Author Response

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

eLife assessment

The authors' finding that PARG hydrolase removal of polyADP-ribose (PAR) protein adducts generated in response to the presence of unligated Okazaki fragments is important for S-phase progression is potentially valuable, but the evidence is incomplete, and identification of relevant PARylated PARG substrates in S-phase is needed to understand the role of PARylation and dePARylation in S-phase progression. Their observation that human ovarian cancer cells with low levels of PARG are more sensitive to a PARG inhibitor, presumably due to the accumulation of high levels of protein PARylation, suggests that low PARG protein levels could serve as a criterion to select ovarian cancer patients for treatment with a PARG inhibitor drug.

Thank you for the assessment and summary. Please see below for details as we have now addressed the deficiencies pointed out by the reviewers.

We believe that PARP1 is one of the major relevant PARG substrates in S phase cells. Previous studies reported that PARP1 recognizes unligated Okazaki fragments and induces S phase PARylation, which recruits single-strand break repair proteins such as XRCC1 and LIG3 that acts as a backup pathway for Okazaki fragment maturation (Hanzlikova et al., 2018; Kumamoto et al., 2021). In this study, we revealed that accumulation of PARP1/2-dependent S phase PARylation eventually led to cell death (Fig. 2). Furthermore, we found that chromatin-bound PARP1 as well as PARylated PARP1 increased in PARG KO cells (Fig. S4A and Fig. 4A), suggesting that PARP1 is one of the key substrates of PARG in S phase cells. Of course, PARG may have additional substrates besides PARP1 which are required for its roles in S phase progression, as PARG is known to be recruited to DNA damage sites through pADPr- and PCNA-dependent mechanisms (Mortusewicz et al., 2011). Precisely how PARG regulates S phase progression warrants further investigation.

Public Reviews:

Reviewer #1 (Public Review):

I have a major conceptual problem with this manuscript: How can the full deletion of a gene (PARG) sensitize a cell to further inhibition by its chemical inhibitor (PARGi) since the target protein is fully absent?

Please see below for details about this point. Briefly, we found that PARG is an essential gene (Fig. 7). There was residual PARG activity in our PARG KO cells, although the loss of full-length PARG was confirmed by Western blotting and DNA sequencing (Fig. S9). The residual PARG activity in these cells can be further inhibited by PARG inhibitor, which eventually lead to cell death.

The authors state in the discussion section: "The residual PARG dePARylation activity observed in PARG KO cells likely supports cell growth, which can be further inhibited by PARGi". What does this statement mean? Is the authors' conclusion that their PARG KOs are not true KOs but partial hypomorphic knockdowns? Were the authors working with KO clones or CRISPR deletion in populations of cells?

The reviewer is correct that our PARG KOs are not true KOs. We were working with CRISPR edited KO clones. As shown in this manuscript, we validated our KO clones by Western blotting, DNA sequencing and MMS-induced PARylation. Despite these efforts and our inability to detect full-length PARG in our KO clones, we suspect that our PARG KO cells may still express one or more active fragments of PARG due to alternative splicing and/or alternative ATG usage.

As shown in Fig. 7, we believe that PARG is essential for proliferation. Our initial KO cell lines are not complete PARG KO cells and residual PARG activity in these cells could support cell proliferation. Unfortunately, due to lack of appropriate reagents we could not draw solid conclusions regarding the isoforms or the truncated PARG expressed in these cells (Please see Western blots below).

Are there splice variants of PARG that were not knocked down? Are there PARP paralogues that can complement the biochemical activity of PARG in the PARG KOs? The authors do not discuss these critical issues nor engage with this problem.

There are five reviewed or potential PARG isoforms identified in the Uniprot database. The two sgRNAs (#1 and #2) used to generate initial PARG KO cells in this manuscript target all three catalytically active isoforms (isoforms 1, 2 and 3), and sgRNA#2 used in HeLa cells also targets isoforms 4 and 5, but these isoforms are considered catalytically inactive according to the Uniprot database. However, it is likely that sgRNA-mediated genome editing may lead to the creation of new alternatively spliced PARG mRNAs or the use of alternative ATG, which can produce catalytically active forms of PARG. Instead of searching for these putative spliced PARG RNAs, we used two independent antibodies that recognize the C-terminus of PARG for WB as shown below. Unfortunately, besides full-length PARG, these antibodies also recognized several other bands, some of them were reduced or absent in PARG KO cells, others were not. Thus, we could not draw a clear conclusion which functional isoform was expressed in our PARG KO cells. Nevertheless, we directly measured PARG activity in PARG KO cells (Fig. S9) and showed that we were still able to detect residual PARG activity in these PARG KO cells. These data clearly indicate that residual PARG activity are present and detected in our KO cells, but the precise nature of these truncated forms of PARG remains elusive.

Author response image 1.

These issues have to be dealt with upfront in the manuscript for the reader to make sense of their work.

We thank this reviewer for his/her constructive comments and suggestions. We will include the data above and additional discussion upfront in our revised manuscript to avoid any further confusion by our readers.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Nie et al investigate the effect of PARG KO and PARG inhibition (PARGi) on pADPR, DNA damage, cell viability, and synthetic lethal interactions in HEK293A and Hela cells. Surprisingly, the authors report that PARG KO cells are sensitive to PARGi and show higher pADPR levels than PARG KO cells, which are abrogated upon deletion or inhibition of PARP1/PARP2. The authors explain the sensitivity of PARG KO to PARGi through incomplete PARG depletion and demonstrate complete loss of PARG activity when incomplete PARG KO cells are transfected with additional gRNAs in the presence of PARPi. Furthermore, the authors show that the sensitivity of PARG KO cells to PARGi is not caused by NAD depletion but by S-phase accumulation of pADPR on chromatin coming from unligated Okazaki fragments, which are recognized and bound by PARP1. Consistently, PARG KO or PARG inhibition shows synthetic lethality with Pol beta, which is required for Okazaki fragment maturation. PARG expression levels in ovarian cancer cell lines correlate negatively with their sensitivity to PARGi.

Thank you for your nice comments. The complete loss of PARG activity was observed in PARG complete/conditional KO (cKO) cells. These cKO clones were generated using wild-type cells transfected with sgRNAs targeting the catalytic domain of PARG in the presence of PARP inhibitor.

Strengths:

The authors show that PARG is essential for removing ADP-ribosylation in S-phase.

Thanks!

Weaknesses:

1. This begs the question as to the relevant substrates of PARG in S-phase, which could be addressed, for example, by analysing PARylated proteins associated with replication forks in PARG-depleted cells (EdU pulldown and Af1521 enrichment followed by mass spectrometry).

We believe that PARP1 is one of the major relevant PARG substrates in S phase cells. Previous studies reported that PARP1 recognizes unligated Okazaki fragments and induces S phase PARylation, which recruits single-strand break repair proteins such as XRCC1 and LIG3 that acts as a backup pathway for Okazaki fragment maturation (Hanzlikova et al., 2018; Kumamoto et al., 2021). In this study, we revealed that accumulation of PARP1/2-dependent S phase PARylation eventually led to cell death (Fig. 2). Furthermore, we found that chromatin-bound PARP1 as well as PARylated PARP1 increased in PARG KO cells (Fig. S4A and Fig. 4A), suggesting that PARP1 is one of the key substrates of PARG in S phase cells. Of course, PARG may have additional substrates besides PARP1 which are required for its roles in S phase progression, as PARG is known to be recruited to DNA damage sites through pADPr- and PCNA-dependent mechanisms (Mortusewicz et al., 2011). Precisely how PARG regulates S phase progression warrants further investigation.

1. The results showing the generation of a full PARG KO should be moved to the beginning of the Results section, right after the first Results chapter (PARG depletion leads to drastic sensitivity to PARGi), otherwise, the reader is left to wonder how PARG KO cells can be sensitive to PARGi when there should be presumably no PARG present.

Thank you for your suggestion! However, we would like to keep the complete PARG KO result at the end of the Results section, since this was how this project evolved. Initially, we did not know that PARG is an essential gene. Thus, we speculated that PARGi may target not only PARG but also a second target, which only becomes essential in the absence of PARG. To test this possibility, we performed FACS-based and cell survival-based whole-genome CRISPR screens (Fig. 5). However, this putative second target was not revealed by our CRISPR screening data (Fig. 5). We then tested the possibility that these cells may have residual PARG expression or activity and only cells with very low PARG expression are sensitive to PARGi, which turned out to be the case for ovarian cancer cells. Equipped with PARP inhibitor and sgRNAs targeting the catalytic domain of PARG, we finally generated cells with complete loss of PARG activity to prove that PARG is an essential gene (Fig. 7). This series of experiments underscore the challenge of validating any KO cell lines, i.e. the identification of frame-shift mutations, absence of full-length proteins, and phenotypic changes may still not be sufficient to validate KO clones. This is an important lesson we learned and we would like to share it with the scientific community.

To avoid further misunderstanding, we will include additional statements/comments at the end of “PARG depletion leads to drastic sensitivity to PARGi” section and at the beginning of “CRISPR screens reveal genes responsible for regulating pADPr signaling and/or cell lethality in WT and PARG KO cells”. Hope that our revised manuscript will make it clear.

1. Please indicate in the first figure which isoforms were targeted with gRNAs, given that there are 5 PARG isoforms. You should also highlight that the PARG antibody only recognizes the largest isoform, which is clearly absent in your PARG KO, but other isoforms may still be produced, depending on where the cleavage sites were located.

The two sgRNAs (#1 and #2) used to generate initial PARG KO cells in this manuscript target all three catalytically active isoforms (isoforms 1, 2 and 3), and sgRNA#2 used in HeLa cells also targets isoforms 4 and 5, but these isoforms are considered catalytically inactive according to the Uniprot database. As suggested, we will modify Fig. S1D and the figure legends.

The manufacturer instruction states that the Anti-PARG antibody (66564S) can only recognize isoform 1, this antibody could recognize isoforms 2 and 3 albeit weakly based on Western blot results with lysates prepared from PARG cKO cells reconstituted with different PARG isoforms, as shown below. As suggested, we will add a statement in the revised manuscript and provide the Western blotting data below.

Author response image 2.

To test whether other isoforms were expressed in 293A and/or HeLa cells, we used two independent antibodies that recognize the C-terminus of PARG for WB as shown below. Unfortunately, besides full-length PARG, these antibodies also recognized several other bands, some of them were reduced or absent in PARG KO cells, others were not. Thus, we could not draw a clear conclusion which functional isoforms or truncated forms were expressed in our PARG KO cells.

Author response image 3.

1. FACS data need to be quantified. Scatter plots can be moved to Supplementary while quantification histograms with statistical analysis should be placed in the main figures.

We agree with this reviewer that quantification of FACS data may provide straightforward results in some of our data. However, it is challenging to quantify positive S phase pADPr signaling in some panels, for example in Fig. 3A and Fig. 4C. In both panels, pADPr signaling was detected throughout the cell cycle and therefore it is difficult to know the percentage of S phase pADPr signaling in these samples. Thus, we decide to keep the scatter plots to demonstrate the dramatic and S phase-specific pADPr signaling in PARG KO cells treated with PARGi. We hope that these data are clear and convincing even without any quantification.

1. All colony formation assays should be quantified and sensitivity plots should be shown next to example plates.

As suggested, we will include the sensitivity plot next to Fig. 3D. However, other colony formation assays in this study were performed with a single concentration of inhibitor and therefore we will not provide sensitivity plots for these experiments. Nevertheless, the results of these experiments are straightforward and easy to interpret.

1. Please indicate how many times each experiment was performed independently and include statistical analysis.

As suggested, we will add this information in the revised manuscript.

Reviewer #3 (Public Review):

Here the authors carried out a CRISPR/sgRNA screen with a DDR gene-targeted mini-library in HEK293A cells looking for genes whose loss increased sensitivity to treatment with the PARG inhibitor, PDD00017273 (PARGi). Surprisingly they found that PARG itself, which encodes the cellular poly(ADP-ribose) glycohydrolase (dePARylation) enzyme, was a major hit. Targeted PARG KO in 293A and HeLa cells also caused high sensitivity to PARGi. When PARG KO cells were reconstituted with catalytically-dead PARG, MMS treatment caused an increase in PARylation, not observed when cells were reconstituted with WT PARG or when the PARG KO was combined with PARP1/2 DKO, suggesting that loss of PARG leads to a strong PARP1/2-dependent increase in protein PARylation. The decrease in intracellular NADH+, the substrate for PARP-driven PARylation, observed in PARG KO cells was reversed by treatment with NMN or NAM, and this treatment partially rescued the PARG KO cell lethality. However, since NAD+ depletion with the FK868 nicotinamide phosphoribosyltransferase (NAMPT) inhibitor did not induce a similar lethality the authors concluded that NAD+ depletion/reduction was only partially responsible for the PARGi toxicity. Interestingly, PARylation was also observed in untreated PARG KO cells, specifically in S phase, without a significant rise in γH2AX signals. Using cells synchronized at G1/S by double thymidine blockade and release, they showed that entry into S phase was necessary for PARGi to induce PARylation in PARG KO cells. They found an increased association of PARP1 with a chromatin fraction in PARG KO cells independent of PARGi treatment, and suggested that PARP1 trapping on chromatin might account in part for the increased PARGi sensitivity. They also showed that prolonged PARGi treatment of PARG KO cells caused S phase accumulation of pADPr eventually leading to DNA damage, as evidenced by increased anti-γH2AX antibody signals and alkaline comet assays. Based on the use of emetine, they deduced that this response could be caused by unligated Okazaki fragments. Next, they carried out FACS-based CRISPR screens to identify genes that might be involved in cell lethality in WT and PARG KO cells, finding that loss of base excision repair (BER) and DNA repair genes led to increased PARylation and PARGi sensitivity, whereas loss of PARP1 had the opposite effects. They also found that BER pathway disruption exhibited synthetic lethality with PARGi treatment in both PARG KO cells and WT cells, and that loss of genes involved in Okazaki fragment ligation induced S phase pADPr signaling. In a panel of human ovarian cancer cell lines, PARGi sensitivity was found to correlate with low levels of PARG mRNA, and they showed that the PARGi sensitivity of cells could be reduced by PARPi treatment. Finally, they addressed the conundrum of why PARG KO cells should be sensitive to a specific PARG inhibitor if there is no PARG to inhibit and found that the PARG KO cells had significant residual PARG activity when measured in a lysate activity assay, which could be inhibited by PARGi, although the inhabited PARG activity levels remained higher than those of PARG cKO cells (see below). This led them to generate new, more complete PARG KO cells they called complete/conditional KO (cKO), whose survival required the inclusion of the olaparib PARPi in the growth medium. These PARG cKO cells exhibited extremely low levels of PARG activity in vitro, consistent with a true PARG KO phenotype.

We thank this reviewer for his/her constructive comments and suggestions.

The finding that human ovarian cancer cells with low levels of PARG are more sensitive to inhibition with a small molecule PARG inhibitor, presumably due to the accumulation of high levels of protein PARylation (pADPr) that are toxic to cells is quite interesting, and this could be useful in the future as a diagnostic marker for preselection of ovarian cancer patients for treatment with a PARG inhibitor drug. The finding that loss of base excision repair (BER) and DNA repair genes led to increased PARylation and PARGi sensitivity is in keeping with the conclusion that PARG activity is essential for cell fitness, because it prevents excessive protein PARylation. The observation that increased PARylation can be detected in an unperturbed S phase in PARG KO cells is also of interest. However, the functional importance of protein PARylation at the replication fork in the normal cell cycle was not fully investigated, and none of the key PARylation targets for PARG required for S phase progression were identified. Overall, there are some interesting findings in the paper, but their impact is significantly lessened by the confusing way in which the paper has been organized and written, and this needs to be rectified.

We believe that PARP1 is one of the major relevant PARG substrates in S phase cells. Previous studies reported that PARP1 recognizes unligated Okazaki fragments and induces S phase PARylation, which recruits single-strand break repair proteins such as XRCC1 and LIG3 that acts as a backup pathway for Okazaki fragment maturation (Hanzlikova et al., 2018; Kumamoto et al., 2021). In this study, we revealed that accumulation of PARP1/2-dependent S phase PARylation eventually led to cell death (Fig. 2). Furthermore, we found that chromatin-bound PARP1 as well as PARylated PARP1 increased in PARG KO cells (Fig. S4A and Fig. 4A), suggesting that PARP1 is one of the key substrates of PARG in S phase cells. Of course, PARG may have additional substrates besides PARP1 which are required for its roles in S phase progression, as PARG is known to be recruited to DNA damage sites through pADPr- and PCNA-dependent mechanisms (Mortusewicz et al., 2011). Precisely how PARG regulates S phase progression warrants further investigation.

As suggested, we will revise our manuscript accordingly and provide additional explanation/statement upfront to avoid any misunderstandings.  

Reviewer #1 (Recommendations For The Authors):

1. Figure 1c. Why does the viability of PARG KO cells improve at higher doses of PARGi? How do the authors explain this paradox?

This phenomenon was observed in 293A PARG KO cells and happened in CellTiter-Glo assay, especially with the top three PARGi concentrations (100 µM, 33.33 µM and 11.11 µM). This may due to the low solubility of this PARGi in the medium, since we sometimes observed precipitation at high concentrations when PARGi stock was diluted in medium.

1. Figure 2d. The authors show that PARGi reduced NAD+ level by 20%. This reduction in NAD+ probably does not explain the cell death phenotype observed by parthanatos cell death. What pathway is activated by PARGi to induce cell death?

Since PARG KO cells treated with PARGi led to uncontrolled pADPr accumulation, it is possible that some of these cells may die due to parthanotos. However, we did not observe a dramatic reduction in NAD+ level. A previous study showed that Parg(-/-) mouse ES cells predominantly underwent caspase-dependent apoptosis (Shirai et al., 2013). Indeed, PARP1 cleavage was detected in PARG KO cells with prolonged PARGi treatment, indicating that at least some of these cells die due to apoptosis (Fig. 2A). Cytotoxicity of PARGi in PARG KO cells may due to several mechanisms including apoptosis, parthanatos and NAD+ reduction.

1. The authors refer to FK866 in the text without explaining what this agent is. FK866 is a noncompetitive inhibitor of nicotinamide phosphoribosyltransferase (NAPRT), a key enzyme in the regulation of NAD+ biosynthesis from the natural precursor nicotinamide. The authors should explain experimental tools in the text as they use them for clarity to the reader.

Thanks for the suggestion! We will include additional citations and discuss how FK866 works in our revised manuscript.

1. In addition to these issues, there are significant formatting and textual problems, such that there are multiple gaps in the body of the text that make coherent reading of the manuscript impossible. Examples are: Page 3 line 10. Page 6 line 5 and line 15, Page 7 line 2, 3, and line 8. Page 8, line 1, and line 3 from bottom. Page 9 line 1, line 7 from bottom and line 9 from the bottom, Page 18 of the results in several places, etc. etc. etc. These formatting errors convey the impression that the submitting authors did not adequately review the manuscript for technical problems prior to submission. The authors need to correct these errors.

Sorry, we will edit the text and remove these gaps as suggested.

Reviewer #3 (Recommendations For The Authors):

1. The major problem with this paper is conceptual - namely, how could PARG knockout cells be hypersensitive to a selective PARG small molecular inhibitor. The evidence in Figure 7 that there is measurable residual PARG activity in the so-called PARG KO 293A and HeLa cells provides a partial explanation for why PARG inhibitor treatment might be deleterious to the PARG KO cells, i.e., because PARGi blocks this residual PARG activity. However, although the authors characterized the PARG alleles in the 293A PARG KO cells by sequencing, the molecular origin of the significant level of residual PARG activity remains unclear (see points 7-9).

Yes, in our study we showed that PARGi treatment inhibited the residual PARG activity in PARG KO cells, which mimics complete loss of PARG as PARG is an essential gene. These data agree with a previous study using Parg(-/-) mouse cells (Koh et al., 2004).We attempted to define the molecular origin of the residual PARG activity, unfortunately this was challenging (please see below for additional discussions). Nevertheless, we showed that residual PARG activity could be detected in PARG KO cells and more importantly cells with reduced PARG expression or activity are sensitive to PARGi. These results indicate that PARG expression and/or activity may be used as a biomarker for PARGi-based therapy.

1. Although the most obvious explanation for the PARGi sensitivity data presented in Figures 1-4 is that the PARG KO cells have residual PARG activity, the authors wait until the discussion on page 26 to raise the possibility that the PARG KO cells might have residual PARG activity that renders them sensitive to PARGi. It would be more logical to move the PARG activity data in Figure 7 earlier in the paper as a supplementary figure, so that the reader is not left wondering how a PARG KO cell remains sensitive to a PARG inhibitor. For this reason, it is recommended that the whole paper be reorganized and rewritten to provide a more logical flow that allows the reader to understand what was done, and why it is hard to generate complete PARG KO cells because the accumulation of pADPR adducts is toxic to the cell.

Thank you for your suggestion! However, we would like to keep the complete PARG KO result at the end of the Results section, since this was how this project evolved. Initially, we did not know that PARG is an essential gene. Thus, we speculated that PARGi may target not only PARG but also a second target, which only becomes essential in the absence of PARG. To test this possibility, we performed FACS-based and cell survival-based whole-genome CRISPR screens (Fig. 5). However, this putative second target was not revealed by our CRISPR screening data (Fig. 5). We then tested the possibility that these cells may have residual PARG expression or activity and only cells with very low PARG expression are sensitive to PARGi, which turned out to be the case for ovarian cancer cells. Equipped with PARP inhibitor and sgRNAs targeting the catalytic domain of PARG, we finally generated cells with complete loss of PARG activity to prove that PARG is an essential gene (Fig. 7). This series of experiments underscore the challenge of validating any KO cell lines, i.e. the identification of frame-shift mutations, absence of full-length proteins, and phenotypic changes may still not be sufficient to validate KO clones. This is an important lesson we learned and we would like to share it with the scientific community.

To avoid further misunderstanding, we will include additional statements/comments at the end of “PARG depletion leads to drastic sensitivity to PARGi” section and at the beginning of “CRISPR screens reveal genes responsible for regulating pADPr signaling and/or cell lethality in WT and PARG KO cells”. Hope that our revised manuscript will make it clear.

1. Exactly how PARG activity would be coordinated with PARP1/2 activity during normal S phase to ensure that PARylation can serve its required function, whatever that may be, and is then removed by PARG is unclear - how would this be orchestrated at the level of a replication fork?

PARG is known to be recruited to sites of DNA damage through pADPr- and PCNA-dependent mechanisms (Mortusewicz et al., 2011). Our current hypothesis is that PARP1 is one of the major PARG substrates in S phase cells. Previous studies reported that PARP1 recognizes unligated Okazaki fragments and induces S phase PARylation, which recruits single-strand break repair proteins such as XRCC1 and LIG3 that acts as a backup pathway for Okazaki fragment maturation (Hanzlikova et al., 2018; Kumamoto et al., 2021). In this study, we revealed that accumulation of PARP1/2-dependent S phase PARylation eventually led to cell death (Fig. 2). Furthermore, we found that chromatin-bound PARP1 as well as PARylated PARP1 increased in PARG KO cells (Fig. S4A and Fig. 4A), suggesting that PARP1 is one of the key substrates of PARG in S phase cells. Of course, PARG may have additional substrates besides PARP1 which are required for its roles in S phase progression. Precisely how PARG regulates S phase progression warrants further investigation.

1. Figure 2B: What gRNAs were used to generate the 293A and HeLa PARG knock clones, i.e., where are they located in the PARG gene? If they are not in the catalytic domain it might be possible to generate PARG proteins with N-terminal deletions that are still active (see points 8-10 below).

The two sgRNAs (#1 and #2) used to generate initial PARG KO cells in this manuscript target all three catalytically active isoforms (isoforms 1, 2 and 3), and sgRNA#2 used in HeLa cells also targets isoforms 4 and 5, but these isoforms are considered catalytically inactive according to the Uniprot database. As suggested, we will modify Fig. S1D and the figure legends to show the localization of gRNAs.

We agree with this reviewer that truncated but active forms of PARG exist in these KO cells. We attempted to identify these trunated forms of PARG by using two independent antibodies that recognize the C-terminus of PARG for WB as shown below. Unfortunately, besides full-length PARG, these antibodies also recognized several other bands, some of them were reduced or absent in PARG KO cells, others were not. Thus, we could not draw a clear conclusion which functional isoform/truncated form was expressed in our PARG KO cells. Nevertheless, we directly measured PARG activity in PARG KO cells (Fig. S9) and showed that we were still able to detect residual PARG activity in these PARG KO cells. Based on these results, we stated that the residual PARG activity was detected in our KO cells, but we were not able to specify the truncated variants of PARG in these cells.

Author response image 4.

1. Figure 3B/page 19: The authors state that "emetine, which diminishes Okazaki fragments, greatly inhibited S phase pADPr signaling in PARG KO cells", and from this deduced that Okazaki fragments on the lagging strand activate PARylation. However, emetine is not a specific lagging strand synthesis inhibitor, as implied here, but rather a protein synthesis inhibitor, which inhibits Okazaki fragment formation indirectly (see PMID: 36260751). The authors need to rewrite this section to explain how emetine works in this context.

As suggested, we will cite this reference and discuss how emetine inhibits Okazaki fragment maturation in our revised manuscript. Additionally, we used three different POLA1 inhibitors to diminish Okazaki fragments. As shown in Fig. S3B, all three POLA1 inhibitors significantly abolished S-phase pADPr induced by PARGi in PARG KO cells. Furthermore, POLA1 inhibitors, adarotene and CD437, were able to rescue cell lethality caused by PARGi in PARG KO cells (Fig. 3E).

1. Figure 7: It is not clear why these cells are called PARG complete/conditional KO cells (cKO). Generally, "conditional knockout" refers to a cell or animal in which a gene can be conditionally knocked out by inducible expression of Cre. Here, it appears that "conditional" refers to the fact that the PARG KO cells only grow in the presence of olaparib - is this the case?

Yes, we used the name to separate these cells from our initial PARG KO cells. Moreover, we were only able to obtain and maintain these PARG cKO clones with complete loss of PARG activity in the presence of PARP inhibitor. Therefore, we called them PARG complete/conditional KO (cKO) cells.

1. Figure 7B and D: The level of full-length PARG protein was much lower in the 293A and HeLa cKO cells compared to WT cells consistent with cKO cells representing a more complete PARG KO. The level of PARG protein in the 293A PARG cKO cells was apparently also lower than in the original PARG KO cells, but the KO and cKO samples should be run side by side to demonstrate this conclusively, and the bands need to be quantified. In panel B, it is not clear from the legend what cKO_3 and cKO_4 are, but presumably, they are different clones, and this should be stated.

Full-length PARG was not detected in either PARG KO or PARG cKO cells by WB. The apparent lower level of endogenous PARG in Fig. 7D was due to the fact that reconstituted cells had high exogenous PARG expression and therefore we had to reduce exposure time for WB.

As for cKO_3 and cKO_4 in Fig.7, they are different clones created by different sgRNAs. As suggested, we will include additional information in figure legends to clearly state which sgRNA was used to generate the respective KO and cKO clones.

1. Figure S8: There is not enough information here or in the text to allow the reader to interpret these PARG allele sequences obtained from the PARG KO cells. From the Methods section, it appears that the PARG KO cells were clonal, with sequence data from one clone of each of the 293A and HeLa cell PARG KO cells being shown. If this is right, then in both cell types one out of four PARG alleles is wild type, and therefore one would expect the PARG protein signal to be ~25% of that in WT cells. However, based on the 293A PARG KO cells PARG immunoblot in Figure 2B the PARG protein signal is clearly much lower than 25% (these bands need to be quantified), and this discrepancy needs to be explained. What is the level of PARG protein in the PARG KO HeLa cells? If different PARG KO cell clones are analyzed by sequencing, do they all have an apparently intact PARG allele? Four different gRNA target sites in the PARG gene are shown in panel A in Figure 7, but the description in the text regarding how the four gRNAs were used is totally inadequate - were all four used simultaneously or only the two in the catalytic domain? Were pairs of gRNAs used in an attempt to generate a large intervening deletion - some Southern blots of the PARG gene region in the PARG cKO cells are needed to figure this out. The gRNAs are given numbers in Figure 7A, but it is unclear from the sequences shown in Figures S8 and S9 which gRNA sites are shown. All of this has to be clarified, so that the reader can understand the nature of the KO/cKO cells knockout alleles, and what PARG-related products, if any, they can express.

Yes, all KO and cKO cells used in this study are single clones. As suggested, we will revise figure legends in Fig.7, S8 and S9 to include detailed information. To avoid any further misunderstanding, we will label the allele “WT” to “WT (reference)” in Fig. S8 and S9. We did not detect intact/wild-type PARG sequence in any single KO/cKO clone by DNA sequencing. Sequencing of single KO/cKO clones was performed by using TOP TA Cloning kit. Briefly, genomic DNA was extracted from each single KO/cKO clone. Approximately 300bp surrounding the sgRNA targeting sequence was amplified by PCR. The PCR product was cloned into the vector and approximately 10-15 bacteria clones were extracted and sent for sequencing. If any intact/wild-type PARG sequence was detected in these 10-15 bacteria clones, this KO/cKO clone was considered heterozygous clone and discarded.

HEK293A and HeLa cells are not diploid cells and have complex karyotypes. PARG gene is located on chromosome 10. Karyotyping by M-FISH shows that HeLa cells have 3 copies of chromosome 10 (Landry et al., 2013). HEK293 cells predominantly have 3 copies of chromosome 10 and sometimes 4 copies can be detected by G-banding (Binz et al., 2019). Therefore, it is anticipated that 1 to 4 mutant alleles would be detected in each KO/cKO clone by sequencing.

Only one sgRNA was transfected into cells for the selection of single clones. We did not use paired or multiple sgRNAs in any of these experiments. As shown in Fig. S1D and Fig. 7A, HEK293A derived and HeLa derived PARG KO single clones were generated with the use of different sgRNAs. In addition, the two PARG cKO single clones from HEK293A and HeLa cells were also generated by the use of two different sgRNAs, as shown in Fig. 7A-B. We will include all the information above in the revised manuscript, i.e. in Methods section as well as in figure legends.

1. Figure S9A: The sequences of the 293A PARG alleles in the cKO cells suggest that these cells also have one intact PARG allele, which again does not fit with the very low level of intact PARG protein shown in Figure 7B. How do the authors explain this?

Sorry, this is a misunderstanding. The allele “WT” in Fig. S8 and S9 is the reference sequence. We will change it to “Reference sequence” to avoid further confusion. As mentioned above, we did not detect any intact/wild-type PARG sequence in any of our single KO/cKO clones by sequencing.

1. Figure S9B: These critical lysate activity data show that the PARG KO cells have ~50% of the PARG activity detected in WT cells. However, this is not consistent with the PARG protein level detected in PARG immunoblot in Figure 1B, which appears to be less than 5% of the PARG protein level in WT cells (with one intact PARG allele in these cells one would theoretically expect~ 25%, although this depends on whether all four alleles are expressed equally). One possibility is that active PARG fragments are generated from one or more of the PARG KO alleles in the PARG KO cells. Targeted sequencing of PARG mRNAs might reveal whether there are shorter RNAs that could encode a protein containing the C-terminal catalytic domain (aa 570-910). In addition, the authors need to show the entire immunoblot to determine if there are smaller proteins recognized by the anti-PARG antibodies that might represent shorter PARG gene products (for this we need to know where the epitope against which the PARG antibodies are directed are located within the PARG protein - ideally they authors need to use an antibody directed against an epitope near the C-terminus).

As stated in the Methods section, we incubated cell lysates with substrates overnight to evaluate the maximum level of pADPr hydrolysis, i.e. PARG activity, we were able to detect in this assay. It is very likely that the PARG activity in PARG KO cells was much lower than 50%, due to saturation of signals for lysates isolated from wild-type cells. Thus, the data presented in our manuscript probably underestimate the reduction of PARG activity in PARG KO cells. Nevertheless, these data indicate that residual PARG activity was detected in PARG KO cells, however this activity was absent in PARG cKO cells.

As aforementioned, we used two independent antibodies that recognize the C-terminus of PARG for WB. Unfortunately, we could not draw a clear conclusion which functional isoforms or truncated proteins were expressed in our PARG KO cells. The dePARylation assay used here may be the best way to test the residual PARG activity in our KO and cKO cells.

1. Figure 7D: In this experiment, the level of re-expressed WT PARG protein was much higher than that of the endogenous PARG protein (quantification is needed) - how might this affect the interpretation of these experiments (N.B., WT and catalytically-dead PARG were also re-expressed for the experiments shown in Figure 1, but there are no PARG immunoblots to demonstrate how much the exogenous proteins were overexpressed, or activity measurements). If regulated pADPr signaling is important for a normal S phase, then one would have thought that expressing a very high level of active PARG would create problems.

In Fig. S1E, we blotted endogenous PARG level in control cells and exogenous PARG level in reconstituted cells. The reviewer is correct that exogenous PARG expression was much higher (~10-fold) than that of endogenous PARG in WT control cells. Nevertheless, we did not observe any obvious phenotypes in PARG KO/cKO cells reconstituted with high level of exogeneous PARG, which may reflect excess PARG level/activity in wild-type control cells.

References:

Binz, R. L., Tian, E., Sadhukhan, R., Zhou, D., Hauer-Jensen, M., and Pathak, R. (2019). Identification of novel breakpoints for locus- and region-specific translocations in 293 cells by molecular cytogenetics before and after irradiation. Sci Rep 9, 10554.

Hanzlikova, H., Kalasova, I., Demin, A. A., Pennicott, L. E., Cihlarova, Z., and Caldecott, K. W. (2018). The Importance of Poly(ADP-Ribose) Polymerase as a Sensor of Unligated Okazaki Fragments during DNA Replication. Mol Cell 71, 319-331 e313.

Koh, D. W., Lawler, A. M., Poitras, M. F., Sasaki, M., Wattler, S., Nehls, M. C., Stoger, T., Poirier, G. G., Dawson, V. L., and Dawson, T. M. (2004). Failure to degrade poly(ADP-ribose) causes increased sensitivity to cytotoxicity and early embryonic lethality. Proc Natl Acad Sci U S A 101, 17699-17704.

Kumamoto, S., Nishiyama, A., Chiba, Y., Miyashita, R., Konishi, C., Azuma, Y., and Nakanishi, M. (2021). HPF1-dependent PARP activation promotes LIG3-XRCC1-mediated backup pathway of Okazaki fragment ligation. Nucleic Acids Res 49, 5003-5016.

Landry, J. J., Pyl, P. T., Rausch, T., Zichner, T., Tekkedil, M. M., Stutz, A. M., Jauch, A., Aiyar, R. S., Pau, G., Delhomme, N., et al. (2013). The genomic and transcriptomic landscape of a HeLa cell line. G3 (Bethesda) 3, 1213-1224.

Mortusewicz, O., Fouquerel, E., Ame, J. C., Leonhardt, H., and Schreiber, V. (2011). PARG is recruited to DNA damage sites through poly(ADP-ribose)- and PCNA-dependent mechanisms. Nucleic Acids Res 39, 5045-5056.

Shirai, H., Fujimori, H., Gunji, A., Maeda, D., Hirai, T., Poetsch, A. R., Harada, H., Yoshida, T., Sasai, K., Okayasu, R., and Masutani, M. (2013). Parg deficiency confers radio-sensitization through enhanced cell death in mouse ES cells exposed to various forms of ionizing radiation. Biochem Biophys Res Commun 435, 100-106.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, Kroll et al. conduct an in-depth behavioral analysis of F0 knockouts of 4 genes associated with late-onset Alzheimer's Disease (AD), together with 3 genes associated with early-onset AD. Kroll and colleagues developed a web application (ZOLTAR) to compare sleep-associated traits between genetic mutants with those obtained from a panel of small molecules to promote the identification of affected pathways and potential therapeutic interventions. The authors make a set of potentially important findings vis-à-vis the relationship between AD-associated genes and sleep. First, they find that loss-of-function in late-onset AD genes universally results in night-time sleep loss, consistent with the well supported hypothesis that sleep disruption contributes to Alzheimer's-related pathologies. psen-1, an early-onset associated AD gene, which the authors find is principally responsible for the generation of AB40 and AB42 in zebrafish, also shows a slight increase in activity at night and slight decreases in night-time sleep. Conversely, psen-2 mutations increase daytime sleep, while appa/appb mutations have no impact on sleep. Finally, using ZOLTAR, the authors identify serotonin receptor activity as potentially disrupted in sorl1 mutants, while betamethasone is identified as a potential therapeutic to promote reversal of psen2 knockout-associated phenotypes.

This is a highly innovative and thorough study, yet a handful of key questions remain. First, are night-time sleep loss phenotypes observed in all knockouts for late-onset AD genes in the larval zebrafish a valid proxy for AD risk?

We cannot say, but it is an interesting question. We selected the four late-onset Alzheimer’s risk genes (APOE, CD2AP, CLU, SORL1) based on human genetics data and brain expression in zebrafish larvae, not based on their likelihood to modify sleep behaviour, which we could have tried by searching for overlaps with GWAS of sleep phenotypes, for example. Consequently, we find it remarkable that all four of these genes caused a night-time sleep phenotype when mutated. We also find it reassuring that knockout of appa/appb and psen2 did not cause a night-time sleep phenotype, which largely excludes the possibility that the phenotype is a technical artefact (e.g. caused by the F0 knockout method) or a property of every gene expressed in the larval brain.

Having said that, it could still be a coincidence, rather than a special property of genes associated with late-onset AD. In addition to testing additional late-onset Alzheimer’s risk genes, the ideal way to answer this question would be to test in parallel a random set of genes expressed in the brain at this stage of development. From this random set, one could estimate the proportion of genes that cause a night-time sleep phenotype when mutated. One could then use that information to test whether late-onset Alzheimer’s risk genes are indeed enriched for genes that cause a night-time sleep phenotype when mutated.

For those mutants that cause night-time sleep disturbances, do these phenotypes share a common underlying pathway? e.g. Do 5-HT reuptake inhibitors promote sleep across all 4 late-onset genes in addition to psen1? Can 5-HT reuptake inhibitors reverse other AD-related pathologies in zebrafish? Can compounds be identified that have a common behavioral fingerprint across all or multiple AD risk genes? Do these modify sleep phenotypes?

To attempt to answer these questions, we used ZOLTAR to generate predictions for all the knockout behavioural fingerprints presented in the study, in the same way as for sorl1 in Fig. 5 and Fig. 5–supplement 1. Here are the indications, targets, and KEGG pathways which are shared by the largest number of knockouts (Author response image 1):

– One indication is shared by 4/7 knockouts: “opioid dependence” (significant for appa/appb, psen1, apoea/apoeb, cd2ap).

– Four targets are shared by 4/7 knockouts: “strychnine-binding glycine receptor” (psen1, apoea/apoeb, clu, sorl1); “neuronal acetylcholine receptor beta-2” (psen1, apoea/apoeb, cd2ap, clu); thyroid peroxidase (psen1, apoea/apoeb, cd2ap, clu); carbonic anhydrase IV (appa/appb, psen1, psen2, cd2ap).

– Three KEGG pathways are shared by 5/7 knockouts: “cholinergic synapse” (psen1, apoea/apoeb, cd2ap, clu, sorl1); tyrosine metabolism (psen2, apoea/apoeb, cd2ap, clu, sorl1); and “nitrogen metabolism” (appa/appb, psen1, psen2, apoea/apoeb, cd2ap).

As reminder, we hypothesised that loss of Sorl1 affected serotonin signalling based on the following annotations being significant: indication “depression”, target “serotonin transporter”, and KEGG pathway “serotonergic synapse”. Indication “depression” is only significant for sorl1 knockouts; target “serotonin transporter” is also significant for appa/appb and psen2 knockouts; and KEGG pathway “serotonergic synapse” is also significant for psen2 knockouts. ZOLTAR therefore does not predict serotonin signalling to be a major theme common to all mutants with a night-time sleep loss phenotype.

Particularly interesting is cholinergic signalling appearing in the most common targets and KEGG pathways. Acetylcholine signalling is a major theme in research on AD. For example, the first four drugs ever approved by the FDA to treat AD were acetylcholinesterase inhibitors, which increase acetylcholine signalling by preventing its breakdown by acetylcholinesterase. These drugs are generally considered only to treat symptoms and not modify disease course, but this view has been called into question (Munoz-Torrero, 2008; Relkin, 2007). If, as ZOLTAR suggests, mutations in several Alzheimer’s risk genes affect cholinergic signalling early in development, this would point to a potential causal role of cholinergic disruption in AD.

Author response image 1.

Common predictions from ZOLTAR for the seven Alzheimer’s risk genes tested. Predictions from ZOLTAR which are shared by multiple knockout behavioural fingerprints presented in the study. Only indications, targets, and KEGG pathways which are significant for at least three of the seven knockouts tested are shown, ranked from the annotations which are significant for the largest number of knockouts.

Finally, the web- based platform presented could be expanded to facilitate comparison of other behavioral phenotypes, including stimulus-evoked behaviors.

Yes, absolutely. The behavioural dataset we used (Rihel et al., 2010) did not measure other stimuli than day/night light transitions, but the “SauronX” platform and dataset (MyersTurnbull et al., 2022) seems particularly well suited for this. To provide some context, we and collaborators have occasionally used the dataset by Rihel et al. (2010) to generate hypotheses or find candidate drugs that reverse a behavioural phenotype measured in the sleep/wake assay (Ashlin et al., 2018; Hoffman et al., 2016). The present work was the occasion to enable a wider and more intuitive use of this dataset through the ZOLTAR app, which has already proven successful. Future versions of ZOLTAR may seek to incorporate larger drug datasets using more types of measurements.

Finally, the authors propose but do not test the hypothesis that sorl1 might regulate localization/surface expression of 5-HT2 receptors. This could provide exciting / more convincing mechanistic support for the assertion that serotonin signaling is disrupted upon loss of AD-associated genes.

While working on the Author Response, we made some changes to the analysis ran by ZOLTAR to calculate enrichments (see Methods and github.com/francoiskroll/ZOLTAR, notes on v2). With the new version, 5-HT receptor type 2 is not a significantly enriched target for the sorl1 knockout fingerprint but type 4 is. 5-HT receptor type 4 was also shown to interact with sorting nexin 27, a subunit of retromer, so is a promising candidate (Joubert et al., 2004). Antibodies against human 5-HT receptor type 2 and 4a exist; whether they would work in zebrafish remains to be tested. In our experience, the availability of antibodies suitable for immunohistochemistry in the zebrafish is a serious experimental roadblock.

Note, all the results presented in the “Version of Records” are from ZOLTAR v2.

Despite these important considerations, this study provides a valuable platform for highthroughput analysis of sleep phenotypes and correlation with small-molecule-induced sleep phenotypes.

Strengths:

- Provides a useful platform for comparison of sleep phenotypes across genotypes/drug manipulations.

- Presents convincing evidence that night-time sleep is disrupted in mutants for multiple late onset AD-related genes.

- Provides potential mechanistic insights for how AD-related genes might impact sleep and identifies a few drugs that modify their identified phenotypes

Weaknesses:

- Exploration of potential mechanisms for serotonin disruption in sorl1 mutants is limited.

- The pipeline developed can only be used to examine sleep-related / spontaneous movement phenotypes and stimulus-evoked behaviors are not examined.

- Comparisons between mutants/exploration of commonly affected pathways are limited.

Thank you for these excellent suggestions, please see our answers above.

Reviewer #2 (Public Review):

Summary:

This work delineates the larval zebrafish behavioral phenotypes caused by the F0 knockout of several important genes that increase the risk for Alzheimer's disease. Using behavioral pharmacology, comparing the behavioral fingerprint of previously assayed molecules to the newly generated knockout data, compounds were discovered that impacted larval movement in ways that suggest interaction with or recovery of disrupted mechanisms.

Strengths:

This is a well-written manuscript that uses newly developed analysis methods to present the findings in a clear, high-quality way. The addition of an extensive behavioral analysis pipeline is of value to the field of zebrafish neuroscience and will be particularly helpful for researchers who prefer the R programming language. Even the behavioral profiling of these AD risk genes, regardless of the pharmacology aspect, is an important contribution. The recovery of most behavioral parameters in the psen2 knockout with betamethasone, predicted by comparing fingerprints, is an exciting demonstration of the approach. The hypotheses generated by this work are important stepping stones to future studies uncovering the molecular basis of the proposed gene-drug interactions and discovering novel therapeutics to treat AD or co-occurring conditions such as sleep disturbance.

Weaknesses:

- The overarching concept of the work is that comparing behavioral fingerprints can align genes and molecules with similarly disrupted molecular pathways. While the recovery of the psen2 phenotypes by one molecule with the opposite phenotype is interesting, as are previous studies that show similar behaviorally-based recoveries, the underlying assumption that normalizing the larval movement normalizes the mechanism still lacks substantial support. There are many ways that a reduction in movement bouts could be returned to baseline that are unrelated to the root cause of the genetically driven phenotype. An ideal experiment would be to thoroughly characterize a mutant, such as by identifying a missing population of neurons, and use this approach to find a small molecule that rescues both behavior and the cellular phenotype. If the connection to serotonin in the sorl1 was more complete, for example, the overarching idea would be more compelling.

Thank you for this cogent criticism.

On the first point, we were careful not to claim that betamethasone normalises the molecular/cellular mechanism that causes the psen2 behavioural phenotype. Having said that, yes, to a certain extent that would be the hope of the approach. As you say, every compound which normalises the behavioural fingerprint will not normalise the underlying mechanism, but the opposite seems true: every compound that normalises the underlying mechanism should also normalise the behavioural fingerprint. We think this logic makes the “behaviour-first” approach innovative and interesting. The logic is to discover compounds that normalise the behavioural phenotype first, only subsequently test whether they also normalise the molecular mechanism, akin to testing first whether a drug resolves the symptoms before testing whether it actually modifies disease course. While in practice testing thousands of drugs in sufficient sample sizes and replicates on a mutant line is challenging, the dataset queried through ZOLTAR provides a potential shortcut by shortlisting in silico compounds that have the opposite effect on behaviour.

You mention a “reduction in movement bouts” but note here that the number of behavioural parameters tested is key to our argument. To take the two extremes, say the only behavioural parameter we measured in psen2 knockout larvae was time active during the day, then, yes, any stimulant used at the right concentration could probably normalise the phenotype. In this situation, claiming that the stimulant is likely to also normalise the underlying mechanism, or even that it is a genuine “phenotypic rescue”, would not be convincing. Conversely, say we were measuring thousands of behavioural parameters under various stimuli, such as swimming speed, position in the well, bout usage, tail movements, and eye angles, it seems almost impossible for a compound to rescue most parameters without also normalising the underlying mechanism. The present approach is somewhere inbetween: ZOLTAR uses six behavioural parameters for prediction (e.g. Fig 6a), but all 17 parameters calculated by FramebyFrame can be used to assess rescue during a subsequent experiment (Fig. 6c). For both, splitting each parameter in day and night increases the resolution of the approach, which partly answers your criticism. For example, betamethasone rescued the day-time hypoactivity without causing night-time hyperactivity, so we are not making the “straw man argument” explained above of using any broad stimulant to rescue the hypoactivity phenotype.

Furthermore, for diseases where the behavioural defect is the primary concern, such as autism or bipolar disorder, perhaps this behaviour-first approach is all that is needed, and whether or not the compound precisely rescues the underlying mechanism is somewhat secondary. The use of lithium to prevent manic episodes in bipolar disorder is a good example. It was initially tested because mania was thought to be caused by excess uric acid and lithium can dissolve uric acid (Mitchell and Hadzi-Pavlovic, 2000). The theory is now discredited, but lithium continues to be used without a precise understanding of its mode of action. In this example, behavioural rescue alone, assuming the secondary effects are tolerable, is sufficient to be beneficial to patients, and whether it modulates the correct causal pathway is secondary.

On the second point, we agree that testing first ZOLTAR on a mutant for which we have a fairly good understanding of the mechanism causing the behavioural phenotype could have been a productive approach. Note, however, that examples already exist in the literature (Ashlin et al., 2018; Hoffman et al., 2016). The example from Hoffman et al. (2016) is especially convincing. Drugs generating behavioural fingerprints that positively correlate with the cntnap2a/cntnap2b double knockout fingerprint were enriched with NMDA and GABA receptor antagonists. In experiments analogous to our citalopram and fluvoxamine treatments (Fig. 5c,d and Fig. 5–supplement 1c,d), cntnap2a/cntnap2b knockout larvae were overly sensitive to the NMDA receptor antagonist MK-801 and the GABAA receptor antagonist pentylenetetrazol (PTZ). Among other drugs tested, zolpidem, a GABAA receptor agonist, caused opposite effects on wild-type and cntnap2a/cntnap2b knockout larvae. Knockout larvae were found to have fewer GABAergic neurons in the forebrain. While these studies did not use precisely the same analysis that ZOLTAR runs, they used the same rationale and behavioural dataset to make these predictions (Rihel et al., 2010), which shows that approaches like ZOLTAR can point to causal processes.

On your last point, we hope our experiment testing fluvoxamine, another selective serotonin reuptake inhibitor (SSRI), makes the connection between Sorl1 and serotonin signalling more convincing.

- The behavioral difference between the sorl1 KO and scrambled at the higher dose of the citalopram is based on a small number of animals. The KO Euclidean distance measure is also more spread out than for the other datasets, and it looks like only five or so fish are driving the group difference. It also appears as though the numbers were also from two injection series. While there is nothing obviously wrong with the data, I would feel more comfortable if such a strong statement of a result from a relatively subtle phenotype were backed up by a higher N or a stable line. It is not impossible that the observed difference is an experimental fluke. If something obvious had emerged through the HCR, that would have also supported the conclusions. As it stands, if no more experiments are done to bolster the claim, the confidence in the strength of the link to serotonin should be reduced (possibly putting the entire section in the supplement and modifying the discussion). The discussion section about serotonin and AD is interesting, but I think that it is excessive without additional evidence.

We mostly agree with this criticism. One could interpret the larger spread of the data for sorl1 KO larvae treated with 10 µM citalopram as evidence that the knockout larvae do indeed react differently to the drug at this dose, regardless of being driven by a subset of the animals. The result indeed does not survive removing the top 5 (p = 0.87) or top 3 (p = 0.18) sorl1 KO + 10 µM larvae, but this amounts to excluding 20 (3/14) or 35 (5/14) % of the datapoints as potential outliers, which is unreasonable. In fact, excluding the top 5 sorl1 KO + 10 µM is equivalent to calling any datapoint with z-score > 0.2 an outlier (z-scores of the top 5 datapoints are 0.2–1.8). Applying consistently the same criterion to the scrambled + 10 µM group would remove the top 6 datapoints (z-scores = 0.5–3.9). Comparing the resulting two distributions again gives the sorl1 KO + 10 µM distribution as significantly higher (p = 0.0015). We would also mention that Euclidean distance, as a summary metric for distance between behavioural fingerprints, has limitations. For example, the measure will be more sensitive to changes in some parameters but not others, depending on how much room there is for a given parameter to change. We included this metric to lend support to the observation one can draw from the fingerprint plot (Fig. 5c) that sorl1 mutants respond in an exaggerated way to citalopram across many parameters, while being agnostic to which parameter might matter most.

Given that the HCR did not reveal anything striking, we agree with you that too much of our argument relied on this result being robust. As you and Reviewer #3 suggested, we repeated this experiment with a different SSRI, fluvoxamine (Fig. 5–supplement 1). We cannot readily explain why the result was opposite to what we found with citalopram, but in both cases sorl1 knockout larvae reacted differently than their control siblings, which adds an argument to our claim that ZOLTAR correctly predicted serotonin signalling as a disrupted pathway from the behavioural fingerprint. Accordingly, we mostly kept the Discussion on Sorl1 the same, although we concede that we may not have identified the molecular mechanism.

- The authors suggest two hypotheses for the behavioral difference between the sorl1 KO and scrambled at the higher dose of the citalopram. While the first is tested, and found to not be supported, the second is not tested at all ("Ruling out the first hypothesis, sorl1 knockouts may react excessively to a given spike in serotonin." and "Second, sorl1 knockouts may be overly sensitive to serotonin itself because post-synaptic neurons have higher levels of serotonin receptors."). Assuming that the finding is robust, there are probably other reasons why the mutants could have a different sensitivity to this molecule. However, if this particular one is going to be mentioned, it is surprising that it was not tested alongside the first hypothesis. This work could proceed without a complete explanation, but additional discussion of the possibilities would be helpful or why the second hypothesis was not tested.

There are no strong scientific reasons why this hypothesis was not tested. The lead author (F Kroll) moved to a different lab and country so the project was finalised at that time. We do not plan on testing this hypothesis at this stage. However, we adapted the wording to make it clear this is one possible alternative hypothesis which could be tested in the future. The small differences found by HCR are actually more in line with the new results from the fluvoxamine experiment, so it may also be that both hypotheses (pre-synaptic neurons releasing less serotonin when reuptake is blocked; or post-synaptic neurons being less sensitive) contribute. The fluvoxamine experiment was performed in a different lab (ICM, Paris; all other experiments were done in UCL, London) in a different wild-type strain (TL in ICM, AB x Tup LF in UCL), which complicates how one interprets this discrepancy.

- The authors claim that "all four genes produced a fairly consistent phenotype at night". While it is interesting that this result arose in the different lines, the second clutch for some genes did not replicate as well as others. I think the findings are compelling, regardless, but the sometimes missing replicability should be discussed. I wonder if the F0 strategy adds noise to the results and if clean null lines would yield stronger phenotypes. Please discuss this possibility, or others, in regard to the variability in some phenotypes.

For the first part of this point, please see below our answer to Reviewer #3, point (2) c.

Regarding the F0 strategy potentially adding variability, it is an interesting question which we tested in a larger dataset of behavioural recordings from F0 and stable knockouts for the same genes (unpublished). In summary, the F0 knockout method does not increase clutchto-clutch or larva-to-larva variability in the assay. F0 knockout experiments found many more significant parameters and larger effect sizes than stable knockout experiments, but this difference could largely be explained by the larger sample sizes of F0 knockout experiments. In fact, larger sample sizes within individual clutches appears to be a major advantage of the F0 knockout approach over in-cross of heterozygous knockout animals as it increases sensitivity of the assay without causing substantial variability. We plan to report in more detail on this analysis in a separate paper as we think it would dilute the focus of the present work.

- In this work, the knockout of appa/appb is included. While APP is a well-known risk gene, there is no clear justification for making a knockout model. It is well known that the upregulation of app is the driver of Alzheimer's, not downregulation. The authors even indicate an expectation that it could be similar to the other knockouts ("Moreover, the behavioural phenotypes of appa/appb and psen1 knockout larvae had little overlap while they presumably both resulted in the loss of Aβ." and "Comparing with early-onset genes, psen1 knockouts had similar night-time phenotypes, but loss of psen2 or appa/appb had no effect on night-time sleep."). There is no reason to expect similarity between appa/appb and psen1/2. I understand that the app knockouts could unveil interesting early neurodevelopmental roles, but the manuscript needs to be clarified that any findings could be the opposite of expectation in AD.

On “there is no reason to expect similarity […]”, we disagree. Knockout of appa/appb and knockout of psen1 will both result in loss of Aβ (appa/appb encode Aβ and psen1 cleaves Appa/Appb to release Aβ, cf. Fig. 3e). Consequently, a phenotype caused by the loss of Aβ, or possibly other Appa/Appb cleavage products, should logically be found in both appa/appb and psen1 knockouts.

On “it is well known that the upregulation of APP is the driver of Alzheimer’s, not downregulation”; we of course agree. Among others, the examples of Down syndrome, APP duplication (Sleegers et al., 2006), or mouse models overexpressing human APP show definitely that overexpression of APP is sufficient to cause AD. Having said that, we would not be so quick in dismissing APP knockout as potentially relevant to understanding of AD.

Loss of soluble Aβ due to aggregation could contribute to pathology (Espay et al., 2023). Without getting too much into this intricate debate, links between levels of Aβ and risk of disease are often counter-intuitive too. For example, out of 138 PSEN1 mutations screened in vitro, 104 reduced total Aβ production and 11 even seemingly abolished the production of both Aβ40 and Aβ42 (Sun et al., 2017). In short, loss of soluble Aβ occurs in both AD and in our appa/appb knockout larvae.

We added a sentence in Results (section psen2 knockouts […]) to briefly justify our appa/appb knockout approach. To be clear, we do not want to imply, for example, that the absence of a night-time sleep phenotype for appa/appb is contradictory to the body of literature showing links between Aβ and sleep, including in zebrafish (Özcan et al., 2020). As you say, our experiment tested loss of App, including Aβ, while the literature typically reports on overexpression of APP, as in APP/PSEN1-overexpressing mice (Jagirdar et al., 2021).

Reviewer #3 (Public Review):

In this manuscript by Kroll and colleagues, the authors describe combining behavioral pharmacology with sleep profiling to predict disease and potential treatment pathways at play in AD. AD is used here as a case study, but the approaches detailed can be used for other genetic screens related to normal or pathological states for which sleep/arousal is relevant. The data are for the most part convincing, although generally the phenotypes are relatively small and there are no major new mechanistic insights. Nonetheless, the approaches are certainly of broad interest and the data are comprehensive and detailed. A notable weakness is the introduction, which overly generalizes numerous concepts and fails to provide the necessary background to set the stage for the data.

Major points

(1) The authors should spend more time explaining what they see as the meaning of the large number of behavioral parameters assayed and specifically what they tell readers about the biology of the animal. Many are hard to understand--e.g. a "slope" parameter.

We agree that some parameters do not tell something intuitive about the biology of the animal. It would be easy to speculate. For example, the “activity slope” parameter may indicate how quickly the animal becomes tired over the course of the day. On the other hand, fractal dimension describes the “roughness/smoothness” of the larva’s activity trace (Fig. 2–supplement 1a); but it is not obvious how to translate this into information about the physiology of the animal. We do not see this as an issue though. While some parameters do provide intuitive information about the animal’s behaviour (e.g. sleep duration or sunset startle as a measure of startle response), the benefit of having a large number of behavioural parameters is to compare behavioural fingerprints and assess rescue of the behavioural phenotype by small molecules (Fig. 6c). For this purpose, the more parameters the better. The “MoSeq” approach from Wiltschko et al., 2020 is a good example from literature that inspired our own Fig. 6c. While some of the “behavioural syllables” may be intuitive (e.g. running or grooming), it is probably pointless to try to explain the ‘meaning’ of the “small left turn in place with head motion” syllable (Wiltschko et al., 2020). Nonetheless, this syllable was useful to assess whether a drug specifically treats the behavioural phenotype under study without causing too many side effects. Unfortunately, ZOLTAR has to reduce the FramebyFrame fingerprint (17 parameters) to just six parameters to compare it to the behavioural dataset from Rihel et al., 2010, but here, more parameters would almost certainly translate into better predictions too, regardless of their intuitiveness.

It is true however that we did not give much information on how some of the less intuitive parameters, such as activity slope or fractal dimension, are calculated or what they describe about the dataset (e.g. roughness/smoothness for fractal dimension). We added a few sentences in the legend of Fig. 2–supplement 1.

(2) Because in the end the authors did not screen that many lines, it would increase confidence in the phenotypes to provide more validation of KO specificity. Some suggestions include:

a. The authors cite a psen1 and psen2 germline mutant lines. Can these be tested in the FramebyFrame R analysis? Do they phenocopy F0 KO larvae?

We unfortunately do not have those lines. We investigated the availability of importing a psen2 knockout line from abroad, but the process of shipping live animals is becoming more and more cost and time prohibitive. However, we observed the same pigmentation phenotype for psen2 knockouts as reported by Jiang et al., 2018, which is at least a partial confirmation of phenocopying a loss of function stable mutant.  

b. psen2_KO is one of the larger centerpieces of the paper. The authors should present more compelling evidence that animals are truly functionally null. Without this, how do we interpret their phenotypes?

We disagree that there should be significant doubt about these mutants being truly functionally null, given the high mutation rate and presence of the expected pigmentation phenotype (Jiang et al., 2018, Fig. 3f and Fig. 3–supplement 3a). The psen2 F0 knockouts were virtually 100% mutated at three exons across the gene (mutation rates were locus 1: 100 ± 0%; locus 2: 99.99 ± 0.06%; locus 3: 99.85 ± 0.24%). Additionally, two of the three mutated exons had particularly high rates of frameshift mutations (locus 1: 97 ± 5%; locus 2: 88 ± 17% frameshift mutation rate). It is virtually impossible that a functional protein is translated given this burden of frameshift mutations. Phenotypically, in addition to the pigmentation defect, double psen1/psen2 F0 knockout larvae had curved tails, the same phenotype as caused by a high dose of the γ-secretase inhibitor DAPT (Yang et al., 2008). These double F0 knockouts were lethal, while knockout of psen1 or psen2 alone did not cause obvious morphological defects. Evidently, most larvae must have been psen2 null mutants in this experiment, otherwise functional Psen2 would have prevented early lethality.

Translation of zebrafish psen2 can start at downstream start codons if the first exon has a frameshift mutation, generating a seemingly functional Psen2 missing the N-terminus (Jiang et al., 2020). Zebrafish homozygous for this early frameshift mutation had normal pigmentation, showing it is a reliable marker of Psen2 function even when it is mutated. This mechanism is not a concern here as the alternative start codons are still upstream of two of the three mutated exons (the alternative start codons discovered by Jiang et al., 2020 are in exon 2 and 3, but we targeted exon 3, exon 4, and exon 6).

We understand that the zebrafish community may be cautious about F0 phenotyping compared to stably generated mutants. As mentioned to Reviewer #2, we are planning to assemble a paper that expressly compares behavioural phenotypes measured in F0 vs. stable mutants to allay some of these concerns. Our current manuscript, which combines CRISPR-Cas9 rapid F0 screening with in silico pharmacological predictions, inevitability represents a first step in characterizing the functions of these genes. 

c. Related to the above, for cd2AP and sorl1 KO, some of the effect sizes seem to be driven by one clutch and not the other. In other words, great clutch-to-clutch variability. Should the authors increase the number of clutches assayed?

Correct, there is substantial clutch-to-clutch variability in this behavioural assay. This is not specific to our experiments. Even within the same strain, wild-type larvae from different clutches (i.e. non-siblings) behave differently (Joo et al., 2021). This is why it is essential to compare behavioural phenotypes within individual clutches (i.e. from a single pair of parents, one male and one female), as we explain in Methods (section Behavioural video-tracking) and in the documentation of the FramebyFrame package. We often see two different experimental designs in literature: comparing non-sibling wild-type and mutant larvae, or pooling different clutches which include all genotypes (e.g. pooling multiple clutches from heterozygous in-crosses or pooling wild-type clutches before injecting them). The first experimental design causes false positive findings (Joo et al., 2021), as the clutchto-clutch variability we and others observe gets interpreted as a behavioural phenotype. The second experimental design should not cause false positives but likely decreases the sensitivity of the assay by increasing the spread within genotypes. In both cases, the clutch-to-clutch variability is hidden, either by interpreting it as a phenotype (first case) or by adding it to animal-to-animal variability (second case). Our experimental design is technically more challenging as it requires obtaining large clutches from unique pairs of parents. However, this approach is better as it clearly separates the different sources of variability (clutch-to-clutch or animal-to-animal). As for every experiment, yes, a larger number of replicates would be better, but we do not plan to assay additional clutches at this time. Our work heavily focuses on the sorl1 and psen2 knockout behavioural phenotypes. The key aspects of these phenotypes were effectively tested in four experiments (five to six clutches) as sorl1 knockout larvae were also tracked in the citalopram and fluvoxamine experiments (Fig. 5 and Fig. 5–supplement 1), and psen2 knockout larvae were also tracked in the small molecule rescue experiment (Fig. 6 and Fig. 6–supplement 1).

The psen2 behavioural phenotype replicated well across the six clutches tested (pairwise cosine similarities: 0.62 ± 0.15; Author response image 2a). 5/6 clutches were less active and initiating more sleep bouts during the day, as we claimed in Fig. 3.

In the citalopram experiment, the H₂O-treated sorl1 knockout fingerprint replicated fairly well the baseline recordings in Fig. 4, despite the smaller sample size (cos = 0.30 and 0.78; Author response image 2b, see “KO Fig. 5”). 5/6 of the significant parameters presented in Fig. 4–supplement 4 moved in the same direction, and knockout larvae were also hypoactive during the day but hyperactive at night. Note that two clutches were tracked on the same 96-well plate in this experiment. We calculated each larva’s z-score using the average of its control siblings, then we averaged all the z-scores to generate the fingerprint. The H₂O treated sorl1 knockout clutch from the fluvoxamine experiment did not replicate well the baseline recordings (cos = 0.08 and 0.11; Author response image 2b, see “KO Fig. 5–suppl. 1”). Knockout larvae were hypoactive during the day as expected, but behaviour at night was not as robustly affected. As mentioned above, knockouts were made in a different genetic background (TL, instead of AB x Tup LF used for all other experiments), which could explain the discrepancy.

We also took the opportunity to check whether our SSRI treatments replicated well the data from Rihel et al., 2010. For both citalopram (n = 3 fingerprints in the database) and fluvoxamine (n = 4 fingerprints in the database), replication was excellent (cos ≥ 0.67 for all comparisons of a fingerprint from this study vs. a fingerprint from Rihel et al. 2010; Author response image 2c,d). Note that the scrambled + 10 µM citalopram and + 10 µM fluvoxamine fingerprints correlate extremely well (cos = 0.92; can be seen in Author response image 2c,d), which was predicted by the small molecule screen dataset.

Author response image 2.

Replication of psen2 and sorl1 F0 knockout fingerprints and SSRI treatments from Rihel et al., 2010. a, (left) Every psen2 F0 knockout behavioural fingerprint generated in this study. Each dot represents the mean deviation from the same-clutch scrambled-injected mean for that parameter (z-score, mean ± SEM). From the experiments in Fig. 6, presented is the psen2 F0 knockout + H₂O fingerprints. The fingerprints in grey (“not shown”) are from a preliminary drug treatment experiment we did not include in the final study. These fingerprints are from psen2 F0 knockout larvae treated with 0.2% DMSO, normalised to scrambled-injected siblings also treated with 0.2% DMSO. (right) Pairwise cosine similarities (−1.0–1.0) for the fingerprints presented. b, Every sorl1 F0 knockout behavioural fingerprint, as in a). c, The scrambled-injected + citalopram (10 µM) fingerprints (grey) in comparison to the citalopram (10–15 µM) fingerprints from the Rihel et al., 2010 database (green). d, The scrambled-injected + fluvoxamine (10 µM) fingerprint (grey) in comparison to the fluvoxamine fingerprints from the Rihel et al., 2010 database (pink). In c) and d), the scrambled-injected fingerprints are from the experiments in Fig. 5 and Fig. 5–suppl. 1, but were converted here into the behavioural parameters used by Rihel et al., 2010 for comparison. Parameters: 1, average activity (sec active/min); 2, average waking activity (sec active/min, excluding inactive minutes); 3, total sleep (hr); 4, number of sleep bouts; 5, sleep bout length (min); 6, sleep latency (min until first sleep bout).

(3) The authors make the point that most of the AD risk genes are expressed in fish during development. Is there public data to comment on whether the genes of interest are expressed in mature/old fish as well? Just because the genes are expressed early does not at all mean that early- life dysfunction is related to future AD (though this could be the case, of course). Genes with exclusive developmental expression would be strong candidates for such an early-life role, however. I presume the case is made because sleep studies are mainly done in juvenile fish, but I think it is really a prejy minor point and such a strong claim does not even need to be made.

This is a fair criticism but we do not make this claim (“early-life dysfunction is related to future AD”) from expression alone. The reviewer is probably referring to the following quote:

“[…] most of these were expressed in the brain of 5–6-dpf zebrafish larvae, suggesting they play a role in early brain development or function,” which does not mention future risk of AD. We do suggest that these genes have a function in development. After all, every gene that plays a role in brain development must be expressed during development, so this wording seemed reasonable. Nevertheless, we adapted the wording to address this point and Reviewer #2’s complaint below. As noted, the primary goal was to check that the genes we selected were indeed expressed in zebrafish larvae before performing knockout experiments. Our discussion does raise the hypothesis that mutations in Alzheimer’s risk genes impact brain development and sleep early in life, but this argument primarily relies on our observation that knockout of late-onset Alzheimer’s risk genes causes sleep phenotypes in 7-day old zebrafish larvae and from previous work showing brain structural differences in children at high genetic risk of AD (Dean et al., 2014; Quiroz et al., 2015), not solely on gene expression early in life.

Please also see our answer to a similar point raised by Reviewer #2 below (cf. Author response image 7).

(4) A common quandary with defining sleep behaviorally is how to rectify sleep and activity changes that influence one another. With psen2 KOs, the authors describe reduced activity and increased sleep during the day. But how do we know if the reduced activity drives increased behavioral quiescence that is incorrectly defined as sleep? In instances where sleep is increased but activity during periods during wake are normal or elevated, this is not an issue. But here, the animals might very well be unhealthy, and less active, so naturally they stop moving more for prolonged periods, but the main conclusion is not sleep per se. This is an area where more experiments should be added if the authors do not wish to change/temper the conclusions they draw. Are psen2 KOs responsive to startling stimuli like controls when awake? Do they respond normally when quiescent? Great care must be taken in all models using inactivity as a proxy for sleep, and it can harm the field when there is no acknowledgment that overall health/activity changes could be a confound. Particularly worrisome is the betamethasone data in Figure 6, where activity and sleep are once again coordinately modified by the drug.

This is a fair criticism. We agree it is a concern, especially in the case of psen2 as we claim that day-time sleep is increased while zebrafish are diurnal. We do not rely heavily on the day-time inactivity being sleep (the ZOLTAR predictions or the small molecule rescue do not change whether the parameter is called sleep or inactivity), but our choice of labelling can fairly be challenged.

To address “are psen2 KO responsive to startling stimuli like controls when awake/when quiescent”, we looked at the larvae’s behaviour immediately after lights abruptly switched on in the mornings. Almost every larva, regardless of genotype, responded strongly to every lights-off transition during the experiment. Instead, we chose the lights-on transition for this analysis because it is a weaker startling stimulus for the larvae than the lights-off transition (Fig. 3–supplement 3), potentially exposing differences between genotypes or behavioural states (quiescent or awake). We defined a larva as having reacted to the lights switching on if it made a swimming bout during the second (25 frames) a er the lights-on transition. Across two clutches and two lights-on transitions, an average of 65% (range 52–73%) of all larvae reacted to the stimulus. psen2 knockout larvae were similarly likely, if not more likely, to respond (in average 69% responded, range 60–76%) than controls (60% average, range 44– 75%). When the lights switched on, about half of the larvae (39–51%) would have been classified as asleep according to the one-minute inactivity definition (i.e. the larva did not move in the minute preceding the lights transition). This allowed us to also compare behavioural states, as suggested by the reviewer. For three of the four light transitions, larvae which were awake when lights switched on were more likely to react than asleep larvae, but this difference was not striking (overall, awake larvae were only 1.1× more likely to react; Author response image 3). Awake psen2 knockout larvae were 1.1× (range 1.04–1.11×) more likely to react than awake control larvae, so, yes, psen2 knockout larvae respond normally when awake. Asleep psen2 knockout larvae were 1.4× (range 0.63–2.19×) more likely to react than asleep control larvae, so psen2 knockouts are also more or equally likely to react than control larvae when asleep. In summary, the overall health of psen2 knockouts did not seem to be a significant confound in the experiment. As the reviewer suggested, if psen2 knockout larvae were seriously unhealthy, they would not be as responsive as control larvae to a startling stimulus.

Author response image 3.

psen2 F0 knockouts react normally to lights switching on, indicating they are largely healthy. At each lights-on transition (9 AM), each larva was categorised as awake if it had moved in the preceding one minute or asleep if it had been inactive for at least one minute. Darker tiles represent larvae which performed a swimming bout during the second following lights-on; lighter tiles represent larvae which did not move during that second. The total count of each waffle plot was normalised to 25 so plots can be compared to each other. The real count is indicated in the corner of each plot. Data is from the baseline psen2 knockout trackings presented in Fig. 3 and Fig. 3–suppl. 2.

Next, we compared inactive period durations during the day between psen2 and control larvae. If psen2 knockout larvae indeed sleep more during the day compared to controls, we may predict inactive periods longer than one minute to increase disproportionately compared to the increase in shorter inactive periods. This broadly appeared to be the case, especially for one of the two clutches (Author response image 4). In clutch 1, inactive periods lasting 1–60 sec were equally frequent in both psen2 and control larvae (fold change 1.0× during both days), while inactive periods lasting 1–2 min were 1.5× (day 1) and 2.5× (day 2) more frequent in psen2 larvae compared to control larvae. In clutch 2, 1–60 sec inactive periods were also equally frequent in both psen2 and control larvae, while inactive periods lasting 1–2 min were 3.4× (day 1) and 1.5× (day 2) more frequent in psen2 larvae compared to control larvae. Therefore, psen2 knockouts disproportionately increased the frequency of inactive periods longer than one minute, suggesting they genuinely slept more during the day.

Author response image 4.

psen2 F0 knockouts increased preferentially the frequency of longer inactive bouts. For each day and clutch, we calculated the mean distribution of inactive bout lengths across larvae of same genotype (psen2 F0 knockout or scrambled-injected), then compared the frequency of inactive bouts of different lengths between the two genotypes. For example, in clutch 1 during day 2, 0.01% of the average scrambled-injected larva’s inactive bouts lasted 111–120 seconds (X axis 120 sec) while 0.05% of the average psen2 F0 knockout larva lasted this long, so the fold change was 5×. Inactive bouts lasting < 1 sec were excluded from the analysis. In clutch 2, day 1 plot, two datapoints fall outside the Y axis limit: 140 sec, Y = 32×; 170 sec, Y = 16×. Data is from the baseline psen2 knockout trackings presented in Fig. 3 and Fig. 3–suppl. 2.

Ultimately, this criticism seems challenging to definitely address experimentally. A possible approach could be to use a closed-loop system which, after one minute of inactivity, triggers a stimulus that is sufficient to startle an awake larva but not an asleep larva. If psen2 knockout larvae indeed sleep more during the day, the stimulus should usually not be sufficient to startle them. Nevertheless, we believe the two analyses presented here are consistent with psen2 knockout larvae genuinely sleeping more during the day, so we decided to keep this label. We agree with the reviewer that the one-minute inactivity definition has limitations, especially for day-time inactivity.

(5) The conclusions for the serotonin section are overstated. Behavioural pharmacology purports to predict a signaling pathway disrupted with sorl1 KO. But is it not just possible that the drug acts in parallel to the true disrupted pathway in these fish? There is no direct evidence for serotonin dysfunction - that conclusion is based on response to the drug. Moreover, it is just one drug - is the same phenotype present with another SSRI? Likewise, language should be toned down in the discussion, as this hypothesis is not "confirmed" by the results (consider "supported"). The lack of measured serotonin differences further raises concern that this is not the true pathway. This is another major point that deserves further experimental evidence, because without it, the entire approach (behavioral pharm screen) seems more shaky as a way to identify mechanisms. There are any number of testable hypotheses to pursue such as a) Using transient transgenesis to visualize 5HT neuron morphology (is development perturbed: cell number, neurite morphology, synapse formation); b) Using transgenic Ca reporters to assay 5HT neuron activity.

Regarding the comment, “is it not just possible that the drug acts in parallel to the true disrupted pathway”, we think no, assuming we understand correctly the question. Key to our argument is the fact that sorl1 knockout larvae react differently to the drug(s) than control larvae. As an example, take night-time sleep bout length, which was not affected by knockout of sorl1 (Fig. 4–supplement 4). For the sake of the argument, say only dopamine signalling (the “true disrupted pathway”) was affected in sorl1 knockouts and that serotonin signalling was intact. Assuming that citalopram specifically alters serotonin signalling, then treatment should cause the same increase in sleep bout length in both knockouts and controls as serotonin signalling is intact in both. This is not what we see, however. Citalopram caused a greater increase in sleep bout length in sorl1 knockouts than in scrambled-injected larvae. In other words, the effect is non-additive, in the sense that citalopram did not add the same number of z-scores to sorl1 knockouts or controls. We think this shows that serotonin signalling is somehow different in sorl1 knockouts. Nonetheless, we concede that the experiment does not necessarily say much about the importance of the serotonin disruption caused by loss of Sorl1. It could be, for example, that the most salient consequence of loss of Sorl1 is cholinergic disruption (see reply to Reviewer #1 above) and that serotonin signalling is a minor theme.

Furthermore, we agree with the reviewer and Reviewer #2 that the conclusions were overly confident. As suggested, we decided to repeat this experiment with another SSRI, fluvoxamine. Please find the results of this experiment in Fig. 5–supplement 1. The suggestions to further test the serotonin system in the sorl1 knockouts are excellent as well, however we do not plan to pursue them at this stage.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major Comments:

- Data are presented in a variety of different ways, occasionally making comparisons across figures difficult. Perhaps at a minimum, behavioral fingerprints as in Figure 3 - Supplementary Figure 1 should be presented for all mutants in the main figures.

We like this suggestion! Thank you. We brought the behavioural fingerprints figure (previously Fig. 4–supplement 5) as main Fig. 4, and put the figure focused on the sorl1 knockout behavioural phenotype in supplementary, with the other gene-by-gene figures.

- It is not clear why some data were selected for supplemental rather than main figures. In many cases, detailed phenotypic data is provided for one example mutant in the main figures, and then additional mutants are described in detail in the supplement. Again, to facilitate comparisons between mutants, fingerprints could be provided for all mutants in a main figure, with detailed analyses moved to the supplements.

The logic was to dedicate one main figure to psen2 (Fig. 3) as an example of an early-onset Alzheimer’s risk gene, and one to sorl1 (previously Fig. 4) as an example of a late-onset Alzheimer’s risk gene. We focused on them in main figures as they are both tested again later (Fig. 5 and Fig. 6). Having said that, we agree that the fingerprints may be a better use of main figure space than the parameters plots. In addition to the above (fingerprints of lateonset Alzheimer’s risk genes in main figure), we rearranged the figures in the early-onset AD section to have the psen2 F0 knockout fingerprint in main.

- The explication of the utility of behavioral fingerprinting on page 35 is somewhat confusing. The authors describe drugs used to treat depression as enriched among small molecules anti-correlating with the sorl1 fingerprint. However, in Figure 5 - Supplementary Figure 1, drugs used to treat depression are biased toward positive cosines, which are indicated as having a more similar fingerprint to sorl1. These drugs should be described as more present among compounds positively correlating with the sorl1 fingerprint.

Sorry, the confusion is about “(anti-)correlating”. Precisely, we meant “correlating and/or anti-correlating”, not just anti-correlating. We changed to that wording. In short, the analysis is by design agnostic to whether compounds with a given annotation are found more on the positive cosines side (le side in Fig. 5–supplement 1a) or the negative cosines side (right side). This is because the dataset often includes both agonists and antagonists to a given pathway but these are difficult to annotate. For example, say 10 compounds in the dataset target the dopamine D4 receptor, but these are an unknown mix of agonists and antagonists. In this case, we want ZOLTAR to generate a low p-value when all 10 compounds are found at extreme ends of the list, regardless of which end(s) that is (e.g. top 8 and bottom 2 should give an extremely low p-value). Initially, we were splitting the list, for each annotation, into positive-cosine fingerprints and negative-cosine fingerprints and testing enrichment on both separately, but we think the current approach is better as it reflects better the cases we want to detect and considers all available examples for a given annotation in one test. In sum, yes, in this case drugs used to treat depression were mostly in the positive-cosine side, but the other drugs on the negative-cosine side also contributed to what the p-value is, so it reflects better the analysis to say “correlating and/or anticorrelating”. You can read more about our logic for the analysis in Methods (section Behavioural pharmacology from sorl1 F0 knockout’s fingerprint).

- The authors conclude the above-described section by stating: "sorl1 knockout larvae behaved similarly to larvae treated with small molecules targeting serotonin signaling, suggesting that the loss of Sorl1 disrupted serotonin signaling." Directionality here may be important. Are all of the drugs targeting the serotonin transporter SSRIs or similar? If so, then a correct statement would be that loss of Sorl1 causes similar phenotypes to drugs enhancing serotonin signaling. Finally, based on the correlation between serotonin transporter inhibitor trazodone and the sorl1 crispant phenotype, it is potentially surprising that the SSRI citalopram caused the opposite phenotype from sorl1, that is, increased sleep during the day and night. It is potentially interesting that this result was enhanced in mutants, and suggests dysfunction of serotonin signaling, but the statement that "our behavioral pharmacology approach correctly predicted from behaviour alone that serotonin signaling was disrupted" is too strong a conclusion.

We understand “disrupt” as potentially going either way, but this may not be the common usage. We changed to “altered”.

The point regarding directionality is excellent, however. We tested the proportion of serotonin transporter agonists and antagonists (SSRIs) on each side of the ranked list of small molecule fingerprints. We used the STITCH database for this analysis as it has more drug–target interactions, but likely less curated, than the Therapeutic Target Database (Szklarczyk et al., 2016). As with the Therapeutic Target Database, most fingerprints of compounds interacting with the serotonin transporter SLC6A4 were found on the side of positive cosines (p ~ 0.005 using the custom permutation test), which replicates Fig. 5a with a different source for the drug–target annotations (Author response image 5). On the side of positive cosines (small molecules which generate behavioural fingerprints correlating with the sorl1 fingerprint), there were 2 agonists and 26 antagonists. On the side of negative cosines (small molecules which generate behavioural fingerprints anti-correlating with the sorl1 fingerprint), there were 3 agonists and 2 antagonists. Using a Chi-squared test, this suggests a significant (p = 0.002) over-representation of antagonists (SSRIs) on the positive side (expected count = 24, vs. 26 observed) and agonists on the negative side (expected count = 1, vs. 3 observed). If SLC6A4 antagonists, i.e. SSRIs, indeed tend to cause a similar behavioural phenotype than knockout of sorl1, this would point in the direction of our original interpretation of the citalopram experiment; which was that excessive serotonin signalling is what causes the sorl1 behavioural phenotype.

Author response image 5.

Using the STITCH database as source of annotations also predicts SLC6A4 as an enriched target for the sorl1 behavioural fingerprint. Same figures as Fig. 5a,b but using the STITCH database (Szklarczyk et al., 2016) as source for the drug targets. a, Compounds annotated by STITCH as interacting with the serotonin transporter SLC6A4 tend to generate behavioural phenotypes similar to the sorl1 F0 knockout fingerprint. 40,522 compound–target protein pairs (vertical bars; 1,592 unique compounds) are ranked from the fingerprint with the most positive cosine to the fingerprint with the most negative cosine in comparison with the mean sorl1 F0 knockout fingerprint. Fingerprints of drugs that interact with SLC6A4 are coloured in yellow. Simulated p-value = 0.005 for enrichment of drugs interacting with SLC6A4 at the top (positive cosine) and/or bottom (negative cosine) of the ranked list by a custom permutation test. b, Result of the permutation test for top and/or bottom enrichment of drugs interacting with SLC6A4 in the ranked list. The absolute cosines of the fingerprints of drugs interacting with SLC6A4 (n = 52, one fingerprint per compound) were summed, giving sum of cosines = 15.9. To simulate a null distribution, 52 fingerprints were randomly drawn 100,000 times, generating a distribution of 100,000 random sum of cosines. Here, only 499 random draws gave a larger sum of cosines, so the simulated p-value was p = 499/100,000 = 0.005 **.

If this were true, we would expect, as the reviewer suggested, SSRI treatment (citalopram or fluvoxamine) on control larvae to give a similar behavioural phenotype as knockout of sorl1. However, this generally did not appear to be the case (sorl1 knockout fingerprint vs. SSRI-treated control fingerprint, cosine = 0.08 ± 0.35; Author response image 6).

Author response image 6.

sorl1 F0 knockouts in comparison to controls treated with SSRIs. a, sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H₂O fingerprint from the citalopram experiment) in comparison with the scrambled-injected + citalopram (1 or 10 µM) fingerprints. Each dot represents the mean deviation from the same-clutch scrambled-injected H₂O-treated mean for that parameter (z-score, mean ± SEM). b, As in a), sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H₂O fingerprint from the fluvoxamine experiment) in comparison with the scrambled-injected + fluvoxamine (10 µM) fingerprint.

The comparison with trazodone is an interesting observation, but it is only a weak serotonin reuptake inhibitor (Ki for SLC6A4 = 690 nM, vs. 8.9 nM for citalopram; Owens et al., 1997) and it has many other targets, both as agonist or antagonist, including serotonin, adrenergic, and histamine receptors (Mijur, 2011). In any case, the average trazodone fingerprint does not correlate particularly well to the sorl1 knockout fingerprint (cos = 0.3). Finally, the sorl1 knockout behavioural phenotype could be primarily caused by altered serotonin signalling in the hypothalamus, where we found both the biggest difference in tph1a/1b/2 HCR signal intensity (Fig. 5f) and the highest expression of sorl1 across scRNA-seq clusters (Fig. 1– supplement 2). In this case, it would be correct to expect sorl1 knockouts to react differently to SSRIs than controls, but it would be incorrect to expect SSRI treatment to cause the same behavioural phenotype, as it concurrently affects every other serotonergic neuron in the brain.

Finally, we agree the quoted conclusion was too strong given the current evidence. We since tested another SSRI, fluvoxamine, on sorl1 knockouts.

- Also in reference to Figure 5: in panel c, data are presented as deviation from vehicle treated. Because of this data presentation choice, it's no longer possible to determine whether, in this experiment, sorl1 crispants sleep less at night relative to their siblings. Does citalopram rescue / reverse sleep deficits in sorl1 mutants?

On your first point, please see our response to Reviewer #3 (2)c and Author Response 2b above.

On “does citalopram rescue/reverse sleep deficits in sorl1 mutants”: citalopram (and fluvoxamine) tends to reverse the key aspects of the sorl1 knockout behavioural phenotype by reducing night-time activity (% time active and total Δ pixels), increasing night-time sleep, and shortening sleep latency (Author response image 7). Extrapolating from the hypothesis presented in Discussion, this may be interpreted as a hint that sorl1 knockouts have reduced levels of 5-HT receptors, as increasing serotonin signalling using an SSRI tends to rescue the phenotype. However, we do not think that focusing on the significant behavioural parameters necessarily make sense here. Rather, one should take all parameters into account to conclude whether knockouts react differently to the drug than wild types (also see answer to Reviewer #3, (7) on this). For example, citalopram increased more the night-time sleep bout length of sorl1 knockouts than the one of controls (Fig. 5), but this parameter was not modified by knockout of sorl1 (Fig. 4). To explain the rationale more informally, citalopram is only used as a tool here to probe serotonin signalling in sorl1 knockouts, whether it worsens or rescues the behavioural phenotype is somewhat secondary, the key question is whether knockouts react differently than controls.

Author response image 7.

Comparing untreated sorl1 F0 knockouts vs. treated with SSRIs. a, sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H₂O fingerprint from the citalopram experiment) in comparison with the sorl1 knockout + citalopram (1 or 10 µM) fingerprints. Each dot represents the mean deviation from the same-clutch scrambled-injected H₂O-treated mean for that parameter (z-score, mean ± SEM). b, As in a), sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H₂O fingerprint from the fluvoxamine experiment) in comparison with the sorl1 + fluvoxamine (10 µM) fingerprint.

- Possible molecular pathways targeted by tinidazole, fenoprofen, and betamethasone are not described.

Tinidazole is an antibiotic, fenoprofen is a non-steroidal anti-inflammatory drug (NSAIDs), betamethasone is a steroidal anti-inflammatory drug. Interestingly, long-term use of NSAIDs reduces the risk of AD (in ’t Veld Bas A. et al., 2001). Several mechanisms are possible (Weggen et al., 2007), including reduction of Aβ42 production by interacting with γ-secretase (Eriksen et al., 2003). However, we did not explore the mechanism of action of these drugs on psen2 knockouts so do not feel comfortable speculating. We do not know, for example, whether these findings apply to betamethasone.

Minor Comments:

- On page 25, panel "g" should be labeled as "f".

Thank you!

- On page 35, a reference should be provided for the statement "From genomic studies of AD, we know that mutations in genes such as SORL1 modify risk by disrupting some biological processes.".

Thank you, this is now corrected. There were the same studies as mentioned in Introduction.

- On page 43, the word "and" should be added - "in wild-type rats and mice, overexpressing mutated human APP and PSEN1, AND restricting sleep for 21 days...".

Right, this sentence could be misread, we edited it. “overexpressing […]” only applied to the mice, not the rats (as they are wild-type); and both are sleep-deprived.

- On page 45, a reference should be provided for the statement "SSRIs can generally be used continuously with no adverse effects" and this statement should potentially be softened.

The reference is at the end of that sentence (Cirrito et al., 2011). You are correct though; we reformulated this statement to: “SSRIs can generally be used safely for many years”. SSRIs indeed have side effects.

- On page 54, a 60-minute rolling average is described as 45k rows, but this seems to be a 30-minute rolling average.

Thank you! We corrected. It should have been 90k rows, as in: 25 frames-per-second × 60 seconds × 60 minutes.

Reviewer #2 (Recommendations For The Authors):

"As we observed in the scRNA-seq data, most genes tested (appa, appb, psen1, psen2, apoea, cd2ap, sorl1) were broadly expressed throughout the 6-dpf brain (Fig. 1d and Fig. 1supplement 3 and 4)."

- apoea and appb are actually not expressed highly in the scRNA-seq data, and the apoea in situ looks odd, as if it has no expression. The appb gene mysteriously does not look as though it has high expression in the Raj data, but it is clearly expressed based on the in situ. I had previously noticed the same discrepancy, and I attribute it to the transcriptome used to map the Raj data, as the new DanioCell data uses a new transcriptome and indicates high appb expression in the brain. Please point out the discrepancy and possible explanation, perhaps in the figure legend.

All excellent points, thank you. We included them directly in Results text.

"most of these were expressed in the brain of 5-6-dpf zebrafish larvae, suggesting they play a role in early brain development or function."

- Evidence of expression does not suggest function, particularly not a function in brain development. As one example, almost half of the genome is expressed prior to the maternal-zygotic transition but does not have a function in those earliest stages of development. There are numerous other instances where expression does not equal function. Please change the sentence even as simply as "it is possible that they".

We mostly agree and edited to “[…], so they could play a role […]”.

Out of curiosity, we plotted, for each zebrafish developmental stage, the proportion of Alzheimer’s risk gene orthologues expressed in comparison to the proportion of all genes expressed (Author response image 8). We defined “all genes” as every gene that is expressed in at least one of the developmental stages (n = 24,856), not the complete transcriptome, to avoid including genes that are never expressed in the brain or whose expression is always below detection limit. We counted a gene as “expressed” if at least three cells had detectable transcripts. Using these definitions, 82 ± 7% of genes are expressed during development. For every developmental stage except 5 dpf (so 11/12), a larger proportion of Alzheimer’s risk genes than all genes are expressed (+5 ± 4%).

Author response image 8.

Proportion of Alzheimer’s risk genes orthologues expressed throughout zebrafish development. Proportion of Alzheimer’s risk genes orthologues (n = 42) and all genes (n = 24,856) expressed in the zebrafish brain at each developmental stage, from 12 hours post-fertilisation (hpf) to 15 days post-fertilisation (dpf). “All genes” corresponds to every gene expressed in the brain at any of the developmental stages, not the complete transcriptome. A gene is considered “expressed” (green) if at least three cells had detectable transcripts. Single-cell RNA-seq dataset from Raj et al., 2020.

"This frame-by-frame analysis has several advantages over previous methods that analysed activity data at the one-minute resolution."

- Which methods are these? There are no citations. There are certainly existing methods in the zebrafish field that can produce similar data to the method developed for this project. This new package is useful, as most existing software is not written in R, so it would help scientists who prefer this programming language. However, I would be careful not to oversell its novelty, since many methods do exist that produce similar results.

We added the references. There were referenced above after “we combined previous sleep/wake analysis methods”, but should have been referenced again here.

We are not convinced by this criticism. We would obviously not claim that the FramebyFrame package is as sophisticated and versatile as video-tracking tools like SLEAP or DeepLabCut, but we do think it answers a genuine need that was not addressed by other methods. Specifically, we know of many labs recording pixel count data across multiple days using the Zebrabox or DanioVision (we added support for DanioVision data after submission), but there were no packages to extract behavioural parameters from these data. Other methods involved standalone scripts with no documentation or version tracking. We would concede the FramebyFrame package is mostly targeted at these labs, but we already know of six labs routinely using it and were recently contacted by a researcher tracking Daphnia in the Zebrabox.

"F0 knockouts of both cutches" - "clutches"

Thank you!

Reviewer #3 (Recommendations For The Authors):

I would suggest totally revamping the Introduction section, and being sure to provide readers with the context and background they need for the data that comes thereafter. Key areas to touch on, in no particular order, include:

• Far more detail on the behavioral pharm screen upon which this paper builds, as a brief overview of that approach and the data generated are needed.

Thank you for the suggestion, we added a sentence hinting at this work in the last Introduction paragraph.

• Limitations of current zebrafish sleep/arousal assays that motivated the authors to develop a new, temporally high-resolution system.

We think this is better explained in Results, as is currently. For example, we need to point to Fig. 2–supplement 2a,b,c to explain that one-minute methods were missing sleep bouts and how FramebyFrame resolves this issue.

• A paragraph about sleep and AD, that does a better job of citing work in humans, mammalian, and invertebrate models that motivate the interest in the connection pursued here.

Sorry, we think this would place too much focus on sleep and AD. We want the main topic of the paper to be the behavioural pharmacology approach, not AD or sleep per se. As the Introduction states, we see Alzheimer’s risk genes as a case study for the behavioural pharmacology approach, rather than the reason why the approach was developed. Additionally, presenting sleep and AD in Introduction risks sounding like ZOLTAR is specifically designed for this context, while we conceived of it as much more generalisable and explicitly encourage its use to study genes associated to other diseases. Note that the paragraph you suggest is, we think, mostly present in Discussion (section Disrupted sleep and serotonin signalling […]).

• I modestly suggest eliminating making such a strong case for a gene-first approach being the best way to understand disease. It is not a zero-sum game, and there is plenty to learn from proteomics, metabolomics, etc. I suspect nobody will argue with the authors saying they leveraged the strength of their system and focused on key AD genes of interest.

From your point below, we understand the following quote is the source of the issue: “For finding causal processes, studying the genome, rather than the transcriptome or epigenome, is advantageous because the chronology from genomic variant to disease is unambiguous […]”. We did not want to suggest it is a zero-sum game, but we now understand how it can be read this way. We adapted slightly the wording. What we want to do is highlight the causality argument as the advantage of the genomics approach. We feel we do not read this argument often enough, while it remains a ‘magic power’ of genomics. One essentially does not have to worry about causality when studying a pathogenic germline variant, while it is a constant concern when studying the transcriptome or epigenome (i.e. did the change in this transcript’s level cause disease, or vice-versa?). To take an example in the context of AD, arguments based on genomics (e.g. Down syndrome or APP duplication) are often the definite arbiters when debating the amyloid hypothesis, exactly because their causality cannot be doubted.

Minor comments

(1) The opening of the introduction is perhaps overly broad, spending an entire paragraph on genome vs transcriptome, etc and making the claim that a gene-first approach is the best path. It isn't zero-sum, and the authors could just get right into AD and study genes of interest. Similar issues occur throughout the manuscript, with sentences/paragraphs that are not necessarily needed.

Please see our answer to your previous point. On the introduction being overly broad, we perfectly agree it is broad, but related to your point about presenting sleep and AD in the Introduction, we wish to talk about finding causal processes from genomics findings using behavioural pharmacology. We purposefully present research on AD as one instance of this broader goal, not the primary topic of the paper.

Another example are these sentences, which could be totally removed as the following paragraph starts off making the same point much more succinctly. "From genomic studies of AD, we know that mutations in genes such as SORL1 modify risk by disrupting some biological processes. Presumably, the same processes are disrupted in zebrafish sorl1 knockouts, and some caused the behavioural alterations we observed. Can we now follow the thread backwards and predict some of the biological processes in which Sorl1 is involved based on the behavioural profile of sorl1 knockouts?"

Thanks for the suggestion, but we think these sentences are useful to place back this Results section in the context of the Introduction. Think of the paper as mainly about the behavioural pharmacology approach, not on Alzheimer’s risk genes. The function of the paragraph here is not simply to explain the method by which we decided to study sorl1; it is to reiterate the rationale behind the behavioural pharmacology approach so that the reader understands where this Results section fits in the overall structure.

(2) Related to the above, the authors use lecanemab as an example to support their approach, but there has been a great deal of controversy regarding this drug. I don't think such extensive justification is needed. This study uses AD risk genes as a case study in a newly developed behavioral pharm pipeline. A great deal of the rest of the intro seems to just fill space and could be more focused on the study at hand. Interestingly, a er gene selection, the next step in their pipeline is sleep/wake analysis yet nothing is covered about AD and sleep in the intro. Some justification of that approach (why focus on sleep/wake as a starting point for behavioral pharm rather than learning and memory?) would be a better use of intro space.

There has indeed been controversy about lecanemab, but even the harshest critiques of the amyloid hypothesis concede that it slows down cognitive decline (Espay et al., 2023). That is all that is needed to support our argument, which is that research on AD started primarily from genomics and thereby yielded a disease-modifying drug. The controversy seems mostly focused on whether this effect size is clinically significant, and we think we correctly represent this uncertainty (e.g. “antibodies against Aβ such as lecanemab show promise in slowing down disease progression” and “the beneficial effects from targeting Aβ aggregation currently remain modest”).

Your next point is entirely fair. We mostly answered it above. To explain further, the primary reason why we measured sleep/wake behaviour is to match the behavioural dataset from Rihel et al., 2010 so we can use it to make predictions, not to study sleep in the context of AD per se. Sure, perhaps learning and memory would have been interesting, but we do not know of any study testing thousands of small molecules on zebrafish larvae during a memory task. We understand it can be slightly confusing though, as we then spend a paragraph of Discussion on sleep as a causal process in AD, but we obviously need to discuss this topic given the findings. However, to reiterate, we purposefully designed FramebyFrame and ZOLTAR to be useful beyond studying sleep/wake behaviour. For example, FramebyFrame would not calculate 17 behavioural parameters if the only goal was to measure sleep. We now mention the Rihel et al., 2010 study in the Introduction as you suggested above (“Far more detail on the behavioral pharm screen […]”), as that is the real reason why sleep/wake behaviour was measured in the first place.

(3) Also related to the above, another more relevant point that could be talked about in the intro is the need for more refined approaches to analyze sleep in zebrafish, given the effort that went into the new analysis system described here. Again, I think the context for why the authors developed this system would be more meaningful than the current content.

Thank you, we think we answered this point above (especially below Limitations of current zebrafish sleep/arousal assays […]).

(4) GWAS can stand for Genome-wide associate studies (plural) so I do not think the extra "s" is needed (GWASs) .

Indeed, that seems to be the common usage. Thank you.

(5) AD candidate risk genes were determined from loci using "mainly statistic colocalization". Can the authors add a few more details about what was done and what the "mainly" caveat refers to?

“Mainly” simply refers to the fact that other methods were used by Schwartzentruber et al. (2021) to annotate the GWAS loci with likely causal genes, but that most calls were ultimately made from statistic colocalisation. Readers can refer to this work to learn more about the methods used.

(6) The authors write "The loss of psen1 only had mild effects on behaviour" but I think they mean "sleep behaviors" as there could be many other behaviors that are disrupted but were not assessed. The same issue a few sentences later with "Behaviour during the day was not affected" and at the end of the following paragraph.

Yes, that would be more precise, thank you.

(7) For the Sorl1 pharmacology data, it is very hard to understand what is being measured behaviorally. Are the authors measuring sleep +/- citalopram, or something else, and why the change to Euclidean distance rather than all the measures we were just introduced to earlier in the manuscript?

We understand these plots (Fig. 5c,d) are less intuitive, but it is important that we show the difference in behaviour compared to H₂O-treated larvae of same genotype. The claim is that citalopram has a larger effect on knockouts than on controls, so the reader needs to focus on the effect of the drug on each genotype, not on the effect of sorl1 knockout. We added the standard fingerprints (i.e. setting controls to z-score = 0) here in Author response figures.

Euclidean distance takes as input all the measures we introduced. The point is precisely not to select a single measure. For example, say we were only plotting active bout number during the day, we would conclude that 10 µM citalopram has the same effect on knockouts and controls. Conversely, if we had taken sleep bout length at night, we would conclude 10 µM has a stronger effect on knockouts. What is the correct parameter to select? Using Euclidean distance resolves this by taking all parameters into account, rather than arbitrarily choosing one.

And what exactly is a "given spike in serotonin"? and how is this hypothesis the conclusion based on the lack of evidence for the second hypothesis? As the authors say, there could be other ways sorl1 knockouts are more sensitive to citalopram, so the absence of evidence for one hypothesis certainly does not support the other hypothesis.

We mean a given release of serotonin in the synaptic cleft. We have fixed this wording. 

We tend to disagree on the second point. We can think of two ways that sorl1 knockouts are more sensitive to citalopram: 1) they produce more serotonin, so blocking reuptake causes a larger spike in knockouts; or 2) blocking reuptake causes the same increase in both knockouts and wild-types but knockouts react more strongly to serotonin. We cannot in fact think of another way to explain the citalopram results. Not finding overwhelming evidence for 1) surely supports 2) somewhat, even if we do not have direct evidence for it. As an analogy, if two diagnoses are possible for a patient, testing negative for the first one supports the other one, even before it is directly tested.

(8) Again some language is used without enough care. Fish are referred to as "drowsier" under some drug conditions. How do the authors know the animal is drowsy? The phenotype is more specific - more sleep, less activity.

Thank you, we switched to “Furthermore, fenoprofen worsened the day-time hypoactivity of psen2 knockout larvae […]”.

(9) This sentence is misleading as it gives the impression that results in this manuscript suggest the conclusion: "Our observation that disruption of genes associated with AD diagnosis after 65 years reduces sleep in 7-day zebrafish larvae suggest that disrupted sleep may be a common mechanism through which these genes exert an effect on risk." That idea is widely held in the field, and numerous other previous manuscripts/reviews should be cited for clarity of where this hypothesis came from.

This idea is not widely held in the field. You likely read this point as “disrupted sleep is a risk factor for AD”, which, yes, is widely discussed in the field, but is not precisely what we are saying. We hypothesise that mutations in some of the Alzheimer’s risk genes cause disrupted sleep, possibly from a very early age, which then causes AD decades later. Studies and reviews on sleep and AD rarely make this hypothesis, at least not explicitly. The closest we know of are a few recent human genetics studies, typically using Mendelian Randomisation, finding that higher genetic risk of AD correlates with some sleep phenotypes, such as sleep duration (Chen et al., 2022; Leng et al., 2021). The work of Muto et al. (2021) is particularly interesting as it found correlations between higher genetic risk of AD and some sleep phenotypes in men in their early twenties, which seems unlikely to be a consequence of early pathology (Muto et al., 2021). Note, however, that even these studies do not mention sleep possibly being disrupted early in development, which is what our findings in zebrafish larvae support. As we mention, we think a team should test whether sleep is different in infants at higher genetic risk of AD, essentially performing an analogous, but obviously much more difficult, experiment as we did in zebrafish larvae. We do not know of any study testing this or even raising this idea, so evidently it is not widely held. Having said that, the studies we mention here were not referenced in the Discussion paragraph. We have now corrected this.

Ashlin TG, Blunsom NJ, Ghosh M, Cockcroft S, Rihel J. 2018. Pitpnc1a Regulates Zebrafish Sleep and Wake Behavior through Modulation of Insulin like Growth Factor Signaling. Cell Rep 24:1389–1396. doi:10.1016/j.celrep.2018.07.012

Chen D, Wang X, Huang T, Jia J. 2022. Sleep and LateOnset Alzheimer’s Disease: Shared Genetic Risk Factors, Drug Targets, Molecular Mechanisms, and Causal Effects. Front Genet 13. doi:10.3389/fgene.2022.794202

Cirrito JR, Disabato BM, Restivo JL, Verges DK, Goebel WD, Sathyan A, Hayreh D, D’Angelo G, Benzinger T, Yoon H, Kim J, Morris JC, Mintun MA, Sheline YI. 2011. Serotonin signaling is associated with lower amyloid-β levels and plaques in transgenic mice and humans. Proc Natl Acad Sci U S A 108:14968–14973. doi:10.1073/pnas.1107411108

Dean DC, Jerskey BA, Chen K, Protas H, Thiyyagura P, RoonJva A, O’Muircheartaigh J, Dirks H, Waskiewicz N, Lehman K, Siniard AL, Turk MN, Hua X, Madsen SK, Thompson PM, Fleisher AS, Huentelman MJ, Deoni SCL, Reiman EM. 2014. Brain Differences in Infants at Differential Genetic Risk for Late-Onset Alzheimer Disease A Cross-sectional Imaging Study. JAMA Neurol 71:11–22. doi:10.1001/jamaneurol.2013.4544

Eriksen JL, Sagi SA, Smith TE, Weggen S, Das P, McLendon DC, Ozols VV, Jessing KW, Zavitz KH, Koo EH, Golde TE. 2003. NSAIDs and enantiomers of flurbiprofen target γ-secretase and lower Aβ42 in vivo. J Clin Invest 112:440–449. doi:10.1172/JCI18162

Espay AJ, Herrup K, Kepp KP, Daly T. 2023. The proteinopenia hypothesis: Loss of Aβ42 and the onset of Alzheimer’s Disease. Ageing Res Rev 92:102112. doi:10.1016/j.arr.2023.102112

Hoffman EJ, Turner KJ, Fernandez JM, Cifuentes D, Ghosh M, Ijaz S, Jain RA, Kubo F, Bill BR, Baier H, Granato M, Barresi MJF, Wilson SW, Rihel J, State MW, Giraldez AJ. 2016. Estrogens Suppress a Behavioral Phenotype in Zebrafish Mutants of the AuJsm Risk Gene, CNTNAP2. Neuron 89:725–733. doi:10.1016/j.neuron.2015.12.039

in ’t Veld Bas A, Ruitenberg A, Hofman A, Launer LJ, van Duijn CM, Stijnen T, Breteler MMB, Stricker BHC. 2001. Nonsteroidal Anti inflammatory Drugs and the Risk of Alzheimer’s Disease. N Engl J Med 345:1515–1521. doi:10.1056/NEJMoa010178

Jagirdar R, Fu C-H, Park J, Corbek BF, Seibt FM, Beierlein M, Chin J. 2021. Restoring activity in the thalamic reticular nucleus improves sleep architecture and reduces Aβ accumulation in mice. Sci Transl Med 13:eabh4284. doi:10.1126/scitranslmed.abh4284

Jiang H, Newman M, Lardelli M. 2018. The zebrafish orthologue of familial Alzheimer’s disease gene PRESENILIN 2 is required for normal adult melanotic skin pigmentation. PLOS ONE 13:e0206155. doi:10.1371/journal.pone.0206155

Jiang H, Pederson SM, Newman M, Dong Y, Barthelson K, Lardelli M. 2020. Transcriptome analysis indicates dominant effects on ribosome and mitochondrial function of a premature termination codon mutation in the zebrafish gene psen2. PloS One 15:e0232559. doi:10.1371/journal.pone.0232559

Joo W, Vivian MD, Graham BJ, Soucy ER, Thyme SB. 2021. A Customizable Low-Cost System for Massively Parallel Zebrafish Behavioral Phenotyping. Front Behav Neurosci 14.

Joubert L, Hanson B, Barthet G, Sebben M, Claeysen S, Hong W, Marin P, Dumuis A, Bockaert J. 2004. New sorting nexin (SNX27) and NHERF specifically interact with the 5-HT4a receptor splice variant: roles in receptor targeting. J Cell Sci 117:5367–5379. doi:10.1242/jcs.01379

Leng Y, Ackley SF, Glymour MM, Yaffe K, Brenowitz WD. 2021. Genetic Risk of Alzheimer’s Disease and Sleep Duration in Non-Demented Elders. Ann Neurol 89:177–181. doi:10.1002/ana.25910

Mitchell PB, Hadzi-Pavlovic D. 2000. Lithium treatment for bipolar disorder. Bull World Health Organ 78:515–517.

Mikur A. 2011. Trazodone: properties and utility in multiple disorders. Expert Rev Clin Pharmacol 4:181–196. doi:10.1586/ecp.10.138

Munoz-Torrero D. 2008. Acetylcholinesterase Inhibitors as Disease-Modifying Therapies for Alzheimer’s Disease. Curr Med Chem 15:2433–2455. doi:10.2174/092986708785909067

Muto V, Koshmanova E, Ghaemmaghami P, Jaspar M, Meyer C, Elansary M, Van Egroo M, Chylinski D, Berthomier C, Brandewinder M, Mouraux C, Schmidt C, Hammad G, Coppieters W, Ahariz N, Degueldre C, Luxen A, Salmon E, Phillips C, Archer SN, Yengo L, Byrne E, Collette F, Georges M, Dijk D-J, Maquet P, Visscher PM, Vandewalle G. 2021. Alzheimer’s disease genetic risk and sleep phenotypes in healthy young men: association with more slow waves and daytime sleepiness. Sleep 44. doi:10.1093/sleep/zsaa137

Myers-Turnbull D, Taylor JC, Helsell C, McCarroll MN, Ki CS, Tummino TA, Ravikumar S, Kinser R, Gendelev L, Alexander R, Keiser MJ, Kokel D. 2022. Simultaneous analysis of neuroactive compounds in zebrafish. doi:10.1101/2020.01.01.891432

Owens MJ, Morgan WN, Plok SJ, Nemeroff CB. 1997. Neurotransmiker receptor and transporter binding profile of antidepressants and their metabolites. J Pharmacol Exp Ther 283:1305– 1322.

Özcan GG, Lim S, Leighton PL, Allison WT, Rihel J. 2020. Sleep is bi-directionally modified by amyloid beta oligomers. eLife 9:e53995. doi:10.7554/eLife.53995

Quiroz YT, Schultz AP, Chen K, Protas HD, Brickhouse M, Fleisher AS, Langbaum JB, Thiyyagura P, Fagan AM, Shah AR, Muniz M, Arboleda-Velasquez JF, Munoz C, Garcia G, Acosta-Baena N, Giraldo M, Tirado V, Ramírez DL, Tariot PN, Dickerson BC, Sperling RA, Lopera F, Reiman EM. 2015. Brain Imaging and Blood Biomarker Abnormalities in Children With Autosomal Dominant Alzheimer Disease: A Cross-Sectional Study. JAMA Neurol 72:912–919. doi:10.1001/jamaneurol.2015.1099

Relkin NR. 2007. Beyond symptomatic therapy: a reexamination of acetylcholinesterase inhibitors in Alzheimer’s disease. Expert Rev Neurother 7:735–748. doi:10.1586/14737175.7.6.735

Rihel J, Prober DA, Arvanites A, Lam K, Zimmerman S, Jang S, Haggarty SJ, Kokel D, Rubin LL, Peterson RT, Schier AF. 2010. Zebrafish Behavioral Profiling Links Drugs to Biological Targets and Rest/Wake Regulation. Science 327:348–351. doi:10.1126/science.1183090

Sleegers K, Brouwers N, Gijselinck I, Theuns J, Goossens D, Wauters J, Del-Favero J, Cruts M, van Duijn CM, Van Broeckhoven C. 2006. APP duplication is sufficient to cause early onset Alzheimer’s dementia with cerebral amyloid angiopathy. Brain J Neurol 129:2977–2983. doi:10.1093/brain/awl203

Sun L, Zhou R, Yang G, Shi Y. 2017. Analysis of 138 pathogenic mutations in presenilin-1 on the in vitro production of Aβ42 and Aβ40 peptides by γ-secretase. Proc Natl Acad Sci 114:E476– E485. doi:10.1073/pnas.1618657114

Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. 2016. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44:D380–D384. doi:10.1093/nar/gkv1277

Weggen S, Rogers M, Eriksen J. 2007. NSAIDs: small molecules for prevention of Alzheimer’s disease or precursors for future drug development? Trends Pharmacol Sci 28:536–543. doi:10.1016/j.Jps.2007.09.004

Wiltschko AB, Tsukahara T, Zeine A, Anyoha R, Gillis WF, Markowitz JE, Peterson RE, Katon J, Johnson MJ, Daka SR. 2020. Revealing the structure of pharmacobehavioral space through motion sequencing. Nat Neurosci 23:1433–1443. doi:10.1038/s41593-020-00706-3

Yang T, Arslanova D, Gu Y, Augelli-Szafran C, Xia W. 2008. Quantification of gamma-secretase modulation differentiates inhibitor compound selectivity between two substrates Notch and amyloid precursor protein. Mol Brain 1:15. doi:10.1186/1756-6606-1-15

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In their paper, Zhan et al. have used Pf genetic data from simulated data and Ghanaian field samples to elucidate a relationship between multiplicity of infection (MOI) (the number of distinct parasite clones in a single host infection) and force of infection (FOI). Specifically, they use sequencing data from the var genes of Pf along with Bayesian modeling to estimate MOI individual infections and use these values along with methods from queueing theory that rely on various assumptions to estimate FOI. They compare these estimates to known FOIs in a simulated scenario and describe the relationship between these estimated FOI values and another commonly used metric of transmission EIR (entomological inoculation rate).

This approach does fill an important gap in malaria epidemiology, namely estimating the force of infection, which is currently complicated by several factors including superinfection, unknown duration of infection, and highly genetically diverse parasite populations. The authors use a new approach borrowing from other fields of statistics and modeling and make extensive efforts to evaluate their approach under a range of realistic sampling scenarios. However, the write-up would greatly benefit from added clarity both in the description of methods and in the presentation of the results. Without these clarifications, rigorously evaluating whether the author's proposed method of estimating FOI is sound remains difficult. Additionally, there are several limitations that call into question the stated generalizability of this method that should at minimum be further discussed by authors and in some cases require a more thorough evaluation.

Major comments:

(1) Description and evaluation of FOI estimation procedure.

a. The methods section describing the two-moment approximation and accompanying appendix is lacking several important details. Equations on lines 891 and 892 are only a small part of the equations in Choi et al. and do not adequately describe the procedure notably several quantities in those equations are never defined some of them are important to understand the method (e.g. A, S as the main random variables for inter-arrival times and service times, aR and bR which are the known time average quantities, and these also rely on the squared coefficient of variation of the random variable which is also never introduced in the paper). Without going back to the Choi paper to understand these quantities, and to understand the assumptions of this method it was not possible to follow how this works in the paper. At a minimum, all variables used in the equations should be clearly defined.

We thank the reviewer for this useful comment. We have clarified the method and defined all relevant variables in the revised manuscript (Line 537-573). The reviewer correctly pointed out additional sections and equations in Choi et al., including the derivation of an exact expression for the steady-state queue-length distribution and the two-moment approximation. Since our work directly utilized the two-moment approximation, our previous manuscript included only material on that section. However, we agree that providing additional details on the derivation of the exact expression would benefit readers. Therefore, we have summarized this derivation in the revised manuscript (Line 561-564). Additionally, we clarified the method’s assumptions, particularly those involved in transitioning from the exact expression to the two-moment approximation (Line 565-570).

b. Additionally, the description in the main text of how the queueing procedure can be used to describe malaria infections would benefit from a diagram currently as written it's very difficult to follow.

We thank the reviewer for this suggestion. In the revised manuscript, we included a diagram illustrating the connection between the queueing procedure and malaria transmission (Appendix 1-Figure 8).

c. Just observing the box plots of mean and 95% CI on a plot with the FOI estimate (Figures 1, 2, and 10-14) is not sufficient to adequately assess the performance of this estimator. First, it is not clear whether the authors are displaying the bootstrapped 95%CIs or whether they are just showing the distribution of the mean FOI taken over multiple simulations, and then it seems that they are also estimating mean FOI per host on an annual basis. Showing a distribution of those per-host estimates would also be helpful. Second, a more quantitative assessment of the ability of the estimator to recover the truth across simulations (e.g. proportion of simulations where the truth is captured in the 95% CI or something like this) is important in many cases it seems that the estimator is always underestimating the true FOI and may not even contain the true value in the FOI distribution (e.g. Figure 10, Figure 1 under the mid-IRS panel). But it's not possible to conclude one way or the other based on this visualization. This is a major issue since it calls into question whether there is in fact data to support that these methods give good and consistent FOI estimates.

There seems to be some confusion on what we display in some key figures. Figures 1-2 and 10-14 (labeled as Figure 1-2 and Appendix 1-Figure 11-15 in the revised manuscript) display bootstrapped distributions including the 95% CIs, not the distribution of the mean FOI taken over multiple simulations. To estimate the mean FOI per host on an annual basis, the two proposed methods require either the steady-state queue length distribution (MOI distribution) or the moments of this distribution. Obtaining such a steady-state queue length distribution necessitates either densely tracked time-series observations per host or many realizations at the same sampling time per host. However, under the sparse sampling schemes, we only have two one-time-point observations per host: one at the end of wet/high-transmission and another at the end of dry/low-transmission. This is typically the case for empirical data, although numerical simulations could circumvent this limitation and generate such output. Nonetheless, we have a population-level queue length distribution from both simulation outputs and empirical data by aggregating MOI estimates across all sampled individuals. We use this population-level distribution to represent and approximate the steady-state queue length distribution at the individual level, not explicitly considering any individual heterogeneity due to transmission. The estimated FOI is per host in the sense of representing the FOI experienced by an individual host whose queue length distribution is approximated from the collection of all sampled individuals. The true FOI per host per year in the simulation is the total FOI of all hosts per year divided by the number of hosts. Therefore, our estimator, combined with the demographic information on population size, estimates the total number of Plasmodium falciparum infections acquired by all individual hosts in the population of interest per year. We clarified this point in the revised manuscript in the subsection of the Materials and Methods, entitled ‘Population-level MOI distribution for approximating time-series observation of MOI per host or many realizations at the same sampling time per host’ (Line 623-639).

We evaluated the impact of individual heterogeneity due to transmission on FOI inference using simulation outputs (Line 157-184, Figure 1-2 and Appendix 1-Figure 11-15). Even with significant heterogeneity among individuals (2/3 of the population receiving approximately 94% of all bites whereas the remaining 1/3 receives the rest of the bites), our methods performed comparably to scenarios with homogeneous transmission. Furthermore, our methods demonstrated similar performance for both non-seasonal and seasonal transmission scenarios.

Regarding the second point, we quantitatively assessed the ability of the estimator to recover the truth across simulations and included this information in a supplementary table in the revised manuscript (supplementary file 3-FOImethodsPerformance.xlsx). Specifically, we indicated whether the truth lies within the bootstrap distribution and provided a measure of relative deviation, which is defined as the true FOI value minus the median of the bootstrap distribution for the estimate, normalized by the true FOI value .  This assessment is a valuable addition which enhances clarity, but please note that our previous graphical comparisons do illustrate the ability of the methods to estimate “sensible” values, close to the truth despite multiple sources of errors. “Close” here is relative to the scale of variation of FOI in the field and to the kind of precision that would be useful in an empirical context. From a practical perspective based on the potential range of variation of FOI, the graphical results already illustrate that the estimated distributions would be informative.

We also thank the reviewer for highlighting instances where our proposed methods for FOI inference perform sub-optimally (e.g. Figure 10, Figure 1 under the mid-IRS panel in the previous manuscript). This feedback prompted us to examine these instances more closely and identify the underlying causes related to the stochastic impact introduced during various sampling processes. These include sampling the host population and their infections at a specific sampling depth in the simulated output, matching the depth used for collecting empirical data. In addition, previously, we imputed MOI estimates for treated individuals by sampling only once from non-treated individuals. This time, we conducted 200 samplings and used the final weighted MOI distribution for FOI inference. By doing so, we reduced the impact of extreme single-sampling efforts on MOI distribution and FOI inference. In other words, some of these suboptimal instances correspond to the scenarios where the one-time sampled MOIs from non-treated individuals do not fully capture the MOI distribution of non-treated individuals. We added a section titled ‘Reducing stochastic impact in sampling processes’ to Appendix 1 on this matter (Line 841-849).

The reviewer correctly noted that our proposed methods tend to underestimate FOI (Figure 1-2, 10-14, ‘Estimated All Errors’ and ‘Estimated Undersampling of Var’ panels in the previous manuscript, corresponding to Figure 1-2 and Appendix 1-Figure 11-15 in the revised manuscript). This underestimation arises from the underestimation of MOI. The Bayesian formulation of the varcoding method does not account for the limited overlap between co-infecting strains, an additional factor that reduces the number of var genes detected per individual. We have elaborated on this matter in the Results and Discussion sections of the revised manuscript (Line 142-149, 252-256).

d. Furthermore the authors state in the methods that the choice of mean and variance (and thus second moment) parameters for inter-arrival times are varied widely, however, it's not clear what those ranges are there needs to be a clear table or figure caption showing what combinations of values were tested and which results are produced from them, this is an essential component of the method and it's impossible to fully evaluate its performance without this information. This relates to the issue of selecting the mean and variance values that maximize the likelihood of observing a given distribution of MOI estimates, this is very unclear since no likelihoods have been written down in the methods section of the main text, which likelihood are the authors referring to, is this the probability distribution of the steady state queue length distribution? At other places the authors refer to these quantities as Maximum Likelihood estimators, how do they know they have found the MLE? There are no derivations in the manuscript to support this. The authors should specify the likelihood and include in an appendix an explanation of why their estimation procedure is in fact maximizing this likelihood, preferably with evidence of the shape of the likelihood, and how fine the grid of values they tested is for their mean and variance since this could influence the overall quality of the estimation procedure.

We thank the reviewer for pointing out these aspects of the work that can be further clarified. In response, we maximized the likelihood of observing the population-level MOI distribution in the sampled population (see our responses to your previous comment c), given queue length distributions, derived from the two-moment approximation method for various mean and variance combinations of inter-arrival times. We added a new section to the Materials and Methods in the revised manuscript with an explicit likelihood formulation (Line 574-585).

Additionally, we specified the ranges for the mean and variance parameters for inter-arrival times and provided the grid of values tested in a supplementary table (supplementary file 4-meanVarianceParams.xlsx). Example figures illustrating the shape of the likelihood have also been included in Appendix 1-Figure 9. We tested the impact of different grid value choices on estimation quality by refining the grid to include more points, ensuring the FOI inference results are consistent. The results of the test are documented in the revised manuscript (Line 587-593, Appendix 1-Figure 10).

(2) Limitation of FOI estimation procedure.

a. The authors discuss the importance of the duration of infection to this problem. While I agree that empirically estimating this is not possible, there are other options besides assuming that all 1-5-year-olds have the same duration of infection distribution as naïve adults co-infected with syphilis. E.g. it would be useful to test a wide range of assumed infection duration and assess their impact on the estimation procedure. Furthermore, if the authors are going to stick to the described method for duration of infection, the potentially limited generalizability of this method needs to be further highlighted in both the introduction, and the discussion. In particular, for an estimated mean FOI of about 5 per host per year in the pre-IRS season as estimated in Ghana (Figure 3) it seems that this would not translate to 4-year-old being immune naïve, and certainly this would not necessarily generalize well to a school-aged child population or an adult population.

We thank the reviewer for this useful comment. The reviewer correctly noted the challenge in empirically measuring the duration of infection for 1-5-year-olds and comparing it to that of naïve adults co-infected with syphilis. We nevertheless continued to use the described method for the duration of infection, while more thoroughly acknowledging and discussing the limitations this aspect of the method introduces. We have highlighted this potential limitation in the Abstract, Introduction, and Discussion sections of the revised manuscript (Line 26-28, 99-103, 270-292). It is important to note that the infection duration from the historical clinical data we have relied on has been used, and is still used, in the malaria modeling community as a credible source for this parameter in untreated natural infections of malaria-naïve individuals in endemic settings of Africa (e.g. in the agent-based model OpenMalaria, see 1).

