replaced them by an updated Volcano plot to represent also the VIP values in addition to the q and Cohen’s d values.

(3) The findings on odd-chain lyso-lipids are interesting, and while these have been reported biologically, odd-chain lipids are uncommon and should be validated with authentic standards as available (please provide an XIC of the level 1 peak and standard if possible, e.g., LPC 17:0) or at least a supplementary figure on manual inspection of the negative mode MS2 spectrum showing the putative fatty acid chain fragment. The current assignments are based on positive mode lipid class fragments and accurate mass.

We thank the reviewer for pointing this out and it is correct that the negative MS2 spectrum is essential for lipid identification. Although the current assignments show only positive fragments for many lipids, the fatty acid chain, if reported, has been confirmed from negative mode MS2 spectrum. The supplementary table 3 with peak information has been augmented with fragment information from both negative and positive ionizations if available. Also, reference and experimental MS2 spectra have been provided as separate supplemental file for level 1 identifications, including the odd-chain lyso-lipids LPC 15:0 and 17:0.

(4) Please provide some supplementary information (MS1/MS2 if available) on the untargeted features of interest (up and down-regulated) from Figure 1C, especially the 5 encircled features. If any manual annotation of these features was attempted, please include a brief description in the results/discussion.

All statistically significant features with MS2 data have been subjected to manual annotation and database searches using at least METLIN, HMDB and LipidMaps. Additionally, if the manual inspection failed to provide any identification, in silico fragmentation software MS-FINDER was used to calculate candidate molecular formula. The features were labeled as unknown if all efforts were unsuccessful. The peak characteristics of the key unknowns in Figure 1b have also been included in the supplemental table.

A note of the manual inspection has been included in the result section line 129: “The top-ranked metabolites in Fig. 1b remained unknown regardless of manual curation.”

Reviewer #3:

I think this is an interesting paper with a very solid methodology and an abundance of results. I am not an expert on metabolomics, and I have some very interesting hours here, trying (but sometimes failing) to grasp this paper's content. This paper also needs to be closely read by a reviewer who knows the metabolomics field and can give feedback on the meaning of the results. I have focused purely on the AUD clinical side as this is where I may contribute. My main concern is conceptualizing the aims and what authors want to investigate. As far as I understand, this is a study of the relationship between alcohol use and the metabolome, and in this respect, I think there are some issues.

Just take the abstract that talks about (in the first sentence) alcohol use disorder ("AUD") - a term that generally sometimes refers to harmful use of alcohol and alcohol addiction and sometimes to all F10-diagnosis (and thus an inaccurate term), then the following sentence talks about what leads to alcohol addiction (not dependence) - and this in a mechanistic direction and in the last part of the second sentence talks about metabolomics being able to decipher metabolic events related to AUD. So, even in the first two sentences, it is confusing - is this about correlates, mechanisms, prevention, or treatment? The inaccuracy of terms continues in sentence 4. We have "chronic alcohol abuse" (?) and "severe alcohol use disorder (AUD)" (abbreviated for the second time). Later, only "alcohol abuse" is used and the abstract ends with something about these findings being interesting in "the management of [...] AUD". All this illustrates that there is a large mixture of concepts - what aspect of alcohol use or abuse are you looking at? Moreover, of intention: is it to find correlates, explanations, or targets for interventions? Without clarity in this respect, one can get lost in what all these interesting measures mean - how we should interpret them. This comment is made only for the abstract. However, but it is equally valid and important for the introduction and discussion parts of the ms, where additional terms and formulations are introduced: "heavy alcohol use" (lines 86-7) and "prevent or treat psychiatric disorders such as AUD" (lines 90-1). This is then reflected in the discussion where the authors claim that what they have found is related to "chronic alcohol abuse" (line 188), "heavy alcohol drinkers" (line 191), and "AUD patients" (lines 199 and 202 and further on).  

We thank the reviewer for this useful comment and we apologize for the confusion. We agree that it is important to use the correct terms and definitions. All patients included in this study were diagnosed as severe AUD (for more information on the diagnosis, see answer to the comments related to DSM-IV and DSM5). This manuscript is consequently related to severe AUD and other terms like “alcohol abuse, “alcohol addiction” are therefore not appropriate. In the revised version of the manuscript, we have used severe AUD or the abbreviation sAUD. The figure and legends have been changed accordingly.

In the first paragraph of the results section, ALCOHOLBIS and GUT2BRAIN are compared. It says they are similar on many measures, including craving, but different on some measures, again including craving. It is difficult to grasp this even if the authors try to explain (lines 101-2). This sentence also introduces some discussion in the results section by saying something normative about their finding and relating this to other research (references 12, 13, and 14).

We would like to apologize for the confusion related to first paragraph of the results section. We have indeed indicated that, while the ALCOHOLBIS cohort and the GUT2BRAIN cohort are highly similar in term of biological and psychological features, a significant difference does exist in the compulsive component of the craving score. Indeed, the mean score of compulsion is 11 ± 3 in the ALCOHOLBIS cohort and 14  ± 3 in the GUT2BRAIN cohort. In healthy controls, the mean score of compulsion is 1.5 ± 1.5. Despite the statistically significant difference in craving between both cohorts, we do not think that this difference is relevant in our context since both scores (11 and 14) are considered high compared to the control group. In order to simplify the message, we have revised the first paragraph as follows:

“Both groups of patients were similar in terms of age, gender, smoking and drinking habits and presented with high scores of depression, anxiety and alcohol craving at T1 (Table 1). These biological and psychological similarities allow us to combine both cohorts (and consequently increase sample size) and compare them to a group of heathy controls for metabolomics analysis”.

In line 104 the abbreviation PCA is introduced but needs to be explained. Such objections could be made for many of the abbreviations used (sPLS-DA VIP, LPC, CSF, CNS, LPE, etc.), but of course, they may be made more difficult by the unusual way of stacking the different sections.

We thank the reviewer for pointing these out. Most abbreviations are written out in the figure legends or method section but indeed the organization of the different sections makes it less evident. The abbreviations pointed out have been opened in the results section when they are first used.

Furthermore, they say that the severity of AUD was "evaluated by a psychiatrist using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, fourth edition (DSM-IV) (ALCOHOLBIS cohort) or fifth edition (DSM-5)" (GUT2BRAIN cohort): This makes sense for DSM-5 but needs to be explained more for DSM-IV. They also need to say what levels were included.

We thank the reviewer for this very appropriate remark that deserves some explanations.

While the patients of the GUT2BRAIN cohort were enrolled in 2018-2019 where the DSM5 was applicable, the patients from the ALCOHOLBIS cohort were recruited many years before. The protocol related to the ALCOHOLBIS cohort was written before 2013, and approved by ethical committee, where the DSM-IV was the last version of the DSM used at that moment. 

We therefore totally agree with the reviewer that our sentence “the severity of AUD was "evaluated by a psychiatrist using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, fourth edition (DSM-IV) (ALCOHOLBIS cohort) or fifth edition (DSM-5)" (GUT2BRAIN cohort)” is not correct. Indeed, DSM-IV (before 2013) described two distinct disorders, alcohol abuse and alcohol dependence, while the DSM-5 integrates the two DSM-IV disorders into a single disorder called alcohol use disorder with mild (2 or 3 symptoms), moderate (4 or 5 symptoms) and severe (6 or more symptoms) sub-classifications.

In this present study, we have enrolled patients that received the diagnosis of alcohol dependence (DSM-IV criteria) or severe alcohol use disorder (DSM5 criteria).

We have changed the paragraph related to this issue into this new one:

“The severity of AUD was evaluated by a psychiatrist using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, fourth edition (DSM-IV) (Alcoholbis cohort) or fifth edition (DSM-5) (GUT2BRAIN cohort). Patients evaluated with the DSM-IV received the diagnosis of “alcohol dependence”, while the patients evaluated with the DSM-5 received the diagnosis of “severe alcohol use disorder” (6 or more criteria). To simplify, we used the term “sAUD” (for severe alcohol use disorder) that includes both diagnosis (sAUD and alcohol dependence)”.

I am unsure about the shared first co-authorship and the shared last co-authorship request, but I leave this up to the editors and the journal policies. Also, the order of the different parts may be correct (the M+M placed last) but is unusual for many journals. This is also up to the journal to decide.

As mentioned in the guidelines to authors, the method section should be included at the end of the manuscript.

eLife assessment

This study provides valuable insights and allows for hypothesis generation around diet-microbe-host interactions in alcohol use disorder. The strength of the evidence is convincing: the work is done in a rigorous manner in a well-described cohort of patients with AUD before and after withdrawal. There are several weaknesses, including validating the metabolites identified by metabolomics, the cross-sectional study design, the lack of a healthy control group, and the descriptive nature of such clinical cohort studies. Nevertheless, the study provides a wealth of new data that may be the basis for future studies that test causality and elucidate the role of single metabolites in the psychiatric sequela of AUD.

Reviewer #1 (Public review):

Summary:

This work by Leclercq and colleagues performed metabolomics on biospecimens collected from 96 patients diagnosed with severe alcohol use disorder (AUD). The authors discovery strong alterations in circulating glycerophospholipids, bile acids, and some gut microbe-derived metabolites in AUD patients compared to controls. An exciting part of this work is that metabolomics was also done in post-mortem samples of the frontal cortex and cerebrospinal fluid of heavy alcohol users, and some of the same metabolites were seen to be altered in the central nervous system. This important study will form the basis for hypothesis generation around diet-microbe-host interactions in alcohol use disorder. The work is done in a highly rigorous manner, and the rigorously collected human samples is an evident strength of this work. Overall, this work will provide many new insights, and it is poised to have a high impact on the field.

Strengths:

(1) The rigorously collected patient-derived samples<br /> (2) There is high rigorous in the metabolomics investigation<br /> (3) Statistical analyses are well-described and strong.<br /> (4) The careful control of taking blood samples at the same time to avoid alterations in meal- and circadian-related fluctuations in metabolites is a clear strength.

Weaknesses:

None remaining

Reviewer #2 (Public review):

The authors carried out the current studies with the justification that the biochemical mechanisms that lead to alcohol addiction are incompletely understood. The topic and question addressed here are impactful and indeed deserve further research. To this end, a metabolomics approach toward investigating the metabolic effects of alcohol use disorder and the effect of alcohol withdrawal in AUD subjects is valuable. However, this work is primarily descriptive in nature, and these data alone do not meet the stated goal of investigating biochemical mechanisms of alcohol addiction. The current work's most significant limitation is the cross-sectional study design, though inadequate description and citation of the underlying methodological approaches also hampers interest.

Most of the data are cross-sectional in study design, i.e., alcohol use disorder vs controls. However, it is well established that there is a high degree of interpersonal variation with metabolism, and further, there is somewhat high intra-personal variation in metabolism over time. This means that the relatively small cohort of subjects is unlikely to just reflect the broader condition of interest (AUD/withdrawal). The authors report a comparison of a later time-point after alcohol withdrawal (T2) vs the AUD condition. Nonetheless, without replicate time points from the control subjects it is difficult to assess how much of these changes are due to withdrawal vs the intra-personal variation described above. Overall, insufficient experimental context exists to interpret these findings into a biological understanding. For example, while several metabolites are linked with AUD and associated with microbiome or host metabolism based on existing literature, it is unclear from the current study what function these changes have concerning AUD, if any. The authors also argue that alcohol withdrawal shifts the AUD plasma metabolic fingerprint towards healthy controls (line 153). However, this is hard to assess based on the provided plots since the direction of change of the orange data subset considers AUD T2 vs. T1. In contrast, AUD T2 vs. Control would represent the claimed shift. To substantiate these claims, the authors would better support their argument by showing this comparison in all experimental groups (including control subjects) in their multi-dimensional model (e.g., PCA). The authors attempt to extend the significance of their findings by assessing post-mortem brain tissues from AUD subjects; however, the finding that many of the metabolites changed in T2/T1 are also found in AUD brain tissues is interesting but does not strongly support the authors' claims that these metabolites are markers of AUD (line 173). Concerning the plasma cohort itself, it is unclear how the authors assessed for compliance with alcohol withdrawal or whether the subjects' blood-alcohol levels were independently verified.

The second area of concern is the lack of description of the analytical methodology, the lack of metabolite identification validation evidence, and related statistical questions. The authors cite reference #59 regarding the general methodology. However, this reference from their group is a tutorial/review/protocol focused resource paper and it needs to be clarified how specific critical steps were actually applied to the current plasma study samples, given the range of descriptions provided in the citations. The authors report a variety of interesting metabolites, including their primary fragment intensities, which is appreciated (Supp Table 3), but no MS2 matching scores are provided for level 2 or 3 hits. Further, level 1 hits under their definition are validated by an in-house standard, but not supporting data are provided other than this categorization. Finally, a common risk in such descriptive studies is finding spurious associations, especially considering the many factors as described in the current work. These include AUD, depression, anxiety, craving, withdrawal, etc. The authors describe the use of BH correction for multiple-hypothesis testing. Still, this approach only accounts for the many possible metabolite association tests within each comparison (such as metabolites vs. depression) and does not account for the multi-variate comparisons to the many behavior/clinical factors described above. The authors should employ one of several common strategies, such as linear mixed effects models for these types of multi-variate assessments.

Revised Review after Resubmission:

I thank the authors for their responses and revisions to the figures and data and their clarifications of their results and study goals. However, based on this updated information, it is now more apparent that the paper falls into the common trap of descriptive studies where insufficient experimental design was considered to test the association in question robustly. Further, follow-up initiatives are lacking to test the findings by other experimental means. Despite the authors' responses, the paper still fails to convert or interpret the metabolomics findings into any new biological understanding or meaningfully testable hypotheses, and the results remain descriptive in nature with significant caveats.

The authors clarify that their study's "goal was not to investigate the biochemical mechanisms of AUD but how metabolomics could contribute to the psychological alterations of AUD." However, the 2nd sentence of the abstract remains as follows: "The biochemical mechanisms that lead to this disorder are not yet fully understood, and in this respect, metabolomics represents a promising approach to decipher metabolic events related to AUD."This leads the reader to conclude that the purpose of the current study is to use metabolomics to address this gap, despite their later clarification. In the revised response, the authors walk back their claims of these goals, yet the manuscript text and data is largely unchanged in the revision. The serious caveats pointed out by several reviewers concerning the study as reported significantly reduces the utility of the described findings for the broader scientific community, and the authors largely downplay these limitations without addressing the underlying issues.

The authors also clarified in their response that the study's key purpose of the study is to assess "correlations between the blood metabolome and psychological symptoms developed in AUD patients." This goal is dubious as the vast majority of metabolites are not psychoactive, and it is implausible that the metabolome would affect mental state or vice versa. More biological frameworks and citations are needed for this paradigm. The soundness of the goal is further questioned by the study's simplistic design and the authors' admission that "In this discovery-based approach, the aim was to discover potential candidates linked with psychological symptoms for subsequent work to evaluate causality." Yet, the authors side-step the point about the risk of finding spurious associations and decline to control this risk using widely-accepted approaches such as multi-variate correction, instead continuing to use only BH correction for multiple hypothesis testing. The reviewers previously pointed out that BH correction only accounts for the many possible metabolite association tests within each comparison (such as metabolites vs depression). However, it does not account for the multi-variate comparisons to the many behavior/clinical factors. This issue is ignored in the response because the study's goal is hypothesis generating. Instead, the authors focused their responses on the issue of causality which was not the central point of the criticism.

Further, the authors employ mainly systemic plasma analyses unlikely to reflect brain biochemistry. The authors deny that the purpose of including the post-mortem brain tissue data was to demonstrate that "metabolites significantly correlated with the psychological symptoms - and present in the central nervous system (frontal cortex or CSF) - are "markers of AUD," yet if this is not the goal, the structure of the experiment, and the value of these data, is unclear. Another reviewer pointed out that it is difficult to control cross-sectional post-mortem tissue due to a lack of suitable controls, and the authors again side-step the question by citing the lack of suitable controls and the impossibility of "healthy controls" in post-mortem samples. This is true, but this lack of technical feasibility and the confounding factor of CVD/lipid metabolism does not justify the weak experimental design in this respect. Therefore, it remains unclear what can be understood from these data, given the limitations.

Finally, the authors acknowledge the limitation in their revision that they did not assess a second-time point in the control cohort of samples which could have been used to tease apart intra-personal variation from AUD-associated changes during alcohol-abstinence. Unfortunately, this is not a small caveat to simply acknowledge in the discussion section; it severely limits the interpretation and utility of the reported data more broadly, and the authors do not address this underlying problem.

Reviewer #3 (Public review):

Summary:

The authors have compared different groups of AUD patients at different levels and have examined metabolomics.

Strengths:

A well-written and comprehensive study.

eLife assessment

This important study describes changes in excitability in motor neurons of the peripheral autonomous nervous system during aging. The manuscript provides convincing evidence indicating that sympathetic neurons from aged mice show higher excitability compared to neurons from young mice which was linked to decreased activity of KCNQ2/3 potassium channels. This research has implications for understanding the age-related changes that occur in the peripheral nervous system.

Reviewer #1 (Public review):

Summary:

The authors study age-related changes in the excitability and firing properties of sympathetic neurons, which they ascribe to age-related changes in the expression of KCNQ (Kv7, "M-type") K+ currents in rodent sympathetic neurons, whose regulation by GPCRs has been most thoroughly studied for over 40 years.

Strengths:

The strengths include the rigor of the current-clamp and voltage-clamp experiments and the lovely, crisp presentation of the data, The separation of neurons into tonic, phasic and adapting classes is also interesting, and informative. The ability to successfully isolate and dissociate peripheral ganglia from such older animals is also quite rare and commendable! There is much useful detail here.

Weaknesses:

Whereas the description of the data are very nice, and useful, the manuscript does not provide much in the way of mechanistic insights. As such, the effect is more of an epi-phenomenon of unclear insight, and the authors cannot ascribe changes in signaling mechanisms, such as that of M1 mAChRs to the phenomena that is supported by data.

Comments on latest version:

I do not have any additional issues to be addressed by the authors.

Reviewer #2 (Public review):

Summary:

This research provides compelling and detailed evidence showing that aging influences intrinsic membrane properties of peripheral sympathetic motor neurons, which become hyperexcitable. The authors found that sympathetic motor neurons from old mice exhibit increased firing rates (spontaneous and evoked), more depolarized membrane resting potential, and increased rheobase. Furthermore, the study investigates cellular mechanisms underlying age-associated hyperexcitability and shows solid evidence supporting that a decreased activity of KCNQ2/3 channels during aging is a major contributor to the increased excitability of sympathetic old neurons. The conclusions of this paper are supported by the data.

Strengths:

Detailed and rigorous analysis of electrical responses of peripheral sympathetic motor neurons using electrophysiology (perforated patch and whole-cell recordings). The study identifies a decreased KCNQ2/3 current as a cellular mechanism behind age-induced hyperexcitability in sympathetic motor neurons.

Weaknesses:

The revised version of the manuscript has addressed all my concerns.

Reviewer #3 (Public review):

This revised study described changes in membrane excitability and Na+ and K+ current amplitudes of sympathetic motor neurons in culture. The findings indicate that neurons isolated from aged animals show increased membrane excitability manifested as increased firing rates in response to electrical stimulation and changes in related membrane properties including depolarized resting membrane potential, increased rheobase, and spontaneous firing. By contrast, neuron cultures from young mice show little to no spontaneous firing and relatively low firing rates in response to current injection. These changes in excitability correlate with reductions in the magnitude of KCNQ currents in neurons cultured from aged mice compared to neurons from cultured from young mice. The authors conclude that aging promotes hyperexcitability of sympathetic motor neurons through changes in KCNQ channels.

Author response:

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

Recommendations for the authors:

Please make corrections as suggested by reviewer 1 to improve the manuscript. Specifically, reviewer 1 suggests making changes to p values in Figure 5, and the importance of citing original scholarly works related to effects of increase in excitability of sympathetic neurons by M1 receptors, and the terminology for M currents and KCNQ currents. These changes will improve the manuscript and are strongly recommended.

The section dealing with Aging Reduces KCNQ currents seems to contain a lot of extraneous information especially in the last part of the long paragraph and this section should be rewritten for improved clarity and - the implications or lack thereof - of the correlation of KCNQ with AP firing rates. The apparent lack of correlation between KCNQ current and KCNQ2 protein needs to be better explained. This is a central part of the study and this result undercuts the premise of the paper. Additionally, the poor specificity of Linordipine for KCNQ should be pointed out in the limitations.

Finally, the editor notes that the author response should not contain ambiguities in what was addressed in the revision. In the original summary of consolidated revisions that were requested, one clearly and separately stated point (point 4) was that experiments in slice cultures should be strongly considered to extend the significance of the work to an intact brain preparation. The author response letter seems to imply that this was done, but this is not the case. The author response seems to have combined this point with another separate point (point 3) about using KCNQ drugs, and imply that all concerns were addressed. Authors should be clear about what revisions were in fact addressed.

Summary of recommendations from the three reviewers:

Please make corrections as suggested by reviewer 1 to improve the manuscript.

Specifically, reviewer 1 suggests making changes to p values in Figure 5,

As a team, we have decided to keep p values. Here is our rationale:

Our lab favors reporting p-values for all statistical comparisons to help readers identify what we consider statistically significant. We color-coded the p-values, with red for p-value < 0.05 and black for p-value > 0.05. As a reader, seeing a p-value=0.7 allows me to know that the authors performed an analysis comparing these conditions and found the mean not to be different. Not presenting the p-value makes me wonder whether the authors even analyzed those groups. We value the ability to analyze the data by seeing all p-values than not being distracted by non-significant p-values.

and the importance of citing original scholarly works related to effects of increase in excitability of sympathetic neurons by M1 receptors, and the terminology for M currents and KCNQ currents. These changes will improve the manuscript and are strongly recommended.

We cited original papers on that area and changed the terminology for M current. I kept KCNQ when referring to the channel protein or abundance.

The section dealing with Aging Reduces KCNQ currents seems to contain a lot of extraneous information especially in the last part of the long paragraph and this section should be rewritten for improved clarity… and - the implications or lack thereof - of the correlation of KCNQ with AP firing rates.

I separated the long paragraph in two. I also removed extraneous information in that section. It now reads:

Previous work by our group and others demonstrated that cholinergic stimulation leads to a decrease in M current and increases the excitability of sympathetic motor neurons at young ages.67-71 The molecular determinants of the M current are channels formed by KCNQ2 and KCNQ3 in these neurons.70, 76, 77 Thus, Figure 6A shows a voltage response (measured in current-clamp mode) and a consecutive M current recording (measured in voltage-clamp mode) in the same neuron upon stimulation of cholinergic type 1 muscarinic receptors. It illustrates the temporal correlation between the decrease of M current with the increase in excitability and firing of APs. This strong dependence led us to hypothesize that aging decreases M current, leading to a depolarized RMP and hyperexcitability (Figure 6B). For these experiments, we measured the RMP and evoked activity using perforated patch, followed by the amplitude of M current using a whole-cell voltage clamp in the same cell. We also measured the membrane capacitance as a proxy for cell size. Interestingly, M current density was smaller by 29% in middle age (7.5 ± 0.7 pA/pF) and by 55% in old (4.8 ± 0.7 pA/pF) compared to young (10.6 ± 1.5 pA/pF) neurons (Figure 6C-D). The average capacitance was similar in young (30.8 ± 2.2 pF), middle-aged (27.4 ± 1.2 pF), and old (28.8 ± 2.3 pF) neurons (Figure 6E), suggesting that aging is not associated with changes in cell size of sympathetic motor neurons, and supporting the hypothesis that aging alters the levels of M current. Next, we tested the effect on the abundance of the channels mediating M current. Contrary to our expectation, we observed that KCNQ2 protein levels were 1.5 ± 0.1 -fold higher in old compared to young neurons (Figure 6F-G). Unfortunately, we did not find an antibody to detect consistently KCNQ3 channels. We concluded that the decrease in M current is not caused by a decrease in the abundance of KCNQ2 protein.

B. and - the implications or lack thereof - of the correlation of KCNQ with AP firing rates.

I am not sure to understand the request in the section on the correlation of KCNQ with AP firing rate. I divided the long paragraph.

The apparent lack of correlation between KCNQ current and KCNQ2 protein needs to be better explained. This is a central part of the study and this result undercuts the premise of the paper.

Indeed, total KCNQ2 protein abundance increases while M current decreases. We do not claim in our work that changes in excitability are caused by a reduction in the expression or density of KCNQ2 channels. On the contrary, our current working hypothesis is that the reduction in M current is caused by changes in traffic, degradation, posttranslational modifications, or cofactors for KCNQ2 or KCNQ3 channels. I have modified the description in the results section and discussion to clarify this concept. We also note that the discussion section contains a paragraph discussing this discrepancy.

Additionally, the poor specificity of Linordipine for KCNQ should be pointed out in the limitations.

Thank you for the suggestion. I have added the following sentences to the Limitations section. It reads: “We want to point out that linopirdine has been reported to affect other ionic currents besides M current (Neacsu and Babes, 2010; Lamas et al., 1997). Despite this limitation, the application of linopirdine to young sympathetic motor neurons led to depolarization and firing of action potentials.”

Finally, the editor notes that the author response should not contain ambiguities in what was addressed in the revision. In the original summary of consolidated revisions that were requested, one clearly and separately stated point (point 4) was that experiments in slice cultures should be strongly considered to extend the significance of the work to an intact brain preparation. The author response letter seems to imply that this was done, but this is not the case. The author response seems to have combined this point with another separate point (point 3) about using KCNQ drugs, and imply that all concerns were addressed. Authors should be clear about what revisions were in fact addressed.

We apologize for this omission. After reviewing this comment, I realized I did not respond to the Major points in the section of the Recommendations for the authors from Reviewer 3. We missed that entire section. Our previous responses addressed the Public review of Reviewer 3. When doing so, we did not separate the sentences, omitting the request to perform the experiment in slices.

The proposed experiments will require an upward microscope coupled to an electrophysiology rig; unfortunately, we do not have the equipment to do these experiments. We agree that our findings need to be tested in intact preparations to understand how the hyperactivity of sympathetic motor neurons affects systemic responses and the function of controlling organ function. This is a crucial step to move the field forward. Our laboratory is trying to find the appropriate experimental design to address this problem. We believe we must go beyond redoing these experiments in slices.

Reviewer #1 (Recommendations For The Authors):

(1) The significance values greater than p < 0.05 do not add anything and distract focus from the results that are meaningful. Fig. 5 is a good example. What does p = 0.7 mean? Or p = 0.6? Does this help the reader with useful information?

We thank Reviewer 1 for raising this question. We have attempted different versions of how we report p values, as we want to make sure to address rigor and transparency in reporting data.

Our lab favors reporting p-values for all statistical comparisons to help readers identify what we consider statistically significant. We color-coded the p-values, with red for p-value < 0.05 and black for p-value > 0.05. As a reader, seeing a p-value=0.7 allows me to know that the authors performed an analysis comparing these conditions and found the mean not to be different. Not presenting the p-value makes me wonder whether the authors even analyzed those groups. We value the ability to analyze the data by seeing all p-values than not being distracted by non-significant p-values.

(2) Fig. 1 is not informative and should be removed.

Although we agree with the reviewer that this figure is not informative, it was created to guide the reader in identifying the problem addressed in our manuscript in the physiological context. Our colleagues who read the first drafts of the manuscript recommended this, so we prefer to keep the figure.

(3) The emphasis on a particular muscarinic agonist favored by many ion channel physiologists, oxotremorine, is not meaningful (lines 192, 198). The important point is stimulation of muscarinic AChRs, which physiologically are stimulated by acetylcholine. The particular muscarinic agonist used is unimportant. Unless mandated by eLife, "cholinergic type 1 muscarinic receptors" are usually referred to as M1 mAChRs, or even better is "Gq-coupled M1 mAChRs." I don't think that Kruse and Whitten, 2021 were the first to demonstrate the increase in excitability of sympathetic neurons from stimulation of M1 mAChRs. Please try and cite in a more scholarly fashion.

A) We have modified lines 192 and 198, removing the mention of oxotremorine.

B) We have modified the nomenclature used to refer to cholinergic type 1 muscarinic receptors.

C) We cited references on the role of M current on sympathetic motor neuron excitability.

(4) The authors may want to use the term "M current" (after defining it) as the current produced by KCNQ2&3-containing channels in sympathetic neurons, and reserve "KCNQ" or "Kv7" currents as those made by cloned KCNQ/Kv7 channels in heterologous systems. A reason for this is to exclude currents KCNQ1-containing channels, which most definitely do not contribute to the "KCNQ" current in these cells. I am not mandating this, but rather suggesting it to conform with the literature.

Thank you for the suggestion. I have modified the text to use the term M current. I maintained the use of KCNQ only when referring to KCNQ channel, such as in the section describing the abundance of KCNQ2.

(5) The section in the text on "Aging reduces KCNQ current" is confusing. Can the authors describe their results and their interpretation more directly?

(6) Please explain the meaning of the increase in KCNQ2 abundance with age in Fig. 6G. How is this increase in KCNQ2 expression consistent with an increase in excitability? The explanation of "The decrease in KCNQ current and the increase in the abundance of KCNQ2 protein suggest a potential compensatory mechanism that occurs during aging, which we are actively investigating in an independent study." is rather odd, considering that the entire thesis of this paper is that changes in excitability and firing properties are underlied by changes in KCNQ2/3 channel expression/density. Suddenly, is this not the case?? What about KCNQ3? It would be very enlightening if the authors would just quantify the ratio of KCNQ2:KCNQ3 subunits in M-type channels in young and old mice using simple TEA dose/response curves (see Shapiro et al., JNS, 2000; Selyanko et al., J. Physiol., Hadley et al., Br. J. Pharm., 2001 and a great many more). It is also surprising that the authors did not assess or probe for differences in mAChR-induced suppression of M current between SCG neurons of young and old mice. This would seem to be a fundamental experiment in this line of inquiry.

We have divided this paragraph in sections.

A. Please explain the meaning of the increase in KCNQ2 abundance with age in Fig. 6G. How is this increase in KCNQ2 expression consistent with an increase in excitability? The explanation of "The decrease in KCNQ current and the increase in the abundance of KCNQ2 protein suggest a potential compensatory mechanism that occurs during aging, which we are actively investigating in an independent study." is rather odd, considering that the entire thesis of this paper is that changes in excitability and firing properties are underlied by changes in KCNQ2/3 channel expression/density. Suddenly, is this not the case??

Our interpretation is that the decrease in M current is not caused by a decrease in the abundance of KCNQ (2) channels. We do not claim that changes in excitability are caused by a reduction in the expression or density of KCNQ2 channels. On the contrary, our working hypothesis is that the reduction in M current is caused by changes in traffic, degradation, posttranslational modifications, or cofactors for KCNQ2 or KCNQ3 channels. We have modified the description in the results section to clarify this concept. “We concluded that the decrease in M current is not caused by a decrease in the abundance of KCNQ2 protein.”

B. What about KCNQ3?

Unfortunately, we did not find an antibody to detect KCNQ3 channels. I have added a sentence to state this.

C. KCNQ2: KCNQ3 subunits in M-type channels in young and old mice using simple TEA dose/response curves.

Our laboratory is working to deeply understand the mechanism behind the changes in M current and its regulation by mAChRs in young and old ages. However, it is part of different research to attend to the complexity of the question. We think pharmacology experiments are insufficient to understand the question's complexity as we described in the next answer.

D. It is also surprising that the authors did not assess or probe for differences in mAChR-induced suppression of M current between SCG neurons of young and old mice. This would seem to be a fundamental experiment in this line of inquiry.

As mentioned, our laboratory is working to understand the mechanism behind M current and its regulation in young and old ages deeply. Our preliminary data show that M currents recorded in old neurons show two behaviors with the activation of mAChR: 1) they do not respond (blue line), or 2) they show a smaller and slower current inhibition than young neurons (red line). This data shows the complexity of the mechanism behind the M current in old neurons where changes in basal levels of PIP2, phospholipids metabolism, KCNQ2/3 changes in traffic/degradation, and M current pharmacology need to be addressed together for a proper interpretation. Showing only one part of this set of experiments in this article may lead to misinterpretation of results.

Author response image 1.

(7) Why do the authors use linopirdine instead of XE-991? Both are dirty drugs hardly specific to KCNQ channels at 25 uM concentrations, but linopirdine less so. The Methods section lists the source of XE991 used in the study, not linopirdine. Is there an error?

A. Why do the authors use linopirdine instead of XE-991?

We use linopiridine with the experimental goal of observing the recovery phase during the washout. The main difference between the effects of XE991 and linopiridine on Kv7.2/3 is associated with the recovery phase. Currents under XE991 treatment recover 30% after 10 min compared to 93.4% with linopiridine in expression systems at -30 mV (Greene DL et al., 2017, J Pharmacol Exp Ther). After validation of KCNQ2/3 inhibition by linopirdine (IC50 value of 2.4 µM), we found linopirdine the most appropriate drug for our experiments.

Unfortunately, we were not able to observe a recovery in our experiments. The limited recovery after washout may be associated with the membrane potential of our conditions (-60 to -50 mV).

B. Both are dirty drugs hardly specific to KCNQ channels at 25 uM concentrations, but linopirdine less so.

We understand the concern of the reviewer. The specificity of XE-991 and linopiridine is not absolute. Linopiridine has been reported to activate TRPV1 channels (EC50 =115 µM, Neacsu and Babes, 2010, J Pharmacol Sci) or nicotinic acetylcholine receptors and GABA-induced Cl- currents (EC50 =7.6 µM and 8.1 µM respectively; Lamas et al, 1997, Eur J Neurosci).

To clarify this limitation in the article, we have added the following sentence in the section Limitations and Conclusions. “We want to point out that linopirdine has been reported to affect other ionic currents besides M current (Neacsu and Babes, 2010; Lamas et al., 1997). Despite this limitation, the application of linopirdine to young sympathetic motor neurons led to depolarization and firing of action potentials.”

C. The Methods section lists the source of XE991 used in the study, not linopirdine. Is there an error?

Thank you for pointing out this. We have added information for both retigabine and linopirdine in the Methods section; both were missing.

(8) Can the authors use a more scientific explanation of RTG action than "activating KCNQ channels?" For instance, RTG induces both a negative-shift in the voltage-dependance of activation and a voltage-independent increase in the open probability, both of which differing in detail between KCNQ2 and KCNQ3 subunits. The authors are free to use these exact words. Thus, the degree of "activation" is very dependent upon voltage at any voltages negative to the saturating voltages for channel activation.

We have modified the text to reflect your suggestion. Thank you.

(9) Methods: did the authors really use "poly-l-lysine-coated coverslips?" Almost all investigators use poly-D-lysine as a coating for mammalian tissue-culture cells and more substantial coatings such as poly-D-lysine + laminin or rat-tail collagen for peripheral neurons, to allow firm attachment to the coverslip.

That is correct. We used poly-L-lysine-coated coverslips. Sympathetic motor neurons do not adhere to poly-D-Lysine.

(10) As a suggestion, sampling M-type/KCNQ/Kv7 current at 2 kHz is not advised, as this is far faster than the gating kinetics of the channels. Were the signals filtered?

Signals were not filtered. Currents were sampled at 2KHz. Our conditions are not far from what is reported by others. Some sample at 10KHz and even 50 KHz. Others do not report the sample frequency.

Reviewer #2:

Weaknesses:

None, the revised version of the manuscript has addressed all my concerns.

We are very appreciative and glad that our responses satisfied your previous concerns.

Reviewer #3:

The main weakness is that this study is a descriptive tabulation of changes in the electrophysiology of neurons in culture, and the effects shown are correlative rather than establishing causality.

In the previous revision, Reviewer 3 wrote: “It is difficult to know from the data presented whether the changes in KCNQ channels are in fact directly responsible for the observed changes in membrane excitability.” And suggested the “use of blockers and activators to provide greater relevance.”

Attending this recommendation, we performed experiments in Fig. 8. Young neurons exposed to linopirdine depolarize membrane potential and promote action potential firing. In contrast, the old neurons treated with retigabine repolarize membrane potential and stop firing action potentials. This new set of experiments suggests age-related electrophysiological changes in old neurons are associated with changes in M current. The main finding of our article.

If Reviewer 3 refers to establishing causality between aging and a reduction in M current, I would like to emphasize that our laboratory is working toward a better understanding of the molecular mechanism of how M current is affected by aging; however, it will be part of a different article.  One of our attempts was to reverse aging with rapamycin, but the previous recommendation was to remove those experiments.

… but the specifics of the effects and relevance to intact preparations are unclear.

Additional experiments in slice cultures would provide greater significance on the potential relevance of the findings for intact preparations.

I apologize for missing this point in the previous revision. The proposed experiments will require an upward microscope coupled to an electrophysiology rig. Unfortunately, I do not

have the equipment to do these experiments.

eLife assessment

This study presents useful findings for how sevoflurane anesthesia modulates the activity of corticotropin-releasing hormone neurons in the paraventricular nucleus of the hypothalamus and how manipulation of such PVHCRH neurons influences anesthesia and post-anesthesia responses. The technical approaches are solid and the data presented is largely clear. Whether PVHCRH neurons are critical for the mechanisms of sevoflurane anesthesia is a direction for the future.

Joint Public Review:

This study describes a group of CRH-releasing neurons, located in the paraventricular nucleus of the hypothalamus, which, in mice, affects both the state of sevoflurane anesthesia and a grooming behavior observed after it. PVHCRH neurons showed elevated calcium activity during the post-anesthesia period. Optogenetic activation of these PVHCRH neurons during sevoflurane anesthesia shifts the EEG from burst-suppression to a seemingly activated state (an apparent arousal effect), although without a behavioral correlate. Chemogenetic activation of the PVHCRH neurons delays sevoflurane-induced loss of righting reflex (another apparent arousal effect). On the other hand, chemogenetic inhibition of PVHCRH neurons delays recovery of righting reflex and decreases sevoflurane-induced stress (an apparent decrease in the arousal effect). The authors conclude that PVHCRH neurons "integrate" sevoflurane-induced anesthesia and stress. The authors also claim that their findings show that sevoflurane itself produces a post-anesthesia stress response that is independent of any surgical trauma, such as an incision. In its revised form, the article does not achieve its intended goal and will not have impact on the clinical practice of anesthesiology nor on anesthesiology research.

Strengths:

The manuscript uses targeted manipulation of the PVHCRH neurons with state-of-the-art methods and is technically sound. Also, the number of experiments is substantial.

Weaknesses:

The most significant weaknesses remain: a) overinterpretation of the significance of their findings b) the failure to use another anesthetic as a control, c) a failure to compellingly link their post-sevoflurane measures in mice to anything measured in humans, and d) limitations in the novelty of the findings. These weaknesses are related to the primary concerns described below:

Concerns about the primary conclusion that PVHCRH neurons integrate the anesthetic effects and post-anesthesia stress response of sevoflurane GA:

It is important to compare the effects of sevoflurane with at least one other inhaled ether anesthetic as one step towards elevating the impact of this paper to the level required for a journal such as eLife. Isoflurane, desflurane, and enflurane are ether anesthetics that are very similar to each other, as well as being similar to sevoflurane. For example, one study cited by the authors (Marana et al. 2013) concludes that there is weak evidence for differences in stress-related hormones between sevoflurane and desflurane, with lower levels of cortisol and ACTH observed during the desflurane intraoperative period. It is important to determine whether desflurane activates PVHCRH neurons in the post-anesthesia period, and whether this is accompanied by excess grooming in the mice because this will distinguish whether the effects of sevoflurane generalize to other inhaled anesthestics, or, alternatively, relate to unique idiosyncratic properties of this gas that may not be a part of its anesthetic properties.

Concerns about the clinical relevance of the experiments:

In anesthesiology practice, perioperative stress observed in patients is more commonly related to the trauma of the surgical intervention, with inadequate levels of antinociception or unconsciousness intraoperatively and/or poor post-operative pain control. The authors seem to be suggesting that the sevoflurane itself is causing stress because their mice receive sevoflurane but no invasive procedures, but there is no evidence of sevoflurane inducing stress in human patients. It is important to know whether sevoflurane effectively produces behavioral stress in the recovery room in patients that could be related to the putative stress response (excess grooming) observed in mice. For example, in surgeries or procedures which required only a brief period of unconsciousness that could be achieved by administering sevoflurane alone (comparable to the 30 min administered to the mice), is there clinical evidence of post-operative stress? It is also important to describe a rationale for using a 30 min sevoflurane exposure. What proportion of human surgeries using sevoflurane use exposure times that are comparable to this?

It is the experience of one of the reviewers that human patients who receive sevoflurane as the primary anesthetic do not wake up more stressed than if they had had one of the other GABAergic anesthetics. If there were signs of stress upon emergence (increased heart rate, blood pressure, thrashing movements) from general anesthesia, this would be treated immediately. The most likely cause of post-operative stress behaviors in humans is probably inadequate anti-nociception during the procedure, which translates into inadequate post-op analgesia and likely delirium. It is the case that children receiving sevoflurane do have a higher likelihood of post-operative delirium. Perhaps the authors' studies address a mechanism for delirium associated with sevoflurane, but this is barely mentioned. Delirium seems likely to be the closest clinical phenomenon to what was studied. As noted by the Besnier et al (2017) article cited by the authors, surgery can elevate postoperative glucocorticoid stress hormones, but it generally correlates with the intensity of the surgical procedure. Besnier et al also note the elevation of glucocorticoids is generally considered to be adaptive. Thus, reducing glucocorticoids during surgery with sevoflurane may hamper recovery, especially as it relates to tissue damage, which was not measured or considered here. This paper only considers glucocorticoid release as a negative factor, which causes "immunosuppression", "proteolysis", and "delays postoperative recovery and...leads to increased morbidity".

It is also the case that there are explicit published findings showing that mild and moderate surgical procedures in children receiving sevoflurane (which might be the closest human proxy to the brief 30 minute sevoflurane exposure used here) do not have elevated cortisol (Taylor et al, J Clin Endocrinol Metab, 2013). This again raises the question of whether the enhanced grooming or elevated corticosterone observed in the mice here has any relevance to humans.

Concerns about the novelty of the findings:

The key finding here is that CRH neurons mediate measures of arousal, and arousal modulates sevoflurane anesthesia induction and recovery. However, CRH is associated with arousal in numerous studies. In fact, the authors' own work, published in eLife in 2021, showed that stimulating the hypothalamic CRH cells lead to arousal and their inhibition promoted hypersomnia. In both papers the authors use fos expression in CRH cells during a specific event to implicate the cells, then manipulate them and measure EEG responses. In the previous work, the cells were active during wakefulness; here- they were active in the awake state the follows anesthesia (Figure 1). Thus, the findings in the current work are incremental and not particularly impactful. Claims like "Here, a core hypothalamic ensemble, corticotropin-releasing hormone neurons in the paraventricular nucleus of the hypothalamus, is discovered" are overstated. PVHCRH cell populations were discovered in the 1980s. Suggesting that it is novel to identify that hypothalamic CRH cells regulate post-anesthesia stress is unfounded as well: this PVH population has been shown over four decades to regulate a plethora of different responses to stress. Anesthesia stress is no different. Their role in arousal is not being discovered in this paper. Even their role in grooming is not discovered in this paper.

The activation of CRH cells in PVH has already been shown to result in grooming by Jaideep Bains (a paper cited by the authors). Thus, the involvement of these cells in this behavior is not surprising. The authors perform elaborate manipulations of CRH cells and numerous analyses of grooming and related behaviors. For example, they compare grooming and paw licking after anesthesia with those after other stressors such as forced swim, spraying mice with water, physical attack and restraint. The authors have identified a behavioral phenomenon in a rodent model that does not have a clear correlation with a behavior state observed in humans during the use of sevoflurane as part of an anesthetic regimen. The grooming behaviors are not a model of the emergence delirium or the cognitive dysfunction observed commonly in patients receiving sevoflurane for general anesthesia. Emergence delirium is commonly seen in children after sevoflurane is used as part of general anesthesia and cognitive dysfunction is commonly observed in adults-particularly the elderly -- following general anesthesia.

Author response:

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

Public reviews

This study describes a group of CRH-releasing neurons, located in the paraventricular nucleus of the hypothalamus, which, in mice, affects both the state of sevoflurane anesthesia and a grooming behavior observed after it. PVHCRH neurons showed elevated calcium activity during the post-anesthesia period. Optogenetic activation of these PVHCRH neurons during sevoflurane anesthesia shifts the EEG from burst-suppression to a seemingly activated state (an apparent arousal effect), although without a behavioral correlate. Chemogenetic activation of the PVHCRH neurons delays sevoflurane-induced loss of righting reflex (another apparent arousal effect). On the other hand, chemogenetic inhibition of PVHCRH neurons delays recovery of righting reflex and decreases sevoflurane-induced stress (an apparent decrease in the arousal effect). The authors conclude that PVHCRH neurons "integrate" sevoflurane-induced anesthesia and stress. The authors also claim that their findings show that sevoflurane itself produces a post-anesthesia stress response that is independent of any surgical trauma, such as an incision. In its revised form, the article does not achieve its intended goal and will not have impact on the clinical practice of anesthesiology nor on anesthesiology research.

Thanks for the reviews. Please see our responses to the following comments.

Weaknesses:

The most significant weaknesses remain:

a) overinterpretation of the significance of their findings

b) the failure to use another anesthetic as a control,

c) a failure to compellingly link their post-sevoflurane measures in mice to anything measured in humans, and

d) limitations in the novelty of the findings. These weaknesses are related to the primary concerns described below:

Concerns about the primary conclusion that PVHCRH neurons integrate the anesthetic effects and post-anesthesia stress response of sevoflurane GA

(1) After revision, their remain multiple places where it is claimed that PVHCRH neurons mediate the anesthetic effects of sevoflurane (impact statement: we explain "how sevoflurane-induced general anesthesia works..."; introduction: "the neuronal mechanisms that mediate the anesthetic effects...of sevoflurane GA remain poorly understood" and "PVHCRH neurons may act as a crucial node integrating the anesthetic effect and stress response of sevoflurane").The manuscript simply does not support these statements. The authors show that a short duration exposure to sevoflurane inhibits PVHCRH neurons, but this is followed by hyperexcitability of these neurons for a short period after anesthesia is terminated. They show that the induction and recovery from sevoflurane anesthesia can be modulated by PVHCRH neuronal activity, most likely through changes in brain state (measured by EEG). They also show that PVHCRH neuronal activity modulates corticosterone levels and grooming behavior observed post-anesthesia (which the authors argue are two stress responses).These two things (effects during anesthesia and effects post-anesthesia)may be mechanistically unrelated to each other. None of these observations relate to the primary mechanism of action for sevoflurane. All claims relating to "anesthetic effects" should be removed. Even the term "integration" seems wrong-it implies the PVH is combining information about the anesthetic effect and post-anesthesia stress responses.

As requested, we have removed all claims related to ‘anesthetic effects’ or ‘integration’. Please see the revised manuscript.

(2) lt is important to compare the effects of sevoflurane with at least one other inhaled ether anesthetic as one step towards elevating the impact of this paper to the level required for a journal such as eLife. Isoflurane, desflurane, and enflurane are ether anesthetics that are very similar to each other, as well as being similar to sevoflurane. For example, one study cited by the authors (Marana et al.2013) concludes that there is weak evidence for differences in stress-related hormones between sevoflurane and desflurane, with lower levels of cortisol and ACTH observed during the desflurane intraoperative period. It is important to determine whether desflurane activates PVHCRH neurons in the post-anesthesia period, and whether this is accompanied by excess grooming in the mice, because this will distinguish whether the effects of sevoflurane generalize to other inhaled anesthestics, or, alternatively, relate to unique idiosyncratic properties of this gas that may not be a part of its anesthetic properties.

Thanks for your insightful comments and suggestions. Regarding your request for additional experiments, we acknowledge the value they could add to our study. However, investigating whether the effects of sevoflurane generalize to other inhaled anesthetics is beyond the scope of our current study. There is evidence indicating the prevalence of anesthetic stress caused by inhaled ether anesthetics1,2. The post-anesthesia stress-related behaviors caused by sevoflurane administration are reminiscent of delirium observed clinically. Notably, studies have shown that the use of desflurane for maintenance of anesthesia did not significantly affect the incidence or duration of delirium compared to sevoflurane administration3. This suggests that our observations likely represent a generalized response to inhaled ether anesthetic rather than being specific to sevoflurane.

Concerns about the clinical relevance of the experiments

In anesthesiology practice, perioperative stress observed in patients is more commonly related to the trauma of the surgical intervention, with inadequate levels of antinociception or unconsciousness intraoperatively and/or poor post-operative pain control. The authors seem to be suggesting that the sevoflurane itself is causing stress because their mice receive sevoflurane but no invasive procedures, but there is no evidence of sevoflurane inducing stress in human patients. It is important to know whether sevoflurane effectively produces behavioral stress in the recovery room in patients that could be related to the putative stress response (excess grooming) observed in mice. For example, in surgeries or procedures which required only a brief period of unconsciousness that could be achieved by administering sevoflurane alone (comparable to the 30 min administered to the mice), is there clinical evidence of post-operative stress? It is also important to describe a rationale for using a 30 min sevoflurane exposure. What proportion of human surgeries using sevoflurane use exposure times that are comparable to this?

It is also the case that there are explicit published findings showing that mild and moderate surgical procedures in children receiving sevoflurane (which might be the closest human proxy to the brief 30 minutes sevoflurane exposure used here) do not have elevated cortisol (Taylor et al, J Clin Endocrinol Metab, 2013). This again raises the question of whether the enhanced grooming or elevated corticosterone observed in the mice here has any relevance to humans.

Thanks for the comments. Most ear, nose, and throat surgeries in children involve a short period of anesthesia with sevoflurane alone4-6, which is similar to the 30-minute exposure in our mouse study. In clinical settings, emergence delirium and agitation are common in young children undergoing sevoflurane anesthesia7, often accompanied by troublesome excitation phenomena during induction and awakening8. These clinical observations align with the post-operative stress response (e.g., excessive grooming) we identified in our study.

It is the experience of one of the reviewers that human patients who receive sevoflurane as the primary anesthetic do not wake up more stressed than if they had had one of the other GABAergic anesthetics. If there were signs of stress upon emergence (increased heart rate, blood pressure, thrashing movements) from general anesthesia, this would be treated immediately. The most likely cause of post-operative stress behaviors in humans is probably inadequate anti-nociception during the procedure, which translates into inadequate post-op analgesia and likely delirium. It is the case that children receiving sevoflurane do have a higher likelihood of post-operative delirium. Perhaps the authors' studies address a mechanism for delirium associated with sevoflurane, but this is barely mentioned. Delirium seems likely to be the closest clinical phenomenon to what was studied. As noted by the Besnier et al (2017) article cited by the authors, surgery can elevate postoperative glucocorticoidstress hormones, but it generally correlates with the intensity of the surgical procedure. Besnier et al also note the elevation of glucocorticoids is generally considered to be adaptive. Thus, reducing glucocorticoids during surgery with sevoflurane may hamper recovery, especially as it relates to tissue damage, which was not measured or considered here. This paper only considers glucocorticoid release as a negative factor, which causes "immunosuppression", "proteolysis", and "delays postoperative recovery and leads to increased morbidity".

Thanks for the comments. We agree that the post-anesthetic stress behaviors mentioned in our manuscript are similar to the clinical phenomenon of delirium, which were defined in Cheng Li’s study as ‘sevoflurane-induced post-operative delirium’9. Therefore, we conducted additional behavioral tests for cognitive function, including the Y-maze and novel object recognition test, in mice administrated 30-minute sevoflurane anesthesia. The results demonstrate that chemogenetic inhibition of PVHCRH neurons ameliorated the short-term memory impairment in mice exposed to 30-minute sevoflurane GA (Figure 7-figure supplement 9), suggesting PVHCRH neurons may involve in modulating sevoflurane-induced postoperative delirium.

Concerns about the novelty of the findings:

The key finding here is that CRH neurons mediate measures of arousal, and arousal modulates sevoflurane anesthesia induction and recovery. However, CRH is associated with arousal in numerous studies. In fact, the authors' own work, published in eLife in 2021, showed that stimulating the hypothalamic CRH cells lead to arousal and their inhibition promoted hypersomnia. In both papers the authors use fos expression in CRH cells during a specific event to implicate the cells, then manipulate them and measure EEG responses. In the previous work, the cells were active during wakefulness; here- they were active in the awake state the follows anesthesia (Figure1). Thus, the findings in the current work are incremental and not particularly impactful. Claims like "Here, a core hypothalamic ensemble, corticotropin-releasing hormone neurons in the paraventricular nucleus of the hypothalamus, is discovered" are overstated. PVHCRH cell populations were discovered in the 1980s. Suggesting that it is novel to identify that hypothalamic CRH cells regulate post-anesthesia stress is unfounded as well: this PVH population has been shown over four decades to regulate a plethora of different responses to stress. Anesthesia stress is no different. Their role in arousal is not being discovered in this paper. Even their role in grooming is not discovered in this paper.

Thanks for the comments. As requested, we have revised our manuscript by removing overstated sentences. Please see the revised manuscript. In terms of novelty, our study reveals that PVHCRH neurons are implicated not only in the induction and emergence of sevoflurane general anesthesia but also in sevoflurane-induced post-operative delirium. This finding represents a novel contribution to the field, as it has not been previously reported by other studies.

The activation of CRH cells in PVH has already been shown to result in grooming by Jaideep Bains (a paper cited by the authors). Thus, the involvement of these cells in this behavior is not surprising. The authors perform elaborate manipulations of CRH cells and numerous analyses of grooming and related behaviors. For example, they compare grooming and paw licking after anesthesia with those after other stressors such as forced swim, spraying mice with water, physical attack and restraint. The authors have identified a behavioral phenomenon in a rodent model that does not have a clear correlation with a behavior state observed in humans during the use of sevoflurane as part of an anesthetic regimen. The grooming behaviors are not a model of the emergence delirium or the cognitive dysfunction observed commonly in patients receiving sevoflurane for general anesthesia. Emergence delirium is commonly seen in children after sevoflurane is used as part of general anesthesia and cognitive dysfunction is commonly observed in adults-particularly the elderly-- following general anesthesia. No features of delirium or cognitive dysfunction are measured here.

As requested, behavioral tests for cognitive function have been conducted and displayed in Figure 7-figure supplement 9.

Other concerns:

In Figure 2, cFos was measured in the PVH at different points before, during and after sevoflurane. The greatest cFos expression was seen in Post 2, the latest time point after anesthesia. However, this may simply reflect the fact that there is a delay between activity levels and expression of cFos (as noted by the authors, 2-3 hours). Thus, sacrificing mice 30 minutes after the onset of sevoflurane application would be expected to drive minimal cFos expression, and the cFos observed at 30 minutes would not accurately reflect the activity levels during the sevoflurane. Also, the authors state that the hyperactivity, as measured by cFos, lasted "approximately 1 hours before returning to baseline", but there is no data to support this return to baseline.

Thanks for the comments. We apologize that the protocol we used for c-fos staining may not accurately reflect the activity levels, so we have removed Figure 2F. The sentence ‘lasted approximately 1 hours before returning to baseline’ refers to the calcium signal but not c-fos level.

In Figure 7, the number of animals appears to change from panel to panel even though they are supposed to show animals from the same groups. For example, cort was measured in only 3 saline-treated O2 animals (Fig 7E), but cFos and CRH were assessed in 4 (Fig C,D). Similarly, grooming time and time spent in open arms was measured in 6 saline-treated O2 controls (Fig 7F, H) but central distance was measured in 8(Fig 7G). There are other group number discrepancies in this figure--the number of data points in the plots do not match what is reported in the legend for numerous groups. Similarly, Figure 4 has a mismatch between the Ns reported in the legend and the number of points plotted per bar. For example, there were 10 animals in the hM3Di group; all are shown for the LORR and time to emergence plots, but only8 were used for time to induction. The legends reported N=7 for the mCherry group, yet 9 are shown for the time to emergence panel. No reason for exclusions is cited. These figures (and their statistics) should be corrected.

Thanks for the comments. We have rechecked and corrected our figures and illustrations in the revised manuscript.

Recommendations for the authors:

In Figure 6, the BSR pre-stim data points for panels F and H look exactly identical, even though these data are from two different sets of mice. It seems likely that one of these panels is not depicting the correct pre-stim data points. Please check this.

Thanks for the comments. We have corrected this mistake.

General anesthesia is a combination of behavioral and physiological states induced and maintained primarily by pharmacologic agents. The authors do not provide a definition of general anesthesia.

Thanks for the advice. We have added the definition of general anesthesia in the introduction part.

The first sentence of the abstract closely resembles the first sentence of the abstract of Brown，Purdon and Van Dort,Annu. Rev. Neurosci. 2011,34:601-28 yet, there is no citation.

Thanks for the comments. We have revised the first sentence.

ln the Discussion, the authors cite the research on circuitry that is relevant for emergence from general anesthesia. Conspicuously missing from this section of the paper is the large body of work by Solt and colleagues which has demonstrated that dopamine agonists (such as methylphenidate), electrical stimulation of the ventral tegmental area and optogenetic stimulation of the D1 neurons in the ventral tegmental area can hasten emergence from general anesthesia. Also omitted is the work of Kelzand colleagues and a discussion of neural inertia.

Thanks for the suggestions. We have added these citations as requested.

As regards the weaknesses of p-values for reporting the results of scientific studies, l offer the following reference to the authors. Ronald L. Wasserstein & Nicole A.Lazar (2016)The ASA Statement on p-Values: Context, Process, and Purpose, The American Statistician,70:2,129- 133, DOl:10.1080/00031305.2016.1154108

Thanks for the suggestions. We have revised the manuscript as requested.

The methods for the CRF antibody are unclear. It was previously suggested that the antibody be validated (for example, show an absence of immunostaining with CRF knockdown) because the concentration of antiserum (1:800) is quite high, suggesting either the antibody is not potent or (more concerning) not specific. The methods also indicated that colchicine was infused ICV prior to perfusion for staining of cFos and CRF, but no surgical methods are described that would enable ICV infusion, and it is not clear why colchicine was used. Please clarify.

The anti-CRF antibody is validated by other studies11,12. F For CRF immunostaining, animals' brains were pre-treated with intraventricular injections of colchicine (20 μg in 500 nL saline) 24 hours before perfusion to inhibit fast axonal transport13,14. Additional details regarding these methods have been included in the Method section of the revised manuscript.

Editor's note:

Full statistical reporting including exact p-values alongside summary statistics (test statistic and df) and 95% confidence intervals is lacking.

Thanks for the suggestions. We have added full statistical reporting in the revised manuscript as requested.

Reference

(1) Marana, E. et al. Desflurane versus sevoflurane: a comparison on stress response. Minerva Anestesiol 79, 7-14 (2013).

(2) Yang, L., Chen, Z. & Xiang, D. Effects of intravenous anesthesia with sevoflurane combined with propofol on intraoperative hemodynamics, postoperative stress disorder and cognitive function in elderly patients undergoing laparoscopic surgery. Pak J Med Sci 38, 1938-1944, doi:10.12669/pjms.38.7.5763 (2022).

(3) Driscoll, J. N. et al. Comparing incidence of emergence delirium between sevoflurane and desflurane in children following routine otolaryngology procedures. Minerva Anestesiol 83, 383-391, doi:10.23736/s0375-9393.16.11362-8 (2017).

(4) Galinkin, J. L. et al. Use of intranasal fentanyl in children undergoing myringotomy and tube placement during halothane and sevoflurane anesthesia. Anesthesiology 93, 1378-1383, doi:10.1097/00000542-200012000-00006 (2000).

(5) Greenspun, J. C., Hannallah, R. S., Welborn, L. G. & Norden, J. M. Comparison of sevoflurane and halothane anesthesia in children undergoing outpatient ear, nose, and throat surgery. J Clin Anesth 7, 398-402, doi:10.1016/0952-8180(95)00071-o (1995).

(6) Messieha, Z. Prevention of sevoflurane delirium and agitation with propofol. Anesth Prog 60, 67-71, doi:10.2344/0003-3006-60.3.67 (2013).

(7) Shi, M. et al. Dexmedetomidine for the prevention of emergence delirium and postoperative behavioral changes in pediatric patients with sevoflurane anesthesia: a double-blind, randomized trial. Drug Des Devel Ther 13, 897-905, doi:10.2147/dddt.S196075 (2019).

(8) Veyckemans, F. Excitation and delirium during sevoflurane anesthesia in pediatric patients. Minerva Anestesiol 68, 402-405 (2002).

(9) Xu, Y., Gao, G., Sun, X., Liu, Q. & Li, C. ATPase Inhibitory Factor 1 Is Critical for Regulating Sevoflurane-Induced Microglial Inflammatory Responses and Caspase-3 Activation. Front Cell Neurosci 15, 770666, doi:10.3389/fncel.2021.770666 (2021).

(10) Friedman, E. B. et al. A conserved behavioral state barrier impedes transitions between anesthetic-induced unconsciousness and wakefulness: evidence for neural inertia. PLoS One 5, e11903, doi:10.1371/journal.pone.0011903 (2010).

(11) Giardino, W. J. et al. Parallel circuits from the bed nuclei of stria terminalis to the lateral hypothalamus drive opposing emotional states. Nat Neurosci 21, 1084-1095, doi:10.1038/s41593-018-0198-x (2018).

(12) Yeo, S. H., Kyle, V., Blouet, C., Jones, S. & Colledge, W. H. Mapping neuronal inputs to Kiss1 neurons in the arcuate nucleus of the mouse. PLoS One 14, e0213927, doi:10.1371/journal.pone.0213927 (2019).

(13) de Goeij, D. C. et al. Repeated stress-induced activation of corticotropin-releasing factor neurons enhances vasopressin stores and colocalization with corticotropin-releasing factor in the median eminence of rats. Neuroendocrinology 53, 150-159, doi:10.1159/000125712 (1991).

(14) Yuan, Y. et al. Reward Inhibits Paraventricular CRH Neurons to Relieve Stress. Curr Biol 29, 1243-1251.e1244, doi:10.1016/j.cub.2019.02.048 (2019).

eLife assessment

This valuable study explores a new strategy of lysin-derived antimicrobial peptide-primed screening to find peptidoglycan hydrolases from bacterial proteomes. Using this strategy, the authors identified five peptidoglycan hydrolases from Acinetobacter baumannii, which they tested on various Gram-positive and Gram-negative pathogens for antimicrobial activity. The revised manuscript addressed most of the prior concerns, and the data presented are solid and will be of interest to microbiologists.

