Reviewer #2 (Public Review):

Having previously solved the X-ray crystallographic structure of the polymer adhesin domain (PAD) of PrgB from E. faecalis, the authors looked to build on that work by crystallizing a nearly full-length construct of PrgB. Though they were successful in their crystallization endeavors, the crystal contained only what was previously thought to be two domains with RGD motifs. The authors' high-resolution structure shows that in fact the C-terminal portion of PrgB is made up of four immunoglobulin-like domains. The authors then set out to collect single-particle cryoEM data in a bid to obtain a full-length structure of PrgB, both in the presence and absence of ssDNA. The authors were only able to obtain quite low-resolution data, which they fit their crystal structures into. The authors then used these structures to inform the design of novel deletion mutants and point mutations, as well as to rationalize years of phenotypic data from other published mutants.

The X-ray crystallographic structure is beautiful and in combination with their in vivo data allowed them to propose a model where PrgB positions cells at an appropriate distance for conjugation. The in vivo experiments appear to be done well and the authors' discovery that the Ser-Asn-Glu is not important for generalized aggregation but has an additional yet unknown role in conjugation and biofilm formation is exciting and well supported by their data.

[Editors' note: In response to reviews of a previous version of this manuscript, the authors have carried out additional experiments that have strengthened the already convincing aspects of the work. We commend the authors for responding to questions raised by the reviewers about the inference of interactions of in vivo importance inferred from low-resolution cryo-EM studies by carrying out and reporting on additional experiments that fail to confirm their initial speculative model. The current work is stronger and more convincing as a result.]

Author Response

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

Reviewer #1 (Recommendations For The Authors):

p. 5, l. 87-90: The control of flgM by OmrA/B (PMID 32133913) and the antisense RNA to flhD (PMID 36000733) are other examples of known regulatory RNAs that impact the flagellar regulon.

We thank the reviewer for pointing out these references and have added citations to them (page 5, lines 87-91).

p.11/Fig. 3: it is intriguing that ArcZ and RprA, two of the rpoS-activating sRNAs, repress lrhA. I realize that it is outside of the scope of this study, but have the authors considered the possibility that ArcZ or McaS could have a role in the previously reported repression of rpoS by LrhA (PMID 16621809)?

We agree that it is intriguing that ArcZ and RprA, two of the rpoS-activating sRNAs, repress lrhA, and added mention of this regulatory connection (page 12, lines 247-250).

p. 13/l. 272: I do not understand why the authors say that "r-proteins were almost exclusively found in chimeras with MotR and FliX and no other sRNAs...", given that several other chimeras between r-prot and other sRNAs are found

While some r-proteins encoding genes were found with other sRNAs in RIL-seq datasets, MotR and FliX generally had the highest numbers. The text was revised to better describe the RIL-seq data for r-proteins interaction partners (page 14, lines 291-295), and a new panel showing the S10 operon with all the interacting sRNAs was added to Figure 3—figure supplement 1B.

Fig. 4 and 5: One possible improvement would be to more systematically assess the effect of base-pairing mutants of the sRNAs, such as MotRM1 or FliXM1 on fliC and rps/rpl genes in vivo. This is especially important for the mutants that affected the sRNA effects in the in vitro probing assays, such as UhpU-M2, MotR-M1 and FliX-S-M1 on fliC (Fig. S7)

As suggested, we examined fliC mRNA levels across growth in motR-M1 and fliX-M1 chromosomal mutants. The results of these northern assays, now shown in Figure 8—figure supplement 1, are consistent with our model as we observed delayed expression of fliC mRNA in motR-M1 background and premature expression in fliX-M1 background (page 21, lines 444446, 449-453).

Fig. 5: it may be worth including a schematic of the whole S10 operon to highlight its length and its organization?

As suggested, a schematic representation of the S10 operon was added to Figure 3—figure supplement 1 with a summary of the RIL-seq data for this operon.

Probing data (Fig. 5, S7 and S9): in general, it is difficult to differentiate the thin and thick brackets, and what is indicated by the dashed brackets is not always clear. Maybe using a color-code instead could help? Highlighting the predicted pairing regions on the different gels could be useful as well.

We thank the reviewer for this suggestion and color-coded the brackets (Figure 5, Figure 4figure supplement 2, and Figure 5-figure supplement 2). The correspondences to regions of predicted pairing are described in the figures legends.

Fig. S10: The experimental evidence used to support FliX-dependent degradation of the rpsS mRNA is indirect (primer extension to observe higher levels of cleavage intermediates). It would be nice to be able to observe a decrease in the mRNA levels as well, either by Northern, or primer extension from a region more distant to the FliX pairing site.

The S10 operon is long (~5 KB). We have tried multiple probes for this mRNA and detect many bands with each, likely due to extensive regulation of this operon. We think teasing out the origin of the different bands to appropriately interpret changes in patterns will require a significant amount of work.

legend of Fig. S10: from the gel, it seems that only the plasmids differ in the samples, and it is not clear where the data corresponding to the WT strain mentioned in the legend is shown

The samples shown in this figure are all for the indicated plasmids in the WT strain. We corrected the figure legend.

Table S1: please define the NOR (normalized odds ratio?)

The definition of Normalized Odds Ratio was added to the legend of Supplementary file 1.

Reviewer #2 (Recommendations For The Authors):

Major comments:

Figure 1B. Please add a negative control (which could be in the supplementary section) from a large section showing transcripts that are not directly influenced by Hfq.

We think the flgKLO browser in this figure serves as a negative control; flgK and flgL clearly are not enriched on Hfq in contrast to FlgO. Figure 1B was generated using published datasets that are easily accessible to the readers at a genome browser and show many other examples of transcripts that are not influenced by Hfq: https://genome.ucsc.edu/cgi-bin/hgTracks?hubUrl=https://hpc.nih.gov/~NICHD- core0/storz/trackhubs/ecoli_rilseq/hub.hub.txt&hgS_loadUrlName=https://hpc.nih.gov/~NICHDcore0/storz/trackhubs/ecoli_rilseq/session.txt&hgS_doLoadUrl=submit

Line 158. MotR* is a more abundant version of [the constitutively overexpressed] MotR. Is there a Northern or qPCR to confirm this? While I understand the relevance of these mutated constructs, their high expression can lead to artefactual effects.

This is a valuable point and therefore we provided a northern blot to document the relative levels of MotR and MotR* (Figure 2—figure supplement 1A).

Figure 2. The overexpression of MotR/MotR* from a plasmid is increasing the number of flagella. However, when the MotR gene is deleted, is there a reduction of the number of flagella? Same question with FliX: what happens when the fliX gene is deleted? According to the model described in the manuscript, we should expect fewer flagella in ΔmotR background and an increased number of flagella in ΔfliX background. Both Figure 2 and Figure 8 would benefit from additional experiments with deleted motR and fliX genes.

We agree that experiments regarding the endogenous effects of endogenous sRNAs are important. We provided such data in Figure 8 and Figure 8—figure supplement 1 for MotR and FliX in a variety of assays: flagella numbers by electron microscopy, motility and competition assays, expression of flagellar genes by RT-qPCR and western analysis. The chromosomallyexpressed MotR-M1 and FliX-M1 base pairing mutants did show the expected phenotypes of reduced and increased numbers of flagella, respectively (Figure 8A-B). As suggested by reviewer 1, we added northern analysis that examined fliC mRNA levels across growth in motRM1 and fliX-M1 chromosomal mutants. The results of these northern assays are consistent with our model as we observed delayed expression of fliC mRNA in motR-M1 background and premature expression in fliX-M1 background. We went to the trouble of constructing strains carrying point mutations in the chromosomal copies of these genes rather than deletions to avoid interfering with the expression of motA and fliC given that MotR and FliX encompass the 5’ and 3’ UTRs, respectively.

Figure 3 is key to demonstrating the sRNAs pairing with their specific targets and potential effect on bacterial swimming. However, these results would be more relevant with endogenous expression of the sRNAs and demonstration of their effects on the same targets. A Northern blot showing the overproduced sRNA level compared to endogenous sRNA level could help us appreciate the expression ratio.

The levels of the UhpU, MotR and FliX expressed from the overexpression plasmids are at least 100-fold higher than the endogenous levels. Thus, we agree that assays of chromosomal deletion/point mutants are important experiments. We did construct chromosomal uhpU-M1 and uhpU∆seed sequence mutants. However, under the conditions assayed, the uhpU chromosomal mutations did not result in observable effects on motility or FlhD-SPA protein levels. It is possible we would be able to detect differences between the wild type and uhpU chromosomal mutant strains under different growth conditions or in different assays, but this would require a significant amount of work. For many other sRNA chromosomal mutations have no or only subtle effects, suggesting redundancy between sRNAs or sRNA roles in fine tuning gene expression.

Figure 4. In panel B, the empty plasmid pZE alone seems to positively affect the flagellin expression when compared to the WT background. This can also be seen in Figure 4C. There is no fliC signal with empty plasmid pBR* but a strong fliC signal with empty plasmid pZE. Maybe the authors can explain this in the manuscript.

With respect to panel B and Figure 4—figure supplement 1A, we agree that there is some variation between the levels of flagellin in the WT and pZE control samples, possibly due to the addition of antibiotic to the pZE culture. We added quantification of the bands in Figure 4— figure supplement 1 to better document the changes in flagellin levels.

With respect to panel C, the pBR samples were collected in crl+ background while the pZE samples were collected in crl- background, which explains the lack of fliC signal in the pBR control sample. This is now noted in the figure legend.

In lines 154-157, the justification for using two plasmids is described. An IPTG-inducible Plac promoter, the pBR*, is used because the constitutive overexpression of UhpU is resulting in mutated UhpU clones. These observations suggest a toxic expression level of UhpU that the cell can only tolerate when the UhpU RNA is somewhat deactivated by mutations. This does not seem like a detail and could be discussed further.

We agree with the reviewer that this observation is important and now mention that it suggests at a critical UhpU role (page 8, lines 160-163).

Figure 5E and I. While the bindings of MotR on rpsJ and Flix-S on rpsS are clear, the resolution of both gels in the areas of binding (upper part of both gels) could be improved.

We found it tricky to choose the mRNA fragments for the in vitro structure probing for the regions of predicted pairing internal to CDSs. Given that we hoped to retain native RNA folding, we chose long fragments; for rpsJ, we started with the +1 of S10 leader and for rpsS, we started 147 nt into the CDS, a region that overlaps the region that was cloned to the rpsS-rplV-gfp fusion. Consequently, the region of base pairing is in the upper part of both gels. The gels were already run for an unusually long time. Thus, we do not think the resolution could be improved further. Nevertheless, we think the region of protection is evident for both mRNAs.

Minor comments:

Fig 1B. The promoter symbols are extremely small, please increase the size.

As suggested, we have enlarged the promoter symbols in Figure 1B as well as in Figure 3A.

Line 211. "the lrhA mRNA has an unusually long 5´ UTR". How long exactly?

The 5’ UTR of the lrhA mRNA is 371 nt long. This is now mentioned in the text (page 11, line 224)

Line 320. Should "Fig 9C" be "Fig S9C" instead?

We thank the reviewer for noticing this typo. Callouts to supplementary figures have now been renumbered per eLife format.

Line 384. Something seems to be missing in the sentence "a representative combined class 2 and 3 promoter".

The sentence has been modified to clarify the designation (page 19, lines 409-411).

Reviewer #3 (Recommendations For The Authors):

Recommendation to clarify/strengthen the presentation of science in the paper:

Lines 102-103: Can the authors provide some more information on how the sRNAs were initially discovered to be potentially sigma-28 dependent and selected?

As suggested, we expanded the section discussing the discovery and the selection of these sRNAs (page 6, lines 104-109).

Lines 192-193: It would be helpful to provide a bit more information in the main text about what are the different RIL-seq data sets (18 in total).

As suggested, we now provide more details about the different RIL-seq datasets we used in the analysis (page 10, lines 202-205).

It would be helpful to specify the criteria for "top" interactions in targets retrieved from RIL-seq data (Table S1 and text, e.g., line 273): e.g. number of conditions, number of chimeras, etc.

As suggested, we now more explicitly specify the criteria for selecting targets to characterize (page 10, lines 205-206).

Fig. 4B/ S6 and line 242: The flagellin amount in the empty vector control (pZE) looks higher than in WT, and the stated effect of MotR/MotR* OE on flagellin is not very clear from the blot. The "cross-reacting band" above flagellin also seems to vary among strains. Could the authors include a quantification of flagellin protein amount and normalize relative to a housekeeping protein (e.g., GroEL), instead of Ponceau S as loading control?

We agree that there is some variation between the levels of flagellin in the WT and pZE control sample, possibly due to the addition of antibiotic to the pZE culture. We added quantification of the bands in Figure 4—figure supplement 1 to better document the changes in flagellin levels.

Figure legends: It would be helpful to have a bit more information about the method used/displayed image rather than stating results in the legends.

As suggested, we now provide a bit more information about the methods used/displayed image in the figure legends to allow for easier comprehension of the data presented in the figures (while trying to balance this with the length of the legends).

Fig. 2: Please include a scale for all electron microscopy images or, if it is the same for all panels, state it in the figure legend. Moreover, the same image is used for the pZE control in panel C, E and Figure S4A/C. It would be better to show different fields of bacteria for the pZE sample.

As is now mentioned in the legends to Figure 2, Figure 2—figure supplement 2, and Figure 8, the same scale was used for all panels. We thought it was better to show the same image for the pZE control in the different panels to emphasize that these samples were all analyzed on the same day.

Fig. 2: The sRNA OE strains seem to show some heterogeneity in cell length (pZE-MotR) or width (pZE-FliX). The authors could, e.g., check whether this is a phenotype correlated to sRNA OE by quantifying these parameters for different fields and comparing to WT or comment on this in the text if this is not consistently seen.

We also were intrigued by the slightly different sizes and widths of cells in the EM images. However, our statistical analysis did not reveal significant differences between the different samples. We now comment on this (page 53, lines 1178-1179).

As a follow-up to this study, it would be interesting to assess the impact of MotR and FliX regulation of ribosomal protein synthesis on overall ribosome activity (e.g., via Ribo-seq), also considering that antitermination regulates rRNA transcription. In the case of MotR, the authors suggest that MotR upregulation of S10 protein might not only impact antitermination, but also lead to the formation of more active ribosomes that would increase flagellar protein synthesis (lines 359-362). However, in the RNA-seq performed in OE MotR* several transcripts encoding rRNA and ribosomal proteins are significantly downregulated compared to EVC (Supplementary Table S2). Could the authors comment on this?

We share the reviewer’s enthusiasm for follow-up work and thank for the suggested experiments. We hope we will be able to decipher the full mechanism of MotR and FliX action on ribosomal protein synthesis in future experiments. The observation that some ribosomal protein-coding gene levels are reduced in the RNA-seq experiment with overexpression of MotR* is interesting but we do not have an explanation other than the fact that the samples were collected early in exponential growth. We now mention the observation in the text (page 19, lines 404-407).

Considering that OE of the WT MotR appears to increase fliC mRNA abundance but has no strong impact on flagellin protein levels, can the authors speculate what is the physiological relevance of MotR* for flagellin production?

We agree that while we do see significant increases in the flagella number and fliC mRNA abundance with MotR and MotR* overexpression, the western analysis did not reveal a striking increase in flagellin levels and also wonder how MotR strongly increases the flagella number, which requires flagellin subunits, but only has a weak effect on the intercellular levels of flagellin. One possibility explanation is that it is more difficult to see significant increases for a protein whose levels are high to begin with. These points are now discussed (page 13, lines 264-269).

Fig. 4C: The pZE samples seem to show variable expression of fliC mRNA although the samples are collected at the same timepoints. Try to clarify in the text.

The northern membrane on the bottom was exposed for a longer time due to the lower fliC mRNA levels in the samples with FliX overexpression. We now note these differences in the legends to Figure 4 and Figure 4—figure supplement 1.

Fig. 7/S13: While a volcano plot for MotR is shown in Fig. 7A, quantification of GFP reporter fusion regulation is shown for MotR. Quantifications of MotR are shown in Fig. S13. Maybe swap the figures.

Given that the data for MotR are in the supplement figures for all other figures we would also like to retain this distribution for Figure 7 (aside from the volcano plot since this experiment was only carried out for MotR).

Lines 135-136 (Fig. S1B): on the northern blots, only sRNA levels of MotR are comparable between rich and minimal media (excluding M63 G6P and M63 gal). Most other sRNA seem to be more abundantly expressed in minimal media conditions compared to LB. Maybe rephrase.

As suggested, the text was revised to point out the differences in the sRNA levels for cells grown in different growth media (page 7, lines 140-144).

Lines 229-234: this paragraph seems not directly connected to the aims of the study (i.e., no effect on motility tested of these other sRNAs) and could be removed (or moved to discussion).

We appreciate the reviewer’s suggestion but, considering Reviewer 1’s comments, think that showing the regulation of lrhA by other sRNAs has value in highlighting the complexity of the regulatory circuit. We have revised the text to incorporate Reviewer 1’s suggestions and better explain why these results are intriguing (page 12, lines 247-250).

Line 200 and Fig. S5: For FlgO sRNA only one target was identified in RIL-seq. This gene could be specified and labeled in Fig. S5 and the text. Does FlgO also bind ProQ?

We now mention the single FlgO target (gatC) detected in four datasets (page 10, lines 213215). In Figure 3—figure supplement 1, we labeled only targets that we followed up with in the current study. Therefore, to be consistent, we prefer not to label gatC in the FlgO plot. FlgO was found to co-immunoprecipitate with ProQ but at much lower levels than with Hfq, and to have very few RNA partners (Melamed et al., 2020).

Lines 493-498: It is mentioned that the four sRNAs were also detected in recent RIL-seq experiments of Salmonella and EPEC. Are any of the here identified targets also found in other species or was none detected as analyses were carried out under conditions that do not favor flagella expression?

The targets identified in this study were not detected in the Salmonella and EPEC RIL-seq datasets. However, the Salmonella and EPEC experiments were carried out under different growth conditions. Based on the sequence conservation of the Sigma 28-dependent sRNAs across several bacterial species (Figure 8—figure supplement 2), we do think overlapping targets will be found in other bacterial species under the appropriate growth conditions.

The strongest evidence of MotR dependent target regulation is the one on rpsJ, which does not necessarily require the additional experiments with MotR. Since the authors were able to show upregulation of the rpsJ-gfp reporter upon OE of MotR WT, it would have strengthened the results if they performed the experiments in Fig. S8C with MotR WT. Similary as an increase of flagella number was seen with OE of MotR WT in Fig. 2A, the effect of the OE S10∆loop could be compared to OE MotR instead of OE MotR (Fig. 6A). At least if would be helpful, to briefly comment on why MotR* was used instead of MotR WT for these experiments.

As suggested, we state MotR was used in some assays given the stronger effects for some phenotypes (page 10, lines 196-197). We think, given that we established MotR and MotR cause the same effects, with increased intensity for the latter, it is reasonable to use MotR* in some of the experiments.

p. lines 482-491 and 508-511: The authors discuss that both UhpU sRNAs and RsaG sRNA from S. aureus are derived from the 3'UTR of uhpT, but conclude there is no overlap regarding flagella regulation, suggesting independent evolution of these sRNAs. However, the authors also mention that UhpU sRNA has many additional targets beyond LhrA involved in carbon and nutrient metabolism. Thus, maybe regulation of metabolic traits could be a conserved theme and function for UhpU and RsaG? Maybe try to comment on or better connect these two parts in the discussion.

As suggested, we now comment on the possibility of the regulation of metabolic traits being a conserved theme and function for UhpU and RsaG (page 24, lines 520-527).

Check the text for consistency regarding the use of italics for gene names (e.g., legend of Figs. 7 and 8)

The text was corrected.

Please introduce abbreviations, e.g., G6P (line 139), REP (line 150), ARN (line 258), NOR/U (Table S1 legend)

As suggested, we now introduce the abbreviations for G6P (page 7, line 142), REP (page 8, lines 155-156), and NOR (Supplementary file 1 legend). Regarding ARN, these sequences are already written in parentheses in the same sentence. However, we revised this to “ARN motif sequences” (page 13, line 278).

Fig. S1A: Highlight REP sequence mentioned in text (line 150).

REP sequences are now highlighted in gray in Figure 1—figure supplement 1A.

Fig. S1C: It would be helpful to list number nt positions on the sRNAs based on full-length transcripts.

The corresponding positions based on the full-length transcripts have also been added to this figure.

Fig. S2: Adjust the position of UhpU-S label.

UhpU-S label position was adjusted.