To reduce misspecification in infection duration and fully utilize our proposed methods, future data collection and sampling could prioritize subpopulations with minimal prior infections and an immune profile similar to naïve adults, such as infants and toddlers. As these individuals are also the most vulnerable, prioritizing them aligns with the priority of all intervention efforts in the short term, which is to monitor and protect the most vulnerable individuals from severe symptoms and death. We discuss this aspect in detail in the Discussion section of the revised manuscript (Line 287-292).

In the pre-IRS phase of Ghana surveys, an estimated mean FOI of about 5 per host per year indicates that a 4-year-old child would have experienced around 20 infections, which could suggest they are far from naïve. The extreme diversity of circulating var genes (2) implies, however, that even after 20 infections, a 4-year-old may have only developed immunity to a small fraction of the variant surface antigens (PfEMP1, Plasmodium falciparum erythrocyte membrane protein 1) encoded by this important gene family. Consequently, these children are not as immunologically experienced as it might initially seem. Moreover, studies have shown that long-lived infections in older children and adults can persist for months or even years, including through the dry season. This persistence is driven by high antigenic variation of var genes and associated incomplete immunity. Additionally, parasites can skew PfEMP1 expression to produce less adhesive erythrocytes, enhancing splenic clearance, reducing virulence, and maintaining sub-clinical parasitemia (3, 4, 5). The impact of immunity on infection duration with age for falciparum malaria remains a challenging open question.

Lastly, the FOI for naïve hosts is a key basic parameter for epidemiological models of complex infectious diseases like falciparum malaria, in both agent-based and equation-based formulations. This is because FOI for non-naïve hosts is typically a function of their immune status, body size, and the FOI of naïve hosts. Thus, knowing the FOI of naïve hosts helps parameterize and validate these models by reducing degrees of freedom.

b. The evaluation of the capacity parameter c seems to be quite important and is set at 30, however, the authors only describe trying values of 25 and 30, and claim that this does not impact FOI inference, however it is not clear that this is the case. What happens if the carrying capacity is increased substantially? Alternatively, this would be more convincing if the authors provided a mathematical explanation of why the carrying capacity increase will not influence the FOI inference, but absent that, this should be mentioned and discussed as a limitation.

Thank you for this question. This parameter represents the carrying capacity of the queuing system, or the maximum number of blood-stage strains with which an individual human host can be co-infected. Empirical evidence, estimated using the varcoding method, suggests this value is 20 (2), providing a lower bound for parameter c. However, the varcoding method does not account for the limited overlap between co-infecting strains, which reduces the number of var genes detected in an individual, thereby affecting the basis of MOI estimation. Additional factors, such as the synchronicity of clones in their 48-hour life cycle on alternate days (6) and within-host competition of strains leading to low-parasitemia levels (7, 8), contribute to under-sampling of strains and are not accounted for in MOI estimation (9). To address these potential under-sampling issues, we previously tested values of 25 and 30.

This time, we systematically investigated a wider range of values, including substantially higher ones: 25, 30, 40, and 60. We found that the FOI inference results are similar across these values. Figure 3 in the main text and supplementary figures (Appendix 1-Figure 16-18) illustrates these findings.

The parameter c influences the steady-state queue length distribution based on the two-moment approximation with specific mean and variance combinations, primarily affecting the distribution’s tail when customer or infection flows are high. Smaller values of c lower the maximum possible queue length, making the system more prone to “overflow”. In such cases, customers or infections may find no space available upon their arrival, hence not incrementing the queue length.

Empirical MOI distributions for high-transmission endemic regions center around 4 or 5, mostly remaining below 10, with only a small fraction between 15-20 (2). These distributions do not support parameter combinations resulting in frequent overflow for a system with c equal to 25 or 30. As one increases the value of c further, these parameter combinations would cause the MOI distributions to shift to larger values inconsistent with the empirical MOI distributions. We therefore do not expect substantially higher values for parameter c to noticeably change either the relative shape of the likelihood or the MLE.

We have included a subsection on parameter c in the Materials and Methods section of the revised manuscript (Line 596-612).

Reviewer #2 (Public Review):

Summary:

The authors combine a clever use of historical clinical data on infection duration in immunologically naive individuals and queuing theory to infer the force of infection (FOI) from measured multiplicity of infection (MOI) in a sparsely sampled setting. They conduct extensive simulations using agent-based modeling to recapitulate realistic population dynamics and successfully apply their method to recover FOI from measured MOI. They then go on to apply their method to real-world data from Ghana before and after an indoor residual spraying campaign.

Strengths:

(1) The use of historical clinical data is very clever in this context.

(2) The simulations are very sophisticated with respect to trying to capture realistic population dynamics.

(3) The mathematical approach is simple and elegant, and thus easy to understand.

Weaknesses:

(1) The assumptions of the approach are quite strong and should be made more clear. While the historical clinical data is a unique resource, it would be useful to see how misspecification of the duration of infection distribution would impact the estimates.

We thank the reviewer for bringing up the limitation of our proposed methods due to their reliance on a known and fixed duration of infection distribution from historical clinical data. Please see our response to Reviewer 1, Comment 2a, for a detailed discussion on this matter.

(2) Seeing as how the assumption of the duration of infection distribution is drawn from historical data and not informed by the data on hand, it does not substantially expand beyond MOI. The authors could address this by suggesting avenues for more refined estimates of infection duration.

We thank the reviewer for pointing out a potential improvement to our work. We acknowledge that FOI is inferred from MOI and thus depends on the information contained in MOI. However, MOI by definition is a number and not a rate parameter. FOI for naïve hosts is a fundamental parameter for epidemiological models of complex infectious diseases like falciparum malaria, in both agent-based and equation-based formulations. FOI of non-naïve hosts is typically a function of their immune status, body size, and the FOI of naïve hosts. Thus, knowing the FOI of naïve hosts helps parameterize and validate these models by reducing degrees of freedom. In this sense, we believe the transformation from MOI to FOI is valuable.

Measuring infection duration is challenging, making the simultaneous estimation of infection duration and FOI an attractive alternative, as the referee noted. This, however, would require closely monitored cohort studies or densely sampled cross-sectional surveys to reduce issues like identifiability. For instance, a higher arrival rate of infections paired with a shorter infection duration could generate a similar MOI distribution to a lower arrival rate with a longer infection duration. In some cases, incorrect combinations of rate and duration might even produce an MOI distribution that appears closer to the targeted distribution. Such cohort studies and densely sampled cross-sectional surveys have not been and will not be widely available across different geographical locations and times. This work utilizes more readily available data from sparsely sampled single-time-point cross-sectional surveys, which precludes more sophisticated derivation of time-varying average arrival rates of infections and lacks the resolution to simultaneously estimate arrival rates and infection duration. In the revised manuscript, we have elaborated on this matter and added a paragraph in the Discussion section (Line 306-309).

(3) It is unclear in the example how their bootstrap imputation approach is accounting for measurement error due to antimalarial treatment. They supply two approaches. First, there is no effect on measurement, so the measured MOI is unaffected, which is likely false and I think the authors are in agreement. The second approach instead discards the measurement for malaria-treated individuals and imputes their MOI by drawing from the remaining distribution. This is an extremely strong assumption that the distribution of MOI of the treated is the same as the untreated, which seems unlikely simply out of treatment-seeking behavior. By imputing in this way, the authors will also deflate the variability of their estimates.

We thank the reviewer for pointing out aspects of the work that can be further clarified. Disentangling the effect of drug treatment on measurements like infection duration is challenging. Since our methods rely on the known and fixed distribution of infection duration from historical data of naïve patients with neurosyphilis infected with malaria as a therapy, drug treatment can potentially violate this assumption. In the previous manuscript, we did not attempt to directly address the impact of drug treatment. Instead, we considered two extreme scenarios that bound reality, well summarized by the reviewer. Reality lies somewhere in between these two extremes, with antimalarial treatment significantly affecting measurements in some individuals but not in others. Nonetheless, the results of FOI inference do not differ significantly across both extremes.

The impact of the drugs likely depends on their nature, efficiency, and duration. We note that treatment information was collected via a routine questionnaire, with participant self-reporting that they had received an antimalarial treatment in the previous two-weeks before the surveys (i.e., participants that reported they were sick, sought treatment, and were provided with an antimalarial treatment). No confirmation through hospital or clinic records was conducted, as it was beyond the scope of the study. Additionally, many of these sick individuals seek treatment at local chemists, which may limit the relevance of hospital or clinic records, if they are even available. Consequently, information on the nature, efficiency, and duration of administrated drugs was incomplete or lacking. As this is not the focus of this work, we do not elaborate on the impact of drug treatment in the revised manuscript.

The reviewer correctly noted that this imputation might not add additional information and could reduce MOI variability. Therefore, in the revised manuscript, we reported FOI estimates with drug-treated 1-5-year-olds excluded. Additionally, we discarded the infection status and MOI values of treated individuals and sampled their MOI from non-treated microscopy-positive individuals, imputing a positive MOI for treated and uninfected individuals. We also reported FOI estimates based on these MOI values. This scenario provides an upper bound for FOI estimates. Note that we do not assume that the MOI distribution for treated individuals is the same as that for untreated individuals. Rather, we aim to estimate what their MOI would have been, and consequently, determine what the FOI per individual per year in the combined population would be, had these individuals not received antimalarial treatment. The results of FOI inference do not differ significantly between these two approaches. They can serve as general solutions to antimalarial treatment issues for others applying our FOI inference methods. These details can be found in the revised manuscript (Line 185-210, 462-484).

- For similar reasons, their imputation of microscopy-negative individuals is also questionable, as it also assumes the same distributions of MOI for microscopy-positive and negative individuals.

We thank the reviewer for this comment. The reviewer correctly noted that we imputed the MOI values for microscopy-negative but PCR-positive 1-5-year-olds by sampling from the microscopy-positive 1-5-year-olds, under the assumption that both groups have similar MOI distributions. This approach was motivated by the analysis of our Ghana surveys, which shows no clear relationship between MOI (or the number of var genes detected within an individual host, on the basis of which our MOI values were estimated) and the parasitemia levels of those hosts. Parasitemia levels underlie the difference in detection sensitivity between PCR and microscopy.

In the revised manuscript, we elaborated on this issue and included formal regression tests showing the lack of a relationship between MOI/the number of var genes detected within an individual host and the parasitemia levels of those hosts (Line 445-451, Appendix 1-Figure 7). We also described potential reasons or hypotheses behind this observation (Line 452-461).

Reviewer #3 (Public Review):

Summary:

It has been proposed that the FOI is a method of using parasite genetics to determine changes in transmission in areas with high asymptomatic infection. The manuscript attempts to use queuing theory to convert multiplicity of infection estimates (MOI) into estimates of the force of infection (FOI), which they define as the number of genetically distinct blood-stage strains. They look to validate the method by applying it to simulated results from a previously published agent-based model. They then apply these queuing theory methods to previously published and analysed genetic data from Ghana. They then compare their results to previous estimates of FOI.

Strengths:

It would be great to be able to infer FOI from cross-sectional surveys which are easier and cheaper than current FOI estimates which require longitudinal studies. This work proposes a method to convert MOI to FOI for cross-sectional studies. They attempt to validate this process using a previously published agent-based model which helps us understand the complexity of parasite population genetics.

Weaknesses:

(1) I fear that the work could be easily over-interpreted as no true validation was done, as no field estimates of FOI (I think considered true validation) were measured. The authors have developed a method of estimating FOI from MOI which makes a number of biological and structural assumptions. I would not call being able to recreate model results that were generated using a model that makes its own (probably similar) defined set of biological and structural assumptions a validation of what is going on in the field. The authors claim this at times (for example, Line 153) and I feel it would be appropriate to differentiate this in the discussion.

We thank the reviewer for this comment, although we think there is a mis-understanding on what can and cannot be practically validated in the sense of a “true” measure of FOI that would be free from assumptions for a complex disease such as malaria. We would not want the results to be over-interpreted, and we have extended the discussion of what we have done to test the methods in the revised manuscript (Line 314-328). Performance evaluation via simulation output is common and often necessary for statistical methods. These simulations can come from dynamical or descriptive models, each making their own assumptions to simplify reality. Our stochastic agent-based model (ABM) of malaria transmission, used in this study, has successfully replicated several key patterns from high-transmission endemic regions in the field, including aspects of strain diversity not represented and captured by simpler models (10).

In what sense this ABM makes a set of biological and structural assumptions that are “probably similar” to those of the queuing methods we present is not clear to us. We agree that using models with different structural assumptions from the method being tested is ideal. Our FOI inference methods based on queuing theory require the duration of infection distribution and the MOI distribution among sampled individuals. However, these FOI inference methods are agnostic to the specific biological mechanisms governing these distributions.

Another important point raised by this comment is what would be the “true” FOI value against which to validate our methods. Empirical MOI-FOI pairs from cohort studies tracking FOI directly are still lacking. Direct FOI measurements are prone to errors because differentiating new infections from the temporary absence of an old infection in the peripheral blood and its subsequent re-emergence remains challenging. Reasons for this challenge include the low resolution of the polymorphic markers used in cohort studies, which cannot fully differentiate hyper-diverse antigenic strains, and the complexity of within-host dynamics and competitive interaction of co-infecting strains (6, 8, 9). Alternative approaches also do not provide a “true” FOI estimation free from assumptions. These approaches involve fitting simplified epidemiological models to densely sampled/repeated cross-sectional surveys for FOI inference. In this case, no FOI is measured directly, and thus, there are no FOI values available for benchmarking against fitted FOI values. The evaluation or validation of these model-fitting approaches is typically based on their ability to capture other epidemiological quantities that are easier to sample or measure, such as prevalence or incidence, with criteria such as the Akaike information criterion (AIC). This type of evaluation is similar to the one done in this work. We selected FOI values that maximize the likelihood of observing the given MOI distribution. Furthermore, we paired our estimated FOI values for Ghana surveys with the independently measured EIR (Entomological Inoculation Rate), a common field measure of transmission intensity. We ensured that our resulting FOI-EIR points align with existing FOI-EIR pairs and the relationship between these quantities from previous studies. We acknowledge that, like model-fitting approaches, our validation for the field data is also indirect and further complicated by high variance in the relationship between EIR and FOI from previous studies.

Prompted by the reviewer’s comment, we elaborated on these points in the revised manuscript, emphasizing the indirect nature and existing constraints of our validation with field data in the Discussion section (Line 314-328). Additionally, we clarified certain basic assumptions of our agent-based model in Appendix 1-Simulation data.

(2) Another aspect of the paper is adding greater realism to the previous agent-based model, by including assumptions on missing data and under-sampling. This takes prominence in the figures and results section, but I would imagine is generally not as interesting to the less specialised reader. The apparent lack of impact of drug treatment on MOI is interesting and counterintuitive, though it is not really mentioned in the results or discussion sufficiently to allay my confusion. I would have been interested in understanding the relationship between MOI and FOI as generated by your queuing theory method and the model. It isn't clear to me why these more standard results are not presented, as I would imagine they are outputs of the model (though happy to stand corrected - it isn't entirely clear to me what the model is doing in this manuscript alone).

We thank the reviewer for this comment. Please refer to our response to Reviewer 2, comment (3), as we made changes in the revised manuscript regarding antimalarial drug treated individuals. We reported two sets of FOI estimates. In the first, we excluded these treated individuals from the analysis as suggested by Reviewer 2. In the second, we discarded their infection status and MOI estimates and sampling from non-treated individuals.

The reviewer correctly noted the surprising lack of impact of antimalarial treatment on MOI estimates. This pattern is indeed interesting and counterintuitive. The impact of the drugs likely depends on their nature, efficiency, and duration. We note that treatment information was collected via a routine questionnaire, with participant self-reporting that they had received an antimalarial treatment in the previous two-weeks before the surveys (i.e., participants that reported they were sick, sought treatment, and were provided with an antimalarial treatment). No confirmation through hospital or clinic or pharmacy records was conducted, as it was beyond the scope of the study. Additionally, many of these sick individuals seek treatment at local chemists, which may limit the relevance of hospital or clinic records, if they are even available. Consequently, information on the nature, efficiency, and duration of administrated drugs was incomplete or lacking. As this is not the focus of this work, we do not elaborate on the impact of drug treatment in the revised manuscript.

Regarding the last point of the reviewer, on understanding the relationship between MOI and FOI, we are not fully clear about what was meant. We are also confused about the statement on what the “model is doing in this manuscript alone”. We interpret the overall comment as the reviewer suggesting a better understanding of the relationship between MOI and FOI generated by the two-moment approximation method and the agent-based model. This could involve exploring the relationship between the moments of their distributions, possibly by fitting models such as simple linear regression models. Although this approach is in principle possible, it falls outside the focus of our work. Moreover, it would be challenging to evaluate the performance of this alternative approach given the lack of MOI-FOI pairs from empirical settings with directly measured FOI values (from large cohort studies). Nonetheless, we note that the qualitative relationship between the two quantities is intuitive. Higher FOI values should correspond to higher MOI values. Less variable FOI values should result in more narrow or concentrated MOI distributions, whereas more variable FOI values should lead to more spread-out MOI distributions. We described this qualitative relationship between MOI and FOI in the revised manuscript (Line 499-502).

As mentioned in the response to the reviewer’s previous point (1), we hope that our clarification of the basic assumptions underlying our agent-based model in Appendix 1-Simulation data helps the reviewer gain a better sense of the model. We appreciate agent-based models involve more assumptions and parameters than typical equation-based models in epidemiology, and their description can be difficult to follow. We have extended this description to rely less on previous publications. As for other ABMs, the population dynamics of the disease is followed over time by tracking individual hosts and strains. This allows us to implement specific immune memory to the large number of strains arising from the var multigene family. There is no equation-based formulation of the transmission dynamics that can incorporate immune memory in the presence of such large variation as well as recombination of the strains. We rely on this model because large strain diversity at high transmission underlies superinfection of individual hosts, and therefore, MOI values larger than one. We relied on the estimation of MOI with a method based on var gene sampling, and therefore, simulated such sampling for individual hosts (which requires an ABM and one that represents such genes and resulting strains explicitly).

(3) I would suggest that outside of malaria geneticists, the force of infection is considered to be the entomological inoculation rate, not the number of genetically distinct blood-stage strains. I appreciate that FOI has been used to explain the latter before by others, though the authors could avoid confusion by stating this clearly throughout the manuscript. For example, the abstract says FOI is "the number of new infections acquired by an individual host over a given time interval" which suggests the former, please consider clarifying.

We thank the reviewer for this helpful comment, as it is crucial to avoid any confusion regarding basic definitions. EIR, the entomological inoculation rate, is closely related to the FOI, force of infection, but they are not equivalent. EIR focuses on the rate of arrival of infectious bites and is measured as such by focusing on the mosquito vectors that are infectious and arrive to bite a given host. Not all these bites result in actual infection of the human host. Epidemiological models of malaria transmission clearly make this distinction, as FOI is defined as the rate at which a host acquires infection. This definition comes from more general models of the population dynamics of infectious diseases. For simpler diseases without super-infection, the typical SIR models define FOI as the rate at which a susceptible individual becomes infected. In the context of malaria, FOI refers to the number of new infections acquired by an individual host over a given time interval. This distinction between EIR and FOI is the reason why studies have investigated their relationship, with the nonlinearity of this relationship reflecting the complexity of the underlying biology and how host immunity influences the outcome of an infectious bite.

We added “blood-stage strains” to the definition of FOI in the previous manuscript, as pointed out by the reviewer, for the following reason. After an individual host acquires an infection/strain from an infectious mosquito bite, the strain undergoes a multi-stage life cycle within the host, including the liver stage and asexual blood stage. Liver-stage infections can fail to advance to the blood stage due to immunity or exceeding the blood-stage carrying capacity. Only active blood-stage infections are detectable in all direct measures of FOI. Quantities used in indirect model-fitting approaches for estimating FOI are also based on or reflect these blood-stage strains/infections. Only these blood-stage strains/infections are transmissible to other individuals, impacting disease dynamics. Ultimately, the FOI we seek to estimate is the one defined as specified above, as well as in both the previous and revised manuscripts, consistent with the epidemiological literature. We expanded on this point in the revised manuscript (Line 641-656).

(4) Line 319 says "Nevertheless, overall, our paired EIR (directly measured by the entomological team in Ghana (Tiedje et al., 2022)) and FOI values are reasonably consistent with the data points from previous studies, suggesting the robustness of our proposed methods". I would agree that the results are consistent, given that there is huge variation in Figure 4 despite the transformed scales, but I would not say this suggests a robustness of the method.

We thank the reviewer for this comment and have modified the relevant sentences to use “consistent” instead of “robust” (Line 229-231).

(5) The text is a little difficult to follow at times and sometimes requires multiple reads to understand. Greater precision is needed with the language in a few situations and some of the assumptions made in the modelling process are not referenced, making it unclear whether it is a true representation of the biology.

We thank the reviewer for this comment. As mentioned in the response to Reviewer 1 and in response to your previous points, we have shortened, reorganized and rewritten parts of the text in the revised manuscript to improve clarity and readability.

Reviewer #1 (Recommendations For The Authors):

Minor comments:

Bar graphs in Figures 6 and 7 are not an appropriate way to rigorously compare whether your estimated MOI (under different approaches) is comparable to your true MOIs. Particularly in Figure 6 it is very difficult to clearly compare what is going on. If anything in Figure 7 it looks like as MOI gets higher, Bayesian methods and barcoding are overestimating relative to the truth. The large Excel file that shows KS statistics could be better summarized (and include p-values not in a separate table) and further discussion of how these methods perform on metrics other than the mean value would be important given that MOI distributions can be heavily right skewed and these high MOI values contain a large proportion of genetic diversity which can be highly informative for the purposes of this estimation.

We appreciate the reviewer’s comment. It appears there may have been some misinterpretation of the pattern in Figure 7 in the previous manuscript. We believe the reviewer meant “as MOI gets higher, Bayesian methods and varcoding are UNDERESTIMATING relative to the truth” rather than “OVERESTIMATING”.

We agree with the reviewer that the comparison of MOI distributions can be improved. To better quantify the difference between the MOI distribution from the original varcoding method and its Bayesian formulation relative to true MOIs, we replaced the KS test conducted in the previous manuscript with two alternative, more powerful tests: the Cramer-von Mises Test and the Anderson-Darling Test. The Cramer-von Mises Test quantifies the sum of the squared differences between the two cumulative distribution functions, while the Anderson-Darling Test, a modification of the Cramer-von Mises Test, gives more weight to the tails of the distribution, as noted by the reviewer. We have summarized the results, including test statistics and their associated p-values, in a supplementary table (Line 135-149, Line 862-883, supplementary file 1-MOImethodsPerformance.xlsx and supplementary file 7-BayesianImprovement.xlsx).

Throughout the text the authors use "consistent" to describe their estimation of FOI, I know this is meant in the colloquial use of the word but consider changing this word to replicable or something similar. When talking about estimators, usually, consistency implies asymptotic convergence in probability which we do not know whether the proposed estimator does.

We thank the reviewer for this suggestion. We changed “consistent” to “replicable” in the revised manuscript.

I think there is an issue with the numbering of the figures, they are just numbered continuously between the main text and appendix between 1 and 15, but in the text, there is a different numbering system between the main text and appendix figures.

We thank the reviewer for this comment. We have double-checked to ensure that the numbering of the figures is consistent with the text in the revised manuscript. Figures are numbered continuously between the main text and the appendix. When referring to these figures in the text, we provide a prefix (i.e., Appendix 1) indicating whether the figure is in the main text or Appendix 1, followed by the figure number.

The description of the bootstrap for 95% CI is a bit sparse, did bootstrap distributions look symmetric? If not did authors use a skewness adjustment to ensure good coverage? Also, is the bootstrap unit of resampling at the individual level, the simulation scenario level, population level?

We checked the bootstrap distributions and calculated their skewness. The majority fall within the range of -0.5 to 0.5, with a few exceptions falling within the range of 0.5-0.75 (supplementary file 6-FOIBootstrapSkewness.xlsx). We considered them as fairly symmetric and thus did not use a skewness adjustment.

In Figures 8 and 9 the x-axes seem to imply there are both the true and estimated MOI distributions on the plot but only 1 color of grey is clearly visible. If there are 2 distributions the color or size needs to be changed or if not consider re-labeling the x-axis.

We thank the reviewer for this comment. There was a mistake in the x-axis labels in Figure 8 and 9. Only the estimated MOI distributions were shown because the true ones are not available for the Ghana field surveys. The labels should simply be “Estimated MOIvar”.

Reviewer #2 (Recommendations For The Authors):

(1) Throughout the results section there are lots of vague statements such as "differ only slightly", "exhibit a somewhat larger, but still small, difference", etc. Please include the exact values and ranges within the text where appropriate because it can be difficult to discern from the figure.

We thank the reviewer for this useful comment. In the revised manuscript, we have provided exact values and ranges where appropriate (supplementary file 1- MOImethodsPerformance.xlsx, supplementary file 3- FOImethodsPerformance.xlsx, and supplementary file 7-BayesianImprovement.xlsx).

(2) Truncate decimals to 2 places.

We thank the reviewer for this comment. In the revised manuscript, we have truncated decimals to two places where applicable.

(3) The queueing theory notation in the methods section is unfamiliar, specifically things like "M/M/c/k", please define the variables used.

We thank the reviewer for this useful comment. In the revised manuscript, we have defined all the variables used. Please refer to our responses to Reviewer 1 Point (1) a.

Reviewer #3 (Recommendations For The Authors):

(1) The work takes many of the models and data from a previous paper published in eLife in 2023 (the 4 most senior authors of this previous manuscript are the 4 authors of the current manuscript). This previous paper introduced some new terminology "census population" which was highlighted as being potentially confusing by 2 of the 3 reviewers of the original article. This was somewhat rebuffed by the authors, though their response was ambiguous about whether the terminology would be changed in any potential future revision. The census population terminology does not appear in this manuscript, though the same data is being used. Publication of similar papers with the same data and different terminology could generate confusion, so I would encourage authors to be consistent and make sure the two papers are in line. To this end, it feels like this paper would be better suited to be classified as a "Research Advances" on this original manuscript and linked, which is a nice functionality that eLife offers.

We thank the reviewer for this comment, but we do not think our work would fall under the criteria of “Research Advances” based on our previous paper pointed out by the reviewer. The reviewer correctly noted that the current work and the previous paper used the same datasets. However, they have different goals and are not related in terms of content.

The previous paper examined how epidemiological quantities and diversity measurements of the local parasite population change following the initiation of effective control interventions and subsequently as this control wanes. These quantities included MOI and census population size (MOI was estimated using the Bayesian formulation of the varcoding method, and the census population size was derived from summing MOIvar across individuals in the human population). In contrast, our current work focused on a different goal: inferring FOI based on MOI. We proposed two methods from queuing theory and illustrated them with MOI estimates obtained with the Bayesian formulation of the "varcoding" method. Although the method applied to estimate MOI is indeed the same as that of the paper mentioned by the reviewer, the proposed methods should be applicable to MOI estimates obtained in any other way, as stated in the Abstract in the previous manuscript. That is, the methods we present in the current paper are independent from the way the MOI estimation has been carried out. Our results are not about the MOI values themselves but rather on an illustration of the methods for converting those MOI values to FOI. In fact, there are different ways to obtain MOI estimates for Plasmodium falciparum (9). The most common approach for determining MOI involves size-polymorphic antigenic markers, such as msp1, msp2, msp3, glurp, ama1, and csp. Similarly, microsatellites, also termed simple sequence repeat (SSR), are another type of size-polymorphic marker that can be amplified to estimate MOI by determining the number of alleles detected. Combinations of genome-wide single nucleotide polymorphisms (SNPs) have also been used to estimate MOI.

The result section of the current manuscript begins by evaluating how different kinds of errors/sampling limitations affect the estimation of MOI using the Bayesian formulation of the varcoding method. Only that brief section, which is not the core or primary objective of the manuscript, could be considered an extension and an advancement related to the other paper. We considered the effect of these errors on the resulting estimates of FOI.

We further note that, as the reviewer pointed out, the census population size is not utilized at all in our current work. We are unclear on why this quantity is mentioned here. Our previous paper has been revised and can be found in eLife as such. We have not changed this terminology and have provided a clear explanation for why we chose it. The reviewer seems to have read the previous response to version 1 posted on December 28, 2023 (Note that version 2 and the associated response was posted on November 20, 2024). Regardless, this is not the place for a discussion on another paper on a quantity that is irrelevant to the current work being reviewed.

We understand that the reviewer’s impression may have been influenced by the previous emphasis on the Bayesian formulation of the varcoding method in our manuscript. With the reorganization and rewriting of parts of the manuscript, we hope the revised version will clearly convey the central goal of our work.

(2) Similar statements that could be toned down. 344 ".... two-moment approximation approach and Little's law are shown to provide consistent and good FOI estimates,.....", 374 "Thus, the flexibility and generality of these two proposed methods allow robust estimation of an important metric for malaria transmission"

We thank the reviewer for this comment. We have modified the descriptive terms for the performance of our methods. Please also refer to our responses to Reviewer 1, Point (1) c and your previous Point (1).

(3) Various assumptions seem to have been made which are not justified. For example, heterogeneous mixing is defined as 2/3rd of the population receives 90% of the bites. A reference for this would be good.

In this work, we considered heterogenous transmission arising from 2/3 of the population receiving approximately 94% of all bites, because we believe this distribution introduces a reasonable and sufficient amount of heterogeneity in exposure risk across individuals. We are not aware of field studies justifying this degree of heterogeneity.

(4) The work assumes children under 5 have no immunity (Line 648 says "It is thus safe to consider negligible the impact of immune memory accumulated from previous infections on the duration of a current infection." ). Is there supporting evidence for this and what would happen if this wasn't the case?

We thank the reviewer for this helpful comment. Please refer to our responses to Reviewer 1 Point (2) a.

(5) Similarly, there are a few instances of a need for more copy-editing. The text says "We continue with the result of the heterogeneous exposure risk scenarios in which a high-risk group ( 2/3 of the total population) receives around 94% of all bites whereas a low-risk group ( 1/3 of the total population) receives the remaining bites (Appendix 1-Figure 5C)." whereas the referenced caption says "For example, heterogeneous mixing is defined as 2/3rd of population receives 90% of the bites."

We believe there was a misinterpretation of the legend caption. In the referenced caption, we stated “2/3rd of population receives MORE THAN 90% of the bites”, which aligns with “around 94% of all bites”. Nonetheless, to maintain consistency in the revised manuscript, we have updated the description to uniformly state “approximately 94% of all bites” throughout.

(6) The term "measurement error" is used to describe the missing potential under-sampling of var genes. Given this would only go one way isn't the term "bias" more appropriate?

We understand that, in general English, “bias” might seem more precise for describing a deviation in one direction. However, in malaria epidemiology and in models for malaria and other infectious diseases, “measurement error” is a general term that describes deviations introduced in the process of measurement and sampling, which can confound or add noise to the true values being collected. This term is commonly used, and we have adhered to it in the revised manuscript.

(7) Line 739 "Though FOI and EIR both reflect transmission intensity, the former refers directly to detectable blood-stage infections whereas the latter concerns human-vector contact rates." In my mind this is not true, the EIR is the number of potentially invading parasites (a contact rate between parasites in mosquitoes and humans if you will). The human-vector contact rate is the human biting rate.

We thank the reviewer for this comment. We have clarified the definition regarding FOI and EIR in our response to your previous comment (3) and in the revised manuscript. We agree that the term “human-vector contact rates” was not precise enough for EIR. We intended “human-infectious vector contact rates”, and we have updated the text to reflect this change (Line 644-645).

References and Notes

(1) Maire, N. et al. A model for natural immunity to asexual blood stages of Plasmodium falciparum malaria in endemic areas. Am J Trop Med Hyg., 75(2 Suppl):19-31 (2006).

(2) Tiedje, K. E. et al. Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions. eLife, 12 (2023).

(3) Andrade C. M. et al. Infection length and host environment influence on Plasmodium falciparum dry season reservoir. EMBO Mol Med.,16(10):2349-2375 (2024).

(4) Zhang X. and Deitsch K. W. The mystery of persistent, asymptomatic Plasmodium falciparum infections, Current Opinion in Microbiology, 70:102231 (2022).

(5) Tran, T. M. et al. An Intensive Longitudinal Cohort Study of Malian Children and Adults Reveals No Evidence of Acquired Immunity to Plasmodium falciparum Infection, Clinical Infectious Diseases, 57(1):40–47 (2013).

(6) Farnert, A., Snounou, G., Rooth, I., Bjorkman, A. Daily dynamics of Plasmodium falciparum subpopulations in asymptomatic children in a holoendemic area. Am J Trop Med Hyg., 56(5):538-47 (1997).

(7) Read, A. F. and Taylor, L. H. The Ecology of Genetically Diverse Infections, Science, 292:1099-1102 (2001).

(8) Sondo, P. et al. Genetically diverse Plasmodium falciparum infections, within-host competition and symptomatic malaria in humans. Sci Rep 9(127) (2019).

(9) Labbe, F. et al. Neutral vs. non-neutral genetic footprints of Plasmodium falciparum multiclonal infections. PLoS Comput Biol, 19(1) (2023).

(10) He, Q. et al. Networks of genetic similarity reveal non-neutral processes shape strain structure in Plasmodium falciparum. Nat Commun 9(1817) (2018).

Author response:

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

eLife Assessment

This study has uncovered some important initial findings about cellular responses to aneuploidy through analysis of gene expression in a set of donated human embryos. While the study's findings are in general solid, some experiments lack statistical power due to small sample sizes. The authors should try to get much more insight with their data highlighting the novel findings.

We thank the editor for considering our manuscript for publication at elife, and for the helpful and thorough reviews of our work. Based on the suggestions of the reviewers, we have carried out additional experiments, expanded the sample size and reanalyzed the data. This has resulted in a thoroughly revised manuscript and much improved work, which we are convinced meets the requirements to be published as a version of record. Of note, the experiments for the revision required the support by 2 additional researchers from our lab which are now coauthors.

These are the main changes made to the initial manuscript:

(1) The RNA-seq data (Figures 1+2) is now FDR corrected and been reanalyzed. This has not affected the initial observations on the activation of p53 and apoptosis in aneuploid human embryos, as well as that the transcriptomic changes are driven by gene dosage effects. 

(2) We have included the transcriptome analysis of reversine-treated embryos in the supplementary data.

(3) For validation of novel findings such as the presence of DNA-damage and the expression of DRAM1 in aneuploid embryos, we now include the stainings of 30 human blastocysts (Figure 3o-t). We found absence of DNA-damage in aneuploid embryos and that DRAM1 is increased in the TE but not the ICM of aneuploid embryos. 

(4) We re-analyzed the co-expression of CASP8/HSP70 in reversine-embryos as suggested by reviewer 1 and found that both proteins tend to be co-expressed. 

(5) We have added a new analysis of NANOG expression (Figure 4a,b) of the embryos used in Figure 3o-t and have found retention of NANOG protein in both the TE and ICM.

(6) We have added 6 euploid and 4 aneuploid embryos to Figure 4l-s, which support the conclusions on the absence of autophagy activation in the ICM and failure of PrE formation in aneuploid embryos.

(7) We have significantly changed the layout of the figures, revised the supplementary tables, added source data files and rewritten the discussion.

Regarding the sample size of the study, it is important to emphasize that human embryos are ethically sensitive material and that those with the specific genetic content we used in this study are rare, limiting our ability to expand the sample size. For the revision, we have added 40 human blastocysts to our initial 85 embryos. Compared to similar and high-quality studies using human embryos, our study shows a relatively large sample size (n=125): Victor et al. 2021: 30 human blastocysts for immunostainings1; Martin et al. 2023: 14 human blastocysts2; Martin et al. 2024: 64 human blastocysts3; Domingo-Muelas et al. 2023: 23 human blastocysts4.              

Public Reviews:

Reviewer#1(PublicReview):

This study investigated an important question in human reproduction: why most fully aneuploid embryos is incompatible with normal fetal development. Specifically, the authors investigated the cellular responses to aneuploidy through analysis of gene expression in a set of donated human blastocysts. The samples included uniform aneuploid embryos of meiotic origin and mosaic aneuploid embryos from the SAC inhibitor reversine treatment. The authors relied mainly on low-input RNA sequencing and immunofluorescence staining. Pathway analysis with RNA-seq data of trophectoderm cells suggested activation of p53 and possibly apoptosis, and this cellular signature appeared to be stronger in TE cells with a higher degree of aneuploidy. Immunostaining also found some evidence of apoptosis, increased expression of HSP70 and autophagy in some aneuploid cells. With combinational OCT4 and GATA4 as lineage markers, it appeared that aneuploidy could alter the second lineage segregation and primitive endoderm formation in particular.

Although this study is largely descriptive, it generated valuable RNA-seq data from a set of aneuploid TE cells with known karyotypes. Immunostaining results in general were consistent with findings in mouse embryos and human gastruloids.

We thank the reviewer for the thorough evaluation of our manuscript. We have implemented most of the suggestions, which have further strengthened the original findings.

While there is a scarcity of human embryo materials for research, the lack of single cell level data limits further extension of the presented data on the consequences of mosaic embryos.  

We did not include single cell RNA-seq data of mosaic human embryos in our study because we focused on embryos diagnosed with complex meiotic abnormalities. Our hypothesis was that the cellular consequences of aneuploidy would be strongest in this type of aneuploidies and most evident to identify and would allow us to provide a basis for the mechanisms of elimination of aneuploid cells in human embryos. In the manuscript (lines 596-626) we acknowledge the limitations of the extrapolation of our results to mosaic embryos.

A major concern is that the gene list used for pathway analysis is not FDR controlled. It is also unclear how the many plots generated with the "supervised approach" were actually performed. 

We agree with the concerns about the fact that our differential expression gene list was not FDR but p-value ranked. We followed the suggestion of the reviewer and revised the RNAseq analysis and focused primarily on pathway analysis. We have also added the comparison between aneuploid and reversine treated embryos to the supplementary data and expanded the analysis of high dosage and low dosage embryos. Importantly, the new analysis has not changed the original finding that aneuploid embryos show hallmarks of p53 activation and apoptosis, and that these effects are gene dosage dependent. The manuscript now includes two completely revised and new figures 1 and 2.

Since we discarded the data generated from our previous approach, we do not use the term supervised approach anymore.

The authors also appear to have ignored the possibility that high-dosage group could have a higher mitotic defect.

This is indeed a possibility. In the discussion (lines 504-508) we have now incorporated the notion that the high dosage embryos could have higher mitotic defects, although our data cannot provide any evidence for this. Of note, the gene expression data shows that all aneuploid embryos (including low dosage and reversine embryos) equally show an enrichment for mitotic spindle pathway genes.

Assuming a fully aneuploid embryo, why do only some cells display p53 and autophagy marker? 

This is a very good question, on which we can only speculate, but the answer likely lies in the diversity across cells of the same embryo.

Even in genetically homogenous tissues and cell cultures, individual cells can exhibit different levels of stress responses, such as p53 activation and apoptosis. This variation may be influenced by the local cellular environment, stochastic gene expression, or differences in cell cycle stages. Other studies on fully aneuploid human embryos could also not detect apoptotic responses in every cell1,3.

For instance, p53 activation differs even between cells that have a similar number of DNA breaks, and this activation is influenced by both cell-intrinsic factors and previous exposure to DNA damage5.

Cell cycle tightly regulates the response of cells to different stressors. For instance, cells in G1 or S-phase might be more sensitive to apoptosis signals6, while those in G2/M might escape this response temporarily7.  Autophagy is more induced in G1 and S phases, with reduced activity in G2 and M phases8.

Individual cells may also have different levels of success in the activation of the compensatory pathways, including the unfolded protein response, autophagy, or changes in metabolism, resulting in some cells adapting better than others.

The expression of p53 and the sensitivity to apoptosis could also be influenced by epigenetic differences between cells, which may alter their transcriptional response to aneuploidy. Even in a genetically identical population, cells can have different epigenetic landscapes, leading to heterogeneous gene expression patterns.

The conclusion about proteotoxic stress was largely based on staining of HSP70. It appears from Figure 3 d,h that the same cells exhibited increased HSP70 and CASP8 staining. Since HSP70 is known to have anti-apoptotic effect, could the increased expression of Hsp70 be an anti-apoptotic response?

Our conclusion about proteotoxic stress was not solely based on HSP70 expression. We also stained for LC3B and p62, which are markers for autophagy and when highly expressed indirectly point towards underlying proteotoxic stress in the cells. 

We reanalyzed the imaging of the stainings in the reversine-treated embryos, and found that the same cells were positive for both HSP70 and CASP8 staining while the minority was single positive (shown now in Figure 3k,l). 

HSP70 does indeed not only unfold misfolded and aggregated proteins but does also have a function during cell survival and apoptosis9. HSP70 has been for instance found to inhibit the cleavage of Bid through active CASP8 within the extrinsic apoptosis pathway10. It is thus possible that it temporarily plays this role, and we have acknowledged this in the discussion (lines 623-626). On the other hand, the evidence points at an active apoptosis in the TE, with concomitant cell loss, so if HSP70 is indeed having an anti-apoptotic effect, it is having a limited impact.

Reviewer #2 (Public Review): 

A high fraction of cells in early embryos carry aneuploid karyotypes, yet even chromosomally mosaic human blastocysts can implant and lead to healthy newborns with diploid karyotypes. Previous studies in other models have shown that genotoxic and proteotoxic stresses arising from aneuploidy lead to the activation of the p53 pathway and autophagy, which helps eliminate cells with aberrant karyotypes. These observations have been here evaluated and confirmed in human blastocysts. The study also demonstrates that the second lineage and formation of primitive endoderm are particularly impaired by aneuploidy.

This is a timely and potentially important study. Aneuploidy is common in early embryos and has a negative impact on their development, but the reasons behind this are poorly understood. Furthermore, how mosaic aneuploid embryos with a fraction of euploidy greater than 50 % can undergo healthy development remains a mystery. Most of our current information comes from studies on murine embryos, making a substantial study on human embryos of great importance. However, there are only very few new findings or insights provided by this study. Some of the previous findings were reproduced, but it is difficult to say whether this is a real finding, or whether it is a consequence of a low sample number. The authors could get much more insight with their data.

We thank the reviewer for the thorough evaluation of our manuscript and the valuable suggestions made in the private recommendations. We have expanded the sample size and have carried out additional experiments that have significantly improved the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Instead of using cut off to generate a list, the authors could just rank the entire detected transcriptome for GSEA. This method fits better the authors' intentions of "primarily focused on pathway analysis." The cut-off value "-log10(p-value)<0.05" is not correct. As we can see from the PCA plot, one would not expect many cut off defined DEGs at all. The most obvious transcriptome change is dosage dependent, as the authors cleared showed with InferCNV.

We thank the reviewer for this suggestion and agree that this was an important concern of the study. We have entirely revised the RNA-seq analysis based on the proposed approach (Figure 1 and 2, Supplementary Figure 1). Also, we have included the analysis of aneuploid versus reversine treated embryos, which has allowed us to determine the differences between naturally occurring chromosomal abnormalities and those that are induced using reversine (Supplementary Figure 1). 

We first performed differential gene expression analysis using DESEq2 with a cut-off value for significantly differentially expressed genes of | log2FC | > 1 and an FDR < 0.05. Based on the PCAs and the low number of differentially expressed genes for all comparisons, besides high dosage versus euploid embryos, we focussed primarily on pathway analysis. 

For that, based on the reviewer’s suggestion, we generated a ranked gene list using the GSEA software (version 4.2.2, MSigDatabase) based on the normalized count matrix of the whole transcriptome that was detected after differential gene expression. The ranked gene list was then subjected to the run GSEA function, and we searched the Hallmark and C2 library for significantly enriched pathways. Thus, we could generate normalized enrichment scores, allowing us to predict whether a pathway is activated or suppressed. The details of the new analysis are described in the Material and Methods section (lines 220-232). Significance was determined using a cut-off value of 25% FDR. This cut-off is proposed in the user guide of the GSEA (https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm) especially for incoherent gene expression datasets, as suggested by our PCAs, which allows for hypothesis driven validation of the dataset. 

Indeed, we found that the most important transcriptome changes are aneuploidy dosage dependent. High dosage embryos show signatures of cellular unfitness, while low-dosage embryos still seem to activate survival pathways (lines 349-364). 

This new analysis did not only increase robustness of our results but also introduced novel findings, which pave the road for future studies. 

The validity of our findings is supported by recent work by the Zernicka-Goetz lab. We found that hypoxia is upregulated in low dosage human aneuploid TE cells. In line with our data, the Zernicka-Goetz lab found in a mouse model of low degree chromosomal abnormalities that hypoxia inducible factor 1A (HIF1A) promotes survival of extraembryonic aneuploid cells by reducing levels of DNA damage11.

(2) It would be very helpful if the authors could perform co-staining of multiple stress markers to better understand the origins of apoptosis and autophagy cells. In Fig 3d and 3h, it seems that the same reversine treated embryo was stained with CASP8, LC3B and HSP70. Is there any correlation between CASP8 and HSP70 at the single cell level? Is there any correlation between p53 and LC3B as the authors suggested, possibly through DRAM1?

We decided to use the complex aneuploid embryos that were left at our facility for the validation of novel findings such as upregulation of DRAM1 and presence and consequences of DNA damage in aneuploid embryos. As suggested by the editor and the other reviewer we also added embryos to existing datasets to increase the sample size where necessary. Therefore, we did not include other co-staining’s of multiple stress markers.

Following the reviewer’s suggestion, we reanalyzed the existing stainings and evaluated whether there is a correlation between CASP8 and HSP70 at the single cell level. The reversine-treated embryos were the only embryo group that was co-stained for both CASP8 and HSP70. We quantified the percentage of cells that were single or double positive for CASP8 and HSP70 and found a higher proportion of double positive cells than to single positives. Therefore, we concluded that there is indeed a correlation between both proteins at the single cell level in reversine-treated embryos and included this data in Figure 3k,l. 

During the experiments for the revision, we found that the DRAM1 protein was upregulated in the cytoplasm of TE cells but not in the ICM of aneuploid embryos (Figure 3s,t), which validates the findings of the gene expression analysis. This data also supports our findings that autophagy is active in aneuploid TE cells while not significantly increased in aneuploid pluripotent ICM cells. Unfortunately, we could not stain LC3B and DRAM1 in the same embryo because the antibodies were raised in the same species.

(3) While " the possibilities for functional studies and lineage tracing experiments in human embryos are very limited," the authors can leverage in silico modelling (ie, PMID: 28700688) to address the roles of aneuploidy in blastocyst formation and development. Is there any selfregulating mechanism underlying the ratios of PrE and EPI? Is apoptosis of ICM cells a natural process during PrE formation (PMID: 18725515)?

It is a very interesting proposal to use in silico modelling to address the roles of aneuploidy during human blastocyst formation and lineage segregation. Although this type of analysis would yield very important insights, we are not able to address this point of the revision due to lack of expertise for this type of analysis in our group, requiring setting up a collaboration with experts in this field.  In the discussion we proposed that future studies can leverage our data to be carried out in silico modelling and cited the proposed article (lines 608-610).

On the second part of the question, we would like to discuss the differences between mouse and human embryo studies. Parts of this were included in the discussion on the possible mechanisms of PrE elimination. 

Is there a self-regulating mechanism for EPI/PrE formation?

To extrapolate the knowledge on mouse development to human it is important to bear in mind that (1) human embryos are outbred, as compared to inbred super-fertile laboratory mouse strains and (2) the embryos are donated to research by subfertile couples, which could compromise the EPI/PrE ratios. For instance, Chousal and colleagues found that poor quality blastocysts have a reduced number of PrE cells12. In human embryos the proportion EPI and PrE cells is indeed highly variable (20%-60%) and while the number of EPI cells does not increase between dpf6 and 7, the number of PrE cells does grow13. We found a similar variable number of EPI and PrE in our study on the lineage segregation mechanisms in good quality human embryos, with an absolute number of EPI of 12.1±6.5 cells and 8.4±3.44 PrE cells14.

By comparison, in late mouse blastocysts, the ratio EPI/PrE cells is consistent (2/3)15. Overall, self-regulating mechanisms in the human embryo are not yet studied in detail due to the lack of possible functional testing.

Is apoptosis a natural process during PrE formation?

Yes, in mice apoptosis is a natural process during PrE formation to eliminate misallocated cells of the inner cell mass through cell competition16,17. Yet, in the human embryo there is no evidence of such mechanisms. Although apoptosis is present even in human blastocysts of good quality18, the origin of such apoptotic cells is now still shown, although suboptimal culture conditions are known to increase cellular fragmentation19. Conversely, our data and that of others1,2 supports the notion that the pluripotent inner cell mass in human embryos is more resistant to apoptosis than the trophectoderm, even in karyotypically aberrant cells. 

(4) The "count tables generated from the raw data files" could not be found in the source data files.

This slipped to our attention, we have added now the count tables to the source data files. Our apologies.

(5) Citations on aneuploidy literature were not done in a fully scholarly manner. It appears that authors selectively cite previous papers that are in support of their hypothesis but left out those with alternative conclusions.

We apologize if we missed any literature that contradicts our findings, it is not intentional. We would be grateful if the reviewer could provide such references. 

In the manuscript we describe the alignment and differences of key findings with several studies (listed below) and the limitations of our study are extensively described in lines 596626.

Our findings align with other work on these aspects:

- RNA-sequencing data2,20–26

- Gene dosage effects drive the transcriptome of the aneuploid human embryo27,28

- Aneuploid cells are cleared by sustained proteotoxic stress followed by p53 activation, autophagy and eventually apoptosis29–37.

- p53 is active in constitutional aneuploid cells38

- The ICM is less sensitive to apoptosis1,2

Our findings differ with other work on these points:

- p53 activation is independent from DNA-damage39

- p53 is active in constitutional aneuploid cells40,41

- Apoptosis is only present in the aneuploid TE of aneuploid cells in the embryo29,30,42    

Reviewer #2 (Recommendations For The Authors):

Comments:

(1) The main problem is that there is no substantial novelty. The authors look at previously identified factors affected by chromosome gains and losses, but none of the new one from their analysis. Anything what could be potentially novel is not carefully analyzed (e.g. the difference between reversine-treated and aneuploid samples, or new potential candidates) or explained. This is really a pity.

In the revision, we have further elaborated on the DNA damage aspect by staining for DNA double-stranded breaks and have validated DRAM1 as an activated downstream effector of p53. We have also added the analyses of the gene-expression of the reversine-treated embryos.

(2) Some of the general statements on aneuploidy are confusing and often borderline generalized. E.g. introduction line 106: "If this (proteotoxic stress) remains unresolved by the activation of autophagy..." I am not aware of any publication suggesting that autophagy resolves proteotoxic stress in aneuploid cells. Citations that replication stress causes DNA damage in aneuploid cells are wrong. This link was first shown by Passerini et al. in 2016. etc.

We have clarified these statements in the introduction and added the proposed citations on replication stress that causes DNA damage in aneuploid cells (lines 95-108).

(3) In the figures the authors show a representative image of aneuploid and diploid embryos. Given the aneuploid embryos have widely different karyotypes, it would be important to clarify which of the embryos has been actually shown. Similarly, in the heat maps it is not clear which line is which embryo. This would be very useful.

We added the karyotypes of the aneuploid embryos to the images in figure 3 and 4. Since the heatmaps were removed from the figures we added the karyotypes to the PCAs in all figures.

(4) The authors constantly state that aneuploid embryo accumulate more DNA damage, which is supported by some of their observations, e.g. the DNA damage response is upregulated. It would be great if they would validated this statements with testing some markers for DNA damage.

We agree with the reviewer that this was an important point and addressing it has revealed that our initial assumption was incorrect and has provided new interesting findings. From the revised RNA-seq analysis, we found only one pathway (DNA damage response TP53) to be activated in all aneuploid embryos (Fig.1e). The ATM pathway was also activated specifically in high-dosage embryos. Following this, we set to test if DNA damage was indeed increased in aneuploid embryos by staining for DNA double strand breaks with gH2AX. 

First, we investigated the gH2AX expression in 5dpf embryos in which we induced DNAdamage with Bleomycin. We compared 6 untreated versus 6 Bleomycin treated human embryos (Fig. 3m) and found that gH2AX foci were rarely present in the untreated embryos and that all cells of the treated embryos showed a pan-nuclear gH2AX staining. 

Second, we compared the presence of gH2AX foci in the TE (NANOG negative cells), ICM (NANOG positive cells) and the whole embryo of 7 euploid versus 11 aneuploid embryos. Interestingly, we found no differences in the number of gH2AX foci or pan-nuclear gH2AX nuclei between euploid and aneuploid embryos (Fig 3o). When dividing our aneuploid embryos into high and low dosage embryos we could also not account for differences. Our data now suggests that complex aneuploid human embryonic cells of meiotic origin do not contain more DNA-double strand breaks, precluding DNA-damage as the source of p53 activation. Last, in our previous experiment we found that phosphorylated S15p53 is increased in aneuploid embryos, supporting an active p53 pathway as suggested by our transcriptomic data. Since we could not find DNA-damage in aneuploid human embryos we speculate that p53 is phosphorylated on Serine15 through metabolic stress as suggested by Jones and colleagues43. We also argue that proteotoxic stress might induce p53 expression as proposed by Singla and colleagues29.

(5) The source of embryos is only partially described in a figure legend. This should be expanded and described in the Materials and Methods section. The embryos are named, but this is nowhere explained. One can only assume that T is for trisomy and M is for monosomy.

We have divided the embryos into different experimental series (Experiment 1-4). This is now described in the Material and methods section (lines 157-175). Also, we have added the experiment number of each embryo to the supplementary tables and to the source data. The abbreviation for T = Trisomy and M= Monosomy was initially introduced in the last paragraph of the figure legend of figure 4.  We now added it to every panel.

(6) Recent works from non-embryonic cells suggest that the cellular response to monosomy is different than the response to trisomy. Did the authors try to test this possible difference? For example, one could compare embryos M174/21, M2/19 and M17 with T2/10, T10/22 and T1/15/18/22.

We thank the reviewer for pointing this out. Our RNA-seq. dataset consisted of three embryos that contained trisomies only and four embryos that contained monosomies only. When reanalyzing our data we found different transcriptomic responses between monosomic only and trisomic only cells. Compared to euploid cells, monosomy only cells activate mainly the p53pathway and protein secretion while translation, DNA replication, cell cycle G1/S, DNA synthesis and processing of DNA double strand breaks were inhibited. Trisomy only cells show activated oxidative phosphorylation, ribosome and translation while protein secretion, apoptosis and cell cycle are inhibited. These differences were confirmed by testing transcriptomic differences between trisomic versus monosomic cells. Our results are similar to studies on human embryos20,26 and other monosomic and trisomic cell lines44,45. However, the interpretation of these results is very limited by the small sample size and the comparison of monosomies and trisomies of different chromosomes. Thus, we decided to keep this analysis out of the manuscript.

Author response image 1.

On the protein level, next to the small sample size, our results were also limited by the fact that not all embryos were stained with the same combinations of antibodies. LC3B was the only protein for which all embryos were immunostained. Thus, other protein data could not be re-analyzed due to even lower sample sizes. 

Below we have separated the LC3B puncta per cell counts into euploid, trisomies only, monosomies only and all other aneuploid embryos. We performed a Kruskal Wallis test with multiple comparisons. It is worth noticing that the difference between euploid and monosomies only (and those that contained both) was statistically significant, while the difference between euploid vs trisomies only and trisomies only vs monosomies only was not statistically significant. These differences contradict the studies on monosomic cell lines that found that proteotoxic stress and autophagy are not present and specific to trisomic cell lines. Here we also decided to keep this specific protein expression analysis out of the manuscript due to the above-mentioned limitations.

Author response image 2.

(7) Line 329: "a trisomy 12 meiotic chromosomal abnormality in one reversine-treated embryo." What does it mean? Why meiotic chromosomal abnormality when the reversine treatment was administered 4 days after fertilization? In the discussion, the authors state "presumed meiotic," but this should be discussed and described more clearly.

Since reversine induces mitotic abnormalities of different types leading to chromosomally mosaic embryos, we could not identify these induced abnormalities using inferCNV on the RNAseq of TE biopsies of said embryos. However, we were not aware of the karyotype of the embryos that were used for these experiments, as they were thawed after they had been cryopreserved at day 3 of development and had not been subjected to genetic testing.  This makes it possible that some of those embryos we used for the reversine experiments in fact carried endogenously acquired meiotic and mitotic chromosomal abnormalities. Since we are only able to detect by inferCNV aneuploidies homogeneously present in the majority of the cells of the sequenced biopsy, we only picked up this trisomy 12.  It is possible that this was not a meiotic abnormality but a miotic one originating at the first cleavage and present at a high percentage of cells in the blastocyst. At any rate, the exact origin of this aneuploidy has no further implications for the results of the study. We clarified this in the manuscript (lines 310-315).

(8) Line 422: "The gene expression profiles suggest that the accumulation of autophagic proteins in aneuploid embryos is caused by increased autophagic flux due to differential expression of the p53 target gene DNA Damage Regulated Autophagy Modulator-1 (DRAM1), rather than by inhibition of autophagy (Supplementary Table 2)." This is highly speculative, as the authors do not have any evidence to support this statement.

To validate this finding we have now stained 7 euploid and 11 aneuploid embryos with a DRAM1 antibody. We found DRAM1 protein to be significantly enriched in the cytoplasm of TE cells but not in the ICM of aneuploid embryos when comparing with euploid embryos (Fig. 3s,t). This data is consistent with the finding that autophagy is increased in the TE and not the ICM of aneuploid human embryos. (Fig 4l-o). Potential implications of DRAM1 expression have been mentioned in the discussion.

(9) The figure legends are confusing. They are mixed up with the methods and some key information are missing.

We revised all figure legends accordingly and removed the experimental set-up figures from the manuscript to reduce any confusion. The methods section was revised and expanded.

(10) In Figure 1, what is the difference between "activated" and "deregulated"?

Since we analyzed our RNA-seq dataset with the method proposed by reviewer 1 we now generated normalized enrichment scores. The terms activated and deregulated are thus not present anymore.  

(11) The p62 images are not really clear. There might be more puncta (not obvious, though), but the staining intensity seems lower in the representative images.  

We do not agree with the reviewer that there might be more p62 puncta (purple), however, we agree that it was not clearly visible from the pictures. Below we show an example of the counting mask (in green) of the aneuploid embryo from figure 3i, where one can clearly appreciate that all the puncta are captured by the counting mask. In this case, the software counted 1704 puncta. To further clarify, we now added a zoom of a randomly chose ROI of the p62 staining’s to figure 3i.

Author response image 3.

(12) The authors claim that there are differences between lineages in response to aneuploidy, such as autophagy not being activated in the OCT4+ lineage, etc. However, the differences are very small and based on a small number of embryos. It is difficult to draw far-reaching conclusions based on a small number of experiments (Fig. 4n-r). The authors also claim in the Abstract that they demonstrated "clear differences with previous findings in the mouse", which are however difficult to identify in the text.

We agree with the reviewer that our conclusions on figures 4l-o were based on a small number of embryos. We have increased as much as possible the sample size. This is challenging due to the constrictions in accessing human embryos, and especially the limited number of embryos with meiotic complex aneuploidy. We have performed immunostainings for LC3B, OCT4 and GATA4 of six additional euploid and four additional aneuploid human embryos. This did not change our overall findings that aneuploid embryos upregulate autophagy in the TE rather than the ICM (Figure 4l-o). After the inclusion of additional embryos, we removed our speculation from the manuscript that autophagy is present in ICM cells of already differentiated cells towards EPI/PrE.

We have rephrased the abstract to state that we highlight a few differences with previous findings in the mouse. Here we focused especially on the different transcriptomic response of reversine treated embryos, that aneuploid mouse embryos do not seem to suffer from lineage segregation errors and that the ICM of aneuploid human embryos lacks apoptosis while aneuploid mouse embryos show elimination from the EPI. Likewise, we highlighted the similar stress responses and that we could give novel insights into p53 mediated autophagy and apoptosis activation through DRAM1 in aneuploid TE cells but not the ICM.  

(13) The text needs thorough editing - long sentences, typos, and grammar errors are frequent. Punctuation is largely missing.

We have revised the text.

References

(1) Victor, A. R. et al. One hundred mosaic embryos transferred prospectively in a single clinic: exploring when and why they result in healthy pregnancies. Fertil Steril 111, 280–293 (2019).

(2) Martin, A. et al. Mosaic results after preimplantation genetic testing for aneuploidy may be accompanied by changes in global gene expression. Front Mol Biosci 10, 264 (2023).

(3) Martín, Á. et al. Trophectoderm cells of human mosaic embryos display increased apoptotic levels and impaired differentiation capacity: a molecular clue regarding their reproductive fate? Human Reproduction 39, 709–723 (2024).

(4) Domingo-Muelas, A. et al. Human embryo live imaging reveals nuclear DNA shedding during blastocyst expansion and biopsy. Cell 186, 3166-3181.e18 (2023).

(5) Loewer, A., Karanam, K., Mock, C. & Lahav, G. The p53 response in single cells is linearly correlated to the number of DNA breaks without a distinct threshold. BMC Biol 11, 1–13 (2013).

(6) Kim, H., Watanabe, S., Kitamatsu, M., Watanabe, K. & Ohtsuki, T. Cell cycle dependence of apoptosis photo-triggered using peptide-photosensitizer conjugate. Scientific Reports 2020 10:1 10, 1–8 (2020).

(7) Pollak, N. et al. Cell cycle progression and transmitotic apoptosis resistance promote escape from extrinsic apoptosis. J Cell Sci 134, (2021).

(8) Neufeld, T. P. Autophagy and cell growth--the yin and yang of nutrient responses. J Cell Sci 125, 2359–2368 (2012).

(9) Lanneau, D. et al. Heat shock proteins: essential proteins for apoptosis regulation. J Cell Mol Med 12, 743 (2008).

(10) Gabai, V. L., Mabuchi, K., Mosser, D. D. & Sherman, M. Y. Hsp72 and Stress Kinase cjun N-Terminal Kinase Regulate the Bid-Dependent Pathway in Tumor Necrosis Factor-Induced Apoptosis. Mol Cell Biol 22, 3415 (2002).

(11) Sanchez-Vasquez, E., Bronner, M. E. & Zernicka-Goetz, M. HIF1A contributes to the survival of aneuploid and mosaic pre-implantation embryos. bioRxiv 2023.09.04.556218 (2023) doi:10.1101/2023.09.04.556218.

(12) Chousal, J. N. et al. Molecular profiling of human blastocysts reveals primitive endoderm defects among embryos of decreased implantation potential. Cell Rep 43, (2024).

(13) Corujo-Simon, E., Radley, A. H. & Nichols, J. Evidence implicating sequential commitment of the founder lineages in the human blastocyst by order of hypoblast gene activation. Development (Cambridge) 150, (2023).

(14) Regin, M. et al. Lineage segregation in human pre-implantation embryos is specified by YAP1 and TEAD1. Human Reproduction 38, 1484–1498 (2023).

(15) Saiz, N., Williams, K. M., Seshan, V. E. & Hadjantonakis, A. K. Asynchronous fate decisions by single cells collectively ensure consistent lineage composition in the mouse blastocyst. Nature Communications 2016 7:1 7, 1–14 (2016).

(16) Plusa, B., Piliszek, A., Frankenberg, S., Artus, J. & Hadjantonakis, A. K. Distinct sequential cell behaviours direct primitive endoderm formation in the mouse blastocyst. Development 135, 3081–3091 (2008).

(17) Hashimoto, M. & Sasaki, H. Epiblast Formation by TEAD-YAP-Dependent Expression of Pluripotency Factors and Competitive Elimination of Unspecified Cells. Dev Cell 50, 139-154.e5 (2019).

(18) Hardy, K. Apoptosis in the human embryo. Rev Reprod 4, 125–134 (1999).

(19) Ramos-Ibeas, P. et al. Embryo responses to stress induced by assisted reproductive technologies. Mol Reprod Dev 86, 1292–1306 (2019).

(20) Licciardi, F. et al. Human blastocysts of normal and abnormal karyotypes display distinct transcriptome profiles. Sci Rep 8, 1–9 (2018).

(21) Maxwell, S. M. et al. Investigation of Global Gene Expression of Human Blastocysts Diagnosed as Mosaic using Next-generation Sequencing. Reproductive Sciences 1–11 (2022) doi:10.1007/s43032-022-00899-x.

(22) Groff, A. F. et al. RNA-seq as a tool for evaluating human embryo competence. Genome Res 29, 1705–1718 (2019).

(23) Starostik, M. R., Sosin, O. A. & McCoy, R. C. Single-cell analysis of human embryos reveals diverse patterns of aneuploidy and mosaicism. Genome Res 30, 814–826 (2020).

(24) Vera-Rodriguez, M., Chavez, S. L., Rubio, C., Pera, R. A. R. & Simon, C. Prediction model for aneuploidy in early human embryo development revealed by single-cell analysis. Nat Commun 6, 7601 (2015).

(25) Sanchez-Ribas, I. et al. Transcriptomic behavior of genes associated with chromosome 21 aneuploidies in early embryo development. Fertil Steril 111, 991-1001.e2 (2019).

(26) Fuchs Weizman, N. et al. Towards Improving Embryo Prioritization: Parallel Next Generation Sequencing of DNA and RNA from a Single Trophectoderm Biopsy. Sci Rep 9, 1–11 (2019).

(27) Fernandez Gallardo, E. et al. A multi-omics genome-and-transcriptome single-cell atlas of human preimplantation embryogenesis reveals the cellular and molecular impact of chromosome instability. bioRxiv 2023.03.08.530586 (2023) doi:10.1101/2023.03.08.530586.

(28) Dürrbaum, M. & Storchová, Z. Effects of aneuploidy on gene expression: implications for cancer. FEBS J 283, 791–802 (2016).

(29) Singla, S., Iwamoto-Stohl, L. K., Zhu, M. & Zernicka-Goetz, M. Autophagy-mediated apoptosis eliminates aneuploid cells in a mouse model of chromosome mosaicism. Nat Commun 11, 1–15 (2020).

(30) Bolton, H. et al. Mouse model of chromosome mosaicism reveals lineage-specific depletion of aneuploid cells and normal developmental potential. Nat Commun 7, 1– 12 (2016).

(31) Ohashi, A. et al. Aneuploidy generates proteotoxic stress and DNA damage concurrently with p53-mediated post-mitotic apoptosis in SAC-impaired cells. Nat Commun 6, 1–16 (2015).

(32) Santaguida, S. & Amon, A. Short- and long-term effects of chromosome missegregation and aneuploidy. Nature Reviews Molecular Cell Biology vol. 16 473–485 Preprint at https://doi.org/10.1038/nrm4025 (2015).

(33) Santaguida, S., Vasile, E., White, E. & Amon, A. Aneuploidy-induced cellular stresses limit autophagic degradation. Genes Dev 29, 2010–2021 (2015).

(34) Chunduri, N. K. & Storchová, Z. The diverse consequences of aneuploidy. Nature Cell Biology 2019 21:1 21, 54–62 (2019).

(35) Dürrbaum, M. et al. Unique features of the transcriptional response to model aneuploidy in human cells. BMC Genomics 15, 139 (2014).

(36) Pan, J.-A., Ullman, E., Dou, Z. & Zong, W.-X. Inhibition of protein degradation induces apoptosis through a microtubule-associated protein 1 light chain 3-mediated activation of caspase-8 at intracellular membranes. Mol Cell Biol 31, 3158–70 (2011).

(37) Stingele, S. et al. Global analysis of genome, transcriptome and proteome reveals the response to aneuploidy in human cells. Mol Syst Biol 8, 608 (2012).

(38) Tang, Y.-C., Williams, B. R., Siegel, J. J. & Amon, A. Identification of aneuploidyselective antiproliferation compounds. Cell 144, 499–512 (2011).

(39) Janssen, A., Van Der Burg, M., Szuhai, K., Kops, G. J. P. L. & Medema, R. H. Chromosome segregation errors as a cause of DNA damage and structural chromosome aberrations. Science 333, 1895–1898 (2011).

(40) Li, M. et al. The ATM-p53 pathway suppresses aneuploidy-induced tumorigenesis. Proc Natl Acad Sci U S A 107, 14188–14193 (2010).

(41) Thompson, S. L. & Compton, D. A. Proliferation of aneuploid human cells is limited by a p53-dependent mechanism. J Cell Biol 188, 369–381 (2010).

(42) Yang, M. et al. Depletion of aneuploid cells in human embryos and gastruloids. Nat Cell Biol 23, 314–321 (2021).

(43) Jones, R. G. et al. AMP-activated protein kinase induces a p53-dependent metabolic checkpoint. Mol Cell 18, 283–293 (2005).

(44) Chunduri, N. K., Barthel, K. & Storchova, Z. Consequences of Chromosome Loss: Why Do Cells Need Each Chromosome Twice? Cells 2022, Vol. 11, Page 1530 11, 1530 (2022).

(45) Krivega, M., Stiefel, C. M. & Storchova, Z. Consequences of chromosome gain: A new view on trisomy syndromes. American Journal of Human Genetics vol. 109 2126–2140 Preprint at https://doi.org/10.1016/j.ajhg.2022.10.014 (2022).

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Brdar, Osterburg, Munick, et al. present an interesting cellular and biochemical investigation of different p53 isoforms. The authors investigate the impact of different isoforms on the in-vivo transcriptional activity, protein stability, induction of the stress response, and hetero-oligomerization with WT p53. The results are logically presented and clearly explained. Indeed, the large volume of data on different p53 isoforms will provide a rich resource for researchers in the field to begin to understand the biochemical effects of different truncations or sequence alterations.

Strengths:

The authors achieved their aims to better understand the impact/activity of different p53 is-forms, and their data will support their statements. Indeed, the major strengths of the paper lie in its comprehensive characterization of different p53 isoforms and the different assays that are measured. Notably, this includes p53 transcriptional activity, protein degradation, induction of the chaperone machinery, and hetero-oligomerization with wtp53. This will provide a valuable dataset where p53 researchers can evaluate the biological impact of different isoforms in different cell lines. The authors went to great lengths to control and test for the effect of (1) p53 expression level, (2) promotor type, and (3) cell type. I applaud their careful experiments in this regard.

Weaknesses:

One thing that I would have liked to see more of is the quantification of the various pull-down/gel assays - to better quantify the effect of, e.g., hetero-oligomerization among the various isoforms. In addition, a discussion about the role of isoforms that contain truncations in the IDRs is not available. It is well known that these regions function in an auto-inhibitory manner (e.g. work by Wright/Dyson) and also mediate many PPIs, which likely have functional roles in vivo (e.g. recruiting p53 to various complexes). The discussion could be strengthened by focusing on some of these aspects of p53 as well.

Thank you for these comments. In this paper we have focused on the importance of the integrity of the folded domains of p53 for their function. The unfolded regions in the N- and the C-terminus have not been our main target but the reviewer is right that they play important regulatory functions that are lost in the corresponding isoforms. We have, therefore, added a few sentences in the Discussion section.

With respect to a better quantification, we have re-evaluated the quantification and adjusted where necessary (see also reviewer 2). With respect to the hetero-oligomerization we have run a new mass spectrometry experiment in which we only focus on the p53 peptides. These have been now quantitatively evaluated and the results are provided in this manuscript Fig. 5.

Reviewer #2 (Public review):

Summary:

In this manuscript entitled "p53 isoforms have a high aggregation propensity, interact with chaperones and lack 1 binding to p53 interaction partners", the authors suggest that the p53 isoforms have high aggregation propensity and that they can co-aggregate with canonical p53 (FLp53), p63 and p73 thus exerting a dominant-negative effect.

Strengths:

Overall, the paper is interesting as it provides some characterization of most p53 isoforms DNA binding (when expressed alone), folding structure, and interaction with chaperones. The data presented support their conclusion and bring interesting mechanistic insight into how p53 isoforms may exert some of their activity or how they may be regulated when they are expressed in excess.

Weaknesses:

The main limitation of this manuscript is that the isoforms are highly over-expressed throughout the manuscript, although the authors acknowledge that the level of expression is a major factor in the aggregation phenomenon and "that aggregation will only become a problem if the expression level surpasses a certain threshold level" (lines 273-274 and results shown in Figures S3D, 6E). The p53 isoforms are physiologically expressed in most normal human cell types at relatively low levels which makes me wonder about the physiological relevance of this phenomenon.

Furthermore, it was previously reported that some isoforms clearly induce transcription of target genes which are not observed here. For example, p53β induces p21 expression (Fujita K. et al. p53 isoforms Delta133p53 and p53beta are endogenous regulators of replicative cellular senescence. Nat Cell Biol. 2009 Sep;11(9):1135-42), and Δ133p53α induces RAD51, RAD52, LIG4, SENS1 and SOD1 expression (Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369. / Gong, L. et al. p53 isoform D133p53 promotes the efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281. / Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028. / Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90. / Joruiz et al. Distinct functions of wild-type and R273H mutant Δ133p53α differentially regulate glioblastoma aggressiveness and therapy-induced senescence. Cell Death Dis. 2024 Jun 27;15(6):454.) which demonstrates that some isoforms can induce target genes transcription and have defined normal functions (e.g. Cellular senescence or DNA repair).

However, in this manuscript, the authors conclude that isoforms are "largely unfolded and not capable of fulfilling a normal cellular function" (line 438), that they do not have "well defined physiological roles" (line 456), and that they only "have the potential to inactivate members of the p53 protein family by forming inactive hetero complexes with wtp53" (line 457-458).

Therefore, I think it is essential that the authors better discuss this major discrepancy between their study and previously published research.

This manuscript is not about hunting for the next “signal transduction pathway” that is “regulated” by a specific p53 isoform. For such a project work has indeed to be conducted at the endogenous level. However, our manuscript is about the basic thermodynamic behavior of these isoforms in in vitro assays and in some cell culture assays.

What, however, depends on the expression level is the interaction with chaperones as well as the tendency to aggregate. And this we actually show in our manuscript by using two different promotors with very different strength: Strong overexpression leads to aggregation, much weaker expression to soluble isoforms. For the mass spectrometry experiments we have established stable expressing cell lines and not used transiently overexpressing ones.

The level from which on the chaperone systems of the cell cannot keep these isoforms soluble and they start to aggregate is certainly an important question, and we have experimental evidence that if we use different chaperone inhibitors the percentage of the aggregating isoforms in the insoluble fraction increases.

Proteins have to follow the basic physicochemical rules also in cells. And this manuscript sets the stage for re-interpreting the observed cellular effects – not in terms of specific interaction with certain promoters but as causing a stress response and non-specific interaction with other not-well folded domains of other proteins.

With respect to this discussion about the physiological relevance, it is interesting to look at a study that was published in Cell:

Rohaly, G., Chemnitz, J., Dehde, S., Nunez, A.M., Heukeshoven, J., Deppert, W. and Dornreiter, I. (2005) A novel human p53 isoform is an essential element of the ATR-intra-S phase checkpoint. Cell, 122, 21-32.

This manuscript describes how a specific isoform regulates an important pathway. Two other studies also focused on the same isoform but showed that it lacks the nuclear localization signal and therefore does not enter the nucleus. And even if it would, it would have no transcriptional activity due to the unfolding of the DBD.

Chan, W.M. and Poon, R.Y. (2007) The p53 Isoform Deltap53 lacks intrinsic transcriptional activity and reveals the critical role of nuclear import in dominant-negative activity. Cancer Res, 67, 1959-1969.

Garcia-Alai, M.M., Tidow, H., Natan, E., Townsley, F.M., Veprintsev, D.B. and Fersht, A.R. (2008) The novel p53 isoform "delta p53" is a misfolded protein and does not bind the p21 promoter site. Protein Sci, 17, 1671-1678.

This example shows that it is important to re-consider the basic principles of protein structure and protein folding. And that is exactly what this manuscript is about.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Does the p53g C-terminus (322-346) form cross-beta amyloid structures? The strong fluorescence signal in the presence of ThT suggests this may be forming amyloid. I wonder if any amyloid sequence predictors identify this region as amyloidogenic.

Using the Waltz predictor (https://doi.org/10.1038/nmeth.1432), the amino acids 339-346 have been identified as potentially amyloidogenic. We have added this information to the manuscript.

(2) The chaperone binding results in Figure 5 are interesting and indeed suggest that many p53 isoforms interact with chaperones in vivo to counteract their destabilized nature. For the 5 p53 isoforms shown in Figure 5D, do they present any HSP70-binding motifs that may not exist in wtp53? These motifs can be predicted from the sequence with established software in a similar manner as the authors performed for TANGO.

Author response image 1.

Predicted Chaperon binding sites using the LIMBO prediction tool. (http://www.ncbi.nlm.nih.gov/pubmed/19696878)

We have analyzed the sequence of p53 and the isoforms for potential HSP70 binding sites using the LIMBO prediction tool. The results are shown in the figure above. Wild type p53 has a very strong site that is lost in the β- and ɣ-isoforms. The ɣ-isoform in addition loses another predicted binding site which is replaced with a ɣ-specific one. Overall, this analysis does not provide a very clear picture due to the loss of some and the creation of new, isoform-specific binding sites. We have, therefore, not included this analysis in the manuscript but show it here for the reviewers.

(3) The mixed hetero-tetramers detected by the MS is very interesting. Also the pull-down experiments in Figure 6. However, the extent of hetero-oligomerization is at times hard to follow. Could you more clearly summarize and/or quantify the results of the hetero-oligomerization experiments?

We have conducted a new mass spectrometry experiment that was focused only on the analysis of p53 peptides. These data are now shown in Figure 5 and Supplementary Figure 6. They show that peptides not present in the Δ133p53α isoform and therefore must come from wild type p53 can be detected. For the Δ133p53β isoform these peptides are absent, suggesting that this isoform does not hetero-oligomerize with wild type p53. Furthermore, all β- and ɣ- isoforms do not show peptides derived from wild type p53, again suggesting that they cannot hetero-oligomerize due to the lack of a functional oligomerization domain.

(4) There is a typo in Figure 5. The figure title (top of page) says "Figure 4: Chaperons". Also, "chaperons" appears in the legend.

Thank you for making us aware of this problem. This has been corrected.

(5) The figures are often quite small with a lot of white space. Figure 4 in particular is arranged in a confusing way with A, D, B, C, E, F, G in T->B L->R order. Perhaps some figures could be expanded or re-arranged to make better use of the available space. E.g. could move B, C above panel D, and then shift F, G to be next to E. This would give you A, [B, C, D], [E, F, G] in a 2x2 format.

We have rearranged figures 2, 4, 5 and 6 to be able to enlarge the individual figure panels.

Reviewer #2 (Recommendations for the authors):

(1) Figure 2C: Why is the p21-Luc reporter assay performed in SAOS-2 cells when all other assays are performed in H1299?

The assays we have performed in this study are independent of the cell type because we investigate very basic principles of protein folding and stability. If one removes a third of a folded domain, this domain will no longer fold, independent of the cell type it is in. However, to show, that the cell type indeed does not play any role, we have repeated the experiments in H1299 cells. These data are now shown in Figure 2C and the original data in SAOS cells we have moved to Supplementary Figure 1E.

(2) Figure 3: I find the statistics on this figure very confusing... It looks like every isoform is compared to the "WT", but in that case, in Figure 3B for example, how can the Δ40p53β be ****, Δ133p53γ be *** while the Δ133p53α, more different to WT and narrower error bars is non-specific? I guess this comes from the normalization of the GST expression of each isoform but in this case, the isoforms should not be compared to the WT, but to their respective GST sample.

There was indeed a mistake in the statistics, thank you for pointing this out.

We repeated the statistical analysis and the relative protein level within each sample is now calculated using the ratio between the respective GST sample and the sample containing E6. Significance for each isoform was assessed by comparing the relative protein level to the protein level of the WT.

(3) Figures 3D and 3E: the authors did not perform the assays on Δ40p53 isoforms because they "contain a fully folded DBD" (lines 218-219). This may be true for Δ40p53α as shown by the pAB240 binding figure 3C, but it is speculative for Δ40p53β and Δ40p53γ since these were not tested in Figure 3C either... Furthermore, Figure 3B suggests that there may be differences between Δ40p53α, Δ40p53β and Δ40p53γ and therefore these two isoforms should be tested for pAB240 IP at least (and DARPin as well if the pAB240 IP shows differences). Also, why were the TAp53β and TAp53γ not tested in Figures 3D and 3E?

Here we disagree with the reviewer. The PDB is full of structures of the p53 DNA binding domain. All of them – including many structures of the same domain from other species – span residues ~90 to 294 (or the equivalent residues in other species). That means that the β- and ɣ- versions of p53 contain the full DNA binding domain. In contrast to the DNA binding domain, the oligomerization domain, however, is truncated and therefore does not form functional tetramers. This is the reason for the reduced binding affinity to DNA.

The pAB240 antibody recognizes and binds to an epitope that becomes exposed upon the unfolding of the DBD. This manuscript shows by multiple experiments that the DBD of the β- and the ɣ-isoforms are not compromised but that the oligomerization domain is not functional. In figures 3D and 3E we have not included the TA β- and the ɣ-isoforms, because, again, they have a folded DBD and their inclusion would not provide any additional information compared to TAp53α.

(4) Figures 4B and 4C are small and extremely difficult to read.

We agree and have rearranged and enlarged these and other figures. Please see also answer to comment (5) of reviewer 1.

(5) Figure 5C: the authors claim that "the isoform induced cellular stress that triggers the expression of chaperones" (line 320). However, if the induction of the HSP70 promoter is shown, there is no evidence that this is due to cellular stress. Evidence to support that claim should be shown.

The expression and accumulation of unfolded, aggregation prone sequences is a stress situation for the cell which triggers the expression of chaperones. The expression of isoforms that are not well folded or of p53 mutants that are not well folded increases expression both from the HSP70 promoter and the heat shock promoter. This shows that the expression of unfolded isoforms induces cellular stress.

(6) Figure 5D: why was this experiment performed in SAOS2 cells when the whole paper was otherwise performed in H1299 cells?<br /> Also, about this figure, the authors write "In addition to this common set, Δ133p53α and Δ40p53α showed only very few additional interaction partners. This situation was very different for Δ133α, Δ133β and TAp53γ." (lines 331 to 333). My feeling is that we should instead read "In addition to this common set, TAp53β and Δ40p53α showed only very few additional interaction partners. This situation was very different for Δ133p53α, Δ133p53β and TAp53γ"

Thank you for spotting this mistake. Indeed, the correct wording is TAp53β and Δ40p53α and we have corrected the manuscript.

The mass spectrometry experiments were actually not carried out in SAOS cells, but in U2OS cells. The reason for not using the H1299 cell line was that these cells do not contain functional p53. In contrast, U2OS cells express wild type p53. We have repeated the mass spectrometry analysis and analyzed the data with a special focus on p53 peptides. This information is now added as Figure 5E. In this analysis we show that the Δ133p53α samples contain peptides from the DBD that are not part of this truncated isoform and must therefore originate from wild type p53 with which this isoform hetero-oligomerizes. The corresponding peptides are absent from Δ133p53β, showing that without a functional oligomerization domain this isoform does not interact with wild type p53. Likewise, the data demonstrate that the β- and the ɣ-isoforms do not form hetero-oligomers.

(7) Supplementary Table 2: the authors claim "For Δ133p53α we could identify peptides between amino acids 102 and 132 that must originate from wild type p53". SAOS2 has a WT TP53 gene and expresses all isoforms endogenously. Therefore, peptides between amino acids 102 and 132 can actually originate from "WT p53" but also TAp53β, TAp53γ, Δ40p53α, Δ40p53β or Δ40p53γ (most likely a mix of these).

We have not used SAOS cells but U2OS cells. As mentioned above the data show that the Δ133p53α sample contains peptides from wild type p53 and that these peptides cannot be found in the Δ133p53β sample. In addition, peptides originating from the oligomerization domain are only found in the samples of isoforms containing an oligomerization domain but not in samples of β- and ɣ-isoforms. The data are presented in Figure 5 E-G and Supplementary Figure S5.

Since the Biotin ligase is directly fused to a specific isoform, peptides from other isoforms can only be detected if these directly interact with the isoform fused to the ligase (and contain unique peptides, not present in the isoform fused to the ligase). The data confirm that only isoforms that have a functional oligomerization domain can interact with wild type p53 (or potentially other isoforms with a functional oligomerization domain).

(8) Figure 6: Why not conduct these luciferase reporter assays using the MDM-2 and p21 promoters like in Figure 2B and 2C since there may be promoter-specific regulation?

This would be particularly important for the p21 promoter as TAp53β is known to induce it (Fujita K. et al. p53 isoforms Delta133p53 and p53beta are endogenous regulators of replicative cellular senescence. Nat Cell Biol. 2009 Sep;11(9):1135-42) and the Δ133p53α, Δ133p53β and Δ133p53γ isoforms were shown to reduce p21 transcription by TAp73β when co-expressed in H1299 cells (Zorić A. et al. Differential effects of diverse p53 isoforms on TAp73 transcriptional activity and apoptosis. Carcinogenesis. 2013 Mar;34(3):522-9.). Neither of these regulations appears here on the pBDS2 reporter, which is puzzling.

The main point of this paper is that all isoforms without a complete DNA binding domain and without a complete oligomerization domain do not bind to DNA with high affinity and do not show transcriptional activity and that is independent of the promotor. There might be effects of expressing certain isoforms in some cells, but that is most likely by inducing a stress response via expression of chaperones etc. High affinity sequence specific DNA binding does not play a role here (see results in Figure 2) and we have therefore not conducted these suggested experiments.

Author response:

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

Although the reviewers found our work interesting, they raised several important concerns about our study. To address these concerns, mostly we performed new experiments. The most important changes are highlighted in the summary paragraphs.

First, in response to Reviewer 1’s suggestions, we have conducted the SFN experiments systematically, e.g., we further confirmed the mechanism of SFN-activated TFEB in HeLa NPC1 cells with new experiments including: the effect of BAPTA-AM (a calcium chelator), FK506+CsA (calcineurin inhibitors) and NAC (ROS scavenger) on SFN-induced TFEB-nuclear translocation in HeLa NPC1 cells (New Fig. S3). The effect of SFN on NPC1 expression (New Fig. S5). Particularly, we examined the colocalization of DiO (a PM marker) staining and surface LAMP1 staining in HeLa NPC1 cells under SFN treatment to confirm the PM exocytosis. In main text and figure legends, accuracy of sentence is thoroughly checked and defined. Hence, we have significantly improved the presentation and clarity in the revision.

Second, in response to Reviewer 2’s suggestions, we have performed additional experiments to demonstrate that the role of TFEB in SFN-evoked the lysosomal exocytosis by using TFEB-KO cells (New Fig. S7B). In TFEB KO cells, this increase of surface LAMP1 signal by SFN treatment was significantly reduced, suggestive of SFN-induced exocytosis in a TFEB-dependent manner. We also investigated the effect of U18666A on CF555-dextran endocytosis. By examining the localization of CF-dex and Lamp1, we found that CF555 is present in the lysosome with U18666A treatment (Fig for reviewers only A,B), suggesting that NPC1 deficiency/U18666A treatment has no effect on CF-dex endocytosis.

Third, in response to Reviewer 3’s suggestions, we have performed experiments in addition to response to other reviewers’ suggestion ie. the cytotoxicity of the concentration of SFN used in this study in various cell lines (New Fig.S10).

In addition, according to the reviewers’ suggestions, we made clarifications and corrections wherever appropriate in the manuscript.

Reviewer #1 (Public review):

Summary:

The authors are trying to determine if SFN treatment results in dephosphorylation of TFEB, subsequent activation of autophagy-related genes, exocytosis of lysosomes, and reduction in lysosomal cholesterol levels in models of NPC disease.

Strengths:

(1) Clear evidence that SFN results in translocation of TFEB to the nucleus.

(2) In vivo data demonstrating that SFN can rescue Purkinje neuron number and weight in NPC1^-/- animals.

Thank you for the support!

Weaknesses:

(1) Lack of molecular details regarding how SFN results in dephosphorylation of TFEB leading to activation of the aforementioned pathways. Currently, datasets represent correlations.

Thank you for raising this critical point! The reviewer is right that in this manuscript we did not talk too much about the molecular mechanism of SFN-evoked TFEB activation. Because in our previous study (Li, Shao et al. 2021), we explored the mechanism of SFN-induced TFEB activation. We show that SFN-evoked TFEB activation via a ROS-Ca²⁺-calcineurin dependent but MTOR -independent pathway (Li, Shao et al. 2021). In the current manuscript, we cited this paper, but did not talk the details of the mechanism, which obviously confused the reviewers. Therefore, in the revision manuscript we added more details of the molecular mechanism of SFN-activated TFEB. Also, we further confirmed this mechanism in HeLa NPC1 cells with new experiments including: the effect of BAPTA-AM (a calcium chelator), FK506+CsA (calcineurin inhibitors) and NAC (ROS scavenger) on SFN-induced TFEB-nuclear translocation in NPC cells (New Fig.S3).

(2) Based on the manuscript narrative, discussion, and data it is unclear exactly how steady-state cholesterol would change in models of NPC disease following SFN treatment. Yes, there is good evidence that lysosomal flux to (and presumably across) the plasma membrane increases with SFN. However, lysosomal biogenesis genes also seem to be increasing. Given that NPC inhibition, NPC1 knockout, or NPC1 disease mutations are constitutively present and the cell models of NPC disease contain lysosomes (even with SFN) how could a simple increase in lysosomal flux decrease cholesterol levels? It would seem important to quantify the number of lysosomes per cell in each condition to begin to disentangle differences in steady state number of lysosomes, number of new lysosomes, and number of lysosomes being exocytosed.

Thank you for this constructive comment. From our data, in NPC1 cells SFN reduced the cholesterol levels by inducing lysosomal exocytosis and increasing lysosomal biogenesis. We understand the reviewer’s point that it would be really helpful to differentiate the exact three states of original number of lysosomes, number of new lysosomes, and number of lysosomes being exocytosis. Unfortunately, due to the technique limitation, so far seems there is no appropriate method that could clearly differentiate the lysosomes exactly come from which state. In the future, hopefully we will have technique to explore this mechanism.

(3) Lack of evidence supporting the authors' premise that "SFN could be a good therapeutic candidate for neuropathology in NPC disease".

Suggestion was taken! We removed this sentence. Thanks!

Reviewer #2 (Public review):

(4) The in vivo experiments demonstrate the therapeutic potential of SFN for NPC. A clear dose response analysis would further strengthen the proposed therapeutic mechanism of SFN.

Thank you for this constructive suggestion. We examined the effect of two doses of SFN30 and 50mg/kg on NPC mice. As shown in Fig.6, SFN (50mg/kg), but not 30mg/kg prevents a degree of Purkinje cell loss in the lobule IV/V of cerebellum, suggesting a dose-correlated preventive effect of SFN. In the future study, we will continue optimizing the dosage form and amount of SFN and do a dose-responsive analysis.

(5) Additional data supporting the activation of TFEB by SFN for cholesterol clearance in vivo would strengthen the overall impact of the study.

Thank the reviewer for this constructive comment. We have detected a significant decrease of pS211-TFEB protein in brain tissues of NPC mice upon SFN treatment compared to vehicle, suggesting that SFN activates TFEB in brain tissue for the first time. It is worth to further examine the lysosomal cholesterol levels in brain tissues to show the direct effect of SFN. However, in our hands and in the literatures Filipin seems not suitable for detecting lysosomal cholesterol accumulation in brain tissue. So far there isn’t a good method to directly measure lysosomal cholesterol in tissue.

(6) In Figure 4, the authors demonstrate increased lysosomal exocytosis and biogenesis by SFN in NPC cells. Including a TFEB-KO/KD in this assay would provide additional validation of whether these effects are TFEB-dependent.

Great suggestion! We investigated the role of TFEB in SFN-evoked the lysosomal exocytosis by using TFEB-KO cells. As shown in New Suppl. Fig. 7B, in TFEB KO cells, this increase of surface LAMP1 signal by SFN (15 μM, 12 h) treatment was significantly reduced, suggestive of SFN induced exocytosis in a TFEB-dependent manner.

(7) For lysosomal pH measurement, the combination of pHrodo-dex and CF-dex enables ratiometric pH measurement. However, the pKa of pHrodo red-dex (according to Invitrogen) is ~6.8, while lysosomal pH is typically around 4.7. This discrepancy may account for the lack of observed lysosomal pH changes between WT and U18666A-treated cells. Notably, previous studies (PMID: 28742019) have reported an increase in lysosomal pH in U18666A-treated cells.

We understand the reviewer’s point. But as stated in the methods and main text, we used pHrodo™ Green-Dextran (P35368, Invitrogen), rather than pHrodo Red-dextran. According to the product information from Invitrogen, pHrodo Green-dex conjugates are non-fluorescent at neural pH, but fluorescence bright green at acidic pH around 4, such as those in endosomes and lysosomes. Therefore, pHrodo Green-dex is suitable to monitor the acidity of lysosome (Hu, Li et al. 2022). We also used LysoTracker Red DND-99 (Thermo Scien fic, L7528) to measure lysosomal pH (Fig. 4G, H), which is consistent with results from pHrodo Green/CF measurement.

The reviewer mentioned that previous studies have reported an increase in lysosomal pH in U18666Atreated cells. We understood this concern. But in our hands, from our data with two lysosomal pH sensors, we have not detected lysosomal pH change in U18666A-treated NPC1 cell models.

(7) The authors are also encouraged to perform colocalization studies between CF-dex and a lysosomal marker, as some researchers may be concerned that NPC1 deficiency could reduce or block the trafficking of dextran along endocytosis.

Thank you for raising this important point and suggestion was taken! We investigated the effect of NPC1 deficiency on CF555-dextran trafficking into lysosome by examining the localization of CF-dex and Lamp1. To clearly define whether CF555-dex is present in the lysosome, we first used apilimod to enlarge lysosomes and then examined the relative posi on of CF555-dex and lamp1. As shown in Author response image 1A,B, in HeLa cells treated with U18666A, CF555 signals (red) clearly present inside lysosome (LAMP1 labelled lysosomal membrane, green signal), suggesting that CF555dex endocytosis is not affected by NPC1 deficiency (U18666A treatment).

Author response image 1.

The effect of NPC1 deficiency on CF555 endocytosis. HeLa cells were transiently transfected with LAMP1-GFP plasmid for 24 h. Cells were then treated with apilimod (100 nM) for 2 h to enlarge the lysosomes, and followed by co- treatment of U18666A (2.5 μM, 24 h) and CF555 (12 h). (A)Each panel shows fluorescence images taken by confocal microscopes. (B) Each panel shows the fluorescence intensity of a line scan (white line) through the double labeled object indicated by the white arrow. Scale bar, 20 μm or 2 μm (for zoom-in images).

(9) In vivo data supporting the activation of TFEB by SFN for cholesterol clearance would significantly enhance the impact of the study. For example, measuring whole-animal or brain cholesterol levels would provide stronger evidence of SFN's therapeutic potential.

We really appreciate the reviewer’s comments. Please see response to point #5.

Reviewer #3 (Public review):

(10) The manuscript is extremely hard to read due to the writing; it needs careful editing for grammar and English.

Sorry for the defects in the writing and grammar. We had thoroughly checked grammar and polished the English to improve the manuscript.

(11) There are a number of important technical issues that need to be addressed.

We will address the technical issues mentioned in the following ques ons.

(12) The TFEB influence on filipin staining in Figure 1A is somewhat subtle. In the mCherry alone panels there is a transfected cell with no filipin staining and the mCherry-TFEBS211A cells still show some filipin staining.

Thank you for raising this point. The reviewer is right that not all the mCherry alone cells with the same level of filipin signal and not all mCherry-TFEBS211 transfected cells show completely no filipin signal. The statistical results were from randomly selected cells from 3 independent experiments. To avoid the confusion, we have included more cells in the statistical analysis to cover all the conditions as shown in the new Fig. 1B. Hopefully this helps to clarify the confusion.

(13) Figure 1C is impressive for the upregulation of filipin with U18666A treatment. However, SFN is used at 15 microM. This must be hitting multiple pathways. Vauzour et al (PMID: 20166144) use SFN at 10 nM to 1microM. Other manuscripts use it in the low microM range. The authors should repeat at least some key experiments using SFN at a range of concentrations from perhaps 100 nM to 5 microM. The use of 15 microM throughout is an overall concern.

The reason that we use this concentration of SFN is based on our previous study (Li, Shao et al. 2021). We had shown that SFN (10–15 μM, 2–9 h) induces robust TFEB nuclear translocation in a dose- and time-dependent manner in HeLa cells as well as in other human cell lines without cytotoxicity (Li, Shao et al. 2021). Also, tissue concentrations of SFN can reach 3–30 μM upon broccoli consumption (Hu, Khor et al. 2006), so we used low micromolar concentrations of SFN (15 μM) in our study. Moreover, we further confirmed that SFN (15 μM) induces TFEB nuclear translocation in HeLa NPC1 cells (Fig. 1F, G Fig. 2B, G) and this concentration of SFN has no cytotoxicity (New Fig.S10).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The following comments are designed to improve and focus the authors' work.

(14) Related to data in Figure 1. The mechanism through which TFEB can reduce Filipin in U18 conditions is unclear. Inhibi on of NPC1 results in hyperactivation of mTOR through cholesterol transport at ER-Lysosome contacts (see Zoncu group publications). If mTORC is hyperac ve in NPC disease models, TFEB would be expected to remain cytoplasmic and not enter the nucleus as the representative image in Figure 1A demonstrates.

In our previous study (Li, Shao et al. 2021), we have shown that SFN induces TFEB nuclear translocation in a mTOR-independent manner (Li, Shao et al. 2021). Consistent with this result, in this study we confirmed that SFN-induced TFEB nuclear translocation is mTor-independent in NPC1 cells (Now Fig. S4A, B). Thus, SFN induced TFEB nuclear translocation in various NPC cells (Fig. 1F, G, Fig. 2B, G). Please also see the discussion about the mechanism of SFN in response to point #1.

(15) Therefore, how does overexpression of TFEB, which remains in the cytoplasm, result in a decreased filipin signal? Similar ques ons relate to Figure 1C-H.

Medina et. al (Medina, Fraldi et al. 2011) show that TFEB overexpression (not activation, so overexpressed TFEB is in the cytoplasm) increases the pool of lysosomes in the proximity of the plasma membrane and promotes their fusion with PM by raising intracellular Ca²⁺ levels through lysosomal Ca²⁺ channel MCOLN1, leading to increased lysosomal exocytosis. Hence, TFEB overexpression only (TFEB is not activated) could reduce filipin signal via increasing lysosomal exocytosis. And with TFEB agonist treatment such as TFEB could further boost this increase.

(16) It would seem appropriate to measure the NPC1 and NPC2 proteins using western blot to ensure that SFN-dependent clearance of cholesterol is not due to enhanced expression of the native protein in U18-treated cells or enhanced folding of the protein in patient fibroblasts.

Thank you for this constructive comment! Because NPC1 gene mutation takes about 95% of NPC cases and NPC2 mutation takes about 5% of NPC cases. And in this study we focused on NPC1 deficiency cases. Thus, we measured the effect of SFN on the expression of NPC1 in human NPC1-patient fibroblasts. Western blot analysis showed that SFN (15 μM, 24 h) treatment did not affect NPC1 expression in human NPC1-patient fibroblasts (new Fig. S5).

(17) Related to data in Figures 1C-E. Controls are missing related to the effect SFN has on steady-state cholesterol levels. This may be insightful in providing information on the mode of action of this compound.

Suggestion was taken! We have supplemented the control- SFN only in new Fig. 1C-E.

(18) The mechanism that links SFN to TFEB-dependent translocation is suggested to involve calcineur independent dephosphorylation of TFEB. However, no data is provided. It would seem important to iden fy the mechanism(s) through which SFN positively regulates TFEB location. This would shift the manuscript and its model from correlations to causation. Experiments involving calcineurin inhibitors, or agonists of TRPML1 that have been reported as being a key source of Ca²⁺ for calcineurin activation, may provide molecular insight.

Please see the paragraph in response to point #1.

(19) Related to Figure 4. Using a plasma membrane counterstain to quantify plasma membrane LAMP1 would increase the rigor of the analysis.

Great idea! We examined the colocalization of DiO (a PM marker) staining and LAMP1 staining in HeLa NPC1 cells under SFN treatment. As shown in new Fig.4A, surface LAMP1 signal(red) colocalized with DiO (green), a PM marker.

(20) Related to Figure 5. How do the authors explain the kinetic disparity between SFN treatment for 24 vs 72 hrs? IF TFEB is activated and promoting lysosomal biogenesis and increased lysosomal flux across the PM, why does cholesterol accumulation lag? Perhaps related to this point. Are other cholesterol metabolizing enzymes that may have altered activity in NPC sensitive to SFN? A similar comment applies to the Sterol regulatory element binding protein pathway, which has been shown to be activated in models of NPC disease.

We understand the reviewer’s point. As shown in Fig. 5C, D, in NPC1^-/- MEF cells, SFN treatment for 24 h showed relative weaker cholesterol clearance compared to the effects in human cells (Fig.1C, D, Fig.2.E, I). Thus, we explored a longer treatment of SFN for 72 h (fresh SFN in medium was added every 24 h), and 72h treatment of SFN exhibited substantial cholesterol reduction (Fig. 5C, D). This different effect could be attributed to the continuous action of SFN, which could prolong the exocytosis, leading to more effective cholesterol clearance. As shown in the DMSO-treated MEF cells, the cholesterol levels are similar in both 24 and 72 h, thus 24 h U18666A treatment has reached the upper limit of the accumulated cholesterol, longer treatment me would not change the cholesterol levels. Thus, cholesterol accumulation has no lag.

We did not investigate whether SFN regulates other cholesterol metabolizing enzymes or sterol regulatory element binding proteins although we cannot rule out this possibility. In this study we mainly focus on the cholesterol clearance effect by SFN via TFEB-mediated pathways. From our data, TFEB KO could significantly diminish SFN-evoked cholesterol clearance. Hence, the effect of other cholesterol metabolizing enzymes or sterol regulatory element binding proteins maybe not as important as TFEB, thus out of scope of this study. In the future, we may explore the involvement of possible other pathways on SFN’s effects.

(21) Related to Figure 7. The western blots for pS211-TFEB are poor. It's suggested that whole blots are shown to increase rigor.

Thank you for the comments. We have represented the blots with more spare space to increase the rigor.

(22) Data demonstrating the ability of SFN to improve Purkinje cell survival are exci ng and pair well with the weight analysis, however, to address the overall goal of determining if "SFN could be a good therapeutic candidate for neuropathology in NPC disease" survival analysis should be tested as well.

Please see the paragraph in response to point #3.

Minor

(23) Throughout the manuscript many different Fonts and font sizes are used. This is very jarring to readers. It is suggested that a more uniform approach is taken to presenting these nice datasets.

We are so sorry and apologize for these oversights. We have thoroughly checked all the manuscript to make sure that Fonts and sizes of font are synchronized.

(24) Related to data presentation. In general, there is a lack of alignment and organization of the figures.

So sorry about this. We have reorganized the figures to get them better aligned.

(25) Line 149, SFN is missing.

Corrected!

Reviewer #3 (Recommendations for the authors):

(26) In Figure 3 the authors should use multiple single siRNAs or perform a functional rescue to determine specificity.

We understand the reviewer’s point. We did design several siRNAs and the efficiency of these siRNAs were validated. Finally, we decide use this siRNA whose knockdown efficiency is best in the study and the specificity of the siTFEB has been validated by Western blot as shown in Fig. 3A. Furthermore, we used TFEB knockout cells constructed by CRISPR/Cas9 to further examine the role of TFEB in SFN-induced cholesterol clearance (Fig. 3D). Consistently with the results in the siTFEB-transfected HeLa NPC1 cells (Fig. 3B, C), SFN failed to diminish cholesterol in HeLa TFEB KO cells. The result from TFEB KO cells is even convincing than siRNA experiment. We also performed a functional rescue of re-expressing TFEB in TFEB KO cells, in which SFN-induced cholesterol clearance was restored (Fig. 3E, F). Collectively, these data indicate that TFEB is required for lysosomal cholesterol reduction upon SFN treatment. Thus, we did not repeat this rescue experiment in the siTFEB-transfected HeLa NPC1 cells.

(27) The label for 3D is missing.

Corrected! Thanks!

(28) Figure 4, although the authors use an an body against the luminal domain of LAMP1 there could s ll be some permeabilization. A marker of the plasma membrane would be helpful.

Please see the response to point #19.

(29) Figure 4, cholesterol in the media because of lysosome exocytosis. This is where the high concentration of SFN is of concern. Is there any cell death that could explain the result? The authors should test for cell death with the SFN treatment.

Thank you for raising this important point! We have measured the cytotoxicity of SFN of the concentrations used in this study in various cell lines (New Fig.S10). Please also see the paragraph in response to point #13.

(30) The blot in Figure 6A is unclear. It is very hard to see any change in pS211-TFEB levels, and, the blurry signal is the detection of phospho-TFEB is uncertain.

Please see the summary paragraph in response to point #21.

References:

Hu, M. Q., P. Li, C. Wang, X. H. Feng, Q. Geng, W. Chen, M. Marthi, W. L. Zhang, C. L. Gao, W. Reid, J. Swanson, W. L. Du, R. Hume and H. X. Xu (2022). "Parkinson's disease-risk protein TMEM175 is a proton-activated proton channel in lysosomes." Cell 185(13): 2292-+.

Hu, R., T. O. Khor, G. Shen, W. S. Jeong, V. Hebbar, C. Chen, C. Xu, B. Reddy, K. Chada and A. N. Kong (2006). "Cancer chemoprevention of intestinal polyposis in ApcMin/+ mice by sulforaphane, a natural product derived from cruciferous vegetable." Carcinogenesis 27(10): 2038-2046.

Li, D., R. Shao, N. Wang, N. Zhou, K. Du, J. Shi, Y. Wang, Z. Zhao, X. Ye, X. Zhang and H. Xu (2021). "Sulforaphane Activates a lysosome-dependent transcriptional program to mitigate oxidative stress." Autophagy 17(4): 872-887.

Medina, D. L., A. Fraldi, V. Bouche, F. Annunziata, G. Mansueto, C. Spampanato, C. Puri, A. Pignata, J. A. Martina, M. Sardiello, M. Palmieri, R. Polishchuk, R. Puertollano and A. Ballabio (2011). "Transcriptional activation of lysosomal exocytosis promotes cellular clearance." Dev Cell 21(3): 421-430.

Author Response

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

Reviewer #1:

We thank the reviewer for the positive evaluation of our manuscript. We have closely examined the issues raised, and below we offer a point-by-point response to each comment. In the revised manuscript below, all the introduced changes are marked with red font.

1. There may be a general typo concerning micromolar and millimolar…

Response 1: The reviewer is correct, and during the reformatting of the manuscript, in some portions of the manuscript, the units used to indicate TPEN concentrations, always µM, were switched to mM. We have corrected those mistakes.

1. In Figure 1C/Lines 150-152, the authors use DTPA and EDTA as extracellular chelators for zinc… Was the amount of zinc in the media measured and determined to be below the amount of chelator used? Additionally, these chelators are not specific for zinc, but can bind other divalent cations including calcium. Even though zinc binds more tightly than calcium to these chelators, by mass action calcium and magnesium ions may outcompete DTPA and EDTA, leaving zinc availability unperturbed. How do the authors take these interactions into account to determine that chelation of extracellular zinc has no effect on intracellular calcium oscillations? The best way to test this is to use zinc responsive fluorescent probes in a sample of the calcium- and magnesium-replete medium and see if the addition of the DTPA or EDTA alters zinc fluorescence in the cuvette.

Response 2: We tested several conditions to determine the effect of chelators on the zinc concentration of the monitoring media using commercially available Zn2+ probes. The fluorescent zinc probe FluoZin3 added extracellularly shows high fluorescence, consistent with trace amounts of zinc and possibly non-specific bindings of other cations.

Further, the media tested was replete with the concentrations of Ca2+ and Mg2+ in TLHEPES. To establish if the non-permeable external chelators we used could bind external Zn2+ despite the high concentrations of Ca2+ and Mg2+, we followed the reviewer’s suggestion of adding the chelators to the complete media in the presence of FluoZin3. The addition of EDTA caused a protracted, ~5 min, but significant decrease in FluoZin3’s fluorescence, suggesting it is effective at removing external Zn2+ despite the presence of other divalent cations (Author response image 1A). We used a second approach where we added the chelator in the presence of nominal concentrations of Ca2+ and Mg2+ to increase the chelators’ chances to find and chelate Zn2+ (Author response image 1B). Then, we injected mPlcζ mRNA, which initiated persistent but low-frequency oscillations, as expected due to the lack of external Ca2+. Remarkably, upon restoring it, the responses became of high frequency, and upon increasing Mg2+, they acquired the regular pattern, consistent with Mg2+’s inhibition of channels that mediate Ca2+ influx. These results show that the chelation of extracellular zinc does not replicate TPEN’s effect, which suggests that TPEN’s abrupt and inhibiting ability on Ca2+ oscillations is most likely due to the 43 chelation of internal Zn2+.

Author response image 1.

Cell-impermeable chelators effectively reduce Zn2+ levels in external media but do prevent initiation or continuation of Ca2+ oscillations. (A) A representative trace of FluoZin3 fluorescence in replete monitoring media (TL-HEPES). The media was supplemented with cell-impermeable FluoZin-3, and after initiation of monitoring, the addition of EDTA (100 μM) occurred at the designated point (triangle). (B) The left black trace represents Ca2+ oscillations initiation by injection of mPlcζ mRNA (0.01 μg/μl). The oscillations were monitored in Ca2+ and Mg2+-free media and in the presence of EDTA (110 μM) to chelate residual divalent cations derived from the water source or reagents used to make the media. The right red trace represents the initiation of oscillations as above, but after a period indicated by the black and green bars, Ca2+ and Mg2+ were sequentially added back.

Noteworthy, low EDTA concentrations, 10-µM, have been used to enhance in vitro culture conditions of mammalian embryos. In fact, it is the key ingredient to overcome the two-cell block that initially prevented the in vitro development of zygotes srom inbred strains. It is unknown how EDTA mediates this effect, which is detectable in Ca2+ and Mg2+ replete media and is only effective when placed extracellularly, but it has been attributed to its ability to chelate toxic metals introduced as impurities by other media components; one study demonstrated that the Zn2+ present in the oil used to overlay the culture medium micro drops was the target (Erbach et al., Human Reproduction, 1995, 10, 3248-54). We included some of these points in the revised version of the manuscript and added this figure as Supplementary Figure 1.

1. The reviewer noted that while dKO eggs showed reduced labile zinc levels, the amount of total zinc is not determined. Further, the response to thapsigargin in dKO eggs didn’t phenocopy the profile in eggs treated with TPEN. The reviewer argued that without further experimentation, such as comparing polar body extrusion and egg activation rate between WT and dKO, it seems to be a stretch to state that these eggs are zinc deficient.

Response 3: We agree that the statement, ‘zinc deficient,’ is an overstatement without determining the total zinc levels and associated phenotypes. Therefore, in the revised version of the manuscript, we referred to dKO-derived eggs and embryos as “low-level labile Zn2+ eggs”. Our follow-up studies show that eggs from dKO females seem to undergo egg activation events, such as the timing and rate of second polar body extrusion and pronuclear formation, with a similar dynamic to WT females. Hence, we estimate that the labile Zn2+ levels in dKO eggs are not as low as those of WT eggs treated with TPEN. Consequently, these intermediate zinc levels may have subtle effects, such as changing the Thapsigargin-induced Ca2+ release through the IP3R1 without causing widespread inhibition of cellular events observed after TPEN. We would argue that this approach is significant because it can distinguish how the different cellular events and proteins and enzymes have distinct affinities or zinc requirements and, in this case, start uncovering the channel(s) present in oocytes and eggs that may contribute to regulating zinc homeostasis.

1. The reviewer pointed out that since zinc is not redox active, it is unclear how zinc could be modifying cysteine residues of IP3R1.The reviewer suggested the possibility that excess zinc is binding to the cysteines and preventing their oxidation leading to the inhibition of the IP3R1 by blocking the channel, thereby preventing calcium release.

Response 4: The reviewer correctly points out that the mechanism(s) whereby excess Zn2+ modifies the IP3R1 function is undetermined in our study. Further, our description of ‘modifying’ is ambiguous and could be misinterpreted. Data in the literature, some of which we cite in the manuscript, shows that “oxidation of cysteine residues enhances receptor’s sensitivity to ligands in various cell types”. Zn2+ preferentially binds to reduced cysteine residues, and thus, we agree with the proposed reviewer's suggestion that “excess zinc may occupy reduced cysteine residues, preventing their oxidization required to sensitize the receptor”. As noted by the reviewer, we cannot rule out that it might be directly blocking the IP3R1 channel. We have modified the corresponding paragraphs in the Discussion.

1. Line 80 and 411, there are three other reports demonstrate the zinc reallocation to the egg shell or ejection as the zinc spark; Zebrafish: Converse et al. in Sci. Reports 10, 15673 (2020); X. lavis: Seeler et al. in Nature Chem. 13, 683-691 (2021), C. elegans: Mendoza et al. in Biology of Reproduction 107(2):406-418 (2022).

Response 5: Thank you for pointing this out, and we have added these references.

1. Line 129, when discussing that Zn2+ concentrations are reduced after TPEN as visualized by FluoZin-3, the authors should cite the article in which FluoZin-3 was first reported and this result was demonstrated initially: "Detection and Imaging of Zinc Secretion from Pancreatic β-Cells Using a New Fluorescent Zinc Indicator" by Gee et al. J. Am. Chem. Soc 124, 5, 776-778.

Response 6: Thank you for pointing this out, and we have added this reference.

1. In Figure 1E/Table 1 the authors evaluated if TPEN supplementation affects meiosis and pronuclear formation; however, the timing of TPEN treatment is unclear. When was TPEN introduced? Were the eggs left in the same media containing TPEN following fertilization, or were they transferred to different media?

Response 7: Thank you for pointing this out, and we have noted the time of the addition in the figure and text.

1. Line 1011 and 1012, ZnTP should be ZnPT.

Response 8: Thank you for pointing this out, which is now corrected.

Reviewer #2:

1. The reviewer raises the question of whether a more complex relationship could exist between the levels of zinc in MII eggs by indicating, “a more active relationship such that zinc efflux associated with each calcium spike could be necessary for terminating the Ca spike by depleting cytoplasmic zinc.” The reviewer also states, “Perhaps, rather than simply a permissive role, the normal Zn fluxes during activation may be acutely changing IP3-R gating sensitivity.”

Response 1: We agree that the demonstration that TPEN dose-dependently delays and consistently terminates ongoing Ca2+ rises perhaps reflects a more nuanced relationship between cytoplasmic labile zinc concentrations, Ca2+ oscillations, and IP3R1 function. Uncovering the precise nature of this relationship would require additional studies, such as determining the impact of TPEN on IP3 binding to its cognate receptor, regulation of channel gating, and more in-depth functional-structural experiments. However, these studies will demand time and complex experimental design and are beyond the scope of the current work. Nevertheless, they are excellent suggestions for future studies.

We would argue against the reviewer’s suggestion that “zinc sparks directly contribute to shaping the oscillations.” Zn2+ released during the sparks is not labile, but Zn2+ bound to cortical granules-resident proteins, most of which are inaccessible to the cytosol and hence to IP3R1s and should not perturb its function. We examined (data not shown) that the levels of cytosolic labile Zn2+, as assessed with FluoZin3, remained steady for over three hours of Plcζ mRNA-initiated oscillations. Further, because the Zn2+ sparks cease after the third or fourth Ca2+ rise, it would mean, at the very least, that this mechanism only operates on the first few responses. Thus, while the change of cytosolic Ca2+ concentrations triggers the Zn2+ sparks, we argue that the opposite influence is unlikely to hold true.

1. The reviewer also pointed out that the role of Trpv3 and Trpm7 in Zn2+ homeostasis seems to be minor and that the effects of genetic deletion of those channels are not as clear as those obtained by TPEN. Given that dKO eggs make it to the MII and release more but not less calcium upon thapsigargin than control despite the lowered labile Zn2+ level, the reviewer speculated that the loss of those channels changes calcium gating independent of Zn2+ concentration.

Response 2: TRPV3, TRPM7, and Cav3.2 are the three channels identified to permeate Ca2+ during oocyte maturation and egg activation in mice. We and other groups have observed that in oocytes and eggs, these channels partly compensate for the absence of each other because the deletion of these channels individually has a limited effect on Ca2+ oscillations and fertility. Thus, in the case of oocytes from Trpv3 and Trpm7 dKO animals, the other plasma membrane channel(s), most likely Cav3.2, is plausibly compensating, and its enhanced function underlies the increased Ca2+ response to Thapsigargin.

Nevertheless, the slower time to the peak and the lesser steep rise of the Thapsigargin induced rise suggest a negative impact of the dKO environment on IP3R1’s ability to mediate Ca2+ release. Based on the rest of the results in the manuscript, we attribute this change to the lower levels of labile Zn2+ in dKO eggs.

1. Lastly, the reviewer noted the upregulation of the Fura-2AM following addition of ZnPT. The reviewer indicated that 0.05 uM ZnPT might not increase intracellular Zn2+ to change Fura-2 fluorescence, but it might be sufficient Zn2+ to enter the cell and keep the IP3R1 channels open causing a sustained rise in cytoplasmic calcium and preventing oscillations. Further, if this interpretation holds true, the inhibitory effects of high Zn2+ on IP3R1’s gating shown in figure 7 would be precluded.

Response 3: We acknowledge that the increased levels of Fura-2 fluorescence following the addition of ZnPT could be due to the increased Zn2+ levels acting on IP3R1, increasing its open probability, and elevating cytosolic Ca2+ levels. We have added this consideration to the discussion. Nevertheless, our evidence suggests that this is unlikely because, as shown in Figure 6 H, I, the ER-Ca2+ levels as assessed by D1ER recordings did not change following the addition of ZnPT, whereas Rhod-2 fluorescence did, suggesting that the two events are seemingly uncoupled. Further, constant leak from the ER and extended high cytosolic Ca2+ would lead to egg activation or cell death, neither of which changes were observed.

Reviewer #3:

The reviewer noted that the present study deepened the understanding of the role of zinc in regulating calcium channels and stores at fertilization beyond the previously known Zn2+ requirement in oocyte maturation and the cell cycle progression. We appreciate these comments.

1. Fig. 1. The reviewer wondered why we selected 10 μM TPEN for most of the experiments in the manuscript. The reviewer noted this concentration only stopped the Ca2+oscillations in just half of the eggs after ICSI.

Response 1: We used 10-μM TPEN throughout the study because it blocked ~50% of the oscillations of a robust trigger of Ca2+ responses such as ICSI and reduced the frequency in the remaining eggs. This concentration of TPEN abrogates and prevents the responses by milder stimuli, such as Acetylcholine and SrCl2. Importantly, thimerosal and Plcζ mRNA overcome the inhibition by 10μM but not 50-μM TPEN. However, 50μM TPEN inactivates Emi2, a Zn2+-dependent enzyme, causing parthenogenic activation and cell cycle progression, and we wanted to avoid this confounding factor. Therefore, we determined 10-μM is a “threshold” concentration and selected it for the remaining studies. We also reasoned that it would allow the detection of more subtle effects of reducing the levels of labile zinc, causing a milder inhibition of IP3R1 sensitivity and a progressive delay or modification of the responses to other agonists rather than fully abrogating them, which is the case with higher concentrations.

1. Line131 - no concentration of TPEN stated? Or 'the addition of different concentrations of TPEN"?

Response 2: We have corrected this. We have now added 50-100 µM concentrations.

1. Line 146 - instead of TPEN, all TPEN concentrations?

Response 3: We have added these corrections, as at the concentrations we tested here, 5μM TPEN and above, all caused a reduction in the baseline of Fura-2 fluorescence.

1. Line 1046 - 'We submit'? Propose?

Response 4: We have replaced the word submit for propose. Thank you for the suggestion.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This work describes the mechanism of protein disaggregation by the ClpL AAA+ protein of Listeria monocytogenes. Using several model subtrate proteins the authors first show that ClpL possesses a robust disaggregase activity that does not further require the endogenous DnaK chaperone in vitro. In addition, they found that ClpL is more thermostable than the endogenous L. monocytogenes DnaK and has the capacity to unfold tightly folded protein domains. The mechanistic basis for the robust disaggregase activity of ClpL was also dissected in vitro and in some cases, supported by in vivo data performed in chaperonedeficient E. coli strains. The data presented show that the two AAA domains, the pore-2 site and the N-terminal domain (NTD) of ClpL are critical for its disaggregase activity. Remarkably, grafting the NTD of ClpL to ClpB converted ClpB into an autonomous disaggregase, highlighting the importance of such a domain in the DnaK-independent disaggregation of proteins. The role of the ClpL NTD domain was further dissected, identifying key residues and positions necessary for aggregate recognition and disaggregation. Finally, using sets of SEC and negative staining EM experiments combined with conditional covalent linkages and disaggregation assays the authors found that ClpL shows significant structural plasticity, forming dynamic hexameric and heptameric active single rings that can further form higher assembly states via their middle domains.

Strengths:

The manuscript is well-written and the experimental work is well executed. It contains a robust and complete set of in vitro data that push further our knowledge of such important disaggregases. It shows the importance of the atypical ClpL N-terminal domain in the disaggregation process as well as the structural malleability of such AAA+ proteins. More generally, this work expands our knowledge of heat resistance in bacterial pathogens.

Weaknesses:

There is no specific weakness in this work, although it would have helped to have a drawing model showing how ClpL performs protein disaggregation based on their new findings. The function of the higher assembly states of ClpL remains unresolved and will need further extensive research. Similarly, it will be interesting in the future to see whether the sole function of the plasmid-encoded ClpL is to cope with general protein aggregates under heat stress.

We thank the reviewer for the positive evaluation. We agree with the reviewer that it will be important to test whether ClpL can bind to and process non-aggregated protein substrates. Our preliminary analysis suggests that the disaggregation activity of ClpL is most relevant in vivo, pointing to protein aggregates as main target.

We also agree that the role of dimers or tetramers of ClpL rings needs to be further explored. Our initial analysis suggests a function of ring dimers as a resting state. It will now be important to study the dynamics of ClpL assembly formation and test whether substrate presence shifts ClpL assemblies towards an active, single ring state.

Reviewer #2 (Public Review):

The manuscript by Bohl et al. is an interesting and carefully done study on the biochemical properties and mode of action of potent autonomous AAA+ disaggregase ClpL from Listeria monocytogenes. ClpL is encoded on plasmids. It shows high thermal stability and provides Listeria monocytogenes food-pathogen substantial increase in resistance to heat. The authors show that ClpL interacts with aggregated proteins through the aromatic residues present in its N-terminal domain and subsequently unfolds proteins from aggregates translocating polypeptide chains through the central pore in its oligomeric ring structure. The structure of ClpL oligomers was also investigated in the manuscript. The results suggest that mono-ring structure and not dimer or trimer of rings, observed in addition to mono-ring structures under EM, is an active species of disaggregase.

Presented experiments are conclusive and well-controlled. Several mutants were created to analyze the importance of a particular ClpL domain.

The study's strength lies in the direct comparison of ClpL biochemical properties with autonomous ClpG disaggregase present in selected Gram-negative bacteria and well-studied E. coli system consisting of ClpB disaggregase and DnaK and its cochaperones. This puts the obtained results in a broader context.