Reviewer #1 (Public review):

Summary:

Li Zhang et al. characterized two new Gram-negative endolysins identified through an AMP-targeted search in bacterial proteomes. These endolysins exhibit broad lytic activity against both Gram-negative and Gram-positive bacteria and retain significant antimicrobial activity even after prolonged exposure to high temperatures (100{degree sign}C for 1 hour). This stability is attributed to a temperature-reversible transition from a dimer to a monomer. The authors suggest several potential applications, such as complementing heat sterilization processes or being used in animal feed premixes that undergo high-temperature pelleting, which I agree with.

Strengths:

The claims are well-supported by relevant and complementary assays, as well as extensive bioinformatic analyses.

Weaknesses:

My last comments are minor and nearly all aim to improve the use of English language in the manuscript. However, a section describing the statistical analysis is still lacking. I believe that the presented manuscript can benefit from language editing, but I leave this decision with the editor.

Reviewer #2 (Public review):

Summary:

The study explores a new strategy of lysin-derived antimicrobial peptide-primed screening to find peptidoglycan hydrolases from bacterial proteomes. Using this strategy authors identified five peptidoglycan hydrolases from A. baumannii. They further tested their antimicrobial activities on various Gram positive and Gram-negative pathogens.

Strengths:

Overall, the study is good and adds new members to the peptidoglycan hydrolases family. The authors also show that these lysins have bactericidal activities against both Gram-positive and Gram-negative bacteria. The crystal structure data is good, reveals different thermostablility to the peptidoglycan hydrolases. Structural data also reveals that PhAb10 and PHAb11 form thermostable dimer and data is corroborated by generating variant protein defective in supporting intermolecular bond pairs. The mice bacterial infection shows promise for the use of these hydrolases as antimicrobial agents.

Weaknesses:

While the authors have employed various mechanisms to justify their findings, some aspects are still unclear. Only CFU has been used to test bactericidal activity. This should also be corroborated by live/dead assay. Moreover, SEM or TEM analysis would reveal the effect of these peptidoglycan hydrolases on Gram-negative /Gram-positive cell envelopes. The authors claim that these hydrolases are similar to T4 lysozyme, but they have not correlated their findings with already published findings on T4 lysozyme. T4 lysozyme has C-terminal amphipathic helix with antimicrobial properties. Moreover, heat, denatured lysozyme also shows enhanced bactericidal activity due to the formation of hydrophobic dimeric forms, which are inserted in the membrane. Authors also observe that heat denatured PHAb10 and PHAb11 have bactericidal activity but no enzymatic activity. These findings should be corroborated by studying the effect of these holoenzymes/ truncated peptides on bacterial cell membranes. Also, a quantitative peptidoglycan cleavage assay should be performed in addition to halo assay. Including these details would make the work more comprehensive.

Revised version: The authors have addressed most of the questions in the revised version of the paper.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

Li Zhang et al. characterized two new Gram-negative endolysins identified through an AMPtargeted search in bacterial proteomes. These endolysins exhibit broad lytic activity against both Gram-negative and Gram-positive bacteria and retain significant antimicrobial activity even after prolonged exposure to high temperatures (100{degree sign}C for 1 hour). This stability is attributed to a temperature-reversible transition from a dimer to a monomer. The authors suggest several potential applications, such as complementing heat sterilization processes or being used in animal feed premixes that undergo high-temperature pelleting, which I agree with. 

We appreciate the reviewer’s valuable comments and suggestions.

Strengths: 

The claims are well-supported by relevant and complementary assays, as well as extensive bioinformatic analyses. 

We appreciate the reviewer’s valuable comments and suggestions.

Weaknesses: 

There are numerous statements in the introduction and discussion sections that I currently do not agree with and consider need to be addressed. Therefore, I recommend major revisions. 

Based on your valuable comments and suggestions, we have revised relevant introduction and discussion sections (pages 3-4, lines 82-101; page 21, lines 480-483).

Major comments: 

Introduction and Discussion: 

The introduction and the discussion are currently too general and not focused. Furthermore, there are some key concepts that are missing and are important for the reader to have an overview of the current state-of-the-art regarding endolysins that target gram-negatives. Specifically, the concepts of 'Artilysins', 'Innolysins', and 'Lysocins' are not introduced. Besides this, the authors do not mention other high-throughput mining or engineering strategies for endolysins, such as e.g. the VersaTile platform, which was initially developed by Hans Gerstmans et al. for one of the targeted pathogens in this manuscript (i.e., Acinetobacter baumannii). Recent works by Niels Vander Elst et al. have demonstrated that this VersaTile platform can be used to high-throughput screen and hit-to-lead select endolysins in the magnitude tens of thousands. Lastly, Roberto Vázquez et al. have recently demonstrated with bio-informatic analyses that approximately 30% of Gram-negative endolysin entries have AMP-like regions (hydrophobic short sequences), and that these entries are interesting candidates for further wet lab testing due to their outer membrane penetrating capacities. Therefore, I fully disagree with the statement being made in the introduction that endolysin strategies to target Gram-negatives are 'in its infancy' and I urge the authors to provide a new introduction that properly gives an overview of the Gram-negative endolysin field.   

We thank the reviewer for the valuable suggestions. A new paragraph has been added to the revised manuscript to reflect the concepts and strategies for lysin engineering and discovery against Gram-negative bacteria (pages 3-4, lines 82-101). 

Results: 

It should be mentioned that the halo assay is a qualitative assay for activity testing. I personally do not like that the size of the halos is used to discriminate in endolysin activity. In this reviewer's opinion, the size of the halo is highly dependent on (i) the molecular size of the endolysin as smaller proteins can diffuse further in the agar, and (ii) the affinity of the CBD subdomain of the endolysin for the bacterial peptidoglycan. It should also be said that in the halo assay, there is a long contact time between the endolysin and the bacteria that are statically embedded in the agar, which can result in false positive results. How did the authors mitigate this? 

We quite agree with the reviewer that the halo assay is only a qualitative method for activity testing and may be perturbed by multiple parameters (DOI:

10.3390/antibiotics9090621). In our study, the halo assay was used only as a preliminary method to rapidly distinguish the activities of multiple candidates, and then the candidates with high antibacterial activities were further characterized through a series of in vitro and in vivo assays in this work.

Testing should have been done at equimolar concentrations. If the authors decided to e.g. test 50 µg/mL for each protein, how was this then compensated for differences in molecular weight? For example, if PHAb10 and PHAb11 have smaller molecular sizes than PHAb7, 8, and 9, there is more protein present in 50 µg/mL for the first two compared to the others, and this would explain the higher decrease in bacterial killing (and possibly the larger halos). 

We thank the reviewer for his valuable suggestions and concerns. We agree with the reviewer that when we need to know exactly how much times more active an enzyme is than the another, we should directly compare the performance of the two enzymes at equimolar concentrations. In our previous work, we followed this rule to distinguish novel chimeric lysins from their parental lysins or their variants (DOI: 10.1128/AAC.00311-20; DOI: 10.1128/AAC.01610-19; DOI: 10.1128/AAC.02043-18). In the present work, our initial goal of testing was to reflect the robustness and efficiency of screening strategy initiated by lysinderived antimicrobial peptides. With this in mind, we therefore did not spend more effort to compare the activities of these candidates in detail but continued to clarify their host range and thermo-tolerance mechanisms, and then continued to examine their performance in infection models. Nonetheless, in future work, we will definitely follow your suggestions when it is necessary to quantify the differences between these candidates.  

Reviewer #2 (Public Review): 

Summary: 

The study explores a new strategy of lysin-derived antimicrobial peptide-primed screening to find peptidoglycan hydrolases from bacterial proteomes. Using this strategy authors identified five peptidoglycan hydrolases from A. baumannii. They further tested their antimicrobial activities on various Gram-positive and Gram-negative pathogens.

We appreciate the reviewer’s valuable comments.

Strengths: 

Overall, the study is good and adds new members to the peptidoglycan hydrolases family. The authors also show that these lysins have bactericidal activities against both Gram-positive and Gram-negative bacteria. The crystal structure data is good, and reveals different thermostablility to the peptidoglycan hydrolases. Structural data also reveals that PhAb10 and PHAb11 form thermostable dimers and data is corroborated by generating variant protein defective in supporting intermolecular bond pairs. The mice bacterial infection shows promise for the use of these hydrolases as antimicrobial agents. 

We appreciate the reviewer’s valuable comments and suggestions.

Weaknesses: 

While the authors have employed various mechanisms to justify their findings, some aspects are still unclear. Only CFU has been used to test bactericidal activity. This should also be corroborated by live/dead assay. Moreover, SEM or TEM analysis would reveal the effect of these peptidoglycan hydrolases on Gram-negative /Gram-positive cell envelopes. The authors claim that these hydrolases are similar to T4 lysozyme, but they have not correlated their findings with already published findings on T4 lysozyme. T4 lysozyme has a C-terminal amphipathic helix with antimicrobial properties. Moreover, heat, denatured lysozyme also shows enhanced bactericidal activity due to the formation of hydrophobic dimeric forms, which are inserted in the membrane. Authors also observe that heat-denatured PHAb10 and PHAb11 have bactericidal activity but no enzymatic activity. These findings should be corroborated by studying the effect of these holoenzymes/ truncated peptides on bacterial cell membranes. Also, a quantitative peptidoglycan cleavage assay should be performed in addition to the halo assay. Including these details would make the work more comprehensive. 

We thank the reviewer for his valuable suggestions and concerns. We agree with the reviewer that employing more methods and techniques such as SEM, TEM, live/dead imaging, and GC-MS will provide a deeper understanding of how these peptidoglycan hydrolases interact with the bacterial envelopes and peptidoglycan bones, which will definitely make our study more comprehensive. The principal idea of this study is, however, to test the robustness and effectiveness of the screening strategy triggered by lysin-derived antimicrobial peptide in discovering new peptidoglycan hydrolases. Therefore, we did not put more efforts in charactering the interactions of these peptidoglycan hydrolases with the bacterial envelopes/membranes in multiple assays; instead, we continued to elucidate their host range and thermo-tolerance mechanisms and then continued to examine their performance in infection models. 

We are also very grateful to the reviewers for their suggestions to correlate our results to published findings on lysozymes. Based on these suggestions, we have included an extensive discussion in the Discussion section of the revised manuscript (page 22, lines 502-514).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Abstract and title. 

In my opinion, the current title does not fully cover the work that is presented in the manuscript. 

According to your valuable comment, we have revised the title to “Dimer-monomer transition defines a hyper-thermostable peptidoglycan hydrolase mined from bacterial proteome by lysin-derived antimicrobial peptide-primed screening”.

Please remove the word 'novel' from the title, as well as elsewhere in the manuscript. As it is true that PHAb10 and PHAb11 are new, they are not novel. There are many reports that have been published on endolysins with activity against Gram-negatives, and sometimes even also Gram-positives. 

We have changed the description of PHAb10 and PHAb11 to avoid using the word “novel”, but alternatively, using “new” or “active” in the title and throughout the text in the revised manuscript.

Additional information for the Introduction section in the Public Review: 

DOI: 10.1128/AAC.00285-16  

DOI: 10.1038/s41598-020-68983-3 

DOI: 10.1128/AAC.00342-19  

DOI: 10.1126/sciadv.aaz1136   

DOI: 10.1111/1751-7915.14339 

DOI: 10.1128/JVI.00321-21  G-

We appreciate the reviewer for these selected references and have cited almost all of them in a new paragraph in the Introduction section of the revised manuscript (pages 3-4, lines 82-101).

Minor Comments: 

Line 30. For a lay person it is not clear what is meant by 'unique mechanism of action.' 

These has been replaced by “direct peptidoglycan degradation activity” in the revised manuscript (page 2, lines 30-31).

Line 60 & 62. Please merge these sentences into one as they have the same meaning.

We have deleted one of the sentences based on your suggestion.

Line 67. Replace 'also' with 'simultaneously'. 

Revised as suggested (page 3, line 66).

Line 74. 'Modern clinical practice' should specifically refer to infectious diseases in humans. 

Revised as suggested (page 3, line 73).

Line 76 to 105. There is too much information that is not focused. This section should be rewritten so that it is in line with the focus of the presented work. I would remove this section and replace it with a new section as proposed in my major comments. 

Based on your suggestion, we deleted this section and prepared a new paragraph in the revised manuscript (pages 3-4, lines 82-101).

Line 113. I strongly disagree with the wording 'in its infancy'. Please see my major comment. 

We have rewritten the paragraph as “However, compared with the current progress in the clinical translation of lysins against Gram-positive bacteria, the discovery of lysins against Gram-negative bacteria that meet the needs described in the WHO priority pathogen list is still urgently needed.” according to your valuable comments in the revised manuscript (page 4, lines 98-101).

Line 116. Remove 'on'. 

Revised as suggested (page 4, line 104).

Results. 

Additional information for the Results section in the Public Review: 

DOI: 10.3390/antibiotics9090621

We thank the reviewer for this valuable reference, which has been cited in the Results section and Methods sections of the revised manuscript (page 7, line 159; page 25, line 605).

Minor comments: 

Line 135. Replace 'would' with 'could'.

Revised as suggested (page 5, line 124).

Line 150. Why was this naming decided to go from 11 -> 7, whereas in Figure 1a the clades go from I to V? This way of naming is not clear to me. 

Thank you for the reviewer's question. There are two numbering systems here: 1-11 is the numbering of peptidoglycan hydrolases mined from different bacterial proteomes by lysin-derived peptide primer screening strategy, and the characterization of candidates mined from the proteome of A. baumannii are 7 to 11 (characterization candidates numbered 1 to 6 are from other bacterial proteomes). Whereas the cladistic analysis of all potential candidates in the A. baumannii proteome is regularly labelled by clade I to V. 

Line 250. Replace 'casts doubt' with 'questions'. 

Revised as suggested (page 10, line 244).

Line 252 to 257. I would encourage the authors to mention if there is any homology in between the peptides on the one hand, and in between the lysozyme catalytic domains on the other hand.

This information has been added to the revised manuscript (page 10, lines 249-251).

Line 266. The following sentence should be reworded: 'However, rare lytic activity was observed in P11-NP, suggesting that a potential role for it in functions other than bactericidal

activity.' 

In the revised manuscript (page 11, lines 261-262), the sentence has been revised as “However, rare lytic activity was observed in P11-NP, suggesting that its function remains to be established”.

Line 276. Replace 'asked' with 'questioned'.

Revised as suggested (page 11, line 270).

From 302 onwards. Why was it chosen to solve the crystal structure of PHAb8, and not PHAb7 and 9? This should be briefly mentioned. 

Initially we tried to decipher the structures of all five enzymes, but we finally obtained the crystal structures of only three enzymes, PHAb8, PHAb10, and PHAb11 by Xray crystallography. This reason has been added in the revised manuscript (page 13, lines 300301).

Line 437. Replace 'the burn wound model' with 'a burn wound model'. 

Revised as suggested (page 19, line 433).

Line 445. Replace 'the mouse abscess model' with 'a mouse abscess model'. 

Revised as suggested (page 19, line 441).

Line 449 to 451. Given that the mice received 5 doses of minocycline and no difference was observed with the group that received tris buffer, was it tested if the Acinetobacter baumannii 3437 isolates became resistant against minocycline during the experiment? 

We appreciate the Reviewer for his valuable concern. In our study, we did not explore in detail the reasons why minocycline was ineffective. But we strongly agree with the reviewer that drug resistance may be one of the reasons.

Discussion. 

Minor comments. 

Line 479. Delete this sentence: 'Policy makers, scientists, enterprisers, and investigators have worked together for decades to exploit the 'trojan horse' globally, but new options for treating antimicrobial resistance in the clinic remain to be seen'. 

Revised as suggested.

Line 483. Reformulate as follows: 'unique mechanism of action, potent bactericidal activity, low risks of drug resistance, and ongoing clinical trials targeting Gram-positive bacteria.' To my knowledge, all these clinical trials target S. aureus, but I might be wrong. 

Revised as suggested (page 21, lines 476-478).

Line 486. 'However, for Gram-negative bacteria, the effects of phage-derived lysins were often hampered by their outer membranes, which requires more strategies to overcome this barrier.' After this sentence, the concepts of Artilysins, Innolysins, and Lysocins should be mentioned, in addition to the introduction. These are important engineering strategies and the reader should be informed that your strategy is thus not the only existent one. 

Revised as suggested (page 21, lines 480-482).

Line 491. Please, again refer to the work of Roberto Vázquez et al., who has done very similar work to your work presented. DOI: 10.1128/JVI.00321-21 

We have cited this interesting work in the Introduction section and Discussion section of the revised manuscript (page 4, line 106; page 21, lines 482-483).

Line 499. Reformulate: 'Gram-positive bacteria are primarily killed through the action of the antimicrobial peptides only'. 

According to your suggestion, it was changed to “while Gram-positive bacteria are killed mainly through the action of the intrinsic antimicrobial peptides” in the revised manuscript (pages 21-22, lines 497-498).

Line 500. Delete this sentence, as this is already mentioned in the results and too detailed:

'Interestingly, we noted a difference in the killing of Gram-positive bacteria by PHAb10 and PHAb11, which may be due to the fact that P11-CP had one more basic amino acid than P10CP, so it had stronger bactericidal activity.' 

Revised as suggested.

Line 503. This statement doesn't make sense because you cannot directly compare ug/mL between endolysins, you must compare equimolar concentrations. Furthermore, testing conditions between studies were different, thus making this claim unjustified. 

These statements have been deleted in the revised manuscript.

Line 524. Please delete:' To our knowledge, this is the first time that an enzyme had been found to adapt to ambient temperature by altering its dimerization state.' 

Revised as suggested.

Figures. 

Figure 1a. Please choose a different name for 'dry job' and 'wet job'. 

Following your suggestion, they have been specified as “In silico analysis” and “Experimental verification” in the revised Figure 1a. 

Figure 6. I suggest moving Figure 6e to the supplementary materials and reorganizing Figure 6 with only panels a to d. 

Revised as suggested.

Materials and Methods, References, and Supplementary Materials.  No comments. 

Reviewer #2 (Recommendations For The Authors): 

Most figure labelings are very small and difficult to read. 

All figures in the revised manuscript have been replaced with high-resolution figures, which hopefully will make these labels easier to follow.

The authors should include a data availability statement in the manuscript.

Revised as suggested (page 28, lines 704-706).

eLife assessment

This study presents useful findings on the efficacy and mechanism of linalool protection against Saprolegnia parasitica oomycetes in the grass carp model. The evidence is incomplete since the claims are partially justified, thus there is a need for more experimental data and more rigorous statistical data analysis . Revisions according to the recommendations will improve the work, making it of interest to scientists within the fields of aquaculture, ichthyology, microbiology, and drug discovery.

Reviewer #1 (Public review):

Summary:

The work seeks to investigate the efficacy of linalool as a natural alternative for combating Saprolegnia parasitica infections, which would provide great benefit to aquaculture. This paper shows the effect of linalool in vitro using a variety of techniques including changes in S. parasitica membrane integrity following linalool exposure and alterations in cell metabolism and ribosome function. Additionally, this work goes on to show that prophylactic and concurrent treatment of linalool at the time of S. parasitica infection can improve survival and tissue damage in vivo in their grass carp infection model. The conclusions of the paper are partially supported by the data, cleaning up, clarifying, and elaborating on some aspects of this work is necessary.

(1) Adding microscopy of the untreated group to compare Figure 2A with would further strengthen the findings here.

(2) Quantification of immune infiltration and histological scoring of kidney, liver, and spleen in the various treatment groups would increase the impact of Figure 4.

(3) The data in Figure 6 I is not sufficiently convincing as being significant.

(4) Comparisons of the global transcriptomic analysis of the untreated group to the PC, LP, and LT groups would strengthen the author's claims about the immunological and transcriptomic changes caused by linalool and provide a true baseline.

Reviewer #2 (Public review):

Summary:

In this study, the authors aimed to delineate the antimicrobial activity of linalool and tried to investigate the mode of action of linalool against S. parasitica infection. One of the main focuses of this work was to identify the in vitro and in vivo mechanisms associated with the protective role of linalool against S. parasitica infection.

Strengths:

(1) The authors have used a variety of techniques to prove their hypothesis.

(2) An adequate number of replicates were used in their studies.

(3) Their findings showed a protective role of linalool against oomycetes and makes it an attractive future antibiotic in the aquaculture industry.

Weaknesses:

There are several weaknesses in this manuscript.

(1) The authors have taken for granted that the readers already know the experiments/assays used in the manuscript. There was not enough explanation for the figures as well as figure legends.

(2) The authors missed adding the serial numbers to the references.

(3) The introduction section does not provide adequate rationale for their work, rather it is focused more on the assays done.

(4) Full forms are missing in many places (both in the text and figure legends), also the resolution of the figures is not good. In some figures, the font size is too small.

(5) There is much mislabeling of the figure panels in the main text. A detailed explanation of why and how they did the experiments and how the results were interpreted is missing.

(6) There is not enough experimental data to support their hypothesis on the mechanism of action of linalool. Most of the data comes from pathway analysis, and experimental validation is missing.

Overall, the conclusions drawn by the authors are partially justified by the data. Importantly, this paper has discovered the novelty of the compound linalool as a potent antimicrobial agent and might open up future possibilities to use this compound in the aquaculture industry.

Author response:

Reviewer #1:

(1) Adding microscopy of the untreated group to compare Figure 2A with would further strengthen the findings here.

Thank you for your comments on our manuscript. We will carefully revise this part. Actually, we used a time-lapse method to capture images at 0 minute before any drugs were added. We will change '0 min' to 'untreated,' which will further strengthen our findings.

(2) Quantification of immune infiltration and histological scoring of kidney, liver, and spleen in the various treatment groups would increase the impact of Figure 4.

Thank you for your comments on our manuscript. To further strengthen Figure 4, we will use quantification of immune infiltration and histological scoring of the kidney, liver, and spleen in different groups. Additionally, we will use ImageJ software for molecular immunohistochemistry and determine the ratio of normal to abnormal cells, providing more comprehensive insights into the effects of the treatments.

(3) The data in Figure 6 I is not sufficiently convincing as being significant.

Thank you for your comments on our manuscript. Previous researches have shown that antibiotics and other drugs can cause alterations in gut microbiota. Therefore, we plan to study the effects of linalool on gut microbiota. The results of this part were mostly built on gut microbiota sequencing and correlation analysis, we have tried several times to isolate vital microbes from the gut, but this is a very challenging work and the results were not good. Thus, in this study, we just predicted the effects of linalool on gut microbiota. In the future, we will continue to delve into interesting aspects of how linalool affects gut microbiota.

(4) Comparisons of the global transcriptomic analysis of the untreated group to the PC, LP, and LT groups would strengthen the author's claims about the immunological and transcriptomic changes caused by linalool and provide a true baseline.

Thank you for your comments on our manuscript. We will compare the global transcriptomic analysis of the untreated group with the PC, LP, and LT groups to strengthen the claims about the immunological and transcriptomic changes induced by linalool, thereby providing a true baseline.

Reviewer #2:

(1) The authors have taken for granted that the readers already know the experiments/assays used in the manuscript. There was not enough explanation for the figures as well as figure legends.

Thank you for your comments on our manuscript. We will provide more detailed explanations of the experiments and assays used in the manuscript, as well as enhance the descriptions in the figure legends, to ensure that readers have a clear understanding of the figures and context.

(2) The authors missed adding the serial numbers to the references.

Thank you for your comments on our manuscript. We will add serial numbers to the references to ensure proper citation and improve the clarity of our manuscript.

(3) The introduction section does not provide adequate rationale for their work, rather it is focused more on the assays done.

Thank you for your comments on our manuscript. We will add a section to the introduction that provides a rationale for our work, specifically focusing on the impact of plant extract on immunoregulation.

(4) Full forms are missing in many places (both in the text and figure legends), also the resolution of the figures is not good. In some figures, the font size is too small.

Thank you for your comments on our manuscript. We will ensure that all abbreviations are expanded where necessary, both in the text and figure legends. Additionally, we will improve the resolution of the figures and increase the font size where needed to enhance clarity.

(5) There is much mislabeling of the figure panels in the main text. A detailed explanation of why and how they did the experiments and how the results were interpreted is missing.

Thank you for your comments on our manuscript. We will improve the labeling of the figure panels, provide detailed explanations of the experimental methods, including their rationale and interpretation, and clarify the connections between the methods.

(6) There is not enough experimental data to support their hypothesis on the mechanism of action of linalool. Most of the data comes from pathway analysis, and experimental validation is missing.

Thank you for your comments on our manuscript. We have tried our best to link transcriptomic data, pathway analysis, experimental validation. We carried out many experiments to substantiate the changes inferred from the transcriptomic data as SEM, TEM, CLSM, molecular docking, RT-qPCR, histopathological examinations. The detailed information is listed as follows. (1) As shown in Figure 2, we combined the transcriptomic data related to membrane and organelle with SEM, TEM, and CLSM images. After deep analysis of these data and observation together, we illustrated that cell membrane may be a potential target for linalool. (2) As shown in Figure 3, we carried out molecular docking to explore the specific binding protein of linalool with ribosome which were screen out as potential target of linalool by transcriptomic data. (3) As shown in Figure 5, transcriptomic data illustrated that linalool enhanced the host complement and coagulation system. To substantiate these changes, we carried out RT-qPCR to detect those important immune-related gene expressions, and found that RT-qPCR analysis results were consistent with the expression trend of transcriptome analysis genes. (4) As shown in Figure 4 and 5, transcriptomics data revealed that linalool promoted wound healing tissue repair, and phagocytosis (Figure. 5E). To ensure these, we carried out histopathological examinations, and found that linalool alleviated tissue damage caused by S. parasitica infection on the dorsal surface of grass carp and enhancing the healing capacity (Figure. 4G). But we know the antimicrobial mechanism of linalool need further investigation, we will conduct more experiments to explore the antimicrobial mechanism of action of linalool in the future.

eLife assessment

This study presents a useful finding on the interplay of CCL5 and miR-324-5p during ischemic stroke injury. Despite its importance, the evidence supporting the claims of the authors is incomplete. In particular, the lack of methodological information, inappropriate statistical testing, a flawed culture system, and the temporal mismatch in the expression of CCL5 and miR-324-5p following stroke have hindered further evaluation of the claims. The work will be of interest to neuroscientists working on brain injury such as stroke.

Reviewer #1 (Public review):

Summary:

Here, the authors attempt to show that CCL5 is increased after stroke, possibly due to decreased miR-324, and that this is a modifiable system to decrease stroke damage. By bidirectionally manipulating CCL5 levels through direct injection of CCL5; a CCL5 blocking antibody; miR324; miR324 antagomir; or CCR5-blocking Maraviroc, they broadly show improvement with lower CCL5 levels. This includes infarct size, behavioral analysis, and immunohistochemical analysis of astrocytes, microglia, and neurons. They further try to mechanistically tie miR324 and CCL5 in astrocytes specifically to stroke-induced changes using a neuronal/astrocytic coculture system. They argue that decreasing CCL5 leads to increased ERK and CREB phosphorylation as a potential neuroprotective mechanism. CCL5 is one potential ligand for CCR5, and recent work identified CCR5 as a targetable mechanism by clinically-approved drug Maraviroc to enhance stroke recovery. Particularly given the high level of interest in CCR5 in stroke recovery, the focus on CCL5 - one of CCR5's potential ligands - and its miR regulation is an exciting expansion of this area of stroke biology.

Strengths:

The authors' findings that decreasing CCL5 acutely after stroke shows behavioral improvement appear robust. This broadly replicates work from other groups, although the finding that miR324 manipulation can phenocopy direct CCL5 manipulation is novel and intriguing. However, many of their other claims are difficult to evaluate based on a combination of missing methodological information, inappropriate statistical testing, and a flawed culture system.

Weaknesses:

Broadly speaking, the manuscript takes a zoomed-out view of what is fundamentally highly localized biology.

(1) miRNA-based regulation, by definition, has to include miR and mRNA in the same cell type; as the authors note, CCL5 is expressed in many cells. It is therefore impossible to propose any interaction on the basis of the tissue-level changes described; any evidence of in vivo cell-type specificity would dramatically improve the claims.

(2) The authors treat an extensive area of ipsilesional cortex uniformly as "IP". Astrocytic and microglial responses to localized injuries such as stroke are highly location-dependent and undoubtedly change dramatically within this area. The presented data cannot be interpreted without confirmation that these were taken at identical distances from the injury, and what that distance was. These do not appear to be adjacent to the injury, where the responses would presumably be the most informative. Similarly, it is difficult to interpret the neuronal Sholl and spine data without more information on where within the large IP region these neurons were found.

The authors attempt to narrow in on cell-type specificity via culture. However, astrocytes are notoriously prone to a dramatic change in culture and require careful methods (immunopanning; see eg doi: 10.1016/j.neuron.2011.07.022) to maintain much resemblance to their in vivo counterpart. It is difficult to conclude much about the role of astrocytes in the CCL5 pathway based on the use of this shaking-based culture system, particularly in the absence of cell-type specific validation in vivo.

There is missing methodological information, including infarct size measurements, TUNEL staining, and statistical testing. The TTC figures look very odd, like a collection of overlapping stars have been placed on the images rather than the natural relatively smooth infarct edges one would expect. It is unclear if the infarct volume measurements accounted for edema, as is standard; there is no description of the protocol used for quantification. It is also unclear if the infarct volume measurement comparisons were also done with t-tests vs ANOVA, as the statistical test used is not listed in the figure legends. In numerous cases where statistical testing is listed, repeated t-tests between subgroups are used vs the more appropriate ANOVA (assuming normality; nonparametric testing as appropriate), making it difficult to have confidence in the results.

Reviewer #2 (Public review):

The authors presented evidence from various in vivo and in vitro experiments demonstrating the mutual interaction between CCL5 and astrocytic miR-342-5p in the ipsilateral core of cerebral ischemia. However, miR-342-5p was downregulated only late after MCAO (D3-7). Additionally, this downregulation was observed not only in the ipsilateral core but also in the ipsilateral penumbra and contralateral sides. Therefore, it is not convincing that the upregulation of CCL5 in the ipsilateral core at later time points (D3 and D7) is attributable to the decreased expression of miR-342-5p. In particular, infarct injury was already evident within a short time period (say 24 h) following MCAO.

(1) The temporal and spatial expression patterns of miR-324-5p do not match those of CCL-5, especially at D1 and D3 (see Figure 1C, 1D). Despite the inverse relationship between miR-324-5p and CCL-5 expression at D7 after MCAO, what was the purpose of administering miR-324-5p agomir (or antagomir) at D1 post-MCAO? If the connection cannot be clearly established, the conclusion reached at the end will be difficult to accept.

(2) Would administering miR-342-5p or anti-CCL5 at later time points (e.g., after D3) reduce infarct size or improve functional recovery? If this is not the case, the effect of CCL5 on neuronal cell damage (infarct size formation) must occur within a very short time after MCAO. Additionally, if the increased CCL5 expression is due to the downregulation of miR-342-5p, its impact would likely be less significant.

(3) While the study offers valuable insights into the roles of CCL5 and its connection with the regulation of miR-342-5p (though this connection is somewhat weak), it is recommended that the authors explore potential translational applications of these findings.

Overall, given the experimental designs and results, it is difficult to support the conclusions drawn in the manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors of this study aim to use an optimization algorithm approach, based on the established NelderMead method, to infer polymer models that best match input bulk Hi-C contact data. The procedure infers the best parameters of a generic polymer model that combines loop-extrusion (LE) dynamics and compartmentalization of chromatin types driven by weak biochemical affinities. Using this and DNA FISH, the authors investigate the chromatin structure of the MYC locus in leukemia cells, showing that loop extrusion alone cannot explain local pathogenic chromatin rearrangements. Finally, they study the locus single-cell heterogeneity and time dynamics.

Strengths:

- The optimization method provides a fast computational tool that speeds up the parameter search of complex chromatin polymer models and is a good technical advancement.

- The method is not restricted to short genomic regions, as in principle it can be applied genome-wide to any input Hi-C dataset, and could be potentially useful for testing predictions on chromatin structure.

Weaknesses:

(1) The optimization is based on the iterative comparison of simulated and Hi-C contact matrices using the Spearman correlation. However, the inferred set of the best-fit simulation parameters could sensitively depend on such a specific metric choice, questioning the robustness of the output polymer models. How do results change by using different correlation coefficients?

This is an important question. We have tested several metrics in the process of building the fitting procedure. We now showcase side-by-side comparisons of the fitting results obtained using these different metrics in supplementary figure 2.

(2) The best-fit contact threshold of 420nm seems a quite large value, considering that contact probabilities of pairs of loci at the mega-base scale are defined within 150nm (see, e.g., (Bintu et al. 2018) and  (Takei et al. 2021)).

This is a good point. Unfortunately, there is no established standard distance cutoff to map distances to Hi-C contact frequency data. Indeed, previous publications have used anywhere between 120 nm to 500 nm (see e.g. (Cardozo Gizzi et al. 2019), (Cattoni et al. 2017) , (Mateo et al. 2019), (Hafner et al. 2022), (Murphy and Boettiger 2022), (Takei et al. 2021), (Fudenberg and Imakaev 2017) , (Wang et al. 2016), (Su et al. 2020), (Chen et al. 2022), (Finn et al. 2019)). 

We have included a supplementary table in the revised preprint (supplementary table 3) listing these values to demonstrate the lack of consensus. This large variation could reflect different chromatin compaction levels across distinct model systems, and different spatial resolutions in DNA FISH experiments performed by different labs. The variance in the threshold choice is also likely partially explained by Hi-C experimental details, e.g. the enzyme used for digestion, which biases the effective length scale of interactions detected (Akgol Oksuz et al. 2021). Among commonly used restriction enzymes, HindIII has a relatively low cutting frequency which results in a lower sensitivity to short-range interactions; on the other hand, MboI has a higher cutting frequency which results in a higher sensitivity to short-range interactions (Akgol Oksuz et al. 2021). Because the Hi-C data we used for the Myc locus in (Kloetgen et al. 2020) was generated using HindIII, we chose a distance cutoff close to the larger end of published values (420 nm). 

(3) In their model, the authors consider the presence of LE anchor sites at Hi-C TAD boundaries. Do they correspond to real, experimentally found CTCF sites located at genomic positions, or they are just assumed? A track of CTCF peaks of the considered chromatin loci would be needed.

We apologize this was not clear. The LE anchor sites in the simulation model were chosen because they correspond to experimental CTCF sites and ChIP-seq peaks located at the corresponding genomic positions. Representative CTCF ChIP-seq tracks from (Kloetgen et al. 2020) have been added to figure 2A in the revised preprint version to emphasize this point.

(4) In the model, each TAD is assigned a specific energy affinity value. Do the different domain types (i.e., different colors) have a mutually attractive energy? If so, what is its value and how is it determined? The simulated contact maps (e.g., Figure 2C) seem to allow attractions between different blocks, yet this is unclear.

Sorry this was not explicit. The attraction energy between a pair of monomers in the simulation is determined using the geometric mean of the affinities of the two monomers. This applies to both monomers within the same domain and in different domains. This detail has been clarified in the Methods section: “To optimize the simulation duration to streamline the parameter search (Supp. Fig. 1 B), we computed the autocorrelation function of the TAD2-TAD4 inter-TAD distance using the initial guess simulation parameters of the MYC locus in CUTLL. The simulation was saved every 5 simulation blocks.”

(5) To substantiate the claim that the simulations can predict heterogeneity across single cells, the authors should perform additional analyses. For instance, they could plot the histograms (models vs. experiments) of the TAD2-TAD4 distance distributions and check whether the models can recapitulate the FISH-observed variance or standard deviation. They could also add other testable predictions, e.g., on gyration radius distributions, kurtosis, all-against-all comparison of single-molecule distance matrices, etc,.

We agree that heterogeneity prediction is a key advantage of the simulations. We do note that the histograms (models vs. experiments) of the TAD2-TAD4 distance distributions measured by FISH were plotted in Fig. 3C as empirical cumulative probability distributions (as is standard in the field), side by side with the simulation predictions. Simulations indeed recapitulate the variance observed by FISH. We also had emphasized this important point in the main text: “Importantly, not just the average distances, but the shape of the distance distribution across individual cells closely matches the predictions of the simulations in both cell types, further confirming that the simulations can predict heterogeneity across cells.”

(6) The authors state that loop extrusion is crucial for enhancer function only at large distances. How does that reconcile, e.g., with Mach et al. Nature Gen. (2022) where LE is found to constrain the dynamics of genomically close (150kb) chromatin loci?

This is an interesting question. In (Mach et al. 2022), the authors tracked the physical distance between two fluorescent labels positioned next to either anchor of a ~150 kb engineered topological domain using live-cell imaging. They found that abrogation of the loop anchors by ablation of the CTCF binding motifs, or knock-down of the cohesin subunit Rad21 resulted in increased physical distance between the loci. HMM Modeling of the distance over time traces suggests that the increased distance resulted from rarer and shorter contacts between the anchors. While this might seem at odds with the results of Fig. 4L, we note a key difference between the loci. While (Mach et al. 2022) observed the dynamics of the distance separating two CTCF loop anchors, in our model only the MYC promoter is proximal to a loop anchor, while the position of the second locus is varied, but remains far from the other anchor. The deletion of the CTCF sites at both anchors in (Mach et al. 2022) indeed results in a lowered sensitivity of the physical distance to Rad21 knock-down, reminiscent of the results of Fig. 4L in our work. This result demonstrates that loop extrusion disruption disproportionately impacts distances between loci close to loop anchors, consistent with Hi-C results (Rao et al. 2017; Nora et al. 2017). We therefore believe that the models in our work and (Mach et al. 2022) are not at odds, but simply reflect that loop extrusion perturbations impact distances between loop anchors the most.  Enhancer-Promoter loops are generally distinct from CTCF-mediated loops (Hsieh et al. 2020, 2022). While (Mach et al. 2022) represents a landmark study in our understanding of the dynamics of genomic folding by loop extrusion, we therefore believe that the locus we chose here - which matches the endogenous MYC architecture - may more accurately represent Enhancer-Promoter dynamics than a synthetic CTCF loop.  To better articulate the similarities between model predictions and differences between the two loci, we have simulated a synthetic locus matching that of (Mach et al. 2022) in the revised preprint. Our simulation recapitulates the results obtained by Mach et al, including the sensitivity of contact frequency and duration to in silico cohesin knock-down (supplementary figure 6). We have updated the Results section accordingly: “The dependence of contact dynamics on loop extrusion in our simulations of MYC differs from that previously observed for two TAD boundaries (45). To check whether the different results are the product of different simulation models, we simulated contact dynamics across two TAD boundaries matching the locus of (45). Our simulations recapitulate the distance distribution and loop extrusion dependence previously observed (Supp. Fig. 6), establishing that the differences between the two systems are biological. While loop extrusion controls both the frequency and duration of contacts at TAD boundaries, it exerts a more nuanced effect on the frequency of contacts in loci pairs like the MYC locus that might better reflect typical enhancer-promoter pairs.”

Reviewer #2 (Public Review):

Summary:

The authors Fu et al., developed polymer models that combine loop extrusion with attractive interactions to best describe Hi-C population average data. They analyzed Hi-C data of the MYC locus as an example and developed an optimization strategy to extract the parameters that best fit this average Hi-C data.

Strengths:

The model has an intuitive nature and the authors masterfully fitted the model to predict relevant biology/Hi-C methodology parameters. This includes loop extrusion parameters, the need for self-interaction with specific energies, and the time and distance parameters expected for Hi-C capture.

Weaknesses:

(1) We are no longer in the age in which the community only has access to population average Hi-C. Why was only the population average Hi-C used in this study?

Can single-cell data: i.e. single-cell Hi-C/Dip-C data or chromatin tracing data (i.e. see Tan et al Science 2018 - for Dip-C, Bintu et al Science 2018, Su et al Cell 2020 for chromatin tracing, etc.) or even 2 color DNA FISH data (used here only as validation) better constrain these models? At the very least the simulations themselves could be used to answer this essential question.

I am expecting that the single-cell variance and overall distributions of distances between loci might better constrain the models, and the authors should at least comment on it.

We agree that it is possible to recapitulate single-cell Hi-C or chromatin tracing data with simulations, and that these data modalities have a superior potential to constrain polymer models because they provide an ensemble of single allele structures rather than population-averaged contact frequencies. However, these data remain out of reach for most labs compared to Hi-C. Our goal with this work was to provide an approachable method that anyone interested could deploy on their locus of choice, and reasoned that Hi-C currently remains the data modality available to most. We envision this strategy will help reach labs beyond the small number of groups expert in single cell chromatin architecture, and thus hopefully broaden the impact of polymer simulations in the chromatin organization field. 

Nevertheless, we do agree that the comparison of single-cell chromatin architectures to simulations is a fertile ground for future studies, and have modified the preprint accordingly (Discussion):

“Future work extending this framework to single cell readouts out chromatin architecture (e.g. single-cell Hi-C or chromatin tracing) holds promise to further constrain chromatin models.”

(2) The authors claimed "Our parameter optimization can be adapted to build biophysical models of any locus of interest. Despite the model's simplicity, the best-fit simulations are sufficient to predict the contribution of loop extrusion and domain interactions, as well as single-cell variability from Hi-C data. Modeling dynamics enables testing mechanistic relationships between chromatin dynamics and transcription regulation. As more experimental results emerge to define simulation parameters, updates to the model should further increase its power." The focus on the Myc locus in this study is too narrow for this claim. I am expecting at least one more locus for testing the generality of this model.

We note that we used two distinct loci in the initial version of our study, the MYC locus in leukemia vs T cells (Figs. 2-3) and a representative locus in experiments comparing WT CTCF with a mutant that leads to loss of a subset of CTCF binding sites (Fig. 1L). To further demonstrate generality, we have added to the revised preprint a demonstration of the simulation fitting to other loci acquired in different cell types (supplementary figure 3).

Recommendations for the authors:.

Reviewer #1 (Recommendations For The Authors):

(1) The Methods part of the imaging analysis lacks some quantitative details that could be useful for the readers: what is the frequency of double detections? How "small" is the 3D region around the centroid? How many cells with no spots or more than four spots are excluded?

We have clarified these important analysis parameters in the revised version of the preprint (Methods), including supplementary Table 2, listing the statistics of excluded cells:

“We then cropped out a small 3D region (20x20x10 pixels) around each approximate centroid, and subtracted the surrounding background intensity.”

“Cells with no spots or more than four spots were excluded from the cell cycle analysis (statistics in Supp. Table 2).”

(2) How is the autocorrelation function of chromatin structures computed?

We computed the autocorrelation function of the TAD2-TAD4 inter-TAD distance using the initial guess simulation parameters (Eattr, boundary permeabilities) of the MYC locus in CUTLL. All other simulation parameters are the same as other simulations in the preprint. The structure of the locus was saved every 5 simulation blocks. These structures were used to compute the TAD2-TAD4 inter-TAD distance as a function of time, which was used to calculate the autocorrelation function. This has been clarified in the revised version of the preprint (Methods):

“To optimize the simulation duration to streamline the parameter search (Supp. Fig. 1 B), we computed the autocorrelation function of the TAD2-TAD4 inter-TAD distance using the initial guess simulation parameters of the MYC locus in CUTLL. The simulation was saved every 5 simulation blocks.”

(3) How is the monomer length (35nm) chosen to best compare FISH data?

Because monomer length is difficult to derive from first principles, the standard in the field is to convert the size of a simulated monomer into a physical distance using a reference measurement in the system of choice. Similar to the Hi-C distance threshold, values for monomer size vary throughout the literature, e.g. 53 nm per 3 kbp monomer (Giorgetti et al. 2014), 50 nm per 2.5 kbp monomer (Nuebler et al. 2018), or from 36 to 60 nm per 3 kbp monomer, depending on the cell line or model details (Conte et al. 2022; Conte et al. 2020). 

Here we used the mean of the median TAD2-TAD4 distances in T Cells and CUTLL as our length reference, and converted simulation distances into nm by matching this value. We obtained 35 nm per 2.5 kbp monomer, a value well within the range of the literature values (see above).

Using this simple conversion, the simulated distance distributions recapitulate two independent metrics accessible by DNA FISH: the shift in median distances between T cell and CUTLL, and the width of each distribution. This agreement indicates that simulations recapitulate both the differences between the two cell types, and the single cell heterogeneity within each cell type. 

(4) The main text does not make clear the "known" biophysical parameters that establish the model ground truth.

In the initial validation of the fitting procedure, by “known biophysical parameters”, we meant that we generated simulated Hi-C maps in which we set the left/right permeabilities at each boundary, and Eattr values within each TAD to known values. We then assessed how well the fitting could recover these known ground truth values by trying to match the simulated representative Hi-C map. The specific values chosen are plotted for each set of simulations in Fig.1 F, H, J. The main text has been made more explicit in the revised preprint version (Results):

“We first validated the optimization method using ground truth maps built from simulation runs with known values of StallL, StallR, Eattr for each boundary/domainbiophysical parameters.”

(5) What are the correlation coefficients between experimental and model contact maps in Figure 1L?

We apologize for the oversight. The missing coefficient values have been added in the revised version of the manuscript (Results):

“As expected, the simulation predicted a significant drop of 0.13 in boundary permeability in CTCFmut compared to WT (Fig. 1 L; Spearman Correlation: 0.85±0.02 for CTCFmut, 0.82±0.01 for WT).”

(6) Figure 2A, B: Contact matrices look oversaturated. Next, why do model contact maps have negative values?

We apologize this was not clear. Figure 2 A,B plotted the log value of the contact matrices, thus the negative values. This has been made explicit in the revised version of the preprint (Fig. 2 Legend). 

(7) For model reproducibility, the authors could report the coordinates of the Hi-C TAD boundaries employed for the model.

We have included in the revised version of the preprint an explicit mention of all genomic coordinates of the loci simulated in the Methods section:

“The model used to fit into MYC Hi-C data consists of 1920 monomers representing chr8:126,720,000131,680,000, with the TAD boundaries located at monomer 456 (chr8: 127,840,000 - 127,880,001), monomer

808 (chr8: 128,720,000 - 128,760,001), monomer 1178 (chr8: 130,160,000 - 130,200,001) and monomer 1592 (chr8: 130,680,000 - 130,720,001).”

(8) What is the shaded area in Figure 3C?

The shaded area in Figure 3C is the standard deviation calculated from three independent DNA FISH or simulation replicates for each bin of the histogram. This detail has been clarified in the revised preprint (Figure 3 legend). 

(9) In the Discussion, I suggest changing as follows: "the time- and distance-gated model proposed here recapitulates several observations" -> "the time- and distance-gated model proposed here could recapitulate several observations", as they are speculations.

The sentence has been changed accordingly in the revised preprint (Discussion). Thank you for the suggestion. 

Reviewer #2 (Recommendations For The Authors):

Suggest analyzing the ability of single-cell data to better constrain dynamical models.

While we agree that modeling single-cell distributions is a worthwhile endeavor to be explored in future work, we believe that the tool presented here serves a slightly different purpose: enabling labs that only have access to the most widespread technique at present to perform simulations to interrogate the forces that shape the organization of an arbitrary locus in their model of choice. Analyzing single-cell data is in principle very powerful, but would by necessity be limited to the small number of systems where these cutting-edge techniques have been deployed. 

Suggest selecting another locus other than MYC to demonstrate generality.

We note that we used two distinct loci in the study, the MYC locus in leukemia vs. T cells (Figs. 2-3) and a representative locus in experiments comparing WT CTCF with a mutant that leads to loss of a subset of CTCF binding sites (Fig. 1L). To further demonstrate generality, we have added to the revised preprint a demonstration of the simulation fitting to other loci acquired in different cell types (supplementary figure 3).

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eLife assessment

This study presents a useful optimization algorithm to identify polymer models that best fit population-averaged chromosome contact data that will be of interest to physicists and biologists working on chromatin organization. The conclusions are supported by solid evidence.

Reviewer #1 (Public review):

Summary:

The authors of this study use an optimization algorithm approach, based on the established Nelder-Mead method, to infer polymer models that best match input bulk Hi-C contact data. The procedure infers the best parameters of a generic polymer model that combines loop-extrusion (LE) dynamics and compartmentalisation of chromatin types driven by weak biochemical affinities. Using this and DNA FISH, the authors investigate the chromatin structure of the MYC locus in leukaemia cells, showing that loop extrusion alone cannot explain local pathogenic chromatin rearrangements. Finally, they study the locus single-cell heterogeneity and time dynamics.

In the revised manuscript the authors have adequately addressed my questions and comments. The exception concerns point #5 of my original review:

(5) Besides cumulative probability distributions, I asked the authors to show the TAD2-TAD4 (model vs. exp) distances in Fig. 3c as relative frequency histograms. This allows readers to more accurately evaluate whether model and experimental distributions have same shape and variance.

eLife assessment

This important study investigates the sensitivity to endogenous cosolvents of three families of intrinsically disordered proteins involved with desiccation. The findings, drawn from well-designed experiments and calculations, suggest a functional synergy between sensitivity to small molecule solutes and convergent desiccation protection strategy. The evidence is found to be convincing, and the authors provide appropriate caveats since the study's conclusions are based on a small number of proteins. This work will be of interest to biochemists and biophysicists interested in the conformation-function relationship of intrinsically disordered proteins.

Reviewer #1 (Public Review):

The individual roles of both cosolvents and intrinsically disordered proteins (IDPs) in desiccation have been well established, but few studies have tried to elucidate how these two factors may contribute synergistically. The authors quantify the synergy for the model and true IDPs involved with desiccation and find that only the true IDPs have strong desiccation tolerance and synergy with cosolvents. Using these as model systems, they quantify the local (secondary structure vis-a-vi CD spectroscopy) and global dimensions (vis-a-vi the Rg of SAXS experiments) and find no obvious changes with the co-solvents. Instead, they focus on the gelation of one of the IDPs and, using theory and experiments, suggest that the co-solvents may enable desiccation tolerance, an interesting hypothesis to guide future in vivo desiccation studies. A few minor points that remained unclear to this reviewer and that were noted previously have been reasonably addressed in this revision.

Strengths:

This paper is quite extensive and has significant strengths worth highlighting. Notably, the number and type of methods employed to study IDPs are quite unusual, employing CD spectroscopy, SAXS measurements, and DSC. The use of the TFE is an exciting integration of the physical chemistry of cosolvents into the desiccation field is a nice approach and a clever way of addressing the gap of the lack of conformational changes depending on the cosolvents. Furthermore, I think this is a major point and strength of the paper; the underlying synergy of cosolvents and IDPs may lie in the thermodynamics of the dehydration process.

Figure S6A is very useful. I encourage readers who are confused about the DSC analysis, interpretation, and calculation to refer to it.

Weaknesses:

All minor weaknesses were addressed in this revision.

Reviewer #2 (Public Review):

Summary:

The paper aims to investigate the synergies between desiccation chaperones and small molecule cosolutes, and describe its mechanistic basis. The paper reports that IDP chaperones have stronger synergies with the cosolutes they coexist with, and in one case suggests that this is related to oligomerization propensity of the IDP.

Strengths:

The authors have done a good job improving the paper. The study uses a lot of orthogonal methods and the experiments are technically well done. They are addressing a new question that has not really been addressed previously.

Weaknesses:

The conclusions are still based on a few examples and only partial correlations. However, this is now acknowledged by the authors and the conclusions are presented with appropriate caveats.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The individual roles of both cosolvents and intrinsically disordered proteins (IDPs) in desiccation have been well established, but few studies have tried to elucidate how these two factors may contribute synergistically. The authors quantify the synergy for the model and true IDPs involved with desiccation and find that only the true IDPs have strong desiccation tolerance and synergy with cosolvents. Using these as model systems, they quantify the local (secondary structure vis-a-vi CD spectroscopy) and global dimensions (vis-a-vi the Rg of SAXS experiments) and find no obvious changes with the co-solvents. Instead, they focus on the gelation of one of the IDPs and, using theory and experiments, suggest that the co-solvents may enable desiccation tolerance, an interesting hypothesis to guide future in vivo desiccation studies. A few minor points that remain unclear to this reviewer are noted.

Strengths:

This paper is quite extensive and has significant strengths worth highlighting. Notably, the number and type of methods employed to study IDPs are quite unusual, employing CD spectroscopy, SAXS measurements, and DSC. The use of the TFE is an exciting integration of the physical chemistry of cosolvents into the desiccation field is a nice approach and a clever way of addressing the gap of the lack of conformational changes depending on the cosolvents. Furthermore, I think this is a major point and strength of the paper; the underlying synergy of cosolvents and IDPs may lie in the thermodynamics of the dehydration process.

Figure S6A is very useful. I encourage readers who are confused about the DSC analysis, interpretation, and calculation to refer to it.

Weaknesses:

Overall, the paper is sound and employs strong experimental design and analysis. However, I wish to point out a few minor weaknesses.

Perhaps the largest, in terms of reader comprehension, focuses on the transition between the model peptides and real IDPs in Figures 1 and 2. Notably, little is discussed with respect to the structure of the IDPs and what is known. Notably, I was confused to find out when looking at Table 1 that many of the IDPs are predicted to be largely unordered, which seemed to contrast with some of the CD spectroscopy data. I wonder if the disorder plots are misleading for readers. Can the authors comment more on this confusion? What are these IDPs structurally?

We apologize for the confusion caused here and thank the reviewer for this astute observation. Our CD spectroscopy data suggests all LEA proteins are almost entirely disordered under aqueous conditions, with a single major minimum at 200 nm, although most have a small inflection around 220 nm, indicating a small proportion of helicity (Fig. 3A). The notable exception here is CAHS D, which – in line with our work and the work of many others – possesses a substantial degree of transient helicity in the linker region (residues 100-200), giving rise to a more pronounced minimum at 220 nm. These conclusions are consistent with our SAXS data (Fig. 4), which predict a radius of gyration far larger than a globular folded protein of the same number of residues should have (15-20 Å). The structural predictions (both Metapredict and AlphaFold2), however, imply several of the proteins to be ordered; AvLEA1C and HeLEA68614 are both predicted to have large folded regions based on metapredict disorder scores. We believe this is an erroneous prediction driven by these regions' propensity to acquire helicity in the context of desiccation (Fig 3B) and/or when interacting with clients. As such, our computational analysis is at odds with the experimental data because these proteins are all poised to undergo a coil-to-helix transition, an effect our parallel work has proposed is important for their function (see Biswas et al. Prot. Sci. 2024). The ability of AlphaFold2 to predict bound-state or transient helices has been previously documented (Alderson et al PNAS 2023)

To address this discrepancy, the caption for Table 1 reads: “We note that the reason many of these profiles contain large folded regions is because the amphipathic LEA and CAHS proteins are predicted to form helices, which metapredict infers and incorrectly highlights these regions as ‘folded’ when really they are disordered in isolation”. We have also added additional context and information to the caption for Fig. S9 “We note that the structural predictions from AlphaFold2 contain largely ordered structures. We believe this is due to the propensity of these proteins to form helices in the context of drying or when interacting with a client. This has been shown in cases where an IDR contains residual helicity or is folded upon binding [70].”

Related to the above thoughts, the alpha fold structures for the LEA proteins are predicted (unconfidently) as being alpha-helical in contrast to the CD data. Does this complicate the TFE studies and eliminate the correlation for the LEA proteins?

AlphaFold2 predicted helicity in disordered regions is commonly observed, and thought to indicate a possible “bound” helical state (Alderson et al. PNAS 2023). As shown by the CD data, in aqueous conditions no secondary structure exists. It is only in the desiccated state - the path to which requires proteins to reach excessively high concentrations - that this secondary structure appears. Underlying our TFE model is that the AlphaFold2 predicted secondary structure is indicative of the state the proteins are in at high abundance, which occurs as cells ramp up protectant expression and as water is removed from the system. Under these assumptions, the CD data is in agreement with the AlphaFold2 predictions, and our analysis holds. This is explained in the methods under “Transfer Free Energy (TFE) Calculations” - but we have now also added an additional sentence to this effect in the main text: “Using a similar AlphaFold2-based approach for LEA proteins and for BSA, we can make correlations between the Gtr of the disorder-to-order transition and synergy (Fig. S8F-K). Interestingly, AlphaFold2 predictions of our LEA proteins were broadly helical, which is in contrast to our experimental characterization of these proteins in aqueous solutions. However, this is not unusual for AlphaFold2 predictions and could possibly represent a “bound” conformation for the proteins [70].”

Additionally, the notation that the LEA and BSA proteins do not correlate is unclear to this reviewer, aren't many of the correlations significant, having both a large R^2 and significant p-value?

We thank the reviewer for pointing this out. While BSA and some LEA proteins have values that correlate with synergy, there’s more to consider in assessing the relevance of these correlations. For example, we cannot claim that the value is physiologically relevant without observing an actual structural change in the protein. Furthermore, several of these proteins (BSA and AvLEA1C) were found to be not significantly synergistic in the LDH assay, and any correlation should, therefore, also be considered non-significant. We have added a sentence to the results to clarify this: “For a subset of these proteins, we see a statistically significant correlation between G and synergy. However, this data is purely computational. For CAHS D, we saw our predictions recapitulated in changes in the protein structure, and for the LEA proteins we do not. Thus, we conclude that cosolutes do not induce synergy in our LEA proteins through a change in folding.”

The calculation of synergy seems too simplistic or even problematic to me. While I am not familiar with the standards in the desiccation field, I think the approach as presented may be problematic due to the potential for higher initial values of protection to have lower synergies (two 50%s for example, could not yield higher than 100%).

We acknowledge the reviewer’s concern about our synergy calculation. We would like to highlight the use of sub-optimal protective concentrations in our synergy assays similar to studies previously reported in the desiccation field (Nguyen et al. 2022; Kim et al. 2018).

As the reviewer pointed out, we agree that there is a theoretical 100% threshold in our experiments which if we hit, we cannot distinguish between individual additive vs synergistic effects. To avoid the situation of reaching the near maximal protection levels (~100%), we intentionally select a sub-optimal concentration of the protectants that are below the maximum efficacy level for individual protectants to use in our assays. This limits the potential for initial higher values of the protectants so that their combined effect is not maximized, and there is always the potential for synergy. We would also like to point out that we never actually hit that 100% threshold in any of our synergy experiments, which warrants that any observed increase in protection is attributed to a true synergistic effect between the protectants.

Instead, I would think one would need to really think of it as an apparent equilibrium constant between functional and non-functional LDH (Kapp = [Func]/[Not Func] and frac = Kapp/(1+Kapp) or Kapp = frac/(1-frac) ) Then after getting the apparent equilibrium constants for the IDP and cosolvent (KappIDP and KappCS), the expected additive effect would be frac = (KappIDP+KappCS)/(1+KappIDP+KappCS).

Consequently, the extent of synergy could be instead calculated as KappBOTH-KappIDP-KappCS. Maybe this reviewer is misunderstanding. It is recommended that the authors clarify why the synergy calculation in the manuscript is reasonable.

We thank the reviewer for this suggestion. In the desiccation field, the synergy calculations that we used is the standard method that people use, so that’s what we present in our main manuscript. However, we have now quantified synergy through two new approaches: one, as suggested by the reviewer, using the equilibrium constant (Kapp) as a metric, and the other using the Bliss Independent model, which is a common approach for calculating synergy in drug combination studies. We see minimal differences in terms of the synergy scores using these different methods. We have included the results for these additional methods in supplemental figure S3.

Related to the above, the authors should discuss the utility of using molar concentration instead of volume fraction or mass concentration. Notably, when trehalose is used in concentration, the volume fraction of trehalose is much smaller compared to the IDPs used in Figure 2 or some in Figure 1. Would switching to a different weighted unit impact the results of the study, or is it robust to such (potentially) arbitrary units?

We thank the reviewer for this comment. Indeed, in studies of cosolute effect, concentration units can alter the conclusions of the study (Auton and Bolen 2004). In our case, the relevant figures where we use a concentration scale (1B and 2B) are not germane to the main conclusions: The only use of these PD50 values is to determine a sub-optimal concentration at which ~30% of the LDH is protected. While it is true that the number for the concentration of e.g., trehalose will be dramatically different if we were to use mass fraction units, the rest of the work and all our conclusions would be exactly the same.

Additionally, our use of a molar ratio when discussing synergy is a direct result of the way we think about such synergy: Since the concentration of both protein and cosolute can change by orders of magnitude during drying, it is the copy numbers of both proteins and cosolute that are conserved in this process, and it is this unit that we think is important to the protective effect (rather than the partial molar volume, for example, which would be changing as the system dries).

Reviewer #2 (Public Review):

Summary:

The paper aims to investigate the synergies between desiccation chaperones and small molecule cosolutes, and describe its mechanistic basis. The paper reports that IDP chaperones have stronger synergies with the cosolutes they coexist with, and in one case suggests that this is related to oligomerization propensity of the IDP.

Strengths:

The study uses a lot of orthogonal methods and the experiments are technically well done. They are addressing a new question that has not really been addressed previously.

Weaknesses:

The conclusions are based on a few examples and only partial correlations. While the data support mechanistic conclusions about the individual proteins studied, it is not clear that the conclusions can be generalized to the extent proposed by the authors due to small effect sizes, small numbers of proteins, and only partial correlations.

Thank you for bringing this up. We agree that we should not generalize our results to other systems based on the evidence we have for the proteins used in our study. We have altered our discussion to highlight that this may apply to other IDPs, and that future experiments must be done to support this: “Additionally, we want to point out that our results cannot necessarily be generalized to all desiccation-related IDPs. More experiments will be needed to assess the relevance of cosolute effects to functional synergy and IDP folding in the context of desiccation and beyond. This remains an important future direction for the field.”

The authors pose relevant questions and try to answer them through a systematic series of experiments that are all technically well-conducted. The data points are generally interpreted appropriately in isolation, however, I am a little concerned about a tendency to over-generalize their findings. Many of the experiments give negative or non-conclusive results (not a problem in itself), which means that the overall storyline is often based on single examples.

We agree with the reviewer’s point. As mentioned earlier, we have modified our manuscript to reflect that our findings are based on the six proteins that we studied, and we can only speculate about other desiccation-related IDPs based on our results.

For example, the central conclusion that IDPs interact synergistically with their endogenous co-solute (Figure 2E) is largely driven by one outlier from Arabidopsis. The rest are relatively close to the diagonal, and one could equally well suggest that the cosolutes affect the IDPs equally (which is also the conclusion in 1F).

We appreciate the reviewer’s concern regarding our conclusion in Figures 2E and 1F. We would like to highlight that our conclusions that IDPs interact synergistically with their endogenous cosolute are based on statistical analysis. Our data shows that full-length proteins that were synergistic with both cosolutes are always significantly more synergistic with the endogenous cosolute (Fig. 2E, Fig. S2C-E). For example, the nematode protein is synergistic with both trehalose and sucrose, but is significantly more synergistic with trehalose, the endogenous nematode cosolute, than with sucrose (Fig S2D).