Fig. S6: Include UhpU in the figure title.

UhpU was added to the title.

Fig. S10: It would be helpful to indicate on the figure (or state more clearly in the legend) which RNA was extracted from WT or ΔfliCX background.

The samples shown in the Figure are all in a WT strain. We corrected the figure legend accordingly.

Line 290: the effect is on flagella number, not motility.

This typo is now corrected (page 15, line 312).

Fig. S8: One-way ANOVA (panel A legend)

This typo is now corrected (page 64, line 1433).

Line 320: Fig. S9C instead of 9C

We thank the reviewer for noticing the typo. The numbering of the supplementary figures has now been changed to the eLife format.

It would be helpful to add reference for statement in line 57.

A reference to (Fitzgerald et al., 2014) was added as suggested.

Add PMID:32133913 as reference for post-transcriptional regulation of the flagellar regulon in the introduction (lines 87-91)

The indicated reference was added as suggested (page 5, lines 87-91).

Legend Fig. S6: expand view -> expanded view

This typo is now corrected (page 63, line 1406).

line 513: sRNA -> sRNAs

This typo is now corrected (page 25, line 549).

Fig. 8G: Maybe include lrhA as target of UhpU sRNA at top of the cascade.

As suggested lrhA has been added as a target of UhpU at the top of the cascade.

eLife assessment

This article provides important findings on how bacteria use small RNAs to regulate flagellar expression with implications for multiple fields. The data supporting the conclusions are convincing with a large amount of data that include results from phenotypic analyses, genomics approaches as well as in-vitro and in-vivo target identification and validation methods. This study on the varied effects of three sRNAs (UhpU, FliX and MotR) is of broad interest to RNA biochemists and microbiologists.

Reviewer #1 (Public Review):

Bacteria can adapt to extremely diverse environments via extensive gene reprogramming at transcriptional and post-transcriptional levels. Small RNAs are key regulators of gene expression that participate in this adaptive response in bacteria, and often act as post-transcriptional regulators via pairing to multiple mRNA-targets.

In this study, Melamed et al. identify four E. coli small RNAs whose expression is dependent on sigma 28 (FliA), involved in the regulation of flagellar gene expression. Even though they are all under the control of FliA, expression of these 4 sRNAs peaks under slightly different growth conditions and each has different effects on flagella synthesis/number and motility. Combining RILseq data, structural probing, northern-blots and reporter assays, the authors show that 3 of these sRNAs control fliC expression (negatively for FliX, positively for MotR and UhpU) and two of them regulate r-protein genes from the S10 operon (again positively for MotR, and negatively for FliX). UhpU also directly represses synthesis of the LrhA transcriptional regulator, that in turn regulates flhDC (at the top of flagella regulation cascade). Based on RILseq data, the fourth sRNA (FlgO) has very few targets and may act via a mechanism other than base-pairing.

As r-protein S10 is also implicated in anti-termination via the NusB-S10 complex, the authors further hypothesize that the up-regulation of S10 gene expression by MotR promotes expression of the long flagellar operons through anti-termination. Consistent with this possible connection between ribosome and flagella synthesis, they show that MotR overexpression leads to an increase in flagella number and in the mRNA levels of two long flagellar operons, and that both effects are dependent on the NusB protein. Lastly, they provide data supporting a more general activating and repressing role for MotR and FliX, respectively, in flagellar genes expression and motility, via a still unclear detailed mechanism.

This study brings a lot of new information on the regulation of flagellar genes, from the identification of novel sigma 28-dependent sRNAs to their effects on flagella production and motility. It represents a considerable amount of work; the experimental data are clear and solid and support the conclusions of the paper. Even though mechanistic details underlying the observed regulations by MotR or FliX sRNAs are lacking, the effect of these sRNAs on fliC, several rps/rpl genes, and flagellar genes and motility is convincing.<br /> The connection between r-protein genes regulation and flagellar operons is exciting, and so is the general effect of pMotR or pFliX on the expression of multiple middle and late flagellar genes.

Reviewer #2 (Public Review):

This manuscript discusses the posttranscriptional regulation of flagella synthesis in Escherichia coli. The bacterial flagellum is a complex structure that consists of three major domains, and its synthesis is an energy-intensive process that requires extensive use of ribosomes. The flagellar regulon encompasses more than 50 genes, and the genes are activated in a sequential manner to ensure that flagellar components are made in the order in which they are needed. Transcription of the genes is regulated by various factors in response to environmental signals. However, little is known about the posttranscriptional regulation of flagella synthesis. The manuscript describes four UTR-derived sRNAs (UhpU, MotR, FliX, and FlgO) that are controlled by the flagella sigma factor σ28 (fliA) in Escherichia coli. The sRNAs have varied effects on flagellin protein levels, flagella number, and cell motility, and they regulate different aspects of flagella synthesis.<br /> UhpU corresponds to the 3´ UTR of uhpT.

UhpU is transcribed from its own promoter inside the coding sequence of uhpT.

MotR originates from the 5´ UTR of motA. The promoter for motR is within the flhC CDS and is also the promoter of the downstream motAB-cheAW operon.

FliX originates from the 3´ UTR of fliC. Probably processed from parental mRNA.

FlgO originates from the 3´ UTR of flgL. Probably processed from parental mRNA.

This is a very interesting study that shows how sRNA-mediated regulation can create a complex network regulating flagella synthesis. The information is new and gives a fresh outlook at cellular mechanisms of flagellar synthesis.

Reviewer #3 (Public Review):

Flagella are crucial for bacterial motility and virulence of pathogens. They represent large molecular machines that require strict hierarchical expression control of their components. So far, mainly transcriptional control mechanisms have been described to control flagella biogenesis. While several sRNAs have been reported that are environmentally controlled and regulate motility mainly via control of flagella master regulators, less is known about sRNAs that are co-regulated with flagella genes and control later steps of flagella biogenesis.

In this carefully designed and well-written study, the authors explore the role of four E. coli σ28-dependent 3' or 5' UTR-derived sRNAs in regulating flagella biogenesis. UhpU and MotR sRNAs are generated from their own σ28(FliA)-dependent promoter, while FliX and FlgO sRNAs are processed from the 3'UTRs of flagella genes under control of FliA. The authors provide an impressive amount of data and different experiments, including phenotypic analyses, genomics approaches as well as in-vitro and in-vivo target identification and validation methods, to demonstrate varied effects of three of these sRNAs (UhpU, FliX and MotR) on flagella biogenesis and motility. For example, they show different and for some sRNAs opposing phenotypes upon overexpression: While UhpU sRNA slightly increases flagella number and motility, FliX has the opposite effect. MotR sRNA also increases the number of flagella, with minor effects on motility.

While the mechanisms and functions of the fourth sRNA, FlgO, remain elusive, the authors provide convincing experiments demonstrating that the three sRNAs directly act on different targets (identified through the analysis of previous RIL-seq datasets), with a variety of mechanisms. The authors demonstrate, UhpU sRNA, which derives from the 3´UTR of a metabolic gene, downregulates LrhA, a transcriptional repressor of the flhDC operon encoding the early genes that activate the flagellar cascade. According to their RIL-seq data analyses, UhpU has hundreds of additional potential targets, including multiple genes involved in carbon metabolism. Due to the focus on flagellar biogenesis, these are not further investigated in this study and the authors further characterize the two other flagella-associated sRNAs, FliX and MotR. Interestingly, they found that these sRNAs seem to target coding sequences rather than acting via canonical targeting of ribosome binding sites. The authors show FliX sRNA represses flagellin expression by interacting with the CDS of the fliC mRNA. Both FliX and MotR sRNA turn out to modulate the levels of ribosomal proteins of the S10 operon with opposite effects. MotR, which is expressed earlier, interacts with the leader and the CDS of rpsJ mRNA, leading to increased S10 protein levels and S10-NusB complex mediated anti-termination, promoting readthrough of long flagellar operons. FliX interacts with the CDSs of rplC, rpsQ, rpsS-rplV, repressing the production of the encoded ribosomal proteins. The authors also uncover MotR and FliX affect transcription selected representative flagellar genes, with an unknown mechanism.

Overall, this comprehensive study expands the repertoire of characterized UTR derived sRNAs and integrates new layers of post-transcriptional regulation into the highly complex flagellar regulatory cascade. Moreover, these new flagella regulators (MotR, FliX) act non-canonically, and impact protein expression of their target genes by base-pairing with the CDS of the transcripts. Their findings directly connect flagella biosynthesis and motility, highly energy consuming processes, to ribosome production (MotR and FliX) and possibly to carbon metabolism (UhpU). In their revised version, the authors have addressed many of the previously raised questions and comments. This made their manuscript easier to read and to follow.

eLife assessment

This study based on the use of Cancer Drug Resistance Accelerator (CDRA) chip is valuable as a platform technology to assess chemoresistance mechanisms. The strength is convincing from the technological point of view. However, the use of a single cell line model and the lack of mechanistic insights could be improved.

Reviewer #1 (Public Review):

Lim W et al. investigated the mechanisms underlying doxorubicin resistance in triple negative breast cancer cells (TNBC). They use a new multifluidic cell culture chamber to grow MB-231 TNBC cells in the presence of doxorubicin and identify a cell population of large, resistant MB-231 cells they term L-DOXR cells. These cells maintain resistance when grown as a xenograft model, and patient tissues also display evidence for having cells with large nuclei and extra genomic content. RNA-seq analysis comparing L-DOXR cells to WT MB-231 cells revealed upregulation of NUPR1. Inhibition or knockdown of NUPR1 resulted in increased sensitivity to doxorubicin. NUPR1 expression was determined to be regulated via HDAC11 via promoter acetylation. The data presented could be used as a platform to understand resistance mechanisms to a variety of cancer therapeutics.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors induced large doxorubicin-resistant (L-DOXR) cells by generating DOX gradients using their Cancer Drug Resistance Accelerator (CDRA) chip. The L-DOXR cells showed enhanced proliferation rates, migration capacity, and carcinogenesis. Then the authors identified that the chemoresistance of L-DOXR cells is caused by failed epigenetic control of NUPR1/HDAC11 axis.

Strengths:

- Chemoresistant cancer cells were generated using a novel technique and their oncogenic properties were clearly demonstrated using both in vivo and in vitro analysis.<br /> - The mechanisms of chemoresistance of the L-DOXR cells could be elucidated using in vivo chemoresistant xenograft models, an unbiased genome-wide transcriptome analysis, and a patient data/tissue analysis.<br /> - This technique has great capability to be used for understanding the chemoresistant mechanisms of tumor cells.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, Lim and colleagues use an innovative CDRA chip platform to derive and mechanistically elucidate the molecular wiring of doxorubicin-resistant (DOXR) MDA-MB-231 cells. Given their enlarged morphology and polyploidy, they termed these cells as Large-DOXR (L-DORX). Through comparative functional omics, they deduce the NUPR1/HDAC11 axis to be essential in imparting doxorubicin resistance and, consequently, genetic or pharmacologic inhibition of the NUPR1 to restore sensitivity to the drug.

Strengths:<br /> The study focuses on a major clinical problem of the eventual onset of resistance to chemotherapeutics in patients with triple-negative breast cancer (TNBC). They use an innovative chip-based platform to establish as well as molecularly characterize TNBC cells showing resistance to doxorubicin and uncover NUPR1 as a novel targetable driver of the resistant phenotype.

Weaknesses:<br /> Critical weaknesses are the use of a single cell line model (i.e., MDA-MB-231) for all the phenotypic and functional experiments and absolutely no mechanistic insights into how NUPR1 functionally imparts resistance to doxorubicin. It is imperative that the authors demonstrate the broader relevance of NUPR1 in driving dox resistance using independent disease models.

eLife assessment

The findings in this study are important and have a practical implication for early cancer diagnosis. Furthermore, we found the ML approach and data analysis compelling. However, significant concerns regarding the quality of the source materials used for the analysis have been raised and need to be addressed.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors used machine learning algorithm to analyze published exosome datasets to find biomarkers to differentiate exosomes of different origin.

Strengths:

The performance of the algorithm are generally of good quality.

Weaknesses:

The source datasets are heterogeneous as described in Figure 1 and Figure 2, or Line 72-75; and therefore questionable.

Reviewer #2 (Public Review):

Summary:

This is a fine work on the development of computational approaches to detect cancer through exosomes. Exosomes are an emerging biomarker resource and have attracted considerable interests in the biomedical field. Kalluri and co-workers collected a large sample pool and used random forest to identify a group of protein markers that are universal to exosomes and to cancer exosomes. The results are very exciting and not only added new knowledge in cancer research but also a new and advanced method to detect cancer. Data was presented very nicely and the manuscript was well written.

Strengths:

Identified new biomarkers for cancer diagnosis via exosomes.<br /> Developed a new method to detect cancer non-invasively.<br /> Results were presented nicely and manuscript were well written.

Weaknesses:

N/A.

Reviewer #3 (Public Review):

In the current study, Li et al. address the difficulty in early non-invasive cancer diagnosis due to the limitations of current diagnostic methods in terms of sensitivity and specificity. The study brings attention to exosomes - membrane-bound nanovesicles secreted by cells, containing DNA, RNA, and proteins reflective of their originating cells. Given the prevalence of exosomes in various biological fluids, they offer potential as reliable biomarkers. Notably, the manuscript introduces a new computational approach, rooted in machine learning, to differentiate cancers by analyzing a set of proteins associated with exosomes. Utilizing exosome protein datasets from diverse sources, including cell lines, tissues, and various biological fluids, the study spotlights five proteins as predominant universal exosome biomarkers. Furthermore, it delineates three distinct panels of proteins that can discern cancer exosomes from non-cancerous ones and assist in cancer subtype classification using random forest models. Impressively, the models based on proteins from plasma, serum, or urine exosomes achieve AUROC scores above 0.91, outperforming other algorithms such as Support Vector Machine, K Nearest Neighbor Classifier, and Gaussian Naive Bayes. Overall, the study presents a promising protein biomarker signature tied to cancer exosomes and proposes a machine learning-driven diagnostic method that could potentially revolutionize non-invasive cancer diagnosis.

eLife assessment

This article brings to bear a useful, extensive set of behavioral methods and neural data to report that activity in numerous cortical regions robustly covaries with the complexity of tone sequences encoded in memory. In its current state, the findings are solid but deserve further analysis to arrive at more convincing conclusions.

Reviewer #1 (Public Review):

The manuscript investigates how humans store temporal sequences of tones in working memory. The authors mainly focus on a theory named "Language of thought" (LoT). Here the structure of a stimulus sequence can be stored in a tree structure that integrates the dependencies of a stimulus stored in working memory. To investigate the LoT hypothesis, participants listened to multiple stimulus sequences that varied in complexity (e.g., alternating tones vs. nearly random sequence). Simultaneously, the authors collected fMRI or MEG data to investigate the neuronal correlates of LoT complexity in working memory. Critical analysis was based on a deviant tone that violated the stored sequence structure. Deviant detection behavior and a bracketing task allowed a behavioral analysis.

Results showed accurate bracketing and fast/correct responses when LoT complexity is low. fMRI data showed that LoT complexity correlated with the activation of 14 clusters. MEG data showed that LoT complexity correlated mainly with activation from 100-200 ms after stimulus onset. These and other analyses presented in the manuscript lead the authors to conclude that such tone sequences are represented in human memory using LoT in contrast to alternative representations that rely on distinct memory slot representations.

Strengths

The study provides a concise and easily accessible introduction. The task and stimuli are well described and allow a good understanding of what participants experience while their brain activation is recorded. Results are extensive as they include multiple behavioral investigations and brain activation data from two different measurement modalities. The presentation of the behavioral results is intuitive. The analysis provided a direct comparison of the LoT with an alternative model based on estimating a transition-probability measure of surprise.

For the fMRI data, the whole brain analysis was accompanied by detailed region of interest analyses, including time course analysis, for the activation clusters correlated with LoT complexity. In addition, the activation clusters have been set in relation (overlap and region of interest analyses) to a math and a language localizer. For the MEG data, the authors investigated the LoT complexity effect based on linear regression, including an analysis that also included transitional probabilities and multivariate decoding analysis. The discussion of the results focused on comparing the activation patterns of the task with the localizer tasks. Overall, the authors have provided considerable new data in multiple modalities on a well-designed experiment investigating how humans represent sequences in auditory working memory.

Weaknesses

The primary issue of the manuscript is the missing formal description of the LoT model and alternatives, inconsistencies in the model comparisons, and no clear argumentation that would allow the reader to understand the selection of the alternative model. Similar to a recent paper by similar authors (Planton et al., 2021 PLOS Computational Biology), an explicit model comparison analysis would allow a much stronger conclusion. Also, these analyses would provide a more extensive evidence base for the favored LoT model. Needed would be a clear argumentation for why the transitional probabilities were identified as the most optimal alternative model for a critical test. A clear description of the models (e.g., how many free parameters) and a description of the simulation procedure (e.g., are they trained, etc.) Here it would be strongly advised to provide the scripts that allow others to reproduce the simulations.

Furthermore, the manuscript needs a clear motivation for the type of sequences and some methodological decisions. Central here is the quadratic trend selectively used for the fMRI analysis but not for the other datasets. Also, the description of the linear mixed models is missing (e.g., the random effect structure, e.g., see Bates, D., Kliegl, R., Vasishth, S., & Baayen, H. (2015). Parsimonious mixed models. arXiv preprint arXiv:1506.04967.). Moreover, sample sizes have not been justified by a power analysis.

Reviewer #2 (Public Review):

Any stimulus that enters the human mind is in one way or another other compressed. A drawing with hundreds of lines might be turned into "picture of a seescape", a complex set of harmonically overlapping sine waves might be turned into "sad piano chord", and a weird set of utterances incomprehensible to most animals could be turned into "someone reading a review aloud" if prior experience permits. Understanding this process is essential to understanding the human mind. Understanding compression is even more critical to understanding working memory that - in its limited capacity - can most profit from compression, abstraction, or chunking.

Here, the authors provide some insight into how a sequence of binary pitch might be compressed during encoding into memory. They use a previously developed method to encapsulate sequences of 16 high and low pitches using a math-like description scheme (Planton et al., 2021). One can think of this scheme as a "language", "a categorization model", or "a process of segmenting patterns", but its central role in the experiment is to derive a 'rough' measure of complexity that is shown to covary with behavioral data, here and in prior work (Planton et al., 2021).

This language seems to be particularly useful in the context of this highly regularized task, where the set of possible sequences is limited to 20 (out of an overall number of 65.536 imaginable sequences). Instead of finding structures in random sequences, subjects can be expected to quickly learn that their task is to detect which particular structure (of a fairly limited class) is to be found in the given sequence. It is unclear whether such a language would also be useful for sequences of more natural stimuli that motivate the authors' research (e.g. syllables, tones, or shapes). What both more natural compression and the compression used in this task have in common is that long-term memory might play an instrumental role during the compression.

Thus, the authors provide clear evidence that these sequences are being compressed and some evidence that the compression used shares some features with the compression model employed, here. The neural data are consistent with this interpretation.

Regardless of our disagreement with the interpretation of the results the authors put forward, we find the research presented here elegantly designed, well grounded in a series of prior work, and inspiring. There is little known about the representation of sequences in memory and during perception and we believe that this work is a notable and helpful addition to our understanding of this question.

eLife assessment

This study reports a valuable finding on the discovery and evaluation of a potent small molecule inhibitor for CRM1 that may be important to treat extranodal NK/T cell lympohma (ENKTL). The evidence supporting the claims of the authors is solid that reflects an important finding for novel CRM1 inhibitors to treat ENKTL, although additional experimental evidence is needed. The work will be of interest to cancer biologists working on ENKTL.

Reviewer #1 (Public Review):

The current manuscript by Liu et al entitled "Discovery and biological evaluation of a potent small molecule CRM1 inhibitor for its selective ablation of extranodal NK/T cell lymphoma" reports the identification of a novel CRM1 inhibitor and shows its efficiency against extranodal natural killer/T cell lymphoma cells (ENKTL).

This is a very timely and very original study with potential impact in a variety of pathologies not only in ENKTL. However, the main conclusions of the work are not supported by experimental evidence. The study claims that LFS-1107 reversibly inhibits the nuclear export receptor CRM1 but the authors only show that the compound binds to CRM1 and that the CRM1 substrate IκBα accumulates in the cell nucleus upon LFS-1107 treatment. The evidence is indirect and alternative scenarios are certainly possible. On the other hand, the manuscript is not always well-written and insufficiently referenced. The quality of the images is poor. The figure legends are incomplete. The nuclear translocation in figure 2G is not convincing. The western blot in figure 2G shows that LFS-1107 treatment induces IκBα expression, and both cytoplasmic and nuclear amounts increase in a dose-dependent manner. Together, these data do not support nuclear IκBα accumulation upon LFS-1107 treatment.