We thank the reviewer for the detailed comments. There are no specific weaknesses indicated in the public review.

Reviewer #3 (Public Review):

Summary:

This manuscript details the characterization of ClpL from L. monocytogenes as a potent and autonomous AAA+ disaggregase. The authors demonstrate that ClpL has potent and DnaKindependent disaggregase activity towards a variety of aggregated model substrates and that this disaggregase activity appears to be greater than that observed with the canonical DnaK/ClpB co-chaperone. Furthermore, Lm ClpL appears to have greater thermostability as compared to Lm DnaK, suggesting that ClpL-expressing cells may be able to withstand more severe heat stress conditions. Interestingly, Lm ClpP can provide thermotolerance to E. coli that have been genetically depleted of either ClpB or in cells expressing a mutant DnaK103. The authors further characterized the mechanisms by which ClpL interacts with protein aggregates, identifying that the N-terminal domain of ClpL is essential for disaggregase function. Lastly, by EM and mutagenesis analysis, the authors report that ClpL can exist in a variety of larger macromolecular complexes, including dimer or trimers of hexamers/heptamers, and they provide evidence that the N-terminal domains of ClpL prevent dimer ring formation, thus promoting an active and substrate-binding ClpL complex. Throughout this manuscript the authors compare Lm ClpL to ClpG, another potent and autonomous disaggregase found in gram-negative bacteria that have been reported on previously, demonstrating that these two enzymes share homologous activity and qualities. Taken together this report clearly establishes ClpL as a novel and autonomous disaggregase.

Strengths:

The work presented in this report amounts to a significant body of novel and significant work that will be of interest to the protein chaperone community. Furthermore, by providing examples of how ClpL can provide in vivo thermotolerance to both E. coli and L. gasseri the authors have expanded the significance of this work and provided novel insight into potential mechanisms responsible for thermotolerance in food-borne pathogens.

Weaknesses:

The figures are clearly depicted and easy to understand, though some of the axis labeling is a bit misleading or confusing and may warrant revision. While I do feel that the results and discussion as presented support the authors' hypothesis and overall goal of demonstrating ClpL as a novel disaggregase, interpretation of the data is hindered as no statistical tests are provided throughout the manuscript. Because of this only qualitative analysis can be made, and as such many of the concluding statements involving pairwise comparisons need to be revisited or quantitative data with stats needs to be provided. The addition of statistical analysis is critical and should not be difficult, nor do I anticipate that it will change the conclusions of this report.

We thank the reviewer for the valid criticism. We addressed the major concern of the reviewer and added the requested statistical analysis to all relevant figures. The analysis confirms our conclusions. We also followed the advice of the reviewer and revised axis labeling to increase clarity.

Reviewer #1 (Recommendations For The Authors):

• It would really help to have a model showing how ClpL performs protein disaggregation based on their findings.

We show that ClpL exerts a threading activity that is fueled by ATP hydrolysis in both AAA domains and executed by pore-located aromatic residues. The basic disaggregation mechanism of ClpL therefore does not differ from ClpB and ClpG disaggregases. Similarly, the specificity of ClpL towards protein aggregates is based on simultaneous interactions of multiple N-terminal domains with the aggregate surface. We could recently describe a similar mode of aggregate recognition for ClpG [1]. We therefore prefer not to add a model to the manuscript. We are currently in preparation of a review that includes the characterization of the novel bacterial disaggregases and will present models there as we consider a review article as more appropriate for such illustrations.

• AAA2 domain of ClpL in Fig 3E should be the same color as in Fig 1A.

We used light grey instead of dark grey for the ClpL AAA2 domain in Fig 3E, to distinguish between ClpL and ClpB AAA domains. This kind of illustration allows for clearer separation of both AAA+ proteins and the fusion construct LN-ClpB*. We therefore prefer keeping the color code.

• Partial suppression of the dnaK mutant could be added in the main manuscript Figure.

The main figure 3 is already very dense and we therefore prefer showing respective data as part of a supplementary figure.

• It would have been interesting to know if the robust autonomous disaggregation activity of ClpL would be sufficient to rescue the growth of more severe E. coli chaperone mutants, like dnaK tig for example. Did the authors test this?

We tested whether expression of clpL can rescue growth of E. coli dnaK103 mutant cells at 40°C on LB plates. This experiment is different from the restoration of heat resistance in dnaK103 cells (Figure 3, figure supplement 2A), as continuous growth at elevated temperatures (40°C) is monitored instead of cell survival upon abrupt severe heat shock (49°C). We did not observe rescue of the temperature-sensitive growth phenotype (40°C) of dnaK103 cells upon clpL expression, though expression of clpG complemented the temperature-sensitive growth phenotype (see Author response image 1 below). This finding points to differences in chaperone activities of ClpL and ClpG. It also suggests that ClpL activity is largely restricted to heat-shock generated protein aggregates, enabling ClpL to complement the missing disaggregation function of DnaK but not other Hsp70 activities including folding and targeting of newly synthesized proteins. We believe that dissecting the molecular reasons for differences in ClpG and ClpL complementation activities should be part of an independent study and prefer showing the growth-complementation data only in the response letter.

Author response image 1.

Serial dilutions (10-1 – 10-6) of E. coli dnaK103 mutant cells expressing E. coli dnaK, L. monocytogenes clpL or P. aeruginosa clpG were spotted on LB plates including the indicated IPTG concentrations. Plates were incubated at 30°C or 40°C for 24 h. p: empty vector control.

Reviewer #2 (Recommendations For The Authors):

Based on results presented in Fig. 2B the authors conclude "that stand-alone disaggregases ClpL and ClpG but not the canonical KJE/ClpB disaggregase exhibit robust threading activities that allow for unfolding of tightly folded domains" (page 5 line 209). In this experiment, the threading power of disaggregases was assessed by monitoring YFP fluorescence during the disaggregation of aggregates formed by fusion luciferase-YFP protein. In my opinion, the results of the experiment depend not only on the threading power of disaggregases but also on the substrate recognition by analyzed disaggregating systems and/or processivity of disaggregases. N-terminal domain in the case of ClpL and KJE chaperones in the case of the KJE/ClpB system are involved in recognition. This is not discussed in the manuscript and the obtained result might be misinterpreted. The authors have created the LN-ClpB* construct (N-terminal domain of ClpL fused to derepressed ClpB) (Fig. 3 E and F). In my opinion, this construct should be used as an additional control in the experiment in Fig. 2 B. It possesses the same substrate recognition domain and therefore the direct comparison of disaggregases threading power might be possible.

We performed the requested experiment (new Figure 3 - figure supplement 2D). We did not observe unfolding of YFP by LN-ClpB. Sínce ClpL and LN-ClpB do not differ in their aggregate targeting mechanisms, this finding underlines the differences in threading power between ClpL and activated (derepressed) ClpB. It also suggests that the AAA threading motors and the aggregate-targeting NTD largely function independently.

Presented results suggest that tetramer and dimer of rings might be a "storage form" of disaggregase. It would be interesting to analyze the thermotolerance and/or phenotype of ClpL mutants that do not form tetramer and dimer (E352A). This variant possesses similar to WT disaggregation activity but does not form dimers and tetramers. If in vivo the differences are observed (for example toxicity of the mutant), the "storage form" hypothesis will be probable.

When testing expression of clpL-MD mutants (E352A, F354A), which cannot form dimers and tetramers of ClpL rings, in E. coli ∆clpB cells, we observed reduced production levels as compared to ClpL wildtype and speculated that reduced expression might be linked to cellular toxicity. We therefore compared spotting efficiencies of E. coli ∆clpB cells expression clpL, ∆NclpL or the clpL-MD mutants at different temperatures. Expression of clpL at high levels abrogated colony formation at 42°C (new Figure 6 - figure supplement 3). ClpL toxicity was dependent on its NTD as no effect was observed upon expression of ∆N-clpL. ClpL-MD mutants (E352A, F354A) were expressed at much lower levels and exhibited strongly increased toxicity as compared to ClpL-WT when produced at comparable levels (new Figure 6 – figure supplement 3). This implies a protective role of ClpL ring dimers and tetramers in the cellular environment by downregulating ClpL activity. We envision that the formation of ClpL assemblies restricts accessibility of the ClpL NTDs and reduces substrate interaction. Increased toxicity of ClpL-E352A and ClpL-F354A points to a physiological relevance of the dimers and tetramers of ClpL rings and is in agreement with the proposed function as storage forms. We added this potential role of ClpL ring assemblies to the discussion section. Due to the strongly reduced production levels of ClpL MD mutants and their enhanced toxicity at elevated temperatures we did not test for their ability to restore thermotolerance in E. coli ∆clpB cells.

Figure 6G and Figure 6 -figure supplement 2 - it is not clear what is the difference in the preparation of WT and WTox forms of ClpL.

ClpL WT was purified under reduced conditions (+ 2 mM DTT), whereas WTox was purified in absence of DTT, thus serving as control for ClpL-T355C, which forms disulfide bonds upon purification without DTT. We have added respective information to the figure legend and the materials and methods section.

Page 5 line 250 - wrong figure citation. Instead of Figure 1 - Figure Supplement 2A should be Figure 3 - Figure Supplement 2A.

Page 5 line 251 - wrong figure citation. Instead of Figure 1 - Figure Supplement 2B/C should be Figure 3 - Figure Supplement 2B/C.

Page 7 line 315 - wrong figure citation. Instead of Figure 4F, it should be Figure 4G Figure 1 - Figure Supplement 2E - At first glance, this Figure does not correspond to the text and is confusing. It would be nice to have bars for Lm ClpL activity in the figure. Alternatively, the description of the y-axis might be changed to "relative to Lm ClpL disaggregation activity" instead of "relative disaggregation activity". One has to carefully read the figure legend to find out that 1 corresponds to Lm ClpL activity.

We have corrected all mistakes and changed the description of y-axis (Figure 1 - figure Supplement 2E) as suggested.

Reviewer #3 (Recommendations For The Authors):

(1) While the authors make many experimental comparisons throughout their study, no statistical tests are described or presented with their results or figures, nor are these statistical tests described in the methods. While the data as presented does appear to support the author's conclusions, without these statistical tests no meaningful conclusions from paired analysis can be drawn. Critically, please report these statistical tests. As a general suggestion please include the statistics (p-values) in the results section when presenting this data, as well as in the figure legends, as this will allow the reader to better understand the authors' presentation and interpretation of the data.

We have added statistical tests to all relevant figures. The analysis is confirming our former statements. We have further clarified our approach for the statistical analysis in the methods section. We report p-values in the results section, however, due to the volume of comparisons we did not add individual p-values to the figure legends but used standard labeling with stars.

(2) Some of the axis labels for the presented graphs are a bit misleading or confusing. Many describe a relative (%) disaggregation rate, but it is not clear from the methods or figure legends what this rate is relative to. Is it relative to non-denatured substrates, to no chaperone conditions, etc.? Is it possible to present the figures with the raw data rates/activity (ex. luciferase activity / time) vs. relative rates? I think that labeling these figure axes with "disaggregation rate" is a bit misleading as none of these experiments measure the actual rate of disaggregation of these model substrates per se (say by SEC-MALS or other biophysical measurements), but instead infer the extent of disaggregation by measuring a property of these substrates, i.e. luciferase activity or fluorescence intensity over time. Thus, labeling these figures with the appropriate axis for what is being measured, and then clarifying in the methods and results what is being inferred by these measurements, will help solidify the author's conclusions.

Relative (%) disaggregation rate usually refers to the disaggregation activity of ClpL wildtype serving as reference. We clarified this point in the revised text and respective figure legends. We now also refer to the process measured (e.g. relative refolding activity of aggregated Luciferase instead of relative disaggregation activity) as suggested by the reviewer and added clarifications to text and materials and methods.

Since we have many measurements for our most frequently used assays and have a reasonable estimate for the general variance within these assays, we found it reasonable to show activity data in relation to fixed controls. This reduces the impact of unspecific variance and thereby makes more accurate comparisons between different repetitions. The reference is now indicated in the axis title.

(3) The figures are well presented, clutter-free, and graphically easy to understand. Figure legends have sufficient information aside from the aforementioned statistical information and should include the exact number of independent replicates for each panel/experiment (ex. n=4), not just a greater than 3. While the figures do show each data point along with the mean and error, in some figures it is difficult to determine the number of replicate data points. Example figures 2c, 2d, and 3a. Also, please state whether the error is std. error or SEM.

While we agree, that this is valuable information, we fear that overloading the figure legends with information may take a toll on the readability. We therefore decided to append the number of replicates for each experiment in a separate supplementary table (Table S2). The depicted error is showing the SD and not the SEM, which we also specified in the figure legends.

(4) There are various examples throughout the results where qualitative descriptors are used to describe comparisons. Examples of this are "hardly enhanced" (Figure 1) and "partially reduced" (Figure 6). While this is not necessarily wrong, qualitative descriptions of comparisons in this manner would require further explanation. What is the definition of "hardly" or "partially"? My recommendation is to just state the data quantitatively, such as "% enhanced" or "reduced by x", this way there is no misinterpretation. Examples of this can be found in Figures 6C-G. This would require a full statistical overview and presentation of these stats in the results.

We followed the reviewer`s advice and no longer use the terms criticized (e.g. “hardly enhanced”). We instead provide the requested quantifications in the text.

Questions for Figures:

Figures 1B and 1C:

(1) Is the disaggregase activity of ClpL towards heat-denatured luciferase and GFP ATPdependent? While the authors later in the manuscript show that mutations within the Walker B domains dramatically impair reactivation (disaggregation) of denatured luciferase, this does not rule out an ATP-independent effect of these mutations. Thus, the authors should test whether disaggregase activity is observed when wild-type ClpL is incubated with denatured substrates without ATP present or in the presence of ADP only.

We tested for ClpL disaggregation activity in absence of nucleotide and presence of ADP only (new Figure 1 – figure supplement 2A). We did not observe any activity, demonstrating that ClpL activity depends on ATP binding and hydrolysis (see also Figure 3 – figure supplement 1D: ATPase-deficient ClpL-E197A/E530A is lacking disaggregation activity).

(2) The authors suggest that a reduction in disaggregase activity observed in samples combining Lm ClpL and KJE (Figure 1C, supp. 1C-E) could be due to competition for protein aggregate binding as observed previously with ClpG. Did the authors test this directly by pulldown assay or another interaction-based assay? While ClpL and ClpG appear to work in a similar manner, it would be good to confirm this. Also, clarification on how this competition operates would be useful. Is it that ClpL prevents aggregates from interacting with KJE, or vice versa?

We probed for binding of ClpL to aggregated Malate Dehydrogenase in the presence of L. monocytogenes or E. coli Hsp70 (DnaK + respective J-domain protein DnaJ) by a centrifugation-based assay. Here, we used the ATPase-deficient ClpL-E197A/E530A (ClpLDWB) mutant, ensuring stable substrate interaction in presence of ATP. We observe reduced binding of ClpL-DWB to protein aggregates in presence of DnaK/DnaJ (new Figure 1 – figure supplement 2G). This finding indicates that both chaperones compete for binding to aggregated proteins and explains inhibition of ClpL disaggregation activity in presence of Hsp70.

(3) Related to the above, while incubation of aggregated substrates with ClpL and KJE does appear to reduce aggregase activity towards GFP (Figure 1c), α-glucosidase (Supp. 1C), and MDH (Supp. 1D), this doesn't appear to be the case towards luciferase (Figure 1b, Supp. 1b). Furthermore, ClpL aggregase activity is reduced towards luciferase when combined with E. coli KJE (Supp. 1e) but not with Lm KJE (Figure 1b). The authors provide no commentary or explanation for these observations. Furthermore, these results complicate the concluding statement that "combining ClpL with Lm KJE always led to a strong reduction in disaggregation activity ... ".

We suggest that the differing inhibitory degrees of the KJE system on ClpL disaggregation activities reflect diverse binding affinities of KJE and ClpL to the respective aggregates. While we usually observe strong inhibition of ClpL activity in presence of KJE, this is different for aggregated Luciferase. This points to specific structural features of Luciferase aggregates or the presence of distinct binding sites on the aggregate surface that favour ClpL binding. We have added a respective comment to the revised manuscript.

The former statement that “combining ClpL with Lm KJE always led to a strong reduction in disaggregation activity” referred to aggregated GFP, MDH and α-Glucosidase for which a strong inhibition of ClpL activity was observed. We have specified this point.

Figures 1D and 1E:

(1) The authors conclude that the heat sensitivity of ΔClpL L. gasseri cells is because they do not express the canonical ClpB disaggregase. A good test to validate this would be to express KJE/ClpB in these Lg ΔClpL cells to see if heat-sensitivity could be fully or partially rescued.

We agree that such experiment would further strengthen the in vivo function of ClpL as alternative disaggregase. However, such approach would demand for co-expression of E. coli ClpB with the authentic E. coli DnaK chaperone system (KJE), as ClpB and DnaK cooperate in a species-specific manner [2-4]. This makes the experiment challenging, also because the individual components need to be expressed at a correct stochiometry. Furthermore, the presence of the authentic L. gasseri KJE system, which is likely competing with the E. coli KJE system for aggregate binding, will hamper E. coli KJE/ClpB disaggregation activity in L. gasseri. In view of these limitations, we would like to refrain from conducting such an experiment.

(2) The rationale for investigating Lg ClpL, and the aggregase activity assays are compelling and support the hypothesis that ClpL contributes to thermotolerance in multiple grampositive species. Though, from Figure 1d, why was only Lg ClpL investigated? It appears that S. thermophilus also lacks the canonical ClpB disaggregase and demonstrates ΔClpL heat sensitivity. There is also other Lactobacillus sp. presented that lack ClpB but were not tested for heat sensitivity. Why only test and move forward with L. gasseri? Lastly, L. mesenteroides is ClpB-negative but doesn't demonstrate ΔClpL heat sensitivity. Why?

We wanted to document high, partner-independent disaggregation activity for another ClpL homolog. We chose L. gasseri, as (i) this bacterial species lacks a ClpB homolog and (ii) a ∆clpL mutant exhibit reduced survival upon severe heat shock (thermotolerance phenotype), which is associated with defects in cellular protein disaggregation. The characterization of L. gasseri ClpL as potent disaggregase in vitro represents a proof-of-concept and allows to generalize our conclusion. We therefore did not further test S. thermophilus ClpL. L. mesenteroides encodes for ClpL but not ClpB, yet, a ∆clpL mutant has not yet been characterized in this species to the best of our knowledge. As we wanted to link ClpL in vitro activity with an in vivo phenotype, we did not characterize L. mesenteroides ClpL.

We agree with the reviewer that the characterization of additional ClpL homologs is meaningful and interesting, however, we strongly believe that such analysis should be part of an exhaustive and independent study.

Figures 2A and 2B:

(1) Figure 2B demonstrates that both ClpL and ClpG, but not the canonical KJE/ClpB, are able to unfold YFP during the luciferase disaggregation process, suggesting that ClpL and ClpG exhibit stronger threading activity. A technical question, can luciferase activity be measured alongside in the same assay sample? If so, would you expect to observe a concomitant increase in luciferase activity as YFP fluorescence decreases?

KJE/ClpB can partially disaggregate and refold aggregated Luciferase-YFP without unfolding YFP during the disaggregation reaction [5]. YFP unfolding is therefore not linked to refolding of aggregated Luciferase-YFP. On the other hand, unfolding of YFP during disaggregation can hamper the refolding of the fused Luciferase moiety as observed for the AAA+ protein ClpC in presence of its partner MecA [5]. These diverse effects make the interpretation of LuciferaseYFP refolding experiments difficult as the degree of YFP unfolding activity does not necessarily correlate with the extend of Luciferase refolding. We therefore avoided to perform the suggested experiment.

Figure 2C and 2D:

(1) Thermal shift assays for ClpL, ClpG, and DnaK were completed with various nucleotides. Were these experiments also completed with samples in their nucleotide-free apo state? Also, while all these chaperones are ATPases, the nucleotides used differ, but no explanation is provided. Comparison should be made of these ATPases bound to the same molecules.

We did not monitor thermal stabilities of chaperones without nucleotide as such state is likely not relevant in vivo. We used ATPγS in case of ClpL to keep the AAA+ protein in the ATPconformation. ATP would be rapidly converted to ADP due to the high intrinsic ATPase activity of ClpL. In case of DnaK ATPγS cannot be used as it does not induce the ATP conformation [6]. The low intrinsic ATPase activity of DnaK allows determining the thermal stability of its ATP conformation in presence of ATP. This is confirmed by calculating a reduced thermal stability of ADP-bound DnaK.

(2) The authors suggest that incubation at 55⁰C will cause unfolding of Lm DnaK, but not ClpL, providing ClpL-positive Lm cells disaggregase activity at 55⁰C. While the thermal shift assays in Figures 2C and 2D support this, an experiment to test this would be to heat-treat Lm DnaK and ClpL at 55⁰C then test for disaggregase activity using either aggregated luciferase or GFP as in Figure 1.

We followed the suggestion of the reviewer and incubated Lm ClpL and DnaK at 55-58°C in presence of ATP for 15 min prior to their use in disaggregation assays. We compared the activities of pre-heated chaperones with controls that were incubated at 30°C for 15 min. Notably, we did not observe a loss of DnaK disaggregation activity, suggesting that thermal unfolding of DnaK at this temperature is reversible. We provide these data as Figure 2 -figure supplement 1 and added a respective statement to the revised manuscript.

Figure 3B:

(1) The authors state that ATPase activity of ΔN-ClpL was "hardly affected", but from the data provided it appeared to result in an approximate 35% reduction. As discussed above, no stats are provided for this figure, but given the error bars, it is highly likely that this reduction is significant. Please perform this statistical test, and if significant, please reflect this in the written results as well as the figure. Lastly, if this reduction in ATPase activity is significant, why would this be so, and could this contribute to the reduction in aggregase activity towards luciferase and MDH observed in Figure 3A?

We applied statistical tests as suggested by the reviewer, showing that the reduction in ATPase activity of ∆N-ClpL is statistically significant. N-terminal domains of Hsp100 proteins can modulate ATPase activity as shown for the family member ClpB, functioning as auxiliary regulatory element for fine tuning of ClpB activity [7]. We speculate that the impact of the ClpL-NTD on the assembly state (stabilization of ClpL ring dimers) might affect ClpL ATPase activity. We would like to point out that other ClpL mutants (e.g. NTD mutant ClpL-Y51A; MDmutant ClpL-F354A) have a similarly reduced ATPase activity, yet exhibit substantial disaggregation activity (approx. 2-fold reduced compared to ClpL wildtype). In contrast ∆NClpL does not exhibit any disaggregation activity. This suggests that the loss of disaggregation activity is caused by a substrate binding defect but not by a partial reduction in ATPase activity. We added a comment on the reduced ATPase activity and also discuss its potential reasons in the discussion section.

(2) I think the authors' conclusion that deletion of the ClpL NTD does not contribute to structural defects of ClpL is premature given the apparent reduction in ATPase activity. Did the authors perform any biophysical analysis of ΔN-ClpL to confirm this conclusion? Thermal shift assays, Native-PAGE, or size-exclusion chromatography for aggregates would all be good assays to demonstrate that the wild-type and ΔN-ClpL have similar structural properties. Surprisingly, Figure 6 describes significant macromolecular changes associated with ΔN-ClpL such that it preferentially forms a dimer of rings. Furthermore, in Supp. Figure 6D the authors report that ΔN-ClpL appears to have an increased Tm as compared to WT- or ΔM-ClpL. The authors should reflect these observations as deletion of the ClpL NTD does appear to contribute to structural changes, though perhaps only at the macromolecular scale, i.e. dimerization of the rings.

We have characterized the oligomeric state of ∆N-ClpL by size exclusion chromatography (Figure 6 – figure supplement 1A) and negative staining electron microscopy (Figure 6C), both showing that it forms assemblies similar to ClpL wildtype. We did not observe an increased tendency of ∆N-ClpL to form aggregates and the protein remained fully soluble after several cycles of thawing and freezing. EM data reveal that ∆N-ClpL exclusively form ring dimers, suggesting that the NTDs destabilize MD-MD interactions. The stabilized interaction between two ∆N-ClpL rings can explain the increased thermal stability (Figure 6 – figure supplement 1D). We speculate that the ClpL NTDs either affect MD-MD interactions through steric hindrance or by directly contacting MDs. We have added a respective statement to the discussion section.

Figure 3C and 3D:

(1) Given the larger error in samples expressing ClpG (100) or ClpL (100) statistical analysis with p-values is required to make conclusions regarding the comparison of these samples vs. plasmid-only control. The effect of ΔN-ClpL vs. wild-type ClpL looks compelling and does appear to attenuate the ClpL-induced thermotolerance. This is nicely demonstrated in Figure 3D.

We quantified respective spot tests (new Figure 3E) and tested for statistical significance as suggested by the reviewer. We show that restoration of heat resistance is significant for the first 30 min. While we always observe rescue at later timepoints significance is lost here due to larger deviations in the number of viable cells and thus the degree of complementation.

Figure 3F:

(1) What is the role of the ClpB NTD? It appears to be dispensable for disaggregase activity, assuming that ClpB is co-incubated with KJE. A quick explanation of this domain in ClpB could be useful.

The ClpB NTD is not required for disaggregation activity, as ClpB is recruited to protein aggregates by DnaK, which interacts with the ClpB MDs. Still, two functions have been described for the ClpB NTD. First, it can bind soluble unfolded substrates such as casein [8]. This substrate binding function can increase ClpB disaggregation activity towards some aggregated model substrates (e.g. Glucose-6-phosphate dehydrogenase) [9]. However, NTD deletion usually does not decrease ClpB disaggregation activity and can even lead to an increase [7, 10, 11]. An increased disaggregation activity of ∆N-ClpB correlates with an enhanced ATPase activity, which is explained by NTDs stabilizing a repressing conformation of the ClpB MDs, which function as main regulators of ClpB ATPase activity [7]. We added a short description on the role of the ClpB NTD to the respective results section.

(2) The result of fusing the ClpL NTD to ClpB supports a role for this NTD in promoting autonomous disaggregase activity. What would you expect to observe if the fused Ln-ClpB protein was co-incubated with KJE? Would this further promote disaggregase activity, or potentially impair through competition? This experiment could potentially support the authors' hypothesis that ClpL and ClpB/KJE can compete with each other for aggregated substrates as suggested in Figure 1.

We have performed the suggested experiment using aggregated MDH as model substrate. We did not observe an inhibition of LN-ClpB disaggregation activity in presence of KJE. In contrast ClpL disaggregation activity towards aggregated MDH is inhibited upon addition of KJE due to competition for aggregate binding (Figure 1 – figure supplement 2D/F). Disaggregation activity of LN-ClpB in presence of KJE can be explained by functional cooperation between both chaperone systems, which involves interactions between aggregate-bound DnaK and the ClpB MDs of the LN-ClpB fusion construct. We prefer showing these data only in the response letter but not including them in the manuscript, as respective results distract from the main message of the LN-ClpB fusion construct: the ClpL NTD functions as autonomous aggregatetargeting unit that can be transferred to other Hsp100 family members.

Author response image 2.

LN-ClpB cooperates with DnaK in protein disaggregation. Relative MDH disaggregation activities of indicated disaggregation systems were determined. KJE: DnaK/DnaJ/GrpE. The disaggregation activity of Lm ClpL was set to 1. Statistical Analysis: Oneway ANOVA, Welch’s Test for post-hoc multiple comparisons. Significance levels: **p < 0.001. n.s.: not significant.

Figures 4E and 4F:

(1) While the effect of various NTD mutations follows a similar trend in regard to the impairment of ClpL-mediated disaggregation of luciferase and MDH, the degree of these effects does appear different. For example, patch A and C mutations reduce ClpL disaggregase activity towards luciferase (~60% / 50% reduction) vs. MDH (>90%) respectively. While these results do suggest a critical role for residues in patches A and C of ClpL, these substrate-specific differences are not discussed. Why would we expect a difference in the effect of these patch A/C ClpL mutations on different substrates?

We speculate that the aggregate structure and the presence or distributions of ClpL NTD binding sites differ between aggregated Luciferase and MDH. A difference between both aggregated model substrates was also observed when testing for an inhibitory effect of Lm KJE (and Ec KJE) on ClpL disaggregation activity (see comment above). We speculate that the mutated NTD residues make specific contributions to aggregate recognition. The severity of binding defects (and reduction of disaggregation activities) of these mutants will depend on specific features of the aggregated model substrates. We now point out that ClpL NTD patch mutants can differ in disaggregation activities depending on the aggregated model substrate used and refer to potential differences in aggregate structures.

(2) The authors suggest that the loss of disaggregation activity of selected NTD mutants could be linked to reduced binding to aggregated luciferase. While this is likely given that these mutations do not appear to affect ATPase activity (Supp. 4), it could be possible that these mutants can still bind to aggregated luciferase and some other mechanism may impair disaggregation. A pull-down assay would help to prove whether reduced binding is observed in these NTD ClpL mutants. This also needs to be confirmed for Supp. Figure 4.2H.

We have shown a strong correlation between loss of aggregate binding and disaggregation activity for several NTD mutants (Fig. 4G, Figure 4 – figure supplement 2H). We decided to perform the aggregate binding assay only with mutants that show a full but not a partial disaggregation defect as we made the experience that the centrifugation-based assay provides clear and reproducible results for loss-of-activity mutants but has limitations in revealing differences for partially affected mutants. This might be explained by the use of nonhydrolyzable ATPγS in these experiments, which strongly stabilizes substrate interactions, potentially covering partial binding defects. We agree with the reviewer that some ClpL NTD mutants might have additional effects on disaggregation activity by e.g. controlling substrate transfer to the processing pore site. We have added a respective comment to the revised manuscript.

(3) Supp. Figure 4.2H has no description in the figure legend. The Y-axes states % aggregate bound to chaperone. How was this measured? See the above comments for Figures 4E and 4F.

We apologize and added the description to the figure legend. The determination of % aggregate bound chaperone is based on the quantifications of chaperones present in the supernatant and pellet fractions after sample centrifugation. Background levels of chaperones in the pellet fractions in absence of protein aggregates were subtracted. We added this information to the materials and methods section.

Figure 6G:

The authors observed reduced disaggregase activity and ATPase activity of mutant T355C under both oxidative and reducing conditions. While this observation under oxidative conditions supports the authors' hypothesis, under reducing conditions (+DTT) we would expect the enzyme to behave similarly to wild-type ClpL unless this mutation has other effects. Can the authors please comment on this and provide an explanation or hypothesis?

The reviewer is correct, ClpL-T355C exhibit a reduced disaggregation activity (Figure 6 – figure supplement 2B). We observe a similar reduction in disaggregation activity for the ClpL MD mutant F354A, pointing to an auxiliary function of the MD in protein disaggregation. We have made a respective comment in the discussion section of the revised manuscript. How exactly ClpL MDs support protein disaggregation is currently unclear and will be subject of future analysis in the lab. We strongly believe that such analysis should be part of an independent study.

Discussion:

In the fourth feature, it is discussed that one disaggregase feature of ClpL is that it does not cooperate with the ClpP protease. While a reference is provided for the canonical ClpB, no data in this paper, nor a reference, is provided demonstrating that ClpL does not interact with ClpP. As discussed, it is highly unlikely that ClpL interacts with ClpP given that ClpL does not contain the IGL/F loops that mediate the interaction of ClpP with cochaperones, such as ClpX, but data or a reference is needed to make such a factual statement.

The absence of the IGL/F loop makes an interaction between ClpL and ClpP highly unlikely. However, the reviewer is correct, direct evidence for a ClpP-independent function of ClpL, though very likely, is not provided. We have therefore rephrased the respective statement: “Forth, novel disaggregases lack the specific IGL/F signature motif, which is essential for cooperation of other Hsp100 proteins with the peptidase ClpP. This feature is shared with the canonical ClpB disaggregase [12] suggesting that protein disaggregation is primarily linked to protein refolding.”.

References

(1) Katikaridis P, Simon B, Jenne T, Moon S, Lee C, Hennig J, et al. Structural basis of aggregate binding by the AAA+ disaggregase ClpG. J Biol Chem. 2023:105336.

(2) Glover JR, Lindquist S. Hsp104, Hsp70, and Hsp40: A novel chaperone system that rescues previously aggregated proteins. Cell. 1998;94:73-82.

(3) Krzewska J, Langer T, Liberek K. Mitochondrial Hsp78, a member of the Clp/Hsp100 family in Saccharomyces cerevisiae, cooperates with Hsp70 in protein refolding. FEBS Lett. 2001;489:92-6.

(4) Seyffer F, Kummer E, Oguchi Y, Winkler J, Kumar M, Zahn R, et al. Hsp70 proteins bind Hsp100 regulatory M domains to activate AAA+ disaggregase at aggregate surfaces. Nat Struct Mol Biol. 2012;19:1347-55.

(5) Haslberger T, Zdanowicz A, Brand I, Kirstein J, Turgay K, Mogk A, et al. Protein disaggregation by the AAA+ chaperone ClpB involves partial threading of looped polypeptide segments. Nat Struct Mol Biol. 2008;15:641-50.

(6) Theyssen H, Schuster H-P, Bukau B, Reinstein J. The second step of ATP binding to DnaK induces peptide release. J Mol Biol. 1996;263:657-70.

(7) Iljina M, Mazal H, Goloubinoff P, Riven I, Haran G. Entropic Inhibition: How the Activity of a AAA+ Machine Is Modulated by Its Substrate-Binding Domain. ACS chemical biology. 2021;16:775-85.

(8) Rosenzweig R, Farber P, Velyvis A, Rennella E, Latham MP, Kay LE. ClpB N-terminal domain plays a regulatory role in protein disaggregation. Proc Natl Acad Sci U S A. 2015;112:E6872-81.

(9) Barnett ME, Nagy M, Kedzierska S, Zolkiewski M. The amino-terminal domain of ClpB supports binding to strongly aggregated proteins. J Biol Chem. 2005;280:34940-5.

(10) Beinker P, Schlee S, Groemping Y, Seidel R, Reinstein J. The N Terminus of ClpB from Thermus thermophilus Is Not Essential for the Chaperone Activity. J Biol Chem. 2002;277:47160-6.

(11) Mogk A, Schlieker C, Strub C, Rist W, Weibezahn J, Bukau B. Roles of individual domains and conserved motifs of the AAA+ chaperone ClpB in oligomerization, ATP-hydrolysis and chaperone activity. J Biol Chem. 2003;278:15-24.

(11) Weibezahn J, Tessarz P, Schlieker C, Zahn R, Maglica Z, Lee S, et al. Thermotolerance Requires Refolding of Aggregated Proteins by Substrate Translocation through the Central Pore of ClpB. Cell. 2004;119:653-65.

Author response:

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

Reviewer #1 (Public Review):

Summary - This study was designed to investigate changes in gene expression and associated chromatin accessibility patterns in spermatogonia in mice at different postnatal stages from pups to adults. The objective was to describe dynamic changes in these patterns that potentially correlate with functional changes in spermatogonia as a function of development and reproductive maturation. The potential utility of this information is to serve as a reference against which similar data from animals subjected to various disruptive environmental influences can be compared.

Major Strengths and Weaknesses of the Methods and Results - A strength of the study is that it reviews previously published datasets describing gene expression and chromatin accessibility patterns in mouse spermatogonia. A weakness of the study is that it is not clear what new information is provided by the data provided that was not already known from previously published studies (see below). Specific weaknesses include the following:

• Terminology - in the Abstract and first part of the Introduction the authors use the generic term "spermatogonial cells" in a manner that seems to be referring primarily to spermatogonial stem cells (SSCs) but initially ignores the well-known heterogeneity among spermatogonia - particularly the fact that only a small proportion of developing spermatogonia become SSCs - and ONLY those SSCs and NOT other developing spermatogonia - support steady-state spermatogenesis by retaining the capacity to either self-renew or contribute to the differentiating spermatogenic lineage throughout the male reproductive lifespan. The authors eventually mention other types of developing male germ cells, but their description of prospermatogonial stages that precede spermatogonial stages is deficient in that M-prospermatogonia - which occur after PGCs but before T1-prospermatogonia - are not mentioned. This description also seems to imply that all T2-prospermatogonia give rise to SSCs which is far from the case. It is the case that prospermatogonia give rise to spermatogonia, but only a very small proportion of undifferentiated spermatogonia form the foundational SSCs and ONLY SSCs possess the capacity to either self-renew or give rise to sequential waves of spermatogenesis.

We thank Reviewer 1 for the comments and clarifications. As suggested in the previous revision, we use the term spermatogonial cells (SPGs) to make it clear that our cell preparations do not exclusively contain SSCs but all SPGs since they derive from a FACS enrichment strategy. This is explained in the manuscript. Further, we conducted deconvolution analyses on the datasets to examine the composition of the enriched SPGs preparations and provide new sequencing information confirming the presence of SSCs and differentiating SPGs.

• Introduction - Statements regarding distinguishing transcriptional signatures in spermatogonia at different postnatal stages appear to refer to ALL subtypes of spermatogonia present at each stage collectively, thereby ignoring the well-known fact that there are distinct spermatogonial subtypes present at each postnatal stage and that some of those occur at certain stages but not at others. This brings into question the usefulness of the authors' discussion of what types of genes are expressed and/or what types of changes in chromatin accessibility are detected in spermatogonia at each stage.

We agree that our data do not provide information about the transcriptional program of each subtype of SPGs. Rather they provide information about the dynamics of transcriptional programs in the transition from postnatal stage to adulthood in an enriched population of SPGs. The datasets are comprehensive and contain mRNA and non-coding RNA (with and without a polyA+ tail), which provides more precise transcriptomic information than classical single cell methods.

• Methodology - The authors based recovery (enrichment) of spermatogonia from male pups on FACS sorting for THY1 and RMV-1. While sorting total testis cells for THY1+ cells does enrich for spermaogonia, this approach is now known to not be highly specific for spermatogonia (somatic cells are also recovered) and definitely not for SSCs. There are more effective means for isolating SSCs from total testis cells that have been validated by transplantation experiments (e.g. use of the Id4/eGFP transgene marker).

We acknowledge the technical limitations of our enrichment strategy and made them clear in our revised manuscript.

The authors then used "deconvolution" of bulk RNA-seq data in an attempt to discern spermatogonial subtype-specific transcriptomes. It is not clear why this is necessary or how it is beneficial given the availability of multiple single-cell RNA-seq datasets already published that accomplish this objective quite nicely - as the authors essentially acknowledge. Beyond this concern, a potential flaw with the deconvolution of bulk RNA-seq data is that this is a derivative approach that requires assumptions/computational manipulations of apparent mRNA abundance estimates that may confound interpretation of the relative abundance of different cellular subtypes within the hetergeneous cell population from which the bulk RNA-seq data is derived. Bottom line, it is not clear that this approach affords any experimental advantage over use of the publicly available scRNA-seq datasets and it is possible that attempts to employ this approach may be flawed yielding misleading data.

The deconvolution analyses were necessary to address the question of the cell composition of our preparations raised by reviewers. These analyses were highly beneficial because they clarify the presence of different SPGs including SSCs in the samples. They are also advantageous because the datasets they are conducted upon have significantly higher sequencing coverage than published single cell datasets. They contain the full transcriptome and not just polyA+ transcripts as 10x datasets thus they provide considerably richer and more comprehensive transcriptomic information. This is very important to correctly interpret the results and to gain additional biological information. For the deconvolution analyses, we used state-of-the-art methods with proper computational controls for calibration. We selected published single-cell RNA-seq datasets of the highest quality. These analyses are extremely useful because they confirm the predominance of SSCs in the postnatal and adult cell samples and a minimal contamination by somatic cells. Our approach also provides a useful workflow that can easily be used by other researchers who cannot afford single-cell RNA-seq and allow them gain more information about the cellular composition of their samples. Finally, the execution of any computational analyses, including analyses of single-cell RNA-seq datasets requires to make assumptions during the development and the use of a method. The assumptions made for deconvolution analyses are not special in this respect and do not introduce more confounds than other methods. What is critical for such analyses is to include proper controls for calibration, which we carefully did and validated using our own previously published datasets for Sertoli cells.

• Results & Discussion - In general, much of the information reported in this study is not novel. The authors' discussion of the makeup of various spermatogonial subtypes in the testis at various ages does not really add anything to what has been known for many years on the basis of classic morphological studies. Further, as noted above, the gene expression data provided by the authors on the basis of their deconvolution of bulk RNA-seq data does not add any novel information to what has been shown in recent years by multiple elegant scRNA-seq studies - and, in fact, as also noted above - represents an approach fraught with potential for misleading results. The potential value of the authors' report of "other cell types" not corresponding to major somatic cell types identified in earlier published studies seems quite limited given that they provide no follow-up data that might indicate the nature of these alternative cell types. Beyond this, much of the gene expression and chromatin accessibility data reported by the authors - by their own admission given the references they cite - is largely confirmatory of previously published results. Similarly, results of the authors' analyses of putative factor binding sites within regions of differentially accessible chromatin also appear to confirm previously reported results. Ultimately, it is not at all novel to note that changes in gene expression patterns are accompanied by changes in patterns of chromatin accessibility in either related promoters or enhancers. The discussion of these observations provided by the authors takes on more of a review nature than that of any sort of truly novel results. As a result, it is difficult to discern how the data reported in this manuscript advance the field in any sort of novel or useful way beyond providing a review of previously published studies on these topics.

• Likely impact - The likely impact of this work is relatively low because, other than the value it provides as a review of previously published datasets, the new datasets provided are not novel and so do not advance the field in any significant manner.

We acknowledge that much of the reported information is not novel but this is not necessarily a drawback as sequencing datasets on the same tissues or cells produced by different groups using comparable methods are common. This does not diminish the validity and usefulness of the datasets but rather enriches the respective fields as omics methods and data analyses can deliver different findings. Thus, our study cannot be criticized and disqualified because other datasets have been published but instead it should be acknowledged for providing high resolution full transcriptome information from different stages and adult of SCs that other studies do not provide. In this respect, the subjective nature of Reviewer 1’s statements is of concern. For instance, the statement: “…represents an approach fraught with potential for misleading results”. Such declaration suggests that all studies that previously used enrichment strategies are “fraught with potential for misleading results», which disqualifies the work of many colleagues. Further, this wrongly assumes that newer technologies are exempt of “potential for misleading results» which is not the case. Single-cell RNA-seq methods, extensively used to study SPGs, has been questioned for their limitation and potential biases due to low sequencing coverage, issues with transcript detection, low capture efficiency and higher degree of noise than bulk RNA datasets. Thus, caution is needed to interpret single-cell datasets on SPGs and these datasets also have their biases. For our datasets, we made major efforts to address the criticisms raised by the reviewer and reduce any potential misleading information by conducting additional analyses, by providing more details on the methods and enrichment strategy and by being careful with data interpretation. We would be grateful if these efforts could be acknowledged and the improvements on the manuscript and the value of the datasets be evaluated with objectivity.

Reviewer #2 (Public Review):

This revised manuscript attempts to explore the underlying chromatin accessibility landscape of spermatogonia from the developing and adult mouse testis. The key criticism of the first version of this manuscript was that bulk preparations of mixed populations of spermatogonia were used to generate the data that form the basis of the entire manuscript. To address this concern, the authors applied a deconvolution strategy (CIBERSORTx (Newman et al., 2019)) in an attempt to demonstrate that their multi-parameter FACS isolation (from Kubota 2004) of spermatogonia enriched for PLZF+ cells recovered spermatogonial stem cells (SSCs). PLZF (ZBTB16) protein is a transcription factor known to mark all or nearly all undifferentiated spermatogonia and some differentiating spermatogonia (KIT+ at the protein level) - see Niedenberger et al., 2015 (PMID: 25737569). The authors' deconvolution using single-cell transcriptomes produced at postnatal day 6 (P6) argue that 99% of the PLZF+ spermatogonia at P8 are SSCs, 85% at P15 and 93% in adults. Quite frankly given the established overlap between PLZF and KIT and known identity of spermatogonia at these developmental stages, this is impossible. Indeed - the authors' own analysis of the reference dataset demonstrates abundant PLZF mRNA in P6 progenitor spermatogonia - what is the authors' explanation for this observation? The same is essentially true in the use of adult references for celltype assignment. The authors found 63-82% of SSCs using this different definition of types (from a different dataset), begging the question of which of these results is true.

For full transparency, we provided information about the deconvolution analyses for all libraries that use cell-type specific matrices generated from PND6 and adult single-cell RNA-seq reference datasets in our previous response (Fig1-3, response to reviewer 1). However, we don’t claim “that 99% of the PLZF+ spermatogonia at P8 are SSCs, 85% at P15 and 93% in adults”. Of these percentages, the ones that correspond to our postnatal libraries are the ones reported in our updated manuscript (Please see FigS2). Importantly, we never claimed that these percentages correspond to “PLZF+ spermatogonia», exclusively. Rather, they were inferred using gene expression-specific signature matrices (Fig1-c response to Reviewer 1 as example). As clearly evident in feature maps in FigS2 of our updated manuscript, the cellular population identified as SSCs using the dataset from Hermann et al., 2018 shows overlap for the expression of Ddx4, Zbtb16 (PLZF), Gfra1 and Id4 but minimal Kit. In agreement with the reviewer’s observation, progenitors also show a signal for Zbtb16 but have a different gene expression signature matrix (see Fig.1c and 2c for an example of gene signature matrices from PND6 and adult samples from the same publication).

Regarding the question of which of these results are true, we observed that deconvolution analyses of our postnatal libraries using two different single-cell postnatal RNA-seq reference datasets consistently suggest a high contribution (>90%) by SSCs (defined using cell-specific expression matrices following identification of cell-types that match the closest ones reported by each study (See FigS2 updated manuscript). The analyses of our adult libraries using published adult datasets from the same group (Hermann et al., 2018; Fig1 response to Reviewer 1 and FigS2 updated manuscript) suggest that the contribution of adult SSCs to the cell population is lower than at postnatal stages, but SSCs still are the most abundant cell stage identified in our libraries (FigS2g). We reported these analyses and acknowledge that in our adult samples, we also likely have differentiating SPGs.

In their rebuttal, the authors also raise a fair point about the precision of differential gene expression among spermatogonial subsets. At the mRNA level, Kit is definitely detectable in undifferentiated spermatogonia, but it is never observed at the protein level until progenitors respond to retinoic acid (see Hermann et al., 2015). I agree with the authors that the mRNAs for "cell type markers" are rarely differentially abundant at absolute levels (0 or 1), but instead, there are a multitude of shades of grey in mRNA abundance that "separate" cell types, particularly in the male germline and among the highly related spermatogonial subtypes of interest (SSCs, progenitor spermatogonia and differentiating spermatogonia). That is, spermatogonial biology should be considered as a continuous variable (not categorical), so examining specific cell populations with defined phenotypes (markers, function) likely oversimplifies the underlying heterogeneity in the male germ lineage. But, here, the authors have ignored this heterogeneity entirely by selecting complex populations and examining them in aggregate. We already know that PLZF protein marks a wide range of spermatogonia, complicating the interpretation of aggregate results emerging from such samples. In their rebuttal, the authors nicely demonstrate the existence of these mixtures using deconvolution estimation. What remains a mystery is why the authors did not choose to perform single-cell multiome (RNA-seq + ATAC-seq) to validate their results and provide high-confidence outcomes. This is an accessible technique and was requested after the initial version, but essentially ignored by the authors.

We agree with the reviewer that the male germ lineage should be considered as a continuous variable and that examining specific cell populations with defined features oversimplifies its heterogeneity. Regarding the use of single-cell multiome (RNA-seq + ATAC-seq), we also agree that this technology can provide additional insight by integrating RNA and chromatin accessibility in the same cells. However, it is an refined method that is expensive, time consuming and requires human resources that are beyond our capacity for this project.

A separate question is whether these data are novel. A prior publication by the Griswold lab (Schleif et al., 2023; PMID: 36983846) already performed ATAC-seq (and prior data exist for RNA-seq) from germ cells isolated from synchronized testes. These existing data are higher resolution than those provided in the current manuscript because they examine germ cells before and after RA-induced differentiation, which the authors do not base on their selection methods. Another prior publication from the Namekawa lab extensively examined the transcriptome and epigenome in adult testes (Maezawa et al., 2000; PMID: 32895557; and several prior papers). The authors should explain how their results extend our knowledge of spermatogonial biology in light of the preceding reports.

Our data do extend previous studies because they provide high-resolution transcriptomic (full transcriptome) and chromatin accessibility profiling in postnatal and adult stages. They now also provide an approach for deconvolution analyses of bulk RNA datasets that can be of use to the community. Novelty in the field of omics is usually not a prime feature and it is common that datasets on the same tissues or cells be published by different groups using comparable methods and analyses.

The authors are also encouraged to improve their use of terminology to describe the samples of interest. The mitotic male germ cells in the testis are called spermatogonia (not spermatogonial cells, because spermatogonia are cells). Spermatogonia arise from Prospermatogonia. Spermatogonia are divisible into two broad groups: undifferentiated spermatogonia (comprised of few spermatogonial stem cells or SSCs and many more progenitor spermatogonia - at roughly 1:10 ratio) and differentiating spermatogonia that have responded to RA. The authors also improperly indicate that SSCs directly produce differentiating spermatogonia - indeed, SSCs produce transit-amplifying progenitor spermatogonia, which subsequently differentiate in response to retinoic acid stimulation. Further, the use of Spermatogonial cells (and SPGs) is imprecise because these terms do not indicate which spermatogonia are in question. Moreover, there have been studies in the literature which have used similar terms inappropriately to refer to SSCs, including in culture. A correct description of the lineage and disambiguation by careful definition and rigorous cell type identification would benefit the reader.

Overall, my concern from the initial version of this manuscript stands - critical methodological flaws prevent interpretation of the results and the data are not novel. Readers should take note that results in essentially all Figures do not reflect the biology of any one type of spermatogonium.

We revised and improved the terminology wherever possible and also considering requests from other reviewers about terminology.

Reviewer #3 (Public Review):

In this study, Lazar-Contes and colleagues aimed to determine whether chromatin accessibility changes in the spermatogonial population during different phases postnatal mammalian testis development. Because actions of the spermatogonial population set the foundation for continual and robust spermatogenesis and the gene networks regulating their biology are undefined, the goal of the study has merit. To advance knowledge, the authors used mice as a model and isolated spermatogonia from three different postnatal developmental age points using cell sorting methodology that was based on cell surface markers reported in previous studies and then performed bulk RNA-sequencing and ATAC-sequencing. Overall, the technical aspects of the sequencing analyses and computational/bioinformatics seems sound but there are several concerns with the cell population isolated from testes and lack of acknowledgement for previous studies that have also performed ATAC-sequencing on spermatogonia of mouse and human testes. The limitations, described below, call into question validity of the interpretations and reduce the potential merit of the findings.

I suggest changing the acronym for spermatogonial cells from SC to SPG for two reasons. First, SPG is the commonly used acronym in the field of mammalian spermatogenesis. Second, SC is commonly used for Sertoli Cells.

This was suggested in the previous review by Reviewer 1 and was modified in the revised version of the manuscript.

The authors should provide a rationale for why they used postnatal day 8 and 15 mice. The FACS sorting approach used was based on cell surface proteins that are not germline specific so there was undoubtedly somatic cells in the samples used for both RNA and ATAC sequencing. Thus, it is essential to demonstrate the level of both germ cell and undifferentiated spermatogonial enrichment in the isolated and profiled cell populations. To achieve this, the authors used PLZF as a biomarker of undifferentiated spermatogonia. Although PLZF is indeed expressed by undifferentiated spermatogonia, there have been several studies demonstrating that expression extends into differentiating spermatogonia. In addition, PLZF is not germ cell specific and single cell RNA-seq analyses of testicular tissue has revealed that there are somatic cell populations that express Plzf, at least at the mRNA level. For these reasons, I suggest that the authors assess the isolated cell populations using a germ cell specific biomarker such as DDX4 in combination with PLZF to get a more accurate assessment of the undifferentiated spermatogonial composition. This assessment is essential for interpretation of the RNA-seq and ATAC-seq data that was generated.

A previous study by the Namekawa lab (PMID: 29126117) performed ATAC-seq on a similar cell population (THY1+ FACS sorted) that was isolated from pre-pubertal mouse testes. It was surprising to not see this study referenced to in the current manuscript. In addition, it seems prudent to cross-reference the two ATAC-seq datasets for commonalities and differences. In addition, there are several published studies on scATAC-seq of human spermatogonia that might be of interest to cross-reference with the ATAC-seq data presented in the current study to provide an understanding of translational merit for the findings.

These points have been addressed in our previous response and in the revised manuscript.

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

Reviewer #1:

Weaknesses:

There appears to be a lack of basic knowledge of the process of spermatogenesis. For instance, the statement that "During the first week of postnatal life, a population of SCs continues to proliferate to give rise to undifferentiated Asingle (As), Apaired (Apr) and Aaligned (Aal) cells. The remaining SCs differentiate to form chains of daughter cells that become primary and secondary spermatocytes around postnatal day (PND) 10 to 12." is inaccurate. The Aal cells are the spermatogonial chains, the two are not distinct from one another. In addition, the authors fail to mention spermatogonial stem cells which form the basis for steady-state spermatogenesis. The authors also do not acknowledge the well-known fact that, in the mouse, the first wave of spermatogenesis is distinct from subsequent waves. Finally, the authors do not mention the presence of both undifferentiated spermatogonia (aka - type A) and differentiating spermatogonia (aka - type B). The premise for the study they present appears to be the implication that little is known about the dynamics of chromatin during the development of spermatogonia. However, there are published studies on this topic that have already provided much of the information that is presented in the current manuscript.

Regarding the inaccuracy and incompleteness of some of the statements about spermatogonial cells and spermatogenesis. In the Introduction, we replaced the following statement: "During the first week of postnatal life, a population of SCs continues to proliferate to give rise to undifferentiated Asingle (As), Apaired (Apr) and Aaligned (Aal) cells. The remaining SCs differentiate to form chains of daughter cells that become primary and secondary spermatocytes around postnatal day (PND) 10 to 12." by: “Spermatogonial cells (SPGs) are the initiators and supporting cellular foundation of spermatogenesis in testis in many species, including mammals. In the mammalian testis, the founding germ cells are primordial germ cells (PGCs), which give rise sequentially to different populations of SPGs : primary transitional (T1)-prospermatogonia (ProSG), secondary transitional (T2)-ProSG, and then spermatogonial stem cells (SSCs) (McCarrey, 2013; Rabbani et al., 2022; Tan et al., 2020). The ProSG population is exhausted by postnatal day (PND) 5 (Drumond et al., 2011) and by PND6-8, distinct SPGs subtypes can be distinguished on the basis of specific marker proteins and regenerative capacity (Cheng et al., 2020; Ernst et al., 2019; Green et al., 2018; Hermann et al., 2018; Tan et al., 2020).

SSCs represent an undifferentiated population of SPGs that retain regenerative capacity and divide to either self-renew or generate progenitors that initiate spermatogenic differentiation, giving rise to differentiating SPGs (diff-SPGs ). Diff-SPGs form chains of daughter cells that become primary and secondary spermatocytes around PND10 to 12. Spermatocytes then undergo meiosis and give rise to haploid spermatids that develop into spermatozoa. Spermatozoa are then released into the lumen of seminiferous tubules and continue to mature in the epididymis until becoming capable of fertilization by PND42-48 in mice  (Kubota and Brinster, 2018; Rooij, 2017).”

Regarding the premise and implications of our findings. We clarified the premise of our finding in the revised manuscript. The following statement was included in the Discussion: "our findings complement existing datasets on spermatogonial cells by providing parallel transcriptomic and chromatin accessibility maps at high resolution from the same cell populations at early postnatal, late postnatal and adult stages collected from single individuals (for adults)".  

It is not clear which spermatogonial subtype the authors intended to profile with their analyses. On the one hand, they used PLZF to FACS sort cells. This typically enriches for undifferentiated spermatogonia. On the other hand, they report detection in the sorted population of markers such as c-KIT which is a well-known marker of differentiating spermatogonia, and that is in the same population in which ID4, a well-known marker of spermatogonial stem cells, was detected. The authors cite multiple previously published studies of gene expression during spermatogenesis, including studies of gene expression in spermatogonia. It is not at all clear what the authors' data adds to the previously available data on this subject.

The authors analyzed cells recovered at PND 8 and 15 and compared those to cells recovered from the adult testis. The PND 8 and 15 cells would be from the initial wave of spermatogenesis whereas those from the adult testis would represent steady-state spermatogenesis. However, as noted above, there appears to be a lack of awareness of the well-established differences between spermatogenesis occurring at each of these stages.

We applied computational deconvolution to our bulk RNA-seq datasets, employing publicly available single-cell RNA-seq datasets, to estimate and identify cellular composition. Trained on high-quality RNA-seq datasets from pure or single-cell populations, deconvolution algorithms create expression matrices reflecting the cellular diversity in reference datasets. These cell-type-specific expression matrices are subsequently used to determine the cellular composition of bulk RNA-seq samples with unknown cellular components (Cobos et al., 2023).

For our analysis, we chose CIBERSORTx (Newman et al., 2019), recognized as the most advanced deconvolution algorithm to date, employing it with three high-quality, publicly available single-cell RNA-seq datasets. First, we assessed the cellular composition of all our RNA-seq libraries, using datasets generated by (Hermann et al., 2018) which characterized the single-cell transcriptomes of testicular cells and various populations of spermatogonial progenitor cells (SPGs) in early postnatal (PND6) and adult stages. This enabled us to not only address potential somatic cell contamination but also to analyse the composition of isolated SPGs using a unified dataset source.

Author response image 1.

Deconvolution analysis of bulk RNA-seq samples using PND6 single-cell RNA seq from Hermann et al, 2018 a. Seurat clusters from PND6 single-cell RNA-seq. b. Feature maps of gene expression for markers of SPGs and somatic cells. c. Gene expression signature matrix from PND6  single-cell RNA-seq datasets. d. Barplot of estimated cellular proportions for all bulk RNA-seq libraries reported in this study. e. Dotplot of the average estimated proportion of SSCs in all bulk RNA-seq libraries reported in this study.

By re-analyzing the single-cell RNA-seq datasets, we identified distinct cell-type clusters, marked by specific cellular markers as reported in the original and subsequent studies (Author response image 1a,b and Author response image 2a,b). Then, CIBERSORTx generated gene-expression signature matrices and estimated the cell-type proportions within our 18 bulk RNA-seq libraries. Evaluation of our postnatal libraries (PND8 and 15) against a PND6 signature matrix revealed a predominant derivation from SPGs, with average estimated proportions of spermatogonial stem cells (SSCs) being 0.99 and 0.85 for PND8 and PND15 samples, respectively (Author response image 1c-e). Notably, the analysis of PND15 libraries also suggested the presence of additional SPGs types, including progenitors and differentiating SPGs (Author response image 1d), albeit at lower frequency. 

Similarly, evaluation of our adult RNA-seq libraries, using an adult signature matrix, showed an average SSC proportion of 0.82, indicating a primary derivation from SSC cells. Consistent with the findings from PND15 libraries, our deconvolution analysis also suggests the presence of additional SPG types, including progenitors and differentiating SPGs (Author response image 1d). However, unlike our early and late postnatal stage libraries, the deconvolution analysis of adult libraries indicated the presence of other cell types (labeled "Other"), not corresponding to the major somatic cell types identified by Hermann et al. 2018. The estimated average proportion of these cells was less than 0.05 in two adult libraries and 0.10 in the others. This variance in cellular composition underlines the deconvolution method's effectiveness in dissecting complex cellular compositions in bulk RNA-seq samples.

Author response image 2.

Deconvolution analysis of bulk RNA-seq samples using Adult single-cell RNA seq (Hermann et al, 2018) a. Seurat clusters from Adult single-cell RNA-seq. b. Feature maps of gene expression for markers of SPG and somatic cells. c. Gene expression signature matrix from Adult single-cell RNA-seq datasets. d. Barplot of estimated cellular proportions for all bulk RNA-seq libraries reported in this study. e. Dotplot of the average estimated proportion of SSCs in all bulk RNA-seq libraries reported in this study.

To further validate our observations, we re-analyzed two additional testicular single-cell RNA-seq datasets derived from an early postnatal stage (PND7) (Tan et al., 2020) and adult (Green et al., 2018) (Author response image 3a,b and Author response image 4a,b). We identified distinct cell-type clusters, marked by specific cellular markers (Author response image 3a,b and Author response image 4a,b), and proceeded with the deconvolution analysis using CIBERSORTx. Evaluation of our postnatal libraries (PND8 and 15) against the PND7 signature matrix from Tan et al., 2020 confirmed a derivation from germ cells (Author response image 3d,e), in particular from SSCs (Author response image 3g), with average estimated proportions of SSCs being 0.93 and 0.86 for PND8 and PND15 samples, respectively, and the rest estimated to be in origin from differentiating SPGs (Author response image 3g,h). In the case of the adult samples, evaluation against the adult signature matrix from Green et al., 2018 confirmed a predominant derivation from SSCs, with average estimated proportions of SSCs being 0.79, consistent with the 0.82 estimated proportion from Hermann et al., 2018. 

Author response image 3.

Deconvolution analysis of bulk RNA-seq samples with additional single-cell datasets. Seurat clusters from PND7 single-cell RNA-seq (Tang 2020). b. Barplot of estimated cellular proportions for all bulk RNA-seq libraries reported in this study. c. Dotplot of the average estimated proportion of germ cells in all bulk RNA-seq libraries reported in this study. d. Re-clustering of germ cell cluster shown in a. e. Barplot of estimated cellular proportions for all bulk RNA-seq libraries reported in this study. f. Dotplot of the average estimated proportion of SSCs in all bulk RNA-seq libraries reported in this study. g. Seurat clusters from adult single-cell RNA-seq (Green et al., 2018). h. Barplot of estimated cellular proportions for all bulk RNA-seq libraries reported in this study. i. Dotplot of the average estimated proportion of germ cells in all bulk RNA-seq libraries reported in this study.

To further validate our deconvolution strategy, we interrogated the cellular composition of bulk RNA-seq libraries derived from cellular populations enriched in Sertoli cells, generated by our group using a similar enrichment/sorting strategy (Thumfart et al., 2022). As expected, our results show that all our libraries are mainly composed of Sertoli cells suggesting that the deconvolution strategy employed is accurate in detecting cell-type composition (Author response image 4).

Author response image 4.

Deconvolution analysis of Sertoli bulk RNA-seq samples. Barplots of estimated cellular proportions for bulk RNAseq libraries reported in Thumfart et al., 2022. Expression matrices were derived from the analysis of single-cell RNA-seq datasets used to asses cellular composition of the SPGs bulk libraries.

Author response image 5.

Id4 and Kit are transcribed in SSCs. Seurat clusters from PND6 single-cell RNA-seq (left) and feature maps of gene expression for Id4 (center) and Kit (right). Zoom in into SSCs (red).

Finally, regarding the following observation by the reviewer: "On the other hand, they report detection in the sorted population of markers such as c-KIT which is a well-known marker of differentiating spermatogonia, and that is in the same population in which ID4, a well-known marker of spermatogonial stem cells, was detected." It was recently shown using single-cell RNA that “nearly all differentiating spermatogonia at P3 (delineated as c-KIT+) are ID4-eGFP” (Law et al., 2019).  While this finding does not exclude the fact that we have a mixture of SPGs cells, this finding supports the possibility that SPG cells express both markers of undifferentiated and differentiated cells, particularly in the early stages of postnatal development. Indeed, we observe that some cells labeled as SSC show signals for both Id4 and Kit in single-cell RNA-seq data from Hermann et al., 2018 (Author response image 5).

Therefore, the results from the deconvolution analysis and our immunofluorescence data showing 85-95% PLZF+  cells in our cellular preparations underscore that our bulk RNA-seq libraries are mainly composed of SPGs. The deconvolution analysis also suggests a predominantly cellular composition of SSCs and to a lesser degree of differentiating SPGs. Our adult RNA-seq libraries show a small proportion of somatic cells (<0.10). 

In the revised manuscript, we compiled the deconvolution analyses and present them in a condensed version in Supplementary Fig 2. 

In general, the authors present observational data of the sort that is generated by RNA-seq and ATAC-seq analyses, and they speculate on the potential significance of several of these observations. However, they provide no definitive data to support any of their speculations. This further illustrates the fact that this study contributes little if any new information beyond that already available from the numerous previously published RNA-seq and ATAC-seq studies of spermatogenesis. In short, the study described in this manuscript does not advance the field.

We acknowledge that RNA-seq and ATAC-seq datasets like ours are observational and that their interpretation can be speculative. Nevertheless, our datasets represent an additional useful resource for the community because they are comprehensive and high resolution, and can be exploited for instance, for studies in environmental epigenetics and epigenetic inheritance examining the immediate and long-term effects of postnatal exposure and their dynamics. The depth of our RNA sequencing allowed detect transcripts with a high dynamic range, which has been limited with classical RNA sequencing analyses of spermatogonial cells and with single-cell analyses (which have comparatively low coverage). Further, our experimental pipeline is affordable (more than single cell sequencing approaches) and in the case of adults, provides data per animal informing on the intrinsic variability in transcriptional and chromatin regulation across males. These points will be discussed in the revised manuscript.

In general, the authors present observational data of the sort that is generated by RNA-seq and ATAC-seq analyses, and they speculate on the potential significance of several of these observations. However, they provide no definitive data to support any of their speculations. This further illustrates the fact that this study contributes little if any new information beyond that already available from the numerous previously published RNA-seq and ATAC-seq studies of spermatogenesis. In short, the study described in this manuscript does not advance the field.

Relevant information for both points was included in the Discussion of the revised manuscript.  

The phenomenon of epigenetic priming is discussed, but then it seems that there is some expression of surprise that the data demonstrate what this reviewer would argue are examples of that phenomenon. The authors discuss the "modest correspondence between transcription and chromatin accessibility in SCs." Chromatin accessibility is an example of an epigenetic parameter associated with the primed state. The primed state is not fully equivalent to the actively expressing state. It appears that certain histone modifications along with transcription factors are critical to the transition between the primed and actively expressing states (in either direction). The cell types that were investigated in this study are closely related spermatogenic, and predominantly spermatogonial cell types. It is very likely that the differentially expressed loci will be primed in both the early (PND 8 or 15) and adult stages, even though those genes are differentially expressed at those stages. Thus, it is not surprising that there is not a strict concordance between +/- chromatin accessibility and +/- active or elevated expression.

Relevant information was included in the Discussion of the revised manuscript.

Reviewer #2:

The objective of this study from Lazar-Contes et al. is to examine chromatin accessibility changes in "spermatogonial cells" (SCs) across testis development. Exactly what SCs are, however, remains a mystery. The authors mention in the abstract that SCs are undifferentiated male germ cells and have self-renewal and differentiation activity, which would be true for Spermatogonial STEM Cells (SSCs), a very small subset of total spermatogonia, but then the methods they use to retrieve such cells using antibodies that enrich for undifferentiated spermatogonia encompass both undifferentiated and differentiating spermatogonia. Data in Fig. 1B prove that most (85-95%) are PLZF+, but PLZF is known to be expressed both by undifferentiated and differentiating (KIT+) spermatogonia (Niedenberger et al., 2015; PMID: 25737569). Thus, the bulk RNA-seq and ATAC-seq data arising from these cells constitute the aggregate results comprising the phenotype of a highly heterogeneous mixture of spermatogonia (plus contaminating somatic cells), NOT SSCs. Indeed, Fig. 1C demonstrates this by showing the detection of Kit mRNA (a well-known marker of differentiating spermatogonia - which the authors claim on line 89 is a marker of SCs!), along with the detection of markers of various somatic cell populations (albeit at lower levels).

The reviewer is correct that our spermatogonial cell populations are mixed and include undifferentiated and differentiated cells, hence the name of spermatogonia (SCs), and probably also contains some somatic cells. We acknowledge that this is a limitation of our isolation approach. To circumvent this limitation, we will conduct in silico deconvolution analysis using publicly available single-cell RNA sequencing datasets to obtain information about markers corresponding to undifferentiated and differentiated spermatogonia cells, and somatic cells. These additional analyses will provide information about the cellular composition of the samples and clarify the representation of undifferentiated and differentiated spermatogonial cells and other cells.

This admixture problem influences the results - the authors show ATAC-seq accessibility traces for several genes in Fig. 2E (exhibiting differences between P15 and Adult), including Ihh, which is not expressed by spermatogenic cells, and Col6a1, which is expressed by peritubular myoid cells. Thus, the methods in this paper are fundamentally flawed, which precludes drawing any firm conclusions from the data about changes in chromatin accessibility among spermatogonia (SCs?) across postnatal testis development.

The reviewer raises concern about the lack of correspondence between chromatin accessibility and expression observed for some genes, arguing that this precludes drawing firm conclusions. However, a dissociation between chromatin accessibility and gene expression is normal and expected since chromatin accessibility is only a readout of protein deposition and occupancy e.g. by transcription factors, chromatin regulators, or nucleosomes, at specific genomic loci that does not give functional information of whether there is ongoing transcriptional activity or not. A gene that is repressed or poised for expression can still show a clear signal of chromatin accessibility at regulatory elements. The dissociation between chromatin accessibility and transcription has been reported in many different cells and conditions (PMID: 36069349, PMID: 33098772) including in spermatogonial cells (PMID: 28985528) and in gonads in different species (PMID: 36323261). Therefore, the dissociation between accessibility and transcription is not a reason to conclude that our data are flawed.

In addition, there already are numerous scRNA-seq datasets from mouse spermatogenic cells at the same developmental stages in question.

This is true but full transcriptomic profiling like ours on cell populations provides different transcriptional information that is deeper and more comprehensive. Our datasets identified >17,000 genes while scRNA-seq typically identifies a few thousand of genes. Our analyses also identified full-length transcripts, variants, isoforms, and low abundance transcripts. These datasets are therefore a valuable addition to existing scRNAseq.

Moreover, several groups have used bulk ATAC-seq to profile enriched populations of spermatogonia, including from synchronized spermatogenesis which reflects a high degree of purity (see Maezawa et al., 2018 PMID: 29126117 and Schlief et al., 2023 PMID: 36983846 and in cultured spermatogonia - Suen et al., 2022 PMID: 36509798) - so this topic has already begun to be examined. None of these papers was cited, so it appears the authors were unaware of this work.

We apologize for not mentioning these studies in our manuscript, we will do so in the revised version.

The authors' methodological choice is even more surprising given the wealth of single-cell evidence in the literature since 2018 demonstrating the exceptional heterogeneity among spermatogonia at these developmental stages (the authors DID cite some of these papers, so they are aware). Indeed, it is currently possible to perform concurrent scATAC-seq and scRNA-seq (10x Genomics Multiome), which would have made these data quite useful and robust. As it stands, given the lack of novelty and critical methodological flaws, readers should be cautioned that there is little new information to be learned about spermatogenesis from this study, and in fact, the data in Figures 2-5 may lead readers astray because they do not reflect the biology of any one type of male germ cell. Indeed, not only do these data not add to our understanding of spermatogonial development, but they are damaging to the field if their source and identity are properly understood. Here are some specific examples of the problems with these data:

Fig. 2D - Gata4 and Lhcgr are not expressed by germ cells in the testis.

Fig. 3A - WT1 is expressed by Sertoli cells, so the change in accessibility of regions containing a WT1 motif suggests differential contamination with Sertoli cells. Since Wt1 mRNA was differentially high in P15 (Fig. 3B) - this seems to be the most likely explanation for the results. How was this excluded?

Fig. 3D - Since Dmrt1 is expressed by Sertoli cells, the "downregulation" likely represents a reduction in Sertoli cell contamination in the adult, like the point above. Did the authors consider this?

Regarding concerns about contamination by somatic cells (Transcription). In addition to the results of our deconvolution analysis (see response to Reviewer #1), we addressed the specific concern of the paradoxical expression of genes considered markers of somatic cells in the testis. For instance, we plotted the expression values of Ihh, Lhcgr, Gata4, Col16a, Wt1, and Dmrt1 along with the expression values of Ddx4 and Zbtb16. We observe that the expression level of Ddx4 and Zbtb16, genes expressed predominantly in SPGs, is orders of magnitude higher than the one observed for the rest of the genes with the notable exception of Dmrt1 which is also highly expressed (Fig.6). Indeed, our analysis of publicly available single-cell RNA-seq datasets shows that Dmrt1 is robustly expressed in germ cells (Author response image 7), and as also noted by the reviewer, in Sertoli cells in postnatal stages. Notably, we observe a significant stepwise decrease in the expression of Dmrt1 across the postnatal maturation of SPG cells. This is highly unlikely to be a result of major contamination by Sertoli cells of just our postnatal libraries. We based this statement on three observations. First, the deconvolution analysis of all our RNA-seq libraries using four different expression signature matrices from high-quality single-cell RNAseq from testis showed that our libraries are largely derived from SPGs. Second, the evaluation of our adult libraries with the PND6 signature matrix from Green et al., 2018 suggested that the proportion of Sertoli cells in our adult libraries, if any, would be higher than in our postnatal libraries (Author response image 3d, blue bars). This makes it unlikely that the observed decrease in expression of Dmrt1 in adult samples is due to prominent somatic contamination of the postnatal libraries. Third, the step-wise decrease in Dmrt1 expression seems to correlate with progression during postnatal development (Author response image 7) as feature maps of Dmrt1 expression derived from public single-cell RNA-seq experiments show a reduction in expression in adult SPGs in comparison with early postnatal stages (Author response image 7 last two panels). Then, the observed effects are likely the result of developmental gene regulatory processes that operate during the developmental maturation of SPGs. 

Author response image 6.

Expression of germ and somatic cell markers in our RNA-seq datasets. Boxplots of log2(CPM) (Top) and CPM (Bottom) values for selected genes from our RNAseq datasets. Each point in boxplots represent the expression value of a biological replicate.

Author response image 7.

Expression of germ and somatic cell markers in publicly available single-cell RNA-seq datasets. Seurat clusters from all analyzed single-cell RNA-seq datasets (first column from left) and feature maps of gene expression for Zbtb16, Dmrt1 and Wt1.

Consistent with the reviewer’s observation, Ihh is not expressed in germ cells and indeed we do not detect signal at this locus nor Lhcgr. Furthermore, while we indeed observe a significant increase in the expression of Wt1 in PND15 samples, its expression level is considerably lower than that of SPG markers. This is even more evident when plotting expression data in a linear scale rather than as a log2 transformation of the expression values. Whether such transcriptional profiles reflect developmentally regulated transcription, stochastic effects on gene expression, or potential somatic contamination is difficult to determine. However, based on our deconvolution data we believe it is unlikely that major contamination could account for our observations. 

Notably, while Wt1 is robustly expressed in nearly all Sertoli cells across postnatal development (Author response image 7), it is also detected in other cell types including SPGs -although in fewer cells and with lower expression levels-, consistent with our observations (Author response image 6 and 8). Therefore, the assignment of a gene as a marker of a particular cell type does not imply that such a gene is expressed uniquely in such cell, rather it is expressed in more cells and likely at higher levels. 

Author response image 8.

Expression of Wt1 in publicly available single-cell RNA-seq datasets. Feature maps of gene expression for Wt1. In dashed boxes, a zoom-in into germ cells cluster that show expression of Wt1 at some of these cells.

Regarding concerns about contamination by somatic cells (chromatin accessibility). In Figure 2 of our manuscript, we show the chromatin accessibility landscape of different genes, including genes either not expressed in testicular cells (Ihh) and those believed to be expressed exclusively in somatic cells (Lhcgr, Gata4, Col16a1, Wt1). For some of these genes, we reported changes in chromatin accessibility at specific sites between PND15 and adults (e.g. Wt1 and Col16a1). The observation of "traces of chromatin accessibility" at these loci and the reported changes in accessibility raised concerns of potential contamination which "fundamentally flaw" our results, as stated by the reviewer. While we acknowledge that all enrichment methods have a margin of potential contamination, we fundamentally disagree with the reviewer's observations. 

The term chromatin accessibility can be misleading. In principle, the term accessibility might suggest the literal lack of protein deposition at a given place in the genome. Rather, chromatin accessibility as evaluated by ATAC- seq (as in this case) must be interpreted as a measure of protein occupancy genome-wide (PMID: 30675018). Depending on the type of fragments analyzed we can obtain information regarding the occupancy of transcription factors (TFs), nucleosomes, and other chromatin-associated proteins that are present at genomic locations at a given time within a population of cells. The detection of chromatin accessibility at a given locus does not necessarily indicate transcription of the gene in a given cell type. A gene can be repressed or poised for expression and still show a clear signal of chromatin accessibility at its regulatory elements or along the gene body. For instance, in agreement with the reviewer's observation, neither Ihh nor Lhcgr is expressed in our datasets (Author response image 6 and Author response image 9), however, they show a distinctive pattern of chromatin accessibility in our datasets and publicly available ATAC-seq data derived from undifferentiated (Id4bright) and differentiating SPGs (Id4-dim) (Cheng et al., 2020) (Author response image 9). A similar argument can be applied regarding other loci such as Wt1 and Col6a1 for which we also observe extremely low levels of transcription. Therefore, the lack of transcription does not exclude that these loci display clear patterns of chromatin accessibility (Author response image 9). Notably, while traces of  chromatin accessibility can also be observed in ATAC-seq datasets from embryonic Sertoli cells (Garcia-Moreno et al., 2019) and other somatic stem cells (hematopoietic stem cells; HSCs) (Xiang et al., 2020) (Author response image 9), the pattern of chromatin accessibility markedly differs with that observed in SPG cells. Therefore, the observed changes in chromatin accessibility are unlikely to result from contaminating somatic cells.

To strengthen our observation, we identified regions of chromatin accessibility in SPGs, Sertoli, and HSCs using both our datasets and publicly available ATAC-seq datasets. Overlap analysis revealed at least four groups of ATAC-seq peaks: 1) peaks shared among all analyzed cell types, 2)peaks shared just among SPG cells, 3) peaks specific to Sertoli cells and 4) peaks specific to HSCs (Author response image 10). Peaks shared among all tested cell-types are predominantly located at promoters of genes involved in translation and DNA replication (GO analysis adj p-value<0.05). In contrast, cell-type specific peaks are localized at intergenic and intragenic regions, suggesting localization at enhancer elements (Author response image 10). Indeed, GO analysis of cell-type specific peaks revealed enrichment for genes involved in male meiosis for SPGs, vesicle-mediated transport for Sertoli cells and in immune system process for HSCs, consistent with cell-type specific functions. If contamination by somatic cells, such as Sertoli cells, would be prominent as stated by the reviewer, we would expect to observe prominent ATAC-seq signal from our datasets at peaks specific to Sertoli cells. Notably, we don't observe ATAC-seq signal at peaks specific for Sertoli cells using our ATAC-seq samples. However, we observe robust signals at shared peaks and peaks specific to SPG cells. This observation, strongly argues against the possibility of major contamination by somatic cells. 

Author response image 9.

Chromatin accessibility profiles at specific loci differ between SPG cells and other cell types. Genome-browser tracks for Ihh, Wt1, Col16a1 and Zbtb16. For each gene, an extended locus view is presented with RNA-seq data (this study) and normalized ATAC-seq tracks from our study and public sources (SPG Id4; GSE131657; Sertoli; GSM3346484; HSC; ENCFF204JEE). Public ATAC-seq datasets were generated enrichment methods similar to the one employed in our study.

Author response image 10.

Shared and cell-type specific ATAC-seq peaks among SPGs, Sertoli and HSC. Up, Normalized ATACseq signal heatmaps of shared and unique ATAC-seq peaks. PND15 and Adult samples are derived from our study. ATAC-seq signal is plotted +/- 500bp from peak center. Bottom, pie charts of ATAC-seq peaks genomic distribution.

Reviewer #3:

In this study, Lazar-Contes and colleagues aimed to determine whether chromatin accessibility changes in the spermatogonial population during different phases of postnatal mammalian testis development. Because actions of the spermatogonial population set the foundation for continual and robust spermatogenesis and the gene networks regulating their biology are undefined, the goal of the study has merit. To advance knowledge, the authors used mice as a model and isolated spermatogonia from three different postnatal developmental age points using a cell sorting methodology that was based on cell surface markers reported in previous studies and then performed bulk RNA-sequencing and ATAC-sequencing. Overall, the technical aspects of the sequencing analyses and computational/bioinformatics seem sound but there are several concerns with the cell population isolated from testes and lack of acknowledgment for previous studies that have also performed ATACsequencing on spermatogonia of mouse and human testes. The limitations, described below, call into question the validity of the interpretations and reduce the potential merit of the findings. I suggest changing the acronym for spermatogonial cells from SC to SPG for two reasons. First, SPG is the commonly used acronym in the field of mammalian spermatogenesis. Second, SC is commonly used for Sertoli Cells.

We thank the reviewer for the suggestion and will rename SCs into SPG cells in the revised manuscript.

The authors should provide a rationale for why they used postnatal day 8 and 15 mice.

We will provide a rationale for the use of postnatal 8 and 15 stages in the revised manuscript. Briefly, these stages are interesting to study because early to mid postnatal life is a critical window of development for germ cells during which environmental exposure can have strong and persistent effects. The possibility that changes in germ cells can happen during this period and persist until adulthood is an important area of research linked to disciplines like epigenetic toxicology and epigenetic inheritance.

The FACS sorting approach used was based on cell surface proteins that are not germline-specific so there were undoubtedly somatic cells in the samples used for both RNA and ATAC sequencing. Thus, it is essential to demonstrate the level of both germ cell and undifferentiated spermatogonial enrichment in the isolated and profiled cell populations. To achieve this, the authors used PLZF as a biomarker of undifferentiated spermatogonia. Although PLZF is indeed expressed by undifferentiated spermatogonia, there have been several studies demonstrating that expression extends into differentiating spermatogonia. In addition, PLZF is not germ-cell specific and single-cell RNA-seq analyses of testicular tissue have revealed that there are somatic cell populations that express Plzf, at least at the mRNA level. For these reasons, I suggest that the authors assess the isolated cell populations using a germ-cell specific biomarker such as DDX4 in combination with PLZF to get a more accurate assessment of the undifferentiated spermatogonial composition. This assessment is essential for the interpretation of the RNA-seq and ATAC-seq data that was generated.

In agreement with the reviewer’s observation, Zbtb16 (PLZF) is expressed in germ cells but also in somatic cells, in particular in the dataset derived from Green et al., 2018 (Author response image 11). However, when evaluating the expression patterns of Ddx4, we noticed that similar to Zbtb16, it is expressed both in the germ line and in the somatic compartment (Author response image 11). Notably, we observe expression of Ddx4 in SSC but also in progenitors and differentiating SPGs (Author response image 11g). These observations suggest that at least at the transcript level, both genes are transcribed in germ cells and to a lesser degree in somatic cells. 

Author response image 11.

Single-cell expression of Ddx4 and Zbtb16. Seurat clusters from all analyzed single-cell RNA-seq datasets (a,c,e,g,i) and feature maps of gene expression for Ddx4 and Zbtb16 (b,d,f,j, h).

Finally, our deconvolution analysis using geneexpression signature matrices for different cellular populations suggest that our RNA-seq and ATAC-seq libraries are largely derived from SPG cells and in particular of SSCs.

Furthermore, while this analysis suggested the presence of somatic cells, their proportion is minimal in comparison with germ cells (Author response images 1-4). This is also supported by ATAC-seq analysis of somatic cells from testis (Author response images 9 and 10). 

A previous study by the Namekawa lab (PMID: 29126117) performed ATAC-seq on a similar cell population (THY1+ FACS sorted) that was isolated from pre-pubertal mouse testes. It was surprising to not see this study referenced in the current manuscript. In addition, it seems prudent to cross-reference the two ATAC-seq datasets for commonalities and differences. In addition, there are several published studies on scATACseq of human spermatogonia that might be of interest to cross-reference with the ATAC-seq data presented in the current study to provide an understanding of translational merit for the findings.

We compared our ATAC-seq datasets with the ones from (Maezawa et al., 2017) and those from (Cheng et al., 2020). All these datasets were generated from FACSs sorted cells enriched for undifferentiating and differentiating SPGs. Sequencing files from Cheng et al, 2020 were equally processed as described in out methods section, while our pipeline was adjusted to process files from Maezawa et al., 2018 as they were single-end sequencing files. We generated a reference set of peaks from SPGs and calculated signal scores for all peaks across all samples. Then, calculated the Pearson correlation for all pairwise comparisons and generated a heatmap of correlations (Author response image 12). Two clusters emerge that separate the SPG samples from the pachytene spermatocytes and round spermatids reported by Maezawa et al., 2018. As expected SPG samples clustered together based on study of origin. Consistently, our postnatal samples formed one cluster next to but separated from the adult one. Similarly, the id4-bright samples clustered together and next to the id4-sim and the sample applied for the Thy1 and cKit samples. Notably, our samples and the ones from Cheng et al., 2020 have a higher correlation with each other when compared with the ones from Maezawa et al., 2018. Given the fundamental difference in library sequencing (single-end instead of the widely used paired-end for ATAC-seq experiments) we reasoned a comparison with the Maezawa et al., 2018 datasets is not optimal. Therefore, this data in addition to the one presented before (see response to Reviewer 1 and 2) strongly supports a predominantly SPG derivation of all our sequencing libraries. 

Author response image 12.

Pearson correlation at the peak level among different ATAC-seq datasets. a) Our ATAC-seq libraries and ATAC-seq libraries from b) Cheng et al., 2020 and c) Maezawa et al., 2020. Thy1-1 and cKit libraries correspond to undifferentiated and differentiating SPGs, respectively. PS, pachytene spermatocytes and RS, round spermatids. Correlation analysis was done using Deeptools.

References

Cheng K, Chen I-C, Cheng C-HE, Mutoji K, Hale BJ, Hermann BP, Geyer CB, Oatley JM, McCarrey JR. 2020. Unique Epigenetic Programming Distinguishes Regenerative Spermatogonial Stem Cells in the Developing Mouse Testis. iScience 23:101596. doi:10.1016/j.isci.2020.101596

Cobos FA, Panah MJN, Epps J, Long X, Man T-K, Chiu H-S, Chomsky E, Kiner E, Krueger MJ, Bernardo D di, Voloch L, Molenaar J, Hooff SR van, Westermann F, Jansky S, Redell ML, Mestdagh P, Sumazin P. 2023. Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes. Genome Biol 24:177. doi:10.1186/s13059-023-03016-6

Drumond AL, Meistrich ML, Chiarini-Garcia H. 2011. Spermatogonial morphology and kinetics during testis development in mice: a high-resolution light microscopy approach. Reproduction 142:145–155. doi:10.1530/rep-10-0431

Ernst C, Eling N, Martinez-Jimenez CP, Marioni JC, Odom DT. 2019. Staged developmental mapping and X chromosome transcriptional dynamics during mouse spermatogenesis. Nat Commun 10:1251. doi:10.1038/s41467-019-09182-1

Garcia-Moreno SA, Futtner CR, Salamone IM, Gonen N, Lovell-Badge R, Maatouk DM. 2019. Gonadal supporting cells acquire sex-specific chromatin landscapes during mammalian sex determination. Dev Biol 446:168–179. doi:10.1016/j.ydbio.2018.12.023

Green CD, Ma Q, Manske GL, Shami AN, Zheng X, Marini S, Moritz L, Sultan C, Gurczynski SJ, Moore BB, Tallquist MD, Li JZ, Hammoud SS. 2018. A Comprehensive Roadmap of Murine Spermatogenesis Defined by Single-Cell RNA-Seq. Dev Cell 46:651-667.e10. doi:10.1016/j.devcel.2018.07.025

Hermann BP, Cheng K, Singh A, Cruz LR-DL, Mutoji KN, Chen I-C, Gildersleeve H, Lehle JD, Mayo M, Westernströer B, Law NC, Oatley MJ, Velte EK, Niedenberger BA, Fritze D, Silber S, Geyer CB, Oatley JM, McCarrey JR. 2018. The Mammalian Spermatogenesis Single-Cell Transcriptome, from Spermatogonial Stem Cells to Spermatids. Cell Rep 25:1650-1667.e8. doi:10.1016/j.celrep.2018.10.026

Kubota H, Brinster RL. 2018. Spermatogonial stem cells†. Biol Reprod 99:52–74. doi:10.1093/biolre/ioy077

Law NC, Oatley MJ, Oatley JM. 2019. Developmental kinetics and transcriptome dynamics of stem cell specification in the spermatogenic lineage. Nat Commun 10:2787. doi:10.1038/s41467-019-10596-0

Maezawa S, Yukawa M, Alavattam KG, Barski A, Namekawa SH. 2017. Dynamic reorganization of open chromatin underlies diverse transcriptomes during spermatogenesis. Nucleic Acids Res 46:gkx1052-. doi:10.1093/nar/gkx1052

McCarrey JR. 2013. Toward a More Precise and Informative Nomenclature Describing Fetal and Neonatal Male Germ Cells in Rodents1. Biol Reprod 89:Article 47, 1-9. doi:10.1095/biolreprod.113.110502

Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, Diehn M, Alizadeh AA. 2019. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol 37:773–782. doi:10.1038/s41587-019-0114-2

Rabbani M, Zheng X, Manske GL, Vargo A, Shami AN, Li JZ, Hammoud SS. 2022. Decoding the Spermatogenesis Program: New Insights from Transcriptomic Analyses. Annu Rev Genet 56:339–368.

doi:10.1146/annurev-genet-080320-040045

Rooij DG de. 2017. The nature and dynamics of spermatogonial stem cells. Development 144:3022–3030. doi:10.1242/dev.146571

Tan K, Song H-W, Wilkinson MF. 2020. Single-cell RNAseq analysis of testicular germ and somatic cell development during the perinatal period. Development 147:dev183251. doi:10.1242/dev.183251

Thumfart KM, Lazzeri S, Manuella F, Mansuy IM. 2022. Long-term effects of early postnatal stress on Sertoli cells. Front Genet 13:1024805. doi:10.3389/fgene.2022.1024805

Xiang G, Keller CA, Heuston EF, Giardine BM, An L, Wixom AQ, Miller A, Cockburn A, Sauria MEG, Weaver K, Lichtenberg J, Göttgens B, Li Q, Bodine D, Mahony S, Taylor J, Blobel GA, Weiss MJ, Cheng Y, Yue F, Hughes J, Higgs DR, Zhang Y, Hardison RC. 2020. An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis. Genome Res 30:gr.255760.119. doi:10.1101/gr.255760.119

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Transcriptional readthrough, intron retention, and transposon expression have been previously shown to be elevated in mammalian aging and senescence by multiple studies. The current manuscript claims that the increased intron retention and readthrough could completely explain the findings of elevated transposon expression seen in these conditions. To that end, they analyze multiple RNA-seq expression datasets of human aging, human senescence, and mouse aging, and establish a series of correlations between the overall expression of these three entities in all datasets.

While the findings are useful, the strength of the evidence is incomplete, as the individual analyses unfortunately do not support the claims. Specifically, to establish this claim there is a burden of proof on the authors to analyze both intron-by-intron and gene-by-gene, using internal matched regions, and, in addition, thoroughly quantify the extent of transcription of completely intergenic transposons and show that they do not contribute to the increase in aging/senescence. Furthermore, the authors chose to analyze the datasets as unstranded, even though strand information is crucial to their claim, as both introns and readthrough are stranded, and if there is causality, than opposite strand transposons should show no preferential increase in aging/senescence. Finally, there are some unclear figures that do not seem to show what the authors claim. Overall, the study is not convincing.

Major concerns: 1) Why were all datasets treated as unstanded? Strand information seems critical, and should not be discarded. Specifically, stranded information is crucial to increase the confidence in the causality claimed by the authors, since readthrough and intron retention are both strand specific, and therefore should influence only the same strand transposons and not the opposite-strand ones.

This is an excellent suggestion. Since only one of our datasets was stranded, we did not run stranded analyses for the sake of consistency. We would like to provide two analyses here that consider strandedness:

First, we find that within the set of all expressed transposons (passing minimal read filtering), 86% of intronic transposons match the strand of the intron (3147 out of 3613). In contrast, the number is 51% after permutation of the strands. Similarly, when we randomly select 1000 intronic transposons 45% match the strandedness of the intron (here we select from the set of all transposons). This is consistent with the idea that most transposons are only detectable because they are co-expressed on the sense strand of other features that are highly expressed.

As for the readthrough data, 287 out of 360 transposons (79%) within readthrough regions matched the strand of the gene and its readthrough.

Second, in the model we postulate, the majority of transposon transcription occurs as a co-transcriptional artifact. This applies equally to genic transposons (gene expression), intronic (intron retention) and gene proximal (readthrough or readin) transposons. Therefore, we performed the following analysis for the set of all transposons in the Fleischer et al. fibroblast dataset.

When we invert the strand annotation for transposons, before counting and differential expression, we would expect the counts and log fold changes to be lower compared to using the “correct” annotation file.

Indeed, we show that out of 6623 significantly changed transposons with age only 226 show any expression in the “inverted run” (-96%). (Any expression is defined as passing basic read filtering.)

Out of the 226 transposons that can be detected in both runs most show lower counts (A) and age-related differential expression converging towards zero (B) in the inverted run (Fig. L1).

Author response image 1.

Transposons with inverted strandedness (“reverse”) show lower expression levels (log counts; A) and no differential expression with age (B) when compared to matched differentially expressed transposons (“actual”). For this analysis we selected all transposons showing significant differential expression with age in the actual dataset that also showed at least minimal expression in the strand-inverted analysis (n=226). Data from Fleischer et al. (2018). (A) The log (counts) are clipped because we only used transposons that passed minimal read filtering in this analysis. (B) The distribution of expression values in the actual dataset is bimodal and positive since some transposons are significantly up- or downregulated. This bimodal distribution is lost in the strand-inverted analysis.

2) "Altogether this data suggests that intron retention contributes to the age-related increase in the expression of transposons" - this analysis doesn't demonstrate the claim. In order to prove this they need to show that transposons that are independent of introns are either negligible, or non-changing with age.

We would like to emphasize that we never claimed that intron retention and readthrough can explain all of the age-related increases in transposon expression. In fact, our data is compatible with a multifactorial origin of transposons expression. Age- and senescence-related transposon expression can occur due to: 1/ intron retention, 2/ readthrough, 3/ loss of intergenic heterochromatin. Specifically, we do not try to refute 3.

However, since most transposons are found in introns or downstream of genes, this suggests that intron retention and readthrough will be major, albeit non-exclusive, drivers of age-related changes in transposons expression. Even if the fold-change for intergenic transposons with aging or senescence were higher this would not account for the broadscale expression patterns seen in RNAseq data.

To further illustrate this, we analyzed transposons located in introns, genes, downstream (ds) or upstream (us) of genes (distance to gene < 25 kb) or in intergenic regions (distance to gene > 25 kb). Indeed, we find that although intergenic transposons show similar log-fold changes to other transposon classes (Fig. L2A), their total contribution to read counts is negligible (Fig. L2B, Fig. Fig. S15). We have also now added a more nuanced explanation of this issue to the discussion.

Author response image 2.

We analyzed transposons located in introns, genes, downstream (ds) or upstream (us) of genes (distance to gene < 25 kb) or in intergenic regions (distance to gene > 25 kb). Independent of their location, transposons show similar differential expression with aging or cellular senescence (A). In contrast, the expression of transposons (log counts) is highly dependent on their location and the median log(count) value decreases in the order: genic > intronic > ds > us > intergenic.

Author response image 3.

Total counts are the sum of all counts from transposons located in introns, genes, downstream (ds) or upstream (us) of genes (distance to gene < 25 kb) or in intergenic regions (distance to gene > 25 kb). Counts were defined as cumulative counts across all samples.

3) Additionally, the correct control regions should be intronic regions other than the transposon, which overall contributed to the read counts of the intron.

4) Furthermore, analysis of read spanning intron and partly transposons should more directly show this contribution.

Thank you for this comment. To rephrase this, if we understand correctly, the concern is that an increase in transposon expression could bias the analysis of intron retention since transposons often make up a substantial portion of an intron. We would like to address this concern with the following three points:

First, if the concern is the correlation between log fold-change of transposons vs log fold-change of their containing introns, we do not think that this kind of data is biased. While transposons make up much of the intron, a single transposon on average only accounts for less than 10% of an intron.

Second, to address this more directly, we show here that even introns that do not contain expressed transposons are increased in aging fibroblasts and after induction of cellular senescence (Fig. S8). This shows that intron retention is universal and most likely not heavily biased by the presence or absence of expressed transposons.

Author response image 4.

We split the set of introns that significantly change with cellular aging (A) or cell senescence (B) into introns that contain at least one transposon (has_t) and those that do not contain any transposons (has_no_t). Intron retention is increased in both groups. In this analysis we included all transposons that passed minimal read filtering (n=63782 in A and n=124173 in B). Median log-fold change indicated with a dashed red line for the group of introns without transposons.

Third, we provide an argument based on the distribution of transposons within introns (Fig. L3).

Author response image 5.

The 5’ and 3’ splice sites show the highest sequence conservation between introns, whereas the majority of the intronic sequence does not. This is because these sites contain binding sites for splicing factors such as U1, U2 and SF1 (A). Transposons could affect splicing and we present a biologically plausible mechanism and two ancillary hypotheses here (B). If transposons affect the splicing (retention) of introns the most likely mechanism would be via impairment of splice site recognition because a transposon close to the site forms a secondary structure, binds an effector protein or provides inadequate sequences for pairing. Hypothesis 1: Transposons impair splicing because they are close to the splice site. Hypothesis 2: Transposons do not impair splicing because they are located away from the splice junction. Retained introns should show a similar depletion of transposons around the junction. Image adapted from: Ren, Pingping, et al. "Alternative splicing: a new cause and potential therapeutic target in autoimmune disease." Frontiers in Immunology 12 (2021): 713540.

Consistent with hypothesis 2 (“transposons do not impair splicing”), we show that the distribution of transposons within introns is similar for the set of all transposons and all significant transposons within significantly overexpressed introns (Fig. S7. A and B is similar in the case of aged fibroblasts; D and E is similar in the case of cellular senescence). If transposon expression was causally linked to changes in intron retention, the most likely mechanism would be via an impairment of splicing. We would expect transposons to be located close to the splice junction, which is not what we observed. Instead, the data is more consistent with intron retention as a driver of transposon expression.

Author response image 6.

Transposons are evenly distributed within introns except for the region close to splice junctions (A-E). Transposons appear to be excluded from the splice junction-adjacent region both in all introns (A, D) and in significantly retained introns (B, E). In addition, transposon density of all introns and significantly retained introns is comparable (C, F). We included only introns containing at least one transposon in this analysis. A) Distribution of 2292769 transposons within 163498 introns among all annotated transposons. B) Distribution of 195190 transposons within 14100 introns significantly retained with age. C) Density (transposon/1kb of intron) of transposons in all introns (n=163498) compared to significantly retained introns (n=14100). D) as in (A) E) Distribution of 428130 transposons within 13205 introns significantly retained with induced senescence. F) Density (transposon/1kb of intron) of transposons in all introns (n=163498) compared to significantly retained introns (n=13205).

5) "This contrasts with the almost completely even distribution of randomly permuted transposons." How was random permutation of transposons performed? Why is this contract not trivial, and why is this a good control?

Permutation was performed using the bedtools shuffle function (Quinlan et al. 2010). We use the set of all annotated transposons and all reshuffled transposons as a control. It is interesting to observe that these two show a very similar distribution with transposons evenly spread out relative to genes. In contrast, expressed transposons are found to cluster downstream of genes. This gave rise to our initial working hypothesis that readthrough should affect transposon expression.

6) Fig 4: the choice to analyze only the 10kb-20kb region downstream to TSE for readthrough regions has probably reduced the number of regions substantially (there are only 200 left) and to what extent this faithfully represent the overall trend is unclear at this point.

This is addressed in Suppl. Fig. 7, we repeated the analysis for every 10kb region between 0 and 100kb, showing similar results.

Furthermore, we show below in a new figure that the results are comparable when we measure readthrough in the 0 to 10kb region, while the sample size of readthrough regions is increased.

Finally, it is commonly accepted to remove readthrough regions overlapping genes, which while reducing sample size, increases accuracy for readthrough determination (Rosa-Mercado et al. 2021). Without filtering readthrough regions can overlap neighboring genes which is reflected in an elevated ratio of Readthrough_counts/Genic_counts (Fig. S9).

Author response image 7.

A) Readthrough was determined in a region 0 to 10 kb downstream of genes for a subset of genes that were at least 10 kb away from the nearest neighboring gene (n=684 regions). The log2 ratio of readthrough to gene expression is plotted across five age groups (adolescent n=32, young n=31, middle-aged n=22, old n=37 and very old n=21). B) As in (A) but data is plotted on a per sample basis. C) Readthrough was determined in a region 0 to 10 kb downstream of genes for a subset of genes that were at least 10 kb away from the nearest neighboring gene (n=1045 regions). The log2 ratio of readthrough to gene expression is plotted for the groups comprising senescence (n=12) and the non-senescent group (n=6). D) As in (D) but data is plotted on a per sample basis and for additional control datasets (serum-starved, immortalized, intermediate passage and early passage). N=3 per group.

7) Fig. 5B shows the opposite of the authors claims: in the control samples there are more transposon reads than in the KCl samples.

Thank you for pointing this out. During preparation of the manuscript the labels of Fig. 5B were switched (however, the color matching between Fig. 5A-C is correct). We apologize for this mistake, which we have now corrected.

8) "induced readthrough led to preferential expression of gene proximal transposons (i.e. those within 25 kb of genes), when compared with senescence or aging". A convincing analysis would show if there is indeed preferential proximity of induced transposons to TSEs. Since readthrough transcription decays as a function of distance from TSEs, the expression of transposons should show the same trends if indeed simply caused by readthrough. Also, these should be compared to the extent of transposon expression (not induction) in intergenic regions without any readthrough, in these conditions.

This is a very good suggestion. We now provide two new supplementary figures analyzing the distance-dependence of transposon expression.

In the first figure (Fig. S13) we show that readthrough decreases with distance (A, B) and we show that transposon counts are higher for transposons close to genes, following a similar pattern to readthrough. This is true in fibroblasts isolated from aged donors (A) and with cellular senescence (B).

Author response image 8.

Readthrough counts (rt_counts) decrease exponentially downstream of genes, both in the aging dataset (A) and in the cellular senescence dataset (B). Although noisier, the pattern for transposon counts (transp_cum_counts) is similar with higher counts closer to gene terminals, both in the aging dataset (C) and in the cellular senescence dataset (D). Readthrough counts are the cumulative counts across all genes and samples. Readthrough was determined in 10 kb bins and the values are assigned to the midpoint of the bin for easier plotting. Transposon counts are the cumulative counts across all samples for each transposon that did not overlap a neighboring gene. n=801 in (C) and n=3479 in (D).

In the second figure (Fig. S14) we show that transposons found downstream of genes with high readthrough show a more pronounced log-fold change (differential expression) than transposons downstream of genes with low readthrough (defined based on log-fold change). This is true in fibroblasts isolated from aged donors (A) and with cellular senescence (B). Furthermore, the difference between high and low readthrough region transposons is diminished for transposons that are more than 10 kb downstream of genes, as would be expected given that readthrough decreases with distance.

Author response image 9.

Transposons found downstream of genes with high readthrough (hi_RT) show a more pronounced log-fold change (transp_logfc) than transposons downstream of genes with low readthrough (low_RT). This is true in fibroblasts isolated from aged donors (A) and with cellular senescence (B). Furthermore, the difference between high and low readthrough region transposons is diminished for transposons that are more than 10 kb downstream of genes (“Transp > 10 kb”). Transposons in high readthrough regions were defined as those in the top 20% of readthrough log-fold change. Readthrough was measured between 0 and 10 kb downstream from genes. n=2124 transposons in (A) and n=6061 transposons in (B) included in the analysis.

Reviewer #2 (Public Review):

In this manuscript, the authors examined the role of transcription readout and intron retention in increasing transcription of transposable elements during aging in mammals. It is assumed that most transposable elements have lost the regulatory elements necessary for transcription activation. Using available RNA-seq datasets, the authors showed that an increase in intron retention and readthrough transcription during aging contributes to an increase in the number of transcripts containing transposable elements.

Previously, it was assumed that the activation of transposable elements during aging is a consequence of a gradual imbalance of transcriptional repression and a decrease in the functionality of heterochromatin (de repression of transcription in heterochromatin). Therefore, this is an interesting study with important novel conclusion. However, there are many questions about bioinformatics analysis and the results obtained.

Major comments:

1) In Introduction the authors indicated that only small fraction of LINE-1 and SINE elements are expressed from functional promoters and most of LINE-1 are co-expressed with neighboring transcriptional units. What about other classes of mobile elements (LTR mobile element and transposons)?

We thank the reviewer for this comment. Historically, most repetitive elements, e.g. DNA elements and retrotransposon-like elements, have been considered inactive, having accrued mutations which prevent them from transposition. On the other hand, based on recent data it is indeed very possible that certain LTR elements become active with aging as suggested in several manuscripts (Liu et al. 2023, Autio et al. 2020). However, these elements are not well annotated and our final analysis (Fig. 6) relies on a well-defined distinction between active and inactive elements. (See also question 2 for further discussion.)

Finally, we would like to point out some of the difficulties with defining expression and re-activation of LTR/ERV elements based on RNAseq data that have been highlighted for the Liu manuscript and are concordant with several of our results: https://pubpeer.com/publications/364E785636ADF94732A977604E0256

Liu, Xiaoqian, et al. "Resurrection of endogenous retroviruses during aging reinforces senescence." Cell 186.2 (2023): 287-304.

Autio A, Nevalainen T, Mishra BH, Jylhä M, Flinck H, Hurme M. Effect of ageing on the transcriptomic changes associated with expression at the HERV-K (HML-2) provirus at 1q22. Immun Ageing. 2020;17(1):11.

2) Results: Why authors considered all classes of mobile elements together? It is likely that most of the LTR containing mobile elements and transposons contain active promoters that are repressed in heterochromatin or by KRAB-C2H2 proteins.

We do not consider LTR containing elements because there is uncertainty regarding their overall expression levels and their expression with aging (Nevalainen et al. 2018). Furthermore, we believe that substantial activity of LTR elements in human genomes should have been detectable through patterns of insertional mutagenesis. Yet studies generally show low to negligible levels of LTR (ERV) mutagenesis. Here, for example, at a 200-fold lower rate than for LINEs (Lee et al. 2012).

Importantly, our analysis in Fig. 6 relies on well-annotated elements like LINEs, which is why we do not include LTR or SINE elements that could be potentially expressed. However, for other analyses we did consider element families independently as can be seen in Table S1, for example.

Nevalainen, Tapio, et al. "Aging-associated patterns in the expression of human endogenous retroviruses." PLoS One 13.12 (2018): e0207407.

Lee, Eunjung, et al. "Landscape of somatic retrotransposition in human cancers." Science 337.6097 (2012): 967-971.

3) Fig. 2. A schematic model of transposon expression is not presented clearly. What is the purpose of showing three identical spliced transcripts?

This is indeed confusing. There are three spliced transcripts to schematically indicate that the majority of transcripts will be correctly spliced and that intron retention is rare (estimated at 4% of all reads in our dataset). We have clarified the figure now, please see below:

Author response image 10.

A schematic model of transposon expression. In our model, represented in this schematic, transcription (A) can give rise to mRNAs and pre-mRNAs that contain retained introns when co-transcriptional splicing is impaired. This is often seen during aging and senescence, and these can contain transposon sequences (B). In addition, transcription can give rise to mRNAs and pre-mRNAs that contain transposon sequences towards the 3’-end of the mRNA when co-transcriptional termination at the polyadenylation signal (PAS) is impaired (C, D) as seen with aging and senescence. Some of these RNAs may be successfully polyadenylated (as depicted here) whereas others will be subject to nonsense mediated decay. Image created with Biorender.

4) The study analyzed the levels of RNA from cell cultures of human fibroblasts of different ages. The annotation to the dataset indicated that the cells were cultured and maintained. (The cells were cultured in high-glucose (4.5mg/ml) DMEM (Gibco) supplemented with 15% (vol/vol) fetal bovine serum (Gibco), 1X glutamax (Gibco), 1X non-essential amino acids (Gibco) and 1% (vol/vol) penicillin-streptomycin (Gibco). How correct that gene expression levels in cell cultures are the same as in body cells? In cell cultures, transcription is optimized for efficient division and is very different from that of cells in the body. In order to correlate a result on cells with an organism, there must be rigorous evidence that the transcriptomes match.

We agree and have updated the discussion to reflect this shortcoming. While we do not have human tissue data, we would like to draw the reviewer’s attention to Fig. S3 where we presented some liver data for mice. We now provide an additional supplementary figure (in a style similar to Fig. S2) showing how readthrough, transposon expression and intron retention changes in 26 vs 5-month-old mice (Fig. S4). Indeed, intron, readthrough and transposons increase with age in mice, although this is more pronounced for transposons and readthrough.

Author response image 11.

Intron, readthrough and transposon elements are elevated in the liver of aging mice (26 vs 5-month-old, n=6 per group). Readthrough and transposon expression is especially elevated even when compered to genic transcripts. The percentage of upregulated transcripts is indicated above each violin plot and the median log10-fold change for genic transcripts is indicated with a dashed red line.

Finally, just to elaborate, we used the aging fibroblast dataset by Fleischer et al. for three reasons:

1) Yes, aging fibroblasts could be a model of human aging, with important caveats as you correctly point out,

2) it is one of the largest such datasets allowing us to draw conclusions with higher statistical confidence and do things such as partial correlations

3) it has been analyzed using similar techniques before (LaRocca, Cavalier and Wahl 2020) and this dataset is often used to make strong statements about transposons and aging such as transposon expression in this dataset being “consistent with growing evidence that [repetitive element] transcripts contribute directly to aging and disease”. Our goal was to put these statements into perspective and to provide a more nuanced interpretation.

LaRocca, Thomas J., Alyssa N. Cavalier, and Devin Wahl. "Repetitive elements as a transcriptomic marker of aging: evidence in multiple datasets and models." Aging Cell 19.7 (2020): e13167.

5) The results obtained for isolated cultures of fibroblasts are transferred to the whole organism, which has not been verified. The conclusions should be more accurate.

We agree and have updated the discussion accordingly.

6) The full pipeline with all the configuration files IS NOT available on github (pabisk/aging_transposons).

Thank you for pointing this out, we have now uploaded the full pipeline and configuration files.

7) Analysis of transcripts passing through repeating regions is a complex matter. There is always a high probability of incorrect mapping of multi-reads to the genome. Things worsen if unpaired short reads are used, as in the study (L=51). Therefore, the authors used the Expectation maximization algorithm to quantify transposon reads. Such an option is possible. But it is necessary to indicate how statistically reliable the calculated levels are. It would be nice to make a similar comparison of TE levels using only unique reads. The density of reads would drop, but in this case it would be possible to avoid the artifacts of the EM algorithm.

We thank the reviewer for this suggestion. We show here that mapping only unique alignments (outFilterMultimapNmax=1 in STAR) leads to similar results.

For the aging fibroblast dataset:

Author response image 12.

For the induced senescence dataset:

Author response image 13.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary.

The authors goal was to map the neural circuitry underlying cold sensitive contraction in Drosophila. The circuitry underlying most sensory modalities has been characterized but noxious cold sensory circuitry has not been well studied. The authors achieve their goal and map out sensory and post-sensory neurons involved in this behavior.

Strengths.

The manuscript provides convincing evidence for sensory and post sensory neurons involved in noxious cold sensitive behavior. They use both connectivity data and functional data to identify these neurons. This work is a clear advance in our understanding of noxious cold behavior. The experiments are done with a high degree of experimental rigor.

Positive comments

- Campari is nicely done to map cold responsive neurons, although it doesn't give data on individual neurons.

- Chrimson and TNT experiments are nicely done.

- Cold temperature activates basin neurons, it's a solid and convincing result.

Weaknesses.

Among the few weaknesses in this manuscript is the failure to trace the circuit from sensory neuron to motor neuron; and to ignore analysis of the muscles driving, cold induced contraction. Authors also need to elaborate more on the novel aspects of their work in the introduction or abstract.

We have performed a more thorough em connectivity analysis of the CIII md neuron circuit (Figure 1A, Figure 1 – Figure supplement 1, Figure 10A). We now report all premotor neurons that are connected to CIII md neurons along with two additional projection/commandlike neurons. These additional premotor neurons (A01d3, A02e, A02f, A02g, A27k, and A31k) that are primarily implicated in locomotion were not required for cold nociception (Figure 5 – Figure supplement 2). Collectively, we have tested the requirement in cold nociception for ~94% synapses between CIII md->premotor neurons and all tested premotor with available driver lines. The requirement in cold nociception was also assessed for the two projection/command-like neurons dLIP7 and A02o neurons, which are required for sensory integration and directional avoidance to noxious touch, respectively (Figure 7 – Figure supplement 2) (Hu et al., 2017; Takagi et al., 2017). Silencing dLIP7 neurons resulted in modest reduction in cold-evoked behaviors, meanwhile A02o neurons were not required for cold nociception (Figure 7 – Figure supplement 2). To complete the analysis from thermosensation to evoked behavior, we analyzed cold-evoked Ca²⁺ responses of larval musculature (Figure 10). Premotor neurons, which are connected to CIII md neurons, target multiple muscle groups (DL, DO, LT, VL, and VO) (Figure 10A). Individual larval segments have unique cold-evoked Ca²⁺ responses, where the strongest cold-evoked Ca²⁺ occurs in the central abdominal segments (Figure 10B-D). Inhibiting motor neuron activity or using an anesthetic (ethyl ether), there is a negligible cold-evoked Ca²⁺ response compared to controls (Figure 10 – Figure supplement 1). Analysis of cold-evoked Ca²⁺ in individual muscles reveal unique Ca²⁺ dynamics for individual muscle groups (Figure 10E-H).

Major comments.

- Class three sensory neuron connectivity is known, and role in cold response is known (turner 16, 18). Need to make it clearer what the novelty of the experiments are.

In figure 1, we are trying to guide the audience to CIII md neuron circuitry and emphasize the necessity and sufficiency CIII md neurons in cold nociception. Previously, only transient (GCaMP6) cold-evoked Ca²⁺ were reported (Turner et al., 2016, 2018). However, here using CaMPARI, we performed dendritic spatial (sholl) analysis of cold-evoked Ca²⁺ responses (Figure 1B-C). During the revision, we evaluated both CIII- and cold-evoked CT throughout larval development (Figure 1G, H). All in all, the findings from the first figure reiterate and replicate previous findings for the role of CIII md neuron in cold nociception. CIII md connectivity might be known, however, we investigated the functional and physiological roles of individual circuit neurons.

- Why focus on premotor neurons in mechano nociceptive pathways? Why not focus on PMNs innervating longitudinal muscles, likely involved in longitudinal larval contraction? Especially since chosen premotor neurons have only weak effects on cold induced contraction?

We assessed requirements for all premotor neurons that are connected to CIII md neurons and for which there are validated driver lines. Only premotor neurons (DnB, mCSI and Chair-1), which were previously initially implicated in mechanosensation, were also required for cold nociception. Premotor neurons previously implicated in locomotion (A01d3, A02e, A02f, A02g, A27k, and A31k) are not required for cold-evoked behaviors (Figure 5 – Figure supplement 2).

Reviewer #2 (Public Review):

Patel et al perform the analysis of neurons in a somatosensory network involved in responses to noxious cold in Drosophila larvae. Using a combination of behavioral experiments, Calcium imaging, optogenetics, and synaptic connectivity analysis in the Drosophila larval they assess the function of circuit elements in the somatosensory network downstream of multimodal somatosensory neurons involved in innocuous and noxious stimuli sensing and probe their function in noxious cold processing, Consistent with their previous findings they find the multidendritic class III neurons, to be the key cold sensing neurons that are both required and sufficient for the CT behaviors response (shown to evoked by noxious cold). They further investigate the downstream neurons identified based on literature and connectivity from EM at different stages of sensory processing characterize the different phenotypes upon activating/silencing those neurons and monitor their responses to noxious cold. The work reveals diverse phenotypes for the different neurons studied and provides the groundwork for understanding how information is processed in the nervous system from sensory input to motor output and how information from different modalities is processed by neuronal networks. However, at times the writing could be clearer and some results interpretations more rigorous.

Specific comments

(1) In Figure 1 -supplement 6D-F (Cho co-activation)

The authors find that Ch neurons are cold sensitive and required for cold nociceptive behavior but do not facilitate behavioral responses induced but CIII neurons

The authors show that coactivating mdIII and cho inhibits the CT (a typically observed coldinduced behavioral response) in the second part of the stimulation period, while Cho was required for cold-induced CT. Different levels of activation of md III and Cho (different light intensities) could bring some insights into the observed phenotypes upon Cho manipulation as different levels activate different downstream networks that could correspond to different stimuli. Also, it would be interesting to activate chordotonal during exposure to cold to determine how a behavioral response to cold is affected by the activation of chordotonal sensory neurons.

Modulating both CIII md and Ch activation to assess the contribution of individual sensory neuron’s role in thermosensation would certainly shed unique insights. However, we believe that such analyses are beyond the scope of the current manuscript and better suited to future followup studies.

(2) Throughout the paper the co-activation experiments investigate whether co-activating the different candidate neurons and md III neurons facilitates the md III-induced CT response. However, the cold noxious stimuli will presumably activate different neurons downstream than optogenetic activation of MdIII and thus can reveal more accurately the role of the different candidate neurons in facilitating cold nociception.

We agree that the CIII md neuron activation of the downstream circuitry would be different from the cold-evoked activation of neurons downstream of primary sensory neurons. We believe that our current finding lay foundations for future works to evaluate how multiple sensory neurons work in concert for generating stimulus specific behavioral responses.

(3) Use of blue lights in behavioral and imaging experiments

Strong Blue and UV have been shown to activate MDIV neurons (Xiang, Y., Yuan, Q., Vogt, N. et al. Light-avoidance-mediating photoreceptors tile the Drosophila larval body wall. Nature 468, 921-926 (2010). https://doi.org/10.1038/nature09576) and some of the neurons tested receive input from MdIV.

In their experiments, the authors used blue light to optogenetically activate CDIII neurons and then monitored Calcium responses in Basin neurons, premotor neurons, and ascending neurons and UV light is necessary for photoconversion in Campari Experiments. Therefore, some of the neurons monitored could be activated by blue light and not cdIII activation. Indeed, responses of Basin-4 neurons can be observed in the no ATR condition (Fig 3HI) and quite strong responses of DnB neurons. (Figure 6E) How do authors discern that the effects they see on the different neurons are indeed due to cold nociception and not the synergy of cold and blue light responses could especially be the case for DNB that could have in facilitating the response to cold in a multisensory context (where mdIV are activated by light).

In addition, the silencing of DNB neurons during cold stimulation does not seem to give very robust phenotypes (no significant CT decrease compared to empty GAL4 control).

It would be important to for example show that even in the absence of blue light the DNB facilitates the mdIII activation or cold-induced CT by using red light and Chrimson for example or TrpA activation (for coactivation with md III).

Alternatively, in some other cases, the phenotype upon co-activation could be inhibited by blue light (e.g. chair-1 (Figure 5 H-I)).

More generally, given the multimodal nature of stimuli activating mdIV , MdIII (and Cho) and their shared downstream circuitry it is important to either control for using the blue light in these stimuli or take into account the presence of the stimulus in interpreting the results as the coactivation of for example Cho and mdIII using blue lights also could activate mdIV (and downstream neurons, alter the state of the network that could inhibit the md III induced CT responses.

Assessing the differences in behavioral phenotypes in the different conditions could give an idea of the influence of combining different modalities in these assays. For example, did the authors observe any other behaviors upon co-activation of MDIII and Cho (at the expense of CT in the second part of the stimulation) or did the larvae resume crawling? Blue light typically induces reorientation behavior. What about when co-activating mdIII and Basin-4?

Using Chrimson and red light or TrpA in some key experiments e.g. with Cho, Basin-4, and DNB would clarify the implication of these neurons in cold nociception

We agree that exposure to a bright light source results in avoidance behaviors in Drosophila larvae, which is primarily mediated by CIV md neurons. However, the light intensities used in our assays is much milder than the ones required to activate sensory neurons. Specifically, based on Xiang et al. 470nm light does not evoke any electrical response at the lowest tested light intensity (0.74mWmm^-2), whereas our light intensity used in behavioral experiments was much lower at 0.15mWmm^-2. Additionally, we assessed larval mobility and turning for control conditions ±ATR and also sensory neuron activation. As expected, there is an increase in larval immobility upon CIII md neurons activation (Author response image 1). Only activation of CIV md neurons resulted in light-evoked turning, meanwhile remaining conditions did show stimulus time locked turning response (Author response image 1). Furthermore, we tested whether the intensity of 470nm light used in our behavior experiments was enough to result in light-evoked Ca²⁺ response in CIII md and CIV md neurons. We expressed RCaMP in sensory neurons using a pan-neural driver (GMR51C10^GAL4). There was no detectable increase in light-evoked Ca²⁺ response in either CIII md or CIV md neuron (Author response image 1).

Furthermore, we also tested multiple optogenetic actuators (ChR2, ChR2-H134R, and CsChrimson) and two CIII md driver lines (19-12^Gal4 and R83B04^Gal4). Regardless of the optogenetic actuator used or the wavelength of the light used, we observe light-evoked CT responses (Figure 1– Figure supplement 6). We found using CsChrimson raises several procedural challenges with our current experimental setup. In our hands, CsChrimson showed extreme sensitivity to any amount ambient white light intensities, whereas others have used infrared imaging to counteract ambient light sensitivity. Our imaging setup is equipped with visible spectrum imaging and cannot be retrofitted record infrared light sources. Thus, we have limited the use of CsChrimson to optogenetic-Ca²⁺ imaging experiments, where we are not recording larval behavior.

The use of TrpA1 would require heat stimulation for activating the channels, which in turn would impact downstream circuit neurons that are shared amongst sensory neurons.

For CaMPARI experiments, the PC light was delivered using a similar custom filter cube, which was used in the original CaMPARI paper (Fosque et al., 2015). This filter cube delivers 440nm wavelength as the PC light. PC light exposure in absence of cold stimulus does not result in differential CaMPARI conversion between CIII md and CIV md (F_red/green = 0.086 and 0.097, respectively). For the same condition, Ch neurons have high CaMPARI, but it is expected as they function in proprioception. Therefore, the chances of downstream neurons being solely activated by PC light remain low. The differential baseline CaMPARI F_red/green ratios of individual circuit neurons could be a result of varying resting state cytosolic Ca²⁺ concentrations.

Lastly, for optogenetic-GCaMP experiments, where we use CIII md>CsChrimson and Basin-2/-4 or DnB>GCaMP to visualize CIII md evoked Ca²⁺ responses in downstream neuron. Xiang et al. reported that confocal laser excitation for GCaMP does not activate CIV md neurons, which is consistent with what we have observed as well.

Author response image 1.

(A) For optogenetic experiments, percent turning was assessed in control conditions and sensory neuron activation. Only CIV md neurons activation results in an increase in bending response. Other conditions do not blue light-evoked turning. (A’) We assessed larval turning based on ellipse fitting using FIJI, the aspect ratio of the radii is indicative of larval bending state. We empirically determined that radii ratio of <2.5 represents a larval turning/bending. This method of ellipse fitting has previously been used to identify C. elegans postures using WrMTrck in FIJI (Nussbaum-Krammer et al., 2015). (B) Percent immobility for all control conditions plus sensory activation driver lines. Only CIII md neuron activation leads to sustained stimulus-locked increase in immobility. There’s also no blue light-evoked reductions in mobility, indicating that there was not increase in larval movement due to blue light. (C) We assessed CIII md (ddaF) and CIV md (ddaC) neurons response to blue light with similar light intensity that was used in behavioral optogenetic experiments. There is no blue light evoked increase in RCaMP fluorescence.