This is not the case in 1F. In Fig. 1F, it is to note that not only are the points close to the diagonal, but most points are close to zero along both axes indicating no synergy. In fact, many points have negative synergy (antagonistic effect).

We do recognize that our conclusions are based on the study of a specific set of six IDPs, and we do not want to overreach in our conclusions. To acknowledge this, we have now added text to emphasize that our conclusion is based on the six proteins that we tested, and we speculate it might apply to other systems: “Our data shows that these six IDPs synergize best with their endogenous cosolute to promote desiccation tolerance and we speculate that this may apply to other desiccation-related IDPs”.

Similarly, the mechanistic explanations tend to be based on single examples. This is somewhat unavoidable as biophysical studies cannot be done on thousands of proteins, but the text should be toned down to reflect the strength of the conclusions.

We acknowledge the reviewer’s concern. We have modified our manuscript accordingly to reflect that the mechanistic insights we gained are for the six proteins we tested empirically. These changes can be found throughout the manuscript. None of our experiments rule out the possibility that other LEA proteins or CAHS proteins may show different structural transitions, or that other IDPs may take on structural changes in response to the cosolutes.

The central hypothesis revolves around the interplay between cosolutes and IDP chaperones comparing chaperones from species with different complements of cosolutes. In Table 1, it is mentioned that Arabidopsis uses both trehalose and sucrose as a cosolute, yet experiments are only done with either of these cosolutes and Arabidopsis is counted in the sucrose column. While it makes sense to compare them separately from a biophysical point of view, the ability to test the co-evolution of these systems is somewhat diminished by this. At least it should be discussed clearly.

We appreciate the reviewer’s comment. As is mentioned in Table 1, Arabidopsis uses both trehalose and sucrose as cosolute. As such, we would predict that the Arabidopsis proteins would respond positively to both cosolutes. We would like to point out that Arabidopsis is counted in both trehalose and sucrose columns.

We would also like to emphasize that multiple osmolytes exist in all organisms as a desiccation response and a simple IDP-cosolute system is far from a true recapitulation of a desiccating system. We have touched on this in the discussion and explicitly addressed the presence of both cosolutes in Arabidopsis and the need for further experiments to test for synergistic interactions using both or multiple mediators to illustrate synergy in multiple cosolute systems: “It is important to note that desiccation-tolerant organisms employ multiple cosolutes to counteract the effects of desiccation. The use of a single cosolute-IDP system in our in vitro experiments does not accurately mirror the diverse cosolute changes in desiccating systems. For instance, Arabidopsis seeds enrich both trehalose and sucrose, among other cosolutes. This demands the necessity of future experiments that incorporate both or multiple cosolutes and assess their synergistic effects, thus elucidating the intricate synergy in multi-cosolute systems.”

It would be helpful if the authors could spell out the theoretical basis of how they quantify synergy. I understand what they are doing - and maybe there are no better ways to do it - but it seems like an approach with limitations. The authors identify one in that the calculation only works far from 100%, but to me, it seems there would be an equally strict requirement to be significantly above 0%. This would suggest that it is used wrongly in Figure 6H, where there is no effect of betaine (at least as far as the color scheme allows one to distinguish the different bars). In this case, the authors cannot really conclude synergy or not, it could be a straight non-synergistic inhibition by betaine.

We appreciate the reviewer’s concern about the theoretical basis of how we quantify synergy. We do acknowledge the limitation of our LDH protection/synergy assay only produces interpretable data when our protectant/mixture yields protection levels within the range 0 and below 100%. Betaine was not protective in any of the concentrations we tested in this study. In line with the reviewer’s comment, we also acknowledge that within our experimental procedures, the inhibitory effects of betaine cannot be accurately captured, considering that LDH activity is ~0% without protectants. However, in our positive control in which LDH is co-incubated with betaine or betaine and CAHS D overnight in the hydrated state, we do not see a loss of enzymatic function of LDH nullifying a direct inhibition by betaine. We have added this text in our manuscript: “Glycine betaine on its own is not protective to LDH during drying nor does it inhibit LDH activity (Fig. S8E)”.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The conclusion in lines 195-196 seems overstated as the length dependence could be strongly changed in non-tested concentrations or those that are not possible experimentally. Notably, the IDPs in Figure 2 are around 200AA and only transition in the ranges tested for these peptides. Some other conclusions around this point seem a little overstated.

We acknowledge the reviewer’s concern about the potential variability of the length dependence of the motifs at concentrations beyond those tested. However, we would like to highlight that higher concentrations of the tandem repeats (At22 and At44) inactivated LDH during the incubation period, as was seen with  the 11-mer motifs. This meant we could not evaluate protection by these motifs at concentrations beyond those plotted in Fig. 1A. This behavior was not observed for the full-length proteins. Regardless, we have toned down the conclusion in lines 195-196 to only reflect our results for the 2X and 4X repeats of At11 which now reads “We synthesized 2X (At22) and 4X (At44) tandem repeats of the A. thaliana 11-mer LEA_4 motif (At11). At22 and At44 show minimal potency in preserving in vitro LDH function during drying (Fig. 1A, Fig. S1A).”

Reviewer #2 (Recommendations For The Authors):

Figure 3: The focus on the ratio 222/210 seems inappropriate. That would indeed be useful for telling apart e.g. an alpha-to-beta transition, or formation of coiled coils. However, for a helix-to-coil equilibrium, which is likely to dominate here, it will not be especially sensitive as demonstrated e.g. by BSA in the dry state.

We thank the reviewer for this comment. The use of ratios to measure structural transition is primarily to eliminate the effects of concentrations on the graph. It is clear from Fig. 3A and Fig. 3B that a structural transition occurs between the aqueous and the desiccated state. This is also very clear from the 222/210 ratio that we use (Fig. 3C), for every construct other than BSA - which indeed does not seem to undergo a dramatic structural change in the desiccated state. We have clarified this now in the description of the results: “Using this metric, all LEAs and CAHS D display a clear increase in helical propensity upon being desiccated (Fig. 3C). On the other hand, the helical propensity of BSA remains very similar to its hydrated state, indicating that no dramatic structural change took place (Fig. 3C).

Minor comments:

Figure 1F is not mentioned in the text.

We have included Fig. 1F in the text.

Some technical details missing for SAXS experiments.

We thank the reviewer for pointing this out. We’ve added additional technical details to the main text, and directed readers to the methods for more information.

It is well known that BSA is in a monomer-dimer equilibrium and this is normally taken into account in data analysis as this is often a calibration sample.

We’ve calculated for BSA, and correlated the resulting data with synergy. This can be found in figure S7M and figure S8I.

Line 247: "BSA, which comes from cows, which of course have no capacity for anhydrobiosis" - This seems like a rather strong statement without a reference. Did the authors consider reanimating beef jerky by soaking it in water? ;-)

This is a great idea, and we hope to assign this project to our next rotation student.

Minor suggestions for figures (that are generally very well done):

Figure 1-4: Consider using the color scheme to indicate what the endogenous cosolutes are. Even though this info is in table one, it would still improve readability.

We have added the colored organismal icons for all figures in which the plain black ones were previously used, including supplementals.

Figure 4: consider adding some white space between the two concentration series of solutes to avoid being read as a single concentration series.

We have updated this figure to clearly separate each sample by osmolyte.

Figure 6H: Consider changing the colors for Betaine and CAHS D, so they are easier to distinguish. They are hard to tell apart on a printout.

We have adjusted the colors for betaine and CAHS D.

eLife assessment

This study provides an important insight into the mechanisms of cooperation between Hsp70 and its cochaperones during reactivation of aggregated proteins. Based on convincing evidence, the authors demonstrate that the co-chaperone Hsp110 boosts disaggregation activity by enhancing Hsp70 recruitment to protein aggregates. This work is of broad interest to biochemists and cell biologists working in the protein homeostasis field.

Reviewer #1 (Public review):

Summary:

The manuscript by Sztangierska et al explores how the Hsp70 chaperone together with its JDP-NEF cofactors and Hsp104 disentangle aggregated proteins. Specifically, the study provides mechanistic findings that explain what role the NEF class Hsp110 has in protein disaggregation. The results explain several previous observations related to Hsp110 in protein disaggregation. Importantly, the study provides compelling evidence that Hsp110 acts early in the disaggregation process.

Strengths:

(1) This is a very well performed study with multiple in vitro experiments that provide convincing support for the claims.

(2) An important finding is that the study places Hsp110 function early in the disaggregation process.

(3) The study has an important value in that it picks up on a number of observations in the field that have not been explored or directly tested by experiment. The presented results settle questions and controversy regarding Hsp110 function in disaggregation.

Weaknesses:

(1) While the key finding of this manuscript is that it places Hsp110 early in the disaggregation process, the other findings are advancing the field less.

Reviewer #2 (Public review):

Sztangierska et al. have investigated the impact of the nucleotide exchange (NEF) factor Hsp110 on the Hsp70-dependent dissolution of amorphous aggregates in the presence of representative members of two classes of J-domain protein.<br /> The authors find that the nucleotide exchange factor of the Hsp110 family, sse1, stimulates the disaggregation activity of yeast Hsp70, ssa1, in particular in the presence of the J-domain protein sis1. Linking chaperone-substrate interactions as determined by biolayer interferometry (BLI) to activity assays, they show that sse1 facilitates the loading of more ssa1 onto the aggregate substrate and propose that this is due to active remodelling of the protein aggregate which exposes more chaperone binding sites and thus facilitates reactivation. This study highlights two important facets of Hsp70 biology: different Hsp70 functions rely on the functional cooperation of specific co-chaperone combinations and the stoichiometry of the different players of the Hsp70 system is an important parameter in tuning Hsp70 chaperone activity.

Strengths:

The manuscript presents a systematic analysis of the functional cooperation of sse1 with a class B J-domain protein sis1 in the disaggregation of two different model aggregate substrates, allowing the authors to draw more general conclusions about Hsp70 disaggregation activity.

The authors can pinpoint the role of sse1 to the initial remodeling of aggregates, rather than the later stages of refolding, highlighting the functional specificity of Hsp70 co-chaperones.

They demonstrate the competitive nature of binding to ssa1 between sse1 and sis1 which can explain the poisoning of Hsp70 chaperone activities observed at high NEF concentrations.

Weaknesses:

While structural requirements have been identified that allow sse1, in cooperation with sis1, to facilitates the loading of Hsp70 on the amorphous aggregate substrate, how this is achieved on a mechanistic level remains an open question.

Reviewer #3 (Public review):

Summary:

The authors studied the function of Hsp110 co-chaperones (e.g. yeast Sse1) in Hsp70-dependent protein disaggregation reactions. The study builds on former work by the authors (Wyszkowski et al., 2021, PNAS), analyzing the binding of Hsp70 and J-domain protein (JDP) cochaperones to protein aggregates using bio-layer interferometry (BLI). It was shown before by other groups that Hsp110 enhances Hsp70 disaggregation activity. The mechanism of Hsp110-stimulated disaggregation activity, however, remained poorly defined. Here, the authors show that yeast Hsp110 increases Hsp70 recruitment to the surface of protein aggregates. The effect is largely dependent on J-domain protein (JDP) identity and particularly pronounced for class B JDPs (e.g. yeast Sis1), which are also more effective in disaggregation reactions. The authors also confirm former results, showing inhibition by increased Hsp110 levels and provide novel evidence that the inhibitory effect is caused by competition between Hsp110 and JDPs for Hsp70 binding.

Strengths:

The work represents a very thoroughly executed study, which provides novel insights into the mechanism of Hsp70-mediated protein disaggregation. Key findings established for yeast chaperones are also documented for human counterparts. The observation that Hsp110 might displace JDPs from Hsp70 during the disaggregation reaction is very appealing. It will now become important to validate this initial finding and dissect how it propels the disaggregation reaction.

Weaknesses:

How exactly the interplay between JDPs and Hsp110 orchestrates protein disaggregation remains largely speculative and further analysis is required for a deeper mechanistic understanding.

Author response:

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

Reviewer #1 (Recommendations For The Authors): 

- The title may not reflect the key finding of the paper. It is well established in the field that the disaggregation process is sensitive to perturbations of the levels of the disaggregating factors.

We have changed the title to better reflect the major finding of the work, the importance of the NEF during the initiation of disaggregation. The new title is: Early Steps of Protein Disaggregation by Hsp70 Chaperone and Class B J-Domain Proteins are Shaped by Hsp110.

- Abstract:

Please note that the phrases "stimulation is much limited with class A JDPs", "limited destabilization of the chaperone complex improves disaggregation", and "tuned proportion between the co-chaperones" are hard to understand. Only after having read the manuscript are the meanings of these phrases accessible.

The phrases in the abstract were changed (page 1, lines 10-14).

- The subheading "Sse1 improves aggregate modification by Hsp70" on p. 7 is unclear. What is measured is a decrease in aggregate size dependent on Hsp70-JDP as well as Sse1.

The subheading was changed to include more precise information, into “Sse1 leads to Hsp70-depenent reduction of aggregate size”.

- The subheading "Biphasic effects of Sse1 on the Hsp70 disaggregation activity" does not describe the finding clearly; "Biphasic effects" is a term that is hard to understand.

To avoid phrases that can be understood in many ways, we have changed the subheading into “Hormetic effects of Sse1 in Hsp70 disaggregation activity”

- p.5, last line. Hsp110 typo The typos have been corrected.

Reviewer #2 (Recommendations For The Authors):

(1) The article emphasises multiple times the importance of stoichiometry between the (co-)chaperones. Most figures would benefit from an indication of the used stoichiometry (or all absolute concentrations) to support the points made about the stoichiometry, especially the figures showing titrations of Sse1, Sse1-2, and Sis1 (Fig. 3D, 3E, 4A-C, S2B, S5F, S6A-E).

The information of protein concentrations has been included in all figure captions.

(2) The manuscript includes a summary model. While this model is a plausible hypothesis of the mechanism of disaggregation by Hsp70, in particular when viewed with previous data (Wyszkowski et al., 2021), it focuses rather heavily on the potential remodeling of clients by Hsp70, which is not the primary focus of the data presented in this manuscript. More emphasis could be put on the JDP class/ functional specificity observed.

The model has been changed according to the Reviewer’s comments to better reflect the findings presented in the manuscript (Figure 5).

(3) The methods section is very brief. I recommend including additional details about reaction conditions (temperature, buffer compositions, protein concentrations) even when previously reported elsewhere to improve the readability of the manuscript. Details regarding the DLS experiments performed are missing.

More detailed information on the experimental conditions has been added to the Methods section, as well as to figure legends.

(4) Many experiments incorporate BLI to assess the effect of NEFs on the binding of the Hsp70 and JDP to aggregates. Although appropriate controls are included (no ATP, Hsp70, and JDP only), a control with only Hsp70 and the NEF would be useful to determine to which extent the NEF itself alters the thickness of the (Hsp70-bound) aggregate biolayer.

The suggested controls were added (Figure 1—figure supplement 1 G) and discussed in the manuscript (page 5, lines 23-24).

Reviewer #3 (Recommendations For The Authors):

- The refolding assay makes use of Luciferase denatured in 5 M GdnHCl. These conditions lead to a spontaneous refolding yield of 20% (Figure 3C), which is very high and limits conclusions on the effect of Hsp110 but also JDPs on the refolding process. Typically this assay uses 6 M GdnHCl for Luciferase denaturation and under these conditions, spontaneous refolding of Luciferase is hardly observed (e.g. Laufen et al. PNAS 1999). The authors are therefore asked to repeat key experiments using altered (6M) GdnHCl concentrations.

We based our experiments assessing luciferase refolding on the publication by Imamoglu et al. (2020), in which the authors, using 5 M GdnHCl for luciferase denaturation, demonstrated that spontaneous and chaperone-assisted luciferase refolding strongly depends on luciferase concentration. In this work, a similar degree of luciferase refolding was reported for the same final luciferase concentration (100 nM) as we used in our experiments (Figure 1—figure supplement 1D). As an additional control, we compared the effects of 5 M and 6 M of GdnHCl during denaturation on luciferase refolding under the same conditions (100 nM, 25 °C, 2 h) and we observed no significant differences (Author response image 1).

Author response image 1.

Chaperone-assisted folding of luciferase after denaturation at 5 M or 6 M GdnHCl. Luciferase was denatured in 5 M or 6 M GdnHCl according to the protocol in the Materials and Methods section. Luminescence was monitored alone or after incubation with Luminescence was monitored alone or after incubation with Ssa1-Sis1 or Ssa1-Ydj1. Chaperones were used at 1 µM concentration. Luciferase activity was measured after 2 hours and normalized to the activity of the native protein. Error bars indicate SD from three repeats.

- Figure 1B: The authors are asked to provide binding curves for Ssa1/Sse1 (no Sis1) and Sis1/Sse1 (no Ssa1) as controls. Particularly the latter combination is required as direct cooperation between Hsp110 and JDPs has been suggested in the literature (Mattoo et al., JBC 2013).

We performed the suggested BLI experiment, and the results are presented in the new Figure 1—figure supplement 1 G (page 5, lines 23-24).

- Figure 1B (and other figure parts showing BLI data): it is unclear how often the BLI experiments have been performed. This should be stated in the figure legend. Can the authors add SDs to the respective curves?

We added detailed information about the number of replicates to the figure legends. SD bars were added to the BLI results shown in Figures1-4, apart from the results of titrations, for which, for the sake of clarity, the three replicates are represented in the plots on the right (Figure 3D). In the case of less than 3 repeats of the results presented in the Supplementary Figures, the remaining repeats are added to the provided Source Data file, information about which has been added to the captions of the respective figures. 

- The observation that Hsp110 can interrupt Hsp70 interaction with JDPs is intriguing. Do the authors envision JDP displacement from the aggregate? If so this could be shown in BLI experiments by monitoring the release of fluorescently labeled Sis1 (similar to labeled Ssa1, Fig. S3C). Or will the released JDP immediately rebind to another binding site on the aggregate? The authors should at least discuss the diverse scenarios as they are relevant to the mechanism of protein disaggregation.

The proposed experiment is challenging due to the transient nature of Sis1 binding to aggregate and high background observed with the method using the fluorescently labelled proteins. The aspect of chaperone’s re-binding after their release by Hsp110 proposed by the reviewer has been introduced into the Discussion section (pages 12/13, lines 25-4). We speculate that Hsp110 might release an Hsp70 molecule as well as a JDP molecule that had been bound to the aggregate through Hsp70 (Figure 5).  

- Figure 2B: Ssa1/Sis1/Sse1 strongly decreases the size of Luciferase-GFP aggregates. Yet this activity only allows for limited refolding of aggregated Luciferase and the reaction stays largely dependent on Hsp104. How do the authors envision the role of the hexameric disaggregase in this process? Does it act exclusively on small-sized aggregates after Hsp110-dependent fragmentation?

A question of the Hsp104 activity with the Hsp70-processed aggregates is indeed intriguing and we agree that it should have been discussed more thoroughly. We added to the manuscript the results of the reactivation of luciferase-GFP with and without Hsp104 to emphasize the role of Hsp104 in the active protein recovery (Figure 2—figure supplement 1A) (page 7, lines 24-27). We propose that aggregate fragmentation by Hsp70-JDPB-Hsp110 increases the effective aggregate surface, at which Hsp104 might become engaged. We do not think that Hsp104 acts only on small aggregates, it might be just more effective, when the number of exposed polypeptides is larger. In the cell, where Hsp104 binds to aggregates of various sizes, protein aggregates apparently also need to undergo such Hsp110-boosted pre-processing by Hsp70, based on the finding that Sse1 is not necessary for Hsp104 recruitment to aggregates, but it is required for Hsp104-dependent disaggregation (Kaimal et al., 2017). We have added a comment on this problem to the Discussion section (pages 11/12, lines 33-4) .

- Page 9: The authors state that the Sse1-2 variant is nearly as effective as Sse1 Wt in stimulating substrate dissociation and refer to published work (Polier et al., 2008). It is unclear how the variant should have Wtlike activity in triggering substrate release although its activity in catalyzing nucleotide exchange is reduced to 5% (both activities are coupled). The observation that high Sse1-2 concentrations do not inhibit protein disaggregation does not necessarily exclude the possibility that high Sse1 WT concentration inhibit the reaction by overstimulating substrate release. The latter possibility should be considered by the authors and added to the discussion section.

We agree with the Reviewer that the description of the Sse1-2 variant was misleading, as it was lacking the key information, that according to the published data (Polier et al., 2008), it was 10 times higher the concentration of the Sse1-2 variant than Sse1 WT that had a similar nucleotide-exchange activity to the wild type. We have changed the text (page 9, lines 16-22, page 13, lines 26-28) to avoid confusion as well as the model in the Figure 5, to underline the importance of substrate release as the cause of the Hsp110-dependent inhibition.

- While similar effects are observed for human class A and class B JDP co-chaperones, they are clearly less pronounced. A mechanistic explanation for the difference between yeast and human chaperones is currently missing and the authors are asked to elaborate on this aspect.

There are indeed clear differences between the human and yeasts systems, especially regarding the dependence on the NEF. Hsc70 has been reported to have a lower rate of ADP release (Dragovic et al., 2006) and thus might rely more on Hsp110 than its yeast ortholog. For the same reason, the strong Hsc70 stimulation by Hsp105 is also observed with class A JDP. We have added a comment on these effects in the Discussion section (page 12, lines 17-21).

Minor points

- Figure S1C (right): the disaggregation rate (%GFP/h) is somewhat misleading/confusing as a value of more than 150%/h is determined in the presence of the complete disaggregation system while only approx. 60% GFP is indeed refolded by the system (Figure S1C, left). Showing the rate as %GFP/min seems more rational.

We changed the units according to the Reviewer’s comment (Figure 1—figure supplement 1A, C).

- Figure S5B: Only a single data point is shown for Ssa1/Sis1/Sse1.

We changed the figure to include datapoints from all three repeats (Figure 3—figure supplement 1 B).

- There are several typos throughout the manuscript. A more careful proofreading is recommended

We have corrected the typos.

Reviewer #1 (Public Review):

The experiments differ somewhat in regard to the aggregated protein used. For example, in Figure 1A, FFL is used with only limited reactivation (10% reactivated at the last timepoint and the curve is flattening), while in Figure 2B FFL-EGFP is used to monitor microscopically what appears to be complete disaggregation. Does FFL-EGFP behave the same as FFL in assays such as the one in Figure 1A or are there major differences that may impact how the data should be interpreted?

We added the results of Luc-GFP reactivation (Figure 2—figure supplement 1 B) (discussed on page 7, lines 24-27 of the manuscipt) which agree with the results obtain with Luciferase as a substrate (Figure 1—figure supplement 1 B). They clearly show that the Ssa1-Sis1-Sse1-dependent decrease in aggregate size is not associated with the recovery of active protein.

Reviewer #2 (Public Review):

Experimental data concerning the class A JDPs should be interpreted with caution. These experiments show very small reactivation activities for luciferase in the range of 0-1% without the addition of Hsp104 and 0-15% with the addition of Hsp104. Moreover, since the assay is based on the recovery of luciferase activity, it conflates two chaperone activities, namely disaggregation and refolding. It is possible that the small degree of reactivation observed for the class A JDP reflects a minor subpopulation of the aggregated species that is particularly easy to disaggregate/refold and may thus not be representative of bulk behaviour.

The disaggregation by the Hsp70 system can be enhanced by the addition of small heat shock proteins at the step of substrate aggregation (Rampelt et al., 2012). However, sHsps compete with Hsp70 for binding to the aggregate (Żwirowski et al., 2017) and for that reason we decided not to include sHsps in the experiments presented in the manuscript, as it would introduce another level of complexity. However, as a control, we performed the disaggregation assay with Hsp70 with Ydj1 using luciferase aggregates formed in the presence or absence of sHsp (Author response image 2). In 1 h, the Hsp70 system without Hsp104 yielded 5% of recovered luciferase activity and the system with Hsp104, 23% compared to the native. The impact of Sse1 on Ssa1-Ydj1 and Ssa1-Ydj1-Hsp104 was similar as for luciferase aggregates formed without sHsps (Figure 1A, Figure 1—figure supplement 1 B). Furthermore, according to the Reviewer’s comment, we have changed the Figure 5 to underscore the more prominent role of class A JDPs in the final protein folding than in disaggregation.

Author response image 2.

Disaggregaton of heat-aggregated luciferase – impact of sHsps. Luciferase (2 μM) was denatured with (blue) or without (red) Hsp26 (20 μM) at 45 ̊C for 15 min in the buffer A (Materials and Methods). Upon 100-fold dilution with the buffer A, supplemented wih 5 mM ATP, 2 mM DTT, 1.2 μM creatine kinase, 20 mM creatine phosphate, chaperones indicated in the legend were added to the final concentration of 1 μM, except for Sse1, concentration of which was 0.1 μM. Shown is luciferase activity measured after 1 h of incubation at 25 °C, normalized to the activity of native luciferase.

Reviewer #3 (Public Review):

Enhanced recruitment of Hsp70 in the presence of Hsp110 was shown for amyloid fibrils before (Beton et al., EMBO J 2022) and should be acknowledged. 

We have added the suggested citation with a respective comment (page 11, lines 20-21).

eLife assessment

This fundamental study demonstrates a novel method for imaging glutamate receptors in situ via cryo-ET. The use of cutting-edge methods is well-described and is compelling. This paper is broadly relevant to biophysicists and neuroscientists.

Reviewer #1 (Public review):

Summary:

Matsui et al. present an experimental pipeline for visualizing molecular machinery of synapses in the brain, which includes numerous techniques, starting with generating labeled antibodies and recombinant mice, continuing with HPF and FIB milling and finishing with tilt series collection and 3D image processing. This pipeline represents a breakthrough in preparation of brain tissue for high resolution imaging and can be used in future tomographic research to reconstruct molecular details of synaptic complexes as well as pre- and post-synaptic assemblies. This methodology can also be adapted for a broader range of tissue preparations and signifies the next step towards better structural understanding of how molecular machineries operate in natural conditions.

Strengths:

The manuscript is very well written, contains a detailed description of methodology, provides nice illustrations and will be an outstanding guide for future research.

Weaknesses:

None noted.

Reviewer #2 (Public review):

Summary

The authors present a method that allows for the identification and localization of molecular machinery at chemical synapses in unstained, unfixed native brain tissue slices. They believe that this approach will provide a 3D structural basis for understanding different mechanisms of synaptic transmission, plasticity, and development. To achieve this, the group used genetically engineered mouse lines and generated thin brain slices that underwent high-pressure freezing (HPF) and focused ion beam (FIB) milling. Utilizing cryo-electron tomography (cryo-ET) and integrating it with cryo-fluorescence microscopy, they achieved micrometer resolution in identifying the glutamatergic synapses along with nanometer resolution to locate AMPA receptors GluA2-subunits using Fab-AuNP conjugates. The findings are summarized with detailed examples of successfully prepared substrates for cryo-ET, specific morphological identification and localization, and the detailed structural organization of excitatory synapses, including synaptic vesicle clusters close to the postsynaptic density and in the cleft.

Strengths

The study advances previous work that used cultured neurons or synaptosomes. Combining cryo-electron tomography (cryo-ET) with fluorescence-guided targeting and labeling with Fab-AuNP conjugates enabled the study of synapses and molecular structures in their native environment without chemical fixation or staining. This preserves their near-native state, offering high specificity and resolution. The methods developed are mostly generalizable, allowing adaptation for identifying and localizing other key molecules at glutamatergic synapses and potentially useful for studying a variety of synapses and cellular structures beyond the scope of this research.

Weaknesses

The preparation and imaging techniques are complex and require highly specialized equipment and expertise, potentially limiting their accessibility and widespread adoption.

Additionally, the methods might need further modifications/tweaks to study other types of synapses or molecular structures effectively.

The reliance on genetically engineered mouse lines and monoclonal, high-affinity antibodies/Fab fragments to specifically label receptors/proteins would limit the wider employment of these methods.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Matsui et al. present an experimental pipeline for visualizing the molecular machinery of synapses in the brain, which includes numerous techniques, starting with generating labeled antibodies and recombinant mice, continuing with HPF and FIB milling, and finishing with tilt series collection and 3D image processing. This pipeline represents a breakthrough in the preparation of brain tissue for high-resolution imaging and can be used in future tomographic research to reconstruct molecular details of synaptic complexes as well as pre- and post-synaptic assemblies. This methodology can also be adapted for a broader range of tissue preparations and signifies the next step towards a better structural understanding of how molecular machineries operate in natural conditions.

Strengths:

The manuscript is very well written, contains a detailed description of methodology, provides nice illustrations, and will be an outstanding guide for future research.

Weaknesses:

None noted.

Reviewer #2 (Public Review):

Summary:

The authors present a method that allows for the identification and localization of molecular machinery at chemical synapses in unstained, unfixed native brain tissue slices. They believe that this approach will provide a 3D structural basis for understanding different mechanisms of synaptic transmission, plasticity, and development. To achieve this, the group used genetically engineered mouse lines and generated thin brain slices that underwent high-pressure freezing (HPF) and focused ion beam (FIB) milling. Utilizing cryo-electron tomography (cryo-ET) and integrating it with cryo-fluorescence microscopy, they achieved micrometer resolution in identifying the glutamatergic synapses along with nanometer resolution to locate AMPA receptors GluA2-subunits using Fab-AuNP conjugates. The findings are summarized with detailed examples of successfully prepared substrates for cryo-ET, specific morphological identification and localization, and the detailed structural organization of excitatory synapses, including synaptic vesicle clusters close to the postsynaptic density and in the cleft.

Strengths:

The study advances previous work that used cultured neurons or synaptosomes. Combining cryo-electron tomography (cryo-ET) with fluorescence-guided targeting and labeling with Fab-AuNP conjugates enabled the study of synapses and molecular structures in their native environment without chemical fixation or staining. This preserves their near-native state, offering high specificity and resolution. The methods developed are generalizable, allowing adaptation for identifying and localizing other key molecules at glutamatergic synapses and potentially useful for studying a variety of synapses and cellular structures beyond the scope of this research.

Weaknesses

The preparation and imaging techniques are complex and require highly specialized equipment and expertise, potentially limiting their accessibility and widespread adoption.

Additionally, the methods might need further modifications/tweaks to study other types of synapses or molecular structures effectively.

The reliance on genetically engineered mouse lines may again impact the generalizability of the findings.

Similarly, the requirement of monoclonal, high-affinity antibodies/Fab fragments to specifically label receptors/proteins would limit the wider employment of these methods.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Matsui et al. present an experimental pipeline for visualizing the molecular machinery of synapsis in the brain, which includes numerous techniques, starting with generating labeled antibodies and recombinant mice, continuing with HPF and FIB milling, and finishing with tilt series collection and 3D image processing. This pipeline represents a breakthrough in the preparation of brain tissue for high-resolution imaging and can be used in future tomographic research to reconstruct molecular details of synaptic complexes as well as pre- and post-synaptic assemblies. This methodology can also be adapted for a broader range of tissue preparations and signifies the next step towards a better structural understanding of how molecular machineries operate in natural conditions.

The manuscript is very well written, contains a detailed description of methodology, provides nice illustrations, and will be an outstanding guide for future research. I only have a few suggestions to further improve this excellent manuscript.

The labeling experiment in Supplementary Figure 3 may have a limitation in the accessibility of certain "narrow" regions to 15F1Fabs (both JF646 and AuNP labeled). Would that be more correct to refer to the labeling of accessible GluA2-containing AMPARs rather than the majority of these receptors in the tissue (lines 180-183)?

The text has been modified to reference “accessible GluA2-containing AMPARs”

Minor comments:

(1) Lines 38-39. "natively derived" appears to be unnecessary here and can be deleted.

Done

(2) Line 153. Please specify the 20% dextran cryoprotectant.

Done.

(3) Lines 155-157. Please label the stratum radiatum and stratum lacunosum-moleculare in Figure 3B.

Done

(4) Figures 1C, 2B, 5B, 5D-E. Missing units for Y-axes.

Done

(5) Supplemental Figure 1. Please add band annotation.

Done

(6) Supplemental Figure 3. Scale bars are missing.

Done

(7) Supplemental Video 1 does not play.

The video file has been corrected.

Reviewer #2 (Recommendations For The Authors):

My congratulations to the authors for undertaking this challenging work.

Major concerns that need to be addressed:

It's unclear if the anti-GluA2 15F1 Fab-AuNP conjugate would affect the receptor clustering and localization on the synaptic membranes. It binds at the distal end, which is likely to impact its interactions with other synaptic proteins, which may affect the synaptic organization and function.

Concern addressed in the ‘Discussion’ section.

The hippocampal slices were treated with the anti-GluA2 15F1 Fab-148 AuNP conjugate for 1 hour at room temperature. It might be helpful to discuss the potential affects of Fab-AuNp on synaptic function. It has been demonstrated previously that introducing binders of the receptors ectodomains can affect synaptic function.

Concern also addressed in the ‘Discussion’ section. 

Kunimichi Suzuki et al. Science369,eabb4853(2020).DOI:10.1126/science.abb4853 https://patents.google.com/patent/US20230192810A1/en

eLife assessment

This revised study presents valuable evidence that a combination of endothelial cells, astrocytes, and neuroblastoma cells of human origin can integrate to form an in vitro brain blood barrier, that recapitulates key aspects of its natural counterpart, especially at short times. Convincingly, the mechanism by which neuroblastoma-secreted GDNF increases Claudin-5 and VE-cadherin is described. To substantiate the role of GDNF in vivo, authors demonstrated that knock-down of this neurotrophic factor, increased the permeability of the brain blood barrier in mice. This in vitro system can be used to study the permeability of the human brain blood barrier to different drugs.

Reviewer #1 (Public review):

Summary:

This paper by Yang et al. established an in vitro triple co-culture BBB model and demonstrated its advantages compared with the mono or double co-culture BBB model. Further, the authors used their established in vitro BBB model and combined it with other methodologies to investigate the specific signaling mechanisms that co-culture with astrocytes but also neurons enhancing the integrity of endothelial cells.

Strengths:

The results persuasively demonstrated that the established triple co-culture BBB model well mimicked several important characteristics of BBB compared with the mono-culture BBB model, including better barrier function and in vivo/in vitro correlation. The use of human-derived immortalized cells made the model construction process faster and more efficient and had a better in vivo correlation without the complications of species differences. This model is expected to be a useful high-throughput evaluation tool for CNS drug development.

Moreover, the authors used a variety of experiments to prove that the triple co-culture model also reflected the interactions between NVU cells, including promoting endothelial cell proliferation and the formation of intercellular junctions. Interestingly, the authors found that neurons also released GDNF to promote barrier properties of brain endothelial cells, as most current research has focused on the promoting effect of astrocytes-derived GDNF on BBB. Meanwhile, the author also validated the functions of GDNF for BBB integrity in vivo by silencing GDNF in mouse brains. Overall, the experiments and data presented support the claim that neurons, alongside astrocytes, contribute to the promoting effects of the barrier function of endothelial cells through GDNF secretion.

Weaknesses:

While the authors explained that the use of human-derived immortalized cells has been justified as more reproducible and efficient in constructing the model, the TEER value of the triple co-culture model remains lower than that of the physiological statement. Future research may need to explore additional methods to further enhance the barrier function of the model.

Reviewer #2 (Public review):

Summary:

Yang and colleagues developed a new in vitro blood-brain barrier model that is relatively simple yet outperforms previous models. By incorporating a neuroblastoma cell line, they demonstrated increased electrical resistance and decreased permeability to small molecules

Strengths:

The authors initially elucidated the soluble mediator responsible for enhancing endothelial functionality, namely GDNF. Subsequently, they elucidated the mechanisms by which GDNF upregulates the expression of VE-cadherin and Claudin-5. They further validated these findings in vivo, and demonstrated predictive value for molecular permeability as well. The study is meticulously conducted and easily comprehensible. The conclusions are firmly supported by the data, and the objectives are successfully achieved. This research is poised to advance future investigations in BBB permeability, leakage, dysfunction, disease modeling, and drug delivery, particularly in high-throughput experiments. I anticipate an enthusiastic reception from the community interested in this area. While other studies have produced similar results with tri-cultures (PMID: 25630899), this study notably enhances electrical resistance compared to previous attempts.

Weaknesses:

The power of this system lies in its simplicity, which is enough to study BBB permeability. However, it also lacks some other important cell-cell interactions such as those involving pericytes. Nonetheless, this is still a valuable tool for high throughput screening.

As with many other similar systems, it has lower TEER values compared to the in vivo counterpart, this is an issue that researchers in the field will have to address in future studies

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors established an in vitro triple co-culture BBB model and demonstrated its advantages compared with the mono or double co-culture BBB model. Further, the authors used their established in vitro BBB model and combined it with other methodologies to investigate the specific mechanism that co-culture with astrocytes but also neurons enhanced the integrity of endothelial cells.

Strengths:

The results persuasively showed the established triple co-culture BBB model well mimicked several important characteristics of BBB compared with the mono-culture BBB model, including better barrier function and in vivo/in vitro correlation. The human-derived immortalized cells used made the model construction process faster and more efficient, and have a better in vivo correlation without species differences. This model is expected to be a useful high-throughput evaluation tool in the development of CNS drugs.

Based on the previous experimental results, detailed studies investigated how co-culture with neurons and astrocytes promoted claudin-5 and VE-cadherin in endothelial cells, and the specific signaling mechanisms were also studied. Interestingly, the authors found that neurons also released GDNF to promote barrier properties of brain endothelial cells, as most current research has focused on the promoting effect of astrocytes-derived GDNF on BBB. Meanwhile, the author also validated the functions of GDNF for BBB integrity in vivo by silencing GDNF in mouse brains. Overall, the experiments and data presented support their claim that, in addition to astrocytes, neurons also have a promoting effect on the barrier function of endothelial cells through GDNF secretion.

Weaknesses:

Although the authors demonstrated a highly usable for predicting the BBB permeability, recorded TEER measurements are still far from the human BBB in vivo reported measurements of TEER, and expression of transporters was not promoted by co-culture, which may lead to the model being unsuitable for studying drug transport mediated by transporters on BBB.

Thank the reviewer very much for the opportunity to improve our manuscript. The immortalized human cell lines, hCMEC/D3 cell, have poor barrier properties and differences in the expression of some transporters and metabolic enzymes as well as TEER compared to human physiological BBB. However, the use of human primary BMECs may be restricted by the acquisition of materials and ethical approval. Isolation and purification of human primary BMECs are time-consuming and laborious. Moreover, culture conditions can alter transcriptional activity (PMID: 37076016). All limit the establishment of BBB models based on primary human BMECs for high-throughput screening. Thus, hCMEC/D3 is still widely used to study characteristics of drug transport across BBB and the effects of certain diseases on BBB (PMID: 37076016; 38711118; 31163193) as it is easy to culture and can express a large number of transporters and metabolic enzymes in its physiological state. Therefore, hCMEC/D3 cells were selected to develop our in vitro BBB model.

Reviewer #1 (Recommendations For The Authors):

Point 1: The authors claim that GDNF is mainly released by human neuroblastoma SH-SY5Y cells in the in vitro BBB model, but there are still some differences between the characteristics of cell lines and neurons. The authors should discuss or provide evidence about the distribution and source of GDNF in the brain to support this conclusion.

We greatly appreciate your helpful suggestions. According to your advice, we have revised the “Discussion” in the revised manuscript as follows:

In “Discussion”:

“GDNF is mainly expressed in astrocytes and neurons (Lonka-Nevalaita et al., 2010; Pochon et al., 1997). In adult animals, GDNF is mainly secreted by striatal neurons rather than astrocytes and microglial cells (Hidalgo-Figueroa et al., 2012). The present study also shows that GDNF mRNA levels in SH-SY5Y cells were significantly higher than that in U251 cells. GDNF was also detected in conditioned medium from SH-SY5Y cells. All these results demonstrate that neurons may secrete GDNF”.

Point 2: The authors found that co-culture induced the proliferation of endothelial cells (Figure 1H). I suggest the authors discuss whether the proliferation of endothelial cells would affect their permeability.

Thanks for your suggestion. According to your advice, we have investigated the effect of cell proliferation on the leakage of the cell layer and included the results in Figure 1—figure supplement 1. The present study showed that basic fibroblast growth factor (bFGF) increased cell proliferation of hCMEC/D3 cells but little affected the expression of both claudin-5 and VE-cadherin (in Figure 2F). The hCMEC/D3 cells were incubated with different doses of bFGF and permeabilities of fluorescein (NaF) and FITC-Dextran 3–5 kDa across hCMEC/D3 cell monolayer were measured. The results showed that incubation with bFGF increased cell proliferation and reduced permeabilities of fluorescein and FITC-Dextran across hCMEC/D3 cell monolayer. However, the permeability reduction was less than that by double co-culture with U251 cells or triple co-culture. These results inferred that contribution of cell proliferation to the barrier function of hCMEC/D3 cells was minor. We have made the modifications in “Results” of our manuscript as follows:

In “Result”:

“Furthermore, hCMEC/D3 cells were incubated with basic fibroblast growth factor (bFGF), which promotes cell proliferation without affecting both claudin-5 and VE-cadherin expression (Figure 2F). The results showed that incubation with bFGF increased cell proliferation and reduced permeabilities of fluorescein and FITC-Dex across hCMEC/D3 cell monolayer. However, the permeability reduction was less than that by double co-culture with U251 cells or triple co-culture. These results inferred that contribution of cell proliferation to the barrier function of hCMEC/D3 was minor (Figure 1—figure supplement 1)”.

Point 3: The authors claimed that GDNF induced the expression of claudin-5 and VE-cadherin separately. However, Andrea Taddei et al. reported that VE-cadherin itself also regulates claudin-5 through the inhibitory activity of FoxO1 (Andrea Taddei et al., 2008). The authors did not consider whether the upregulation of claudin-5 is associated with the increase of VE-cadherin.

Thank you for your suggestion. We also investigated whether VE-cadherin affected claudin-5 expression in hCMEC/D3 cells transfected with VE-cadherin siRNA. It was not consistent with the report by Taddei et al. that silencing VE-cadherin only slightly decreased the mRNA level of claudin-5 without significant difference. Furthermore, basal and GDNF-induced claudin-5 protein levels were unaltered by silencing VE-cadherin. The discrepancies may come from characteristics of the tested cells. Endothelial cells derived from murine embryonic stem cells with homozygous null mutation were used in Taddei’s study, while we transfected immortalized brain microvascular endothelial cells with siRNA. Several reports have demonstrated different mechanisms regulating expression of claudin-5 and VE-cadherin. In retinal endothelial cells, hyperglycemia remarkably reduced claudin-5 expression (but not VE-cadherin) (PMID: 24594192). However, in hCMEC/D3 cells, hypoglycemia significantly decreased claudin-5 (not VE-cadherin) expression but hyperglycemia increased VE-cadherin expression (not claudin 5) (PMID: 24708805). Therefore, the roles of VE-cadherin in regulation of claudin-5 in BBB should be further investigated.

Following your valuable suggestion, we have modified the “Results”, “Discussion” and “Figure 4—figure supplement 1” in the revised manuscript as follows:

In “Result”:

“It was reported that VE-cadherin also upregulates claudin-5 via inhibiting FOXO1 activities (Taddei et al, 2008). Effect of VE-cadherin on claudin-5 was studied in hCMEC/D3 cells silencing VE-cadherin. It was not consistent with the report by Taddei et al. that silencing VE-cadherin only slightly decreased the mRNA level of claudin-5 without significant difference. Furthermore, basal and GDNF-induced claudin-5 protein levels were unaltered by silencing VE-cadherin (Figure 4—figure supplement 1). Thus, the roles of VE-cadherin in regulation of claudin-5 in BBB should be further investigated.”

In “Discussion”:

“Claudin-5 expression is also regulated by VE-cadherin (Taddei et al., 2008). Differing from the previous reports, silencing VE-cadherin with siRNA only slightly affected basal and GDNF-induced claudin-5 expression. The discrepancies may come from different characteristics of the tested cells. Several reports have supported the above deduction. In retinal endothelial cells, hyperglycemia remarkably reduced claudin-5 expression (but not VE-cadherin) (Saker et al., 2014). However, in hCMEC/D3 cells, hypoglycemia significantly decreased claudin-5 expression but hyperglycemia increased VE-cadherin expression (Sajja et al., 2014)”.

“Figure 4—figure supplement 1: The contribution of VE-Cadherin on the GDNF-induced claudin-5 expression. Effects of the VE-Cadherin siRNA (siVE-Cad) on mRNA expression of VE-cadherin (A) and claudin-5 (B). Effects of siVE-Cad and GDNF on claudin-5 and VE-cadherin protein expression (C). NC: negative control plasmids. The above data are shown as the mean ± SEM. Four biological replicates per group. Two technical replicates for A and B, and one technical replicate for C. Statistical significance was determined using unpaired Student’s t-test or one-way ANOVA test followed by Fisher’s LSD test.”

Point 4:  The annotation of significance with the p-values in the figures might not be visually concise and clear. It is recommended to provide the p-values in the legends or raw data.

Thank you for your valuable suggestion. We have revised our figures in our revised manuscript. The specific p-values and statistical methods were summarized in the source data files of each figure.

Point 5: The authors need to note the material of the Transwell membrane used to increase the reproducibility of experiments, because different materials may cause differences in permeability and TEER (DianeM. Wuest et al., 2013).

We greatly appreciate your valuable suggestions. According to your advice, we have provided the information on the material of the Transwell membrane in the “Materials and Methods” in the revised manuscript as follows:

In “Materials and Methods”:

“U251 cells were seeded at 2 × 104 cells/cm2 on the bottom of Transwell inserts (PET, 0.4 µm pore size, SPL Life Sciences, Pocheon, Korea) coated with rat-tail collagen (Corning Inc., Corning, NY, USA)”.

Point 6: It is not necessary to abbreviate "in vitro/in vivo correlation" in the legend of Figure 7 as it was not mentioned again in the following text.

Thank you for your valuable suggestion. We have deleted the abbreviation of "Figure 7" of the revised manuscript.

In “Figure 7”

“Figure 7. In vitro/in vivo correlation assay of BBB permeability."

Reviewer #2 (Public Review):

Summary:

Yang and colleagues developed a new in vitro blood-brain barrier model that is relatively simple yet outperforms previous models. By incorporating a neuroblastoma cell line, they demonstrated increased electrical resistance and decreased permeability to small molecules.

Strengths:

The authors initially elucidated the soluble mediator responsible for enhancing endothelial functionality, namely GDNF. Subsequently, they elucidated the mechanisms by which GDNF upregulates the expression of VE-cadherin and Claudin-5. They further validated these findings in vivo, and demonstrated predictive value for molecular permeability as well. The study is meticulously conducted and easily comprehensible. The conclusions are firmly supported by the data, and the objectives are successfully achieved. This research is poised to advance future investigations in BBB permeability, leakage, dysfunction, disease modeling, and drug delivery, particularly in high-throughput experiments. I anticipate an enthusiastic reception from the community interested in this area. While other studies have produced similar results with tri-cultures (PMID: 25630899), this study notably enhances electrical resistance compared to previous attempts.

Weaknesses:

(A) Considerable effort has been directed towards developing in vitro models that more closely resemble their in vivo counterparts, utilizing stem cell-derived NVU cells. Although these examples are currently rudimentary, they offer better BBB mimicry than Yang's study.

Thank you very much for your valuable comments. Indeed, hCMEC/D3 cells, have poor barrier properties and low TEER compared to human physiological BBB. The human pluripotent stem cells BBB models (hPSC-BBB models) make it possible to provide a robust and scalable cell source for BBB modeling, although many challenges remain, particularly concerning reproducibility and recreation of multifaceted phenotypes in vitro with increasing complexity. Moreover, the hPSC-derived BBB models are highly dependent upon the heterogeneous incorporation of hPSC-derived BMEC origins, cells derived from different protocols are not well validated and standardized in the BBB models. Thus, the hPSC-BBB models are still being developed and their clinic applications are still at an early stage (PMID: 34815809; 35755780). The hCMEC/D3 cell line is still widely used to study characteristics of drug transport across BBB and the effects of certain diseases on BBB (PMID: 37076016; 38711118; 31163193) as it is easy to culture and can express a large number of transporters and metabolic enzymes in its physiological state. Therefore, hCMEC/D3 cells were selected to develop our in vitro BBB model.

(B) Additionally, some instances might benefit from more robust statistical tests; nonetheless, I do not think this would significantly alter the experimental conclusions.

Thank you for your valuable suggestions on the statistical methods used in our study, which made us realize our lack of rigor in selecting statistical methods. We have made modifications to statistical methods, and all statistical results showed the manuscript have been updated accordingly.

(C) Similar experiments with tri-cultures yielding analogous results have been reported by other authors (PMID: 25630899). TEER values are a bit higher than the aforementioned experiments; however, this study has values at least one order of magnitude lower than physiological levels.

Thank your advice. We also noticed that TEER values in the present study were different from previous reports, which may come from types of BEMCs, astrocytes, and neurons.

Reviewer #2 (Recommendations For The Authors):

Point 1: If you've already decided to enhance the model by incorporating additional cell types, why not include pericytes as well? As mentioned in the public review, other studies have explored tri-culture models; adding pericytes or other cell types could provide valuable insights.

We greatly appreciate your helpful suggestions. As you mentioned, the barrier function of our model still needs further improvement, which is also a limitation of our current model. In our future research, we will aim to optimize our model by incorporating other NVU cells. Beyond drug screening, we also hope that our in vitro BBB model can serve as a versatile tool to investigate underlying factors associated with neuropathological disorders. According to your advice, we have modified “Discussion” in the revised manuscript as follows:

In “Discussion”:

“However, the study also has some limitations. In addition to neurons and astrocytes, other cells such as microglia, pericytes, and vascular smooth muscle cells, especially pericytes, may also affect BBB function. How pericytes affect BBB function and interaction among neurons, astrocytes, and pericytes needs further investigation.”

Point 2: The decline in TEER after 6 days is concerning. Have you extended your experiments beyond day 7? If so, what were the outcomes? Did the system degrade, leading to decreased resistance, or did cell death occur?

We greatly appreciate your helpful recommendation. We also observed that the TEER of our culture system began to decline on day 7. To ensure the reliability of our experiments, our experiments were conducted on day 6 of co-cultivation and did not extend beyond day 7. We speculate that the reason for the decrease in TEER values may be due to excessive cell contact, which could inhibit cell proliferation and long-term cultivation may lead to cell aging. Similar results showing a decrease in TEER of i_n vitro_ BBB models after prolonged culture have been reported in other studies (PMID: 31079318; 8470770). To eliminate misunderstandings, we have made the following modifications to our manuscript:

In “Result”:

“TEER values were measured during the co-culture (Figure 1B). TEER values of the four in vitro BBB models gradually increased until day 6. On day 7, the TEER values showed a decreasing trend. Thus, six-day co-culture period was used for subsequent experiments”.

In “In vitro BBB permeability study” of “Materials and Methods”:

“On day 7, the TEER values of BBB models showed a decreasing trend. Therefore, the subsequent experiments were all completed on day 6”.

Point 3: It is standard practice for figures to be referenced in the order they appear in the manuscript. However, Figures 1A and 1B are not mentioned until the end of the methods section. Adding a brief sentence at the beginning of the main body referencing these figures would improve the clarity of the experimental approach.

Thank you for your valuable suggestion. We had made modifications to Figure 1, and the details of the cell model establishment process had been included in Figure 9 which is mentioned in the “Materials and Methods” section.

Point 4: To strengthen the evidence supporting the proliferative effect of GDNF, consider incorporating additional measures beyond cell count alone. While an increase in cell count could be attributed to reduced cell death (given GDNF's pro-survival properties), proliferation effects have also been shown (PMID: 28878618). I suggest demonstrating proliferation with markers or cell cycle analysis would provide more robust evidence.

Thank you for your helpful suggestion. We used EdU incorporation and CCK-8 assays to further detect the proliferation of hCMEC/D3 cells, and corresponding results were added in the revised Figure 1H and Figure 1I. The description of results is shown as follows:

In “Results”:

“Co-culture with SH-SY5Y, U251, and U251 + SH-SY5Y cells also enhanced the proliferation of hCMEC/D3 cells. Moreover, the promoting effect of SH-SY5Y cells was stronger than that of U251 cells (Figure 1G-1I).”

Point 5: Could you specify the use of technical replicates in your experiments? How many?

Thank you for your helpful suggestion, and we apologize for the issue you pointed out. We have now specified the technical replicates of experiments in the legends of the revised manuscript. In general, the technical replicate number of ELISA and qPCR is two, and that of the rest experiments is one. And we have also made the following modifications to our manuscript:

In “Statistical analyses” of “Materials and Methods”:

“All results are presented as mean ± SEM. The average of technical replicates generated a single independent value that contributes to the n value used for comparative statistical analysis”.

Point 6: Given the sample size of 4 in most experiments, it may be insufficient for passing a normality test. Therefore, it's advisable to employ non-parametric tests such as the Kruskal-Wallis test, followed by appropriate post-hoc tests.

Thank you for your valuable and useful suggestion. We apologize for our initial oversight regarding statistics. Based on your suggestion, we have thoroughly reviewed and revised the statistical methods and statistical results in the manuscript. Referring to the ‘Statistics Guide’ of GraphPad (H. J. Motulsky, "The power of nonparametric tests", GraphPad Statistics Guide. Accessed 20 June 2024. https://www.graphpad.com/guides/prism/latest/statistics/stat_the_power_of_nonparametric_tes.htm), the Kruskal-Wallis test is more robust when the data does not follow a normal distribution or homogeneity of variance. However, due to its reliance on ranks, it may have lower sensitivity in detecting small differences. If the total sample size is tiny, the Kruskal-Wallis test will always give a P value greater than 0.05 no matter how much the groups differ. To address this, we first used the Shapiro-Wilk test to assume whether the samples come from Gaussian distributions. For samples meeting this criterion, parametric tests were employed. For samples that do not follow the Gaussian distribution, as per your advice, we utilized the non-parametric tests. We have modified the “Statistical analyses” in the revised manuscript as follows:

In “Statistical analyses” of “Materials and Methods”:

“The data were assessed for Gaussian distributions using Shapiro-Wilk test. Brown-Forsythe test was employed to evaluate the homogeneity of variance between groups. For comparisons between two groups, statistical significance was determined by unpaired 2-tailed t-test. The acquired data with significant variation were tested using unpaired t-test with Welch's correction, and non-Gaussian distributed data were tested using Mann-Whitney test. For multiple group comparisons, one-way ANOVA followed by Fisher’s LSD test was used to determine statistical significance. The acquired data with significant variation were tested using Welch's ANOVA test, and non-Gaussian distributed data were tested using Kruskal-Wallis test. P < 0.05 was considered statistically significant. The simple linear regression analysis was used to examine the presence of a linear relationship between two variables. Data were analyzed using GraphPad Prism software version 8.0.2 (GraphPad Software, La Jolla, CA, USA)”.

eLife assessment

The present study provides valuable evidence on the neurochemical mechanisms underlying working memory in obesity. The authors' approach considering specific working memory operations (maintenance, updating) and putative dopaminergic genes is solid, though the inclusion of a more direct measure of dopamine signaling would have strengthened the work.

Reviewer #1 (Public review):

Herzog and colleagues investigated the interactions between working memory (WM) task condition (updating, maintenance) and BMI (body-mass-index), while considering selected dopaminergic genes (COMT, Taq1A, C957T, DARPP-32). Emerging evidence suggest that there might be a specific negative association with BMI in the updating but not maintenance condition, with potential bearings to reversal reward learning in obesity. The inclusion of multiple dopaminergic genes is a strength in the present study, considering the complexity of the interactions between tonic and phasic dopamine across the brain that may distinctly associate with the component processes of WM. Here, the finding was that BMI was negatively associated with WM performance regardless of the condition (updating, maintenance), but in models including moderation by either Taq1A or DARPP-32 (but not by COMT and C957T) an interaction by task condition was observed. Furthermore, a two-way interaction effect between BMI and genotype was observed exclusively in the updating condition. These findings are in line with the accounts by which striatal dopamine as reflected by Taq1A and DARPP-32 play an important role in working memory updating, while cortical dopamine as reflected by COMT is mainly associated with maintenance. The authors conclude that the genetic moderation reflects a compound effect of having high BMI and an advantageous allele in Taq1A or DARPP-32 to working memory updating specifically.

These data increment the accumulating evidence that the dopamine system plays an important role in obesity. The result that Taq1A and DARPP-32 moderated the interaction between WM condition and BMI required intricate post hoc analysis to understand the bearings to updating. The authors found that Taq1A or DARPP-32 genotype moderated the negative association between BMI and WM exclusively in update condition (significant two-way interaction effect), suggesting that the BMI-WM associations in other conditions were similar across genotypes. Importantly, visual inspection of the relationship between WM and BMI (Fig 4 & 5) suggests more prevalent positive effects of the putatively advantageous Taq1A-A1 and DARPP-32-AA genotypes to the overall negative relationship between WM and BMI in updating, but not in the other conditions. Given that an overall negative relationship was statistically supported across all conditions (model 1), a plausible interpretation would be that updating condition stands out in terms of a positive moderation by putative advantageous genotypes, rather than compound negative consequences of BMI and genotype in updating. Statistical testing stratified by Taq1A genotype confirmed that the interaction with task condition was driven by the carriers of the advantageous genotype, whereas stratification by DARPP-32 genotype revealed a significant task-condition interaction in both A/A- and G-carriers. Taken together, the present results highlight inter-subject variability in the associations between obesity, dopamine, and working memory, which can sometimes be captured using blood-based dopamine markers. This finding indicates that not all individuals with obesity show the same patterns of dopamine-related alterations and underscores the necessity to address inter-individual variability in future research and treatment efforts.

Reviewer #2 (Public review):

Summary:

The authors investigated if obesity is associated with elevated working memory deficits. Prior theorizing would suggest that individuals with a higher BMI would be worse at working memory updating, potentially due to impaired dopaminergic signaling in the striatum. However, the authors find that higher BMI was associated with worse working memory performance, irrespective of having to ignore or update new information. To further explore the putative dopaminergic mechanisms, participants are stratified according to genetic polymorphisms in COMT, Taq1A, DARPP and C957T and the ratio of the amino acids phenylalanine and tyrosine, all implicated in dopamine-signaling. They find that carrying specific alleles of Taq1A and DARPP, but not of COMT and C957T, mitigated the otherwise negative relationship between BMI and working memory for updating, but not for maintenance.

The authors put forward several possible mechanistic explanations of these observations, including imbalances in the striatal go/no-go dopamine pathways. However, only future, more direct measures of dopamine signaling can provide a confirmation of these hypotheses.

Strengths:

Differentiating between working memory maintenance (ignoring) and updating is a powerful way to get a deeper insight into specific working memory deficits in individuals with obesity. This way of assessing working memory could potentially be applied to various populations at risk for cognitive or working memory deficits.

By pooling data from three studies, the authors reached a relatively large sample of 320 participants, which enables the assessment of more subtle effects on working memory, including the differentiation between updating and ignoring.

Working memory gating has long implicated striatal dopamine signaling. This paper shows that a specific combination of a high BMI and specific dopamine-related genotypes can selectively moderate working memory updating. More insight into how these risk factors interact can ultimately lead to more tailor-made treatments.

Weaknesses:

The introduction mentions that specific alleles can alter dopamine signaling in various ways. However, the authors are less clear on how they expect these alterations to subsequently affect working memory updating and maintenance in the current study. While I understand that the complexity of these mechanisms might make it challenging to form specific predictions, it would be helpful if the authors acknowledged this uncertainty and clarified that their analyses are exploratory in nature, and they will therefore refrain from any directional hypotheses regarding the genotypes.

The majority of participants seems to fall within the normal BMI-range, whereas the interaction between BMI and genetic variations or amino acid ratio particularly surfaces at higher BMI. As genetic variations are usually associated with small effect sizes, the effective sample size, although large for a behavioral analysis only, might have been too small to detect meaningful effects of particular alleles of COMT and C957T.

The relationships between genetic variations, BMI and specific disturbances in dopamine signaling are complex, as compensating mechanisms might be at play to mitigate any detrimental effects. Future studies that apply more direct measures or manipulations of dopaminergic processes could therefore aid in mechanistically explaining the results.

Author response:

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

Reviewer #1 (Public Review):

In particular, theoretical analysis of the extant evidence and formulation of the hypothesis remains elusive in terms of the potential mechanisms of updating/maintaining balance in obesity

We thank the reviewer for their feedback regarding the theoretical analysis and hypothesis formulation in our manuscript. We have attempted to build our hypothesis based on established correlations between dopamine levels and working memory capabilities, as seen in various populations affected by dopaminerelated changes (e.g. Parkinson’s disease (Fallon et al. 2017), older individuals (Podell et al., 2012), or more generally, in individuals with lower dopamine synthesis capacity (Colzato et al., 2013)). Our hypothesis — that individuals with higher BMI might show impaired updating — is an extrapolation from observed patterns in these conditions. We recognize that the evidence connecting obesity to similar neuropsychological profiles may seem preliminary. We have tried to elaborate more clearly on how we reached our hypotheses in the revised version of the introduction. 