Reviewer #2 (Public Review):

Indeed, ENKTL is a rather deadly tumor with unmet medical needs. The work is novel in the sense that they designed and identified a very potent inhibitor homing at CRM1 via a deep-reinforcement learning model to suppress the overactivation of NF-κB signaling, an underlying mechanism of ENKTL pathogenesis. The authors demonstrated that LFS-1107 binds more strongly with CRM1 (approximately 40-fold) as compared to KPT-330, an existing CRM1 inhibitor. Another merit of the small-molecule inhibitor is that LFS-1107 can selectively eliminate ENKTL cells while sparing normal blood cells. Their animal results clearly demonstrated that the small-molecule inhibitor was able to extend mouse survival and eliminate tumor cells considerably. Overall, the manuscript may provide a possible therapeutic strategy to treat ENKTL with a good safety profile. The manuscript is also well-written. The weakness of the manuscript is that some details for the design and evaluation of the small-molecular inhibitor are missing.

eLife assessment

This paper measures the neural activity in reach-to-grasp and reach-only tasks using intrinsic optical imaging. The paper describes these in the relationship to the Intra-cortical micro stimulation maps of the same animals. The dataset is unique and potentially highly important. However, the claim of "clustered neural activity" is not tested against any quantifiable alternative hypothesis of non-clustered activity, and support for this idea is therefore currently incomplete.

Reviewer #1 (Public Review):

This paper describes the neural activity, measured by intrinsic optical imaging in reach-to-grasp, and reach-only conditions in relation to the Intra-cortical micro stimulation maps. The paper mostly describes a relatively unique and potentially useful data set. However, in the current version, no real hypotheses about the organization of M1 and PMd are tested convincingly. For example, the claim of "clustered neural activity" is not tested against any quantifiable alternative hypothesis of non-clustered activity, and support for this idea is therefore incomplete.

The combination of intrinsic optical imaging and intra-cortical micro-stimulation of the motor system of two macaque monkeys promised to be a unique and highly interesting dataset. The experiments are carefully conducted. In the analysis and interpretation of the results, however, the paper was disappointing to me. The two main weaknesses in my mind were:

a) The alternative hypotheses depicted in Figure 1B are not subjected to any quantifiable test. When is an activity considered to be clustered and when is it distributed? The fact that the observed actions only activate a small portion of the forelimb area (Figure 5G, H) is utterly unconvincing, as this analysis is highly threshold-dependent. Furthermore, it could be the case that the non-activated regions simply do not give a good intrinsic signal, as they are close to microvasculature (something that you actually seem to argue in Figure 6b). Until the authors can show that the other parts of the forelimb area are clearly activated for other forelimb actions (as you suggest on line 625), I believe the claim of cluster neural activity stands unsupported.

b) The most interesting part of the study (which cannot be easily replicated with human fMRI studies) is the correspondence between the evoked activity and intra-cortical stimulation maps. However, this is impeded by the subjective and low-dimensional description of the evoked movement during stimulation (mainly classifying the moving body part), and the relatively low-dimensional nature (4 conditions) of the evoked activity.

c) Many details about the statistical analysis remain unclear and seem not well motivated.

Reviewer #2 (Public Review):

Chehade and Gharbawie investigated motor and premotor cortex in macaque monkeys performing grasping and reaching tasks. They used intrinsic signal optical imaging (ISOI) covering an exceedingly large field-of-view extending from the IPS to the PS. They compared reaching and fine/power-grip grasping ISOI maps with "motor" maps which they obtained using extensive intracranial microstimulation. The grasping/reaching-induced activity activated relatively isolated portions of M1 and PMd, and did not cover the entire ICM-induced 'motor' maps of the upper limbs. The authors suggest that small subzones exist in M1 and PMd that are preferentially activated by different types of forelimb actions. In general, the authors address an important topic. The results are not only highly relevant for increasing our basic understanding of the functional architecture of the motor-premotor cortex and how it represents different types of forelimb actions, but also for the development of brain-machine interfaces. These are challenging experiments to perform and add to the existing yet complementary electrophysiology, fMRI, and optical imaging experiments that have been performed on this topic - due to the high sensitivity and large coverage of the particular IOSI methods employed by the authors. The manuscript is generally well written and the analyses seem overall adequate - but see below for some additional analyses that should be done. Although I'm generally enthusiastic about this manuscript, there are two major issues that should be clarified. These major questions relate mainly to potential thresholding issues and clustering issues.

Major:

1) The main claim of the authors is that specific forelimb actions activate only a small fraction of what they call the motor map (i.e., those parts of M1/PMd that evoke muscle contractions upon ICM). The action-related activity is measured by ISOI. When looking a the 'raw' reflectance maps, it is rather clear that relatively wide portions of the exposed cortex are activated by grasping/reaching, especially at later time points after the action. In fact, another reading of the results may be that there are two zones of 'deactivation' that split a large swath of motor-premotor cortex being activated by the grasping/reaching actions. (e.g. at 6 seconds after the cue in Fig 3A, 5A). At first sight, the 'deactivated' regions seem to be located in the cortex representing the trunk/shoulder/face - hence regions not necessarily activated (or only weakly) during the grasping/reaching actions. If true, this means that most of the relevant M1/PMd cortex IS activated during the latter actions - opposing the 'clustering' claims of the authors. This raises the question of whether the 'granularity' claimed by the authors is<br /> a. threshold dependent. In this context, the authors should provide an analysis whereby 'granularity' is shown independent of statistical thresholds of the ISOI maps.<br /> b. dependent on the time-point one assesses the maps. Given the sluggish hemodynamic responses, it is unclear which part of the ISOI maps conveys the most information relative to the cue and arm/hand movements. I suspect that timepoints > 6 s will reveal even larger 'homogeneous' activations compared to the maps < 6s.<br /> In fact, Fig 5F (which is highly thresholded) shows a surprisingly good match between the different forelimb actions, which argues against the existence of small subzones that are preferentially activated by different types of forelimb actions -the main claim of the authors.

2) Related to the previous point, the ROI selections/definitions for the time course analyses seem highly arbitrary. As indicated in the introduction, the clustering hypothesis dictates that "an arm function would be concentrated in subzones of the motor arm zones. Neural activity in adjacent subzones would be tuned for other arm functions." To test this hypothesis directly in a straightforward manner, the authors could use the results from the ICM experiment to construct independent ROIs and to evaluate the ISOI responses for the different actions. In that case, the authors could do a straightforward ANOVA (if the data permits parametric analyses) with ROI, action, and time point (and possibly subject) as factors.

Reviewer #1 (Public Review):

This paper evaluates the effect of knocking out CST7(Cystatin 5) on the APPNL-G-F Alzheimer's disease mouse model. They found sexually dimorphic outcomes, with differential transcriptional responses, increased phagocytosis (but interestingly a higher plaque burden) in females and suppressed inflammatory microglial activation in males (but interestingly no change in plaque burden). This study offers new insight into the functional role of CST7 that is upregulated in a subset of disease- associated microglia in AD models and human brain. Despite the discovery of disease-associated microglia several years ago, there has been little effort in understanding the function of the different genes that make up this profile, making this paper especially timely. Overall, the experiments are well-controlled and the data support the main conclusions and the manuscript could be strengthened by addressing the below comments and clarifying questions that could impact the interpretation of their data/ findings.

1. In the first section discussing CST7 expression levels in AD models, it would be good to involve a discussion of levels of CST7 change in human AD samples. There are sufficient available datasets to look at this, and it would help us understand how comparable the animal models are to human patients. For example, while in mice CST7 is highly enriched in microglia/macrophages, in human datasets it seems like it is not quite so specific to microglia - it is equally expressed in endothelial cells. This might have a significant impact on the interpretation of the data, and it would be good to introduce and assess the findings in mice through the human subjects lens. There is a discussion of the human data in the discussion section, but it would be more appropriately assessed in the same way as the mouse data and comparatively presented in the results section. The authors could also include the data from Gerrits et al. 2021 in their first figure.<br /> 2. The differential RNAseq data is perhaps one of the most striking results of this paper; however it is difficult to see exactly how similar the male v female APPNL-G-F profiles are, in addition to the genes shared or not between the KO condition. Venn diagrams, in addition to statistical tests, would enhance this part of the paper and add more clarity.<br /> 3. A major argument in the paper is a continuation of Sala-Frigiero 2019 which says that the female phenotype is an acceleration of the male phenotype. Does this mean that if males were assessed at later timepoints, they would be more similar to the females? Or are there intrinsic differences that never resolve? It would be helpful to see a later timepoint for males to get at the difference between these two options<br /> 4. If the central argument is that CST7 in females decreases phagocytosis and in males increases microglia activation, are there changes in amyloid plaque burden or structure in the APPNL-G-F /CST 7 KO mice compared to APPNL-G-F/CST7 WT that reflect these changes? Please address. If not, how does this affect the functional interpretation of differential expression observed in phagocytic/reactive microglia genes? Pieces of this are discussed but it could be clearer<br /> 5. It is confusing that increased phagocytosis in the APPNL-G-F/CST7 KO females leads to greater plaque burden, considering proteolysis is not affected. What might explain this observation? Additionally, it is interesting that suppression of microglial activation doesn't lead to an increase in plaques in the male APPNL-G-F/CST7 KO mice. How does the profile of phagocytic microglia in the male APPNL-G-F/CST7 KO mice differ from the APPNL-G-F males?<br /> 6. Seems that the authors have potentially discovered an unusual mechanism for how CST7 could regulate cell autonomous function without impacting its canonical protease target. The authors deal with this extensively in the discussion but an ELISA or ICC to localize CST7 to microglia in vitro or in vitro would help address this point.<br /> 7. The authors focus on plaques in their final figure, however dysregulated microglial phagocytosis could impact many other aspects of brain health. Simple immunohistochemistry for synapses and myelin/oligodendrocytes (especially given the results of the in vitro phagocytosis assay) could provide more insight here.

Reviewer #2 (Public Review):

In this article, Daniels et al evaluate the function of Cst7, a gene previously shown to be strongly expressed when microglia respond to Alzheimer's-like pathology. The reported findings include evidence for a sexually dimorphic role of Cst7 in microglia, including differences in lysosomal activity and ability to phagocytose. Some questions remain as to how many of these effects are 1) disease-independent, 2) age-dependent, and 3) ultimately affecting cognition

Strengths:<br /> -The approach taken here is sound, knocking out Cst7 in an animal model of Alzheimer's-like pathology, and analysing a range of variables associated with the pathology.<br /> -The authors have made good use of existing datasets, evidencing the advantages of data sharing and open data mining.<br /> -Data reporting is also excellent, as we can see the individual data points, and also observe how optimal group numbers were used. This adds solidity to the study.<br /> -The results are very well connected, with experiments focusing on the in vivo and in vitro lysosomal/phagocytic function<br /> -Exploring the effect of sex, as an independent variable, is a refreshing approach and clearly an important one by looking at the findings reported here.

Weaknesses:<br /> -The basis for the hypothesis of Cst7 displaying sexual dymorphism is not as strong as indicated by the text. Data presented in Figure 1 supports 1/2 models have statistically significant differences in expression of Cst7 between males and females.<br /> -As presented, it is hard to disentangle the differential impact of sex, in isolation, compared to the accelerated pathology/ageing observed in females. In other words, Cst7 could be playing a differential role in females not because that particular gene has sexually dimorphic roles, but because female microglia are generally more advanced in their phenotype and prone to Cst7-dependent effects that their younger counterparts (or male microglia) would not suffer. We also lack context when it comes to baseline effects of Cst7-/- compared to disease-related effects, since a crucial control (non-AD Cst7-/-) is missing from analyses, key in Figure 2 for example.<br /> -It is unclear how the knockout of Cst7 would selectively affect microglia. The expression of Cst7 is definitely very high in microglia in AD, but it's less clear whether other cells express this gene as well. If so, the effects of Cst7-/- could be microglia-independent in part.<br /> -Considering the large number of mice used in these studies, and the effort that very likely went into these, it is disappointing that we do not have any measure of cognition or any other behavioural task associated with the molecular data. Ultimately, changes in amyloid, for example, could or could not correlate with real pathology in APP models.

Reviewer #3 (Public Review):

In this manuscript, Daniels et al explored the role of Cystatin F in an A-driven mouse model of Alzheimer's disease. By crossing a constitutive knockout mouse lacking the gene that encodes Cystatin F, Cst7, to the AppNL-G-F mouse line, the authors describe impairments in microglial gene expression and phagocytic function that emerge more prominently in females versus males lacking Cst7. A strength of the study is its focus: given mounting evidence that microglia are a hub of neurological dysfunction with particular potential to trigger or exacerbate neurodegenerative disorders, it is essential to determine the changes in microglia that occur pathologically to promote disease progression. Similarly, the wide-spread identification of the gene in question, Cst7, as upregulated in AD models makes this gene a good target for mechanistic studies.

The paper in its current form also has several weaknesses which limit the insights derived, weaknesses that are largely related to the experimental tools and approaches chosen by the authors to test their hypotheses. For example, the paper begins with a figure replotting data from previous studies showing that Cst7 is upregulated in mouse models of Alzheimer's disease. Though relevant to the current study, there are no new insights provided here. Next, the authors perform bulk RNA-sequencing on microglia isolated from male and female mice in the Cst7-/-; AppNL-G-F mouse line. In the methods, it is unclear whether the authors took precautions to preserve the endogenous transcriptional state of these cells given evidence that microglia can acquire a DAM-like signature simply due to the process of dissociation (Marsh et al, Nature Neuroscience, 2022). If the authors did not control for this, their results may not support the conclusions they draw from the data. Relatedly, it appears the authors pooled all microglia together here, instead of just isolating DAMs specifically or analyzing microglia at single-cell resolution, which could reveal the heterogeneous nature of the role of Cst7 in microglia. In addition to losing information about heterogeneity, another concern is that they could be diluting out the major effects of the model on microglial function by including all microglia. Overall, the biggest issue I have with the RNA-sequencing data is the lack of validation of the gene expression changes identified using a different method that does not require dissociation, like immunohistochemistry or fluorescence in situ hybridization. Especially given the limited number of genes they found to be mis-regulated (see Fig. 2 E and G), I worry that these changes might simply be noise, especially since the authors provide no further evidence of their mis-regulation. Without further validation, the data presented are not sufficient to support the authors' claims.

In assessing the changes in microglial function and A pathology that occur in males and females of the Cst7-/-; AppNL-G-F line, the authors identify some differences between how females and males are affected by the loss of Cst7. While the statistical analyses the authors perform as given in the figure legends appear to be correct, the plots do not show significant changes between males and females for a given parameter. Take for example Figure 3H. Loss of Cst7 decreases IBA+Lamp+ microglia in males but increases this parameter in females. However, it does not appear that there is a significant difference in IBA+Lamp+ microglia in male versus female mice lacking Cst7. If there is no absolute difference between males and females, can the differential effects of Cst7 knockout on the sexes really be so relevant to the sexual dimorphism observed in the disease? I question this connection, but perhaps a greater discussion of what the result might mean by the authors would be helpful for placing this into context.

Finally, the use of in vitro assays of microglial function can be helpful as secondary analyses when coupled with in vivo or ex vivo approaches, but are not on their own sufficient to support the authors' conclusions. Quantitative engulfment assays (see Schafer et al, Neuron, 2012) on brain tissue showing that male and female microglia lacking Cst7 engulf different amounts of material (e.g. plaques, synapses, myelin) in the intact brain would be more convincing.

In general, a major limitation to the insights that can be derived in the study is the decision of the authors to perform all experiments at a single late-stage time point of 12 months of age. As this is quite far into disease progression for many AD models, phenotypic changes identified by the authors could arise due to the downstream effects of plaque deposition and therefore may not implicate Cst7 as a mechanism driving neurodegeneration rather than one of many inflammatory changes that accompany AD mouse models nearing the one-year time point. A related problem is that the study uses a constitutive KO mouse that has lacked Cst7 expression throughout life, not just during disease processes that increase with aging. In summary, the topic of the article is important and timely, but the connection between the data and the authors' conclusions is not as strong as it could be.

eLife assessment

This study uncovers a unique feature of the nucleotide binding domain interface in human CFTR, offering valuable insights into the effects of different non-hydrolytic mutations on CFTR gating. While the evidence presented is solid, a more thorough examination of the non-hydrolytic mutants of zebrafish CFTR for comparison would strengthen the authors' claims. In the current form, more cautious interpretations of some of the data are needed. This study will be of interest to researchers in the fields of cystic fibrosis and proteins in the ATP Binding Cassette (ABC) transporter family.

Reviewer #1 (Public Review):

The CFTR ion channel belongs to the family of ABC transporters, alternating between inward-facing (IF) and outward-facing (OF) conformations driven by binding and hydrolysis of ATP. ABC transporters are involved in a wide variety of physiologically essential transport processes. In contrast to all other ABC transporters, the OF conformation of CFTR includes an anion-conducting transmembrane pore, which has enabled investigators to use single-channel patch clamp electrophysiology to characterize the energetics of most of the relevant transitions that can be observed during a gating cycle. A transition that had remained elusive to quantify is the non-hydrolytic closing rate in which the channel switches back to an IF conformation even though both nucleotide-binding domain sites are occupied by non-hydrolyzed ATP molecules. The reason for this is that the rate of closure due to ATP hydrolysis occurs much faster, such that the non-hydrolytic closing rate cannot be quantified in WT channel recordings. Further, channels with mutations that are expected to disrupt ATP hydrolysis exhibit high variability between mutants in the non-hydrolytic closing rates, precluding an extrapolation for this rate onto the WT channel. It is presently unclear whether the large spread in the rates is caused by distinct degrees of remnant hydrolytic activity in each of the mutant channels. Regardless of this uncertainty, several of these mutations have been successfully employed in structural studies to stabilize the channel in an OF conformation with two ATP molecules bound. In the present manuscript, Márton A. Simon and collaborators use patch-clamp electrophysiology to measure the rates of non-hydrolytic channel closure in human CFTR channels containing single and double mutations expected to disrupt ATP hydrolysis. First, they find that the E1371Q but not the E1371S mutation significantly stabilizes the OF state and slows channel closure in the human but not the zebrafish channel. Looking at the structures of both human and zebrafish E1371Q mutant channels, a non-native hydrogen bond is identified between the side-chain of E1371Q and the main chain at position G576 that is observed only in the human structure and would be expected to stabilize the OF state. Double mutant cycle analysis is then utilized to compare the effect of removal of either of the hydrogen-bonding partners in the human CFTR, and found to be consistent with a large energy of interaction between the two sites that is interpreted to occur selectively in the OF state. Notably, none of these perturbations altered the intra-burst activity of the CFTR, indicating that those closures do not involve major changes in the NBD. The rates of closure for other mutants are then investigated, and it is found that combining two hydrolysis-disrupting mutations that retain relatively fast closure rates does not slow closure any further, suggesting that their fast closure rates are not due to residual ATP hydrolytic activity. Further, this observation also suggests that these mutations do not affect the intrinsic non-hydrolytic closing rate, allowing their rates to be used as a measure of what occurs in WT channels. The experiments presented are high-quality, the conclusions are well supported by the data and the findings clarify a series of questions that had remained in the field, in addition to finding and precisely quantitating the role of a hydrogen bond in stabilizing a conformation state of this channel. The results could have implications for other members of the ABC family that are harder to study because they do not produce ionic currents. Some methodological details could be explained better, such as the voltage at which each of the recordings was performed, and how data was normalized, which is presently unclear. Additional testing of the hypothesis could have been carried out through double mutant cycle analysis with E1371Q + G576Δ in the zebrafish receptor or the other non-hydrolytic mutants.