(4) Basins

- Page 17 line 442-3 "Neural silencing of all Basin (1-4) neurons, using two independent driver lines (R72F11GAL4 and R57F07^GAL4).

Did the authors check the expression profile of the R57F07 line that they use to probe "all basins"? The expression profile published previously (Ohyama et al, 2015, extended data) shows one basin neuron (identified as basin-4 ) and some neurons in the brain lobes. Also, the split GAL4 that labels Basin-4 (SS00740) is the intersection between R72F11 and R57F07 neurons. Thus the R57F07 likely labels Basin-4 and if that is the case the data in Figure 2 9 and supplement) and Figure 3 related to this driver line, should be annotated as Basin-4, and the results and their interpretation modified to take into account the different phenotypes for all basins and Basin-4 neurons.

Due to the non-specific nature of R57F07^GAL4 in labeling Basin-4 and additional neuron types, we have decided to remove the driver line from our current analysis. We would need to perform further independent investigations to identify the other cell types and validate their role in cold nociception.

Page 19 l. 521-525 I am confused by these sentences as the authors claim that Basin-4 showed reduced Calcium responses upon repetitive activation of CDIII md neurons but then they say they exhibit sensitization. Looking at the plots in FIG 3 F-I the Basin-4 responses upon repeated activation seem indeed to decrease on the second repetition compared to the first. What is the sensitization the authors refer to?

We have rephrased this section.

On Page 47-In this section of the discussion, the authors emit an interesting hypothesis that the Basin-1 neuron could modulate the gain of behavioral responses. While this is an interesting idea, I wonder what would be the explanation for the finding that co-activation of Cho and MDIII does not facilitate cold nociceptive responses. Would activation of Basin-1 facilitate the cold response in different contexts (in addition to CH0-mediated stimuli)?

Page 48 Thus the implication of the inhibitory network in cold processing should be better contextualized.

The authors explain the difference in the lower basin-2 Ca- response to Cold/ mdIII activation (compared to Basin-4) despite stronger connectivity, due a stronger inputs from inhibitory neurons to Basin-2 (compared to Basin-4). The previously described inhibitory neurons that synapse onto Basin-2 receive rather a small fraction of inputs from the class III sensory neurons. The differences in response to cold could be potentially assigned to the activation of the inhibitory neurons by the cold-sensing cho- neurons. However, that cannot explain the differences in responses induced by class III neurons. Do the authors refer to additional inhibitory neurons that would receive significant input from MdIII?

Alternative explanations could exist for this difference in activation: electrical synapses from mdIII onto Basin-4, and by stronger inputs from mdIV (compared to Basin-2 in the case of responses to Cold stimulus (Cold induces responses in md IV sensory neurons). Different subtypes of CD III may differentially respond to cold and the cold-sensing ones could synapse preferentially on basin-4 etc.

A possible explanation for lack of CT facilitation when Ch and CIII md neurons are both activated are likely the competing sensory inputs going into Basins and yet unknown role of the inhibitory network between sensory and Basin neurons in cold nociception (Jovanic et al., 2016). Mechanical activation of Ch leads to several behavioral responses (hunch, back-up, pause, crawl, and/or bend) and transition between behaviors (Kernan et al., 1994; Tsubouchi et al., 2012; Zhang et al., 2015; Turner et al., 2016, 2018; Jovanic et al., 2019; Masson et al., 2020).

Meanwhile, primary CIII md-/cold-evoked is CT (Turner et al., 2016, 2018, Patel et al., 2022, Himmel et al., 2023). Certain touch- versus cold- evoked behaviors are mutually exclusive, where co-activation of Ch and CIII md likely leads to competing neural impulses leading to lack of any single behavioral enhancement. Furthermore, the mini circuit motif between Ch and Basins consisting of feedforward, feedback and lateral inhibitory neurons that play a role in behavioral selection and transitions might impact the overall output of Basin neurons. Upon Ch and CIII md neuron co-activation, the cumulative Basin neuronal output may be biased towards increased behavioral transitions instead of sustained singular behavior response.

While we posited one possible mechanism explaining the differences between cold- or CIII mdevoked Ca²⁺ responses in Basin 2 and 4 neurons, where we suggest the differences in evoked Ca²⁺ responses may arise due to differential connectivity of TePns and inhibitory network neurons to Basin 2 and/or 4. Furthermore, ascending A00c neurons are connected to descending feedback SEZ neuron, SeIN128, which have connectivity to Basins (1-3 and strongest with Basin 2), A02o, DnB, Chair-1 and A02m/n (Ohyama et al., 2015; Zhu et al., 2024). However, how the 5 different subtypes of CIII md neurons respond to cold is unknown. Electrical recordings of the dorsal CIII md neurons revealed that within & between neuron subtypes there’s variability in temperature sensitivity of individual neurons, where population coding results in fine-tuned central temperature representation (Maksymchuk et al., 2022). Evaluating the role of how individual CIII md subtypes Basin activation could reveal important insights into the precise relationship between CIII md and multisensory integration Basin neurons. However, as of yet there are no known CIII md neuron driver lines that mark a subset of CIII md neurons thus limiting further clarification on how primary sensory information is transduced to integration neurons.

(5) A00c

Page 26 Figure 4F-I line While Goro may not be involved in cold nociception the A00c (and A05q) seems to be.

A00c could convey information to other neurons other than Goro and thus be part of a pathway for cold-induced CT.

A deeper look into A00c connectivity reveals that there is a reciprocal relationship between A00c and SEZ descending neuron, SeIN128 (Ohyama et al., 2015; Zhu et al., 2024). Additionally, this feedback SEZ descending neuron synapse onto A02o, A05q, Basins (highest connectivity to Basin 2 and weak connectivity to Basin 1 & 3), and select premotor neurons (Chair-1, DnB, and A02m/n) (Ohyama et al., 2015; Zhu et al., 2024). Interestingly, SEZ feedback neuron likely plays a role in the observed cold-/CIII md neuron evoked differential calcium activity and behavioral requirement amongst Basin-2 and -4 in cold nociception. We have added this to our discussion section.

(6) Page 31 766-768 the conclusion that "premotor function is required for and can facilitate cold nociception" seems odd to stress as one would assume that some premotor neurons would be involved in controlling the behavioral responses to a stimulus. It would be more pertinent in the summary to specify which premotor neurons are involved and what is their function

We have updated the section regarding premotor neurons’ role in cold nociception and now there’s a more specific concluding statement.

(7) There are several Split GAL4 used in the study (with transgenes inserted in attP40 et attP2 site). A recent study points to a mutation related to attP40 that can have an effect on muscle function: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750024/. The controls used in behavioral experiments do not contain the attP40 site. It would be important to check a control genotype bearing an attP40 site and characterize the different parameters of the CT behavior to cold and take this into account in interpreting the results of the experiments using the SplitGAL4 lines

We have performed control experiments bearing empty attP40;attP2 sites in our neural silencing experiments. The observed muscle phenotypes were present in larvae bearing homozygous copies attP40/attP40 (van der Graaf et al., 2022). However, in our experiments, none of the larvae that we tested behaviorally had homozygous attP40;attP2 insertions. We have updated Table 1 to now include insertion sites.

Reviewer #3 (Public Review):

Summary:

The authors follow up on prior studies where they have argued for the existence of cold nociception in Drosophila larvae. In the proposed pathway, mechanosensitive Class III multidendritic neurons are the noxious cold responding sensory cells. The current study attempts to explore the potential roles of second and third order neurons, based on information of the Class III neuron synaptic outputs that have been obtained from the larval connectome.

Strengths:

The major strength of the manuscript is the detailed discussion of the second and third order neurons that are downstream of the mechanosensory Class III multidendritic neurons. These will be useful in further studies of gentle touch mechanosensation and mechanonociception both of which rely on sensory input from these cells. Calcium imaging experiments on Class III

activation with optogenetics support the wiring diagram.

Weaknesses:

The scientific premise is that a full body contraction in larvae that are exposed to noxious cold is a sensorimotor behavioral pathway. This premise is, to start with, questionable. A common definition of behavior is a set of "orderly movements with recognizable and repeatable patterns of activity produced by members of a species (Baker et al., 2001)." In the case of nociception behaviors, the patterns of movement are typically thought to play a protective role and to protect from potential tissue damage.

Does noxious cold elicit a set of orderly movements with a recognizable and repeatable pattern in larvae? Can the patterns of movement that are stimulated by noxious cold allow the larvae to escape harm? Based on the available evidence, the answer to both questions is seemingly no. In response to noxious cold stimulation many, if not all, of the muscles in the larva, simultaneously contract (Turner et al., 2016), and as a result the larva becomes stationary. In response to cold, the larva is literally "frozen" in place and it is incapable of moving away. This incapacitation by cold is the antithesis of what one might expect from a behavior that protects the animals from harm.

Extensive literature has investigated the physiological responses of insects to cold (reviewed in Overgaard and MacMillan, 2017). In numerous studies of insects across many genera (excluding cold adapted insects such as snow flies), exposure to very cold temperatures quickly incapacitates the animal and induces a state that is known as a chill coma. During a chill coma, the insect becomes immobilized by the cold exposure, but if the exposure to cold is very brief the insect can often be revived without apparent damage. Indeed, it is common practice for many laboratories that use adult Drosophila for studies of behavior to use a brief chilling on ice as a form of anesthesia because chilling is less disruptive to subsequent behaviors than the more commonly used carbon dioxide anesthesia. If flies were to perceive cold as a noxious nociceptive stimulus, then this "chill coma" procedure would likely be disruptive to behavioral studies but is not. Furthermore, there is no evidence to suggest that larval sensation of "noxious cold" is aversive.

The insect chill coma literature has investigated the effects of extreme cold on the physiology of nerves and muscles and the consensus view of the field is that the paralysis that results from cold is due to complex and combined action of direct effects of cold on muscle and on nerves (Overgaard and MacMillan, 2017). Electrophysiological measurements of muscles and neurons find that they are initially depolarized by cold, and after prolonged cold exposure they are unable to maintain potassium homeostasis and this eventually inhibits the firing of action potentials (Overgaard and MacMillan, 2017). The very small thermal capacitance of a Drosophila larva means that its entire neuromuscular system will be quickly exposed to the effect of cold in the behavioral assays under consideration here. It would seem impossible to disentangle the emergent properties of a complex combination of effects on physiology (including neuronal, glial, and muscle homeostasis) on any proposed sensorimotor transformation pathway.

Nevertheless, the manuscript before us makes a courageous attempt at attempting this. A number of GAL4 drivers tested in the paper are found to affect parameters of contraction behavior (CT) in cold exposed larvae in silencing experiments. However, notably absent from all of the silencing experiments are measurements of larval mobility following cold exposure. Thus, it is not known from the study if these manipulations are truly protecting the larvae from paralysis following cold exposure, or if they are simply reducing the magnitude of the initial muscle contraction that occurs immediately following cold (ie reducing CT). The strongest effect of silencing occurs with the 19-12-GAL4 driver which targets Class III neurons (but is not completely specific to these cells).

Optogenetic experiments for Class III neurons relying on the 19-12-GAL4 driver combined with a very strong optogenetic acuator (ChETA) show the CT behavior that was reported in prior studies. It should be noted that this actuator drives very strong activation, and other studies with milder optogenetic stimulation of Class III neurons have shown that these cells produce behavioral responses that resemble gentle touch responses (Tsubouchi et al 2012 and Yan et al 2013). As well, these neurons express mechanoreceptor ion channels such as NompC and Rpk that are required for gentle touch responses. The latter makes the reported Calcium responses to cold difficult to interpret in light of the fact that the strong muscle contractions driven by cold may actually be driving mechanosensory responses in these cells (ie through deformation of the mechanosensitive dendrites). Are the cIII calcium signals still observed in a preparation where cold induced muscle contractions are prevented?

A major weakness of the study is that none of the second or third order neurons (that are downstream of CIII neurons) are found to trigger the CT behavioral responses even when strongly activated with the ChETA actuator (Figure 2 Supplement 2). These findings raise major concerns for this and prior studies and it does not support the hypothesis that the CIII neurons drive the CT behaviors.

Later experiments in the paper that investigate strong CIII activation (with ChETA) in combination with other second and third order neurons does support the idea activating those neurons can facilitate body-wide muscle contractions. But many of the co-activated cells in question are either repeated in each abdominal neuromere or they project to cells that are found all along the ventral nerve cord, so it is therefore unsurprising that their activation would contribute to what appears to be a non-specific body-wide activation of muscles along the AP axis. Also, if these neurons are already downstream of the CIII neurons the logic of this coactivation approach is not particularly clear. A more convincing experiment would be to silence the different classes of cells in the context of the optogenetic activation of CIII neurons to test for a block of the effects, a set of experiments that is notably absent from the study.

The authors argument that the co-activation studies support "a population code" for cold nociception is a very optimistic interpretation of a brute force optogenetics approach that ultimately results in an enhancement of a relatively non-specific body-wide muscle convulsion.

We have responded extensively to reviewer 3’s comments in our provisional response to address the critiques regarding conceptual merit of this paper.

Author response:

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

Public Review:

We would like to thank the reviewers for providing constructive feedback on the manuscript. To address their concerns, we have performed additional experiments, analyzed the new data, and revised the manuscript.

(1) The utility of a pipeline depends on the generalization properties.

While the proposed pipeline seems to work for the data the authors acquired, it is unclear if this pipeline will actually generalize to novel data sets possibly recorded by a different microscope (e.g. different brand), or different imagining conditions (e.g. illumination or different imagining artifacts) or even to different brain regions or animal species, etc.

The authors provide a 'black-box' approach that might work well for their particular data sets and image acquisition settings but it is left unclear how this pipeline is actually widely applicable to other conditions as such data is not provided.

In my experience, without well-defined image pre-processing steps and without training on a wide range of image conditions pipelines typically require significant retraining, which in turn requires generating sufficient amounts of training data, partly defying the purpose of the pipeline.

It is unclear from the manuscript, how well this pipeline will perform on novel data possibly recorded by a different lab or with a different microscope.

To address the generalizability of our DL segmentation model, we have performed several validation experiments with deploying our model on out-of-distribution data that 1) had distinct channels  2) were acquired in different species (rat) with a different vascular fluorescent label and a different imaging protocol, and 3) were acquired on a different microscope and with a different vascular label. We first used our model to segment images (507x507um lateral FOV, 170-250 um axial range) from three C57BL/6 mice imaged on the same two-photon fluorescent microscope following the same imaging protocol. The vasculature was labelled by intravenous injection of the Texas Red dextran (70 kDa MW, Thermo Fisher Scientific Inc, Waltham MA), as in the current experiment. In lieu of the EYFP signal from pyramidal neurons that was present in the original data, we added Gaussian noise with a mean and standard deviation identical to the acquired vascular channel in the out-of-distribution dataset. Second, we applied our model to images (507x507um lateral FOV, 300-400 um axial range) from two Fischer rats that were injected with 2000-kDa Alexa680-dextran via a tail vein catheter. These rats were imaged on the same two-photon fluorescence microscope, but with Galvano scanners (instead of resonant scanners). As before, a second channel of Gaussian noise was added to simulate the missing EYFP signal. Finally, we segmented an image of vasculature from an ex-vivo cleared mouse brain (1665x1205x780 um) acquired on a light sheet fluorescence microscope (Miltenyi UltraMicroscope Blaze), with a Lectin-DyLight 649 labelling the vessel walls.  The Dice Score, Precision, Recall, Hausdorff 95%, and Mean surface distance were reported for segmentations of 2PFM data sets, following the generation of ground truth images by assisted manual segmentation in ilastik. Examples of the generated segmentation masks are presented in Supplementary figure 9 for visual comparison. We have described the image pre-processing steps/transforms before model inference in the revised Methods section. In general, should the segmentation results on a data set be deemed unsatisfactory, our model can be further fine-tuned on out-of-distribution data. Furthermore, the image analyses downstream from segmentation are applicable irrespective of the method utilized to arrive at a robust vascular segmentation.

Author response table 1.

Dataset performance comparison for UNETR

(2) Some of the chosen analysis results seem to not fully match the shown data, or the visualization of the data is hard to interpret in the current form.

We have updated the visualizations to make them more accessible and ensure close correspondence between tables and figures.

(3) Additionally, some measures seem not fully adapted to the current situation (e.g. the efficiency measure does not consider possible sources or sinks). Thus, some additional analysis work might be required to account for this.

Thank you for your comment. The efficiency metric was selected as it does not consider sources or sinks. We do agree that accounting for vessel subtypes in the analysis (thus classifying larger vessels as either suppliers/sources or drainers/sinks) would be very useful: notwithstanding, this classification is extremely laborious, as we have noted in our prior work1 . We are therefore leveraging machine learning in a parallel project to afford vessel classification by type. Notwithstanding, the source/sink analysis based on in vivo 2PFM data is confounded by the small FOV.

(4) The authors apply their method to in vivo data. However, there are some weaknesses in the design that make it hard to accept many of the conclusions and even to see that the method could yield much useful data with this type of application. Primarily, the acquisition of a large volume of tissue is very slow. In order to obtain a network of vascular activity, large volumes are imaged with high resolution. However, the volumes are scanned once every 42 seconds following stimulation. Most vascular responses to neuronal activation have come and gone in 42 seconds so each vessel segment is only being sampled at a single time point in the vascular response. So all of the data on diameter changes are impossible to compare since some vessels are sampled during the initial phase of the vascular response, some during the decay, and many probably after it has already returned to baseline. The authors attempt to overcome this by alternating the direction of the scan (from surface to deep and vice versa). But this only provides two sample points along the vascular response curve and so the problem still remains.

We thank the Reviewer for bringing up this important point. Although vessels can show relatively rapid responses to perturbation, vascular responses to photostimulation of ChannelRhodopsin-2 in neighbouring neurons are long-lasting: they do not come and go in 42 seconds. To demonstrate this point, we acquired higher temporal-resolution images of smaller volumes of tissue over 5 minutes preceding and 5 minutes following the 5-s photoactivation with the original photostimulation parameters. The imaging protocol was different in that we utilized a piezoelectric motor, a smaller field of view (512um x (80-128)um x (34-73)um), and only 3x frame averaging, resulting in a temporal resolution of 1.57-3.17 seconds per frame. This acquisition was repeated at different cortical depths in three Thy1-ChR2 mice and the vascular radii were estimated using our presented pipeline. Significantly responding vessels here were selected via an F-test of radius estimates before vs. after stimulation. LOESS fits to the time-dependent radius of significantly responding vessels are shown in Supplementary Figure 5. Vessels shorter than 20 um in length were excluded from the analysis so as to focus on vessel segments where averaging the vascular radius over many vertices was possible. A video of one of the acquisitions is shown along with the timecourses of select vessels’ calibre changes in Author response image 1. The vascular calibre changes following photostimulation persisted for several minutes, consistent with earlier observations by us and others2–5. These small-volume acquisitions demonstrated that dilations were repeatedly longer than the 42 seconds (i.e. our original temporal resolution).

Our temporal sampling was chosen to permit a large field of view acquisition while still being well within the span of the vascular response to look at larger scale vascular coordination that has not previously been studied. The pipeline readily adapts to smaller fields of view at a finer temporal sampling, though such an acquisition precludes the study of the response coordination across hundreds of vessels. While a greater number of baseline frames would help with the baseline variability estimation, maintaining animals under anesthesia during prolonged imaging is exceedingly difficult, precluding us from extending our total acquisition time.

Author response image 1.

Estimated vascular radius at each timepoint for select vessels from the imaging stack shown in the following video: https://flip.com/s/kB1eTwYzwMJE

(5) A second problem is the use of optogenetic stimulation to activate the tissue. First, it has been shown that blue light itself can increase blood flow (Rungta et al 2017). The authors note the concern about temperature increases but that is not the same issue. The discussion mentions that non-transgenic mice were used to control for this with "data not shown". This is very important data given these earlier reports that have found such effects and so should be included.

We have updated the manuscript to incorporate the data on volumetric scanning in (nontransgenic) C57BL/6 mice undergoing blue light stimulation, with identical parameters as those used in Thy-ChR2 mice (Supplementary Figure 8). As before, responders were identified as vessels that following blue light stimulation showed a radius change greater than 2 standard deviations of their baseline radius standard deviation: their estimated radii changes are shown in Supplementary Figure 8.  There was no statistical difference between the radii distributions of any of the photostimulation conditions and pre-photostimulation baseline.

(6) Secondly, there doesn't seem to be any monitoring of neural activity following the photo-stimulation. The authors repeatedly mention "activated" neurons and claim that vessel properties change based on distance from "activated" neurons. But I can't find anything to suggest that they know which neurons were active versus just labeled. Third, the stimulation laser is focused at a single depth plane. Since it is single-photon excitation, there is likely a large volume of activated neurons. But there is no way of knowing the spatial arrangement of neural activity and so again, including this as a factor in the analysis of vascular responses seems unjustified.

Given the high fidelity of Channel-Rhodpsin2 activation with blue light photostimulation found by us and others3, we assume that all labeled neurons within the volume of photostimulation are being activated. Depending on their respective connectivities, their postsynaptic neurons (whether or not they are labeled) may also get activated. We therefore agree with the reviewer that the spatial distribution of neuronal activation is not well defined. The manuscript has been revised to update the terminology from activated to labeled neurons and stress in the Discussion that the motivation for assessing the distance to the closest labeled neuron as one of our metrics is purely to demonstrate the possibility of linking vascular response to activations in their neighbouring neurons and including morphological metrics in the computational pipeline.

(7) The study could also benefit from more clear illustration of the quality of the model's output. It is hard to tell from static images of 3-D volumes how accurate the vessel segmentation is. Perhaps some videos going through the volume with the masks overlaid would provide some clarity. Also, a comparison to commercial vessel segmentation programs would be useful in addition to benchmarking to the ground truth manual data.

We generated a video demonstrating the deep-learning model outputs and have made the video available here: https://flip.com/s/_XBs4yVxisNs. We aimed to develop an open-source method for the research community as the vast majority of groups do not have access to commercial software for vessel segmentation.

(8) Another useful metric for the model's success would be the reproducibility of the vessel responses. Seeing such a large number of vessels showing constrictions raises some flags and so showing that the model pulled out the same response from the same vessels across multiple repetitions would make such data easier to accept.

We have generated a figure demonstrating the repeatability of the vascular responses following photostimulation in a volume and presented them next to the corresponding raw acquisitions for visual inspection (Supplementary figure 6). It is important to note that there is a significant biological variability in vessels’ responses to repeated stimulation, as described previously 3,6: a well-performing model should be able to quantify biological heterogeneity as it of itself may be of interest. Constrictions have been reported in the literature by our group and others 1,2,4,5,7, though their prevalence has not been systematically studied to date. Concerning the reproducibility of our analysis, we have demonstrated model reproducibility (as a metric of its success) on a dataset where vessels visually appeared to dilate consistently following 452 nm light stimulation: these results are now presented in Supplementary Figure 6 of the revised Manuscript. We thus observed that the model repeatedly detected the vessels - that appeared to dilate on visual inspections - as dilating. Examples of vessels constricting repeatedly were also examined and maximal intensity projections of the vessel before and after photostimulation inspected, confirming their repeated constriction (Author response image 2).

It is also worth noting that while the presence of the response (defined as change above 2 standard deviations of the radius across baseline frames) was infrequent (2107 vessels responded at least once, out of a total of 10,552 unique vessels imaged), the direction of the response was highly consistent across trials. Given twice the baseline variability as the threshold for response, of the vessels that responded more than once, 31.7% dilated on some trials while constricting on others; 41.1% dilated on each trial; and 27.2% constricted on each trial. (Note that some trials use 1.1 vs. 4.3 mW/mm2 and some have opposite scanning directions).

Author response image 2.

Sample capillaries constrictions from maximum intensity projections at repeated time points following optogenetic stimulation. Baseline (pre-stimulation) image is shown on the left and the post-stimulation image, is on the right, with the estimated radius changes listed to the left.

(9) A number of findings are questionable, at least in part due to these design properties. There are unrealistically large dilations and constrictions indicated. These are likely due to artifacts of the automated platform. Inspection of these results by eye would help understand what is going on.

Some of the dilations were indeed large in magnitude. We present select examples of large dilations and constrictions ranging in magnitude from 2.08 to 10.80 um for visual inspection (Author response image 3) (for reference, average, across vessel and stimuli, the magnitude of radius changes were 0.32 +/- 0.54 um). Diameter changes above 5 um were visually inspected.

Author response image 3.

Additional views of diameter change in maximum intensity projections ranging in magnitude from 2.08 um to 10.80 um.

(10) In Figure 6, there doesn't seem to be much correlation between vessels with large baseline level changes and vessels with large stimulus-evoked changes. It would be expected that large arteries would have a lot of variability in both conditions and veins much less. There is also not much within-vessel consistency. For instance, the third row shows what looks like a surface vessel constricting to stimulation but a branch coming off of it dilating - this seems biologically unrealistic.

We now plot photostimulation-elicited vessel-wise radius changes vs. their corresponding baseline radius standard deviations (Author response image 4). The Pearson correlation coefficient between the baseline standard deviation and the radius change was 0.08 (p<1e-5) for  552nm 4.3 mW/mm^2 stimulation,  -0.08 (p<1e-5) for  458nm 1.1 mW/mm^2 stimulation, and -0.04 (p<1e-5) for  458nm 4.3 mW/mm^2 stimulation. For non-control (i.e. blue) photostimulation conditions, the change in the radius is thus negatively correlated to the vessel’s baseline radius standard deviation: this small negative correlation indicates that there is little correlation between vessel radius change and the baseline variability in the vessel radius. Classification of vessels by type (arteries vs. veins) is needed before we can comment on differences between these vascular components. The between-vessel (i.e. between parent vessels and their daughter branches separated by branch points) consistency is explicitly evaluated by the assortativity metric, in Figure 9: vessels do somewhat tend to react similarly to their downstream branches: we observed a mean assortativity of 0.4. As for the instance of a surface vessel constricting while a downstream vessel dilates, it is important to remember that the 2PFM FOV restricts us to imaging a very small portion of the cortical microvascular network: one (among many) daughter vessels showing changes in the opposite direction to the parent vessel is not violating the conservation of mass; in addition, mural cells on adjacent branches can respond differently.

Author response image 4.

Vessel radius change elicited by photostimulation vs. baseline radius standard deviation across all vessels. The threshold level for response identification is shown as the black line.

(11) As mentioned, the large proportion of constricting capillaries is not something found in the literature. Do these happen at a certain time point following the stimulation? Did the same vessel segments show dilation at times and constriction at other times? In fact, the overall proportion of dilators and constrictors is not given. Are they spatially clustered? The assortativity result implies that there is some clustering, and the theory of blood stealing by active tissue from inactive tissue is cited. However, this theory would imply a region where virtually all vessels are dilating and another region away from the active tissue with constrictions. Was anything that dramatic seen?

The kinetics of the vascular responses are not accessible via the current imaging protocol and acquired data; however, this computational pipeline can readily be adapted to test hypotheses surrounding the temporal evolution of the vascular responses, as shown in Supplementary Figure 2 (with higher temporal-resolution data). Some vessels dilate at some time points and constrict at others as shown in Supplementary Figure 2. As listed in Table 2, 4.4% of all vessels constrict and 7.5% dilate for 452nm stimulation at 4.3 mW/mm^2. There was no obvious spatial clustering of dilators or constrictors: we expect such spatial patterns to be more common with different modes of stimulation and/or in the presence of pathology. The assortativity peaked at 0.4 (quite far from 1 where each vessel’s response exactly matches that of its neighbour).

(12) Why were nearly all vessels > 5um diameter not responding >2SD above baseline? Did they have highly variable baselines or small responses? Usually, bigger vessels respond strongly to local neural activity.

In Author response image 5, we now present the stimulation-induced radius changes vs. baseline radius variability across vessels with a radius greater than 5 um. The Pearson correlation between the radius change and the baseline radius standard deviation across time was low: r=0.05 (p=0.5) for  552nm 4.3 mW/mm^2 stimulation,  r=-0.27 (p<1e-5) for  458nm 1.1 mW/mm^2 stimulation, and r=-0.31 (p<1e-5) for 458nm 4.3 mW/mm^2 stimulation. These results demonstrate that the changes following optogenetic stimulation are lower than twice the baseline standard deviation across time for most of these vessels. The pulsatility of arteries results in significant variability in their baseline radius8; in turn, literature to date suggests very limited radius changes in veins. Both of these effects could contribute to the radius response not being detected in many larger vessels.

Author response image 5.

The change in the vessel radius elicited by photostimulation vs. baseline vessel radius standard deviation in vessels with a baseline radius greater than 5 um. The threshold level for response identification is shown as the black line.

References

(1) Mester JR, Rozak MW, Dorr A, Goubran M, Sled JG, Stefanovic B. Network response of brain microvasculature to neuronal stimulation. NeuroImage. 2024;287:120512. doi:10.1016/j.neuroimage.2024.120512

(2) Alarcon-Martinez L, Villafranca-Baughman D, Quintero H, et al. Interpericyte tunnelling nanotubes regulate neurovascular coupling. Nature. 2020;kir 2.1(7823):91-95. doi:10.1038/s41586-020-2589-x

(3) Mester JR, Bazzigaluppi P, Weisspapir I, et al. In vivo neurovascular response to focused photoactivation of Channelrhodopsin-2. NeuroImage. 2019;192:135-144. doi:10.1016/j.neuroimage.2019.01.036

(4) O’Herron PJ, Hartmann DA, Xie K, Kara P, Shih AY. 3D optogenetic control of arteriole diameter in vivo. Nelson MT, Calabrese RL, Nelson MT, Devor A, Rungta R, eds. eLife. 2022;11:e72802. doi:10.7554/eLife.72802

(5) Hartmann DA, Berthiaume AA, Grant RI, et al. Brain capillary pericytes exert a substantial but slow influence on blood flow. Nat Neurosci. Published online February 18, 2021:1-13. doi:10.1038/s41593-020-00793-2

(6) Mester JR, Bazzigaluppi P, Dorr A, et al. Attenuation of tonic inhibition prevents chronic neurovascular impairments in a Thy1-ChR2 mouse model of repeated, mild traumatic brain injury. Theranostics. 2021;11(16):7685-7699. doi:10.7150/thno.60190

(7) Hall CN, Reynell C, Gesslein B, et al. Capillary pericytes regulate cerebral blood flow in health and disease. Nature. 2014;508(7494):55-60. doi:10.1038/nature13165

(8) Meng G, Zhong J, Zhang Q, et al. Ultrafast two-photon fluorescence imaging of cerebral blood circulation in the mouse brain in vivo. Proc Natl Acad Sci U S A. 2022;119(23):e2117346119. doi:10.1073/pnas.2117346119

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Line 207: a superfluous '.' before the references.

This has been corrected.

Line 273 ff:

While the metrics are described in mathematical terms which is very useful, the appearing distances (d) and mathematical symbols are not. While mostly intuitively clear, precise definitions of all symbols introduced should be given to avoid ambiguities.

The description has been clarified.

This applies to all formulas appearing in the manuscript and the authors might want to check them carefully.

We have updated them wherever needed.

The mean surface distance seems not to reflect the mean MINIMAL surface distance but just the overall mean surface distance. Or a different definition of the appearing symbols is used, highlighting the need for introducing every mathematical symbol carefully.

The definitions have been updated for clarity, specifying the distinction between Hausdorff 95% distance and mean surface distance.

Line 284:

It is unclear to me why center-line detection was performed in MATLAB and not Python. Using multiple languages/software packages and in addition relying on one that is not freely available/open source makes this tool much less attractive as a real open-source tool for the community. The authors stress in the manuscript abstract that their pipeline is an open and accessible tool, the use of MATLAB defies this logic to some extent in my view.

Centerline detection for large volumetric data is available in Python, see e.g. Scipy packages as well for large data sets via ClearMap or VesselVio.

We tested the centerline detection in Python, scipy (1.9.3) and Matlab. We found that the Matlab implementation performed better due to its inclusion of a branch length parameter for the identification of terminal branches, which greatly reduced the number of false branches; the Python implementation does not include this feature (in any version) and its output had many more such “hair” artifacts. Clearmap skeletonization uses an algorithm by Palagyi & Kuba(1999) to thin segmentation masks, which does not include hair removal. Vesselvio uses a parallelized version of the scipy implementation of Lee et al. (1994) algorithm which does not do hair removal based on a terminal branch length filter; instead, Vesselvio performs a threshold-based hair removal that is frequently overly aggressive (it removes true positive vessel branches), as highlighted by the authors.

Moreover, the authors mention that robust center-line detection was critical. In my view, robust center-line extraction typically requires some additional processing of the binarized data, e.g. using a binary smoothing step. Various binary smoothers are available in the literature and as Python code.

Indeed, binary smoothing was performed: background “holes” located within the vasculature were filled; the masks were dilated (3x) and then eroded to the centreline. Scipy’s binary closing function smoothes the morphology of binary segmentation masks by dilating and then eroding the segmentation masks (as a part of the selected skeletonization algorithm).

Line 303:

'RBC' is not defined (red blood cells?)

This has been updated.

Line 398:

pPhotonsimulation -> Photostimulation

This has been corrected.

Line 400 ff: Efficiency:

I am not sure how useful the measure really is without any information about the 'sources' (i.e. arteries) and sinks (i.e. veins) as blood does not need to be moved between any two arbitrary nodes.

While blood reversals are observed, blood is typically not moved arbitrarily between two arbitrary nodes in capillary networks.

We agree with the reviewer that classifying the vessels by type is important and are currently working on deep learning-based algorithms for the classification of microvasculature into arterioles and venules for future work.

In addition, short paths between two nodes with low resistivity will potentially dominate the sum and the authors excluded vessels 10um and above. This threshold seems arbitrary.

The 10-um diameter threshold was not applied in the computation of the network metrics. The 10-um thresholding was restricted to “capillary” identification in Figure 8: the 10-um cutoff for referring to a vessel as a capillary has long been applied in the literature [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11].

Figure 3:

It's unclear what the units are for the Mean Surface and Harsdorf Distances (pixel or um?).

The units have now been specified (um).

Figure 4:

The binarized data, and particularly the crops are difficult to interpret in black and white. It would be much more useful to present the segmentation results in a way that is interpretable (e.g. improving the rendering of the 3d information, particularly in the crops by using shadows or color codes for depth, etc).

We have updated these visualizations and shaded them based on cortical depth.

Panel C indicates that the illastik is performing badly due to changes in imagining conditions (much higher background level). As pointed out before, in my view, a reasonable pipeline should start by removing and standardizing background levels as well as dynamic ranges and possibly other artifacts before performing a more detailed analysis. This would also make the pipeline more robust against data from other microscopes etc as only a few preprocessing parameters might need to be adjusted.

I wonder whether after such a pre-processing step, UNET / UNETR would still perform in a way that was superior to ilastik, as ground truth data was generated with the aid of illastiks initially.

The Ilastik model is based on semi-automatically generated foreground labels in small batches. We had to break it up into small groups during manual labelling as larger groups were not able to run due to the computational limits of Ilastik. Ilastik is typically trained in an iterative fashion on a few patches at a time because it takes 2-3 hours per patch to train and the resulting model does not generalize on the remaining patches or out-of-distribution data - even with image pre-processing steps. On the reviewer's comment, we did try inputting normalized images into Ilastik, but this did not improve its results. UNET and UNETR inputs have been normalized for signal intensities.

Typical pre-processing/standard computer vision techniques with parameter tuning do not generalize on out-of-distribution data with different image characteristics, motivating the shift to DL-based approaches.

Figure 5:

This is a validation figure that might be better shown in an appendix or as a supplement.

Since this is a methodological paper, we think it is important to highlight the validation of the proposed method.

Line 476:

It's surprising that the number of vessel segments almost doubles when taking the union. Is the number of RBC plugs expected to be so high?

The etiology of discontinuities includes, but is not limited to, RBC plugs; we expect discontinuities to arise also from a very short pixel dwell time (0.067us) of the resonant scanning and have indeed observed apparent vessel discontinuities on resonant scanning that are not present with Galvano scanning using a pixel dwell time of 2us.

Section 4.4 / 4.5 :

The analysis in these sections provides mostly tables with numbers that are more difficult to read and hides possible interesting structures in the distribution of the various measures/quantities. For example, why is 5um a good choice to discriminate between small and large vessels, why not resolve this data more precisely via scatter plots?

Some distributions are shown in the appendix and could be moved to the main analysis.

Generally, visualizing the data and providing more detailed insights into the results would make this manuscript more interesting for the general reader.

The radius of vessel segments drops off after 5.0 um, as shown in Supplementary Figure 4A. The 10-um diameter thresholding is based on prior literature [1], [12], [13], [14], [15], [16], [17], [18], [19] and is used to segregate different vessel types in a conservative manner. The smallest capillaries are expected to have pericytes on their vessel walls whereas arteries are expected to have smooth muscle cells on their vessel walls. These differences in mural cells also may lead to differences in respective vessels’ reactivity.

The data summarized in Tables 1 and 2 are shown as scatter plots in Figures 8, Supplementary Fig 4 and Supplementary Fig 5.

Line 556:

The authors deem a certain change in radius as the relevant measure for responding vessels. They deem a vessel responding if it dilates by twice the std deviation in the radius.

Based on this measure they find that large vessels rarely respond.

However, I think this analysis might obscure some interesting effects:

(1) The standard deviation of the radius depends on the correct estimation of the center point. Given the limited spatial resolution the center point (voxel) obtained from the binarization and skeletonization might not lie in the actual center of the vessel. This effect will be stronger for larger vessels. Center point coordinates should thus be corrected to minimize the std in radius.

(2) Larger vessels will not necessarily have a perfectly circular shape, and thus the std measure is not necessarily a good measure of 'uncertainty' of estimating the actual radius.

(3) The above reasons possibly contribute to the fact that from Figure 6 it seems vessels with larger radii have higher std in general (as indicated above some more detailed visualization of the data instead of plain tables could reveal such effects better, e.g. scatter radius vs std). This higher std is making it harder to detect changes in larger vessels. However, with respect to the blood flow, the critical factor is the cross-section of the vessel that scales with the radius squared. Thus, a fixed change in radius for a vessel (say 1um) will induce a larger increase in the flow rate in larger vessels as the change in cross-section is also proportional to the radius of the vessel.

Thus, larger vessels to be deemed responders should probably have lower thresholds, thresholds should be taken on the cross-section change, or at least thresholds should not be higher for larger vessels as it is the case now using the higher std.

(1) The radius estimate does not depend on the precise placement of the center point as the radius is not being estimated by the distance from the center point to the boundary of the vessel. Instead, our strategy is to estimate the cross-sectional area (A) of the vessel by the Riemann sum of the sectors with the apex at the center point; the radius is then quoted as sqrt(A/pi) (Supplementary figure 3B). Thus, estimated vessel radius estimates in each cross-sectional plane are then averaged across the cross-sectional planes placed every ~1um along the vessel length. The uncertainty in the cross-sectional plane’s vessel radius, the uncertainty in the vessel radius (upon averaging the cross-sectional planes), and the uncertainty in the radius estimate across repeated measures of a state (i.e. across different samples of the baseline vs, post-photostimulation states) are all reported, and the last one used to define responding vessels.

To demonstrate the insensitivity to the precise placement of the vessel’s centrepoint, we have jittered the centerline in the perpendicular plane to the vessel tangent plane at each point along the vessel and then estimated the mean radius in 71 cross-sectional planes of larger vessels (mean radius > 5 um). The percent difference in the estimated radius at our selected vessel centrepoints vs. the jittered centrepoints is plotted above. The percent difference in the mean radius estimated was 0.64±3.44%  with 2.45±0.30 um centerpoint jittering. (In contrast, photostimulation was estimated to elicit an average 25.4±18.1% change in the magnitude of the radius of larger vessels, i.e. those with a baseline radius >5um.)

(2) Indeed, the cross-sectional areas of either large or small vessels are not circles. Consequently, we are placing the vessel boundary, following other published work[20], at the minimum of the signal intensity gradients computed along thirty-six spokes emanating from the centrepoint (cf Figure 2H,K). The cross-sectional area of the vessel in the said cross-sectional plane is then estimated by summing the areas of the sectors flanked by neighbouring spokes. We do not make an assumption about the cross-sectional area being circular. We report radii of circles with the equivalent area as that of the cross-sectional areas merely for ease of communication (as most of the literature to date reports vessel radii, rather than vessel cross-sectional areas.)

To demonstrate the robustness of this approach, we show the sensitivity of vessel-wise radius estimate on the number of spokes used to estimate the radius in Supplementary Figure 3a. The radius estimate converges after 20 spokes have been used for estimation. Our pipeline utilizes 36 spokes and then excludes minima that lie over 2 STD away from the mean radius estimate across those 36 spokes. With 36 spokes, the vesselwise mean radius estimation was within 0.24±0.62% of the mean of radius estimates using 40-60 spokes.

(3) Across-baseline sample uncertainty in vessel radius is not dependent on baseline vessel caliber (i.e. this uncertainty is not larger in larger vessels).

Supplementary Figure 5 shows vessel radius changes for large vessels without a threshold defining responding or non-responding vessels. To explore the dependence of the outcomes on the threshold used to identify the responding vessels, we have explored an alternative strategy, whereby responding small vessels are identified as those vessels that show a post-photostimulation (vs. baseline) radius change of more than 10%. These data are now plotted in Supplementary Figure 10, for capillaries which is in agreement with Figure 8. These points are now also discussed in the Discussion section of the revised manuscript:

“Additionally, alternative definitions of responding vessels may be useful depending on the end goal of a study (e.g., this could mean selecting a threshold for the radius change based on a percentage change from the baseline level).”

Section 4.5.1

Why is the distance to the next neuron a good measure here? If two or more neurons are just a bit further away there will be twice or multiple times the 'load' while the measure would only indicate the distance to the shortest neuron. I wonder how the results change if those 'ensemble' effects are taken into account.

In this direction, looking for network-level effects with respect to the full spatial organization of the neurons would be very interesting to look at.

We agree with the review that this question is interesting; however, it is not addressable using present data: activated neuronal firing will have effects on their postsynaptic neighbors, yet we have no means of measuring the spread of activation using the current experimental model.

Figure 8

The scatter plots shown are only partly described (e.g. what's the line with error bars in C, why does it only appear for the high-intensity stimulation?).

Quadratic polynomial fit is shown only in C as the significant response was observed only for this condition, i.e. for the higher intensity blue photostimulation.

From the scatter plots as shown it is not clear to me why dilations happen on average further away. This might be a density effect not well visible in this representation. The data does not seem to show a clear relationship between neuron distance and Delta R.

Particularly in the right panel (high stimulation) there seems to be a similar number of close by neurons responding in both directions, but possibly a few more contracting at larger distances?

So, the overall effect does not seem as 'simple' as suggested in the title of section 4.5.1 in my view, but rather more cells start to contract at larger distances while there seems to be a more intricate balance nearby.

A more thorough analysis and visualization of the densities etc. might be needed to clarify this point.

The language has been revised to:

458-nm photostimulation resulted in a mix of constrictions and dilations with 44.1% of significantly responding vessels within 10 um of a labelled pyramidal neuron constricting and 55.1% dilating, while 53.3% of vessels further than 30 um constricted and 46.7% dilated. The cutoff distances from the closest labelled neuron were based on estimates of cerebral metabolic rate of oxygen consumption that showed a steep gradient in oxygen consumption with distance from arteries, CMRO2 being halved by 30 μm away

We added a probability density plot for significant constrictors and dilators to Figure 8 and Supplementary Figure 5.

Figure 8 Panel D / Section 4.5.2

This is a very interesting result in my view found in this study.

I am unclear how to interpret the effect. The authors state that dilators tend to be closer to the surface. Looking at the scatter plot (without real density information except the alpha value) it seems again the number of responders in both directions is about the same, but in deeper regions the contraction is just larger? This would be different, than how the authors interpret the data. It is unclear from the provided analysis/plots what is actually the case.

We added a probability density function plot of the constrictors and dilators, which shows a greater incidence of constrictions (vs. dilations). The text of the paper was then clarified to include the proportion of significant constrictors/ dilators closer than 10 um vs. further than 30 um away from the closest labeled neuron.

For the analyses above involving $Delta R$ I recommend also look how those results change when looking at changes in cross section instead, i.e. taking into account the actual vessel radius as well as discussed above.

It would be interesting to speculate here or in the discussion on a reason why vessels in deeper regions might need to contract more?

Unaddressed is the question if e.g. contraction in a vessel for small stimulation is predictive of contractions for larger stimulation or any other relationships?

Thank you for your comment. Given its hierarchical organization and high within-vessel response heterogeneity, we believe that the vasculature is best analyzed as a network. Our radius estimates come from averaged cross-sectional estimates allowing us to examine heterogeneity within individual vessel segments.

The discussion has been updated to include reasons as to why deeper vessels may contract more:

“As the blue light stimulation power increased, the mean depth of both constricting and dilating vessels increased, likely resulting from higher intensity light reaching ChR2-expressing neurons deeper in the tissue and exciting superficial neurons (and thus their postsynaptic neurons) to a greater level [21], [22]. The blue light would be expected to excite a lower number of neurons farther from the cortical surface at lower powers.”

Also, how consistent are contractions/dilations observed at a particular vessel etc.

To look at the consistency of a particular vessel's response to the 1.1 or 4.3 mW/mm^2 blue light photostimulation, we categorized all significant responses as constrictions or dilations, defining a responding vessel as that showing a change that is either > 2 x baseline vessel radius variability or >10% of the vessel’s mean baseline radius.

Given twice the baseline variability as the threshold for response, of the vessels that responded more than once, 31.7% dilated on some trials while constricting on others; 41.1% dilated on each trial; and 27.2% constricted on each trial. (Note that some trials use 1.1 vs. 4.3 mW/mm2 and some have opposite scanning directions).

Section 4.5.3

The results in assortativity are interesting. It would be interesting to look at how the increase in assortativity is mediated. For, example, is this in localized changes in some parts of the graph as visible in A or are there other trends? Do certain sub-graphs that systematically change their radius have certain properties (e.g. do activated neurons cluster there) or are these effects related to some hotspots that also show a coordinated change in control conditions (the assortativity seems not zero there)?

I already discussed if the efficiency measure is necessarily the best measure to use here without taking into account 'sources' and 'sinks'.

We plan to address this in future work once we have successfully trained models for the classification of vessels into arteries, veins, and capillaries. Capillaries will be classified based on their branch order from parent arteries to specify where in the network changes are occurring.

Figure 9

It's unclear to me why the Ohm symbol needs to be bold?

It is not bolded (just the font’s appearance).

Line 707:

"458-nm photostimulation caused capillaries to dilate when pyramidal neurons were close, and constrict when they were further away."

In my view, this interpretation is too simple, given the discussion above. A more detailed analysis could clarify this point.

The discussion on this point has been revised to:

458-nm photostimulation resulted in a mix of constrictions and dilations, with 44.1% of significantly responding vessels within 10 μm of a labelled pyramidal neuron constricting, and 55.1% dilating; while 53.3% of vessels further than 30 μm constricted and 46.7% dilated. The cutoff distances from the closest labelled neuron were based on estimates of cerebral metabolic rate of oxygen consumption that showed a steep gradient in oxygen consumption with distance from arteries, CMRO2 being halved by 30 μm away [23].

Line 740:

"The network efficiency here can be thought of as paralleling mean transit time, i.e., the time it takes blood to traverse the capillary network from the arteries to the veins".

The network efficiency as defined by the authors seems not to rely on artery/vein information and thus this interpretation is not fully correct in my view.

The authors might want to reconsider this measure for one that accounts for sources and sinks, if they like to interpret their results as in this line.

Yes, the efficiency described does not account for sources and sinks. It estimates the resistivity of capillaries, as a proxy for the ease of moving through the observed capillary nexus. Looking at the efficiency metric from graph theory does not require knowledge of the direction of blood flow, and can comment on the resistivity changes across capillary networks.

For future work, we are investigating methods of classifying vessels as arteries, capillaries, or veins. This type of analysis will provide more detailed information on paths between arteries and veins; it will not provide insight into large-scale network-wide modifications, as those require larger fields of view. 

Line 754 Pipeline Limitations and Adaptability

I think the additional 'problem' of generating new training data for novel data sets or data from other microscopes etc should be addressed or the pipeline tested on such data sets.

Generating training data is typically the biggest time investment when adapting pipelines.

The generalization properties of the current pipeline are not discussed (e.g. performance on a different microscope / different brain area / different species etc.).

The public response to reviews has been updated with out-of-distribution data from other imaging protocols, microscopes, and species showing generalizability. These results have also been added to the paper as Supplementary Table 4, and Figure 6. The performance of our pipeline on these out-of-distribution data is now discussed in the updated Discussion section.

Line 810

Code availability should be coupled with the publication of this paper as it seems the main contribution. I don't see how the code can be made available after publication only. It should be directly available once the manuscript is published and it could help to make it available to the reviewers before that. It can be updated later of course.

The code is being made available.

Reviewer #2 (Recommendations For The Authors):

This analytical pipeline could be quite useful but it needs to be better demonstrated. If faster volumetric imaging is not possible, perhaps using it over a small volume would still demonstrate its utility at a smaller but more believable scale.

The higher temporal resolution scans (over smaller tissue volumes) have now been performed and the results of applying our pipeline to these data are summarized in Supplementary Figure 2.

Using sensory stimuli for neuronal activation might be a better idea than optogenetic stimulation. It isn't necessary but it would avoid the blue light issue.

The pipeline is readily applicable for analysis of vasoreactivity following different perturbers; however, the robustness of vessels’ response is higher with blue light photostimulation of ChR2 than with sensory stimuli [24]. Notwithstanding, an example of the vascular response to electrical stimulation of the contralateral forepaw is now included in Supplementary Figure 2.

This tool could be quite useful even without neural activity mapping. It obviously makes it even more powerful, but again, the utility could be demonstrated with just vascular data or even anatomical neuronal data without function.

We agree with both points, and have emphasized them in the revised discussion section.

Line 559 says the average capillary diameter change was 1.04 um. The next sentence and the table below all have different values so this is unclear.

The wording was updated to make this clearer.

Line 584 - should 458 be 552?

458 is correct.

Figure 1 - the schematic doesn't seem right - the 650 LPF with the notches is positioned to pass short light and reflect long wavelengths and the notch bands.

The figure has been updated to reflect this. The original layout was done for compactness.

References

(1) D. A. Hartmann, V. Coelho-Santos, and A. Y. Shih, “Pericyte Control of Blood Flow Across Microvascular Zones in the Central Nervous System,” Annu. Rev. Physiol., vol. 84, no. Volume 84, 2022, pp. 331–354, Feb. 2022, doi: 10.1146/annurev-physiol-061121-040127.

(2) J. Batista, “An adaptive gradient-based boundary detector for MRI images of the brain,” in 7th International Conference on Image Processing and its Applications, Manchester, UK: IEE, 1999, pp. 440–444. doi: 10.1049/cp:19990360.

(3) Y. Le, X. Xu, L. Zha, W. Zhao, and Y. Zhu, “Tumor boundary detection in ultrasound imagery using multi-scale generalized gradient vector flow,” J. Med. Ultrason., vol. 42, no. 1, pp. 25–38, Jan. 2015, doi: 10.1007/s10396-014-0559-3.

(4) X. Ren, “Multi-scale Improves Boundary Detection in Natural Images,” in Computer Vision – ECCV 2008, D. Forsyth, P. Torr, and A. Zisserman, Eds., Berlin, Heidelberg: Springer, 2008, pp. 533–545. doi: 10.1007/978-3-540-88690-7_40.

(5) C. Grigorescu, N. Petkov, and M. A. Westenberg, “Contour and boundary detection improved by surround suppression of texture edges,” Image Vis. Comput., vol. 22, no. 8, pp. 609–622, Aug. 2004, doi: 10.1016/j.imavis.2003.12.004.

(6) J. Tang and S. T. Acton, “Vessel Boundary Tracking for Intravital Microscopy Via Multiscale Gradient Vector Flow Snakes,” IEEE Trans. Biomed. Eng., vol. 51, no. 2, pp. 316–324, Feb. 2004, doi: 10.1109/TBME.2003.820374.

(7) J. Merkow, A. Marsden, D. Kriegman, and Z. Tu, “Dense Volume-to-Volume Vascular Boundary Detection,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, Eds., Cham: Springer International Publishing, 2016, pp. 371–379. doi: 10.1007/978-3-319-46726-9_43.

(8) F. Orujov, R. Maskeliūnas, R. Damaševičius, and W. Wei, “Fuzzy based image edge detection algorithm for blood vessel detection in retinal images,” Appl. Soft Comput., vol. 94, p. 106452, Sep. 2020, doi: 10.1016/j.asoc.2020.106452.

(9) M. E. Martinez-Perez, A. D. Hughes, S. A. Thom, A. A. Bharath, and K. H. Parker, “Segmentation of blood vessels from red-free and fluorescein retinal images,” Med. Image Anal., vol. 11, no. 1, pp. 47–61, Feb. 2007, doi: 10.1016/j.media.2006.11.004.

(10) A. M. Mendonca and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Trans. Med. Imaging, vol. 25, no. 9, pp. 1200–1213, Sep. 2006, doi: 10.1109/TMI.2006.879955.

(11) A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” in Medical Image Computing and Computer-Assisted Intervention — MICCAI’98, W. M. Wells, A. Colchester, and S. Delp, Eds., Berlin, Heidelberg: Springer, 1998, pp. 130–137. doi: 10.1007/BFb0056195.

(12) K. Bisht et al., “Capillary-associated microglia regulate vascular structure and function through PANX1-P2RY12 coupling in mice,” Nat. Commun., vol. 12, no. 1, p. 5289, Sep. 2021, doi: 10.1038/s41467-021-25590-8.

(13) Y. Wu et al., “Quantitative relationship between cerebrovascular network and neuronal cell types in mice,” Cell Rep., vol. 39, no. 12, p. 110978, Jun. 2022, doi: 10.1016/j.celrep.2022.110978.

(14) T. Kirabali et al., “The amyloid-β degradation intermediate Aβ34 is pericyte-associated and reduced in brain capillaries of patients with Alzheimer’s disease,” Acta Neuropathol. Commun., vol. 7, no. 1, p. 194, Dec. 2019, doi: 10.1186/s40478-019-0846-8.

(15) X. Ren et al., “Linking cortical astrocytic neogenin deficiency to the development of Moyamoya disease–like vasculopathy,” Neurobiol. Dis., vol. 154, p. 105339, Jul. 2021, doi: 10.1016/j.nbd.2021.105339.

(16) J. Steinman, M. M. Koletar, B. Stefanovic, and J. G. Sled, “3D morphological analysis of the mouse cerebral vasculature: Comparison of in vivo and ex vivo methods,” PLOS ONE, vol. 12, no. 10, p. e0186676, Oct. 2017, doi: 10.1371/journal.pone.0186676.

(17) A.-A. Berthiaume et al., “Dynamic Remodeling of Pericytes In Vivo Maintains Capillary Coverage in the Adult Mouse Brain,” Cell Rep., vol. 22, no. 1, pp. 8–16, Jan. 2018, doi: 10.1016/j.celrep.2017.12.016.

(18) S. Katz, R. Gattegno, L. Peko, R. Zarik, Y. Hagani, and T. Ilovitsh, “Diameter-dependent assessment of microvascular leakage following ultrasound-mediated blood-brain barrier opening,” iScience, vol. 26, no. 6, p. 106965, Jun. 2023, doi: 10.1016/j.isci.2023.106965.

(19) J. Drouin-Ouellet et al., “Cerebrovascular and blood-brain barrier impairments in Huntington’s disease: Potential implications for its pathophysiology,” Ann. Neurol., vol. 78, no. 2, pp. 160–177, Aug. 2015, doi: 10.1002/ana.24406.

(20) K. P. McDowell, A.-A. Berthiaume, T. Tieu, D. A. Hartmann, and A. Y. Shih, “VasoMetrics: unbiased spatiotemporal analysis of microvascular diameter in multi-photon imaging applications,” Quant. Imaging Med. Surg., vol. 11, no. 3, pp. 969–982, Mar. 2021, doi: 10.21037/qims-20-920.

(21) E. L. Johnson et al., “Characterization of light penetration through brain tissue, for optogenetic stimulation.” bioRxiv, p. 2021.04.08.438932, Apr. 08, 2021. doi: 10.1101/2021.04.08.438932.

(22) S. I. Al-Juboori, A. Dondzillo, E. A. Stubblefield, G. Felsen, T. C. Lei, and A. Klug, “Light scattering properties vary across different regions of the adult mouse brain,” PloS One, vol. 8, no. 7, p. e67626, 2013, doi: 10.1371/journal.pone.0067626.

(23) P. Mächler et al., “Baseline oxygen consumption decreases with cortical depth,” PLOS Biol., vol. 20, no. 10, p. e3001440, Oct. 2022, doi: 10.1371/journal.pbio.3001440.

(24) J. R. Mester et al., “In vivo neurovascular response to focused photoactivation of Channelrhodopsin-2,” NeuroImage, vol. 192, pp. 135–144, May 2019, doi: 10.1016/j.neuroimage.2019.01.036.

Author response:

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

eLife Assessment

This study uses state-of-the-art methods to label endogenous dopamine receptors in a subset of Drosophila mushroom body neuronal types. The authors report that DopR1 and Dop2R receptors, which have opposing effects in intracellular cAMP, are present in axons termini of Kenyon cells, as well as those of two classes of dopaminergic neurons that innervate the mushroom body indicative of autocrine modulation by dopaminergic neurons. Additional experiments showing opposing effects of starvation on DopR1 and DopR2 levels in mushroom body neurons are consistent with a role for dopamine receptor levels increasing the efficiency of learned food-odour associations in starved flies. Supported by solid data, this is a valuable contribution to the field.

We thank the editors for the assessment, but request to change “DopR2” to “Dop2R”. The dopamine receptors in Drosophila have confusing names, but what we characterized in this study are called Dop1R1 (according to the Flybase; aka DopR1, dDA1, Dumb) and Dop2R (ibid; aka Dd2R). DopR2 is the name of a different dopamine receptor.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is an important and interesting study that uses the split-GFP approach. Localization of receptors and correlating them to function is important in understanding the circuit basis of behavior.

Strengths:

The split-GFP approach allows visualization of subcellular enrichment of dopamine receptors in the plasma membrane of GAL4-expressing neurons allowing for a high level of specificity.

The authors resolve the presynaptic localization of DopR1 and Dop2R, in "giant" Drosophila neurons differentiated from cytokinesis-arrested neuroblasts in culture as it is not clear in the lobes and calyx.

Starvation-induced opposite responses of dopamine receptor expression in the PPL1 and PAM DANs provide key insights into models of appetitive learning.

Starvation-induced increase in D2R allows for increased negative feedback that the authors test in D2R knockout flies where appetitive memory is diminished.

This dual autoreceptor system is an attractive model for how amplitude and kinetics of dopamine release can be fine-tuned and controlled depending on the cellular function and this paper presents a good methodology to do it and a good system where the dynamics of dopamine release can be tested at the level of behavior.

Weaknesses:

LI measurements of Kenyon cells and lobes indicate that Dop2R was approximately twice as enriched in the lobe as the average density across the whole neuron, while the lobe enrichment of Dop1R1 was about 1.5 times the average, are these levels consistent during different times of the day and the state of the animal. How were these conditions controlled and how sensitive are receptor expression to the time of day of dissection, staining, etc.

To answer this question, we repeated the experiment in two replicates at different times of day and confirmed that the receptor localization was consistent (Figure 3 – figure supplement 1); LI measurements showed that Dop2R is enriched more in the lobe and less in the calyx compared to Dop1R1 (Figure 3D). The states of animals that could affect LI (e.g. feeding state and anesthesia for sorting, see methods) were kept constant. 

The authors assume without discussion as to why and how presynaptic enrichment of these receptors is similar in giant neurons and MB.

In the revision, we added a short summary to recapitulate that the giant neurons exhibit many characteristics of mature neurons (Lines #152-156): "Importantly, these giant neurons exhibit characteristics of mature neurons, including firing patterns (Wu et al., 1990; Yao & Wu, 2001; Zhao & Wu, 1997) and acetylcholine release (Yao et al., 2000), both of which are regulated by cAMP and CaMKII signaling (Yao et al., 2000; Yao & Wu, 2001; Zhao & Wu, 1997)." In addition, we found punctate Brp accumulations localized to the axon terminals of the giant neurons (former Figure 4D and 4E). Therefore, the giant neuron serves as an excellent model to study the presynaptic localization of dopamine receptors in isolated large cells.

Figures 1-3 show the expensive expression of receptors in alpha and beta lobes while Figure 5 focusses on PAM and localization in γ and β' projections of PAM leading to the conclusion that presynaptic dopamine neurons express these and have feedback regulation. Consistency between lobes or discussion of these differences is important to consider.

In the revised manuscript, we show data in the γ KCs (Figure 4C, Figure 5 - figure supplement 1) in addition to α/β KCs, and demonstrate the consistent synaptic localization of Dop1R1 and Dop2R as in α/β KCs (Figure 4B and 5A). 

Receptor expression in any learning-related MBONs is not discussed, and it would be intriguing as how receptors are organized in those cells. Given that these PAMs input to both KCs and MBONs these will have to work in some coordination.

The subcellular localization of dopamine receptors in MBONs indeed provides important insights into the site of dopaminergic signaling in these neurons (Takemura et al., 2017; Pavlowsky et al., 2018; Pribbenow et al., 2022). Therefore, we added new data for Dop1R1 and Dop2R in MBON-γ1pedc>αβ (Figure 6). Interestingly, these receptors are localized to in the dendritic projection in the γ1 compartment as well as presynaptic boutons (Figure 6). 

Although authors use the D2R enhancement post starvation to show that knocking down receptors eliminated appetitive memory, the knocking out is affecting multiple neurons within this circuit including PAMs and KCs. How does that account for the observed effect? Are those not important for appetitive learning? 

In the appetitive memory experiment (Figure 9C), we knocked down Dop2R only in the select neurons of the PPL1 cluster, and this manipulation does not directly affect Dop2R expression in PAMs and KCs.

Starvation-induced enhancement of Dop2R expression in the PPL1 neurons (Figure 8F) would attenuate their outputs and therefore disinhibit expression of appetitive memory in starved flies (Krashes et al., 2009). Consistently, Dop2R knock-down in PPL1 impaired appetitive memory in starved flies (Figure 9C). We revised the corresponding text to make this point clearer (Lines #224227).

The evidence for fine-tuning is completely based on receptor expression and one behavioral outcome which could result from many possibilities. It is not clear if this fine-tuning and presynaptic feedback regulation-based dopamine release is a clear possibility. Alternate hypotheses and outcomes could be considered in the model as it is not completely substantiated by data at least as presented.

The reviewer’s concern is valid, and the presynaptic dopamine tuning by autoreceptors may need more experimental support. We therefore additionally discussed another possibility (Lines #289-291): “Alternatively, these presynaptic receptors could potentially receive extrasynaptic dopamine released from other DANs. Therefore, the autoreceptor functions need to be experimentally clarified by manipulating the receptor expression in DANs.”

Reviewer #2 (Public Review):

Summary:

Hiramatsu et al. investigated how cognate neurotransmitter receptors with antagonizing downstream effects localize within neurons when co-expressed. They focus on mapping the localization of the dopaminergic Dop1R1 and Dop2R receptors, which correspond to the mammalian D1- and D2-like dopamine receptors, which have opposing effects on intracellular cAMP levels, in neurons of the Drosophila mushroom body (MB). To visualize specific receptors in single neuron types within the crowded MB neuropil, the authors use existing dopamine receptor alleles tagged with 7 copies of split GFP to target reconstitution of GFP tags only in the neurons of interest as a read-out of receptor localization. The authors show that both Dop1R1 and Dop2R, with differing degrees, are enriched in axonal compartments of both the Kenyon Cells cholinergic presynaptic inputs and in different dopamine neurons (DANs), which project axons to the MB. Co-localization studies of dopamine receptors with the presynaptic marker Brp suggest that Dop1R1 and, to a larger extent Dop2R, localize in the proximity of release sites. This localization pattern in DANs suggests that Dop1R1 and Dop2R work in dual-feedback regulation as autoreceptors. Finally, they provide evidence that the balance of Dop1R1 and Dop2R in the axons of two different DAN populations is differentially modulated by starvation and that this regulation plays a role in regulating appetitive behaviors.

Strengths:

The authors use reconstitution of GFP fluorescence of split GFP tags knocked into the endogenous locus at the C-terminus of the dopamine receptors as a readout of dopamine receptor localization. This elegant approach preserves the endogenous transcriptional and post-transcriptional regulation of the receptor, which is essential for studies of protein localization.

The study focuses on mapping the localization of dopamine receptors in neurons of the mushroom body. This is an excellent choice of system to address the question posed in this study, as the neurons are well-studied, and their connections are carefully reconstructed in the mushroom body connectome. Furthermore, the role of this circuit in different behaviors and associative memory permits the linking of patterns of receptor localization to circuit function and resulting behavior. Because of these features, the authors can provide evidence that two antagonizing dopamine receptors can act as autoreceptors within the axonal compartment of MB innervating DANs. The differential regulation of the balance of the two receptors under starvation in two distinct DAN innervations provides evidence of the role that regulation of this balance can play in circuit function and behavioral output.

Weaknesses:

The approach of using endogenously tagged alleles to study localization is a strength of this study, but the authors do not provide sufficient evidence that the insertion of 7 copies of split GFP to the C terminus of the dopamine receptors does not interfere with the endogenous localization pattern or function. Both sets of tagged alleles (1X Venus and 7X split GFP tagged) were previously reported (Kondo et al., 2020), but only the 1X Venus tagged alleles were further functionally validated in assays of olfactory appetitive memory. Despite the smaller size of the 7X split-GFP array tag knocked into the same location as the 1X venus tag, the reconstitution of 7 copies of GFP at the C terminus of the dopamine receptor, might substantially increase the molecular bulk at this site, potentially impeding the function of the receptor more significantly than the smaller, single Venus tag. The data presented by Kondo et al. 2020, is insufficient to conclude that the two alleles are equivalent.

In the revision, we validated the function of these engineered receptors by a new set of olfactory learning experiments. Both these receptors in KCs were shown to be required for aversive memory (Kim et al., 2007, Scholz-Kornehl et al., 2016). As in the anatomical experiments, we induced GFP110 expression in KC of the flies homozygous for 7xGFP₁₁-tagged receptors using MB-Switch and 3 days of RU486 feeding o. We confirmed STM performance of these flies were not significantly different from the control (Figure 2 – figure supplement 1). Thus, these fusion receptors are functional.

The authors' conclusion that the receptors localize to presynaptic sites is weak. The analysis of the colocalization of the active zone marker Brp whole-brain staining with dopamine receptors labeled in specific neurons is insufficient to conclude that the receptors are localized at presynaptic sites. Given the highly crowded neuropil environment, the data cannot differentiate between the receptor localization postsynaptic to a dopamine release site or at a presynaptic site within the same neuron. The known distribution of presynaptic sites within the neurons analyzed in the study provides evidence that the receptors are enriched in axonal compartments, but co-labeling of presynaptic sites and receptors in the same neuron or super-resolution methods are needed to provide evidence of receptor localization at active zones.  The data presented in Figures 5K-5L provides compelling evidence that the receptors localize to neuronal varicosities in DANs where the receptors could play a role as autoreceptors.

Given the highly crowded environment of the mushroom body neuropil, the analysis of dopamine receptor localization in Kenyon cells is not conclusive. The data is sufficient to conclude that the receptors are preferentially localizing to the axonal compartment of Kenyon cells, but co-localization with brain-wide Brp active zone immunostaining is not sufficient to determine if the receptor localizes juxtaposed to dopaminergic release sites, in proximity of release sites in Kenyon cells, or both.

To better resolve the microcircuits of KCs, we triple-labeled the plasma membrane and DAR::rGFP in KCs, and Brp, and examined their localizations with high-resolution imaging with  Airyscan. This strategy revealed the receptor clusters associated with Brp accumulation within KCs (Figure 4). To further verify the association of DARs and active zones within KCs, we co-expressed Brp^short::mStraw and GFP_1-10 and confirmed their colocalization (Figure 5A), suggesting presynaptic localization of DARs in KCs. With these additional characterizations, we now discuss the significance of receptors at the presynaptic sites of KCs.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

This is an important and interesting study that uses the split-GFP approach. Localization of receptors and correlating them to function is important in understanding the circuit basis of behavior.

For Figure 1, the authors show PAM, PPL1 neurons, and the ellipsoid body as a validation of their tools (Dop1R1-T2A-GAL4 and Dop2R-T2A-GAL4) and the idea that these receptors are colocalized. However, it appears that the technique was applied to the whole brain so it would be great to see the whole brain to understand how much labelling is specific and how stochastic. Methods could include how dissection conditions were controlled and how sensitive are receptor expression to the time of day of dissection, staining, etc.

The expression patterns of the receptor T2A-GAL4 lines (Figure 1A and 1B) are consistent in the multiple whole brains (Kondo et al., 2020, Author response image 1).

Author response image 1.

The significance of the expression of these two receptors in an active zone is not clearly discussed and presynaptic localization is not elaborated on. Would something like expansion microscopy be useful in resolving this? It would be important to discuss that as giant neurons in culture don't replicate many aspects of the MB system.

In the revised manuscript, we elaborated discussion regarding the function of the two antagonizing receptors at the AZ (Lines #226-275).

Does MB-GeneSwitch > GFP1-1 reliably express in gamma lobes? Most of the figures show alpha/beta lobes.

Yes. MB-GeneSwitch is also expressed in γ KCs, but weakly. 12 hours of RU486 feeding, which we did in the previous experiments, was insufficient to induce GFP reconstitution in the γ KCs. By extending the time of transgene induction, we visualized expression of Dop1R1 and Dop2R more clearly in γ KCs. Their localization is similar to that in the α/β KCs (Figure 4C, Figure 5 - figure supplement 1).

Figure 6, y-axis says protein level. At first, I thought it was related to starvation so maybe authors can be more specific as the protein level doesn't indicate any aspect of starvation.

We appreciate this comment, and the labels on the y-axis were now changed to “rGFP levels” (Figure 8C and 8F, Figure 8 - figure supplement 1B, 1D and 1F).

Reviewer #2 (Recommendations For The Authors):

Title:

The title of the manuscript focuses on the tagging of the receptors and their synaptic enrichment.

Given that the alleles used in the study were generated in a previously published study (Kondo et al, 2020), which describes the receptor tagging and that the data currently provided is insufficient to conclude that the receptors are localizing to synapses, the title should be changed to reflect the focus on localizing antagonistic cognate neurotransmitter receptors in the same neuron and their putative role as autoreceptors in DANs.

Following this advice, we removed the methodology from the title and revised it to “Synaptic enrichment and dynamic regulation of the two opposing dopamine receptors within the same neurons”.

Minor issues with text and figures:

Figure 1

A conclusion from Figure 1 is that the two receptors are co-expressed in Kenyon cells. Please provide panels equivalent to the ones shown in D-G, with Kenyon cells cell bodies, or mark these cells in the existing panels, if present. Line 111 refers to panel 1D as the Kenyon cells panel, which is currently a PAM panel.

We added images for coexpression of these receptors in the cell bodies of KCs (Figure 1 - figure supplement 1) and revised the text accordingly (Lines #89-90).

Given that most of the study centers on visualizing receptor localization, it would benefit the reader to include labels in Figure 1 that help understand that these panels reflect expression patterns rather than receptor localization. For instance, rCD2::GFP could be indicated in the Dop1R1-LexA panels.

As suggested, labels were added to indicate the UAS and lexAop markers (Figure 1D, 1E, 1G-1I and Figure 1 – figure supplement 1).

Given that panels D-E focus on the cell bodies of the neurons, it could be beneficial for the reader to present the ellipsoid body neurons using a similar view that only shows the cell bodies. Similarly, one could just show the glial cell bodies .

We now show the cell bodies of ring neurons (Figure 1G) and ensheathing glia (Figure 1I).

For panel 1E, please indicate the subset of PPL1 neurons that both expressed Dop1R1 and Dop2R, as indicated in the text, as it is currently unclear from the image.

Dop1R1-T2A-LexA was barely detected in all PPL1 (Figure 1E). We corrected the confusing text (Lines #95-96).

Figure 2

The cartoon of the cell-type-specific labeling should show that the tag is 7XFP-11 and the UAScomponent FP-10, as the current cartoon leads the reader to conclude that the receptors are tagged with a single copy of split GFP. The detail that the receptors are tagged with 7 copies of split GFP is only provided through the genotype of the allele in the resource table.  This design aspect should be made clear in the figure and the text when describing the allele and approach used to tag receptors in specific neuron types.

We now added the construct design in the scheme (Figure 2A) and revised the corresponding text (Line #101-103).

Panel A. The arrow representing the endogenous promoter in the yellow gene representation should be placed at the beginning of the coding sequence. Currently, the different colors of what I assume are coding (yellow) and non-coding (white) transcript regions are not described in the legend.  I would omit these or represent them in the same color as thinner boxes if the authors want to emphasize that the tag is inserted at the C terminus within the endogenous locus.

The color scheme was revised to be more consistent and intuitive (Figure 2A).

Figure 3

Labels of the calyx and MB lobes would benefit readers not as familiar with the system used in the study. In addition, it would be beneficial to the reader to indicate in panel A the location of the compartments analyzed in panel H (e.g., peduncle, α3).

Figure 3A was amended to clearly indicate the analyzed MB compartments.

Adding frontal and sagittal to panels B-E, as in Figure 2, would help the reader interpret the data. 

In Figure 3B, “Frontal” and “Sagittal” were indicated.

Panel F-G. A scale bar should be provided for the data shown in the insets. Could the author comment on the localization of Dop1R1 in KCs? The data in the current panel suggests that only a subset of KCs express high levels of receptors in their axons, as a portion of the membrane is devoid of receptor signals. This would be in line with differential dopamine receptor expression in subsets of Kenyon cells, as shown in Kondo et al., 2020, which is currently not commented on in the paper. 

We confirmed that the majority of the KCs express both Dop1R1 and Dop2R genes (Figure 1 - figure supplement 1). LIs should be compared within the same cells rather than the differences of protein levels between cell types as they also reflect the GAL4 expression levels. 

Panel H. Some P values are shown as n.s. (p> 0.05). Other non-significant p values in this panel and in other figures throughout the paper are instead reported (e.g. peduncle P=0.164). For consistency, please report the values as n.s. as indicated in the methods for all non-significant tests in this panel and throughout the manuscript.

We now present the new dataset, and the graph represents the appropriate statistical results (Figure 3D; see the methods section for details).

The methods of labeling the receptors through the expression of the GeneSwitch-controlled GFP1-10 in Kenyon cells induced by RU486 are not provided in the methods. Please provide a description of this as referenced in the figure legend and the genotypes used in the analysis shown in the panels.

The method of RU486 feeding has been added. We apologize for the missing method.

Figure 4

Please provide scale bars for the inset in panels A-B.

Scale bars were added to all confocal images.

The current analysis cannot distinguish between postsynaptic and presynaptic dopamine receptors in KCs, and the figure title should reflect this.

We now present the new data dopamine receptors in KCs and clearly distinguish Brp clusters of the KCs and other cell types (Figure 4, Figure 5).

The reader could benefit from additional details of using the giant neuron model, as it is not commonly used, and it is not clear how to relate this to interpret the localization of dopaminergic receptors within Kenyon cells. The use of the venus-tagged receptor variant should be introduced in the text, as using a different allele currently lacks context. Figures 4F-4J show that the receptor is localizing throughout the neuron. Quantifying the fraction of receptor signal colocalizing with Brp could aid in interpreting the data.  However, it would still not be clear how to interpret this data in the context of understanding the localization of the receptors in neurons within fly brain circuits. In the absence of additional data, the data provided in Figure 4 is inconclusive and could be omitted, keeping the focus of the study on the analysis of the two receptors in DANs. Co-expressing a presynaptic marker in Kenyon cells (e.g., by expressing Brp::SNAP)  in conjunction with rGFP labeled receptor would provide additional evidence of the relationship of release sites in Kenyon cells and tagged dopamine receptors in these same cells and could add evidence in support to the current conclusion.

Following the advice, we added a short summary to recapitulate that the giant neurons exhibit many characteristics of mature neurons (Lines #152-156): "Importantly, these giant neurons exhibit characteristics of mature neurons, including firing patterns (Wu et al., 1990; Yao & Wu, 2001; Zhao & Wu, 1997) and acetylcholine release (Yao et al., 2000), both of which are regulated by cAMP and CaMKII signaling (Yao et al., 2000; Yao & Wu, 2001; Zhao & Wu, 1997)." Therefore, the giant neuron serves as an excellent model to study the presynaptic localization in large cells in isolation.

To clarify polarized localization of Brp clusters and dopamine receptors but not "localizing throughout the neuron", we now show less magnified data (Figure 5C). It clearly demonstrates punctate Brp accumulations localized to the axon terminals of the giant neurons (former Figure 4D and 4E). This is the same membrane segment where Dop1R1 and Dop2R are localized (Figure 5C). Therefore, the association of Brp clusters and the dopamine receptors in the isolated giant neurons suggests that the subcellular localization in the brain neurons is independent of the circuit context. 

As the giant neurons do not form intermingled circuits, venus-tagged receptors are sufficient for this experiment and simpler in genetics.

Following the suggestion to clarify the AZ association of the receptors in KCs, we coexpressed Brpshort-mStraw and GFP1-10 in KCs and confirmed their colocalization (Figure 5A).

Figure 6

The data and analysis show that starvation induces changes in the α3 compartment in PPL1 neurons only, while the data provided shows no significant change for PPL1 neurons innervating other MB compartments. This should be clearly stated in lines 174-175, as it is implied that there is a difference in the analysis for compartments other than α3. Panel L of Figure 6 - supplement 1 shows no significant change for all three compartments analyzed and should be indicated as n.s. in all instances, as stated in the methods. 

We revised the text to clarify that the starvation-induced differences of Dop2R expression were not significant (Lines #217-219). The reason to highlight the α3 compartment is that both Dop1R1 and Dop2R are coexpressed in this PPL1 neuron (Figure 8D).

Additional minor comments:

There are a few typos and errors throughout the manuscript. The text should be carefully proofread to correct these. Here are the ones that came to my attention:

Please reference all figure panels in the text. For instance, Figure 3A is not mentioned and should be revised in line 112 as Figure 3A-E.

Lines 103-104. The sentence "LI was visualized as the color of the membrane signals" is unclear and should be revised. 

Figure 4 legend - dendritic claws should likely be B and C and not B and E.

Lines 147 - Incorrect figure panels, should be 5C-L or 5D-E.

Line 241 - DNAs should be DANs.

Methods - please define what the abbreviation CS stands for.

We really appreciate for careful reading of this reviewer. All these were corrected.

Author response:

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

eLife Assessment

This valuable study investigates how the neural representation of individual finger movements changes during the early period of sequence learning. By combining a new method for extracting features from human magnetoencephalography data and decoding analyses, the authors provide incomplete evidence of an early, swift change in the brain regions correlated with sequence learning, including a set of previously unreported frontal cortical regions. The addition of more control analyses to rule out that head movement artefacts influence the findings, and to further explain the proposal of offline contextualization during short rest periods as the basis for improvement performance would strengthen the manuscript.

We appreciate the Editorial assessment on our paper’s strengths and novelty. We have implemented additional control analyses to show that neither task-related eye movements nor increasing overlap of finger movements during learning account for our findings, which are that contextualized neural representations in a network of bilateral frontoparietal brain regions actively contribute to skill learning. Importantly, we carried out additional analyses showing that contextualization develops predominantly during rest intervals.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study addresses the issue of rapid skill learning and whether individual sequence elements (here: finger presses) are differentially represented in human MEG data. The authors use a decoding approach to classify individual finger elements and accomplish an accuracy of around 94%. A relevant finding is that the neural representations of individual finger elements dynamically change over the course of learning. This would be highly relevant for any attempts to develop better brain machine interfaces - one now can decode individual elements within a sequence with high precision, but these representations are not static but develop over the course of learning.

Strengths:

The work follows a large body of work from the same group on the behavioural and neural foundations of sequence learning. The behavioural task is well established and neatly designed to allow for tracking learning and how individual sequence elements contribute. The inclusion of short offline rest periods between learning epochs has been influential because it has revealed that a lot, if not most of the gains in behaviour (ie speed of finger movements) occur in these socalled micro-offline rest periods. The authors use a range of new decoding techniques, and exhaustively interrogate their data in different ways, using different decoding approaches. Regardless of the approach, impressively high decoding accuracies are observed, but when using a hybrid approach that combines the MEG data in different ways, the authors observe decoding accuracies of individual sequence elements from the MEG data of up to 94%.

We have previously showed that neural replay of MEG activity representing the practiced skill was prominent during rest intervals of early learning, and that the replay density correlated with micro-offline gains (Buch et al., 2021). These findings are consistent with recent reports (from two different research groups) that hippocampal ripple density increases during these inter-practice rest periods, and predict offline learning gains (Chen et al., 2024; Sjøgård et al., 2024). However, decoder performance in our earlier work (Buch et al., 2021) left room for improvement. Here, we reported a strategy to improve decoding accuracy that could benefit future studies of neural replay or BCI using MEG.

Weaknesses:

There are a few concerns which the authors may well be able to resolve. These are not weaknesses as such, but factors that would be helpful to address as these concern potential contributions to the results that one would like to rule out. Regarding the decoding results shown in Figure 2 etc, a concern is that within individual frequency bands, the highest accuracy seems to be within frequencies that match the rate of keypresses. This is a general concern when relating movement to brain activity, so is not specific to decoding as done here. As far as reported, there was no specific restraint to the arm or shoulder, and even then it is conceivable that small head movements would correlate highly with the vigor of individual finger movements. This concern is supported by the highest contribution in decoding accuracy being in middle frontal regions - midline structures that would be specifically sensitive to movement artefacts and don't seem to come to mind as key structures for very simple sequential keypress tasks such as this - and the overall pattern is remarkably symmetrical (despite being a unimanual finger task) and spatially broad. This issue may well be matching the time course of learning, as the vigor and speed of finger presses will also influence the degree to which the arm/shoulder and head move. This is not to say that useful information is contained within either of the frequencies or broadband data. But it raises the question of whether a lot is dominated by movement "artefacts" and one may get a more specific answer if removing any such contributions.

Reviewer #1 expresses concern that the combination of the low-frequency narrow-band decoder results, and the bilateral middle frontal regions displaying the highest average intra-parcel decoding performance across subjects is suggestive that the decoding results could be driven by head movement or other artefacts.

Head movement artefacts are highly unlikely to contribute meaningfully to our results for the following reasons. First, in addition to ICA denoising, all “recordings were visually inspected and marked to denoise segments containing other large amplitude artifacts due to movements” (see Methods). Second, the response pad was positioned in a manner that minimized wrist, arm or more proximal body movements during the task. Third, while online monitoring of head position was not performed for this study, it was assessed at the beginning and at the end of each recording. The head was restrained with an inflatable air bladder, and head movement between the beginning and end of each scan did not exceed 5mm for all participants included in the study.

The Reviewer states a concern that “it is conceivable that small head movements would correlate highly with the vigor of individual finger movements”. We agree that despite the steps taken above, it is possible that minor head movements could still contribute to some remaining variance in the MEG data in our study. However, such correlations between small head movements and finger movements could only meaningfully contribute to decoding performance if: (A) they were consistent and pervasive throughout the recording (which might not be the case if the head movements were related to movement vigor and vigor changed over time); and (B) they systematically varied between different finger movements, and also between the same finger movement performed at different sequence locations (see 5-class decoding performance in Figure 4B). The possibility of any head movement artefacts meeting all these conditions is unlikely. Alternatively, for this task design a much more likely confound could be the contribution of eye movement artefacts to the decoder performance (an issue raised by Reviewer #3 in the comments below).

Remember from Figure 1A in the manuscript that an asterisk marks the current position in the sequence and is updated at each keypress. Since participants make very few performance errors, the position of the asterisk on the display is highly correlated with the keypress being made in the sequence. Thus, it is possible that if participants are attending to the visual feedback provided on the display, they may generate eye movements that are systematically related to the task. Since we did record eye movements simultaneously with the MEG recordings (EyeLink 1000 Plus; Fs = 600 Hz), we were able to perform a control analysis to address this question. For each keypress event during trials in which no errors occurred (which is the same time-point that the asterisk position is updated), we extracted three features related to eye movements: 1) the gaze position at the time of asterisk position update (triggered by a KeyDown event), 2) the gaze position 150ms later, and 3) the peak velocity of the eye movement between the two positions. We then constructed a classifier from these features with the aim of predicting the location of the asterisk (ordinal positions 1-5) on the display. As shown in the confusion matrix below (Author response image 1), the classifier failed to perform above chance levels (overall cross-validated accuracy = 0.21817):

Author response image 1.

Confusion matrix showing that three eye movement features fail to predict asterisk position on the task display above chance levels (Fold 1 test accuracy = 0.21718; Fold 2 test accuracy = 0.22023; Fold 3 test accuracy = 0.21859; Fold 4 test accuracy = 0.22113; Fold 5 test accuracy = 0.21373; Overall cross-validated accuracy = 0.2181). Since the ordinal position of the asterisk on the display is highly correlated with the ordinal position of individual keypresses in the sequence, this analysis provides strong evidence that keypress decoding performance from MEG features is not explained by systematic relationships between finger movement behavior and eye movements (i.e. – behavioral artefacts) (end of figure legend).

Remember that the task display does not provide explicit feedback related to performance, only information about the present position in the sequence. Thus, it is possible that participants did not actively attend to the feedback. In fact, inspection of the eye position data revealed that on majority of trials, participants displayed random-walk-like gaze patterns around a central fixation point located near the center of the screen. Thus, participants did not attend to the asterisk position on the display, but instead intrinsically generated the action sequence. A similar realworld example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks) as provided in the study task – feedback which is typically ignored by the user.

The minimal participant engagement with the visual task display observed in this study highlights another important point – that the behavior in explicit sequence learning motor tasks is highly generative in nature rather than reactive to stimulus cues as in the serial reaction time task (SRTT). This is a crucial difference that must be carefully considered when designing investigations and comparing findings across studies.

We observed that initial keypress decoding accuracy was predominantly driven by contralateral primary sensorimotor cortex in the initial practice trials before transitioning to bilateral frontoparietal regions by trials 11 or 12 as performance gains plateaued. The contribution of contralateral primary sensorimotor areas to early skill learning has been extensively reported in humans and non-human animals.(Buch et al., 2021; Classen et al., 1998; Karni et al., 1995; Kleim et al., 1998) Similarly, the increased involvement of bilateral frontal and parietal regions to decoding during early skill learning in the non-dominant hand is well known. Enhanced bilateral activation in both frontal and parietal cortex during skill learning has been extensively reported (Doyon et al., 2002; Grafton et al., 1992; Hardwick et al., 2013; Kennerley et al., 2004; Shadmehr & Holcomb, 1997; Toni, Ramnani, et al., 2001), and appears to be even more prominent during early fine motor skill learning in the non-dominant hand (Lee et al., 2019; Sawamura et al., 2019). The frontal regions identified in these studies are known to play crucial roles in executive control (Battaglia-Mayer & Caminiti, 2019), motor planning (Toni, Thoenissen, et al., 2001), and working memory (Andersen & Buneo, 2002; Buneo & Andersen, 2006; Shadmehr & Holcomb, 1997; Toni, Ramnani, et al., 2001; Wolpert et al., 1998) processes, while the same parietal regions are known to integrate multimodal sensory feedback and support visuomotor transformations (Andersen & Buneo, 2002; Buneo & Andersen, 2006; Shadmehr & Holcomb, 1997; Toni, Ramnani, et al., 2001; Wolpert et al., 1998), in addition to working memory (Grover et al., 2022). Thus, it is not surprising that these regions increasingly contribute to decoding as subjects internalize the sequential task. We now include a statement reflecting these considerations in the revised Discussion.

A somewhat related point is this: when combining voxel and parcel space, a concern is whether a degree of circularity may have contributed to the improved accuracy of the combined data, because it seems to use the same MEG signals twice - the voxels most contributing are also those contributing most to a parcel being identified as relevant, as parcels reflect the average of voxels within a boundary. In this context, I struggled to understand the explanation given, ie that the improved accuracy of the hybrid model may be due to "lower spatially resolved whole-brain and higher spatially resolved regional activity patterns".

We disagree with the Reviewer’s assertion that the construction of the hybrid-space decoder is circular for the following reasons. First, the base feature set for the hybrid-space decoder constructed for all participants includes whole-brain spatial patterns of MEG source activity averaged within parcels. As stated in the manuscript, these 148 inter-parcel features reflect “lower spatially resolved whole-brain activity patterns” or global brain dynamics. We then independently test how well spatial patterns of MEG source activity for all voxels distributed within individual parcels can decode keypress actions. Again, the testing of these intra-parcel spatial patterns, intended to capture “higher spatially resolved regional brain activity patterns”, is completely independent from one another and independent from the weighting of individual inter-parcel features. These intra-parcel features could, for example, provide additional information about muscle activation patterns or the task environment. These approximately 1150 intra-parcel voxels (on average, within the total number varying between subjects) are then combined with the 148 inter-parcel features to construct the final hybrid-space decoder. In fact, this varied spatial filter approach shares some similarities to the construction of convolutional neural networks (CNNs) used to perform object recognition in image classification applications (Srinivas et al., 2016). One could also view this hybrid-space decoding approach as a spatial analogue to common timefrequency based analyses such as theta-gamma phase amplitude coupling (θ/γ PAC), which assess interactions between two or more narrow-band spectral features derived from the same time-series data (Lisman & Jensen, 2013).

We directly tested this hypothesis – that spatially overlapping intra- and inter-parcel features portray different information – by constructing an alternative hybrid-space decoder (Hybrid_Alt) that excluded average inter-parcel features which spatially overlapped with intra-parcel voxel features, and comparing the performance to the decoder used in the manuscript (Hybrid_Orig). The prediction was that if the overlapping parcel contained similar information to the more spatially resolved voxel patterns, then removing the parcel features (n=8) from the decoding analysis should not impact performance. In fact, despite making up less than 1% of the overall input feature space, removing those parcels resulted in a significant drop in overall performance greater than 2% (78.15% ± 7.03% SD for Hybrid_Orig vs. 75.49% ± 7.17% for Hybrid_Alt; Wilcoxon signed rank test, z = 3.7410, p = 1.8326e-04; Author response image 2).

Author response image 2.

Comparison of decoding performances with two different hybrid approaches. Hybrid_Alt: Intra-parcel voxel-space features of top ranked parcels and inter-parcel features of remaining parcels. Hybrid_Orig: Voxel-space features of top ranked parcels and whole-brain parcel-space features (i.e. – the version used in the manuscript). Dots represent decoding accuracy for individual subjects. Dashed lines indicate the trend in performance change across participants. Note, that Hybrid_Orig (the approach used in our manuscript) significantly outperforms the Hybrid_Alt approach, indicating that the excluded parcel features provide unique information compared to the spatially overlapping intra-parcel voxel patterns (end of figure legend).

Firstly, there will be a relatively high degree of spatial contiguity among voxels because of the nature of the signal measured, i.e. nearby individual voxels are unlikely to be independent. Secondly, the voxel data gives a somewhat misleading sense of precision; the inversion can be set up to give an estimate for each voxel, but there will not just be dependence among adjacent voxels, but also substantial variation in the sensitivity and confidence with which activity can be projected to different parts of the brain. Midline and deeper structures come to mind, where the inversion will be more problematic than for regions along the dorsal convexity of the brain, and a concern is that in those midline structures, the highest decoding accuracy is seen.

We agree with the Reviewer that some inter-parcel features representing neighboring (or spatially contiguous) voxels are likely to be correlated, an important confound in connectivity analyses (Colclough et al., 2015; Colclough et al., 2016), not performed in our investigation.

In our study, correlations between adjacent voxels effectively reduce the dimensionality of the input feature space. However, as long as there are multiple groups of correlated voxels within each parcel (i.e. – the rank is greater than 1), the intra-parcel spatial patterns could meaningfully contribute to the decoder performance, as shown by the following results:

First, we obtained higher decoding accuracy with voxel-space features (74.51% ± 7.34% SD) compared to parcel space features (68.77% ± 7.6%; Figure 3B), indicating individual voxels carry more information in decoding the keypresses than the averaged voxel-space features or parcel space features. Second, individual voxels within a parcel showed varying feature importance scores in decoding keypresses (Author response image 3). This finding shows that correlated voxels form mini subclusters that are much smaller spatially than the parcel they reside within.

Author response image 3.:

Feature importance score of individual voxels in decoding keypresses: MRMR was used to rank the individual voxel space features in decoding keypresses and the min-max normalized MRMR score was mapped to a structural brain surface. Note that individual voxels within a parcel showed different contribution to decoding (end of figure legend).

Some of these concerns could be addressed by recording head movement (with enough precision) to regress out these contributions. The authors state that head movement was monitored with 3 fiducials, and their time courses ought to provide a way to deal with this issue. The ICA procedure may not have sufficiently dealt with removing movement-related problems, but one could eg relate individual components that were identified to the keypresses as another means for checking. An alternative could be to focus on frequency ranges above the movement frequencies. The accuracy for those still seems impressive and may provide a slightly more biologically plausible assessment.

We have already addressed the issue of movement related artefacts in the first response above. With respect to a focus on frequency ranges above movement frequencies, the Reviewer states the “accuracy for those still seems impressive and may provide a slightly more biologically plausible assessment”. First, it is important to note that cortical delta-band oscillations measured with local field potentials (LFPs) in macaques is known to contain important information related to end-effector kinematics (Bansal et al., 2011; Mollazadeh et al., 2011) muscle activation patterns (Flint et al., 2012) and temporal sequencing (Churchland et al., 2012) during skilled reaching and grasping actions. Thus, there is a substantial body of evidence that low-frequency neural oscillatory activity in this range contains important information about the skill learning behavior investigated in the present study. Second, our own data shows (which the Reviewer also points out) that significant information related to the skill learning behavior is also present in higher frequency bands (see Figure 2A and Figure 3—figure supplement 1). As we pointed out in our earlier response to questions about the hybrid space decoder architecture (see above), it is likely that different, yet complimentary, information is encoded across different temporal frequencies (just as it is encoded across different spatial frequencies) (Heusser et al., 2016). Again, this interpretation is supported by our data as the highest performing classifiers in all cases (when holding all parameters constant) were always constructed from broadband input MEG data (Figure 2A and Figure 3—figure supplement 1).

One question concerns the interpretation of the results shown in Figure 4. They imply that during the course of learning, entirely different brain networks underpin the behaviour. Not only that, but they also include regions that would seem rather unexpected to be key nodes for learning and expressing relatively simple finger sequences, such as here. What then is the biological plausibility of these results? The authors seem to circumnavigate this issue by moving into a distance metric that captures the (neural network) changes over the course of learning, but the discussion seems detached from which regions are actually involved; or they offer a rather broad discussion of the anatomical regions identified here, eg in the context of LFOs, where they merely refer to "frontoparietal regions".

The Reviewer notes the shift in brain networks driving keypress decoding performance between trials 1, 11 and 36 as shown in Figure 4A. The Reviewer questions whether these shifts in brain network states underpinning the skill are biologically plausible, as well as the likelihood that bilateral superior and middle frontal and parietal cortex are important nodes within these networks.

First, previous fMRI work in humans assessed changes in functional connectivity patterns while participants performed a similar sequence learning task to our present study (Bassett et al., 2011). Using a dynamic network analysis approach, Bassett et al. showed that flexibility in the composition of individual network modules (i.e. – changes in functional brain region membership of orthogonal brain networks) is up-regulated in novel learning environments and explains differences in learning rates across individuals. Thus, consistent with our findings, it is likely that functional brain networks rapidly reconfigure during early learning of novel sequential motor skills.

Second, frontoparietal network activity is known to support motor memory encoding during early learning (Albouy et al., 2013; Albouy et al., 2012). For example, reactivation events in the posterior parietal (Qin et al., 1997) and medial prefrontal (Euston et al., 2007; Molle & Born, 2009) cortex (MPFC) have been temporally linked to hippocampal replay, and are posited to support memory consolidation across several memory domains (Frankland & Bontempi, 2005), including motor sequence learning (Albouy et al., 2015; Buch et al., 2021; F. Jacobacci et al., 2020). Further, synchronized interactions between MPFC and hippocampus are more prominent during early as opposed to later learning stages (Albouy et al., 2013; Gais et al., 2007; Sterpenich et al., 2009), perhaps reflecting “redistribution of hippocampal memories to MPFC” (Albouy et al., 2013). MPFC contributes to very early memory formation by learning association between contexts, locations, events and adaptive responses during rapid learning (Euston et al., 2012). Consistently, coupling between hippocampus and MPFC has been shown during initial memory encoding and during subsequent rest (van Kesteren et al., 2010; van Kesteren et al., 2012). Importantly, MPFC activity during initial memory encoding predicts subsequent recall (Wagner et al., 1998). Thus, the spatial map required to encode a motor sequence memory may be “built under the supervision of the prefrontal cortex” (Albouy et al., 2012), also engaged in the development of an abstract representation of the sequence (Ashe et al., 2006). In more abstract terms, the prefrontal, premotor and parietal cortices support novice performance “by deploying attentional and control processes” (Doyon et al., 2009; Hikosaka et al., 2002; Penhune & Steele, 2012) required during early learning (Doyon et al., 2009; Hikosaka et al., 2002; Penhune & Steele, 2012). The dorsolateral prefrontal cortex DLPFC specifically is thought to engage in goal selection and sequence monitoring during early skill practice (Schendan et al., 2003), all consistent with the schema model of declarative memory in which prefrontal cortices play an important role in encoding (Morris, 2006; Tse et al., 2007). Thus, several prefrontal and frontoparietal regions contributing to long term learning (Berlot et al., 2020) are also engaged in early stages of encoding. Altogether, there is strong biological support for the involvement of bilateral prefrontal and frontoparietal regions to decoding during early skill learning. We now address this issue in the revised manuscript.

If I understand correctly, the offline neural representation analysis is in essence the comparison of the last keypress vs the first keypress of the next sequence. In that sense, the activity during offline rest periods is actually not considered. This makes the nomenclature somewhat confusing. While it matches the behavioural analysis, having only key presses one can't do it in any other way, but here the authors actually do have recordings of brain activity during offline rest. So at the very least calling it offline neural representation is misleading to this reviewer because what is compared is activity during the last and during the next keypress, not activity during offline periods. But it also seems a missed opportunity - the authors argue that most of the relevant learning occurs during offline rest periods, yet there is no attempt to actually test whether activity during this period can be useful for the questions at hand here.

We agree with the Reviewer that our previous “offline neural representation” nomenclature could be misinterpreted. In the revised manuscript we refer to this difference as the “offline neural representational change”. Please, note that our previous work did link offline neural activity (i.e. – 16-22 Hz beta power (Bonstrup et al., 2019) and neural replay density (Buch et al., 2021) during inter-practice rest periods) to observed micro-offline gains.

Reviewer #2 (Public review):

Summary

Dash et al. asked whether and how the neural representation of individual finger movements is "contextualized" within a trained sequence during the very early period of sequential skill learning by using decoding of MEG signal. Specifically, they assessed whether/how the same finger presses (pressing index finger) embedded in the different ordinal positions of a practiced sequence (4-1-3-2-4; here, the numbers 1 through 4 correspond to the little through the index fingers of the non-dominant left hand) change their representation (MEG feature). They did this by computing either the decoding accuracy of the index finger at the ordinal positions 1 vs. 5 (index_OP1 vs index_OP5) or pattern distance between index_OP1 vs. index_OP5 at each training trial and found that both the decoding accuracy and the pattern distance progressively increase over the course of learning trials. More interestingly, they also computed the pattern distance for index_OP5 for the last execution of a practice trial vs. index_OP1 for the first execution in the next practice trial (i.e., across the rest period). This "off-line" distance was significantly larger than the "on-line" distance, which was computed within practice trials and predicted micro-offline skill gain. Based on these results, the authors conclude that the differentiation of representation for the identical movement embedded in different positions of a sequential skill ("contextualization") primarily occurs during early skill learning, especially during rest, consistent with the recent theory of the "micro-offline learning" proposed by the authors' group. I think this is an important and timely topic for the field of motor learning and beyond.

Strengths

The specific strengths of the current work are as follows. First, the use of temporally rich neural information (MEG signal) has a large advantage over previous studies testing sequential representations using fMRI. This allowed the authors to examine the earliest period (= the first few minutes of training) of skill learning with finer temporal resolution. Second, through the optimization of MEG feature extraction, the current study achieved extremely high decoding accuracy (approx. 94%) compared to previous works. As claimed by the authors, this is one of the strengths of the paper (but see my comments). Third, although some potential refinement might be needed, comparing "online" and "offline" pattern distance is a neat idea.

Weaknesses

Along with the strengths I raised above, the paper has some weaknesses. First, the pursuit of high decoding accuracy, especially the choice of time points and window length (i.e., 200 msec window starting from 0 msec from key press onset), casts a shadow on the interpretation of the main result. Currently, it is unclear whether the decoding results simply reflect behavioral change or true underlying neural change. As shown in the behavioral data, the key press speed reached 3~4 presses per second already at around the end of the early learning period (11th trial), which means inter-press intervals become as short as 250-330 msec. Thus, in almost more than 60% of training period data, the time window for MEG feature extraction (200 msec) spans around 60% of the inter-press intervals. Considering that the preparation/cueing of subsequent presses starts ahead of the actual press (e.g., Kornysheva et al., 2019) and/or potential online planning (e.g., Ariani and Diedrichsen, 2019), the decoder likely has captured these future press information as well as the signal related to the current key press, independent of the formation of genuine sequential representation (e.g., "contextualization" of individual press). This may also explain the gradual increase in decoding accuracy or pattern distance between index_OP1 vs. index_OP5 (Figure 4C and 5A), which co-occurred with performance improvement, as shorter inter-press intervals are more favorable for the dissociating the two index finger presses followed by different finger presses. The compromised decoding accuracies for the control sequences can be explained in similar logic. Therefore, more careful consideration and elaborated discussion seem necessary when trying to both achieve high-performance decoding and assess early skill learning, as it can impact all the subsequent analyses.

The Reviewer raises the possibility that (given the windowing parameters used in the present study) an increase in “contextualization” with learning could simply reflect faster typing speeds as opposed to an actual change in the underlying neural representation.

We now include a new control analysis that addresses this issue as well as additional re-examination of previously reported results with respect to this issue – all of which are inconsistent with this alternative explanation that “contextualization” reflects a change in mixing of keypress related MEG features as opposed to a change in the underlying representations themselves. As correct sequences are generated at higher and higher speeds over training, MEG activity patterns related to the planning, execution, evaluation and memory of individual keypresses overlap more in time. Thus, increased overlap between the “4” and “1” keypresses (at the start of the sequence) and “2” and “4” keypresses (at the end of the sequence) could artefactually increase contextualization distances even if the underlying neural representations for the individual keypresses remain unchanged. One must also keep in mind that since participants repeat the sequence multiple times within the same trial, a majority of the index finger keypresses are performed adjacent to one another (i.e. - the “4-4” transition marking the end of one sequence and the beginning of the next). Thus, increased overlap between consecutive index finger keypresses as typing speed increased should increase their similarity and mask contextualization related changes to the underlying neural representations.

We addressed this question by conducting a new multivariate regression analysis to directly assess whether the neural representation distance score could be predicted by the 4-1, 2-4 and 4-4 keypress transition times observed for each complete correct sequence (both predictor and response variables were z-score normalized within-subject). The results of this analysis also affirmed that the possible alternative explanation that contextualization effects are simple reflections of increased mixing is not supported by the data (Adjusted R² = 0.00431; F = 5.62). We now include this new negative control analysis in the revised manuscript.

We also re-examined our previously reported classification results with respect to this issue. We reasoned that if mixing effects reflecting the ordinal sequence structure is an important driver of the contextualization finding, these effects should be observable in the distribution of decoder misclassifications. For example, “4” keypresses would be more likely to be misclassified as “1” or “2” keypresses (or vice versa) than as “3” keypresses. The confusion matrices presented in Figures 3C and 4B and Figure 3—figure supplement 3A display a distribution of misclassifications that is inconsistent with an alternative mixing effect explanation of contextualization.

Based upon the increased overlap between adjacent index finger keypresses (i.e. – “4-4” transition), we also reasoned that the decoder tasked with separating individual index finger keypresses into two distinct classes based upon sequence position, should show decreased performance as typing speed increases. However, Figure 4C in our manuscript shows that this is not the case. The 2-class hybrid classifier actually displays improved classification performance over early practice trials despite greater temporal overlap. Again, this is inconsistent with the idea that the contextualization effect simply reflects increased mixing of individual keypress features.

In summary, both re-examination of previously reported data and new control analyses all converged on the idea that the proximity between keypresses does not explain contextualization.

We do agree with the Reviewer that the naturalistic, generative, self-paced task employed in the present study results in overlapping brain processes related to planning, execution, evaluation and memory of the action sequence. We also agree that there are several tradeoffs to consider in the construction of the classifiers depending on the study aim. Given our aim of optimizing keypress decoder accuracy in the present study, the set of trade-offs resulted in representations reflecting more the latter three processes, and less so the planning component. Whether separate decoders can be constructed to tease apart the representations or networks supporting these overlapping processes is an important future direction of research in this area. For example, work presently underway in our lab constrains the selection of windowing parameters in a manner that allows individual classifiers to be temporally linked to specific planning, execution, evaluation or memory-related processes to discern which brain networks are involved and how they adaptively reorganize with learning. Results from the present study (Figure 4—figure supplement 2) showing hybrid-space decoder prediction accuracies exceeding 74% for temporal windows spanning as little as 25ms and located up to 100ms prior to the KeyDown event strongly support the feasibility of such an approach.

Related to the above point, testing only one particular sequence (4-1-3-2-4), aside from the control ones, limits the generalizability of the finding. This also may have contributed to the extremely high decoding accuracy reported in the current study.

The Reviewer raises a question about the generalizability of the decoder accuracy reported in our study. Fortunately, a comparison between decoder performances on Day 1 and Day 2 datasets does provide insight into this issue. As the Reviewer points out, the classifiers in this study were trained and tested on keypresses performed while practicing a specific sequence (4-1-3-2-4). The study was designed this way as to avoid the impact of interference effects on learning dynamics. The cross-validated performance of classifiers on MEG data collected within the same session was 90.47% overall accuracy (4-class; Figure 3C). We then tested classifier performance on data collected during a separate MEG session conducted approximately 24 hours later (Day 2; see Figure 3 — figure supplement 3). We observed a reduction in overall accuracy rate to 87.11% when tested on MEG data recorded while participants performed the same learned sequence, and 79.44% when they performed several previously unpracticed sequences. Both changes in accuracy are important with regards to the generalizability of our findings. First, 87.11% performance accuracy for the trained sequence data on Day 2 (a reduction of only 3.36%) indicates that the hybrid-space decoder performance is robust over multiple MEG sessions, and thus, robust to variations in SNR across the MEG sensor array caused by small differences in head position between scans. This indicates a substantial advantage over sensor-space decoding approaches. Furthermore, when tested on data from unpracticed sequences, overall performance dropped an additional 7.67%. This difference reflects the performance bias of the classifier for the trained sequence, possibly caused by high-order sequence structure being incorporated into the feature weights. In the future, it will be important to understand in more detail how random or repeated keypress sequence training data impacts overall decoder performance and generalization. We strongly agree with the Reviewer that the issue of generalizability is extremely important and have added a new paragraph to the Discussion in the revised manuscript highlighting the strengths and weaknesses of our study with respect to this issue.

In terms of clinical BCI, one of the potential relevance of the study, as claimed by the authors, it is not clear that the specific time window chosen in the current study (up to 200 msec since key press onset) is really useful. In most cases, clinical BCI would target neural signals with no overt movement execution due to patients' inability to move (e.g., Hochberg et al., 2012). Given the time window, the surprisingly high performance of the current decoder may result from sensory feedback and/or planning of subsequent movement, which may not always be available in the clinical BCI context. Of course, the decoding accuracy is still much higher than chance even when using signal before the key press (as shown in Figure 4 Supplement 2), but it is not immediately clear to me that the authors relate their high decoding accuracy based on post-movement signal to clinical BCI settings.

The Reviewer questions the relevance of the specific window parameters used in the present study for clinical BCI applications, particularly for paretic patients who are unable to produce finger movements or for whom afferent sensory feedback is no longer intact. We strongly agree with the Reviewer that any intended clinical application must carefully consider the specific input feature constraints dictated by the clinical cohort, and in turn impose appropriate and complimentary constraints on classifier parameters that may differ from the ones used in the present study. We now highlight this issue in the Discussion of the revised manuscript and relate our present findings to published clinical BCI work within this context.

One of the important and fascinating claims of the current study is that the "contextualization" of individual finger movements in a trained sequence specifically occurs during short rest periods in very early skill learning, echoing the recent theory of micro-offline learning proposed by the authors' group. Here, I think two points need to be clarified. First, the concept of "contextualization" is kept somewhat blurry throughout the text. It is only at the later part of the Discussion (around line #330 on page 13) that some potential mechanism for the "contextualization" is provided as "what-and-where" binding. Still, it is unclear what "contextualization" actually is in the current data, as the MEG signal analyzed is extracted from 0-200 msec after the keypress. If one thinks something is contextualizing an action, that contextualization should come earlier than the action itself.

The Reviewer requests that we: 1) more clearly define our use of the term “contextualization” and 2) provide the rationale for assessing it over a 200ms window aligned to the KeyDown event. This choice of window parameters means that the MEG activity used in our analysis was coincident with, rather than preceding, the actual keypresses. We define contextualization as the differentiation of representation for the identical movement embedded in different positions of a sequential skill. That is, representations of individual action elements progressively incorporate information about their relationship to the overall sequence structure as the skill is learned. We agree with the Reviewer that this can be appropriately interpreted as “what-and-where” binding. We now incorporate this definition in the Introduction of the revised manuscript as requested.

The window parameters for optimizing accurate decoding individual finger movements were determined using a grid search of the parameter space (a sliding window of variable width between 25-350 ms with 25 ms increments variably aligned from 0 to +100ms with 10ms increments relative to the KeyDown event). This approach generated 140 different temporal windows for each keypress for each participant, with the final parameter selection determined through comparison of the resulting performance between each decoder. Importantly, the decision to optimize for decoding accuracy placed an emphasis on keypress representations characterized by the most consistent and robust features shared across subjects, which in turn maximize statistical power in detecting common learning-related changes. In this case, the optimal window encompassed a 200ms epoch aligned to the KeyDown event (t₀ = 0 ms). We then asked if the representations (i.e. – spatial patterns of combined parcel- and voxel-space activity) of the same digit at two different sequence positions changed with practice within this optimal decoding window. Of course, our findings do not rule out the possibility that contextualization can also be found before or even after this time window, as we did not directly address this issue in the present study. Future work in our lab, as pointed out above, are investigating contextualization within different time windows tailored specifically for assessing sequence skill action planning, execution, evaluation and memory processes.

The second point is that the result provided by the authors is not yet convincing enough to support the claim that "contextualization" occurs during rest. In the original analysis, the authors presented the statistical significance regarding the correlation between the "offline" pattern differentiation and micro-offline skill gain (Figure 5. Supplement 1), as well as the larger "offline" distance than "online" distance (Figure 5B). However, this analysis looks like regressing two variables (monotonically) increasing as a function of the trial. Although some information in this analysis, such as what the independent/dependent variables were or how individual subjects were treated, was missing in the Methods, getting a statistically significant slope seems unsurprising in such a situation. Also, curiously, the same quantitative evidence was not provided for its "online" counterpart, and the authors only briefly mentioned in the text that there was no significant correlation between them. It may be true looking at the data in Figure 5A as the online representation distance looks less monotonically changing, but the classification accuracy presented in Figure 4C, which should reflect similar representational distance, shows a more monotonic increase up to the 11th trial. Further, the ways the "online" and "offline" representation distance was estimated seem to make them not directly comparable. While the "online" distance was computed using all the correct press data within each 10 sec of execution, the "offline" distance is basically computed by only two presses (i.e., the last index_OP5 vs. the first index_OP1 separated by 10 sec of rest). Theoretically, the distance between the neural activity patterns for temporally closer events tends to be closer than that between the patterns for temporally far-apart events. It would be fairer to use the distance between the first index_OP1 vs. the last index_OP5 within an execution period for "online" distance, as well.

The Reviewer suggests that the current data is not enough to show that contextualization occurs during rest and raises two important concerns: 1) the relationship between online contextualization and micro-online gains is not shown, and 2) the online distance was calculated differently from its offline counterpart (i.e. - instead of calculating the distance between last Index_OP5 and first Index_OP1 from a single trial, the distance was calculated for each sequence within a trial and then averaged).

We addressed the first concern by performing individual subject correlations between 1) contextualization changes during rest intervals and micro-offline gains; 2) contextualization changes during practice trials and micro-online gains, and 3) contextualization changes during practice trials and micro-offline gains (Figure 5 – figure supplement 4). We then statistically compared the resulting correlation coefficient distributions and found that within-subject correlations for contextualization changes during rest intervals and micro-offline gains were significantly higher than online contextualization and micro-online gains (t = 3.2827, p = 0.0015) and online contextualization and micro-offline gains (t = 3.7021, p = 5.3013e-04). These results are consistent with our interpretation that micro-offline gains are supported by contextualization changes during the inter-practice rest periods.

With respect to the second concern, we agree with the Reviewer that one limitation of the analysis comparing online versus offline changes in contextualization as presented in the original manuscript, is that it does not eliminate the possibility that any differences could simply be explained by the passage of time (which is smaller for the online analysis compared to the offline analysis). The Reviewer suggests an approach that addresses this issue, which we have now carried out. When quantifying online changes in contextualization from the first Index_OP1 the last Index_OP5 keypress in the same trial we observed no learning-related trend (Figure 5 – figure supplement 5, right panel). Importantly, offline distances were significantly larger than online distances regardless of the measurement approach and neither predicted online learning (Figure 5 – figure supplement 6).

A related concern regarding the control analysis, where individual values for max speed and the degree of online contextualization were compared (Figure 5 Supplement 3), is whether the individual difference is meaningful. If I understood correctly, the optimization of the decoding process (temporal window, feature inclusion/reduction, decoder, etc.) was performed for individual participants, and the same feature extraction was also employed for the analysis of representation distance (i.e., contextualization). If this is the case, the distances are individually differently calculated and they may need to be normalized relative to some stable reference (e.g., 1 vs. 4 or average distance within the control sequence presses) before comparison across the individuals.

The Reviewer makes a good point here. We have now implemented the suggested normalization procedure in the analysis provided in the revised manuscript.

Reviewer #3 (Public review):

Summary:

One goal of this paper is to introduce a new approach for highly accurate decoding of finger movements from human magnetoencephalography data via dimension reduction of a "multiscale, hybrid" feature space. Following this decoding approach, the authors aim to show that early skill learning involves "contextualization" of the neural coding of individual movements, relative to their position in a sequence of consecutive movements. Furthermore, they aim to show that this "contextualization" develops primarily during short rest periods interspersed with skill training and correlates with a performance metric which the authors interpret as an indicator of offline learning.

Strengths:

A clear strength of the paper is the innovative decoding approach, which achieves impressive decoding accuracies via dimension reduction of a "multi-scale, hybrid space". This hybrid-space approach follows the neurobiologically plausible idea of the concurrent distribution of neural coding across local circuits as well as large-scale networks. A further strength of the study is the large number of tested dimension reduction techniques and classifiers (though the manuscript reveals little about the comparison of the latter).

We appreciate the Reviewer’s comments regarding the paper’s strengths.

A simple control analysis based on shuffled class labels could lend further support to this complex decoding approach. As a control analysis that completely rules out any source of overfitting, the authors could test the decoder after shuffling class labels. Following such shuffling, decoding accuracies should drop to chance level for all decoding approaches, including the optimized decoder. This would also provide an estimate of actual chance-level performance (which is informative over and beyond the theoretical chance level). Furthermore, currently, the manuscript does not explain the huge drop in decoding accuracies for the voxel-space decoding (Figure 3B). Finally, the authors' approach to cortical parcellation raises questions regarding the information carried by varying dipole orientations within a parcel (which currently seems to be ignored?) and the implementation of the mean-flipping method (given that there are two dimensions - space and time - what do the authors refer to when they talk about the sign of the "average source", line 477?).

The Reviewer recommends that we: 1) conduct an additional control analysis on classifier performance using shuffled class labels, 2) provide a more detailed explanation regarding the drop in decoding accuracies for the voxel-space decoding following LDA dimensionality reduction (see Fig 3B), and 3) provide additional details on how problems related to dipole solution orientations were addressed in the present study.

In relation to the first point, we have now implemented a random shuffling approach as a control for the classification analyses. The results of this analysis indicated that the chance level accuracy was 22.12% (± SD 9.1%) for individual keypress decoding (4-class classification), and 18.41% (± SD 7.4%) for individual sequence item decoding (5-class classification), irrespective of the input feature set or the type of decoder used. Thus, the decoding accuracy observed with the final model was substantially higher than these chance levels.

Second, please note that the dimensionality of the voxel-space feature set is very high (i.e. – 15684). LDA attempts to map the input features onto a much smaller dimensional space (number of classes – 1; e.g. – 3 dimensions, for 4-class keypress decoding). Given the very high dimension of the voxel-space input features in this case, the resulting mapping exhibits reduced accuracy. Despite this general consideration, please refer to Figure 3—figure supplement 3, where we observe improvement in voxel-space decoder performance when utilizing alternative dimensionality reduction techniques.

The decoders constructed in the present study assess the average spatial patterns across time (as defined by the windowing procedure) in the input feature space. We now provide additional details in the Methods of the revised manuscript pertaining to the parcellation procedure and how the sign ambiguity problem was addressed in our analysis.

Weaknesses:

A clear weakness of the paper lies in the authors' conclusions regarding "contextualization". Several potential confounds, described below, question the neurobiological implications proposed by the authors and provide a simpler explanation of the results. Furthermore, the paper follows the assumption that short breaks result in offline skill learning, while recent evidence, described below, casts doubt on this assumption.

We thank the Reviewer for giving us the opportunity to address these issues in detail (see below).

The authors interpret the ordinal position information captured by their decoding approach as a reflection of neural coding dedicated to the local context of a movement (Figure 4). One way to dissociate ordinal position information from information about the moving effectors is to train a classifier on one sequence and test the classifier on other sequences that require the same movements, but in different positions (Kornysheva et al., 2019). In the present study, however, participants trained to repeat a single sequence (4-1-3-2-4). As a result, ordinal position information is potentially confounded by the fixed finger transitions around each of the two critical positions (first and fifth press). Across consecutive correct sequences, the first keypress in a given sequence was always preceded by a movement of the index finger (=last movement of the preceding sequence), and followed by a little finger movement. The last keypress, on the other hand, was always preceded by a ring finger movement, and followed by an index finger movement (=first movement of the next sequence). Figure 4 - Supplement 2 shows that finger identity can be decoded with high accuracy (>70%) across a large time window around the time of the key press, up to at least +/-100 ms (and likely beyond, given that decoding accuracy is still high at the boundaries of the window depicted in that figure). This time window approaches the keypress transition times in this study. Given that distinct finger transitions characterized the first and fifth keypress, the classifier could thus rely on persistent (or "lingering") information from the preceding finger movement, and/or "preparatory" information about the subsequent finger movement, in order to dissociate the first and fifth keypress. Currently, the manuscript provides no evidence that the context information captured by the decoding approach is more than a by-product of temporally extended, and therefore overlapping, but independent neural representations of consecutive keypresses that are executed in close temporal proximity - rather than a neural representation dedicated to context.

Such temporal overlap of consecutive, independent finger representations may also account for the dynamics of "ordinal coding"/"contextualization", i.e., the increase in 2-class decoding accuracy, across Day 1 (Figure 4C). As learning progresses, both tapping speed and the consistency of keypress transition times increase (Figure 1), i.e., consecutive keypresses are closer in time, and more consistently so. As a result, information related to a given keypress is increasingly overlapping in time with information related to the preceding and subsequent keypresses. The authors seem to argue that their regression analysis in Figure 5 - Figure Supplement 3 speaks against any influence of tapping speed on "ordinal coding" (even though that argument is not made explicitly in the manuscript). However, Figure 5 - Figure Supplement 3 shows inter-individual differences in a between-subject analysis (across trials, as in panel A, or separately for each trial, as in panel B), and, therefore, says little about the within-subject dynamics of "ordinal coding" across the experiment. A regression of trial-by-trial "ordinal coding" on trial-by-trial tapping speed (either within-subject or at a group-level, after averaging across subjects) could address this issue. Given the highly similar dynamics of "ordinal coding" on the one hand (Figure 4C), and tapping speed on the other hand (Figure 1B), I would expect a strong relationship between the two in the suggested within-subject (or group-level) regression. Furthermore, learning should increase the number of (consecutively) correct sequences, and, thus, the consistency of finger transitions. Therefore, the increase in 2-class decoding accuracy may simply reflect an increasing overlap in time of increasingly consistent information from consecutive keypresses, which allows the classifier to dissociate the first and fifth keypress more reliably as learning progresses, simply based on the characteristic finger transitions associated with each. In other words, given that the physical context of a given keypress changes as learning progresses - keypresses move closer together in time and are more consistently correct - it seems problematic to conclude that the mental representation of that context changes. To draw that conclusion, the physical context should remain stable (or any changes to the physical context should be controlled for).

The issues raised by Reviewer #3 here are similar to two issues raised by Reviewer #2 above. We agree they must both be carefully considered in any evaluation of our findings.

As both Reviewers pointed out, the classifiers in this study were trained and tested on keypresses performed while practicing a specific sequence (4-1-3-2-4). The study was designed this way as to avoid the impact of interference effects on learning dynamics. The cross-validated performance of classifiers on MEG data collected within the same session was 90.47% overall accuracy (4class; Figure 3C). We then tested classifier performance on data collected during a separate MEG session conducted approximately 24 hours later (Day 2; see Figure 3—supplement 3). We observed a reduction in overall accuracy rate to 87.11% when tested on MEG data recorded while participants performed the same learned sequence, and 79.44% when they performed several previously unpracticed sequences. This classification performance difference of 7.67% when tested on the Day 2 data could reflect the performance bias of the classifier for the trained sequence, possibly caused by mixed information from temporally close keypresses being incorporated into the feature weights.

Along these same lines, both Reviewers also raise the possibility that an increase in “ordinal coding/contextualization” with learning could simply reflect an increase in this mixing effect caused by faster typing speeds as opposed to an actual change in the underlying neural representation. The basic idea is that as correct sequences are generated at higher and higher speeds over training, MEG activity patterns related to the planning, execution, evaluation and memory of individual keypresses overlap more in time. Thus, increased overlap between the “4” and “1” keypresses (at the start of the sequence) and “2” and “4” keypresses (at the end of the sequence) could artefactually increase contextualization distances even if the underlying neural representations for the individual keypresses remain unchanged (assuming this mixing of representations is used by the classifier to differentially tag each index finger press). If this were the case, it follows that such mixing effects reflecting the ordinal sequence structure would also be observable in the distribution of decoder misclassifications. For example, “4” keypresses would be more likely to be misclassified as “1” or “2” keypresses (or vice versa) than as “3” keypresses. The confusion matrices presented in Figures 3C and 4B and Figure 3—figure supplement 3A in the previously submitted manuscript do not show this trend in the distribution of misclassifications across the four fingers.