“Based on the above considerations these inconsistencies may be due to prior studies not clearly differentiating between distractor-resistant maintenance and updating in the context of working memory. This distinction may be crucial, however, as indirect evidence hints at potential specific alteration in these two sub-processes in obesity. For instance, obesity has been associated with aberrant dopamine transmission, with there being an abundance of literature linking obesity to changes in D2 receptor availability in the striatum (see e.g. Horstmann et al., 2015). However, results are not consensual, with studies reporting decreased, increased, or unchanged D2 receptor availability in obesity (Ribeiro et al., 2023; Janssen & Horstmann, 2022; see Darcey et al. (2023) for a potential explanation). Additionally, there are reports of differences in dopamine transporter (DAT) availability in both obese humans (Chen et al., 2008; but also see Pak et al., 2023) and rodents (Narayanaswami et al., 2013; Jones et al., 2021; Hamamah et al., 2023). The observed changes in dopamine are often interpreted as being due to chronic dopaminergic overstimulation resulting from overeating (Volkow & Wise, 2005; Volkow et al., 2008) and altered reward sensitivity as a consequence thereof (Blum et al., 1996). Considering that working memory gating is highly dependent on dopamine signaling, such changes could theoretically alter the balance between maintenance and updating processes in obesity. Next to this, obesity has frequently been associated with functional and structural changes in WM gating-related brain areas, implying another pathway through which working memory gating might get affected. At the level of the prefrontal cortex (PFC), studies have reported reduced gray matter volume and compromised white matter microstructure in individuals with obesity (Debette et al., 2014; Kullmann et al., 2016; Morys et al., 2024; Lv et al., 2024), and functional changes become evident with frequent reports of decreased activity in the dorsolateral PFC during tasks requiring cognitive control (e.g., Morys et al., 2018; Xu et al., 2017). Notably, Han et al. (2022) observed significantly lower spontaneous dlPFC activity during rest, potentially indicating reduced baseline dlPFC activity in obesity. On the level of the striatum, gray matter volume seems to correlate positively with measures of obesity (Horstmann et al., 2011), and individuals with obesity show greater activation of the dorsal striatum in response to high-calorie food stimuli compared to normal-weight individuals, indicating a stronger dopamine-dependent reward response to food cues (Stice et al., 2008; Small et al., 2003). Additionally, changes in connectivity between and within the striatum and PFC in obesity, both structurally (Li et al., 2023) and functionally (Verdejo-Román et al., 2017a, 2017b; Contreras-Rodríguez et al., 2017) have been reported. Although these studies mostly investigate brain function in relation to food and reward processing, changes in these areas may also impair the ability to adequately engage in working memory gating processes, as activity in affective (reward) and cognitive fronto-striatal loops immensely overlap (Janssen et al., 2019). On the behavioral level, individuals with obesity consistently demonstrate impairments in food-specific (Janssen et al., 2017) but also non-food specific goal-directed behavioral control (Janssen et al., 2020) and reinforcement learning (Weydmann et al., 2023). It seems that difficulties with integrating negative feedback may be central to these alterations (Mathar et al., 2017; Kastner et al., 2017), which could explain a potential insensitivity to the negative consequences associated with (over) eating. Crucially, in humans, a substantial contribution to (reward) learning is mediated by working memory processes (Moustafa et al., 2008; Collins & Frank, 2012, 2018; Collins et al., 2014, 2017; Westbrook et al., 2024). The observed difficulties in reward learning in obesity may hence partly be rooted in a failure to update working memory with new reward information, suggesting cognitive issues that extend beyond mere difficulties in valuation processes. However, empirical support for this interpretation is currently lacking. A more nuanced understanding of the effects of obesity on working memory is crucial, however, as it could lead to more targeted intervention options.”

The result that Taq1A and DARPP-32 moderated the interaction between WM condition and BMI requires intricate post hoc analysis to understand the bearings to update. The authors found that Taq1A or DARPP32 genotype moderated the negative association between BMI and WM exclusively in the update condition (significant two-way interaction effect), suggesting that the BMI-WM associations in other conditions were similar across genotypes. Importantly, visual inspection of the relationship between WM and BMI (Fig 4 & 5) suggests more prevalent positive effects of the putatively advantageous Taq1A-A1 and DARPP-32-AA genotypes to the overall negative relationship between WM and BMI in updating, but not in the other conditions. Given that an overall negative relationship was statistically supported across all conditions (model 1), a plausible interpretation would be that the updating condition stands out in terms of a positive moderation by putative advantageous genotypes, rather than compound negative consequences of BMI and genotype in updating. Critically, this interpretation stands in stark contrast with the interpretation put forth by the authors suggesting a specifically negative association between BMI and WM updating.

We are grateful for the reviewers’ thorough review and insightful comments. We appreciate the attention to detail and the opportunity to improve our manuscript. We agree that further examination of the relationship between Taq1A, DARPP-32, and BMI, particularly in the update condition, is crucial for a comprehensive understanding of our results. In response to your feedback, we have conducted additional post hoc analyses, which indeed revealed the effects anticipated by the reviewer. Accordingly, we have revisited our discussion and conclusions to ensure that they accurately reflect the complexities of our findings, particularly regarding the positive moderation by putative advantageous genotypes in the update condition. Once again, we appreciate your thoughtful review and are grateful for the opportunity to strengthen the manuscript based on your feedback.

In the results section we added: 

“Further post hoc examination of the effects on updating revealed that, the association between BMI and performance was significant for A1-carriers (95%CIs: -0.488 to -0.190), with 33.9% lower probability to score correctly per unit change in BMI, but non-significant for non-A1-carriers (95%CIs: -0.153 to 0.129; 1.22% lower probability). Interestingly, compared to all other conditions, in the update condition, the negative association between BMI and task performance was weakest for non-A1-carriers (estimate = -0.012, SE = 0.072, but strongest for A1-carriers (estimate = -0.339, SE = 0.076; see Figure 3 and Table S6), emphasizing that genotype impacts this condition the most. To further check if this difference in slope was statistically significant across conditions, we stratified the sample into Taq1A subgroups (A1+ vs. A1-) and assessed whether BMI affected task performance differently across conditions separately for each subgroup. This analysis revealed no significant difference in the relationship between BMI and task performance across conditions among A1+ individuals (pBMI*condition = 0.219). However, within the A1- subgroup, a significant interaction effect between BMI and condition emerged (pBMI*condition = 0.049). Collectively, these findings suggest that the absence of the A1-allele is linked to improved task performance, particularly in the context of updating, where it seems to mitigate the otherwise negative effects of BMI.” 

“Once more, further examination of the observed DARPP-32, BMI, and condition interaction showed that, in the update condition, the negative association between BMI and task performance was weakest and nonsignificant for A/A (estimate = -0.044, SE = 0.066; 95%CIs: -0.174 to 0.086), but strongest and significant for G-carrying individuals (estimate = -0.324, SE = 0.079; 95%CIs: -0.478 to – 0.170). See Table S7 and Figure 5.  Splitting the sample in to DARPP subgroups (A/A vs. G-carrier) revealed that in both subgroups, there was significant interaction effect of BMI and condition on task performance (pA/A = 0.034, pG-carrier = 0.003). In the case of DARPP, it hence appears that carrying the disadvantageous G-allele could exacerbate the negative effects of BMI, while the more advantageous allele (A/A) might mitigate them - once again particularly in the context of updating.” 

Following from this, we added the following text snippets to the discussion:

“Noteworthy, our data revealed that differences in updating appeared to be driven by the non-risk allele groups. Despite increasing BMI, performance remained stable.” 

“However, as BMI increases, the possession of a greater D2 receptor density seems to become advantageous, as evidenced by the lack of a negative correlation between BMI and updating performance in non-A carriers. We speculate that this phenomenon could potentially be attributed to the compensating effects of this genotype. While individuals with fewer D2 receptors (A1+) may have quicker saturation of receptors regardless of dopamine levels, in those with more D2 receptors (A1-) saturation may be slower. This could contribute to a more finely tuned balance between "go" and "no-go" signaling, despite potential alterations in dopamine tone in obesity (Horstmann et al., 2015; but also see Darcey et al., 2023 or Janssen & Horstmann, 2022). Clearly, the current data cannot provide empirical evidence for these speculations, and further discrete research is needed to establish firm conclusions. 

Regarding DARPP, we found that carrying the G-allele significantly exacerbated the negative effects of BMI, while the more advantageous allele (A/A) mitigated them, once again particularly in the context of updating.”

“Collectively, our observations hint at the potential of advantageous genotypes to moderate the adverse impacts of high BMI on cognitive functions.” 

In conclusion, in its current form the title of the present work is ambivalent in terms of 1) the use of the term "impaired" in the context of cognitively normal individuals, 2) a BMI group difference specifically in the updating condition, and 3) the dopaminergic mechanisms based on observational data

Given the results of the additional post hoc analyses, we agree with the reviewer and have refined the title of our work to be less misleading. The title now reads:     

“Working Memory Gating in Obesity is Moderated by Striatal Dopaminergic Gene Variants” 

Reviewer #1 (Recommendations for the Authors):

Beyond the issues raised in the public review, I recommend the authors adjust the use of pathologizing terminology in the context of a clinically healthy population. In particular, terms like "dopaminergic abnormalities" and "working memory deficits/impairment" seem pathologizing in a healthy, non-morbidly obese cohort. To that end, despite a negative continuous association between BMI and WM, there are high and low-performing individuals in all BMI segments, and group differences (high vs low BMI; not reported) do not seem as dramatic as between healthy controls and say Parkinson's disease patients. Furthermore, owing to the observational design of the present study the authors should pay attention to the use of terms suggesting causal relationships, such as "influence" in the context of statistical associations. Also, sentences like "Our study is the first to show such selective effects" seem problematic not only in terms of claims of primacy, but also in terms of the selectivity of the effects (associations). See the public review for an alternative interpretation of selectivity to updating conditions.   

Of minor importance are the occasional spelling errors, that should be carefully checked by the authors. Also, I would like the authors to double-check the model configurations reported in the main text and the supplementary material. According to the supplement model 1 contains task condition by subject as a random effect (random slope model), whereas the main text states that this model configuration didn't converge and therefore only subject-specific intercepts are included. Hence, there seems to be discordance between the model descriptions in the main text and supplement. To that end, it would seem appropriate to briefly motivate the use of LME and the random effect for subject (within-subject correlation between conditions). Also, the origin of the odds ratios (OR) reported in the results section is not explicitly defined in the methods or results.

We appreciate the reviewer's thoughtful recommendations and have taken several steps to address the concerns raised:

(1) We have revised our manuscript to ensure that the language is less pathologizing and avoids suggesting causal relationships where only associations are indicated.  

For example: 

In the abstract, we replaced “abnormalities” with “alterations”:   

“Dopaminergic alterations have emerged as a potential mediator. However, current models suggest these alterations should only shift the balance in working memory tasks, not produce overall deficits”

In the introduction we replaced “impairments” with “alterations”:               

“This distinction may be crucial, however, as indirect evidence hints at potential specific alteration in these two sub-processes in obesity.

Generally, we took care to replace terms like 'dopaminergic abnormalities' and 'working memory deficits/impairments' with more neutral descriptors suitable for a clinically healthy population in the whole manuscript. 

(2) We have modified primacy statements to be more nuanced. In the discussion, for example, we now say “This finding is compelling as it demonstrates a rarely observed selective effect.” Instead of “This finding is compelling as we are the first to show such selective effects.”

(3) We have conducted an additional thorough review of our manuscript to correct any spelling errors.

(4) Upon reevaluation, we corrected the inconsistencies with respect to the random structure of model 1. We therefore have revised the supplementary material to now accurately reflect that the model did not converge when including condition as a random factor, and thus, only subject-specific intercepts are included.

(5) We have expanded our methods section to better explain the use of linear mixed effects models (LMEs) and the inclusion of random effects for subjects to account for within-subject correlation between conditions. We added the following text:

“Given the within-subject design of our study, we used generalized linear mixed models (GLM) […]” and

“The random structure of the model was thus reduced to include the factor ‘subject’ only, thereby accounting for the repeated measures taken from each subject.”

(6) We have clearly defined the derivation of the odds ratios reported in our results in the methods section of our manuscript. We added the following text to the methods section:

“Reported odds ratios (OR) are retrieved from exponentiating the log-odds coefficients called with the summary() function.”

Reviewer #2 (Public Review):

The majority of participants seem to fall within the normal BMI range, whereas the interaction between BMI and genetic variations or amino acid ratio particularly surfaces at higher BMI. As genetic variations are usually associated with small effect sizes, the effective sample size, although large for a behavioral analysis only, might have been too small to detect meaningful effects of risk alleles of COMT and C957T.

We thank the reviewer for the valuable feedback. We concur that the effective sample size may have posed a limitation in detecting meaningful effects of COMT and C957T, particularly given the skewness of our data towards participants within the normal BMI range. In response to the reviewer’s comments, we have refined the relevant paragraph in the limitations section of our manuscript, emphasizing the importance of recruiting a more balanced sample, including individuals with higher BMI, in future studies.

“Furthermore, an additional limitation is that our data is slightly skewed towards participants within the normal BMI range. The effective sample size to detect meaningful genotype effects (e.g. for COMT or C957T) might thus have been too small, particularly at higher BMI levels. Future studies may address this limitation by recruiting a more balanced sample, including more individuals with higher BMI.”

The relationships between genetic variations, BMI, and specific disturbances in dopamine signaling are complex, as compensating mechanisms might be at play to mitigate any detrimental effects. The results would therefore benefit from more direct measures or manipulations of dopaminergic processes.

We thank the reviewer for this valuable input. We acknowledge the potential benefits of employing a more direct measure, or ideally, a dopaminergic manipulation, to establish a clearer causal link between dopamine processes and working memory gating in the context of obesity. In response to the reviewers' constructive feedback, we have addressed this limitation in the discussion section of our manuscript, emphasizing the need for further research in this area:

“Additionally, the correlational nature of our findings highlights the need for more direct experimental manipulations of dopaminergic processes in obesity. Previous studies have established a causal link between dopamine and WM gating through drug manipulations (Fallon et al., 2017, 2019). Applying a similar approach to an obese sample could help establish a clearer causal link between dopamine activity and WM gating in the context of obesity.”

The introduction could benefit from a more elaborate description of the predicted effects: into which direction (better or worse updating) would the authors predict each effect to go and why? This is clearly explained for COMT, but not for e.g. DARPP-32.

We thank the reviewer for their valuable feedback. We appreciate the suggestion to provide a more detailed description of the predicted effects for each genetic marker in the introduction. We would like to note, however, that the analyses involving markers such as DARPP-32 were inherently exploratory in nature. Consequently, we intentionally refrained from formulating directed hypotheses, as our primary aim was to observe and report any emergent patterns.

Reviewer #2 (Recommendations for the Authors):              

To what extent are the polymorphisms or amino acid ratios associated with BMI? For example, when including C957T polymorphism in the analysis, the detrimental effect of BMI on working memory is no longer statistically significant. Could this be due to a relatively strong relationship between C957T polymorphism and BMI? Could the authors provide figures showing how BMI relates to the genetic polymorphisms and amino acid ratio?

We appreciate the reviewer's insightful comment and have thoroughly investigated the potential relationship between the polymorphism and BMI. Our analysis did not reveal any direct association between C957T and BMI. We have included this analysis in our manuscript. The reviewer’s comment strengthened the comprehensiveness of our study.

“Because the main effect of BMI dissipated when including C957T in the model, we ran an additional exploratory analysis to check whether this polymorphism directly related to BMI. Linear regression, predicting BMI by genotype, showed no association between the two (p = 0.2432), indicating that BMI effect is probably not masked by the presence of the C957T polymorphism. See Table S8.”

eLife assessment

This valuable manuscript reports alterations in autophagy present in dopaminergic neurons differentiated from iPSCs of patients with WDR45 mutations. The authors identified compounds that improved the defects present in mutant cells by generating isogenic iPSC without the mutation and performing an automated drug screening. The methodological approaches are solid, but the claims still need to be completed; showing the effects of the identified compounds on iron-related alterations is crucial. The effects of these drugs in vivo would be a great addition to the study.

Reviewer #1 (Public Review):

In the current study, Papandreou et al. developed an iPSC-based midbrain dopaminergic neuronal cell model of Beta-Propeller Protein-Associated Neurodegeneration (BPAN), which is caused by mutations in the WDR45 gene and is known to impair autophagy. They also noted defective autophagy and abnormal BPAN-related gene expression signatures. Further, they performed a drug screening and identified five cardiac glycosides. Treatment with these drugs effectively in improved autophagy defects and restored gene expression. Seeing the autophagy defects and impaired expression of BPAN-related genes adds strength to this study. Importantly, this work shows the value of iPSC-based modeling in studying disease and finding therapeutic strategies for genetic disorders, including BPAN.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors aim to demonstrate that cardiac glycosides restore autophagy flux in an iPSC-derived mDA neuronal model of WDR45 deficiency. They established a patient-derived induced pluripotent stem cell (iPSC)-based midbrain dopaminergic (mDA) neuronal model and performed a medium-throughput drug screen using high-content imaging-based IF analysis. Several compounds were identified that ameliorate disease-specific phenotypes in vitro.

Strengths:

This manuscript engaged in an important topic and yielded some interesting data.

Author response:

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

eLife assessment 

This valuable manuscript reports alterations in autophagy present in dopaminergic neurons differentiated from iPSCs in patients with WDR45 mutations. The authors identified compounds that improved the defects present in mutant cells by generating isogenic iPSC without the mutation and performing an automated drug screening. The methodological approaches are solid, but the claims still need to be completed: showing the effects of the identified compounds on iron-related alterations is crucial. The effects of these drugs in vivo would be a great addition to the study. 

Thank you for this assessment. We agree that further hit validation would be a great addition to the study. At present, we provide this through RNAseq data but not at the protein level. Further validation using in vivo models would also be warranted but is beyond the scope of the current work.

Public Reviews:

Reviewer #1 (Public Review): 

Summary: 

In the current study, Papandreou et al. developed an iPSC-based midbrain dopaminergic neuronal cell model of Beta-Propeller Protein-Associated Neurodegeneration (BPAN), which is caused by mutations in the WDR45 gene and is known to impair autophagy. They also noted defective autophagy and abnormal BPAN-related gene expression signatures. Further, they performed a drug screening and identified five cardiac glycosides. Treatment with these drugs effectively in improved autophagy defects and restored gene expression. 

Strengths: 

Seeing the autophagy defects and impaired expression of BPAN-related genes adds strength to this study. Importantly, this work shows the value of iPSC-based modeling in studying disease and finding therapeutic strategies for genetic disorders, including BPAN. 

Weaknesses: 

It is unclear whether these cells show iron metabolism defects and whether treatment with these drugs can ameliorate the iron metabolism phenotypes. 

We are pleased to ascertain that the reviewer feels the work is an important step in the field for BPAN. We also absolutely agree that secondary hit validation assays showing cardiac glycoside efficacy in restoring patient-related in vitro phenotypes would be very valuable. 

We set up  assays to investigate iron metabolism phenotypes, including  western blotting for Ferritin Heavy Chain 1, Transferrin and Ferroportin 1 (SLC40A1) at day 65 of differentiation, but found no significant difference when comparing patient lines to controls (data not shown). 

We also performed cell viability studies using the Alamar Blue assay on Day 11 ventral midbrain progenitors after 24 hour exposure to a) glucose starvation, b) media with no antioxidants (L-ascorbic acid and B-27 supplement), c) oxidative stressors MPP+ 1mM and FeCl3 100 uM (MPP+ and FeCl3 as suggested by  Seibler et al  (Brain 2018 PMID: 30169597). We found no difference in cell viability between patients, age-matched controls and CRISPR lines (data not shown). Additionally, we examined lysosomal function in BPAN Day 11 progenitors (2 age-matched controls, 3 patient lines, 2 isogenic controls); again, using the autophagy flux treatments mentioned above) via LAMP1 high content imaging immunofluorescence. We have seen no difference in LAMP1 puncta production between patient lines and controls and, therefore, have not included this data in our revision.

Overall, we agree with the reviewer that  more validation of the compound hits’ ability to restore robust BPAN-related in vitro and in vivo phenotypes (including studies of iron metabolism/ homeostasis) will be needed in the future – this could be undertaken in more mature 2D culture systems, 3D organoid models and disease-relevant animal models.

Reviewer #2 (Public Review): 

Summary: 

In this manuscript, the authors aim to demonstrate that cardiac glycosides restore autophagy flux in an iPSC-derived mDA neuronal model of WDR45 deficiency. They established a patientderived induced pluripotent stem cell (iPSC)-based midbrain dopaminergic (mDA) neuronal model and performed a medium-throughput drug screen using high-content imaging-based IF analysis. Several compounds were identified to ameliorate disease-specific phenotypes in vitro. 

Strengths: 

This manuscript engaged in an important topic and yielded some interesting data. 

Weaknesses: 

This manuscript failed to provide solid evidence to support the conclusion. 

We are pleased that the reviewer assesses the work as conceptually important and interesting. We also agree that more work to understand the pathophysiology underpinning BPAN, and the mechanisms through which cardiac glycosides help restore affected intracellular pathways are warranted. More validation of the compound hits’ ability to restore broader disease-specific in vitro and in vivo phenotypes is also needed in future studies. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Overall, this is a nicely executed study. Here are my suggestions:

(1) Showing the iron phenotypes in these cells and testing if treatment with these drugs rescues iron-related phenotypes will add significant value to this work. 

We absolutely agree that secondary hit validation assays showing  glycoside efficacy in restoring disease-related in vitro phenotypes is warranted. The main issue here is identifying how WDR45 deficiency leads to cellular dysfunction or dyshomeostasis and early death. Unfortunately, the mechanism by which this happens is not yet delineated, and more relevant future work is needed. 

In our lab, we set up such assays. Regarding iron metabolism-related phenotypes, we performed western blotting for Ferritin Heavy Chain 1, Transferrin and Ferroportin 1 (SLC40A1) but found no significant difference when comparing patient lines to controls (data not shown). We also performed cell viability studies using the Alamar Blue assay on Day 11 ventral midbrain progenitors after 24 hour exposure to a) glucose starvation, b) media with no antioxidants (L-ascorbic acid and B-27 supplement), c) oxidative stressors MPP+ 1mM and FeCl3 100 uM (MPP+ and FeCl3, as suggested by the Seibler et al paper, Brain 2018 PMID: 30169597). We found no difference in cell viability between patients, age-matched controls and CRISPR lines (data not shown). Additionally, we examined lysosomal function in BPAN Day 11 progenitors (2 age-matched controls, 3 patient lines, 2 isogenic controls; again, using the autophagy flux treatments mentioned above) via LAMP1 high content imaging immunofluorescence. We have seen no difference in LAMP1 puncta production between patient lines and controls and, therefore, have not included this data in our revision.

(2) Assessing the effects of these drugs in an in vivo model will strengthen this study. 

This is a valid point, and we agree that further validation using in vivo models such as the reported BPAN mouse models, would be warranted in the future.

Reviewer #2 (Recommendations For The Authors): 

While this manuscript engaged in an important topic and yielded exciting data, there are still some concerns for the authors to address. 

(1) The biggest concern is that the characterization of autophagic flux solely with LC3 is not convincing enough. Although ATG2A and ATG2B are required for phagophore formation during autophagy, their interaction with WDR45 seems dispensable for phagophore formation for a mild autophagy defect observed in WDR45 knockout cell models and mouse models. All wdr45/- mice are born normally and survive the postnatal starvation period, unlike mice lacking essential ATG proteins, like ATG5, ATG7, and VMP1. The functional relevance of WDR45 and autophagy remains to be fully established. Overall, this manuscript failed to provide solid evidence to support the conclusion. 

This is a valid point. We have looked at autophagy flux in fibroblasts and Day 11 ventral midbrain stage. For fibroblasts, 1 control line and three patient lines were used; for Day 11 progenitors, 2 control lines, 2 patient lines and one isogenic control were used. Cells from different lines were cultured on the same 96-well plates, at the same plating density, and treated concurrently to minimise fluctuations in flux due to unaccounted factors, e.g., confluence, incubator temperature etc. Treatments consisted of a) DMSO (basal condition), b) Bafilomycin A1 (flux inhibition via autophagosome/ lysosome fusion blockage), c) Torin A1 (mTOR inhibitor, flux inducer) and d) combination of Bafilomycin A1 and Torin 1, for a total of 3 hours. In all these conditions, LC3 puncta production in BPAN lines was reduced when compared to controls. We believe that these results indicate defective autophagy flux in BPAN in different cell types.

Moreover, we have demonstrated defects in autophagy-related gene (ATG) expression through RNA sequencing, that is restored after CRISPR/Cas9-mediated correction of the disease-causing mutation in a patient derived line, but also after treatments with torin 1 and digoxin. These results suggest a dysregulated ATG network in WDR45 deficiency. 

(2) WDR45 is linked to BPAN. Do the authors detect any iron accumulation in DA progenitors or mDA neurons? 

Regarding iron metabolism-related phenotypes, we performed western blotting for Ferritin Heavy Chain 1, Transferrin and Ferroportin 1 (SLC40A1) but found no significant difference when comparing patient lines to controls (data not shown). We agree that more studies into the links between WDR45 deficiency, iron metabolism and neurodegeneration are needed. 

(3) It is necessary to detect LC3 protein levels by western blot to distinguish LC3I and LC3II and gain a more accurate understanding for the process of LC3 - marked autophagosome. 

Thank you for this valid point. 

Due to the very dynamic nature of autophagy, and many factors influencing flux , we have not been able to meaningfully examine autophagy-related markers in an iPSC-derived system that is also inherently prone to variability.  Therefore, LC3 and p62 values exhibited high variability, and hence we are unable to adequately interpret them (data not shown). Instead, in this manuscript we have focused on high-content assays with cells cultured and treated simultaneously at Day 11 of differentiation, which have shown autophagy flux defects.

We have looked at autophagy flux in fibroblasts and at Day 11 ventral midbrain stage. For fibroblasts, 1 control line and three patient lines were used; for Day 11 progenitors, 2 control lines, 2 patient lines and one isogenic control were used. Cells from different lines were cultured on the same 96-well plates, at the same plating density, and treated concurrently to minimise fluctuations in flux due to unaccounted factors, e.g., confluence, incubator temperature etc. Treatments consisted of a) DMSO (basal condition), b) Bafilomycin A1 (flux inhibition via autophagosome/ lysosome fusion blockage), c) Torin A1 (mTOR inhibitor, flux inducer) and d) combination of Bafilomycin A1 and Torin 1, for a total of 3 hours. In all these conditions, LC3 puncta production in BPAN lines was reduced when compared to controls. We believe that these results indicate defective autophagy flux in BPAN in different cell types.

(4)  Some methodological details need to be included - detailed descriptions of various quantifications for IF staining should be provided. For example, it is unclear how "% cells+ ve for marker combination" (Fig.1B) was quantified, and there are many unconventional units such as "% cells+ ve for marker combination "; please check and correct them. 

Thank you for pointing this out. We have changed the legends in Figure 1B and Supplementary Figure 2C to ‘percentage of cells positive for marker combination’. Moreover, in our Methods section (Immunocytochemistry sub-section), we have updated the text as follows, to give more clarification on the process of marker quantification (Page 25, Paragraph 2): ‘For quantification, 4 random fields were imaged from each independent experiment. Subsequently, 1200 to 1800 randomly selected nuclei were quantified using ImageJ (National Institutes of Health). Manual counting for nuclear (DAPI) staining and co-staining with the marker of interest was performed, and percentages of cells expressing combinations of markers were calculated as needed.’

(5) In Figure 3 and Figure 4, the quantifications for IF images were inconsistent with the shown IF image, for example, the representative IF image for detection of LC3 with Tor1 treatment. 

Due to space restrictions, we have not included representative images from all patient lines, and every treatment condition depicted in the graphs. In Figure 3 (describing the set-up of the LC3 screening assay), only one control line and one patient line is shown in basal (DMSO-treated) conditions. In Supplementary Figure 4D, only one patient line and the corresponding isogenic control line are depicted after Torin 1 treatments.

Quantification of the LC3 puncta in this assay (20 fields per well, each condition in a technical duplicate, n=8 biological replicates) was automated, using ImageJ and R Studio, with subsequent statistical significance calculation on GraphPad Prism. Hence, the immunofluorescence figures depict a reduction in LC3 puncta per nuclei numbers in patient-derived lines versus controls, but not the exact difference after automated image analysis. We have detailed this in the Methods section (High content imaging-based immunofluorescence subsection) of our manuscript (Page 26, Paragraph 2): ‘For all high content imaging-based experiments, the PerkinElmer Opera Phenix microscope was used for imaging. 20 fields were imaged per well, at 40 x magnification, Numerical Aperture 1.1, Binning 1. Image analysis was performed using ImageJ and R Studio.60 For the drug screen, puncta values were normalised according to positive and negative controls from each plate and Z-scores for each compound screened were generated.  Statistical significances were calculated on GraphPad Prism V.

8.1.2. software (GraphPad Software, Inc.; https://www.graphpad.com/scientific-software/prism/).’

(6)  In Figure 4C, LC3 should be co-stain with the DA progenitor maker to indicate that the intercellular LC3 level within the projectors. 

Thank you for raising this point. The images from Figure 4C were obtained during the medium throughput drug screen, where the FOXA2 co-stain was not used. The FOXA2 stain was only used during the initial set-up of the LC3 screening assay, to confirm that the Day 11 cells had ventral midbrain identities. Indeed, most of the Day 11 cells used in the high content imaging-related experiments were FOXA2-positive, as shown in Figure 3 and Supplementary Figure 4.

(7) Examining P62, one of the most important indicators for autophagic flux, should be parallel with LC3 detection. In Figure 5A, P62 accumulation seems not significant in patient 02 Day 11 ventral midbrain projectors; how about that in Day 65? 

The reviewer is raising a valid point. We have not examined p62 and LC3 staining in parallel in high content imaging-based experiments but agree that this would be good to examine in future studies. 

Some other minor points 

(8) It needs to give a more detailed description of the tested compounds you mentioned in the text. 

Thank you for this point. We have elaborated on the contents of the Prestwick library used for the screening, as below (Page 9, Paragraph 3): ‘We then utilised this high-content imaging LC3 assay to identify novel compounds of potential therapeutic interest for BPAN by screening the Prestwick Chemical Library containing 1,280 compounds, of which more than 95% FDA/ EMA approved.’

In the Methods Section, Page 25, Paragraph 5, we also detail the library as follows: ‘For drug screening, the Prestwick Chemical Library (1,280 compounds, 95% FDA/ EMA approved, 10 mM in DMSO, https://www.prestwickchemical.com/screening-libraries/prestwick-chemical-library/) was used; cells were treated with compounds for 24 hours at 10 μM final concentration.’

(9) Please pay attention to the abbreviation; many gene names only have abbreviations without full names when they first appear in the context. 

Thank you for this point. We have corrected this in various places throughout the manuscript and especially in the introduction section.

(10) Almost all figures have the problem of insufficient image resolution, or the font of the indicated words needs to be bigger to be distinguished clearly, like in Fig.1B, 1C, 1E. 

Thank you for this point, we have ensured that all figures have adequate image resolution as specified by the journal requirements. 

(11) The sample size or biological repeated times should be given in figure legends. 

Thank you for this point. We have now indicated numbers of biological replicates where appropriate.

eLife assessment

The aim of this valuable study is to identify novel genes involved in sleep regulation and memory consolidation. It combines transcriptomic approaches following memory induction with measurements of sleep and memory to discover molecular pathways underlying these interlinked behaviors. The authors explore transcriptional changes in specific mushroom body neurons and suggest roles for two genes involved in RNA processing, Polr1F and Regnase-1, in the regulation of sleep and memory. Although this work exploits convincing and validated methodology, the strength of the evidence is incomplete to support the main claim that these two genes establish a definitive link between sleep and memory consolidation.

Reviewer #2 (Public review):

Prior work by the Sehgal group has shown that a small group of neurons in the fly brain (anterior posterior (ap) α'β' mushroom body neurons (MBNs)) promote sleep and sleep-dependent appetitive memory specifically under fed conditions (Chouhan et al., (2021) Nature). Here, Li, Chouhan et al. combine cell-specific transcriptomics with measurements of sleep and memory to identify molecular processes underlying this phenomenon. They define transcriptional changes in ap α'β' MBNs and suggest a role for two genes downregulated following memory induction (Polr1F and Regnase-1) in regulating sleep and memory.

The transcriptional analyses in this manuscript are impressive. The authors have now included additional experiments that define acute and developmental roles for Polr1F and Regnase-1 respectively in regulating sleep. They have also provided additional data to strengthen their conclusion that Polr1F knockdown in α'β' mushroom body neurons enhances sleep.

The resubmitted work represents a convincing investigation of two novel sleep-regulatory proteins that may also play important roles in memory formation.

The authors have comprehensively addressed my comments, which I very much appreciate. I congratulate them on this excellent work.

Reviewer #3 (Public review):

Previous work (Chouhan et al., 2022) from the Sehgal group investigated the relationship between sleep and long-term memory formation by dissecting the role of mushroom body intrinsic neurons, extrinsic neurons, and output neurons during sleep-dependent and sleep-independent memory consolidation. In this manuscript, Li et al., profiled transcriptome in the anterior-posterior (ap) α'/β' neurons and identified genes that are differentially expressed after training in fed condition, which supports sleep-dependent memory formation. By knocking down candidate genes systematically, the authors identified Polr1F and Regnase-1 as two important hits that play potential roles in sleep and memory formation. What is the function of sleep and how to create a memory are two long-standing questions in science. The present study used a new approach to identify novel components that may link sleep and memory consolidation in a specific type of neuron. Importantly, these components implicated that RNA processing may play a role in these processes.

While I am enthusiastic about the innovative approach employed to identify RNA processing genes involved in sleep regulation and memory consolidation, I feel that the data presented in the manuscript is insufficient to support the claim that these two genes establish a definitive link between sleep and memory consolidation. First, the developmental role of Regnase-1 in reducing sleep remains unclear because knocking down Regnase-1 using the GeneSwitch system produced neither acute nor chronic sleep loss phenotype. In the revised manuscript, the author used the Gal80ts to restrict the knockdown of Regnase-1 in adult animals and concluded that Regnase-1 RNAi appears to affect sleep through development. Conducting overexpression experiments of Regnase-1 would lend some credibility to the phenotypes, however, this is not pursued in the revised manuscript. Second, while constitutive Regnase-1 knockdown produced robust phenotypes for both sleep-dependent and sleep-independent memory, it also led to a severe short-term memory phenotype. This raises the possibility that flies with constitutive Regnase-1 knockdown are poor learners, thereby having little memory to consolidate. The defect in learning could be simply caused by chronic sleep loss before training. Thus, this set of results does not substantiate a strong link between sleep and long-term memory consolidation. Lastly, the discussion on the sequential function of training, sleep, and RNA processing on memory consolidation appears speculative based on the present data.

Reviewer #4 (Public review):

Summary:

Li and Chouhan et al. follow up on a previous publication describing the role of anterior-posterior (ap) and medial (m) ɑ′/β′ Kenyon cells in mediating sleep-dependent and sleep-independent memory consolidation, respectively, based on feeding state in Drosophila melanogaster. The authors sequenced bulk RNA of ap ɑ′/β′ Kenyon cells 1h after flies were either trained-fed, trained-starved or untrained-fed and find a small number of genes (59) differentially expressed (3 upregulated, 56 downregulated) between trained-fed and trained-starved conditions. Many of these genes encode proteins involved in the regulation of gene expression. The authors then screened these differentially expressed genes for sleep phenotypes by expressing RNAi hairpins constitutively in ap ɑ′/β′ Kenyon cells and measuring sleep patterns. Two hits were selected for further analysis: Polr1F, which promoted sleep, and Regnase-1, which reduced sleep. The pan-neuronal expression of Polr1F and Regnase-1 RNAi constructs was then temporally restricted to adult flies using the GeneSwitch system. Polr1F sleep phenotypes were still observed, while Regnase-1 sleep phenotypes were not, indicating developmental defects. Appetitive memory was then assessed in flies with constitutive knockdown of Polr1F and Regnase-1 in ap ɑ′/β′ Kenyon cells. Polr1F knockdown did not affect sleep-dependent or sleep-independent memory, while Regnase-1 knockdown disrupted sleep-dependent memory, sleep-independent memory, as well as learning. Polr1F knockdown increased pre-ribosomal RNA transcripts in the brain, as measured by qPCR, in line with its predicted role as part of the RNA polymerase I complex. A puromycin incorporation assay to fluorescently label newly synthesized proteins also indicated higher levels of bulk translation upon Polr1F knockdown. Regnase-1 knockdown did not lead to observable changes in measurements of bulk translation.

Strengths:

The proposed involvement of RNA processing genes in regulating sleep and memory processes is interesting, and relatively unexplored. The methods are satisfactory.

Weaknesses:

The main weakness of the paper is in the overinterpretation of their results, particularly relating to the proposed link between sleep and memory consolidation, as stated in the title. Constitutive Polr1F knockdown in ap ɑ′/β′ Kenyon cells had no effect on appetitive long-term memory, while constitutive Regnase-1 knockdown affected both learning and memory. Since the effects of constitutive Regnase-1 knockdown on sleep could be attributed to developmental defects, it is quite plausible that these same developmental defects are what drive the observed learning and memory phenotypes. In this case, an alternative explanation of the authors' findings is that constitutive Regnase-1 knockdown disrupts the entire functioning of ap ɑ′/β′ Kenyon cells, and as a consequence behaviors involving these neurons (i.e. learning, memory and sleep) are disrupted. It will be important to provide further evidence of the function of RNA processing genes in memory in order to substantiate the memory link proposed by the authors.

Author response:

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Below, I will list the points that should be addressed by the authors:

(1) Line 139: The authors conclude that the lack of a phenotype induced by knockdown of Polr1F is due to reduced baseline sleep because of the leakiness of the Genswitch system. However, it is not clear why the argument of the SybGS being leaky should not apply to all experiments done with this tool. The authors should comment on that aspect. Furthermore, this claim is testable since it should be detectable against genetic controls. An alternative explanation to the proposed scenario is that the Polr1F sleep phenotype observed in the constitutive knockdown experiment is based on developmental defects. The authors should provide additional evidence to explain the discrepancy.

We appreciate the reviewer’s insightful feedback. We assume the reviewer is referring to Regnase-1 RNAi (and not Polr1F) as Regnase-1 RNAi flies exhibit reduced sleep before dusk, potentially hindering further detection of sleep reduction. The leaky sleep reduction was based upon comparison with genetic controls in that experiment. Nevertheless, to discern whether our observations stem from developmental effects, we conducted adult-specific knockdowns of both Polr1F and Regnase-1 using the TARGET system. We generated the R35B12-Gal4:TubGal80ts line and crossed it with the UAS-Polr1FRNAi and UAS-Regnase-1RNAi lines. We confirmed that Polr1F RNAi promotes sleep when knocked down in adults (Figure 3 - supplemental figure 1). Conversely, Regnase-1 showed no effect on sleep in the adult stage, which is consistent with our nSyb-GS experiments, and suggests, as noted by the reviewer, that the Regnase-1 RNAi sleep effect is likely developmental (Figure 3 – supplemental figure 3).

(2) Line 170: Regnase1 knockdown affects all memory types, including short-term and long-term memory. The authors conclude that these genes are involved in consolidation. However, besides consolidation, it has been shown that α′β′ KCs are involved in short-term appetitive memory retrieval. Thus, an equally possible explanation is that the knockdown impairs the neuronal function per se, which would lead to a defect in all behaviors related to α′β′ KCs, rather than a specific role for consolidation. The authors have to provide additional evidence to substantiate their claim.

The exact role of Regnase-1 in the α′β′ KCs remains unclear.  We acknowledge the reviewer’s concern and have amended our conclusion to include this potential explanation suggested by the reviewer.

(3) Line 87-88: For the protocol used, it was reported that GFPnls cannot be used for FACS sorting. The authors might want to comment/clarify that aspect. https://star-protocols.cell.com/protocols/1669.

For our RNA-seq experiments, we conducted single cell isolation by FACS sorting cells, instead of nuclei, labeled with GFP.nls. The protocol mentioned that GFP.nls is not effective for single nuclear RNA-seq as it is not specific for nuclei, but for our cell sorting purposes that did not matter.

(4) Line 131: The authors should report the concentration of RU486.

Sorry, this is now in methods.

(5) Line 155: Is that really 42 hours? This might be a typo. If not, it would be good to justify the prolonged re-starvation period.

Flies fed after training form sleep-dependent memories but did not show robust long-term memory after 30 h of restarvation. As starvation is a requisite for appetitive memory retrieval (Krashes and Waddell 2008), the low memory scores after 30 h could be due to inadequate starvation. Therefore, we starved flies for 42h, which is similar to the sleep-independent memory paradigm in which flies are starved for 18 h before training and then tested 24 h after training; this protocol resulted in robust long-term memory performance. These flies were fine and able to make choices in a T-maze after 42 h starvation.

(6) I will be listing mistakes/unclear points in the figures. However, all figures should be checked very carefully for clarity.

Thanks for these valuable comments. We have gone over the figures carefully and fixed any issues we found.

(7) Figure 1C: It is not entirely clear to me how this heatmap was created and what the values mean.

The 59 differentially expressed genes (DEGs) were selected based on DESeq2 described in the methods. For the heatmap, Transcripts per million (TPM) of these 59 DEGs were log-transformed and then scaled row-wise and plotted with IDEP v0.95 (http://bioinformatics.sdstate.edu/idep95/).

(8) Figures 2A and 2B: The units might be missing. For Supplementary Figure 2, it is not clear what the different groups are without looking at the main figure.

Fixed.

(9) Figure 3: The panel arrangement is confusing. Furthermore, the "B)" is cut. The same issue is present in the Supplementary Figure.

Sorry! We rearranged the panels, and fixed the issue in both figures.

(10) Figure 5B: It is not clear what the scale bar means.

Now indicated

(11) Line 119: The citation "Marygold et al n.d."?

Fixed

(12) Line 620: I'm not sure that the rate and localization of nascent peptide synthesis are measured.

Great point. We used the puromycin assay to estimate significant changes in translation. However, we did not measure the absolute translational rate or the localization of newly synthesized proteins. We rephrased this in the updated manuscript.

(13) Line 627, the authors should give the NA of the objective, further the authors should double-check the information they provide on the resolution.

Fixed, it was 20X.

(14) Line 629 "Fuji" is unclear, it might refer to the Fiji software, and in that case, it should be listed in the used software. Further, the authors have to check on the information they provide on the intensity, e.g. is that GFP fluorescence?

Yes, it was Fiji and GFP. The manuscript has been updated accordingly.

(15) Line 634, It is stated that two concentrations of CX-5461 are used, however, as far as I can see only data for the 0.2 mM.

We apologize for the confusion. Data are indeed only shown for 0.2 mM. We also tested 0.4 mM and 0.6 mM under fed conditions once and 0.1 mM under starved conditions twice. Since all effects were not significant, we only presented the complete 0.2 mM results in the supplementary figure.

(16) Line 352 "Marygold et al nd" is probably a glitch in the citation?

It’s a citation tool issue and has been fixed.

(17) The authors use apostrophe rather than a prime in describing the α "prime" β "prime" KCs

We have corrected this.

Reviewer #2 (Recommendations For The Authors):

The authors have generated an interesting study that promises to advance the understanding of how context-dependent changes in sleep and memory are executed at the molecular level. The manuscript is well-written and the statistical analyses appear robust. Major and minor comments are detailed below.

Overall, I would suggest that the authors try to obtain additional evidence that Pol1rF modulates sleep and test the effect of acute adult-stage knockdown of Polr1F and Regnase-1 specifically in ap α'β' MBNs rather than pan-neuronally.

Major comments

(1) In Figures 2 and 3 and associated supplemental figures, the authors first test for a role for Polr1F and Regnase-1 specifically in ap α'β' MBNs (Fig. 2), then test for an acute role for these proteins via pan-neuronal drug inducible expression (Fig. 3). Because the former manipulation is cell-specific and the latter is pan-neuronal, it is hard for the reader to draw conclusions pertaining to ap α'β' MBNs from the second dataset. Perhaps Regnase-1 indeed acutely regulates sleep in ap α'β' MBNs, but that effect is masked by counteracting roles in other neurons? Conversely, it remains possible that Polr1F and Regnase-1 act during development in ap α'β' MBNs to modulate sleep. Indeed, since silencing the output of ap α'β' MBNs using temperature-sensitive shibire does not alter baseline sleep (Chouhan et al., (2021) Nature), the notion that Regnase-1 could act acutely in ap α'β' MBNs to reduce baseline sleep is somewhat surprising.

The authors could address this by using a method such as TARGET (temperature-sensitive GAL80) to acutely reduce Polr1F and Regnase-1 expression specifically in ap α'β' MBNs and test how this impacts sleep.

Thanks for the very helpful suggestions. We have done the suggested experiments and discuss them above in response to Reviewer 1. They are included in the manuscript as Figure 3 – supplemental figure 1 and figure 3 – supplemental figure 3.

(2) Figure 4 presents data examining whether Polr1F and Regnase-1 knockdown suppresses training-induced increases in sleep. For the untrained flies, based on the data in Fig. 2C, E I expected that Polr1F knockdown flies would exhibit more sleep than their respective controls (Fig. 4E), but this was not the case. These data suggest that more evidence may be warranted to strengthen the link between Polr1F (and potentially Regnase-1) knockdown and sleep. Could the authors use independent RNAi constructs or cell-specific CRISPR (all available from current stock centres) to validate their current results? Related to this, it would be useful to know whether the authors outcrossed any of their transgenic reagents into a defined genetic background.

The untrained flies in figure 4E are not equivalent to flies tested for Polr1F effects on sleep in figure 2C. In Figure 4E, flies were starved for 18 h and then exposed to sucrose without an odor at ZT6. Following sucrose exposure, flies were moved to sucrose locomotor tubes, and sleep was assessed only in the ZT8-12 interval. Sleep was not significantly different between untrained R35B12>Polr1FRNAi and Polr1FRNAi/+ flies, and while it was higher in R35B12>Polr1FRNAi than in R35B12/+ untrained flies, the data overall indicate that Polr1F downregulation has no impact on sleep under these conditions and at this time. Similarly, in fully satiated settings (Figure 2C), we found no difference in sleep during the ZT8-12 period between R35B12>Polr1FRNAi flies and genetic controls. We did not outcross our transgenic lines but have now tested another available Polr1F RNAi (VDRC: v103392) (Figure 3 – supplemental figure 1). As shown in the figure, adult-specific knockdown of Polr1F by this RNAi line promoted sleep, as did the initial RNAi line.

(3) Could the authors provide additional evidence that Polr1F knockdown in ap α'β' MBNs does not enhance sleep by reducing movement? A separate assay such as climbing would be beneficial. Alternatively, examining peak activity levels at dawn/dusk from the 12L: 12D DAM data.

We checked the peak activity per minute per day for adult specific knockdown of PorlF1 and Regnase-1 (data shown in Figure 3 – supplemental figure 4). The results show that Polr1F knockdown in ap α'β' MBNs does not enhance sleep by reducing movement.

(4) In terms of validating their proposed model, over-expressing of Polr1F during appetitive training might be predicted to suppress training-induced sleep increases and potentially long-term memory. Do the authors have any evidence for this?

We were unable to find any Pol1rF overexpression line. However, we obtained the Regnase-1 over-expression line from Dr. Ryuya Fukunaga’s lab and found that Regnase-1 OE does not affect sleep (Figure 4 – supplemental figure 1).

Minor comments

(1) Abstract: can the authors please define 'ap' as anterior posterior?

Fixed.

(2) Figure 2 Supplemental 1: can the authors please denote the genotypes each color refers to in?

Fixed.

(3) In Figure 3 Supplemental 1, the authors state that acute Regnase-1 knockdown did not reduce sleep, but sleep during the night period does appear to be reduced (panel A). Was this quantified?

We quantified this, and it was not significant.

(4) Discussion, line 234: the heading of this section is 'Polr1F regulates ribosome RNA synthesis and memory' but the data presented in Figure 4 suggests that Polr1F does not affect memory. Can the authors clarify this?

We made an adjustment to the title and acknowledge that at the present time we cannot say Polr1F affects memory.

(5) Methods, Key Resource Table: can the authors please identify which fly lines were used for Polr1F and Regnase-1 knockdown experiments?

Fixed. Fly line BDSC64553 was used for Polr1F RNAi except in Figure 3 – supplemental figure 1 and 4, where VDRC 103392 was used. VDRC 27330 was used for Regnase-1 knockdown experiments.

Reviewer #3 (Recommendations For The Authors):

(1) Figure 1B: This plot is currently labelled as PCA of DEGs, which I believe is inaccurate, as such a plot is a quality control that examines the overall clustering of samples by using all read counts (not just the DEGs). In addition, the color key value of this Figure 1B is not provided.

Thank you for the insightful suggestion. The reviewer’s comment here that typically PCA plots are used for overall clustering of RNA-seq samples is indeed valid. We've acknowledged that our samples, due to their high similarity in cell populations and mild treatments, do not exhibit clear separation when we use all genes. However, we show a PathwayPCA plot of all DEGs. We aim to highlight that RNA processing pathways enriched among the DEGs account for much of the separation of the groups.

(2) A reviewer token is not provided to examine the sequencing data set.

The RNA-seq data has been submitted to the Sequence Read Archive (SRA) with NCBI BioProject accession number PRJNA1132369. The reviewer token is https://dataview.ncbi.nlm.nih.gov/object/PRJNA1132369?reviewer=cvqkddp8rjuebsjefk0f19556r.

(3) In the discussion, the author pointed out that many of the 59 DEGs have implicated functions in RNA processing. To strengthen the statement, it would be beneficial to conduct the Gene Ontology analysis to test whether the DEGs are enriched for RNA processing-related GO terms.

We have included the GO analysis results in Figure1 and another GO analysis of all DEGs in Figure 1 – supplemental figure 1.

(4) Figure 4E presents an intriguing finding because it shows that the untrained R35B12>Polr1FRNAi flies exhibit reduced sleep (instead of increased sleep) when compared to untrained Polr1/+ control flies.

Please see above response to reviewer #2 question2.

(5) For the memory assay method, the identity of odor A and odor B is not provided.

We used 4-methylcyclohexanol and 3-octanol; this information has been added into the methods section.

(6) Female flies were used for the sleep assay. However, it is not clear whether only female flies were used for the memory assay.

Mixed sexes are used for memory assays because a huge number of files is needed for these experiments. We added this information in the methods.

(7) It is important to provide olfactory acuity data on control and experimental animals to rule out that the learning/memory phenotype is caused by defects in sensing the odor used for training and testing.

Since Polr1F RNAi flies perform well, odor acuity is not an issue. Regnase1RNAi affects both short-term and long-term memories, but this seems to be a developmental issue, so we did not do the odor acuity experiments here.

(8) Line 20: "ap alpha'/beta'" neurons should be spelled as "anterior posterior (ap) alpha'/beta' neurons", as this is the first time that this anatomical name appears in this manuscript.

Fixed.

(9) Figure 2C and 2D labelling: R35B12>control; UAS control should be changed to R35B12/+ control; UAS-RNAi/+ control.

Fixed.

(10) Line 155: it is unclear why the flies were re-starved for 42hr before testing. Is this a different protocol from the 30hr re-starvation that was used by Chouhan et al., 2021?

We have explained the rationale above. The starvation period was increased to get better memory scores.

(11) Line 160: it is stated that knocking down Polr1F did not affect memory, which is consistent with Polr1f levels typically decreasing during memory consolidation. Is there a reference demonstrating that Polr1f levels typically decrease during memory consolidation?

It’s from our RNA-seq dataset from Figure1C. The level of Polr1F decreased in fed trained flies compared with other control flies.

(12)  Genotype labeling in Figure 4F is inconsistent with the rest of the manuscript.

Fixed.

eLife assessment

Through anchored phylogenomic analyses, this important study offers fresh insights into the evolutionary history of the plant diet and geographic distribution of Belidae weevil beetles. Employing robust methodological approaches, the authors propose that certain belid lineages have maintained a continuous association with Araucaria hosts since the Mesozoic era. Although the biogeographical analysis is somewhat limited by uncertainties in vicariance explanations, this convincing study enhances our understanding of Belidae's evolutionary dynamics and provides new perspectives on ancient community ecology.

Reviewer #1 (Public review):

This is a very nice study of Belidae weevils using anchored phylogenomics that presents a new backbone for the family and explores, despite a limited taxon sampling, several evolutionary aspects of the group. I find that the methodology is appropriate, and all analytical steps are well presented. The paper is well written and presents interesting aspects of Belidae systematics and evolution. The major weakness of the study being the very limited taxon sampling that has deep implications for the discussion of ancestral estimations.

Reviewer #2 (Public review):

Summary:

The authors used a combination of anchored hybrid enrichment and Sanger sequencing to construct a phylogenomic data set for the weevil family Belidae. Using evidence from fossils and previous studies they are able to estimate a phylogenetic tree with a range of dates for each node - a timetree. They use this to reconstruct the history of the belids' geographic distributions and associations with their hostplants. They infer that the belids' association with conifers pre-dates the rise of the angiosperms. They offer an interpretation of belid history in terms of the breakup of Gondwanaland, but acknowledge that they cannot rule out alternative interpretations that invoke dispersal.

Strengths:

The strength of any molecular-phylogenetic study hinges on four things: the extent of the sampling of taxa; the extent of the sampling of loci (DNA sequences) per genome; the quality of the analysis; and - most subjectively - the importance and interest of the evolutionary questions the study allows the authors to address. The first two of these, sampling of taxa and loci, impose a tradeoff: with finite resources, do you add more taxa or more loci? The authors follow a reasonable compromise here, obtaining a solid anchored-enrichment phylogenomic data set (423 genes, >97 kpb) for 33 taxa, but also doing additional analyses that included 13 additional taxa from which only Sanger sequencing data from 4 genes was available. The taxon sampling was pretty solid, including all 7 tribes and a majority of genera in the group. The analyses also seemed to be solid - exemplary, even, given the data available.

This leaves the subjective question of how interesting the results are. The very scale of the task that faces systematists in general, and beetle systematists in particular, presents a daunting challenge to the reader's attention: there are so many taxa, and even a sophisticated reader may never have heard of any of them. Thus it's often the case that such studies are ignored by virtually everyone outside a tiny cadre of fellow specialists. The authors of the present study make an unusually strong case for the broader interest and importance of their investigation and of its focal taxon, the belid weevils.

The belids are of special interest because - in a world churning with change and upheaval, geologically and evolutionarily - relatively little seems to have been going on with them, at least with some of them, for the last hundred million years or so. The authors make a good case that the Araucaria-feeding belid lineages found in present-day Australasia and South America have been feeding on Araucaria continuously since the days when it was a dominant tree taxon nearly worldwide, before it was largely replaced by angiosperms. Thus these lineages plausibly offer a modern glimpse of an ancient ecological community.

Comments on current version:

The MS was already in pretty good shape last time around, and the authors have made most of the minor revisions and copy-edits suggested by the reviewers. There may be a few remaining points of disagreement with the reviewers, but these seem to be minor matters of opinion and nothing that ought to delay publication.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This is a very nice study of Belidae weevils using anchored phylogenomics that presents a new backbone for the family and explores, despite a limited taxon sampling, several evolutionary aspects of the group. The phylogeny is useful to understand the relationships between major lineages in this group and preliminary estimation of ancestral traits reveals interesting patterns linked to host-plant diet and geographic range evolution. I find that the methodology is appropriate, and all analytical steps are well presented. The paper is well-written and presents interesting aspects of Belidae systematics and evolution. The major weakness of the study is the very limited taxon sampling which has deep implications for the discussion of ancestral estimations.

Thank you for these comments.

The taxon sampling only appears limited if counting the number of species. However, 70 % of belid species diversity belongs to just two genera. Moreover, patterns of host plant and host organ usage and distribution are highly conserved within genera and even tribes. Therefore, generic-level sampling is a reasonable measure of completeness. Although 60 % of the generic diversity was sampled in our study, we acknowledge that our discussion of ancestral estimations would be stronger if at least one genus of

Afrocorynina and the South American genus of Pachyurini could be included.

Reviewer #2 (Public Review):

Summary:

The authors used a combination of anchored hybrid enrichment and Sanger sequencing to construct a phylogenomic data set for the weevil family Belidae. Using evidence from fossils and previous studies they can estimate a phylogenetic tree with a range of dates for each node - a time tree. They use this to reconstruct the history of the belids' geographic distributions and associations with their host plants. They infer that the belids' association with conifers pre-dates the rise of the angiosperms. They offer an interpretation of belid history in terms of the breakup of Gondwanaland but acknowledge that they cannot rule out alternative interpretations that invoke dispersal.

Strengths:

The strength of any molecular-phylogenetic study hinges on four things: the extent of the sampling of taxa; the extent of the sampling of loci (DNA sequences) per genome; the quality of the analysis; and - most subjectively - the importance and interest of the evolutionary questions the study allows the authors to address. The first two of these, sampling of taxa and loci, impose a tradeoff: with finite resources, do you add more taxa or more loci? The authors follow a reasonable compromise here, obtaining a solid anchored-enrichment phylogenomic data set (423 genes, >97 kpb) for 33 taxa, but also doing additional analyses that included 13 additional taxa from which only Sanger sequencing data from 4 genes was available. The taxon sampling was pretty solid, including all 7 tribes and a majority of genera in the group. The analyses also seemed to be solid - exemplary, even, given the data available.

This leaves the subjective question of how interesting the results are. The very scale of the task that faces systematists in general, and beetle systematists in particular, presents a daunting challenge to the reader's attention: there are so many taxa, and even a sophisticated reader may never have heard of any of them. Thus it's often the case that such studies are ignored by virtually everyone outside a tiny cadre of fellow specialists. The authors of the present study make an unusually strong case for the broader interest and importance of their investigation and its focal taxon, the belid weevils.

The belids are of special interest because - in a world churning with change and upheaval, geologically and evolutionarily - relatively little seems to have been going on with them, at least with some of them, for the last hundred million years or so. The authors make a good case that the Araucaria-feeding belid lineages found in present-day Australasia and South America have been feeding on Araucaria continuously since the days when it was a dominant tree taxon nearly worldwide before it was largely replaced by angiosperms. Thus these lineages plausibly offer a modern glimpse of an ancient ecological community.

Weaknesses:

I didn't find the biogeographical analysis particularly compelling. The promise of vicariance biogeography for understanding Gondwanan taxa seems to have peaked about 3 or 4 decades ago, and since then almost every classic case has been falsified by improved phylogenetic and fossil evidence. I was hopeful, early in my reading of this article, that it would be a counterexample, showing that yes, vicariance really does explain the history of *something*. But the authors don't make a particularly strong claim for their preferred minimum-dispersal scenario; also they don't deal with the fact that the range of Araucaria was vastly greater in the past and included places like North America. Were there belids in what is now Arizona's petrified forest? It seems likely. Ignoring all of that is methodologically reasonable but doesn't yield anything particularly persuasive.

Thank you for these comments.

The criticism that the biogeographical analysis is “not very compelling” is true to a degree, but it is only a small part of the discussion and, as stated by the reviewer, cannot be made more “persuasive”, in part because of limitations in taxon sampling but also because of uncertainties of host associations (e.g. with ferns). We tried to draw persuasive conclusions while not being too speculative at the same time. Elaborating on our short section here would only make it much more speculative — and dispersal scenarios more so than vicariance ones (at least in Belinae).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I have a few comments relative to this last point of a more general nature:

- I think it would be informative in Figure 1 to present family names for the outgroups.

Family names for outgroups have been added to Figure 1.

- There is a summary of matrix composition in the results but I think a table would be better listing all necessary information for each dataset (number of taxa, number of taxa with only Sanger data, parsimony informative sites, GC content, missing data, etc...).

We added Table S4 with detailed information about the matrices.

- Perhaps I missed it, but I didn't find how fossil calibrations were implemented in BEAST (which prior distribution was chosen and with which parameters).

We used uniform priors, this has been added to the Methods section.

- I am worried that the taxon sampling (ca. 10% of the family) is too low to conduct meaningful ancestral estimations, without mentioning the moderately supported relationships among genera and large time credibility intervals. This should be better acknowledged in the paper and perhaps should weigh more into the discussion.

Belidae in general are a rare group of weevils, and it has been a huge effort and a global collaboration to sample all tribes and over 60 % of the generic diversity in the present study. A high degree of conservation of host plant associations, host plant organ usage and distribution are observed within genera and even tribes. Therefore, we feel strongly that the resulting ancestral states are meaningful.

Moreover, 70 % of the belid species diversity belongs to only two genera, Rhinotia and Proterhinus. Our species sampling is about 36 % if we disregard the 255 species of these two genera.

However, we acknowledge that our results could be improved by sampling more genera of Afrocorynina and Pachyurini. However, these taxa are very hard to collect. We have acknowledged the limitation of our taxon sampling, branching supports and timetree credibility intervals in the discussion to minimize speculative in conclusions.

- It might be nice to have a more detailed discussion of flanking regions. In my experience and from the literature there seems to be increasing concern about the use of these regions in phylogenomic inferences for multiple solid reasons especially the more you go back in time (complex homology assessment, overall gappyness, difficulty to partition the data, etc...)

We tested the impact of flanking regions on the results of our analyses and showed this data did not having a detrimental impact. We added more details about this to the results section of the paper, including information about the cutoffs we used to trim the flanking regions.

Reviewer #2 (Recommendations For The Authors):

Line 42, change "recent temporal origins" to "recent origins".

Modified in the text.

Line 97-98, "phylogenetic hypotheses have been proposed for all genera" This is ambiguous. The syntax makes it sound like these were separate hypotheses for each genus - the relationships of the species within them, maybe. However, the context implies that the hypotheses relate to the relationships between the genera. Clarify. "A phylogenetic hypothesis is available for generic relationships in each subfamily. . . " or something.

Modified in the text.

Line 162, ". . . all three subtribes (Agnesiotinidi, Belini. . . " Something's wrong here. Change "subtribes" to "tribes"?

Modified in the text.

Line 219, the comma after "unequivocally" needs to be a semicolon.

Modified in the text.

Line 327 and elsewhere, the abbreviation "AHE" is used but never spelled out; spell out what it stands for at first use. Or why not spell it out every single time? You hardly ever use it and scientists' habit of using lots of obscure abbreviations is a bad one that's worth resisting, especially now that it no longer requires extra ink and paper to spell things out.

Modified in the text.

Author response:

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

Reviewer #1:

Minor

(MN1) The segregants should be referred to as F2 segregants as they are derived from an F1 cross.

We thank the reviewer for pointing out this important oversight. We indeed analyzed segregants of an F1 cross and have corrected this in the text.

(MN2) The connections to eQTLs in other organisms should be addressed in the introduction and conclusion. For example, in humans, there has been little evidence for trans eQTLs in contrast to what has been found in yeast.

We thank the reviewer for pointing this out and improved our introduction and conclusion with such connections.

(M3) The authors state that an advantage of scRNAseq over bulk is that it captures rare cell populations (line 79), but this advantage is not exploited in this study.

While we did not explicitly demonstrate the effect of using scRNA-seq on capturing variation in rare cell populations, the referenced literature (21, 40) provides evidence that pooled scRNA-seq captures important expression heterogeneity (which implicitly contains potentially rare expression states). In our study, this is leveraged on F2 segregants to assess expression variation within the same lineage (genotype). This impacts the partitioning of expression variance from genotype.

Thus, we mentioned this point to further support the choice of using scRNA-seq for this analysis and showed that even a few single cells enable the reconstruction of the genome and expression profile of rare cell types.

(MN4) The authors use ~5% of the lineages from the original study. There is no rationale for why this is an appropriate sample size. Is there an argument for using more cells in eQTL mapping or conversely could the authors ask if fewer cells would provide similar conclusions by downsampling?