Reviewer #2 (Public Review):

Gating of the CFTR chloride channel is controlled by its nucleotide binding domains (NBDs) where ATP binding-induced dimerization leads to channel opening and ATP hydrolysis in the catalytic ATP binding site terminates CFTR's opening burst. Mutations that diminish ATP hydrolysis, including Walker A mutation K1250A, Walker B mutation D1370N, and catalytic glutamate mutations E1371Q and E1371S, have been used extensively to trap the channel in the open state by researchers studying CFTR function. The E1371Q human CFTR (hCFTR) has an extremely longer burst duration than all the other hydrolysis-deficient mutants, including E1371S hCFTR. An unexpected finding that the E-to-Q and E-to-S mutants of zebrafish CFTR (zCFTR) have similar non-hydrolytic closing rates inspired Simon et al to investigate the underlying mechanism for this discrepancy between the human and zebrafish CFTR orthologs, and examine how hydrolysis deficient mutations have differential effects on the CFTR's burst duration. Their data support the idea that all the above mutations completely abolish ATP hydrolysis. The closing rate of K1250A and E1371S CFTR represents the true non-hydrolytic closing rate of wildtype CFTR, while the closing rate of D1370N is accelerated presumably due to the lack of interaction between the negatively charged aspartate and magnesium ion in the ATP binding site. On the other hand, an artificial H-bond between the G576-Q1371 of hCFTR, which is absent in zCFTR, stabilizes the NBD dimer and slowers non-hydrolytic closure.

The conclusions of this paper are mostly well supported by the data, but some additional experiments will strengthen the claim on the role of the artificial inter-NBD hydrogen bond (point 1 below). Some aspects of data interpretation need to be further clarified (point 2-5 below).

1) The author hypothesized that in hCFTR an artificial H-bond between the side-chain of glutamine at position 1371 (i.e., in E1371Q mutant) and the backbone carbonyl at G576 of the D-loop stabilizes the NBD dimer. Such H-bond is absent in E1372Q zCFTR. The authors employed mutant cycle analysis on the G576Δ-E1371S mutation pair to demonstrate an energetic coupling between the hG576 and hE1371Q. However, how the deletion of G576 might alter the local structure is unpredictable. The result does not directly address the discrepancy between zCFTR and hCFTR, either. The D-loop is highly conserved across species with a consensus sequence PFGYLD (residue 574-579 in hCFTR), but in zCFTR the analogous sequence is PFTHLD. The backbone carbonyl oxygen could therefore be harder to access in zCFTR. A simple yet critical experiment would have strengthened the authors' claim that the interaction between Q1371 and G576 stabilizes the dimer: introducing mutation in the D-loop of zCFTR to match the sequence of hCFTR (and vice versa). The authors' hypothesis would predict that zCFTR with hCFTR's D-loop sequence should recapitulate hCFTR's phenotype: the E-to-Q mutation on the catalytic glutamate would further lengthen the burst duration compared to the E-to-S mutation.

2) The authors speculated that the reason for D1370N's relatively fast closing rate compared to other non-hydrolytic mutants is the loss of interaction between Mg2+ and the negatively charged aspartate. However, this reasoning fails to explain why non-hydrolytic closure of wildtype CFTR in the absence of Mg2+ (e.g., Levring et al. 2023 Extended Data Fig. 7g) is even slower than the non-hydrolytic closure of D1370N CFTR opened by MgATP, where at least the Mg2+ is present. The authors should caution the readers that so far no definitive experimental evidence can explain the destabilizing effect of D1370N.

3) Based on the results that the double mutant E1371S/K1250A hCFTR has similar burst duration as single mutant E1371S and K1250A, the authors made a strong claim that both mutations completely abolish ATP hydrolysis. Similar reasoning was applied to D1370N. The limitations in such interpretations should be discussed. The authors made the assumption that the termination of a burst is solely controlled by site 2 (Figure 1C). However, when hydrolysis is significantly diminished, binding of ATP in site 2 is very stable, and thus dissociation of ATP from site 2 versus site 1 becomes hard to distinguish. Whether all hydrolysis-deficient mutants share the same open-to-close transition by releasing ATP from site 2 but retaining ATP in site 1 is still a question. As the authors have elaborated in the text, it is known that mutations in the degenerate site 1 can affect non-hydrolytic closing. When mutations are introduced to site 2, they might as well result in allosteric effects on the stability of ATP binding in site 1, which could subsequently alter the channel's closing rate. The authors might want to make the readers aware of the complicated relationship between channel closure and CFTR's two ATP binding sites, and that the estimation of the "true non-hydrolytic closing rate" is based on an oversimplified gating scheme shown in Figure 1C.

4) It is known that non-hydrolytic closing rate of CFTR is phosphorylation dependent, which the authors briefly mentioned in the Discussion. Vergani et al. (2003) documented that τburst of K1250A and D1370N in PKA is ~80 s and ~4 s respectively, but both are reduced by roughly twofold when PKA was removed. In this study the burst durations of K1250A (~30 s, Figure 4C) and D1370N (~2 s, Figure 4E) indicate that these channels are not strongly phosphorylated. Similarly, the τburst of E1371S in PKA is over 100 s (Bompadre et al. 2005), significantly longer than that in the current study. Although it is unclear how a different degree of R domain phosphorylation affects non-hydrolytic closing, the fact that it does again suggests that the simplified scheme used as the base for data interpretation may have its limitation. The Discussion would benefit from a more cautionary note on the oversimplification of the IB1↔B1 transition, and clarify that channels are not strongly phosphorylated in the current experimental condition.

5) The τburst of E1371Q CFTR is over 400 second while the τburst of K1250A-E1371Q double mutant is shortened to ~200 second (Figure 3B, black vs Figure 4C, black). The K1250A-E1371S CFTR also seems to have a shorter τburst than E1371S CFTR (Figure 4C, blue vs Figure 3B, blue). Although the effect of the K1250A mutation on shortening τburst of E1371Q and E1371S CFTR is not as dramatic as the D1370N mutation, the authors might want to clearly state if there is indeed a significant difference and address how K1250A mutation has such destabilizing effect.

Reference:<br /> Bompadre, S. G., Cho, J. H., Wang, X., Zou, X., Sohma, Y., Li, M., and Hwang, T. C. (2005) CFTRgating II: Effects of nucleotide binding on the stability of open states. J Gen Physiol 125, 377-394

Levring,J., Terry,D.S., Kilic,Z., Fitzgerald,G., Blanchard,S.C., and Chen,J. (2023). CFTR function,<br /> pathology and pharmacology at single-molecule resolution. Nature 616, 606-614.

Vergani,P., Nairn,A.C., and Gadsby,D.C. (2003). On the mechanism of MgATP-dependent gating of CFTR Cl- channels. J. Gen. Physiol 121, 17-36.

Reviewer #3 (Public Review):

CFTR is an anion-selective channel that plays important roles in epithelial physiology. In this paper, Simon and colleagues focus on the step of the CFTR gating cycle that opens the pore. But the authors are particularly interested in the reversal of this opening step. Wild-type (WT) CFTR channels do not usually close by reversal of the opening step, as closure via this "non-hydrolytic" pathway is slow. Instead, hydrolysis of the ATP molecule bound at site 2 destabilizes the open (or bursting) channel and triggers rapid "hydrolytic" channel closure - before the open channel has time to overcome the energetic barrier on the non-hydrolytic pathway. While it is generally (but not universally) accepted that such a non-equilibrium kinetic scheme underlies CFTR gating, how tightly gating and ATPase cycles are coupled is still quite controversial.

Here, combining simple electrophysiology measurements on mutant channels with solid arguments, the authors provide an improved estimate for the backward rate on the opening transition (rate k-1) in WT-CFTR channels. It turns out that this rate is indeed slow, compared to the rate of the hydrolytic step (k1) allowing authors to conclude that WT CFTR channels close via reversal of the opening step only less than once every 100 gating cycles. In addition, results of thermodynamic mutant cycles and careful analysis of cryo-EM structures are used to support plausible molecular mechanisms that explain why different mutations in CFTR's catalytic site slow down, speed up or barely affect non-hydrolytic closure.

The strength of this study is twofold. First, the methods are sound, and the effects seen are clear-cut. Records are competently acquired, with a high number of repeats, are well analysed and very clearly presented. Second, the authors interpret their results with interdisciplinary competence, drawing on structural knowledge of ABC transporter catalytic mechanism, as well as on an in-depth understanding of studies investigating kinetics and thermodynamics of CFTR gating. This study, bringing together conclusions obtained in many previous studies, is a useful step forward towards a comprehensive description of the energetic landscape CFTR channel proteins wander through when gating. The Csanády lab has greatly contributed to developing this over the past years, and this paper reads as a "capstone".

However the reliance on previous conclusions is, in some ways, also a weakness. Many of the inferences made in interpreting the data depend on assumptions being met. There is evidence supporting the validity of these, but more clarity in stating implicit assumptions, and why the authors believe them to be valid, could improve the manuscript. The results fit well within the conceptual framework of CFTR's non-equilibrium gating. But some scientists, still sceptical of its basic premises, will not be convinced by these new results.

Within this context, the authors achieve their aim of estimating the microscopic rate constant for non-hydrolytic closure. The study will be of interest not only to scientists studying CFTR gating, but also to those wishing to understand how small-molecule drugs affect such gating. The mechanism of action of ivacaftor (currently taken by thousands of people for treatment of cystic fibrosis) is still not completely clear, and some evidence suggests that it stabilizes the pre-hydrolytic bursting state investigated here. Aspects of CFTR's conformational dynamics will probably also be true for some of its phylogenetic relatives. Thus, those studying other ABC transporters, many of which have medical relevance, will find it interesting to learn how CFTR couples its gating and hydrolytic cycles. This is especially true now, when cryogenic electron microscopy and other methods allow detailed structural comparisons between related ABC transporters, which can be correlated with differences in their function. Now more than ever CFTR could be a "model ABC protein".

eLife assessment

This paper will be of considerable interest to population geneticists and other scholars in the field of paleogenomics. The study provides an impressive dataset containing 200+ novel human ancient genome sequences and a very creative, robust, and novel approach for studying human migration across time using ancient DNA. The authors find that the population structure in Europe has been remarkably stable over time. The conclusions are well supported by the data and the methods used are thoughtful and rigorous.

Reviewer #1 (Public Review):

This paper presents an interesting data set from historic Western Eurasia and North Africa. Overall, I commend the authors for presenting a comprehensive paper that focuses the data analysis of a large project on the major points, and that is easy to follow and well-written. Thus, I have no major comments on how the data was generated, or is presented. Paradoxically, historical periods are undersampled for ancient DNA, and so I think this data will be useful. The presentation is clever in that it focuses on a few interesting cases that highlight the breadth of the data.

The analysis is likewise innovative, with a focus on detecting "outliers" that are atypical for the genetic context where they were found. This is mainly achieved by using PCA and qpAdm, established tools, in a novel way. Here I do have some concerns about technical aspects, where I think some additional work could greatly strengthen the major claims made, and lay out if and how the analysis framework presented here could be applied in other work.

## clustering analysis<br /> I have trouble following what exactly is going on here (particularly since the cited Fernandes et al. paper is also very ambiguous about what exactly is done, and doesn't provide a validation of this method). My understanding is the following: the goal is to test whether a pair of individuals (lets call them I1 and I2) are indistinguishable from each other, when we compare them to a set of reference populations. Formally, this is done by testing whether all statistics of the form F4(Ref_i, Ref_j; I1, I2) = 0, i.e. the difference between I1 and I2 is orthogonal to the space of reference populations, or that you test whether I1 and I2 project to the same point in the space of reference populations (which should be a subset of the PCA-space). Is this true? If so, I think it could be very helpful if you added a technical description of what precisely is done, and some validation on how well this framework works.

An independent concern is the transformation from p-values to distances. I am in particular worried about i) biases due to potentially different numbers of SNPs in different samples and ii) whether the resulting matrix is actually a sensible distance matrix (e.g. additive and satisfies the triangle inequality). To me, a summary that doesn't depend on data quality, like the F2-distance in the reference space (i.e. the sum of all F4-statistics, or an orthogonalized version thereof) would be easier to interpret. At the very least, it would be nice to show some intermediate results of this clustering step on at least a subset of the data, so that the reader can verify that the qpWave-statistics and their resulting p-values make sense.

The methodological concerns lead me to some questions about the data analysis. For example, in Fig2, Supp 2, very commonly outliers lie right on top of a projected cluster. To my understanding, apart from using a different reference set, the approach using qpWave is equivalent to using a PCA-based clustering and so I would expect very high concordance between the approaches. One possibility could be that the differences are only visible on higher PCs, but since that data is not displayed, the reader is left wondering. I think it would be very helpful to present a more detailed analysis for some of these "surprising" clustering where the PCA disagrees with the clustering so that suspicions that e.g. low-coverage samples might be separated out more often could be laid to rest.

One way the presentation could be improved would be to be more consistent in what a suitable reference data set is. The PCAs (Fig2, S1 and S2, and Fig6) argue that it makes most sense to present ancient data relative to present-day genetic variation, but the qpWave and qpAdm analysis compare the historic data to that of older populations. Granted, this is a common issue with ancient DNA papers, but the advantage of using a consistent reference data set is that the analyses become directly comparable, and the reader wouldn't have to wonder whether any discrepancies in the two ways of presenting the data are just due to the reference set.

## PCA over time<br /> It is a very interesting observation that the Fst-vs distance curve does not appear to change after the bronze age. However, I wonder if the comparison of the PCA to the projection could be solidified. In particular, it is not obvious to me how to compare Fig 6 B and C, since the data in C is projected onto that in Fig B, and so we are viewing the historic samples in the context of the present-day ones. Thus, to me, this suggests that ancient samples are most closely related to the folks that contribute to present-day people that roughly live in the same geographic location, at least for the middle east, north Africa and the Baltics, the three regions where the projections are well resolved.<br /> Ideally, it would be nice to have independent PCAs (something F-stats based, or using probabilistic PCA or some other framework that allows for missingness). Alternatively, it could be helpful to quantify the similarity and projection error.

Reviewer #2 (Public Review):

Antonio, Weiss, Gao, Sawyer, et al. provide new ancient DNA (aDNA) data for 200 individuals from Europe and the Mediterranean from the historical period, including Iron Age, Late Antiquity, Middle Ages, and early modernity. These data are used to characterize population structure in Europe across time and identify first-generation immigrants (roughly speaking, those who present genetic ancestry that is significantly different from others in the same archaeological site). Authors provide an estimate of an average across regions of >8% of individuals being first-generation immigrants. This observation, coupled with the observed genetic heterogeneity across regions, suggests high mobility of individuals during the historical period in Europe. In spite of that, Principal Component Analysis (PCA) indicates that the overall population structure in Europe has been rather stable in the last 3,000 years, i.e., the levels of genetic differentiation across space have been relatively stable. To understand whether population structure stability is compatible with a large number (>8%) of long-distance immigrants, authors use spatially-explicit Wright-Fisher simulations. They conclude these phenomena are incompatible and provide a thoughtful and convincing explanation for that.

Overall I think this manuscript is very well written and provides an exciting take-home message. The dataset with 200+ novel ancient human genomes will be a great resource for population genetics and paleogenomic studies. Methods are robust and well-detailed. Although the methods used are well-known and standard in the field of paleogenomics, the way the authors use these methods is very creative, insightful, and refreshing. Results provide a comprehensive and novel assessment of historical population genetic structure in Europe, including characterizing genetic heterogeneity within populations and interactions/migration across regions. Conclusions are fully supported by the data.

A few of the strengths of this manuscript are its dataset containing a large number of ancient human genomes, the novel insights about human migration provided by the results, the creative approach to characterize migration and population structure across time using aDNA, and the excellent figures describing research results. I see no major issues with this paper.

eLife assessment

This important study examines the existence of a fear memory engram in acetylcholine neurons of the basal forebrain and seeks to link this to modulation of amygdala for fear expression. Using a combination of techniques including genetic access to cFos expressing neurons, in-vivo chemogenetics, and optical detection of acetylcholine (ACh), the authors present solid evidence that posteriorly-located amygdala projecting basal forebrain cholinergic neurons participate in cue-specific threat learning and memory. This paper will be of interest to those studying circuit-level mechanisms of learning and emotion regulation.

Reviewer #3 (Public Review):

This paper examines the existence of a fear memory engram in acetylcholine neurons of the basal forebrain and seeks to link this to the modulation of the amygdala for fear expression. Using genetically encoded ACh sensors, they show that ACh is released in the basolateral amygdala (BLA) in response to cues that had been paired with aversive shock (CS+) and by shock itself. They then use a cfos activity capture specifically of ACh neurons approach to show that an overlapping population of basal forebrain ACh neurons are activated during learning and recall, that chemogenetically silencing them reduced aversive memory recall, and that these cells have enhanced excitability. Moving on to examining the role of basal forebrain ACh neurons in regulating BLA, the authors show that chemogenetically inhibiting BLA projecting ACh neurons reduces memory recall-induced Fos activity in BLA neurons. Finally, they demonstrate the importance of these cells in producing freezing responses to both learned and innate aversive stimuli, though from different ACh populations.

The identification of specific activity-defined acetylcholine neurons for aversive memory expression as well as the role of basal forebrain ACh neurons in regulating BLA to produce expression of defensive behaviors is important and interesting. However, the paper is missing important control groups and experiments that are necessary to adequately support the authors' claims.

eLife assessment

This is an important study of the mechanism of how binding of the fatty acid myristic acid (MYR) inhibits the activity of the kinase c-Abl, a critical regulator of many cellular processes. While the general aspects of this regulation are known from structure determination and biochemical studies, the exact molecular mechanism and the nature of the allosteric inhibition were not known. The authors use MD simulation to close this gap and provide a detailed mechanistic description of the inhibitory mechanism, although some of the evidence remains incomplete.

Reviewer #1 (Public Review):

The authors used MD simulations to investigate the role of N-terminal myristoylation and the presence of two SH domains on the allosteric regulation of c-Abl kinase. Standard established MD simulation methods and analyses were applied, including the force distribution analysis (FDA) method developed by Grater et al. some time ago.

The system is large and the conformational changes are complicated. In light of this, and aggravated by the fact that direct comparison with - and critical testing against - experimental data is not possible in the present case, I consider the overall simulation times to be rather short (several repeats, but only 500 ns). So there might be statistical convergence issues. Especially also because at least some of the starting structures were generated from available experimental structures after some modifications/modelling, and they might thus be out of equilibrium and need some time to fully relax during the MD simulations.

Unfortunately, I cannot find any convergence tests concerning the length of the simulations, which are usually considered to be standard analyses (Appendix Fig. 5 shows the effect of different thermostats and capping of the peptide chain, but no tests concerning simulation time). This could be critical in the present case, where the authors acknowledge themselves (e.g., on p. 4) that there are only subtle differences between the different simulation systems and the variations within a given system are larger than the relevant (putative) differences between systems (Fig. 1 C, D, E).

Issues with statistical convergence are expected not only for the standard MD simulations but also for the umbrella sampling simulations, as 50 ns sampling per window is nowadays not considered state of the art and is likely insufficient for quantitative binding free energy calculation, especially for membranes (see, e.g., DOI 10.1021/ct200316w). However, worrying about this latter aspect might neither be useful nor needed, because in our view the statement that myristoyl groups can bind to the membrane and that they can compete with binding in the hydrophobic protein pocket can hardly be considered a surprise and would not have required any simulation at all in my view because the experimental K_D values are available (Table 1). The very unfavourable K_d values for unbinding of Myr from both the hydrophobic protein pocket as well as from the membrane in fact show that this is not how it is expected to work in reality. The fully solvated state will be avoided due to its high free energy. Instead, isn't the myristoyl expected to directly transition from the pocket into the membrane, after membrane binding of the kinase in a proper orientation?

Concerning the metadynamics simulations, these are usually done to obtain a free energy landscape. Why was this not attempted here? In the present case, the authors seemed to have used metadynamics only for generating starting structures, with different degrees of helicity of the alpha_I part, for subsequent standard MD simulations. Not surprisingly, nothing much happened during the latter, and conformers with kinked/partially unfolded alpha_I as well as conformers with straight alpha_I were both found to be "stable", at least on the short simulation time scale. It could also not be expected that the SH domain would spontaneously detach in response to helix straightening - again, this would require much longer simulation times than 500 ns. Nevertheless, alpha_I straightening might very well reduce the binding affinity towards SH - this can only be explicitly studied with free energy simulations, however.

Reviewer #2 (Public Review):

The manuscript aims at understanding how the fatty acid ligand MYR inhibits the activity of Abl kinase. Despite a wealth of structural and biochemical data, a key mechanistic understanding of how MYR binding could inactive Abl was missing.

The authors used equilibrium and enhanced molecular dynamics (MD) simulations to masterfully answer open questions left by extensive experimental data in the mechanistic understanding of this system. The authors took advantage of several state-of-the-art simulation techniques and carefully planned simulations to extract a coherent understanding from a wealth of experimental facts.

The manuscript convincingly identifies an allosteric regulation by MYR. Allostery is often a source of confusion and sometimes is used as a magic catch-it-all explanation for poorly understood phenomena. Here, the authors show very compelling evidence of the existence of an allosteric mechanism. Also, they identify the physical origin of the allosteric pathway, providing a clear mechanistic understanding at the residue-level resolution. This is an impressive achievement.