Following this logic, it’s also possible that if the ordinal coding is largely driven by this mixing effect, the increased overlap between consecutive index finger keypresses during the 4-4 transition marking the end of one sequence and the beginning of the next one could actually mask contextualization-related changes to the underlying neural representations and make them harder to detect. In this case, a decoder tasked with separating individual index finger keypresses into two distinct classes based upon sequence position might show decreased performance with learning as adjacent keypresses overlapped in time with each other to an increasing extent. However, Figure 4C in our previously submitted manuscript does not support this possibility, as the 2-class hybrid classifier displays improved classification performance over early practice trials despite greater temporal overlap.

As noted in the above reply to Reviewer #2, we also conducted a new multivariate regression analysis to directly assess whether the neural representation distance score could be predicted by the 4-1, 2-4 and 4-4 keypress transition times observed for each complete correct sequence (both predictor and response variables were z-score normalized within-subject). The results of this analysis affirmed that the possible alternative explanation put forward by the Reviewer is not supported by our data (Adjusted R² = 0.00431; F = 5.62). We now include this new negative control analysis result in the revised manuscript.

Finally, the Reviewer hints that one way to address this issue would be to compare MEG responses before and after learning for sequences typed at a fixed speed. However, given that the speed-accuracy trade-off should improve with learning, a comparison between unlearned and learned skill states would dictate that the skill be evaluated at a very low fixed speed. Essentially, such a design presents the problem that the post-training test is evaluating the representation in the unlearned behavioral state that is not representative of the acquired skill. Thus, this approach would miss most learning effects on a task in which speed is the main learning metrics.

A similar difference in physical context may explain why neural representation distances ("differentiation") differ between rest and practice (Figure 5). The authors define "offline differentiation" by comparing the hybrid space features of the last index finger movement of a trial (ordinal position 5) and the first index finger movement of the next trial (ordinal position 1). However, the latter is not only the first movement in the sequence but also the very first movement in that trial (at least in trials that started with a correct sequence), i.e., not preceded by any recent movement. In contrast, the last index finger of the last correct sequence in the preceding trial includes the characteristic finger transition from the fourth to the fifth movement. Thus, there is more overlapping information arising from the consistent, neighbouring keypresses for the last index finger movement, compared to the first index finger movement of the next trial. A strong difference (larger neural representation distance) between these two movements is, therefore, not surprising, given the task design, and this difference is also expected to increase with learning, given the increase in tapping speed, and the consequent stronger overlap in representations for consecutive keypresses. Furthermore, initiating a new sequence involves pre-planning, while ongoing practice relies on online planning (Ariani et al., eNeuro 2021), i.e., two mental operations that are dissociable at the level of neural representation (Ariani et al., bioRxiv 2023).

The Reviewer argues that the comparison of last finger movement of a trial and the first in the next trial are performed in different circumstances and contexts. This is an important point and one we tend to agree with. For this task, the first sequence in a practice trial is pre-planned before the first keypress is performed. This occurs in a somewhat different context from the sequence iterations that follow, which involve temporally overlapping planning, execution and evaluation processes. The Reviewer is concerned about a difference in the temporal mixing effect issue raised above between the first and last keypresses performed in a trial. Please, note that since neural representations of individual actions are competitively queued during the pre-planning period in a manner that reflects the ordinal structure of the learned sequence (Kornysheva et al., 2019), mixing effects are most likely present also for the first keypress in a trial.

Separately, the Reviewer suggests that contextualization during early learning may reflect preplanning or online planning. This is an interesting proposal. Given the decoding time-window used in this investigation, we cannot dissect separate contributions of planning, memory and sensory feedback to contextualization. Taking advantage of the superior temporal resolution of MEG relative to fMRI tools, work under way in our lab is investigating decoding time-windows more appropriate to address each of these questions.

Given these differences in the physical context and associated mental processes, it is not surprising that "offline differentiation", as defined here, is more pronounced than "online differentiation". For the latter, the authors compared movements that were better matched regarding the presence of consistent preceding and subsequent keypresses (online differentiation was defined as the mean difference between all first vs. last index finger movements during practice). It is unclear why the authors did not follow a similar definition for "online differentiation" as for "micro-online gains" (and, indeed, a definition that is more consistent with their definition of "offline differentiation"), i.e., the difference between the first index finger movement of the first correct sequence during practice, and the last index finger of the last correct sequence. While these two movements are, again, not matched for the presence of neighbouring keypresses (see the argument above), this mismatch would at least be the same across "offline differentiation" and "online differentiation", so they would be more comparable.

This is the same point made earlier by Reviewer #2, and we agree with this assessment. As stated in the response to Reviewer #2 above, we have now carried out quantification of online contextualization using this approach and included it in the revised manuscript. We thank the Reviewer for this suggestion.

A further complication in interpreting the results regarding "contextualization" stems from the visual feedback that participants received during the task. Each keypress generated an asterisk shown above the string on the screen, irrespective of whether the keypress was correct or incorrect. As a result, incorrect (e.g., additional, or missing) keypresses could shift the phase of the visual feedback string (of asterisks) relative to the ordinal position of the current movement in the sequence (e.g., the fifth movement in the sequence could coincide with the presentation of any asterisk in the string, from the first to the fifth). Given that more incorrect keypresses are expected at the start of the experiment, compared to later stages, the consistency in visual feedback position, relative to the ordinal position of the movement in the sequence, increased across the experiment. A better differentiation between the first and the fifth movement with learning could, therefore, simply reflect better decoding of the more consistent visual feedback, based either on the feedback-induced brain response, or feedback-induced eye movements (the study did not include eye tracking). It is not clear why the authors introduced this complicated visual feedback in their task, besides consistency with their previous studies.

We strongly agree with the Reviewer that eye movements related to task engagement are important to rule out as a potential driver of the decoding accuracy or contextualizaton effect. We address this issue above in response to a question raised by Reviewer #1 about the impact of movement related artefacts on our findings.

First, the assumption the Reviewer makes here about the distribution of errors in this task is incorrect. On average across subjects, 2.32% ± 1.48% (mean ± SD) of all keypresses performed were errors, which were evenly distributed across the four possible keypress responses. While errors increased progressively over practice trials, they did so in proportion to the increase in correct keypresses, so that the overall ratio of correct-to-incorrect keypresses remained stable over the training session. Thus, the Reviewer’s assumptions that there is a higher relative frequency of errors in early trials, and a resulting systematic trend phase shift differences between the visual display updates (i.e. – a change in asterisk position above the displayed sequence) and the keypress performed is not substantiated by the data. To the contrary, the asterisk position on the display and the keypress being executed remained highly correlated over the entire training session. We now include a statement about the frequency and distribution of errors in the revised manuscript.

Given this high correlation, we firmly agree with the Reviewer that the issue of eye movement related artefacts is still an important one to address. Fortunately, we did collect eye movement data during the MEG recordings so were able to investigate this. As detailed in the response to Reviewer #1 above, we found that gaze positions and eye-movement velocity time-locked to visual display updates (i.e. – a change in asterisk position above the displayed sequence) did not reflect the asterisk location above chance levels (Overall cross-validated accuracy = 0.21817; see Author response image 1). Furthermore, an inspection of the eye position data revealed that most participants on most trials displayed random walk gaze patterns around a center fixation point, indicating that participants did not attend to the asterisk position on the display. This is consistent with intrinsic generation of the action sequence, and congruent with the fact that the display does not provide explicit feedback related to performance. As pointed out above, a similar real-world example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks), which is typically ignored by the user.

The minimal participant engagement with the visual display in this explicit sequence learning motor task (which is highly generative in nature) contrasts markedly with behavior observed when reactive responses to stimulus cues are needed in the serial reaction time task (SRTT). This is a crucial difference that must be carefully considered when comparing findings across studies using the two sequence learning tasks.

The authors report a significant correlation between "offline differentiation" and cumulative microoffline gains. However, it would be more informative to correlate trial-by-trial changes in each of the two variables. This would address the question of whether there is a trial-by-trial relation between the degree of "contextualization" and the amount of micro-offline gains - are performance changes (micro-offline gains) less pronounced across rest periods for which the change in "contextualization" is relatively low? Furthermore, is the relationship between micro-offline gains and "offline differentiation" significantly stronger than the relationship between micro-offline gains and "online differentiation"?

In response to a similar issue raised above by Reviewer #2, we now include new analyses comparing correlation magnitudes between (1) “online differentiation” vs micro-online gains, (2) “online differentiation” vs micro-offline gains and (3) “offline differentiation” and micro-offline gains (see Figure 5 – figure supplement  4, 5 and 6). These new analyses and results have been added to the revised manuscript. Once again, we thank both Reviewers for this suggestion.

The authors follow the assumption that micro-offline gains reflect offline learning.

We disagree with this statement. The original (Bonstrup et al., 2019) paper clearly states that micro-offline gains do not necessarily reflect offline learning in some cases and must be carefully interpreted based upon the behavioral context within which they are observed. Further, the paper lays out the conditions under which one can have confidence that micro-offline gains reflect offline learning. In fact, the excellent meta-analysis of (Pan & Rickard, 2015), which re-interprets the benefits of sleep in overnight skill consolidation from a “reactive inhibition” perspective, was a crucial resource in the experimental design of our initial study (Bonstrup et al., 2019), as well as in all our subsequent work. Pan & Rickard state:

“Empirically, reactive inhibition refers to performance worsening that can accumulate during a period of continuous training (Hull, 1943 . It tends to dissipate, at least in part, when brief breaks are inserted between blocks of training. If there are multiple performance-break cycles over a training session, as in the motor sequence literature, performance can exhibit a scalloped effect, worsening during each uninterrupted performance block but improving across blocks(Brawn et al., 2010; Rickard et al., 2008 . Rickard, Cai, Rieth, Jones, and Ard (2008 and Brawn, Fenn, Nusbaum, and Margoliash (2010 (Brawn et al., 2010; Rickard et al., 2008 demonstrated highly robust scalloped reactive inhibition effects using the commonly employed 30 s–30 s performance break cycle, as shown for Rickard et al.’s (2008 massed practice sleep group in Figure 2. The scalloped effect is evident for that group after the first few 30 s blocks of each session. The absence of the scalloped effect during the first few blocks of training in the massed group suggests that rapid learning during that period masks any reactive inhibition effect.”

Crucially, Pan & Rickard make several concrete recommendations for reducing the impact of the reactive inhibition confound on offline learning studies. One of these recommendations was to reduce practice times to 10s (most prior sequence learning studies up until that point had employed 30s long practice trials). They state:

“The traditional design involving 30 s-30 s performance break cycles should be abandoned given the evidence that it results in a reactive inhibition confound, and alternative designs with reduced performance duration per block used instead (Pan & Rickard, 2015 . One promising possibility is to switch to 10 s performance durations for each performance-break cycle Instead (Pan & Rickard, 2015 . That design appears sufficient to eliminate at least the majority of the reactive inhibition effect (Brawn et al., 2010; Rickard et al., 2008 .”

We mindfully incorporated recommendations from (Pan & Rickard, 2015) into our own study designs including 1) utilizing 10s practice trials and 2) constraining our analysis of micro-offline gains to early learning trials (where performance monotonically increases and 95% of overall performance gains occur), which are prior to the emergence of the “scalloped” performance dynamics that are strongly linked to reactive inhibition effects.

However, there is no direct evidence in the literature that micro-offline gains really result from offline learning, i.e., an improvement in skill level.

We strongly disagree with the Reviewer’s assertion that “there is no direct evidence in the literature that micro-offline gains really result from offline learning, i.e., an improvement in skill level.” The initial (Bonstrup et al., 2019) report was followed up by a large online crowd-sourcing study (Bonstrup et al., 2020). This second (and much larger) study provided several additional important findings supporting our interpretation of micro-offline gains in cases where the important behavioral conditions clarified above were met (see Author response image 4 below for further details on these conditions).

Author response image 4.

This Figure shows that micro-offline gains o ser ed in learning and nonlearning contexts are attri uted to different underl ing causes. Micro-offline and online changes relative to overall trial-by-trial learning. This figure is based on data from (Bonstrup et al., 2019). During early learning, micro-offline gains (red bars) closely track trial-by-trial performance gains (green line with open circle markers), with minimal contribution from micro-online gains (blue bars). The stated conclusion in Bönstrup et al. (2019) is that micro-offline gains only during this Early Learning stage reflect rapid memory consolidation (see also (Bonstrup et al., 2020)). After early learning, about practice trial 11, skill plateaus. This plateau skill period is characterized by a striking emergence of coupled (and relatively stable) micro-online drops and micro-offline increases. Bönstrup et al. (2019) as well as others in the literature (Brooks et al., 2024; Gupta & Rickard, 2022; Florencia Jacobacci et al., 2020), argue that micro-offline gains during the plateau period likely reflect recovery from inhibitory performance factors such as reactive inhibition or fatigue, and thus must be excluded from analyses relating micro-offline gains to skill learning. The Non-repeating groups in Experiments 3 and 4 from Das et al. (2024) suffer from a lack of consideration of these known confounds (end of Fig legend).

Evidence documented in that paper (Bonstrup et al., 2020) showed that micro-offline gains during early skill learning were: 1) replicable and generalized to subjects learning the task in their daily living environment (n=389); 2) equivalent when significantly shortening practice period duration, thus confirming that they are not a result of recovery from performance fatigue (n=118); 3) reduced (along with learning rates) by retroactive interference applied immediately after each practice period relative to interference applied after passage of time (n=373), indicating stabilization of the motor memory at a microscale of several seconds consistent with rapid consolidation; and 4) not modified by random termination of the practice periods, ruling out a contribution of predictive motor slowing (N = 71) (Bonstrup et al., 2020). Altogether, our findings were strongly consistent with the interpretation that micro-offline gains reflect memory consolidation supporting early skill learning. This is precisely the portion of the learning curve (Pan & Rickard, 2015) refer to when they state “…rapid learning during that period masks any reactive inhibition effect”.

This interpretation is further supported by brain imaging evidence linking known memory-related networks and consolidation mechanisms to micro-offline gains. First, we reported that the density of fast hippocampo-neocortical skill memory replay events increases approximately three-fold during early learning inter-practice rest periods with the density explaining differences in the magnitude of micro-offline gains across subjects (Buch et al., 2021). Second, Jacobacci et al. (2020) independently reproduced our original behavioral findings and reported BOLD fMRI changes in the hippocampus and precuneus (regions also identified in our MEG study (Buch et al., 2021)) linked to micro-offline gains during early skill learning. These functional changes were coupled with rapid alterations in brain microstructure in the order of minutes, suggesting that the same network that operates during rest periods of early learning undergoes structural plasticity over several minutes following practice (Deleglise et al., 2023). Crucial to this point, Chen et al. (2024) and Sjøgård et al (2024) provided direct evidence from intracranial EEG in humans linking sharp-wave ripple density during rest periods (which are known markers for neural replay (Buzsaki, 2015)) in the human hippocampus (80-120 Hz) to micro-offline gains during early skill learning.

Thus, there is now substantial converging evidence in humans across different indirect noninvasive and direct invasive recording techniques linking hippocampal activity, neural replay dynamics and offline performance gains in skill learning.

On the contrary, recent evidence questions this interpretation (Gupta & Rickard, npj Sci Learn 2022; Gupta & Rickard, Sci Rep 2024; Das et al., bioRxiv 2024). Instead, there is evidence that micro-offline gains are transient performance benefits that emerge when participants train with breaks, compared to participants who train without breaks, however, these benefits vanish within seconds after training if both groups of participants perform under comparable conditions (Das et al., bioRxiv 2024).

The recent work of (Gupta & Rickard, 2022, 2024) does not present any data that directly opposes our finding that early skill learning (Bonstrup et al., 2019) is expressed as micro-offline gains during rest breaks. These studies are an extension of the Rickard et al (2008) paper that employed a massed (30s practice followed by 30s breaks) vs spaced (10s practice followed by 10s breaks) experimental design to assess if recovery from reactive inhibition effects could account for performance gains measured after several minutes or hours. Gupta & Rickard (2022) added two additional groups (30s practice/10s break and 10s practice/10s break as used in the work from our group). The primary aim of the study was to assess whether it was more likely that changes in performance when retested 5 minutes after skill training (consisting of 12 practice trials for the massed groups and 36 practice trials for the spaced groups) had ended reflected memory consolidation effects or recovery from reactive inhibition effects. The Gupta & Rickard (2024) follow-up paper employed a similar design with the primary difference being that participants performed a fixed number of sequences on each trial as opposed to trials lasting a fixed duration. This was done to facilitate the fitting of a quantitative statistical model to the data.

To reiterate, neither study included any analysis of micro-online or micro-offline gains and did not include any comparison focused on skill gains during early learning trials (only at retest 5 min later). Instead, Gupta & Rickard (2022), reported evidence for reactive inhibition effects for all groups over much longer training periods than early learning. In fact, we reported the same findings for trials following the early learning period in our original 2019 paper (Bonstrup et al., 2019) (Author response image 4). Please, note that we also reported that cumulative microoffline gains over early learning did not correlate with overnight offline consolidation measured 24 hours later (Bonstrup et al., 2019) (see the Results section and further elaboration in the Discussion). We interpreted these findings as indicative that the mechanisms underlying offline gains over the micro-scale of seconds during early skill learning versus over minutes or hours very likely differ.

In the recent preprint from (Das et al., 2024), the authors make the strong claim that “micro-offline gains during early learning do not reflect offline learning” which is not supported by their own data. The authors hypothesize that if “micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”. The study utilizes a spaced vs. massed practice groups between-subjects design inspired by the reactive inhibition work from Rickard and others to test this hypothesis.

Crucially, their design incorporates only a small fraction of the training used in other investigations to evaluate early skill learning (Bonstrup et al., 2020; Bonstrup et al., 2019; Brooks et al., 2024; Buch et al., 2021; Deleglise et al., 2023; F. Jacobacci et al., 2020; Mylonas et al., 2024). A direct comparison between the practice schedule designs for the spaced and massed groups in Das et al., and the training schedule all participants experienced in the original Bönstrup et al. (2019) paper highlights this issue as well as several others (Author response image 5):

Author response image 5.

This figure shows (A) Comparison of Das et al. Spaced & Massed group training session designs, and the training session design from the original (Bonstrup et al., 2019) paper. Similar to the approach taken by Das et al., all practice is visualized as 10-second practice trials with a variable number (either 0, 1 or 30) of 10-second-long inter-practice rest intervals to allow for direct comparisons between designs. The two key takeaways from this comparison are that (1) the intervention differences (i.e. – practice schedules) between the Massed and Spaced groups from the Das et al. report are extremely small (less than 12% of the overall session schedule) (gaps in the red shaded area) and (2) the overall amount of practice is much less than compared to the design from the original Bönstrup report (Bonstrup et al., 2019) (which has been utilized in several subsequent studies). (B) Group-level learning curve data from Bönstrup et al. (2019) (Bonstrup et al., 2019) is used to estimate the performance range accounted for by the equivalent periods covering Test 1, Training 1 and Test 2 from Das et al (2024). Note that the intervention in the Das et al. study is limited to a period covering less than 50% of the overall learning range (end of figure legend).

Participants in the original (Bonstrup et al., 2019) experienced 157.14% more practice time and 46.97% less inter-practice rest time than the Spaced group in the Das et al. study (Author response image 5). Thus, the overall amount of practice and rest differ substantially between studies, with much more limited training occurring for participants in Das et al.

In addition, the training interventions (i.e. – the practice schedule differences between the Spaced and Massed groups) were designed in a manner that minimized any chance of effectively testing their hypothesis. First, the interventions were applied over an extremely short period relative to the length of the total training session (5% and 12% of the total training session for Massed and Spaced groups, respectively; see gaps in the red shaded area in Author response image 5). Second, the intervention was applied during a period in which only half of the known total learning occurs. Specifically, we know from Bönstrup et al. (2019) that only 46.57% of the total performance gains occur in the practice interval covered by Das et al Training 1 intervention. Thus, early skill learning as evaluated by multiple groups (Bonstrup et al., 2020; Bonstrup et al., 2019; Brooks et al., 2024; Buch et al., 2021; Deleglise et al., 2023; F. Jacobacci et al., 2020; Mylonas et al., 2024), is in the Das et al experiment amputated to about half.

Furthermore, a substantial amount of learning takes place during Das et al’s Test 1 and Test 2 periods (32.49% of total gains combined). The fact that substantial learning is known to occur over both the Test 1 (18.06%) and Test 2 (14.43%) intervals presents a fundamental problem described by Pan and Rickard (Pan & Rickard, 2015). They reported that averaging over intervals where substantial performance gains occur (i.e. – performance is not stable) inject crucial artefacts into analyses of skill learning:

“A large amount of averaging has the advantage of yielding more precise estimates of each subject’s pretest and posttest scores and hence more statistical power to detect a performance gain. However, calculation of gain scores using that strategy runs the risk that learning that occurs during the pretest and (or posttest periods (i.e., online learning is incorporated into the gain score (Rickard et al., 2008; Robertson et al., 2004 .”

The above statement indicates that the Test 1 and Test 2 performance scores from Das et al. (2024) are substantially contaminated by the learning rate within these intervals. This is particularly problematic if the intervention design results in different Test 2 learning rates between the two groups. This in fact, is apparent in their data (Figure 1C,E of the Das et al., 2024 preprint) as the Test 2 learning rate for the Spaced group is negative (indicating a unique interference effect observable only for this group). Specifically, the Massed group continues to show an increase in performance during Test 2 and 4 relative to the last 10 seconds of practice during Training 1 and 2, respectively, while the Spaced group displays a marked decrease. This post-training performance decrease for the Spaced group is in stark contrast to the monotonic performance increases observed for both groups at all other time-points. One possible cause could be related to the structure of the Test intervals, which include 20 seconds of uninterrupted practice. For the Spaced group, this effectively is a switch to a Massed practice environment (i.e., two 10-secondlong practice trials merged into one long trial), which interferes with greater Training 1 interval gains observed for the Space group. Interestingly, when statistical comparisons between the groups are made at the time-points when the intervention is present (Figure 1E) then the stated hypothesis, “If micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”, is confirmed.

In summary, the experimental design and analyses used by Das et al does not contradict the view that early skill learning is expressed as micro-offline gains during rest breaks. The data presented by Gupta and Rickard (2022, 2024) and Das et al. (2024) is in many ways more confirmatory of the constraints employed by our group and others with respect to experimental design, analysis and interpretation of study findings, rather than contradictory. Still, it does highlight a limitation of the current micro-online/offline framework, which was originally only intended to be applied to early skill learning over spaced practice schedules when reactive inhibition effects are minimized (Bonstrup et al., 2019; Pan & Rickard, 2015). Extrapolation of this current framework to postplateau performance periods, longer timespans, or non-learning situations (e.g. – the Nonrepeating groups from Das et al. (2024)), when reactive inhibition plays a more substantive role, is not warranted. Ultimately, it will be important to develop new paradigms allowing one to independently estimate the different coincident or antagonistic features (e.g. - memory consolidation, planning, working memory and reactive inhibition) contributing to micro-online and micro-offline gains during and after early skill learning within a unifying framework.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) I found Figure 2B too small to be useful, as the actual elements of the cells are very hard to read.

We have removed the grid colormap panel (top-right) from Figure 2B. All of this colormap data is actually a subset of data presented in Figure 2 – figure supplement 1, so can still be found there.

Reviewer #2 (Recommendations for the authors):

(1) Related to the first point in my concerns, I would suggest the authors compare decoding accuracy between correct presses followed by correct vs. incorrect presses. This would clarify if the decoder is actually taking the MEG signal for subsequent press into account. I would also suggest the authors use pre-movement MEG features and post-movement features with shorter windows and compare each result with the results for the original post-movement MEG feature with a longer window.

The present study does not contain enough errors to perform the analysis proposed by the Reviewer. As noted above, we did re-examine our data and now report a new control regression analysis, all of which indicate that the proximity between keypresses does not explain contextualization effects.

(2) I was several times confused by the author's use of "neural representation of an action" or "sequence action representations" in understanding whether these terms refer to representation on the level of whole-brain, region (as defined by the specific parcellation used), or voxels. In fact, what is submitted to the decoder is some complicated whole-brain MEG feature (i.e., the "neural representation"), which is a hybrid of voxel and parcel features that is further dimension-reduced and not immediately interpretable. Clarifying this point early in the text and possibly using some more sensible terms, such as adding "brain-wise" before the "sequence action representation", would be the most helpful for the readers.

We now clarified this terminology in the revised manuscript.

(3) Although comparing many different ways in feature selection/reduction, time window selection, and decoder types is undoubtedly a meticulous work, the current version of the manuscript seems still lacking some explanation about the details of these methodological choices, like which decoding method was actually used to report the accuracy, whether or not different decoding methods were chosen for individual participants' data, how training data was selected (is it all of the correct presses in Day 1 data?), whether the frequency power or signal amplitude was used, and so on. I would highly appreciate these additional details in the Methods section.

The reported accuracies were based on linear discriminant analysis classifier. A comparison of different decoders (Figure 3 – figure supplement 4) shows LDA was the optimal choice.

Whether or not different decoding methods were chosen for individual participants' data

We selected the same decoder (LDA) performance to report the final accuracy.

How training data was selected (is it all of the correct presses in Day 1 data?),

Decoder training was conducted as a randomized split of the data (all correct keypresses of Day 1) into training (90%) and test (10%) samples for 8 iterations.

Whether the frequency power or signal amplitude was used

Signal amplitude was used for feature calculation.

(4) In terms of the Methods, please consider adding some references about the 'F1 score', the 'feature importance score,' and the 'MRMR-based feature ranking,' as the main readers of the current paper would not be from the machine learning community. Also, why did the LDA dimensionality reduction reduce accuracy specifically for the voxel feature?

We have now added the following statements to the Methods section that provide more detailed descriptions and references for these metrics:

“The F1 score, defined as the harmonic mean of the precision (percentage of true predictions that are actually true positive) and recall (percentage of true positives that were correctly predicted as true) scores, was used as a comprehensive metric for all one-versus-all keypress state decoders to assess class-wise performance that accounts for both false-positive and false-negative prediction tendencies [REF]. A weighted mean F1 score was then computed across all classes to assess the overall prediction performance of the multi-class model.”

and

“Feature Importance Scores

The relative contribution of source-space voxels and parcels to decoding performance (i.e. – feature importance score) was calculated using minimum redundant maximum relevance (MRMR) and highlighted in topography plots. MRMR, an approach that combines both relevance and redundancy metrics, ranked individual features based upon their significance to the target variable (i.e. – keypress state identity) prediction accuracy and their non-redundancy with other features.”

As stated in the Reviewer responses above, the dimensionality of the voxel-space feature set is very high (i.e. – 15684). LDA attempts to map the input features onto a much smaller dimensional space (number of classes-1; e.g. – 3 dimensions for 4-class keypress decoding). It is likely that the reduction in accuracy observed only for the voxel-space feature was due to the loss of relevant information during the mapping process that resulted in reduced accuracy. This reduction in accuracy for voxel-space decoding was specific to LDA. Figure 3—figure supplement 3 shows that voxel-space decoder performance actually improved when utilizing alternative dimensionality reduction techniques.

(5) Paragraph 9, lines #139-142: "Notably, decoding associated with index finger keypresses (executed at two different ordinal positions in the sequence) exhibited the highest number of misclassifications of all digits (N = 141 or 47.5% of all decoding errors; Figure 3C), raising the hypothesis that the same action could be differentially represented when executed at different learning state or sequence context locations."

This does not seem to be a fair comparison, as the index finger appears twice as many as the other fingers do in the sequence. To claim this, proper statistical analysis needs to be done taking this difference into account.

We thank the Reviewer for bringing this issue to our attention. We have now corrected this comparison to evaluate relative false negative and false positive rates between individual keypress state decoders, and have revised this statement in the manuscript as follows:

“Notably, decoding of index finger keypresses (executed at two different ordinal positions in the sequence) exhibited the highest false negative (0.116 per keypress) and false positive (0.043 per keypress) misclassification rates compared with all other digits (false negative rate range = [0.067 0.114]; false positive rate range = [0.020 0.037]; Figure 3C), raising the hypothesis that the same action could be differentially represented when executed within different contexts (i.e. - different learning states or sequence locations).”

(6) Finally, the authors could consider acknowledging in the Discussion that the contribution of micro-offline learning to genuine skill learning is still under debate (e.g., Gupta and Rickard, 2023; 2024; Das et al., bioRxiv, 2024).

We have added a paragraph in the Discussion that addresses this point.

Reviewer #3 (Recommendations for the authors):

In addition to the additional analyses suggested in the public review, I have the following suggestions/questions:

(1) Given that the authors introduce a new decoding approach, it would be very helpful for readers to see a distribution of window sizes and window onsets eventually used across individuals, at least for the optimized decoder.

We have now included a new supplemental figure (Figure 4 – figure Supplement 2) that provides this information.

(2) Please explain in detail how you arrived at the (interpolated?) group-level plot shown in Figure 1B, starting from the discrete single-trial keypress transition times. Also, please specify what the shading shows.

Instantaneous correct sequence speed (skill measure) was quantified as the inverse of time (in seconds) required to complete a single iteration of a correctly generated full 5-item sequence. Individual keypress responses were labeled as members of correct sequences if they occurred within a 5-item response pattern matching any possible circular shifts of the 5-item sequence displayed on the monitor (41324). This approach allowed us to quantify a measure of skill within each practice trial at the resolution of individual keypresses. The dark line indicates the group mean performance dynamics for each trial. The shaded region indicates the 95% confidence limit of the mean (see Methods).

(3) Similarly, please explain how you arrived at the group-level plot shown in Figure 1C. What are the different colored lines (rows) within each trial? How exactly did the authors reach the conclusion that KTT variability stabilizes by trial 6?

Figure 1C provides additional information to the correct sequence speed measure above, as it also tracks individual transition speed composition over learning. Figure 1C, thus, represents both changes in overall correct sequence speed dynamics (indicated by the overall narrowing of the horizontal speed lines moving from top to bottom) and the underlying composition of the individual transition patterns within and across trials. The coloring of the lines is a shading convention used to discriminate between different keypress transitions. These curves were sampled with 1ms resolution, as in Figure 1B. Addressing the underlying keypress transition patterns requires within-subject normalization before averaging across subjects. The distribution of KTTs was normalized to the median correct sequence time for each participant and centered on the mid-point for each full sequence iteration during early learning.

(4) Maybe I missed it, but it was not clear to me which of the tested classifiers was eventually used. Or was that individualized as well? More generally, a comparison of the different classifiers would be helpful, similar to the comparison of dimension reduction techniques.

We have now included a new supplemental figure that provides this information.

(5) Please add df and effect sizes to all statistics.

Done.

(6) Please explain in more detail your power calculation.

The study was powered to determine the minimum sample size needed to detect a significant change in skill performance following training using a one-sample t-test (two-sided; alpha = 0.05; 95% statistical power; Cohen’s D effect size = 0.8115 calculated from previously acquired data in our lab). The calculated minimum sample size was 22. The included study sample size (n = 27) exceeded this minimum.

This information is now included in the revised manuscript.

(7) The cut-off for the high-pass filter is unusually high and seems risky in terms of potential signal distortions (de Cheveigne, Neuron 2019). Why did the authors choose such a high cut-off?

The 1Hz high-pass cut-off frequency for the 1-150Hz band-pass filter applied to the continuous raw MEG data during preprocessing has been used in multiple previous MEG publications (Barratt et al., 2018; Brookes et al., 2012; Higgins et al., 2021; Seedat et al., 2020; Vidaurre et al., 2018).

(8) "Furthermore, the magnitude of offline contextualization predicted skill gains while online contextualization did not", lines 336/337 - where is that analysis?

Additional details pertaining to this analysis are now provided in the Results section (Figure 5 – figure supplement 4).

(9) How were feature importance scores computed?

We have now added a new subheading in the Methods section with a more detailed description of how feature importance scores were computed.

(10)  Please add x and y ticks plus tick labels to Figure 5 - Figure Supplement 3, panel A

Done

(11) Line 369, what does "comparable" mean in this context?

The sentence in the “Study Participants” part of the Methods section referred to here has now been revised for clarity.

(12) In lines 496/497, please specify what t=0 means (KeyDown event, I guess?).

Yes, the KeyDown event occurs at t = 0. This has now been clarified in the revised manuscript.

(13) Please specify consistent boundaries between alpha- and beta-bands (they are currently not consistent in the Results vs. Methods (14/15 Hz or 15/16 Hz)).

We thank the Reviewer for alerting us to this discrepancy caused by a typographic error in the Methods. We have now corrected this so that the alpha (8-14 Hz) and beta-band (15-24 Hz) frequency limits are described consistently throughout the revised manuscript.

References

Albouy, G., Fogel, S., King, B. R., Laventure, S., Benali, H., Karni, A., Carrier, J., Robertson, E. M., & Doyon, J. (2015). Maintaining vs. enhancing motor sequence memories: respective roles of striatal and hippocampal systems. Neuroimage, 108, 423-434. https://doi.org/10.1016/j.neuroimage.2014.12.049

Albouy, G., King, B. R., Maquet, P., & Doyon, J. (2013). Hippocampus and striatum: dynamics and interaction during acquisition and sleep-related motor sequence memory consolidation. Hippocampus, 23(11), 985-1004. https://doi.org/10.1002/hipo.22183 Albouy, G., Sterpenich, V., Vandewalle, G., Darsaud, A., Gais, S., Rauchs, G., Desseilles, M., Boly, M., Dang-Vu, T., Balteau, E., Degueldre, C., Phillips, C., Luxen, A., & Maquet, P. (2012). Neural correlates of performance variability during motor sequence acquisition. NeuroImage, 60(1), 324-331. https://doi.org/10.1016/j.neuroimage.2011.12.049

Andersen, R. A., & Buneo, C. A. (2002). Intentional maps in posterior parietal cortex. Annu Rev Neurosci, 25, 189-220. https://doi.org/10.1146/annurev.neuro.25.112701.142922 112701.142922 [pii]

Ashe, J., Lungu, O. V., Basford, A. T., & Lu, X. (2006). Cortical control of motor sequences. Curr Opin Neurobiol, 16(2), 213-221. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=16563734

Bansal, A. K., Vargas-Irwin, C. E., Truccolo, W., & Donoghue, J. P. (2011). Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices. J Neurophysiol, 105(4), 1603-1619. https://doi.org/10.1152/jn.00532.2010

Barratt, E. L., Francis, S. T., Morris, P. G., & Brookes, M. J. (2018). Mapping the topological organisation of beta oscillations in motor cortex using MEG. NeuroImage, 181, 831-844. https://doi.org/10.1016/j.neuroimage.2018.06.041

Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci U S A, 108(18), 7641-7646. https://doi.org/10.1073/pnas.1018985108

Battaglia-Mayer, A., & Caminiti, R. (2019). Corticocortical Systems Underlying High-Order Motor Control. J Neurosci, 39(23), 4404-4421. https://doi.org/10.1523/JNEUROSCI.2094-18.2019

Berlot, E., Popp, N. J., & Diedrichsen, J. (2020). A critical re-evaluation of fMRI signatures of motor sequence learning. Elife, 9. https://doi.org/10.7554/eLife.55241

Bonstrup, M., Iturrate, I., Hebart, M. N., Censor, N., & Cohen, L. G. (2020). Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data. NPJ Sci Learn, 5, 7. https://doi.org/10.1038/s41539-020-0066-9

Bonstrup, M., Iturrate, I., Thompson, R., Cruciani, G., Censor, N., & Cohen, L. G. (2019). A Rapid Form of Offline Consolidation in Skill Learning. Curr Biol, 29(8), 1346-1351 e1344. https://doi.org/10.1016/j.cub.2019.02.049

Brawn, T. P., Fenn, K. M., Nusbaum, H. C., & Margoliash, D. (2010). Consolidating the effects of waking and sleep on motor-sequence learning. J Neurosci, 30(42), 13977-13982. https://doi.org/10.1523/JNEUROSCI.3295-10.2010

Brookes, M. J., Woolrich, M. W., & Barnes, G. R. (2012). Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. NeuroImage, 63(2), 910-920. https://doi.org/10.1016/j.neuroimage.2012.03.048

Brooks, E., Wallis, S., Hendrikse, J., & Coxon, J. (2024). Micro-consolidation occurs when learning an implicit motor sequence, but is not influenced by HIIT exercise. NPJ Sci Learn, 9(1), 23. https://doi.org/10.1038/s41539-024-00238-6

Buch, E. R., Claudino, L., Quentin, R., Bonstrup, M., & Cohen, L. G. (2021). Consolidation of human skill linked to waking hippocampo-neocortical replay. Cell Rep, 35(10), 109193. https://doi.org/10.1016/j.celrep.2021.109193

Buneo, C. A., & Andersen, R. A. (2006). The posterior parietal cortex: sensorimotor interface for the planning and online control of visually guided movements. Neuropsychologia, 44(13), 2594-2606. https://doi.org/10.1016/j.neuropsychologia.2005.10.011

Buzsaki, G. (2015). Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning. Hippocampus, 25(10), 1073-1188. https://doi.org/10.1002/hipo.22488

Chen, P.-C., Stritzelberger, J., Walther, K., Hamer, H., & Staresina, B. P. (2024). Hippocampal ripples during offline periods predict human motor sequence learning. bioRxiv, 2024.2010.2006.614680. https://doi.org/10.1101/2024.10.06.614680

Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012). Neural population dynamics during reaching. Nature, 487(7405), 51-56. https://doi.org/10.1038/nature11129

Classen, J., Liepert, J., Wise, S. P., Hallett, M., & Cohen, L. G. (1998). Rapid plasticity of human cortical movement representation induced by practice. J Neurophysiol, 79(2), 1117-1123. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=9463469

Colclough, G. L., Brookes, M. J., Smith, S. M., & Woolrich, M. W. (2015). A symmetric multivariate leakage correction for MEG connectomes. NeuroImage, 117, 439-448. https://doi.org/10.1016/j.neuroimage.2015.03.071

Colclough, G. L., Woolrich, M. W., Tewarie, P. K., Brookes, M. J., Quinn, A. J., & Smith, S. M. (2016). How reliable are MEG resting-state connectivity metrics? NeuroImage, 138, 284-293. https://doi.org/10.1016/j.neuroimage.2016.05.070

Das, A., Karagiorgis, A., Diedrichsen, J., Stenner, M.-P., & Azanon, E. (2024). “Micro-offline gains” convey no benefit for motor skill learning. bioRxiv, 2024.2007.2011.602795. https://doi.org/10.1101/2024.07.11.602795

Deleglise, A., Donnelly-Kehoe, P. A., Yeffal, A., Jacobacci, F., Jovicich, J., Amaro, E., Jr., Armony, J. L., Doyon, J., & Della-Maggiore, V. (2023). Human motor sequence learning drives transient changes in network topology and hippocampal connectivity early during memory consolidation. Cereb Cortex, 33(10), 6120-6131. https://doi.org/10.1093/cercor/bhac489

Doyon, J., Bellec, P., Amsel, R., Penhune, V., Monchi, O., Carrier, J., Lehéricy, S., & Benali, H. (2009). Contributions of the basal ganglia and functionally related brain structures to motor learning. [Review]. Behavioural brain research, 199(1), 61-75. https://doi.org/10.1016/j.bbr.2008.11.012

Doyon, J., Song, A. W., Karni, A., Lalonde, F., Adams, M. M., & Ungerleider, L. G. (2002). Experience-dependent changes in cerebellar contributions to motor sequence learning. Proc Natl Acad Sci U S A, 99(2), 1017-1022. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=11805340

Euston, D. R., Gruber, A. J., & McNaughton, B. L. (2012). The role of medial prefrontal cortex in memory and decision making. Neuron, 76(6), 1057-1070. https://doi.org/10.1016/j.neuron.2012.12.002

Euston, D. R., Tatsuno, M., & McNaughton, B. L. (2007). Fast-forward playback of recent memory sequences in prefrontal cortex during sleep. Science, 318(5853), 1147-1150. https://doi.org/10.1126/science.1148979

Flint, R. D., Ethier, C., Oby, E. R., Miller, L. E., & Slutzky, M. W. (2012). Local field potentials allow accurate decoding of muscle activity. J Neurophysiol, 108(1), 18-24. https://doi.org/10.1152/jn.00832.2011

Frankland, P. W., & Bontempi, B. (2005). The organization of recent and remote memories. Nat Rev Neurosci, 6(2), 119-130. https://doi.org/10.1038/nrn1607

Gais, S., Albouy, G., Boly, M., Dang-Vu, T. T., Darsaud, A., Desseilles, M., Rauchs, G., Schabus, M., Sterpenich, V., Vandewalle, G., Maquet, P., & Peigneux, P. (2007). Sleep transforms the cerebral trace of declarative memories. Proc Natl Acad Sci U S A, 104(47), 1877818783. https://doi.org/10.1073/pnas.0705454104

Grafton, S. T., Mazziotta, J. C., Presty, S., Friston, K. J., Frackowiak, R. S., & Phelps, M. E. (1992). Functional anatomy of human procedural learning determined with regional cerebral blood flow and PET. J Neurosci, 12(7), 2542-2548.

Grover, S., Wen, W., Viswanathan, V., Gill, C. T., & Reinhart, R. M. G. (2022). Long-lasting, dissociable improvements in working memory and long-term memory in older adults with repetitive neuromodulation. Nat Neurosci, 25(9), 1237-1246. https://doi.org/10.1038/s41593-022-01132-3

Gupta, M. W., & Rickard, T. C. (2022). Dissipation of reactive inhibition is sufficient to explain post-rest improvements in motor sequence learning. NPJ Sci Learn, 7(1), 25. https://doi.org/10.1038/s41539-022-00140-z

Gupta, M. W., & Rickard, T. C. (2024). Comparison of online, offline, and hybrid hypotheses of motor sequence learning using a quantitative model that incorporate reactive inhibition. Sci Rep, 14(1), 4661. https://doi.org/10.1038/s41598-024-52726-9

Hardwick, R. M., Rottschy, C., Miall, R. C., & Eickhoff, S. B. (2013). A quantitative metaanalysis and review of motor learning in the human brain. NeuroImage, 67, 283-297. https://doi.org/10.1016/j.neuroimage.2012.11.020

Heusser, A. C., Poeppel, D., Ezzyat, Y., & Davachi, L. (2016). Episodic sequence memory is supported by a theta-gamma phase code. Nat Neurosci, 19(10), 1374-1380. https://doi.org/10.1038/nn.4374

Higgins, C., Liu, Y., Vidaurre, D., Kurth-Nelson, Z., Dolan, R., Behrens, T., & Woolrich, M. (2021). Replay bursts in humans coincide with activation of the default mode and parietal alpha networks. Neuron, 109(5), 882-893 e887. https://doi.org/10.1016/j.neuron.2020.12.007

Hikosaka, O., Nakamura, K., Sakai, K., & Nakahara, H. (2002). Central mechanisms of motor skill learning. Curr Opin Neurobiol, 12(2), 217-222. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12015240

Jacobacci, F., Armony, J. L., Yeffal, A., Lerner, G., Amaro, E., Jr., Jovicich, J., Doyon, J., & Della-Maggiore, V. (2020). Rapid hippocampal plasticity supports motor sequence learning. Proc Natl Acad Sci U S A, 117(38), 23898-23903. https://doi.org/10.1073/pnas.2009576117

Jacobacci, F., Armony, J. L., Yeffal, A., Lerner, G., Amaro Jr, E., Jovicich, J., Doyon, J., & DellaMaggiore, V. (2020). Rapid hippocampal plasticity supports motor sequence learning.

Proceedings of the National Academy of Sciences, 117(38), 23898-23903. Karni, A., Meyer, G., Jezzard, P., Adams, M. M., Turner, R., & Ungerleider, L. G. (1995). Functional MRI evidence for adult motor cortex plasticity during motor skill learning. Nature, 377(6545), 155-158. https://doi.org/10.1038/377155a0

Kennerley, S. W., Sakai, K., & Rushworth, M. F. (2004). Organization of action sequences and the role of the pre-SMA. J Neurophysiol, 91(2), 978-993. https://doi.org/10.1152/jn.00651.2003 00651.2003 [pii]

Kleim, J. A., Barbay, S., & Nudo, R. J. (1998). Functional reorganization of the rat motor cortex following motor skill learning. J Neurophysiol, 80, 3321-3325.

Kornysheva, K., Bush, D., Meyer, S. S., Sadnicka, A., Barnes, G., & Burgess, N. (2019). Neural Competitive Queuing of Ordinal Structure Underlies Skilled Sequential Action. Neuron, 101(6), 1166-1180 e1163. https://doi.org/10.1016/j.neuron.2019.01.018

Lee, S. H., Jin, S. H., & An, J. (2019). The difference in cortical activation pattern for complex motor skills: A functional near- infrared spectroscopy study. Sci Rep, 9(1), 14066. https://doi.org/10.1038/s41598-019-50644-9

Lisman, J. E., & Jensen, O. (2013). The theta-gamma neural code. Neuron, 77(6), 1002-1016. https://doi.org/10.1016/j.neuron.2013.03.007

Mollazadeh, M., Aggarwal, V., Davidson, A. G., Law, A. J., Thakor, N. V., & Schieber, M. H. (2011). Spatiotemporal variation of multiple neurophysiological signals in the primary motor cortex during dexterous reach-to-grasp movements. J Neurosci, 31(43), 15531-15543. https://doi.org/10.1523/JNEUROSCI.2999-11.2011

Molle, M., & Born, J. (2009). Hippocampus whispering in deep sleep to prefrontal cortex--for good memories? Neuron, 61(4), 496-498. https://doi.org/10.1016/j.neuron.2009.02.002

Morris, R. G. M. (2006). Elements of a neurobiological theory of hippocampal function: the role of synaptic plasticity, synaptic tagging and schemas. [Review]. The European journal of neuroscience, 23(11), 2829-2846. https://doi.org/10.1111/j.1460-9568.2006.04888.x

Mylonas, D., Schapiro, A. C., Verfaellie, M., Baxter, B., Vangel, M., Stickgold, R., & Manoach, D. S. (2024). Maintenance of Procedural Motor Memory across Brief Rest Periods Requires the Hippocampus. J Neurosci, 44(14). https://doi.org/10.1523/JNEUROSCI.1839-23.2024

Pan, S. C., & Rickard, T. C. (2015). Sleep and motor learning: Is there room for consolidation? Psychol Bull, 141(4), 812-834. https://doi.org/10.1037/bul0000009

Penhune, V. B., & Steele, C. J. (2012). Parallel contributions of cerebellar, striatal and M1 mechanisms to motor sequence learning. Behav. Brain Res., 226(2), 579-591. https://doi.org/10.1016/j.bbr.2011.09.044

Qin, Y. L., McNaughton, B. L., Skaggs, W. E., & Barnes, C. A. (1997). Memory reprocessing in corticocortical and hippocampocortical neuronal ensembles. Philos Trans R Soc Lond B Biol Sci, 352(1360), 1525-1533. https://doi.org/10.1098/rstb.1997.0139

Rickard, T. C., Cai, D. J., Rieth, C. A., Jones, J., & Ard, M. C. (2008). Sleep does not enhance motor sequence learning. J Exp Psychol Learn Mem Cogn, 34(4), 834-842. https://doi.org/10.1037/0278-7393.34.4.834

Robertson, E. M., Pascual-Leone, A., & Miall, R. C. (2004). Current concepts in procedural consolidation. Nat Rev Neurosci, 5(7), 576-582. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=15208699

Sawamura, D., Sakuraba, S., Suzuki, Y., Asano, M., Yoshida, S., Honke, T., Kimura, M., Iwase, Y., Horimoto, Y., Yoshida, K., & Sakai, S. (2019). Acquisition of chopstick-operation skills with the non-dominant hand and concomitant changes in brain activity. Sci Rep, 9(1), 20397. https://doi.org/10.1038/s41598-019-56956-0

Schendan, H. E., Searl, M. M., Melrose, R. J., & Stern, C. E. (2003). An FMRI study of the role of the medial temporal lobe in implicit and explicit sequence learning. Neuron, 37(6), 1013-1025. https://doi.org/10.1016/s0896-6273(03)00123-5

Seedat, Z. A., Quinn, A. J., Vidaurre, D., Liuzzi, L., Gascoyne, L. E., Hunt, B. A. E., O'Neill, G. C., Pakenham, D. O., Mullinger, K. J., Morris, P. G., Woolrich, M. W., & Brookes, M. J. (2020). The role of transient spectral 'bursts' in functional connectivity: A magnetoencephalography study. NeuroImage, 209, 116537. https://doi.org/10.1016/j.neuroimage.2020.116537

Shadmehr, R., & Holcomb, H. H. (1997). Neural correlates of motor memory consolidation. Science, 277, 821-824.

Sjøgård, M., Baxter, B., Mylonas, D., Driscoll, B., Kwok, K., Tolosa, A., Thompson, M., Stickgold, R., Vangel, M., Chu, C., & Manoach, D. S. (2024). Hippocampal ripples mediate motor learning during brief rest breaks in humans. bioRxiv. https://doi.org/10.1101/2024.05.02.592200

Srinivas, S., Sarvadevabhatla, R. K., Mopuri, K. R., Prabhu, N., Kruthiventi, S. S. S., & Babu, R. V. (2016). A Taxonomy of Deep Convolutional Neural Nets for Computer Vision [Technology Report]. Frontiers in Robotics and AI, 2. https://doi.org/10.3389/frobt.2015.00036

Sterpenich, V., Albouy, G., Darsaud, A., Schmidt, C., Vandewalle, G., Dang Vu, T. T., Desseilles, M., Phillips, C., Degueldre, C., Balteau, E., Collette, F., Luxen, A., & Maquet, P. (2009). Sleep promotes the neural reorganization of remote emotional memory. J Neurosci, 29(16), 5143-5152. https://doi.org/10.1523/JNEUROSCI.0561-09.2009

Toni, I., Ramnani, N., Josephs, O., Ashburner, J., & Passingham, R. E. (2001). Learning arbitrary visuomotor associations: temporal dynamic of brain activity. Neuroimage, 14(5), 10481057. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=11697936

Toni, I., Thoenissen, D., & Zilles, K. (2001). Movement preparation and motor intention. NeuroImage, 14(1 Pt 2), S110-117. https://doi.org/10.1006/nimg.2001.0841

Tse, D., Langston, R. F., Kakeyama, M., Bethus, I., Spooner, P. A., Wood, E. R., Witter, M. P., & Morris, R. G. (2007). Schemas and memory consolidation. Science, 316(5821), 76-82. https://doi.org/10.1126/science.1135935

van Kesteren, M. T., Fernandez, G., Norris, D. G., & Hermans, E. J. (2010). Persistent schemadependent hippocampal-neocortical connectivity during memory encoding and postencoding rest in humans. Proc Natl Acad Sci U S A, 107(16), 7550-7555. https://doi.org/10.1073/pnas.0914892107

van Kesteren, M. T., Ruiter, D. J., Fernandez, G., & Henson, R. N. (2012). How schema and novelty augment memory formation. Trends Neurosci, 35(4), 211-219. https://doi.org/10.1016/j.tins.2012.02.001

Vidaurre, D., Hunt, L. T., Quinn, A. J., Hunt, B. A. E., Brookes, M. J., Nobre, A. C., & Woolrich, M. W. (2018). Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks. Nat Commun, 9(1), 2987. https://doi.org/10.1038/s41467-01805316-z

Wagner, A. D., Schacter, D. L., Rotte, M., Koutstaal, W., Maril, A., Dale, A. M., Rosen, B. R., & Buckner, R. L. (1998). Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. [Comment]. Science (New York, N.Y.), 281(5380), 1188-1191. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=9712582 &retmode=ref&cmd=prlinks

Wolpert, D. M., Goodbody, S. J., & Husain, M. (1998). Maintaining internal representations: the role of the human superior parietal lobe. Nat Neurosci, 1(6), 529-533. https://doi.org/10.1038/2245

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors investigate the role of microtubule dynamics and its effects on neuronal aging. Using C. elegans as a model, the authors investigate the role of evolutionarily conserved Hippo pathway in microtubule dynamics of touch receptor neurons (TRNs) in an age-dependent manner. Using genetic, molecular, behavioral, and pharmacological approaches, the authors show that age-dependent loss of microtubule dynamics might underlie structural and functional aging of TRNs. Further, the authors show that the Hippo pathway specifically functions in these neurons to regulate microtubule dynamics. Specifically, authors show that hyperactivation of YAP-1, a downstream component of the Hippo pathway that is usually inhibited by the kinase activity of the upstream components of the pathway, results in microtubule stabilization and that might underlie the structural and functional decline of TRNs with age. However, how the Hippo pathway regulates microtubule dynamics and neuronal aging was not investigated by the authors.

Strengths:

This is a well-conducted and well-controlled study, and the authors have used multiple approaches to address different questions.

Weaknesses:

There are no major weaknesses identified, except that the effect of the Hippo pathway seems to be specific to only a subset of neurons. I would like the authors to address the specificity of the effect of the Hippo pathway in TRNs, in their resubmission.

Although our genetic experiments, including TRNs-specific rescue/overexpression of YAP-1 and knockdown of WTS-1, strongly suggest that a cell-autonomous function of WTS-1-YAP-1 axis in TRNs, the Hpo pathway could have broader roles in neuroprotection. While this pathway may regulate microtubules stability in multiple neurons, other characteristics of TRNs, such as their anatomical localization near the cuticle or their long projections along body axis, could contribute to their susceptibilities to age-related deformation. Otherwise, the Hpo pathway may be truly TRNs-specific. TRNs have unique microtubules in both terms of composition and structure. Among nine α-, six β-tubulin genes in C. elegans, one α-tubulin (mec-12) and one β-tubulin (mec-7) showed highly enriched expression in TRNs [1, 2] and TRNs contain special 15-protofilament microtubule structure, while all other neurons in C. elegans have 11-protofilament microtubules [3]. Transcriptional regulation through YAP-1 may affect the specific microtubule structure of TRNs, leading to premature neuronal deformation. We have included this in the discussion section of the revised manuscript.

Reviewer #2 (Public review):

Summary:

This study examines a novel role of the Hpo signaling pathway, specifically of wts-1/LATS and the downstream regulator of gene expression, yap, in age-related neurodegeneration in C. elegans touch-responsive mechanosensory neurons, ALM and PLM. The study shows that knockdown or deletion of wts-1/LATS causes age-associated morphological abnormalities of these neurons, accompanied by functional loss of touch responsiveness. This is further associated with enhanced, abnormal, microtubule stabilization in these neurons.

Strengths:

This study examines a novel role of the Hpo signaling pathway, specifically of wts-1/LATS and the downstream regulator of gene expression, yap, in age-related neurodegeneration in C. elegans touch-responsive mechanosensory neurons, ALM and PLM. The study shows that knockdown or deletion of wts-1/LATS causes age-associated morphological abnormalities of these neurons, accompanied by functional loss of touch responsiveness. This is further associated with enhanced, abnormal, microtubule stabilization in these neurons. Strong pharmacological and especially genetic manipulations of MT-stabilizing or severing proteins show a strong genetic link between yap and regulation of MTs stability. The study is strong and uses robust approaches, especially strong genetics. The demonstrations on the aging-related roles of the Hpo signaling pathway, and the link to MTs, are novel and compelling. Nevertheless, the study also has mechanistic weaknesses (see below).

Weaknesses:

Specific comments:

(1) The study demonstrates age-specific roles of the Hpo pathway, specifically of wts-1/LATS and yap, specifically in TRN mechanosensory neurons, without observing developmental defects in these neurons, or effects in other neurons. This is a strong demonstration. Nevertheless, the study does not address whether there is a correlation of Hpo signaling pathway activity decline specifically in these neurons, and not other neurons, and at the observed L4 stage and onwards (including the first day of adulthood, 1DA stage). Such demonstrations of spatio-temporal regulation of the Hpo signaling pathway and its activation seem important for linking the Hpo pathway with the observed age-related neurodegeneration. Can this age-related response be correlated to indeed a decline in Hpo signaling during adulthood? Especially at L4 and onwards? It will be informative to measure this by examining the decline in wts1 as well as yap levels and yap nuclear localization.

As described above, we have included possible explanations for the specificity of the Hpo pathway in TRNs. Since components of the Hpo pathway are expressed in various tissues, including the intestine and hypodermis, this pathway could have broader neuroprotective roles across multiple neurons. Alternatively, it could function in TRNs. Given that the TRNs possess unique microtubules in both structure and composition, and that Hpo pathway has crucial roles in microtubule stability regulation, the roles of the Hpo pathway may indeed be TRNs-specific. As we described in the manuscript, our observations, along with those of others, indicate that neuronal deformation of TRNs begins around the 4th day of adulthood. Additionally, the degree of morphological deformation in wts-1 mutants at the L4 stage is comparable to that of aged wild-type worms on the 15th day of adulthood. Therefore, to assess the functional decline of WTS-1 or nuclear localization of YAP-1, observations should begin in 4-day-old animals. Using fluorescence-tagged YAP-1 under the mec-4 promoter, we couldn’t detect a significant increase in nuclear YAP-1 in TRNs of 4-day-old adult. Additionally, we were unable to assess YAP-1 intercellular localization in older animals, such as 10-day-old animals, possibly due to the small cell size of neurons or morphological alteration along with aging of TRNs. Although we did not detect functional decline of WTS-1 or increased nuclear YAP-1 in TRNs, nuclear localization of YAP-1 increases with age in other tissues, such as the intestine and hypodermis (Author response image. 1). This may result from inactivation of the Hippo (Hpo) pathway, an indirect consequence of structural and functional decline—such as tissue stiffness associated with aging—or a combination of both. Additionally, given that morphological deformation of TRNs appears to begin around fourth day of adulthood, nuclear localization of YAP-1 in the intestine and hypodermis seems to have a later onset and be more moderate. It is possible that YAP-1 nuclear localization in TRNs occurs earlier or that other factors contribute early-stage touch neuronal deformation.

Author response image 1.

Quantification of the proportion of worms exhibiting nuclear localization of YAP-1. We used GFP-tagged YAP-1 driven by its own 4 kb promoter. A total of 90 animals were observed each day.

(2) The Hpo pathway eventually activates gene expression via yap. Although the study uses robust genetic manipulations of yap and wts-1/LATS, it is not clear whether the observed effects are attributed to yap-mediated regulation of gene expression (see 3).

Given that the neuronal deformation in the wts-1 mutant was completely restored by the loss of yap-1 or egl-44, it strongly suggests that YAP-TEAD-mediated transcriptional regulation is responsible for the premature neuronal degeneration of the wts-1 mutant. However, in this study, we were unable to identify specific transcriptional target genes associated with these phenomena, which represents a limitation of our research (please see below).

(3) The observations on the abnormal MT stabilization, and the subsequent genetic examinations of MT-stability/severing genes, are a significant strength of the study. Nevertheless, despite the strong genetic links to yap and wts-1/LATS, it is not clear whether MT-regulatory genes are regulated by transcription downstream of the Hpo pathway, thus not enabling a strong causal link between MT regulation and Hpo-mediated gene expression, making this strong part of the study mechanistically circumstantial. Specifically, it will be good to examine whether the genes addressed herein, for example, Spastin, are transcriptionally regulated downstream of the Hpo pathway. This comment is augmented by the finding that in the wts-1/ yap-1 double mutants, MT abnormality, and subsequent neuronal morphology and touch responses are restored, clearly indicating that there is an associated transcriptional regulation

If the target genes of YAP-1 are not identified, it will be difficult to fully understand how YAP-1 regulates microtubule stability. Microtubule-stabilizing genes, whose knockdown alleviates wts-1 mutant neuronal deformation, could be potential transcriptional targets of YAP-1. Among these genes, PTRN-1 and DLK-1 contain MCAT sequences (CATTCCA/T), a well-conserved DNA motif recognized by the TEAD transcription factor, in their promoters near the transcription start site (TSS). We hypothesized that the expression of fluorescence-tagged reporters of promoter regions containing these MCAT sequences would be enhanced in the absence of wts-1 activity. Although both reporters were expressed in TRNs, they did not show significant changes in the wts-1 mutant background. We also focused on spv-1, a worm homolog of ARHGAP29, which negatively regulates RhoA. YAP is known to modulate actin cytoskeleton rigidity through transcriptional regulation of ARHGAP29 [4]. The promoter of spv-1 contains 2 MCAT sequences and loss of spv-1 mitigated neuronal deformation of the wts-1 mutant. However, reporters of promoter regions containing MCAT sequences only weakly expressed in the process of TRNs. More importantly, ectopic expression of dominant-negative form of rho-1/rhoA did not lead to significant deformation of TRNs. While YAP typically functions as a transcriptional co-activator, it has also been reported to repress target gene expression, such as DDIT4 and Trail, in collaborated with TEAD transcriptional factor [5].  As a reviewer pointed out, spas-1 might be transcriptionally repressed by yap-1, given that its loss leads to premature deformation of TRNs. However, since the phenotype of the spas-1 mutant has a later onset than the wts-1 mutant and is relatively restricted to ALM, we excluded it from our candidate gene search. Despite extensive genetic approaches, we were unable to establish a strong causal link between YAP-1 and the regulation of microtubule stability. Unbiased screenings, such as tissue-specific transcriptome analysis, may help address the remaining questions. We have outlined the limitations of this study in the discussion section of the revised manuscript.

Other comments:

(1) The TRN-specific knockdown of wts-1 and yap-1 is a clear strength. Nevertheless, these do not necessarily show cell-autonomous effects, as the yap transcription factor may regulate the expression of external cues, secreted or otherwise, thus generating non-cell autonomous effects. For example, it is known that yap regulates TGF-beat expression and signaling.

In the absence of LATS1/2 activity, activated YAP has been reported to drive biliary epithelial cell lineage specification by directly regulating TGF-β transcription during and after liver development [6]. Even when functioning in an autocrine manner, TGF-β can exhibit non-cell autonomous effects. While it primarily acts on the same cell that secretes it, some molecules may also affect neighboring cells, leading to paracrine effects. Additionally, TGF-β can modify the extracellular matrix (ECM), indirectly affecting surrounding cells. Similarly, if YAP regulates transcription of secretory protein in TRNs, the resulting extracellular factors or surrounding cells may influence touch neuronal microtubules in a non-cell-autonomous manner. Although our genetic data strongly suggest a cell-autonomous function of WTS-1-YAP-1 in TRNs, we could not exclude the possibility that YAP-1 functions non-cell-autonomously, as we were unable to identify its transcriptional targets. We have included this in the discussion section of the revised manuscript.

(2) Continuing from comment (3) above, it seems that many of the MT-regulators chosen here for genetic examinations were chosen based on demonstrated roles in neurodegeneration in other studies. It would be good to show whether these MT-associated genes are directly regulated by transcription by the Hpo pathway.

As we described above, several MT-associated genes­­, such as ptrn-1, dlk-1 and spv-1, contain MCAT sequences in their promoter and their knockdown alleviated wts-1-induced neuronal deformation. These genes were tested to determine whether they were directly regulated by WTS-1-YAP-1. Based on our findings, we concluded that they were unlikely to be regulated by the Hpo pathway in TRNs.

(3) The impairment of the touch response may not be robust: it is only a 30-40% reduction at L4, and even less reduction at 1DA. It would be good to offer possible explanations for this finding.

As pointed out by the reviewer, the impairment of touch responses of wts-1 mutants showed an approximately 33% reduction at both L4 and 1DA compared to age-matched wild-type animals. At the L4 stage, control worms responded to nearly every gentle touch (94%), whereas wts-1 mutants responded to only 60% of stimuli. By 1DA, control worms exhibited slightly decline in touch responses compared to L4 (82.5%), whereas wts-1 mutants displayed more pronounced impairment (55.7%) (Fig 1E). Regarding the severity and frequency of structural degeneration of wts-1 mutant at both stages, it appears to be relatively moderate. As we noted in the manuscript, our observations, along with those of others, indicate that structural abnormalities in ALM and PLM neurons begin to appear around the fourth day of adulthood and progressively worsen as the worms age [7]. In a previous study, Tank et al. categorized day 10-aged worms into two groups based on their movement ability and then assessed structural deformation in each animal to determine whether structural and functional degeneration of TRNs were correlated. In this same group of animals, they examined the gentle touch response and found that animals responded to gentle touch 46 ± 5.1 %, 84 ± 12.2 %, respectively [8]. It could be said that, on average, day 10 animals had 65% touch response on average, which is consistent with our observation in day 10 animals (Fig. 5E, 56.3%). Given these observations, the function of TRNs of wts-1 mutant or aged animals appears to be preserved despite severe structure failures. The gentle touch response evokes an escape behavior in which animals quickly move away from the stimulus; thus proper touch responses are essential for avoiding predators and ensuring survival. It has been reported to be necessary for evading fungal predation, such as escaping from a constricting hyphal ring [9]. Given that the gentle touch response is crucial for survival, its function is likely well preserved despite structural abnormalities, such as age-related deformation.

Reviewer #1 (Recommendations for the authors):

Major comments:

(1) Why is the effect of the Hippo pathway on microtubule dynamics specific to TRNs? Is it the structure of TRNs that makes them prone to the effects of age-dependent decline in microtubule dynamics? The authors are advised to discuss it in their resubmission.

As described above, we have included possible explanations for the tissue specificity of the Hpo pathway in TRNs and the vulnerability of TRNs to age-associated decline in the discussion section of the revised manuscript.

(2) The authors are advised to explain the shorter life span of wts-1; yap-1 double mutants (with restored TRNs) compared to wts-1 single mutants in Figure 2F. The life span of yap-1 single mutants should be included in Figure 2F. Further, based on the data, the shorter lifespan of wts-1 mutants cannot be attributed to abnormal TRNs as the lifespan of wts-1; yap-1 double mutants is even shorter. The authors are advised to explain the shorter life span of wts-1 mutants compared to wild-type controls.

wts-1 is known to be involved in various developmental processes, including the maintenance of apicobasal polarity in the intestine, growth rate control, and dauer formation [10-12]. Since WTS-1 activity is restored in the intestine of the mutant used for lifespan measurement, the shorter lifespan of the wts-1 mutant may result from the loss of WTS-1 in tissues other than the intestine. Although we were unable to include lifespan data for the yap-1 mutant, recent studies indicate that the yap-1(tm1416) mutant or yap-1 RNAi treated worms exhibit a shortened lifespan [13, 14]. Thus, our data showing a slightly shorter lifespan of the wts-1; yap-1 mutant compared with the wts-1 mutant may result from the synergistic action of yap-1 and yap-1-independent downstream factors of wts-1. While this study does not provide an explanation for the shortened lifespan of wts-1 or wts-1; yap-1 mutants, the fact that the wts-1; yap-1 double mutant with restored TRNs still have a shorter lifespan compared with the wts-1 mutant strongly suggests that premature deformation of the wts-1 neurons appear to be a touch neuron-specific event, rather than being associated with whole body, as described in the manuscript..

Minor comments:

(1) In the abstract, please provide definitions for LATS and YAP. Authors can mention that LATS is a kinase and YAP a transcriptional co-activator in the Hippo pathway.

(2) In the last paragraph on page 9, change "these function" to "this function", and change "knock-downed" to "knocked down".

(3) On page 10, paragraph 2, change "regarding the action mechanism" to "regarding the mechanism of action".

(4) On page 11, paragraph 1, change "endogenous WTS-1 could inhibits" to "endogenous WTS-1 could inhibit".

(5) On page 16, paragraph 1, change "consistent to the hypothesis" to "consistent with this hypothesis".

(6) Overall, the paper is well written. However, there is still room to improve the language and diction used by the authors.

We have revised all minor comments suggested by the reviewer in the revised manuscript.

References

(1) Hamelin M, Scott IM, Way JC, Culotti JG. The mec-7 beta-tubulin gene of Caenorhabditis elegans is expressed primarily in the touch receptor neurons. EMBO J. 1992;11(8):2885-93. Epub 1992/08/01. doi: 10.1002/j.1460-2075.1992.tb05357.x. PubMed PMID: 1639062; PubMed Central PMCID: PMCPMC556769.

(2) Fukushige T, Siddiqui ZK, Chou M, Culotti JG, Gogonea CB, Siddiqui SS, et al. MEC-12, an alpha-tubulin required for touch sensitivity in C. elegans. J Cell Sci. 1999;112 ( Pt 3):395-403. Epub 1999/01/14. doi: 10.1242/jcs.112.3.395. PubMed PMID: 9885292.

(3) Chalfie M, Thomson JN. Structural and functional diversity in the neuronal microtubules of Caenorhabditis elegans. J Cell Biol. 1982;93(1):15-23. Epub 1982/04/01. doi: 10.1083/jcb.93.1.15. PubMed PMID: 7068753; PubMed Central PMCID: PMCPMC2112106.

(4) Qiao Y, Chen J, Lim YB, Finch-Edmondson ML, Seshachalam VP, Qin L, et al. YAP Regulates Actin Dynamics through ARHGAP29 and Promotes Metastasis. Cell Rep. 2017;19(8):1495-502. Epub 2017/05/26. doi: 10.1016/j.celrep.2017.04.075. PubMed PMID: 28538170.

(5) Kim M, Kim T, Johnson RL, Lim DS. Transcriptional co-repressor function of the hippo pathway transducers YAP and TAZ. Cell Rep. 2015;11(2):270-82. Epub 2015/04/07. doi: 10.1016/j.celrep.2015.03.015. PubMed PMID: 25843714.

(6) Lee DH, Park JO, Kim TS, Kim SK, Kim TH, Kim MC, et al. LATS-YAP/TAZ controls lineage specification by regulating TGFbeta signaling and Hnf4alpha expression during liver development. Nat Commun. 2016;7:11961. Epub 2016/07/01. doi: 10.1038/ncomms11961. PubMed PMID: 27358050; PubMed Central PMCID: PMCPMC4931324.

(7) Toth ML, Melentijevic I, Shah L, Bhatia A, Lu K, Talwar A, et al. Neurite sprouting and synapse deterioration in the aging Caenorhabditis elegans nervous system. J Neurosci. 2012;32(26):8778-90. Epub 2012/06/30. doi: 10.1523/JNEUROSCI.1494-11.2012. PubMed PMID: 22745480; PubMed Central PMCID: PMCPMC3427745.

(8) Tank EM, Rodgers KE, Kenyon C. Spontaneous age-related neurite branching in Caenorhabditis elegans. J Neurosci. 2011;31(25):9279-88. Epub 2011/06/24. doi: 10.1523/JNEUROSCI.6606-10.2011. PubMed PMID: 21697377; PubMed Central PMCID: PMCPMC3148144.

(9) Maguire SM, Clark CM, Nunnari J, Pirri JK, Alkema MJ. The C. elegans touch response facilitates escape from predacious fungi. Curr Biol. 2011;21(15):1326-30. Epub 2011/08/02. doi: 10.1016/j.cub.2011.06.063. PubMed PMID: 21802299; PubMed Central PMCID: PMCPMC3266163.

(10) Cai Q, Wang W, Gao Y, Yang Y, Zhu Z, Fan Q. Ce-wts-1 plays important roles in Caenorhabditis elegans development. FEBS Lett. 2009;583(19):3158-64. Epub 2009/09/10. doi: 10.1016/j.febslet.2009.09.002. PubMed PMID: 19737560.

(11) Kang J, Shin D, Yu JR, Lee J. Lats kinase is involved in the intestinal apical membrane integrity in the nematode Caenorhabditis elegans. Development. 2009;136(16):2705-15. Epub 20090715. doi: 10.1242/dev.035485. PubMed PMID: 19605499.

(12) Lee H, Kang J, Ahn S, Lee J. The Hippo Pathway Is Essential for Maintenance of Apicobasal Polarity in the Growing Intestine of Caenorhabditis elegans. Genetics. 2019;213(2):501-15. Epub 20190729. doi: 10.1534/genetics.119.302477. PubMed PMID: 31358532; PubMed Central PMCID: PMCPMC6781910.

(13) Teuscher AC, Statzer C, Goyala A, Domenig SA, Schoen I, Hess M, et al. Longevity interventions modulate mechanotransduction and extracellular matrix homeostasis in C. elegans. Nat Commun. 2024;15(1):276. Epub 2024/01/05. doi: 10.1038/s41467-023-44409-2. PubMed PMID: 38177158; PubMed Central PMCID: PMCPMC10766642.

(14) Saul N, Dhondt I, Kuokkanen M, Perola M, Verschuuren C, Wouters B, et al. Identification of healthspan-promoting genes in Caenorhabditis elegans based on a human GWAS study. Biogerontology. 2022;23(4):431-52. Epub 2022/06/25. doi: 10.1007/s10522-022-09969-8. PubMed PMID: 35748965; PubMed Central PMCID: PMCPMC9388463.

Author response:

The following is the authors’ response to the original reviews

Public Review:

Reviewer #1 (Public review): 

Summary: 

Odor- and taste-sensing are mediated by two different systems, the olfactory and gustatory systems, and have different behavioral roles. In this study, Wei et al. challenge this dichotomy by showing that odors can activate gustatory receptor neurons (GRNs) in Drosophila to promote feeding responses, including the proboscis extension response (PER) that was previously thought to be driven only by taste. While previous studies suggested that odors can promote PER to appetitive tastants, Wei et al. go further to show that odors alone cause PER, this effect is mediated through sweet-sensing GRNs, and sugar receptors are required. The study also shows that odor detection by bitter-sensing GRNs suppresses PER. The authors' conclusions are supported by behavioral assays, calcium imaging, electrophysiological recordings, and genetic manipulations. The observation that both attractive and aversive odors promote PER leaves an open question as to why this effect is adaptive. Overall, the study sheds new light on chemosensation and multimodal integration by showing that odor and taste detection converge at the level of sensory neurons, a finding that is interesting and surprising while also being supported by another recent study (Dweck & Carlson, Sci Advances 2023).

Strengths: 

(1) The main finding that odors alone can promote PER by activating sweet-sensing GRNs is interesting and novel.

(2) The study uses video tracking of the proboscis to quantify PER rather than manual scoring, which is typically used in the field. The tracking method is less subjective and provides a higherresolution readout of the behavior.

(3) The study uses calcium imaging and electrophysiology to show that odors activate GRNs. These represent complementary techniques that measure activity at different parts of the GRN (axons versus dendrites, respectively) and strengthen the evidence for this conclusion. 

(4) Genetic manipulations show that odor-evoked PER is primarily driven by sugar GRNs and sugar receptors rather than olfactory neurons. This is a major finding that distinguishes this work from previous studies of odor effects on PER and feeding (e.g., Reisenman & Scott, 2019; Shiraiwa, 2008) that assumed or demonstrated that odors were acting through olfactory neurons.

We appreciate the reviewer’s positive assessment of the novelty and significance of our work.

Weaknesses/Limitations: 

(1) The authors may want to discuss why PER to odors alone has not been previously reported, especially as they argue that this is a broad effect evoked by many different odors. Previous studies testing the effect of odors on PER only observed odor enhancement of PER to sugar (Oh et al., 2021; Reisenman & Scott, 2019; Shiraiwa, 2008) and some of these studies explicitly show no effect of odor alone or odor with low sugar concentration; regardless, the authors likely would have noticed if PER to odor alone had occurred. Readers of this paper may also be aware of unpublished studies failing to observe an effect of PER on odor alone (including studies performed by this reviewer and unrelated work by other colleagues in the field), which of course the authors are not expected to directly address but may further motivate the authors to provide possible explanations.

We appreciate the reviewer’s comment. We believe that the difference in genotype is likely the largest reason behind this point. This is because the strength varied widely across genotypes and was quite weak in some strains including commonly used w[1118] empty Gal4 and w[1118] empty spit Gal4 as shown in Figure1- figure supplement 3 (Figure S3 in original submission). However, given that we observed odor-evoked PER in various genotypes (many in main Figures and three in Figure1- figure supplement 3 including Drosophila simulans), the data illustrate that it is a general phenomenon in Drosophila. Indeed, although Oh et al. (2021) did not emphasize it in the text, their Fig. 1E showed that yeast odor evoked PER at a probability of 20%, which is much higher than the rate of spontaneous PER in many genotypes. Therefore, this literature may represent another support for the presence of odor-evoked PER. We have expanded our text in the Discussion to describe these issues.

Another possibility is our use of DeepLabcut to quantitatively track the kinematics of proboscis movement, which may have facilitated the detection of PER.

(2) Many of the odor effects on behavior or neuronal responses were only observed at very high concentrations. Most effects seemed to require concentrations of at least 10-2 (0.01 v/v), which is at the high end of the concentration range used in olfactory studies (e.g., Hallem et al., 2004), and most experiments in the paper used a far higher concentration of 0.5 v/v. It is unclear whether these are concentrations that would be naturally encountered by flies.

We acknowledge that the concentrations used are on the higher side, suggesting that GRNs may need to be stimulated with relatively concentrated odors to induce PER. Although it is difficult to determine the naturalistic range of odor concentration, it is at least widely reported that olfactory neurons including olfactory receptor neurons and projection neurons do not saturate, and exhibit odor identity-dependent responses at the concentration of 10^-2 where odor-evoked PER can be observed. Furthermore, we have shown in Figure 6 that low concentration (10^-4) of banana odor, ethyl butyrate, and 4-methycyclohexanol all significantly increased the rate of odor-taste multisensory PER even in olfactory organs-removed flies, suggesting that low concentration odors can influence feeding behavior via GRNs in a natural context where odors and tastants coexist at food sites. Finally, we note that odors were further diluted by a factor of 0.375 by mixing the odor stream with the main air stream before being applied to the flies as described in Methods.

(3) The calcium imaging data showing that sugar GRNs respond to a broad set of odors contrasts with results from Dweck & Carlson (Sci Adv, 2023) who recorded sugar neurons with electrophysiology and observed responses to organic acids, but not other odors. This discrepancy is not discussed.  

As the reviewer points out, Dweck and Carlson (Sci Adv, 2023) reported using single sensillum electrophysiology (base recording) that sugar GRNs only respond to organic acids whereas we found using calcium imaging from a group of axons and single sensillum electrophysiology (tip recording) that these GRNs respond to a wide variety of odors. Given that we observed odor responses using two methods, the discrepancy is likely due to the differences in genotype examined. We now have discussed this point in the text.

(4) Related to point #1, it would be useful to see a quantification of the percent of flies or trials showing PER for the key experiments in the paper, as this is the standard metric used in most studies and would help readers compare PER in this study to other studies. This is especially important for cases where the authors are claiming that odor-evoked PER is modulated in the same way as previously shown for sugar (e.g., the effect of starvation in Figure S4).

For starved flies, we would like to remind the reviewer that the percentage of trials showing PER is reported in Fig. 1E, which shows a similar trend as the integrated PER duration. For fed flies, we have analyzed the percentage of PER and added the result to Figure 2-figure supplement 1C (Figure S4 in original submission).

(5) Given the novelty of the finding that odors activate sugar GRNs, it would be useful to show more examples of GCaMP traces (or overlaid traces for all flies/trials) in Figure 3. Only one example trace is shown, and the boxplots do not give us a sense of the reliability or time course of the response. A related issue is that the GRNs appear to be persistently activated long after the odor is removed, which does not occur with tastes. Why should that occur? Does the time course of GRN activation align with the time course of PER, and do different odors show differences in the latency of GRN activation that correspond with differences in the latency of PER (Figure S1A)?

Following the reviewer’s suggestion, we now report GCaMP responses for all the trials in all the flies (both Gr5a>GCaMP and Gr66a>GCaMP flies), where the time course and trial-to-trial/animal-toanimal variability of calcium responses can be observed (Figure 3-figure supplement 2).

Regarding the second point, we recorded responses to both sucrose and odors in some flies and found that calcium responses of GRNs are long-lasting not only to odors but also to sucrose, as shown in Author response image 1. This may be due in part to the properties of GCaMP6s and slower decay of intracellular calcium concentration as compared to spikes.

Author response image 1.

Example calcium responses to sucrose and odor (MCH) in the same fly (normalized by the respective peak responses to better illustrate the time course of responses). Sucrose (blue) and odor (orange) concentrations are 100 mM, and 10^-1 respectively. Odor stimulation begins at 5 s and lasts for 2 s. Sucrose was also applied at the same timing for the same duration although there was a limitation in controlling the precise timing and duration of tastant application. Because of this limitation, we did not quantify the off time constant of two responses.

To address whether the time course of GRN activation aligns with the time course of PER, and whether different odors evoke different latencies of GRN activation that correspond to latencies of PER, we plotted the time course of GRN responses and PER, and further compared the response latencies across odors and across two types of responses in Gr5a>GCaMP6s flies. As shown in Author response image 2, no significant differences were found in response latency between the six odors for PER and odor responses. Furthermore, Pearson correlation between GRN response latencies and PER latencies was not significant (r = 0.09, p = 0.872).

Author response image 2.

(A) PER duration in each second in Gr5a-Gal4>UAS-GCaMP6s flies. The black lines indicate the mean and the shaded areas indicate standard error of the mean. n = 25 flies. (B) Time course of calcium responses (ΔF/F) to nine odors in Gr5a GRNs. n = 5 flies. (C) Latency to the first odor-evoked PER in Gr5a-Gal4>UAS-GCaMP6s flies. Green bar indicates the odor application period. p = 0.67, one-way ANOVA. Box plots indicate the median (orange line), mean (black dot), quartiles (box), and 5-95% range (bar). Dots are outliers. (D) Latency of calcium responses (10% of rise to peak time) in Gr5a GRNs. Green bar indicates the odor application period. p = 0.32, one-way ANOVA. Box plots indicate the median (orange line), mean (black dot), quartiles (box), and 5-95% range (bar). Dots are outliers.

(6) Several controls are missing, and in some cases, experimental and control groups are not directly compared. In general, Gal4/UAS experiments should include comparisons to both the Gal4/+ and UAS/+ controls, at least in cases where control responses vary substantially, which appears to be the case for this study. These controls are often missing, e.g. the Gal4/+ controls are not shown in Figure 2C-G and the UAS/+ controls are not shown in Figure 2J-L (also, the legend for the latter panels should be revised to clarify what the "control" flies are). For the experiments in Figure S5, the data are not directly compared to any control group. For several other experiments, the control and experimental groups are plotted in separate graphs (e.g., Figure 2C-G), and they would be easier to visually compare if they were together. In addition, for each experiment, the authors should denote which comparisons are statistically significant rather than just reporting an overall p-value in the legend (e.g., Figure 2H-L).

We thank the reviewer for the input. We have conducted additional experiments for four Gal4/+controls in Figure 2 and added detailed information about control flies in the figure legend (Figure 2C-F).

For the RNAi flies shown in Figure 2 and Figure 2-figure supplement 3, we used the recommended controls suggested by the VDRC. These control flies were crossed with tubulin-Gal4 lines to include both Gal4 and UAS control backgrounds.

Regarding Figure S5 in original submission (current Figure 2-figure supplement 2), we now present the results of statistical tests which revealed that PER to certain odors is statistically significantly stronger than that to the solvent control (mineral oil) for both wing-removed and wing-leg-removed flies.

For Figure 2C-F, we now plot the results for experimental and control groups side by side in each figure.

Regarding the results of statistical tests, we have provided more information in the legend and also prepared a summary table (supplemental table). 

(7) Additional controls would be useful in supporting the conclusions. For the Kir experiments, how do we know that Kir is effective, especially in cases where odor-evoked PER was not impaired (e.g., Orco/Kir)? The authors could perform controls testing odor aversion, for example. For the Gr5a mutant, few details are provided on the nature of the control line used and whether it is in the same genetic background as the mutant. Regardless, it would be important to verify that the Gr5a mutant retains a normal sense of smell and shows normal levels of PER to stimuli other than sugar, ruling out more general deficits. Finally, as the method of using DeepLabCut tracking to quantify PER was newly developed, it is important to show the accuracy and specificity of detecting PER events compared to manual scoring.  

A previous study (Sato, 2023, Front Mol Neurosci) showed that the avoidance to 100 μM 2methylthiazoline was abolished, and the avoidance to 1 mM 2MT was partially impaired in Orco>Kir2.1 flies. However, because Orco-Gal4 does not label all the ORNs and we have more concrete results on flies in which all the olfactory organs are removed as well as specific GRNs and Gr are manipulated, we decided to remove the data for Orco>kir2.1 flies and have updated the text and Figure 2 accordingly.

For the Gr5a mutant and its control, we have added detailed information about the genotype in the figure legend and in the Methods. We have used the exact same lines as reported in Dahanukar et al. (2007) by obtaining the lines from Dr. Dahanukar. Dahanukar et al. has already carefully examined that Gr5a mutant loses responses only to certain types of sugars (e.g. it even retains normal responses to some other sugars), demonstrating that Gr5a mutants do not exhibit general deficits.

As for the PER scoring method, we manually scored PER duration and compared the results with those obtained using DeepLabCut in wild type flies for the representative data. The two results were similar (no statistical difference). We have reported the result in Figure1-figure supplement 1C.

(8) The authors' explanation of why both attractive and aversive odors promote PER (lines 249-259) did not seem convincing. The explanation discusses the different roles of smell and taste but does not address the core question of why it would be adaptive for an aversive odor, which flies naturally avoid, to promote feeding behavior.  

We have extended our explanation in the Discussion by adding the following possibility: “Enhancing PER to aversive odors might also be adaptive as animals often need to carry out the final check by tasting a trace amount of potentially dangerous substances to confirm that those should not be further consumed.”

Reviewer #2 (Public review): 

Summary: 

A gustatory receptor and neuron enhances an olfactory behavioral response, proboscis extension. This manuscript clearly establishes a novel mechanism by which a gustatory receptor and neuron evokes an olfactory-driven behavioral response. The study expands recent observations by Dweck and Carlson (2023) that suggest new and remarkable properties among GRNs in Drosophila. Here, the authors articulate a clear instance of a novel neural and behavioral mechanism for gustatory receptors in an olfactory response.

Strengths: 

The systematic and logical use of genetic manipulation, imaging and physiology, and behavioral analysis makes a clear case that gustatory neurons are bona fide olfactory neurons with respect to proboscis extension behavior.

Weaknesses: 

No weaknesses were identified by this reviewer.  

We appreciate the reviewer’s recognition of the novelty and significance of our work.

Reviewer #3 (Public review): 

Summary: 

Using flies, Kazama et al. combined behavioral analysis, electrophysiological recordings, and calcium imaging experiments to elucidate how odors activate gustatory receptor neurons (GRNs) and elicit a proboscis extension response, which is interpreted as a feeding response. 

The authors used DeepLabCut v2.0 to estimate the extension of the proboscis, which represents an unbiased and more precise method for describing this behavior compared to manual scoring.

They demonstrated that the probability of eliciting a proboscis extension increases with higher odor concentrations. The most robust response occurs at a 0.5 v/v concentration, which, despite being diluted in the air stream, remains a relatively high concentration. Although the probability of response is not particularly high it is higher than control stimuli. Notably, flies respond with a proboscis extension to both odors that are considered positive and those regarded as negative.

The authors used various transgenic lines to show that the response is mediated by GRNs.

Specifically, inhibiting Gr5a reduces the response, while inhibiting Gr66a increases it in fed flies. Additionally, they find that odors induce a strong positive response in both types of GRNs, which is abolished when the labella of the proboscis are covered. This response was also confirmed through electrophysiological tip recordings.

Finally, the authors demonstrated that the response increases when two stimuli of different modalities, such as sucrose and odors, are presented together, suggesting clear multimodal integration.

Strengths: 

The integration of various techniques, that collectively support the robustness of the results.

The assessment of electrophysiological recordings in intact animals, preserving natural physiological conditions.

We appreciate the reviewer’s recognition of the novelty and significance of our work.

Weaknesses: 

The behavioral response is observed in only a small proportion of animals.  

We acknowledge that the probability of odor-evoked PER is lower compared to sucrose-evoked PER, which is close to 100 % depending on the concentration. To further quantify which proportion of animals exhibit odor-evoked PER, we now report this number besides the probability of PER for each odor shown in Fig. 1E. We found that, in wild type Dickinson flies, 73% and 68 % of flies exhibited PER to at least one odor presented at the concentration of 0.5 and 0.1.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

Minor comments/suggestions: 

- Define "MO" in Figure 1D.  

We have defined it as mineral oil in the figure legend.

- Clarify how peak response was calculated for GCaMP traces (is it just the single highest frame per trial?).

We extended the description in the Methods as follows: “The peak stimulus response was quantified by averaging ΔF/F across five frames at the peak, followed by averaging across three trials for each stimulus. Odor stimulation began at frame 11, and the frames used for peak quantification were 12 to 16.” We made sure that information about the image acquisition frame rate was provided earlier in the text.

- Clarify how the labellum was covered in Figure 3 and show that this does not affect the fly's ability to do PER (e.g., test PER to sugar stimulation on tarsus) - otherwise one might think that gluing the labella could affect PER.

In Figure 3, only calcium responses were recorded, and PER was not recorded simultaneously from the same flies. To ensure stable recording from GRN axons in the SEZ, we kept the fly’s proboscis in an extended position as gently as possible using a strip of parafilm. In some of the imaging experiments, we covered the labellum with UV curable glue, whose purpose was not to fix the labellum in an extended position but to prevent the odors from interacting with GRNs on the labellum. We have added a text in the Methods to explain how we covered the labellum.

- Clarify how the coefficients for the linear equation were chosen in Figure 3G.  

We used linear regression (implemented in Python using scikit-learn) to model the relationship between neural activity and behavior, aiming to predict the PER duration based on the calcium responses of two GRN types, Gr5a and Gr66a. The coefficients were estimated using the LinearRegression function. We added this description to the Methods. 

- Typo in "L-type", Figure 4A.  

We appreciate the reviewer for pointing out this error and have corrected it.

- Clarify over what time period ephys recordings were averaged to obtain average responses.

We have modified the description in the Methods as follows: “The average firing rate was quantified by using the spikes generated between 200 and 700 ms after the stimulus contact following the convention to avoid the contamination of motion artifact (Dahanukar and Benton, 2023; Delventhal et al., 2014; Hiroi et al., 2002).

- The data and statistics indicate that MCH does not enhance feeding in Figure 6G, so the text in lines 207-208 is not accurate.

We have modified the text as follows: “A similar result was observed with ethyl butyrate, and a slight, although not significant, increase was also observed with 4-methylcyclohexanol (Figure 6G).”

- P-value for Figure S9 correlation is not reported.  

We appreciate the reviewer for pointing this out. The p-value is 0.00044, and we have added it to the figure legend (current Figure 5-figure supplement 1).

Reviewer #2 (Recommendations for the authors): 

Honestly, I have no recommendations for improvement. The manuscript is extremely well-written and logical. The experiments are persuasive. A lapidary piece of work.

We appreciate the reviewer for the positive assessment of our work.

Reviewer #3 (Recommendations for the authors): 

- I suggest explaining the rationale for selecting a 4-second interval, beginning 1 second after the onset of stimulation.

Integrated PER duration was defined as the sum of PER duration over 4 s starting 1 s after the odor onset. This definition was set based on the following data.

(1) We used a photoionization detector (PID) to measure the actual time that the odor reaches the position of a tethered fly, which was approximately 1.1 seconds after the odor valve was opened. Therefore, we began analyzing PER responses 1 second after the odor onset (valve opening) to align with the actual timing of stimulation.

(2) As shown in Fig.1D and 1F, the majority of PER occurred within 4 s after the odor arrival.

We have now added the above rationale in the Methods.

- I could not find the statistical analysis for Figures 1E and 1G. If these figures are descriptive, I suggest the authors revise the sentences: 'Unexpectedly, we found that the odors alone evoked repetitive PER without an application of a tastant (Figures 1D-1G, and Movie S1). Different odors evoked PER with different probability (Figure 1E), latency (Figure S1A), and duration (Figures 1F, 1G, and S2)'.

We have added the results of statistical analysis to the figure legend.

- In Figure 2, the authors performed a Scheirer-Ray-Hare test, which, to my knowledge, is a nonparametric test for comparing responses across more than two groups with two factors. If this is the case, please provide the p-values for both factors and their interaction

We now show the p-values for both factors, odor and group as well as their interaction in the supplementary table. 

- In line 83, I suggest the authors avoid claiming that 'these data show the olfactory system modulates but is not required for odor-evoked PER,' as they are inhibiting most, but not all olfactory receptor neurons. In this regard, is it possible to measure the olfactory response to odors in these flies?  

We thank the reviewer for the comment. Because Orco-Gal4 does not label all the ORNs and because we have more concrete results on flies in which all the olfactory organs are removed as well as specific GRNs and Gr are manipulated, we decided to remove the data for Orco>kir2.1 flies and have updated the text and Figure 2 accordingly.

- In Figure 2, I wonder if there are differences in the contribution of various receptors in detecting different odors. A more detailed statistical analysis might help address this question.

Although it might be possible to infer the contribution of different gustatory receptors by constructing a quantitative model to predict PER, it is a bit tricky because the activity of individual GRNs and not Grs are manipulated in Figure 2 except for Gr5a. The idea could be tested in the future by more systematically manipulating many Grs that are encoded in the fly genome.

- For Figures 2J-L, please clarify which group serves as the control.  

We have added this information to the legend. 

- In Figure 3, I recommend including an air control in panels D and F to better appreciate the magnitude of the response under these conditions.

The responses to all three controls, air, mineral oil and water, were almost zero. As the other reviewer suggested to present trial-to-trial variability as well, we now show responses to all the controls in all the trials in all the animals tested in Figure 3-figure supplement 2.

- I had difficulty understanding Figure 3G. Could the authors provide a more detailed explanation of the model?

We used linear regression (implemented in Python using scikit-learn) to model the relationship between neural activity and behavior, aiming to predict the PER duration based on the calcium responses of two GRN types, Gr5a and Gr66a. The weights for GRNs were estimated using the LinearRegression function. The weight for Gr5a and Gr66a was positive and negative, respectively, indicating that Gr5a contributes to enhance whereas Gr66a contributes to reduce PER.

To evaluate the model performance, we calculated the coefficient of determination (R²), which was 0.81, meaning the model explained 81% of the variance in the PER data.

The scatter plot in Fig. 3G shows a tight relationship between the predicted PER duration (y-axis) plotted against the actual PER duration (x-axis), demonstrating a strong predictive power of the model.

We added the details to the Methods.

- In Figure S4a, the reported p-value is 0.88, which seems to be a typo, as the text indicates that PER is enhanced in a starved state.

Thank you for pointing this out. We have modified the figure legend to describe that PER was enhanced in a starved state only for the experiments conducted with odors at 10^-1 concentration (current Figure 2-figure supplement 1).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, Basha and colleagues aim to test whether the thalamic nucleus reuniens can facilitate the hippocampus/prefrontal cortex coupling during sleep. Considering the importance of sleep in memory consolidation, this study is important to understand the functional interaction between these three majorly involved regions. This work suggests that the thalamic nucleus reuniens has a functional role in synchronizing the hippocampus and prefrontal cortex.

Strengths:

The authors performed recordings in naturally sleeping cats, and analysed the correlation between the main slow wave sleep oscillatory hallmarks: slow waves, spindles, and hippocampal ripples, and with reuniens' neurons firing. They also associated intracellular recordings to assess the reuniens-prefrontal connectivity, and computational models of large networks in which they determined that the coupling of oscillations is modulated by the strength of hippocampal-thalamic connections.

Thank you for your positive evaluation.

Weaknesses:

The authors' main claim is made on slow waves and spindle coupling, which are recorded both in the prefrontal cortex and surprisingly in reuniens. Known to be generated in the cortex by cortico-thalamic mechanisms, the slow waves and spindles recorded in reuniens show no evidence of local generation in the reuniens, which is not anatomically equipped to generate such activities. Until shown differently, these oscillations recorded in reuniens are most likely volume-conducted from nearby cortices. Therefore, such a caveat is a major obstacle to analysing their correlation (in time or frequency domains) with oscillations in other regions.

(1) We fully agree with the reviewer that reuniens likely does not generate neither slow waves nor spindles. We do not make such claim, which we clearly stated in the discussion (lines 319-324). We propose that Reuniens neurons mediate different forms of activity. In the model, we introduced MD nucleus only because without MD we were unable to generate spindles. While the slow waves and spindles are generated in other thalamocortical regions, the REU neurons show these rhythms due to long-range projections from these regions to REU as has been shown in the model.

(2) Definitely, we cannot exclude some influence of volume conductance on obtained LFP recordings in REU nucleus. However, we show modulation of spiking activity within REU by spindles. Spike modulation cannot be explained by volume conductance but can be explained by either synaptic drive (likely the case here) or some intrinsic neuronal processes (like T-current).

(3) In our REU recordings for spike identification we used tetrode recordings. If slow waves and spindles are volume conducted, then slow waves and spindles recorded with tetrodes should have identical shape. Following reviewer comment, we took these recordings and subtracted one channel from another. The difference in signal during slow waves is in the order 0.1 mV. Considering that the distance between electrodes is in the order of 20 um, such a difference in voltage is major and can only be explained by local extracellular currents, likely due to synaptic activities originating in afferent structures.

Finally, the choice of the animal model (cats) is the best suited one, as too few data, particularly anatomical ones regarding reuniens connectivity, are available to support functional results.

(1) Thalamus of majority of mammals (definitely primates and carnivores, including cats) contain local circuit interneurons (about 30 % of all neurons). A vast majority of studies in rodents (except LGN nucleus) report either absence or extremally low (i.e. Jager P, Moore G, Calpin P, et al. Dual midbrain and forebrain origins of thalamic inhibitory interneurons. eLife. 2021; 10: e59272.) number of thalamic interneurons. Therefore, studies on other species than rodents are necessary, and bring new information, which is impossible to obtain in rodents.

(2) Cats’ brain is much larger than the brain of mice or rats, therefore, the effects of volume conductance from cortex to REU are much smaller, if not negligible. The distance between REU and closest cortical structure (ectosylvian gyrus) in cats is about 15 mm.

(3) Indeed, there is much less anatomical data on cats as opposed to rodents. This is why, we performed experiments shown in the figure 1. This figure contains functional anatomy data. Antidromic responses show that recorded structure projects to stimulated structure. Orthodromic responses show that stimulated structure projects to recorded structure.

Reviewer #2 (Public Review):

Summary:

The interplay between the medial prefrontal cortex and ventral hippocampal system is critical for many cognitive processes, including memory and its consolidation over time. A prominent idea in recent research is that this relationship is mediated at least in part by the midline nucleus reuniens with respect to consolidation in particular. Whereas the bulk of evidence has focused on neuroanatomy and the effects of temproary or permanent lesions of the nucleus reuniens, the current work examined the electrophysiology of these three structures and how they inter-relate, especially during sleep, which is anticipated to be critical for consolidation. They provide evidence from intercellular recordings of the bi-directional functional connectivity among these structures. There is an emphasis on the interactions between these regions during sleep, especially slow-wave sleep. They provide evidence, in cats, that cortical slow waves precede reuniens slow waves and hippocampal sharp-wave ripples, which may reflect prefrontal control of the timing of thalamic and hippocampal events, They also find evidence that hippocampal sharp wave ripples trigger thalamic firing and precede the onset of reuniens and medial prefrontal cortex spindles. The authors suggest that the effectiveness of bidirectional connections between the reuniens and the (ventral) CA1 is particularly strong during non-rapid eye movement sleep in the cat. This is a very interesting, complex study on a highly topical subject.

Strengths:

An excellent array of different electrophysiological techniques and analyses are conducted. The temporal relationships described are novel findings that suggest mechanisms behind the interactions between the key regions of interest. These may be of value for future experimental studies to test more directly their association with memory consolidation.

We thank this reviewer for very positive evaluation of our study.

Weaknesses:

Given the complexity and number of findings provided, clearer explanation(s) and organisation that directed the specific value and importance of different findings would improve the paper. Most readers may then find it easier to follow the specific relevance of key approaches and findings and their emphasis. For example, the fact that bidirectional connections exist in the model system is not new per se. How and why the specific findings add to existing literature would have more impact if this information was addressed more directly in the written text and in the figure legends.

Thank you for this comment. In the revised version, we will do our best to simplify presentation and more clearly explain our findings.

Reviewing Editor (Recommendations for Authors):

Please discuss the ability of reuniens to generate spindles?

We briefly discussed this in previous version. We now extended the discussion (p. 18).

For population data, how many cats were used in acute and chronic experiments, where does the population data originate in Fig. 2? How repeatable were the findings across animals? Was histology verified in each animal?

As previously stated in the beginning of method section we totally used 20 cats: 16 anesthetized (or acute) and 4 non-anesthetized (or chronic). We added number of cats in appropriate places in the result section. Population data in figure 2 comes from 48, 49 or 52 recording sessions (depending on the type of analysis, and indicated in the figure legend) from 4 chronic cats; we clarified this information in the legend. Results were highly repeatable across animals. Histology was verified in all chronic and acute animals, we added a sentence in the method section.

Explanation of figures is very poor, values in figures should be reported in results so they can be compared in the context of the description.

In this revised version, we report most numbers present in figures and their legend to the main text (result section).

The depth of the recording tungsten electrodes are meaningless without the AP and ML coordinates given how heterogenous mPFC is. What is the ventromedial wall of the mPFC in the cat?

We added the ML and AP coordinates in the method section. We corrected ventromedial wall for ventroposterior part of the mPFC.

What are the two vertical lines in 1F?

This was an error while preparing the figure. The panel was corrected.

Line 90 mean +-SD of what? There are no numbers.

Thanks, we now indicate the values.

Panel 2L does not show increased spindling in reuniens prior to PFC as indicated in the results, please explain. It does show SWR in the hippocampus prior to spindles, what is the meaning of such a time relationship?

Panel 2L did show an increased spindling reuniens prior to mPFC, but indeed at the time scale shown, it was not very clear. In this revised manuscript, we added an inset zooming around time zero to make this point clearer.

Panel 2L indeed show an increase in SWR prior to the increase in spindle in both Reuniens and mPFC.

As stated in the discussion, ‘We found that hippocampal SWRs trigger thalamic firing and precede the onset of reuniens and mPFC spindles, which points to SWRs as one of candidate events for spindle initiation.’

It is unclear what the slow waves of PFC mean, these represent filtered PFC lfp, but is this a particular oscillation? They continue to occur during the spindle, while the slow waves supposedly trigger the spindle. Please explain and clarify.

We recently published a review article involving several scientists studying both human and animal sleep that has inserted Box. 1 (Timofeev I, Schoch S, LeBourgeois M, Huber R, Riedner B, Kurth S. Spatio-temporal properties of sleep slow waves and implications for development. Current Opinion in Physiology. 2020; 15: 172–182). In this box among other terms, we provide current definition of slow waves vs slow oscillation. Briefly, if slow waves are repeated with a given rhythm, they typically form slow oscillation. However, if they occur in isolation or are not rhythmic, they remain slow waves, but cannot be called slow oscillation.

Regarding relation of spindles and slow oscillation. We are currently systematically analyzing data on spindles and slow waves obtained from head-restrained and freely behaving cats. One of the main findings is that a majority of ‘cortical’ spindles are local. Local to the extent that spindles can occur in alternation in two neighboring cortical cells. Largely, LFP sleep spindles occur more or less synchronously within suprasylvian gyrus of cats where indeed a large majority of them was triggered by slow waves. The synchrony between LFP spindles in suprasylvian vs other other cortical areas is much less clear. So, it is not surprizing that spindles in one bran region can occur when there is a slow wave present in some other brain region. Something of a kind was also shown in human (Mölle M, Bergmann TO, Marshall L, Born J. Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing. Sleep. 2011; 34 (10): 1411-1421).

In this regard, we are not ready to include modifications in the manuscript.

Line 134, where is spindle amplitude shown? Plots report power within the spindle frequency band, which obviously captures more than just spindles.

No, plots of figure 3 B, C show the phase-amplitude coupling (PAC) strength. These were calculated with detected spindles, therefore, while we cannot exclude some false spindle detections, we are confident that the false spindle detections are at a negligible level. We modified text and instead of spindle amplitude, we describe SW-spindle amplitude coupling. This reflects our analysis with exactitude.

The discussion must include the medio dorsal nucleus which is the largest thalamic input to the prefrontal cortex and also receives input from the hippocampus. In particular, the case must be made for why reuniens would play a more important or different role than MD? (For example: Occurrence of Hippocampal Ripples is Associated with Activity Suppression in the Mediodorsal Thalamic Nucleus - PMC (nih.gov)).

We cited the suggested study. We cannot say whether reuniens plays a more or less important role. What is clear is that hippocampal ripples at the onset of spindles trigger increased firing in both MD and reuniens. Our extracellular recordings (Fig. 4, K) suggest that the increased firing is associated with spike-bursts. We also have a parallel unpublished study done on anesthetized mice showing SWR triggered inhibitory potentials in both reuniens and MD that reverses around -65mV - -70 mV. Because the majority of SWR occurred at the onset of cortical up state, a relative role of cortico-thalamic vs hippocampo-thalamic drive is not easy to separate. We hope, we will convincingly do this in our forthcoming study, with the limitation that it was done on anesthetized mice.

Reviewer #1 (Recommendations For The Authors):

I strongly encourage the authors to perform current source density analyses on the LFP signals recorded in the nucleus reuniens to make sure that the observed oscillations are indeed locally generated. So far, the anatomical organisation in reuniens cannot support the local generation of oscillations, such as spindles and slow wave. At least in rodents (the cat reuniens does not seem too different, until shown differently), there were no oscillators found in reuniens, and at least not arranged like in cortical areas, allowing the summation in time, and particularly space, of rhythmic input currents. Bipolar recordings with pairs of twisted electrodes might also be useful to assess the local existence of spindles and slow waves.

Current source density calculation is possible when one knows the exact distance between recording sites. As we used tetrodes made with 4 twisted platinum-iridium wires, we know more or less the range of distance between recording sites, but not the exact distance between any given pair of electrodes.

Then, the physical distance between the reuniens and any cortical structure is about 8-9 mm. Therefore, with such distances, volume conductance is expected to be negligible. If slow waves and spindles are volume conducted, then slow waves and spindles recorded with tetrodes should have identical shape. Following reviewer comment, we took these recordings and subtracted one channel from another. The difference in signal during slow waves is in the order 0.1 mV. Considering that the distance between electrodes is in the order of 20 um, such a difference in voltage is major and can only be explained by local extracellular currents, likely due to synaptic activities originating in afferent structures.

Below, we plotted the voltage of one channel of the tetrode versus another channel of the same tetrode. If the signal was simply volume conducted, one would expect to see the vast majority of points on the x=y line (red).

Author response image 1.

Below is a segment of mPFC LFP recording (upper black trace), mPFC LFP filtered for spindle frequency (7-15 Hz) and the spindle detected (black lines above the filtered trace. Then two LFP traces from a tetrode in the Reuniens (orange and light blue) are overlayed. The second trace (Blue) from bottom represents the substraction of Reuniens 1 minus Reuniens 2 channel, and just below (lower Blue trace) is this susbtraction trace filtered for spindle frequency (7-15 Hz) showing clear voltage difference in the spindle range between the two electrodes. Note also that around time 179-179.5 s, there is clear spindle oscillation in the mPFC recording which is not present in the Reuniens recordings.

Author response image 2.

Therefore, we are convinced that in our recordings, volume conductance did not play any significant role.

Another concern regarding delays between events, like slow waves, measured between two regions (as exemplified by Figure 3). It appears that the delays were calculated from the filtered signal. Figure 3G shows a delay between the peak of the mPFC slow wave between the raw and the filtered signal, which might be artifactual of the processing. It is though not (or less) visible for the reuniens recording. Such mismatch might explain the observed differences in delays.

Thanks for this comment. We recomputed the analysis using the original signal (smoothed) and obtained very similar results. Panels H and I of figure 3 were updated using the new analysis performed on original signal.

The overall analyses of LFP-triggered reuniens MUA activity lack of statistics (at least z-scored firing to normalise the firings).

Fig. 2 H and I are representative examples for histograms; statistical data are shown in circular plots as explained in the legend. Fig. 2 L, shows populational data and we provide now standard error. Fig. 4 C and D show individual example. Fig. 4 E shows histograms of activity of all identified putative single units. Units that show significant modulation are displayed above white line. Fig. 4 F shows populational data for significantly modified units.  

A last point of detail in the model, which surprisingly shows reuniens to excitatory hippocampal cells' connectivity. Recent literature reports that reuniens only connect hippocampal interneurons, and not principal cells (at least in rodents, I could not find any report in cats). I wonder how changing this parameter would affect the results of the computational investigation, particularly the results shown in Figure 6.

There are several studies in the literature showing a direct excitation from the Reuniens to pyramidal cells in the CA1, here are three of them:

Goswamee, P., et al. (2021). "Nucleus Reuniens Afferents in Hippocampus Modulate CA1 Network Function via Monosynaptic Excitation and Polysynaptic Inhibition." Frontiers in Cellular Neuroscience 15.

Dolleman-Van der Weel MJ, Lopes da Silva FH, Witter MP (1997) Nucleus Reuniens Thalami Modulates Activity in Hippocampal Field CA1 through Excitatory and Inhibitory Mechanisms. The Journal of Neuroscience 17:5640.

Dolleman-van der Weel MJ, Lopes da Silva FH, Witter MP (2017) Interaction of nucleus reuniens and entorhinal cortex projections in hippocampal field CA1 of the rat. Brain Structure and Function 222:2421-2438.

Because this is not a review paper, we opted to not cite all the papers describing connectivity between mPFC, hippocampus and thalamus.

Reviewer #2 (Recommendations For The Authors):

I respectively suggest that the earlier (public) comments listed above should be addressed. In addition, it would be useful to make it clearer when non-rapid eye movement sleep was being addressed and when rapid eye movement was being addressed. Is it of value to use a single term instead of adding "slow wave sleep" or else clarify when either term is used? The addition of more subheadings might help. Moreover, the relative contribution/value of evidence from these two sleep states was not addressed or was not very clear.

We tried to make it clearer when NREM and when REM was analysed.

We replaced slow-wave sleep with NREM sleep in the figure 5 title.

We added several subheadings in the discussion.

Relative contribution of NREM vs REM sleep was not addressed? Sorry but we do not clearly understand your question. Figs. 2 and 3 deal mainly with NREM sleep (Fig 2.B has an example of REM sleep). Fig. 4 essentially describes results obtained during REM sleep.

I was not sure if the Abstract summarised the key take-home messages from the large amount of evidence provided. Some choices are needed, of course, but "evidence of bidirectional connectivity" struck me as less novel than other evidence provided. Given the huge amount of findings provided, which is commendable, it is still useful to present it perhaps in a more digestible fashion. For example, the headings or the first sentence(s) below headings could indicate the aim or the outcome of the specific method/analysis/findings.

We rewrote abstract and we also added some conclusion to highlight major findings and their meaning.

It is more common to use NRe or Re, rather than REU.

We avoided using RE as, for decades, we used RE to abbreviate the thalamic reticular nucleus in several publications. In this revised version, we spell at full - Reuniens.

Line 49 mentions "short-term" memory. Please specify this more clearly as it is otherwise ambiguous. Also, line 303.

We rephrased the sentence: In particular, the hierarchical coupling of slow waves, spindles and SWRs is thought to play a key role in memory consolidation.

Line 303 was likely about the ventromedial wall: we corrected that sentence.

Line 62: the word, "required" (for memory function) is too strong because there is evidence that it is not always required.

We modified the sentence for plays a major role.

The focus within the medial prefrontal cortex could be specified more clearly / earlier.

The mPFC is mentioned in the second sentence of the abstract and in the first sentence of the introduction.

Line 134: The heading states "determine" and then mentions modulation. These terms may not be interchangeable or they need clarification.

We changed it to slow wave-spindle amplitude coupling. This represents exactly our analysis.

Line 204: Does "cortical network" mean prefrontal cortex network"?

Yes, as described in lines 192-193, the two cortical networks (N1 and N2) of the model represent the mPFC layer 5 and 6 respectively.

Lines 283 to 289: These were not very clear to me.

These lines described the potential mechanisms for the responses to hippocampal and reuniens stimulation recorded intracellularly (results in figure 1). We modified this paragraph for clarity.

Line 296: Specify the "claim".

We modified the sentence for “[…] provides supporting evidence for this claim that nucleus Reuniens might synchronize the activity of ventral hippocampus and mPFC.”

The discussion naturally focuses on the thalamic nucleus reuniens, but also occasionally mentions the thalamic mediodorsal nucleus. The distinction, assuming this is highly relevant, could be expressed more clearly (direct comparison with their previous papers).

We never published a study on the mediodorsal nucleus. We do have some unpublished results from recordings in the MD nucleus and they reveal the presence of an inhibitory component at the beginning of cortical active states, therefore behaving in a similar way to first order nuclei. It is then possible that spindles recorded in the reuniens are actually generated in the MD nucleus and then transmitted to Reuniens through the thalamic reticular nucleus, as both MD and reuniens are connected to the rostral thalamic reticular nucleus. We added some discussion about this.

Figure 1B: Do the authors have any additional evidence of the placements in the reuniens, because the photo provided suggests a large area beyond the reuniens boundary. Also, please confirm is the CEM between Rh and Re in the cat (I think the Rh and Re are adjacent in the rat).

Figure 1B is from an electrolytic lesion, which is necessarily bigger than the tip of the electrode. Therefore the center of the electrolytic lesion indicates where the electrode tip was located which is well within the reuniens nucleus.

Also, yes CE (Nucleus centralis thalami, pars medialis) is located between the reuniens and rhomboid in cats. This can be found in two cat atlas:  

Reinoso-Suárez, F. (1961). Topographischer Hirnatlas der Katze für experimental-physiologische Untersuchungen (Merck).

Berman AL, Jones EG (1982) The Thalamus and Basal Telencephalon of the Cat: A Cytoarchitectonic Atlas with Stereotaxic Coordinates: University of Wisconsin Press.

The first mention of hippocampus in the figure legends should remind the reader by stating "ventral hippocampus".

In this revised version, we added “ventral” in several instances both in the main text and in figure legend.

Figure 2: It seems unusual to mention "unusually short NREM". Presumably, things are the same otherwise - if so, perhaps mention that, especially if some of the effects reflect an "unusual" episode.

We display this particular segment because we want to show continuous recording in which still individual elements characterizing specific states are still visible.

Some effects look like they are strong and others perhaps weaker. If so, how do these impact the final conclusions?

Sorry, we did not understand clearly what is meant here by the reviewer. In general, if any effect has statistically significant difference (old fashion 0.05) we consider it as significant. Any other cases are described on individual basis.

Perhaps "MAD" should be in full on the first occasion, if not already.

It was spelled out at line 659, but we now spell it out also in the results section and in figure 2 legend.

Methods: the key question is the use of rodent recordings to classify cat recordings. It would be good to have a reference indicating that this can be directly used for cats, which may have different sleep cycles and patterns compared to rats.

We did not use rodent recordings to classify cat recordings, however we did used a state detection script that was developed with rodent recordings. As mentioned in the method section, we adapted the script to cat mPFC recordings and then manual corrections were made to correctly detect REM episodes. Respectfully, our lab investigates sleep-wake in non-anesthetized animals for a few decades; we developed state detection algorithm in mice, cats, marmosets when needed (to analyse months of recordings), and we have an extensive expertise in identifying states of vigilance from electrophysiological recordings.

Author response:

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

Reviewer #1 (Public Review):

Weaknesses:

INTRODUCTION & THEORY

(1) Can the authors please clarify why the first trial of extinction in a standard protocol does NOT produce the retrieval-extinction effect? Particularly as the results section states: "Importantly, such a short-term effect is also retrieval dependent, suggesting the labile state of memory is necessary for the short-term memory update to take effect (Fig. 1e)." The importance of this point comes through at several places in the paper:

1A. "In the current study, fear recovery was tested 30 minutes after extinction training, whereas the effect of memory reconsolidation was generally evident only several hours later and possibly with the help of sleep, leaving open the possibility of a different cognitive mechanism for the short-term fear dementia related to the retrieval-extinction procedure." ***What does this mean? The two groups in study 1 experienced a different interval between the first and second CS extinction trials; and the results varied with this interval: a longer interval (10 min) ultimately resulted in less reinstatement of fear than a shorter interval. Even if the different pattern of results in these two groups was shown/known to imply two different processes, there is absolutely no reason to reference any sort of cognitive mechanism or dementia - that is quite far removed from the details of the present study.

Indeed, the only difference between the standard extinction paradigm and the retrieval-extinction paradigm is the difference between the first and second CS extinction trials. It has been shown before that a second CS+ presented 1 hour after the initial retrieval CS+ resulted in the dephosphorylation of GluR1 in rats, which was indicative of memory destabilization. The second CS+ presented only 3 minutes after the initial retrieval CS+, as in the standard extinction training, did not cause the GluR1 dephosphorylation effect (Monfils et al., 2009). Therefore, an isolated presentation of the CS+ seems to be important in preventing the return of fear expression. Behaviorally, when the CSs were presented in a more temporally spaced (vs. mass presentation) or a more gradual manner in the extinction training, the fear amnesia effects were more salient (Cain et al., 2003, Gershman et al., 2013). It has also been suggested that only when the old memory and new experience (through extinction) can be inferred to have been generated from the same underlying latent cause, the old memory can be successfully modified (Gershman et al., 2017). On the other hand, if the new experiences are believed to be generated by a different latent cause, then the old memory is less likely to be subject to modification. Therefore, the way the first and 2nd CS are temporally organized (retrieval-extinction or standard extinction) might affect how the latent cause is inferred and lead to different levels of fear expression from a theoretical perspective. These findings, together with studies in both fear and drug memories using the retrieval-extinction paradigm (Liu et al., 2014, Luo et al., 2015, Schiller et al., 2010, Xue et al., 2012), seem to suggest that the retrieval-extinction and the standard extinction procedures engage different cognitive and molecular mechanisms that lead to significant different behavioral outcomes. 

In our study, we focus on the short-term and long-term amnesia effects of the retrieval-extinction procedure but also point out the critical role of retrieval in eliciting the short-term effect.

1B. "Importantly, such a short-term effect is also retrieval dependent, suggesting the labile state of memory is necessary for the short-term memory update to take effect (Fig. 1e)." ***As above, what is "the short-term memory update"? At this point in the text, it would be appropriate for the authors to discuss why the retrieval-extinction procedure produces less recovery than a standard extinction procedure as the two protocols only differ in the interval between the first and second extinction trials. References to a "short-term memory update" process do not help the reader to understand what is happening in the protocol.

Sorry for the lack of clarity here. By short-term memory update we meant the short-term amnesia in fear expression.

(2) "Indeed, through a series of experiments, we identified a short-term fear amnesia effect following memory retrieval, in addition to the fear reconsolidation effect that appeared much later."

***The only reason for supposing two effects is because of the differences in responding to the CS2, which was subjected to STANDARD extinction, in the short- and long-term tests. More needs to be said about how and why the performance of CS2 is affected in the short-term test and recovers in the long-term test. That is, if the loss of performance to CS1 and CS2 is going to be attributed to some type of memory updating process across the retrieval-extinction procedure, one needs to explain the selective recovery of performance to CS2 when the extinction-to-testing interval extends to 24 hours. Instead of explaining this recovery, the authors note that performance to CS1 remains low when the extinction-to-testing interval is 24 hours and invoke something to do with memory reconsolidation as an explanation for their results: that is, they imply (I think) that reconsolidation of the CS1-US memory is disrupted across the 24-hour interval between extinction and testing even though CS1 evokes negligible responding just minutes after extinction.

In our results, we did not only focus on the fear expression related to CS2. In fact, we also demonstrated that the CS1 related fear expression diminished in the short-term memory test but re-appeared in the long-term memory after the CS1 retrieval-extinction training.

The “…recovery of performance to CS2 when the extinction-to-testing interval extends to 24 hours…” is a result that has been demonstrated in various previous studies (Kindt and Soeter, 2018, Kindt et al., 2009, Nader et al., 2000, Schiller et al., 2013, Schiller et al., 2010, Xue et al., 2012). That is, the reconsolidation framework stipulates that the pharmacological or behavioral intervention during the labile states of the reconsolidation window only modifies the fear memory linked to the reminded retrieval cue, but not for the non-reminded CS-US memory expression (but also see (Liu et al., 2014, Luo et al., 2015) for using the unconditioned stimulus as the reminder cue and the retrieval-extinction paradigm to prevent the return of fear memory associated with different CS).  In fact, we hypothesized the temporal dynamics of CS1 and CS2 related fear expressions were due to the interplay between the short-term and long-term (reconsolidation) effects of the retrieval-extinction paradigm in the last figure (Fig. 6). 

(3) The discussion of memory suppression is potentially interesting but, in its present form, raises more questions than it answers. That is, memory suppression is invoked to explain a particular pattern of results but I, as the reader, have no sense of why a fear memory would be better suppressed shortly after the retrieval-extinction protocol compared to the standard extinction protocol; and why this suppression is NOT specific to the cue that had been subjected to the retrieval-extinction protocol.

We discussed memory suppression as one of the potential mechanisms to account for the three characteristics of the short-term amnesia effects: cue-independence, temporal dynamics (short-term) and thought-control-ability relevance. According to the memory suppression theory, the memory suppression effect is NOT specific to the cue and this effect was demonstrated via the independent cue test in a variety of studies (Anderson and Floresco, 2022, Anderson and Green, 2001, Gagnepain et al., 2014, Zhu et al., 2022). Therefore, we suggest in the discussion that it might be possible the CS1 retrieval cue prompted an automatic suppression mechanism and yielded the short-term fear amnesia consistent with various predictions from the memory suppression theory:

“In our experiments, subjects were not explicitly instructed to suppress their fear expression, yet the retrieval-extinction training significantly decreased short-term fear expression. These results are consistent with the short-term amnesia induced with the more explicit suppression intervention (Anderson et al., 1994; Kindt and Soeter, 2018; Speer et al., 2021; Wang et al., 2021; Wells and Davies, 1994). It is worth noting that although consciously repelling unwanted memory is a standard approach in memory suppression paradigm, it is possible that the engagement of the suppression mechanism can be unconscious. For example, in the retrieval-induced forgetting (RIF) paradigm, recall of a stored memory impairs the retention of related target memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner (Imai et al., 2014). Moreover, subjects with trauma histories exhibited more suppression-induced forgetting for both negative and neutral memories than those with little or no trauma (Hulbert and Anderson, 2018). Similarly, people with higher self-reported thought-control capabilities showed more severe cue-independent memory recall deficit, suggesting that suppression mechanism is associated with individual differences in spontaneous control abilities over intrusive thoughts (Küpper et al., 2014). It has also been suggested that similar automatic mechanisms might be involved in organic retrograde amnesia of traumatic childhood memories (Schacter et al., 2012; Schacter et al., 1996).”

3A. Relatedly, how does the retrieval-induced forgetting (which is referred to at various points throughout the paper) relate to the retrieval-extinction effect? The appeal to retrieval-induced forgetting as an apparent justification for aspects of the present study reinforces points 2 and 3 above. It is not uninteresting but needs some clarification/elaboration.

We introduced the retrieval-induced forgetting (RIF) to make the point that RIF was believed to be related to the memory suppression mechanism and the RIF effect can appear relatively early, consistent with what we observed in the short-term amnesia effect. We have re-written the manuscript to make this point clearer:

“It is worth noting that although consciously repelling unwanted memory is a standard approach in memory suppression paradigm, it is possible that the engagement of the suppression mechanism can be unconscious. For example, in the retrieval-induced forgetting (RIF) paradigm, recall of a stored memory impairs the retention of related target memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner (Imai et al., 2014). Moreover, subjects with trauma histories exhibited more suppression-induced forgetting for both negative and neutral memories than those with little or no trauma (Hulbert and Anderson, 2018). Similarly, people with higher self-reported thought-control capabilities showed more severe cue-independent memory recall deficit, suggesting that suppression mechanism is associated with individual differences in spontaneous control abilities over intrusive thoughts (Küpper et al., 2014).”