Although scRNA-seq is highly scalable, it has limitations in terms of throughput. Indeed, a single library with 10x Genomics generates data in the order of 10^4 wellcovered cells. With these limitations, our choice of ~5% of the lineages of the original study stems from the need to recover the same lineage multiple times within these 10^4 cells (in our study, each lineage is recovered on average 4 times). 

While it is possible to run multiple libraries and sequencing lanes, budget limitations prevent us from running more libraries, especially since we expect power to scale with the square-root of the number of lineages (there is diminishing returns). 

(MN5) I do not agree that the use of UMIs overcomes the challenges of low sequencing depth. UMIs mitigate the possible technical artifacts due to massive PCR amplification.

We thank the reviewer for this comment and will clarify this in the manuscript. Indeed, we intended to refer to the breadth of coverage (instead of the depth), which would usually manifest with massive PCR amplification of few transcripts.

(MN6) There is an inadequate reference to prior work on scRNAseq in yeast that established the methods used by the authors and eQTL mapping in human cells using scRNAseq.

We thank the reviewer for this and have added more context on scRNA-seq methods benchmark in yeast (drop-seq etc) and sc-eQTL in human. Additionally, we have cited Jariani et al. (2020) in eLife where similar techniques were employed for scRNA-seq in yeast.

(MN7) The use of empty quotes in Figure 4A is confusing and an alternative presentation method should be used.

We will remove these empty quotes characters and replace them with a more meaningful representation like “none”.

(MN8) The authors speculate about the use of predicted fitness instead of observed fitness, but this is something they could explicitly address in their current study.

We thank the reviewer for this comment but have decided not to perform a whole new bulk-segregant analysis experiment (X-QTL) to identify QTL that way. However, we do agree that we could in principle use the QTL that were identified in our previous study (Nguyen Ba et al, 2022). Despite this, we do not see the need for this because the predicted fitness is the overlap between genotype and phenotype (within the variance partitioning framework, it is the ‘narrow-sense heritability’ if one ignores epistasis). Thus, the use of predicted fitness when partitioning for expression variation would be constrained to that overlap (as opposed to the real observed fitness). This means that within the variance partitioning framework, the overlap of genotype, expression, and fitness is fully recapitulated by using predicted fitness instead (given that this predicted fitness is accurate to the narrow-sense heritability). In our previous study, we found that the QTL essentially predict all of the narrow-sense heritability. We believe it is therefore evident that the use of predicted fitness would be sufficient if and only if the expression variation independent of genotype is not associated with observed fitness.

We note that our study cannot generalize whether the overlap between genotype and expression fully captures fitness variation explained by expression. Indeed, we believe this is not generalizable to many other contexts (for example, in development). Thus, at present, the use of predicted fitness remains a speculation.

Major:

(MJ1) There is insufficient information provided about the nature of data. At a minimum, the following information should be provided to enable assessment of the study: What is the total library size, how many genes are identified per cell, how many UMIs are found per cell, what is the doublet rate, and how are doublets identified (e.g. on the basis of heterozygous calls at polymorphic loci?), how many times is each genotype observed, and how many polymorphic sites are identified per cell that are the basis of genotype inferences?

We understand that these metrics are relevant to the reader to have an idea of the power of our approach and integrate them in the manuscript in Table 1.

(MJ2) The prior study analyzed 18 different conditions, whereas this study only assays expression in a single condition. However, the power of the authors' approach is that its efficiency enables testing eQTLs in multiple conditions. The study would be greatly strengthened through analysis of at least one more condition, and ideally several more conditions. The previous fitness study would be a useful guide for choosing additional conditions as identifying those conditions that result in the greatest contrasts in fitness QTL would be best suited to testing the generalizations that can be drawn from the study.

We agree that a major strength of our approach is that it rapidly allows eQTL mapping in several conditions. While the experiments presented here are likely less expensive than the classical eQTL mapping experiments, the cost of 10x genomics and sequencing is still an important consideration. The pleiotropy analysis of the prior study was substantially difficult to interpret and put in context, and thus we decided to focus on a proof of concept and leave room for a more thorough analysis of multiple environments for a future study. We acknowledge that this is a main weakness of our manuscript.

(MJ3) Alternatively, the authors could demonstrate the power of their approach by applying it to a cross between two other yeast strains. As the cross between BY and RM has been exhaustively studied, applying this approach to a different cross would increase the likelihood of making novel biological discoveries.

We thank the reviewers for this suggestion, and it is indeed something that our lab is considering. Currently, one of our main point of the manuscript still relies on growth measurements of segregants (the fitness), which we cannot obtain from segregants and scRNA-seq alone. 

Unfortunately, in this experimental design, it is difficult to obtain the fitness of cells and the genotype simultaneously because the barcode of the segregant is not expressed and not frequently read during genotyping. Thus, we still need to perform a whole QTL panel for a new cross without substantial re-engineering. 

That being said, we are working on this but feel that including a new panel in this study is beyond the scope of our manuscript. 

(MJ4) Figure 1 is misleading as A presents the original study from 2022 without important details such as how genotypes were identified. It is unclear what the barcode is in this study and how it is used in the analysis. Is the barcode for each lineage transcribed so that it is identified in the scRNA-seq data? Or, does the barcode in B refer to the cell index barcode? A clearer presentation and explanation of terms are needed to understand the method.

Because F2 segregant lineage barcodes are not expressed, the barcode indicated in Figure 1B refers to cell barcodes from 10x Genomics. Our present study does not make use of the lineage barcode. We clarified this in the figure clarifying that panel A refers to the original study from 2022 and explicitly mentioning ‘cell barcodes’. 

(MJ5) The rationale for the analysis reported in Figure 2B is unclear. The fitness data are from the previous study and the goal is to estimate the heritability using the genotyping data from the scRNA-Seq data. What is the explanation for why the data don't agree for only one condition, i.e. 37C? And, what are we to understand from the overall result?

The rationale of Figure 2A/B is to show that cell lineage genotyping with scRNA-seq yields consistent results with previous genotype-phenotype analyses of the same cross. While Figure 2A shows that the single-cell imputed genotypes resemble the reference panel (sequenced in the Nguyen Ba 2022 study), Figure 2B shows that the variance partitioning to associate genotype to phenotype can be performed using the single-cell genotypes themselves (bypassing the reference panel). We believe this is an interesting result given that the reads obtained by scRNA-seq are constrained to a subset of SNP. However, we note that if the imputed single-cell genotypes were perfectly matching with the reference panel, it would not be surprising that one could do genotype-phenotype mapping from the single-cell genotypes.

In Figure 2B, we tested whether the similarity of the single-cell imputed genotypes to the reference panel was enough to estimate heritabilities (another summary statistic). 

In the remaining paragraphs of that result section, we further discuss that the single-cell lineage genotypes can be used for QTL mapping as well, recapitulating many of the QTL identified in the reference panel (provided that one controls for power). This result did not make it as a main Figure but is included in Figure S4.

That being said, we decided to update the figure by comparing the estimates in subsamples of batch1 scRNA-seq to subsamples of batch 1 reference panel and subsamples of the full reference panel. Subsamples were performed to control for power in the variance partitioning. We also noticed that the fitness of several F2 segregants is missing for the phenotypes 33C, 35C and 37C in the original study so we decided to exclude these environments.

(MJ6) Figure 3 presents an analysis of variance partitioning as a Venn diagram. This summarized result is very hard to understand in the absence of any examples of what the underlying raw data look like. For example, what does trait variation look like if only genotype explains the variance or if only gene expression explains the variance? The presented highly summarized data is not intuitive and its presentation is poor - the result that is currently provided would be easier to read in a table format, but the reader needs more information to be able to interpret and understand the result.

The Venn diagram is largely adopted in the context of variance partitioning (see Cohen, Jacob, and Patricia Cohen. 1975. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences.) but we realize that it has not been used often for displaying heritability estimates. To this end, we have added explanatory labels for the biological meaning of the areas or components of the diagram in the Figure and in the text. 

(MJ7) I am concerned about the conclusions that can be drawn about expression heritability. The authors claim that expression heritability is correlated with expression levels. It seems likely that this reflects differing statistical power. How can this possibility be excluded?

We thank the reviewer for highlighting this. We now explicitly acknowledge this potential confounding factor in the manuscript.

(MJ8) Conversely, the authors claim that the genes with the lowest heritability are genes involved in the cell cycle. However, uniquely in scRNA-seq, cell cycle regulated genes appear to have the highest variance in the data as they are only expressed in a subset of cells. Without incorporating this fact one would erroneously conclude that the variation is not heritable. To test the heritability of cell cycle regulation genes the authors should partition the cells into each cell cycle stage based on expression.

The reviewer is right to say that the low heritability of cell cycle control genes could be explained by the fact that these genes are only expressed in a subset of the dataset. Indeed, a high transcriptomic variance does not necessarily imply a low expression heritability: the cell cycle could be the residual of the expression heritability model, i.e. it explains expression variance with low association to genetic mutation.

That being said, our result is consistent with results obtained from yeast bulk RNA-seq (Albert et al. 2018), in which cell cycle is averaged out. 

In our study, we also average out the cell-cycle as we use the consensus expression and the consensus genome to estimate the heritability.

(MJ9) I do not understand Figure S5 and how eQTL sites are assigned to these specific classes given that the authors say that causative variation cannot be resolved because of linkage disequilibrium.

The rationale for Figure S5 is to show that the QTL model obtained from single-cell data is consistent with the reference panel QTL mapping experiment. Although there is uncertainty around the exact position of the QTL, we relied on the loci with the highest likelihood and showed that the datasets have consistent features. This is enabled by the fact that the QTL identified using the scRNA-seq genotypes are the ones with largest effect size in the reference panel, and are thus more likely to be mapped accurately.

(MJ10) The paragraph starting at line 305 is very confusing. In particular, the authors state that they identify a hotspot of regulation at the mating type locus. It is not obvious why this would be the case. Moreover, they claim that they find evidence for both MATa and MATalpha gene expression. Information is not provided about how segregants were isolated, but assuming that the authors did not dissect 25,000 tetrads to obtain 100,000 segregants I would infer that random spore using SGA was used. In that case, all cells should be MATa. The authors should clarify and explain this observation.

Although most of the cells have the MATa mating type (as selected by random spore using SGA), it is well known and discussed in Nguyen Ba et al. paper that there are few lineages with other mating types or diploids (they are leakers in the selection process). 

Indeed, we verified that we can detect a small number of MATalpha cells or diploids within this pool.

(MJ11) Ultimately, it is not clear what new biological findings the authors have made. There are no novel findings with respect to causative variation underlying eQTLs and I would encourage the authors to make clearer statements in their abstract, introduction, and conclusion about the key discoveries. E.g. What are the "new associations between phenotypic and transcriptomic variations" mentioned in the abstract?

This paper focuses more on the proof of concept that scRNA-seq can help integrate expression data in GPM analysis to reveal broad scale associations between fitness and expression. Indeed, novel findings include new hotspots of expression regulation in the RM/BY genetic background, we find that trans-regulation of expression has more impact than cis-regulation on fitness and evaluate the strength of the association between the genome, the transcriptome and fitness (in one environment). Additionally, the analysis reveals biological questions that cannot be answered even by increasing the experimental scale of eQTL mapping experiments. For example, we find that most of the missing heritability is not explained by expression. These key points will be clarified in the abstract, introduction and conclusion as suggested by the editors.

Reviewer #2:

(MJ1) Most of the figures center on methods development and validation for the authors' single-cell RNA-seq in the yeast cross […] One potential novelty of the study is the methods per se: that is, showing that scRNA-seq works for concomitant genotyping and gene expression profiling in the natural variation context. The authors' rigor and effort notwithstanding: in my view, this can be described as modest in terms of principles. That is, the authors did a good job putting the scRNA-seq idea into practice, but their success is perhaps not surprising or highly relevant for work outside of yeast (as the discussion says).

Although the scope of the method is limited, we think that it can apply to any largescale dataset in which transcription variance and genetic diversity are not small. This can help reduce the lack of associations between trait heritability and expression regulation, which is frequent as these two parameters are often not measured within the same dataset. 

We can, however, think of some other settings where a similar experiment may be interesting. This includes, for example, pooling cells from different human individuals (with enough genetic diversity) and applying the same scRNA-seq method to back-identify the individuals and matching them to a particular phenotype. We believe our proof of concept is therefore an important contribution as these other experiments might have broad implications.

(MJ2) The more substantive claim by the authors for the impact of the study is that they make new observations about the role of expression in phenotype (lines 333-335). The major display item of the manuscript on this theme is Figure 4A, reporting which loci that control growth phenotype (from an earlier paper) also control expression. This is solid but I regret to say that the results strike me as modest.

This paper focuses more on the proof of concept that scRNA-seq can help integrate expression data in GPM analysis to reveal broad scale associations between fitness and expression. Indeed, novel findings include new hotspots of expression regulation in the RM/BY genetic background, we find that trans-regulation of expression has more impact than cis-regulation on fitness and evaluate the strength of the association between the genome, the transcriptome and fitness (in one environment). Additionally, the analysis reveals biological questions that cannot be answered even by increasing the experimental scale of eQTL mapping experiments. For example, we find that most of the missing heritability is not explained by expression. These key points will be clarified in the abstract, introduction and conclusion as suggested by the editors.

(MJ3) The discussion makes some perhaps fairly big claims that the work has helped "bridge understanding of how genetic variation influences transcriptomic variation" and ultimately cellular phenotype. But with the data as they stand, the authors have missed an opportunity to crystallize exactly how a given variant affects expression (perhaps in waves of regulators affecting targets that affect more regulators) and then phenotype, except for the speculations in the text on lines 305-319. The field started down this road years ago with Bayesian causality inference methods applied to eQTL and phenotype mapping (via e.g. the work of Eric Schadt). The authors could now try Mendelian randomization-type fine-grained detailed models for more firepower toward the same end, and/or experimental tests of the genotype-to-expression-to-phenotype relationship. I would see these directions, motivated by fundamental questions that are relevant to the field at large, as leading to a major advance for this very crowded field. As it stands, I felt their absence in this manuscript especially if the authors are selling principles about linking expression and phenotype as their take-home.

We thank the reviewer for this suggestion and agree that the analysis of the genotypeto-expression-to-phenotype relationship would benefit from a more fine-grain model. While we are interested in exploring this, we decided to limit the scope of this manuscript to the proof of concept that scRNA-seq can help gain insights about the genotypephenotype map at a broader scale.

(MN1) I also wonder whether the co-mapping of expression and growth traits in Figure 4A would have been possible with e.g. the bulk RNA-seq from Albert et al., 2018, and I recommend that the authors repeat the Figure 4A-type analyses with the latter to justify their statement that their massive scRNA data set would actually be necessary for them to bear fruit (lines 386-388).

By repeating our eQTL hotspot analysis with Albert et al. (2018) data, we observed a non-significant association between eQTL hotspot and QTL (χ2 p = 0.50). That being said, there are some differences in the Albert et al. Experiment that preclude us from conclusively saying whether the bulk RNA-seq experiments by Alberts would not bear fruit. Indeed, that experiment is only 4 times smaller in scale and so we would not expect dramatic differences. To highlight power differences, the Albert et al. Paper identified about 6 eQTL per gene, while our study identified about 21 which is consistent with the power differences.

This highlights that this scRNA-seq experiment is scalable, so the technique may be useful for further studies. In addition, this pooled scRNA-seq strategy enables analysis of the association of transcription with phenotype.

(MN2) I also read the discussion of the manuscript as bringing to the fore some of the challenges a reader has in judging the current state of the results to be of actionable impact. The discussion, and the manuscript, will be improved if the authors can put the work in context, posing concrete questions from the field and stating how they are addressed here and what's left to do.

We agree with the reviewer and have summarized our answers to some of the questions in the field in the discussion section.

All that being said, we acknowledge the limitations of our study.

eLife assessment

This useful study describes expression profiling by scRNA-seq of thousands of cells of recombinant yeast genotypes from a system that models natural genetic variation. The rigorous new method presented here shows promise for improving the efficiency of genotype-to-phenotype mapping in yeast, providing convincing evidence for its efficacy. This revised manuscript focuses on overcoming technical challenges with this approach and identifies several new biological insights that build upon the field of genotype-to-phenotype mapping, a central question of interest to geneticists and evolutionary biologists.

Reviewer #1 (Public review):

In the revision of their paper, N'Guessan et al have improved the report of their study of expression QTL (eQTL) mapping in yeast using single cells. The authors make use of advances in single cell RNAseq (scRNAseq) in yeast to increase the efficiency with which this type of analysis can be undertaken. Building on prior research led by the senior author that entailed genotyping and fitness profiling of almost 100,000 cells derived from a cross between two yeast strains (BY and RM) they performed scRNAseq on a subset of ~5% (n = 4,489) individual cells. To address the sparsity of genotype data in the expression profiling they used a Hidden Markov Model (HMM) to infer genotypes and then identify the most likely known lineage genotype from the original dataset. To address the relationship between variance in fitness and gene expression the authors partition the variance to investigate the sources of variation. They then perform eQTL mapping and study the relationship between eQTL and fitness QTL identified in the earlier study.

This paper seeks to address the question of how quantitative trait variation and expression variation are related. scRNAseq represents an appealing approach to eQTL mapping as it is possible to simultaneously genotype individual cells and measure expression in the same cell. As eQTL mapping requires large sample sizes to identify statistical relationships, the use of scRNAseq is likely to dramatically increase the statistical power of such studies. However, there are several technical challenges associated with scRNAseq and the authors' study is focused on addressing those challenges. Most of the points raised by my review of the initial version have been addressed. However, one point remains and one additional point should be considered.

(1) Given that the authors overcame many technical and analytical challenges in the course of this research, the study would be greatly strengthened through analysis of at least one, and ideally several, more conditions which would expand the conclusions that could be drawn from the study and demonstrate the power of using scRNAseq to efficiently quantify expression in different environments.

(2) In this version the authors have introduced the use of data imputation using a published algorithm, DISCERN. This has greatly increased the variation explained by their model as presented in figure 3. However, it is possible that the explained variance is now an overestimation as a result of using the imputed expression data. I think that it would be appropriate to present figure 3 using the sparse data presented in the initial version of the paper and the newly presented imputed data so that the reader can draw their own conclusions about the interpretation.

Reviewer #2 (Public review):

The authors now say the main take-home for their work is (1) they have established methods for linkage mapping with scRNA-seq and that these (2) "can help gain insights about the genotype-phenotype map at a broader scale." My opinion in this revision is much the same as it was in the first round: I agree that they have met the first goal, and the second theme has been so well explored by other literature that I'm not convinced the authors' results meet the bar for novelty and impact. To my mind, success for this manuscript would be to support the claim that the scRNA-seq approach helps "reveal hidden components of the yeast genotype-to-phenotype map." I'm not sure the authors have achieved this. I agree that the new Figure 3 is a nice addition-a result that apparently hasn't been reported elsewhere (30% of growth trait variation can't be explained by expression). The caveats are that this is a negative result that needs to be interpreted with caution; and that it would be useful for the authors to clarify whether the ability to do this calculation is a product of the scRNA-seq method per se or whether they could have used any bulk eQTL study for it. Beside this, I regret to say that I still find that the results in the revision recapitulate what the bulk eQTL literature has already found, especially for the authors' focal yeast cross: heritability, expression hotspots, the role of cis and trans-acting variation, etc.

Likewise, when in the first round of review I recommended that the authors repeat their analyses on previous bulk RNA-seq data from Albert et al., my point was to lead the authors to a means to provide rigorous, compelling justification for the scRNA-seq approach. The response to reviewers and the text (starting on line 413) says the comparison in its current form doesn't serve this purpose because Albert et al. studied fewer segregants. Wouldn't down-sampling the current data set allow a fair comparison? Again, to my mind what the current manuscript needs is concrete evidence that the scRNA-seq method per se affords truly better insights relative to what has come before.

I also recommend that the authors take care to improve the main text for readability and professionalism. It would benefit from further structural revision throughout (especially in the figure captions) to allow high-impact conclusions to be highlighted and low-impact material to be eliminated. Figure 4 and the results text sections from line 319 onward could be edited for concision or perhaps moved to supplementary if they obscure the authors' case for the scRNA-seq approach. The text could also benefit from copy editing (e.g. three clauses starting with "while" in the paragraph starting on line 456; "od ratio" on line 415). I appreciate the authors' work on the discussion, including posing big picture questions for the field (lines 426-429), but I don't see how they have anything to do with the current scRNA-seq method.

eLife assessment

This study investigated the involvement of programmed cell death (PCD) in Arabidopsis thaliana root cap cells and its effect on microbial colonization. The authors have reported the importance of timely corpse clearance in the root cap and a root cap-specific transcription factor in controlling microbial colonization by beneficial fungi. By demonstrating the connection between transcriptional control of PCD and microbial colonization, this study provides fundamental insights into how relationships are established and regulated at the root-microbiome interface. The strength of the evidence presented is convincing, providing a foundation for further research concerning the spatial and temporal dynamics of microbiome recruitment along the root axis.

Reviewer #1 (Public Review):

Summary:

The study investigated how root cap cell corpse removal affects the ability of microbes to colonize Arabidopsis thaliana plants. The findings demonstrate how programmed cell death and its control in root cap cells affect the establishment of symbiotic relationships between plants and fungi. Key details on molecular mechanisms and transcription factors involved are also given. The study suggests reevaluating microbiome assembly from the root tip, thus challenging traditional ideas about this process. While the work presents a key foundation, more research along the root axis is recommended to gain a better understanding of the spatial and temporal aspects of microbiome recruitment.

Comments on revised version:

The authors have positively addressed all the critical points I raised in the previous review.

Reviewer #2 (Public Review):

Summary:

The authors identify the root cap as an important key region for establishing microbial symbioses with roots. By highlighting for the first time the crucial importance of tight regulation of a specific form of programmed cell death of root cap cells and the clearance of their cell corpses, they start unraveling the molecular mechanisms and its regulation at the root cap (e.g. by identifying an important transcription factor) for the establishment of symbioses with fungi (and potentially also bacterial microbiomes).

Strengths:

It is often believed that the recruitment of plant microbiomes occurs from bulk soil to rhizosphere to endosphere. These authors demonstrate that we have to re-think microbiome assembly as a process starting and regulated at the root tip and proceeding along the root axis.

Comments on revised version:

The authors have addressed all critical points in their revision.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study investigated how root cap cell corpse removal affects the ability of microbes to colonize Arabidopsis thaliana plants. The findings demonstrate how programmed cell death and its control in root cap cells affect the establishment of symbiotic relationships between plants and fungi. Key details on molecular mechanisms and transcription factors involved are also given. The study suggests reevaluating microbiome assembly from the root tip, thus challenging traditional ideas about this process. While the work presents a key foundation, more research along the root axis is recommended to gain a better understanding of the spatial and temporal aspects of microbiome recruitment.

We thank Reviewer #1 for their positive evaluation and critical feedback.

Reviewer #2 (Public Review):

Summary:

The authors identify the root cap as an important key region for establishing microbial symbioses with roots. By highlighting for the first time the crucial importance of tight regulation of a specific form of programmed cell death of root cap cells and the clearance of their cell corpses, they start unraveling the molecular mechanisms and its regulation at the root cap (e.g. by identifying an important transcription factor) for the establishment of symbioses with fungi (and potentially also bacterial microbiomes).<br /> Strengths:

It is often believed that the recruitment of plant microbiomes occurs from bulk soil to rhizosphere to endosphere. These authors demonstrate that we have to re-think microbiome assembly as a process starting and regulated at the root tip and proceeding along the root axis.

Weaknesses:

The study is a first crucial starting point to investigate the spatial recruitment of beneficial microorganisms along the root axis of plants. It identifies e.g. an important transcription factor for programmed cell death, but more detailed investigations along the root axis are now needed to better understand - spatially and temporally - the orchestration of microbiome recruitment.

We appreciate Reviewers #2 insightful comments and agree that further investigations are needed to gain a deeper understanding of the intricate interplay between the spatial and temporal recruitment of the microbiome and developmental cell death in future studies.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

- Given that the smb-3 altered PCD phenotype has already been reported in several publications, the aim of using Evans blue staining to highlight LRC cell corpses along the root surface of smb-3 is not clear. Maybe S1 would be more informative as main figure.

As an indicator of membrane integrity loss and cell death, Evans blue staining was used to characterize all dPCD mutants described in this study and their interactions with S. indica. To avoid redundancies with other publications, we restructured Figure 1, incorporating panel S1A to provide an introductory overview of the smb-3 phenotype. The former Figure 1B is now located in Figure S1.

- It is not clear how the analysis of protein aggregates fits into the rationale, why analyze these formations? What role should they have in the process of PCD or interaction with microbes?

The manuscript has been modified the following way to clarify the analysis of protein aggregates in the dPCD mutants: “The transcription factor SMB promotes the expression of various dPCD executor genes, including proteases that break down and clear cellular debris and protein aggregates following cell death induction. In the LRCs of smb-3 mutants, the absence of induction of these proteases potentially explains the accumulation of protein aggregates in uncleared dead LRC cells.”.

- Is the accumulation of misfolded and aggregated proteins also present during physiological PCD of LRC cells in the WT?

The biochemical mechanisms underlying PCD can vary depending on the affected cell types and tissues. Within the root tip of Arabidopsis, two different modes of PCD have been described, differentiating between columella root cap cells and LRC cells. For clarification the manuscript has been adjusted the following way:” Under physiological conditions in WT roots, we previously observed protein aggregate accumulation in sloughed columella cell packages, but not during dPCD of distal LRC clearance (Llamas et al., 2021). This aligns with the findings that dPCD of the columella is affected by the loss of autophagy, while dPCD of the LRC is not (Feng et al., 2022).”.

- I suggest being more careful when using the term "root cap" instead of "LRC" to reduce ambiguity (i.e. lines 56; 137), maybe you need to double-check the text.

We agree with the reviewer that a clear distinction between “root cap” and “LRC” is very important. We have adjusted the manuscript to avoid any misunderstandings.

- A technical question regarding qPCR sample preparation: doesn't washing the smb-3 roots cause a loss of LRC stretched cells and would it therefore lead to an alteration of the results?

The mechanical washing of roots is essential to ensure a clear distinction between intraradical fungal growth and accommodation around roots. While we cannot exclude the possibility that mechanical washing removes LRC cells, intraradical quantification of fungal biomass aims to measure S. indica growth in the epidermal and cortical cell layers, underneath the uncleared LRC cells. Thus, we complemented this assay with extraradical colonization assays to quantify external fungal biomass with intact LRC cells.

- It is not clear if S. indica promotes PCD in wt and/or in smb-3, could you comment on it?

It remains an open question whether and to what extent S. indica promotes PCD, although there are strong indications that this fungus activates different cell death pathways at various developmental stages, including dAdo mediated cell death. We posit that certain microbes have evolved to regulate and manipulate different dPCD processes to enhance colonization, implicating a complex crosstalk between various PCD pathways. We have adjusted the manuscript to underscore this perspective the following way:” Transcriptomic analysis of both established and predicted key dPCD marker genes revealed diverse patterns of upregulation and downregulation during S. indica colonization. These findings provide a valuable foundation for future studies investigating the dynamics of dPCD processes during beneficial symbiotic interactions and the potential manipulation of these processes by symbiotic partners.”.

- How analysis of BFN1 expression in whole root confirms its downregulation at the onset of cell death in S. indica-colonized plants. Moreover, is the transcriptional regulation of BFN1 important for PCD, or is the BFN1 protein level correlated with the establishment of cell death?

BFN1 gene expression in Arabidopsis shows a transient decrease around 6–8 days after S. indica inoculation, coinciding with the proposed onset of S. indica-induced cell death. While we can only speculate on a potential correlation between BFN1 downregulation and the onset of S. indica-induced cell death, we have described other pathways through which S. indica induces cell death. For example, it produces small metabolites such as dAdo through the synergistic activity of two secreted fungal effector proteins (Dunken et al., 2023). This suggests that S. indica recruits different pathways to induce cell death, which may vary depending on the host plant and interact with each other as shown for many other immunity related cell death pathways which share some components.

Regarding the second part of the question, BFN1 expression correlates positively with cells primed for dPCD (Olvera-Carrillo et al., 2015). BFN1 protein accumulates in the ER lumen and is released into the cytoplasm upon cell death induction to exert its DNase functions (Fendrych et al., 2014). If accumulation of BFN1 is cause or consequence of cell death remains to be validated.

- Line 190: there is a typo "in the nucleus", this is superfluous given that the reporter is nuclear.

The manuscript has been adjusted accordingly; see line L208. However, we consider the distinction important as we aim to emphasize the difference between the nuclear localization of the fluorescent signal in "healthy" cells and the dispersed fluorescent signal spreading in the cytoplasm of cells priming or undergoing dPCD.

- Line 255: there is a typo, stem cells can not differentiate.

The manuscript has been adjusted.

- During root hair development some epidermal cells undergo PCD to allow the emergence of root hairs. Furthermore, during plant defense against pathogens, epidermal cells undergo cell death to prevent further colonization. Have these cell death events been reported to occur under physiological conditions during development?

Plant defence responses in roots and the hypersensitive response (HR) still remain largely unexplored. The HR is a defence mechanism that consists of a localized and rapid cell death at the site of pathogen invasion. It is triggered by pathogenic effector proteins, usually recognized by intracellular immune receptors (NLRs), and accompanied by other features such as ROS signalling, Ca2+ bursts and cell wall modifications (Balint-Kurti, 2019). Notably, HR has been widely described in leaves, but no strong evidence has been shown for the occurrence of HR in plant roots (Hermanns et al., 2003, Radwan et al., 2005). Additionally, previous studies have not shown any transcriptional parallels between common dPCD marker genes and HR PCD in Arabidopsis (Olvera-Carrillo et al., 2015; Salguero-Linares et al., 2022).

While S. indica is a beneficial root endophyte that does not induce classical hypersensitive response (HR) in host plants, the impact of dPCD on S. indica colonization should not be overlooked. S. indica promotes root hair formation in its hosts (Saleem et al., 2022), and in Arabidopsis, root hair cells naturally undergo cell death 2–3 weeks after emergence (Tan et al., 2016). This aspect could be particularly relevant for understanding the dynamics of S. indica colonization.

- Showing the analysis of pBFN1 in smb-3 would help in validating the idea that the downregulation of BFN1 by S. indica is regulated independently of SMB.

SMB is known to be a root cap specific transcription factor (Willemsen et al., 2008; Fendrych et al., 2014). The pBFN1:tdTOMATO reporter line shows that BFN1 expression occurs in many different tissues undergoing dPCD, above and below ground, where SMB is not expressed or present. Therefore, we can postulate that the downregulation of BFN1 by S. indica in the differentiation zone is regulated independently of SMB.

- A question of great interest still remains open: is it the microbe that induces the regulation of BFN1 causing a delay in cell clearance and favoring the infection or is it the plant that reduces BFN1 to favor the interaction with the microbe? In other words, is the mechanism a response to stress or a consolidation of the interaction with the host?

We agree with this reviewer that this question remains open. Whether active interference by fungal effector proteins, fungal-derived signaling molecules, or a systemic response of Arabidopsis roots underlies BFN1 downregulation during S. indica colonization remains to be investigated. Yet, it is noteworthy that the downregulation of BFN1 in Arabidopsis is not specific to S. indica but also occurs during interactions with other beneficial microbes such as S. vermifera and two bacterial synthetic communities. This suggests that it could be a broader plant response to microbial presence. However, at this stage, we can only speculate on these possibilities. We therefore changed some of the statements in the paper to moderate our conclusions: e.g. “Expression of plant nuclease BFN1, which is associated with senescence, is modulated to facilitate root accommodation of beneficial microbes” to leave open who exactly is controlling BFN1, the plant or the microbes.

Reviewer #2 (Recommendations For The Authors):

This is a straightforward study, well executed and well written. I have only a few specific comments, and some concern the statistics which is a bit more serious and where I would like to get answers first. Looking at the figures, I am sure that the authors can easily clarify the issues in the manuscript.

We appreciate the positive feedback and included clarifications in the statistical section in the material and methods.

Statistics:

- The statistics are not detailed in Material and Methods, but are only briefly indicated in the headings of the figures. Include a statistics section in Material and Methods.

We added an extra paragraph with statistical analysis in the Material and Method section for clarifications, which reads as follows:” All statistical analyses, except for the transcriptomic analysis, were performed using Prism8. Individual figures state the applied statistical methods, as well as p and F values. p-values and corresponding asterisks are defined as following, p<0.05 *, p<0.01**, p<0.001***.”.

- Figure 1/ Figure S3, etc: First of all, a **** with p< 0.00001 does not exist! Significance in statistics just means that we assume that there is a difference with some kind of probability that has been defined as p<0.05 *, p<0.01**, p<0.001***, and NOT more! Even if p<0.000001, it is still p<0.001***. Stating the meaning of asterisks in a separate Statistics section in Materials and Methods would also avoid repetitive explanations (e.g. Figure 4, L68: 'Asterisk indicates significantly different...').

We agree and have updated the manuscript accordingly. See comment above.  

- Also, it is advisable to reduce the digits of the p-values to a meaningful length (e.g. Figure 2 L 36: (*P<0.0466) should be (F[1, ?] = ?; p<0.047). The * is not necessary in the text, as p<0.05 is already given. We do not obtain more information by a more exact p-value, because all we need to know is that p<0.05.

We adjusted the p-values accordingly throughout the manuscript.

- It is NOT sufficient to communicate just the p-value of a statistical analysis. What is always needed is the F-value (student test and ANOVA) with both nominator and denominator degrees of freedom (e.g. F[2, 10] =) AND the p-value.

We included F-values throughout the manuscript in all main and supplemental figures to provide more clarity for the readers.

- The reason becomes clear in Fig. 2D where the authors state that they used 3 biological replicates, each with 40 plants. I assume the statistics was wrongly based on calculating with 120 plants (F[1,120] =) as technical replicates instead of correctly the biological replicates (3 means of 40 technical replicates each, (F[1,3] =))?? If F-value and df had been given, errors like this would be immediately visible - for any reviewer/reader, but also to the authors.<br /> \=>Please re-analyze the statistics correctly.

To assess S. indica-induced growth promotion, we measured and compared the root length of Arabidopsis plants under S. indica colonization or mock conditions at three different time points. Each genotype and treatment combination involved measuring 50 plants, with each plant serving as an independent biological replicate inoculated with the same S. indica spore solution. For comprehensive statistical analysis, we conducted the experiment a total of 3 times, using fresh fungal inoculum each time, originally referred to as "three biological replicates." We maintain that including all plant measurements is essential for a thorough statistical analysis of our growth promotion experiment. However, in order to avoid confusion, we have updated the figure legend to clarify the experimental set-up as following: “(D) Root length measurements of WT plants and smb-3 mutant plants, during S. indica colonization (seed inoculated) or mock treatment. 50 plants for each genotype and treatment combination were observed and individually measured over a time period of two weeks. WT roots show S. indica-induced growth promotion, while growth promotion of smb-3 mutants was delayed and only observed at later stages of colonization. This experiment was repeater 2 more independent times, each time with fresh fungal material. Statistical analysis was performed via one-way ANOVA and Tukey’s post hoc test (F [11, 1785] = 1149; p < 0.001). For visual representation of statistical relevance each time point was additionally evaluated via one-way ANOVA and Tukey’s post hoc test at 8dpi (F [3, 593] = 69.24; p < 0.001), 10dpi (F [3, 596] = 47.59; p < 0.001) and 14dpi (F [3, 596] = 154.3; p < 0.001).”

- Figure 2, L 18; Figure 5, L 95, Figure S5 L53, etc: I am worried about executing a statistical test 'before normalization' - what does it mean?? WHY was a normalization necessary, WHAT EXACTLY was normalized and do we see normalized plots that do NOT correspond to the data on which the statistics was based? At least this implies 'before normalization'! Please explain, and/or re-analyze the statistics correctly.

We agree that the phrasing “before normalization” may lead to confusion, as the normalization of data to the mean of the control group does not alter the statistical analysis. Normalization was performed to achieve a clearer visual representation. Additionally, Evans blue staining is quantified by measuring the mean grey value, which does not correspond to a specific unit. Normalizing the data allows for the representation of relative staining intensities. The manuscript has been adjusted accordingly throughout.

- Statistics in Figure 1: L8/9: 'in reference to B' is unclear, I guess the mean of the control was used as a reference? This would also explain the variation in relative staining intensity (Figure 1C). if normalization was carried out (see above) all control (WT) values should be exactly 1, but they are not. I guess it was normalized to the mean of the control?

“In reference to X” or “corresponding to X” typically means that Figure X shows an example image from the dataset on which the statistical quantification is based. We have updated the manuscript throughout the main and supplemental figure legends to use “refers to image shown in X” to avoid confusion.  

Figure S4, L 42: '(corresponding to A)', see comment above.

See comment above.

Figure 5B, L 87: '(in reference to A)'; L93: (in reference to C), etc. - see above. Unclear how A was used as a reference. Was it the mean of A? BUT again only 3 biological replicates! So it has to be the mean of 3 reps that was used as control! OR can we at least say that the 10 measured roots were independent of each other (crucial (!) precondition for executing student's test or ANOVA? Then you would have at least 10 replicates (mean of 4 pictures taken per root for each).

Quantification of Evans blue staining intensity involved taking 4 pictures along the main root axis of each plant. We re-evaluated the statistical analysis correctly with the averaged datapoints for each plant root. We adjusted main figures (Fig.1C and 5B) and supplementary figures (Fig. S1C and S4B) and changed the material and methods section of the manuscript as following: “4 pictures were taken along the main root axis of each plant and averaged together, for an overview of cell death in the differentiation zone.”.

- Statistics in Figure 4, L 69: what means 'adjusted p-value'? Which analysis?

The material and method section of the manuscript has been adjusted as following for clarification: “Differential gene expression analysis was performed using the R package DESeq2 (Love et al., 2014). Genes with an FDR adjusted p-value < 0.05 were considered as differentially expressed genes (DEGs). The adjusted p-value refers to the transformation of the p-value obtained with the Wald test after considering multiple testing. To visualize gene expression, genes expression levels were normalized as Transcript Per kilobase million (TPM).”.

- Statistics in Figure 5, L102-105: see above! Were the statistics correctly calculated with 7 reps, or wrongly with 30? # I guess each time point was normalized to the mean of WT? By the way, it is not clear if repeated measurements were done on the same plants. If repeated measurements were done on the SAME plants, then these data are statistically not independent anymore (time-series analysis), and e.g. MANOVA must be used and significant (!) before proceeding to ANOVA and Tukey.

The statistics for quantifying intraradical colonization of Arabidopsis roots were calculated with 7 replicates. For each replicate, 30 plants were pooled to obtain sufficient material for RNA extraction and cDNA synthesis. Plants from the same genotype were harvested separately for each time point, ensuring that the time points are statistically independent from one another.

Statistics Fig. S1, L 11-12: see above, '5 plants were imaged for each mock and ..., evaluating 4 pictures ...' That means you have means of 4 pictures for 5 biological replicates - the figure shows 20 replicates. However, the statistics must be based on 5 reps! You may indicate the 4 pictures per root by different colours. Change throughout all figures and calculate the statistics correctly (show this by indicating the correct df in your statistics as discussed above).

We have conducted a re-evaluation of the statistical analysis of Evans blue staining for all figures presented throughout the manuscript. See comment above.

Statistics Fig. S3, L 31: 'Relative quantification of ...' see above, relative to what? Explain this also clearly in Statistics in Materials and Methods.

Relative quantification refers to normalizing data to the mean of the corresponding control group. Figure legends have been revised to clarify this point.

Statistics Fig. S5, L 57/58: 'Genes are clustered using spearmen correlation as distance measure'. If I understand it correctly, Spearman correlation is NOT a distance measure. You used Spearman correlation to cluster gene expression. Now it would be interesting to know WHICH clustering method was used, e.g. a hierarchical or non-hierarchical clustering method? and which one, e.g. single linkage, complete linkage? The outcome depends very much on the clustering method. Therefore, this information is important.

To perform gene clustering, we set the option “clustering_distance_rows = "spearman" “ of the Heatmap function included in the ComplexHeatmap package. The function first computes the distance matrix using the formula 1 - cor(x, y, method) with Spearman as correlation method. It then performs hierarchical clustering using the complete linkage method by default.

# Arabidopsis is a genus name and by convention, this has to be written throughout the MS in italics - even if the authors define Arabidopsis thaliana (in italics) = Arabidopsis (without).

# typos

L 24: smb-3 mutants (must be explained)

L 83 insert: ...two well-characterized SMB loss-of-function ...

While smb-3 is a SMB loss-of-function mutant bfn1-1 is a BFN1 loss-of-function mutant, independent of SMB.

L 93: The switch between the biotrophic..

L 119: distal border

L 125: aggregates in the smb-3 mutant

L 132: between the meristematic

L 177/178: was observed at 6 dpi in Arabidopsis colonized by S. indica.

L 250: colonization stages by S. indica.

L 288: and root cell death (RCD)

L 289: and towards...

L 296: dPCD protects the

L 304: This raises the

L 351: to remove loose

All the above-mentioned typos have been addressed in the manuscript.

Materials and Methods

L 327: give composition and supplier of MYP medium

L 344 name supplier of MS medium

L 338 name supplier of PNM medium

L 353: replace 'Following,..' with 'Subsequently, ..'

L 360: replace 'on plate' with 'on the agar plate' - change throughout the Materials and methods!

L 360: name supplier of Alexa Fluor 488

L 363: name supplier of (MS) square plate

L 377: insert comma: After cleaning, the roots...

L 394: explain the acronym and name supplier of PBS

L 399: explain the acronym and name supplier of TBST

All the above-mentioned comments in the material and methods have been addressed in the manuscript.  

Figure 2G) x-axis, change order: Hoechst/Proteostat

Figure 3, L53: propidium iodide: name supplier

Figure 4, L68: Asterisks

L 60: explain LRC

L 67, L69, L70: explain the acronym TPM and how expression values were measured in Materials and Methods, the brief explanation in the figure is unclear and not sufficient

All the above-mentioned comments in the figure legends have been addressed.

Figure S5, L50: explain 'SynComs'

L 51: corrects 30 plans => 30 plants

L 56: vaules => values

L 57: use capital letter: Spearman correlation

All the above-mentioned comments in the supplemental figure legends have been addressed.

eLife assessment

This important manuscript presents several structures of the Kv1.2 voltage-gated potassium channel, based on state-of-the-art cryoEM techniques and algorithms. The authors present solid evidence for structures of an inactivating mutant of Kv1.2, DTX-bound Kv1.2 and of Kv1.2 in potassium-free solution (with presumably sodium ions bound within the selectivity filter). These structures advance our knowledge of the molecular basis of the slow inactivation process of potassium channels.

eLife assessment

This manuscript describes valuable findings on the expression pattern of orexin receptors in the midbrain and how manipulating this system influences several behaviors, such as context-induced locomotor activity and exploration. The overall strength of evidence - which includes anatomical, viral manipulation studies, and brain imaging - is solid and broadly supports claims in the paper, however, there are several areas in which the conclusions are only partially supported by the statistical evidence. These results have implications for understanding the neural underpinnings of reward and will be of interest to neuroscientists and cognitive scientists with an interest in the neurobiology of reward.

Reviewer #1 (Public review):

In this manuscript, the role of orexin receptors in dopamine transmission is studied. It extends previous findings suggesting an interplay of these two systems in regulating behaviour by first characterising the expression of orexin receptors in the midbrain and then disrupting orexin transmission in dopaminergic neurons by deleting its predominant receptor, OX1R (Ox1R fl/fl, Dat-Cre tg/wt mice). Electrophysiological and calcium imaging data suggest that orexin A acutely and directly stimulates SN and VTA dopaminergic neurons, but does not seem to induce c-Fos expression. Behavioural effects of depleting OX1R from dopaminergic neurons includes enhanced novelty-induced locomotion and exploration, relative to littermate controls (Ox1R fl/fl, Dat-Cre wt/wt). However, no difference between groups is observed in tests that measure reward processing, anxiety, and energy homeostasis. To test whether depletion of OX1R alters overall orexin-triggered activation across the brain, PET imaging is used in OX1R∆DAT knockout and control mice. This analysis reveals that several regions show a higher neuronal activation after orexin injection in OX1R∆DAT mice, but the authors focus their follow up study on the dorsal bed nucleus of the stria terminalis (BNST) and lateral paragigantocellular nucleus (LPGi). Dopaminergic inputs and expression of dopamine receptors type-1 and -2 (DRD1 & DRD2) is assessed and compared to control demonstrating moderate decrease of DRD1 and DRD2 expression in BNST of OX1R∆DAT mice and unaltered expression of DRD2, with absence of DRD1 expression in LPGi of both groups. Overall, this study is valuable for the information it provides on orexin receptor expression and function on behaviour and for the new tools it generated for the specific study of this receptor in dopaminergic circuits.

Strengths:

The use of a transgenic line that lacks OX1R in dopamine-transporter expressing neurons is a strong approach to dissect the direct role of orexin in modulating dopamine signalling in the brain. The battery of behavioural assays to study this line provides a valuable source of information for researchers interested in the role of orexin in animal physiology.

Weaknesses:

This study falls short in providing evidence for an anatomical substrate of the altered behaviour observed in mice lacking orexin receptor subtype 1 in dopaminergic neurons. How orexin transmission in dopaminergic neurons regulates the expression of postsynaptic dopamine receptors (as observed in BNST of OX1R∆DAT mice) is an intriguing question poorly discussed. Whether disruption of orexin activity alters dopamine release in target areas is an important point not addressed.

Reviewer #2 (Public review):

Summary:

This manuscript examines expression of orexin receptors in midbrain - with a focus on dopamine neurons - and uses several fairly sophisticated manipulation techniques to explore the role of this peptide neurotransmitter in reward-related behaviors. Specifically, in situ hybridization is used to show that dopamine neurons predominantly express orexin receptor 1 subtype and then go on to delete this receptor in dopamine transporter-expressing using a transgenic strategy. Ex vivo calcium imaging of midbrain neurons is used to show that, in the absence of this receptor, orexin is no longer able to excite dopamine neurons of the substantia nigra.

The authors proceed to use this same model to study the effect of orexin receptor 1 deletion on a series of behavioral tests, namely, novelty-induced locomotion and exploration, anxiety-related behavior, preference for sweet solutions, cocaine-induced conditioned place preference, and energy metabolism. Of these, the most consistent effects are seen in the tests of novelty-induced locomotion and exploration in which the mice with orexin 1 receptor deletion are observed to show greater levels of exploration, relative to wild-type, when placed in a novel environment, an effect that is augmented after icv administration of orexin.

In the final part of the paper, the authors use PET imaging to compare brain-wide activity patterns in the mutant mice compared to wildtype. They find differences in several areas both under control conditions (i.e., after injection of saline) as well as after injection of orexin. They focus in on changes in dorsal bed nucleus of stria terminalis (dBNST) and the lateral paragigantocellular nucleus (LPGi) and perform analysis of the dopaminergic projections to these areas. They provide anatomical evidence that these regions are innervated by dopamine fibers from midbrain, are activated by orexin in control, but not mutant mice, and that dopamine receptors are present. Thus, they argue these anatomical data support the hypothesis that behavioral effects of orexin receptor 1 deletion in dopamine neurons are due to changes in dopamine signaling in these areas.

Strengths:

Understanding how orexin interacts with the dopamine system is an important question and this paper contains several novel findings along these lines. Specifically:

(1) Distribution of orexin receptor subtypes in VTA and SN is explored thoroughly.<br /> (2) Use of the genetic model that knocks out a specific orexin receptor subtype from dopamine-transporter-expressing neurons is a useful model and helps to narrow down the behavioral significance of this interaction.<br /> (3) PET studies showing how central administration of orexin evokes dopamine release across the brain is intriguing, especially that two key areas are pursued - BNST and LPGi - where the dopamine projection is not as well described/understood.

Weaknesses:

The role of the orexin-dopamine interaction is not explored in enough detail. The manuscript presents several related findings, but the combination of anatomy and manipulation studies do not quite tell a cogent story. Ideally, one would like to see the authors focus on a specific behavioral parameter and show that one of their final target areas (dBNST or LPGi) was responsible or at least correlated with this behavioral readout.

In many places in the Results, insufficient explanation and statistical reporting is provided. Throughout the Results - especially in the section on behavior although not restricted to this part - statements are made without statistical tests presented to back up the claims, e.g., "Compared to controls, Ox1RΔDAT 143 mice did not show significant changes in spontaneous locomotor activity in home cages" (L143) and "In a hole-board test, female Ox1RΔDAT mice showed increased nose pokes into the holes in early (1st and 2nd) sessions compared to control mice" (L151). In other places, ANOVAs are mentioned but full results including main effects and interactions are not described in detail, e.g., in F3-S3, only a single p-value is presented and it is difficult to know if this is the interaction term or a post hoc test (L205). These and all other statements need statistics included in the text as support. Addition of these statistical details was also requested by the editor.

In the presentation of reward processing this is particularly important as no statistical tests are shown to demonstrate that controls show a cocaine-induced preference or a sucrose preference. Here, one option would be to perform one-sample t-tests showing that the data were different to zero (no preference). As it is, the claim that "Both of the control and Ox1RΔDAT groups showed a preference for cocaine injection" is not yet statistically supported.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the role of orexin receptors in dopamine neurons is studied. Considering the importance of both orexin and dopamine signalling in the brain, with critical roles in arousal and drug seeking, this study is important to understand the anatomical and functional interaction between these two neuromodulators. This work suggests that such interaction is direct and occurs at the level of SN and VTA, via the expression of OX1R-type orexin receptors by dopaminergic neurons.

Strengths:

The use of a transgenic line that lacks OX1R in dopamine-transporter-expressing neurons is a strong approach to dissecting the direct role of orexin in modulating dopamine signalling in the brain. The battery of behavioural assays to study this line provides a valuable source of information for researchers interested in the role of orexin-A in animal physiology.

We thank the reviewer for summarizing the importance and significance of our study. 

Weaknesses:

The choice of methods to demonstrate the role of orexin in the activation of dopamine neurons is not justified and the quantification methods are not described with enough detail. The representation of results can be dramatically improved and the data can be statistically analysed with more appropriate methods.

We have further improved our description of the methods in the revised reviewed preprint, and here in the response letter, we respond point-by-point to ‘Reviewer #1 (Recommendations For The Authors)’ below. 

Reviewer #2 (Public Review):

Summary:

This manuscript examines the expression of orexin receptors in the midbrain - with a focus on dopamine neurons - and uses several fairly sophisticated manipulation techniques to explore the role of this peptide neurotransmitter in reward-related behaviors. Specifically, in situ hybridization is used to show that dopamine neurons predominantly express the orexin receptor 1 subtype and then go on to delete this receptor in dopamine neurons using a transgenic strategy. Ex vivo calcium imaging of midbrain neurons is used to show that in the absence of this receptor orexin is no longer able to excite dopamine neurons of the substantia nigra.

The authors proceed to use this same model to study the effect of orexin receptor 1 deletion on a series of behavioral tests, namely, novelty-induced locomotion and exploration, anxiety-related behavior, preference for sweet solutions, cocaine-induced conditioned place preference, and energy metabolism. Of these, the most consistent effects are seen in the tests of novelty-induced locomotion and exploration in which the mice with orexin 1 receptor deletion are observed to show greater levels of exploration, relative to wild-type, when placed in a novel environment, an effect that is augmented after icv administration of orexin.

In the final part of the paper, the authors use PET imaging to compare brain-wide activity patterns in the mutant mice compared to wildtype. They find differences in several areas both under control conditions (i.e., after injection of saline) as well as after injection of orexin. They focus on changes in the dorsal bed nucleus of stria terminalis (dBNST) and the lateral paragigantocellular nucleus (LPGi) and perform analysis of the dopaminergic projections to these areas. They provide anatomical evidence that these regions are innervated by dopamine fibers from the midbrain, are activated by orexin in control, but not mutant mice, and that dopamine receptors are present. Thus, they argue these anatomical data support the hypothesis that behavioral effects of orexin receptor 1 deletion in dopamine neurons are due to changes in dopamine signaling in these areas.

Strengths:

Understanding how orexin interacts with the dopamine system is an important question and this paper contains several novel findings along these lines. Specifically:

(1) The distribution of orexin receptor subtypes in VTA and SN is explored thoroughly.

(2) Use of the genetic model that knocks out a specific orexin receptor subtype from only dopamine neurons is a useful model and helps to narrow down the behavioral significance of this interaction.

(3) PET studies showing how central administration of orexin evokes dopamine release across the brain is intriguing, especially since two key areas are pursued - BNST and LPGi - where the dopamine projection is not as well described/understood.

We thank the reviewer for the careful summary and highlighting the novelty of our study.

Weaknesses:

The role of the orexin-dopamine interaction is not explored in enough detail. The manuscript presents several related findings, but the combination of anatomy and manipulation studies does not quite tell a cogent story. Ideally, one would like to see the authors focus on a specific behavioral parameter and show that one of their final target areas (dBNST or LPGi) was responsible or at least correlated with this behavioral readout. In addition, some more discussion on what the results tell us about orexin signaling to dopamine neurons under normal physiological conditions would be very useful. For example, what is the relevance of the orexin-dopamine interaction blunting noveltyinduced locomotion under wildtype conditions?

We agree that focusing on some orexin-dopamine targeting areas, such as dBNST or LPGi, is important to further reveal the anatomy-behavior links and underlying mechanisms. While we are very interested in further investigations, in the present manuscript we mainly aim to give an overview of the behavioral roles of orexin-dopamine interaction and to propose some promising downstream pathways in a relatively broad and systematical way. 

We have explained the physiological meanings of our results in more detail in the discussion in the revised reviewed preprint (lines 282-293, 318-332, ). Novelty-induced behavioral response should be at proper levels under normal physiological conditions. The orexin-dopamine interaction blunting novelty-induced locomotion could be important to keep attention on the main task without being distracted too much by other random stimuli in the environment. When this balance is disrupted, behavioral deficit may happen, such as attention deficit and hyperactivity disorder (ADHD).  

In some places in the Results, insufficient explanation and reporting is provided. For example, when reporting the behavioral effects of the Ox1 deletion in two bottle preference, it is stated that "[mutant] mice showed significant changes..." without stating the direction in which preference was affected.

For the reward-related behaviors described in this study, we did not find significant changes between [mutant] and control mice. We agree that it will be helpful for readers by describing the behavioral tests in more details. In the revised reviewed preprint, we have described in more detail in the results and Materials and Methods section how the control and [mutant] mice behave to the reward (lines 162-165, 171-181, 526-528).  

The cocaine CPP results are difficult to interpret because it is unclear whether any of the control mice developed a CPP preference. Therefore, it is difficult to conclude that the knockout animals were unaffected by drug reward learning. Similarly, the sucrose/sucralose preference scores are also difficult to interpret because no test of preference vs. water is performed (although the data appear to show that there is a preference at least at higher concentrations, it has not been tested).

We described the CPP analysis in the Materials and Methods section (lines 523-528 ) as below: ‘The percentage of time spent in the reward-paired compartment was calculated: 100 x time spent in the compartment / (total time - time spent in the middle area). The CPP score was then analyzed using the calculated percentage of time: 100 x (time on the test day – time on pre-test days)/ time on pre-test days. The pre-test and test days were before and after the conditioning, respectively. Thus, the CPP score above zero indicates that the CPP preference has developed.’ In Figure 2—figure supplement 4 C and F, it was shown that most control and knockout mice had a CPP score above zero. The control and knockout groups both developed a preference and there was no significant difference between the groups. 

For the sucrose/sucralose preference tests, in Figure 2—figure supplement 4 A and D, we present values as the percentages of sucrose/sucralose consumption in total daily drinking amount (sucrose/sucralose solution + water). Thus, percentages above 50% indicates mice prefer sucrose/sucralose to water. As shown in the figure, male mice only showed weak preference of 0.5% sucrose, compared to water, and under all other tested conditions, the mice showed strong preference of the sweet solution. There was no significant difference between control and knockout mice. 

We have described this in more details in the Results and Materials and Methods section in the revised reviewed preprint. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Figure 1, A-I. It is difficult to depict the anatomical subdivision of VTA in Figure 1, panels A and B. It is recommended to add a panel showing a schematic illustration of the SNc and subregions of VTA: PN, PIF, PBP, IF (providing more detail than in Figure 1, panel J). It is also recommended to show lower magnification images (as in Figure 1 - supplement 1), including both hemispheres, and to delineate the outline of the different subregions using curved lines, based on reference atlases (similar to Figure 1, panel I, please include distance from bregma). It would be helpful to indicate in Figure 1 that panel A is a control mouse and panel B is a Ox1RΔDAT mouse and include C-F letters to show corresponding insets. Anatomically, the paraintrafasicular nucleus (PIF) is positioned between the paranigral nucleus (PN) and the parabrachial pigmented nucleus (PBP). The authors have depicted the PIF ventral to the PN in Figure 1 panels A, B, and I. These panels and the quantification of Ox1R/2R positive cells within the different subdivisions need to be corrected accordingly. The image analysis method used to quantify RNAscope fluorescent images is not described in sufficient detail. Please expand this section.

According to the reviewer’s suggestions, we have refined Figure 1 in the revised reviewed preprint. We are now showing the schematic illustration of the SN and subregions of VTA in panel I, with blue squares to label the regions shown in panels A and B, and the distance from bregma is included. The outlines to delineate SN and the subregions of VTA are adjusted from straight to curved lines based on reference atlases. As suggested, we have also indicated panel A is a control and panel B is a Ox1RΔDAT mouse and included C-F letters to show corresponding insets. We apologize for the mistake about labeling PIF and PN positions in Figure A. We have corrected the labeling of their positions and double checked the quantification accordingly. This does not change our discussion or conclusion since both PIF and PN are the medial part of VTA, where both Ox1R and Ox2R are observed. The description of the image analysis in Matierials and Methods section has been improved (lines 378-385). We decided not to show lower magnification images than in Figure 1—supplement 1 to include both hemispheres, in the interests of clarity and reader-friendliness.  

(2) Figure 1, J-L. The claim that orexin activates dopaminergic SN and VTA neurons is weakly supported by the data provided. Calcium imaging of SN dopaminergic neurons in control mice suggests a discrete effect of 100 nM orexin-A application compared to baseline. Application of 300 nM shows a slightly bigger effect, but none of these results are statistically analysed. 

We are surprised by this comment and thank the reviewer for pointing out our apparent lack of clarity in the previous version (lines 96-106 and legend of Figure 1K, L). In more detail, we explain the data analysis in the new version (lines 119-133, 451-465) and the legend of Figure 1K, L and Figure 1-figure supplement 3).

The main goal of this part of the project was to functionally validate the Ox1R knockout in dopaminergic (DAT-expressing) neurons. This was a prerequisite for the behavioral and PET imaging experiments. We used GCaMP-mediated Ca2+ imaging in acute brain slices to reach this goal. This analysis was performed on the dopaminergic SN neurons, which we used as an "indicator population" because a large number of these neurons express Ox1R, but only a few express Ox2R. 

The analysis consisted of two parts:

a) For each neuron, we tested whether it responded to orexin A. At the single cell level, a neuron was considered orexin A-responsive if the change in fluorescence induced by orexin A was three times larger than the standard deviation (3 σ criterion) of the baseline fluorescence, corresponding to a Zscore of 3. We found that 56% of the neurons tested responded to orexin A, while 44% of the neurons did not respond to orexin A (Figure 1L, top). These data agree with the number of Ox1R-expressing neurons (Figure 1J). 

b) We also determined the orexin A-induced GCaMP fluorescence for each neuron, expressed as a percentage of GCaMP fluorescence induced upon application of high K+ saline. Accordingly, the "population response" of all analyzed neurons was expressed as the mean ± SEM of these responses. The significance of this mean response was tested for each group (control and Ox1R KO) using a onesample t-test. We found a marked and highly significant (p < 0.0001, n = 71) response of control neurons to 100 nM orexin A, while the Ox1R KO neurons did not respond (p = 0.5, n = 86). Note that, as described in a), 44% of the neurons contributing to the mean do not respond to orexin. Thus, the orexin responses of most responders are significantly higher than the mean. This is also evident in the example recordings in Figure 1K and Figure1—figure supplement 3. The orexin A-induced change in fluorescence was increased by increasing the orexin A concentration to 300 nM.

Note: As mentioned above, the orexin A response was expressed for each neuron individually as a percentage of its high K+saline-induced GCaMP fluorescence. This value is a solid reference point, reflecting the GCaMP fluorescence at maximal voltage-activated Ca2+ influx. Obviously, the Ca2+ concentration at this point is extremely high and not typically reached under physiological conditions. Therefore, as shown in Figure1—figure supplement 3 for completeness, the physiologically relevant responses may appear relatively minor at first glance when presented together in one figure (compare Figure1—figure supplement 3 A and B).

The authors should provide more evidence of the orexin-induced activation of dopaminergic neurons in the SN to support this claim and investigate whether a similar activation is observed in VTA neurons. 

Following the reviewer's suggestion, we confirmed orexin A-induced activation of dopaminergic neurons in the mouse SN by using perforated patch clamp recordings (Figure1—figure supplement 2).

This finding is consistent with previous extracellular in vivo recordings in rats (Liu et al., 2018).

The activation of dopaminergic neurons in the mouse VTA by orexin A has been shown repeatedly in earlier studies (e.g., Baimel et al., 2017; Korotkova et al., 2003; Tung et al., 2016).

In addition, Figure 3-Figure Supplement 2 shows that injection of orexin does not induce c-Fos expression in SN and VTA dopaminergic neurons of control and Ox1RΔDAT mice, which further weakens the claim made by the authors.

Figure 3—Figure Supplement 2 in the original submission is now Figure 3—Figure Supplement 3 in the revised reviewed preprint. It shows low c-Fos expression in SN and VTA dopaminergic neurons, and orexin-induced c-Fos expression was observed in Th-negative cells in SN and VTA. 

Technically relatively straightforward, Fos analysis is widely (and successfully) used in studies to reveal neuronal activation. However, this approach has limitations, e.g., regarding sensitivity and temporal resolution. Electrophysiological or optical imaging techniques can circumvent these shortcomings. The electrophysiological and Ca2+ imaging studies presented here, along with previous electrophysiological studies by others, clearly show that orexin A acutely and directly stimulates SN and VTA dopaminergic neurons.