By leaving a pocket in the protein, MYR enables the protein's activation. But MYR is a highly hydrophobic molecule surrounded by water. Where could it go rather than quickly binding back to the protein pocket? By asking this reasonable question, the authors propose an exciting mechanistic hypothesis. The physical proximity of Abl kinase to a cellular membrane could lead to a competition between the protein and the membrane for MYR, leading to a novel layer of regulation for this kinase. Free energy calculations performed by the authors show that this hypothesis is reasonable from the thermodynamic point of view.

From a broader perspective, this manuscript is an important contribution to the discussion of four outstanding topics. 1) myristoylation is an example of lipidation, a post-translational modification where an acyl chain is covalently linked to a protein. The role of post-translational modifications has been greatly underappreciated and investigated in the MD community. However, as all the work on Sars-Cov2 and this contribution show, post-translational modifications can be crucial to understanding function. Ignoring them could lead to severely biased results. 2) the debate on the nature of allostery is still on the rage. Some authors claim that looking for a residue-level mechanistic chain of events that explains the allosteric action does not make sense and that the only way of thinking about allostery is as a sudden global change of the conformational landscape. Here, the authors show that instead, it is possible and leads to an essential understanding. 3) The authors hypothesize a novel crosstalk between the Abl and cellular membranes mediated by MYR. This exciting and far-reaching hypothesis opens the door to new complex layers of regulation. I suspect that these crosstalks between cytosolic proteins, or the soluble domain of membrane-tethered proteins and membranes, are much more ubiquitous than what has been appreciated so far. 4) From a methodological point of view, this manuscript represents a masterful use of simulations to put existing experimental data in a coherent picture. It is an example of the use of MD simulations at its best, where the simulations make sense of experiments, integrate existing data into a unified picture, and lead to new hypotheses that can be tested in future experiments.

It would be superb if the authors could propose precise predictions that could inspire future experiments. Now that they present a residue-resolution allosteric pathway, can they suggest point mutations that would interrupt it?

Author Response

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

Reviewer #1 (Recommendations For The Authors):

• The improvement of the gene annotations of the ferret genome was an important part of this study, and so I would recommend that the authors have a results section and figure dedicated to documenting this.

Thank you so much for appreciating our efforts on improving gene models, which was indeed a critical part in this study. According to the reviewer’s suggestion, we added a new section to the main text, “Improvement of the gene model for scRNA-seq of ferrets” with a figure (Fig.1 C, D, E).

• Are the references to figure S8A, B alright (line 306)? In fact, that entire figure was not well described or out of place. In general, unlike the rest of the manuscript, the section dealing with the human-ferret comparison was a little bit confusing, and the figure legends were not extremely helpful. Could the authors please revisit the main text and figure legends of this section for clarity?

We agree with the reviewer’s recommendation. We removed references to Figure S8A, B. In place of that, we explained the reason more carefully; “We chose a recently published human dataset (Bhaduri et al, 2021) for comparison, because this study containing GW25 dataset which included more tRG cells than previous studies that did not contain GW25 data. Furthermore, we used only data at GW25”

We also revised several parts in this section to understand more easily by additional explanations as well as in the legends of Fig. 7 and Fig. S8.

Reviewer #2 (Recommendations For The Authors):

I have a few very minor comments on the manuscript.

• I would caution the authors against claiming that they have demonstrated bona fide generation of ependymal cells from tRG cells. While the expression of FOXJ1 is a very good indication, they have not demonstrated the morphological transformation of a tRG cell into an ependymal cell.

We agree the reviewer’s opinion. We have never thought that we proved that tRG differentiates ependymal cells, but we consider that this is highly likely the case (We use the term “suggest” in the abstract). To prove this genetically, we extensively tried to knock the EGFP gene into the CRYAB gene by the CRISPR/Cas9 method, to be able to show the lineage relationship between tRG and ependymal cells. However, we have so far failed to do this for a year trial. We also tried to just label tRG with EGFP and follow it in the slice culture.

However, we failed to keep the slice in the culture until we observed the transition from tRG shape to the ependymal shape. It seems to be a slow process. What we could do was to observe the transition from single cilia to multi-cilia, which is part of the morphological transition from epithelial neural stem cells such as Radial Glia to an ependymal-like sheet form. To prove this transition from tRG to ependymal cells (and also astrocytes) is one of the most important issue which needs some new idea, technique or strategy.

• There are several typos throughout the manuscript that I would recommend fixing for example, page 5 line 123 says "OLIGO2" instead of "OLIG2"

Thank you so much. We carefully read and corrected typos. We wish we corrected all of them.

Besides these two points, the manuscript is already prepared to a high standard.

I really appreciate reviewersʼ efforts to finish reviews in a short time, responding to our request related to the first authorʼs thesis application.

Reviewer #1 (Public Review):

In this manuscript, Bilgic et al aim to identify the progenitor types (and their specific progeny) that underlie the expanded nature of gyrencephalic brains. To do this, they take a comparative scRNAseq (single cell transcriptomics) approach between neurodevelopment of the gyrencephalic ferret, and previously published primary human brain and organoid data.

They first improve gene annotations of the ferret genome and then collect a time series of scRNAseq data of 6 stages of the developing ferret brain spanning both embryonic and post-natal development. Among the various cell types they identify are a small proportion of truncated radial glial cells (tRGs), a population known to be enriched in humans and macaques that emerges late in neurogenesis as the RGC scaffold splits into an oRGC that contact the pial surface and a tRG that contacts the ventricular surface. They find that the tRGs consist of three distinct subpopulations two of which are committed to ependymal and astroglial fates.

By integrating these data with publicly available data of developing human brains and human brain organoids they make some important observations. Human and ferret tRGs have very similar transcriptional states, suggesting that the human tRGs too give rise to ependymal and astroglial fates. They also find that the current culture conditions of human brain organoids seem to lack tRGs, something that will need to be addressed if they are to be used to study tRGs. While the primary human data set did contain tRGs, the stage or the region sampled were likely not appropriate, and therefore, the number of cells they could retrieve was low.

The authors have spent considerable efforts in improving gene modeling of the ferret genome, which will be important for the field. They've generated valuable time series data for the developing ferret brain, and have proposed the lineal progeny for the tRGs in the human brain. Whether tRGs actually do give rise to the ependymal and astrogial fates needs to be validated in future studies.

Reviewer #2 (Public Review):

Bilgic et al first explored cellular diversity in the developing cerebral cortex of ferret, honing in on progenitor cell diversity by employing FACS sorting of HES5-positive cells. They have generated a novel single cell transcriptomic dataset capturing the diversity of cells in the developing ferret cerebral cortex, including diverse radial glial and excitatory neuron populations. Unexpectedly, this analysis revealed the presence of CRYAB-positive truncated radial glia previously described only in humans. Using bioinformatic analyses, the investigators proposed that truncated radial glia produce ependymal cells, astrocytes, and to a lesser degree, neurons. Of particular interest to the field, they identify enriched expression of FOXJ1 in late truncated radial glia strongly indicating that towards the end of neurogenesis, these cells likely give rise to ependymal cells. This study represents a major advancement in the field of cortical development and a valuable dataset for future studies of ferret cortical development.

eLife assessment

This is an important study that improves gene models for the ferret genome and identifies neural progenitors that are comparable to those found in developing human brains. The data are convincing and clearly presented. Of particular interest to the field, the work identifies enriched expression of FOXJ1 in late truncated radial glia, strongly indicating that towards the end of neurogenesis, these cells likely give rise to ependymal cells. The work is of interest to anyone studying the development of the nervous system, especially colleagues studying the evolution of development.

Author Response

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

eLife assessment

This is a valuable investigation of the chromatin dynamics throughout the cell cycle by using fluorescence signals and patterns of GFP-PCNA and CY3-dUTP, which labels newly synthesized DNA. The authors report reduced chromatin mobility in S relative to G1 phase. The technology and methods used are solid, but the significance of the work is reduced by the model system employed, the HeLa cell line, which has a greatly abnormal genome.

We have obtained data from a diploid human cell that validates the reduction of S-phase chromatin mobility.

Public Review:

The manuscript presented by Pabba et al. studied chromatin dynamics throughout the cell cycle. The authors used fluorescence signals and patterns of GFP-PCNA (GFP tagged proliferating cell nuclear antigen) and CY3-dUTP (which labels newly synthesized DNA but not the DNA template) to determine cell cycle stages in asynchronized HeLa (Kyoto) cells and track movements of chromatin domains. PCNA binds to replication forks and form replication foci during the S phase. The major conclusions are: (1) Labeled chromatin domains were more mobile in G1/G2 relative to the S-phase. (2) Restricted chromatin motion occurred at sites in proximity to DNA replication sites. (3) Chromatin motion was restricted by the loading of replisomes, independent of DNA synthesis. This work is based on previous work published in 2015, entitled "4D Visualization of replication foci in mammalian cells corresponding to individual replicons," in which the labeling method was demonstrated to be sound. Although interesting, reduced chromatin mobility in S relative to G1 phase is not new to the field.

It was first shown in yeast (Heun et al. 2001; DOI:10.1126/science.1065366) that the S-phase mobility is reduced compared to the G1 phase. This was followed by other papers showing the same in yeast [(Gasser 2002; DOI: 10.1126/science.1067703), (Smith et al. 2019; DOI: 10.1091/mbc.E19-08-0469)]. The relation between chromatin motion and cell cycle progression in the mammalian genome is less studied. Over recent years there have been a few studies that addressed chromatin mobility and cell cycle progression but from a different perspective. In the publication Nozaki et al. (2017; DOI:10.1016/j.molcel.2017.06.018) chromatin motion analysis was performed on single histones. The study did not find a significant change of histone/nucleosome mobility measured during cell cycle progression. Using CRISPR/dCas9 to label random DNA loci, Ma et al. (2019; DOI:10.1083/jcb.201807162) found that chromatin motion in S-phase was significantly lower than in the G1 phase. However, most of the studies measure the chromatin motion using either insertion of ectopic loci or proteins marking the loci (dCas9) or histones. Using either ectopic loci addition or CRISPR/dCas9 might have an effect on the chromatin mobility itself and measuring single histone motion is not equivalent to measuring the motion of DNA segments. We, therefore, opted to label the DNA directly using the replication of the DNA. In this manner we preserve the native chromatin structure and, thus, motion.

Importantly, in addition to measuring decreased DNA motion in S-phase, our study indicates that it is not the DNA synthesis per se but the loading of replisomes onto chromatin that slows down its motion. This allowed us to propose a mechanism on how chromatin motion is affected by DNA replication in S-phase.

The genome in HeLa cells is greatly abnormal with heterogeneous aneuploidy, which makes quantification complicated and weakens the conclusions.

We agree that the HeLa cells are aneuploid and we have addressed the heterogeneity of HeLa Kyoto within our detection methods (for clarification see point 3). To validate our conclusions in normal diploid human cells, we performed the chromatin mobility analysis using human fibroblasts (IMR90 cells in figures 2, 3 and S2) and plotted the MSD curves for different cell cycle stages. The outcome of this analysis showed that the mobility of chromatin in diploid fibroblasts in S-phase is lower than in G1 and G2. In fact, this effect is stronger in IMR90 cells than in HeLa Kyoto cells. Hence, this is not an aneuploid tumor cell phenomenon.

The manuscript is difficult to follow in places due to insufficient clarity. The manuscript should be written in a way that can be understood without referencing previous articles. Overall, the work is moderately impactful to the field.

Major recommendations:

1) In Figure 1B, the illustration and images for S phase are confusing. The author should specify which is early S and which is late S. Do the yellow circles represent GFP-PCNA foci? How did the authors distinguish mid S from early S and late S (in Figure 2)? Are all images in Figure 1 scaled to the same contrast threshold?

The yellow circles correspond to the colocalized signal of GFP-PCNA and Cy3-dUTP that overlap and represent the labeled chromatin sites that are replicated in the next cell cycle.

We clarified all the points mentioned above and updated figure 1 and figure 2 accordingly.

2) In Figure 2B, the y-axis is marked as "Frequency of cells" but the equation listed below is counting DNA (per focus). How to convert DNA (per focus) to DNA (per cell)? The x-axis is marked as "Genome size" without any unit (e.g., kb? Mb?) The x-axis seems to be the C factor, not the genome size.

To determine the amount of DNA present in each labeled DNA focus, we first segmented the whole nucleus and measured the total intensity of DAPI (DNA amount) which is called IDNA TOTAL. Then the labeled replication foci are segmented and the intensity of label present in each segmented foci is measured (IRFi). Throughout the S-phase progression the amount of DNA increases twofold from early to late S-phase. The cells at each cell cycle stage were determined using the PCNA pattern. By plotting the frequency (number of cells) and the relative genome content normalized to the G1 stage we calculated the relative genome size otherwise called cell cycle correction factor for each stage from G1 to G2. The ratio of DNA intensity in labeled replication (IRFi)/ to the total DNA intensity of DAPI (IDNA total) gives the fraction of DNA present in each foci compared to the whole nucleus. This ratio was then multiplied by the genome size (Kbp) of HeLa Kyoto cells which was measured and published in Chagin et al. (2016; DOI:10.1038/ncomms11231). This gives us the approximate amount of DNA present in each labeled replication foci in Kbp. Since the genome duplicates over cell cycle stages, the measured DNA content in IRFi was corrected to the cell cycle stage (determined by PCNA) by multiplying the cell cycle correction factor.

3) HeLa cells are known to be highly heterogeneous and heavily aneuploidy. Cells in one sample have different numbers of chromosomes ranging from 50 - 80. Therefore, GS (genome size) for each cell should not be the same. Using one constant GS in the equation for every cell introduces errors. Has the cell-to-cell variation been considered and corrected in the data? If not, the authors should provide information regarding cell-to-cell variations, such as the intensity variation of nuclear DAPI signals in synchronized cells.

It is true that the HeLa genome is aneuploid. However, the heterogeneity of the genome is true, if one compares different HeLa strains as studied in Frattini et al. (2015; DOI:10.1038/srep15377), where they show the variability of genome and RNA expression profiles and small genomic rearrangements among different HeLa strains. However, to our knowledge, it is not studied extensively or shown whether the heterogeneity and aneuploidy would also be a cell to cell variation. Therefore, we performed a control experiment to verify the variability between HeLa Kyoto cells, where we either synchronized or not and stained with DAPI and the DNA content profiles of all cells were plotted as a histogram (supplementary figure 1B) to show that cell to cell variations is not present and by synchronizing, we see that the cell population in G1, has similar DNA content showing that the cell to cell variability is negligible in our detection methods. Nonetheless, we have obtained data using normal diploid human fibroblasts, which validated our outcome.

STABLE:

Macville, Merryn, et al. "Comprehensive and definitive molecular cytogenetic characterization of HeLa cells by spectral karyotyping." Cancer research 59.1 (1999): 141-150.

UNSTABLE:

Liu, Yansheng, et al. "Multi-omic measurements of heterogeneity in HeLa cells across laboratories." Nature biotechnology 37.3 (2019): 314-322.

Landry, Jonathan JM, et al. "The genomic and transcriptomic landscape of a HeLa cell line." G3: Genes, Genomes, Genetics 3.8 (2013): 1213-1224.

4) The chromatin foci are in a variety of sizes and intensities. How were boundaries of foci determined? Weak foci were picked up in one image but not in another. This is a concern because the size of the chromatin domain could influence mobility measurement. The authors should provide control experiments or better explanations for detecting and selecting chromatin foci.

The method for detecting chromatin foci is described in “Materials and Methods” section “Automated tracking of chromatin structures in time-lapse videos”. “Chromatin structures are detected by the spot-enhancing filter (SEF) (Sage et al., 2005; doi:10.1109/TIP.2005.852787) which consists of a Laplacian-of-Gaussian (LoG) filter followed by thresholding the filtered image and determination of local maxima. The threshold is automatically determined by the mean of the absolute values of the filtered image plus a factor times the standard deviation.” For reasons of consistency, we used the same threshold factor for all images of an image sequence. Therefore, depending on the intensity distribution in an image, it can happen that weak foci are not detected in some images. Alternatively, one could manually adapt the threshold factor for all single images, which, however, would be subjective. We now added the information that we used the same threshold factor for all images of an image sequence.

5) In Figure 3, the authors combined MSD from G1 and G2 in one group. Has any published data suggested that chromatin dynamics are the same in G1 and G2?

To clarify this we separated G1 and G2 mobility measurements in supplementary figure S2 and updated the figures and text accordingly.

6) In Figure 3B, cytoplasmic CY3-dUTP foci are found in the G1/G2 and S images. Are these CY3-dUTP aggregates? If so, are they also found in the nucleus? What is the mobility of the cytoplasmic CY3-dUTP foci?

These are aggregates and not found in the nucleus. These foci were excluded from the analysis by using a nuclear mask based on the PCNA signal. This information was added to the figure 3B legend.

7) In Figure 4, how is colocalization defined? 1.8 um is approximately the size of a chromosome territory, which is much larger than 0.5 Mb. Two foci that are 1.8 um apart should not be considered in the same chromosome.

We agree that colocalized would indeed mean that the signals are overlapping. Therefore, we updated the figures and text as center to center distance or proximity analysis.

Minor comments:

1) Figure 3D should be presented by a box and whisker plot. The histogram does not show an actual distribution of the data.

The histograms shown in figure 3D is the average mean square displacement measurement value for different cell cycle stages. These are the same data shown in the table. Therefore, the histogram is removed and the table in figure 3C is retained.

2) Please explain Figure 3C error bars in the figure legend. Are they SD?

The error bars of the MSD curves (highlighted in bright color around the curves) in figure 3C show the standard error of the mean (SEM) representing the deviations between the MSD curves for an image sequence. We clarified this in the legend of Figure 3C.

3) In Figure 5C, some western blotting results seem to be assembled from replicate experiments. Comparing signals from one experiment with the same background is suggested.

We made sure that the western blots from the same replicates are cropped and the information is also added to the respective figure legends.

eLife assessment

This is a valuable investigation of the chromatin dynamics throughout the cell cycle by using fluorescence signals and patterns of GFP-PCNA and CY3-dUTP, which labels newly synthesized DNA. The authors report reduced chromatin mobility in S relative to G1 phase. The technology and methods used are solid. The data will be of interest to researchers working on chromatin dynamics.

Joint Public Review:

The manuscript presented by Pabba et al. studied chromatin dynamics throughout the cell cycle. The authors used fluorescence signals and patterns of GFP-PCNA (GFP tagged proliferating cell nuclear antigen) and CY3-dUTP (which labels newly synthesized DNA but not the DNA template) to determine cell cycle stages in asynchronized HeLa (Kyoto) cells and track movements of chromatin domains. PCNA binds to replication forks and form replication foci during the S phase. The major conclusions are: (1) Labeled chromatin domains were more mobile in G1/G2 relative to the S-phase. (2) Restricted chromatin motion occurred at sites in proximity to DNA replication sites. (3) Chromatin motion was restricted by the loading of replisomes, independent of DNA synthesis. This work is based on previous work published in 2015, entitled "4D Visualization of replication foci in mammalian cells corresponding to individual replicons," in which the labeling method was demonstrated to be sound.

Comments on latest version: The revised manuscript has included data from a diploid cell line IMR90 (fibroblasts isolated from normal lung tissue) to strengthen the conclusions. Overall, quality of the work is substantially improved.

Author Response

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

We are very grateful to the reviewers for their thorough assessment of our study, and their acknowledgment of its strengths and weaknesses. We did our best below to address the weaknesses raised in their public review, and to comply with their recommendations.

Reviewer #1 (Public Review):

Segas et al. present a novel solution to an upper-limb control problem which is often neglected by academia. The problem the authors are trying to solve is how to control the multiple degrees of freedom of the lower arm to enable grasp in people with transhumeral limb loss. The proposed solution is a neural network based approach which uses information from the position of the arm along with contextual information which defines the position and orientation of the target in space. Experimental work is presented, based on virtual simulations and a telerobotic proof of concept

The strength of this paper is that it proposes a method of control for people with transhumeral limb loss which does not rely upon additional surgical intervention to enable grasping objects in the local environment. A challenge the work faces is that it can be argued that a great many problems in upper limb prosthesis control can be solved given precise knowledge of the object to be grasped, its relative position in 3D space and its orientation. It is difficult to know how directly results obtained in a virtual environment will translate to real world impact. Some of the comparisons made in the paper are to physical systems which attempt to solve the same problem. It is important to note that real world prosthesis control introduces numerous challenges which do not exist in virtual spaces or in teleoperation robotics.