(4) Given the reports by Chalkia, van Oudenhove & Beckers (2020) and Chalkia et al (2020), some qualification needs to be inserted in relation to reference 6. That is, reference 6 is used to support the statement that "during the reconsolidation window, old fear memory can be updated via extinction training following fear memory retrieval". This needs a qualifying statement like "[but see Chalkia et al (2020a and 2020b) for failures to reproduce the results of 6]."

https://pubmed.ncbi.nlm.nih.gov/32580869/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115860/

We have incorporated the reviewer’s suggestion into the revised manuscript in both the introduction:

“Pharmacological blockade of protein synthesis and behavioral interventions can both eliminate the original fear memory expression in the long-term (24 hours later) memory test ( Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), resulting in the cue-specific fear memory deficit (Debiec et al., 2002; Lee, 2008; Nader, Schafe, & LeDoux, 2000). For example, during the reconsolidation window, retrieving a fear memory allows it to be updated through extinction training (i.e., the retrieval-extinction paradigm (Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), but also see (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; D. Schiller, LeDoux, & Phelps, 2020)”

And in the discussion:

“It should be noted that while our long-term amnesia results were consistent with the fear memory reconsolidation literatures, there were also studies that failed to observe fear prevention (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; Schroyens et al., 2023). Although the memory reconsolidation framework provides a viable explanation for the long-term amnesia, more evidence is required to validate the presence of reconsolidation, especially at the neurobiological level (Elsey et al., 2018). While it is beyond the scope of the current study to discuss the discrepancies between these studies, one possibility to reconcile these results concerns the procedure for the retrieval-extinction training. It has been shown that the eligibility for old memory to be updated is contingent on whether the old memory and new observations can be inferred to have been generated by the same latent cause (Gershman et al., 2017; Gershman and Niv, 2012). For example, prevention of the return of fear memory can be achieved through gradual extinction paradigm, which is thought to reduce the size of prediction errors to inhibit the formation of new latent causes (Gershman, Jones, et al., 2013). Therefore, the effectiveness of the retrieval-extinction paradigm might depend on the reliability of such paradigm in inferring the same underlying latent cause. Furthermore, other studies highlighted the importance of memory storage per se and suggested that memory retention was encoded in the memory engram cell ensemble connectivity whereas the engram cell synaptic plasticity is crucial for memory retrieval (Ryan et al., 2015; Tonegawa, Liu, et al., 2015; Tonegawa, Pignatelli, et al., 2015). It remains to be tested how the cue-independent short-term and cue-dependent long-term amnesia effects we observed could correspond to the engram cell synaptic plasticity and functional connectivity among engram cell ensembles (Figure 6). This is particularly important, since the cue-independent characteristic of the short-term amnesia suggest that either different memory cues fail to evoke engram cell activities, or the retrieval-extinction training transiently inhibits connectivity among engram cell ensembles. Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

5A. What does it mean to ask: "whether memory retrieval facilitates update mechanisms other than memory reconsolidation"? That is, in what sense could or would memory retrieval be thought to facilitate a memory update mechanism?

It is widely documented in the literatures that memory retrieval renders the old memory into a labile state susceptible for the memory reconsolidation process. However, as we mentioned in the manuscript, studies have shown that memory reconsolidation requires the de novo protein synthesis and usually takes hours to complete. What remains unknown is whether old memories are subject to modifications other than the reconsolidation process. Our task specifically tested the short-term effect of the retrieval-extinction paradigm and found that fear expression diminished 30mins after the retrieval-extinction training. Such an effect cannot be accounted for by the memory reconsolidation effect.

5B. "First, we demonstrate that memory reactivation prevents the return of fear shortly after extinction training in contrast to the memory reconsolidation effect which takes several hours to emerge and such a short-term amnesia effect is cue independent (Study 1, N = 57 adults)."

***The phrasing here could be improved for clarity: "First, we demonstrate that the retrieval-extinction protocol prevents the return of fear shortly after extinction training (i.e., when testing occurs just min after the end of extinction)." Also, cue-dependence of the retrieval-extinction effect was assessed in study 2.

We thank the reviewer and have modified the phrasing of the sentence:

“First, we demonstrate that memory retrieval-extinction protocol prevents the return of fear expression shortly after extinction training and this short-term effect is memory reactivation dependent (Study 1, N = 57 adults).”

5C. "Furthermore, memory reactivation also triggers fear memory reconsolidation and produces cue-specific amnesia at a longer and separable timescale (Study 2, N = 79 adults)." ***In study 2, the retrieval-extinction protocol produced a cue-specific disruption in responding when testing occurred 24 hours after the end of extinction. This result is interesting but cannot be easily inferred from the statement that begins "Furthermore..." That is, the results should be described in terms of the combined effects of retrieval and extinction, not in terms of memory reactivation alone; and the statement about memory reconsolidation is unnecessary. One can simply state that the retrieval-extinction protocol produced a cue-specific disruption in responding when testing occurred 24 hours after the end of extinction.

We have revised the text according to the reviewer’s comment.

“Furthermore, across different timescales, the memory retrieval-extinction paradigm triggers distinct types of fear amnesia in terms of cue-specificity and cognitive control dependence, suggesting that the short-term fear amnesia might be caused by different mechanisms from the cue-specific amnesia at a longer and separable timescale (Study 2, N = 79 adults).”

5D. "...we directly manipulated brain activities in the dorsolateral prefrontal cortex and found that both memory retrieval and intact prefrontal cortex functions were necessary for the short-term fear amnesia."

***This could be edited to better describe what was shown: E.g., "...we directly manipulated brain activities in the dorsolateral prefrontal cortex and found that intact prefrontal cortex functions were necessary for the short-term fear amnesia after the retrieval-extinction protocol."

Edited:

“Finally, using continuous theta-burst stimulation (Study 3, N = 75 adults), we directly manipulated brain activity in the dorsolateral prefrontal cortex, and found that both memory reactivation and intact prefrontal cortex function were necessary for the short-term fear amnesia after the retrieval-extinction protocol.”

5E. "The temporal scale and cue-specificity results of the short-term fear amnesia are clearly dissociable from the amnesia related to memory reconsolidation, and suggest that memory retrieval and extinction training trigger distinct underlying memory update mechanisms."

***The pattern of results when testing occurred just minutes after the retrieval-extinction protocol was different from that obtained when testing occurred 24 hours after the protocol. Describing this in terms of temporal scale is unnecessary, and suggesting that memory retrieval and extinction trigger different memory update mechanisms is not obviously warranted. The results of interest are due to the combined effects of retrieval+extinction and there is no sense in which different memory update mechanisms should be identified with retrieval (mechanism 1) and extinction (mechanism 2).

We did not argue for different memory update mechanisms for the “retrieval (mechanism 1) and extinction (mechanism 2)” in our manuscript. Instead, we proposed that the retrieval-extinction procedure, which was mainly documented in the previous literatures for its association with the reconsolidation-related fear memory retention (the long-term effect), also had a much faster effect (the short-term effect). These two effects differed in many aspects, suggesting that different memory update mechanisms might be involved.

5F. "These findings raise the possibility of concerted memory modulation processes related to memory retrieval..."

***What does this mean?

As we mentioned in our response to the previous comment, we believe that the retrieval-extinction procedure triggers different types of memory update mechanisms working on different temporal scales.

(6) "...suggesting that the fear memory might be amenable to a more immediate effect, in addition to what the memory reconsolidation theory prescribes..."

***What does it mean to say that the fear memory might be amenable to a more immediate effect?

We intended to state that the retrieval-extinction procedure can produce a short-term amnesia effect and have thus revised the text.

(7) "Parallel to the behavioral manifestation of long- and short-term memory deficits, concurrent neural evidence supporting memory reconsolidation theory emphasizes the long-term effect of memory retrieval by hypothesizing that synapse degradation and de novo protein synthesis are required for reconsolidation."

***This sentence needs to be edited for clarity.

We have rewritten this sentence:

“Corresponding to the long-term behavioral manifestation, concurrent neural evidence supporting memory reconsolidation hypothesis emphasizes that synapse degradation and de novo protein synthesis are required for reconsolidation.”

(8) "previous behavioral manipulations engendering the short-term declarative memory effect..."

***What is the declarative memory effect? It should be defined.

We meant the amnesia on declarative memory research, such as the memory deficit caused by the think/no-think paradigms. Texts have been modified for clarity:

“On the contrary, previous behavioral manipulations engendering the short-term amnesia on declarative memory, such as the think/no-think paradigm, hinges on the intact activities in brain areas such as dorsolateral prefrontal cortex (cognitive control) and its functional coupling with specific brain regions such as hippocampus (memory retrieval) (Anderson and Green, 2001; Wimber et al., 2015).”

(9) "The declarative amnesia effect emerges much earlier due to the online functional activity modulation..."

***Even if the declarative memory amnesia effect had been defined, the reference to online functional activity modulation is not clear.

We have rephrased the sentence:

“The declarative amnesia effect arises much earlier due to the more instant modulation of functional connectivity, rather than the slower processes of new protein synthesis in these brain regions.”

(10) "However, it remains unclear whether memory retrieval might also precipitate a short-term amnesia effect for the fear memory, in addition to the long-term prevention orchestrated by memory consolidation."

***I found this sentence difficult to understand on my first pass through the paper. I think it is because of the phrasing of memory retrieval. That is, memory retrieval does NOT precipitate any type of short-term amnesia for the fear memory: it is the retrieval-extinction protocol that produces something like short-term amnesia. Perhaps this sentence should also be edited for clarity.

We have changed “memory retrieval” to “retrieval-extinction” where applicable.

I will also note that the usage of "short-term" at this point in the paper is quite confusing: Does the retrieval-extinction protocol produce a short-term amnesia effect, which would be evidenced by some recovery of responding to the CS when tested after a sufficiently long delay? I don't believe that this is the intended meaning of "short-term" as used throughout the majority of the paper, right?

By “short-term”, we meant the lack of fear expression in the test phase (measured by skin conductance responses) shortly after the retrieval-extinction procedure (30 mins in studies 1 & 2 and 1 hour in study 3). It does not indicate that the effect is by itself “short-lived”.

(11) "To fully comprehend the temporal dynamics of the memory retrieval effect..."<br /> ***What memory retrieval effect? This needs some elaboration.

We’ve changed the phrase “memory retrieval effect” to “retrieval-extinction effect” to refer to the effect of retrieval-extinction on fear amnesia.

(12) "We hypothesize that the labile state triggered by the memory retrieval may facilitate different memory update mechanisms following extinction training, and these mechanisms can be further disentangled through the lens of temporal dynamics and cue-specificities."

***What does this mean? The first part of the sentence is confusing around the usage of the term "facilitate"; and the second part of the sentence that references a "lens of temporal dynamics and cue-specificities" is mysterious. Indeed, as all rats received the same retrieval-extinction exposures in Study 2, it is not clear how or why any differences between the groups are attributed to "different memory update mechanisms following extinction".

As the reviewer mentioned, if only one time point data were collected, we cannot differentiate whether different memory update mechanisms are involved. In study 2, however, the 3 groups only differed on the time onsets the reinstatement test was conducted. Accordingly, our results showed that the fear amnesia effects for CS1 and CS2 cannot be simply explained by forgetting: different memory update mechanisms must be at work to explain the characteristics of the SCR related to both CS1 and CS2 at three different time scales (30min, 6h and 24h). It was based on these results, together with the results from the TMS study (study 3), that we proposed the involvement of a short-term memory update mechanism in addition to the reconsolidation related fear amnesia (which should become evident much later) induced by the retrieval-extinction protocol.

(13) "In the first study, we aimed to test whether there is a short-term amnesia effect of fear memory retrieval following the fear retrieval-extinction paradigm."

***Again, the language is confusing. The phrase, "a short-term amnesia effect" implies that the amnesia itself is temporary; but I don't think that this implication is intended. The problem is specifically in the use of the phrase "a short-term amnesia effect of fear memory retrieval." To the extent that short-term amnesia is evident in the data, it is not due to retrieval per se but, rather, the retrieval-extinction protocol.

We have changed the wordings and replaced “memory retrieval” with “retrieval-extinction” where applicable.

(14) The authors repeatedly describe the case where there was a 24-hour interval between extinction and testing as consistent with previous research on fear memory reconsolidation. Which research exactly? That is, in studies where a CS re-exposure was combined with a drug injection, responding to the CS was disrupted in a final test of retrieval from long-term memory which typically occurred 24 hours after the treatment. Is that what the authors are referring to as consistent? If so, which aspect of the results are consistent with those previous findings? Perhaps the authors mean to say that, in the case where there was a 24-hour interval between extinction and testing, the results obtained here are consistent with previous research that has used the retrieval-extinction protocol. This would clarify the intended meaning greatly.

Our 24 hour test results after the retrieval-extinction protocol was consistent with both pharmacological and behavioral intervention studies in fear memory reconsolidation studies (Kindt and Soeter, 2018, Kindt et al., 2009, Liu et al., 2014, Luo et al., 2015, Monfils et al., 2009, Nader et al., 2000, Schiller et al., 2013, Schiller et al., 2010, Xue et al., 2012) since the final test phase typically occurred 24 hours after the treatment. At the 24-hour interval, the memory reconsolidation effect would become evident either via drug administration or behavioral intervention (extinction training).

DATA

(15) Points about data:

5A. The eight participants who were discontinued after Day 1 in study 1 were all from the no-reminder group. Can the authors please comment on how participants were allocated to the two groups in this experiment so that the reader can better understand why the distribution of non-responders was non-random (as it appears to be)?

15B. Similarly, in study 2, of the 37 participants that were discontinued after Day 2, 19 were from Group 30 min, and 5 were from Group 6 hours. Can the authors comment on how likely these numbers are to have been by chance alone? I presume that they reflect something about the way that participants were allocated to groups, but I could be wrong.

We went back and checked out data. As we mentioned in the supplementary materials, we categorized subjects as non-responders if their SCR response to any CS was less than 0.02  in Day 1 (fear acquisition). Most of the discontinued participants (non-responders) in the no-reminder group (study 1) and the 30min & 24 h groups (study 2) were when the heating seasons just ended or were yet to start, respectively. It has been documented that human body thermal conditions were related to the quality of the skin conductance response (SCR) measurements (Bauer et al., 2022, Vila, 2004). We suspect that the non-responders might be related to the body thermal conditions caused by the lack of central heating.

15C. "Post hoc t-tests showed that fear memories were resilient after regular extinction training, as demonstrated by the significant difference between fear recovery indexes of the CS+ and CS- for the no-reminder group (t26 = 7.441, P < 0.001; Fig. 1e), while subjects in the reminder group showed no difference of fear recovery between CS+ and CS- (t29 = 0.797, P = 0.432, Fig. 1e)."

***Is the fear recovery index shown in Figure 1E based on the results of the first test trial only? How can there have been a "significant difference between fear recovery indexes of the CS+ and CS- for the no-reminder group" when the difference in responding to the CS+ and CS- is used to calculate the fear recovery index shown in 1E? What are the t-tests comparing exactly, and what correction is used to account for the fact that they are applied post-hoc?

As we mentioned in the results section of the manuscript, the fear recovery index was defined as “the SCR difference between the first test trial and the last extinction trial of a specific CS”. We then calculated the “differential fear recovery index” (figure legends of Fig. 1e) between CS+ and CS- for both the reminder and no-reminder groups. The post-hoc t-tests were used to examine whether there were significant fear recoveries (compare to 0) in both the reminder (t₂₉ = 0.797, P = 0.432, Fig. 1e) and no-reminder (t₂₆ = 7.441, P  < 0.001; Fig. 1e) groups. We realize that the description of Bonferroni correction was not specified in the original manuscript and hence added in the revision where applicable.

15D. "Finally, there is no statistical difference between the differential fear recovery indexes between CS+ in the reminder and no reminder groups (t55 = -2.022, P = 0.048; Fig. 1c, also see Supplemental Material for direct test for the test phase)."

***Is this statement correct - i.e., that there is no statistically significant difference in fear recovery to the CS+ in the reminder and no reminder groups? I'm sure that the authors would like to claim that there IS such a difference; but if such a difference is claimed, one would be concerned by the fact that it is coming through in an uncorrected t-test, which is the third one of its kind in this paragraph. What correction (for the Type 1 error rate) is used to account for the fact that the t-tests are applied post-hoc? And if no correction, why not?

We are sorry about the typo.  The reviewer was correct that we meant to claim here that “… there is a significant difference between the differential fear recovery indexes between CS+ in the reminder and no-reminder groups (t₅₅ =- 2.022, P = 0.048; Fig. 1e)”.  Note that the t-test performed here was a confirmatory test following our two-way ANOVA with main effects of group (reminder vs. no-reminder) and time (last extinction trial vs. first test trial) on the differential CS SCR response (CS+ minus CS-) and we found a significant group x time interaction effect (F_1.55 = 4.087, P = 0.048, η² = 0.069). The significant difference between the differential fear recovery indexes was simply a re-plot of the interaction effect mentioned above and therefore no multiple correction is needed. We have reorganized the sequence of the sentences such that this t-test now directly follows the results of the ANOVA:

“The interaction effect was confirmed by the significant difference between the differential fear recovery indexes between CS1+ and CS2+ in the reminder and no-reminder groups (t₅₅ \= -2.022, P \= 0.048; Figure 1E, also see Supplemental Material for the direct test of the test phase).”

15E. In study 2, why is responding to the CS- so high on the first test trial in Group 30 min? Is the change in responding to the CS- from the last extinction trial to the first test trial different across the three groups in this study? Inspection of the figure suggests that it is higher in Group 30 min relative to Groups 6 hours and 24 hours. If this is confirmed by the analysis, it has implications for the fear recovery index which is partly based on responses to the CS-. If not for differences in the CS- responses, Groups 30 minutes and 6 hours are otherwise identical.

Following the reviewer’s comments, we went back and calculated the mean SCR difference of CS- between the first test trial and the last extinction trial for all three studies (see Author response image 1 below). In study 1, there was no difference in the mean CS- SCR (between the first test trial and last extinction trial) between the reminder and no-reminder groups (Kruskal-Wallis test , panel a), though both groups showed significant fear recovery even in the CS- condition (Wilcoxon signed rank test, reminder: P = 0.0043, no-reminder: P = 0.0037). Next, we examined the mean SCR for CS- for the 30min, 6h and 24h groups in study 2 and found that there was indeed a group difference (one-way ANOVA,F_2.76 = 5.3462, P = 0.0067, panel b), suggesting that the CS- related SCR was influenced by the test time (30min, 6h or 24h). We also tested the CS- related SCR for the 4 groups in study 3 (where test was conducted 1 hour after the retrieval-extinction training) and found that across TMS stimulation types (PFC vs. VER) and reminder types (reminder vs. no-reminder) the ANOVA analysis did not yield main effect of TMS stimulation type (F_1.71 = 0.322, P = 0.572) nor main effect of reminder type (F_1.71 = 0.0499, P = 0.824, panel c). We added the R-VER group results in study 3 (see panel c) to panel b and plotted the CS- SCR difference across 4 different test time points and found that CS- SCR decreased as the test-extinction delay increased (Jonckheere-Terpstra test, P = 0.00028). These results suggest a natural “forgetting” tendency for CS- related SCR and highlight the importance of having the CS- as a control condition to which the CS+ related SCR was compared with.

Author response image 1.

15F. Was the 6-hour group tested at a different time of day compared to the 30-minute and 24-hour groups; and could this have influenced the SCRs in this group?

For the 30min and 24h groups, the test phase can be arranged in the morning, in the afternoon or at night. However, for the 6h group, the test phase was inevitably in the afternoon or at night since we wanted to exclude the potential influence of night sleep on the expression of fear memory (see Author response table 1 below). If we restricted the test time in the afternoon or at night for all three groups, then the timing of their extinction training was not matched.

Author response table 1.

Nevertheless, we also went back and examined the data for the subjects only tested in the afternoon or at nights in the 30min and 24h groups to match with the 6h group where all the subjects were tested either in the afternoon or at night. According to Author response table 1 above, we have 17 subjects for the 30min group (9+8),18 subjects for the 24h group (9 + 9) and 26 subjects for the 6h group (12 + 14). As Author response image 2 shows, the SCR patterns in the fear acquisition, extinction and test phases were similar to the results presented in the original figure.

Author response image 2.

15G. Why is the range of scores in "thought control ability" different in the 30-minute group compared to the 6-hour and 24-hour groups? I am not just asking about the scale on the x-axis: I am asking why the actual distribution of the scores in thought control ability is wider for the 30-minute group?

We went back and tested whether the TCAQ score variance was the same across three groups. We found that there was significant difference in the variance of the TCAQ score distribution across three groups (F_2.155 = 4.324, P = 0.015, Levene test). However, post-hoc analyses found that the variance of TCAQ is not significantly different between the 30min and 6h groups (F_26.25 = 0.4788, P = 0.0697), nor between the 30min and 24h groups (i>F_26.25 = 0.4692, P = 0.0625). To further validate our correlational results between the TCAQ score and the fear recovery index, we removed the TCAQ scores that were outside the TCAQ score range of the 6h & 24h groups from the 30min group (resulting in 4 “outliner” TCAQ scores in the 30min group, panel a in Author response image 3 below) and the Levene test confirmed that the variance of the TCAQ scores showed no difference across groups after removing the 4 “outliner” data points in the 30min group (i>F_2.147 = 0.74028, P = 0.4788). Even with the 4 “outliers” removed from the 30min group, the correlational analysis of the TCAQ scores and the fear recovery index still yielded significant result in the 30min group (beta = -0.0148, t = -3.731, P = 0.0006, see panel b below), indicating our results were not likely due to the inclusion of subjects with extreme TCAQ scores.

Author response image 3.

(16) During testing in each experiment, how were the various stimuli presented? That is, was the presentation order for the CS+ and CS- pseudorandom according to some constraint, as it had been in extinction? This information should be added to the method section.

We mentioned the order of the stimuli in the testing phase in the methods section “… For studies 2 & 3, …a pseudo-random stimulus order was generated for fear acquisition and extinction phases of three groups with the rule that no same trial- type (CS1+, CS2+ and CS-) repeated more than twice. In the test phase, to exclude the possibility that the difference between CS1+ and CS2+ was simply caused by the presentation sequence of CS1+ and CS2+, half of the participants completed the test phase using a pseudo-random stimuli sequence and the identities of CS1+ and CS2+ reversed in the other half of the participants.”

(17) "These results are consistent with previous research which suggested that people with better capability to resist intrusive thoughts also performed better in motivated dementia in both declarative and associative memories."

***Which parts of the present results are consistent with such prior results? It is not clear from the descriptions provided here why thought control ability should be related to the present findings or, indeed, past ones in other domains. This should be elaborated to make the connections clear.

In the 30min group, we found that subjects’ TCAQ scores were negatively correlated with their fear recovery indices. That is, people with better capacity to resist intrusive thoughts were also less likely to experience the return of fear memory, which are consistent with previous results. Together with our brain stimulation results, the short-term amnesia is related to subject’s cognitive control ability and intact dlPFC functions. It is because of these similarities that we propose that the short-term amnesia might be related to the automatic memory suppression mechanism originated from the declarative memory research. Since we have not provided all the evidence at this point of the results section, we briefly listed the connections with previous declarative and associative memory research.

Reviewer #2 (Public Review):

The fear acquisition data is converted to a differential fear SCR and this is what is analysed (early vs late). However, the figure shows the raw SCR values for CS+ and CS- and therefore it is unclear whether the acquisition was successful (despite there being an "early" vs "late" effect - no descriptives are provided).

As the reviewer mentioned, the fear acquisition data was converted to a differential fear SCR and we conducted a two-way mixed ANOVA (reminder vs. no-reminder) x time (early vs. late part of fear acquisition) on the differential SCRs. We found a significant main effect of time (early vs. late; F_1.55 = 6.545, P = 0.013, η² = 0.106), suggesting successful fear acquisition in both groups. Fig. 1c also showed the mean differential SCR for the latter half of the acquisition phase in both the reminder and no-reminder groups and there was no significant difference in acquired SCRs between groups (early acquisition: t₅₅ = -0.063, P = 0.950; late acquisition: t₅₅ = -0.318, P = 0.751; Fig. 1c).

In Experiment 1 (Test results) it is unclear whether the main conclusion stems from a comparison of the test data relative to the last extinction trial ("we defined the fear recovery index as the SCR difference between the first test trial and the last extinction trial for a specific CS") or the difference relative to the CS- ("differential fear recovery index between CS+ and CS-"). It would help the reader assess the data if Figure 1e presents all the indexes (both CS+ and CS-). In addition, there is one sentence that I could not understand "there is no statistical difference between the differential fear recovery indexes between CS+ in the reminder and no reminder groups (P=0.048)". The p-value suggests that there is a difference, yet it is not clear what is being compared here. Critically, any index taken as a difference relative to the CS- can indicate recovery of fear to the CS+ or absence of discrimination relative to the CS-, so ideally the authors would want to directly compare responses to the CS+ in the reminder and no-reminder groups. The latter issue is particularly relevant in Experiment 2, in which the CS- seems to vary between groups during the test and this can obscure the interpretation of the result.

In all the experiments, the fear recovery index (FRI) was defined as the SCR difference between the first test trial and the last extinction trial for any CS. Subsequently, the differential fear recovery index (FRI) was defined between the FRI of a specific CS+ and the FRI of the CS-. The differential FRI would effectively remove the non-specific time related effect (using the CS- FRI as the baseline). We have revised the text accordingly.

As we responded to reviewer #1, the CS- fear recovery indices (FIR) for the reminder and no-reminder groups were not statistically different (Kruskal-Wallis test , panel a, Author response image 1), though both groups showed significant fear recovery even in the CS- condition (Wilcoxon signed rank test, reminder: P = 0.0043, no-reminder: P = 0.0037, panel a). Next, we examined the mean SCR for CS- for the 30min, 6h and 24h groups in study 2 and found that there was indeed a group difference (one-way ANOVA,  one-way ANOVA,F_2.76 = 5.3462, P = 0.0067, panel b), suggesting that the CS- SCR was influenced by the test time delay. We also tested the CS- SCR for the 4 groups in study 3 and found that across TMS stimulation types (PFC vs. VER) and reminder types (reminder vs. no-reminder) the ANOVA analysis did not yield main effect of TMS stimulation type (F_1.71 = 0.322, P = 0.572) nor main effect of reminder type (F_1.71 = 0.0499, P = 0.824, panel c). We added the R-VER group results in study 3 (see panel c) to panel b and plotted the CS- SCR difference across 4 different test time points and found that CS- SCR decreased as the test-extinction delay increased (Jonckheere-Terpstra test, P = 0.00028). These results suggest a natural “forgetting” tendency for the CS- fear recovery index and highlight the importance of having the CS- as a control condition to compare the CS+ recovery index with (resulting in the Differential recovery index). Parametric and non-parametric analyses were adopted based on whether the data met the assumptions for the parametric analyses.

In Experiment 1, the findings suggest that there is a benefit of retrieval followed by extinction in a short-term reinstatement test. In Experiment 2, the same effect is observed on a cue that did not undergo retrieval before extinction (CS2+), a result that is interpreted as resulting from cue-independence, rather than a failure to replicate in a within-subjects design the observations of Experiment 1 (between-subjects). Although retrieval-induced forgetting is cue-independent (the effect on items that are suppressed [Rp-] can be observed with an independent probe), it is not clear that the current findings are similar. Here, both cues have been extinguished and therefore been equally exposed during the critical stage.

We appreciate the reviewer’s insight on this issue. Although in the discussion we raised the possibility of memory suppression to account for the short-term amnesia effect, we did not intend to compare our paradigm side-by-side with retrieval-induced forgetting. In our previous work (Wang et al., 2021), we reported that active suppression effect of CS+ related fear memory during the standard extinction training generalized to other CS+, yielding a cue-independent effect. In the current experiments, we did not implement active suppression; instead, we used the CS+ retrieval-extinction paradigm. It is thus possible that the CS+ retrieval cue may function to facilitate automatic suppression. Indeed, in the no-reminder group (standard extinction) of study 1, we did observe the return of fear expression, suggesting the critical role of CS+ reminder before the extinction training. Based on the results mentioned above, we believe our short-term amnesia results were consistent with the hypothesis that the retrieval CS+ (reminder) might prompt subjects to adopt an automatic suppress mechanism in the following extinction training, yielding cue-independent amnesia effects.

The findings in Experiment 2 suggest that the amnesia reported in Experiment 1 is transient, in that no effect is observed when the test is delayed by 6 hours. The phenomena whereby reactivated memories transition to extinguished memories as a function of the amount of exposure (or number of trials) is completely different from the phenomena observed here. In the former, the manipulation has to do with the number of trials (or the total amount of time) that the cues are exposed to. In the current study, the authors did not manipulate the number of trials but instead the retention interval between extinction and test. The finding reported here is closer to a "Kamin effect", that is the forgetting of learned information which is observed with intervals of intermediate length (Baum, 1968). Because the Kamin effect has been inferred to result from retrieval failure, it is unclear how this can be explained here. There needs to be much more clarity on the explanations to substantiate the conclusions.

Indeed, in our studies, we did not manipulate the amount of exposure (or number of trials) but only the retention interval between extinction and test. Our results demonstrated that the retrieval-extinction protocol yielded the short-term amnesia on fear memory, qualitatively different from the reconsolidation related amnesia proposed in the previous literatures. After examining the temporal dynamics, cue-specificity and TCAQ association with the short-term amnesia, we speculated that the short-term effect might be related to an automatic suppression mechanism. Of course, further studies will be required to test such a hypothesis.

Our results might not be easily compared with the “Kamin effect”, a term coined to describe the “retention of a partially learned avoidance response over varying time intervals” using a learning-re-learning paradigm (Baum, 1968, Kamin, 1957). However, the retrieval-extinction procedure used in our studies was different from the learning-re-learning paradigm in the original paper (Kamin, 1957) and the reversal-learning paradigm the reviewer mentioned (Baum, 1968).

There are many results (Ryan et al., 2015) that challenge the framework that the authors base their predictions on (consolidation and reconsolidation theory), therefore these need to be acknowledged. Similarly, there are reports that failed to observe the retrieval-extinction phenomenon (Chalkia et al., 2020), and the work presented here is written as if the phenomenon under consideration is robust and replicable. This needs to be acknowledged.

We thank the reviewer pointing out the related literature and have added a separate paragraph about other results in the discussion (as well as citing relevant references in the introduction) to provide a full picture of the reconsolidation theory to the audience:

“It should be noted that while our long-term amnesia results were consistent with the fear memory reconsolidation literatures, there were also studies that failed to observe fear prevention (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; Schroyens et al., 2023). Although the memory reconsolidation framework provides a viable explanation for the long-term amnesia, more evidence is required to validate the presence of reconsolidation, especially at the neurobiological level (Elsey et al., 2018). While it is beyond the scope of the current study to discuss the discrepancies between these studies, one possibility to reconcile these results concerns the procedure for the retrieval-extinction training. It has been shown that the eligibility for old memory to be updated is contingent on whether the old memory and new observations can be inferred to have been generated by the same latent cause (Gershman et al., 2017; Gershman and Niv, 2012). For example, prevention of the return of fear memory can be achieved through gradual extinction paradigm, which is thought to reduce the size of prediction errors to inhibit the formation of new latent causes (Gershman, Jones, et al., 2013). Therefore, the effectiveness of the retrieval-extinction paradigm might depend on the reliability of such paradigm in inferring the same underlying latent cause. Furthermore, other studies highlighted the importance of memory storage per se and suggested that memory retention was encoded in the memory engram cell ensemble connectivity whereas the engram cell synaptic plasticity is crucial for memory retrieval (Ryan et al., 2015; Tonegawa, Liu, et al., 2015; Tonegawa, Pignatelli, et al., 2015). It remains to be tested how the cue-independent short-term and cue-dependent long-term amnesia effects we observed could correspond to the engram cell synaptic plasticity and functional connectivity among engram cell ensembles (Figure 6). This is particularly important, since the cue-independent characteristic of the short-term amnesia suggest that either different memory cues fail to evoke engram cell activities, or the retrieval-extinction training transiently inhibits connectivity among engram cell ensembles. Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

The parallels between the current findings and the memory suppression literature are speculated in the general discussion, and there is the conclusion that "the retrieval-extinction procedure might facilitate a spontaneous memory suppression process". Because one of the basic tenets of the memory suppression literature is that it reflects an "active suppression" process, there is no reason to believe that in the current paradigm, the same phenomenon is in place, but instead, it is "automatic". In other words, the conclusions make strong parallels with the memory suppression (and cognitive control) literature, yet the phenomena that they observed are thought to be passive (or spontaneous/automatic).

Ultimately, it is unclear why 10 mins between the reminder and extinction learning will "automatically" suppress fear memories. Further down in the discussion, it is argued that "For example, in the well-known retrieval-induced forgetting (RIF) phenomenon, the recall of a stored memory can impair the retention of related long-term memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner". I did not follow with the time delay between manipulation and test (20 mins) would speak about whether the process is controlled or automatic.

In our previous research, we showed that the memory suppression instruction together with the extinction procedure successfully prevented the return of fear expression in the reinstatement test trials 30mins after the extinction training (Wang et al., 2021). In the current experiments, we replaced the suppression instruction with the retrieval cue before the extinction training (retrieval-extinction protocol) and observed similar short-term amnesia effects. These results prompted us to hypothesize in the discussion that the retrieval cue might facilitate an automatic suppression process. We made the analogy to RIF phenomenon in the discussion to suggest that the suppression of (competing) memories could be unintentional and fast (20 mins), both of which were consistent with our results. We agree with the reviewer that this hypothesis is more of a speculation (hence in the discussion), and more studies are required to further test such a hypothesis. However, what we want to emphasize in this paper is the report of the short-term amnesia effects which were clearly not related to the memory reconsolidation effect in a variety of aspects.

Among the many conclusions, one is that the current study uncovers the "mechanism" underlying the short-term effects of retrieval extinction. There is little in the current report that uncovers the mechanism, even in the most psychological sense of the mechanism, so this needs to be clarified. The same applies to the use of "adaptive".

Whilst I could access the data on the OFS site, I could not make sense of the Matlab files as there is no signposting indicating what data is being shown in the files. Thus, as it stands, there is no way of independently replicating the analyses reported.

We have re-organized data on the OFS site, and they should be accessible now.

The supplemental material shows figures with all participants, but only some statistical analyses are provided, and sometimes these are different from those reported in the main manuscript. For example, the test data in Experiment 1 is analysed with a two-way ANOVA with the main effects of group (reminder vs no-reminder) and time (last trial of extinction vs first trial of the test) in the main report. The analyses with all participants in the sup mat used a mixed two-way ANOVA with a group (reminder vs no reminder) and CS (CS+ vs CS-). This makes it difficult to assess the robustness of the results when including all participants. In addition, in the supplementary materials, there are no figures and analyses for Experiment 3.

We are sorry for the lack of clarity in the supplementary materials. We have supplementary figures Fig. S1 & S2 for the data re-analysis with all the responders (learners + non-learners). The statistical analyses performed on the responders in both figures yielded similar results as those in the main text. For other analyses reported in the supplementary materials, we specifically provided different analysis results to demonstrate the robustness of our results. For example, to rule out the effects we observed in two-way ANOVA in the main text may be driven by the different SCR responses on the last extinction trial, we only tested the two-way ANOVA for the first trial SCR of test phase and these analyses provided similar results. Please note we did not include non-learners in these analyses (the texts of the supplementary materials).

Since we did not exclude any non-learners in study 3, all the results were already reported in the main text.

One of the overarching conclusions is that the "mechanisms" underlying reconsolidation (long term) and memory suppression (short term) phenomena are distinct, but memory suppression phenomena can also be observed after a 7-day retention interval (Storm et al., 2012), which then questions the conclusions achieved by the current study.

As we stated before, the focus of the manuscript was to demonstrate a novel short-term fear amnesia effect following the retrieval-extinction procedure. We discussed memory suppression as one of the potential mechanisms for such a short-term effect. In fact, the durability of the memory suppression effect is still under debate. Although Storm et al. (2012) suggested that the retrieval-induced forgetting can persist for as long as a week, other studies, however, failed to observe long-term forgetting (after 24 hrs; (Carroll et al., 2007, Chan, 2009). It is also worth noting that Storm et al. (2012) tested RIF one week later using half of the items the other half of which were tested 5 minutes after the retrieval practice. Therefore, it can be argued that there is a possibility that the long-term RIF effect is contaminated by the test/re-test process on the same set of (albeit different) items at different time onsets (5mins & 1 week).

Reviewer #3 (Public Review):

(1) The entire study hinges on the idea that there is memory 'suppression' if (1) the CS+ was reminded before extinction and (2) the reinstatement and memory test takes place 30 minutes later (in Studies 1 & 2). However, the evidence supporting this suppression idea is not very strong. In brief, in Study 1, the effect seems to only just reach significance, with a medium effect size at best, and, moreover, it is unclear if this is the correct analysis (which is a bit doubtful, when looking at Figure 1D and E). In Study 2, there was no optimal control condition without reminder and with the same 30-min interval (which is problematic, because we can assume generalization between CS1+ and CS2+, as pointed out by the authors, and because generalization effects are known to be time-dependent). Study 3 is more convincing, but entails additional changes in comparison with Studies 1 and 2, i.e., applications of cTBS and an interval of 1 hour instead of 30 minutes (the reason for this change was not explained). So, although the findings of the 3 studies do not contradict each other and are coherent, they do not all provide strong evidence for the effect of interest on their own.

Related to the comment above, I encourage the authors to double-check if this statement is correct: "Also, our results remain robust even with the "non-learners" included in the analysis (Fig. S1 in the Supplemental Material)". The critical analysis for Study 1 is a between-group comparison of the CS+ and CS- during the last extinction trial versus the first test trial. This result only just reached significance with the selected sample (p = .048), and Figures 1D and E even seem to suggest otherwise. I doubt that the analysis would reach significance when including the "non-learners" - assuming that this is what is shown in Supplemental Figure 1 (which shows the data from "all responded participants").

Our subjects were categorized based on the criteria specified in supplementary table S1. More specifically, we excluded the non-responders (Mean CS SCR < 0.02 uS  in the fear acquisition phase), and non-learners and focused our analyses on the learners. Non-responders were dismissed after day 1 (the day of fear acquisition), but both learners and non-learners finished the experiments. This fact gave us the opportunity to examine data for both the learners and the responders (learners + non-learners). What we showed in fig. 1D and E were differential SCRs (CS+ minus CS-) of the last extinction trials and the differential fear recovery indices (CS+ minus CS-), respectively. We have double checked the figures and both the learners (Fig. 1) and the responders (i.e. learners and non-learners, supplementary Fig. 1) results showed significant differences between the reminder and no-reminder groups on the differential fear recovery index.

Also related to the comment above, I think that the statement "suggesting a cue-independent short-term amnesia effect" in Study 2 is not correct and should read: "suggesting extinction of fear to the CS1+ and CS2+", given that the response to the CS+'s is similar to the response to the CS-, as was the case at the end of extinction. Also the next statement "This result indicates that the short-term amnesia effect observed in Study 2 is not reminder-cue specific and can generalize to the non-reminded cues" is not fully supported by the data, given the lack of an appropriate control group in this study (a group without reinstatement). The comparison with the effect found in Study 1 is difficult because the effect found there was relatively small (and may have to be double-checked, see remarks above), and it was obtained with a different procedure using a single CS+. The comparison with the 6-h and 24-h groups of Study 2 is not helpful as a control condition for this specific question (i.e., is there reinstatement of fear for any of the CS+'s) because of the large procedural difference with regard to the intervals between extinction and reinstatement (test).

In Fig. 2e, we showed the differential fear recovery indices (FRI) for the CS+ in all three groups. Since the fear recovery index (FRI) was calculated as the SCR difference between the first test trial and the last extinction trial for any CS, the differential fear recovery indices (difference between CS+ FRI and CS- FRI) not significantly different from 0 should be interpreted as the lack of fear expression in the test phase. Since spontaneous recovery, reinstatement and renewal are considered canonical phenomena in demonstrating that extinction training does not really “erase” conditioned fear response, adding the no-reinstatement group as a control condition would effectively work as the spontaneous recovery group and the comparison between the reinstatement and no-instatement groups turns into testing the difference in fear recovery using different methods (reinstatement vs. spontaneous recovery).

(2) It is unclear which analysis is presented in Figure 3. According to the main text, it either shows the "differential fear recovery index between CS+ and CS-" or "the fear recovery index of both CS1+ and CS2+". The authors should clarify what they are analyzing and showing, and clarify to which analyses the ** and NS refer in the graphs. I would also prefer the X-axes and particularly the Y-axes of Fig. 3a-b-c to be the same. The image is a bit misleading now. The same remarks apply to Figure 5.

We are sorry about the lack of clarity here. Figures 3 & 5 showed the correlational analyses between TCAQ and the differential fear recovery index (FRI) between CS+ and CS-. That is, the differential FRI of CS1+ (CS1+ FRI minus CS- FRI) and the differential FRI of CS2+ (CS2+ FRI minus CS- FRI).

We have rescaled both X and Y axes for figures 3 & 5 (please see the revised figures). 

(3) In general, I think the paper would benefit from being more careful and nuanced in how the literature and findings are represented. First of all, the authors may be more careful when using the term 'reconsolidation'. In the current version, it is put forward as an established and clearly delineated concept, but that is not the case. It would be useful if the authors could change the text in order to make it clear that the reconsolidation framework is a theory, rather than something that is set in stone (see e.g., Elsey et al., 2018 (https://doi.org/10.1037/bul0000152), Schroyens et al., 2022 (https://doi.org/10.3758/s13423-022-02173-2)).

In addition, the authors may want to reconsider if they want to cite Schiller et al., 2010 (https://doi.org/10.1038/nature08637), given that the main findings of this paper, nor the analyses could be replicated (see, Chalkia et al., 2020 (https://doi.org/10.1016/j.cortex.2020.04.017; https://doi.org/10.1016/j.cortex.2020.03.031).

We thank the reviewer’s comments and have incorporated the mentioned papers into our revised manuscript by pointing out the extant debate surrounding the reconsolidation theory in the introduction:

“Pharmacological blockade of protein synthesis and behavioral interventions can both eliminate the original fear memory expression in the long-term (24 hours later) memory test ( Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), resulting in the cue-specific fear memory deficit (Debiec et al., 2002; Lee, 2008; Nader, Schafe, & LeDoux, 2000). For example, during the reconsolidation window, retrieving a fear memory allows it to be updated through extinction training (i.e., the retrieval-extinction paradigm (Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), but also see (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; D. Schiller, LeDoux, & Phelps, 2020). ”

As well as in the discussion:

“It should be noted that while our long-term amnesia results were consistent with the fear memory reconsolidation literatures, there were also studies that failed to observe fear prevention (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; Schroyens et al., 2023). Although the memory reconsolidation framework provides a viable explanation for the long-term amnesia, more evidence is required to validate the presence of reconsolidation, especially at the neurobiological level (Elsey et al., 2018). While it is beyond the scope of the current study to discuss the discrepancies between these studies, one possibility to reconcile these results concerns the procedure for the retrieval-extinction training. It has been shown that the eligibility for old memory to be updated is contingent on whether the old memory and new observations can be inferred to have been generated by the same latent cause (Gershman et al., 2017; Gershman and Niv, 2012). For example, prevention of the return of fear memory can be achieved through gradual extinction paradigm, which is thought to reduce the size of prediction errors to inhibit the formation of new latent causes (Gershman, Jones, et al., 2013). Therefore, the effectiveness of the retrieval-extinction paradigm might depend on the reliability of such paradigm in inferring the same underlying latent cause. Furthermore, other studies highlighted the importance of memory storage per se and suggested that memory retention was encoded in the memory engram cell ensemble connectivity whereas the engram cell synaptic plasticity is crucial for memory retrieval (Ryan et al., 2015; Tonegawa, Liu, et al., 2015; Tonegawa, Pignatelli, et al., 2015). It remains to be tested how the cue-independent short-term and cue-dependent long-term amnesia effects we observed could correspond to the engram cell synaptic plasticity and functional connectivity among engram cell ensembles (Figure 6). This is particularly important, since the cue-independent characteristic of the short-term amnesia suggest that either different memory cues fail to evoke engram cell activities, or the retrieval-extinction training transiently inhibits connectivity among engram cell ensembles. Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

Relatedly, it should be clarified that Figure 6 is largely speculative, rather than a proven model as it is currently presented. This is true for all panels, but particularly for panel c, given that the current study does not provide any evidence regarding the proposed reconsolidation mechanism.

We agree with the reviewer that Figure 6 is largely speculative. We realize that there are still debates regarding the retrieval-extinction procedure and the fear reconsolidation hypothesis. We have provided a more elaborated discussion and pointed out that figure 6 is only a working hypothesis and more work should be done to test such a hypothesis:

“Although mixed results have been reported regarding the durability of suppression effects in the declarative memory studies (Meier et al., 2011; Storm et al., 2012), future research will be needed to investigate whether the short-term effect we observed is specifically related to associative memory or the spontaneous nature of suppression (Figure 6C).”

Lastly, throughout the paper, the authors equate skin conductance responses (SCR) with fear memory. It should at least be acknowledged that SCR is just one aspect of a fear response, and that it is unclear whether any of this would translate to verbal or behavioral effects. Such effects would be particularly important for any clinical application, which the authors put forward as the ultimate goal of the research.

Again, we agree with the reviewer on this issue, and we have acknowledged that SCR is only one aspect of the fear response and caution should be exerted in clinical application:

“Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

(4) The Discussion quite narrowly focuses on a specific 'mechanism' that the authors have in mind. Although it is good that the Discussion is to the point, it may be worthwhile to entertain other options or (partial) explanations for the findings. For example, have the authors considered that there may be an important role for attention? When testing very soon after the extinction procedure (and thus after the reminder), attentional processes may play an important role (more so than with longer intervals). The retrieval procedure could perhaps induce heightened attention to the reminded CS+ (which could be further enhanced by dlPFC stimulation)?

We thank the reviewer for this suggestion and have added more discussion on the potential mechanisms involved. Unfortunately, since the literature on attention and fear recovery is rather scarce, it is even more of a speculation given our study design and results are mainly about subjects’ skin conductance responses (SCR).

(5) There is room for improvement in terms of language, clarity of the writing, and (presentation of the) statistical analyses, for all of which I have provided detailed feedback in the 'Recommendations for the authors' section. Idem for the data availability; they are currently not publicly available, in contrast with what is stated in the paper. In addition, it would be helpful if the authors would provide additional explanation or justification for some of the methodological choices (e.g., the 18-s interval and why stimulate 8 minutes after the reminder cue, the choice of stimulation parameters), and comment on reasons for (and implications of) the large amount of excluded participants (>25%).

We have addressed the data accessibility issue and added the justifications for the methodological choices as well as the excluded participants. As we mentioned in the manuscript and the supplementary materials, adding the non-learners into data analysis did not change the results. Since the non-responders discontinued after Day 1 due to their non-measurable spontaneous SCR signals towards different CS, it’s hard to speculate whether or how the results might have changed. However, participants’ exclusion rate in the SCR studies were relatively high (Hu et al., 2018, Liu et al., 2014, Raio et al., 2017, Schiller et al., 2010, Schiller et al., 2012, Wang et al., 2021). The non-responders were mostly associated with participants being tested in the winter in our tasks. Cold weather and dry skins in the winter are likely to have caused the SCR hard to measure (Bauer et al., 2022, Vila, 2004). Different intervals between the reinstating US (electric shock) and the test trials were used in the previous literature such as 10min (Schiller et al., 2010, Schiller et al., 2013) and 18 or 19s (Kindt and Soeter, 2018, Kindt et al., 2009, Wang et al., 2021). We stuck with the 18s reinstatement interval in the current experiment. For the cTBS stimulation, since the stimulation itself lasted less than 2mins, we started the cTBS 8min after the onset of reminder cue to ensure that any effect caused by the cTBS stimulation occurred during the hypothesized time window, where the old fear memory becomes labile after memory retrieval. All the stimulation parameters were determined based on previous literature, which showed that with the transcranial magnetic stimulation (TMS) on the human dorsolateral prefrontal cortex could disrupt fear memory reconsolidation (Borgomaneri et al., 2020, Su et al., 2022).

Finally, I think several statements made in the paper are overly strong in light of the existing literature (or the evidence obtained here) or imply causal relationships that were not directly tested.

We have revised the texts accordingly.

Reviewer #2 (Recommendations For The Authors):

On numerous occasions there are typos and the autocorrect has changed "amnesia" for "dementia".

We are sorry about this mistake and have revised the text accordingly.

Reviewer #3 (Recommendations For The Authors):

*"Neither of the studies reported in this article was preregistered. The data for both studies are publicly accessible at https://osf.io/9agvk". This excerpt from the text suggests that there are 2 studies, but there are 3 in the paper. Also, the data are only accessible upon request, not publicly available. I haven't requested them, as this could de-anonymize me as a reviewer.

We are sorry for the accessibility of the link. The data should be available to the public now.

*Please refrain from causal interpretations when they are not supported by the data:

- Figure 3 "thought-control ability only affected fear recovery"; a correlation does not provide causal evidence.

- "establishing a causal link between the dlPFC activity and short-term fear amnesia." I feel this statement is too strong; to what extent do we know for sure what the applied stimulation of (or more correct: near) the dlPFC does exactly?

We thank the reviewer for the suggestion and have changed the wording related to figure 3. On the other hand, we’d like to argue that the causal relationship between the dlPFC activity and short-term fear amnesia is supported by the results from study 3. Although the exact functional role of the TMS on dlPFC can be debated, the fact that the TMS stimulation on the dlPFC (compared to the vertex group) brought back the otherwise diminished fear memory expression can be viewed as the causal evidence between the dlPFC activity and short-term fear amnesia.

*The text would benefit from language editing, as it contains spelling and grammar mistakes, as well as wording that is vague or inappropriate. I suggest the authors check the whole text, but below are already some excerpts that caught my eye:

"preludes memory reconsolidation"; "old fear memory can be updated"; "would cause short-term memory deficit"; "the its functional coupling"; "Subjects (...) yielded more severe amnesia in the memory suppression tasks"; "memory retrieval might also precipitate a short-term amnesia effect"; "more SEVERE amnesia in the memory suppression tasks"; "the effect size of reinstatement effect"; "the previous literatures"; "towards different CS"; "failed to show SCR response to the any stimuli"; "significant effect of age of TMS"; "each subject' left hand"; "latter half trials"; "Differntial fear recovery"; "fear dementia"; "the fear reinstatement effects at different time scale is related to"; "fear reocery index"; "thought-control abiliites"; "performed better in motivated dementia"; "we tested that in addition to the memory retrieval cue (reminder), whether the"; "during reconsolidation window"; "consisitent with the short-term dementia"; "low level of shock (5v)"

We thank the reviewer for thorough reading and sorry about typos in the manuscript. We have corrected typos and grammar mistakes as much as we can find.

*In line with the remark above, there are several places where the text could still be improved.

- The last sentence of the Abstract is rather vague and doesn't really add anything.

- Please reword or clarify: "the exact functional role played by the memory retrieval remains unclear".

- Please reword or clarify: "the unbinding of the old memory trace".

- "suggesting that the fear memory might be amenable to a more immediate effect, in addition to what the memory reconsolidation theory prescribes" shouldn't this rather read "in contrast with"?

We have modified the manuscript.

- In the Introduction, the authors state: "Specifically, memory reconsolidation effect will only be evident in the long-term (24h) memory test due to its requirement of new protein synthesis and is cue-dependent". They then continue about the more immediate memory update mechanisms that they want to study, but it is unclear from how the rationale is presented whether (and why (not)) they also expect this mechanism to be cue-dependent.

Most of the previous studies on the fear memory reconsolidation using CS as the memory retrieval cues have demonstrated that the reconsolidation effect is cue-dependent (Kindt and Soeter, 2018, Kindt et al., 2009, Monfils et al., 2009, Nader et al., 2000, Schiller et al., 2013, Schiller et al., 2010, Xue et al., 2012). However, other studies using unconditioned stimulus retrieval-extinction paradigm showed that such protocol was able to prevent the return of fear memory expression associated with different CSs (Liu et al., 2014, Luo et al., 2015). In our task, we used CS+ as the memory retrieval cues and our results were consistent with results from previous studies using similar paradigms.

- "The effects of cTBS over the right dlPFC after the memory reactivation were assessed using the similar mixed-effect four-way ANOVA". Please clarify what was analyzed here.<br /> - "designing novel treatment of psychiatric disorders". Please make this more concrete or remove the statement.

This sentence was right after a similar analysis performed in the previous paragraph. While the previous graph focused on how the SCRs in the acquisition phase were modulated by factors such as CS+ (CS1+ and CS2+), reminder (reminder vs. no-reminder), cTBS site (right dlPFC vs. vertex) and trial numbers, this analysis focused instead on the SCR responses in the extinction training phase. We have made the modifications as the reviewer suggested.

*I have several concerns related to the (presentation) of the statistical analyses/results:<br /> - Some statistical analyses, as well as calculation of certain arbitrary indices (e.g., differential fear recovery index) are not mentioned nor explained in the Methods section, but only mentioned in the Results section.

We have added the explanation of the differential fear recovery index into the methods section:

“To measure the extent to which fear returns after the presentation of unconditioned stimuli (US, electric shock) in the test phase, we defined the fear recovery index as the SCR difference between the first test trial and the last extinction trial for a specific CS for each subject. Similarly, in studies 2 and 3, differential fear recovery index was defined as the difference between fear recovery indices of CS+ and CS- for both CS1+ and CS2+.”

- Figure 1C-E: It is unclear what the triple *** mean. Do they have the same meaning in Figure 1C and Figure 1E? I am not sure that that makes sense. The meaning is not explained in the figure caption (I think it is different from the single asterisk*) and is not crystal clear from the main text either.

We explained the triple *** in the figure legend (Fig. 1): ***P < 0.001. The asterisk placed within each bar in Figure 1C-E indicates the statistical results of the post-hoc test of whether each bar was significant. For example, the *** placed inside bars in Figure 1E indicates that the differential fear recovery index is statistically significant in the no-reminder group (P < 0.001).

- Supplemental Figure 1: "with all responded participants" Please clarify how you define 'responded participants' and include the n's.

We presented the criteria for both the responder/non-responder and the learner/non-learner in the table of the supplementary materials and reported the number of subjects in each category (please see supplement Table 1).

- "the differential SCRs (difference between CS+ and CS-) for the CS+". Please clarify what this means and/or how it is calculated exactly.

Sorry, it means the difference between the SCRs invoked by CS+ and CS- for both CS1+ (CS1+ minus CS-) and CS2+ (CS2+ minus CS-).

*I suggest that the authors provide a bit more explanation about the thought-control ability questionnaire. For example, the type of items, etc, as this is not a very commonly used questionnaire in the fear conditioning field.

We provided a brief introduction to the thought-control ability questionnaire in the methods section:

“The control ability over intrusive thought was measured by the 25-item Thought-Control Ability Questionnaire (TCAQ) scle(30). Participants were asked to rate on a five-point Likert-type scale the extent to which they agreed with the statement from 1 (completely disagree) to 5 (completely agree). At the end of the experiments, all participants completed the TCAQ scale to assess their perceived control abilities over intrusive thoughts in daily life(17).”

We have added further description of the item types to the TCAQ scale.

*The authors excluded more than 25% of the participants. It would be interesting to hear reasons for this relatively large number and some reflection on whether they think this selection affects their results (e.g., could being a (non)responder in skin conductance influence the susceptibility to reactivation-extinction in some way?).

Participants exclusion rate in the SCR studies were relatively high (Hu et al., 2018, Liu et al., 2014, Raio et al., 2017, Schiller et al., 2010, Schiller et al., 2012, Wang et al., 2021). The non-responders were mostly associated with participants being tested in the winter in our tasks. Cold weather and dry skins in the winter are likely to have caused the SCR hard to measure (Bauer et al., 2022, Vila, 2004).

*Minor comments that the authors may want to consider:

- Please explain abbreviations upon first use, e.g., TMS.

- In Figure 6, it is a bit counterintuitive that the right Y-axis goes from high to low.

We added the explanation of TMS:

“Continuous theta burst stimulation (cTBS), a specific form of repetitive transcranial magnetic stimulation (rTMS)…”

We are sorry and agree that the right Y-axis was rather counterintuitive. However, since the direction of the fear recovery index (which was what we measured in the experiment) and the short/long-term amnesia effect are of the opposite directions, plotting one index from low to high would inevitably cause the other index to go from high to low.

Reference:

Anderson, M. C. and Floresco, S. B. 2022. Prefrontal-hippocampal interactions supporting the extinction of emotional memories: The retrieval stopping model. Neuropsychopharmacology, 47, 180-195.

Anderson, M. C. and Green, C. 2001. Suppressing unwanted memories by executive control. Nature, 410, 366-9.

Bauer, E. A., Wilson, K. A. and Macnamara, A. 2022. 3.03 - cognitive and affective psychophysiology. In: ASMUNDSON, G. J. G. (ed.) Comprehensive clinical psychology (second edition). Oxford: Elsevier.

Baum, M. 1968. Reversal learning of an avoidance response and the kamin effect. J Comp Physiol Psychol, 66, 495-7.

Borgomaneri, S., Battaglia, S., Garofalo, S., Tortora, F., Avenanti, A. and Di Pellegrino, G. 2020. State-dependent tms over prefrontal cortex disrupts fear-memory reconsolidation and prevents the return of fear. Curr Biol, 30, 3672-3679.e4.

Cain, C. K., Blouin, A. M. and Barad, M. 2003. Temporally massed cs presentations generate more fear extinction than spaced presentations. J Exp Psychol Anim Behav Process, 29, 323-33.

Carroll, M., Campbell-Ratcliffe, J., Murnane, H. and Perfect, T. 2007. Retrieval-induced forgetting in educational contexts: Monitoring, expertise, text integration, and test format. European Journal of Cognitive Psychology, 19, 580-606.

Chan, J. C. K. 2009. When does retrieval induce forgetting and when does it induce facilitation? Implications for retrieval inhibition, testing effect, and text processing. Journal of Memory and Language, 61, 153-170.

Gagnepain, P., Henson, R. N. and Anderson, M. C. 2014. Suppressing unwanted memories reduces their unconscious influence via targeted cortical inhibition. Proc Natl Acad Sci U S A, 111, E1310-9.

Gershman, S. J., Jones, C. E., Norman, K. A., Monfils, M. H. and Niv, Y. 2013. Gradual extinction prevents the return of fear: Implications for the discovery of state. Front Behav Neurosci, 7, 164.

Gershman, S. J., Monfils, M. H., Norman, K. A. and Niv, Y. 2017. The computational nature of memory modification. Elife, 6.

Hu, J., Wang, W., Homan, P., Wang, P., Zheng, X. and Schiller, D. 2018. Reminder duration determines threat memory modification in humans. Sci Rep, 8, 8848.

Kamin, L. J. 1957. The retention of an incompletely learned avoidance response. J Comp Physiol Psychol, 50, 457-60.

Kindt, M. and Soeter, M. 2018. Pharmacologically induced amnesia for learned fear is time and sleep dependent. Nat Commun, 9, 1316.

Kindt, M., Soeter, M. and Vervliet, B. 2009. Beyond extinction: Erasing human fear responses and preventing the return of fear. Nat Neurosci, 12, 256-8.

Liu, J., Zhao, L., Xue, Y., Shi, J., Suo, L., Luo, Y., Chai, B., Yang, C., Fang, Q., Zhang, Y., Bao, Y., Pickens, C. L. and Lu, L. 2014. An unconditioned stimulus retrieval extinction procedure to prevent the return of fear memory. Biol Psychiatry, 76, 895-901.

Luo, Y.-X., Xue, Y.-X., Liu, J.-F., Shi, H.-S., Jian, M., Han, Y., Zhu, W.-L., Bao, Y.-P., Wu, P., Ding, Z.-B., Shen, H.-W., Shi, J., Shaham, Y. and Lu, L. 2015. A novel ucs memory retrieval-extinction procedure to inhibit relapse to drug seeking. Nature Communications, 6, 7675.

Monfils, M. H., Cowansage, K. K., Klann, E. and Ledoux, J. E. 2009. Extinction-reconsolidation boundaries: Key to persistent attenuation of fear memories. Science, 324, 951-5.

Nader, K., Schafe, G. E. and Le Doux, J. E. 2000. Fear memories require protein synthesis in the amygdala for reconsolidation after retrieval. Nature, 406, 722-6.

Raio, C. M., Hartley, C. A., Orederu, T. A., Li, J. and Phelps, E. A. 2017. Stress attenuates the flexible updating of aversive value. Proc Natl Acad Sci U S A, 114, 11241-11246.

Schiller, D., Kanen, J. W., Ledoux, J. E., Monfils, M. H. and Phelps, E. A. 2013. Extinction during reconsolidation of threat memory diminishes prefrontal cortex involvement. Proc Natl Acad Sci U S A, 110, 20040-5.

Schiller, D., Monfils, M. H., Raio, C. M., Johnson, D. C., Ledoux, J. E. and Phelps, E. A. 2010. Preventing the return of fear in humans using reconsolidation update mechanisms. Nature, 463, 49-53.

Schiller, D., Raio, C. M. and Phelps, E. A. 2012. Extinction training during the reconsolidation window prevents recovery of fear. J Vis Exp, e3893.

Su, S., Deng, J., Yuan, K., Gong, Y., Zhang, Y., Li, H., Cao, K., Huang, X., Lin, X., Wu, P., Xue, Y., Bao, Y., Shi, J., Shi, L. and Lu, L. 2022. Continuous theta-burst stimulation over the right dorsolateral prefrontal cortex disrupts fear memory reconsolidation in humans. iScience, 25, 103614.

Vila, J. 2004. Psychophysiological assessment. In: SPIELBERGER, C. D. (ed.) Encyclopedia of applied psychology. New York: Elsevier.

Wang, Y., Zhu, Z., Hu, J., Schiller, D. and Li, J. 2021. Active suppression prevents the return of threat memory in humans. Commun Biol, 4, 609.

Xue, Y. X., Luo, Y. X., Wu, P., Shi, H. S., Xue, L. F., Chen, C., Zhu, W. L., Ding, Z. B., Bao, Y. P., Shi, J., Epstein, D. H., Shaham, Y. and Lu, L. 2012. A memory retrieval-extinction procedure to prevent drug craving and relapse. Science, 336, 241-5.

Zhu, Z., Anderson, M. C. and Wang, Y. 2022. Inducing forgetting of unwanted memories through subliminal reactivation. Nature communications, 13, 6496-6496.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Many drugs have off-target effects on the gut microbiota but the downstream consequences for drug efficacy and side effect profiles remain unclear. Herein, Wang et al. use a mouse model of liver injury coupled to antibiotic and microbiota transplantation experiments. Their results suggest that metformin-induced shifts in gut microbial community structure and metabolite levels may contribute to drug efficacy. This study provides valuable mechanistic insights that could be dissected further in future studies, including efforts to identify which specific bacterial species, genes, and metabolites play a causal role in drug response. Importantly, although some pilot data from human subjects is shown, the clinical relevance of these findings for liver disease remain to be determined.

Thank you for reviewing our manuscript. We appreciate your valuable feedback. We agree that the downstream consequences of off-target effects on the gut microbiota by various drugs remain unclear. Our study aimed to shed light on this aspect by utilizing a mouse model of liver injury and conducting antibiotic and microbiota transplantation experiments. Our findings suggest that shifts in the structure and metabolite levels of the gut microbial community induced by metformin play a role in the drug’s efficacy. We believe that these mechanistic insights provide a strong foundation for further investigations. Specifically, future studies could focus on identifying the specific bacterial species, genes, and metabolites that have a causal role in drug response. While we have included some pilot data from human subjects, we acknowledge that the clinical relevance of our findings in the context of liver disease still requires further determination. In fact, we focused on the alteration of microbiota and metabolism caused by metformin in human bodies, which could capture the characteristics of changes in a more composite clinical direction, elucidating the potential role of metformin. We appreciate your attention to this aspect and thank you again for your thoughtful review and valuable suggestions.

The major strength of this work is its scope, including detailed mouse phenotyping, inter-disciplinary methods, and numerous complementary experiments. The antibiotic depletion and FMT experiments provide support for a role of the gut microbiota in this mouse model.

A major limitation is the lack of studies narrowing down which microbes are responsible. Sequencing data is shown, but no follow-up studies are done with bacterial isolates or defined communities.

We acknowledge the limitation of our study in not narrowing down the specific microbes responsible for the observed effects. We hold the opinion that metformin exerts its effects through modulation of specific metabolic pathways unique to the microbial community. Previous study has shown that metformin can inhibit microbial folate metabolism, leading to longevity-promoting effects that are not attributed to a single colony or strain[1]. Similarly, the impact of metformin on amino acid metabolism in the microbial community appears to be widespread. While further investigations with bacterial isolates or defined communities are needed, our findings suggest that metformin's effects on microbial metabolism are complex and involve multiple members of the microbial community.

The link to GABA is also somewhat tenuous. While it does match the phenotypic data, there are no targeted experiments in which GABA producing microbial communities/strains are compared to a control community/strain. As such, it seems difficult to know how much of the effects in this model are due to GABA vs. other metabolites.

We agree with your point regarding the tenuous link to GABA in our study. While we did observe an increase in GABA as the only amino acid following metformin treatment, and this finding has not been reported previously, we acknowledge the need for targeted experiments comparing GABA-producing microbial communities/strains to control communities/strains. Previous literatures suggest that metformin's modulation of the microbiota can vary significantly depending on the disease context, with different microbial populations exhibiting differential responses[2-4]. Given this complexity, we opted to study the overall microbial community response to metformin rather than focusing on specific strains. Additionally, our detection of key enzymes involved in GABA synthesis at the community level further supports our findings.

My major recommendation would be to revise the title, abstract, and discussion to provide more qualification and to consider alternative interpretations.

We appreciate your feedback and understand your concern regarding the need for more qualification and consideration of alternative interpretations. We hope to have more specific and detailed suggestions you may have to enhance the clarity and qualification of our title and abstract. Furthermore, we have tried to revise discussion in order to enhance the scientific rigor and logical coherence of our study. If you have any specific recommendations or insights, we would be more than willing to make further revisions to address those concerns.

Some key controls are also missing, which could be addressed by repeat experiments in the mouse model.

We appreciate your suggestion to include additional key controls in the mouse model experiments. We have conducted repeat experiments to test the effect of antibiotics in the absence of metformin to differentiate between the effects of the model itself and the interaction of metformin with antibiotics. As results of liver injury indicators shown, there were no significance among Control, Control+Met, Control+FMT and Control+Abx groups, revealing that metformin and its treated feces, and antibiotics had no effect on liver function in normal mice (Figure 1).

Author response image 1.

Figure1 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level.

The antibiotic depletion experiment would be improved by testing the effect of antibiotics in the absence of metformin, to see if the effect is just driven by the model itself as opposed to an interaction between metformin and antibiotics.

For the antibiotic depletion experiment, we had used antibiotics (Abx) for the mice of modeling, and the survival rate and liver function detection suggested that Abx had no extra effect on liver, which demonstrated that the effect is just driven by the model itself as opposed to an interaction between metformin and antibiotics (Figure 2).

Author response image 2.

Figure2 a: Survival rate between IR and IR + Abx group; b: Serum ALT level; c: Serum AST level.

References

[1] CABREIRO F, AU C, LEUNG K Y, et al. Metformin Retards Aging in C. elegans by Altering Microbial Folate and Methionine Metabolism [J]. Cell, 2013, 153(1): 228-39.

[2] LIANG H, SONG H, ZHANG X, et al. Metformin attenuated sepsis-related liver injury by modulating gut microbiota [J]. Emerg Microbes Infect, 2022, 11(1): 815-28.

[3] SUN L, XIE C, WANG G, et al. Gut microbiota and intestinal FXR mediate the clinical benefits of metformin [J]. Nat Med, 2018, 24(12): 1919-29.

[4] ZHAO H Y, LYU Y J, ZHAI R Q, et al. Metformin Mitigates Sepsis-Related Neuroinflammation via Modulating Gut Microbiota and Metabolites [J]. Frontiers in Immunology, 2022, 13:797312.

Reviewer #2 (Public Review):

The authors examine the use of metformin in the treatment of hepatic ischemia/reperfusion injury (HIRI) and suggest the mechanism of action is mediated in part by the gut microbiota and changes in hepatic ferroptosis. While the concept is intriguing, the experimental approaches are inadequate to support these conclusions.

The histological and imaging studies were considered a strength and reveal a significant impact of metformin post-HIRI.

Thank you for reviewing our paper titled “Gut microbiota-derived gamma-aminobutyric acid from metformin treatment reduces hepatic ischemia/reperfusion injury through inhibiting ferroptosis”. We appreciate your insightful comments and suggestions, which have provided valuable insights into improving the quality and credibility of my research. We agree with your assessment that the experimental approaches used in this study may have limitations in supporting the conclusions drawn, and we appreciate your recognition of the strength of our histological and imaging studies, which clearly demonstrate the impact of metformin post-HIRI.

Weaknesses largely stem from the experimental design. First, use of the iron chelator DFO would be strengthened using the ferroptosis inhibitor, liproxstatin.

Your suggestion to employ the ferroptosis inhibitor, liproxstatin, in addition to the iron chelator DFO is well-taken. Incorporating liproxstatin into our experimental setup would provide a more comprehensive understanding of the involvement of hepatic ferroptosis in the mechanism of action of metformin. Therefore, we employed liproxstatin to inhibit HIRI and detected some core indicators of liver injury. As figure 3 shown, liproxstatin can reduce liver injury, restore liver GSH level and inhibit Fe accumulation, suggesting that ferroptosis plays an important role in HIRI. We hope this modification will enhance the credibility of our conclusions.

Author response image 3.

Figure3 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level; d: Liver GSH level; e: Liver Fe level.

Second, the impact of metformin on the microbiota is profound resulting in changes in bile acid, lipid, and glucose homeostasis. Throughout the manuscript no comparisons are made with metformin alone which would better capture the metformin-specific effects.

Thank you for raising an important point regarding the impact of metformin on the microbiota and its potential effects on bile acid, lipid, and glucose homeostasis. It has well known that that the effects of metformin on normal blood glucose and lipid metabolism are minimal. Metformin primarily exerts its effects in cases of impaired glucose tolerance, which is why it is widely used for non-diabetic conditions. Regarding the changes in bile acid metabolism and chronic cholesterol and lipid elevation, these associations are typically observed in chronic liver disease models. Since our study focuses on an acute model of HIRI, we did not specifically investigate these changes.

Lastly, the absence of proper controls including germ free mice, metformin treated mice, FMT treated mice, etc make it difficult to understand the outcomes and to properly reproduce the findings in other labs.

Lastly, we acknowledge your concern regarding the absence of proper controls, including germ-free mice, metformin-treated mice, and FMT -treated mice. We understand that these controls are essential for robustly interpreting and reproducing our findings. Therefore, we have added a batch of experiments for verification. As results shown, there were no significance among Control, Control+Met, Control+FMT and Control+Abx groups, revealing that metformin and its treated feces, and antibiotics had no effect on liver function in normal mice (Figure 1). We hope the result of these controls could address your valid point and provide a more comprehensive framework for understanding the outcomes.

Author response image 4.

Figure1 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level.

Overall, while the concept is interesting and has the potential to better understand the pleiotropic functions of metformin, the limitations with the experimental design and lack of key controls make it challenging to support the conclusions.

We genuinely appreciate your constructive criticism and the time you have taken to evaluate my work. Your feedback has shed light on the limitations of our experimental design and the need for key controls, which we have addressed in revised manuscript. If you have any further recommendations or concerns, we would be more than willing to incorporate them into my future work.

Reviewer #3 (Public Review):

The study presented in this paper explores the role of gut microbiota in the therapeutic effect of metformin on HIRI, as supported by fecal microbiota transplantation (FMT) experiments. Through high throughput sequencing and HPLC-MS/MS, the authors have successfully demonstrated that metformin administration leads to an increase in GABA-producing bacteria. Moreover, the study provides compelling evidence for the beneficial impact of GABA on HIRI.

Thank you for your valuable feedback on our paper exploring the role of gut microbiota in the therapeutic effect of metformin on hepatic ischemia-reperfusion injury (HIRI). We appreciate your positive remarks and suggestions for improvement. In response to your comments, we have revised the manuscript accordingly. We have included additional details on the high throughput sequencing and HPLC-MS/MS methods used to analyze the gut microbiota and GABA levels. This should provide readers with a clearer understanding of our experimental approach and the evidence supporting our findings.

Regarding your suggestion to further investigate the mechanisms underlying the beneficial impact of GABA on HIRI, we agree that this is an important direction for future research. We plan to conduct additional studies to explore the specific mechanisms by which GABA exerts its protective effects on HIRI in the future. We also supplemented discussion of potential therapeutic strategies targeting GABAergic pathways in the discussion section.

Thank you once again for your insightful comments. We believe that these revisions have strengthened the manuscript and improved its scientific rigor. We hope that you find the revised version to be satisfactory and look forward to your further feedback.

Reviewer #1 (Recommendations For The Authors):

The writing could be improved. Multiple typos are found throughout and there is an overuse of adverbs like "expectedly". You should let the reader decide what is or is not expected. Try to avoid terms like "confirmed" or "validated", which only applies if you knew the result a priori. Remove underscores in species names. The Results section is also very difficult to interpret given the lack of explanation of experimental design. For example, the human study is only briefly mentioned within a larger paragraph on mouse data, without any explanation as to the study design. Similar issues are true for the transcriptomics and amplicon sequencing - it would help the reader to explain what samples were processed, the timepoints, etc.

Thank you for your valuable feedback on our manuscript entitled “Gut microbiota-derived gamma-aminobutyric acid from metformin treatment reduces hepatic ischemia/reperfusion injury through inhibiting ferroptosis” We appreciate your constructive comments and insightful suggestions for improvement.

We have carefully reviewed your comments and have made several revisions to enhance the clarity and readability of the manuscript. We have addressed the issue of multiple typos and have removed the overuse of adverbs, such as “expectedly,” to allow readers to draw their own conclusions from the results. Additionally, we have eliminated terms like “confirmed” or “validated” that may imply a priori knowledge of the results.

We apologize for the lack of clarity regarding the experimental design in the Results section. We have now provided a more detailed explanation of the study design for the human study, transcriptomics, and amplicon sequencing experiments. This includes information on the samples processed, timepoints, and other relevant details, to aid readers in understanding the experimental procedures.

In response to your comment about removing underscores in species names, we have revised the text accordingly to ensure consistency and accuracy in the species nomenclature used throughout the manuscript.

Once again, we sincerely appreciate your valuable input, which has helped us improve the quality of our manuscript. We hope that the revised version now meets your expectations and look forward to any further feedback you may have.

Thank you for your time and attention.

Line 53 - prebiotics aren't "microbial agents"

We apologize for this error, which we have corrected. (line 55: “Microbial agents, such as synbioticsprebiotics and probiotics…”)

Line 88 - sequencing doesn't "verify the critical role of gut microbiota"

We apologize for this error, which we have corrected. (line 90: “In order to verifyclarify the critical role of gut microbiota in the pleiotropic actions of metformin,22-24 fecal samples were collected from the mice to perform 16S rRNA sequencing.

Line 92 - missing a citation for the "microbiota-gut-liver axis theory"

We have corrected it in manuscript. (line 93: “Next, as the microbiota-gut-liver axis theory indicates,25 HIRI-induced dysfunction of the gut barrier may aggravate liver damage by disrupting the gut microbiota.”)

Line 112 - it's very surprising to me that FMT led to lower alpha diversity, which seems impossible.

We understand your surprise regarding the observed decrease in alpha diversity after FMT. Our findings indeed deviate from the commonly observed pattern of increased alpha diversity post-FMT. We have carefully re-examined our data and conducted additional analyses to ensure the accuracy of our results. After thorough investigation, we have identified a potential reason for this unexpected outcome, which we believe could shed light on this phenomenon. We hypothesize that the lower alpha diversity observed in our study might be attributed to the specific characteristics of the donor microbiota used for FMT. While the donor microbiota exhibited certain beneficial properties associated with the therapeutic effect on HIRI, it could have presented a limited diversity compared to the recipient’s original gut microbiota. This discrepancy in diversity could have contributed to the observed decrease in alpha diversity following FMT.

To further support our hypothesis, we have included a discussion on this unexpected finding in the revised manuscript. We believe that this addition will provide a more comprehensive understanding of the results and help contextualize the observed decrease in alpha diversity following FMT.

Line 117 - Antibiotics don't "identify the function of gut microbes." Need to specify which antibiotics were used and for how long.

We have corrected it in manuscript. (line 119: “To further identify the function of gut microbes, experiments were designed, and combination treatment of antibiotics (1 mg/mL penicillin sulfate, 1 mg/mL neomycin sulfate, 1 mg/mL metronidazole and 0.16 mg/mL gentamicin) and metformin were employed for 1 week before IR treated.”)

Line 120 - this experiment shows that the gut microbiota (or antibiotics more precisely) matters, not the "reshaped gut microbiota"

We have corrected it in manuscript. (line 124: “The results confirmed that reshaped gut microbiota is critical for the effect of metformin against HIRI.”)

Line 122 - need to reword this subheading and the concluding sentence. The main takeaway is that the FMT improved markers of ferroptosis, but no additional causal links are provided here.

We have revised in manuscript. (line 125: “FMT alleviates HIRI-induced ferroptosis through reshaped fecal microbiota.”)

Line 141 - need to explain what transcriptomics data was generated and how it was analyzed.

We have revised in manuscript. (line 144: “To elucidate the molecular mechanisms through which pathway participates metformin-treated IR injury, we analysed gene expression profiles of each group mice. Transcriptome sequencing analysis revealed that 9697 genes were in common among four groups (Supplementary Figure 6). Therefore, we used these common genes for KEGG analysis, showing that The transcriptome analysis of liver tissues showed that similar mRNA changes between Met group and FMT group are mainly concentrated in the three top pathways: lipid metabolism, carbohydrate metabolism, and amino acid metabolism (Fig 4a).”)

Line 150 - change to "16S rRNA gene sequencing". Typo: "mice microbes".

We have revised in manuscript. (line 156: “Moreover, it was observed that the genus of Bacteroides had a significant increase based on the 16s rRNA gene sequencing of metformin-treated mice microbes.”)

Line 152 - upregulated refers to gene expression, change to enriched.

We have revised in manuscript. (line 171: “Detailedly, the species of Bacteroides containing Bacteroides thetaiotaomicron, Bacteroides unifomis, and Bacteroides salyersiae, were enriched in human gut after metformin administration (Fig. 4i).”)

Line 159 - typo: "prokaryotes"

We have revised in manuscript. (line 165: “In order to further identify the increased GABA originates from gut microbiota, two key enzymes of prokaryotes protokaryotic GABA synthesis, GAD and PAT, were detected on DNA level, finding that both of them are significantly increased in the feces from IR+Met and IR+FMT groups (Fig. 4h).”)

Line 161 - the human study should be under a new sub-heading and provide more details.

We have revised in manuscript. (line 168: In order to clarify the specific effects of metformin on microbiota, given the big safety margin, healthy volunteers were recruited for a 1 week of daily oral 500mg dose of metformin trial. Fecal samples were collected before and after oral administration of metformin for metagenomic analysis .”)

Line 197 - It's unclear why the current study conflicts with prior literature. Is it due to the disease model, the starting microbiota, something else? Please add more discussion.

Thank you for bringing this important point to our attention, and we appreciate your valuable input. We agree that it is important to discuss the potential reasons for the discrepancy between our findings and prior literature on metformin-reshaped microbiota. In our study, we used a disease model of HIRI, which may have unique characteristics compared to other disease models. It is possible that the specific disease model influenced the response of the gut microbiota. Additionally, the starting microbiota of the recipients and the characteristics of the donor microbiota used for FMT could also play a role in the disparity. We have expanded the discussion section of our revised manuscript to further address these potential factors and their implications. We hope that this additional information will provide a more comprehensive explanation for the discrepancy between our study and prior literature.

Figure 1a - change to Kaplan Meier not ANOVA. Specify the contrast - which groups are being compared?

We have revised in Figure 1a.

Figure 1e, alpha diversity - relabel "sobs" with "observed OTUs". Change to 3 bars with error and add statistics.

We have revised in Figure 1e.

Figure 1e, PCA - this should be a separate panel (1f). Color of big red circle doesn't match the points. Add PERMANOVA p-value/R2. Change to OTUs not genera. Better yet, use amplicon sequence variants from DADA2.

We have revised in Figure 1e..

Figure 2a - Change to Kaplan Meier. Also, it's unclear if residual metformin could be in the donor samples.

We have revised in Figure 2a.

Figure 2f, alpha diversity - relabel "sobs" with "observed OTUs". Change to 3 bars with error and add statistics.

We have revised in Figure 2f.

Figure 2f, PCA - this should be a separate panel (2g). Color of big orange circle doesn't match the points. Add PERMANOVA p-value/R2. Change to OTUs not genera. Better yet, use amplicon sequence variants from DADA2.

We have revised in Figure 2f.

Figure 4b - check units, shouldn't this be ng/mg (i.e. weight not volume).

We have revised in Figure 4b.

Figure 4c,d - need more explanation in the legend and Results as to what is shown here.

We have revised in Figure 4c,d.

Figure 4d - unclear why only Bacteroides are shown here or if the p-values are adjusted for multiple comparisons.

Thank you for your comment regarding Figure 4d in our manuscript. We apologize for the confusion caused. The reason why only Bacteroides is shown in Figure 4d is because we specifically wanted to investigate the changes in Bacteroides abundance following metformin treatment.

In the mouse experiments, we observed a significant increase in Bacteroides after metformin treatment. To investigate if a similar change occurs in healthy volunteers, we examined the levels of Bacteroides in fecal samples before and after oral administration of metformin. We found that the abundance of Bacteroides also increased in the human gut after metformin administration, consistent with the results from the animal experiments. Regarding the p-values, we apologize for not mentioning whether they were adjusted for multiple comparisons in the figure legend. In our revised manuscript, we have provided a clarification stating that the p-values were adjusted using the appropriate method. We appreciate your feedback and hope that this explanation clarifies the rationale behind Figure 4d. Thank you for your valuable input.

Reviewer #2 (Recommendations For The Authors):

Below I've listed several suggestions to improve the paper.

1. Controls - the authors should include metformin only treated mice, FMT only treated mice, etc. Additionally, germ free mice treated with metformin and HIRI would be helpful to better implicate the gut microbiome in these beneficial effects.

Thank you for your suggestion regarding the inclusion of additional control groups in our study. We agree that including metformin only treated mice, FMT only treated mice, and germ-free mice treated with metformin and HIRI would provide valuable insights into the role of the gut microbiome in the observed beneficial effects.

Therefore, we have included metformin only treated mice, FMT only treated mice and Abx only treated mice as supplement to better assess the specific contribution to the observed effects. As results shown, there were no significance among Control, Control+Met, Control+FMT and Control+Abx groups, revealing that metformin and its treated feces, and antibiotics had no effect on liver function in normal mice (figure1).

We appreciate your input and believe that the inclusion of these additional control groups will strengthen our study and provide a more comprehensive understanding of the role of the gut microbiome in the therapeutic effects observed.

Author response image 5.

Figure1 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level.

1. More thorough characterization of metabolite pools. Metformin is known to influence many pathways including bile acids and lipids. These important molecules should be measures as they likely play a key role in the observed protective effect. In fact, many of the key changes displayed in Figure 3H are involved in lipid metabolism.

Thank you for your valuable feedback regarding the characterization of metabolite pools in our study. We appreciate your suggestion to measure the influence of metformin on bile acids and lipid metabolism, as they are crucial pathways that may play a significant role in the observed protective effect.

Regarding bile acids, we agree that they are important in the context of metformin’s influence on metabolic pathways. However, it is important to note that the impact of metformin on bile acids appears to be more prominent in chronic liver disease models. In our acute model, the changes in bile acids were not as significant. Instead, our results primarily indicate a close association between lipid changes and hepatic ferroptosis. Metformin significantly modulates lipid metabolism, thereby alleviating liver ferroptosis.

Additionally, we have conducted metagenomic sequencing on the gut microbiota of healthy volunteers before and after oral administration of metformin. While analyzing the data, we did not observe significant changes in key genes involved in regulating bile acid variations. This might be attributed to the healthy volunteers used in our study, where significant changes in bile acids were not induced.

We appreciate your insightful comments and suggestions, which have shed light on the importance of characterizing bile acids and lipid metabolism in our study. While the impact of bile acids may be more evident in chronic liver disease models, our findings highlight the significant influence of metformin on lipid metabolism, closely related to hepatic ferroptosis. We will take your suggestions into account for future studies to further explore the role of bile acids and their regulation by metformin.

1. Imaging of lipid ROS is not quantitative. The authors should conduct more standard assays with BODIPY 581/591 C11 using cell lysates.

We appreciate your suggestion to conduct more standard assays using BODIPY 581/591 C11 with cell lysates.

We would like to clarify that we did indeed utilize assays with BODIPY 581/591 C11 to detect and measure lipid ROS in our study. The detailed description of these assays can be found in the Methods section of our paper. We followed established protocols and guidelines to ensure accurate and reliable measurements of lipid ROS levels.

We acknowledge that imaging techniques may have limitations in providing quantitative data. However, we employed BODIPY 581/591 C11 assays as a widely accepted and commonly used method to assess lipid ROS levels. This allowed us to obtain qualitative and semi-quantitative information on the changes in lipid ROS levels in response to metformin treatment.

1. Liproxstatin may be a better drug choice or at the very least should be used to compare with the DFO data

Thank you for your suggestion. We have taken your advice into consideration and conducted an evaluation of Liproxstatin as a ferroptosis inhibitor. Our findings indicate that Liproxstatin significantly improves HIRI (Figure C). We believe that incorporating Liproxstatin in our research will provide valuable insights and allow for a comprehensive comparison with the DFO data.

Author response image 6.

Figure3 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level; d: Liver GSH level; e: Liver Fe level.

1. The rationale for how GABA was selected is not clear. I am surprised that there were not more significant metabolite changes. It might be better to show a volcano plot of heatmap of the significantly changed features.

Thank you for raising an important question regarding the rationale for selecting GABA as the focus metabolite in our study. Initially, we also had concerns about the limited number of significant metabolite changes observed. However, through our comprehensive metabolomic profiling, we identified GABA as the most significantly altered metabolite following HIRI.

It is worth noting that we specifically focused on the measurement of 22 essential amino acids in our analysis. While it is possible that changes in non-essential amino acids may have occurred, we did not examine them in this study. Nevertheless, we have since used additional methods to validate the upregulation of GABA levels, and the biological effects observed support the specific role of GABA in protecting against HIRI. Based on the fact that GABA was the only significant amino acid, the volcano plot was of little significance, so we did not supplement this plot.

We appreciate your valuable input and thank you for bringing up this important issue.

1. The manuscript needs to be proofread and edited. There are a variety of typos and grammar issues throughout.

Thank you for your feedback. We acknowledge that the manuscript requires proofreading and editing, as we have identified several typos and grammar issues. We will try to ensure that the necessary revisions are made to improve the overall quality of the manuscript.

Reviewer #3 (Recommendations For The Authors):

However, I have some major concerns for the manuscript.

1. Line 26 16S rRNA and metagenomic sequencing alone can't accurately confirm the improvement effect of GABA producing bacteria on HIRI. In fact, transcriptome analysis, HPLC-MS/MS and other methods were also used in this paper, so the language expression here is not appropriate

Thank you for pointing out the language expression issue in line 26 of the manuscript. We apologize for any confusion caused. You are correct in stating that 16S rRNA and metagenomic sequencing alone may not accurately confirm the improvement effect of GABA-producing bacteria on HIRI. In our study, we employed a combination of multiple methods, including transcriptome analysis, HPLC-MS/MS, especially detection of bacteria GABA key synthetases, PAT and GAD, to comprehensively investigate the impact of GABA-producing bacteria on HIRI.

We have revised the language in line 26 to reflect the broader range of methods used in our study to support the conclusions regarding the improvement effect of GABA-producing bacteria on HIRI.

1. The Introduction section needs to add a description of the previous research on the association between HIRI and ferroptosis

Thank you for your suggestion regarding the inclusion of a description of the association between HIRI and ferroptosis in the Introduction section. We agree that this is an important aspect to address. However, upon further consideration, we have decided to move the discussion of ferroptosis and its potential role in HIRI to the Discussion section, as it aligns better with the logical flow of the manuscript. This allows us to discuss the potential implications and future directions in a more organized and coherent manner.

1. Authors should provide quantified figure or table next to the results of western blot that are more convenient to understand.

We have revised in manuscript. (See sfigure 7)

1. In this paper, FMT experiments are used to verify that metformin remodeled gut microbiota can play a role in improving HIRI. The operation steps of FMT should be described more specifically in the method part

*What is the fecal donor information for FMT?

*Line272 Did the IR + FMT group put the transplanted microbiota of FMT directly into the drinking water like the other treatment groups? Will such an operation affect the quality and quantification of the transplanted microbiota and lead to the loss of microbiota species? It is crucial for the authors to provide a clear and thorough clarification regarding these matters within the context of their FMT experiment.

Thank you for your feedback regarding the need for a more detailed description of the fecal microbiota transplantation (FMT) procedure and clarification regarding the IR + FMT group in our manuscript. We appreciate your suggestions and we have taken them into consideration.

In our study, the fecal donor for FMT was obtained from mice that had been orally administered metformin. The fecal microbiota was collected and processed to remove any residual metformin before transplantation. Specifically, the microbiota for the IR + FMT group was administered through gavage, as stated in line 272. This method does not affect the quality or quantity of the transplanted microbiota, nor does it lead to a loss of microbiota species. We understand the importance of providing clear and thorough clarification regarding these matters. Therefore, we have included additional specific details of the FMT procedure in the revised version of the manuscript. We hope that this clarification addresses your concerns and provides a more comprehensive understanding of our FMT experiment.

1. The presentation of transcriptomic analysis results in the manuscript is insufficiently comprehensive and specific, as they are solely depicted through Fig 4a. Relying solely on Fig 4a is inadequate to establish the definitive roles of the met group and FMT group in ferroptosis compared to other groups. Therefore, the authors should provide additional transcriptomic analysis results to ascertain the specific effects of the met group and FMT group in ferroptosis, as well as their comparison with other groups.

Thank you for your feedback regarding the comprehensiveness of our transcriptomic analysis results in the manuscript. We understand your concerns and appreciate your suggestion. In our study, we have provided additional data beyond Fig 4a to support the specific effects of the met group and FMT group in ferroptosis, as well as their comparison with other groups. Specifically, in Figure 3, we have included Western blot (WB) and quantitative real-time polymerase chain reaction (qRT-PCR) data to confirm the involvement of ferroptosis in HIRI and the role of metformin in attenuating ferroptosis. Moreover, we have presented transcriptomic analysis results in Figure 3h, which includes a heatmap of genes related to lipid metabolism. These findings can strengthen our conclusions regarding the importance of ferroptosis in HIRI and the protective effects of metformin against ferroptosis. We hope that these data address your concerns and provide a more comprehensive understanding of our research findings.

Author Response

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

Reviewer #1

Recommendation 1: The authors reasoned upon the presence of a differential basal hydraulic stress in waves' valleys vs hills at first from the observation of "domes" formation upon 48h cultivation. I suggest performing a quantification to support the statement as a good scientific practice. Furthermore, it would strengthen the concept when the formation of domes was compared between the waves' dimensions as a different grade of cell extrusion was quantified. i.e., 50, 100, and 200 µm.

Response 1: Upon seeing the phenomenon (Author response image 1 A), we performed a count for domes on the 100 µm and saw a significant effect. We refrained from including the results as it is the subject of ongoing research in our lab. In response to the reviewer’s suggestion, we have included a graph (Author response image 1 B) showing the increasing number of domes over 48 hours from three 100 µm wave samples.

We have updated Figure 2A and B in the manuscript to include the new graph.

Author response image 1.

(A) shows dome (white arrows) over a 100 µm wave substrate. (B) is the number of accumulated domes in valley and hill regions, for 3 independent samples, over 48 hours.

Recommendation 2: Using RICM microscopy to quantify the cell basal separation with the substrate and hydraulic stress is very clever. Nevertheless, I am in doubt if the different intensity reported for the hills vs valley (Fig. 2G and H) is a result of the signal reduction at deeper Z levels. Since there is no difference in extrusion and forces between valleys and hills in the 200 µm waves but only in 50µm and 100µm, I would add this to the quantification. I would expect no intensity difference from RICM for the 200 µm sample if this is not an artefact of imaging.

Response 2: We performed additional experiments on blank wave substrates (both 100 and 200 µm) to ascertain the extent of reflection intensity drop (Author response image 2A). And, as correctly pointed out by Reviewer #1, there was a drop in intensity even without cells. On the 100 µm waves, hill reflections are on average ~27 % dimmer than valley reflections. Whereas, on the 200 µm waves, hill reflections are on average ~39 % dimmer.

Using this information, we performed a calibration on the RICM results obtained from both the 100 and 200 µm waves (Author response image 3B). The calibrated 100 µm data showed residual signatures of difference, whereas the calibrated 200 µm distributions appeared very similar. We noticed large cross- sample variations in the registered intensities, which will negatively impact effect size if not accounted for. To do this, we subsequently normalized both hill and valley intensities against planar region intensities for each sample. As shown by the final output (Author response image 3C), we were able to remove the skewness in the distributions. Moreover, 1-way ANOVA followed by a post hoc analysis with BH correction revealed a significant reduction in 100 µm hill/flat intensity ratio compared to 100 µm valley/flat intensity ratios (Δ~-23 %). Conversely, no significance was observed for the same comparison on the 200 µm waves.

Author response image 2.

(A). RICM from blank wave samples reveal a reduction in reflection intensity in hill regions compared to flat and valley regions.

Author response image 3.

(B) shows the RICM intensities after adjusting for the inherent reflection intensity drop shown in (A). (C) show the RICM intensities after normalization against planar region signals; this removes cross-sample variations and improve effect size of differences.

We have updated the manuscript Figure 2I and text accordingly. The blank wave results are included in Figure 2-figure supplement 1 along with updated text and summary data table in Supplementary File 4.

Recommendation 3: To measure 3D forces on top of the hills and valleys, the use of PAA gels is necessary. Since in Fig 3B, the authors show a difference in cell extrusion number between substrates and stiffnesses, I think it is necessary to confirm the presence of more extrusion in valleys vs hills on PAA gels. This would ensure the conclusion between normal forces and extrusion.

Response 3: We do have time-lapse data with monolayers on the PAA waves. However, we felt results from the flat regions were sufficient in supporting the point being made in the text. Specifically, our original intention with PAA gels was to show that the extrusion reductions seen in osmotic perturbations were by virtue of removing basal stress and not some cryptic osmotic response. Hydrogels were chosen because they can effectively dilute basal solute concentration and thereby reduce the osmotically induced water transport. Moreover, as fluid could freely move within the gel, the fluid stress can quickly equilibrate across the basal surface. In contrast, poorly water/solute permeable substrates could lead to localized spikes in solute concentration and transient basal regions with high fluid stress.

To get a sense of the potential difference in basal solute concentration between the two materials, we can do a quick hand-waving estimation. For monolayers on non-water/solute permeable PDMS of 20x20 mm and using the laser wavelength (640 nm) for RICM as an extreme estimate of basal separation, we should expect ~0.25 µl of total basal water content. On the other hand, we typically produce our PAM gel slabs using ~150 µl of precursor solutions. This means that, given similar amounts of solute, PAM gels will lead to monolayer basal osmolarity that is around 3 orders of magnitude lower than monolayers on PDMS, producing significantly lower osmotic potential. This implies from the outset that we should expect high survivability of cells on these substrates irrespective of curvature domains. Indeed, later immunoblotting experiments showed MDCKs exhibiting hyper activated FAK and Akt on PAM gels.

In response to Reviewer #1’s suggestion then, we have added another supporting time-lapse (Video 19) showing typical response of MDCK monolayers on 100 µm PAA waves (Author response image 4). Evident from the time-lapses, like the planar regions, cell extrusions were very rare. This supports the idea that on PAM gels the effects of basal hydraulic stress and asymmetric forces are marginal against the strong survival signals. And the response is similar to hyper-osmotic perturbations; there, we did not see a significant difference between valley and hill extrusions.

Author response image 4.

Time-lapse snapshot showing negligible MDCK extrusions 24 hours after confluency over PAM gel wave substrates.

Recommendation 4: Before proceeding with the FAK inhibitor experiment, the authors should better justify why the 4.1 wt % sucrose vs DMSO or NaCl is the most inert treatment. This can be done by citing relevant papers or showing time-lapses (as it is done for the higher FAKI14 dose).

Response 4: Although some cells have recently been shown to be able to transport and utilize sucrose, mammalian cells generally cannot directly take up polysaccharides for metabolism and this is frequently mentioned in literature: see (Ref. R1) for example. Without special enzymes to break sucrose down into monosaccharides, such as sucrase found in the gut, the sugars should remain spectators in the culture medium, contributing only to osmotic effects.

DMSO on the other hand, besides changing osmolarity, can also be integrated into cell membrane and pass through cells over time. It has been reported to chronically affect cell membrane properties and gene expressions (Ref. R2).

Finally, it is well known that both sodium and chloride ions are readily taken up and transported by cells (Ref R3). They help to regulate the transmembrane potential, which in turn can affect membrane bound proteins and biochemical reactions within a cell.

Hence, comparing the 3 hyper-osmotic perturbations, adding sucrose should have the least off- target effects on both the inhibitor study and the subsequent immunoblotting. And, in response to the reviewer’s recommendation, we have updated the text accordingly and included new references to support our statement.

Ref R1. H. Meyer, O. Vitavska, H. Wieczorek; Identification of an animal sucrose transporter. Journal of Cell Science 124, 1984–1991 (2011). Doi: 10.1242/jcs.082024

Ref R2. B. Gironi, Z. Kahveci, B. McGill, B.-D. Lechner, S. Pagliara, J. Metz, A. Morresi, F. Palombo, P. Sassi, P. G. Petrov; Effect of DMSO on the Mechanical and Structural Properties of Model and Biological Membranes. Biophysical Journal 119, 274-286 (2020). Doi: doi.org/10.1016/j.bpj.2020.05.037

Ref R3. X. Zhang, H. Li; Interplay between the electrostatic membrane potential and conformational changes in membrane proteins. Protein Science 28, 502-512 (2019). Doi: 10.1002/pro.3563

Recommendation 5: The data showing a FAK-dependent phosphorylation of AKT responsible for a higher cell survival rate in the hills is not yet completely convincing. Please show a reduced AKT phosphorylation level after FAK inhibition in high osmolarity levels. Furthermore, the levels of AKT activation seem to increase slightly upon substrate softening independently of FAK activation or osmotic pressure (i.e., Fig. 4E, Soft PDMS). The authors should comment on this in connection with the results shown for PAA gels.

Response 5: For the additional immunoblotting experiments, work is currently underway. We could not, however, complete these experiments in time for this revision, as both Cheng-Kuang and Xianbin will shortly be taking on new jobs elsewhere. David will continue with the immunoblotting studies and should be able to include the results in an update in the coming months. As for the apparent elevated levels of AKT seen on soft silicones, we speculate that it is because we cannot immunoblot cells that have died and were inevitably washed out at the start of the procedure. Inferring from the higher extrusion rates on these soft substrates, we could be missing a significant portion of stats. Specifically, we are missing all the cells that would have lowered AKT activation but died, and had we been able to collect those statistics, perhaps both the FAK and AKT should have shown lower levels. We risk committing survival bias on the results if we read too much into the data as is.

Alternatively, another explanation could be that, by virtue of survival of the fittest, we might have effectively selected a subpopulation of cells that were able to survive on lower FAK signals, or completely irrespectively of it.

At any rate, to prove our foregoing hypothesis would require us to perform comprehensive immunoblotting and total transcriptome analysis over different duration conditions. Unfortunately, we do not have the time to do that for the current article, but it could be developed into a stand-alone molecular biology investigation in future. We have included similar discussion in the main text.

Recommendation 6: In the discussion, the authors suggest the reported findings be especially relevant for epithelia that significantly separate compartments and regulate water and soluble transport. These are for example kidney epithelia (i.e., MDCK is the best experimental choice), retinal epithelium or intestinal epithelium. I would suggest that some proof-of-concept experiments could be done to support this concept. For example, I would expect keratinocytes (i.e., HaCaT) not to show a strong difference in extrusion rate between valleys and hills since the monolayer is not so sealed as kidney epithelium. In general, this kind of experiment would significantly strengthen the finding of this work.

Response 6: As recommended, we tracked the behavior of retina pigment epithelial cells (hTERT RPE-1 from ATCC) which do not form tight monolayers like MDCKs (Ref. R4). We did not detect extrusion events occurring from monolayers of these cells (Author response image 5). This is true even for portions of monolayers over waved regions.

Author response image 5.

Time-lapse snapshot showing non-existent o cell extrusions from RPE monolayers confluent for over 21 hours.

We have updated these findings in the main text discussions and included a new supporting time- lapse (Video 15) in our article.

Ref R4 F. Liu, T. Xu, S. Peng, R. A. Adelman, L. I. Rizzolo; Claudins regulate gene and protein expression of the retinal pigment epithelium independent of their association with tight junctions. Experimental Eye Research 198, 108157 (2020). Doi: 10.1016/j.exer.2020.108157

Recommendation 7 (minor point): Figure S1 needs to have clear notes indicating in each step what is what. i.e., where is glass, PDMS, NOA73, etc? A more detailed caption will help the figure's comprehension. Also "Cy52" should be changed to "soft silicone" to be consistent with the text (or Cy52 should be mentioned in the text).

Response 7 (minor point): Changes were made to Figure 1-figure supplement 1 to improve comprehension accordingly. CY52 was added to the main-text, next to the first appearance of the word soft silicone, to be consistent with the figures.

Recommendation 8 (minor point): The authors often mentioned that epithelial monolayers are denser on PAA gels. Please add a reference(s) to this statement.

Response 8 (minor point): The statement is an inference from visually comparing monolayers on PAM gels and PDMS. The difference is quite evident (Author response image 6). The density difference is in spite of the fact that the substrates share similar starting cell numbers.

To address the reviewer’s comment, we have combined time-lapses of monolayers on silicones and PAM gels side-by-side in Video 17 to facilitate convenient comparisons.

Author response image 6.

Time-lapse snapshot at 24 hours after confluence, showing conspicuously higher density of MDCK monolayers on PAM gel compared to those on silicon elastomer.

Reviewer #2

Recommendation 1: The sinusoidal wavy substrate that the authors use in their investigation is interesting and relevant, but it is important to realize that this is a single-curved surface (also known as a developable surface). This means that the Gaussian curvature is zero and that monolayers need to undergo (almost) no stretching to conform to the curvature. The authors should at least discuss other curved surfaces as an option for future research, and highlight how the observations might change. Convex and concave hemispherical surfaces, for example, might induce stronger differences than observed on the sinusoidal substrates, due to potentially higher vertical resultant forces that the monolayer would experience. The authors could discuss this geometry aspect more in their manuscript and potentially link it to some other papers exploring cell-curvature interactions in more complex environments (e.g. non-zero Gaussian curvature).

Response 1: In response to reviewer #2’s recommendation we have highlighted in the discussion of our text that our waves constitute a developable surface and that cells will experience little stretching for the most part. Based on our knowledge of how curvature can modulate forces and thus osmotic effects, we included some rudimentary analysis of what one would expect on hemispherical surfaces of two types: one that is periodic and contiguous (Ref. R5), and another with delineating flat regions (Ref. R6).

For epithelial monolayers in the first scenario, and on poorly solute/water permeable substrates, we should also expect to see a relatively higher likelihood of extrusions from concave regions compared to convex ones. Moreover, as the surfaces are now curved in both principal directions (producing larger out-of-plane forces), we should see the onset of differential extrusions seen in this study, but at larger length scales. For example, the effects seen on 100 µm hemicylindrical waves might now happen at larger feature size for hemispherical waves. Furthermore, as this kind of surface would invariably contain hyperbolic regions (saddle points), we might expect an intermediate response from these locations. If the forces in both principal directions offset each other, the extrusion response may parallel planar regions. On the other hand, if one dominates over the other, we may see extrusion responses tending to the dominating curvature (concave of convex).

On the other hand, on curved landscapes with discrete convex or concave regions, we should expect, within the curved surface, extrusion behaviors paralleling findings in this study. What would be interesting would be to see what happens at the rims (or skirt regions) of the features. At these locations we effectively have hyperbolically curved surfaces, and like before, we should expect some sort of competing effect between the forces generated from the principal directions. So, for dome skirts, we should see fewer extrusions when the domes are small, and vice versa, when they are larger. Meanwhile, for pit rims, we should see a reversed behavior. It should also be noted that the transitioning curvature between convex/concave and planar regions would also modulate the effect.

These effects might have interesting developmental implications. For instance, in developing pillar like tissues (e.g., villi) structures, the strong curvatures of nascent lumps would favor accumulation of cell numbers. However, once the size of the lumps reaches some critical value, epithelial cell extrusions might begin to appear at the roots of the developing structures, offsetting cell division, and eventually halting growth.

Ref R5. L. Pieuchot, J. Marteau, A. Guignandon, T. Dos Santos, I. Brigaud, P. Chauvy, T. Cloatre, A. Ponche, T. Petithory, P. Rougerie, M. Vassaux, J. Milan, N. T. Wakhloo, A. Spangenberg, M. Bigerelle, K. Anselme, Curvotaxis directs cell migration through cell-scale curvature landscapes. Nature Communications 9, 3995 (2018). Doi: 10.1038/s41467-018-06494-6

Ref R6. M. Werner, S. B.G. Blanquer, S. P. Haimi, G. Korus, J. W. C. Dunlop, G. N. Duda, D. W. Grijpma, A. Petersen, Surface curvature differentially regulates stem cell migration and differentiation via altered attachment morphology and nuclear deformation. Advanced Science 4, 1–11 (2017). Doi: 10.1002/advs.201600347

Recommendation 2: The discussion of the experiments on PAM gels is rather limited. The authors describe that cells on the PAM gels experience fewer extrusions than on the PDMS substrates, but this is not discussed in sufficient detail (e.g. why is this the case). Additionally, the description of the 3D traction force microscopy and its validation is quite limited and should be extended to provide more convincing evidence that the measured force differences are not an artefact of the undulations of the surface.

Response 2: We first saw a significant reduction in cell extrusions when we performed hyper-osmotic perturbations, and to eliminate possible off-target effects of the compounds used to increase osmolarity, we used three different compounds to be sure. In spite of this, we felt it would further support our argument, that basal accumulation of fluid stress was responsible for the extrusions, if we had some other independent means of removing fluid stress without directly tuning osmolarity through addition of extraneous solutes. We hence thought of culturing MDCK monolayers on hydrogels.

Hydrogels were chosen because they can effectively dilute basal solute concentration (for reference ions (Na+) are continuously pumped out basally by the monolayer) and thereby reduce the associated osmotically induced water transport. Moreover, as fluid could freely move within the gel, the fluid stress can quickly equilibrate across the basal surface. In contrast, poorly water/solute permeable substrates will lead to localized spikes in solute concentration and transient basal regions with high fluid stress.

To get a sense of the extent of difference in basal solute concentration between the two materials, we can do a quick hand-waving estimation. For monolayers on non-water-permeable PDMS of 20x20 mm, and using the laser wavelength (640 nm) for RICM as an extreme estimate of basal separation, we should expect ~0.25 µl of total basal water content. On the other hand, we typically produce our PAM gel slabs using ~150 µl of precursor solutions. This means that, given similar amounts of solute, PAM gels will lead to monolayer basal osmolarity that is around 3 orders of magnitude lower than monolayers on PDMS, producing significantly lower osmotic potential. This implies from the outset that we should expect high survivability of cells on these substrates. Indeed, later immunoblotting experiments showed MDCKs exhibiting hyper activated FAK and Akt on PAM gels.

As for the 3D TFM used in this study, it is actually implemented from a well-established finite element method to solve inverse problems in engineering and has been repeatedly validated in larger scale engineering contexts (Ref. R7). The novelty and contribution of our article is in its adaptation to reconstruct cellular forces at microscopic scales.

In brief, soft materials, such as hydrogels used in our case, are doped with fluorescent particles, coated with ECM, and then seeded with cells. The cells would exert forces that deform the soft substrate, thereby displacing the fluorescent particles from their equilibrium positions. This particle displacement can be extracted by producing an image pair with microscopy; first one with the cells, and subsequent one of relaxed gel after removal of cells with acutely cytotoxic reagents, such as SDS. There are several ways in which the displacement field can be extracted from the image pair. These include particle tracking velocimetry, particle image velocimetry, digital volume correlation, and optical flow.

We employed 3D Farneback optical flow in our study for its superior computational performance. The method was validated using synthetically generated images from Sample 14 of the Society for Experimental Mechanics DIC challenge. The accuracy of the calculated displacements using the 3D Farneback optical flow was then compared to the provided ground truth displacements. For the highest frequency displacement image pairs, an x-component root-mean-square-error (RMSE) value of 0.0113 was observed. This was lower than the 0.0141 RMSE value for the Augmented Lagrangian Digital Volume Correlation method. This suggested that the 3D Farneback optical flow is capable of accurately calculating the displacement between two bead images.

The displacement fields are then fed into a finite element suite (ANSYS in our case) along with the model and mesh of the underlying substrate structure to obtain node specific displacements. This is required because mech nodes do not typically align with voxel positions of displacements. With these node specific displacements, we subsequently solve the inverse problem for the forces using Tikhonov regularization (Ref. R8). The outcome is a vector of node specific forces.

In light of the above, to physically validate the method in our context would require the generation of a known ground truth force on the scale of pico- to nano-newtons and subsequently image the particle displacements from this force using confocal microscopy. The force must then be released in situ in order for the relaxed gel to be imaged again. This is not a straightforward feat at this scale, and a method that immediately springs to mind is magnetic tweezers. Unfortunately, this is a tool that we cannot develop within reasonable timeframes, as the method will have to be seamlessly integrated with our spinning-disk confocal. However, as a compromise, we have included an in-silico validation with our revised manuscript.

Specifically, given a finite element model with a predefined curvature, a known force was applied to the surface of the model (Author response image 7A). The resulting displacements were then calculated from the finite element solution. A 10% random noise is then added to the resulting displacement. The traction force recovery (Fig. R2-1 B) was then performed using the in-silico noisy displacements. To evaluate the accuracy of the recovery, the cosine similarity along with the mean norm of the force vectors were calculated. A value closer to 1 for both evaluation metrics indicates a more accurate reconstruction of the simulated traction force. The cosine similarity of the recovered traction forces to the original applied force was 0.977±0.056 while the norm of the recovered traction forces as a proportion of the original applied force was 1.016±0.165. As both values are close to 1 (i.e., identical), this suggested that the traction forces could be satisfactorily recovered using the finite-element based method.

In response to the reviewer’s recommendations then, additional content has been included in the main text to explain the use of PAM gels and the workings of our 3D TFM pipeline.

Ref R7. James F. Doyle, Modern Experimental Stress Analysis: Completing the Solution of Partially Specified Problems (John Wiley & Sons, Chichester, 2004).

Ref R8. Per Christian Hansen, Discrete Inverse Problems: Insight and Algorithms (siam, Philadelphia, 2010).

Author response image 7.

(A) shows simulated force field to generate simulated displacements. (B) shows force field reconstructed from simulated displacements with noise.

Recommendation 3: The authors show nuclear deformation on the hills and use this as evidence for a resultant downward-pointing force vector. This has, indeed, also been observed in other works referenced by the authors (e.g. Werner et al.), and could be interesting evidence to support the current observations, provided the authors also show a nuclear shape on the concave and flat regions. The authors could potentially also characterize this shape change better using higher-resolution data.

Response 3: We characterized nucleus deformation using Hoechst-stained samples as per recommendation. The deformation is estimated by dividing segmented nuclei volumes by best-fit ellipsoid volumes of same objects. In this way, objects exhibiting minimal bending will lead to values close to 1.0. The obtained graph is shown in figure Author response image 8B (and manuscript Figure 3D).

Author response image 8.

(A) an example of deformed nuclei on 50 µm wave hill region. (B) a Violin plot of calculated nuclear deformations across dimensions and features using segmented volume normalized against best-fit ellipsoid volume.

Our quantifications show a statistically significant difference in nuclei deformation measure medians between hill and valley cells on the 50 µm (0.973 vs 0.982) and 100 µm (0.971 vs 0.979) waves; this indicates that cells on the hills tend to have more deformed nuclei compared to cells in the valleys. Meanwhile, no significant difference was found for a similar comparison on 200 µm (0.978 vs 0.978) samples. For reference, the median found for cells pooled from planar regions was 0.975.

In response to the reviewer’s suggestions Figure 3 of our manuscript has been updated to include the new results on nuclei deformation. The text has also been updated to account for the new information to support our claims. The statistics are included in a new summary data table in Supplementary File 6.

Recommendation 4: The U-net for extrusion detection is a central tool used within this study, though the explanation and particularly validation of the tool are somewhat lacking. More clarity in the explanation and more examples of good (or bad) detections would help establish this tool as a more robust component of the data collection (on all geometries).

Response 4: The architecture of the neural network used in this study is outlined in supplementary figure S5a. To validate the performance of the model, a test dataset consisting of 200 positive examples and 100 negative examples were fed into the network and the resulting prediction was obtained from model. The confusion matrix of the model is shown in supplementary figure S5c. The weighted precision and recall of the model are 0.958 and 0.953 respectively.

Additionally, we have included examples of false positive and false negative detections in Figure 1-figure supplement 5 (Author response image 8). For false positive detections, these were typically observed to be extrusions that were labelled to have occurred the frame prior to the frame of interest (Author response image 9 bottom sequence). However, as the extrusion process is incomplete in the prior frame, there are still changes in the extruded cell body and the network falsely predicts this as a detection.

Author response image 9.

Examples of false negative and false positive extrusions registration.

Recommendation 5: The authors study the involvement of FAK in the observed curvature-dependent and hydraulic stress-dependent spatial regulation of cell extrusion. In one of the experiments, the authors supplement the cell medium with FAK inhibitors, though only in a hyper-osmotic medium. They show that FAK inhibition counteracts the extrusion-suppressing effect of a hyper-osmotic medium. However, no data is shown on the effect of FAK inhibitors within the control medium. Would the extrusion rates be even higher then?

Response 4: We proceeded, as suggested by the reviewer, to explore the effects of the FAK inhibitor on MDCK monolayers in our control medium. The results revealed that, at the 3 µM FAK concentration, where cells in sucrose media showed an elevated extrusion rate, monolayers in control medium quickly suffered massive cell death (Author response image 10) similar to what was seen when 6 µM FAK was introduced to sucrose medium.

This finding suggests that osmolarity protects against FAK inhibitors in a dose dependent manner. Moreover, as cell extrusions require an intact monolayer, its rates cannot increase indefinitely: a point will be reached where an intact monolayer can no longer be maintained.

We have updated the main text of our article to mention this observation, and also included a new time-lapse (Video 22) to demonstrate the effect.

Author response image 10.

Timelapse snapshot of MDCK monolayers over waves 4 hours after inclusion of focal adhesion kinase inhibitor.

Recommendation 6: The supplementary videos show two fields of view next to each other, which is not immediately clear to the viewer. I strongly advise the authors to add a clear border between the two panels, so that it is clear that the cells from one panel are not migrating into the next panel.

Response 6: A distinctive border has been added to the movies to separate panels showing different focal planes of the same stack.

Recommendation 7: The general quality and layout of the figures could be improved. Some figures would benefit from higher-resolution or larger cell images (e.g. Figure 2A, C, D), and the organisation of subpanels could be improved (e.g. especially in Figure 2). The box plots and bar graphs are also not consistent throughout the manuscript in terms of colouring and style, which should be improved.

Response 7: We have enlarged the figures in question accordingly, at the cost of reducing some information. However, the full scope of the sub-figures remains accessible in the supplementary movies. We have also tried to change the placement of the panels to improve readability. We have also adjusted the valley, hill, and flat coloring scheme for the extrusion boxplots in Figures 1 and 2 to make them consistent.

Recommendation 8: The graphs in Figures 3E and F are confusing and difficult to interpret. The x-axis states "Position along curve in radians" but it is unclear how to relate this to the position on the wavy substrate. The graphs also have a second vertical axis on the right ("valley-interface-hill"), which adds to the confusion. I would recommend the authors provide more explanation and consider a different approach of plotting this.

Response 8: We have removed the confusing plot of cross-sectional profile from the force graphs. To indicate positions on the waves, we have augmented radian values with Hill, Interface, and Valley accordingly.

Recommendation 9: Specify which silicone was used for the low-stiffness silicone substrates in the methods and in the main text.

Response 9: CY52 has been added to the main-text, next to the first appearance of the word soft silicone, to be consistent with the figures.

Recommendation 10: The flow lines that are plotted over the RICM data make it difficult to see the underlying RICM images. I would advise to also show the RICM images without the flow lines.

Response 10: The original movie S15 (now Video 16) showing the RICM overlapped with optical flow paths has now been replaced by a movie showing the same, but with the flow paths and RICM in separate panels.

Recommendation 11: In the first paragraph of the discussion, the authors write: "And this difference was both dependent on the sense (positive or negative)...". This is superfluous since the authors already mentioned earlier in the paragraph that the convex and concave regions (i.e. different signs of curvature) show differences in extrusion rates.

Response 11: The sentence has been changed to “And this difference was also dependent on the degree of curvature.”

Recommendation 12: In the second paragraph of the discussion, the authors mention that "basal fluid spaces under monolayers in hill regions were found consistently smaller than those in valley regions". Is this data shown in the figures of the manuscript? If so, a reference should be made because it was unclear to me.

Response 12: This statement is an inference from the comparison of the hill and valley RICM grey values. Specifically, RICM intensities are direct surrogates for basal separations (i.e., fluid space (as there cannot be a vacuum)) by virtue of the physics underlying the effect. To be more precise then, “inferred from RICM intensity differences (Figure 2I)” has been added to support the statement.

Recommendation 13: On page 7 of the discussion, the authors talk about positively and negatively curved surfaces. This type of description should be avoided, as this depends on the definition of the surface normal (i.e. is positive convex or concave?). Rather use convex and concave in this context.

Response 13: The wording has been changed accordingly.

Recommendation 14: The label of Table 8 reads "Table 2".

Response 14: The error has been corrected.

Reviewer #3

Recommendation 1: The central finding seems to be opposite to an earlier report (J Cell Sci (2019) 132, jcs222372), where MDCK cells in curved alginate tubes exhibit increased extrusion on a convex surface. I suggest that you comment on possible explanations for the different behaviors.

Response 1: The article in question primarily reported the phenomenon of MDCK and J3B1A monolayers detaching from the concave alginate tube walls coated with Matrigel. The authors attributed this to the curvature induced out-of-plane forces towards the center of the tubes. Up to this point, the findings and interpretation are consistent with our current study where we also find a similar force trend in concave regions.

To further lend support to the importance of curvature in inducing detachment, the authors cleverly bent the tubes to introduce asymmetry in curvature between outer and inner surfaces. Specifically, the outside bend is concave in both principal directions, whereas the inside bend is convex in one of its principal directions. As expected, the authors found that detachment rates from the outer surface were much larger compared to the inner one. Again, the observations and interpretations are consistent with our own findings; the convex direction will generate out-of-plane forces pointing into the surface, serving to stabilize the monolayer against the substrate. It should be noted however, since the inner-side tube is characterized by both convex and concave curvatures in its two principal directions, the resulting behavior of overlaying monolayers will depend on which of the two resulting forces become dominant. So, for gradual bends, one should expect the monolayers to still be able to detach from the inner tube surface. This is what was reported in their findings.

For their extrusion observations, I am surprised. Because their whole material (hydrogels) is presumably both solute and water permeable, I would be more inclined to expect very few extrusions irrespective of curvature. This is indeed the case with our study of MDCKs on PAM hydrogels, where the hydrogel substrate effectively buffers against the quick build-up of solute concentration and basal hydraulic stress. Without the latter, concave monolayer forces alone are unlikely to be able to disrupt cell focal adhesions. Indeed, the detachments seen in their study are more likely by exfoliation of Matrigel rather than pulling cells off Matrigel matrix entirely.

My guess is that the extrusions seen in their study are solely of the canonical crowding effect. If this was the case, then the detached monolayer on the outside bend could buffer against crowding pressure by buckling. Meanwhile, the monolayer on the inside bend, being attached to the surface, can only regulate crowding pressure by removing cells through extrusions. This phenomenon should be particular to soft matrices such as Matrigel. Using stiffer and covalently bonded ECM should be sufficient to prevent monolayers from detaching, leading to similar extrusion behaviors. In response to the reviewer’s recommendation then, we have included a short paragraph to state the points discussed in this response.

Recommendation 2: Fig 3E, F: The quantities displayed on the panels are not forces, but have units of pressure (or stress).

Response 2: we have changed “force” to “stress” according to the reviewer’s suggestion. The reason we kept the use of force in the original text was due to the fact that we were reconstructing forces. Due to discretization, the resulting forces will inevitably be assigned to element nodes. In between the nodes, in the faces, there will be no information. So, in order to have some form of continuity to plot, the face forces are obtained by averaging the 4 nodes around the element face. Unfortunately, element face areas are not typically of the same size, therefore the average forces obtained needs to be further normalized against the face area, leading to a quantity that has units of stress.

Recommendation 3: Fig 2D: Asterisks are hard to see.

Response 3: the color of the asterisks has been changed to green for better clarity against a B&W background.

Recommendation 4: p 19, l 7: Word missing in "the of molding"

Response 4: the typo has been amended to “the molding of”.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Wang et al. generate XAP5 and XAP5L knockout mice and find that they are male infertile due to meiotic arrest and reduced sperm motility, respectively. RNA-Seq was subsequently performed and the authors concluded that XAP5 and XAP5L are antagonistic transcription factors of cilliogenesis (in XAP5-KO P16 testis: 554 genes were unregulated and 1587 genes were downregulated; in XAP5L-KO sperm: 2093 genes were unregulated and 267 genes were downregulated).

We are grateful for the comprehensive summary.

Strengths:

Knockout mouse models provided strong evidence to indicate that XAP5 and XAP5L are critical for spermatogenesis and male fertility.

Thank you for your positive comment.

Weaknesses:

The key conclusions are not supported by evidence. First, the authors claim that XAP5 and XAP5L transcriptionally regulate sperm flagella development; however, detailed molecular experiments related to transcription regulation are lacking. How do XAP5 and XAP5L regulate their targets? Only RNA-Seq is not enough. Second, the authors declare that XAP5 and XAP5L are antagonistic transcription factors; however, how do XAP5 and XAP5L regulate sperm flagella development antagonistically? Only RNA-Seq is not enough. Third, I am concerned about whether XAP5 really regulates sperm flagella development. XAP5 is specifically expressed in spermatogonia and XAP5-cKO mice are in meiotic arrest, indicating that XAP5 regulates meiosis rather than sperm flagella development.