In vivo, the injection of orexin A induced a pronounced c-Fos activity in non-dopaminergic cells of the VTA and SN but not in dopaminergic neurons. This result shows that the detection of c-Fos has worked in principle. Whether the absent c-Fos staining in dopaminergic neurons is due to lack of sensitivity, whether other IEGs would have worked better here, or whether there are other, e.g., cell type-specific reasons for the absence of staining, cannot be determined from the current data.

(3) Figure 2, I-L. The fact that ICV injection of both saline and orexin causes a sustained increase of locomotion (around 20 minutes in males, and over 30 minutes in females) is problematic and could mask the effects of orexin, particularly in females. It is unclear what panels J and L are showing. To be appropriately analysed, the authors should plot the pre- and post-injection AUC data for all groups and analyse it as a two-way mixed ANOVA, with the within-subjects factor "pre/post injection activity" and between-subjects factor "group". The authors can only warrant a statistically meaningful hyperlocomotor effect in Ox1RΔDAT mice if a significant interaction is found.

Though mice were habituated to the injection, it still makes sense to see the injection-induced increase in locomotion to some extent. We described in the figure legend that the AUC was calculated for the period after orexin injection, which meant 5 – 90 min in Figure 2 I, K. We have clearly observed significant differences between genotypes and between saline and orexin application, which means the genotype and orexin impact is strong enough to pop up despite of the injection effect. 

As the reviewer’s suggests, we have now plotted the pre- and post-injection AUC data for all groups and analyzed it as a two-way mixed ANOVA, with the within-subjects factor "pre/post injection activity" and between-subjects factor "group". To match the pre- and post-injection duration, we are now comparing AUC for around 60 min before and after the injection. A significant interaction is found here. Panels I-L are renewed, and the differences induced by Ox1R knockout and orexin confirmed the results shown in the initially submitted manuscript.  

(4) Figure 3. The literature has robustly shown that one of the main projection areas of VTA and SN dopaminergic neurons is the striatum, in particular its ventral part. It is surprising to see that this region is not affected by the lack of OX1R or by the injection of orexin. How can the authors explain that identified regions with significantly different activity include neighbouring brain structures with heterogenous composition? See for example, in panel A, section bregma 0.62mm, a significant region is seen expanding across the cortex, corpus callosum, and striatum. While the data from PET studies is potentially interesting, it may not be adequate to provide enough resolution to allow examination of the anatomical distribution of orexin-mediated neuronal activation.

While the striatum is a major projection area of dopaminergic neurons in VTA and SN, the projection and function of Ox1R-positive dopaminergic neurons is not clear. We have improved the description of dopamine function diversity in the revised reviewed preprint (lines 46-58), and it was reported before that the projection-defined dopaminergic populations in the VTA exhibited different responses to orexin A (Baimel et al., 2017). Moreover, the striatum activity is modulated by the indirect effect via other brain regions affected by Ox1R-positive dopaminergic neurons. It is unknown how the striatum activity should change after Ox1R deletion in dopaminergic neurons. We could not rule out the possibility that the striatum is indeed modulated by the Ox1R-positive dopaminergic neurons, though there was only a trend of genotype difference (Ox1RΔDAT vs. ctrl) in the ventral striatum in the section bregma 1.42 mm in Figure 3A. The ICV injection of orexin is potentially acting on Ox1R and Ox2R in the whole brain, so projections from other brain regions to the striatum also affect striatum activity and could have masked the effect of Ox1R-positive dopaminergic neurons. 

The spatial resolution of the PET data is in the order of ~1 mm^3. As we also explained in the Materials and Methods section, the size of a voxel in the original PET data is 0.4mm x 0.4mm x 0.8 mm. All calculations were performed on this grid. The higher-resolved images shown in Figure 3 are for presentation purposes only inspired by a request of the reviewer who asked us to show this in the Jais et al. 2016 manuscript. To make this clearer we now added the p-map images with the original voxel size to the supplement (Figure 3—figure supplement 1). For the interest in specific brain areas, more precise identification of anatomical sub-regions requires using methods with higher spatial resolution such as staining of brain slices for c-Fos-positive cells as we do in Figure 4.

PET is a powerful tool to identify global regions of activation/inhibition. In the manuscript, we have described in the results and discussion section that the activity in brain regions with related functions were changed. In panel A, Ox1RΔDAT showed activity increase in MPA, Pir and endopiriform claustrum, which are important for olfactory sensation; spinal trigeminal nucleus, sp5, and IRt, which regulates mastication and sensation of the oral cavity and the surface of the face; SubCV and Gi, which regulates sleeping and motion-related arousal and motivation. In panel B, changes in HDB, MCPO, Pir, DEn, S1, V2L and V1 are related to sensation, and changes in BNST, LPGi and M2 are important for emotion, exploration, and action selection. 

(5) Figure 4. As in Figure 1, the authors should consider including a schematic illustration of the brain areas that are being analysed using a reference atlas. It is also recommended to provide more details describing the quantification of the images. Without such information, the data is not convincing, in particular, the claim that Ox1R depletion causes a decrease in DRD1 in BNST is unclear. Additional unbiased quantitative approaches could be used to strengthen this point.

We have added Figure 4—figure supplement 1 as a schematic illustration of the brain areas that were being analyzed using a reference atlas. More details describing the unbiased quantification of the images have been added to Materials and Methods. We have added Figure 4—figure supplement 3, to show DRD1, DRD2 and the merged signal separately.  

(6) The discussion starts by stating that the main findings of this study are based on RNAscope and optophysiological experiments, however, the latter are not presented anywhere in the manuscript. This sentence (line 192) should be revised. The authors state in line 193 that OX1R is the only orexin receptor in the SN, but they show in Figure 1 that in the SN, 3% of neurons express OX2R and 2% co-express both receptors. 

We thank the reviewer for the input. We have rephrased the beginning of the discussion to clarify the objectives (lines 238 - 246). In doing so, we changed "optophysiological experiments" and "single orexin receptor" (lines 192 and 193 in the original manuscript) to " Ca2+ imaging experiments" and "main subtype of orexin receptors ", respectively. In this context, it should be noted that Ca2+ imaging is considered an optophysiological method - optophysiology generally refers to techniques that combine optical methods with physiological measurements.

The results of LPGi and BNST dopamine receptors in control and Ox1RΔDAT mice are poorly discussed. The authors should justify why these two regions were selected for further validation and how these may be related to the behavioural effects found in Ox1RΔDAT regarding exposure to a novel context.

Ox1RΔDAT mice exhibited increased novelty- and orexin-induced locomotion compared to control mice. After orexin injection, PET imaging shows that the neural activity of BNST and LPGi was lower or higher than in control mice, respectively. We selected BNST and LPGi for further validation because we think their key functional roles in regulating emotion, exploratory behaviors and locomotor speed are related to novelty-induced locomotion. We confirmed changes in neural activity change by c-Fos staining and investigated the expression patterns of dopamine receptors in BNST and LPGi. Our findings suggested that Ox1R deletion in dopaminergic neurons results in the disinhibition of neural activity in LPGi via dopaminergic pathways and the decrease of dopamine-mediated neural activity in BNST. Emotion perception affects the decision of how to respond to the novelty. It is possible that novelty activates the orexin system and Ox1R signaling in dopaminergic neurons promotes emotion perception and inhibits exploration. Of course, further careful investigation is necessary to test this hypothesis in the future experiments. We have improved the rational description and discussion in the

‘Results’ and ‘Discussion’ section in the revised reviewed preprint (lines 210-213, 259-270, 293-308). 

Reviewer #2 (Recommendations For The Authors):

A major recommendation - if possible - would be to directly show that one or both of the two target areas - dBNST and LPGi - are associated with the behavioral effects caused by the deletion of the orexin receptor 1 in dopamine neurons.

We completely agree that it would be very valuable to directly show dBNST and LPGi are associated with the behavioral effects caused by the deletion of Ox1R in dopaminergic neurons. While we are very interested in carefully investigating specific orexin-dopamine targeting areas and related neural circuits in the future, in the present manuscript, we mainly aim to give an overview of the behavioral roles of orexin-dopamine interaction and propose some promising downstream pathways. 

The authors should state if data are corrected for multiple comparisons, e.g., in the PET study of different regions.

We have included information about the post-hoc tests for all 2-way ANOVA analyses in the submitted manuscript. For the PET study, the p-values in the p-maps were not corrected for multiple comparison, Figure 3—figure supplement 2 shows the raw data of each mouse and the analysis method (t-test). In the revised reviewed preprint, we include the information on the analysis method in the figure legends of Figure 3. 

We consider that saline and orexin injections mimic the resting and active state of mice, respectively, and would like to study genotype effect under each condition. Doing 2-way ANOVA takes in count the difference between orexin and saline injection, which could mask the genotype effect under a certain condition. Therefore, we decided to perform t-tests for each condition in Figure 3. While we provide readers with full information in Figure 3—figure supplement 2 with the raw data of each individual mouse, below we present the p-maps after multiple comparisons (Sidak’s post hoc test). After multiple comparisons, we could see changes in similar brain regions as in Figure 3, though significant values are reduced by the correction for multiple comparisons, and under orexin-injection condition, we fail to see significantly higher activity around the lateral paragigantocellular nucleus (LPGi), nucleus of the horizontal limb of the diagonal band (HDB) and magnocellular preoptic nucleus (MCPO) in Ox1RΔDAT mice. In order to more precisely identify the anatomical locations, we performed additional experiments to confirm the changes revealed by PET. For example, LPGi is a relatively small region confirmed and identified more precisely by c-Fos immunostaining (Figure 4A, C). 

Author response image 1.

PET imaging studies comparing Ox1RΔDAT and control mice, with post-hoc t-test to correct for multiple comparisons. 3D maps of p-values in PET imaging studies comparing Ox1RΔDAT and control mice, after intracerebroventricular (ICV) injection of (A) saline (NS) and (B) orexin A. Control-NS, n = 8; control-orexin, n = 6; Ox1RΔDAT, n = 8. M2, secondary motor cortex; MPA, medial preoptic area; Pir, piriform cortex; IEn, intermediate endopiriform claustrum; DEn, dorsal endopiriform claustrum; VEn, ventral endopiriform claustrum; LSS, lateral stripe of the striatum; BNST, the dorsal bed nucleus of the stria terminalis; S1Sh, primary somatosensory cortex, shoulder region; S1HL, primary somatosensory cortex, hindlimb region; S1BF, primary somatosensory cortex, barrel field; S1Tr, primary somatosensory cortex, trunk region; V1, primary visual cortex; V2L, secondary visual cortex, lateral area; SubCV, subcoeruleus nucleus, ventral part; Gi, gigantocellular reticular nucleus; IRt, intermediate reticular nucleus; sp5, spinal trigeminal tract.

Provide a rationale for following up on BNST and LPGi and not any of the regions identified in the PET study.

We thank the reviewer for the careful reading and important input. Ox1RΔDAT mice exhibited increased novelty- and orexin-induced locomotion compared to control mice. After orexin injection, PET imaging shows that the neural activity of BNST and LPGi was lower or higher than control mice, respectively.

We selected BNST and LPGi for further validation because we think their key functional roles in regulating emotion, exploratory behaviors and locomotor speed are related to novelty-induced locomotion. We confirmed the neural activity change by c-Fos staining and investigated the expression patterns of dopamine receptors in BNST and LPGi. Our findings suggested that Ox1R deletion in dopaminergic neurons results in the disinhibition of neural activity in LPGi via dopaminergic pathways and the decrease of dopamine-mediated neural activity in BNST. Emotion perception affects the decision how to respond to the novelty. It is possible that novelty activates the orexin system and Ox1R signaling in dopaminergic neurons promotes emotion perception and inhibits exploration. Of course, further investigation is necessary to test this hypothesis in future. We have improved the rational description and discussion in the ‘Results’ and ‘Discussion’ section in the revised reviewed preprint (lines 210-213, 259-270, 293-308). 

Heatmap in Fig. 1K should not have smoothing across the y-axis, individual cells should be discrete.

We thank the reviewer for bringing this issue to our attention. The data had not been intentionally smoothed (neither across the x-axis nor the y-axis), but it was probably a formatting issue. We have corrected this and separated individual cell traces with lines (Figure 1K, Figure 1—figure supplement 3).

Dopamine cells are well known to lack Fos expression in most cases. Did the authors consider using another IEG to show neural activation, e.g., pERK?

We did not use another IEG. The electrophysiological and Ca2+ imaging studies presented here, along with previous electrophysiological studies by others, clearly show that orexin A acutely and directly stimulates SN and VTA dopaminergic neurons. Please see also the response to a related comment of Reviewer 1.

Consider adding a lower magnification section to anatomical figures to aid the reader in orienting and identifying the location.

We have added the schematic illustration of SN, VTA, BNST and LPGi in Figure 1I and Figure 4— figure supplement 1. We hope this helps the reader in orienting and identifying the location.  

Data availability should be stated.

There are no restrictions on data availability. We have added this section to the revised reviewed preprint.

Line 50. Some more references both historical and recent could be given to support this statement about the function of dopamine.

We have improved the description and references to support the statement about dopamine function (lines 46-58). We have cited recent studies and some reviews in the revised reviewed preprint (lines 4658). 

The PET data (Fig. 3) might be easier to visualize and interpret if a white background was used. In addition, is there a more refined way of presenting the data in Fig 3, S1?

It is common to present imaging data such as PET and MRI on a black background. We also have already applied this color scheme in multiple publications and would therefore prefer to stick to this color scheme. 

While Figure 3 is the concise way to present PET data, we aim to show the original individual results of mice in Figure 3—figure supplement 2 and to demonstrate how we performed the statistical analysis. Therefore, we take an example voxel of the respective brain area, perform the t-test, and present the data as bars with individual dots. 

Line 97. State what type of Ca imaging here, e.g., "we performed Ca imaging in ex vivo slices of VTA and SN".

As the reviewer suggested, we have specified the type of Ca2+ imaging (line 112).

Line 165. State which groups this post-mortem analysis was performed on and if any differences were to be found (not expected to find differences in this anatomical tracing experiment but good to report this as both groups were used).

Postmortem analysis of c-Fos staining revealed low c-Fos expression in dopaminergic neurons in the VTA and SN of Ox1RΔDAT and control mice after ICV injection of saline or orexin A (1 nmol). No obvious changes were observed among the groups. We have improved the description in the revised reviewed preprint (lines 202-208).

Line 192. What do you mean by optophysiological here? The Ca imaging (which is a fairly small, confirmatory element of the manuscript).

We have changed ‘optophysiological experiments’ (line 192 in initial submitted manuscript) to ‘calcium imaging experiments’ and rephrased the beginning of the discussion to clarify the objectives (lines 238246).

The protein level in the diet is substantially higher than in most rodent diets (34% here vs 14-20% in most commercial rodent chows). Please comment on this.

This diet is for rat and mouse maintenance, purchased from ssniff Spezialdiäten GmbH (product V1554).

The percentage of calories supplied by protein is affected by the calculation methods. The company calculated with pig equation before and the value was 34% in the old instruction data sheet. They have updated the value to 23% in the new data sheet with calculations by Atwater factors. We thank the reviewer for reminding us and have updated the values in the revised reviewed preprint (lines 314-316). 

Editor's note:

Should you choose to revise your manuscript, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

We have provided the source data and the statistical reporting for each Figure with the revision

References

Baimel, C., Lau, B. K., Qiao, M., & Borgland, S. L. (2017). Projection-target-defined effects of orexin and dynorphin on VTA dopamine neurons. Cell Rep, 18(6), 1346-1355.  https://doi.org/10.1016/j.celrep.2017.01.030

Korotkova, T. M., Eriksson, K. S., Haas, H. L., & Brown, R. E. (2002). Selective excitation of GABAergic neurons in the substantia nigra of the rat by orexin/hypocretin in vitro. Regul Pept, 104(1-3), 83-89. https://doi.org/10.1016/s0167-0115(01)00323-8 

Korotkova, T. M., Sergeeva, O. A., Eriksson, K. S., Haas, H. L., & Brown, R. E. (2003). Excitation of ventral tegmental area dopaminergic and nondopaminergic neurons by orexins/hypocretins. J Neurosci, 23(1), 7-11. https://www.ncbi.nlm.nih.gov/pubmed/12514194

Liu, C., Xue, Y., Liu, M. F., Wang, Y., Liu, Z. R., Diao, H. L., & Chen, L. (2018). Orexins increase the firing activity of nigral dopaminergic neurons and participate in motor control in rats. J Neurochem, 147(3), 380-394. https://doi.org/10.1111/jnc.14568 

Tung, L. W., Lu, G. L., Lee, Y. H., Yu, L., Lee, H. J., Leishman, E., Bradshaw, H., Hwang, L. L., Hung, M. S., Mackie, K., Zimmer, A., & Chiou, L. C. (2016). Orexins contribute to restraint stress-induced cocaine relapse by endocannabinoid-mediated disinhibition of dopaminergic neurons. Nat Commun, 7, 12199. https://doi.org/10.1038/ncomms12199

Author response:

Public Reviews:

Reviewer #1 (Public review): 

Summary: 

The authors investigated the anatomical features of the synaptic boutons in layer 1 of the human temporal neocortex. They examined the size of each synapse, the macular or perforated appearance, the size of the synaptic active zone, the number and volume of the mitochondria, and the number of synaptic and dense core vesicles, also differentiating between the readily releasable, the recycling, and the resting pool of synaptic vesicles. The coverage of the synapse by astrocytic processes was also assessed, and all the above parameters were compared to other layers of the human temporal neocortex. The authors conclude that the subcellular morphology of the layer 1 synapses are suitable for the functions of the neocortical layer, i.e. the synaptic integration within the cortical column. The low glial coverage of the synapses might allow increased glutamate spillover from the synapses, enhancing synaptic crosstalk within this cortical layer. 

Strengths: 

The strengths of this paper are the abundant and very precious data about the fine structure of the human neocortical layer 1. Quantitative electron microscopy data (especially that derived from the human brain) are very valuable since this is a highly time- and energy-consuming work. The techniques used to obtain the data, as well as the analyses and the statistics performed by the authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion. 

We would like to thank reviewer#1 for his very positive comments on our manuscript stating that such data about the fine structure of the human neocortex are are highly relevant.

Weaknesses: 

There are several weaknesses in this work. First, the authors should check and review extensively for improvements to the use of English. Second, several additional analyses performed on the existing data could substantially elevate the value of the data presented. Much more information could be gained from the existing data about the functions of the investigated layer, of the cortical column, and about the information processing of the human neocortex. Third, several methodological concerns weaken the conclusions drawn from the results. 

We would like to thank the reviewer for his critical and thus helpful comments on our manuscript. We took the first comment of the reviewer concerning the English and have thus improved our manuscript by rephrasing and shortening sentences. Secondly, according to the reviewer several additional analyses should be performed on the existing data, which could substantially elevate the value of the data presented. We will implement some of the suggestions in the improved version of the manuscript where appropriate. We will address a more detailed answer to the reviewer’s queries in her/his suggestions to the authors (see below). However, the reviewer states himself: “The techniques used to obtain the data, as well as the analyses and the statistics performed by the authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion”.

Reviewer #2 (Public review): 

Summary: 

The study of Rollenhagen et al. examines the ultrastructural features of Layer 1 of the human temporal cortex. The tissue was derived from drug-resistant epileptic patients undergoing surgery, and was selected as far as possible from the epilepsy focus, and as such considered to be non-epileptic. The analyses included 4 patients with different ages, sex, medication, and onset of epilepsy. The manuscript is a follow-on study with 3 previous publications from the same authors on different layers of the temporal cortex: 

Layer 4 - Yakoubi et al 2019 eLife

Layer 5 - Yakoubi et al 2019 Cerebral Cortex

Layer 6 - Schmuhl-Giesen et al 2022 Cerebral Cortex.

They find, that the L1 synaptic boutons mainly have a single active zone, a very large pool of synaptic vesicles, and are mostly devoid of astrocytic coverage. 

Strengths: 

The manuscript is well-written and easy to read. The Results section gives a detailed set of figures showing many morphological parameters of synaptic boutons and glial elements. The authors provide comparative data of all the layers examined by them so far in the Discussion. Given that anatomical data in the human brain are still very limited, the current manuscript has substantial relevance. The work appears to be generally well done, the EM and EM tomography images are of very good quality. The analysis is clear and precise.

We would like to thank the reviewer for his very positive evaluation of our paper and the comments that such data have a substantial relevance, in particular in the human neocortex. In contrast to reviewer#1, this reviewer’s opinion is that the manuscript is well written and easy to read.

Weaknesses: 

One of the main findings of this paper is that "low degree of astrocytic coverage of L1 SBs suggests that glutamate spillover and as a consequence synaptic cross-talk may occur at the majority of synaptic complexes in L1". However, the authors only quantified the volume ratio of astrocytes in all 6 layers, which is not necessarily the same as the glial coverage of synapses. In order to strengthen this statement, the authors could provide 3D data (that they have from the aligned serial sections) detailing the percentage of synapses that have glial processes in close proximity to the synaptic cleft, that would prevent spillover. 

We agree with the reviewer that we only quantified the volume ratio of the astrocytic coverage but not necessarily the percentage of synapses that may or not contribute to the formation of the ‘tripartite’ synapse. As suggested, we will re-analyze our material with respect to the percentage of coverage for individual synaptic boutons in each layer and will implement the results in the improved version of the manuscript. However, since this is a completely new analysis that is time-consuming we would like to ask the reviewer for additional time to perform this task.

A specific statement is missing on whether only glutamatergic boutons were analyzed in this MS, or GABAergic boutons were also included. There is a statement, that they can be distinguished from glutamatergic ones, but it would be useful to state it clearly in the Abstract, Results, and Methods section what sort of boutons were analyzed. Also, what is the percentage of those boutons from the total bouton population in L1? 

We would like to thank the reviewer for this comment. Although our title clearly states, we focused on quantitative 3D-models of excitatory synaptic boutons, we will point out that more clearly in the Methods and Result chapters. Our data support recent findings by others (see for example Cano-Astorga et al. 2023, 2024; Shapson-Coe et al. 2024) that have evaluated the ratio between excitatory vs. inhibitory synaptic boutons in the temporal lobe neocortex, the same area as in our study, which was between 10-15% inhibitory terminals but with a significant layer and region specific difference. We will include the excitatory vs. inhibitory ratio and the corresponding citations in the Results section.

Synaptic vesicle diameter (that has been established to be ~40nm independent of species) can properly be measured with EM tomography only, as it provides the possibility to find the largest diameter of every given vesicle. Measuring it in 50 nm thick sections results in underestimation (just like here the values are ~25 nm) as the measured diameter will be smaller than the true diameter if the vesicle is not cut in the middle, (which is the least probable scenario). The authors have the EM tomography data set for measuring the vesicle diameter properly. 

We partially disagree with the reviewer on this point. Using high-resolution transmission electron microscopy, we measured the distance from the outer-to-outer membrane only on those synaptic vesicles that were round in shape with a clear ring-like structure to avoid double counts and discarded all those that were only partially cut according to criteria developed by Abercrombie (1946) and Boissonnat (1988). We assumed that within a 55±5 nm thick ultrathin section (silver to gray interference contrast) all clear-ring-like vesicles were distributed in this section assuming a vesicle diameter between 25 to 40nm. For large DCVs, double-counts were excluded by careful examination of adjacent images and were only counted in the image where they appeared largest.

In addition, we have measured synaptic vesicles using TEM tomography and came to similar results. We will address this in Material and Methods that both methods were used.

It is a bit misleading to call vesicle populations at certain arbitrary distances from the presynaptic active zone as readily releasable pool, recycling pool, and resting pool, as these are functional categories, and cannot directly be translated to vesicles at certain distances. Indeed, it is debated whether the morphologically docked vesicles are the ones, that are readily releasable, as further molecular steps, such as proper priming are also a prerequisite for release.

We thank the reviewer for this comment. However, nobody before us tried to define a morphological correlate for the three functionally defined pools of synaptic vesicles since synaptic vesicles normally are distributed over the entire nerve terminal. As already mentioned above, after long and thorough discussions with Profs. Bill Betz, Chuck Stevens, Thomas Schikorski and other experts in this field we tried to define the readily releasable (RRP), recycling (RP) and resting pools by measuring the distance of each synaptic vesicle to the presynaptic density (PreAZ). Using distance as a criterion, we defined the RRP including all vesicles that were located within a distance (perimeter) of 10 to 20 nm from the PreAZ that is less than an average vesicle diameter (between 25 to 40 nm). The RP was defined as vesicles within a distance of 60-200 nm away, still quite close but also rapidly available on demand and the remaining ones beyond 200 nm were suggested to belong to the resting pool. This concept was developed for our first publication (Sätzler et al. 2002) and this approximation since then is very much acknowledged by scientist working in the field of synaptic neuroscience and computational neuroscientist. We were asked by several labs worldwide whether they can use our data of the perimeter analysis for modeling. We agree that our definition of the three pools can be seen as arbitrary but we never claimed that our approach is the truth but nothing as the truth. Concerning the debate whether only docked vesicles or also those very close the PreAZ should constitute the RRP we have a paper in preparation using our perimeter analysis, EM tomography and simulations trying to clarify this debate. Our preliminary results suggest that the size of the RRP should be reconsidered.

Tissue shrinkage due to aldehyde fixation is a well-documented phenomenon that needs compensation when dealing with density values. The authors cite Korogod et al 2015 - which actually draws attention to the problem comparing aldehyde fixed and non-fixed tissue, still the data is non-compensated in the manuscript. Since all the previous publications from this lab are based on aldehyde fixed non-compensated data, and for this sake, this dataset should be kept as it is for comparative purposes, it would be important to provide a scaling factor applicable to be able to compare these data to other publications.

We thank the reviewer for his suggestion. However, for several reasons we did not correct for shrinkage caused by aldehyde fixation. There are papers by Eyre et al. (2007) and the mentioned paper by Korogod et al. 2015 that have demonstrated that cryo-fixation reveals larger numbers of docked synaptic vesicles, a smaller glial volume, and a less intimate glial coverage of synapses and blood vessels compared to chemical fixation. Other structural subelements such as active zone size and shape and the total number of synaptic vesicles remained unaffected. In two further publications Zhao et al. (2012a, b) investigating hippocampal mossy fiber boutons using cryo-fixation and substitutions came to similar results with respect to bouton and active zone size and number and diameter of synaptic vesicles compared to aldehyde-fixation as described by Rollenhagen et al. 2007 for the same nerve terminal. This was one of the reasons not correcting for shrinkage. In addition, all cited papers state that chemical fixation in general provides a much better ultrastructural preservation of tissue samples when compared with cryo-fixation and substitution where optimal preservation is only regional within a block of tissue and therefore less suitable for large-scale ultrastructural analyses as we performed.

Reviewer #3 (Public review): 

Summary: 

Rollenhagen et al. offer a detailed description of layer 1 of the human neocortex. They use electron microscopy to assess the morphological parameters of presynaptic terminals, active zones, vesicle density/distribution, mitochondrial morphology, and astrocytic coverage. The data is collected from tissue from four patients undergoing epilepsy surgery. As the epileptic focus was localized in all patients to the hippocampus, the tissue examined in this manuscript is considered non-epileptic (access) tissue. 

Strengths: 

The quality of the electron microscopic images is very high, and the data is analyzed carefully. Data from human tissue is always precious and the authors here provide a detailed analysis using adequate approaches, and the data is clearly presented. 

We are very thankful to the reviewer upon his very positive comments about our data analysis and presentation.

Weaknesses: 

The study provides only morphological details, these can be useful in the future when combined with functional assessments or computational approaches. The authors emphasize the importance of their findings on astrocytic coverage and suggest important implications for glutamate spillover. However, the percentage of synapses that form tripartite synapses has not been quantified, the authors' functional claims are based solely on volumetric fraction measurements. 

We thank the reviewer for his critical comments on our findings concerning the layer-specific astrocytic coverage as also suggested by reviewer#2. As already stated above we will analyze the astrocytic coverage and the layer-specific percentage of astrocytic contribution to the ‘tripartite’ synapse in more detail. We are, however, a bit puzzled about the comment that structural anatomists usually receive that our study only provides morphological details. Our thorough analysis of structural and synaptic parameters of synaptic boutons underlie and might even predict the function of synaptic boutons in a given microcircuit or network and will thus very much improve our understanding and knowledge about the functional properties of these structures, in particular in the human brain where such studies are still quite rare. The main goal of our studies in the human neocortex was the quantitative morphology of synaptic boutons and thus the synaptic organization of the cortical column, layer by layer which to our knowledge is the first such detailed study undertaken in the human brain. Our efforts have set a golden standard in the analysis of synaptic boutons embedded in different microcircuits und is meanwhile internationally very well accepted.

The distinction between excitatory and inhibitory synapses is not clear, they should be analyzed separately. 

As already stated above in response to reviewer#1 our study focused on excitatory synaptic boutons since they represent the majority of synapses. However, in the improved version of our manuscript in the Material and Method section we included a paragraph with structural criteria to distinguish excitatory from inhibitory terminals (see also our comment to reviewer#1 concerning this point) including appropriate citations.

The text connects functional and morphological characteristics in a very direct way. For example, connecting plasticity to any measurement the authors present would be rather difficult without any additional functional experiments. References to various vesicle pools based on the location of the vesicles are also more complex than suggested in the manuscript. The text should better reflect the limitations of the conclusions that can be drawn from the authors' data. 

We thank the reviewer for this comment. However, it has been shown by meanwhile numerous publications that the shape and size of the active zone together with the pool of synaptic vesicles and the astrocytic coverage critically determines synaptic transmission and synaptic strength, but can also contribute to the modulation of synaptic plasticity (see also citations within the text). It has been shown that synaptic boutons can switch upon certain stimulation conditions to different modes of release (uni- vs. multiquantal, uni- vs multivesicular release) and from asynchronous to synchronous release leading also to the modulation of synaptic short- and long-term plasticity. To the second comment: When we started with our first paper about the Calyx of Held – principal neuron synapse in the MNTB (Sätzler et al. 2002) we tried to define a morphological correlate for the three functionally defined pools. As already mentioned above in our reply to the other two reviewers, this is rather difficult since synaptic vesicles are normally distributed over the entire nerve terminal. After long and thorough discussions with Bill Betz, Chuck Stevens and other leading scientist in the field of synaptic neuroscience, we together with Bert Sakmann tried to define a morphological correlate for the functionally defined pools using a perimeter analysis. We defined the readily releasable pool as vesicles 10 to 20 nm away from the presynaptic active zone, the recycling pool as those in 60-200 nm distance and the remaining as those belonging to the resting pool. However, it has been shown by capacitance measurements (see for example Hallermann et al 2003), FM1-43 investigations (see for example Henkel et al. 1996) and high-resolution electron microscopy (see for example Schikorski and Stevens 2001; Schikorski 2014) that our estimate of the RRP nearly perfectly matches with the functionally defined pools at hippocampal and cortical synapses (Silver et al. 2003). In addition, in one of our own papers (Rollenhagen et al. 2018) we also estimated the RP functionally from trains of EPSPs using an exponential fit analysis and came to similar results upon its size using the perimeter analysis.

Of course, as stated by the reviewer the scenario could be more complex, using other criteria but we never claimed that our morphologically defined pools are the truth but nothing as the truth but we believe it offers a quite good approximation.

eLife assessment

This is a useful study depicting the ultrastructural features of layer 1 of the human temporal cortex, the authors assess various synaptic parameters, astrocytic volumetric ratio, and mitochondrial morphology. The data were collected using a solid methodology, however, the analysis of the functional vesicle pools is incomplete, and reliance solely on electron microscopy limits the scope of the work to structural observation. The work will be of interest to neuroscientists and computational researchers investigating cortical and network function.

Reviewer #1 (Public review):

Summary:

The authors investigated the anatomical features of the synaptic boutons in layer 1 of the human temporal neocortex. They examined the size of each synapse, the macular or perforated appearance, the size of the synaptic active zone, the number and volume of the mitochondria, and the number of synaptic and dense core vesicles, also differentiating between the readily releasable, the recycling, and the resting pool of synaptic vesicles. The coverage of the synapse by astrocytic processes was also assessed, and all the above parameters were compared to other layers of the human temporal neocortex. The authors conclude that the subcellular morphology of the layer 1 synapses are suitable for the functions of the neocortical layer, i.e. the synaptic integration within the cortical column. The low glial coverage of the synapses might allow increased glutamate spillover from the synapses, enhancing synpatic crosstalk within this cortical layer.

Strengths:

The strengths of this paper are the abundant and very precious data about the fine structure of the human neocortical layer 1. Quantitative electron microscopy data (especially that derived from the human brain) are very valuable since this is a highly time- and energy-consuming work. The techniques used to obtain the data, as well as the analyses and the statistics performed by the authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion.

Weaknesses:

There are several weaknesses in this work. First, the authors should check and review extensively for improvements to the use of English. Second, several additional analyses performed on the existing data could substantially elevate the value of the data presented. Much more information could be gained from the existing data about the functions of the investigated layer, of the cortical column, and about the information processing of the human neocortex. Third, several methodological concerns weaken the conclusions drawn from the results.

Reviewer #2 (Public review):

Summary:

The study of Rollenhagen et al. examines the ultrastructural features of Layer 1 of the human temporal cortex. The tissue was derived from drug-resistant epileptic patients undergoing surgery, and was selected as far as possible from the epilepsy focus, and as such considered to be non-epileptic. The analyses included 4 patients with different ages, sex, medication, and onset of epilepsy. The manuscript is a follow-on study with 3 previous publications from the same authors on different layers of the temporal cortex:

Layer 4 - Yakoubi et al 2019 eLife<br /> Layer 5 - Yakoubi et al 2019 Cerebral Cortex<br /> Layer 6 - Schmuhl-Giesen et al 2022 Cerebral Cortex.

They find, that the L1 synaptic boutons mainly have a single active zone, a very large pool of synaptic vesicles, and are mostly devoid of astrocytic coverage.

Strengths:

The manuscript is well-written and easy to read. The Results section gives a detailed set of figures showing many morphological parameters of synaptic boutons and glial elements. The authors provide comparative data of all the layers examined by them so far in the Discussion. Given that anatomical data in the human brain are still very limited, the current manuscript has substantial relevance.

The work appears to be generally well done, the EM and EM tomography images are of very good quality. The analysis is clear and precise.

Weaknesses:

One of the main findings of this paper is that "low degree of astrocytic coverage of L1 SBs suggests that glutamate spillover and as a consequence synaptic cross-talk may occur at the majority of synaptic complexes in L1". However, the authors only quantified the volume ratio of astrocytes in all 6 layers, which is not necessarily the same as the glial coverage of synapses. In order to strengthen this statement, the authors could provide 3D data (that they have from the aligned serial sections) detailing the percentage of synapses that have glial processes in close proximity to the synaptic cleft, that would prevent spillover.

A specific statement is missing on whether only glutamatergic boutons were analysed in this MS, or GABAergic boutons were also included. There is a statement, that they can be distinguished from glutamatergic ones, but it would be useful to state it clearly in the Abstract, Results, and Methods section what sort of boutons were analysed. Also, what is the percentage of those boutons from the total bouton population in L1?

Synaptic vesicle diameter (that has been established to be ~40nm independent of species) can properly be measured with EM tomography only, as it provides the possibility to find the largest diameter of every given vesicle. Measuring it in 50 nm thick sections results in underestimation (just like here the values are ~25 nm) as the measured diameter will be smaller than the true diameter if the vesicle is not cut in the middle, (which is the least probable scenario). The authors have the EM tomography data set for measuring the vesicle diameter properly.

It is a bit misleading to call vesicle populations at certain arbitrary distances from the presynaptic active zone as readily releasable pool, recycling pool, and resting pool, as these are functional categories, and cannot directly be translated to vesicles at certain distances. Indeed, it is debated whether the morphologically docked vesicles are the ones, that are readily releasable, as further molecular steps, such as proper priming are also a prerequisite for release.

Tissue shrinkage due to aldehyde fixation is a well-documented phenomenon that needs compensation when dealing with density values. The authors cite Korogod et al 2015 - which actually draws attention to the problem comparing aldehyde fixed and non-fixed tissue, still the data is non-compensated in the manuscript. Since all the previous publications from this lab are based on aldehyde fixed non-compensated data, and for this sake, this dataset should be kept as it is for comparative purposes, it would be important to provide a scaling factor applicable to be able to compare these data to other publications.

Reviewer #3 (Public review):

Summary:

Rollenhagen et al. offer a detailed description of layer 1 of the human neocortex. They use electron microscopy to assess the morphological parameters of presynaptic terminals, active zones, vesicle density/distribution, mitochondrial morphology, and astrocytic coverage. The data is collected from tissue from four patients undergoing epilepsy surgery. As the epileptic focus was localized in all patients to the hippocampus, the tissue examined in this manuscript is considered non-epileptic (access) tissue.

Strengths:

The quality of the electron microscopic images is very high, and the data is analyzed carefully. Data from human tissue is always precious and the authors here provide a detailed analysis using adequate approaches, and the data is clearly presented.

Weaknesses:

The study provides only morphological details, these can be useful in the future when combined with functional assessments or computational approaches. The authors emphasize the importance of their findings on astrocytic coverage and suggest important implications for glutamate spillover. However, the percentage of synapses that form tripartite synapses has not been quantified, the authors' functional claims are based solely on volumetric fraction measurements.

The distinction between excitatory and inhibitory synapses is not clear, they should be analyzed separately.

The text connects functional and morphological characteristics in a very direct way. For example, connecting plasticity to any measurement the authors present would be rather difficult without any additional functional experiments. References to various vesicle pools based on the location of the vesicles are also more complex than suggested in the manuscript. The text should better reflect the limitations of the conclusions that can be drawn from the authors' data.

eLife assessment

This fundamental work provides creative and thoughtful analysis of rodent foraging behavior and its dependence on body reference frame-based vs world reference frame-based cues. It compellingly demonstrates that a robust map, capable of supporting taking novel shortcuts, is learned primarily if not exclusively based on self-motion cues, which has rarely if ever been reported outside of the human literature. The work, which will be of interest to a broad audience of neuroscientists, provides a rich discussion about the role of the hippocampus in supporting the behavior that should guide future neurophysiological investigations.

Reviewer #1 (Public review):

Assessment:

This important work advances our understanding of navigation and path integration in mammals by using a clever behavioral paradigm. The paper provides compelling evidence that mice are able to create and use a cognitive map to find "short cuts" in an environment, using only the location of rewards relative to the point of entry to the environment and path integration, and need not rely on visual landmarks.

Summary:

The authors have designed a novel experimental apparatus called the 'Hidden Food Maze (HFM)' and a beautiful suite of behavioral experiments using this apparatus to investigate the interplay between allothetic and idiothetic cues in navigation. The results presented provide a clear demonstration of the central claim of the paper, namely that mice only need a fixed start location and path integration to develop a cognitive map. The experiments and analyses conducted to test the main claim of the paper -- that the animals have formed a cognitive map -- are conclusive. While I think the results are quite interesting and sound, one issue that needs to be addressed is the framing how landmarks are used (or not), as discussed below, although I believe this will be a straight forward issue for the authors to address.

Strengths:

The 90 degree rotationally symmetric design and use of 4 distal landmarks and 4 quadrants with their corresponding rotationally equivalent locations (REL) lends itself to teasing apart the influence of path integration and landmark-based navigation in a clever way. The authors use a really complete set of experiments and associated controls to show that mice can use a start location and path integration to develop a cognitive map and generate shortcut routes to new locations.

Weaknesses:

There were no major weaknesses identified that were not addressed during revisions.

Reviewer #3 (Public review):

Summary:

How is it that animals find learned food locations in their daily life? Do they use landmarks to home in on these learned locations or do they learn a path based on self-motion (turn left, take ten steps forward, turn right, etc.). This study carefully examines this question in a well designed behavioral apparatus. A key finding is that to support the observed behavior in the hidden food arena, mice appear to not use the distal cues that are present in the environment for performing this task. Removal of such cues did not change the learning rate, for example. In a clever analysis of whether the resulting cognitive map based on self-motion cues could allow a mouse to take a shortcut, it was found that indeed they are. The work nicely shows the evolution of the rodent's learning of the task, and the role of active sensing in the targeted reduction of uncertainty of food location proximal to its expected location.

Strengths:

A convincing demonstration that mice can synthesize a cognitive map for the finding of a static reward using body frame-based cues. Showing that uncertainty of final target location is resolved by an active sensing process of probing holes proximal to the expected location. Showing that changing the position of entry into the arena rotates the anticipated location of the reward in a manner consistent with failure to use distal cues.

Weaknesses:

The task is low stakes, and thus the failure to use distal cues at most costs the animal a delay in finding the food; this delay is likely unimportant to the animal, and the pre-training procedure is likely to make it clear to the animal's that distal cues are unreliable even if desirable to use. Thus, it is unclear whether this result would generalize to a situation where the animal may be under some time pressure, urgency due to food (or water) restriction, or due to predatory threat, or situations where distal cues are reliable. In such cases, the use of distal cues to make locating the reward robust to changing start locations may be more likely to be observed.

Author response:

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

We would like to thank the reviewers and editors for their careful assessment and review of our article. The many detailed comments, questions and suggestions were very helpful in improving our analyses and presentation of data. In particular, our Discussion benefited enormously from the comments. 

Below we respond in detail to every point raised. 

We especially note that Reviewer #3’s small query on “trial where learning is defined to have occurred, we were not given the quantitative criterion operationalizing "learning" - please provide” led to deeper analyses and insights and a lengthy response.

This analysis prompted the addition of a sentence (red) to the Abstract. 

“Animals navigate by learning the spatial layout of their environment. We investigated spatial learning of mice in an open maze where food was hidden in one of a hundred holes. Mice leaving from a stable entrance learned to efficiently navigate to the food without the need for landmarks. We developed a quantitative framework to reveal how the mice estimate the food location based on analyses of trajectories and active hole checks. After learning, the computed “target estimation vector” (TEV) closely approximated the mice’s route and its hole check distribution. The TEV required learning both the direction and distance of the start to food vector, and our data suggests that different learning dynamics underlie these estimates. We propose that the TEV can be precisely connected to the properties of hippocampal place cells. Finally, we provide the first demonstration that, after learning the location of two food sites, the mice took a shortcut between the sites, demonstrating that they had generated a cognitive map. ”

Note: we added, at the end of the manuscript, the legends for the Shortcut video (Video 1) and the main text figure legends; these are with a larger font and so easier to read. 

Reviewer #1 (Public Review):

Assessment:

This important work advances our understanding of navigation and path integration in mammals by using a clever behavioral paradigm. The paper provides compelling evidence that mice are able to create and use a cognitive map to find "short cuts" in an environment, using only the location of rewards relative to the point of entry to the environment and path integration, and need not rely on visual landmarks.

Thank you.

Summary:

The authors have designed a novel experimental apparatus called the 'Hidden Food Maze (HFM)' and a beautiful suite of behavioral experiments using this apparatus to investigate the interplay between allothetic and idiothetic cues in navigation. The results presented provide a clear demonstration of the central claim of the paper, namely that mice only need a fixed start location and path integration to develop a cognitive map. The experiments and analyses conducted to test the main claim of the paper -- that the animals have formed a cognitive map -- are conclusive. While I think the results are quite interesting and sound, one issue that needs to be addressed is the framing of how landmarks are used (or not), as discussed below, although I believe this will be a straightforward issue for the authors to address.

We have now added detailed discussion on this important point. See below.

Strengths:

The 90-degree rotationally symmetric design and use of 4 distal landmarks and 4 quadrants with their corresponding rotationally equivalent locations (REL) lends itself to teasing apart the influence of path integration and landmark-based navigation in a clever way. The authors use a really complete set of experiments and associated controls to show that mice can use a start location and path integration to develop a cognitive map and generate shortcut routes to new locations.

Weaknesses:

I have two comments. The second comment is perhaps major and would require rephrasing multiple sentences/paragraphs throughout the paper.

(1) The data clearly indicate that in the hidden food maze (HFM) task mice did not use external visual "cue cards" to navigate, as this is clearly shown in the errors mice make when they start trials from a different start location when trained in the static entrance condition. The absence of visual landmark-guided behavior is indeed surprising, given the previous literature showing the use of distal landmarks to navigate and neural correlates of visual landmarks in hippocampal formation. While the authors briefly mention that the mice might not be using distal landmarks because of their pretraining procedure - I think it is worth highlighting this point (about the importance of landmark stability and citing relevant papers) and elaborating on it in greater detail. It is very likely that mice do not use the distal visual landmarks in this task because the pretraining of animals leads to them not identifying them as stable landmarks. For example, if they thought that each time they were introduced to the arena, it was "through the same door", then the landmarks would appear to be in arbitrary locations compared to the last time. In the same way, we as humans wouldn't use clouds or the location of people or other animate objects as trusted navigational beacons. In addition, the animals are introduced to the environment without any extra-maze landmarks that could help them resolve this ambiguity. Previous work (and what we see in our dome experiments) has shown that in environments with 'unreliable' landmarks, place cells are not controlled by landmarks - https://www.sciencedirect.com/science/article/pii/S0028390898000537, https://pubmed.ncbi.nlm.nih.gov/7891125/. This makes it likely that the absence of these distal visual landmarks when the animal first entered the maze ensured that the animal does not 'trust' these visual features as landmarks.

Thank you. We have added many references and discussion exactly on this point including both direct behavioral experiments as well as discussion on the effects of landmark (in)stability of place cell encoding of “place”.  See Page 18 third paragraph.

“An alternate factor might be the lack of reliability of distal spatial cues in predicting the food location. The mice, during pretraining trials, learned to find multiple food locations without landmarks. In the random trials, the continuous change of relative landmark location may lead the mice to not identifying them as “stable landmarks”. This view is supported by behavioral experiments that showed the importance of landmark stability for spatial learning (32-34) and that place cells are not controlled by “unreliable landmarks” (35-38). Control experiments without landmarks (Fig. S6A,B) or in the dark (Fig. S6C-F) confirmed that the mice did not need landmarks for spatial learning of the food location.”

(2) I don't agree with the statement that 'Exogenous cues are not required for learning the food location'. There are many cues that the animal is likely using to help reduce errors in path integration. For example, the start location of the rat could act as a landmark/exogenous cue in the sense of partially correcting path integration errors. The maze has four identical entrances (90-degree rotationally symmetric). Despite this, it is entirely plausible that the animal can correct path integration errors by identifying the correct start entrance for a given trial, and indeed the distance/bearing to the others would also help triangulate one's location. Further, the overall arena geometry could help reduce PI error. For example, with a food source learned to be "near the middle" of the arena, the animal would surely not estimate the position to be near the far wall (and an interesting follow-on experiment would be to have two different-sized, but otherwise nearly identical arenas). As the rat travels away from the start location, small path integration errors are bound to accumulate, these errors could be at least partially corrected based on entrance and distal wall locations. If this process of periodically checking the location of the entrance to correct path integration errors is done every few seconds, path integration would be aided 'exogenously' to build a cognitive map. While the original claim of the paper still stands, i.e. mice can learn the location of a hidden food size when their starting point in the environment remains constant across trials. I would advise rewording portions of the paper, including the discussion throughout the paper that states claims such as "Exogenous cues are not required for learning the food location" to account for the possibility that the start and the overall arena geometry could be used as helpful exogenous cues to correct for path integration errors.

We agree with the referee that our claim was ill-phrased. Surely the behavior of the mouse must be constrained by the arena size to some extent. To minimize potential geometric cues from the arena, we carefully analyzed many preliminary experiments (each with a unique batch of 4 mice) having the target positioned at different locations. We added a paragraph to the section “Further controls” where we explain our choice for the target position. Page 12 last paragraph; Page 13 “Arena geometry” paragraph.

Also, following the suggestion from the reviewer, we probed whether the hole checks accumulated near the center of the arena for the random entrance mice, as a potential sign that some spatial learning is going on. In fact, neither the density of hole checks, nor the distance of the hole checks to the center of the arena change with learning: panel A below shows the probability density of finding a hole check at a given distance from the center of the arena; both trial 1 and trial 14 have very similar profiles. Panel B shows the density of hole checks near (<20cm) and far (>20cm) from the arena’s center.

Author response image 1.

It also doesn’t show any significant differences between trials 1 and 14.

So even though there’s some trend (in panel A, the peak goes from 60cm to a double peak, one at 30cm away from the center, and the other still at 60cm), the distance from the center is still way too large compared to the mouse’s body size and to the average inter-hole distance (<10cm). These panels are now in the Supplementary Figure S8B.

Finally, we enhanced the wording in our claim. We now have a new section entitled: “What cues are required for learning the food location?”. There, we systematically cover all possible cues and how they might be affected by their stability under the perturbation of maze floor rotation. 

Reviewer #2 (Public Review):

Summary:

This manuscript reports interesting findings about the navigational behavior of mice. The authors have dissected this behavior in various components using a sophisticated behavioral maze and statistical analysis of the data.

Strengths:

The results are solid and they support the main conclusions, which will be of considerable value to many scientists.

Thank you.

Weaknesses:

Figure 1: In some trials the mice seem to be doing thigmotaxis, walking along the perimeter of the maze. This is perhaps due to the fear of the open arena. But, these paths along the perimeter would significantly influence all metrics of navigation, e.g. the distance or time to reward.

Perhaps analysis can be done that treats such behavior separately and the factors it out from the paths that are away from the perimeter.

In Page 4, we added a small section entitled: “Pretraining trials”. Our reference was suggested by Reviewer #3 (noted as “Golani” with first author “Fonio”). Our preliminary experiments used naïve mice and they typically took greater than 2 days before they ventured into the arena center and found the single filled hole. This added unacceptable delays and the Pretraining trials greatly diminished the extensive thigmotaxis (not quantified). The “near the walls” trajectories did continue in the first learning trial (Fig. 2A, 3A) but then diminished in subsequent trials. We found no evidence that thigmotaxis (trajectories adjacent to the wall) were a separate category of trajectory. 

Figure 1c: the color axis seems unusual. Red colors indicate less frequently visited regions (less than 25%) and white corresponds to more frequently visited places (>25%)? Why use such a binary measure instead of a graded map as commonly done?

Thank you; you are completely correct. We have completely changed the color coding. 

Some figures use linear scale and others use logarithmic scale. Is there a scientific justification? For example, average latency is on a log scale and average speed is on a linear scale, but both quantify the same behavior. The y-axis in panel 1-I is much wider than the data. Is there a reason for this? Or can the authors zoom into the y-axis so that the reader can discern any pattern?

We use logarithmic scale with the purpose of displaying variables that have a wide range of variation (mainly, distance, latency, and number of hole checks, since it linearly and positively correlates with both distance and latency – see new Fig. S4B,C). For example, Latency goes from hundreds of seconds (trial 1) to just a few seconds (trial 14). Similarly, the total distance goes from hundreds of centimeters (trial 1, sometimes more than 1000cm, see answer about the 10-fold variation of distance below) to just the start-target distance (which is ~100cm). These variables vary over a few orders of magnitude. We display speed in a linear axis because it does not increase for more than one order of magnitude.

Moreover, fitting the wide-ranged data (distance, latency, nchecks) yields smaller error in logscale [i.e., fitting log(y) vs. trial, instead of y vs. trial]. In these cases, the log-scale also helps visualizing how well the data was fitted by the curve. Thus, presenting wide-ranged data in linear scale could be misleading regarding goodness of fit.

We now zoomed into the Y axis scale in Panels I of Fig. 2 and Fig. 3. We kept it in log-scale, but linear Y scale produces Author response image 2 for Figs. 3I and 2I, respectively.

Author response image 2.

Thus, we believe that the loglog-scale in these panels won’t compromise the interpretation of the phenomenon. In fact, the loglog of the static case suggests that the probability of hole checking distance increases according to a power law as the mouse approaches the target (however, we did not check this thoroughly, so we did not include this point in the discussion). Power law behavior is observed in other animals (e.g, ants: DOI: 10.1371/journal.pone.0009621) and is sometimes associated with a stochastic process with memory.

1F shows no significant reduction in distance to reward. Does that mean there is no improvement with experience and all the improvement in the latency is due to increasing running speed with experience?

Correct and in the section “Random Entrance experiments” under “Results” (Page 5) we explicitly note this point.

“We hypothesize that the mice did not significantly reduce their distance travelled (Fig. 2A,B,F) because they had not learned the food location - the decrease in latency (Fig. 2D) was due to its increased running speed and familiarity with non-spatial task parameters.”

Figure 3: The distance traveled was reduced by nearly 10-fold and speed increased by by about 3fold. So, the time to reach the reward should decrease by only 3 fold (t=d/v) but that too reduced by 10fold. How does one reconcile the 3fold difference between the expected and observed values?

The traveled distance is obtained by linearly interpolating the sampled trajectory points. In other words, the software samples a discrete set of positions, for each recorded instant 𝑡. The total distance is 

where is the Euclidean distance between two consecutively sampled points. However, the same result (within a fraction of cm error) can be obtained by integrating the sampled speed over time 𝑣! using the Simpson method

Since Latency varies by 10-fold, it is just expected that, given 𝑑 = 𝑣𝑡, the total distance will also vary by 10-fold (since 𝑣 is constant in each time interval Δ𝑡; replacing 𝑣! in the integral yields the discrete sum above).

The correctness of our kinetic measurements can be simply verified by multiplying the data from the Latency panel with the data from the Velocity panel. If this results in the Distance plot, then there is no discrepancy. 

In Author response image 3, we show the actual measured distance, 𝑑_total_, for both conditions (random and static entrance), calculated with the discrete sum above (black filled circles). 

Author response image 3.

We compare this with two quantities: (a) average speed multiplied by average latency (red squares); and (b) average of the product of speed by latency (blue inverted triangles). The averages are taken over mice. Notice that if the multiplication is taken before the average (as it should be done), then the product 〈𝑣𝑡〉45*( is indistinguishable from the total distance obtained by linear interpolation. Even taking the averages prior to the multiplication (which is physically incorrect, since speed and latency and properties of each individual mouse), yields almost exactly the same result (well within 1 standard deviation).

The only thing to keep in mind here is that the Distance panel in the paper presents the normalized distance according to the target distance to the starting point. This is necessary because in the random entrance experiments, each mouse can go to 1 of 4 possible targets (each of which has a different distance to the starting point).

Figure 4: The reader is confused about the use of a binary color scheme here for the checking behavior: gray for a large amount of checking, and pink for small. But, there is a large ellipse that is gray and there are smaller circles that are also gray, but these two gray areas mean very different things as far as the reader can tell. Is that so? Why not show the entire graded colormap of checking probability instead of such a seemingly arbitrary binary depiction?

Thank you. Our coloring scheme was indeed poorly thought out and we have changed it. Hopefully the reviewer now finds it easier to interpret. The frequency of hole checks is now encoded into only filled circles of varying sizes and shades of pink. Small empty circles represent the arena holes (empty because they have no food); The large transparent gray ellipse is the variance of the unrestricted spatial distribution of hole checks.

Figure 4C: What would explain the large amount of checking behavior at the perimeter? Does that occur predominantly during thigmotaxis?

Yes. As mentioned above, thigmotaxis still occurs in the first trial of training. The point to note is that the hole checking shown in Fig. 4C is over all the mice so that, per mice, it does not appear so overwhelming. 

Was there a correlation between the amount of time spent by the animals in a part of the maze and the amount of reward checking? Previous studies have shown that the two behaviors are often positively correlated, e.g. reference 20 in the manuscript. How does this fit with the path integration hypothesis?

We thank the reviewer for pointing this out. Indeed, the time spent searching & the hole checking behavior are correlated. We added a new panel C to Fig. S4 showing a raw correlation plot between Latency and number of checks. 

Also, in the last paragraph of the “Revealing the mouse estimate of target position from behavior” section under “Results”), we now added a sentence relating the findings in Fig. 4H and 4K (spatial distribution of hole checks, and density of checks near the target, respectively) to note that these findings are in agreement with Fig 3C (time spent searching in each quadrant).

“The mean position of hole checks near (20cm) the target is interpreted as the mouse estimated target (Fig. 4C,D,G,H; green + sign=mean position; green ellipses = covariance of spatial hole check distribution restricted to 20cm near the target). This finding together with the displacement and spatial hole check maps (Figs. 4F and 4H, respectively) corroborates the heatmap of time spent in the target quadrant (Fig. 3C), suggesting a positive correlation between hole checks and time searching (see also Fig. S4C).”

"Scratches and odor trails were eliminated by washing and rotating the maze floor between trials." Can one eliminate scratches by just washing the maze floor? Rotation of the maze floor between trials can make these cues unreliable or variable but will not eliminate them. Ditto for odor cues.

The upper arena floor is rotated between trials so that any scratches will not be stable cues. We clarified this in the Discussion about potential cues. 

See “What cues are required for learning the food location?”

"Possible odor gradient cues were eliminated by experiments where such gradients were prevented with vacuum fans (Fig. S6E)" What tests were done to ensure that these were *eliminated* versus just diminished?

"Probe trials of fully trained mice resulted in trajectories and initial hole checking identical to that of regular trials thereby demonstrating that local odor cues are not essential for spatial learning." As far as the reader can tell, probe trials only eliminated the food odor cues but did not eliminate all other odors. If so, this conclusion can be modified accordingly.

We were most worried about odor cues guiding the mice and as now described at great length, we tried to mitigate this problem in many ways. As the reviewer notes, it is not possible to have absolute certainty that there are no odor cues remaining. The most difficult odor to eliminate was the potential odor gradient emanating from the mouse’s home cage. However, the 2 vacuum fans per cage were very powerful in first evacuating the cage air (150x in 5 minutes) and then drawing air from the arena, through the cage and out its top for the duration of each trial. We believe that we did at least vastly reduce any odor cues and perhaps completely eliminated them.

The interpretation of direction selectivity is a bit tricky. At different places in this manuscript, this is interpreted as a path integration signal that encodes goal location, including the Consync cells. However, studies show that (e.g. Acharya et al. 2016) direction selectivity in virtual reality is comparable to that during natural mazes, despite large differences in vestibular cues and spatial selectivity. How would one reconcile these observations with path integration interpretation?

Thank you. We had not been serious enough in considering the VR studies and their implications for optic flow as a cue for spatial learning. We now have a section (Optic flow cues) in the Discussion that acknowledges the potential role of such cues in spatial learning in our maze. 

However, spatial learning in our maze can also occur in the dark. The next small section (Vestibular and proprioceptive cues) addresses this point. We cannot be certain about the precise cues used by the mouse to effectively learn to locate food in our maze, but it will take further behavioral and electrophysiological studies to go deeper into these questions. 

An extended discussion is found in the sections entitled “What cues are required for learning the food location” and “A fixed start location and self-motion cues are required for spatial learning”.  We may have missed some references or ideas regarding VR maze learning with optic flow signals – the Acharya et al reference was an excellent starting point, and we would be grateful for additional pointers that would improve our discussion of this point.

The manuscript would be improved if the speculations about place cells, grid cells, BTSP, etc. were pared down. I could easily imagine the outcome of these speculations to go the other way and some claims are not supported by data. "We note that the cited experiments were done with virtual movement constrained to 1D and in the presence of landmarks. It remains to be shown whether similar results are obtained in our unconstrained 2D maze and with only self-motion cues available." There are many studies that have measured the evolution of place cells in non- virtual mazes, look up papers from the 1990s. Reference 43 reports such results in a 2D virtual maze.

We understand the reviewer’s concerns with the length of the manuscript. However, both the first and third reviewer did find this extensive section useful. We did not add the many papers on the evolution of place fields in real world mazes simply to prevent even greater expansion of the discussion, but relied on the very thorough review of Knierim and Hamilton instead. 

Reviewer #3 (Public Review):

Summary:

How is it that animals find learned food locations in their daily life? Do they use landmarks to home in on these learned locations or do they learn a path based on self-motion (turn left, take ten steps forward, turn right, etc.). This study carefully examines this question in a well-designed behavioral apparatus. A key finding is that to support the observed behavior in the hidden food arena, mice appear to not use the distal cues that are present in the environment for performing this task. Removal of such cues did not change the learning rate, for example. In a clever analysis of whether the resulting cognitive map based on self-motion cues could allow a mouse to take a shortcut, it was found that indeed they are. The work nicely shows the evolution of the rodent's learning of the task, and the role of active sensing in the targeted reduction of uncertainty of food location proximal to its expected location.

Strengths:

A convincing demonstration that mice can synthesize a cognitive map for the finding of a static reward using body frame-based cues. This shows that the uncertainty of the final target location is resolved by an active sensing process of probing holes proximal to the expected location. Showing that changing the position of entry into the arena rotates the anticipated location of the reward in a manner consistent with failure to use distal cues.

Thank you.

Weaknesses:

The task is low stakes, and thus the failure to use distal cues at most costs the animal a delay in finding the food; this delay is likely unimportant to the animal. Thus, it is unclear whether this result would generalize to a situation where the animal may be under some time pressure, urgency due to food (or water) restriction, or due to predatory threat. In such cases, the use of distal cues to make locating the reward robust to changing start locations may be more likely to be observed.

We have added “Combining trajectory direction and hole check locations yields a Target Estimation Vector” a section summarizing our main hypotheses and this section includes noting exactly this point + including the reference to the excellent MacIver paper on “robot aggression”.

The main point here follows the Knierim and Hamilton review and assumes that learning “heading direction” and “distance from start to food” require different cues and extraction mechanisms.  “Here we follow a review by Knierim and Hamilton (12) suggesting independent mechanisms for extraction of target direction versus target distance information. Averaging across trajectories gave a mean displacement direction, an estimate of the average heading direction as the mouse ran from start to food. The heading direction must be continuously updated as the mice runs towards the food, given that the mean displacement direction remains straight despite the variation across individual trajectories. Heading direction might be extracted from optic flow and/or vestibular system and be encoded by head direction cells. However, the distance from home to food is not encoded by head direction signals.”

And

“We hypothesize that path integration over trajectories is used to estimate the distance from start to food. The stimuli used for integration might include proprioception or acceleration (vestibular) signals as neither depends on visual input. Our conclusion is in accord with a literature survey that concluded that the distance of a target from a start location was based on path integration and separate from the coding of target heading direction (12). Our “in the dark” experiments reveal the minimal stimuli required for spatial learning – an anchoring starting point and directional information based on vestibular and perhaps proprioceptive signals. This view is in accord with recent studies using VR (47, 48). Under more naturalistic conditions, animals have many additional cues available that can be used for flexible control of navigation under time or predation pressure (51).”.