We agree that the precise knowledge of the object to grasp is an issue for real world application, and that real world prosthesis control introduces many challenges not addressed in our experiments. Those were initially discussed in a dedicated section of the discussion (‘Perspectives for daily-life applications’), and we have amended this section to integrate comments by reviewers that relate to those issues (cf below).

The authors claim that the movement times obtained using their virtual system, and a teleoperation proof of concept demonstration, are comparable to natural movement times. The speed of movements obtained and presented are easier to understand by viewing the supplementary materials prior to reading the paper. The position of the upper arm and a given target are used as input to a classifier, which determines the positions of the lower arm, wrist and the end effector. The state of the virtual shoulder in the pick and place task is quite dynamic and includes humeral rotations which would be challenging to engineer in a real physical prosthesis above the elbow. Another question related to the pick and place task used is whether or not there are cases where both the pick position and the place position can be reached via the same, or very similar, shoulder positions? i.e. with the shoulder flexion-extension and abduction-adduction remaining fixed, can the ANN use the remaining five joint angles to solve the movement problem with little to no participant input, simply based on the new target position? If this was the case, movements times in the virtual space would present a very different distribution to natural movements, while the mean values could be similar. The arguments made in the paper could be supported by including individual participant data showing distributions of movement times and the distances travelled by the end effector where real movements are compared to those made by an ANN.

In the proposed approach users control where the hand is in space via the shoulder. The position of the upper arm and a given target are used as input to a classifier, which determines the positions of the lower arm, wrist and the effector. The supplementary materials suggest the output of the classifier occurs instantaneously, in that from the start of the trial the user can explore the 3D space associated with the shoulder in order to reach the object. When the object is reached a visual indicator appears. In a virtual space this feedback will allow rapid exploration of different end effector positions which may contribute to the movement times presented. In a real world application, movement of a distal end-effector via the shoulder is not to be as graceful and a speed accuracy trade off would be necessary to ensure objects are grasped, rather than knocked or moved.

As correctly noted by the reviewer and easily visible on videos, the distal joints predicted by the ANN are realized instantaneously in the virtual arm avatar, and a discontinuity occurs at each target change whereby the distal part of the arm jumps to the novel prediction associated with the new target location. As also correctly noted by the reviewer, there are indeed some instances where minimal shoulder movements are required to reach a new target, which in practice implies that on those instances, the distal part of the arm avatar jumps instantaneously close to the new target as soon as this target appears. Please note that we originally used median rather than mean movement times per participant precisely to remain unaffected by potential outliers that might come from this or other situations. We nevertheless followed the reviewer’s advice and have now also included individual distributions of movement times for each condition and participant (cf Supplementary Fig. 2 to 4 for individual distributions of movement time for Exp1 to 3, respectively). Visual inspection of those indicates that despite slight differences between participants, no specific pattern emerges, with distributions of movement times that are quite similar between conditions when data from all participants are pooled together.

Movement times analysis indicates therefore that the overall participants’ behavior has not been impacted by the instantaneous jump in the predicted arm positions at each of the target changes. Yet, those jumps indicate that our proposed solution does not satisfactorily reproduce movement trajectory, which has implications for application in the physical world. Although we introduced a 0.75 s period before the beginning of each trial for the robotic arm to smoothly reach the first prediction from the ANN in our POC experiment (cf Methods), this would not be practical for a real-life scenario with a sequence of movements toward different goals. Future developments are therefore needed to better account for movement trajectories. We are now addressing this explicitly in the manuscript, with the following paragraph added in the discussion (section ‘Perspectives of daily-life applications’):

“Although our approach enabled participants to converge to the correct position and orientation to grasp simple objects with movement times similar to those of natural movements, it is important to note that further developments are needed to produce natural trajectories compatible with real-world applications. As easily visible on supplementary videos 2 to 4, the distal joints predicted by the ANN are realized instantaneously such that a discontinuity occurs at each target change, whereby the distal part of the arm jumps to the novel prediction associated with the new target location. We circumvented problems associated with this discontinuity on our physical proof of concept by introducing a period before the beginning of each trial for the robotic arm to smoothly reach the first prediction from the ANN. This issue, however, needs to be better handled for real-life scenarios where a user will perform sequences of movements toward different objects.”

Another aspect of the movement times presented which is of note, although it is not necessarily incorrect, is that the virtual prosthesis performance is close too perfect. In that, at the start of each trial period, either pick or place, the ANN appears to have already selected the position of the five joints it controls, leaving the user to position the upper arm such that the end effector reaches the target. This type of classification is achievable given a single object type to grasp and a limited number of orientations, however scaling this approach to work robustly in a real world environment will necessitate solving a number of challenges in machine learning and in particular computer vision which are not trivial in nature. On this topic, it is also important to note that, while very elegant, the teleoperation proof of concept of movement based control does not seem to feature a similar range of object distance from the user as the virtual environment. This would have been interesting to see and I look forward to seeing further real world demonstrations in the authors future work.

According to this comment, the reviewer has the impression that the ANN had already selected a position of the five joints it controls at the start of each trial, and maintained those fixed while the user operates the upper arm so as to reach the target. Although the jumps at target changes discussed in the previous comment might give this impression, and although this would be the case should we have used an ANN trained with contextual information only, it is important to stress that our control does take shoulder angles as inputs, and produced therefore changes in the predicted distal angles as the shoulder moves.

To substantiate this, we provide in Author response image 1 the range of motion (angular difference at each joint between the beginning and the end of each trial) of the five distal arm angles, regrouped for all angles and trials of Exp1 to 3 (one circle and line per participant, representing the median of all data obtained by that participant in the given experiment and condition, as in Fig. 3 of the manuscript). Please note that those ranges of motion were computed on each trial just after the target changes (i.e., after the jumps) for conditions with prosthesis control, and that the percentage noted on the figure below those conditions correspond to the proportion of the range of motion obtained in the natural movement condition. As can be seen, distal angles were solicited in all prosthesis control conditions by more than half the amount they moved in the condition of natural movements (between 54 and 75% depending on conditions).

Author response image 1.

With respect to the last part of this comment, we agree that scaling this approach to work robustly in a real world environment will necessitate solving a number of challenges in machine learning and in particular computer vision. We address those in a specific section of the discussion (‘Perspectives for daily-life application’) which has been further amended in response to the reviewers’ comments. As also mentioned earlier and at the occasion of our reply to other reviewers’ comments, we also agree that our physical proof of concept is quite preliminary, and we are looking forward to conduct future work in order to solve some of the issues discussed and get closer to real world demonstrations.

Reviewer #2 (Public Review):

Segas et al motivate their work by indicating that none of the existing myoelectric solution for people with transhumeral limb difference offer four active degrees of freedom, namely forearm flexion/extension, forearm supination/pronation, wrist flexion/extension, and wrist radial/ulnar deviation. These degrees of freedom are essential for positioning the prosthesis in the correct plan in the space before a grasp can be selected. They offer a controller based on the movement of the stump.

The proposed solution is elegant for what it is trying to achieve in a laboratory setting. Using a simple neural network to estimate the arm position is an interesting approach, despite the limitations/challenges that the approach suffers from, namely, the availability of prosthetic hardware that offers such functionality, information about the target and the noise in estimation if computer vision methods are used. Segas et al indicate these challenges in the manuscript, although they could also briefly discuss how they foresee the method could be expanded to enable a grasp command beyond the proximity between the end-point and the target. Indeed, it would be interesting to see how these methods can be generalise to more than one grasp.

Indeed, we have already indicated those challenges in the manuscript, including the limitation that our control “is suitable to place the hand at a correct position and orientation to grasp objects in a wide workspace, but not for fine hand and grasp control ...” (cf 4th paragraph of the ‘Perspectives for daily-life applications’ section of the discussion). We have nevertheless added the following sentence at the end of this paragraph to stress that our control could be combined with recently documented solutions for multiple grasp functions: “Our movement-based approach could also be combined with semi-autonomous grasp control to accommodate for multiple grasp functions39,42,44.”

One bit of the results that is missing in the paper is the results during the familiarisation block. If the methods in "intuitive" I would have thought no familiarisation would be needed. Do participants show any sign of motor adaptation during the familiarisation block?

Please note that the familiarization block indicated Fig. 3a contains approximately half of the trials of the subsequent initial acquisition block (about 150 trials, which represents about 3 minutes of practice once the task is understood and proficiently executed), and that those were designed to familiarize participants with the VR setup and the task rather than with the prosthesis controls. Indeed, it is important that participants were made familiar with the setup and the task before they started the initial acquisition used to collect their natural movements. In Exp1 and 2, there was therefore no familiarization to the prosthesis controls whatsoever (and thus no possible adaptation associated with it) before participants used them for the very first time in the blocks dedicated to test them. This is slightly different in Exp3, where participants with an amputated arm were first tested on their amputated side with our generic control. Although slight adaptation to the prosthesis control might indeed have occurred during those familiarization trials, this would be difficult in practice to separate from the intended familiarization to the task itself, which was deemed necessary for that experiment as well. In the end, we believe that this had little impact on our data since that experiment produced behavioral results comparable to those of Exp1 and 2, where no familiarization to the prosthesis controls could have occurred.

In Supplementary Videos 3 and 4, how would the authors explain the jerky movement of the virtual arm while the stump is stationary? How would be possible to distinguish the relative importance of the target information versus body posture in the estimation of the arm position? This does not seem to be easy/clear to address beyond looking at the weights in the neural network.

As discussed in our response to Reviewer1 and now explicitly addressed in the manuscript, there is a discontinuity in our control, whereby the distal joints of the arm avatar jumps instantaneously to the new prediction at each target change at the beginning of a trial, before being updated online as a function of ongoing shoulder movements for the rest of that trial. In a sense, this discontinuity directly reflects the influence of the target information in the estimation of the distal arm posture. Yet, as also discussed in our reply to R1, the influence of proximal body posture (i.e., Shoulder movements) is made evident by substantial movements of the predicted distal joints after the initial jumps occurring at each target change. Although those features demonstrate that both target information and proximal body posture were involved in our control, they do not establish their relative importance. While offline computation could be thought to quantify their relative implication in the estimation of the distal arm posture, we believe that further human-in-the-loop experiments with selective manipulation of this implication would be necessary to establish how this might affect the system controllability.

I am intrigued by how the Generic ANN model has been trained, i.e. with the use of the forward kinematics to remap the measurement. I would have taught an easier approach would have been to create an Own model with the native arm of the person with the limb loss, as all your participants are unilateral (as per Table 1). Alternatively, one would have assumed that your common model from all participants would just need to be 'recalibrated' to a few examples of the data from people with limb difference, i.e. few shot calibration methods.

AR: Although we could indeed have created an Own model with the native arm of each participant with a limb loss, the intention was to design a control that would involve minimal to no data acquisition at all, and more importantly, that could also accommodate bilateral limb loss. Indeed, few shot calibration methods would be a good alternative involving minimal data acquisition, but this would not work on participants with bilateral limb loss.

Reviewer #3 (Public Review):

This work provides a new approach to simultaneously control elbow and wrist degrees of freedom using movement based inputs, and demonstrate performance in a virtual reality environment. The work is also demonstrated using a proof-of-concept physical system. This control algorithm is in contrast to prior approaches which electrophysiological signals, such as EMG, which do have limitations as described by the authors. In this work, the movements of proximal joints (eg shoulder), which generally remain under voluntary control after limb amputation, are used as input to neural networks to predict limb orientation. The results are tested by several participants within a virtual environment, and preliminary demonstrated using a physical device, albeit without it being physically attached to the user.

Strengths:

Overall, the work has several interesting aspects. Perhaps the most interesting aspect of the work is that the approach worked well without requiring user calibration, meaning that users could use pre-trained networks to complete the tasks as requested. This could provide important benefits, and if successfully incorporated into a physical prosthesis allow the user to focus on completing functional tasks immediately. The work was also tested with a reasonable number of subjects, including those with limb-loss. Even with the limitations (see below) the approach could be used to help complete meaningful functional activities of daily living that require semi-consistent movements, such as feeding and grooming.

Weaknesses:

While interesting, the work does have several limitations. In this reviewer's opinion, main limitations are: the number of 'movements' or tasks that would be required to train a controller that generalized across more tasks and limbpostures. The authors did a nice job spanning the workspace, but the unconstrained nature of reaches could make restoring additional activities problematic. This remains to be tested.

We agree and have partly addressed this in the first paragraph of the ‘Perspective for daily life applications’ section of the discussion, where we expand on control options that might complement our approach in order to deal with an object after it has been reached. We have now amended this section to explicitly stress that generalization to multiple tasks including more constrained reaches will require future work: “It remains that generalizing our approach to multiple tasks including more constrained reaches will require future work. For instance, once an intended object has been successfully reached or grasped, what to do with it will still require more than computer vision and gaze information to be efficiently controlled. One approach is to complement the control scheme with subsidiary movements, such as shoulder elevation to bring the hand closer to the body or sternoclavicular protraction to control hand closing26, or even movement of a different limb (e.g., a foot45). Another approach is to control the prosthesis with body movements naturally occurring when compensating for an improperly controlled prosthesis configuration46.”

The weight of a device attached to a user will impact the shoulder movements that can be reliably generated. Testing with a physical prosthesis will need to ensure that the full desired workspace can be obtained when the limb is attached, and if not, then a procedure to scale inputs will need to be refined.

We agree and have now explicitly included this limitation and perspective to our discussion, by adding a sentence when discussing possible combination with osseointegration: “Combining those with osseointegration at humeral level3,4 would be particularly relevant as this would also restore amplitude and control over shoulder movements, which are essential for our control but greatly affected with conventional residual limb fitting harness and sockets. Yet, testing with a physical prosthesis will need to ensure that the full desired workspace can be obtained with the weight of the attached device, and if not, a procedure to scale inputs will need to be refined.”

The reliance on target position is a complicating factor in deploying this technology. It would be interesting to see what performance may be achieved by simply using the input target positions to the controller and exclude the joint angles from the tracking devices (eg train with the target positions as input to the network to predict the desired angles).

Indeed, the reliance on precise pose estimation from computer vision is a complicating factor in deploying this technology, despite progress in this area which we now discuss in the first paragraph of the ‘Perspective for daily life applications’ section of the discussion. Although we are unsure what precise configuration of input/output the reviewer has in mind, part of our future work along this line is indeed explicitly dedicated to explore various sets of input/output that could enable coping with availability and reliability issues associated with real-life settings.

Treating the humeral rotation degree of freedom is tricky, but for some subjects, such as those with OI, this would not be as large of an issue. Otherwise, the device would be constructed that allowed this movement.

We partly address this when referring to osseointegration in the discussion: “Combining those with osseointegration at humeral level3,4 would be particularly relevant as this would also restore amplitude and control over shoulder movements, which are essential for our control but greatly affected with conventional residual limb fitting harness and sockets.” Yet, despite the fact that our approach proved efficient in reconstructing the required humeral angle, it is true that realizing it on a prosthesis without OI is an open issue.

Overall, this is an interesting preliminary study with some interesting aspects. Care must be taken to systematically evaluate the method to ensure clinical impact.

Reviewer #1 (Recommendations For The Authors):

Page 2: Sentence beginning: "Here, we unleash this movement-based approach by ...". The approach presented utilises 3D information of object position. Please could the authors clarify whether or not the computer vision references listed are able to provide precise 3D localisation of objects?

While the references initially cited in this sentence do support the view that movement goals could be made available in the context of prosthesis control through computer vision combined with gaze information, it is true that they do not provide the precise position and orientation (I.e., 6d pose estimation) necessary for our movementbased control approach. Six-dimensional object pose estimation is nevertheless a very active area of computer vision that has applications beyond prosthesis control, and we have now added to this sentence two references illustrating recent progress in this research area (cf. references 30 and 31).

Page 6: Sentence beginning: "The volume spread by the shoulder's trajectory ...".

• Page 7: Sentence beginning: "With respect to the volume spread by the shoulder during the Test phases ...".

• Page 7: Sentence beginning: "Movement times with our movement-based control were also in the same range as in previous experiments, and were even smaller by the second block of intuitive control ...".

On the shoulder volume presented in Figure 3d. My interpretation of the increased shoulder volume in Figure 3D Expt 2 shown in the Generic ANN was that slightly more exploration of the upper arm space was necessary (as related to the point in the public review). Is this what the authors mean by the action not being as intuitive? Does the reduction in movement time between TestGeneric1 and TestGeneric 2 not suggest that some degree of exploration and learning of the solution space is taking place?

Indeed, the slightly increased shoulder volume with the Generic ANN in Exp2 could be interpreted as a sign that slightly more exploration of the upper arm space was necessary. At present, we do not relate this to intuitiveness in the manuscript. And yes, we agree that the reduction in movement time between TestGeneric1 and TestGeneric 2 could suggest some degree of exploration and learning.

Page 7: Sentence beginning: "As we now dispose of an intuitive control ...". I think dispose may be a false friend in this context!

This has been replaced by “As we now have an intuitive control…”.

Page 8: Section beginning "Physical Proof of Concept on a tele-operated robotic platform". I assume this section has been added based on suggestions from a previous review. Although an elegant PoC the task presented in the diagram appears to differ from the virtual task in that all the targets are at a relatively fixed distance from the robot. In respect to the computer vision ML requirements, this does not appear to require precise information about the distance between the user and an object. Please could this be clarified?

Indeed, the Physical Proof of Concept has been added after the original submission in order to comply with requests formulated at the editorial stage for the paper to be sent for review. Although preliminary and suffering from several limitations (amongst which a reduced workspace and number of trials as compared to the VR experiments), this POC is a first step toward realizing this control in the physical world. Please note that as indicated in the methods, the target varied in depth by about 10 cm, and their position and orientation were set with sensors at the beginning of each block instead of being determined from computer vision (cf section ‘Physical Proof of Concept’ in the ‘Methods’: “The position and orientation of each sponge were set at the beginning of each block using a supplementary sensor. Targets could be vertical or tilted at 45 and -45° on the frontal plane, and varied in depth by about 10 cm.”).

Page 10: Sentence beginning: "This is ahead of other control solutions that have been proposed ...". I am not sure what this sentence is supposed to convey and no references are provided. While the methods presented appear to be a viable solution for a group of upper-limb amputees who are often ignored by academic research, I am not sure it is appropriate for the authors to compare the results obtained in VR and via teleoperation to existing physical systems (without references it is difficult to understand what comparison is being made here).

The primary purpose of this sentence is to convey that our approach is ahead of other control solutions proposed so far to solve the particular problem as defined earlier in this paragraph (“Yet, controlling the numerous joints of a prosthetic arm necessary to place the hand at a correct position and orientation to grasp objects remains challenging, and is essentially unresolved”), and as documented to the best we could in the introduction. We believe this to be true and to be the main justification for this publication. The reviewer’s comment is probably directed toward the second part of this sentence, which states that performances of previously proposed control solutions (whether physical or in VR) are rarely compared to that of natural movements, as this comparison would be quite unfavorable to them. We soften that statement by removing the last reference to unfavorable comparison, but maintained it as we believe it is reflecting a reality that is worth mentioning. Please note that after this initial paragraph, and an exposition of the critical features of our control, most of the discussion (about 2/3) is dedicated to limitations and perspectives for daily-life application.

Page 10: Sentence: "Here, we overcame all those limitations." Again, the language here appears to directly compare success in a virtual environment with the current state of the art of physical systems. Although the limitations were realised in a virtual environment and a teleoperation PoC, a physical implementation of the proposed system would depend on advances in machine vision to include movement goal. It could be argued that limitations have been traded, rather immediately overcome.

In this sentence, “all those limitations” refers to all three limitations mentioned in the previous sentences in relation to our previous study which we cited in that sentence (Mick et al., JNER 2021), rather than to limitations of the current state of the art of physical systems. To make this more explicit, we have now changed this sentence to “Here, we overcome those three limitations”.

Page 11: Sentence beginning: "Yet, impressive progresses in artificial intelligence and computer vision ...".

• Page 11: Sentence beginning: "Prosthesis control strategies based on computer vision ..."

The science behind self-driving cars is arguably of comparable computational complexity to the real-world object detection and with concurrent real-time grasp selection. The market for self-driving cars is huge and a great deal of R&D has been funded, yet they are not yet available. The market for advanced upper-limb prosthetics is very small, it is difficult to understand who would deliver this work.