Thank you for the critical comments. To strengthen our conclusions, we have included XAP5/XAP5L CUT&Tag data in our revised manuscript. This highly sensitive method has allowed us to identify direct target genes of XAP5 and XAP5L (Table S1, Figure S6). Notably, our results demonstrate that both FOXJ1 and RFX2 are occupied by XAP5 (Figure 4G). Additionally, real-time PCR validation confirmed that RFX2 is also associated with XAP5L, even though enriched peaks for the RFX2 gene were not detected in the initial CUT&Tag data (Figure 4G). These findings indicate that XAP5 and XAP5L regulate the expression of FOXJ1 and RFX2 by directly binding to these genes. De novo motif analyses revealed that XAP5 and XAP5L shared a conserved binding sequence (CCCCGCCC/GGGCGGGG) (Figure S6C), and the bound regions of FOXJ1 and RFX2 contain this sequence. Further analysis shows that many XAP5L target genes are also targets of XAP5 (Figure S6G), despite the limited number of identified XAP5L target genes. This differential binding and regulation of shared target genes underscore the antagonistic relationship between XAP5 and XAP5L. Collectively, these findings provide additional support for the idea that XAP5 and XAP5L function as antagonistic transcription factors, acting upstream of transcription factor families, including FOXJ1 and RFX factors, to coordinate ciliogenesis during spermatogenesis.

While we agree that XAP5 primarily regulates meiosis during spermatogenesis, our data also indicate that many cilia-related genes, including key transcription regulators of spermiogenesis such as RFX2 and SOX30, are downregulated in XAP5-cKO mice and are bound by XAP5 (Figure 4, Figures S4 and S6). It is important to note that genes coding for flagella components are expressed sequentially and in a germ cell-specific manner during development. When we refer to "regulating sperm flagella development", we mean the spatiotemporal regulation. We have revised the manuscript to clarify this point.

Reviewer #2 (Public Review):

In this study, Wang et al., report the significance of XAP5L and XAP5 in spermatogenesis, involved in transcriptional regulation of the ciliary gene in testes. In previous studies, the authors demonstrate that XAP5 is a transcription factor required for flagellar assembly in Chlamydomonas. Continuing from their previous study, the authors examine the conserved role of the XAP5 and XAP5L, which are the orthologue pair in mammals.

XAP5 and XAP5L express ubiquitously and testis specifically, respectively, and their absence in the testes causes male infertility with defective spermatogenesis. Interestingly, XAP5 deficiency arrests germ cell development at the pachytene stage, whereas XAP5L absence causes impaired flagellar formation. RNA-seq analyses demonstrated that XAP5 deficiency suppresses ciliary gene expression including Foxj1 and Rfx family genes in early testis. By contrast, XAP5L deficiency abnormally remains Foxj1 and Rfx genes in mature sperm. From the results, the authors conclude that XAP5 and XAP5L are the antagonistic transcription factors that function upstream of Foxj1 and Rfx family genes.

This reviewer thinks the overall experiments are performed well and that the manuscript is clear. However, the current results do not directly support the authors' conclusion. For example, the transcriptional function of XAP5 and XAP5L requires more evidence. In addition, this reviewer wonders about the conserved XAP5 function of ciliary/flagellar gene transcription in mammals - the gene is ubiquitously expressed despite its functional importance in flagellar assembly in Chlamydomonas. Thus, this reviewer thinks authors are required to show more direct evidence to clearly support their conclusion with more descriptions of its role in ciliary/flagellar assembly.

Thank you for your thoughtful review of our work. We appreciate your positive feedback on the overall quality of the experiments and the clarity of the manuscript. In response to your concerns, we have included new experimental data and made revisions to the manuscript (lines 193-217) to better support our conclusions, particularly regarding the transcriptional function of XAP5 and XAP5L. Additionally, we have expanded on the role of XAP5 in ciliary and flagellar assembly to provide more direct evidence for its functional importance. Thank you for your insights.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The title (Control of ciliary transcriptional programs during spermatogenesis by antagonistic transcription factors) is not specific and does tend to exaggerate.

Thank you for the comment, and we appreciate the opportunity to clarify the appropriateness of the title. Our paper extensively investigates the transcriptional regulation of ciliary genes during spermatogenesis. It demonstrates that XAP5/XAP5L are key transcription factors involved in this process. The title reflects our primary focus on the transcriptional programs that govern ciliary gene expression. Moreover, our paper shows that XAP5 positively regulates the expression of ciliary genes, particularly during the early stages of spermatogenesis, while XAP5L negatively regulates these genes. This antagonistic relationship is a crucial aspect of the study and is effectively conveyed in the title. In addition, our revised paper provides detailed insights into how XAP5/XAP5L control ciliary gene expression during spermatogenesis.

Figure 4C: FOXJ1 and RFX2 are absent in sperm from WT mice. Are you sure? They are highly expressed in WT testes.

Thank you for your careful review. While FOXJ1 and RFX2 are indeed highly expressed in the testes of wild-type (WT) mice, our data show that they are not detectable in mature sperm. This observation is consistent with published single-cell RNA-seq data(Jung et al., 2019), which indicate that FOXJ1 and RFX2 are primarily expressed in spermatocytes but not in spermatids (Figure S7). This expression pattern aligns with that that of IFT-particle proteins, which are essential for the formation but not the maintenance of mammalian sperm flagella(San Agustin, Pazour, & Witman, 2015).

XAP5 is specifically expressed in spermatogonia and XAP5-cKO mice are in meiotic arrest, indicating that XAP5 regulates meiosis rather than sperm flagella development.

We appreciate your insightful comments. As mentioned above, we agree that XAP5 primarily regulates meiosis during spermatogenesis. When we mentioned "regulating sperm flagella development," we were referring to the spatiotemporal regulation of these processes. We have revised the manuscript to clarify this distinction. Thank you for your understanding.

The title of Figure 2 (XAP5L is required for normal sperm formation) is not accurate because the progress of spermatogenesis and sperm count is normal in XAP5L-KO mice (only sperm motility is reduced).

We apologize for any confusion caused by the previous figure. It did not accurately convey the changes in sperm count. In the revised Figure 2B, we clearly demonstrate that the sperm count in XAP5L-KO mice is indeed lower than that in WT mice. This revision aims to provide a more accurate representation of the effects of XAP5L deficiency on spermatogenesis. Thank you for bringing this to our attention.

Reviewer #2 (Recommendations For The Authors):

(1) Although XAP5 and XAP5L deficiency alters the transcription of Foxj1 and Rfx family genes, which are the essential transcription factors for the ciliogenesis, current data do not directly support that XAP5 and XAP5L are the upstream transcription factors. The authors need to show more direct evidence such as CHIP-Seq data.

Thank you for your valuable feedback! In this revised manuscript, we have included data identifying candidate direct targets of XAP5 and XAP5L using the highly sensitive CUT&Tag method (Kaya-Okur et al., 2019). Our results show that XAP5 occupies both FOXJ1 and RFX2 (Figure 4G). Furthermore, real-time PCR validation of the CUT&Tag experiments confirmed that RFX2 is also occupied by XAP5L (Figure 4G), despite the initial CUT&Tag data not revealing enriched peaks for the RFX2 gene (Table S1). Unfortunately, the limited number of enriched peaks identified for XAP5L (Table S1) suggests that the XAP5L antibody used in the CUT&Tag experiment might have suboptimal performance, which prevented us from detecting occupancy on the FOXJ1 promoter. Nevertheless, these additional data provide strong evidence that XAP5 and XAP5L function as upstream transcription factors for FOXJ1 and RFX family genes, supporting their essential roles in ciliogenesis.

(2) Shared transcripts that are altered by the absence of either XAP5 or XAP5L do not clearly support they are antagonistic transcription factors.

Thank you for your insightful comment. In our revised manuscript, we performed CUT&Tag analysis to identify target genes of XAP5 and XAP5L. Motif enrichment analysis revealed conserved binding sequences for both factors (Figures S6C), indicating a subset of shared downstream genes between XAP5 and XAP5L. Among the downregulated genes in XAP5 cKO germ cells, 891 genes were bound by XAP5 (Figure S6D). Although the number of enriched peaks identified for XAP5L was limited, 75 of the upregulated genes in XAP5L KO sperm were bound by XAP5L (Figure S6E). Importantly, of these 75 XAP5L target genes, approximately 30% (22 genes) were also identified as targets of XAP5 (Figure S6G), further support the idea that XAP5 and XAP5L function as antagonistic transcription factors.

(3) XAP5 seems to be an ancient transcription factor for cilia and flagellar assembly. However, XAP5 expresses ubiquitously in mice. How can this discrepancy be explained? Is it also required for primary cilia assembly? Are their expression also directly linked to ciliogenesis in other types of cells?

Thank you for the thoughtful questions. The ubiquitous expression of XAP5 in mice can be understood in light of its role as an ancient transcription factor for cilia and flagellar assembly. Given that cilia are present on nearly every cell type in the mammalian body (O'Connor et al., 2013), this broad expression pattern makes sense. In fact, XAP5 serves not only as a master regulator of ciliogenesis but also as a critical regulator of various developmental processes (Kim et al., 2018; Lee et al., 2020; Xie et al., 2023).

Our current unpublished work demonstrates that XAP5 is essential for primary cilia assembly in different cell lines. The loss of XAP5 protein results in abnormal ciliogenesis, further supporting its vital role in ciliary formation across different cell types.

We believe that the widespread expression of XAP5 reflects its fundamental importance in multiple cellular processes, including ciliogenesis, development, and potentially other cellular functions yet to be discovered.

(4) XAP5L causes impairs flagellar assembly. Have the authors observed any other physiological defects in the absence of XAP5L in mouse models? Such as hydrocephalus and/or tracheal defects?

Thank you for the questions. We have carefully examined XAP5L KO mice for other physiological defects. To date, we have not observed any additional physiological abnormalities. Specifically, we assessed the condition of tracheal cilia in XAP5L KO mice and found no significant differences compared to wild-type (WT) mice, as illustrated in Author response image 1 below.

Author response image 1.

References

Jung, M., Wells, D., Rusch, J., Ahmad, S., Marchini, J., Myers, S. R., & Conrad, D. F. (2019). Unified single-cell analysis of testis gene regulation and pathology in five mouse strains. Elife, 8. doi:10.7554/eLife.43966

Kaya-Okur, H. S., Wu, S. J., Codomo, C. A., Pledger, E. S., Bryson, T. D., Henikoff, J. G., . . . Henikoff, S. (2019). CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun, 10(1), 1930. doi:10.1038/s41467-019-09982-5

Kim, Y., Hur, S. W., Jeong, B. C., Oh, S. H., Hwang, Y. C., Kim, S. H., & Koh, J. T. (2018). The Fam50a positively regulates ameloblast differentiation via interacting with Runx2. J Cell Physiol, 233(2), 1512-1522. doi:10.1002/jcp.26038

Lee, Y.-R., Khan, K., Armfield-Uhas, K., Srikanth, S., Thompson, N. A., Pardo, M., . . . Schwartz, C. E. (2020). Mutations in FAM50A suggest that Armfield XLID syndrome is a spliceosomopathy. Nature Communications, 11(1). doi:10.1038/s41467-020-17452-6

O'Connor, A. K., Malarkey, E. B., Berbari, N. F., Croyle, M. J., Haycraft, C. J., Bell, P. D., . . . Yoder, B. K. (2013). An inducible CiliaGFP mouse model for in vivo visualization and analysis of cilia in live tissue. Cilia, 2(1), 8. doi:10.1186/2046-2530-2-8

San Agustin, J. T., Pazour, G. J., & Witman, G. B. (2015). Intraflagellar transport is essential for mammalian spermiogenesis but is absent in mature sperm. Mol Biol Cell, 26(24), 4358-4372. doi:10.1091/mbc.E15-08-0578

Xie, X., Li, L., Tao, S., Chen, M., Fei, L., Yang, Q., . . . Chen, L. (2023). Proto-Oncogene FAM50A Can Regulate the Immune Microenvironment and Development of Hepatocellular Carcinoma In Vitro and In Vivo. Int J Mol Sci, 24(4). doi:10.3390/ijms24043217

Author Response

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

We would like to extend our gratitude to the reviewers for their meticulous analysis and constructive feedback on our manuscript. We have revised our paper based on the suggestions regarding supporting literature and the theory behind CAPs along with detailed insights regarding our methods. Their suggestions have been extremely useful in strengthening the clarity and rigor of our manuscript.

Reviewer #1 (Recommendations For The Authors):

(1) There are no obvious problems with this paper and it is relatively straightforward. There are some challenges that I would like to suggest. These variants have multiple mutations, so it would be interesting if you could drill down to find out which mutation is the most important for the collective changes reported here. I would like to see a sequence alignment of these variants, perhaps in the supplemental material, just to get some indication of the extent of mutations involved.

Finding the most important mutation within a set is a tricky question, as each mutation changes the way future mutations will affect function due to epistasis. Indeed, this is what we aim to explore in this work. To illustrate this point, we included a new supplementary figure S5A. Three critical mutations that emerged quickly, and were frequently observed in other dominant variants, were S477N, T478K, and N501Y. Thus, we computed the EpiScore values of these three mutations, with several critical residues contributing to hACE2 binding. The EpiScore distribution indicates that residues 477, 478, and 501 have strong epistatic (i.e., non-additive) interactions, as indicated by EpiScore values above 2.0.

To further investigate these epistatic interactions, we first conducted MD simulations and computed the DFI profile of these three single mutants. We analyzed how different the DFI scores of the hACE2 binding interface residues of the RBD are, across three single mutants with Omicron, Delta, and Omicron XBB variants (Fig S5B). Fig S5B shows how mutations at these particular sites affect the binding interface DFI in various backgrounds, as the three mutations are also observed in the Omicron, XBB, and XBB 1.5 variants. If the difference in the DFI profile of the mutant and the given variant is close to 0, then we could safely state that this mutation affected the variant the most. However, what we observe is quite the opposite: the DFI profile of the mutation is significantly different in different variant backgrounds. While these mutations may change overall behavior, their individual contributions to overall function are more difficult to pin down because overall function is dependent on the non-additive interactions between many different residues.

Author response image 1.

(A) Three critical mutations that emerged quickly, and were frequently observed in other dominant variants, were S477N, T478K, and N501Y. EpiScores of sites 477, 478, and 501 with one another are shown with k = the binding interface of the open chain. These residues are highly epistatic, producing higher responses than expected when perturbed together. (B) The difference in the dynamic flexibility profiles between the single mutants and the most common variants for the hACE2 binding residues of the RBD. DFI profiles exhibit significant variation from zero, and also show different flexibility in each background variant, highlighting the critical non-additive interactions of the other mutation in the given background variant. Thus, these three critical mutations, impacting binding affinity, do not solely contribute to the binding. There are epistatic interactions with the other mutations in VOCs that shape the dynamics of the binding interface to modulate binding affinity with hACE2.

As we discussed above, while the epistatic interactions are crucial and the collective impact of the mutations shape the mutational landscape of the spike protein, we would like note that mutation S486P is one of the critical mutations we identify, modulating both antibody and hACE2 binding and our analysis reveals the strong non-additive interactions with the other mutational sites. This mutational site appears in both XBB1.5 and earlier Omicron strains which highlights its importance in functional evolution of the spike protein. CAPs 346R, 486F, and 498Q also may be important, as they have a high EpiScore, indicating critical epistatic interaction with many mutation sites.

Regarding to the suggestion about presenting the alignment of the different variants, we have attached a mutation table, highlighting the mutated residues for each strain compared to the reference sequence as supplemental Figure S1 along with the full alignment file.

(2) Also, I am wondering if it would be possible to insert some of these flexibilities and their correlations directly into the elastic network models to enable a simpler interpretation of these results. I realize this is beyond the scope of the present work, but such an effort might help in understanding these relatively complex effects.

This is great suggestion. A similar analysis has been performed for different proteins by Mcleash (See doi: 10.1016/j.bpj.2015.08.009) by modulating the spring constants of specific position to alter specific flexibility and evaluate change in elastic free energy to identify critical mutation (in particular, allosteric mutation) sites. We will be happy to pursue this as future work.

Minor

(3) 1 typo on line 443 - should be binding instead of biding.

Fixed, thanks for spotting that.

(4) The two shades of blue in Fig. 4B were not distinguishable in my version.

To fix this, we have changed the overlapping residues between Delta and Omicron to a higher contrast shade of blue.

(5) Compensatory is often used in an entirely different way - additional mutations that help to recover native function in the presence of a deleterious mutation.

Although our previous study (Ose et al. 2022, Biophysical Journal) shows that compensatory mutations were generally additive, the two ideas are not one and the same. We thank the reviewer for pointing this out. Therefore, to clarify, we have now described our results in terms of dynamic additivity, rather than compensation.

Reviewer #2 (Recommendations For The Authors):

(1) The authors note that the identified CAPs overlap with those of others (Cagliani et al. 2020; Singh and Yi 2021; Starr, Zepeda, et al. 2022). In itself, this merits a deeper discussion and explicit indication of which positions are not identified. However, there is one point that I believe may represent a fundamental flaw in this study in that the calculation of EP from the alignment of S proteins ignores entirely the differences in the interacting interface with which S for different coronaviruses in the alignment interact in the different receptors in each host species. This may be the reason why so many "CAPs" are in the RBD. The authors should at the very least make a convincing case of why they are not simply detecting constraints imposed by the different interacting partners, at least in the case of positions within the RBD interface with ACE2. Another point that the authors should discuss is that ACE2 is not the only receptor that facilitates infection, TMPRSS2 and possibly others have been identified as well. The results should be discussed in light of this.

To begin with, we have now explicitly noted (on line 135) that “sites 478, 486, 498, and 681 have already been implicated in SARS-CoV-2 evolution, leaving the remaining 11 CAPs as undiscovered candidate sites for adaptation.” Evolutionary analyses are done using orthologous protein sequences, so there is no way to integrate information on different receptors in each host species in the calculation of EPs. However, we appreciate that the preponderance of CAPs in the RBD is likely due to different binding environments. We have added the following text (on line 83) to clarify our point: “Adaptation in this case means a virus which can successfully infect human hosts. As CAPs are unexpected polymorphisms under neutral theory, their existence implies a non-neutral effect. This can come in the form of functional changes (Liu et al. 2016) or compensation for functional changes (Ose et al. 2022). Therefore, we suspect that these CAPs, being unexpected changes from coronaviruses across other host species with different binding substrates, may be partially responsible for the functional change of allowing human infection.” This hypothesis is supported by the overlap of CAPs we identified with the positions identified in other studies (e.g., 478, 486, 498, and 681). Binding to TMPRSS2 and other substrates are also covered by this analysis as it is a measure of overall evolutionary fitness, rather than binding to any specific substrate. Our paper does focus on discussing hACE2 binding and mentions furin cleavage, but indeed lacks discussion on the role of TMPRSS2. We have added the following text to line 157: “Another host cell protease, TMPRSS2, facilitates viral attachment to the surface of target cells upon binding either to sites Arg815/Ser816, or Arg685/Ser686 which overlaps with the furin cleavage site 676-689, further emphasizing the importance of this area (Hoffmann et al. 2020b; Fraser et al. 2022).”

(2) Turning now to the computational methods utilized to study dynamics, I have serious reservations about the novelty of the results as well as the validity of the methodology. First of all, the authors mention the work of Teruel et al. (PLOS Comp Bio 2021) in an extremely superficial fashion and do not mention at all a second manuscript by Teruel et al. (Biorxiv 2021.12.14.472622 (2021)). However, the work by Teruel et al. identifies positions and specific mutations that affect the dynamics of S and the evolution of the SARS-CoV-2 virus in light of immune escape, ACE2 binding, and open and closed state dynamics. The specific differences in approach should be noted but the results specifically should be compared. This omission is evident throughout the manuscript. Several other groups have also published on the use of nomal-mode analysis methods to understand the Spike protein, among them Verkhivker et al., Zhou et al., Majumder et al., etc.

Thank you for your suggestions. Upon further examination of the listed papers, we have added citations to other groups employing similar methods. However, it's worth noting that the results of Teruel et al.'s studies are generally not directly comparable to our own. Particularly, they examine specific individual mutations and overall dynamical signatures associated with them, whereas our results are always considered in the context of epistasis and joint effects with CAPs, and all mutations belong to the common variants. Although important mutations may be highlighted in both cases, it is for very different reasons. Nevertheless, we provide a more detailed mention of the results of both studies. See lines 178, 255, and 393.

(3) The last concern that I have is with respect to the methodology. The dynamic couplings and the derived index (DCI) are entirely based on the use of the elastic network model presented which is strictly sequence-agnostic. Only C-alpha positions are taken into consideration and no information about the side-chain is considered in any manner. Of course, the specific sequence of a protein will affect the unique placement of C-alpha atoms (i.e., mutations affect structure), therefore even ANM or ENM can to some extent predict the effect of mutations in as much as these have an effect on the structure, either experimentally determined or correctly and even incorrectly modelled. However, such an approach needs to be discussed in far deeper detail when it comes to positions on the surface of a protein such that the reader can gauge if the observed effects are the result of modelling errors.

We would like to clarify that most of our results do not involve simulations of different variants, but rather how characteristic mutation sites for those variants contribute to overall dynamics. For the full spike, we operate on only two simulations: open and closed. When we do analyze different variants, starting on line 438, the observed difference does not come from the structure, but from the covariance matrix obtained from molecular dynamics (MD) simulations, which are sensitive to single amino acid changes.

Reviewer #3 (Recommendations For The Authors):

(1) On line 99 there is a misspelling, 'withing'.

It has been fixed. Thanks for spotting that.

(2) Some graphical suggestions to make the figures easier to read:

In Figure 1C, a labeled circle around the important sites, the receptor binding domain, and the Furin cleavage site, would help the reader orient themselves. Moreover, it would make clear which CAPs are NOT in the noteworthy sites described in the text.

Good idea. We have added transparent spheres and labels to show hACE2 binding sites and Furin cleavage sites.

In Figure 2C the colors are a bit low contrast; moreover, there are multiple text sizes on the same figure which should perhaps be avoided to ensure legibility.

We have made yellow brighter and standardized font sizes.

Figure 3 is a bit dry, perhaps indicating in which bins the 'interesting' sites could be informative.

Thank you for the suggestion, but the overall goal of Figure 3 is to illustrate that the mutational landscape is governed by the equilibrium dynamics in which flexible sites undergo more mutations during the evolution of the CoV2 spike protein. Therefore, adding additional positional information may complicate our message.

Figure 4, the previous suggestions about readability apply.

We ensured same sized text and higher contrast colors.

Figure 5B, the residue labels are too small.

We increased the font size of the residue labels.

In Figure 8 maybe adding Delta to let the reader orient themselves would be helpful to the discussion.

Unfortunately, there is no single work that has experimentally quantified binding affinities towards hACE2 for all the variants. When we conducted the same analysis for the Delta variant in Figure 8, the experimental values were obtained from a different source (doi: 10.1016/j.cell.2022.01.001) and the values were significantly different from the experimental work we used for Omicron (Yue et al. 2023). When we could adjust based on the difference in experimentally measured binding affinity values of the original Wuhan strain in these two separate studies, we observed a similar correlation, as seen below. However, we think this might not be a proper representation. Therefore, we chose to keep the original figure.

Author response image 2.

The %DFI calculations for variants Delta, Omicron, XBB, and XBB 1.5. (A) %DFI profile of the variants are plotted in the same panel. The grey shaded areas and dashed lines indicate the ACE2 binding regions, whereas the red dashed lines show the antibody binding residues. (B) The sum of %DFI values of RBD-hACE2 interface residues. The trend of total %DFI with the log of Kd values overlaps with the one seen with the experiments. (C) The RBD antibody binding residues are used to calculate the sum of %DFI. The ranking captured with the total %DFI agrees with the susceptibility fold reduction values from the experiments.

(3) Replicas of the MD simulations would make the conclusions stronger in my opinion.

We ran a 1µs long simulation and performed convergence analysis for the MD simulations using the prior work (Sawle L, Ghosh K. 2016.) More importantly, we also evaluated the statistical significance of computed DFI values as explained in detail below (Please see the answer to question 3 of Reviewer #3 (Public Review):)

Reviewer #3 (Public Review):

(1) A longer discussion of how the 19 orthologous coronavirus sequences were chosen would be helpful, as the rest of the paper hinges on this initial choice.

The following explanation has been added on line 114: EP scores of the amino acid variants of the S protein were obtained using a Maximum Likelihood phylogeny (Kumar et al. 2018) built from 19 orthologous coronavirus sequences. Sequences were selected by examining available non-human sequences with a sequence identity of 70% or above to the human SARS CoV-2’s S protein sequence. This cutoff allows for divergence over evolutionary history such that each amino acid position had ample time to experience purifying selection, whilst limiting ourselves to closely related coronaviruses. (Figure 1A).

(2) The 'reasonable similarity' with previously published data is not well defined, nor there was any comment about some of the residues analyzed (namely 417-484). We have revised this part of the manuscript and add to the revised version.

We removed the line about reasonable similarity as it was vague, added a line about residues 417-484, and revised the text accordingly, starting on line 354.

(3) There seem to be no replicas of the MD simulations, nor a discussion of the convergence of these simulations. A more detailed description of the equilibration and production schemes used in MD would be helpful. Moreover, there is no discussion of how the equilibration procedure is evaluated, in particular for non-experts this would be helpful in judging the reliability of the procedure.

We opted for a single, extended equilibrium simulation to comprehensively explore the longterm behavior of the system. Given the specific nature of our investigation and resource constraints, a well-converged, prolonged simulation was deemed a practical and scientifically valid approach, providing a thorough understanding of the system's dynamics. (doi: 10.33011/livecoms.1.1.5957, https://doi.org/10.1146/annurev-biophys-042910-155255 )

We updated our methods section starting on line 605 with extended information about the MD simulations and the converge criteria for the equilibrium simulations. We also added a section that explains our analysis to check statistical significance of obtained DFI values.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, Millard and colleagues investigated if the analgesic effect of nicotine on pain sensitivity, assessed with two pain models, is mediated by Peak Alpha Frequency (PAF) recorded with resting state EEG. The authors found indeed that nicotine (4 mg, gum) reduced pain ratings during phasic heat pain but not cuff pressor algometry compared to placebo conditions. Nicotine also increased PAF (globally). However, mediation analysis revealed that the reduction in pain ratings elicited by the phasic heat pain after taking nicotine was not mediated by the changes in PAF. Also, the authors only partially replicated the correlation between PAF and pain sensitivity at baseline (before nicotine treatment). At the group-level no correlation was found, but an exploratory analysis showed that the negative correlation (lower PAF, higher pain sensitivity) was present in males but not in females. The authors discuss the lack of correlation.

In general, the study is rigorous, methodology is sound and the paper is well-written. Results are compelling and sufficiently discussed.

Strengths:

Strengths of this study are the pre-registration, proper sample size calculation, and data analysis. But also the presence of the analgesic effect of nicotine and the change in PAF.

Weaknesses:

It would even be more convincing if they had manipulated PAF directly.

We thank Reviewer #1 for their positive and constructive comments regarding our study. We appreciate the view that the study was rigorous and methodologically sound, that the paper was well-written, and that the strengths included our pre-registration, sample size calculation, and data analysis.

In response to the reviewer's comment about more directly manipulating Peak Alpha Frequency (PAF), we agree that such an approach could provide a more direct investigation of the role of PAF in pain processing. We chose nicotine to modulate PAF as the literature suggested it was associated with a reliable increase in PAF speed. As mentioned in our Discussion, there are several alternative methods to manipulate PAF, such as non-invasive brain stimulation techniques (NIBS) like transcranial alternating current stimulation (tACS) or neurofeedback training. These approaches could help clarify whether a causal relationship exists between PAF and pain sensitivity. Although methods such as NIBS still require further investigation as there is little evidence for these approaches changing PAF (Millard et al., 2024).

Reviewer #2 (Public Review):

Summary:

The study by Millard et al. investigates the effect of nicotine on alpha peak frequency and pain in a very elaborate experimental design. According to the statistical analysis, the authors found a factor-corrected significant effect for prolonged heat pain but not for alpha peak frequency in response to the nicotine treatment.

Strengths:

I very much like the study design and that the authors followed their research line by aiming to provide a complete picture of the pain-related cortical impact of alpha peak frequency. This is very important work, even in the absence of any statistical significance. I also appreciate the preregistration of the study and the well-written and balanced introduction. However, it is important to give access to the preregistration beforehand.

Weaknesses:

The weakness of the study revolves around three aspects:

(1) I am not entirely convinced that the authors' analysis strategy provides a sufficient signal-tonoise ratio to estimate the peak alpha frequency in each participant reliably. A source separation (ICA or similar) would have been better suited than electrode ROIs to extract the alpha signal. By using a source separation approach, different sources of alpha (mu, occipital alpha, laterality) could be disentangled.

(2) Also, there's a hint in the literature (reference 49 in the manuscript) that the nicotine treatment may not work as intended. Instead, the authors' decision to use nicotine to modulate the peak alpha frequency and pain relied on other, not suitable work on chronic pain and permanent smokers. In the present study, the authors use nicotine treatment and transient painful stimulation on nonsmokers.

(3) In my view, the discussion could be more critical for some aspects and the authors speculate towards directions their findings can not provide any evidence. Speculations are indeed very important to generate new ideas but should be restricted to the context of the study (experimental pain, acute interventions). The unfortunate decision to use nicotine severely hampered the authors' aim of the study.

Impact:

The impact of the study could be to show what has not worked to answer the research questions of the authors. The authors claim that their approach could be used to define a biomarker of pain. This is highly desirable but requires refined methods and, in order to make the tool really applicable, more accurate approaches at subject level.

We thank reviewer #2 for their recognition of the study’s design, the importance of this research area, and the pre-registration of our study. In response to the weaknesses highlighted:

(1) We appreciate the reviewer’s suggestion to improve the signal-to-noise ratio by applying source separation techniques, such as ICA, which have now been performed and incorporated into the manuscript. Our original decision to use sensor-level ROIs followed the precedent set in previous studies, our rationale being to improve reproducibility and avoid  biases from picking individual electrodes or manually picking sources. We have  added analyses using an automated pipeline that selects components based on the presence of a peak in the alpha range and alignment with a predefined template topography representing sensorimotor sites. Here again we found no significant differences in the mediation results that used a sensor space sensorimotor ROI, further supporting the robustness of the chosen approach. ICA could still potentially disentangle different sources of alpha, such as occipital alpha and mu rhythm, and provide new insights into the PAF-pain relationship. We have now added a discussion in the manuscript about the potential advantages of source separation techniques and suggest that the possible contributions of separate alpha sources be investigated and compared to sensor space PAF as a direction for future research.

(2) We recognise the reviewer's concern regarding our choice of nicotine as a modulator of pain and alpha peak frequency (PAF). The meta-analysis by Ditre et al. (2016) indeed points to small effect sizes for nicotine's impact on experimental pain and highlights the potential for publication bias. However, our decision to use nicotine in this study was not primarily based on its direct analgesic effects, but rather on its well-documented ability to modulate PAF, in smoking and non-smoker populations, as outlined in our study aims.

In this regard, the intentional use of nicotine was to assess whether changes in PAF could mediate alterations in pain. This approach aligns with the broader concept that a direct effect of an intervention is not necessary to observe indirect effects (Fairchild & McDaniel, 2017). We have, however, revised our introduction to further clarify this rationale, highlighting that nicotine was used as a tool for PAF modulation, not solely for its potential analgesic properties.

(3) We agree with the reviewer’s observation that certain aspects of the Discussion could be more cautious, particularly regarding speculations about nicotine’s effects and PAF as a biomarker of pain. We have revised the Discussion to ensure that our interpretations are better grounded in the data from this study, clearly stating the limitations and avoiding overgeneralization. This revision focuses on a more critical evaluation of the potential relationships between PAF, nicotine, and pain sensitivity based solely on our experimental context.

Finally, We also apologize for not providing access to the preregistration earlier. This was an oversight on our end, and we will ensure that future preregistrations are made available upfront.

Reviewer #3 (Public Review):

In this manuscript, Millard et al. investigate the effects of nicotine on pain sensitivity and peak alpha frequency (PAF) in resting state EEG. To this end, they ran a pre-registered, randomized, double-blind, placebo-controlled experiment involving 62 healthy adults who received either 4 mg nicotine gum (n=29) or placebo (n=33). Prolonged heat and pressure were used as pain models. Resting state EEG and pain intensity (assessed with a visual analog scale) were measured before and after the intervention. Additionally, several covariates (sex at birth, depression and anxiety symptoms, stress, sleep quality, among others) were recorded. Data was analyzed using ANCOVAequivalent two-wave latent change score models, as well as repeated measures analysis of variance. Results do not show *experimentally relevant* changes of PAF or pain intensity scores for either of the prolonged pain models due to nicotine intake.

The main strengths of the manuscript are its solid conceptual framework and the thorough experimental design. The researchers make a good case in the introduction and discussion for the need to further investigate the association of PAF and pain sensitivity. Furthermore, they proceed to carefully describe every aspect of the experiment in great detail, which is excellent for reproducibility purposes. Finally, they analyse the data from almost every possible angle and provide an extensive report of their results.

The main weakness of the manuscript is the interpretation of these results. Even though some of the differences are statistically significant (e.g., global PAF, pain intensity ratings during heat pain), these differences are far from being experimentally or clinically relevant. The effect sizes observed are not sufficiently large to consider that pain sensitivity was modulated by the nicotine intake, which puts into question all the answers to the research questions posed in the study.

We would like to express our gratitude to Reviewer #3 for their thoughtful and constructive review, including the positive feedback on the strengths of our study's conceptual framework, experimental design, and thorough methodological descriptions.

We acknowledge the concern regarding the experimental and clinical relevance of some statistically significant results (e.g., global PAF and pain intensity during heat pain) and agree that small effect sizes may limit their practical implications. However, our primary goal was to assess whether nicotine-induced changes in PAF mediate pain changes, rather than to demonstrate large direct effects on pain sensitivity. Nicotine was chosen for its known ability to modulate PAF, and our focus was on the mechanistic role of PAF in pain perception. To clarify this, we have revised the discussion to better differentiate between statistical significance, experimental relevance, and clinical applicability. We emphasize that this study represents a preliminary step towards understanding PAF’s mechanistic role in pain, rather than a direct clinical application.

We appreciate the suggestion to refine our interpretation. We have adjusted our language to ensure it aligns with the effect sizes observed and made recommendations for future research, such as testing different nicotine doses, to potentially uncover stronger or more clinically relevant effects.

Although modest, we believe these findings offer valuable insights into the potential mechanisms by which nicotine affects alpha oscillations and pain. We have also discussed how these small effects could become more pronounced in different populations (e.g., chronic pain patients) and over time, offering guidance for future research on PAF modulation and pain sensitivity.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I have a number of points that the authors may want to consider for this or future work.

(1) By reviewing the literature provided by the authors in the introduction I think that using nicotine as a means to modulate pain and alpha peak frequency was a mistake. The only work that may give a hint on whether nicotine can modulate experimental pain is the meta-analysis by Ditre and colleagues (2016). They suggest that their small effect may contain a publication bias. I think the other "large body of evidence" is testing something else than analgesia.

Thank you for your consideration of our choice of nicotine in the study. The meta-analysis by Ditre and colleagues (2016) suggests small effect sizes for nicotine's impact on experimental pain, compared to the moderate effects claimed in some papers, especially when accounting for the potential publication bias you mentioned. However, our selection of nicotine was primarily driven by its documented ability to modulate PAF rather than its direct analgesic effects, as clearly stated in our aims. Therefore, we do not view our decision to use nicotine as a mistake; instead, it was aligned with our goal of assessing whether changes in PAF mediate alterations in pain and thus served as a valuable tool. This perspective aligns with the broader concept that a direct effect is not a prerequisite for observing indirect effects of an intervention on an outcome (Fairchild &

McDaniel, 2017). To further enhance clarity, we've revised the introduction to emphasize the role of nicotine in manipulating PAF in relation to our study's aims.

Previously we wrote: “A large body of evidence suggests that nicotine is an ideal choice for manipulating PAF, as both nicotine and smoking increase PAF speed [37,40–47] as well as pain thresholds and tolerance [48–52].” This has been changed to read: “Because evidence suggests that nicotine can modulate PAF, where both nicotine and smoking increase PAF speed [37,40–47], we chose nicotine to assess our aim of whether changes in PAF mediate changes in pain in a ‘mediation by design’ approach [48]. In addition, given evidence that nicotine may increase experimental pain thresholds and tolerance [49–53], nicotine could also influence pain ratings during tonic pain.”

(2) As mentioned above, the OSF page is not accessible.

We apologise for this. We had not realised that the pre-registration was under embargo, but we have now made it available.

(3) I generally struggle with the authors' approach to investigating alpha. With the approach the authors used to detect peak alpha frequency it might be that the alpha signal may just show such a low amplitude that it is impossible to reliably detect it at electrode level. In my view, the approach is not accurate enough, which can be seen by the "jagged" shape of the individual alpha peak frequency. In my view, a source separation technique would have been more useful. I wonder which of the known cortical alphas contributes to the effects the authors have reported previously: occipital, mu rhythms projections or something else? A source separation approach disentangles the different alphas and will increase the SNR. My suggestion would be to work on ICA components or similar approaches. The advantage is that the components are almost completely free of any artefacts. ICAs could be run on the entire data or separately for each individual. In the latter case, it might be that some participants do not exhibit any alpha component.

We appreciate your thoughtful consideration of our approach to investigating alpha. The calculation of PAF involves various methods and analysis steps across the literature (Corcoran et al., 2018; Gil Avila et al., 2023; McLain et al., 2022). Your query about which known cortical alphas contribute to reported effects is important. Initially focusing on a sensorimotor component from an ICA in Furman et al., 2018, subsequent work from our labs suggested a broader relationship between PAF and pain across the scalp (Furman et al., 2019; Furman et al., 2020; Millard et al., 2022), and a desire to conduct analyses at the sensor level in order to improve the reproducibility of the methods (Furman et al., 2020). However, based on your comment we have made several additions to the manuscript, including: explaining why we did not use manual ICA methods, suggest this for future research, and added an exploratory analysis using a recently developed automated pipeline that selects components based on the presence of a peak in the alpha range and alignment with a predefined template topography representing activity from occipital or motor sites.

While we acknowledge that ICA components can offer a better signal-to-noise ratio (SNR) and possibly smoother spectral plots, we opted for our chosen method to avoid potential bias inherent in deciding on a component following source separation. The desire for a quick, automated, replicable, and unbiased pipeline, crucial for potential clinical applications of PAF as a biomarker, influenced this decision. At the time of analysis registration, automated methods for deciding which alpha components to extract following ICA were not apparent. We have now added this reasoning to Methods.

“Contrary to some previous studies that used ICA to isolate sensory region alpha sources (Furman et al., 2018; De Martino et al., 2021; Valentini et al., 2022), we used pre-determined sensor level ROIs to improve reproducibility and reduce the potential for bias when individually selecting ICA components. Using sensor level ROIs may decrease the signal-to-noise ratio of the data; however, this approach has still been effective for observing the relationship between PAF and experimental pain (Furman et al., 2019; Furman et al., 2020).”

We have also added use of ICA and development of methods as a suggestion for future research in the discussion:

“Additionally, the use of global PAF may have introduced mediation measurement error into our mediation analysis. The spatial precision used in the current study was based on previous literature on PAF as a biomarker of pain sensitivity, which have used global and/or sensorimotor ROIs (Furman et al., 2018; Furman et al., 2020). Identification and use of the exploratory electrode clusters found in this study could build upon the current work (e.g., Furman et al., 2021). However, exploratory analysis of the clusters found in the present analysis demonstrated no influence on mediation analysis results (Supplementary Materials 3.8-3.10). Alternatively, independent component analysis (ICA) could be used to identify separate sources of alpha oscillations (Choi et al., 2005), as used in other experimental PAF-pain studies (Furman et al., 2018; Valentini et al., 2022), which could aid to disentangle the potential relevance of different alpha sources in the PAFpain relationship. Although this comes with the need to develop more reproducible and automated methods for identifying such components.”

The specific location or source of PAF that relates to pain remains unclear. Because of this, we did employ an exploratory cluster-based permutation analysis to assess the potential for variations in the presence of PAF changes across the scalp at sensor level, and emphasise that location of PAF change could be explored in future. However, we have now conducted the mediation analysis (difference score 2W-LCS model) using averages from the data-driven parietal cluster, frontal cluster, and both clusters together. For these we see a stronger effect of gum on PAF change, which was expected given the data driven approach of picking electrodes. There was still a total and direct effect of nicotine on pain during the PHP model, but still no indirect effect via change in PAF. For the CPA models, there were still no significant total, direct, or indirect effects of nicotine on CPA ratings. Therefore, using these data-driven clusters did not alter results compared to the model using the global PAF variable.

The reader has been directed to this supplementary material so:

“The potential mediating effect of this change in PAF on change in PHP and CPA was explored (not pre-registered) by averaging within each cluster (central-parietal: CP1, CP2, Cpz, P1, P2, P3, P4, Pz, POz; right-frontal: F8, FT8, FT10) and across both clusters. This averaging across electrodes produced three new variables, each assessed in relation to mediating effects on PHP and CPA ratings. The resulting in six exploratory mediation analysis (difference score 2W-LCS) models demonstrated minimal differences from the main analysis of global PAF (8-12 Hz), except for the

expected stronger effect of nicotine on change in PAF (bs = 0.11-0.14, ps < .003; Supplementary

Materials 3.8-3.10).”

Moreover, our team has been working on an automated method for selecting ICA components, so in response to your comment we assessed whether using this method altered the results of the current analysis. The in-depth methodology behind this new automatic pipeline will be published with a validation from some co-authors in the current collaboration in due course. At present, in summary, this automatic pipeline conducts independent component analysis (ICA) 10 times for each resting state, and selects the component with the highest topographical correlation to a template created of a sensorimotor alpha component from Furman et al., (2018). 

The results of the PHP or CPA mediation models were not substantially different using the PAF calculated from independent components than that using the global PAF. For the PHP model, the total effect (b = -0.648, p \= .033) and direct effects (b = -0.666, p \= .035) were still significant, and there was still no significant indirect effect (b = 0.018, p \= .726). The general fit was reduced, as although the CFI was above 0.90, akin to the original model, the RMSEA and SRMR were not below 0.08, unlike the original models (Little, 2013). For the CPA model, there were still no significant total (b = -0.371, p \= .357), direct (b = -0.364, p \= .386), or indirect effects (b = -0.007, p \= .906), and the model fit also decreased, with CFI below 0.90 and RMSEA and SRMR above 0.08. See supplementary material (3.11). Note that still no correlations were seen between this IC sensorimotor PAF and pain (PHP: r = 0.11, p = .4; CPA: r \= -0.064, p = .63).

Interestingly, in both models, there was now no longer a significant a-path (PHP: b = 0.08, p =

0.292; CPA: b = 0.039, p = 0.575), unlike previously observed (PHP: b = 0.085, p = 0.018; CPA: b = 0.089, p = 0.011). We interpret this as supporting the previously highlighted difference between finding an effect on PAF globally but not in a sensorimotor ROI (and now a sensorimotor IC), justifying the exploratory CBPA and the suggestion in the discussion to explore methodology.

We understand that this analysis does not fully uncover the reviewer’s question in which they wondered which of the known cortical alphas contributes to the effects reported in our previous work. However, we consider this exploration to be beyond the scope of the current paper, as it would be more appropriately addressed with larger datasets or combinations of datasets, potentially incorporating MEG to better disentangle oscillatory sources. The highlighted differences seen between global PAF, sensorimotor ROI PAF, sensorimotor IC PAF, as well as the CBPA of PAF changes provide ample directions for future research to build upon: 1) which alpha (sensor or source space) are related to pain, 2) how are these alpha signals represented robustly in a replicable way, and 3) which alpha (sensor or source space) are manipulable through interventions. These are all excellent questions for future studies to investigate.

The below text has been added to the Discussion:

In-house code was developed to compare a sensorimotor component to the results presented in this manuscript (Supplementary Material 3.11), showing similar results to the sensorimotor ROI mediation analysis presented here. However, examination of which alpha - be it sensor or source space - are related to pain, how they can be robustly represented, and how they can be manipulated are ripe avenues for future study.

(4) I have my doubts that you can get a reliable close to bell-shaped amplitude distribution for every participant. The argument that the peak detection procedure is hampered by the high-amplitude lower frequency can be easily solved by subtracting the "slope" before determining the peak. My issue is that the entire analysis is resting on the assumption that each participant has a reliable alpha effect at electrode level. This is not the case. Non-alpha participants can severely distort the statistics. ICA-based analyses would be more sensitive but not every participant will show alpha. You may want to argue with robust group effects but In my view, every single participant counts, particularly for this type of data analysis, where in the case of a low SNR the "peak" can easily shift to the extremes. In case there is an alpha effect for a specific subject, we should see a smooth bump in the frequency spectrum between 8 and 12 12Hz. Anything beyond that is hard to believe. The long stimulation period allows a broad FFT analysis window with a good frequency resolution in order to detect the alpha frequency bump.

The reviewer is correct that non-alpha participants can distort the statistics. We did visually assess the EEG of each individual’s spectra at baseline to establish the presence of global peaks, as we believe this is good practice to aid understanding of the data. Please see Author response image 1 for individual spectra seen at baseline. Although not all participants had a ‘smooth bump in the frequency spectrum between 8 and 12 Hz’, we prefer to not apply/necessitate this assumption to our data. Chiang et al., (2011) suggest that ~3% of individuals do not have a discernible alpha peak, and in our data we observed only one participant without a very obvious spectral peak (px-39). But, this participant does have enough activity within the alpha range to identify PAF by the CoG method (i.e. not just flat spectra and activity on top of 1/f characteristics). Without a pre-registered and standardised decision process to remove such a participant in place, we opted to not remove any participants to avoid curation of our data.

Author response image 1.

(5) I find reports on frequent channel rejections reflect badly on the data quality. Bad channels can be avoided with proper EEG preparation. EEG should be continuously monitored during recording in order to obtain best data quality. Have any of the ROI channels been rejected?

We appreciate your attention to the channel rejection. We believe that the average channels removed (0.94, 0.98, 0.74, and 0.87 [range: 0-4] for each of the four resting states out of 64 channels) does not suggest overly frequent rejection, as it was less than one electrode on average and the numbers are below the accepted number of bad channels to remove/interpolate (i.e. 10%) in EEG pipelines (Debnath et al., 2020; Kayhan et al., 2022). To maintain data quality, consistently poor channels were identified and replaced over time. We hope you will accept our transparency on this issue and note that by stating how channel removal decisions were made (i.e. 8 or more deviations) and reporting the number of channels removed, we adhere to the COBIDAS guidelines (Pernet et al., 2018; 2020).

During analysis, cases of sensorimotor ROI channels being rejected were noted and are now specified in our manuscript. “Out of 248 resting states recorded, 14 resting states had 4 ROI channels instead of 5. Importantly, no resting state had fewer than 4 channels for the sensorimotor ROI.”

Note, we also realised that we had not specified that we did interpolate channels for the cluster based permutation analysis. This has been corrected with the following sentence:

“Removed channels were not interpolated for the pre-registered global and sensorimotor ROI averaged analyses, but were interpolated for an exploratory cluster based permutation analysis using the nearest neighbour average method in `Fieldtrip`.”

(6) I have some issues buying the authors' claims that there is an effect of nicotine on prolonged pain. By looking at the mean results for the nicotine and placebo condition, this can not be right. What was the point in including the variables in the equation? In my view, in this within-subject design the effect of nicotine should be universal, no matter what gender, age, or depression. The unconditional effect of nicotine is close to zero. I can not get my head around how any of the variables can turn the effects into significance. There must be higher or lower variable scores that might be related to a higher or lower effect on nicotine. The question is not to consider these variables as a nuisance but to show how they modulate the pain-related effect of nicotine treatment. Still, the overall nicotine effect of the entire group is basically zero.

Another point is that for within-subject analyses even tiny effects can become statistically significant if they are systematically in one direction. This might be the case here. There might be a significant effect of nicotine on pain but the actual effect size (5.73 vs. 5.78) is actually not interpretable. I think it would be interesting for the reader how (in terms of pain rating difference) each of the variables can change the effect of nicotine.

Thank you for your comments. We recognize the concern about interpreting the effect of nicotine on prolonged pain solely based on mean results, and in fact wish to discourage this approach. It's crucial to note that both PAF and pain are highly individual measures (i.e. high inter-individual variance), necessitating the use of random intercepts for participants in our analyses to acknowledge the inherent variability at baseline across participants. Including random intercepts rather than only considering the means helps address the heterogeneity in baseline levels among participants. We also recognise that displaying the mean PHP ratings for all participants in Table 2 could be misleading, firstly because these means do not have weight in an analysis that takes into account a random-effects intercept for participants, and secondly because two participants (one from each group) did not have post-gum PHP assessments and were not included in the mediation analysis due to list-wise deletion of missing data. Therefore, to reduce the potential for misinterpretation, we have added extra detail to display both the full sample and CPA mediation analysis (i.e. N=62) and the data used for PHP mediation analysis (i.e. n=60) in Table 2. We hope that the extra details added to this table will help the readers interpretation of results.

In light of this, we have also altered the PAF Table 3 to reflect both the pre-post values used for the CPA mediation and baseline correlations with CPA and PHP pain (i.e. N=62), and the pre-post values used for the PHP mediation (i.e. n=60).

It is inherently difficult to visualise the findings of a mediation analysis with confounding variables that also used latent change scores (LCS) and random-effect intercepts for participants. LCS was specifically used because of issues of regression to the mean that occur if you calculate a straightforward ‘difference-score’, therefore calculating the difference in order to demonstrate the results of the statistical model in a figure, for example, does not provide a full description of the data assessed (Valente & McKinnon, 2017). Nevertheless, if we look at the data descriptively with this in mind, then calculating the change in PHP ratings does indicate that, for the nicotine group, the mean change in PHP ratings was -0.047 (SD = 1.05, range: -4.13, 1.45). Meanwhile, for the placebo group the mean change in PHP ratings was 0.33 (SD = 0.75, range: -1.37, 1.66). Therefore suggesting a slight decrease in pain ratings on average for the nicotine group compared to a slight increase on average for the placebo group. With control for pre-determined confounders, we found that the latent change score was -0.63 lower for the nicotine group compared to the control group (i.e. the direct effect of nicotine on change in pain).

If the reviewer is only discussing the effect of nicotine on pain, we do not believe that this effect ‘should be universal’. There is clear evidence that effects of nicotine on other measures can vary greatly across individuals (Ettinger et al., 2009; Falco & Bevins, 2015; Pomerleau et al., 1995). Our intention would not be to propose a universal effect but to understand how these variables may influence nicotine's impact on pain for individuals. Here we focus on the effects of nicotine on PAF and pain sensitivity, but attempted to control for the potential influence of these other confounding factors. Therefore, our statistical approach goes beyond mean values, incorporating variables like sex at birth, age, and depression to control for and explore potential modulating factors. Control for confounding factors is an important aspect of mediation analysis (Lederer et al., 2019; VanderWeele, 2019).

Regarding the seemingly small effect size, we understand your concern. Indeed ‘tiny effects can become statistically significant if they are systematically in one direction’, which may be what we see in this analysis. We do not agree that the effect is ‘not interpretable’, rather that it should be interpreted in light of its small effect size (effect size being the beta coefficient in our analysis, rather than the mean group difference). We agree on the importance of considering practical significance alongside statistical significance and hope to conduct additional experiments and analyses in future to elucidate the contribution of each variable to the subtle and therefore not entirely conclusive overall effect you mention.

Your feedback on this is valuable, and we have ensured a more detailed discussion in the revised manuscript on how these factors should be interpreted alongside some additional post-hoc analyses of confounding factors that were significant in our mediation, with the note that investigation of these interactions is exploratory. We had already discussed the potential contribution of sex on the effect of nicotine on PAF, with exploratory post-hoc analysis on this included in supplementary materials. In addition, we have now added an exploratory post-hoc analysis on the potential contribution of stress on the effect of nicotine on pain. This then shows the stratified effects by the covariates that our model suggest are influencing change in PAF and pain.

Results edits:

“There was also a significant effect of perceived stress at baseline on change in PHP ratings when controlling for group allocation and other confounding variables (b = -0.096, p = .048, bootstrapped 95% CI: [-0.19, -0.000047]), where higher perceived stress resulted in larger decreases in PHP ratings (see Supplementary Material 3.3 for post-hoc analysis of stress).”

Supplementary material addition:

“3.3 Exploratory analysis of the influence of perceived stress on the effects of nicotine on change in PHP ratings “

“Due to the significant estimated effects of perceived stress on change in PHP ratings in the 2WLCS mediation model, we also explored post-hoc effects of stress on change in PHP ratings. We found that there is strong evidence for a negative correlation between stress and change in PHP rating within the nicotine group (n = 28, r = −0.39, BF10 = 13.65; Figure 3) that is not present in the placebo group, with equivocal evidence (n = 32, r = −0.14, BF10 = 0.46). This suggests that those with higher baseline stress who had nicotine gum experienced greater decreases in PHP ratings. Note that there was less, but still sufficient evidence for this relationship within the nicotine group when the participant who was a potential outlier for change in PHP rating was removed (n = 27, r = −0.32, BF10 = 1.45). “

Author response image 2.

Spearman correlations od baseline perceived stress with the change in phasic heat pain (PHP) ratings, suggest strong evidence for a negative relationship for the nicotine gum groupin orange (n=28; BF₁₀=13.65) but not for the placebo group in grey (n=32; BF₁₀=0.46). Regression lines and 95% confidence intervals.

Discussion edits:

“For example, in addition to the effect of nicotine on prolonged heat pain ratings, our results suggest an effect of stress on changes in heat pain ratings, with those self-reporting higher stress at baseline having greater reductions in pain. Our post-hoc analysis suggested that this relationship between higher stress and larger decrease in PHP ratings was only present for the nicotine group (Supplementary Material 3.3). As stress is linked to nicotine use [69,70] and pain [71–73], these interactions should be explored in future.”

(7) Is the differential effect of nicotine vs. placebo based on the pre vs. post treatment effect of the placebo condition or on the pre vs. post effect of the nicotine treatment? Can the mediation model be adapted and run for each condition separately? The placebo condition seems to have a stronger effect and may have driven the result.

Thank you for your comments. In our mediation analysis, the differential effect of nicotine vs. placebo is assessed as a comparison between the pre-post difference within each condition. A latent change score (i.e. pre-post) is calculated for each condition (nicotine and placebo), and then the effect of being in the nicotine group (dummy coded as 1) is compared to being in the placebo group (dummy coded as 0). The comparison between conditions is needed for this model (Valente & MacKinnon, 2017), as we are assessing the change in PAF and pain in the nicotine group compared to the change in the placebo group.

However, to address your response, it is possible to simplify and assess the relationship between the change in peak alpha frequency (PAF) and change in pain within each gum group (nicotine and placebo) independently, without including the intervention as a factor. To do this, the mediation model can be simplified to regression analysis with latent change scores that focus purely on these relationships. The results of this can help to understand whether change in PAF influences change in pain within each group separately. As with the main analysis, we see no significant influence of change in PAF on change in pain while controlling for the same confounding variables within the nicotine group (Beta = -0.146 +/- 1.105, p = 0.895, 95% CI: -2.243, 2.429) or the placebo group (Beta = 0.730 +/- 2.061, p = 0.723, 95% CI: -4.177, 3.625).

When suggesting that the “the placebo condition seems to have a stronger effect and may have driven the result”, we believe you are referring to the increase in mean PHP ratings within the placebo group from pre (5.51 +/- 2.53) to post-placebo gum (5.84 +/- 2.67). Indeed there was a significant increase in pain ratings pre to post chewing placebo gum (t(31) = -2.53, p = 0.0165, 95% CI: -0.603, -0.0653), that was not seen after chewing nicotine gum (t(27) = 0.237, p = 0.81, 95% CI: -0.358, 0.452). In lieu of a control where no gum was chewed (i.e. simply a second pain assessment ~30 minutes after the first), we assume the gum without nicotine is a good reference that controls for the effect of time plus expectation of chewing nicotine gum. With this in mind, as we describe in our results, the change in PHP ratings is reduced in the nicotine group compared to the placebo group. Note that this phrasing keeps the effect of placebo on pain as our reference from which to view the effect of nicotine on pain. However, you are correct that we need to ensure we emphasise that the change in pain in the PHP group is reduced in comparison to the change seen after placebo.

We have not included these extra statistics in our revised manuscript, but hope that they aid the your understanding and interpretation of the included analyses and have highlighted these nuances in the discussion.

“However, we note that the observed effect of nicotine on pain was small in magnitude, and most prominent in comparison to the effect of placebo, where pain ratings increased after chewing, which brings into question whether this reduction in pain is meaningful in practice.”

(8) I would not dare to state that nicotine can function as an acute analgesic. Acute analgesics need to work for everyone. The average effect here is close to zero.

In light of your feedback, we have refined our language to avoid a sweeping assertion of universal analgesic effects and emphasize individual variability. Nicotine's role as a coping strategy for pain is acknowledged in the literature (Robinson et al., 2022), with the meta-analysis by Ditre et al. (2016) discussing its potential as an acute analgesic in humans, along with some evidence from animal research (Zhang et al., 2020). Our revised discussion underscores the need for further exploration into factors influencing nicotine's potential impact on pain. We have also specified the short-term nature of nicotine use in this context to distinguish acute effects from potential opposing effects after long-term use (Zhang et al., 2020).

“Short-term nicotine use is thought to have acute analgesic properties in experimental settings, with a review reporting that nicotine increased pain thresholds and pain tolerance [49]. In addition, research in a rat model suggests analgesic effects on mechanical thresholds after short-term nicotine use (Zhang et al., 2020). However, previous research has not assessed the acute effects of nicotine on prolonged experimental pain models. The present study found that 4 mg of nicotine reduced heat pain ratings during prolonged heat pain compared to placebo for our human participants, but that prolonged pressure pain decreased irrespective of which gum was chewed. Our findings are thus partly consistent with the idea that nicotine may have acute analgesic properties [49], although further research is required to explore factors that may influence nicotine’s potential impact on a variety of prolonged pain models. We further advance the literature by reporting this effect in a

model of prolonged heat pain, which better approximates the experience of clinical pain than short lasting models used to assess thresholds and tolerance [50]. However, we note that the observed effect of nicotine on pain was small in magnitude, and most prominent in comparison to the effect of placebo, where pain ratings increased after chewing, which brings into question whether this reduction in pain is meaningful in practice. Future research should examine whether effects on pain increase in magnitude with different nicotine administration regimens (i.e. dose and frequency).”

(9) Figures 2E and 2F are not particularly intuitive. Usually, the colour green in "jet" colour coding is being used for "zero" values. I would suggest to cut off the blue and use only the range between red green and red.

We have chosen to retain the current colour scale for several reasons. In our analysis, green represents the middle of the frequency range (approx 10 Hz in this case), and if we were to use green as zero, it would effectively remove both blue and green from the plot, resulting in only red shades. Additionally, we have provided a clear colour scale for reference next to the plot, which allows readers to interpret the data accurately. Our intention is to maintain clarity and precision in representing the data, rather than conforming strictly to conventional practices in color coding.

We believe that the current representation effectively conveys the results of our study while allowing readers to interpret the data within the context provided. Thank you again for your suggestion, and we hope you understand our reasoning in this matter.

(10) Did the authors do their analysis on the parietal ROI or on the pre-registerred ROI?

The analysis was conducted on the pre-registered sensorimotor ROI and on the global values. We have now also conducted the analysis with the regions suggested with the cluster based permutation analysis as requested by reviewer 2, comment 3.

(11) Point 3.2 in the discussion. I would be very cautious to discuss smoking and chronic pain in the context of the manuscript. The authors can not provide any additional knowledge with their design targeting non-smokers, acute nicotine and experimental pain. The information might be interesting in the introduction in order to provide the reader with some context but is probably misleading in the discussion.

We appreciate your perspective and agree with your caution regarding the discussion of smoking and chronic pain. While our study specifically targets non-smokers and focuses on acute nicotine effects in experimental pain, we understand the importance of contextual clarity. We have removed these points from the discussion to not mislead the reader.

Previously we wrote, and have removed: “For those with chronic pain, smoking and nicotine use is reported as a coping strategy for pain [52]; abstinence can increase pain sensitivity [48,50], and pain is thus seen as a barrier to smoking cessation due to fear of worsening pain [51,52]. Therefore, continued understanding of the acute effects of nicotine on models of prolonged pain could improve understanding of the role of nicotine and smoking use in chronic pain [49,51,52].”

(12) I very much appreciate section 3.3 of the discussion. I would not give up on PAF as a target to modulate pain. A modulation might not be possible in such a short period of experimental intervention. PAF might need longer and different interventions to gradually shift in order to attenuate the intensity of pain. As discussed by the authors themselves, I would also consider other targets for alpha analysis (as mentioned above not other electrodes or ROIs but separated sources.)

Thank you for your comments on section 3.3. We appreciate your recognition of the potential significance of PAF as a target for pain modulation. Your insights align with our considerations that the experimental intervention duration or type might be a limiting factor in observing substantial shifts in PAF to attenuate pain intensity. We had mentioned the use of the exploratory electrode clusters in future work, but have now also mentioned that the use of ICA to identify separate ICA sources may provide an alternative approach. See responses to your previous ICA comment regarding separate sources.

REFERENCES for responses to reviewer 2

Chiang, A. K. I., Rennie, C. J., Robinson, P. A., Van Albada, S. J., & Kerr, C. C. (2011). Age trends and sex differences of alpha rhythms including split alpha peaks. Clinical Neurophysiology, 122(8), 1505-1517.

Debnath, R., Buzzell, G. A., Morales, S., Bowers, M. E., Leach, S. C., & Fox, N. A. (2020). The Maryland analysis of developmental EEG (MADE) pipeline. Psychophysiology, 57(6), e13580.

Ettinger, U., Williams, S. C., Patel, D., Michel, T. M., Nwaigwe, A., Caceres, A., ... & Kumari, V. (2009). Effects of acute nicotine on brain function in healthy smokers and non-smokers: estimation of inter-individual response heterogeneity. Neuroimage, 45(2), 549-561.

Falco, A. M., & Bevins, R. A. (2015). Individual differences in the behavioral effects of nicotine: a review of the preclinical animal literature. Pharmacology Biochemistry and Behavior, 138, 80-90.

Kayhan, E., Matthes, D., Haresign, I. M., Bánki, A., Michel, C., Langeloh, M., ... & Hoehl, S. (2022). DEEP: A dual EEG pipeline for developmental hyperscanning studies. Developmental cognitive neuroscience, 54, 101104.

Lederer, D. J., Bell, S. C., Branson, R. D., Chalmers, J. D., Marshall, R., Maslove, D. M., ... & Vincent, J. L. (2019). Control of confounding and reporting of results in causal inference studies. Guidance for authors from editors of respiratory, sleep, and critical care journals. Annals of the American Thoracic Society, 16(1), 22-28.

Little TD. Longitudinal structural equation modeling. Guilford press; 2013.

Pernet, C., Garrido, M., Gramfort, A., Maurits, N., Michel, C. M., Pang, E., ... & Puce, A. (2018). Best practices in data analysis and sharing in neuroimaging using MEEG.

Pernet, C., Garrido, M. I., Gramfort, A., Maurits, N., Michel, C. M., Pang, E., ... & Puce, A. (2020). Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nature neuroscience, 23(12), 1473-1483.

Pomerleau, O. F. (1995). Individual differences in sensitivity to nicotine: implications for genetic research on nicotine dependence. Behavior genetics, 25(2), 161-177.

Robinson, C. L., Kim, R. S., Li, M., Ruan, Q. Z., Surapaneni, S., Jones, M., ... & Southerland, W. (2022). The Impact of Smoking on the Development and Severity of Chronic Pain. Current Pain and Headache Reports, 26(8), 575-581.

Xia, J., Mazaheri, A., Segaert, K., Salmon, D. P., Harvey, D., Shapiro, K., ... & Olichney, J. M. (2020). Event-related potential and EEG oscillatory predictors of verbal memory in mild cognitive impairment. Brain communications, 2(2), fcaa213.

VanderWeele, T. J. (2019). Principles of confounder selection. European journal of epidemiology, 34, 211-219.

Valente, M. J., & MacKinnon, D. P. (2017). Comparing models of change to estimate the mediated effect in the pretest–posttest control group design. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 428-450.

Vimolratana, O., Aneksan, B., Siripornpanich, V., Hiengkaew, V., Prathum, T., Jeungprasopsuk, W., ... & Klomjai, W. (2024). Effects of anodal tDCS on resting state eeg power and motor function in acute stroke: a randomized controlled trial. Journal of NeuroEngineering and Rehabilitation, 21(1), 1-15.

Zhang, Y., Yang, J., Sevilla, A., Weller, R., Wu, J., Su, C., ... & Candiotti, K. A. (2020). The mechanism of chronic nicotine exposure and nicotine withdrawal on pain perception in an animal model. Neuroscience letters, 715, 134627.

Reviewer #3 (Recommendations For The Authors):

Introduction

(1) Rationale and link to chronic pain. I am not sure I agree with the statement "The ability to identify those at greater risk of developing chronic pain is limited". I believe there is an abundance of literature associating risk factors with the different instances of chronic pain (e.g., Mills et al., 2019). The fact that the authors cite studies involving potential neuroimaging biomarkers leads me to believe that they perhaps did not intend to make such a broad statement, or that they wanted to focus on individual prediction instead of population risk.

We thank the reviewer for the thought put into this comment. We did indeed wish to refer to individual prediction, but also realise that the focus on predicting pain might not be the most appropriate opening for this manuscript. Therefore, we have adjusted the below sentence to refer to the need to identify modifiable factors rather than the need to predict pain.

“Identifying modifiable factors that influence pain sensitivity could be a key step in reducing the presence and burden of chronic pain (van der Miesen et al., 2019; Davis et al., 2020; Tracey et al., 2021).”

(2) The statement "Individual peak alpha frequency (PAF) is an electro-physiological brain measure that shows promise as a biomarker of pain sensitivity, and thus may prove useful for predicting chronic pain development" is a non sequitur. PAF may very well be a biomarker of pain sensitivity, but the best measures of pain sensitivity we have (selfreported pain intensity ratings) in general are not in themselves predictive of the development of chronic pain. Conversely, features that are not related to pain sensitivity could be useful for predicting chronic pain (e.g., Tanguay-Sabourin et al., 2023).

We agree that it is essential to acknowledge that self-reported pain intensity ratings alone are not definitive predictors of chronic pain development. To align with this, we have revised the sentence, removing the second clause to avoid overstatement. The adjusted sentence now reads, "Individual peak alpha frequency (PAF) is an electrophysiological brain measure that shows promise as a biomarker of pain sensitivity."

(3) Finally, some of the statements in the discussion comparing a tonic heat pain model with chronic neuropathic pain might be an overstatement. Whereas it is true that some of the descriptors are similar, the time courses and mechanisms are vastly different.

We appreciate this comment, and agree that it is difficult to compare the heat pain model used to clinical neuropathic pain. This was an oversight and with further understanding we have removed this comment from the introduction and the discussion:

“In parallel, we saw no indication of a relationship between PAF and pain ratings during CPA. The introduction of the CPA model, specifically calibrated to a moderate pain threshold, provides further support for the notion that the relationship between PAF and pain is specific to certain pain types [17,28]. Prolonged heat pain was pre-dominantly described as moderate/severe shooting, sharp, and hot pain, whereas prolonged pressure pain was predominantly described as mild/moderate throbbing, cramping, and aching in the present study. It is possible that the PAF–pain relationship is specific to particular pain models and protocols [12,17].”

Methodology

(4) or the benefit of good science. However, I am compelled to highlight that I could not access the preregistered files, even though I waited for almost two weeks after requesting permission to do so. This was a problem on two levels: the main one is that I could not check the hypothesized effect sizes of the sample size estimation, which are not only central to my review, and in general negate all the benefits that should go with preregistration (i.e., avoiding phacking, publication bias, data dredging, HARKing, etc.). The second one is that I had to provide an email address to request access. This allows the authors to potentially identify the reviewers. Whereas I have no issues with this and I support transparent peer review practices (https://elifesciences.org/inside-elife/e3e90410/increasingtransparency-in-elife-s-review-process), I also note that this might condition other reviewers.

We apologise for this. We had not realised that the pre-registration was under embargo, but we have now made it available.

Interpretation of results

(5)To be perfectly clear, I trust the results of this study more than some of the cited studies regarding nicotine and pain because it was preregistered, the sample size is considerably larger, and it seems carefully controlled. I just do not agree with the interpretation of the results, stated in the first paragraph of the Discussion. Quoting J. Cohen, "The primary product of a research inquiry is one or more measures of effect size, not P values" (Cohen, 1990). As I am sure the authors are aware of, even tiny differences between conditions, treatments or groups will eventually be statistically significant given arbitrarily large sample sizes. What really matters then is the magnitude of these differences. In general, the authors hypothesize on why there were no differences on the pressure pain model, and why decreases in heat pain were not mediated by PAF, but do not seem to consider the possibility that the intervention just did not cause the intended effect on the nociceptive system, which would be a much more straightforward explanations for all observations.

While acknowledging and agreeing with the concern that 'even tiny differences between conditions, treatments, or groups will eventually be statistically significant given arbitrarily large sample sizes,' it's crucial to clarify that our sample size of N=62 does not fall into the category of arbitrarily large. We carefully considered the observed outcomes in the pressure pain model and the lack of PAF mediation in heat pain, as dictated by our statistical approach and the obtained results.

The suggestion of a straightforward explanation aligning with the intervention not causing the intended effect on the nociceptive system is a valid consideration. We did contemplate the possibility of a false positive, emphasising this in the limitations of our findings and the need for replication to draw stronger conclusions to follow up this initial study.

(6) In this regard, I do not believe that an average *increase* of 0.05 / 10 (Nicotine post - pre) can be considered a "reduction of pain ratings", regardless of the contrast with placebo (average increase of 0.24 / 10). This tiny effect size is more relevant in the context of the considerable inter-individual variation, in which subjects scored the same heat pain model anywhere from 1 to 10, and the same pressure pain model anywhere from 1 to 8.5. In this regard, the minimum clinically or experimentally important differences (MID) in pain ratings varies from study to study and across painful conditions but is rarely below 1 / 10 in a VAS or NRS scale, see f. ex. (Olsen et al., 2017). It is not my intention to question whether nicotine can function as an acute analgesic in general (as stated in the Discussion), but instead, if it worked as such under these very specific experimental conditions. I also acknowledge that the authors note this issue in two lines in the Discussion, but I believe that this is not weighed properly.

We appreciate your perspective on the interpretation of the effect size, and we understand the importance of considering it in the context of individual variation.

As also discussed in response to comment 6 From reviewer 2, we recognize the concern about interpreting the effect of nicotine on prolonged pain solely based on mean results, and in fact wish to discourage this approach. It's crucial to note that both PAF and pain are highly individual measures (i.e. high inter-individual variance), necessitating the use of random intercepts for participants in our analyses to acknowledge the inherent variability at baseline across participants. Including random intercepts rather than only considering the means helps address the heterogeneity in baseline levels among participants. We also recognise that displaying the mean PHP ratings for all participants in Table 2 could be misleading, firstly because these means do not have weight in an analysis that takes into account a random-effects intercept for participants, and secondly because two participants (one from each group) did not have post-gum PHP assessments and were not included in the mediation analysis due to list-wise deletion of missing data. Therefore, to reduce the potential for misinterpretation, we have added extra detail to display both the full sample and CPA mediation analysis (i.e. N=62) and the data used for PHP mediation analysis (i.e. n=60) in Table 2. We hope that the extra details added to this table will help the readers interpretation of results.

Moreover, we have made sure refer to the comparison with the placebo group when discussing the reduction or decrease in pain seen in the nicotine group, for example:

“2) nicotine reduced prolonged heat pain intensity but not prolonged pressure pain intensity compared to placebo gum;”

“The nicotine group had a decrease in heat pain ratings compared to the placebo group and increased PAF speed across the scalp from pre to post-gum, driven by changes at central-parietal and right-frontal regions.”

We have kept our original comment of whether this effect on pain is meaningful in practice to refer to the minimum clinically or experimentally important differences in pain ratings as highlighted by Olsen et al., 2017.

“While acknowledging the modest effect size, it’s essential to consider the broader context of our study’s focus. Assessing the clinical relevance of pain reduction is pertinent in applications involving the use of any intervention for pain management [69]. However, from a mechanistic standpoint, particularly in understanding the implications of and relation to PAF, the specific magnitude of the pain effect becomes less pivotal. Nevertheless, future research should examine whether effects on pain increase in magnitude with different nicotine administration regimens (i.e. dose and frequency).”

(7) In line with the topic of effect sizes, average effect sizes for PAF in the study cited in the manuscript range from around 1 Hz (Boord et al., 2008; Wydenkeller et al., 2009; Lim et al., 2016), to 2 Hz (Foulds et al., 1994), compared with changes of 0.06 Hz (Nicotine post - pre) or -0.01 Hz (Placebo post - pre). MIDs are not so clearly established for peak frequencies in EEG bands, but they should be certainly larger than some fractions of a Hertz (which is considerably below the reliability of the measurement).

We appreciate your care of these nuances. We acknowledge the differences in effect sizes between our study and those referenced in the manuscript. Given the current state of the literature, it's noteworthy that ‘MIDs’ for peak frequencies in EEG bands, particularly PAF changes, are not clearly established, other than a recent publication suggesting that even small changes in PAF are reliable and meaningful (Furman et al., 2021). In light of this, we have addressed the uncertainty around the existence and determination of MIDs in our revision, highlighting the need for further research in this area.

In addition, our study employed a greater frequency resolution (0.2 Hz) compared to some of the referenced studies, with approximately 0.5 Hz resolution (Boord et al., 2008; Wydenkeller et al., 2009; Foulds et al., 1994). This improved resolution allows for a more precise measurement of changes in PAF. Considering this, it is plausible that studies with lower resolution might have conflated increases in PAF, and our higher resolution contributes to a more accurate representation of the observed changes.

We have also incorporated this insight into the manuscript, emphasising the methodological advancements in our study and their potential impact on the interpretation of PAF changes. Thank you for your thoughtful feedback.

“The ability to detect changes in PAF can be considerably impacted by the frequency resolution used during Fourier Transformations, an element that is overlooked in recent methodological studies on PAF calculation [16,95]. Changes in PAF within individuals might be obscured or conflated by lower frequency resolutions, which should be considered further in future research.”

(8) The authors also ran alternative statistical models to analyze the data and did not find consistent results in terms of PHP ratings (PAF modulation was still statistically significantly different). The authors attribute this to the necessity of controlling for covariates. Now, considering the effects sizes, aren't these statistically significant differences just artifacts stemming from the inclusion of too many covariates (Simmons et al., 2011)? How much influence should be attributable to depression and anxiety symptoms, stress, sleep quality and past pain, considering that these are healthy volunteers? Should these contrasting differences call the authors to question the robustness of the findings (i.e., whether the same data subjected to different analysis provides the same results), particularly when the results do not align with the preregistered hypothesis (PAF modulation should occur on sensorimotor ROIs)?

Thank you for your comments on our alternative statistical models. By including these covariates, we aim to provide a more nuanced understanding of the complexities within our data by considering their potential impact on the effects of interest. The decision to include covariates was preregistered (apologies again that this was not available) and made with consideration of balancing model complexity and avoiding potential confounding. Moreover, we hope that the insights gained from these analyses will offer valuable information about the behaviour of our data and aid future research in terms of power calculations, expected variance, and study design.

(9) Beyond that, I believe in some cases that the authors overreach in an attempt to provide explanations for their results. While I agree that sex might be a relevant covariate, I cannot say whether the authors are confirming a pre-registered hypothesis regarding the gender-specific correlation of PAF and pain, or if this is just a post hoc subgroup analysis. Given the large number of analyses performed (considering the main document and the supplementary files), caution should be exercised on the selective interpretation of those that align with the researchers' hypotheses.

We chose to explore the influence of sex on the correlation between PAF and pain, because this has also been investigated in previous publications of the relationship (Furman et al., 2020).  We state that the assessment by sex is exploratory in our results on p.17: “in an exploratory analysis of separate correlations in males and females (Figure 5, plot C)”. For clarity regarding whether this was a pre-registered exploration or not, we have adjusted this to be: “in an exploratory analysis (not pre-registered) of separate correlations in males and females (Figure 5, plot C), akin to those conducted in previous research on this topic (Furman et al., 2020),

We have made sure to state this in the discussion also. Therefore, when we previously said on p.22:

“Regarding the relationship between PAF and pain at baseline, the negative correlation between PAF and pain seen in previous work [7–11,15] was only observed here for male participants during the PHP model for global PAF.” We have now changed this to: “Regarding the relationship between PAF and pain at baseline, the negative correlation between PAF and pain seen in previous work [7– 11,15] was only observed here for male participants during the PHP model for global PAF in an exploratory analysis.”

Please also note that we altered the colour and shape of points on the correlation plot (Figure 5 in initial submission), the male brown was changed to a dark brown as we realised that the light brown colour was difficult to read. The shape was then changed for male points so that the two groups can be distinguished in grey-scale.

Overall, your thoughtful feedback is instrumental in refining the interpretation of our findings, and we look forward to presenting a more comprehensive and nuanced discussion. Thank you for your comments.

REFERENCES for responses to reviewer 3

Arendt-Nielsen, L., & Yarnitsky, D. (2009). Experimental and clinical applications of quantitative sensory testing applied to skin, muscles and viscera. The Journal of Pain, 10(6), 556-572.

Chowdhury, N. S., Skippen, P., Si, E., Chiang, A. K., Millard, S. K., Furman, A. J., ... & Seminowicz, D. A. (2023). The reliability of two prospective cortical biomarkers for pain: EEG peak alpha frequency and TMS corticomotor excitability. Journal of Neuroscience Methods, 385, 109766.

Fishbain, D. A., Lewis, J. E., & Gao, J. (2013). Is There Significant Correlation between SelfReported Low Back Pain Visual Analogue Scores and Low Back Pain Scores Determined by Pressure Pain Induction Matching?. Pain practice, 13(5), 358-363.

Furman, A. J., Prokhorenko, M., Keaser, M. L., Zhang, J., Chen, S., Mazaheri, A., & Seminowicz, D. A. (2021). Prolonged pain reliably slows peak alpha frequency by reducing fast alpha power.

bioRxiv, 2021-07.

Heitmann, H., Ávila, C. G., Nickel, M. M., Dinh, S. T., May, E. S., Tiemann, L., ... & Ploner, M. (2022). Longitudinal resting-state electroencephalography in patients with chronic pain undergoing interdisciplinary multimodal pain therapy. Pain, 163(9), e997.

McLain, N. J., Yani, M. S., & Kutch, J. J. (2022). Analytic consistency and neural correlates of peak alpha frequency in the study of pain. Journal of neuroscience methods, 368, 109460.

Ngernyam, N., Jensen, M. P., Arayawichanon, P., Auvichayapat, N., Tiamkao, S., Janjarasjitt, S., ... & Auvichayapat, P. (2015). The effects of transcranial direct current stimulation in patients with neuropathic pain from spinal cord injury. Clinical Neurophysiology, 126(2), 382-390.

Parker, T., Huang, Y., Raghu, A. L., FitzGerald, J., Aziz, T. Z., & Green, A. L. (2021). Supraspinal effects of dorsal root ganglion stimulation in chronic pain patients. Neuromodulation: Technology at the Neural Interface, 24(4), 646-654.

Petersen-Felix, S., & Arendt-Nielsen, L. (2002). From pain research to pain treatment: the role of human experimental pain models. Best Practice & Research Clinical Anaesthesiology, 16(4), 667680.

Sarnthein, J., Stern, J., Aufenberg, C., Rousson, V., & Jeanmonod, D. (2006). Increased EEG power and slowed dominant frequency in patients with neurogenic pain. Brain, 129(1), 55-64.

Sato, G., Osumi, M., & Morioka, S. (2017). Effects of wheelchair propulsion on neuropathic pain and resting electroencephalography after spinal cord injury. Journal of Rehabilitation Medicine, 49(2), 136-143.

Sufianov, A. A., Shapkin, A. G., Sufianova, G. Z., Elishev, V. G., Barashin, D. A., Berdichevskii, V. B., & Churkin, S. V. (2014). Functional and metabolic changes in the brain in neuropathic pain syndrome against the background of chronic epidural electrostimulation of the spinal cord. Bulletin of experimental biology and medicine, 157(4), 462-465.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study probes the role of the NF-κB inhibitor IκBa in the regulation of pluripotency in mouse embyronic stem cells (mESCs). It follows from previous work that identified a chromatin-specific role for IκBa in the regulation of tissue stem cell differentiation. The work presented here shows that a fraction of IκBa specifically associates with chromatin in pluripotent stem cells. Using three Nfkbia-knockout lines, the authors show that IκBa ablation impairs the exit from pluripotency, with embryonic bodies (an in vitro model of mESC multi-lineage differentiation) still expressing high levels of pluripotency markers after sustained exposure to differentiation signals. The maintenance of aberrant pluripotency gene expression under differentiation conditions is accompanied by pluripotency-associated epigenetic profiles of DNA methylation and histone marks. Using elegant separation of function mutants identified in a separate study, the authors generate versions of IκBa that are either impaired in histone/chromatin binding or NF-κB binding. They show that the provision of the WT IκBa, or the NF-κB-binding mutant can rescue the changes in gene expression driven by loss of IκBa, but the chromatin-binding mutant can not. Thus the study identifies a chromatin-specific, NF-κB-independent role of IκBa as a regulator of exit from pluripotency.

Strengths:

The strengths of the manuscript lie in: (a) the use of several orthogonal assays to support the conclusions on the effects of exit from pluripotency; (b) the use of three independent clonal Nfkbia-KO mESC lines (lacking IκBa), which increase confidence in the conclusions; and (c) the use of separation of function mutants to determine the relative contributions of the chromatin-associated and NF-κB-associated IκBa, which would otherwise be very difficult to unpick.

Weaknesses:

In this reviewer's view, the term "differentiation" is used inappropriately in this manuscript. The data showing aberrant expression of pluripotency markers during embryoid body formation are supported by several lines of evidence and are convincing. However, the authors call the phenotype of Nfkbia-KO cells a "differentiation impairment" while the data on differentiation markers are not shown (beyond the fact that H3K4me1, marking poised enhancers, is reduced in genes underlying GO processes associated with differentiation and organ development). Data on differentiation marker expression from the transcriptomic and embryoid body immunofluorescent experiments, for example, should be at hand without the need to conduct many more experiments and would help to support the conclusions of the study or make them more specific. The lack of probing the differentiation versus pluripotency genes may be a missed opportunity in gaining in-depth understanding of the phenotype associated with loss of the chromatin-associated function of IκBa.

Reviewer #2 (Public review):

Summary:

This manuscript investigates the role of IκBα in regulating mouse embryonic stem cell (ESC) pluripotency and differentiation. The authors demonstrate that IκBα knockout impairs the exit from the naïve pluripotent state during embryoid body differentiation. Through mechanistic studies using various mutants, they show that IκBα regulates ESC differentiation through chromatin-related functions, independent of the canonical NFκB pathway.

Strengths:

The authors nicely investigate the role of IκBα in pluripotency exit, using embryoid body formation and complementing the phenotypic analysis with a number of genome-wide approaches, including transcriptomic, histone marks deposition, and DNA methylation analyses. Moreover, they generate a first-of-its-kind mutant set that allows them to uncouple IκBα's function in chromatin regulation versus its NF-κB-related functions. This work contributes to our understanding of cellular plasticity and development, potentially interesting a broad audience including developmental biologists, chromatin biology researchers, and cell signaling experts.

Weaknesses:

- The study's main limitation is the lack of crucial controls using bona fide naïve cells across key experiments, including DNA methylation analysis, gene expression profiling in embryoid bodies, and histone mark deposition. This omission makes it difficult to evaluate whether the observed changes in IκBα-KO cells truly reflect naïve pluripotency characteristics.

- Several conclusions in the manuscript require a more measured interpretation. The authors should revise their statements regarding the strength of the pluripotency exit block, the extent of hypomethylation, and the global nature of chromatin changes. - From a methodological perspective, the manuscript would benefit from additional orthogonal approaches to strengthen the knockout findings, which may be influenced by clonal expansion of ES cells.

Overall, this study makes an important contribution to the field. However, the concerns raised regarding controls, data interpretation, and methodology should be addressed to strengthen the manuscript and support the authors' conclusions.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I have the following comments and suggestions for the authors to consider:

(1) Fig, 1D: the number of replicates for this experiment is not mentioned. It would be good to see if the apparent accumulation of IκBa on chromatin of S/L cells is reproducible. If it is, does the accumulation of IκBa "prime" chromatin for differentiation?

We apologize for missing this information in the figure legend. We have repeated the experiment two independent times, and confirmed the localization of IκBα in the chromatin fraction of mESCs cultured in Serum/LIF (S/L). We have included the information in the figure legend.

Regarding the second question, we do believe that the presence of IκBα primes mESCs to exit from differentiation. Previous data from the lab (Mulero et al Cancer Cell 2012; Marruecos et al EMBO Reports 2020) demonstrated that IκBα regulates important developmental genes (Hox genes and differentiation-related genes), which become dysregulated upon IκBα depletion. Based on those previous results, together with our results that demonstrated that lack of IκBα hyperactivates the pluripotency network, we conclude that IκBα is a crucial element to attenuate pluripotency programs, allowing a successful exit from naïve pluripotency and differentiation.

(2) Fig. 1E: From what is shown, Rela doesn't agree (i.e. no enrichment in EpiSCs in the Atlasi data). Are the culture conditions in Atlasi 2020 the same as in this paper (base medium etc.)? Also, why not label all genes/proteins that are shown in 1C?

Differences observed between our data and the in-silico data might be due to differences in culture conditions used in Atlasi and colleagues. In particular, Atlasi et al. cultured the mESCs in 2i/LIF for 2 consecutive months, whereas we induced ground state of naïve pluripotency (2i/LIF) for only 96h. In the case of EpiSC differentiation, similar protocols are used in both our work and in Atlasi et al. Nevertheless, despite existing differences, in both studies IκBα is enriched in the ground state of naive pluripotency. 

The reason why some proteins that are missing in Figure 1E but appearing in Figure 1C is because they are not detected in the mass spectrometry experiment.

(3) Fig. 1F: The word "clustering" here is misleading. While Nfkbia shows similar dynamics as pluripotency genes, clustering should not be used unless clusters of genes are shown in the same heatmap (and the transcripts naturally cluster together). The figure would be even more informative if all the genes from the 4 different categories were presented on the same heatmap.

As suggested by the reviewer, we have generated a heatmap where the  genes from the different four categories (Figure 1F) are displayed  and clustered together:

Author response image 1.

Heatmap including all the genes from Figure 1F of the manuscript and clustering is simultaneously conducted over the four categories.

As shown in previous heatmap, we can confirm that most of the Nf-kB genes (except for Nfkbia and Nfkbid) clustered together with differentiation markers.   

Nonetheless, to be more conservative with original Figure 1F and for clarity upon gene categories,  we have updated the figure  with a combined heatmap, sliced by gene categories.  In this updated version, we can observe how IkBα gene, though classified by the biological process where it classically belongs (NF-kB pathway), is higher at pluripotency, whereas it decreases upon differentiation induction, similarly as most of the pluripotency genes.

We have also changed the text accordingly and have added the following sentences in the main text (lines 121-125): “The expression pattern of Nfkbia was similar to the pluripotency genes whereas most of the NF-κB genes were upregulated upon differentiation, showing an analogous expression dynamics as developmental genes, as previously described”.

(4) This reviewer felt that the statement "Notably, several polycomb elements were highly expressed in mESCs, consistent with the possibility that chromatin-bound IκBα modulates PRC2 activity in the pluripotent state" (p.5, lines 125-127) is premature here. While similar expression dynamics may be consistent with a linked function, they in no way suggest this. This can be more accurately stated to point out that Nfkbia shows similar expression dynamics in pluripotency and differentiation as Polycomb component      genes.

We agree that the statement is premature and we have changed it by: “Previous reports have demonstrated that chromatin-bound IκBα modulates PRC2 activity in different adult stem cell models [27]. Interestingly, we observed that most of the Polycomb target genes follow a similar expression pattern of Nfkbia and pluripotency, with higher expression in mESCs (Figure 1F).” (lines 125-128 in the manucript).

(5) Top of p. 6: the results are mis-attributed to Fig. 1, it should be Fig. 2.

We thank the reviewer for this observation. We have corrected it in the main text.

(6) Fig. 1B and Fig. 5I: the images of the AP stains are very difficult to see, better resolution images should be used.

We have increased both the resolution and the size of the AP colonies.

(7) Line 142 (p.6): Fig. S1B should be S1C. In general the manuscript would benefit from review of the order and labeling of the figure panels as there are a number of inconsistencies.

We have better organized the figures in the new version of the manuscript. In particular, we have reorganized the Figure S1 to have a more logical order. We have done the same for the Figure 2 and Figure 5 and they are updated in the new version of the reviewed manuscript.

(8) The authors call the phenotype of Nfkbia-KO cells a "differentiation impairment". Do the EBs shown in Fig. 2 also express differentiation markers? Do they fail to up-regulate those markers or just fail to down-regulate pluripotency markers? At the transcriptomic level the Nfkbia-KO cells still change significantly upon provision of differentiation signals (Fig. 2C), what types of gene processes underlie the differences between WT and KO cells and which processes are common? Also, based on this figure, the phenotype looks to be more of a delay than a failure in differentiation, as the cells still follow the same trajectory but lag behind the WT cells. It is difficult to discern whether this is the case based on Fig. 2E-G as we don't see the later time point (up to Day 9).

In general, with the data presented in Fig. 2C and Fig. S1, the authors show that many of the hallmarks of exit from pluripotency are impaired in Nfkbia-KO cells, as well as the general "transcriptional status" of the cells, but they don't show differentiation markers (which would be necessary to conclude an impairment in differentiation). The data should be readily available in the datasets that are in the manuscript already and it will be informative to extract and present them. The data are not currently publicly accessible (unavailable until July 2025) so it was not possible to mine them.

We appreciate the observation, and we have included more data to confirm that the IκBα-KO cells show a differentiation impairment. In the first version of the manuscript, differentiation markers are displayed from Figures 2E-G, where genes from the three germ layers (ectoderm, mesoderm and endoderm) are not activated in IκBα-KO EBs at 48h and 96h. Moreover, the volcano plot displayed in Figure S1F of the first version clearly shows a downregulation of important differentiation genes such as a T, Eomes, Lhx1 and Foxa2. We agree that 96h EBs is an early time point to talk about differentiation impairment. For that reason, we have also included the same pluripotent and differentiation genes in 216h EBs (Figures S1F-G of the newer version of the manuscript). It is clearly observed that IκBα-KO 216h EBs maintain an upregulation of pluripotency programs which negatively correlate with a lower differentiation capability. Moreover, the impairment in the differentiation with a higher expression of pluripotency markers is confirmed by the presence of high SSEA-1 expression in IκBα-KO 216h EBs (Figure S1C of the manuscript) and alkaline phosphatase (AP) staining (Figure 2C of the manuscript). Lastly, the fact that IκBα-KO teratomas contain higher proportion of OCT3/4+ cells further confirming that IκBα-KO cells cannot differentiate because of the inability to exit from pluripotency.

Finally, generated data (and deposited in GEO repository with SuperSeries id GSE239565) is already publicly available. 

(9) Fig. 5A: even if there are no global changes in NF-κB target genes, could a small subset of NF-κB target genes still mediate the IκBa effects?

We have analyzed the whole NF-κB signature, and we have identified a small cluster of genes that are differentially expressed at 96h EBs between IκBα-KO and IκBα-WT (Author response image 2). Interestingly, what we observed is the opposite as expected since we see un downregulation of that subset in the IκBα-KO 96h EBs (Author response image 3). For that reason, detected changes in the NF-κB target gene expression after deletion of Nfkbia do not support an NF-κB inhibitory role for IkBa in pluripotent ESC.

Author response image 2.

Heatmap of NF-κB genes expression at the different time points of differentiation (mESCs, 48h EBs, 96h EBs). Highlighted region marks the genes that are differentially expressed between both genotypes at 96h EBs.

Author response image 3.

Violin plot of genes from the NF-κB pathway which are differentially expressed at 96h EBs.

(10) Lines 233-238, the part of the text is repeated.

We appreciate the observation and have deleted the repeated part.

(11) The data in Fig. 5D-E make it difficult to be sure whether the conclusions on the relative subcellular localisations of the different mutants are accurate, as the chromatin-binding mutant seems to be less abundant than the other mutants (judging from the Input in Fig. 5C and also from the tubulin loading controls in Fig. 5D-E). Showing the IκBa levels in total extracts would make the interpretation of these data more robust. The authors do mention that the chromatin-binding mutant IκBa protein is consistently expressed at lower levels but they do not comment on how this may affect the data interpretation - could the lack of rescue be due to lower levels of the chromatin-binding mutant IκBa relative to the wild-type IκBa? This should be addressed in the Discussion, if not tested formally by normalising the expression levels of the different forms of IκBa in the rescue experiments.

Although protein stability is different among the SOF mutants, IκBα^ΔChromatin is exclusively detected in the cytoplasm, with lack of detection in the chromatin compartment (Figures 5D-E of the reviewed manuscript). For this reason, we believe that the quantitative differences in protein levels of the different mutants cannot explain the subcellular localization differences and the phenotype observed.

Nonetheless, we cannot discard that differences in the protein levels between SOF mutants can affect the rescue phenotype, and we have specified so in the discussion section of the manuscript. 

(12) Lines 260-261: "Induction of i-IκBαWT and i-IκBαΔNF-κB reduced the expression levels of the naive pluripotent genes Zfp42, Klf2, Sox2 and Tbx3, which were increased by i-IκBαΔChromatin (Figure 5F)." This is not an accurate statement. The expression was not reduced by the ΔChrom mutant in the same way as it was by the WT and the ΔNF-κB mutant, but it was not increased.

We have better specified the description of the results displayed in Figure 5F (lines 258-261 of the main manuscript):

“Induction of i-IκBα^WT and i-IκBα^ΔNF-κB reduced the expression levels of the naïve pluripotent genes Zfp42, Klf2, Sox2 and Tbx3. On the other hand, the same genes either do not change their expression (Zfp42, Sox2, Klf2) or increase their levels (Tbx3) upon i-IκBα^ΔChromatin  induction (Figure 5F).”

(13) In Fig. 5J the images will ideally be shown before and after Doxycycline treatment, to better support the conclusions.

We have included a new panel in Figure S4 (Figure S4E in the reviewed manuscript) where the No doxycycline control 216 EBs between the different conditions (i-IκBα^WT, i-IκBα^ΔChrom and i-IκBα^ΔNF-κB) are included.

Reviewer #2 (Recommendations for the authors):

- The PCA analysis in Figure 2 appears to contradict the authors' conclusions about global transcriptome changes in KO cells. Furthermore, there is a discrepancy between immunofluorescence data showing near-complete methylation loss and the methylation array analysis results.

Although there is a differentiation block in the IkBa KO EBs, this is not complete and they show some differentiation trend after 96h (Fig 2C), moreover, acquisition of differentiation genes from all three germ layers is strongly affected (Figure 2E of the reviewed manuscript) and these programs remain downregulated and pluripotency genes are still expressed in IκBα-KO EBs at later time points (216h) (Fig 2B). Altogether demonstrates that the lack of IκBα impairs differentiation and the silencing of the pluripotency network.

Discrepancies between methylation array and immunofluorescence are expected since immunofluorescence is not quantitative and the methylation array is very precise.  

- The authors should revise their statements regarding the strength of the pluripotency exit block, the extent of hypomethylation, and the global nature of chromatin changes. For example, the observed chromatin changes, including H3K27ac modifications, appear relatively modest and should be described as such. - The manuscript would benefit from additional orthogonal approaches to strengthen the knockout findings, which may be influenced by clonal expansion of ES cells. Additionally, the emphasis on overlapping H3K4me3 and H3K27me3 regions should be reduced, as these represent a minor fraction of the affected regions (only 41 regions).

We have revised the text and have included it in the discussion section the following text (lines 327-331 in the reviewed manuscript):

“Although IκBα KO  mESCs  exhibit a transcriptional phenotype and hypomethylation state  that resembles the ground state of naïve pluripotency, there are only modest changes on histone marks associated to enhancers (H3K27Ac) or gene regulation (H3K4me3 and H3K27me3). Altogether indicates that further experiments are required to fully elucidate the effect of chromatin IκBα.”

We have also included Fig S3E-S3F to show that similar differences as WT and KO in H3K4me3 and H3K27me3 are observed in a serum/LIF and 2i conditions, further supporting the fact that KO cells in Serum/LIF resemble WT cells in 2i condition.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Amaral et al. presents a study investigating the mesoscale modelling and dynamics of bolalipids.

Strengths:

The figures in this paper are exceptional. Both those to outline and introduce the lipid types, but also the quality and resolution of the plots. The data held within also appears to be outstanding and of significant (hopefully) general interest.

We thank the reviewer for their kind words and the appreciation of our work.

Weaknesses:

In the introduction, I would like to have read more specifics on the biological role of bolalipids. Archaea are mentioned, but this kingdom is huge - there must be specific species that can be discussed where bolalipids are integral to archaeal life. The authors should go beyond ’extremophiles’. In short, they should unpack why the general audience should be interested in these lipids, within a subset of organisms that are often forgotten about.

Following the reviewer’s advice we have revised the introduction of the manuscript, in which we now discuss specific species (Sulfolobus acidocaldarius and Thermococcus kodakarensis) and how in these species bolalipids are integral to archaeal life. We explain that the ratio between bilayer and bolalipids, and the number of cyclopentane rings contained within bolalipids can change to adapt to the environment. The revised parts of the introduction read (p.1 ):

“Like for bacteria and eukaryotes, archaea must keep their lipid membranes in a fluid state (homeoviscous adaptation). This is important even under extreme environmental conditions, such as hot and cold temperatures, or high and low pH values [7]. Because of this, many archaea adapt to changes in their environment by tuning the lipid composition of their membranes: altering the ratio between bola- and bilayer lipids in their membranes [8, 9] and/or by changing the number of cyclopentane rings in their lipid tails, which are believed to make lipid molecules more rigid [5]. For example, Thermococcus kodakarensis increases its tetraether bolalipid ratio from around 50% to over 80% when the temperature of the environment increases from 60 to 85 C [10]. Along the same lines, the cell membrane of Sulfolobus acidocaldarius, can contain over 90 % of bolalipids with up to 8 cyclopentane rings at 70 C and pH 2.5 [5, 11]. It is worth mentioning that in exceptional cases bacteria also synthesise bolalipids in response to high temperatures [12], highlighting that the study of bolalipid membranes is relevant not only for archaeal biology but also from a general membrane biophysics perspective.”

Reviewer #2 (Public review):

Summary:

The authors aimed to understand the biophysical properties of archeal membranes made of bolalipids. Bacterial and eukaryotic membranes are made of lipids that self-assemble into bilayers. Archea, instead, use bolalipids, lipids that have two headgroups and can span the entire bilayer. The authors wanted to determine if the unique characteristics of archaea, which are often extremophiles, are in part due to the fact that their membranes contain bolalipids.

The authors develop a minimal computational model to compare the biophysics of bilayers made of lipids, bolalipids, and mixtures of the two. Their model enables them to determine essential parameters such as bilayer phase diagrams, mechanical moduli, and the bilayer behaviour upon cargo inclusion and remodelling.

The author demonstrates that bolalipid bilayers behave as binary mixtures, containing bolalipids organized either in a straight conformation, spanning the entire bilayer, or in a u-shaped one, confined to a single leaflet. This dynamic mixture allows bolalipid bilayers to be very sturdy but also provides remodelling. However, remodelling is energetically more expensive than with standard lipids. The authors speculate that this might be why lipids were more abundant in the evolutionary process. Strengths:

This is a wonderful paper, a very fine piece of scholarship. It is interesting from the point of view of biology, biophysics, and material science. The authors mastered the modelling and analysis of these complex systems. The evidence for their findings is really strong and complete. The paper is written superbly, the language is precise and the reading experience is very pleasant. The plots are very well-thought-out.

Weaknesses:

I would not talk about weaknesses, because this is really a nice paper. If I really had to find one, I would have liked to see some clear predictions of the model expressed in such a way that experimentalists could design validation experiments.

We thank the reviewer for their very kind assessment. We incorporated their recommendations regarding experimental validation in the discussion section, as follows (p.14):

“Our model makes a number of predictions that could be tested by experiment either in cells or in vitro. First, it predicts that a small increase in the fraction of archaeal bilayer lipids should be sufficient to soften a bolalipid-rich membrane. While this could be tested in the future, so far only very few studies have yet reported experimental analysis of archaeal membrane mixtures [18, 50]. Second, we observed that membranes with moderate bolalipid molecular rigidity k_bola exhibit curvature-dependent bending rigidity. To experimentally verify this, one could extrude membrane tethers from cells while controlling for membrane tension. Finally, to get to the core mechanism underlying our findings, it will be important to develop experimental methods that will allow the fraction of U-shaped bolalipid conformers per leaflet to be imaged and measured.”

Reviewer #3 (Public review):

Summary:

The authors have studied the mechanics of bolalipid and archaeal mixed-lipid membranes via comprehensive molecular dynamics simulations. The Cooke-Deserno 3-bead-per-lipid model is extended to bolalipids with 6 beads. Phase diagrams, bending rigidity, mechanical stability of curved membranes, and cargo uptake are studied. Effects such as the formation of U-shaped bolalipids, pore formation in highly curved regions, and changes in membrane rigidity are studied and discussed. The main aim has been to show how the mixture of bolalipids and regular bilayer lipids in archaeal membrane models enhances the fluidity and stability of these membranes.

Strengths:

The authors have presented a wide range of simulation results for different membrane conditions and conformations. For the most part, the analyses and their results are presented clearly and concisely. Figures, supplementary information, and movies very well present what has been studied. The manuscript is well-written and is easy to follow.

We thank the reviewer for the detailed assessment of our work and their constructive feedback.

Major issues

R3.Q1: The Cooke-Deserno model, while very powerful for biophysical analysis of membranes at the mesoscale, is very much void of chemical information. It is parametrized such that it is good in producing fluid membranes and predicting values for bending rigidity, compressibility, and even thermalexpansioncoefficientfallingintheacceptedrangeofvaluesforbilayermembranes. But it still represents a generic membrane. Now, the authors have suggested a similar model for the archaeal bolalipids, which have chemically different lipids (the presence of cyclopentane rings for one), and there is no good justification for using the same pairwise interactions between their representative beads in the coarse-grained model. This does not necessarily diminish the worth of all the authors’ analyses. What is at risk here is the confusion between ”what we observe this model of bolalipidor mixed-membranes do” and ”how real bolalipid-containing archaeal membranes behave at these mechanical and thermal conditions.”.

As the reviewer correctly notes, Cooke and Deserno used a minimal model, devoid of chemical detail, to represent fluid lipid membranes composed of bilayer lipids. Indeed archaeal lipids are chemically different compared to non-archaeal lipids, but just like non-archaeal lipids, they can be very different from one another. Given the chemical diversity of bolalipids between each other, instead of representing their complexity in a complicated model with many experimentally unconstrained parameters, we here defined a minimal model for bolalipids. The power of this minimal model is to represent the key physical/geometrical characteristics of archaeal membranes, namely the fact that lipid heads on two sides of the membrane are often connected, that bolalipids can exhibit a conformational change, and that bolalipids mix with some percentage of bilayer molecules. We then ask a general question: how do these unique geometrical characteristics of archaeal membranes influence their mechanics and reshaping? The reviewer is however right in pointing out that a model, regardless of its level of details (atomistic, coarse-grained, minimal), is still a model.

Our approach of extending an established coarse-grained model for bilayer lipids to bolalipids is further supported by experimental observations, which report that archaeal bilayer lipids can form membranes of comparable bending rigidity to those of non-archaeal bilayer membranes [53]. Hence, different lipid linkages (archaeal vs. non-archaeal) give rise to fluid, deformable membranes of not too dissimilar rigidities, suggesting that both archaeal and non-archaeal bilayer lipids can be represented by a similar minimal coarse-grained model for the purpose of mesoscopic biophysical investigations. Since archaeal bolalipids have the same core chemical structure as two archaeal bilayer lipids joined by their tail ends, similarly we model a bolalipid by joining two bilayer lipids. Such an approach also efficiently enables us to compare bolalipid with bilayer membranes, and connect to the large body of knowledge on the physics of bilayer membranes.

To conclude, our coarse-grained model is indeed intended to capture the main physical properties of bolalipid membranes, and not their chemical diversity.

R3.Q2: Another more specific, major issue has to do with using the Hamm-Kozlov model for fitting the power spectrum of thermal undulations. The 1/q² term can very well be attributed to membrane tension. While a barostat is indeed used, have the authors made absolutely sure that the deviation from 1/q⁴ behaviour does not correspond to lateral tension?

To the casual observer, any 1/q² trend might point at membrane tension. However, the precise functional form is relevant as it determines whether the 1/q² dominates the 1/q⁴ trend for small or large values of the wave number q in the fitted power spectrum.

The first model (including lipid tilt) exhibits the functional form 1/(kq⁴) + 1/(kq²). In contrast, the second model (including membrane tension) exhibits the functional form 1/(kq⁴ + ∑q²). Importantly, the two models obey a different functional form. Here k and k_θ, are the bending and tilt moduli, which are assumed positive, and ∑ is the membrane tension, which can be either positive or negative. For the first model (with tilt), while for small q the amplitude is proportional to q^-4, for large q the amplitude is proportional to q^-2. In contrast, for the second model (with positive tension) while for small q the amplitude is proportional to q^-2, for large q the amplitude is proportional to q^-4. If membrane tension were to be negative in the second model, the slope would cross from negative infinity for small q to -4 for large q. The functional dependencies are summarized in Author response image 1A.

For rigid bolalipid membranes, it is clearly visible that the slope of the power spectrum plotted against the wave number q decreases with increasing q (Author response image 1B). While the slope initially assumes a value close to 4, it gradually approaches 2 for larger values of q. We conclude that only the model including lipid tilt can fit the power spectrum of membrane fluctuations appropriately (solid-dashed line), whereas the model with tension fails to fit the data (dashed line). We note that the combined model containing both lipid tilt and membrane tension does not give a better fit (dotted line).

To demonstrate that the tension model cannot fit the data, we included the best fits for both models for rigid bolalipid membranes in the new SI section 16 (p. S22) and show that only the tilt model leads to acceptable fits. We also measured the projected membrane tension - , where P_x,P_y are respectively the pressure in x and y direction and  L_z is the dimension of the simulation box in z axis. We found the projected membrane tension to give a negligible value similarly to the one that we indirectly measured by fitting a combined model with both tension and tilt, further confirming our conjecture.

Author response image 1.

(A) Schematic showing the decay of the power spectrum as a function of the wave number q in the tilt model (top), in the tension model with positive membrane tension (middle), and in the tension model with negative membrane tension (bottom). (B) Fitted power spectrum as a function of q for rigid bolalipid membranes (k_bola=5k_BT). The fit shows that while the model with tension (dashed line) cannot fit the data, the model with tilt nicely fits the spectrum (solid-dashed line). The combined model including both tension and tilt does not fit the spectrum any better (dotted line).

R3.Q3: I got more worried when I noticed in the SI that the simulations had been done with combined ”fix langevin” and ”fix nph” LAMMPS commands. This combination does not result in a proper isothermal-isobaric ensemble. The importance of tilt terms for bolalipids is indeed very interesting, but I believe more care is needed to establish that.

In what follows, we show that there is no reason to worry. First of all we want to clarify that the physical setup we simulate is that of a membrane contained in a heat bath under negligible tension with correct diffusional dynamics. To achieve this physical setup, for which we use a Langevin thermostat combined with pressure control via an overdamped barostat, which we implement in LAMMPS by combining ”fix langevin” and ”fix nph”.

In more detail: we simulated particles in an implicit solvent, for which we use a Langevin thermostat to get the right diffusional dynamics. To apply the theory of fitting fluctuation spectrums the simulation box length needs to be (near) constant. However, simulating membranes at a fixed box size results in an average non-zero membrane tension, making it hard to measure bending rigidity. The reason is that the effect of membrane tension is most influential on the largest wavelength modes, which are also most decisive when determining mechanical membrane properties like membrane rigidity. To minimize the effect of tension, we perform our simulation with an overdamped barostat (𝜏_baro = 10 𝜏 _langevin), which keeps the membrane near tensionless, as also done before [32]. In the revised manuscript, we have clarified the statement on the physical ensemble used (p.S2):

“For simulating flat membrane patches of bolalipids, we combined the previously used Langevin thermostat with relaxation time of 1𝜏 with a Nosé–Hoover barostat with relaxation time of 10𝜏. In LAMMPS this amounts to combining the commands ’fix langevin’ with ’fix nph’. We configured the barostat to set lateral pressure P_xy to zero by re-scaling the simulation box in the x-y plane. We compare this setup to a fixed box length setup, and an NPT ensemble setup, in SI section 17.”

To connect our results with statistical mechanics ensemble theory we tested alternative setups. Similar setups, including the formal isothermal-isobaric ensemble, where N,P,T are kept constant using Nose-Hoover style equations for thermostating and barostating with modern corrections [34], which the reviewer refers to, result in very similar fluctuation spectrums. Consequently, our measurements of bending and tilt modulus hold true regardless of the integration scheme. However, such a setup does not correctly capture implicit solvent and diffusional dynamics.

In even more detail: we tested our setup (implemented via ”fix langevin”+”fix nph”) versus a isothermal-isobaric ensemble (implemented via ”fix npt”). We measured volume mean and standard deviation, and found them matching for a reference LJ gas.

To be completely sure, and to please the reviewer, we have performed additional verifications in the new SI section 17, which we summarize in the following. We simulated three representative membranes with different integration schemes: ”fix npt”, ”fix langevin”+”fix nph”, and ”fix langevin” (Langevin dynamics with projected area fixed at the average value obtained from a ”langevin+nph”). We checked that the ”fix nph” barostat is merely equilibrating the membrane to a tensionless configuration, after which the projected membrane area (A_p = L_xL<sub<y</sub>) is practically constant. Consequently, the different schemes resulted in minor changes in the longest wavelength modes that we tracked down to small changes in the negligible tension. The resulting measurements of bending modulus change by less than 10%, and our main text conclusions do not change. Author response image 2 compares the fluctuation spectrums for the different integration schemes.

Author response image 2.

Height fluctuation spectrum, for a bilayer membrane at T_eff =1.1, simulated with Langevin dynamics (pink, ‘langevin‘), our setup (purple, ‘nph+langevin‘), and under an isothermal-isobaric ensemble (blue, ‘npt‘); fits are shown as dotted lines.

R3.Q4: This issue is reinforced when considering Figure 3B. These results suggest that increasing the fraction of regular lipids increases the tilt modulus, with the maximum value achieved for a normal Cooke-Deserno bilayer void of bolalipids. But this is contradictory. For these bilayers, we don’t need the tilt modulus in the first place.

We understand the concern why this might be counter-intuitive, and we thank the reviewer for pointing it out. We first want to stress that the tilt modulus can also be measured for bilayer membranes even if it is not needed to fit the fluctuation spectrum. If we measure the tilt modulus for a bilayer membrane, we obtain a value similar to the previously measured one [36]. Importantly, here we also report measurements for the tilt modulus for bolalipid membranes.

To understand the seemingly contradictory behaviour of the tilt modulus, it is insightful to rewrite the expression for the fluctuation spectrum as done in Eq. (1):

where is a characteristic length scale related to tilt, which we call the tilt persistence length. From the last equation it is easy to see that the tilt modulus 𝜅_𝜃 becomes relevant for the fluctuation spectrum if the tilt persistence length l_𝜃  is not negligible. In other words, this means that we have to consider the tilt modulus 𝜅_𝜃 as relevant, if it is sufficiently small compared to the bending rigidity 𝜅.

However, this is not only counter-intuitive, but also difficult to communicate graphically. Per the excellent reviewer’s suggestion, to make the interpretation more accessible, we converted in the main text and its figures the tilt modulus to the more directly interpretable tilt persistence length l_𝜃, as this is small when tilt is irrelevant (for bilayer lipids and flexible bolalipids) and large otherwise (for rigid bolalipids). This includes changes to the main text on p.6 and p.8 , and to the insets in Figs. 2C and 3B. We note that for completeness we also report the tilt modulus 𝜅_𝜃  in the SI.

R3.Q5: Also, from the SI, I gathered that the authors have neglected the longest wavelength mode because it is not equilibrated. If this is indeed the case, it is a dangerous thing to do, because with a small membrane patch, this mode can very well change the general trend of the power spectrum. As a lot of other analyses in the manuscript rely on these measurements, I believe more elaboration is in order.

We thank the reviewer for the careful examination of our supplementary material. For each fluctuation spectrum measurement, we ran multiple replicas. We observed that the largest wavelength modes were not fully equilibrated. In the simulations the first mode of the fluctuation spectrum is probed at different amplitudes and phases. We thus expected the potential systematic error would show up clearly when comparing spectrums of the different replicas. As we saw no correlation in these systematic offsets between replicas, we concluded that the simulations are sufficiently equilibrated and we could safely exclude the first mode of the fluctuation spectrum from our analysis.

To show without doubt that this procedure does not randomly bias our results, we also ran simulations for three representative membranes until all modes were equilibrated. On the modes previously equilibrated, the resulting spectrums agree with our previous shorter simulations. On the largest wavelength modes that were previously not fully equilibrated, we noticed a small deviation from theory, specifically for flexible membranes (small bending modulus). These small deviations can be explained by including a negligible negative tension. Importantly, however, the resulting bending modulus σ stays nearly the same. We note that the small negative tension disappears when we halve the timestep (see Author response image 3). This verification is shown in SI section 17.

R3.Q6: The authors have found that ”there is a strong dependency of the bending rigidity on the membrane mean curvature of stiffer bolalipids.” The effect is negative, with the membrane becoming less stiff at higher mean curvatures. Why is that? I would assume that with more flexible bolalipids, the possibility of reorganization into U-shaped chains should affect the bending rigidity more (as Figure 2E suggests). While for a stiff bolalipid, not much would change if you increase the mean curvature. This should be either a tilt effect, or have to do with asymmetry between the leaflets. But on the other hand, the tilt modulus is shown to decrease with increasing bolalipid rigidity. The authors get back to this issue only on page 10, when they consider U-shaped lipids in the inner and outer leaflets and write, ”this suggested that an additional membrane-curving mechanism must be involved.” But then again, in the Discussion, the authors write, ”It is striking that membranes made from stiffer bolalipids showed a curvature-dependent bending modulus, which is a clear signature that bolalipid membranes exhibit plastic behaviour during membrane reshaping,” adding to the confusion.

Author response image 3.

Height fluctuation spectrum, for a bilayer membrane at T_eff =1.1, as simulated in the main text (grey, for 60⇥10³τ), for longer duration (1_.44⇥10⁶τ) (pink), and with the longer duration and halved timestep =0.005_τ(purple); fits are shown as dotted lines (tension and tilt) or dash-dot lines (tilt only).

We thank the reviewer for asking this important question. Membrane bending rigidity in bolalipid membranes decreases dramatically once a small fraction of U-shapes is allowed to form, but then plateaus once this U-shape fraction reaches 20%. In a curved bolalipid membrane, U-shapes must accumulate in the outer leaflet to accommodate for area difference. Together, the bending rigidity non-linear dependence on U-shape fraction, and the promotion of U-shapes by curvature, explain why in a membrane made of moderately stiff bolalipids (k_bola = 1k_BT), which contain very few U-shapes in the flatstate, the bending rigidity of the membrane decreases as curvature increases. While in a membrane made of flexible bolalipid molecules (k_bola = 0), where many U-shapes are present in the flat membrane, the bending rigidity does not change with curvature.

Bending rigidity 𝜅 in flat membranes composed of bolalipids decreases dramatically once a small fraction of U-shapes is allowed to form, but plateaus once more than 20% of U-shaped bolalipids are present. In details, our data shows that with an increasing bolalipid molecular rigidity k_bola, both the number of U-shaped bolalipids decreases (Fig. 2B) and the membrane rigidity 𝜅 increases (Fig. 2C). Thus, the correlation suggests that U-shaped bolalipids soften the membrane, in a non-linear way where most of the change in membrane bending rigidity happens for U-shaped bolalipid fraction < 20% (Figure S11).

Separately, membrane curvature affects the area difference between curved membrane leaflets and thus drives U-shape accumulation. To be specific, a cylindrical membrane with area A, mean curvature H and thickness h has the outer leaflet with area A(1 + Hh) and the inner leaflet with smaller area A(1 Hh). This can be large, in our simulations up to an area change of Hh \= 25%. For pure bolalipid membranes, straight bolalipids occupy the same space in each leaflet. Area difference can then be achieved only by having a different amount of U-shaped bolalipids in each leaflet, which can result in a different U-shape fraction between leaflets and thus ’asymmetry between leaflets’. Figure S10 confirms U-shape head fraction asymmetry that increases with curvature, for both flexible (k_bola = 0) and moderately stiff bolalipids (k_bola = 1k_BT).

Together, these two effects result in membrane softening under curvature for the moderately stiff bolalipids, but constant rigidity for flexible bolalipids (Fig. 2F). In details: for membranes composed of moderately stiff bolalipid molecules (k_bola = 1k_BT), the U-shape bolalipid head fraction only increases in the outer leaflet, goingfrom10to20%(Figure S10). This is in the high sensitivity region where the bending rigidity is expected to change the most (Figure S11). We hypothesize that the molecular rigidity of a U-shaped bolalipid creates compression on the outer leaflet that stabilizes the membrane curvature and thus causes membrane softening. We suspect that for membranes composed of rigid bolalipids (k > 1k_BT), the effect is likely not present due to the absence of U-shape formation even under strong bending.

By contrast, for membranes composed of flexible bolalipids (k = 0), the U-shaped bolalipid head fraction changes relatively little from its value for flat membranes (from 50% to respectively 60 and 40% for the outer and inner leaflet, Figure S10). This is in the region where the membrane bending rigidity is expected to respond weakly to U-shape fraction (Figure S11). Additionally, the change is symmetric, so presumably the outer leaflet becomes softer as the inner leaflet becomes stiffer, thus creating opposing effects and only weakly affecting the membrane bending rigidity as a whole. We note that the distinction between the U-shape head fraction that we plot (Figure S10) and U-shape fraction (Figure S11) matters little for this analysis.

We have added this deduction and its plots to SI section 8, and revised the corresponding statement in the main text accordingly (p.7 ).

“Changing membrane curvature alters the area differently in the two membrane leaflets. To adapt to the area difference, we thus expect the fraction of U-shaped bolalipids to change as the membrane curvature changes. Moreover, the results of Fig. 2B and Fig. 2C showed that the U-shaped bolalipid fraction and the membrane bending rigidity are correlated. As a result, we predict that the fraction of straight versus U-shaped bolalipids in a membrane will change in response to membrane bending, in a way that makes the bending rigidity of a bolalipid membrane curvature dependent.”

R3.Q7: This issue is repeated when the authors study nanoparticle uptake. They write: ”to reconcile these seemingly conflicting observations we reason that the bending rigidity, similar to Figure 2F, is not constant but softens upon increasing membrane curvature, due to dynamic change in the ratio between bolalipids in straight and U-shaped conformation. Hence, bolalipid membranes show stroking plastic behaviour as they soften during reshaping.” But the softening effect that they refer to, as shown in Figure 4B, occurs for very stiff bolalipids, for which not much switching to U-shaped conformation should occur.

We thank the reviewer for locating a particularly dense sentence. We changed the text to explicitly refer to the range k 2 [0,2] k_BT for which there is significant change in U-shape fraction (p.8 ):

“To reconcile these seemingly conflicting observations we reason that the bending rigidity κ, similar to Fig. 2F, is not constant but softens in the range k 2 [0,2] k_BT, upon increasing membrane curvature. This is due to the dynamic change in the ratio between bolalipids in straight and U-shaped conformation.”

As for Fig. 4B, for k > 2k_BT, pores form thus explaining the plateau in adsorption energy.

R3.Q8: Another major issue is with what the authors refer to as the ”effective temperature”. While plotting phase diagrams for kT/eps value is absolutely valid, I’m not a fan of calling this effective temperature. It is a dimensionless quantity that scales linearly with temperature, but is not a temperature. It is usually called a ”reduced temperature”. Then the authors refer to their findings as studying the stability of archaeal membranes at high temperatures. I have to disagree because eps is not the only potential parameter in the simulations (there are at least space exclusion and angle-bending stiffnesses) so one cannot identify changing eps with changing the global simulation temperature. This only works when you have one potential parameter, like an LJ gas.

We indeed thought about this before and found that it makes little difference in our set-up. To thoroughly show that the distinction matters very little, per reviewer’s question, we computed our phase diagrams by scaling temperature T explicitly (and not lipid tail interactions T_eff = k_BT /ϵ_p). We added these results to the SI section 14 and found no significant difference when comparing scaling tail interactions (Figure S15A) with scaling temperature explicitly (Figure S15B).

We also computed Fig. 2A-C for scaling interactions (Figure S17A) and scaling temperature explicitly (Figure S17B). We found a slightly increased U-shaped bolalipid fraction for low k when comparing scaling interactions (Figure S17A) with temperature scaling (Figure S17B). The reason is that the U-shaped fraction depends on temperature, as with higher temperature bolalipids can easier transition into the U-shape. Most importantly, however, we found no qualitative changes on the liquid region or the mechanical membrane properties when we compared the different scaling variants.

The reason why both scaling variants match so well can be understood easily. All pair potentials, including volume exclusion interactions between head beads and other membrane beads, were also scaled in the same manner as tail-to-tail interactions, as described in the SI. In contrast, the energy scales for maintaining the lipid bonds, the bilayer lipid angles and the bolalipid angles are relatively large compared to the energy scales involved in tail-to-tail interactions. This separation of energy scales guarantees that there will be little effect when increasing global temperature. Regarding nomenclature, we take the reviewer’s advice and have added ’reduced temperature’ as an alias for T_eff in the main text.

In the revised version of the manuscript, we mention these observations in the SI section 14 and point towards these results in the main text (p.4 ):

“This interaction strength governs the membrane phase behaviour and can be interpreted as the effective temperature or reduced temperature T_eff = k_BT /ϵ_p. As the distinction between scaling interactions (T_eff) or temperature (T) is not important for our analysis (see Supplemental Information (SI) section 14), for simplicity we refer to T_eff as temperature in the following.”

Minor issues

R3.Q9: As the authors have noted, the fact that the membrane curvature can change the ratio of U-shaped to straight bolalipids would render the curvature elasticity non-linear (though the term ”plastic” should not be used, as this is still structurally reversible when the stress is removed. Technically, it is hypoelastic behaviour, possibly with hysteresis.) With this in mind, when the authors use essentially linear elastic models for fluctuation analysis, they should make a comparison of maximum curvatures occurring in simulations with a range that causes significant changes in bolalipid conformational ratios.

We thank the reviewer for their suggestion on calling the non-linear behaviour of the curvature elasticity hypoelastic. We have edited the main text accordingly (p.8 ):

“In an elastic material, the strain modulus holds constant and deformation is reversible. For bolalipid membranes at k = 1k_BT, however, the bending modulus decreases when deformation increases, rendering bolalipid membranes hypoelastic.”

Moreover, regarding the maximum curvatures occurring in the fluctuation simulations: We first note that the ensemble average of the mean curvature H from the fluctuation measurements is indicated as a vertical line in Fig. 2F. As the average value is nearly zero, the membrane can be considered as flat in good approximation. To investigate the question in more detail, we extended the SI with a careful analysis of the validity of the maximum membrane curvature and the validity of the Monge gauge approximation (SI section 15).