Furthermore, we added panel G do Fig S4, where we show the evolution of the heading angle along the trajectory, plotted as a function of the trials. We see that the mouse only steer towards the target in the last segment of the trajectory, consistent with having the head direction being continuously updated along the path to the food.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

All three reviewers agreed during the consultation that the context in which distal cues are described in the manuscript would benefit significantly from refinement. The distal cues may be made completely useless from an ethological perspective e.g. if they are seen as "moving" relative to the entrance point (i.e. if the animal were to think it were entering the same location), then the cues would appear as unstable in the random entrance. As such, they may be so unlike natural experiences as to be potentially confusing to the animal. Moreover, as reported in some of the reviews, the animals may be using the entrances and boundaries as cues to help refine path integration. The results are still very interesting, but more refinement in the text on the interpretation of cues would greatly improve the manuscript. Thus, we recommend that you revise your manuscript to address the reviews.

Thank you. We agree with this recommendation of the reviewers have greatly expanded our discussion on cue stability as already indicated above. 

Should you choose to revise your manuscript, pleasse ensure the manuscript include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

Done

Lastly, I want to personally apologize for the long delay in editing this manuscript. All three reviews were unfortunately quite delayed, including my own review. I want to thank you for submitting your work to eLife and hope that we can be more efficient in editing your work in the future.

It was a long review process, but we also appreciate that our article was dense and difficult to read. We tried to be comprehensive in our controls and analyses and we appreciate the considerable effort it must have taken to carefully review our paper.

Reviewer #3 (Recommendations For The Authors):

I quite enjoyed this paper and have some suggestions for further improvement.

First, while I appreciate that the format of the journal has Methods at the end, there are some key details that need to be moved forward in the study for proper appreciation of the results. These include:

(1) Location and size of distal cues.

Done

(2) Use of floor washing between mice.  

Done

(3) Use of food across the subfloor to provide some masking of the location of the food reward.

Done

(4) A scale bar on one of the early figures showing the apparatus would be beneficial.

Done for Figure 1 where we also provide arena diameter and area.

(5) Motivational state of the mouse with respect to the food reward (in this case, not food restricted, correct?).

Done

Although we are told the trial where learning is defined to have occurred, we were not given the quantitative criterion operationalizing "learning" - please provide (unless I missed it!).

Thank you.  This question turned out to be of importance and led to more detailed analyses and related Discussion. We therefore answer in depth.

We now realize that learning the distance to food versus learning the direction to food must be analyzed separately.

On Page 5 second paragraph we provide a definition of “learning distance to food”.

“Fitting the function dtotal \= B*exp(-Trial/K) reveals the characteristic timescale of learning, K, in trial units (Fig. 2F). We obtained K= 26±24 giving a coefficient of variation (CV) of 0.92. The mean, K=26, is therefore very uncertain and far greater than the actual number of trials. Thus, we hypothesize that the mice did not significantly reduce their distance travelled (Fig. 2A,B,F) because they had not learned the food location – the decrease in latency (Fig. 2D) was due to its increased running speed and familiarity with non-spatial task parameters. ”

On Page 7 second paragraph the same analysis gives:

“Now the fitting of the function dtotal\=B exp(-Trial/K) yielded K\=5.6±0.5 with a CV = 0.08; the mean is therefore a reliable estimate of total distance travelled. We interpret this to indicate that it takes a minimum number of K= 6 trials for learning the distance to the target (see also Fig. S4D,E,F,G).

Learning is still not complete because it takes 14 trials before the trajectories become near optimal.”

Learning of distance to food is evident by Trial 6 but is not complete.

On Page 9 third paragraph we give a very precise answer to time taken to learn the direction from start to food. This was already very clear from Fig. 4I but we had missed the significance of this result. 

“We compared the deviation between the TEV and the true target vector (that points from start directly to the food hole; Fig. 4I). While the random entrance mice had a persistent deviation between TEV and target of more than 70o, the static entrance mice were able to learn the direction of the target almost perfectly by trial 6 (TEV-target deviation in first trial mean±SD = 57.27o ± 41.61o; last trial mean±SD = 5.16o ± 0.20o; P=0.0166). A minimum of 6 trials is sufficient for learning both the direction and distance to food (Fig. 4I) (Fig. 3F) (see Discussion). The kinetics of learning direction to food are clearly different from learning distance to food since the direction to food remains stable after Trial 6 while the distance to food continues to approach the optimal value.”

Learning the direction from start to food is completely learned by Trial 6. 

These analyses led to an addition to the Discussion on Page 20 (following the Heading).

“Here we follow a review by Knierim and Hamilton (12) that hypothesized independent mechanisms for extraction of target direction versus target distance information. Our data strongly supports their hypothesis. Target direction is nearly perfectly estimated at trial 6 (Fig. 4I and Results). The deviation of the TEV from the start to food vector is rapidly reduced to its minimal value (5.16o) and with minimal variability (SD=0.20o). Learning the distance from start to food is also evident at trial 6 but only reaches an asymptotic near optimal value at trial 14 (Fig. 3F). The learning dynamics are therefore very different for target direction versus target distance. As noted below, the food direction is likely estimated from the activity of head direction cells. The neural mechanisms by which distance from start to food is estimated are not known (but see (49)).”

We believe that this small addition summarizes the complicated answer to the reviewer’s question and is helpful in better connecting the Knierim and Hamilton paper to our data. However, if the reviewers and editors feel that we have gone too far or that this discussion is not clear, we can remove or alter the extra sentences as per any comments. 

Reference #49 is to a review paper on spatial learning in weakly electric fish in the dark (https://doi.org/10.1016/j.conb.2021.07.002). The review summarizes data on a neural “time stamp” mechanism for estimating distance from start to food. In this review article, we explicitly hypothesized that rodents might utilize such a time stamp mechanism for finding food. We did not include this in the discussion because it was too distracting and would likely confuse readers but put in the reference in case some readers did want to access the “time stamp” hypothesis for spatial learning in the dark. 

Second, the discussion was thoughtful and rich. I particularly enjoyed the segment describing the likely computations of the hippocampus. There are a few thoughts I have for the authors to think about that might be useful to potentially add to the discussion:

"The remaining one, mouse 34, went from B to the start location and then, to A."

This out-and-back pattern has been seen in the literature, such as multiple papers by Golani (here's one: https://www.pnas.org/doi/full/10.1073/pnas.0812513106). Would the authors speculate, given their suggested algorithm, what the significance of out and back may be? Is there something about the cell's encoding of direction and distance that requires a return to the start location, and would this be different if representation is based on self-motion versus based on distal cues in an allocentric representation?

We do discuss this for pretraining trials but have no idea what this mouse is doing in this case.

In a low-stakes task environment, for an animal that has a low acuity visual system, where the penalty for not using distal cues is at most some additional (likely enriching in itself to these mice who live a fairly unenriched life in small cages) search/learning/exploration time, perhaps it is not so surprising that body-frame cues are used. Considering the ethology of the animal, if it had multiple exits of an underground burrow, it might need to use distal cues to avoid confusion. The scenario you provide to the animal is essentially a deceptive one where it has no way of telling it is coming out to the arena from a different burrow hole, modulo some small landmarks on an otherwise uniform cylinder of space. This might be asking too much of an animal where the space it would enter normally would not be a uniform cylinder.

What happens with a higher-stakes case? This is clearly a different study, but you may find some recent work with a mobile predatory robot of interest (https://www.sciencedirect.com/science/article/pii/S2211124723016820). Visual cues are crucial in the avoidance of threats in this case. Re-routing, as shown by multiple videos of that study, is after a brief pause, and seemingly takes into account the likely future position of the threat.

Done. A fascinating paper that illustrates the unexpected “high level” behavior a rodent is capable of when placed in more naturalistic situations. I think our “two food location” experiments are along the same direction – unexpected rich behavior when the mouse are challenged.

Connected to the low-stakes vs high-stakes point, it might be nice for the paper to discuss situations in which cognitive-map-based spatial problem solutions make sense versus not.

Here is an example of such a discussion, around page 496:

https://www.dropbox.com/scl/fi/ayoo5w4jgnkblgfu7mpad/MacI09a_situated_cog.pdf?

rlkey=2qhh89ii7jbkavt6ivevarvdk&dl=0.

Right a very relevant discussion by MacIver. However, when I tried to write it in it took nearly half a page of dense writing to connect to the themes of our article. I figured that the already long discussion will try the patience of most readers and so decided to not include this extra discussion.

Minor points/ queries

Why the increase in sample density at about the 1/4 radius of arena distance? Static, trial 14, Figure 3I, shown also maybe Figure 4 H.

We were also puzzled when this occurred but have no explanation. And there are, in our figures, many other examples of the mice hole checking near their exit site. See next answer.

Why was the hole proximal to start so often probed in 7B?

We were also puzzled when this occurred but have no explanation.

Check Video 1 to exactly see this behavior. The mouse exits its home and immediately checks a nearby hole. It proceeds to Site B (empty) and then Site A (empty) with many hole checks along the way. After leaving Site A, the mouse proceeds to the wall located far from an entrance and does another hole check. The near the wall holes that are checked are in no way remarkable: a) they have never contained food; b) they are rotated between trials, and we wash the floor carefully, so they do not “smell” any particular hole; c) the food on the lower level floor is in no way “clumped” under that hole, etc.

We have discussed this phenomenon quite a lot and LM was able to come up with only one hypothesis for this behavior. In analogy to the electric fish work (responses of diencephalic neurons to “leaving or encountering a landmark”), the “near the entrance” hole check might be an active sensing probe to “time stamp” the exit from home while finding food would “time stamp” the end of a successful trajectory. Path integration between time stamps would then provide the estimate for time/distance from start to food – exactly our hypothesis for weakly electric fish spatial learning in the dark. This hypothesis is exceedingly speculative and so we do not want to include it.  

Normally I would cite a line number. Since I do not see line numbers, I will leave it to you to do a search:

"A than the expected by chance" -> "than expected"

Done. I apologize for the lack of line numbers. I have, so far, been unable to get Word to confine line numbers to selected text and not run over onto the Figure Legends. I have put in page numbers and hope this helps.

RW, VR, MWM, etc - please expand the acronym on first use.

Done

It might be interesting to see differences in demand/reliance on active sensing in the individuals who learn the task less well than the animals who learn the task well. If the point is to expunge uncertainty, then does the need for such expunging increase with the poverty of internal representation resolution / fewer decimal places on the internal TEV calculation?

We do have variation in the mice learning time but the numbers are not sufficient for this interesting extension. This is just one of many follow up studies we hope to carry out.

eLife assessment

This study describes the formation of a penetration ring in the rice blast fungus Magnaporthe oryzae during host cell invasion. The work provides useful insights into how the penetration ring facilitates the transition of penetration pegs into invasive hyphae, which leads to a better understanding of plant-pathogen interactions. However, the evidence supporting the function of this novel infection structure remains incomplete and further work is needed to help clarify the exact role of the penetration ring in the infection process.

Reviewer #1 (Public review):

Summary:

This study focuses on characterizing a previously identified gene, encoding the secreted protein Ppe1, that may play a role in rice infection by the blast fungus Magnaporthe oryzae. Magnaporthe oryzae is a hemibiotrophic fungus that infects living host cells before causing disease. Infection begins with the development of a specialized infection cell, the appressorium, on the host leaf surface. The appressorium generates enormous internal turgor that acts on a thin penetration peg at the appressorial base, forcing it through the leaf cuticle. Once through this barrier, the peg elaborates into bulbous invasive hyphae that colonizes the first infected cell before moving to neighboring cells via plasmodesmata. During this initial biotrophic growth stage, invasive hyphae invaginate the host plasma membrane, which surrounds growing hyphae as the extra-invasive hyphae membrane (EIHM). To avoid detection, the fungus secretes apoplastic effectors into the EIHM matrix via the conventional ER-Golgi secretion pathway. The fungus also forms a plant-derived structure called the biotrophic interfacial complex (BIC) that receives cytoplasmic effectors through an unconventional secretion route before they are delivered into the host cell. Together, these secreted effector proteins act to evade or suppress host innate immune responses. Here the authors contribute to our understanding of M. oryzae infection biology by showing how Ppe1, which localizes to both the appressorial penetration peg and to the appressorial-like transpressoria associated with invasive hyphal movements into adjacent cells, maximizes host cell penetration and disease development and is thus a novel contributor to rice blast disease.

Strengths:

A major goal of M. oryzae research is to understand how the fungus causes disease, either by determining the physiological underpinnings of the fungal infection cycle or by identifying effectors and their host targets. Such new knowledge may point the way to novel mitigation strategies. Here, the authors make an interesting discovery that bridges both fungal physiology and effector biology research by showing how a secreted protein Ppe1, initially considered an effector with potential host targets, associates with its own penetration peg (and transpressoria) to facilitate host invasion. In a previous study, the authors had identified a small family of small secreted proteins that may function as effectors. Here they suggest Ppe1 (and, later in the manuscript, Ppe2/3/5) localizes outside the penetration peg when appressoria develops on surfaces that permit penetration, but not on artificial hard surfaces that prevent peg penetration. Deleting the PPE1 gene reduced (although did not abolish) penetration, and a fraction of those that penetrated developed invasive hyphae that were reduced in growth compared to WT. Using fluorescent markers, the authors show that Ppe1 forms a ring underneath appressoria, likely where the peg emerges, which remained after invasive hyphae had developed. The ring structure is smaller than the width of the appressorium and also lies within the septin ring known to form during peg development. This so-called penetration ring also formed at the transpressorial penetration point as invasive hyphae moved to adjacent cells. This structure is novel, and required for optimum penetration during infection. Furthermore, Ppe1, which carries a functional signal peptide, may form on the periphery of the peg, together suggesting it is secreted and associated with the peg to facilitate penetration. Staining with aniline blue also suggests Ppe1 is outside the peg. Together, the strength of the work lies in identifying a novel appressorial penetration ring structure required for full virulence.

Weaknesses:

The main weakness of the paper is that, although Ppe1 is associated with the peg and optimizes penetration, the function of Ppe1 is not known. The work starts off considering Ppe1 a secreted effector, then a facilitator of penetration by associating with the peg, but what role it plays here is only often speculated about. For example, the authors consider at various times that it may have a structural role, a signaling role orchestrating invasive hyphae development, or a tethering role between the peg and the invaginated host plasma membrane (called throughout the host cytoplasmic membrane, a novel term that is not explained). However, more effort should be expended to determine which of these alternative roles is the most likely. Otherwise, as it stands, the paper describes an interesting phenomenon (the appressorial ring) but provides no understanding of its function.

The inability to nail down the function of Ppe1 likely stems from two underlying assumptions with weak support. Firstly, the authors assume that Ppe1 is secreted and associated with the peg to form a penetration ring between the plant cell wall and cytoplasm membrane. However, the authors do not demonstrate it is secreted (for instance by blocking Ppe1 secretion and its association with the peg using brefeldin A). Also, they do not sufficiently show that Ppe1 localizes on the periphery of the peg. This is because confocal microscopy is not powerful enough to see the peg. The association they are seeing (for example in Figure 4) shows localization to the bottom of the appressorium and around the primary hyphae, but the peg cannot be seen. Here, the authors will need to use SEM, perhaps in conjunction with gold labeling of Ppe1, to show it is associating with the peg and, indeed, is external to the peg (rather than internal, as a structural role in peg rigidity might predict). It would also be interesting to repeat the microscopy in Figure 4C but at much earlier time points, just as the peg is penetrating but before invasive hyphae have developed - Where is Ppe1 then? Finally, the authors speculate, but do not show, that Ppe1 anchors penetration pegs on the plant cytoplasm membrane. Doing so may require FM4-64 staining, as used in Figure 2 of Kankanala et al, 2007 (DOI: 10.1105/tpc.106.046300), to show connections between Ppe1 and host membranes. Note that the authors also do not show that the penetration ring is a platform for effector delivery, as speculated in the Discussion.

Secondly, the authors assume Ppe1 is required for host infection due to its association with the peg. However, its role in infection is minor. The majority of appressoria produced by the mutant strain penetrate host cells and elaborate invasive hyphae, and lesion sizes are only marginally reduced compared to WT (in fact, the lesion density of the 70-15 WT strain itself seems reduced compared to what would be expected from this strain). The authors did not analyze the lesions for spores to confirm that the mutant strains were non-pathogenic (non-pathogenic mutants sometimes form small pinprick-like lesions that do not sporulate). Thus, the pathogenicity phenotype of the knockout mutant is weak, which could contribute to the inability to accurately define the molecular and cellular function of Ppe1.

In summary, it is important that the role of Ppe1 in infection be determined.

Reviewer #2 (Public review):

The article focuses on the study of Magnaporthe oryzae, the fungal pathogen responsible for rice blast disease, which poses a significant threat to global food security. The research delves into the infection mechanisms of the pathogen, particularly the role of penetration pegs and the formation of a penetration ring in the invasion process. The study highlights the persistent localization of Ppe1 and its homologs to the penetration ring, suggesting its function as a structural feature that facilitates the transition of penetration pegs into invasive hyphae. The article provides a thorough examination of the infection process of M. oryzae, from the attachment of conidia to the development of appressoria and the formation of invasive hyphae. The discovery of the penetration ring as a structural element that aids in the invasion process is a significant contribution to the understanding of plant-pathogen interactions. The experimental methods are well-documented, allowing for reproducibility and validation of the results.

eLife assessment

This valuable work discusses the phylogenetic conservation of the hippocampal region and primary sensory cortical regions in mammalian species. The authors propose that species-specific differences in behavior and mnemonic functions may be due to differences in cortico-hippocampal connectivity patterns. However, the manuscript, in its present form, is speculative, and the strength of evidence for this proposition is incomplete.

Reviewer #1 (Public Review):

The paper itself has a reasonable aim, to compare the inputs to the hippocampus from cortical regions across mammals. But for some reason, the conclusions that are reached are very limited. We know for example that the main laboratory rodents investigated, rats and mice, are nocturnal, live in underground tunnels, and have a very wide field of view with no fovea. In contrast, primates have a highly developed cortical system for vision and a fovea, and so have very different capabilities to rodents, as they have an ability to identify people or objects at a distance, and to remember where they have been seen. Despite this major difference in the visual cortical processing in these different mammals, somehow important points are missed in this paper about how the cortical processing is organised in these different mammals, and how this is reflected in the anatomy.

Reviewer #2 (Public Review):

Summary:

The manuscript emphasizes a phylogenetic conservation of the hippocampal region and primary sensory cortical regions in mammalian species. The authors then propose that the evident species-specific differences in behavior and memory-related functions may be due to differences in type and amount of cortico-hippocampal connectivity.

Strengths:

The authors are well-established researchers with a long history of excellent results and publications. The question (co-influence of cortical and hippocampal connections) is potentially interesting.

Weaknesses:

The treatment is very broad and macro scale, ignoring the likelihood that hippocampal-cortical connectivity and behavioral outcomes result from multiple differences at a more micro-scale. The designated "mammalian" sample is also broad. Thus, it can appear incomplete as a sample, and incompletely discussed.

eLife assessment

This research investigates the precision of numerosity perception in two different tasks and concludes that human performance aligns with an efficient coding model optimized for current environmental statistics and task goals. The findings may have important implications for our understanding of numerosity perception as well as the ongoing debate on different efficient coding models. However, the evidence presented in the paper to support the conclusion is still incomplete and could be strengthened by further modeling analysis or experimental data that can address potential confounds.

Reviewer #1 (Public review):

Summary:

The "number sense" refers to an imprecise and noisy representation of number. Many researchers propose that the number sense confers a fixed (exogenous) subjective representation of number that adheres to scalar variability, whereby the variance of the representation of number is linear in the number.

This manuscript investigates whether the representation of number is fixed, as usually assumed in the literature, or whether it is endogenous. The two dimensions on which the authors investigate this endogeneity are the subject's prior beliefs about stimuli values and the task objective. Using two experimental tasks, the authors collect data that are shown to violate scalar variability and are instead consistent with a model of optimal encoding and decoding, where the encoding phase depends endogenously on prior and task objectives. I believe the paper asks a critically important question. The literature in cognitive science, psychology, and increasingly in economics, has provided growing empirical evidence of decision-making consistent with efficient coding. However, the precise model mechanics can differ substantially across studies. This point was made forcefully in a paper by Ma and Woodford (2020, Behavioral & Brain Sciences), who argue that different researchers make different assumptions about the objective function and resource constraints across efficient coding models, leading to a proliferation of different models with ad-hoc assumptions. Thus, the possibility that optimal coding depends endogenously on the prior and the objective of the task, opens the door to a more parsimonious framework in which assumptions of the model can be constrained by environmental features. Along these lines, one of the authors' conclusions is that the degree of variability in subjective responses increases sublinearly in the width of the prior. And importantly, the degree of this sublinearity differs across the two tasks, in a manner that is consistent with a unified efficient coding model.

Comments:

(1) Modeling and implementation of estimation task

The biggest concern I have with the paper is about the experimental implementation and theoretical account of the estimation task. The salient features of the experimental data (Figure 1C) are that the standard deviations of subjects' estimated quantities are hump-shaped in the true stimulus x and that the standard deviation, conditional on the true stimulus x, is increasing in prior width. The authors attribute these features to a Bayesian encoding and decoding model in which the internal representation of the quantity is noisy, and the degree of noise depends on the prior - as in models of efficient coding (Wei and Stocker 2015 Nature Neuro; Bhui and Gershman 2018 Psych Review; Hahn and Wei 2024 Nature Neuro).

The concern I have is about the final "step" in the model, where the authors assume there is an additional layer of motor noise in selecting the response. The authors posit that the subject's selection of the response is drawn from a Gaussian with a mean set to the optimally decoded estimate x*(r), and variance set to a free parameter sigma_0^2. However, the authors also assume that the Gaussian distribution is "truncated to the prior range." This truncation is a nontrivial assumption, and I believe that on its own, it can explain many features of the data.

To see this, assume that there is no noise in the internal representation of x, there is only motor noise. This corresponds to a special case of the authors' model in which υ is set to 0. The model then reduces to a simple account in which responses are drawn from a Gaussian distribution centered at the true value of x, but with asymmetric noise due to the truncation. I simulated such a model with sigma_0=7. The resulting standard deviations of responses for each value of x (based on 1000 draws for each value of x), across the three different priors, reproduce the salient patterns of the standard deviation in Figure 1C: i) within each condition, the standard deviation is hump-shaped and peaks at x=60 and ii) conditional on x, standard deviation increases in prior width. The takeaway is that this simple model with only truncated motor noise - and without any noisy or efficient coding of internal representations - provides an alternative channel through which the prior affects behavior.

Of course, this does not imply that subjects' coding is not described by the efficient encoding and decoding model posited by the authors. However, it does suggest an important alternative mechanism for the authors' theoretical results in the estimation task. Moreover, some of the quantitative conclusions about the differences in behavior with the discrimination task would be greatly affected by the assumption of truncated motor noise.

Turning to the experiment, a basic question is whether such a truncation was actually implemented in the design. That is, was the range of the slider bar set to the range of the prior? (The methods section states that the size on the screen of the slider was proportional to the prior width, but it was unclear whether the bounds of the slider bar changed with the prior). If the slider bar range did depend on the prior, then it becomes difficult to interpret the data. If not, then perhaps one can perform analyses to understand how much the motor noise is responsible for the dependence of the standard deviation on both x and the prior width. Indeed, the authors emphasize that their model is best fit at α=0.48, which would seem to imply that the best fitting value of υ is strictly positive. However, it would be important to clarify whether the estimation procedure allowed for υ=0, or whether this noise parameter was constrained to be positive (i.e., clarify whether the estimation assumed noisy and efficient coding of internal representations).

(2) Differences across tasks

A main takeaway from the paper is that optimal coding depends on the expected reward function in each task. This is the explanation for why the degree of sublinearity between standard deviation and prior width changes across the estimation and discrimination task. But besides the two different reward functions, there are also other differences across the two tasks. For example, the estimation task involves a single array of dots, whereas the discrimination task involves a pair of sequences of Arabic numerals. Related to the discussion above, in the estimation task the response scale is continuous whereas in the discrimination task, responses are binary. Is it possible that these other differences in the task could contribute to the observed different degrees of sublinearity? It is likely beyond the scope of the paper to incorporate these differences into the model, but such differences across the two tasks should be discussed as potential drivers of differences in observed behavior.

If it becomes too difficult to interpret the data from the estimation task due to the slider bar varying with the prior range, then which of the paper's conclusions would still follow when restricting the analysis to the discrimination task?

(3) Placement literature

One closely related experiment to the discrimination task in the current paper can be found in Frydman and Jin (2022 Quarterly Journal of Economics). Those authors also experimentally vary the width of a uniform prior in a discrimination task using Arabic numerals, in order to test principles of efficient coding. Consistent with the current findings, Frydman and Jin find that subjects exhibit greater precision when making judgments about numbers drawn from a narrower distribution. However, what the current manuscript does is it goes beyond Frydman and Jin by modeling and experimentally varying task objectives to understand and test the effects on optimal coding. This contribution should be highlighted and contrasted against the earlier experimental work of Frydman and Jin to better articulate the novelty of the current manuscript.

Reviewer #2 (Public review):

Summary:

This paper provides an ingenious experimental test of an efficient coding objective based on optimization as a task success. The key idea is that different tasks (estimation vs discrimination) will, under the proposed model, lead to a different scaling between the encoding precision and the width of the prior distribution. Empirical evidence in two tasks involving number perception supports this idea.

Strengths:

- The paper provides an elegant test of a prediction made by a certain class of efficient coding models previously investigated theoretically by the authors.

The results in experiments and modeling suggest that competing efficient coding models, optimizing mutual information alone, may be incomplete by missing the role of the task.

Weaknesses:

- The claims would be more strongly validated if data were present at more than two widths in the discrimination experiment.

- A very strong prediction of the model -- which determines encoding entirely from prior and task -- is that Fisher Information is uniform throughout the range, strongly at odds with the traditional assumption of imprecision increasing with the numerosity (Weber/Fechner law). This prediction should be checked against the data collected. It may not be trivial to determine this in the Estimation experiment, but should be feasible in the Discrimination experiment in the Wide condition: Is there really no difference in discriminability at numbers close to 10 vs numbers close to 90? Figure 2 collapses over those, so it's not evident whether such a difference holds or not. I'd have loved to look into this in reviewing, but the authors have not yet made their data publicly available - I strongly encourage them to do so.

Importantly, the inverse u-shaped pattern in Figure 1 is itself compatible with a Weber's-law-based encoding, as shown by simulation in Figure 5d in Hahn&Wei [1]. This suggests a potential competing variant account, in apparent qualitative agreement with the findings reported: the encoding is compatible with Fisher's law, and only a single scalar, the magnitude of sensory noise, is optimized for the task for the loss function (3). As this account would be substantially more in line with traditional accounts of numerosity perception - while still exhibiting task-dependence of encoding as proposed by the authors - it would be worth investigating if it can be ruled out based on the data gathered for this paper.

References:

[1] Hahn & Wei, A unifying theory explains seemingly contradictory biases in perceptual estimation, Nature Neuroscience 2024

Reviewer #3 (Public review):

Summary:

This work demonstrates that people's imprecision in numeric perception varies with the stimulus context and task goal. By measuring imprecision across different widths of uniform prior distributions in estimation and discrimination tasks, the authors find that imprecision changes sublinearly with prior width, challenging previous range normalization models. They further show that these changes align with the efficient encoding model, where decision-makers balance expected rewards and encoding costs optimally.

Strengths:

The experimental design is straightforward, controlling the mean of the number distribution while varying the prior width. By assessing estimation errors and discrimination accuracy, the authors effectively highlight how imprecision adjusts across conditions.

The model's predictions align well with the data, with the exponential terms (1/2 and 3/4) of imprecision changes matching the empirical results impressively.

Weaknesses:

Some details in the model section are unclear. Specifically, I'm puzzled by the Wiener process assumption where r∣x∼N(m(x)T,s^2T). Does this imply that both the representation of number x and the noise are nearly zero at the beginning, increasing as observation time progresses? This seems counterintuitive, and a clearer explanation would be helpful.

The authors explore range normalization models with Gaussian representation, but another common approach is the logarithmic representation (Barretto-García et al., 2023; Khaw et al., 2021). Could the logarithmic representation similarly lead to sublinearity in noise and distribution width?

Additionally, Heng et al. (2020) found that subjects did not alter their encoding strategy across different task goals, which seems inconsistent with the fully adaptive representation proposed here. I didn't find the analysis of participants' temporal dynamics of adaptation. The behavioral results in the manuscript seem to imply that the subjects adopted different coding schemes in a very short period of time. Yet in previous studies of adaptation, experimental results seem to be more supportive of a partial adaptive behavior (Bujold et al., 2021; Heng et al., 2020), which might balance experimental and real-world prior distributions. Analyzing temporal dynamics might provide more insight. Noting that the authors informed subjects about the shape of the prior distribution before the experiment, do the results in this manuscript suggest a top-down rapid modulation of number representation?

Barretto-García, M., De Hollander, G., Grueschow, M., Polanía, R., Woodford, M., & Ruff, C. C. (2023). Individual risk attitudes arise from noise in neurocognitive magnitude representations. Nature Human Behaviour, 7(9), 1551-1567. https://doi.org/10.1038/s41562-023-01643-4

Bujold, P. M., Ferrari-Toniolo, S., & Schultz, W. (2021). Adaptation of utility functions to reward distribution in rhesus monkeys. Cognition, 214, 104764. https://doi.org/10.1016/j.cognition.2021.104764

Heng, J. A., Woodford, M., & Polania, R. (2020). Efficient sampling and noisy decisions. eLife, 9, e54962. https://doi.org/10.7554/eLife.54962

Khaw, M. W., Li, Z., & Woodford, M. (2021). Cognitive Imprecision and Small-Stakes Risk Aversion. The Review of Economic Studies, 88(4), 1979-2013. https://doi.org/10.1093/restud/rdaa044

eLife assessment

This important study reports that slow fluctuations of serotonin release during wakefulness and non-REM sleep correspond to periods of either increased arousal or enhanced offline information processing. The evidence supporting the claim is convincing, and the methodology used in the study will benefit many in the field. The work will be of interest to neuroscientists working on sleep, memory, and neuromodulation.

Reviewer #1 (Public review):

Summary:

In this work, the authors recorded the dynamics of the 5-HT with fiber photometry from CA1 in one hemisphere and LFP from CA1 in the other hemisphere. They observed an ultra-slow oscillation in the 5-HT signal during both wakefulness and NREM sleep. The authors have studied different phases of the ultra-slow oscillation to examine the potential difference in the occurrence of some behavioral state-related physiological phenomena (hippocampal ripples, EMG, and inter-area coherence).

Strengths:

The relation between the falling/rising phase of the ultra-slow oscillation and the ripples is sufficiently shown. There are some minor concerns about the observed relations that should be addressed with some further analysis.

Systematic observations have started to establish a strong relation between the dynamics of neural activity across the brain and measures of behavioral arousal. Such relations span a wide range of temporal scales that are heavily inter-related. Ultra-slow time scales are specifically understudied due to technical limitations and neuromodulatory systems are the strongest mechanistic candidates for controlling/modulating the neural dynamics at these time scales. The hypothesis of the relation between a specific time scale and one certain neuromodulator (5-HT in this manuscript) could have a significant impact on the understanding of the hierarchy in the temporal scales of neural activity.

Weaknesses:

One major caveat of the study is that different neuromodulators are strongly correlated across all time scales and related to this, the authors need to discuss this point further and provide more evidence from the literature (if any) that suggests similar ultra-slow oscillations are weaker or lack from similar signals recorded for other neuromodulators such as Ach and NA.

A major question that has been left out from the study and discussion is how the same level of serotonin before and after the peak could be differentially related to the opposite observed phenomenon. What are the possible parallel mechanisms for distinguishing between the rising and falling phases? Any neurophysiological evidence for sensing the direction of change in serotonin concentration (or any other neuromodulator), and is there any physiological functionality for such mechanisms?

Reviewer #2 (Public review):

Summary:

In their study, Cooper et al. investigated the spontaneous fluctuations in extracellular 5-HT release in the CA1 region of the hippocampus using GRAB5-HT3.0. Their findings revealed the presence of ultra-low frequency (less than 0.05 Hz) oscillations in 5-HT levels during both NREM sleep and wakefulness. The phase of these 5-HT oscillations was found to be related to the timing of hippocampal ripples, microarousals, electromyogram (EMG) activity, and hippocampal-cortical coherence. In particular, ripples were observed to occur with greater frequency during the descending phase of 5-HT oscillations, and stronger ripples were noted to occur in proximity to the 5-HT peak during NREM. Microarousal and EMG peaks occurred with greater frequency during the ascending phase of 5-HT oscillations. Additionally, the strongest coherence between the hippocampus and cortex was observed during the ascending phase of 5-HT oscillations. These patterns were observed in both NREM sleep and the awake state, with a greater prevalence in NREM. The authors posit that 5-HT oscillations may temporally segregate internal processing (e.g., memory consolidation) and responsiveness to external stimuli in the brain.

Strengths:

The findings of this research are novel and intriguing. Slow brain oscillations lasting tens of seconds have been suggested to exist, but to my knowledge they have never been analyzed in such a clear way. Furthermore, although it is likely that ultra-slow neuromodulator oscillations exist, this is the first report of such oscillations, and the greatest strength of this study is that it has clarified this phenomenon both statistically and phenomenologically.

Weaknesses:

As with any paper, this one has some limitations. While there is no particular need to pursue them, I will describe ten of them below, including future directions:

(1) Contralateral recordings: 5-HT levels and electrophysiological recordings were obtained from opposite hemispheres due to technical limitations. Ipsilateral simultaneous recordings may show more direct relationships.

(2) Sample size: The number of mice used in the experiments is relatively small (n=6). Validation with a larger sample size would be desirable.

(3) Lack of causality: The observed associations show correlations, not direct causal relationships, between 5-HT oscillations and neural activity patterns.

(4) Limited behavioral states: The study focuses primarily on sleep and quiet wakefulness. Investigation of 5-HT oscillations during a wider range of behavioral states (e.g., exploratory behavior, learning tasks) may provide a more complete understanding.

(5) Generalizability to other brain regions: The study focuses on the CA1 region of the hippocampus. It's unclear whether similar 5-HT oscillation patterns exist in other brain regions.

(6) Long-term effects not assessed: Long-term effects of ultra-low 5-HT oscillations (e.g., on memory consolidation or learning) were not assessed.

(7) Possible species differences: It's uncertain whether the findings in mice apply to other mammals, including humans.

(8) Technical limitations: The temporal resolution and sensitivity of the GRAB5-HT3.0 sensor may not capture faster 5-HT dynamics.

(9) Interactions with other neuromodulators: The study does not explore interactions with other neuromodulators (e.g., norepinephrine, acetylcholine) or their potential ultraslow oscillations.

(10) Limited exploration of functional significance: While the study suggests a potential role for 5-HT oscillations in memory consolidation and arousal, direct tests of these functional implications are not included.

Reviewer #3 (Public review):

Summary:

The activity of serotonin (5-HT) releasing neurons as well as 5-HT levels in brain structures targeted by serotonergic axons are known to fluctuate substantially across the animal's sleep/wake cycle, with high 5-HT levels during wakefulness (WAKE), intermediate levels during non-REM sleep (NREM) and very low levels during REM sleep. Recent studies have shown that during NREM, the activity of 5-HT neurons in raphe nuclei oscillates at very low frequencies (0.01 - 0.05 Hz) and this ultraslow oscillation is negatively coupled to broadband EEG power. However, how exactly this 5-HT oscillation affects neural activity in downstream structures is unclear.

The present study addresses this gap by replicating the observation of the ultraslow oscillation in the 5-HT system, and further observing that hippocampal sharp wave-ripples (SWRs), biomarkers of offline memory processing, occur preferentially in barrages on the falling phase of the 5-HT oscillation during both wakefulness and NREM sleep. In contrast, the raising phase of the 5-HT oscillation is associated with microarousals during NREM and increased muscular activity during WAKE. Finally, the raising 5-HT phase was also found to be associated with increased synchrony between the hippocampus and neocortex. Overall, the study constitutes a valuable contribution to the field by reporting a close association between raising 5-HT and arousal, as well as between falling 5-HT and offline memory processes.

Strengths:

The study makes compelling use of the state-of-the-art methodology to address its aims: the genetically encoded 5-HT sensor used in the study is ideal for capturing the ultraslow 5-HT dynamics and the novel detection method for SWRs outperforms current state-of-the-art algorithms and will be useful to many scientists in the field. Explicit validation of both of these methods is a particular strength of this study.

The analytical methods used in the article are appropriate and are convincingly applied, the use of a general linear mixed model for statistical analysis is a particularly welcome choice as it guards against pseudoreplication while preserving statistical power.

Overall, the manuscript makes a strong case for distinct sub-states across WAKE and NREM, associated with different phases of the 5-HT oscillation.

Weaknesses:

All of the evidence presented in the study is correlational. While the study mostly avoids claims of causality, it would still benefit from establishing whether the 5-HT oscillation has a direct role in the modulation of SWR rate via e.g. optogenetic activation/inactivation of 5-HT axons. As it stands, the possibility that 5-HT levels and SWRs are modulated by the same upstream mechanism cannot be excluded.

eLife assessment

This valuable study aims to understand the function of ProSAP-interacting protein 1 (Prosapip1) in the brain. Using a conditional Prosapip1 KO mouse (floxed prosapip1 crossed with Syn1-Cre line), the authors performed analysis including protein biochemistry, synaptic physiology, and behavioral learning. Solid evidence from this study supports a role of Prosapip 1 in synaptic protein composition, synaptic NMDA responses, LTP, and spatial memory. Addressing some of the technical and methodological weaknesses may further improve the significance of the study.

Reviewer #1 (Public review):

Summary:

In the manuscript by Hoisington et al., the authors utilized a novel conditional neuronal prosap2-interacting protein 1 (Prosapip1) knockout mouse to delineate the effects of both neuronal and dorsal hippocampal (dHP)-specific knockout of Prosapip1 impacts biochemical and electrophysiological neuroadaptations within the dHP that may mediate behaviors associated with this brain region.

Strengths:

(1) Methodological Strengths

a. The generation and use of a conditional neuronal knockout of Prosapip1 is a strength. These mice will be useful for anyone interested in studying or comparing and contrasting the effects of loss of Prosapip1 in different brain regions or in non-neuronal tissues.

b. The use of biochemical, electrophysiological, and behavioral approaches are a strength. By providing data across multiple domains, a picture begins to emerge about the mechanistic role for Prosapip1. While questions still remain, the use of the 3 domains is a strength.

c. The use of both global, constitutive neuronal loss of Prosapip1 and postnatal dHP-specific knockout of Prosapip1 help support and validate the behavioral conclusions.

(2) Strengths of the results

a. It is interesting that loss of Prosapip1 leads to specific alterations in the expression of GluN2B and PSD95 but not GluA1 or GluN2A in a post-homogenization fraction that the author's term a "synaptic" fraction. Therefore, these results suggest protein-specific modulation of glutamatergic receptors within a "synaptic" fraction.

b. The electrophysiological data demonstrate an NMDAR-dependent alteration in measures of hippocampal synaptic plasticity, including long-term potentiation (LTP) and NMDAR input/output. These data correspond with the biochemical data demonstrating a biochemical effect on GluN2B localization. Therefore, the conclusion that loss of Prosapip1 influences NMDAR function is well supported.

c. The behavioral data suggest deficits in memory in particular novel object recognition and spatial memory, in the Prosapip1 knockout mice. These data are strongly bolstered by both the pan-neuronal knockout and the dHP Cre transduction.

Weaknesses:

(1) Methodological Weaknesses

a. The synapsin-Cre mice may more broadly express Cre-recombinase than just in neuronal tissues. Specifically, according to Jackson Laboratories, there is a concern with these mice expressing Cre-recombinase germline. As the human protein atlas suggests that Prosapip1 protein is expressed extraneuronally, validation of neuron or at least brain-specific knockout would be helpful in interpreting the data. Having said that, the data demonstrating that the brain region-specific knockout has similar behavioral impacts helps alleviate this concern somewhat; however, there are no biochemical or electrophysiological readouts from these animals, and therefore an alternative mechanism in this adult knockout cannot be excluded.

b. The use of the word synaptic and the crude fractionation make some of the data difficult to interpret/contextualize. It is unclear how a single centrifugation that eliminates the staining of a nuclear protein can be considered a "synaptic" fraction. This is highlighted by the presence of GAPDH in this fraction which is a cytosolically-enriched protein. While GAPDH may be associated with some membranes it is not a synaptic protein. There is no quantification of GAPDH against total protein to validate that it is not enriched in this fraction over control. Moreover, it should not be used as a loading control in the synaptic fraction. There are multiple different ways to enrich membranes, extrasynaptic fractions, and PSDs and a better discussion on the caveats of the biochemical fractionation is a minimum to help contextualize the changes in PSD95 and GluN2B.

c. Also, the word synaptosomal on page 7 is not correct. One issue is this is more than synaptosomes and another issue is synaptosomes are exclusively presynaptic terminals. The correct term to use is synaptoneurosome, which includes both pre and postsynaptic components. Moreover, as stated above, this may contain these components but is most likely not a pure or even enriched fraction.

d. The age at which the mice underwent injection of the Cre virus was not mentioned.

(2) Weaknesses of results

a. There were no measures of GluN1 or GluA2 in the biochemical assays. As GluN1 is the obligate subunit, how it is impacted by the loss of Prosapip1 may help contextualize the fact that GluN2B, but not GluN2A, is altered. Moreover, as GluA2 has different calcium permeance, alterations in it may be informative.

b. While there was no difference in GluA1 expression in the "synaptic" fraction, it does not mean that AMPAR function is not impacted by the loss of Prosapip1. This is particularly important as Prosapip1 may interact with kinases or phosphatases or their targeting proteins. Therefore, measuring AMPAR function electrophysiologically or synaptic protein phosphorylation would be informative.

c. There is a lack of mechanistic data on what specifically and how GluN2B and PSD95 expression is altered. This is due to some of the challenges with interpreting the biochemical fractionation and a lack of results regarding changes in protein posttranslational modifications.

d. The loss of social novelty measures in both the global and dHP-specific Prosapip1 knockout mice were not very robust. As they were consistently lost in both approaches and as there were other consistent memory deficits, this does not impact the conclusions, but may be important to temper discussion to match these smaller deficits within this domain.

e. Alterations in presynaptic paired-pulse ratio measures are intriguing and may point to a role for Prosapip1 in synapse development, as discussed in the manuscript. It would be interesting to delineate if these PPR changes also occur in the adult knockout to help detail the specific Prosapip1-induced neuroadaptations that link to the alterations in novelty-induced behaviors.

Reviewer #2 (Public review):

Summary:

The authors provide valuable findings characterizing a Prosapip1 conditional knockout mouse and the effects of knockout on hippocampal excitatory transmission, NMDAR transmission, and several learning behaviors. Furthermore, the authors selectively and conditionally knockout Prosapip1 in the dorsal hippocampus and show that it is required for the same spatial learning and memory assessed in the conditional knockout mice. The study uncovers how Prosapip1 is involved PSD organization and is a functional and critical player in dorsal Hippocampal LTP via its interaction with GluN2B subunits.

Strengths:

The study is well-controlled and detailed, and the data in the paper match the conclusions.

Weaknesses:

Some statistical information is lacking.

eLife assessment

This study presents a fundamental finding to the field interested in recurrent processing and its neuromodulatory underpinnings, finding unexpectedly that memantine (blocking NMDA-receptors) enhances the decoding of features thought to rely on NMDA-receptors. The evidence is solid and would be improved by further persuading the readership of the likely functional underpinnings of this direction of result and why there was no behavioural effect. These findings will be of interest to a wide community of researchers studying consciousness, sensory processing, attention, and neurotransmitters.

Reviewer #1 (Public review):

The authors investigate the function and neural circuitry of reentrant signals in the visual cortex. Recurrent signaling is thought to be necessary to common types of perceptual experience that are defined by long-range relationships or prior expectations. Contour illusions - where perceptual objects are implied by stimuli characteristics - are a good example of this. The perception of these illusions is thought to emerge as recurrent signals from higher cortical areas feedback onto the early visual cortex, to tell the early visual cortex that it should be seeing object contours where none are actually present.

The authors test the involvement of reentrant cortical activity in this kind of perception using a drug challenge. Reentrance in the visual cortex is thought to rely on NMDAR-mediated glutamate signalling. The authors accordingly employ an NMDA antagonist to stop this mechanism, looking for the effect of this manipulation on visually evoked activity recorded in EEG.

The motivating hypothesis for the paper is that NMDA antagonism should stop recurrent activity and that this should degrade perceptual activity supporting the perception of a contour illusion, but not other types of visual experience. Results in fact show the opposite. Rather than degrading cortical activity evoked by the illusion, memantine makes it more likely that machine learning classification of EEG will correctly infer the presence of the illusion.

On the face of it, this is confusing, and the paper currently does not entirely resolve this confusion. But there are relatively easy ways to improve this. The authors would be well served by entertaining more possible outcomes in the introduction - there's good reason to expect a positive effect of memantine on perceptual brain activity, and I provide details on this below. The authors also need to further emphasize that the directional expectations that motivated E1 were, of course, adapted after the results from this experiment emerged. The authors presumably at least entertained the notion that E2 would reproduce E1 - meaning that E2 was motivated by a priori expectations that were ultimately met by the data.

I broadly find the paper interesting, graceful, and creative. The hypotheses are clear and compelling, the techniques for both manipulation of brain state and observation of that impact are cutting edge and well suited, and the paper draws clear and convincing conclusions that are made necessary by the results. The work sits at the very interesting crux of systems neuroscience, neuroimaging, and pharmacology. I believe the paper can be improved in revision, but my suggestions are largely concerning presentation and nuance of interpretation.

(1) I miss some treatment of the lack of behavioural correlate. What does it mean that metamine benefits EEG classification accuracy without improving performance? One possibility here is that there is an improvement in response latency, rather than perceptual sensitivity. Is there any hint of that in the RT results? In some sort of combined measure of RT and accuracy?

(2) An explanation is missing, about why memantine impacts the decoding of illusion but not collinearity. At a systems level, how would this work? How would NMDAR antagonism selectively impact long-range connectivity, but not lateral connectivity? Is this supported by our understanding of laminar connectivity and neurochemistry in the visual cortex?

(3) The motivating idea for the paper is that the NMDAR antagonist might disrupt the modulation of the AMPA-mediated glu signal. This is in line with the motivating logic for Self et al., 2012, where NMDAR and AMPAR efficacy in macacque V1 was manipulated via microinfusion. But this logic seems to conflict with a broader understanding of NMDA antagonism. NMDA antagonism appears to generally have the net effect of increasing glu (and ACh) in the cortex through a selective effect on inhibitory GABA-ergic cells (eg. Olney, Newcomer, & Farber, 1999). Memantine, in particular, has a specific impact on extrasynaptic NMDARs (that is in contrast to ketamine; Milnerwood et al, 2010, Neuron), and this type of receptor is prominent in GABA cells (eg. Yao et al., 2022, JoN). The effect of NMDA antagonists on GABAergic cells generally appears to be much stronger than the effect on glutamergic cells (at least in the hippocampus; eg. Grunze et al., 1996).

This all means that it's reasonable to expect that memantine might have a benefit to visually evoked activity. This idea is raised in the GD of the paper, based on a separate literature from that I mentioned above. But all of this could be better spelled out earlier in the paper, so that the result observed in the paper can be interpreted by the reader in this broader context.

To my mind, the challenging task is for the authors to explain why memantine causes an increase in EEG decoding, where microinfusion of an NMDA antagonist into V1 reduced the neural signal Self et al., 2012. This might be as simple as the change in drug... memantine's specific efficacy on extrasynaptic NMDA receptors might not be shared with whatever NMDA antagonist was used in Self et al. 2012. Ketamine and memantine are already known to differ in this way.

(4) The paper's proposal is that the effect of memantine is mediated by an impact on the efficacy of reentrant signaling in visual cortex. But perhaps the best-known impact of NMDAR manipulation is on LTP, in the hippocampus particularly but also broadly. Perception and identification of the kanisza illusion may be sensitive to learning (eg. Maertens & Pollmann, 2005; Gellatly, 1982; Rubin, Nakayama, Shapley, 1997); what argues against an account of the results from an effect on perceptual learning? Generally, the paper proposes a very specific mechanism through which the drug influences perception. This is motivated by results from Self et al 2012 where an NMDA antagonist was infused into V1. But oral memantine will, of course, have a whole-brain effect, and some of these effects are well characterized and - on the surface - appear as potential sources of change in illusion perception. The paper needs some treatment of the known ancillary effects of diffuse NMDAR antagonism to convince the reader that the account provided is better than the other possibilities.

(5) The cross-decoding approach to data analysis concerns me a little. The approach adopted here is to train models on a localizer task, in this case, a task where participants matched a kanisza figure to a target template (E1) or discriminated one of the three relevant stimuli features (E2). The resulting model was subsequently employed to classify the stimuli seen during separate tasks - an AB task in E1, and a feature discrimination task in E2. This scheme makes the localizer task very important. If models built from this task have any bias, this will taint classifier accuracy in the analysis of experimental data. My concern is that the emergence of the kanisza illusion in the localizer task was probably quite salient, respective to changes in stimuli rotation or collinearity. If the model was better at detecting the illusion to begin with, the data pattern - where drug manipulation impacts classification in this condition but not other conditions - may simply reflect model insensitivity to non-illusion features.

I am also vaguely worried by manipulations implemented in the main task that do not emerge in the localizer - the use of RSVP in E1 and manipulation of the base rate and staircasing in E2. This all starts to introduce the possibility that localizer and experimental data just don't correspond, that this generates low classification accuracy in the experimental results and ineffective classification in some conditions (ie. when stimuli are masked; would collinearity decoding in the unmasked condition potentially differ if classification accuracy were not at a floor? See Figure 3c upper, Figure 5c lower).

What is the motivation for the use of localizer validation at all? The same hypotheses can be tested using within-experiment cross-validation, rather than validation from a model built on localizer data. The argument may be that this kind of modelling will necessarily employ a smaller dataset, but, while true, this effect can be minimized at the expense of computational cost - many-fold cross-validation will mean that the vast majority of data contributes to model building in each instance.

It would be compelling if results were to reproduce when classification was validated in this kind of way. This kind of analysis would fit very well into the supplementary material.

Reviewer #2 (Public review):

Summary:

In this paper, the authors investigate the role of NMDA-receptors in recurrent processing. In doing so, the authors present data from two studies, where they attempt to decode different stimulus features, namely contrast, collinearity, and illusory contours. The latter of which the authors claim relies uniquely on recurrent processing. Therefore, to test whether NMDA receptors are particularly involved in recurrent processing they administer a NMDA-antagonist to see whether the decoding of illusory contours is specifically perturbed, and leaves the decoding of other features intact. They further aim to disentangle the role of NMDA-receptors by manipulating visibility and task relevance of the decoded features

In the first experiment, the authors decode two targets, the first was always presented clearly, the second's visibility was manipulated by presenting it after a short lag rather than a long lag (inducing attentional blink), as well as masking the target on half the trials. First, they find for target 1 clear evidence for the NMDA-receptor increasing (rather than decreasing) decoding performance of illusory contours. They move on to analyse target 2 to explore the manipulations of lag and masking. Here they find that masking reduced decoding of all three stimulus features, but only the lag reduced decoding of illusory contours. Importantly, the NMDA-antagonist improved decoding only in the unmasked, long lag condition, in the cluster analyses. However, the interaction with the lag condition was not significant, and the effect on decoding was primarily present in the later decoding time window, and not significant when exploring the peak of the decoding time window.

The second experiment was highly similar, but got rid of the lag manipulation, and replaced it with a manipulation of task relevance. Notably, masking did not abolish the decoding of illusory contours completely, in contrast to the first experiment. More importantly, they find that the NMDA-receptor now clearly increases decoding of illusory contours, particularly when the illusory contours are not masked. No effect of task relevance is found.

Taken together the authors state that evidence is found for NMDA-receptors role in recurrent processing.

Strengths:

This is an interesting study using state-of-the-art methods in combination with drug manipulation to study recurrent processing. Their analysis methods are state-of-the-art, and the question that they are trying to address is topical and interesting to a wide research audience, encompassing both researchers interested in visual perception and consciousness, as well as those interested in perturbed vision as found in psychiatric disorders.

Weaknesses:

The experimental design is somewhat complicated, which can make it difficult to match the authors' claims to the actual evidence that is provided. I have some reservations about the paper which are born out of a few issues.<br /> (1) The title, abstract, and introduction hide their counterintuitive finding of increased decoding, presumably as it was unexpected.<br /> (2) Their analysis choices are sometimes unclear, making it difficult to assess whether the analyses are sensible.<br /> (3) The appropriate tests for the interactions that the authors claim they found are often lacking.

To start off, I think the reader is being a bit tricked when reading the paper. Perhaps my priors are too strong, but I assumed, just like the authors, that NMDA-receptors would disrupt recurrent processing, in line with previous work. However, due to the continuous use of the ambiguous word 'affected' rather than the more clear increased or perturbed recurrent processing, the reader is left guessing what is actually found. That's until they read the results and discussion finding that decoding is actually improved. This seems like a really big deal, and I strongly urge the authors to reword their title, abstract, and introduction to make clear they hypothesized a disruption in decoding in the illusion condition, but found the opposite, namely an increase in decoding. I want to encourage the authors that this is still a fascinating finding.

Apologies if I have missed it, but it is not clear to me whether participants were given the drug or placebo during the localiser task. If they are given the drug this makes me question the logic of their analysis approach. How can one study the presence of a process, if their very means of detecting that process (the localiser) was disrupted in the first place? If participants were not given a drug during the localiser task, please make that clear. I'll proceed with the rest of my comments assuming the latter is the case. But if the former, please note that I am not sure how to interpret their findings in this paper.

The main purpose of the paper is to study recurrent processing. The extent to which this study achieves this aim is completely dependent to what extent we can interpret decoding of illusory contours as uniquely capturing recurrent processing. While I am sure illusory contours rely on recurrent processing, it does not follow that decoding of illusory contours capture recurrent processing alone. Indeed, if the drug selectively manipulates recurrent processing, it's not obvious to me why the authors find the interaction with masking in experiment 2. Recurrent processing seems to still be happening in the masked condition, but is not affected by the NMDA-receptor here, so where does that leave us in interpreting the role of NMDA-receptors in recurrent processing? If the authors can not strengthen the claim that the effects are completely driven by affecting recurrent processing, I suggest that the paper will shift its focus to making claims about the encoding of illusory contours, rather than making primary claims about recurrent processing.

An additional claim is being made with regards to the effects of the drug manipulation. The authors state that this effect is only present when the stimulus is 1) consciously accessed, and 2) attended. The evidence for claim 1 is not supported by experiment 1, as the masking manipulation did not interact in the cluster-analyses, and the analyses focussing on the peak of the timing window do not show a significant effect either. There is evidence for this claim coming from experiment 2 as masking interacts with the drug condition. Evidence for the second claim (about task relevance) is not presented, as there is no interaction with the task condition. A classical error seems to be made here, where interactions are not properly tested. Instead, the presence of a significant effect in one condition but not the other is taken as sufficient evidence for an interaction, which is not appropriate. I therefore urge the authors to dampen the claim about the importance of attending to the decoded features. Alternatively, I suggest the authors run their interactions of interest on the time-courses and conduct the appropriate cluster-based analyses.

How were the length of the peak-timing windows established in Figure 1E? My understanding is that this forms the training-time window for the further decoding analyses, so it is important to justify why they have different lengths, and how they are determined. The same goes for the peak AUC time windows for the interaction analyses. A number of claims in the paper rely on the interactions found in these post-hoc analyses, so the 223- to 323 time window needs justification.

Reviewer #3 (Public review):

Summary:

In this study, Stein and colleagues use a clever masking/attentional blink paradigm using Kanisza stimuli, coupled with EEG decoding and the NMDA antagonist memantine, to isolate putative neural markers of feedforward, lateral, and feedback processing.

In two elegant experiments, they show that memantine selective influences EEG decoding of only illusory Kanisza surfaces (but not contour continuation or raw contrast), only when unmasked, only when attention is available (not when "blinked"), and only when task-relevant.

This neatly implicates NMDA receptors in the feedback mechanisms that are believed to be involved in inferring illusory Kanisza surfaces, and builds a difficult bridge between the large body of human perceptual experiments and pharmacological and neurophysiological work in animals.

Strengths:

Three key strengths of the paper are<br /> (1) The elegant and thorough experimental design, which includes internal replication of some key findings.<br /> (2) The clear pattern of results across the full set of experiments.<br /> (3) The clear writing and presentation of results.

The paper effectively reports a 4-way interaction, with memantine only influencing decoding of surfaces (1) that are unmasked (2), with attention available (3) and task-relevant (4). Nevertheless, the results are very clear, with a clear separation between null effects on other conditions and quite a strong (and thus highly selective) effect on this one intersection of conditions. This makes the pattern of findings very convincing.

Weaknesses:

Overall this is an impressive and important paper. However, to my mind, there are two minor weaknesses.

First, despite its clear pattern of neural effects, there is no corresponding perceptual effect. Although the manipulation fits neatly within the conceptual framework, and there are many reasons for not finding such an effect (floor and ceiling effects, narrow perceptual tasks, etc), this does leave open the possibility that the observation is entirely epiphenomenal, and that the mechanisms being recorded here are not actually causally involved in perception per se.

Second, although it is clear that there is an effect on decoding in this particular condition, what that means is not entirely clear - particularly since performance improves, rather than decreases. It should be noted here that improvements in decoding performance do not necessarily need to map onto functional improvements, and we should all be careful to remain agnostic about what is driving classifier performance. Here too, the effect of memantine on decoding might be epiphenomenal - unrelated to the information carried in the neural population, but somehow changing the balance of how that is electrically aggregated on the surface of the skull. *Something* is changing, but that might be a neurochemical or electrical side-effect unrelated to actual processing (particularly since no corresponding behavioural impact is observed.)

eLife assessment

This study explores the role of protein synthesis in spinal cord neurons in the regulation of chronic pain. Using innovative techniques, this valuable study outlines cell-type specific gene changes that occur in the spinal cord in the early and late phases of nerve injury. The presented evidence and methods used are, however, incomplete: there are several major technical and analysis issues that need to be addressed, and in addition, deeper gene expression analysis and additional controls would have strengthened the conclusions. This work will be of broad interest to biologists studying pathological plasticity in circuits.

Reviewer #1 (Public review):

Summary:

This study investigated the role of transcriptional and translational controls of gene expression in dorsal root ganglia and lumbar spinal cord in neuropathic pain in mice. Using ribosome profiling (Ribo-seq) and translating ribosome affinity purification (TRAP), they show changes in transcriptomic and translational gene expression at the peripheral and central levels rapidly after nerve injury. While translational changes in gene expression remained elevated for more than two months in both DRGs and the spinal cord, transcriptomic regulation was absent in the spinal cord long after the onset of neuropathy. Disrupting mRNA translation in dorsal horn neurons using antisense oligonucleotides reduced mechanical withdrawal threshold and facial expression of pain. Using fluorescent noncanonical amino acid tagging (FUNCAT), the authors further show that de novo protein expression primarily occurs in inhibitory neurons in the superficial dorsal horn after nerve injury. Accordingly, a selective increase in translational control of gene expression in spinal inhibitory neurons, or a subset of mainly inhibitory neurons expressing parvalbumin (PV), using transgenic mice, led to a decrease in the excitability of PV neurons and mechanical allodynia. In contrast, decreasing the translational control of spinal PV neurons prevented the alteration of the electrophysiological properties of the PV cells induced by nerve injury.

Strengths:

This is a well-written article that uncovers a previously unappreciated role of gene expression control in PV neurons, which seems to play an important part in the loss of inhibitory control of spinal circuits typically seen after peripheral nerve injury. The conclusions are generally well supported by the data.

Weaknesses:

The study would benefit from further clarifications in the methods section and a deeper analysis of gene expression changes in mRNA expression and ribosomal footprint observed after nerve injury.

Antisense oligonucleotides used to reduce translation by disrupting eIF4E expression were administered i.c.v. It is unknown if the authors controlled for locomotor deficits, which might add confounds in the interpretation of behavioral results. A more local route should have been preferable to avoid targeting brain regions, which could potentially affect behavior.

Only female mice were used for Ribo-Seq, TRAP, FUNCAT, and electrophysiology, but both sexes were used for behavior experiments.

The conditional KO of 4E-BP1 using transgenic animals should be total in the targeted cells. However, only a partial reduction is reported in Figure S2 in GAD2, PV, Vglut2, or Tac1 cells. Again, proper methods for quantification of fluorescence in these experiments are lacking.

The elegant knockdown of eIF4E using AAV-mediated shRNAmir shows a recovery of the electrophysiological intrinsic properties of PV neurons after injury. It is unclear if such manipulation would be sufficient to reverse mechanical allodynia in vivo.

Reviewer #2 (Public review):

Summary:

I reviewed the manuscript titled "Translational Control in the Spinal Cord Regulates Gene Expression and Pain Hypersensitivity in the Chronic Phase of Neuropathic Pain." This manuscript compares transcription and translation in the spinal cord during the acute and chronic phases of neuropathic pain induced by surgical nerve injury. The authors chose to focus their investigation on translation in the chronic phase due to its greater impact on gene expression in the spinal cord compared to transcription.

(1) The study is significant because the molecular mechanisms underlying chronic pain remain elusive. The role of translational regulation in the spinal cord has not been investigated in neuroplasticity and chronic pain mouse models. The manuscript is innovative and technically robust. The authors employed several cutting-edge techniques such as Rio-seq, TRAP-seq, slice electrophysiology, and viral approaches. Despite the technical complexity, the manuscript is well-written. The authors demonstrated that inhibition of eIF4E alleviates pain hypersensitivity, that de novo protein synthesis is more pronounced in inhibitory interneurons, and that manipulating mTOR-eIF4E pathways alters mechanical sensitivity and neuroplasticity.

(2) Strengths: innovation (conceptual and technical levels), data support the conclusions.

Weakness:

Confusion about the sex of the animals. It is unclear whether eIF4E ASO affects translation and which cells. It is not determined that modulating translation in PV+ neurons impacts neuropathic pain behaviors.

Reviewer #3 (Public review):

Summary:

This study provides evidence for translational changes in inhibitory spinal dorsal horn neurons following chronic nerve injury. Gene expression changes have been widely studied in the context of pain induction and provided key insights into the adaptation of the nervous system in the early phases of chronic pain. Whereas this is interesting biologically, most patients will arrive in the clinic beyond the acute phase of their injury, thus limiting the translational relevance of these studies. Recent studies have extended this work to highlight the difference between acute and chronic pain states, potentially explaining the cascading factors leading to chronic pain, and hopefully how to prevent this in vulnerable populations. The present study suggests that translational changes within spinal inhibitory populations could underlie long-term chronic pain, leading to decreased inhibition and heightened pain thresholds.