We agree that the market for self-driving cars is much higher than that for advanced upper-limb prosthetics. Yet, as mentioned in our reply to a previous comment, 6D object pose estimation is a very active area of computer vision that has applications far beyond prosthesis control (cf. in robotics and augmented reality). We have added two references reflecting recent progress in this area in the introduction, and have amended the discussion accordingly: “Yet, impressive progress in artificial intelligence and computer vision is such that what would have been difficult to imagine a decade ago appears now well within grasp38. For instance, we showed recently that deep learning combined with gaze information enables identifying an object that is about to be grasped from an egocentric view on glasses33, and this even in complex cluttered natural environments34. Six-dimensional object pose estimation is also a very active area of computer vision30,31, and prosthesis control strategies based on computer vision combined with gaze and/or myoelectric control for movement intention detection are quickly developing39–44, illustrating the promises of this approach.”

Page 15: Sentence beginning: "From this recording, 7 signals were extracted and fed to the ANN as inputs: ...".

• Page 15: Sentence beginning: "Accordingly, the contextual information provided as input corresponded to the ...".

The two sentences appear to contradict one another and it is difficult to understand what the Own ANN was trained on. If the position and the orientation of the object were not used due to overfitting, why claim that they were used as contextual information? Training on the position and orientation of the hand when solving the problem would not normally be considered contextual information, the hand is not part of the environment or setting, it is part of the user. Please could this section be made a little bit clearer?

The Own ANN was trained using the position and the orientation of a hypothetic target located within the hand at any given time. This approach has been implemented to increase the amount of available data. However, when the ANN is utilized to predict the distal part of the virtual arm, the position and orientation of the current target are provided. We acknowledge that the phrasing could be misleading, so we have added the following clarification to the first sentence: "… (3 Cartesian coordinates and 2 spherical angles that define the position and orientation of the hand as if a hypothetical cylindrical target was placed in it at any time, see an explanation for this choice in the next paragraph)".

Page 16: Sentence beginning: "A trial refers to only one part of this process: either ...". Would be possible to present these values separately?

Although it would be possible to present our results separately for the pick phase and for the place phase, we believe that this would overload the manuscript for little to no gain. Indeed, nothing differentiates those two phases other than the fact that the bottle is on the platform (waiting to be picked) in the pick phase, and in the hand (waiting to be placed) in the place phase. We therefore expect to have very similar results for the pick phase and for the place phase, which we verified as follows on Movement Time: Author response image 2 shows movement time results separated for the pick phase (a) and for the place phase (b), together with the median (red dotted line) obtained when results from both phases are polled together. As illustrated, results are very similar for both phases, and similar to those currently presented in the manuscript with both phases pooled (Fig3C).

Author response image 2.

Page 19: Sentence beginning "The remaining targets spanned a roughly ...". Figure 2 is a very nice diagram but it could be enhanced with a simple visual representation of this hemispherical region on the vertical and horizontal planes.

We made a few attempts at enhancing this figure as suggested. However, the resulting figures tended to be overloaded and were not conclusive, so we opted to keep the original.

Page 19: Sentence beginning "The Movement Time (MT) ..."

• Page 19: Sentence beginning "The shoulder position Spread Volume (SV) ..." Would it be possible to include a traditional timing protocol somewhere in the manuscript so that readers can see the periods over which these measures calculated?

We have now included Fig. 5 to illustrate the timing protocol and the periods over which MT and SV were computed.

Reviewer #2 (Recommendations For The Authors):

Minor comments

Page 6: "Yet, this control is inapplicable "as is" to amputees, for which recording ..." -> "Yet, this control is inapplicable "as is" to amputees, for WHOM recording ... "

This has been modified as indicated.

Throughout: "amputee" -> "people with limb loss" also "individual with limb deficiency" -> "individual with limb difference"

We have modified throughout as indicated.

It would have been great to see a few videos from the tele-operation as well. Please could you supply these videos?

Although we agree that videos of our Physical Proof of Concept would have been useful, we unfortunately did not collect videos that would be suitable for this purpose during those experimental phases. Please note that this Physical Proof of Concept was not meant to be published originally, but has been added after the original submission in order to comply with requests formulated at the editorial stage for the paper to be sent for review.

Reviewer #3 (Recommendations For The Authors):

Consider using the terms: intact-limb rather than able-bodied, residual limb rather than stump, congenital limb different rather than congenital limb deficiency.

We have modified throughout as indicated.

Reviewer #1 (Public Review):

Segas et al. present a novel solution to an upper-limb control problem which is often neglected by academia. The problem the authors are trying to solve is how to control the multiple degrees of freedom of the lower arm to enable grasp in people with transhumeral limb loss. The proposed solution is a neural network based approach which uses information from the position of the arm along with contextual information which defines the position and orientation of the target in space. Experimental work is presented, based on virtual simulations and a telerobotic proof of concept.

The strength of this paper is that it proposes a method of control for people with transhumeral limb loss which does not rely upon additional surgical intervention to enable grasping objects in the local environment. A challenge the work faces is that it can be argued that a great many problems in upper limb prosthesis control can be solved given precise knowledge of the object to be grasped, its relative position in 3D space and its orientation. It is difficult to know how directly results obtained in a virtual environment will translate to real world impact. Some of the comparisons made in the paper are to physical systems which attempt to solve the same problem. It is important to note that real world prosthesis control introduces numerous challenges which do not exist in virtual spaces or in teleoperation robotics.

The authors claim that the movement times obtained using their virtual system, and a teleoperation proof of concept demonstration, are comparable to natural movement times. The speed of movements obtained and presented are easier to understand by viewing the supplementary materials prior to reading the paper. The position of the upper arm and a given target are used as input to a classifier, which determines the positions of the lower arm, wrist and the end effector. The state of the virtual shoulder in the pick and place task is quite dynamic and includes humeral rotations which would be challenging to engineer in a real physical prosthesis above the elbow. Another question related to the pick and place task used is whether or not there are cases where both the pick position and the place position can be reached via the same, or very similar, shoulder positions? i.e. with the shoulder flexion-extension and abduction-adduction remaining fixed, can the ANN use the remaining five joint angles to solve the movement problem with little to no participant input, simply based on the new target position? If this was the case, movements times in the virtual space would present a very different distribution to natural movements, while the mean values could be similar. The arguments made in the paper could be supported by including individual participant data showing distributions of movement times and the distances travelled by the end effector where real movements are compared to those made by an ANN.

In the proposed approach users control where the hand is in space via the shoulder. The position of the upper arm and a given target are used as input to a classifier, which determines the positions of the lower arm, wrist and the effector. The supplementary materials suggest the output of the classifier occurs instantaneously, in that from the start of the trial the user can explore the 3D space associated with the shoulder in order to reach the object. When the object is reached a visual indicator appears. In a virtual space this feedback will allow rapid exploration of different end effector positions which may contribute to the movement times presented. In a real world application, movement of a distal end-effector via the shoulder is not to be as graceful and a speed accuracy trade off would be necessary to ensure objects are grasped, rather than knocked or moved.

Another aspect of the movement times presented which is of note, although it is not necessarily incorrect, is that the virtual prosthesis performance is close too perfect. In that, at the start of each trial period, either pick or place, the ANN appears to have already selected the position of the five joints it controls, leaving the user to position the upper arm such that the end effector reaches the target. This type of classification is achievable given a single object type to grasp and a limited number of orientations, however scaling this approach to work robustly in a real world environment will necessitate solving a number of challenges in machine learning and in particular computer vision which are not trivial in nature. On this topic, it is also important to note that, while very elegant, the teleoperation proof of concept of movement based control does not seem to feature a similar range of object distance from the user as the virtual environment. This would have been interesting to see and I look forward to seeing further real world demonstrations in the authors future work.

Reviewer #2 (Public Review):

Segas et al motivate their work by indicating that none of the existing myoelectric solution for people with trans-humeral limb difference offer four active degrees of freedom, namely forearm flexion/extension, forearm supination/pronation, wrist flexion/extension, and wrist radial/ulnar deviation. These degrees of freedom are essential for positioning the prosthesis in the correct plan in the space before a grasp can be selected. They offer a controller based on the movement of the stump.

The proposed solution is elegant for what it is trying to achieve in a laboratory setting. Using a simple neural network to estimate the arm position is an interesting approach, despite the limitations/challenges that the approach suffers from, namely, the availability of prosthetic hardware that offers such functionality, information about the target and the noise in estimation if computer vision methods are used. Segas et al indicate these challenges in the manuscript, although they could also briefly discuss how they foresee the method could be expanded to enable a grasp command beyond the proximity between the end-point and the target. Indeed, it would be interesting to see how these methods can be generalise to more than one grasp.

One bit of the results that is missing in the paper is the results during the familiarisation block. If the methods in "intuitive" I would have thought no familiarisation would be needed. Do participants show any sign of motor adaptation during the familiarisation block?

In Supplementary Videos 3 and 4, how would the authors explain the jerky movement of the virtual arm while the stump is stationary? How would be possible to distinguish the relative importance of the target information versus body posture in the estimation of the arm position? This does not seem to be easy/clear to address beyond looking at the weights in the neural network.

I am intrigued by how the Generic ANN model has been trained, i.e. with the use of the forward kinematics to remap the measurement. I would have taught an easier approach would have been to create an Own model with the native arm of the person with the limb loss, as all your participants are unilateral (as per Table 1). Alternatively, one would have assumed that your common model from all participants would just need to be 'recalibrated' to a few examples of the data from people with limb difference, i.e. few shot calibration methods.

Reviewer #3 (Public Review):

This work provides a new approach to simultaneously control elbow and wrist degrees of freedom using movement based inputs, and demonstrate performance in a virtual reality environment. The work is also demonstrated using a proof-of-concept physical system. This control algorithm is in contrast to prior approaches which electrophysiological signals, such as EMG, which do have limitations as described by the authors. In this work, the movements of proximal joints (eg shoulder), which generally remain under voluntary control after limb amputation, are used as input to neural networks to predict limb orientation. The results are tested by several participants within a virtual environment, and preliminary demonstrated using a physical device, albeit without it being physically attached to the user.

Strengths:<br /> Overall, the work has several interesting aspects. Perhaps the most interesting aspect of the work is that the approach worked well without requiring user calibration, meaning that users could use pre-trained networks to complete the tasks as requested. This could provide important benefits, and if successfully incorporated into a physical prosthesis allow the user to focus on completing functional tasks immediately. The work was also tested with a reasonable number of subjects, including those with limb-loss. Even with the limitations (see below) the approach could be used to help complete meaningful functional activities of daily living that require semi-consistent movements, such as feeding and grooming.

Weaknesses:<br /> While interesting, the work does have several limitations. In this reviewer's opinion, main limitations are: the number of 'movements' or tasks that would be required to train a controller that generalized across more tasks and limb-postures. The authors did a nice job spanning the workspace, but the unconstrained nature of reaches could make restoring additional activities problematic. This remains to be tested.

The weight of a device attached to a user will impact the shoulder movements that can be reliably generated. Testing with a physical prosthesis will need to ensure that the full desired workspace can be obtained when the limb is attached, and if not, then a procedure to scale inputs will need to be refined.

The reliance on target position is a complicating factor in deploying this technology. It would be interesting to see what performance may be achieved by simply using the input target positions to the controller and exclude the joint angles from the tracking devices (eg train with the target positions as input to the network to predict the desired angles).

Treating the humeral rotation degree of freedom is tricky, but for some subjects, such as those with OI, this would not be as large of an issue. Otherwise, the device would be constructed that allowed this movement.

Overall, this is an interesting preliminary study with some interesting aspects. Care must be taken to systematically evaluate the method to ensure clinical impact.

Author Response

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

REVIEWER #1:

The authors present a carefully controlled set of experiments that demonstrate an additional complexity for GPCR signaling in that endosomal signaling make be different when b-arrestin is or isn't associated with a G protein-bound V2R vasopressin receptor. It uses state of the art biosensorbased approaches and b-arrestin KO lines to assess this. It adds to a growing body of evidence that G proteins and b-arrestin can associate with GPCR complexes simultaneously. They also demonstrate the possibility that Gaq might also be activated by the V2R receptor. My sense is one thing they may need to be considered is the possibility of such "megacomplexes" might actually involve receptor dimers or oligomers.

1.1 Can the authors please review the data that describes the concept of "GPCR megacomplexes"? I feel this is missing from the introduction. The notion means different things to different people. As you will see from my other comments, you should especially focus on evidence at the level of the single receptor.

We appreciate the reviewer’s comments and have now included a more wholesome description of the GPCR megacomplex, or ‘megaplex’, concept in the introduction (page 2, 1st paragraph).

1.2 The authors use mini-G proteins to conclude that V2R receptors interact with Gaq (in addition to Gas). I would prefer if there were a more direct measure of this. Can the authors show that the receptor interacts with full length Gaq (and not the other G proteins in Figure)? Is there a signaling phenotype associated with Gaq coupling? Is it sensitive to Gaq inhibition?

Excellent point and we are happy to expand further on this. The ability of the V2R to activate Gq/11 has already been demonstrated before (Zhu, X. et al. Mol Pharmacol 46(3):460-9 (1994); Lykke, K. et al. Physiol Rep. 3(8):e12519 (2015); Avet, C. et al. eLife 11: e74101 (2022); Heydenreich, F.M. et al. Mol Pharmacol 102(3):139-49 (2022). Therefore, we did not attempt to document this activation using more traditional assays. On the other hand, to demonstrate an interaction between V2R and Ga subunit in cells is challenging for several reasons. First, the full-length Ga subunit is already located at the plasma membrane at basal state, and thus, generates high background signals in proximity assays. Second, upon receptor activation, the Ga subunit interaction with V2R is so transient that it is difficult, if not impossible, to catch this transient moment in a proximity assay. Although the miniG proteins are highly engineered, coupling specificity of the different subtypes (Gas, Gai/o, Gaq/11, and Ga12/13) to GPCRs is maintained. In addition, as they are homogenously expressed in the cytosol under basal states rather than at the membrane, they generate low background noise. Upon agonist stimulation, miniG proteins are recruited from the cytosol to the V2R at the plasma membrane, resulting in a robust signal in proximity assays. Thus, miniG proteins are unique in that they can actually detect GPCR–G protein interactions in cellular proximity assays, which is very challenging using full-length Ga subunits.

That being said, we fully understand the reviewer’s concern and greatly value the effort in enhancing robustness of our study. Therefore, we have now monitored downstream signaling events of Gaq/11 in the absence or presence of the selective Gaq/11 inhibitor YM-254890 as a secondary method of documenting Gaq/11 activity. Specifically, we used a newly developed biosensor to measure diacylglycerol (DAG) production, a downstream second messenger of Gaq/11 activation, at both the plasma membrane and endosomes. Using a second biosensor, we detect general protein kinase C (PKC) activation, which is another downstream signaling event of Gaq/11 activation. Together, we demonstrated that AVP-stimulation leads to DAG production at both the plasma membrane and endosomes (Fig. 1C-D) as well as PKC activation (Fig. 1E), which all are sensitive to YM-254890 inhibition (Fig. 1C-D and E). Together these results rigorously suggest that the V2R interacts with and activates Gaq/11.

1.3 I raise a similar concern with Gaq coupling in endosomes.

For similar reasons that miniG proteins are excellent tools for demonstrating V2R interaction with G proteins at the plasma membrane, miniG proteins can also be used to detect V2R interaction with G proteins at endosomes by measuring proximity between miniG and an endosomal marker in response to agonist challenge. However, to ensure that the endosomal recruitment of miniGsq to the V2R demonstrated in our study corresponds to endosomal Gaq/11 activation, we monitored the production of DAG at the early endosomes in a similar way to which we detected DAG production at the plasma membrane. As shown in Fig. 1D, stimulation of V2R with AVP induces recruitment of the DAG-binding biosensor to the early endosomal marker Rab5. Pre-treatment of the cells with the selective Gaq/11 inhibitor YM-254890 abrogated this response, confirming that V2R activation leads to production of DAG at the early endosomes in a Gaq/11-dependent manner (Fig. 1D).

1.4 Can the confocal data be shown for Gai and Ga12?

Yes, we can certainly show this data as negative control. We have now included the confocal data using Halo-mGsi as a negative control for confocal microscopy (Fig. 2). As seen on this figure, mGsi does not colocalize with Lck (plasma membrane), nor with EEA1 (early endosomes) upon stimulation of cells with AVP in line with a receptor that does not couple to Gai/o.

We did not include data using Halo-mG12, as this G protein subtype, similar to Gi/o, does not couple functionally to V2R. Therefore, it is highly unlikely we would obtain different results from the experiments using Halo-mGsi.

1.5 The authors want us to believe that there is simultaneous binding of G proteins and b-arrestin. This is never demonstrated and is at odds with the structural basis of G protein and b-arrestin binding. Have the authors considered that "simultaneous" occupancy might simply reflect binding at distinct GPCR monomers in the context of dimeric or oligomeric receptors? They could I suppose provide data at the level of a single receptor rather than using the bulk BRET approaches used.

We appreciate the comment and opportunity to highlight some of our previous work, which address the megacomplexes at the level of a single receptor. First, we have characterized the megacomplex biochemically and structurally at a low resolution (Thomsen ARB et al. 2016, Cell 166(4):907-19). The results unequivocally demonstrate that a single GPCR interacts simultaneously with heterotrimeric G protein, at the receptor core, and with b-arrestin via the phosphorylated receptor carboxy-terminal. We also documented functionality of the megacomplex as the receptor can interact with and activate the G protein, which were shown by 3 different biochemical approaches (Thomsen ARB et al. 2016, Cell 166(4):907-19). In addition, we solved a high-resolution cryo-EM structure of a megacomplex further highlighting the architecture of this complex (Nguyen AH et al. 2019, Nat Struct Mol Biol 26:1123-31). As both biochemical and structural analyses were done in vitro in which the receptor was embedded in a detergent micelle, we also confirmed that the megacomplex structural architecture fits naturally within the context of a membrane in molecular dynamics simulation experiments (Nguyen AH et al. 2019, Nat Struct Mol Biol 26:1123-31).

In cells, we and others have also showed that GPCRs such as the V2R can bind b-arrestins exclusively via the phosphorylated carboxy-terminal tail as it does in the megacomplex (Kumari P et al. 2016, Nat Commun 7:13416; Cahill III TJ et al. 2017, PNAS 114(10):2562-67; Kumari P et al. 2017, Mol Biol Cell 28(8):1003-10; Chen K et al. 2023, Nature (online doi: https://doi.org/10.1038/s41586-023-06420-x). In addition, we and others have used BRET and confocal microscopy to show that the V2R and other GPCRs recruit G protein and b-arrestin simultaneously and that the three components colocalize in endosomes upon prolonged agonist exposure (Thomsen ARB et al. 2016, Cell 166(4):907-19; Chen K et al. 2023, Nature (online doi: https://doi.org/10.1038/s41586-023-06420-x). As the reviewer correctly points out, in these cellular experiments (as well as in single molecule microscopy), the working resolution is not high enough to rule out that the receptors that co-recruit G protein and b-arrestin in endosomes could be dimeric instead of monomeric. Thus, we conducted a series of experiments with GPCR–b-arrestin fusions where the two proteins are covalently attached at the receptor carboxy-terminal tail. We showed that despite the GPCR–b-arrestin coupling being fully functional (in respect to b-arrestin promoting a highaffinity state of the receptor for agonist binding and constitutively internalizing the receptor) the receptor could still activate G proteins (Thomsen ARB et al. 2016, Cell 166(4):907-19; Nguyen AH et al. 2019, Nat Struct Mol Biol 26:1123-31), which demonstrates that the single receptor megaplex can physically form in cells.

We have now included an extra paragraph in the discussion to go over these megaplex-related considerations (5th paragraph in the discussion), and we thank the reviewer for raising this point.

1.6 Please introduce abbreviations when you first use this- this was not done consistently.

Thank you for noticing these errors, which we now have corrected.  

REVIEWER #2:

This manuscript by Daly et al., probes the emerging paradigm of GPCR signaling from endosomes using the V2R as a model system with an emphasis on Gaq/11 and b-arrestins. The study employs cellular imaging, enzyme complementation assays and energy transfer-based sensors to probe the potential formation of GPCR-G-protein-b-arrestin megaplexes. While the study is certainly very interesting, it appears to be very preliminary at many levels, and clearly requires further development in order to make robust conclusions. The authors should consider expanding on this work further to make the points more convincingly to make the work solid and impactful. The two corresponding authors are among the leaders in the field having demonstrated the existence of megaplexes, and building on the work in a systematic fashion should certainly move the paradigm forward. As the work presented in the current manuscript is already pre-printed, the authors should take this opportunity to present a completer and more comprehensive story to the field.