In short, we found that the involved membrane curvatures are small and therefore are unlikely to trigger any significant changes of the bending modulus. Moreover, since we are dealing with two bolalipid conformations, we also tested the homogeneity of the membrane. In our simulations of flat membrane patches we did not observe clustering or phase separation between the two bolalipid conformations beyond the [2,3]σ range. Furthermore, we get good agreement between our fluctuation measurement and the cylinder simulations in Fig. 2F. We now mention this verification in the revised version of the manuscript (p.8 ):

“Fortunately, this dependency on curvature does not invalidate our fluctuation results, where the curvature is small enough that its effect on the bending modulus is negligible (SI section 15).”

Last but least, simulating bending/unbending cycles of an arc-shaped membrane (frozen endpoints) shows agreement with cylinder membrane simulations, and no hysteresis at the rates of deformation employed (cf. M. Amaral’s thesis [54], soon to be out of the embargo period).

R3.Q10: The Introduction section of the manuscript is written with a biochemical approach, with very minor attention to the simulation works on this system. Some molecular dynamics works are only cited as existing previous work, without mentioning what has already been studied in archaeal membranes. While some information, like the binding of ESCRT proteins to archaeal membranes, though interesting, helps little to place the study within the discipline. The Introduction should be revised to show what has already been studied with simulations (as the authors mention in the Discussion) and how the presented research complements it.

The present research for the first time covers archaeal membranes with a single coarse-grained model capable of assuming both bolalipid in-membrane conformations and sweeps through temperature, membrane composition, and molecular rigidity. The work shows the first curvature dependent bending modulus for pure bolalipid membranes. It also investigates systematically bending modulus and Gaussian modulus, and tests the model in an all-encompassing budding simulation that incorporates topology changes. Existing atomistic or coarse-grained MD simulations (MARTINI or similar force fields) are limited to small patches of membrane, with no study of large-scale deformations or topology changes; plus, they rely on force fields that were parametrized for bilayer membranes.

To give a comprehensive overview of the field, we revised the introduction section of the manuscript, in which we now discuss previous computational work investigating membrane diffusivity, U-shaped lipid fraction, and bending rigidity (p.3 ):

“By contrast, only a few studies have investigated bolalipid membranes applying computational or theoretical tools [24, 25]. Specifically, the pore closure time in bolalipid membranes, and the role of cyclopentane rings for membrane properties has been investigated using all-atom simulations, showing decreased lateral mobility, reduced permeability to water, and increased lipid packing [26–28]. Moreover, using coarse-grained simulations, it was suggested that bolalipid membranes are thicker [29], exhibit a gel-to-liquid phase transition at higher temperature [30], and exhibit a reduced diffusivity [31]. However, little research has been devoted to investigating mechanics and reshaping of bolalipid membranes at the mesoscale despite the obvious importance of this question from evolutionary, biophysics, and biotechnological perspectives and although different membrane physics is expected to manifest.”

Following the reviewer’s advice and to keep the introduction concise and focused on bolalipid membranes, we have removed the paragraph on ESCRT-III proteins in the revised manuscript.

R3.Q11: The authors have been a bit loose with using the term ”stability”. I’d like to see the distinction in each case, as in ”chemical/thermal/mechanical/conformational stability”.

We have clarified when applicable the type of stability throughout the manuscript. In all other instances, if not clear from context, we mean simply that the membrane persists being a membrane. At our coarse-grained level, this means the membrane does not disassemble into a gas phase.

R3.Q12: In the original Cooke-Deserno model, a so-called ”poorman’s angle-bending term” is used, which is essentially a bond-stretching term between the first and third particle. However, I notice the authors using the full harmonic angle-bending potential. This should be mentioned.

This is made clear in the SI (Eq. (S3)). Cooke and Deserno mention the harmonic angle potential as a valid alternative in their original publication. We now also added this detail to the main text (p.3 ):

“The angle formed by the chain of three beads is kept near 180° via an angular potential with strength k₀, instead of the approximation by a bond between end beads of the original model [32].”

R3.Q13: The analysis of energy of U-shaped lipids with the linear model E \= c₀ + c₁k is indeed very interesting. I am curious, can this also be corroborated with mean energy measurements? The minor issue is calling the source of the favorability of U-shaped lipids ”entropic”, while clearly an energetic contribution is found. The two conformations, for example, might differ in the interactions with the neighbouring lipids.

We were also curious and thank the reviewer for the suggestion of mean energy measurements. We concluded that there must be either an entropic contribution to the free energy or an intermolecular interaction energy favouring U-shaped bolalipids. We have now included these measurements in SI section 6 (p.S5 ):

“By splitting the average potential energy between an internal contribution (bonds, angles and pair interactions between particles in the same molecule) and an external contribution (pair interactions between a molecule and its neighbours), we determined the transition energy from straight to U-shaped bolalipids in detail. We found that this transition lowers the internal potential energy of the bolalipid while increasing its interaction energy. In total, we obtained an energy barrier for the transition of ΔE_s→u = 0.79±0.01k_BT. Since the fit indicates, however, that the U-shaped bolalipid conformation is preferred over the straight conformation, we conclude that there must be either an entropic contribution to the free energy or an intermolecular interaction energy favouring U-shaped bolalipids.”

We refer to these measurements in the main text (p.6 ):

“For the fit it appears that c₀ < 0, which implies that bolalipids in U-shape conformation are slightly favoured over straight bolalipids at k = 0 (explored in SI section 6).”

R3.Q14: The authors write in the Discussion, ”In any case, our results indicate that membrane remodelling, such as membrane fission during membrane traffic, is much more difficult in bolalipid membranes [34].” Firstly, I’m not sure if studying the dependence of budding behaviour on adhesion energy with nanoparticles is enough to make claims about membrane fission. Secondly, why is the 2015 paper by Markus Deserno cited here?

We thank the reviewer for giving us the opportunity to clarify. We make an energetic argument on membrane fission based on the observed difference in the ratio of .

Splitting a spherical membrane vesicle into two spherical vesicles (fission) increases the bending energy by 8𝜋𝜅 and decreases the energy related to the Gaussian bending modulus by . The second part of the argument is given for example in the review by Markus Deserno (p.23, right column), that’s why we cite the paper here. Together, this gives an energy barrier, required for membrane fission in the considered geometry of ∆E_fission = . We found that is around 0.5 for bolalipid membranes and around 1 for bilayer membranes. Since 𝜅 was typically larger in bolalipid membranes we thus expect the energy barrier for fission ∆E_fission to be larger for bolalipid membranes. We therefore predict that membrane remodelling, such as membrane fission during membrane trafficking, is harder in bolalipid membranes. We explain our reasoning in the discussion of the revised manuscript (p.13 ):

“Membrane remodelling, such as the fission of one spherical vesicle into two, increases the bending energy by 8πκ but decreases the energy related to the Gaussian modulus by – [39], giving rise to a fission energy barrier of ∆E_fission = . Our results indicated that while in bolalipid membranes 𝜅 is larger, is smaller compared to bilayer membranes. Our results thus predict a larger energy barrier for membrane fission ∆E_fission in bolalipid membranes compared to bilayer membranes.”

R3.Q15: In the SI, where the measurement of the diffusion coefficient is discussed, the expression for D is missing the power 2 of displacement.

We thank the reviewer for spotting this oversight. We corrected it in the revised version of the SI (p.S5 ).

R3.Q16: Where cargo uptake is discussed, the term ”adsorption energy” is used. I think the more appropriate term would be ”adhesion energy”.

For the sake of simplicity, we changed the term to adhesion energy (caption of Fig. 4, and p.10). We do not have a strong opinion on this, but we believe that adsorption energy would be equally correct as we describe the adsorption of many lipid head beads to a nanoparticle.

R3.Q17: Typos:

Page 1, paragraph 2: Adaption → Adaptation. Page 10, paragraph 1: Stroking → Striking.

We thank the reviewer for spotting these typos which we have corrected in the revised version of the manuscript.

Recommendations for the authors

Reviewer #1 (Recommendations for the authors):

A few thoughts (likely out of the scope of this paper but possibly to consider upon revision):

R1.Q1: Do bolalipids always have the same headgroup? I don’t recall reading this in the introduction/discussion. R1 and R2 are in Figure 1, but I don’t know whether there are standard types. Could this be expanded upon? Is the model able to take these differences into account?

We thank the reviewer for raising this important question. Similar to bacteria and eukaryotes, in archaea there is a huge variety in terms of the different head groups that lipids can contain and thus also lipid variety. Most archaeal lipids have head groups that contain either phosphate groups or sugar residues. Typically, archaeal bolalipids are asymmetric and contain a phosphatidyl and a sugar moiety at the two ends of the lipid molecule. Within the membrane the lipid is oriented such that the phosphatidyl moiety points towards the interior of the cell whereas the sugar moiety points towards the outside of the cell as it occupies more space [5].

In our computational model, however, we consider symmetric bolalipids for the sake of simplicity and to decouple the role of ”connected geometry” from other effects. In principle, we could investigate the effect of lipid asymmetry by increasing the size of one of the lipid head beads. However, this investigation exceeds the scope of the present study and therefore requires future work.

In the revised version of the manuscript, we now clarify that bolalipids can have different headgroups (p.1 and the caption of Fig. 1):

“The hydrophilic heads can be composed of different functional groups with phosphatidyl and sugar being the most relevant moieties. For bolalipids the two head groups at either end of the molecule are typically distinct (Fig. 1A right) [5].”

“The hydrophilic head of a bolalipid can be composed of different functional groups represented by R1 and R2 (right).”

We also explicitly state that we neglect lipid head group asymmetry for the sake of simplicity (p.4 ):

“To decouple the effect of the connected geometry of the bolalipids from that of lipid asymmetry, we assume both head beads of a bolalipid to share the same properties.”

R1.Q2: Is it possible to compare the mesoscale models to either Coarse-grained or even all-atom lipid models? Have simulations previously been performed for bolalipids at those levels of description?

A few studies have investigated bolalipids membranes in simulations previously. These studies either used all-atom or coarse-grained simulations. However, none of these studies investigated how bolalipids respond to membrane deformations. Therefore, it is currently not possible to directly compare our results to studies in the literature. However, to recapitulate our predictions experimentally is certainly something that could and should be done in the future. As a reply to this reviewer and reviewer 3, we discuss the current state of modelling bolalipid membranes in simulations in the revised version of the manuscript (p.3 ):

“By contrast, only a few studies have investigated bolalipid membranes applying computational or theoretical tools [24, 25]. Specifically, the pore closure time in bolalipid membranes, and the role of cyclopentane rings for membrane properties has been investigated using all-atom simulations, showing decreased lateral mobility, reduced permeability to water, and increased lipid packing [26–28]. Moreover, using coarse-grained simulations, it was suggested that bolalipid membranes are thicker [29], exhibit a gel-to-liquid phase transition at higher temperature [30], and exhibit a reduced diffusivity [31]. However, little research has been devoted to investigating mechanics and reshaping of bolalipid membranes at the mesoscale despite the obvious importance of this question from evolutionary, biophysics, and biotechnological perspectives and although different membrane physics is expected to manifest.”

We want to mention, however, that we do compare membrane diffusivity, U-shaped lipid fraction, and bending rigidity to the behaviour and values that have been previously measured in simulations in the discussion section. In general, we find good agreement between our results and previously reported behaviour/values (p.13 ):

“While flexible bolalipid membranes are liquid under the same conditions as bilayer membranes, we found that stiff bolalipids form membranes that operate in the liquid regime at higher temperatures. These results agree well with previous molecular dynamics simulations that suggested that bolalipid membranes are more ordered and have a reduced diffusivity compared to bilayer membranes [24, 29]. In our simulations, this is due to the fact that completely flexible bolalipids molecules adopt both straight (transmembrane) as well as the U-shaped (loop) conformation with approximately the same frequency. In contrast, stiff bolalipids typically only take on the straight conformation when assembled in a membrane. These results agree with the previous coarse-grained molecular dynamics simulations using the MARTINI force field which showed that the ratio of straight to U-shaped bolalipids increased upon stiffening the linker between the lipid tails [29].

[...]

When we determined the bending rigidity of bolalipid membranes by measuring their response to thermal fluctuations, we found that membranes made from flexible bolalipids are only slightly more rigid than bilayer membranes. This result is consistent with previous atomistic simulations, which showed that the membrane rigidity was similar for membranes composed of bilayer lipids and flexible synthetic bolalipids [45].”

R1.Q3: How would membrane proteins alter the behaviour of bolalipids? Either those integral to the membrane or those binding peripherally?

The reviewer asks an important question. However, the question is difficult to answer due to its scope and the gaps in the current literature. Important examples of integral or peripheral membrane proteins that alter the behaviour of bolalipids and archaeal bolalipid membranes are involved in cell homeostasis, cell division, membrane trafficking, and lipid synthesis.

The cells of many archaeal species are enclosed in a paracrystalline protein layer called the Slayer, which is attached to the lipid membrane [4, 55]. The main function of the S-layer is to keep the cell’s shape and to protect it against osmotic stress. Due to the embedding of the S-layer in the membrane at specific locations, it is to be expected that the membrane properties are influenced by the S-layer. Furthermore, archaea execute cell division by locally reshaping the membrane using FtsZ and ESCRT-III proteins [56]. While Asgard archaeal genomes encode proteins with homology to those regulating aspects of eukaryotic membrane remodelling and trafficking [57], they have yet to be observed undergoing a process like endocytosis [58]. In addition, it has been speculated that the proteins that drive the synthesis of two diether lipids into a tetraether lipid are either membrane associated or integral membrane proteins [59].

However, to the best of our knowledge it is not known how membrane proteins specifically alter the behaviour of bolalipids. Future work will need to be executed to answer this question. Following the advice of reviewer 3 and to keep the introduction concise and focused on bolalipid membranes, we do not mention these observations in the revised manuscript.

R1.Q4: Is there a mechanism in cells to convert or switch bolalipids from a straight to a u-shaped description? Does this happen spontaneously or are there enzymes responsible for this?

We thank the reviewer for bringing up this important point. Despite the relevance of the question, little is currently known about the mechanism that make bolalipids transition between a straight and a U-shaped configuration mainly because there is to date no established experimental method.

Besides our own results, most of what we know comes from coarse-grained molecular dynamics simulations, which showed that bolalipids can spontaneously transition between the straight and U-shaped configuration [29]. In addition, by using comparative genomic analysis, it has been predicted that many archaeal species contain flippases, i.e., membrane proteins that are able, upon the consumption of energy, to transfer (flipflop) bilayer lipids between the two membrane leaflets [43]. Moreover, it has been shown that Halobacterium salinarum (an archaeon with a bilayer lipid membrane) [44] contains scramblases, which are membrane proteins that passively transfer bilayer lipids from one membrane leaflet to the other. It is therefore tempting to speculate that similar proteins might exist for bolalipids which could facilitate the straight to U-shaped transition.

In addition, it has been reported that vesicles composed of bolalipid membranes can undergo fusion with enveloped influenza viruses [17]. In this context, it has been suggested that the influenza fusion protein hemagglutinin may locally induce U-shaped bolalipids to facilitate membrane fusion. However, all these hints are by far no proof of a mechanism that can drive the straight to U-shaped bolalipid transition, and further work needs to be done to investigate this question in detail.

In the revised version of the manuscript, we now discuss what is known about potential mechanisms to facilitate the straight to U-shaped transition in the discussion section (p.13 ):

“While previous coarse-grained simulations predicted that bolalipids spontaneously transition between the straight and U-shaped conformations [29], how this happens in archaeal membranes and whether membrane proteins are involved in this conformational transition needs to be clarified in the future. Experimental studies suggest that archaeal membranes contain flippases and scramblases for the transitioning of bilayer lipids between membrane leaflets [43, 44], raising the possibility that similar proteins could also facilitate conformational transitions in bolalipids. In addition, it has been suggested that the viral fusion protein hemagglutinin could cause a transition from straight to U-shaped bolalipid conformation during the fusion of bolalipid vesicles with influenza viruses [17]. However, future investigation is required.”

R1.Q5: Ideally, coordinates and any parameter files required to run the molecular simulations should be included for reproducibility.

We absolutely share the reviewer’s concern with reproducibility and as such have included in the original submission as part of our data availability section a link to a code repository (available at: https://doi.org/10.5281/zenodo.13934991 [51]) that allows initializing and simulating flat membrane patches, with user control of the parameters explored in this paper (𝜔,T_eff,k_bola,f^bi).

Reviewer #2 (Recommendations for the authors):

This is a great paper and I congratulate the authors for writing such a fine piece of scholarship. The only nitty-gritty feedback that I have is summarized in the following three points:

R2.Q1: In the introduction the authors talk about archaea adapting their membrane to retain membrane fluidity. However, homeoviscous adaptation is also fundamental in bacteria and eukaryotes.

The reviewer is correct, like archaea the membranes of bacteria and eukaryotes must balance between flexibility and stability. Moreover, the cell membranes in all 3 domains of life need to maintain membrane fluidity and provide mobility to the embedded lipids and membrane proteins (homeoviscous adaptation). The general idea is that these organisms change the ratio of different lipids to change membrane properties and thereby optimally adapt to their environments [10]. Importantly, however, there are differences of how homeoviscous adaptation is maintained across the different domains of life. As a reply to this reviewer and reviewer 3, we now discuss the underlying mechanisms in the revised parts of the introduction (p.1 ):

“Like for bacteria and eukaryotes, archaea must keep their lipid membranes in a fluid state (homeoviscous adaptation). This is important even under extreme environmental conditions, such as hot and cold temperatures, or high and low pH values [7]. Because of this, many archaea adapt to changes in their environment by tuning the lipid composition of their membranes: altering the ratio between bola- and bilayer lipids in their membranes [8, 9] and/or by changing the number of cyclopentane rings in their lipid tails, which are believed to make lipid molecules more rigid [5]. For example, Thermococcus kodakarensis increases its tetraether bolalipid ratio from around 50% to over 80% when the temperature of the environment increases from 60 to 85 C [10]. Along the same lines, the cell membrane of Sulfolobus acidocaldarius, can contain over 90 % of bolalipids with up to 8 cyclopentane rings at 70 C and pH 2.5 [5, 11]. It is worth mentioning that in exceptional cases bacteria also synthesise bolalipids in response to high temperatures [12], highlighting that the study of bolalipid membranes is relevant not only for archaeal biology but also from a general membrane biophysics perspective.”

R2.Q2: Uncertainties in Gaussian rigidity modulus estimates are not properly reported.

The large uncertainties in the Gaussian rigidity modulus were due to the fact how they were calculated. In short, is determined in cap folding simulations [41] (SI section 9), by using the measured values of the dimensionless parameter 𝜉, related to the folding probability, the bending modulus 𝜅, the membrane line tension , and the cap radius R. In our case, the main source of uncertainty for determining comes from the uncertainty in the measurement of the bending rigidity 𝜅. To obtain 𝜅, previously, we fitted fluctuation spectra for different seeds and only then averaged the obtained values. In the revised version of the manuscript, we now first pool the fluctuation spectra of the different simulation seeds before we fit all spectra at the same time. This new approach results in smaller uncertainties for the bending rigidity 𝜅 and also the Gaussian rigidity modulus .

As a consistency check, in addition to the simulations that we previously performed at T_eff = 1.3, we have repeated the cap folding and line tension simulations at T_eff = 1.2, resulting in similar values for . In the revised version of the manuscript, we report the newly calculated values and uncertainties for at T_eff  = 1.2 in the main text (p.8 ):

“At T_eff  = 1.2, we obtained = 4.30±0.22kBT and thus a ratio of = 0.89±0.04 for bilayer membranes, similar to what has been reported previously [41]. For flexible bolalipid membranes, we got a slightly smaller value for = 5.04 ± 0.37kBT. Due to the larger bending modulus, however, flexible bolalipid membranes show a significantly smaller ratio = 0.64± 0.04 (k = 0). At larger temperature (Teff = 1.3), the ratio can be even smaller = 0.45 ± 0.07 (see SI section 9).”

In addition, we report the values at T_eff = 1.3 and T_eff = 1.2 in the SI (p.S15 , Tabl. S4):

We have also adapted the discussion of the Gaussian bending modulus accordingly (p.13 ):

“Another marked difference between bilayer and flexible bolalipid membranes is the ratio of the Gaussian rigidity to the bending modulus. Instead of being around 1 as for bilayer membranes [41], it is around 1/2 and therefore only half of that of bilayer lipids.”

Reviewer #3 (Recommendations for the authors):

While I think the bulk of the work presented is useful, some of the issues that I raised in my review are indeed major. Without properly addressing them, it is hard to accept the conclusions of the manuscript. I hope the authors can address them by revising their analysis.

We thank the reviewer for their constructive feedback, which helped us to improve the manuscript. We have addressed all points raised by the reviewer in our detailed point-by-point response to the reviewer (see above). We hope the reviewer will now find it easier to accept our conclusions.

(1) R. Phillips, J. Kondev, J. Theriot, and H. Garcia, Physical biology of the cell (Garland Science, New York, 2012).

(2) H. T. McMahon and J. L. Gallop, Membrane curvature and mechanisms of dynamic cell membrane remodelling, Nature 438, 590 (2005).

(3) S. B. Gould, Membranes and evolution, Curr. Biol. 28, R381 (2018).

(4) S.-V. Albers and B. H. Meyer, The archaeal cell envelope, Nat. Rev. Microbiol. 9, 414 (2011).

(5) P. M. Oger and A. Cario, Adaptation of the membrane in Archaea, Biophys. Chem. 183, 42 (2013).

(6) K. Rastädter, D. J. Wurm, O. Spadiut, and J. Quehenberger, The Cell Membrane of Sulfolobus spp.—Homeoviscous Adaption and Biotechnological Applications, International Journal of Molecular Sciences 21, 3935 (2020).

(7) P. L.-G. Chong, Archaebacterial bipolar tetraether lipids: Physico-chemical and membrane properties, Chem. Phys. Lipids 163, 253 (2010).

(8) M. Tourte, P. Schaeffer, V. Grossi, and P. M. Oger, Functionalized Membrane Domains: An Ancestral Feature of Archaea?, Front. Microbiol. 11, 526 (2020).

(9) Y. H. Kim, G. Leriche, K. Diraviyam, T. Koyanagi, K. Gao, D. Onofrei, J. Patterson, A. Guha, N. Gianneschi, G. P. Holland, M. K. Gilson, M. Mayer, D. Sept, and J. Yang, Entropic effects enable life at extreme temperatures, Sci. Adv. 5, eaaw4783 (2019).

(10) M. F. Siliakus, J. van der Oost, and S. W. M. Kengen, Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure, Extremophiles 21, 651 (2017).

(11) D. W. Grogan, Phenotypic characterization of the archaebacterial genus sulfolobus: comparison of five wild-type strains, J. Bacteriol. 171, 6710 (1989).

(12) D. X. Sahonero-Canavesi, M. F. Siliakus, A. Abdala Asbun, M. Koenen, F. von Meijenfeldt, S. Boeren, N. J. Bale, J. C. Engelman, K. Fiege, L. Strack van Schijndel, J. S. Sinninghe Damsté, and L. Villanueva, Disentangling the lipid divide: Identification of key enzymes for the biosynthesis of membrane-spanning and ether lipids in Bacteria, Sci. Adv. 8, eabq8652 (2022).

(13) M. van Wolferen, A. A. Pulschen, B. Baum, S. Gribaldo, and S.-V. Albers, The cell biology of archaea, Nat. Microbiol. 10.1038/s41564-022-01215-8 (2022).

(14) U. Bakowsky, U. Rothe, E. Antonopoulos, T. Martini, L. Henkel, and H.-J. Freisleben, Monomolecular organization of the main tetraether lipid from Thermoplasma acidophilum at the water–air interface, Chem. Phys. Lipids 105, 31 (2000).

(15) C. Jeworrek, F. Evers, M. Erlkamp, S. Grobelny, M. Tolan, P. L.-G. Chong, and R. Winter, Structure and Phase Behavior of Archaeal Lipid Monolayers, Langmuir 27, 13113 (2011).

(16) D. P. Brownholland, G. S. Longo, A. V. Struts, M. J. Justice, I. Szleifer, H. I. Petrache, M. F. Brown, and D. H. Thompson, Phase Separation in Binary Mixtures of Bipolar and Monopolar Lipid Dispersions Revealed by 2H NMR Spectroscopy, Small Angle X-Ray Scattering, and Molecular Theory, Biophysical Journal 97, 2700 (2009).

(17) A. Bhattacharya, I. D. Falk, F. R. Moss, T. M. Weiss, K. N. Tran, N. Z. Burns, and S. G. Boxer, Structure–function relationships in pure archaeal bipolar tetraether lipids, Chem. Sci. 15, 14273 (2024).

(18) V. Vitkova, D. Mitkova, V. Yordanova, P. Pohl, U. Bakowsky, G. Staneva, and O. Batishchev, Elasticity and phase behaviour of biomimetic membrane systems containing tetraether archaeal lipids, Colloids Surf. A Physicochem. Eng. Asp. 601, 124974 (2020).

(19) E. Chang, Unusual thermal stability of liposomes made from bipolar tetraether lipids, Biochem. Biophys. Res. Commun. 202, 673 (1994).

(20) O. V. Batishchev, A. S. Alekseeva, D. S. Tretiakova, T. R. Galimzyanov, A. Y. Chernyadyev, N. R. Onishchenko, P. E. Volynsky, and I. A. Boldyrev, Cyclopentane rings in hydrophobic chains of a phospholipid enhance the bilayer stability to electric breakdown, Soft Matter 16, 3216 (2020).

(21) U. Seifert, Configurations of fluid membranes and vesicles, Adv. Phys. 46, 13 (1997).

(22) H. Noguchi, Membrane Simulation Models from Nanometer to Micrometer Scale, J. Phys. Soc. Jpn. 78, 041007 (2009).

(23) F. Frey and T. Idema, More than just a barrier: using physical models to couple membrane shape to cell function, Soft Matter 17, 3533 (2021).

(24) C. Huguet, S. Fietz, A. Rosell-Melé, X. Daura, and L. Costenaro, Molecular dynamics simulation study of the effect of glycerol dialkyl glycerol tetraether hydroxylation on membrane thermostability, Biochimica et Biophysica Acta (BBA) - Biomembranes 1859, 966 (2017).

(25) T. R. Galimzyanov, P. I. Kuzmin, P. Pohl, and S. A. Akimov, Elastic deformations of bolalipid membranes, Soft Matter 12, 2357 (2016).

(26) T. R. Galimzyanov, P. E. Volynsky, and O. V. Batishchev, Continuum elasticity and molecular dynamics of a pore in archaeal bolalipid membranes, Soft Matter 21, 687 (2025).

(27) A. O. Chugunov, P. E. Volynsky, N. A. Krylov, I. A. Boldyrev, and R. G. Efremov, Liquid but Durable: Molecular Dynamics Simulations Explain the Unique Properties of Archaeal-Like Membranes, Sci. Rep. 4, 7462 (2015).

(28) L. F. Pineda De Castro, M. Dopson, and R. Friedman, Biological Membranes in Extreme Conditions: Simulations of Anionic Archaeal, PLoS One 11, e0155287 (2016).

(29) M. Bulacu, X. Périole, and S. J. Marrink, In Silico Design of Robust Bolalipid Membranes, Biomacromolecules 13, 196 (2012).

(30) C. H. Davis, H. Nie, and N. V. Dokholyan, Insights into thermophilic archaebacterial membrane stability from simplified models of lipid membranes, Phys. Rev. E 75, 051921 (2007).

(31) S. Dey and J. Saha, Minimal Coarse-Grained Modeling toward Implicit Solvent Simulation of Generic Bolaamphiphiles, J. Phys. Chem. B 124, 2938 (2020).

(32) I. R. Cooke and M. Deserno, Solvent-free model for self-assembling fluid bilayer membranes: Stabilization of the fluid phase based on broad attractive tail potentials, J. Chem. Phys. 123, 224710 (2005).

(33) P. L.-G. Chong, U. Ayesa, V. Prakash Daswani, and E. C. Hur, On Physical Properties of Tetraether Lipid Membranes: Effects of Cyclopentane Rings, Archaea 2012, 1 (2012).

(34) A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in ’t Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton, LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales, Comput. Phys. Commun. 271, 108171 (2022).

(35) A. Stukowski, Visualization and analysis of atomistic simulation data with ovito–the open visualization tool, Modelling and Simulation in Materials Science and Engineering 18, 015012 (2009).

(36) E. R. May, A. Narang, and D. I. Kopelevich, Role of molecular tilt in thermal fluctuations of lipid membranes, Physical Review E 76, 021913 (2007).

(37) W. Helfrich, Elastic Properties of Lipid Bilayers: Theory and Possible Experiments, Z. Naturforsch. C 28, 693 (1973).

(38) M. Hamm and M. Kozlov, Elastic energy of tilt and bending of fluid membranes, Eur. Phys. J. E 3, 323 (2000).

(39) M. Deserno, Fluid lipid membranes: From differential geometry to curvature stresses, Chemistry and Physics of Lipids 185, 11 (2015).

(40) V. A. Harmandaris and M. Deserno, A novel method for measuring the bending rigidity of model lipid membranes by simulating tethers, The Journal of Chemical Physics 125, 204905 (2006).

(41) M. Hu, J. J. Briguglio, and M. Deserno, Determining the Gaussian Curvature Modulus of Lipid Membranes in Simulations, Biophys. J. 102, 1403 (2012).

(42) M. Deserno, Elastic deformation of a fluid membrane upon colloid binding, Phys. Rev. E 69, 031903 (2004), arXiv: cond-mat/0303656.

(43) K. S. Makarova, M. Y. Galperin, and E. V. Koonin, Comparative genomic analysis of evolutionarily conserved but functionally uncharacterized membrane proteins in archaea: Prediction of novel components of secretion, membrane remodeling and glycosylation systems, Biochimie 118, 302 (2015).

(44) A. Verchère, W.-L. Ou, B. Ploier, T. Morizumi, M. A. Goren, P. Bütikofer, O. P. Ernst, G. Khelashvili, and A. K. Menon, Light-independent phospholipid scramblase activity of bacteriorhodopsin from Halobacterium salinarum, Sci. Rep. 7, 9522 (2017).

(45) T. B. H. Schroeder, G. Leriche, T. Koyanagi, M. A. Johnson, K. N. Haengel, O. M. Eggenberger, C. L. Wang, Y. H. Kim, K. Diraviyam, D. Sept, J. Yang, and M. Mayer, Effects of lipid tethering in extremophile-inspired membranes on H(+)/OH(-) flux at room temperature, Biophys. J. 110, 2430 (2016).

(46) R. Xu, A. Dehghan, A.-C. Shi, and J. Zhou, Elastic property of membranes self-assembled from diblock and triblock copolymers, Chem. Phys. Lipids 221, 83 (2019).

(47) Z. Dogic and S. Fraden, Ordered phases of filamentous viruses, Curr. Opin. Colloid Interface Sci. 11, 47 (2006).

(48) E. Barry and Z. Dogic, Entropy driven self-assembly of nonamphiphilic colloidal membranes, Proc. Natl. Acad. Sci. U.S.A. 107, 10348 (2010).

(49) A. J. Balchunas, R. A. Cabanas, M. J. Zakhary, T. Gibaud, S. Fraden, P. Sharma, M. F. Hagan, and Z. Dogic, Equation of state of colloidal membranes, Soft Matter 15, 6791 (2019).

(50) M. Saracco, P. Schaeffer, M. Tourte, S.-V. Albers, Y. Louis, J. Peters, B. Demé, S. Fontanay, and P. M. Oger, Bilayer-Forming Lipids Enhance Archaeal Monolayer Membrane Stability, Int. J. Mol. Sci. 26, 3045 (2025).

(51) M. Amaral, archaeal_membranes : code and examples (2024), available at https://doi.org/10.5281/zenodo. 13934991.

(52) M. F. Ergüder and M. Deserno, Identifying systematic errors in a power spectral analysis of simulated lipid membranes, The Journal of Chemical Physics 154, 214103 (2021).

(53) J. Genova, N. Ulrih, V. Kralj-Iglič, A. Iglič, and I. Bivas, Bending Elasticity Modulus of Giant Vesicles Composed of Aeropyrum Pernix K1 Archaeal Lipid, Life 5, 1101 (2015).

(54) M. Amaral, Archaeal Membranes: In Silico Modelling and Design, Ph.D. thesis, Institute of Science and Technology Austria (2024).

(55) M. Pohlschroder, F. Pfeiffer, S. Schulze, and M. F. A. Halim, Archaeal cell surface biogenesis, FEMS Microbiol. Rev. 42, 694 (2018).

(56) K. S. Makarova, N. Yutin, S. D. Bell, and E. V. Koonin, Evolution of diverse cell division and vesicle formation systems in Archaea, Nat. Rev. Microbiol. 8, 731 (2010).

(57) C. W. Stairs and T. J. Ettema, The Archaeal Roots of the Eukaryotic Dynamic Actin Cytoskeleton, Curr. Biol. 30, R521 (2020).

(58) B. Baum and D. A. Baum, The merger that made us, BMC Biol. 18, 72 (2020).

(59) Z. Zeng, H. Chen, H. Yang, Y. Chen, W. Yang, X. Feng, H. Pei, and P. V. Welander, Identification of a protein responsible for the synthesis of archaeal membrane-spanning GDGT lipids, Nat. Commun. 13, 1545 (2022).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript by Liu et al explores the role of the UPR and immune regulators in the evaluation of nutritional quality in C. elegans. They identify neuronal UPR activation and the MAPK PMK-1 as key responders to low food quality. In particular, the data suggest that these pathways are activated by low levels of vitamin C synthesis that result from the low sugar levels present in heat-killed E. coli.

Strengths:

The results are intriguing and expand our understanding both of physiological food evaluation systems, and of the known roles of stress response pathways in organismal physiology. The authors use a range of techniques, encompassing imaging, metabolomic analysis, gene expression analysis, and behavioural assays, to support their claims.

Thank you for your thorough review and acknowledgment of the strengths of our study.

Weaknesses:

There is limited mechanistic analysis in the study. In particular, how does low vitamin C trigger UPR activation? This is an intriguing finding that, if followed up, could potentially reveal a novel mechanism of UPR activation. In addition, how is the activation of the PMK-1 pathway driven by/coordinated with UPR activation? The data in some figures is not as convincing as it could be: the magnitude of the effect size is small in the supplementation experiments, and the statistical tests used are not always appropriate to enable multiple comparisons.

(1) There is limited mechanistic analysis in the study. In particular, how does low vitamin C trigger UPR activation? This is an intriguing finding that, if followed up, could potentially reveal a novel mechanism of UPR activation. 

Thank you for highlighting the need for further mechanistic analysis in our study. We appreciate the opportunity to clarify the process by which low vitamin C triggers UPR activation.

Our investigation revealed that the vitamin C content in heat-killed E. coli (HK-E. coli) is comparable to that of live E. coli or HK-yfbR mutant E. coli (Figure 4-figure supplement 1A), indicating that the induction of unfolded protein response (UPR) in C. elegans by HK-E. coli is not solely attributed to low vitamin C levels but rather involves other unidentified factors.

Through metabolomic analysis, we observed significant decreases in sugar levels, including lactose, D-(+)-sucrose, and D-(+)-glucose, in HK-E. coli (Figure 3B, Table S1). Notably, supplementing D-(+)-glucose effectively inhibited UPRER, immune response, and avoidance behavior induced by HK-E. coli (Figure 3E-H). These findings suggest that the deficiency in sugars in HK-E. coli triggers a stress response and avoidance behavior in animals, which can be alleviated by D-(+)-glucose supplementation.

Furthermore, when comparing heat-killed E. coli mutant yfbR (HK-yfbR) to HK-E. coli, we observed significantly higher sugar levels, including lactose and D-(+)-sucrose, in HK-yfbR (Figure 3B). This was accompanied by reduced UPRER in animals feeding on HK-yfbR (Figure 3-figure supplement 1B), indicating that higher sugar levels may inhibit the induction of UPRER by low-quality food.

Considering that the synthesis of vitamin C (VC) occurs through the glucuronate pathway, utilizing D-glucose as a precursor 1, 2 (Figure 4A), we investigated whether the vitamin C biosynthesis pathway is involved in evaluating low-quality food using D-glucose. Contrary to our initial hypothesis, animals fed live E. coli did not exhibit higher glucose levels compared to those fed low-quality food (HK_-E. coli_). Our results indicate that animals maintain similar VC levels when fed ideal food (live E. coli) compared to low-quality food (HK-E. coli) (Figure 4B), suggesting that animals do not stimulate VC biosynthesis under favorable food conditions. However, supplementation of D-GlcA or E. coli-yfbR mutation in HK-E. coli significantly improved VC levels when animals were fed low-quality food (HK-OP50) (Figure 4B, 4C). Moreover, VC or D-glucuronate (D-GlcA) supplementation inhibited HK-E. coli-induced UPRER (Figure 4D), indicating that glucose boosts the animal's ability to adapt to unfavorable food environments by increasing VC levels, thereby inhibiting UPRER, but not under favorable food conditions.

These findings shed light on the complex interplay between vitamin C, sugar levels, and UPR activation, providing valuable insights into the mechanisms underlying food evaluation and stress response pathways in organisms.

Overall, we are grateful for the reviewer's constructive feedback, which motivates us to continue our efforts to understanding how the UPR response contributes to the complexities of food evaluation and behavioral responses in organisms.

(2) In addition, how is the activation of the PMK-1 pathway driven by/coordinated with UPR activation?

Thank you for your insightful inquiry. In our discussion section, we have addressed this question by integrating new data and discussion to provide insights into the coordination between PMK-1 pathway activation and UPR activation.

Previous studies have demonstrated that activating innate immunity, specifically the PMK-1 MAPK pathway, results in a reduction in translation3, as well as a shutdown of food digestion in animals4, likely aimed at reducing protein translation and cellular metabolism. To further investigate this relationship, we measured the translation level of animals fed with heat-killed E. coli (HK-E. coli) and found a significant reduction in total translation ability in these animals (Figure 5-figure supplement 1D). This observation suggests that activating innate immunity through the PMK-1 MAPK pathway may serve as a mechanism to slow down translation progress, thereby alleviating the pressure on the unfolded protein response (UPR) and preventing excessive UPRER activation.

By integrating these findings, we propose a model wherein activation of the PMK-1 pathway coordinates with UPR activation to regulate translation and cellular metabolism in response to low-quality food. This coordinated response likely serves to maintain cellular homeostasis and prevent detrimental effects associated with excessive UPRER activation.

These insights contribute to our understanding of the intricate interplay between innate immunity, cellular stress responses, and metabolic regulation in organisms facing nutritional challenges.

(3) The data in some figures is not as convincing as it could be: the magnitude of the effect size is small in the supplementation experiments, and the statistical tests used are not always appropriate to enable multiple comparisons.

We appreciate the reviewers' concerns regarding the data presentation and statistical analyses in some of our figures. In response to this feedback, we have made revisions to improve the robustness and clarity of our statistical methods.

All statistical analyses were conducted using GraphPad Prism 8.0 software. Specifically, a two-tailed unpaired t-test was employed for the statistical analysis of two groups of samples, while one-way or two-way ANOVA was utilized for the statistical analysis of more than two groups of samples. These adjustments ensure appropriate statistical comparisons and enhance the reliability of our findings.

Reviewer #2 (Public Review):

Summary:

In this work, the authors aim to better understand how C. elegans detects and responds to heat-killed (HK) E. coli, a low-quality food. They find that HK food activates two canonical stress pathways, ER-UPR, and innate immunity, in the nervous system to promote food aversion. Through the creative use of E. coli genetics and metabolomics, the authors provide evidence that the altered carbohydrate content of HK food is the trigger for the activation of these stress responses and that supplementation of HK food with sugars (or their biosynthetic product, vitamin C), reduces stress pathway induction and food avoidance. This work makes a valuable addition to the literature on metabolite detection as a mechanism for the evaluation of nutritional value; it also provides some new insight into the physiologically relevant roles of well-known stress pathways in modulating behavior.

Strengths:

-The work addresses an important question by focusing on understanding how the nervous system evaluates food quality and couples this with behavioral change. -The work takes full advantage of the tools available in this powerful system and builds on extensive previous studies on feeding behavior and stress responses in C. elegans.

-Creative use of E. coli genetics and metabolite profiling enabled the identification of carbohydrate metabolism as a candidate source of food-quality signals.

-For the most part, the studies are rigorous and logically designed, providing good support for the authors' model.

We deeply appreciate the reviewer's insightful assessment of our study's strengths. 

Weaknesses:

-It is not clear how the mechanism identified here is connected to previously described, related processes. In particular, it is not clear whether this mechanism has a role in the detection of other low-quality foods. Further, the specificity of the ability of sugar/vitamin C to suppress stress pathway induction is unclear (i.e., does sugar/vitamin C have any effect on the activation of these pathways through other means?). Additionally, the relationship of this pathway to the vitamin B2-sensing mechanism previously described by the senior author is unclear. These issues do not weaken confidence in the authors' conclusions, but they do reduce the potential significance of the work.

(1) In particular, it is not clear whether this mechanism has a role in the detection of other low-quality foods. 

Thank you for your valuable feedback. In response to your inquiry, we investigated whether the UPRER (IRE-1/XBP-1) - Innate immunity (PMK-1/p38 MAPK) axis is specific to evaluating low-quality food (HK-E. coli) or if it plays a broader role in food detection.

We conducted behavioral assays using N2, pmk-1, and xbp-1 mutant animals fed with normal E. coli food, inedible food (Saprophytic staphylococci)4, and pathogenic food (Pseudomonas aeruginosa-PA14)5. We found that N2, pmk-1, and xbp-1 mutant worms did not exhibit avoidance behavior when presented with normal food (OP50). However, both N2 and xbp-1 mutant worms were able to escape from inedible food (N2 was predominantly found on the border areas of the bacterial lawn and xbp-1 mutant worms on border and in), Saprophytic staphylococci, whereas pmk-1 mutant worms did not exhibit this avoidance behavior. Notably, N2 and xbp-1 mutant worms exhibited even more pronounced avoidance behavior when exposed to Pseudomonas aeruginosa, whereas pmk-1 mutant worms were more susceptible to infection by this pathogen (Figure 2-figure supplement 2C). These findings suggest that the UPR-Immunity pathway plays a crucial role in helping animals avoid low-quality food (HK-E. coli) by triggering an avoidance response. In contrast, the Innate immunity pathway, mediated by PMK-1/p38 MAPK, appears to play a key role in evaluating unfavorable food sources, such as HK-E. coli, Saprophytic staphylococci, and Pseudomonas aeruginosa, and helping animals avoid these environments.

(2) Further, the specificity of the ability of sugar/vitamin C to suppress stress pathway induction is unclear (i.e., does sugar/vitamin C have any effect on the activation of these pathways through other means?). 

Thank you for your inquiry regarding the specificity of the ability of sugar/vitamin C to suppress stress pathway induction. We aimed to address this question by investigating whether high levels of VC inhibit other stress-induced UPRER pathways.

Previous studies have shown that both Tunicamycin6 and pathogenic bacteria, such as Pseudomonas aeruginosa-PA145, induce UPRER in C. elegans. In response to your query, we conducted experiments to examine whether VC supplementation inhibits UPRER induced by these stressors. Our findings indicate that VC supplementation does not inhibit UPRER induced by either Tunicamycin or PA14 (Author response image 1).

These results suggest that while sugar/vitamin C may suppress stress pathway induction in the context of low-quality food, its effects may not extend to other stressors that induce UPRER through different mechanisms. This insight helps clarify the specificity of sugar/vitamin C's role in modulating stress pathway activation, contributing to a better understanding of the broader regulatory networks involved in stress response in C. elegans.

Author response image 1.

VC supplementation does not inhibit Tunicamycin or PA14-induced UPRER.

(3) Additionally, the relationship of this pathway to the vitamin B2-sensing mechanism previously described by the senior author is unclear.

In response to your comment, we would like to clarify the relationship of our pathway to the previously described vitamin B2-sensing mechanism we found. Previous studies have demonstrated that heat-killed E. coli (HK-E. coli) serves as a low-quality food source incapable of supporting the growth of C. elegans larvae, whereas supplementation with vitamin B2 (VB2) can restore animal growth7

This study investigates the role of sugar deficiency in HK-E. coli, which induces the UPRER-immune response and avoidance behavior in C. elegans. Surprisingly, our findings indicate that supplementing HK-E. coli with carbohydrates such as D-Glc and D-GlcA does not promote animal development (Figure 3-figure supplement 2G), suggesting that carbohydrates are not essential for supporting animal growth on this food source. However, we did observe that carbohydrates play a critical role in inhibiting the UPRER-immune response induced by sugar deficiency in HK-E. coli.

-The authors claim that the induction of the innate immune pathway reporter irg-5::GFP is "abolished" in pmk-1(RNAi) animals, but Figure S2K seems to show a clear GFP signal when these animals are fed HK-OP50. Similarly, the claim that feeding WT animals HK-OP50 enriches phospho-PMK-1 levels (Fig 2E) is unconvincing - only one western blot is shown, with no quantification, and there is a smear in the critical first lane.

(1) The authors claim that the induction of the innate immune pathway reporter irg-5::GFP is "abolished" in pmk-1(RNAi) animals, but Figure S2K seems to show a clear GFP signal when these animals are fed HK-OP50. 

We sincerely appreciate the reviewer's attention. To address this concern, we have replaced the images with higher resolution, larger ones in Figure 2-figure supplement 1-I. These updated images provide a clearer representation of the data, ensuring that all details are readily visible and enabling a more accurate interpretation of the results.

(2) Similarly, the claim that feeding WT animals HK-OP50 enriches phospho-PMK-1 levels (Fig 2E) is unconvincing - only one western blot is shown, with no quantification, and there is a smear in the critical first lane.

Thank you, following reviewer’s suggestion, we also repeated some of the western. We now replace the Figure 2E and quantified relative intensity of pPMK-1/tublin. We also provide the uncropped western blots images as source data ( “raw-data WB” file). 

-The rationales for some of the paper's hypotheses could be improved. For example, the rationale for screening the E. coli mutant library is that some mutants, when heat-killed, may be missing a metabolite that induces the ER-UPR. A more straightforward hypothesis might be that some mutant E. coli strains aberrantly induce the ER-UPR when *not* heat-killed, because they are missing a metabolite that prevents stress pathway induction. This is not in itself a major concern, but it would be useful for the authors to provide a rationale for their hypothesis.

Thank you for the insightful suggestion. We acknowledge the importance of providing a clear rationale for our hypotheses in the paper. In response to this feedback, we have enhanced the discussion section to better elucidate the rationale behind our hypotheses.

One limitation of our study is the lack of explanation for why HK-E. coli activates UPRER and immunity. We hypothesized that when heat-killed, HK-E. coli may lack or contain altered levels of certain metabolites that either activate or inhibit UPRER and immunity, respectively. Additionally, we speculated that E. coli mutants killed by heat may lack metabolites that activate UPRER and immunity, or conversely, have increased levels of metabolites that inhibit these pathways.

Fortunately, our investigation led to the discovery of the E. coli mutant yfbR, which inhibits UPRER and immunity by increasing carbohydrates that aid in resisting these stress pathways. Moving forward, we intend to further explore the intricate relationship between HK-E. coli and UPRER-immunity. This will be a key focus of our future research efforts.

-The authors do not provide any explanation for some unexpected results from the E. coli screen. Earlier in the paper, the authors found that innate immune signaling is downstream of ER-UPR activation. However, of the 20 E. coli mutants that, when heat-killed, "did not induce... the UPR-ER reporter," 9 of them still activate the innate immune response. This seems at odds with the authors' simple model since it suggests that low-quality food can induce innate immune signaling independently of the ER-UPR. Further, only one of the 9 has an effect on behavior, even though failure to activate the innate immune pathway might be expected to lead to a behavioral defect in all of these.

Thank you for your understanding, and we apologize for any confusion caused by our earlier statement. To provide clarification, our study revealed that out of the 20 E. coli mutants examined, none activated the UPRER. Among these mutants, 9 did not induce immunity, and interestingly, one out of these 9 mutants demonstrated the ability to inhibit avoidance behavior.

This diversity in phenotypic outcomes can be attributed to the varied metabolites present in different E. coli mutants. To thoroughly evaluate the effects of these mutants, we conducted a comprehensive three-step screening process, utilizing UPRER marker, immunity marker, and avoidance behavior assays.

Through this rigorous approach, we identified the E. coli mutant, yfbR, which exhibited the desired inhibitory effects on UPRER, immunity, and avoidance behavior.

Subsequently, we conducted a metabolomics analysis of various food qualities (HK-K12, HK-yfbR, and Live-K12). Our findings revealed higher sugar levels in

HK-yfbR and Live-K12 compared to HK-K12 (Figure 3B, Figure 3-figure supplement 2A, and Table S1), indicating that sugar deficiency might trigger the UPRER, immunity responses, and subsequent avoidance behavior. 

-In a number of places, the writing style can make the authors' arguments difficult to follow.

Thanks for the reviewer’s efforts. We changed all of these errors and polish the language of this paper. 

-Some of the effect sizes observed by the authors are exceedingly small (e.g, the suppression of hsp-4::gfp induction by sugar supplementation in Figs 3C-E), raising some concern about the biological significance of the effect.

Thank you for your feedback. In response to your concern, we have included additional clarification in the manuscript.

We have added the following statement: “While sugar effectively inhibits the HK-E. coli-induced UPRER and immune response, it does not fully suppress it to the extent observed with live-E. coli (Figure 3C-F). This implies that additional nutrients present in live-E. coli might also contribute to the inhibition of UPRER and immune response.”

This addition helps to address the observation that some effect sizes appear small, providing context and suggesting potential factors that may influence the outcomes. 

-In some cases, there is a discrepancy between the fluorescence images and their quantitation (e.g., Figure 3E, where the effect of glucose on GFP fluorescence seems much stronger in the image than in the graph).

Thank you for your valuable suggestion. In response, we have revised our image selection process to ensure impartiality. We now randomly select images to ensure they accurately represent the quantified data without bias. More details regarding this update can be found in Author response image 2.

Author response image 2.

More original picture corresponding to Figure 3E 

Reviewer #3 (Public Review):

Summary:

Animals can evaluate food quality in many ways. In contrast to the rapid sensory evaluation with smell and taste, the mechanism of slow nutrient sensation and its impact on food choice is unexplored. The authors utilize C. elegans larvae and their bacterial food as an elegant model to tackle this question and reveal the detailed molecular mechanism to avoid nutrient-poor foods.

Strengths:

The strength of this study is that they identified the molecular identities of the critical players in bacterial food and C. elegans using unbiased approaches, namely metabolome analysis, E. coli mutant screening, and RNA sequencing. Furthermore, they strengthen their findings by thorough experiments combining multiple methods such as genetics, fluorescent reporter analysis, and Western blot.

Thank you for highlighting the strengths of our study. 

Weaknesses:

The major caveat of this study is the reporter genes. The transcriptional reporters were used to monitor the UPRER and immune responses in the intestine of C. elegans.

However, their tissue-specific rescue experiments suggest that the genes in the UPRER and immune response function in the neurons. Thus, we should carefully interpret the results of the reporter genes.

Thank you for your insightful comment. We appreciate the opportunity to address your concerns regarding the interpretation of our reporter gene data.

Upon reevaluation, we observed strong induction of the UPRER reporter

(Phsp-4::GFP)8 and immunity reporter (Pirg-5::GFP)9 both in the intestine (Figure 1F-G) and in neurons (Figure 1-figure supplement 2A) in response to feeding unfavorable food (HK-E. coli). This suggests that both the UPRER and immune pathways may indeed respond to low-quality food (HK-E. coli) in multiple tissues of C. elegans. While we acknowledge that our tissue-specific rescue experiments suggest a role for these pathways in neurons, the intestinal fluorescence of Phsp-4::GFP or Pirg-5::GFP is easily observable and scorable. Therefore, we chose to focus our further analyses on the intestine for practical reasons.

Overall, this work provides convincing data to support their model. In the C. elegans field, the behaviors of larvae are not well studied compared to adults. This work will pose an interesting question about the difference between larvae and adults in nutrition sensing in C. elegans and provide a framework and candidate molecules to be studied in other organisms.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major suggestions:

(1) My major overall comment is that the paper would be substantially strengthened by more mechanistic analysis. In particular, how does low vitamin C trigger UPR activation? This is an intriguing finding and it would be important to see it more fully explored.  

Our study revealed that the vitamin C content in HK_-E. coli_ is comparable to that of live E. coli or HK-yfbR (Figure 4-figure supplement 1A), suggesting that the induction of unfolded protein response (UPR) in C. elegans by HK-E. coli is not attributed to low vitamin C levels, but rather to unknown factors.

Metabolomic analysis showed that the sugar levels, including lactose, D-(+)-sucrose, and D-(+)-glucose, were significantly decreased in HK-E. coli (Figure 3B, Table S1).

Furthermore, we found that supplementing D-(+)-glucose effectively inhibited UPRER (Figure 3E), immune response (Figure 3F, 3G, and Figure 3-figure supplement 2D), and avoidance behavior (Figure 3H) induced by HK-E. coli. Our findings suggest that the deficiency in sugars in HK-E. coli triggers a stress response and avoidance behavior in animals, which can be alleviated by D-(+)-glucose supplementation.

Notably, when E. coli was heat-killed, we observed that the sugar levels, including lactose and D-(+)-sucrose, were significantly higher in the heat-killed E. coli mutant yfbR (HK-yfbR) compared to HK-E. coli (Figure 3B). Moreover, we found that UPRER was reduced in animals feeding HK-yfbR (Figure 3-figure supplement 1B), indicating that higher sugar levels may inhibit the induction of UPRER by low-quality food.

The synthesis of vitamin C (VC) occurs through the glucuronate pathway, utilizing D-glucose as a precursor 1, 2 (Figure 4A). This led us to investigate whether the vitamin C biosynthesis pathway is involved in evaluating low-quality food by using D-glucose. In this study, we found that animals feeding live E. coli, which should produce more VC, exhibit higher glucose levels. However, our results show that animals maintain similar VC levels when fed ideal food (live E. coli) compared to low-quality food (HK-E. coli) (Figure 4B), suggesting that animals do not stimulate VC biosynthesis under favorable food conditions. In contrast, when animals are fed low-quality food (HK-OP50), we found that supplementing D-GlcA (Figure 4C) or E. coli-yfbR mutation (Figure 4B) in HK-E. coli can improve VC levels. Moreover, we found that VC or D-glucuronate (D-GlcA) supplementation inhibited HK-E. coli induced UPRER (Figure 4D). These data indicate that glucose boosts the animal's ability to adapt to unfavorable food environments by increasing VC levels, thereby inhibiting UPRER, but not in favorable food conditions.

In addition,we asked whether high level of VC inhibits other stress induced UPRER. Previous study shown that Tunicamycin6 and pathogenic bacteria-Pseudomonas aeruginosa-PA145 induce UPRER in C. elegans. We found that VC supplementation does not inhibit Tunicamycin or PA14-induced URPER (Author response image 3). 

Author response image 3.

VC supplementation does not inhibit Tunicamycin or PA14-induced UPRER.

In addition, how is the activation of the PMK-1 pathway driven by/coordinated with UPR activation? 

If the authors do not want to pursue these directions experimentally in this study, the discussion would be strengthened by considering these questions and identifying candidate regulatory mechanisms for further exploration.

In this study, we found that heat-killed E. coli (HK-E. coli), a low-sugar food, triggers cellular unfolded protein response (UPRER) and immune response. We also demonstrated that 1) the activation of UPRER by low-quality food depends on the IRE-1/XBP-1, 2) activation of immune response (PMK-1) is downstream of XBP-1 in responding to low-quality food.

how is the activation of the PMK-1 pathway driven by/coordinated with UPR activation? 

In our discussion part, we added new data and discussion to answer reviewer’s question. 

A previous study has shown that activating innate immunity (PMK-1 MAPK) leads to a reduction in translation 3. Our own previous research has also demonstrated that PMK-1 activation causes a shutdown of food digestion in animals4, likely to reduce protein translation and cellular metabolism. To investigate this further, we measured the translation level of animals fed with HK-E. coli and found that total translation ability is significantly reduced in these animals (Figure 5-figure supplement 1D). This finding suggests that activating innate immunity (PMK-1 MAPK) may serve as a mechanism to slow down translation progress, thereby alleviating the pressure on the unfolded protein response (UPR) and preventing excessive UPRER activation.

(2) Figure 2C: The data shows that xbp-1 mutants are significantly more likely to leave heat-killed E. coli. However, no other conditions are examined. Is this avoidance defect specific to heat-killed E. coli, or is it a more general effect of xbp-1 mutants - that is, are other conditions that evoke avoidance also affected by mutation of xbp-1? Is feeding behavior on regular E. coli altered in this background? The finding would be more relevant if the authors could clarify or provide more context for their claims here.

We then asked whether UPRER (IRE-1/XBP-1) - Innate immunity (PMK-1/p38 MAPK) axis is specific to evaluate low-quality food (HK-E. coli). We examined the avoidance behavior phenotype of wild-type and mutant L1 animals by placing them on various food conditions, including normal E. coli food, inedible food (Saprophytic staphylococci) and pathogenic food (Pseudomonas aeruginosa-PA14), for a 24-hour period. We found that N2, pmk-1, and xbp-1 mutant worms did not exhibit avoidance behavior when presented with normal food (OP50). However, both N2 and xbp-1 mutant worms were able to escape from inedible food, Saprophytic staphylococci, whereas pmk-1 mutant worms did not show this avoidance. Notably, xbp-1 mutant worms exhibited even more pronounced avoidance behavior when exposed to Pseudomonas aeruginosa, whereas pmk-1 mutant worms were more susceptible to infection by this pathogen (Figure 2-figure supplement 2C). These findings suggest that the UPR-Immunity pathway plays a crucial role in helping animals avoid low-quality food by triggering an avoidance response. In contrast, the Innate immunity pathway, which is mediated by PMK-1/p38 MAPK, appears to play a key role in evaluating unfavorable food sources, such as HK-E. coli, Saprophytic staphylococci, and Pseudomonas aeruginosa, and helping animals avoid these environments.

(3) Figure 3C-F: The magnitude of the changes between conditions shown in these panels is small. To what extent does this supplementation represent a full rescue? The findings would be strengthened if figures/images for the control condition (non-HK E. coli) were shown for comparison to allow the reader to assess the extent to which UPR/PMK-1 activation is rescued.

In response to a reviewer's suggestion, we included live-E. coli as a control in our study. Notably, our data revealed that the addition of lactose, D-(+)-sucrose, and D-(+)-glucose partially inhibited the HK-E. coli-induced unfolded protein response (UPRER) and immune response, suggesting that other nutrients present in live-E. coli may also play a role in inhibiting UPRER.

We added this in manuscript: “While sugar effectively inhibits the HK-E. coli-induced UPRER and immune response, it does not fully suppress it to the extent observed with live-E. coli (Figure 3C-F). This implies that additional nutrients present in live-E. coli might also contribute to the inhibition of UPRER and immune response.” 

(4) Figure 5B-D: The magnitude of changes shown between conditions here again appear to be very small, even those labelled as statistically significant. It is important to ensure that the correct statistical tests have been used to assess the significance of these differences (see below).

All statistical analyses were performed in Graphpad prism 8.0. Two-tailed unpaired t test was used for statistical analysis of two groups of samples，one-way or two-way ANOVA was used for statistical analysis of more than two groups of samples.

(5) Methods: In the "Statistical analysis" section, the authors state that "All statistical analyses were performed using Student's t-test". However, this is not the appropriate test to use in experiments where multiple comparisons are made, which is true in several instances across the paper. In these cases, a more appropriate statistical test should be used.

All statistical analyses were performed in Graphpad prism 8.0. Two-tailed unpaired t test was used for statistical analysis of two groups of samples，one-way or two-way ANOVA was used for statistical analysis of more than two groups of samples.

Minor suggestions:

(1) Figure S2: RNAi is usually delivered in a different E. coli strain, HT115. Is this the case with the RNAi knockdowns in Figure S2, and given that diet can influence UPR activation, is it possible that this different diet could change the phenotypes observed?

This should be clarified by the authors.

In this study, all RNAi experiments involved bleaching adult animals under RNAi strain culture conditions to obtain L1 animals. Subsequently, L1 animals were transferred to HK-E. coli OP50 for phenotype analysis. In response to a reviewer's suggestion, we observed that L1 animals obtained from mothers fed E. coli strains OP50, HT115, or K12 exhibited similar UPR induction under HK-E. coli OP50 feeding conditions (Author response image 4). These findings suggest that variations in diet did not alter the UPR phenotypes.

Author response image 4.

L1 animals obtained from mothers fed E. coli strains OP50, HT115, or K12 exhibited similar UPR induction under HK-E. coli OP50 feeding conditions 

Reviewer #2 (Recommendations For The Authors):

Line 182: "irg-5::GFP" should be "hsp-4::gfp".

Thanks for the reviewer’s efforts. We have changed this error.

Reviewer #3 (Recommendations For The Authors):

Major comments:

(1) The reporter genes of UPRER and immune response were analyzed in the intestine throughout the study. On the other hand, their rescue experiments suggest that these pathways function in the neurons. They should provide the fluorescence data in the neurons at least for Figures 1F and 1G to confirm that the intestinal response matches the neuronal response and mention that further analyses were done in the intestine for easy scoring.

Consistent with the results of the RNA sequencing (RNA-seq) analysis, the UPRER reporter (Phsp-4::GFP)8 and immunity reporter (Pirg-5::GFP)9 were strongly induced in intestinal (Figure 1F-G) and neurons (Figure 1-figure supplement 2A) by feeding unfavorable food (HK-E. coli), suggesting that UPRER and immune pathways may respond to low-quality food (HK-E. coli). As intestinal fluorescence (Phsp-4::GFP or Pirg-5::GFP) is easy observation and scoring, the further analyses were done in the intestine. 

(2) I have concerns about the interpretation of the p-PMK-1 data. Although the authors described that "p-PMK-1 is prominently increased" in the text (Line 150), it is unclear on the data (Figure 2E). Similarly, the authors' statement "p-PMK-1 is decreased in animals with D-GlcA (F).." was not fully supported by the data in Figure 4F. The experiment should be repeated and quantified. Moreover, pPMK-1 showed single bands in Figure 2E, but double bands in Figure 3G, 4F, and 4G. The authors should explain why that is the case and which band we should look at for Figures 3G, 4F, and 4G.

As reviewer’s suggestion, we also repeated some of the western. We found that after longer expose, there are two bands for pPMK-1 (Figure 2E, new data; and “raw-data WB” file). The VHP-1 phosphatase is known to inhibit PMK-13. In our previous study, we found that worms treated with vhp-1(RNAi), which hyperactivates p-PMK-1 (lower band) 4. In contrast, the two bands are disappeared in pmk-1 mutant (Author response image 5). Thus, the lower band indicates the pPMK-1. We now replace the Figure 2E and quantified relative intensity of pPMK-1/tublin. We also provide the uncropped western blots images as source data ( “raw-data WB” file). 

Author response image 5.

In our previous study, we found that worms treated with vhp-1(RNAi), which hyperactivates p-PMK-1 (lower band) 4. In contrast, the two bands are disappeared in pmk-1 mutant. These pictures are extracted from our previous study4.

(3) Heat-killed E. coli (HK-E. coli) is low-quality because the lack of sugar cannot support the growth of C. elegans larvae (Qi and Han, Cell, 2018). Thus, animals do not show the UPRER-immune response and avoidance when HK-E. coli is supplemented with sugars such as glucose (Line 225-227). If these sugars are the key, C. elegans larvae should be able to grow better with HK-E. coli supplemented with glucose. Authors should address this possibility.

Previous studies have shown that heat-killed E. coli (HK-E. coli) is a low-quality food source that cannot support the growth of C. elegans larvae7. Here, we found that sugar deficiency in HK-E. coli induces the UPRER-immune response and avoidance behavior in C. elegans. Given this, we investigated whether sugar supplementation could promote animal growth when fed HK-E. coli. To our surprise, supplementing HK-E. coli with carbohydrates (D-Glc, D-GlcA) did not support animal development (Figure 3-figure supplement 2G), suggesting that carbohydrates are not essential for supporting animal growth on this food source. However, we did find that carbohydrates are critical for inhibiting the UPRER-immune response induced by sugar deficiency in HK-E. coli.

(4) Line 884: Instead of the Student's t-test, the ANOVA should be used for multiple comparisons.

All statistical analyses were performed in Graphpad prism 8.0. Two-tailed unpaired t test was used for statistical analysis of two groups of samples，one-way or two-way ANOVA was used for statistical analysis of more than two groups of samples.

(5) Although the results are interesting and convincing, the manuscript needs some careful editing and proofreading. As far as I could catch, there are more than 100 errors and typos, as I summarized in minor comments. I recommend the authors proofread thoroughly to make this work easier to read.

Thanks for the reviewer’s efforts. We changed all of these errors and polish the language of this paper. 

Minor comments:

(1) Line 30: nature -> natural

(2) Line 86: elegnas -> elegans

(3) Line 93: the17h -> the 17h

(4) Line 97: response -> respond

(5) Line106: responded -> respond

(6) Lien 107-109: Add references for the three reporters

(7) Line 114: immune -> immune pathway

(8) Line 118: immune depended -> immune-dependent

(9) Line 128, 594, 596: deferentially -> differentially

(10) Line 131: Explain what IRE-1-mediated splicing of xbp-1 with references

(11) Line 170: XPB-1 -> XBP-1

(12) Line 179: URP -> UPR

(13) Line 181: hsp-4::GFP -> Phsp-4::GFP

(14) Line 183: Italicize E. coli; mutant -> mutants

(15) Line 184: irg-5::GFP -> Pirg-5::GFP (2 places)

(16) Line 197, 203, 206, 207: Lactose -> lactose

(17) Line 206, 209, 217, 225, 228, 232, 237, 262, 442, 445, 604, 739: Glucose -> glucose

(18) Line 218: Sugars deficiency -> sugar deficiency

(19) Line 229: found contribute to -> found to contribute to

(20) Line 235, 537, 539, 587, 599, 642, 855: Italicize E. coli

(21) Line 236: same -> the same

(22) Line 239: I recommend adding "in C. elegans". This study uses both E. coli and C.

elegans genetics. Sometimes, it is confusing which organism was mentioned. It should be applied where it is necessary.

(23) Line 240: additional -> addition

(24) Line 339, 642: Italicize kgb-1

(25) Line 390: Italicize Pseudomonas aeruginosa, Bacillus thuringiensis,

Staphylococcus aureus, and Serratia marcescens

(26) Line 394: wiht -> with

(27) Line 400, 550: Change ER to superscript; Italicize ire-1, xbp-1, and pmk-1

(28) Line 415: xpb-1 -> xbp-1

(29) Line 460, 525, 531, 532, 617, 655: Italicize yfbR

(30) Line 457, 468, 472, 475, 482, 497, 513, 624, 629, 633, 733. 758: Vitamin -> vitamin

(31) Line 459: Make it clear what is the relationship between vitamin C and TAA

(32) Line 527: Do not italicize mutant

(33) Line 538: Phsp-6:GFP -> Phsp-6::GFP (to match other descriptions)

(34) Line 540: Phsp-4:GFP -> Phsp-4::GFP (to match other descriptions)

(35) Line 540: Italicize hsp-4

(36) Line 543: Pirg-5:GFP -> Pirg-5::GFP (to match other descriptions) and italicize irg-5

(37) Line 550, 881: Innate -> innate

(38) Line 557, 560, 564, 838: Do not italicize HK

(39) Line 561: Remove the extra space before "three"

(40) Line 575, 577: Reporter -> reporter

(41) Line 575, 607: Italicize Phsp-4::GFP

(42) Line 577: immunity -> Immunity; Italicize Pirg-5::GFP

(43) Line 585, 653: keio -> Keio

(44) Line 586: hsp-4::GFP -> Phsp-4::GFP

(45) Line 586, 589 (2 places): irg-5::GFP -> Pirg-5::GFP

(46) Line 597: Remove "all"

(47) Line 600: Trehalose -> trehalose

(48) Line 609: Italicize Pirg-5::GFP

(49) Line 615: critically -> critical

(50) Line 636: Remove "+"

(51) Line 656 (2 places), 682: Do not italicize OP50

(52) Line 664: Lead -> lead

(53) Line 681: Describe the composition of NGM or show the reference. Since this paper examines nutrition, the composition of the medium is crucial.

(54) Line 686-706: Italicize all allele names. Be consistent with how to write the promoter to avoid confusion (e.g., ttx-3p -> Pttx-3). Be consistent with how to describe the transgene (e.g., Phsp-4::GFP(zcIs4) -> zcIs4[Phsp-4::GFP])

(55) Line 710: Describe the composition of LB or show the reference. Since this paper examines nutrition, the composition of the medium is crucial.

(56) Line 709, 856 (2 places), 858: Do not italicize K12 to make it consistent

(57) Line 719: Podr-1p:RFP -> Podr-1::RFP

(58) Line 722, 724: Italicize ges-1 and xbp-1

(59) Line 723: Pges-1:xbp-1::GFP -> Pges-1::xbp-1::GFP

(60) Line 735: Glucuronic -> glucuronic

(61) Line 748: I believe it is 5 mm instead of 0.5 mm

(62) Line 750: The equation should be (5 mm)2/(17.5 mm)2

(63) Line 759: Remove the period after "pattern".

(64) Line 766: Describe how they were synchronized

(65) Line 774: Italicize Psysm-1p::GFP

(66) Line 785: Insert a space before "until"

(67) Line 787: the mutant -> mutant

(68) Line 789, 792, 793, 795 (2 places): GPF -> GFP

(69) Line 791: next -> Next; an -> a

(70) Line 799: Remove a space before "MRC".

(71) Line 804: I do not understand what "until adulthood" means in this context;

Remove a space before "by". (I recommend searching double space and correcting it.)

(72) Line 853: Metabolome -> metabolome

(73) Line 893-1082: Species and gene names should be italicized in Reference

(74) Figures 1F, 1G, S2F, S2G: The panels' order should match the bar graphs' order. The apparent difference in the representative data does not match the marginal difference in the bar graph in Fig. 1G. The authors should double-check the results.

(75) Figure 1F, 2A, 2B, 3C, 3D, 3E, 4D, 4I, S1J, S2A, S2B, S2I, S3B, S3F, S3H: hsp-4::GFP -> Phsp-4::GFP

(76)  Figure 1G, 2D, 3F, 4E, 4J, S1K, S2H, S3C, S3I: irg-5::GFP -> Pirg-5::GFP

(77)  Figure 6: Liquids -> Lipids; Italicize ire-1, xbp-1, pmk-1

(78)  Figure S1I: hsp-6::GFP -> Phsp-6::GFP

(79)  In the legend for Figure S1 after Figure S1, (A), (B)... were duplicated. It is OK in the corresponding main text (Line 530)

(80)  Figure S2F, S3G, S4C, S4D: sysm-1::GFP -> Psysm-1::GFP

(81)  Figure S2G: irg-1::GFP -> Pirg-1::GFP

(82)  Figure S3H and S3I: Describe which ones are Glu + conditions

References: 

(1) Patananan AN, Budenholzer LM, Pedraza ME, Torres ER, Adler LN, Clarke SG. The invertebrate Caenorhabditis elegans biosynthesizes ascorbate. Arch Biochem Biophys 569, 32-44 (2015).

(2) Yabuta Y_, et al. L-Ascorbate Biosynthesis Involves Carbon Skeleton Rearrangement in the Nematode Caenorhabditis elegans. _Metabolites 10,  (2020).

(3) Weaver BP, Weaver YM, Omi S, Yuan W, Ewbank JJ, Han M. Non-Canonical Caspase Activity Antagonizes p38 MAPK Stress-Priming Function to Support Development. Dev Cell 53, 358-369 e356 (2020).

(4) Geng S_, et al. Gut commensal E. coli outer membrane proteins activate the host food digestive system through neural-immune communication. _Cell Host Microbe 30, 1401-1416 e1408 (2022).

(5)  Richardson CE, Kooistra T, Kim DH. An essential role for XBP-1 in host protection against immune activation in C. elegans. Nature 463, 1092-1095 (2010).

(6) Harding HP_, et al. An Integrated Stress Response Regulates Amino Acid Metabolism and Resistance to Oxidative Stress. _Molecular Cell 11, 619-633 (2003).

(7) Qi B, Kniazeva M, Han M. A vitamin-B2-sensing mechanism that regulates gut protease activity to impact animal’s food behavior and growth. eLife 6, e26243 (2017).

(8) Calfon M_, et al. IRE1 couples endoplasmic reticulum load to secretory capacity by processing the XBP-1 mRNA. _Nature 415, 92-96 (2002).

(9) Bolz DD, Tenor JL, Aballay A. A Conserved PMK-1/p38 MAPK Is Required in Caenorhabditis elegans Tissue-specific Immune Response to Yersinia pestis Infection*. The Journal of Biological Chemistry 285, 10832 - 10840 (2010).

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

In Ryu et al., the authors use a cortical mouse astrocyte culture system to address the functional contribution of astrocytes to circadian rhythms in the brain. The authors' starting point is transcriptional output from serum-shocked culture, comparative informatics with existing tools and existing datasets. After fairly routine pathway analyses, they focus on the calcium homeostasis machinery and one gene, Herp, in particular. They argue that Herp is rhythmic at both mRNA and protein levels in astrocytes. They then use a calcium reporter targeted to the ER, mitochondria, or cytosol and show that Herp modulates calcium signaling as a function of circadian time. They argue that this occurs through the regulation of inositol receptors. They claim that the signaling pathway is clock-controlled by a limited examination of Bmal1 knockout astrocytes. Finally, they switch to calcium-mediated phosphorylation of the gap junction protein Connexin 43 but do not directly connect HERP-mediated circadian signaling to these observations. While these experiments address very important questions related to the critical role of astrocytes in regulating circadian signaling, the mechanistic arguments for HERP function, its role in circadian signaling through inositol receptors, the connection to gap junctions, and ultimately, the functional relevance of these findings is only partially substantiated by experimental evidence. 

Strengths: 

- The paper provides useful datasets of astrocyte gene expression in circadian time. 

- Identifies HERP as a rhythmic output of the circadian clock. 

- Demonstrates the circadian-specific sensitivity of ATP -> calcium signaling. 

- Identifies possible rhythms in both Connexin 43 phosphorylation and rhythmic movement of calcium between cells. 

Weaknesses: 

- It is not immediately clear why the authors chose to focus on Ca2+ homeostasis or Herp from their initial screens as neither were the "most rhythmic" pathways in their primary analyses. 

We appreciate the reviewer’s comment. We chose to focus on Ca2+ homeostasis processes because intracellular Ca2+ signaling plays crucial role in numerous astrocyte functions and is notably associated with sleep/wake status of animals, which is our primary interest (Bojarskaite et al., 2020; Ingiosi et al., 2020; Blum et al., 2021; Szabó et al., 2017). Among the genes involved in calcium ion homeostasis, Herp exhibited the most robust rhythmicity (supplementary table 1). The rationale for our focus on Ca2+ homeostasis and Herp is explained in the results section (line 143-150). We hope this provides a clear justification for our focus.

- It would have been interesting (and potentially important) to know whether various methods of cellular synchronization would also render HERP rhythmic (e.g., temperature, forskolin, etc). If Herp is indeed relatively astrocyte-specific and rhythmic, it should be easy to assess its rhythmicity in vivo. 

Thank you for the reviewer’s insightful comment. In response, we examined HERP expression in cultured astrocytes synchronized using either Dexamethasone or Forskolin treatment. We found that Herp exhibited rhythmic expression at both the the mRNA and protein levels under these conditions. These results have been added to Figure S3 and are explained in the manuscript (lines 173-175).

Additionally, we measured HERP levels in the prefrontal cortex of mice at CT58 and CT70 and found no rhythmicity, as shown in Author response image 1. Given that Herp is expressed in various brain cell types, including microglia, endothelial cells, neurons, oligodendrocytes, and the astrocytes- with the highest expression in microglia(Cahoy et al., 2008), we reason that the potential rhythmic expression of HERP in astrocytes might be masked by its continuous expression in other cell types. Nonetheless, to assess HERP rhythmicity specifically in astrocytes in vivo, we attempted immunostaining using several anti-HERP antibodies, but none were successful. Consequently, we were unable to determine whether HERP exhibits rhythmic expression in astrocytes in vivo.

Author response image 1.

HERP levels were constant at CT58 and CT70. (A, B) Mice were entrained under 12h:12h LD cycle and maintained in constant dark. Prefrontal cortices were harvested at indicated time and processed for Western blot analysis. Representative image shows three independent samples. (B) Quantification of HERP levels normalized to VINCULIN. Values in graphs are mean ± SEM (*p < 0.05, **p < 0.005, ***p < 0.0005, and ****p < 0.00005; t-test)

- The authors show that Herp suppression reduces ATP-mediated suppression of calcium whereas it initially increases Ca2+ in the cytosol and mitochondria and then suppresses it. The dynamics of the mitochondrial and cytosolic responses are not discussed in any detail and it is unclear what their direct relationship is to Herp-mediated ER signaling. What is the explanation for Herp (which is thought to be ER-specific) to calcium signaling in other organelles? 

Our examination of cytosolic and mitochondrial Ca2+ responses was aimed at corroborating HERP’s effect on ER Ca2+ response. Upon ATP stimulation, Ca2+ is released from the ER via IP3R receptors (IP3Rs) and subsequently transmitted to other organelles including mitochondria (Carreras-Sureda et al., 2018; Giorgi et al., 2018). Ca2+ is directly transferred to the cytosol by IP3Rs located on the ER membrane, and to the mitochondria through a complex formed by IP3R and the voltage-dependent anion channel (VDAC) on the mitochondria (Giorgi et al., 2018).  Consistent with previous reports, we observed an increase of cytosolic and mitochondrial Ca2+ levels accompanied by decrease in ER Ca2+ levels following ATP treatment (See Fig. 3B, E, H, control siRNA). The ATP-stimulated ER Ca2+ release was enhanced by Herp knockdown. We reasoned that if Ca2+ release was enhanced, then cytosolic and mitochondrial Ca2+ uptakes would also be enhanced. The results were consistent with our hypothesis (See Fig. 3B, E, H, Herp siRNA). These observations are described in the Results section (lines 202-208) and in the Discussion (lines 333-348). We hope this explanation clarifies the relationship between Herp-mediated ER Ca2+ response and Ca2+ response in other organelles. Thank you for your consideration.

- What is the functional significance of promoting ATP-mediated suppression of calcium in ER? 

In astrocytes, intracellular Ca2+ plays crucial role in regulating several processes. In this study, among various downstream effects of intracellular Ca2+, we examined the gap junction channel (GJC) conductance, which affects astrocytic communication. As discussed in the manuscript (lines 357-381), circadian variation in HERP results in rhythmic Cx43 (S368) phosphorylation linked with GJC conductance. We propose that during the subjective night phase, heightened ATP induced ER Ca2+ release reduces GJC conductance, uncoupling astrocytes from the syncytium, making them better equipped for localized response. On the other hand, during the subjective day phase, increased GJC conductance may allow astrocytes to control a larger area for synchronous neuronal activity which is a key feature of sleep.

- The authors then nicely show that the effect of ATP is dependent on intrinsic circadian timing but do not explain why these effects are antiphase in cytosol or mitochondria.

Moreover, the ∆F/F for calcium in mitochondria and cytosol both rise, cross the abscissa, and then diminish - strongly suggesting a biphasic signaling event. Therefore, one wonders whether measuring the area under the curve is the most functionally relevant measurement of the change. 

We appreciate the reviewer’s insightful comments. As explained in our previous response, Ca2+ released from the ER is transferred to the cytosol and mitochondria. This transfer explains why the fluorescent intensities of cytosolic and mitochondrial Ca2+ indicators show anti-phasic responses to those of the ER.

We agree that cytosolic and mitochondrial Ca2+ responses may be biphasic. The decrease below the abscissa in mitochondria and cytosol likely reflects Ca2+ extrusion from these organelles. However, our primary focus was on the initial uptake of Ca2+ following ER Ca2+ release. Thus, when calculating the area under the curve (AUC), we measured the area between the ∆F/F graph and the y=0 (X-axis) for both mitochondria and cytosol. We reason that the measuring the area under the curve (above the abscissa) fits with our objective.

While addressing your concerns, we noticed errors in the Y-axis labels of Fig. 3C, 4D, and 5C. For the ER Ca2+ dynamics, we measured the area above curve. These mistakes have now been corrected.

- Why are mitochondrial and cytosolic calcium not also demonstrated for Bmal1 KO astrocytes? 

In two sets of experiments (Fig. 3 and Fig. 4), we demonstrated that the increase in cytosolic and mitochondrial Ca2+ aligns with ER Ca2+ release. Since there were no circadian time differences in ER Ca2+ release in the Bmal1 KO cultures, we concluded that it was unnecessary to measure Ca2+ levels in the mitochondria and cytosol. Additionally, our primary focus is on the ER Ca2+ response rather than the Ca2+ dynamics in subcellular organelles. We hope this clarifies our rationale and maintains the focus of our study.

- The authors claim that Herp acts by regulating the degradation of ITPRs but this hypothesis - rather central to the mechanisms proposed in this study - is not experimentally substantiated. 

We appreciate the reviewer’s insightful comments regarding the role of HERP in the degradation of IP3Rs. In the original manuscript, we demonstrated that treating cells with Herp siRNA leads to an increase in the levels of ITPR1 and ITPR2, suggesting that HERP might be involved in the regulation of IP3Rs stability. This observation is consistent with previous studies, which showed that Herp siRNA treatment increases ITPR levels in HeLa and cardiac cells (Paredes et al., 2016; Torrealba et al., 2017). Torrealba et al. also showed that HERP regulates the polyubiquitination of IP3Rs. Based on our results and previous reports, we hypothesized that HERP similarly regulates ITPR degradation in cultured astrocytes.

However, as the reviewer rightly pointed out, further evidence is needed to confirm that HERP specifically regulates ITPR degradation. To address this, we conducted new experiments examining the effect of XesC, an inhibitor of IP3Rs, on ER Ca2+ release. The treatment of XesC reduced the ER Ca2+ release and abolished the enhancement of ER Ca2+ release by Herp KD. These results demonstrated that HERP influences ER Ca2+ response through IP3Rs. These new findings have been added to Fig. 3N – 3P and explained in the Results section (lines 217-221).

We believe these additional experiments and clarifications strengthen our hypothesis that HERP regulates IP3R degradation, thereby modulating ER Ca2+ responses.

- There is no clear demonstration of the functional relevance of the circadian rhythms of ATP-mediated calcium signaling.

As mentioned in the previous response, we examined Cx43 phosphorylation linked with GJC conductance in the context of ATP-mediated Ca2+ signaling. Our results demonstrated circadian variations in Cx43 Ser368 phosphorylation leading to variations of gap junction channel (GJC) conductance (Fig. 6C – F and Fig. 7D - I). We have discussed the significance of this circadian rhythm in ATP driven ER Ca2+ signaling concerning astrocytic function during sleep/wake states in the manuscript (lines 357 – 382) as follows.

“ATP-stimulated Cx43 (S368) phosphorylation is higher at 30hr (subjective night phase) than at 42hr (subjective day phase) (Fig. 6C and 6D.), a finding further supported by in vivo experiments showing higher pCx43(S368) levels in the prefrontal cortex during the subjective night than during the day (Fig. 6E and 6F). What are the implications of this day/night variation in Cx43 (S368) phosphorylation? We reasoned that the circadian variation in Cx43 phosphorylation could significantly impact astrocyte functionality within the syncytium. Indeed, our cultured astrocytes exhibited circadian phase-dependent variation in gap junctional communication (Fig.7D – 7F). Astrocytes influence synaptic activity through the release of gliotransmitters such as glutamate, GABA, D-serine, and ATP, triggered by increases in intracellular Ca2+ in response to the activity of adjacent neurons and astrocytes (Verkhratsky & Nedergaard, 2018). Importantly, this increase in Ca2+ spreads to adjacent astrocytes through GJCs (Fujii et al., 2017), influencing a large area of the neuronal network. Considering that Cx43 Ser368 phosphorylation occurs to uncouple specific pathways in the astrocytic syncytium to focus local responses (Enkvist & McCarthy, 1992), our findings suggest that astrocytes better equipped for localized responses when presented with a stimulus during the active phase in mice. Conversely, during the rest period, characterized by more synchronous neuronal activity across broad brain areas (Vyazovskiy et al., 2009) higher GJC conductance might allow astrocytes to exert control over a larger area. In support of this idea, recent study showed that synchronized astrocytic Ca2+ activity advances the slow wave activity (SWA) of the brain, a key feature of non-REM sleep (Szabó et al., 2017). Blocking GJC was found to reduce SWA, further supporting this interpretation. However, conflicting findings have also been reported. For instance, Ingiosi et al. (Ingiosi et al., 2020) found that astrocytic synchrony was higher during wakefulness than sleep in the mouse frontal cortex. Whether these differing results in astrocyte synchrony during resting and active periods are attributable to differences in experimental context (e.g., brain regions, sleep-inducing condition) remains unclear. Indeed, astrocyte Ca2+ dynamics during wakefulness/sleep vary according to brain regions (Tsunematsu et al., 2021). While the extent of astrocyte synchrony might differ depending on brain region and/or stimulus, on our results suggest that the baseline state of astrocyte synchrony, which is affected by GJC conductance, varies with the day/night cycle.”

Reviewer #2 (Public Review): 

Summary: 

The article entitled "Circadian regulation of endoplasmic reticulum calcium response in mouse cultured astrocytes" submitted by Ryu and colleagues describes the circadian control of astrocytic intracellular calcium levels in vitro. 

Strengths: 

The authors used a variety of technical approaches that are appropriate 

We appreciate the reviewer’s acknowledgement of the strengths of our manuscript.

Weaknesses: 

Statistical analysis is poor and could lead to a misinterpretation of the data 

Thank you for the comment. We have carefully reviewed our statistical analyses and applied appropriate methods where necessary. Please see below for the specific revisions and improvements made.

For Fig. 2D-E, we initially used a t-test. However, after adding more replicates and conducting a normality test, we found that the data did not follow a normal distribution. Therefore, we switched to the Mann-Whitney U test. In Fig. 5D-E, we originally used a repeated measures two-way ANOVA, but we have now changed it to a standard two-way ANOVA. For Fig. 7C and I, we also observed non-normal distribution in the normality test and consequently replaced the t-test with the Mann-Whitney U test. For other analyses not specifically mentioned, normality tests confirmed normal distribution, allowing us to use t-tests or ANOVA as appropriate for statistical analysis.

Several conceptual issues have been identified. 

We have addressed the reviewer’s concerns. Please see our detailed point-by-point responses below.

Overinterpretation of the data should be avoided. This is a mechanistic paper done completely in vitro, all references to the in vivo situation are speculative and should be avoided. 

We appreciate the reviewer’s insightful comment. Following the reviewer’s suggestion, we have removed the interpretations of GO pathways in the context of in vivo situation.

Reviewer #3 (Public Review): 

Astrocyte biology is an active area of research and this study is timely and adds to a growing body of literature in the field. The RNA-seq, Herp expression, and Ca2+ release data across wild-type, Bmal1 knockout, and Herp knockdown cellular models are robust and lend considerable support to the study's conclusions, highlighting their importance. Despite these strengths, the manuscript presents a gap in elucidating the dynamics of HERP and the involvement of ITPR1/2 in modulating Ca2+ release patterns and their circadian variations, which remains insufficiently supported and characterized. While the Connexin data underscore the importance of rhythmic Ca2+ release triggered by ATP, the relationship here appears correlational and the role of HERP and ITPR in Cx function remains to be characterized. Moreover, enhancing the manuscript's clarity and readability could significantly benefit the presentation and comprehension of the findings. 

We appreciate the reviewer’s acknowledgement of the strengths of our manuscript. Regarding the identified gaps, we have conducted several new experiments to clearly demonstrate the HERP-ITPR-Cx phosphorylation axis. Please see our detailed point-by-point responses below.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

- While HERP appears to be a clock-controlled gene and its protein levels appear to demonstrate rhythmicity as well, the data quality of the western blotting in Bmal1 knockout raises some concern about the accuracy of HERP protein quantification. 

We understand the reviewer’s concern regarding the proximity of the HERP band to a nonspecific band in the Western blotting for the Bmal1 knockout. However, we took great care to ensure the accuracy of our HERP band quantification. We meticulously selected only the specific HERP band, excluding nonspecific band. Therefore, we are confident in the accuracy of our HERP protein measurements.

- If HERP is rhythmic and ITPRs are not, if their model is correct, might we expect HERP suppression to result in 'unmasking' an ITPR rhythm? 

Our model suggests that both HERP and ITPRs are rhythmic, with HERP regulating the degradation of ITPR proteins and driving their rhythms. Consistent with this, we observed that day/night variations in ITPR2 levels (Fig. 4N and 4O). Therefore, we concluded that circadian variations in HERP are sufficient to drive ITPR2 rhythms. We have explained this in detail in the Result section (lines 236-241) and the Discussion section (lines 324-332).

- The authors make a rather abrupt switch to examining gap junctions and connexin 43 phosphorylation. While the data demonstrating that the phosphorylation of S368 may indeed be rhythmic - the authors do not connect these data to the rest of the manuscript by showing a connection to HERP-mediated calcium signaling, limiting the coherence of the narrative. 

Thank you for the reviewer’s insightful comments. To address the reviewer's concern regarding the connection between Herp and the phosphorylation of CX43 at S368, we have conducted new experiments to test whether KD of Herp abolishes the rhythms of Cx43 phosphorylation at S368. We found that the phosphorylation of Cx43 at S368 is significantly enhanced at 30hrs post sync compared with 42hrs post sync in control siRNA-treated astrocytes consistent with our previous results (Fig. 6C & 6D). On the other hand, this circadian phase dependent difference in phosphorylation was abolished in Herp siRNA treated astrocytes. These results clearly indicate that circadian variations in Cx43 phosphorylation are driven by the HERP. These new results are now included in Fig. 6G and 6H and explained in the Results section (lines 276-281).

- Comment on data presentation: the authors repeatedly present histograms with attached lines between data points - from my understanding of the experiments, this is inappropriate unless these were repeated measures from the same cells. Otherwise, the lines connecting one data point to another between different conditions (e.g., Ctrl or Herp knockdown) are arbitrary and possibly misleading (i.e., Figure 3K, 3M, 4L, 6D). 

Thank you for the reviewer’s comment. We have updated the figures by removing the lines connecting data points in the relevant figures (Fig.3K, M, Fig4.N and Fig.6D)).

Reviewer #2 (Recommendations For The Authors): 

Most of the suggestions of this reviewer are related to the conceptual interpretation and presentation of the data and to the statistical analysis 

In Figure 1 the authors analyzed the rhythmic transcriptome of cortical astrocytes synchronized with a serum shock in two different ways. The authors need to discuss what is the difference between the two methods used to detect rhythmic transcripts and make sense of them. 

Following the reviewer’s suggestion, we have provided a more detailed explanation about MetaCycle and BioCycle, as well as the rationale for using both packages in our analysis as follows: “Various methods have been used to identify periodicity in time-series data, such as Lomb-Scargle (Glynn et al., 2006), JTK_CYCLE (Hughes et al., 2010) and ARSER (Yang & Su, 2010), each with distinct advantages and limitations. MetaCycle, integrates these three methods, facilitating the evaluation of periodicity in time-series data without requiring the selection of an optimal algorithm (Wu et al., 2016). Additionally, BioCycle has been developed using a deep neural network trained with extensive synthetic and biological time series datasets (Agostinelli et al., 2016). Because MetaCycle and Biocycle identify periodic signal based on different algorithms, we applied both packages to identify periodicity in our time-series transcriptome data. BioCycle and MetaCycle analyses detected 321 and 311 periodic transcripts, respectively (FDR corrected, q-value < 0.05) (Fig. 1B). Among these, 220 (53.4%) were detected by both methods, but many transcripts did not overlap. MetaCycle is known for its inability to detect asymmetric waveforms (Mei et al., 2020). In our analysis, genes with increasing waveforms like Adora1 and Mybph were identified as rhythmic only by BioCycle, while Plat and Il34 were identified as rhythmic only by MetaCycle (Fig. S1C). Despite these discrepancies, the clear circadian rhythmic expression profiles of these genes led us to conclude that using the union of the two lists compensates for the limitations of each algorithm.”

Please refer to lines 105-117 in the Results section.

The reasoning for comparing CT0 with the phase of the clock 8 hs after SS needs to be explained. Circadian time (CT) conceptually refers to the clock phase in the absence of entrainment cues in vivo, the direct transformation of "time after synchronization" in vitro to CT is misleading. 

Thank you for the reviewer’s insightful comments. Initially, we believed that transforming TASS to CT, despite being in vitro data, might provide a more intuitive and physiologically relevant interpretation of our results. However, we agree that this approach might be misleading. Following the reviewer’s suggestion, we have revised our terminology by changing “CT” to “Time post sync (hr)”. Nonetheless, in Fig. 1F for circular peak phase map, we set 8hrs post sync to ZT0 based on a phase comparison result in Fig. 1D for physiologically relevant interpretation. We hope these revisions clarify our approach.

Moreover, also by definition a CT cannot be defined in terms of "dark" or "light". Figure 6M needs to be changed. 

Following the reviewer’s suggestion, we removed the labels CT22 and CT34. Instead. we have labeled the respective periods as “30hr post sync” and “42hr post sync”.

In Figure 1D, the authors present a gene ontology analysis that is certainly interesting, however, it should not be overinterpreted when trying to explain processes that take place only in vivo (e.g. wound repair). 

Thank you for the insightful comment. Following the reviewer’s feedback, we have removed the paragraph interpreting the cell migration process in relation to wound repair and have focused instead on Ca2+ ion homeostasis.

In Figure 2A the relative expression of clock genes and Herp is again misleading by a white/grey shading indicating subjective night and subjective day when the system under study is a cell culture. 

We understand the reviewer’s concern that a cell culture system is not equivalent to light/dark entrainment condition. However, we apply time-synchronizing stimuli to recapitulate in vivo entrainment. In addition, by comparing our data with CircaDB, we defined 8hrs post sync as corresponding to ZT0, thus aligning it with the beginning of the day. We have retained the shading to facilitate easier interpretation of our data in relation to in vivo situations. However, in response to the reviewer’s concern, we have revised the shading from white/grey to light grey/dark grey. We hope this adjustment addresses the reviewer’s concern, but if the reviewer still believes it is inappropriate, please let us know, we will gladly update it.

In the Figure 2A legend, it is indicated that rhythmicity is assessed using MetaCycle with mean values obtained from n=2. The authors need to make clear whether this n=2 mean: 2 biological replicates or 2 technical replicates. This difference is relevant because it would make the analysis statistically valid or invalid, respectively. 

Thank you for your feedback. n=2 refers to 2 biological replicates. Therefore, the analysis is statistically valid.

In Figures 2C and D the authors applied a T-test, a parametric statistical test for one-to-one comparison that requires normality distribution of the data to be tested first. To test normality, the authors need at least 4 biological replicates. The suggestion of this reviewer is that these experiments have to be repeated and proper statistics applied. 

Thank you for your feedback. In response to the reviewer's suggestion, we conducted additional experiments to increase the number of biological replicates to 4. After verifying the normality of the data, we applied a t-test for Figure 2C and a Mann-Whitney test for Figure 2D and 2E. These tests confirmed significant statistical difference between groups.

Further evidence of Bmal1-dependent control of HERP circadian expression authors could check the presence of E-Box elements in the Herp promoter. 

Thank you for the reviewer’s insightful comment. In the original version of our manuscript's Discussion section, we mentioned the absence of a canonical E-Box in the upstream of Herp gene. However, following the reviewer’s suggestion and considering the potential role of non-canonical E-Boxes, we conducted an additional analysis. This analysis identified several non-canonical E-Boxes within the 6 kb upstream region of the Herp gene (Table S2). Notably, we found one non-canonical E-Box, “CACGTT,” known to regulate circadian expression (Yoo et al., 2005) is close to the transcription start site (chr8:94386194-94386543). Moreover, this element is evolutionarily conserved across various mammals, including humans, rats, mice, dogs, and opossums (See Author response image 2). Therefore, we reasoned that these non-canonical E boxes might drive the CLOCK/BMAL1 dependent expression of Herp. We have updated the Discussion to reflect these findings in lines 315-319.

Author response image 2.

The calcium experiments shown in Figures 3A-I, could be more convincing if the authors showed that the different Ca2+ sensors are compartment-specific by showing co-localization with a subcellular marker. In the pictures shown it is not even possible to recognize the cell dimensions. 

Following the reviewer’s suggestion, we performed co-staining experiments with organelle specific Ca2+ indicators and organelle markers. First, astrocytes were co-transfected with G-CEPIA1er, an ER specific Ca2+ indicator and ER targeted DsRed2 (with Calreticulin signal sequence). Live imaging analysis showed that the fluorescent intensities of G-CEPIA1er and DsRed2-ER-5 significantly overlapped in co-transfected cells. Secondly, astrocytes were transfected with Mito-R-GECO1 and Mitotracker, a cell permeable mitochondria dye, was applied. The fluorescent intensities of Mito-R-GECO1 and Mitotracker also significantly overlapped. These new data are included in Figure S4 and explained in the Result section (lines 194-195).

Data analysis in Figure 3 K and M is misleading. According to the explanations of the results, each of the experiments to assess ITRP1 or 2 is run independently. Then it is not clear why the relative levels obtained with control or Herp siRNA are plotted as pairs. Same comment as above for Figure 4L and Figure 6D. 

Thank you for the reviewer’s insightful comments. Reviewer1 raised similar issues. Following the reviewers’ suggestions, we have removed the lines connecting the data points in Fig. 3K, 3M, 4L, and 6D.

In Figure 5E the authors need to explain why they consider that repeated measures 2-way ANOVA is the right statistical test to apply. According to the explained experimental design, cells transfected, synchronized, and then harvested independently at the indicated time after synchronization. 

Thank you for the reviewer’s insightful comment. Upon reviewing the statistical methods as suggested, we have revised our approach. Instead of using repeated measures 2-way ANOVA, we have now applied a standard 2-way ANOVA, which is more appropriate given the experimental procedures were independent, as the reviewer pointed out.

The English language needs to be revised throughout the text. 

We have thoroughly revised the English language throughout the text.

Reviewer #3 (Recommendations For The Authors): 

(1) Figure 3. Clarify the physiological importance of 100 µM ATP. Would the Herp rhythm warrant Ca2+ release rhythms under basal conditions? In 3J-K, the relatively weak effect of Herp knockdown on ITPR1/2 levels, albeit statistically significant, may not be physiologically significant. This calls into question the claimed Herp-ITPR axis that underlies the Ca2+ release phenotype. Further, the correlation certainly exists but further characterization of Herp KD cells would be required to address the mechanism. 

As previously reported, a broad range of ATP concentrations can induce Ca2+ activity in the astrocytes (Neary et al., 1988). Originally, we conducted an ATP dose-response analysis to observe ER Ca2+ release in our primary astrocyte culture. Our results show that ER Ca2+ release begins at 50 µM ATP and plateaus at 500 µM. Please refer to Author response image 3. We selected 100µM ATP for our experiments because it induces a medium level of ER Ca2+ response. Importantly, although measuring ATP concentrations at the synapse in vivo is challenging(Tan et al., 2017), estimates suggest synaptic ATP concentrations range from 5-500 µM (Pankratov et al., 2006). Thus, 100µM ATP is a physiologically relevant concentration that can affect nearby cells, including astrocytes, in the nervous system.

Author response image 3.

Cultured astrocytes were transfected with G-CEPIA1er ER and at 48hrs post transfection, cultured astrocytes were treated with various concentrations of ATP and Ca2+ imaging analysis was performed. (A) ΔF/F0 values over time following ATP application. (B) Area above curve values. Values in graphs are mean ± SEM (*p < 0.05, **p < 0.005, ***p < 0.0005, and ****p < 0.00005; one-way ANOVA).

Regarding the comment on Ca2+ release rhythms under basal conditions, we interpret this as referring Ca2+ release in the absence of a stimulus. We typically observe Ca2+ release only upon stimulation, such as ATP treatment. However, we acknowledge that the modest effects of HERP knockdown on ITPR1/2 levels could question the HERP-ITPR axis’s role in ER Ca2+ release.

To address this, we analyzed whether Herp KD induced increases in ER Ca2+ release were mediated through ITPRs by treating cells with Xestospongin C (XesC), an IP3R inhibitor. XesC treatment reduced ATP-induced ER Ca2+ release and eliminated the differences in ER Ca2+ release between control and Herp KD astrocytes (Fig. 3N – 3P). These results clearly indicate that HERP-ITPR axis plays critical role in controlling ER Ca2+ release. These new experiments have been included in Fig. 3 and explained in the result section (lines 217-221).

Furthermore, following the reviewer’s suggestion, we examined whether HERP rhythms underlie the rhythms of ER Ca2+ response by analyzing ER Ca2+ response in Herp KD astrocyte in two different times following synchronization. In control astrocytes, ATP-induced ER Ca2+ responses vary depending on time, whereas these time-dependent variations were abolished in Herp KD astrocytes. These new experiments have been included in Fig. 4K – 4M and explained in the Results section (lines 232-235).

Collectively, these results indicate that HERP rhythms lead to time-dependent differences in ER Ca2+ response through ITPRs.

(2) Figure 4K-L. As data suggested the involvement of ITPR1 and ITPR2 (circadian effect), a reasonable next step is to determine their involvement, but the study did not pursue the hypothesis. 

Thank you for your insightful comment. Our results indeed suggest that rhythms in ITPR2 levels may drive the time-dependent variations in ATP-induced ER Ca2+ release following synchronization. The newly conducted experiments demonstrated that treatment with the ITPR inhibitor XesC suppressed ATP-induced ER Ca2+ release at both control and Herp siRNA treatment conditions (Fig. 3). Based on these findings, we now further confirm that rhythms of ITPR levels, specifically ITPR2 underlie the circadian variations in ER Ca2+ release. While examining the effect of ITPR2 siRNA would directly prove the involvement of ITPR2, we have decided to pursue this experiment in the future studies.

(3) Figure 5A-C. Data from WT cells should be included side by side with Bmal1-/- cells for comparison which is expected to be consistent with the HERP levels as in 5D-E. Again, the role of ITPR2 is suggested but not demonstrated. 

Following the reviewer's suggestion, we conducted additional experiments including both WT and Bmal1-/- cultured astrocytes side-by-side. The results were consistent with our previous findings: WT astrocytes showed rhythms of ER Ca2+ release while Bmal1-/- astrocytes did not. We have updated the Figure 5A to 5C and the corresponding Results section in lines 242-245 accordingly.<br /> Regarding second comment, as mentioned in our previous response, we plan to examine the role of ITPR2 in further studies.

(4) Figure 6. The Connexin data seems an addon and is correlative with the Ca2+ release. The role of Herp and Itpr in Connexin function is not addressed. Figure 6E-F was not called out in the results section. Suggest providing additional data to support the role of the HERP-ITPR axis in regulating Ca2+ release and Connexin activity. 

We agree that additional data are needed to support the role of HERP in regulating CX43 phosphorylation. Therefore, we have conducted further experiments to determine whether rhythms of Cx43 phosphorylation are regulated by HERP. In the control astrocytes, ATP treatment induced time-dependent variations in Cx43 phosphorylation. However, these rhythms were abolished in Herp KD astrocytes. These results indicate that rhythms in HERP levels contribute to the time-dependent variations in Cx43 phosphorylation. These new experiments have included in Fig. 6G and 6H and explained in the results section (lines 276-281).

Regarding second comment, we have corrected our oversight by properly referencing figures 6E-F in the results section. Please refer to lines 357-359 for clarification.

(5) Discussion. This section should focus on noteworthy points to discuss, not repeating the results. 

Based on the reviewer's valuable suggestions, we have revised the Discussion section to minimize repetition of the results. Thank you for your guidance.

(6) The manuscript exhibits numerous grammatical and textual inaccuracies that necessitate careful revision by the authors. My observations here are confined to the title and the abstract alone. I recommend altering the title from "mouse cultured astrocytes" to "cultured mouse astrocytes" for clarity and grammatical correctness. The abstract, meanwhile, needs enhancements both in terms of its content and language. It should incorporate the results of the partitioning among the ER, cytoplasm, and mitochondria, and provide clear definitions for some of the critical terms used. It's worth noting that the abstract's second sentence contains a grammatical error. 

Thank you for the reviewer’s valuable feedback. We have carefully revised the title, abstract, and main text to address the grammatical and textual issues. The title has been changed to “cultured mouse astrocytes”. Additionally, the abstract now includes results related to cytoplasmic Ca2+ dynamics and has been revised in several places. We appreciate your insights and have worked to enhance the content and language accordingly.

Reference

Agostinelli, F., Ceglia, N., Shahbaba, B., Sassone-Corsi, P., & Baldi, P. (2016). What time is it? Deep learning approaches for circadian rhythms. Bioinformatics, 32(12), i8-i17. https://doi.org/10.1093/bioinformatics/btw243

Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., Thompson, W. J., & Barres, B. A. (2008). A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neurosci, 28(1), 264-278. https://doi.org/10.1523/JNEUROSCI.4178-07.2008

Carreras-Sureda, A., Pihán, P., & Hetz, C. (2018). Calcium signaling at the endoplasmic reticulum: fine-tuning stress responses. Cell Calcium, 70, 24-31. https://doi.org/10.1016/j.ceca.2017.08.004

Enkvist, M. O., & McCarthy, K. D. (1992). Activation of protein kinase C blocks astroglial gap junction communication and inhibits the spread of calcium waves. J Neurochem, 59(2), 519-526. https://doi.org/10.1111/j.1471-4159.1992.tb09401.x

Fujii, Y., Maekawa, S., & Morita, M. (2017). Astrocyte calcium waves propagate proximally by gap junction and distally by extracellular diffusion of ATP released from volume-regulated anion channels. Scientific Reports, 7(1), 13115. https://doi.org/10.1038/s41598-017-13243-0

Giorgi, C., Marchi, S., & Pinton, P. (2018). The machineries, regulation and cellular functions of mitochondrial calcium. Nature Reviews Molecular Cell Biology, 19(11), 713-730. https://doi.org/10.1038/s41580-018-0052-8

Glynn, E. F., Chen, J., & Mushegian, A. R. (2006). Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms. Bioinformatics, 22(3), 310-316. https://doi.org/10.1093/bioinformatics/bti789

Hughes, M. E., Hogenesch, J. B., & Kornacker, K. (2010). JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. J Biol Rhythms, 25(5), 372-380. https://doi.org/10.1177/0748730410379711

Ingiosi, A. M., Hayworth, C. R., Harvey, D. O., Singletary, K. G., Rempe, M. J., Wisor, J. P., & Frank, M. G. (2020). A Role for Astroglial Calcium in Mammalian Sleep and Sleep Regulation. Curr Biol, 30(22), 4373-4383.e4377. https://doi.org/10.1016/j.cub.2020.08.052

Mei, W., Jiang, Z., Chen, Y., Chen, L., Sancar, A., & Jiang, Y. (2020). Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines. Briefings in Bioinformatics, 22(3). https://doi.org/10.1093/bib/bbaa135

Neary, J. T., van Breemen, C., Forster, E., Norenberg, L. O., & Norenberg, M. D. (1988). ATP stimulates calcium influx in primary astrocyte cultures. Biochem Biophys Res Commun, 157(3), 1410-1416. https://doi.org/10.1016/s0006-291x(88)81032-5

Pankratov, Y., Lalo, U., Verkhratsky, A., & North, R. A. (2006). Vesicular release of ATP at central synapses. Pflugers Arch, 452(5), 589-597. https://doi.org/10.1007/s00424-006-0061-x

Paredes, F., Parra, V., Torrealba, N., Navarro-Marquez, M., Gatica, D., Bravo-Sagua, R., Troncoso, R., Pennanen, C., Quiroga, C., Chiong, M., Caesar, C., Taylor, W. R., Molgó, J., San Martin, A., Jaimovich, E., & Lavandero, S. (2016). HERPUD1 protects against oxidative stress-induced apoptosis through downregulation of the inositol 1,4,5-trisphosphate receptor. Free Radic Biol Med, 90, 206-218. https://doi.org/10.1016/j.freeradbiomed.2015.11.024

Szabó, Z., Héja, L., Szalay, G., Kékesi, O., Füredi, A., Szebényi, K., Dobolyi, Á., Orbán, T. I., Kolacsek, O., Tompa, T., Miskolczy, Z., Biczók, L., Rózsa, B., Sarkadi, B., & Kardos, J. (2017). Extensive astrocyte synchronization advances neuronal coupling in slow wave activity in vivo. Scientific Reports, 7(1), 6018. https://doi.org/10.1038/s41598-017-06073-7

Tan, Z., Liu, Y., Xi, W., Lou, H. F., Zhu, L., Guo, Z., Mei, L., & Duan, S. (2017). Glia-derived ATP inversely regulates excitability of pyramidal and CCK-positive neurons. Nat Commun, 8, 13772. https://doi.org/10.1038/ncomms13772

Torrealba, N., Navarro-Marquez, M., Garrido, V., Pedrozo, Z., Romero, D., Eura, Y., Villalobos, E., Roa, J. C., Chiong, M., Kokame, K., & Lavandero, S. (2017). Herpud1 negatively regulates pathological cardiac hypertrophy by inducing IP3 receptor degradation. Sci Rep, 7(1), 13402. https://doi.org/10.1038/s41598-017-13797-z

Tsunematsu, T., Sakata, S., Sanagi, T., Tanaka, K. F., & Matsui, K. (2021). Region-specific and state-dependent astrocyte Ca²⁺ dynamics during the sleep-wake cycle in mice. The Journal of Neuroscience, JN-RM-2912-2920. https://doi.org/10.1523/jneurosci.2912-20.2021

Verkhratsky, A., & Nedergaard, M. (2018). Physiology of Astroglia. Physiol Rev, 98(1), 239-389. https://doi.org/10.1152/physrev.00042.2016

Vyazovskiy, V. V., Olcese, U., Lazimy, Y. M., Faraguna, U., Esser, S. K., Williams, J. C., Cirelli, C., & Tononi, G. (2009). Cortical firing and sleep homeostasis. Neuron, 63(6), 865-878. https://doi.org/10.1016/j.neuron.2009.08.024

Wu, G., Anafi, R. C., Hughes, M. E., Kornacker, K., & Hogenesch, J. B. (2016). MetaCycle: an integrated R package to evaluate periodicity in large scale data. Bioinformatics, 32(21), 3351-3353. https://doi.org/10.1093/bioinformatics/btw405

Yang, R., & Su, Z. (2010). Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation. Bioinformatics, 26(12), i168-174. https://doi.org/10.1093/bioinformatics/btq189

Yoo, S. H., Ko, C. H., Lowrey, P. L., Buhr, E. D., Song, E. J., Chang, S., Yoo, O. J., Yamazaki, S., Lee, C., & Takahashi, J. S. (2005). A noncanonical E-box enhancer drives mouse Period2 circadian oscillations in vivo. Proc Natl Acad Sci U S A, 102(7), 2608-2613. https://doi.org/10.1073/pnas.0409763102

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Yang, Hu et al. examined the molecular mechanisms underlying astrocyte activation and its implications for multiple sclerosis. This study shows that the glycolytic enzyme PKM2 relocates to astrocyte nuclei upon activation in EAE mice. Inhibiting PKM2's nuclear import reduces astrocyte activation, as evidenced by decreased proliferation, glycolysis, and inflammatory cytokine release. Crucially, the study identifies TRIM21 as pivotal in regulating PKM2 nuclear import via ubiquitination. TRIM21 interacts with PKM2, promoting its nuclear translocation and enhancing its activity, affecting multiple signaling pathways. Confirmatory analyses using single-cell RNA sequencing and immunofluorescence demonstrate TRIM21 upregulation in EAE astrocytes. Modulating TRIM21 expression in primary astrocytes impacts PKM2-dependent glycolysis and proliferation. In vivo experiments targeting this mechanism effectively mitigate disease severity, CNS inflammation, and demyelination in EAE.

The authors supported their claims with various experimental approaches, however, some results should be supported with higher-quality images clearly depicting the conclusions and additional quantitative analyses of Western blots.

Thanks for the reviewer’s comments. We agree with the reviewer and have added higher magnification images, for example Fig.2A to better visualize the localization of PKM2 in DASA-treated conditions, and Fig. 3A and Fig.3B to better visualize the pSTAT3 and pp65. Moreover, we have added quantitative analyses of Western blots for some key experiments, for example quantitative results for Fig.2D is added in Fig.S3 to show the change of PKM2 and p-c-myc in DASA-58-treated conditions and quantitative results for Fig. 3D are added in Fig.S4B and S4C to show the change of nuclear and cytoplasmic PKM2, STAT3 and NF-κB in different conditions.

Strength:

This study presents a comprehensive investigation into the function and molecular mechanism of metabolic reprogramming in the activation of astrocytes, a critical aspect of various neurological diseases, especially multiple sclerosis. The study uses the EAE mouse model, which closely resembles MS. This makes the results relevant and potentially translational. The research clarifies how TRIM21 regulates the nuclear import of PKM2 through ubiquitination by integrating advanced techniques. Targeting this axis may have therapeutic benefits since lentiviral vector-mediated knockdown of TRIM21 in vivo significantly reduces disease severity, CNS inflammation, and demyelination in EAE animals.

We thank the reviewer for their positive and constructive comments on the manuscript.

Weaknesses:

The authors reported that PKM2 levels are elevated in the nucleus of astrocytes at different EAE phases compared to cytoplasmic localization. However, Figure 1 also shows elevated cytoplasmic expression of PKM2. The authors should clarify the nuclear localization of PKM2 by providing zoomed-in images. An explanation for the increased cytoplasmic PKM2 expression should provided. Similarly, while PKM2 translocation is inhibited by DASA-58, in addition to its nuclear localization, a decrease in the cytoplasmic localization of PKM2 is also observed. This situation brings to mind the possibility of a degradation mechanism being involved when its nuclear translocation of PKM2 is inhibited.

According to the results of immunofluorescence staining of PKM2 in spinal cord of EAE mice and in cultured primary astrocytes, in addition to the observation of PKM2 nuclear translocation in EAE conditions, we showed an elevated expression of PKM2 in astrocytes, including the cytoplasmic and nuclear expression. In neurological diseases, various studies showed consistent results, for example, following spinal cord injury (SCI), not only the upregulated expressing of PKM2 but also nuclear translocation was observed in astrocytes (Zhang et al., 2015). In EAE conditions, CNS inflammation is elevated and several proinflammatory cytokines and chemokines might contribute to the upregulated expression of PKM2 in astrocytes. We have tested TNFα and IL-1β, which are recognized to play important roles in EAE and MS (Lin and Edelson, 2017, Wheeler et al., 2020), and results from western blots showed the increased expression of PKM2 upon stimulation with TNFα and IL-1β (Author response image 1). Moreover, according to the reviewer’s suggestions, we have added zoomed-in images for figure 2A.

Additionally, the reviewer has noted the decrease in the cytoplasmic PKM2 level, degradation-related mechanism and other mechanisms might be involved in this process.

Author response image 1.

Upregulated expression of PKM2 in astrocytes following stimulation with TNF-α and IL-1β. Primary astrocytes were stimulated with TNF-α and IL-1β (50 ng/mL) for 48 h and western blotting analysis were performed.

In Figure 3D, the authors claim that PKM2 expression causes nuclear retention of STAT3, p65, and p50, and inhibiting PKM2 localization with DASA-58 suppresses this retention. The western blot results for the MOG-stimulated group show high levels of STAT3, p50, and p65 in nuclear localization. However, in the MOG and DASA-58 treated group, one would expect high levels of p50, p65, and STAT3 proteins in the cytoplasm, while their levels decrease in the nucleus. These western blot results could be expanded. Additionally, intensity quantification for these results would be beneficial to see the statistical difference in their expressions, especially to observe the nuclear localization of PKM2.

We agree with the reviewer’s comments and we have incorporated the quantification of STAT3，p50 and p65 for Fig.3D and Fig.S4B and Fig.S4C. Nevertheless, given that DASA-58 did not trigger a notable increase in the cytoplasmic level of PKM2, we did not detect an upregulation of STAT3, p50, or p65 in the cytoplasm of the MOG and DASA-58-treated groups. With the quantification results, it is more obvious to see the changes of these proteins in different conditions.

The discrepancy between Figure 7A and its explaining text is confusing. The expectation from the knocking down of TRIM21 is the amelioration of activated astrocytes, leading to a decrease in inflammation and the disease state. The presented results support these expectations, while the images showing demyelination in EAE animals are not highly supportive. Clearly labeling demyelinated areas would enhance readers' understanding of the important impact of TRIM21 knockdown on reducing the disease severity.

Thank you for pointing this out. We sincerely apologize for our carelessness. Based on your comments, we have made the corrections in the manuscript. As there is indeed a statistical difference in the mean clinical scores between shTRIM21-treated group and shVec group, we have accordingly revised the sentence for Figure 7A to state, “At the end time point at day 22 p.i., shTRIM21-treated group showed reduced disease scores compared to control groups (Fig. 7A).” .

Additionally, we have added the whole image of the spinal cord for MBP in Author Response image 2. Moreover, we have labelled the demyelinated areas to facilitate readers’ understanding.

Author response image 2.

MBP staining of the whole spinal cord in EAE mice from shVec and shTRIM21 group. Scale bar: 100 μm. Demyelinated areas are marked with dashed lines.

Reviewer #2 (Public Review):

This study significantly advances our understanding of the metabolic reprogramming underlying astrocyte activation in neurological diseases such as multiple sclerosis. By employing an experimental autoimmune encephalomyelitis (EAE) mouse model, the authors discovered a notable nuclear translocation of PKM2, a key enzyme in glycolysis, within astrocytes.

Preventing this nuclear import via DASA 58 substantially attenuated primary astrocyte activation, characterized by reduced proliferation, glycolysis, and inflammatory cytokine secretion.<br /> Moreover, the authors uncovered a novel regulatory mechanism involving the ubiquitin ligase TRIM21, which mediates PKM2 nuclear import. TRIM21 interaction with PKM2 facilitated its nuclear translocation, enhancing its activity in phosphorylating STAT3, NFκB, and c-myc. Single-cell RNA sequencing and immunofluorescence staining further supported the upregulation of TRIM21 expression in astrocytes during EAE.

Manipulating this pathway, either through TRIM21 overexpression in primary astrocytes or knockdown of TRIM21 in vivo, had profound effects on disease severity, CNS inflammation, and demyelination in EAE mice. This comprehensive study provides invaluable insights into the pathological role of nuclear PKM2 and the ubiquitination-mediated regulatory mechanism driving astrocyte activation.

The author's use of diverse techniques, including single-cell RNA sequencing, immunofluorescence staining, and lentiviral vector knockdown, underscores the robustness of their findings and interpretations. Ultimately, targeting this PKM2-TRIM21 axis emerges as a promising therapeutic strategy for neurological diseases involving astrocyte dysfunction.

While the strengths of this piece of work are undeniable, some concerns could be addressed to refine its impact and clarity further; as outlined in the recommendations for the authors.

Thanks for the reviewer’s comment and positive evaluation of our present work. We have further answered each question in recommendations section.

Reviewer #3 (Public Review):

Summary:

Pyruvate kinase M2 (PKM2) is a rate-limiting enzyme in glycolysis and its translocation to the nucleus in astrocytes in various nervous system pathologies has been associated with a metabolic switch to glycolysis which is a sign of reactive astrogliosis. The authors investigated whether this occurs in experimental autoimmune encephalomyelitis (EAA), an animal model of multiple sclerosis (MS). They show that in EAA, PKM2 is ubiquitinated by TRIM21 and transferred to the nucleus in astrocytes. Inhibition of TRIM21-PKM2 axis efficiently blocks reactive gliosis and partially alleviates symptoms of EAA. Authors conclude that this axis can be a potential new therapeutic target in the treatment of MS.

Strengths:

The study is well-designed, controls are appropriate and a comprehensive battery of experiments has been successfully performed. Results of in vitro assays, single-cell RNA sequencing, immunoprecipitation, RNA interference, molecular docking, and in vivo modeling etc. complement and support each other.

Weaknesses:

Though EAA is a valid model of MS, a proposed new therapeutic strategy based on this study needs to have support from human studies.

We agree that although we have clarified the therapeutic potential of targeting TRIM21 or PKM2 in the treatment of EAE, a mouse model of MS, the application in human studies warrants further studies. While considering the use of TRIM21 as a target for treating multiple sclerosis in clinical trials, several issues need to be addressed to ensure the safety, efficacy and feasibility. One such aspect is the development of drug that specifically target TRIM21 in brain, capable of crossing the blood-brain barrier and have minimal off-target effects. The translation of preclinical finding into clinical trials poses a significant challenge. To provide evidence for the similarities between the EAE model and multiple sclerosis, we have screened GEO databases (Author response image 3). In GSE214334 which analyzed transcriptional profiles of normal-appearing white matter from non-MS and different subtypes of disease (RRMS, SPMS and PPMS). Although no statistical difference was observed among different groups, the TRIM21 expression has tendency to increase in SPMS (secondary progressive MS) and PPMS (primary progressive MS) patients. In GSE83670, astrocytes from 3 control white matter and 4 multiple sclerosis normal appearing white matter (NAWM) were analyzed. TRIM21 mRNA expression is higher in MS group (78.73 ± 10.44) compared to control group (46.67 ± 24.15). Although these two GEO databases did not yield statistically significant differences, TRIM21 expression appears to be elevated in the white matter of MS patients compared to controls.

To address this limitation, we have incorporated the following statement in the discussion section: “However, whether TRIM21-PKM2 could potentially serve as therapeutic targets in multiple sclerosis warrants further studies.”

Author response image 3.

TRIM21 expression in control and MS patients based on published GEO database. (A) The expression of TRIM21 in normal-appearing white matter in non-MS (Ctl) and different clinical subtypes of MS (RRMS, SPMS, PPMS) based on GSE214334 (one-way ANOVA). (B) The expression of TRIM21 from multiple sclerosis normal appearing white matter (NAWM) and control WM based on GSE83670. RRMS, relapsing--remitting MS; SPMS, secondary progressive MS; PPMS, primary progressive MS (unpaired Student's t test). Data are represented as the means ± SEM.

Reviewer #4 (Public Review):

Summary:

The authors report the role of the Pyruvate Kinase M2 (PKM2) enzyme nuclear translocation as fundamental in the activation of astrocytes in a model of autoimmune encephalitis (EAE). They show that astrocytes, activated through culturing in EAE splenocytes medium, increase their nuclear PKM2 with consequent activation of NFkB and STAT3 pathways. Prevention of PKM2 nuclear translocation decreases astrocyte counteracts this activation. The authors found that the E3 ubiquitin ligase TRIM21 interacts with PKM2 and promotes its nuclear translocation. In vivo, either silencing of TRIM21 or inhibition of PKM2 nuclear translocation ameliorates the severity of the disease in the EAE model.

Strengths:

This work contributes to the knowledge of the complex action of the PKM2 enzyme in the context of an autoimmune-neurological disease, highlighting its nuclear role and a novel partner, TRIM21, and thus adding a novel rationale for therapeutic targeting.

Weaknesses:

Despite the relevance of the work and its goals, some of the conclusions drawn would require more thorough proof:

I believe that the major weakness is the fact that TRIM21 is known to have per se many roles in autoimmune and immune pathways and some of the effects observed might be due to a PKM2-independent action. Some of the experiments to link the two proteins, besides their interaction, do not completely clarify the issue. On top of that, the in vivo experiments address the role of TRIM21 and the nuclear localisation of PKM2 independently, thus leaving the matter unsolved.