Strengths:

The approaches used and the broad outcomes of the manuscript are interesting and could be an exciting development in the field. The authors are using approaches more common in molecular biology and extending these into neuroscientific research, getting into the detail of how pathology could impact gene expression differentially across the course of an injury. This could open up new areas of research to selectively target not only defined populations but additionally help alleviate pain symptoms once an injury has already reached the maintenance phase. There is an opportunity to delve into what must be a very large data set and learn more about what genes are differentially translated and how this could affect circuit function.

Weaknesses:

Whereas the authors approach a key question in pain chronicity, the manuscript falls a little short of providing any conclusive data.

The manuscript was in some areas very difficult to follow. Terminology was not always consistent or clear, and the flow of the manuscript could use some attention to highlight key areas. Whereas the overall message is clear in the summary, this would not necessarily be the case when reading the manuscript alone.

The study claims to show that translational control mechanisms in the spinal cord play a role in mediating neuropathic pain hypersensitivity, but the studies presented do not fully support this statement. The authors instead provide some correlation between translation and behavioural reflex excitability (namely vfh and Hargreaves).

It is difficult to fully interpret the work, as there are a number of inconsistencies, namely the range of timings pre- and post-injury, lack of controls for manipulations, the use of shmiRNA versus lineage deletions, and lack of detailed somatosensory testing. It is not completely clear how this work could be translatable as is, without a deeper understanding of how translational control affects circuit function and whether all of this is necessarily bad for the system, or whether this is a positive homeostatic adaptation to the hyperexcitability of the circuit following injury.

A large portion of the work is focussed on showing an inhibitory-selective change in translation following chronic nerve injury. The evidence for this is however lacking. Statistics to show that translational effects are restricted to inhibitory subpopulations are inadequate. The author's choice of transgenic lines is not clear and seems to rely on availability rather than hypothesis.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review):

In the article by Dearlove et al., the authors present evidence in strong support of nucleotide ubiquitylation by DTX3L, suggesting it is a promiscuous E3 ligase with capacity to ubiquitylate ADP ribose and nucleotides. The authors include data to identify the likely site of attachment and the requirements for nucleotide modification. 

While this discovery potentially reveals a whole new mechanism by which nucleotide function can be regulated in cells, there are some weaknesses that should be considered. Is there any evidence of nucleotide ubiquitylation occurring cells? It seems possible, but evidence in support of this would strengthen the manuscript. The NMR data could also be strengthened as the binding interface is not reported or mapped onto the structure/model, this seems of considerable interest given that highly related proteins do have the same activity. 

The paper is for the most part well well-written and is potentially highly significant 

Comments on revised version: 

The revised manuscript has addressed many of the concerns raised and clarified a number of points. As a result the manuscript is improved. 

The primary concern that remains is the absence of biological function for Ub-ssDNA/RNA and the inability to detect it in cells. Despite this the manuscript will be of interest to those in the ubiquitin field and will likely provoke further studies and the development of tools to better assess the cellular relevance. As a result this manuscript is important. 

We agree with the reviewer’s assessment.

Minor issue: 

Figure 1A - the authors have now included the constructs used but it would be more informative if the authors lined up the various constructs under the relevant domains in the full-length protein. 

Figure 1 will be fixed in the Version of Record.

Reviewer #2 (Public Review):

The manuscript by Dearlove et al. entitled "DTX3L ubiquitin ligase ubiquitinates single-stranded nucleic acids" reports a novel activity of a DELTEX E3 ligase family member, DTX3L, which can conjugate ubiquitin to the 3' hydroxyl of single-stranded oligonucleotides via an ester linkage. The findings that unmodified oligonucleotides can act as substrates for direct ubiquitylation and the identification of DTX3 as the enzyme capable of performing such oligonucleotide modification are novel, intriguing, and impactful because they represent a significant expansion of our view of the ubiquitin biology. The authors perform a detailed and diligent biochemical characterization of this novel activity, and key claims made in the article are well supported by experimental data. However, the studies leave room for some healthy skepticism about the physiological significance of the unique activity of DTX3 and DTX3L described by the authors because DTX3/DTX3L can also robustly attach ubiquitin to the ADP ribose moiety of NAD or ADP-ribosylated substrates. The study could be strengthened by a more direct and quantitative comparison between ubiquitylation of unmodified oligonucleotides by DTX3/DTX3L with the ubiquitylation of ADP-ribose, the activity that DTX3 and DTX3L share with the other members of the DELTEX family.

Comment on revised version:

In my opinion, reviewers' comments are constructively addressed by the authors in the revised manuscript, which further strengthens the revised submission and makes it an important contribution to the field. Specifically, the authors perform a direct quantitative comparison of two distinct ubiquitylation substrates, unmodified oligonucleotides and fluorescently labeled NADH and report that kcat/Km is 5-fold higher for unmodified oligos compared to NADH. This observation suggests that ubiquitylation of unmodified oligos is not a minor artifactual side reaction in vitro and that unmodified oligonucleotides may very well turn out to be the physiological substrates of the enzyme. However, the true identity of the physiological substrates and the functionally relevant modification site(s) remain to be established in further studies. 

We agree with the reviewer’s assessment.

---

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

Public Reviews: 

Reviewer #1 (Public Review): 

In the article by Dearlove et al., the authors present evidence in strong support of nucleotide ubiquitylation by DTX3L, suggesting it is a promiscuous E3 ligase with capacity to ubiquitylate ADP ribose and nucleotides. The authors include data to identify the likely site of attachment and the requirements for nucleotide modification. 

While this discovery potentially reveals a whole new mechanism by which nucleotide function can be regulated in cells, there are some weaknesses that should be considered. Is there any evidence of nucleotide ubiquitylation occurring cells? It seems possible, but evidence in support of this would strengthen the manuscript. The NMR data could also be strengthened as the binding interface is not reported or mapped onto the structure/model, this seems of considerable interest given that highly related proteins do have the same activity. 

The paper is for the most part well well-written and is potentially highly significant, but it could be strengthened as follows: 

(1) The authors start out by showing DTX3L binding to nucleotides and ubiquitylation of ssRNA/DNA. While ubiquitylation is subsequently dissected and ascribed to the RD domains, the binding data is not followed up. Does the RD protein alone bind to the nucleotides? Further analysis of nucleotide binding is also relevant to the Discussion where the role of the KH domains is considered, but the binding properties of these alone have not been analysed. 

We thank the reviewer for the suggestion. We have tested DTX3L RD for ssDNA binding using NMR (see Figure 4A and Figure S2), which showed that DTX3L RD binds ssDNA. We have now tested the DTX3L KH domains for RNA/ssDNA binding using an FP experiment. However, the FP experiment did not show significant changes upon titrating RNA/ssDNA, suggesting that the KH domains alone are not sufficient to bind RNA/ssDNA. We have opted to put this data in the response-to-review as future investigation will be required to examine whether other regions of DTX3L cooperate with RD to bind RNA/ssDNA. We have revised the Discussion on the KH domains. We now state that “Our findings show the DTX3L DTC domain binds nucleic acids but whether the KHL domains contribute to nucleic acid binding requires further investigation.”

Author response image 1.

Fold change of fluorescence polarisation of 6-FAM-labelled ssDNA D4 upon titrating with DTX3L variants. DTX3L KH domain fragments were expressed with a N-terminal His-MBP tag to increase the molecular weight to enhance the signal.

(2) With regard to the E3 ligase activity, can the authors account for the apparent decreased ubiquitylation activity of the 232-C protein in Figure 1/S1 compared to FL and RD? 

We found that the 232-C protein batch used in the assay was not pure and have subsequently re-purified the protein. We have repeated the ubiquitination of ssDNA and RNA (Fig. 1H and 1I) and 232-C exhibited similar activity as WT. Furthermore, we performed autoubiquitination (Fig. S1G) and E2~Ub discharge assay (Fig. S1H) to compare the activity. 232-C was slower in autoubiquitination (Fig. S1G), but showed similar activity in the E2~Ub discharge assay as WT. These findings suggest that the RING domain in 232-C is functional and 232-C likely lacks ubiquitination site(s) present in 1-231 region necessary for autoubiquitination.

(3) Was it possible to positively identify the link between Ub and ssDNA/RNA using mass spectrometry? This would overcome issues associated with labels blocking binding rather than modification. 

We have tried to use mass spectrometry to detect the linkage between Ub and ssDNA/RNA, but was unable to do so. We suspect that the oxyester linkage might be labile, posing a challenge for mass spectrometry techniques. Similarly, a recent preprint from Ahel lab, which utilises LC-MS, detects the Ub-NMP product rather than the linkage (https://www.biorxiv.org/content/10.1101/2024.04.19.590267v1.full.pdf).

(4) Furthermore, can a targeted MS approach be used to show that nucleotides are ubiquitylated in cells? 

This will require future development and improvement of the MS approach, specifically the isolation of labile oxyester-linked products from cells and the optimisation of the MS detection method.

(5) Do the authors have the assignments (even partial?) for DTX3L RD? In Figure 4 it would be helpful to identify the peaks that correspond to the residues at the proposed binding site. Also do the shifts map to a defined surface or do they suggest an extended site, particularly for the ssDNA.

We only collected HSQC spectra which was insufficient for assignments. We have performed a competition experiment using ADPr and labelled ssDNA, showing that ADPr competes against the ubiquitination of ssDNA (Figure 4D). We have also provided an additional experiment showing that ssDNA with a blocked 3’-OH can compete against ubiquitination of ADPr (Figure 4E). These data, together with our NMR analysis, further strengthen the evidence that ssDNA and ADPr compete the same binding pocket in DTX3L RD. Understanding how DTX3L RD binds ssDNA/RNA is an ongoing research in the lab.

(6) Does sequence analysis help explain the specificity of activity for the family of proteins? 

We have performed sequence alignment and structure comparison of DTX proteins using both RING and DTC domains (Fig. S3). These analyses showed that DTX3 and DTX3L RING domains lack a N-terminal helix and two loop insertions compared to DTX1, DTX2 and DTX4. These additions make DTX1, DTX2 and DTX4 RING domain larger than DTX3L and DTX3. It is not clear how these would influence the orientation of the recruited E2~Ub. Comparison of the DTC domain showed that DTX1, DTX2 and DTX4 contain an Ala-Arg motif, which causes a bulge at one end of DTC pocket. In the absence of Ala-Arg motif, DTC pockets of DTX3 and DTX3L contain an extended groove which might accommodate one or more of the nucleotides 5' to the targeted terminal nucleotide. It seems that both features of RING and DTC domains might attribute to the specificity of DTX3L and DTX3. We have included these comparisons in the discussion and suggested that future structural characterization is necessary to unveil the specificity.

(7) While including a summary mechanism (Figure 5I) is helpful, the schematic included does not necessarily make it easier for the reader to appreciate the key findings of the manuscript or to account for the specificity of activity observed. While this figure could be modified, it might also be helpful to highlight the range of substrates that DTX3L can modify - nucleotide, ADPr, ADPr on nucleotides etc. 

We have modified this Figure to include the range of substrates.

Reviewer #2 (Public Review): 

Summary: 

The manuscript by Dearlove et al. entitled "DTX3L ubiquitin ligase ubiquitinates single-stranded nucleic acids" reports a novel activity of a DELTEX E3 ligase family member, DTX3L, which can conjugate ubiquitin to the 3' hydroxyl of single-stranded oligonucleotides via an ester linkage. The findings that unmodified oligonucleotides can act as substrates for direct ubiquitylation and the identification of DTX3 as the enzyme capable of performing such oligonucleotide modification are novel, intriguing, and impactful because they represent a significant expansion of our view of the ubiquitin biology. The authors perform a detailed and diligent biochemical characterization of this novel activity, and key claims made in the article are well supported by experimental data. However, the studies leave room for some healthy skepticism about the physiological significance of the unique activity of DTX3 and DTX3L described by the authors because DTX3/DTX3L can also robustly attach ubiquitin to the ADP ribose moiety of NAD or ADP-ribosylated substrates. The study could be strengthened by a more direct and quantitative comparison between ubiquitylation of unmodified oligonucleotides by DTX3/DTX3L with the ubiquitylation of ADP-ribose, the activity that DTX3 and DTX3L share with the other members of the DELTEX family. 

Strengths: 

The manuscript reports a novel and exciting observation that ubiquitin can be directly attached to the 3' hydroxyl of unmodified, single-stranded oligonucleotides by DTX3L. The study builds on the extensive expertise and the impactful previous studies by the Huang laboratory of the DELTEX family of E3 ubiquitin ligases. The authors perform a detailed and diligent biochemical characterization of this novel activity, and all claims made in the article are well supported by experimental data. The manuscript is clearly written and easy to read, which further elevates the overall quality of submitted work. The findings are impactful and will help illuminate multiple avenues for future follow-up investigations that may help establish how this novel biochemical activity observed in vitro may contribute to the biological function of DTX3L. The authors demonstrate that the activity is unique to the DTX3/DTX3L members of the DELTEX family and show that the enzyme requires at least two single-stranded nucleotides at the 3' end of the oligonucleotide substrate and that the adenine nucleotide is preferred in the 3' position. Most notably, the authors describe a chimeric construct containing RING domain of DTX3L fused to the DTC domain DTX2, which displays robust NAD ubiquitylation, but lacks the ability to ubiquitylate unmodified oligonucleotides. This construct will be invaluable in the future cell-based studies of DTX3L biology that may help establish the physiological relevance of 3' ubiquitylation of nucleic acids. 

Weaknesses: 

The main weakness of the study is in the lack of direct evidence that the ubiquitylation of unmodified oligonucleotides reported by the authors plays any role in the biological function of DTX3L. The study leaves plenty of room for natural skepticism regarding the physiological relevance of the reported activity, because, akin to other DELTEX family members, DTX3 and DTX3L can also catalyze attachment of ubiquitin to NAD, ADP ribose and ADP-ribosylated substrates. Unfortunately, the study does not offer any quantitative comparison of the two distinct activities of the enzyme, which leaves plenty of room for doubt. One is left wondering, whether ubiquitylation of unmodified oligonucleotides is just a minor and artifactual side activity owing to the high concentration of the oligonucleotide substrates and E2~Ub conjugates present in the in-vitro conditions and the somewhat lower specificity of the DTX3 and DTX3L DTC domains (compared to DTX2 and other DELTEX family members) for ADP ribose over other adenine-containing substrates such as unmodified oligonucleotides, ADP/ATP/dADP/dATP, etc. The intriguing coincidence that DTX3L, which is the only DTX protein capable of ubiquitylating unmodified oligonucleotides, is also the only family member that contains nucleic acid interacting domains in the N-terminus, is suggestive but not compelling. A recently published DTX3L study by a competing laboratory (PMID: 38000390), which is not cited in the manuscript, suggests that ADP-ribose-modified nucleic acids could be the physiologically relevant substrates of DTX3L. That competing hypothesis appears more convincing than ubiquitylation of unmodified oligonucleotides because experiments in that study demonstrate that ubiquitylation of ADP-ribosylated oligos is quite robust in comparison to ubiquitylation of unmodified oligos, which is undetectable. It is possible that the unmodified oligonucleotides in the competing study did not have adenine in the 3' position, which may explain the apparent discrepancy between the two studies. In summary, a quantitative comparison of ubiquitylation of ADP ribose vs. unmodified oligonucleotides could strengthen the study. 

We thank the reviewer for the constructive feedback. We agree that evidence for the biological function is lacking. While we have tried to detect Ub-ssDNA/RNA from cells, we found that isolating and detecting labile oxyester-linked Ub-ssDNA/RNA products remain challenging due to (1) low levels of Ub-ssDNA/RNA products, (2) the presence of DUBs and nucleases that rapidly remove the products during the experiments, and (3) our lack of a suitable MS approach to detect the product. For these reasons, we feel that discovering the biological function will require future effort and expertise and is beyond the scope of our current manuscript.

In the manuscript (PMID: 38000390), the authors used PARP10 to catalyse ADP-ribosylation onto 5’-phosphorylated ssDNA/RNA. They used the following sequences which lacks 3’-adenosine, which could explain the lack of ubiquitination.

E15_5′P_RNA [Phos]GUGGCGCGGAGACUU

E15_5′P_DNA [Phos]GTGGCGCGGAGACTT

We have performed the experiment using this sequence to verify this (see Author response image 2 below). We have cited this manuscript but for some reasons, Pubmed has updated its published date from mid 2023 to Jan 2024. We have updated the Endnote in the revised manuscript.

Author response image 2.

Fluorescently detected SDS-PAGE gel of in vitro ubiquitination catalysed by DTX3L-RD in the presence ubiquitination components and 6-FAM-labelled ssDNA D4 or D31.

We agree that it is crucial to compare ubiquitination of oligonucleotides and ADPr by DTX3L to find its preferred substrate. We have challenged oligonucleotide ubiquitination by adding excess ADPr and found that ADPr efficiently competes with oligonucleotide (Figure 4D). We have also performed an experiment showing that ssDNA with a blocked 3’-OH can compete against ubiquitination of ADPr (Figure 4E). These data support that ADPr and ssDNA compete for the same binding site on DTX3L.

We also performed kinetic analysis of ubiquitination of fluorescently labelled ssDNA (D4) and NAD+ by DTX3L-RD (Fig. 4F and Fig. S2D–G) to assess substrate preferences. Here, we used fluorescent-labelled NAD+ (F-NAD+) in place of ADPr as labelled NAD+ is commercially available. With the known concentration of fluorescently labelled ssDNA and NAD+ as the standard, we could estimate the rate of ubiquitinated product formation across different substrate concentrations. We have included this finding in the main text “DTX3L-RD displayed _k_cat value of 0.0358 ± 0.0034 min-1 and a _K_m value of 6.56 ± 1.80 mM for Ub-D4 formation, whereas the Michaelis-Menten curve did not reach saturation for Ub-F-NAD+ formation (Fig. 4F and fig. S2, D-G). Comparison of the estimated catalytic efficiency (_k_cat/_K_m = 5457  M-1 min-1 for D4 and estimated _k_cat/_K_m = 1190  M-1 min-1 for F-NAD+; Fig. 4F) suggested that DTX3L-RD exhibited 4.5-fold higher catalytic efficiency for D4 than F-NAD+. This difference primarily results from a better _K_m value for D4 compared to F-NAD+. Although DTX3L-RD showed weak _K_m for F-NAD+, it displays a higher rate for converting F-NAD+ to Ub-F-NAD+ at higher substrate concentration (Fig. 4F). Thus, substrate concentration will play a role in determining the preference.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Writing/technical points: 

(1) The introduction is relatively complex and the last paragraph, which reviews the discoveries on the paper, is long. It may be helpful to highlight the significance and frame the experiments as what they have addressed, rather than detailing each set of experiments completed. 

We have modified the last paragraph in the introduction to highlight the major discovery of our work.

(2) Line 24, Abstract. 'Its N-terminal region' is not obvious 

We have changed “Its N-terminal region” to “the N-terminal region of DTX3L”.

(3) Line 44 - split sentence to emphasize E3 ligase point? 

We have modified the sentence as suggested.

(4) Figures 1B and 1C could be larger - currently they are not very helpful. Also atoms (ADPr?) are shown, but not indicated in the legend or labelled on the panel. 

We have enlarged Figures 1B and 1C and indicated RNA on the structure.

(5) The structure of the D2 domain of DTX3L has recently been reported (Vela-Rodriguez et al). It might be helpful to comment on this manuscript. 

We have now commented on D2 domain in the results section and in the discussion.

(6) It would be helpful to indicate the DTX3L constructs used in Figure 1a. 

We have included all DTX3L constructs used in Figure 1a.

(7) Interpretation of Figure 4A is difficult, the authors may wish to consider other ways to visualize the data. 

We have now removed the black arrow in Figure 4A as it was confusing. Instead, we drew a black box on the cross-peak where the close-up views are shown in Figures 4B and 4C.

(8) Figure 4A. Please indicate which binding partner is highlighted by red/black arrows. 

We have removed black arrow. The red arrows indicate cross-peaks which undergo chemical shift perturbation when DTX3L-RD was titrated with ssDNA or ADPr, highlighting their binding sites on DTX3L-RD overlap.

(9) Line 284 - please indicate the bulge in Figure S3. 

We have indicated the bulge on Figure S3.

(10) Aspects of the discussion are speculative, given that evidence of Ub conjugated to nucleotides in cells is yet to be obtained and the functional consequences of modification are uncertain. 

We understand that the discussion on the potential roles of ubiquitination of ssNAs is speculative. We have now modified it to: “Based on the known functions of the DTX3L/PARP9 complex and the findings of this study, we propose several hypotheses for future research”, so that readers will understand that these are speculative.

(11) Line 295 onwards - this paragraph discusses the role of the KH domains in nucleotide binding, but it is not clear that the authors have directly demonstrated that the KH domains bind nucleotides as all constructs used in the binding experiments in Figure 1/S1 include the RING-DTC domains. 

We found that KH domains alone did not bind ssDNA or RNA. We have modified line 295. This section now reads “Typically, KH domains contain a GXXG motif within the loop between the first and second α helix (22). However, analysis of the sequence of the KHL domains in DTX3L shows these domains lack this motif. Multiple studies have shown that mutation in this motif abolishes binding to nucleic acids (23-26). Our findings show the DTX3L DTC domain binds nucleic acids but whether the KHL domains contribute to nucleic acid binding requires further investigation. Additionally, the structure of the first KHL domain was recently reported and shown to form a tetrameric assembly (20). Our analysis with DTX3L 232-C, which lacks the first KHL domain and RRM, indicate that it can still bind ssDNA and ssRNA. Despite this, a more detailed analysis will be required to determine whether oligomerization plays a role in nucleic acid binding and ubiquitination.”

eLife assessment

This important study reports the discovery of a novel nucleotide ubiquitylation activity by the DTX3L E3 ligase. Solid evidence is presented for ubiquitin attachment to single-stranded oligonucleotides. This very interesting biochemical finding can be used as a starting point for studies to establish relevance in a physiological setting.

Reviewer #1 (Public Review):

In the article by Dearlove et al., the authors present evidence in strong support of nucleotide ubiquitylation by DTX3L, suggesting it is a promiscuous E3 ligase with capacity to ubiquitylate ADP ribose and nucleotides. The authors include data to identify the likely site of attachment and the requirements for nucleotide modification.

While this discovery potentially reveals a whole new mechanism by which nucleotide function can be regulated in cells, there are some weaknesses that should be considered. Is there any evidence of nucleotide ubiquitylation occurring cells? It seems possible, but evidence in support of this would strengthen the manuscript. The NMR data could also be strengthened as the binding interface is not reported or mapped onto the structure/model, this seems of considerable interest given that highly related proteins do have the same activity.

The paper is for the most part well well-written and is potentially highly significant

Comments on revised version:

The revised manuscript has addressed many of the concerns raised and clarified a number of points. As a result the manuscript is improved.

The primary concern that remains is the absence of biological function for Ub-ssDNA/RNA and the inability to detect it in cells. Despite this the manuscript will be of interest to those in the ubiquitin field and will likely provoke further studies and the development of tools to better assess the cellular relevance. As a result this manuscript is important.

Minor issue:<br /> Figure 1A - the authors have now included the constructs used but it would be more informative if the authors lined up the various constructs under the relevant domains in the full-length protein.

Reviewer #2 (Public Review):

The manuscript by Dearlove et al. entitled "DTX3L ubiquitin ligase ubiquitinates single-stranded nucleic acids" reports a novel activity of a DELTEX E3 ligase family member, DTX3L, which can conjugate ubiquitin to the 3' hydroxyl of single-stranded oligonucleotides via an ester linkage. The findings that unmodified oligonucleotides can act as substrates for direct ubiquitylation and the identification of DTX3 as the enzyme capable of performing such oligonucleotide modification are novel, intriguing, and impactful because they represent a significant expansion of our view of the ubiquitin biology. The authors perform a detailed and diligent biochemical characterization of this novel activity, and key claims made in the article are well supported by experimental data. However, the studies leave room for some healthy skepticism about the physiological significance of the unique activity of DTX3 and DTX3L described by the authors because DTX3/DTX3L can also robustly attach ubiquitin to the ADP ribose moiety of NAD or ADP-ribosylated substrates. The study could be strengthened by a more direct and quantitative comparison between ubiquitylation of unmodified oligonucleotides by DTX3/DTX3L with the ubiquitylation of ADP-ribose, the activity that DTX3 and DTX3L share with the other members of the DELTEX family.

Comment on revised version:

In my opinion, reviewers' comments are constructively addressed by the authors in the revised manuscript, which further strengthens the revised submission and makes it an important contribution to the field. Specifically, the authors perform a direct quantitative comparison of two distinct ubiquitylation substrates, unmodified oligonucleotides and fluorescently labeled NADH and report that kcat/Km is 5-fold higher for unmodified oligos compared to NADH. This observation suggests that ubiquitylation of unmodified oligos is not a minor artifactual side reaction in vitro and that unmodified oligonucleotides may very well turn out to be the physiological substrates of the enzyme. However, the true identity of the physiological substrates and the functionally relevant modification site(s) remain to be established in further studies.

eLife assessment

This important study investigates the molecular mechanisms underpinning how the tumor necrosis factor alpha-induced protein, TIPE, regulates aerobic glycolysis to promote tumor growth in melanoma. Solid data using multiple independent approaches provide new insights into the molecular mechanisms underpinning aerobic glycolysis, also known as the Warburg Effect, in melanoma cells. However, further investigation of a potential oncogenic effect of TIPE in melanoma patients is warranted and more advanced metabolomic and bioenergetic assays could be employed. The work will be of interest to biomedical researchers working in cancer and metabolism.

Reviewer #1 (Public review):

Summary:

Tian et al. describes how TIPE regulates melanoma progression, stemness, and glycolysis. The authors link high TIPE expression to increased melanoma cell proliferation and tumor growth. TIPE causes dimerization of PKM2, as well as translocation of PKM2 to the nucleus, thereby activating HIF-1alpha. TIPE promotes the phosphorylation of S37 on PKM2 in an ERK-dependent manner. TIPE is shown to increase stem-like phenotype markers. The expression of TIPE is positively correlated with the levels of PKM2 Ser37 phosphorylation in murine and clinical tissue samples. Taken together, the authors demonstrate how TIPE impacts melanoma progression, stemness, and glycolysis through dimeric PKM2 and HIF-1alpha crosstalk.

The authors manipulated TIPE expression using both shRNA and overexpression approaches throughout the manuscript. Using these models, they provide strong evidence of the involvement of TIPE in mediating PKM2 Ser37 phosphorylation and dimerization. The authors also used mutants of PKM2 at S37A to block its interaction with TIPE and HIF-1alpha. In addition, an ERK inhibitor (U0126) was used to block the phosphorylation of Ser37 on PKM2. The authors show how dimerization of PKM2 by TIPE causes nuclear import of PKM2 and activation of HIF-1alpha and target genes. Pyridoxine was used to induce PKM2 dimer formation, while TEPP-46 was used to suppress PKM2 dimer formation. TIPE maintains stem cell phenotypes by increasing expression of stem-like markers. Furthermore, the relationship between TIPE and Ser37 PKM2 was demonstrated in murine and clinical tissue samples.

The evaluation of how TIPE causes metabolic reprogramming can be better assessed using isotope tracing experiments and improved bioenergetic analysis.

Reviewer #2 (Public review):

In this article, Tian et al present a convincing analysis of the molecular mechanisms underpinning TIPE-mediated regulation of glycolysis and tumor growth in melanoma. The authors begin by confirming TIPE expression in melanoma cell lines and identify "high" and "low" expressing models for functional analysis. They show that TIPE depletion slows tumour growth in vivo, and using both knockdown and over expression approaches, show that this is associated with changes in glycolysis in vitro. Compelling data using multiple independent approaches is presented to support an interaction between TIPE and the glycolysis regulator PKM2, and over-expression of TIPE promoted nuclear translocation of PKM2 dimers. Mechanistically, the authors also demonstrate that PKM2 is required for TIPE-mediated activation of HIF1a transcriptional activity, as assessed using an HRE-promoter reporter assay, and that TIPE-mediated PKM2 dimerization is p-ERK dependent. Finally, the dependence of TIPE activity on PKM2 dimerization was demonstrated on tumor growth in vivo and in regulation of glycolysis in vitro, and ectopic expression of HIF1a could rescue inhibition of PKM2 dimerization in TIPE overexpressing cells and reduced induction of general cancer stem cell markers, showing a clear role for HIF1a in this pathway.

The detailed mechanistic analysis of TIPE mediated regulation of PKM2 to control aerobic glycolysis and tumor growth is a major strength of the study and provides new insights into the molecular mechanisms that underpin the Warburg effect in melanoma cells. The main conclusions of this paper are well supported by data, however further investigation of a potential oncogenic effect of TIPE in melanoma patients is warranted to support the tumor promoting role of TIPE identified in the experimental models. Analysis of patient samples showed a significant increase in TIPE protein levels in primary melanoma compared to benign skin tumours, and a further increase upon metastatic progression. Moreover, TIPE levels correlate with proliferation (Ki67) and hypoxia gene sets in the TCGA melanoma patient dataset. However, the authors note in the discussion that high TIPE expression associates with better survival outcomes in the TCGA melanoma patients and these data should be included in this paper. Further investigation of how TIPE-mediated regulation of glycolysis contributes to melanoma progression is warranted to confirm the authors claims of a potential oncogenic function. Regardless, the new insights into the molecular mechanisms underpinning TIPE-mediated aerobic glycolysis in melanoma are convincing and will likely generate interest in the cancer metabolism field.

Author response:

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

Reviewer #1 (Public Review):

Summary:

Tian et al. describe how TIPE regulates melanoma progression, stemness, and glycolysis. The authors link high TIPE expression to increased melanoma cell proliferation and tumor growth. TIPE causes dimerization of PKM2, as well as translocation of PKM2 to the nucleus, thereby activating HIF-1alpha. TIPE promotes the phosphorylation of S37 on PKM2 in an ERK-dependent manner. TIPE is shown to increase stem-like phenotype markers. The expression of TIPE is positively correlated with the levels of PKM2 Ser37 phosphorylation in murine and clinical tissue samples. Taken together, the authors demonstrate how TIPE impacts melanoma progression, stemness, and glycolysis through dimeric PKM2 and HIF-1alpha crosstalk.

Strengths:

The authors manipulated TIPE expression using both shRNA and overexpression approaches throughout the manuscript. Using these models, they provide strong evidence of the involvement of TIPE in mediating PKM2 Ser37 phosphorylation and dimerization. The authors also used mutants of PKM2 at S37A to block its interaction with TIPE and HIF-1alpha. In addition, an ERK inhibitor (U0126) was used to block the phosphorylation of Ser37 on PKM2. The authors show how dimerization of PKM2 by TIPE causes nuclear import of PKM2 and activation of HIF-1alpha and target genes. Pyridoxine was used to induce PKM2 dimer formation, while TEPP-46 was used to suppress PKM2 dimer formation. TIPE maintains stem cell phenotypes by increasing the expression of stem-like markers. Furthermore, the relationship between TIPE and Ser37 PKM2 was demonstrated in murine and clinical tissue samples.

Weaknesses:

The evaluation of how TIPE causes metabolic reprogramming can be better assessed using isotope tracing experiments and improved bioenergetic analysis.

Thank you very much for your suggestions. Unfortunately, we cannot complete the isotope tracing experiments due to the lack of instruments, nor with the help of the company after consulting several companies. We are very sorry for this imperfect experiment, and we have discussed this disadvantage in our manuscripts. Moreover, due to our negligence, there was only three metabolites were presented in the previous manuscripts. However, we have performed the routine untargeted metabolomics to demonstrate how TIPE causes metabolic reprogramming. We have added the detailed results as a new figure named as Figure S3, in which, the glycolysis pathway particularly pyruvate and lactic acid is decreased after TIPE interference.

Reviewer #2 (Public Review):

In this article, Tian et al present a convincing analysis of the molecular mechanisms underpinning TIPE-mediated regulation of glycolysis and tumor growth in melanoma. The authors begin by confirming TIPE expression in melanoma cell lines and identify "high" and "low" expressing models for functional analysis. They show that TIPE depletion slows tumour growth in vivo, and using both knockdown and over-expression approaches, show that this is associated with changes in glycolysis in vitro. Compelling data using multiple independent approaches is presented to support an interaction between TIPE and the glycolysis regulator PKM2, and the over-expression of TIPE-promoted nuclear translocation of PKM2 dimers. Mechanistically, the authors also demonstrate that PKM2 is required for TIPE-mediated activation of HIF1a transcriptional activity, as assessed using an HRE-promoter reporter assay, and that TIPE-mediated PKM2 dimerization is p-ERK dependent. Finally, the dependence of TIPE activity on PKM2 dimerization was demonstrated on tumor growth in vivo and in the regulation of glycolysis in vitro, and ectopic expression of HIF1a could rescue the inhibition of PKM2 dimerization in TIPE overexpressing cells and reduced induction of general cancer stem cell markers, showing a clear role for HIF1a in this pathway. The main conclusions of this paper are well supported by data, but some aspects of the experiments need clarification and some data panels are difficult to read and interpret as currently presented.

The detailed mechanistic analysis of TIPE-mediated regulation of PKM2 to control aerobic glycolysis and tumor growth is a major strength of the study and provides new insights into the molecular mechanisms that underpin the Warburg effect in cancer cells. However, despite these strengths, some weaknesses were noted, which if addressed will further strengthen the study.

(1) The analysis of patient samples should be expanded to more directly measure the relationship between TIPE levels and melanoma patient outcome and progression (primary vs metastasis), to build on the association between TIPE levels and proliferation (Ki67) and hypoxia gene sets that are currently shown.

Thanks for your suggestions, we have added the relationship between TIPE levels and progression (non-lymph node metastasis vs lymph node metastasis). In addition, we added the association between TIPE and Ki67 or LDH levels as your advised, as shown in Figure 7.

However, the relationship between TIPE levels and melanoma patient outcome is not presented in this article. One reason is that the tissue microarray lack of the survival data. Interestingly, the TCGA dataset showed that the higher TIPE expression has a favorable prognosis for melanoma. We are also very curious about this. Our following study indicated that TIPE might serve as a positive regulator of PD-L1. Therefore, the higher expression of TIPE presents more sensitive tendency to immunotherapy, resulting in a favorable prognosis in melanoma. The detailed mechanisms will be discussed in our following article, and we hope that it might as a continuous research topic for TIPE in melanoma.

We just only disclose a little information that TIPE has a similar survival and immune signature to PD-L1 and PD-1 in melanoma as following:

Author response image 1.

(2) The duration of the in vivo experiments was not clearly defined in the figures, however, it was clear from the tumor volume measurements that they ended well before standard ethical endpoints in some of the experiments. A rationale for this should be provided because longer-duration experiments might significantly change the interpretation of the data. For example, does TIPE depletion transiently reduce or lead to sustained reductions in tumor growth?

Thanks for your suggestions. Actually, we have performed a pre-experiment before the formal experiments, and all the time points were referred to this. Furthermore, we have added the detailed time points into the figure legends as you suggested.

(3) The analysis of general cancer stem cell markers is solid and interesting, however inclusion of neural crest stem cell markers that are more relevant to melanoma biology would greatly strengthen this aspect of the study.

Thanks for your advices. We have selected two neural crest stem cell markers including Nestin and Sox10 to test their expression after overexpression of TIPE in G361 cells or interference of TIPE in A375 cells.

(4) The authors should take care that all data panels are clearly readable in the figures to facilitate appropriate interpretation by the reader.

Thanks for your suggestions. We have amended the data panels according to you advises to ensure it is clear and professionally presented.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major points

(1) In Figure 1D, glucose, pyruvate, and lactate were measured at a steady state. However, metabolites at steady state do not accurately depict changes in pathway activity. An isotope tracing experiment (i.e., using labelled 13C glucose) can be used to study glucose catabolism into pyruvate, as well as tracing into lactate or into the TCA cycle following changes in TIPE expression. In addition, although the authors point towards changes in metabolic reprogramming, only three metabolites were measured. The use of isotope tracing to monitor metabolites from more than one pathway would be suggested to support the claim that metabolism is being reprogrammed due to TIPE.

Thank you very much for your suggestions. Unfortunately, we cannot complete the isotope tracing experiments due to the lack of instruments, nor with the help of the company after consulting several companies. We are very sorry for this imperfect experiment, and we have discussed this disadvantage in our manuscripts. Moreover, due to our negligence, there was only three metabolites were presented in the previous manuscripts. However, we have performed the routine untargeted metabolomics to demonstrate how TIPE causes metabolic reprogramming. We have added the detailed results as a new figure named as Figure S3, in which, the glycolysis pathway particularly pyruvate and lactic acid is decreased after TIPE interference.

(2) In Figure 1H, extracellular acidification was used to determine glycolytic activity. However, bicarbonate secretion can also greatly affect pH, and should be considered (PMID 25449966). Although total ATP content was measured, the contribution of ATP from glycolysis can be also determined (see PMID 28270511) to provide a more accurate representation of glycolytic ATP production.

Thanks for your suggestions again. As described at the above, we will improve our measurement methods in the future, and we have discussed our weakness in the manuscripts.

(3) On page 5, lines 108-111, the authors show that "This process represents an important regulator of the TIPE family switching between oxidative phosphorylation and aerobic glycolysis, paving the way for cancer-specific metabolism in response to low-oxygen challenge." However, there is no data on oxidative phosphorylation. What is the effect of TIPE on oxygen consumption?

Thanks for your careful and professional advices. We have conducted a thorough review of the manuscript for language accuracy and corrected this term to eliminate confusion and ensure the text is clear and professionally presented.

Minor points

(1) On page 3, line 68, it is unclear what is increasing lactate levels, as lactate can be transported inside of cells.

Thanks for your suggestions, we have corrected this misdescription to improve the overall quality and readability of the manuscript.

(2) In Figure 1B, RNA sequencing was performed on TIPE overexpressing G361 cells. The "ribosome" pathway has the highest count and lowest p-value. However, there is no mention of this in the text.

Thanks for your suggestions, we selected aerobic glycolysis as our major story comprehensively according to the transcriptomics, metabolomics and the Co-IP/MS results. Anyway, the "ribosome" pathway as you pointed might is our next research topic in the future.

(3) It would be helpful to include the cell line in Figure S1B-C as well as in the figure legend.

Thanks for your suggestions, we have added the cell line into Figure S1B-C as well as in the figure legend.

(4) Concerning supplementary figures, it would be helpful to include the panel numbers when referring to them in the main text (see line 120 or 122 as an example).

Thanks for your suggestions, we have added the panel numbers when referring to them in the main text.

(5) The sentence on lines 127-131 is very confusing.

Thanks for your suggestions, we have corrected the improper descriptions as you mentioned.

(6) In Figure S3, qPCR is misspelled in the figure legend. Also, it would be helpful to include what is meant by "relative expression" on the y-axis of Figure S3A.

Thanks for your suggestions, we have corrected the errors as you pointed. Due to the y-axis represents the expression both of TIPE and HIF-1α, the present description might be more suitable.

(7) There is an extra space on line 196.

Thanks for your suggestions, we have corrected as you pointed.

(8) In Figure 7E LDH staining was performed. Which isoform of LDH was detected?

Actually, we stained total LDH in Figure 7E.

(9) On line 931, Warburg is misspelled.

Thanks for your suggestion, we have corrected all mentioned typos, including " Warburg " in lines 931.

Reviewer #2 (Recommendations For The Authors):

Major comments:

- Supplementary Figure 2G. Unit of time measurement for tumor growth panel needs to be defined. If this refers to days, 5 days is a relatively short period to assess tumor growth differences in vivo, and indeed, 1000-1200mm3 is a standard ethical end-point for these types of models, and this experiment was concluded well before reaching these tumor sizes. Can the authors explain why they ended this experiment at this timepoint?

Thanks for your suggestions. As you suggested, we have added the detailed time points into the figure legends. Actually, we have performed a pre-experiment before the formal experiments, and all the time points were referred to this.

- Supplementary Figure 2j - Correlation analysis between TIPE expression and overall survival outcome in melanoma patients is more relevant to support the experimental observations described in the paper than the correlation with Ki67. This analysis should also be provided. In addition, is there any difference in TIPE expression between primary and metastatic melanoma patients which would then more directly link TIPE with melanoma progression in patients?

The relationship between TIPE levels and melanoma patient outcome is not presented in this article. One reason is that the tissue microarray lack of the survival data. Interestingly, the TCGA dataset showed that the higher TIPE expression has a favorable prognosis for melanoma. We are also very curious about this. Our following study indicated that TIPE might serve as a positive regulator for PD-L1. Therefore, the higher expression of TIPE presents more sensitive tendency to immunotherapy, resulting in a favorable prognosis in melanoma. The detailed mechanisms will be discussed in our following article, and we hope that it might as a continuous research topic for TIPE in melanoma.

Furthermore, we have added the relationship between TIPE levels and progression (non-lymph node metastasis vs lymph node metastasis), and Ki67 in Figure 7.

- Figure 2 - The A2 domain protein represents a substantial reduction in the size of PKM2, which would likely have other structural effects that could affect interactions with TIPE. This should be discussed by the authors because, in this reviewer's opinion, the data presented do not shed light on the specific TIPE domain requirements for the interaction with PKM2.

Thanks for your suggestions. We have discussed this phenomenon in our manuscripts.

- Figure 4: The authors show that PKM2 recruitment to the promoters of GLUT1 and LDHA is induced by TIPE expression. Is HIF1a recruitment also induced by TIPE? This is a key gap in the detailed molecular analysis provided by the authors.

Thanks for your suggestions. This phenomenon you mentioned is very interesting, however, the expression of GLUT1 and LDHA was completely decreased when we overexpression of TIPE and PKM2 (S37A) compared to overexpression of TIPE and wild PKM2. Therefore, we believe that the higher expression of GLUT1 and LDHA was primarily promoted by TIPE-induced PKM2 recruitment.

- Figure 6: The authors present nice data for general pluripotency/stem cell markers however given melanocytes arise from the neural crest, and neural crest markers are expressed during melanoma initiation and response to therapies, analysis of neural crest stem cell markers would be appropriate to include in this analysis. For example, Sox10, Pax3, NGFR, and AQP2 have all been identified as neural crest stem cell markers expressed in both melanoma patients and experimental models.

Thanks for your advices. We have selected two neural crest stem cell markers including Nestin and Sox10 to test their expression after overexpression of TIPE in G361 cells or interference of TIPE in A375 cells.

Minor comments:

- All Figure and Supplementary Figure legends should indicate how many replicate experiments the data represents, and all error bars should be defined (StDev vs SEM).

We have added as you suggested.

- Supplementary Figure S1C - can the authors confirm the densitometry values on the western, as the band looks to be considerably larger than 1.6 fold higher compared to the control?

We redone the densitometry measurement by ImageJ. However, the result still the same.

- FACs panels in Supplementary Figure 2C-D are unreadable and should be enlarged.

- Supplementary Figure S2i - quantification of Ki67 images appears warranted.

- Supplementary Figure S2j - The text in the figure panel is too small and needs to be increased so the data can be interpreted accurately. Also, the authors should confirm the data is specifically from melanoma patients in the figure legend.

We have improved the quality of the figures and revised their descriptions for greater clarity and coherence, ensuring that they effectively highlight the key results of our study.

- Figure 1A - text on the heat map cannot be read. Gene-level information can be removed, and sample labels should be made larger. In panel D, no statistical analysis is shown for the metabolomics analysis. These should be added, or the authors should modify the text when referring to these data.

We have improved the quality of the figures and revised their descriptions for greater clarity and coherence, ensuring that they effectively highlight the key results of our study.

- Line 127: RNAseq data does not indicate a change in metabolites; text should be changed to say "TIPE dramatically promoted expression of genes...".

We have corrected as you suggested.

- Supplementary Figure S3c - Labels and correlation values are not readable.

- Figure 2A - The text and details in the figure are difficult to read.

- Figure S4 D-H - text in figure panels too small to read.

Thank you for above three questions, we have carefully reviewed the entire document to ensure all figures are clear and correctly cited, preventing any confusion and maintaining the integrity of our research findings.

- Figure 3 - the legend restates the major observations and interpretations of the figure, however does not contain enough information about what the data represents or how it was generated. The interpretation of the data should be made in the main text. For example, in panel 3. F-G the number of individual cells quantified for the analysis should be stated. In addition, given the data are generated from two completely independent cell lines, it would be more appropriate to have separate graphs for the A375 cells and G361 cells. The signal levels in the respective controls at baseline are very different, and plotted together without clear labels, making the reader question the validity of the data when this just reflects different basal signals in different cell models.

We have separated the graphs for the A375 cells and G361 cells.

- Figure 4 B-C - IgG controls are missing in Co-IP experiments.

We have added the IgG controls as you suggested.

- Figure 5F - The unit of measure of time should be indicated on the axes; is this days?

We measured the tumor volumes for 7 times every 5 days. We have added the detailed description in the materials and methods section.

- Line 348: error in text, mammosphere which should presumably be tumorsphere if from melanoma cells.

Thanks for your suggestions, we have corrected this term to "tumorsphere" and conducted a thorough language and grammar review of the manuscript to ensure its professional presentation.

- Methods: more experimental details for the transcriptomic, mass spec, and metabolomics studies should be provided. There are insufficient details if readers wish to repeat these experiments.

Thanks for your suggestions, we have corrected as you advised.

eLife assessment

In this study, Ferling and colleagues provide convincing evidence demonstrating that myeloid cells exert distinct, cargo-dependent responses during and after phagocytosis. These important findings establish previously unrecognized insights into the function(s) of myeloid cells in immunosurveillance and are thus likely to be broadly impactful across the spectrum of biomedical disciplines including immunology and cell biology. Notwithstanding these clear strengths of the article, some minor issues were noted pertinent to the relative opaqueness of the mechanisms underpinning context-specific RhoA activation.

Reviewer #1 (Public review):

Summary:

This manuscript uses PS-coated and IgG-opsonized targets to model the engulfment of apoptotic cells and pathogens. It demonstrates that differential activation of the respiratory burst accounts for variations in cell morphology, adhesion, and migration following phagocytosis of different particles. Specifically, reactive oxygen species produced by phagosomes containing IgG-opsonized targets activate Rho GTPases. This activation triggers Formin- and ERM-dependent compaction of the cortical actin network, leading to rounded cell morphology, reduced membrane ruffling, disassembly of podosomes, and decreased migration. Some of these findings are validated in cells exposed to pathogens or soluble MAMPs.

Strengths:

The manuscript presents well-executed and controlled experiments. It proposes an intriguing model to explain the distinct behaviors of myeloid cells when confronted with different phagocytic cargoes and offers fresh insights into immune surveillance.

Weaknesses:

Certain aspects of the proposed model require further experimental evidence. The significance of the cellular behavioral differences in response to various phagocytic cargoes warrants further exploration within physiological contexts.

Specific comments:

How do reactive oxygen species lead to an increase in Rho activation while simultaneously reducing Rac activity? The underlying molecular mechanisms remain unresolved, although potential regulatory pathways are discussed.

Given that the number of phagocytosed particles affects cell behavior (SF1), it is important to ensure that an equivalent number of particles are phagocytosed when comparing cells treated with PS-beads and IgG-beads (Figure 1a). How was this experimentally controlled, and how many particles are phagocytosed under each condition?

Why were experiments conducted in BMDM, Raw264.7, and PMN cells under different conditions? For Raw264.7 and PMN cells, cell behavior was only compared between those treated with IgG-RBC and untreated cells. What occurs to these cells when they are exposed to PS-beads as opposed to IgG-beads?

How long does it take for cells treated with IgG-beads to recover and regain their mobility and surveillance activity? Does this recovery occur following a reduction in reactive oxygen species production?

A contractile actin cortex usually requires the activity of both Formin and myosin II. It is a bit surprising that inhibitors of ROCK and myosin II, when added to Raw cells engulfing IgG-RBC, did not affect podosome disassembly. Is the cytoskeletal rearrangement observed in Figure 2 also independent of myosin II activity?

Reviewer #2 (Public review):

Summary:

The manuscript by Ferling et al. describes how phagocytosis of IgG but not PS-opsonized targets induces the cells to round up and disassemble their podosomes. The mechanism downstream of the FcR is then dissected. The authors show that RhoA-mediated actin polymerization is involved, as well as actin nucleators of the Formin family, but not ROCK or Myosin II. ERM proteins and ROS production play a role in podosome loss and RhoA activation. Similar observations were made after cells were put in contact with Candida albicans or with soluble LPS.

Strengths:

The manuscript is of very good scientific standards, based on solid cell biology and biochemistry approaches, both in a murine macrophage cell line and in murine primary macrophages. It reaches the criteria for a significant advance in the field.

Author response:

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

Reviewer 1:

Lines 43 to 46 cannot be referred to as methodology: 

"to investigate a) determinants of attribution; b) patterns of investigated events, including species and breed affected, history of previous abortion and recent stressful events, and the seasonality of cases; c) determinants of reporting, investigation and attribution; (d) cases in which zoonotic pathogens were detection". 

The above should be deleted from the methodology.  

The text is in the abstract and describes, in brief, analyses that we performed and the rationale for these analyses, which we consider relevant for understanding the approach.  As such, we think the text should remain.   

Italicize et al. in the citations

This has been done.

Reviewer 2: 

Data Presentation: While the analysis is comprehensive, the presentation of data could be enhanced with the use of more visual aids such as tables, graphs, or charts to illustrate key findings. 

While further visualisation of findings would be possible, we consider the key results are captured effectively in the existing figures and tables.  Open access to the data also allows for further analyses that might be of interest to readers. 

Discussion Section: The paper could benefit from a more in-depth discussion of the implications of the findings for disease control strategies and policy formulation in Tanzania. 

We thank the reviewer for this important comment.  In most of the paragraphs of the Discussion we discuss the implications of the findings with specific reference, where relevant, to disease control in Tanzania.  For example, in the paragraph regarding human capacity building, we discuss how LFOs might be incentivised to report health events and how this could improve the reach and sensitivity of future surveillance platforms.  Similarly, these issues are discussed in other paragraphs of the Discussion. 

Future Directions: Including recommendations for future research or areas for further investigation would add depth to the paper.

This suggestion has been acted upon and we have added text in the conclusion to describe recommendations for future research.

Reviewer 3:

The thoughts of the authors on the topic and its significance are implied, and the methodological approach needs further clarity.  The number of wards in the study area, statistical selection of wards, type of questionnaire ie open or close-ended. Statistical analyses of outcomes were not clearly elucidated in the manuscript. 

The number of wards and how they were selected (from randomly selected wards included in earlier cross-sectional exposure studies (Bodenham et al. 2021)) is described in the Abortion Surveillance Platform section of the Methods.  We have added description of the questionnaire to indicate that it was a mixture of open and closed questions. We have reviewed the statistical analyses and consider that they have been fully and appropriately described and so have not changed this. 

Fifteen wards were mentioned in the text but 13 used what were the exclusion criteria. 

As described, the study focussed on fifteen wards however two wards did not report any cases. As such, investigations only took place in thirteen of the fifteen wards and this has been described in the text. 

Observations were from pastoral, agropastoral, and smallholder agroecological farmers. No sample numbers or questionnaires were attributed to the above farming systems to correlate findings with management systems. 

As described, the 15 wards comprised five wards that were expected to be predominantly pastoral, three were expected to be predominantly agropastoral and seven expected to predominantly smallholder, and these categories were assigned by the research team following discussion with local experts (typically the district level veterinary officer) (Bodenham et al. 2021). As such, we consider this to be described sufficiently.  

The impacts of the research investigation output are not clearly visible as to warrant intervention methods. 

The aim of this paper was to provide insights on the feasibility and value of establishing a livestock abortion surveillance platform. The aetiological data that could be used to inform specific disease control measures or interventions was the focus of a previous paper (Thomas et al. 2022) as described in the text.    

What were the identified pathogens from laboratory investigation, particularly with the use of culture and PCR not even mentioning the zoonotic pathogens encountered if any? 

An earlier published paper describing the aetiology of the cases was mentioned (Thomas et al. 2022).  This paper fully describes the identified pathogens and the methods used for identification and attribution. Additionally, in the Sample Analysis section we describe the pathogens that were tested and the methods used.  In the section Exposure to Zoonotic Pathogens we specifically list Brucella spp. C. burnetiid, T. gondii and RVFV and so we consider that we have sufficiently described the pathogens tested for, the methods and the zoonotic pathogens detected. 

The public health importance of any of the abortifacient agents was not highlighted. 

The Introduction provides background information on the public health importance of abortifacient agents and we dedicate a whole section (Exposure to Zoonotic Pathogens) to the public health implications of the number of cases in which zoonotic pathogens were detected. Additionally, we discuss the implications of this in the Discussion. 

Comments in manuscript itself:

Line 230: Why are you estimating. The study was supposed to be based on real time abortion events or at least abortion events within 72 hours

We were estimating the sensitivity of the platform by dividing the number of investigated abortion cases by the number of abortions for the livestock population in each of the study wards that would have been expected over the study period.  Because the denominator in this calculation was an expected number, and not a measured count, we can only estimate.

236: In areas where there was no reported abortion event why will you estimate. This action will lead to false conclusion of abortion event in area that did have an event.

We think there has been some misunderstanding of what this section of text was describing. We were not attributing a case to an area where there was none. Rather, as mentioned above, the aim of this particular analysis was to estimate the sensitivity of the platform. To achieve this, we needed to estimate what the expected number of abortion cases in each ward would have been. 

279: Give a brief description of R

A citation and some explanatory text have been added.

348: Table 1: Your table did not show cases where estimate values were used

We think this comment has resulted from the confusion described above regarding estimated cases.  Table 1 has summary data for the actual cases that were reported in the study and does not have the data for the estimated number of abortions that were expected to have occurred in each ward.  As described in line 247, this data is given in Supplementary Materials 3.

404: Not clear, please rephase

This sentence has been re-drafted to improve clarity

467: Why are you numbering the findings of your investigation in your discussion? You have not told us about the previous abortion event in your study area prior to this study and why you embarked on this study in this regions. The current abortion event situation in your country based on other researchers work is missing and how your findings is important as it related to similar investigation elsewhere.

We number the key findings for clarity and to make each finding distinct and so prefer to retain it. 

The study area was chosen because it was the site of an earlier cross-sectional exposure study within which the wards were randomly selected.  As a result, thirteen of the fifteen wards targeted in the reported study were randomly selected.  Two additional wards were selected purposively because of strong existing relationships with the livestock-keeping community.  This was explained in the Methods in Lines 161 – 164. 

Regarding livestock abortion in Tanzania, as explained in the Introduction (lines 112-114), there is little data on abortion in livestock in Tanzania and elsewhere. Nonetheless, in the Discussion, we do describe the results with respect to other abortion studies carried out in

Ethiopia, Nigeria and India (lines 592-598). Moreover, as described in the Introduction (line 90-94), the implementation of syndromic or event-based surveillance in livestock is rare and to the authors’ knowledge has mostly been implemented in Europe, North America or Australasia with only a single pilot project identified in Africa.  

494: Why will you use an estimate for abortion event that were not reported

As described above, this comment reflects a misunderstanding of what was being described.  As written in line 494, an attempt was made to gauge the sensitivity of the surveillance platform by estimating the percentage of expected abortions that the investigated cases represented. That is, to estimate the percentage of abortions that the surveillance platform managed to detect, we divided the number of investigated abortions by the expected number of abortions (in each ward).  The method for this estimation was described in lines 228-238.  

511: Why was farming pattern excluded. Livestock rearing condition is equally critical for this type of investigation example an animal reared intensive system farming method will definitely experience different stress than livestock on nomadic free range system

We agree with the reviewer that livestock rearing system might be expected to impact both the aetiology and incidence of livestock abortion.  However, because the number of wards was small and the distribution across system not equal, any association between investigated cases and and livestock rearing system could not be assessed.  We have made this clearer with additional text in the same paragraph of the Discussion.

529: Nothing was mentioned about educating the farmers or livestock owners to assist in some instances on possible sample collection during this abortion events and

sending these samples as quickly as possible to the central laboratory in suitable condition for investigation and result of the finding communicated back to the farmers

Because abortions can be caused by zoonotic pathogens, we did not involve livestock keepers in the collection of samples.  Rather, sample collection was carried out by the research team and livestock field officers who had received appropriate training.  In addition, results were reported back to the livestock keepers within 10 days of the investigation and, where pathogens were detected, more specific advice provided as to management strategies that could minimise further transmission to livestock and people. This is all described in the Methods (lines 181-199).

540: The livestock owner can be taught how to collect vaginal swab and send samples under suitable condition to the laboratory and the findings reported back to them.

Please see above response.

549: Please summerise.

Line 549-581 succinctly describes the attribution of cases to specific pathogens.  The text given is required for comprehension and any further summarisation could impact understanding. Consequently, we have left the text as it is. 

584: Please summerise.

Line 584-626 describes the patterns of livestock abortion in Tanzania.  The text given is required to fully discuss the findings and any further reduction in text could impact understanding. Consequently, we have left the text as it is.

eLife assessment

This important study reports the use of a surveillance approach in identifying emerging diseases, monitoring disease trends, and informing evidence-based interventions in the control and prevention of livestock abortions, as it relates to their public health implications. The data support the convincing finding that abortion incidence is higher during the dry season, and occurs more in cross-bred and exotic livestock breeds. Aetiological and epidemiological data can be generated through established protocols for sample collection and laboratory diagnosis. These findings are of potential interest to the fields of veterinary medicine, public health, and epidemiology.

Reviewer #1 (Public review):

Summary:

The paper examined livestock abortion, as it is an important disease syndrome that affects productivity and livestock economies. If livestock abortion remains unexamined it poses risks to public health.

Several pathogens are associated with livestock abortions but across Africa however the livestock disease surveillance data rarely include information from abortion events, little is known about the aetiology and impacts of livestock abortions, and data are not available to inform prioritisation of disease interventions. Therefore the current study seeks to examine the issue in detail and proposes some solutions.

The study took place in 15 wards in northern Tanzania spanning pastoral, agropastoral and smallholder agro-ecological systems. The key objective is to investigate the causes and impacts of livestock abortion.

The data collection system was set up such that farmers reported abortion cases to the field officers of the Ministry of Livestock and Fisheries livestock<br /> The reports were made to the investigation teams. The team only included abortion of those that the livestock field officers could attend to within 72 hours of the event occurring.

Also a field investigation was carried out to collect diagnostic samples from aborted materials. In addition aborting dams and questionnaires were administer to collect data on herd/flock management. Laboratory diagnostic tests were carried out for a range of abortigenic pathogens

Over the period of the study 215 abortion events in cattle (n=71), sheep (n=44) and goats (n=100) were investigated. In all 49 investigated cases varied widely across wards, with three .The Aetiological attribution, achieved for 19.5% of cases through PCR-based diagnostics, was significantly affected by delays in field investigation.

The result also revealed that vaginal swabs from aborting dams provided a practical and sensitive source of diagnostic material for pathogen detection.

Livestock abortion surveillance can generate valuable information on causes of zoonotic disease outbreaks, and livestock reproductive losses and can identify important pathogens that are not easily captured through other forms of livestock disease surveillance. The study demonstrated the feasibility of establishing an effective reporting and investigation system that could be implemented across a range of settings, including remote rural areas,

Strengths:

The paper combines both science and socio economic methodology to achieve the aim of the study.

The methodology was well presented and the sequence was great. The authors explain where and how the data was collected. Figure 2 was used to describe the study area which was excellently done. The section on Investigation of cases was well written. The sample analysis was also well written. The authors devoted a section to summarizing the investigated cases and description of the livestock 221-study population. The logic model has been well presented

Weaknesses:

All the weaknesses identified have been resolved by the the authors

Reviewer #2 (Public review):

The paper provides a comprehensive analysis of the importance of livestock abortion surveillance in Tanzania. The authors aim to highlight the significance of this surveillance system in identifying disease priorities and guiding interventions to mitigate the impact of livestock abortions on both animal and human health.

Summary:

The paper begins by discussing the context of livestock farming in Tanzania and the significant economic and social impact of livestock abortions. The authors then present a detailed overview of the livestock abortion surveillance system in Tanzania, including its objectives, methods, and data collection process. They analyze the data collected from this surveillance system over a specific period to identify the major causes of livestock abortions and assess their public health implications.

Evaluation:

Overall, this paper provides valuable insights into the importance of livestock abortion surveillance as a tool for disease prioritization and intervention planning in Tanzania. The authors effectively demonstrate the utility of this surveillance system in identifying emerging diseases, monitoring disease trends, and informing evidence-based interventions to control and prevent livestock abortions.

Strengths:

(1) Clear Objective: The paper clearly articulates its objective of highlighting the value of livestock abortion surveillance in Tanzania.

(2) Comprehensive Analysis: The authors provide a thorough analysis of the surveillance system, including its methodology, data collection process, and findings as seen in the supplementary files.

(3) Practical Implications: The paper discusses the practical implications of the surveillance system for disease control and public health interventions in Tanzania.

(4) Well-Structured: The paper is well-organized, with clear sections and subheadings that facilitate understanding and navigation.

All suggestions made for improvement of the manuscript have been appropriately effected.

Final Recommendation:

Overall, this paper makes a significant contribution to the literature on livestock abortion surveillance and its implications for disease control in Tanzania.

Reviewer #3 (Public review):

The authors delved into an important aspect of abortifacient diseases of livestock in Tanzania. The thoughts of the authors on the topic and its significance have been clarified. The number of wards in the study area, statistical selection of wards, type of questionnaire ie open or close ended. and statistical analyses of outcomes have been clearly elucidated in the manuscript. The exclusion criteria for two wards out of the fifteen wards mentioned in the text are clearly stated. Observations were from pastoral, agro-pastoral and small holder agro ecological farmers. Sample numbers or questionnaires attributed to the above farming systems correlate findings with management systems. The impacts of the research investigation output are clearly visible as to warrant intervention methods. The identified pathogens from laboratory investigation, particularly with the use of culture and PCR, as well as the zoonotic pathogens encountered are stated in the manuscript and the supplementary files.

In conclusion, based on the intent of the authors and content of this research, and the weight of the research topic, the seeming weaknesses in the critical data analysis observed have been clarified, to demonstrate cause, effect and impact.

The authors have carried out the necessary corrections.

The findings do imply that identification of some of the abortifacient of livestock in Tanzania will necessitate important interventions in the control of the diseases in the study area