We are grateful for the time and efforts the reviewer has put into reviewing our work. We are certainly excited to learn that the reviewer finds our work “very interesting”. Regarding the robustness, we have added extra control experiments to increase the completeness of the study. These experiments include:

• Measurements of AVP-stimulated diacylglycerol production, a signaling event downstream of Gaq/11 activation. These measurements were conducted both at plasma membrane (Fig. 1C) and early endosomes (Fig. 1D) using a newly developed DAG-binding biosensor, and demonstrate that the V2R activates Gaq/11 at both of these subcellular locations.

• Monitoring AVP-promoted protein kinase C activation, another downstream signaling effect of Gaq/11 activation (Fig. 1E). The result of this approach shows in another way that V2R activates of Gaq/11.

• Inhibition of signaling events downstream of Gaq/11 activation using the selective of Gaq/11 inhibitor YM254890. YM-254890 inhibits both AVP-stimulated DAG production at plasma membrane and endosomes as well as PKC activation (Fig. 1C-E), which strongly confirms that these signaling outputs are results of Gaq/11 activation.

• We have also included the confocal data using Halo-mGsi as a negative control for confocal microscopy (Fig. 2). As seen in this figure, mGsi does not translocate to the plasma membrane or early endosomes upon stimulation with AVP, which validates that V2R activation does not couple to and activate Gai/o.

Finally, we would like to kindly remind the reviewer that the production of the pre-print manuscript is part of the peer-review process in eLife.

2.1 The use of miniG proteins in these experiments is a major concern as these are highly engineered and may not represent the true features of G proteins. While these have been used as a readout in other publications, their use in demonstrating megaplex formation is sub-optimal, and native, full-length G proteins should be used.

We are a bit unsure as to what the reviewer means by using native full-length G proteins. If the reviewer is suggesting to co-immunoprecipitate V2R with native unlabeled G protein and b-arrestin, it should be considered that the G protein interaction with the receptor is extremely transient and unlikely to survive the pull-down procedure unless stabilized by a nanobody or crosslinking. Although the b-arrestin interaction with the receptor is more stable of nature, co-immunoprecipitation with the receptor requires crosslinking or stabilization with a Fab/nanobody. Therefore, we do not think this approach can be used as a more accurate way of detecting native megaplexes.

If the reviewer is suggesting the use of full-length G proteins in our cell-based proximity assays instead of miniG proteins, we would like to highlight that this approach is somewhat prone to false-positive responses. The major reason behind this is that G proteins are located at regions in membranes close to the receptor whereas b-arrestins are distributed throughout the cytosol. Upon activation of the V2R, barrestins translocate to the receptor at the plasma membrane, which results in enhanced BRET between V2R-coupled G protein subtypes and b-arrestins (see Author response image 1 below of preliminary data). This translocation also results in non-specific BRET signals between b-arrestins and G protein subtypes at the plasma membrane that do not couple to V2R but are located in close proximity to the receptor. As these nonspecific BRET signals do not report on the formation of functional V2R megaplexes (see Author response image 1), we have purposely not used this approach.

Author response image 1.

To overcome this technical hurdle in detection of functional megaplexes, we have replaced full-length G proteins by miniG proteins as the latter are located in the cytosol at resting states and only translocate to the membrane area if a receptor adopts an active conformation. This replacement is advantageous since activation of megaplex-forming receptors such as the V2R results in simultaneous translocation of miniG proteins and b-arrestins from the cytosol to the receptor at the plasma membrane, which produces a highly specific proximity signal (see Author response image 2 below of preliminary data). When stimulating the V2R, we only observe increases in proximity between b-arrestin1 and miniG proteins that are activated by the V2R (miniGs and miniGsq) but not the miniG proteins that are not activated by this receptor (miniGsi and miniG12) (see Author response image 2). Therefore, usage of miniG proteins offers a more accurate experimental approach to detect functional megaplexes as compared to the usage of full-length G proteins.

Author response image 2.

2.2 The interpretation of complementation (NanoLuc) or proximity (BRET) as evidence of signaling is not appropriate, especially when overexpression system and engineered constructs are being used.

We thank the reviewer for raising this concern. We have previously demonstrated global Gas activation and Gas signaling in form of cAMP stimulated by internalized V2R (Thomsen ARB et al. 2016, Cell 166(4):907-19). As mentioned previously, in the current updated manuscript we have now included experiments to document downstream signaling events in response to Gaq/11 activation. These experiments include measurement of production of DAG at the plasma membrane (Fig. 1C) and early endosomes (Fig. 1D), as well as phosphorylation/activation of PKC (Fig. 1E). Pre-incubation with the selective Gaq/11 inhibitor YM-254890, abrogated all these downstream signals and confirms that the V2R stimulates Gaq/11 protein signaling at both the plasma membrane and endosomes (Fig. 1C-E).

2.3 After the original work from the same corresponding authors on megaplex formation, the major challenge in the field is to demonstrate the existence and relevance of megaplex formation at endogenous levels of components, and the current study focuses solely on showing the proximity of Gaq and b-arrestins.

We completely agree with the reviewer that it will be important to demonstrate functionality endogenous megaplexes and we are currently working on this in other studies using different receptor systems. However, doing this is not trivial and we will have to overcome major technical barriers that we feel is somewhat out of the scope of the current study. The goal of our V2R study is to demonstrate that V2R megaplexes form with Gaq/11 resulting to Gaq/11 activation at endosomes, and that endosomal G protein activation by the V2R can occur independently of b-arrestin, which we in our humble opinion accomplish.

2.4 The study lacks a coherent approach, and the assays are often shifted back and forth between the two b-arrestin isoforms (1 and 2), for example, confocal vs. complementation etc.

We understand the reviewer’s concern. However, as opposed to the β2-adrenergic receptor that binds βarrestin2 with higher affinity than β-arrestin1, V2R has a strong affinity for both β-arrestin1 and β-arrestin2 (Oakley et al. 2000, JBC 275(22):17201-10). The V2R’s almost identical affinity for β-arrestin1 and βarrestin2 is well illustrated in Fig. 3B. Thus, although different β-arrestin isoforms were used in some experiments, it is very unlikely that the overall results and conclusions from this study will change by adding extra experiments to ensure that both β-arrestin isoforms are used in every experiment.

2.5 In every assay, only the G proteins and b-arrestins are monitored without a direct assessment of the presence of receptor, and absent that data, it is difficult to justify calling these entities megaplexes.

Mini G proteins and b-arrestin come into close proximity upon agonist stimulation of the V2R. Using confocal microscopy, we observed this co-recruitment of miniGs/miniGsq and b-arrestin in response to prolonged V2R stimulation at endosomes specifically (Fig. 3D-F). In absence of GPCR stimulation, both miniG and b-arrestin would be homogenously distributed throughout the cytosol, and thus, the only reason to why both proteins have been recruited to endosomes in response to AVP challenge is that they are recruited to internalized and active V2R. This point was obviously not adequately described in the original manuscript, and thus, we have now clarified this further in the updated manuscript at the 8th sentence of the last paragraph of the "The V2R recruits Gas/Gaq and barrs simultaneously" section.

REVIEWER #3:

The manuscript by Daly et al. examines endosomal signaling of the vasopressin type 2 receptors using engineered mini G protein (mG proteins) and a number of novel techniques to address if sustained G protein signaling in the endosomal compartment is enhanced by b-arrestin. Employing these interesting techniques they have how V2R could activates Gas and Gaq in the endosomal compartments and how this modulation could occur in arrestin-dependent and -independent manner. Although the phenomenon of endosomal signaling is complex to address the authors have tried their best to examine these using a number of well controlled set of experiments. Though this is an interesting and well carried out study of endosomal signaling of G proteins, my concerns are:

3.1 The study is done in overexpressed HEK 293 cells with these engineered constructs making me wonder if the kinetics would be the same in primary cells?

The reviewer raises an interesting and valid point. It is possible that in the context of primary cells the kinetic would differ slightly and it would definitely be interesting to address this in a subsequent study. However, despite being an interesting aspect of our study, the kinetic itself is not our major take home message, but rather the subcellular localization of the G protein activation and the role of β-arrestin in these events. We have now highlighted this aspect in our updated manuscript (1st paragraph of the discussion) and we thank the reviewer for addressing this.

3.2 The use of the phrase "G protein activation independent of b-arrestins to a minor degree" would make me question its physiological relevance. The authors should discuss the relevance of their findings in physiological or pathological context.

We are glad that the reviewer focuses on this point, and we would like to highlight that other GPCRs including the glucagon-like peptide-1 receptor (GLP1R) internalizes in a β-arrestin-independent manner (Claing A et al. 2000 PNAS 97(3):1119-24), while signaling through Gas from endosomes. In the case of the GLP1R, this endosomal Gas signaling promotes glucose-stimulated insulin secretion in pancreatic βcells (Kuna RS et al. 2013 Am J Physiol Endocrinol Metab 305:E161-70). Consequently, β-arrestinindependent endosomal G protein signaling appears to have some physiological relevance. Similarly, in a very recent pre-print from the von Zastrow group (Blythe EE and von Zastrow M 2023 BioRxiv https://doi.org/10.1101/2022.09.07.506997), it was reported that endogenously-expressed vasoactive intestinal peptide receptor 1 (VIPR1), which regulates gastro-intestinal functions, promotes robust G protein signaling from endosomes in a completely β-arrestin-independent fashion. This again suggest that endogenously expressed GPCRs can internalize and activate G proteins from endosomes independently from β-arrestin to produce physiological responses. We have now discussed about these studies in the 6th paragraph of the discussion.

3.3 The confocal colocalization studies shown in Figure 2 and their conclusion "suggesting a certain level of endosomal Gas/Gaq signaling despite the absence of barr2" seems rather inconclusive.

As opposed to V2R a receptor that retains β-arrestin in endosomes upon internalization, β-arrestin quickly dissociates from V2b2AR after internalization due to the low affinity of the carboxy-terminal of β2AR for βarrestin. In the previous Fig. 2 (now Fig. 3), after 45 minutes of AVP stimulation, no β-arrestin is visible at endosomes in cells expressing V2b2AR as β-arrestin has already dissociated from the receptor and translocated back to the cytosol. However, clear green clusters of mGs and mGsq are still visible at endosomes indicating the presence of active receptor interacting with Gas or Gaq despite the fact that βarrestin is back to the cytosol. We quantified the percentage of the green mGs or mGsq clusters that do not colocalize with β-arrestin and have added this information to the updated version of the manuscript (Fig. 3G). In V2R-expressing cells, almost all active receptors that interact with Gas or Gaq/11 also associate with β-arrestin (Fig. 3G). In contrast, in V2b2AR-expressing cells, approximately 75% of the active receptors do not interact with β-arrestin (Fig. 3G). This suggests that β-arrestin binding to V2R is not an absolute requirement for endosomal Gas and Gaq activation by V2R. This point was obviously not addressed adequately in the original manuscript, and thus, we have now elaborated further on this in the updated version in the last paragraph of the "The V2R recruits Gas/Gaq and βarrs simultaneously" section.

3.4 Though a novel observation it is not clear to me how V2R would internalize after activation without arrestin. Is it some sort of generalized microcytosis occurring in these overexpressed cells? Should discuss.

This is certainly a very interesting observation and something other research laboratories also have seen recently – in particular, in context to endosomal G protein signaling (Blythe EE and von Zastrow M 2023 BioRxiv https://doi.org/10.1101/2022.09.07.506997). The main and best characterized pathway for GPCR internalization is clathrin-dependent where receptors most commonly are associated with β-arrestins. However, for some GPCRs, the β-arrestin association is not required for clathrin-mediated internalization. One example is the apelin receptor that can internalize via clathrin-coated pits, but in β-arrestinindependent manner (Pope GR et al. 2016 Moll Cell Endocrinol. 437:108-19). Alternatively, GPCRs can also internalize independently of any clathrin and β-arrestin associations via caveolae or fast endophilinmediated endocytosis (FEME). We have now expanded our discussion of possible mechanisms for βarrestin-independent receptor internalization in the updated manuscript in the 6th paragraph of the discussion, and we thank the reviewer for the suggestion.

3.5 Is use of mini G protein a good representation? The authors should justify.

Excellent point and something we have comprehensively discussed in our response to reviewer 1 and 2 (points 1.2 and 2.1).

eLife assessment

This is an important study that contributes to our understanding of the role of beta-arrestins in endosomal activation of the vasopressin type 2 receptors. While the use of a minigene as a tool is a weakness, the evidence is overall convincing and makes for significant findings whose theoretical and practical implications extend to other GPCRs.

Joint Public Review:

The authors present a carefully controlled set of experiments that demonstrate an additional complexity for GPCR signaling in that endosomal signaling may be different when beta-arrestin is or isn't associated with a G protein-bound V2 vasopressin receptor. It uses state of the art biosensor-based approaches and beta-arrestin KO lines to assess this. It adds to a growing body of evidence that G proteins and beta-arresting can associate with GPCR complexes simultaneously. They also demonstrate the possibility that Gq might also be activated by the V2 receptor. My sense is one thing they may need to be considered is the possibility of such "megacomplexes" might actually involve receptor dimers or oligomers. They have added significant amounts of new data to address concerns I had about the sole use of mini-genes to assess G protein coupling and broadened discussions about the possible mechanisms underlying their observations. I would still argue that receptor oligomers are a more obvious way for such megacomplexes to be organized around.

Reviewer #1 (Public Review):

Summary:<br /> The current study examines the necessity of estrogen receptor alpha (ESR1) in GABA neurons located in the anteroventral and preoptic periventricular nuclei and the medial preoptic nucleus of the hypothalamus. This brain area is implicated in regulating the pre-ovulatory LH surge in females, but the identity of the estrogen-sensitive neurons that are required remains unknown. The data indicate that approximately 70% knockdown of ESR1 in GABA neurons resulted in variable reproductive phenotypes. However, when the ESR1 knockdown also results in a decrease in kisspeptin expression by these cells, the females had disrupted LH surges, but no alterations in pulsatile LH release. These data support the hypothesis that kisspeptin cells in this region are critical for the pre-ovulatory LH surge in females.

Strengths:<br /> The current study examined the efficacy of two guide RNAs to knockdown ESR1 in GABA neurons, resulting in an approximate 70% reduction in ESR1 in GABA neurons. The efficacy of this knockdown was confirmed in the brain via immunohistochemistry and the reproductive outcomes were analyzed several ways to account for differences in guide RNAs or the precise brain region with the ESR1 knockdown. The analysis was taken one step further by grouping mice based on kisspeptin expression following ESR1 knockdown and examining the reproductive phenotypes. Overall, the aims of the study were achieved, the methods were appropriate, and the data were analyzed extensively. This data supports the hypothesis that kisspeptin neurons in the anterior hypothalamus are critical for the preovulatory LH surge.

Weaknesses: One minor weakness in this study is the conclusion that the guide RNAs didn't seem to have unique effects on GnRH cFos expression or the reproductive phenotypes. Though the data indicate a 60-70% knockdown for both gRNA2 and gRNA3, 3 of the 4 gRNA2 mice had no cFos expression in GnRH neurons during the time of the LH surge, whereas all mice receiving gRNA3 had at least some cFos/GnRH co-expression. In addition, when mice were re-categorized based on reduction (>75%) in kisspeptin expression, most of the mice in the unilateral or bilateral groups received gRNA2, whereas many of the mice that received gRNA3 were in the "normal" group with no disruption in kisspeptin expression. Thus, additional experiments with increased sample sizes are needed, even if the efficacy of the ESR1 knockdown was comparable before concluding these 2 gRNAs don't result in unique reproductive effects.

eLife assessment

This important study provides convincing evidence of the criticality of estradiol - estrogen receptor-mediated upregulation of kisspeptin within neurons of the preoptic area to generate an ovulation-inducing luteinizing hormone surge. The use of in vivo CRIPSR-Cas9 is novel in this system and provides a road map for future studies in reproductive neuroendocrinology. This paper will be of interest to reproductive neuroscientists and endocrinologists.

Reviewer #2 (Public Review):

Clarkson et al investigated the impact of in vivo ESR1 gene disruption selectively in preoptic area GABA neurons on the estrogen regulation of LH secretion. The hypothalamic pathways by which estradiol controls the secretion of gonadotrophins are incompletely understood and relevant to a better understanding of the mechanisms driving fertility and reproduction. Using CRISPR-Cas9 methodology, the authors were able to effectively reduce the expression of estrogen receptor (ER)-alpha in GABA neurons located in the preoptic area of adult female mice. The results obtained were rather variable except in the animals with concomitant suppression of kisspeptin in the rostral periventricular region of the third ventricle (RP3V), which displayed interruption of ovarian cyclicity and an altered estradiol-induced LH surge. The experimental approach used allowed for a cell-selective, temporally-controlled suppression of ER-alpha expression, providing further evidence of the critical role of RP3V kisspeptin neurons in the estrogen positive-feedback effect. Nevertheless, the assessment of the estradiol-induced LH surge was limited to only one terminal blood collection. The preovulatory LH surge is a variable phenomenon and would require serial blood sampling for a conclusive evaluation of the surge occurrence or alteration, such as in shape, amplitude, or timing. The animals were not assessed for ovulation either, which might be a functional readout for the effectiveness of the LH surge. Thus, the actual effect on the preovulatory LH surge was not fully characterized. Finally, the study leaves unanswered the role of GABA itself. As there was no evident phenotype for the ESR1 knockdown in GABA neurons that do not coexpress kisspeptin, this suggests that GABA neurotransmission in the preoptic area is not involved in the estrogen regulation of LH secretion.

Reviewer #3 (Public Review):

Summary:<br /> Although the key role of estrogen receptor alpha (ERα ; encoded by ESR1 gene) in the control of reproduction has been known for more than two decades, the identity of the neuronal population(s) underlying the control of the negative and positive feedbacks exerted by estrogens on the hypothalamic pituitary ovary axis has been more complicated to pinpoint. Among the factors contributing to the difficulty in this endeavor are the cellular heterogeneity of the preoptic area and hypothalamus and the pulsatile activity of the axis. Several neuronal populations have been identified that control the activity of GnRH neurons, the hypothalamic headmasters of the axis. Among them, the kisspeptin neurons of the rostral periventricular aspect of the third ventricle (RP3V) have been considered the major candidates to convey preovulatory estrogen signals to GnRH neurons, which do not express ERα. Yet, the existence of other populations of kisspeptin neurons (notably in the arcuate nucleus) has made it difficult to selectively ablate ESR1 in one population. A first study (Wang et al., 2019) reported that knocking down ESR1 specifically in RP3V kisspeptin neurons led to decreased excitability of Kp neurons, blunted spontaneous as well estrogen-induced preovulatory LH surge, and reduced or absent corpora lutea indicative of impaired ovulation, but cyclicity was left unaltered.

As GABAergic afferences to GnRH neurons are also implicated in mediating the effects of estrogens on the HPG axis, the present study sought to investigate their role in the positive feedback using genetically driven Crispr-Cas9 mediated knockdown of ERα in VGAT expressing neurons in a specific subregion of the preoptic area. To this end, they stereotaxically delivered viral vectors expressing validated guide RNA into the preoptic area and evaluated their impact on estrus cyclicity and the ability of mice to mount an LH surge induced by estrogens and associated activation of GnRH neurons assessed by the co-expression of Fos. The results demonstrate that knocking down ESR1 in preoptic gabaergic neurons leads to an absence of LH surge and acyclicity when associated with severely reduced kisspeptin expression suggesting that a subpopulation of neurons co-expressing Kp and VGAT neurons are key for LH surge since total absence of Kp is associated with an absence of GnRH neuron activation and reduced LH surge (although this was not confirmed by the post-hoc). Although the implication of kisspeptin neurons was highly suspected already, the novelty of these results lies in the fact that estrogen signaling is necessary for only a selected fraction of them to maintain both regular cycles and LH surge capacity.

Strengths:<br /> Remarkable aspects of this study are, its dataset which allowed them to segregate animals based on distinct neuronal phenotypes matching specific physiological outcomes, the transparency in reporting the results (e.g. all statistical values being reported, all grouping variables being clearly defined, clarity about animals that were excluded and why) and the clarity of the writing. This allows the reader to understand clearly what has been done and how the analyses have been carried out. The same applies to the discussion which describes clearly possible interpretations as well as the limitations of this study based on a single in vivo experiment.

Another remarkable feature of this work lies in the analysis of the dataset. As opposed to the cre-lox approach which theoretically allows for the complete ablation of specific neuronal populations, but may lack specificity regarding timing of action and location, genetically driven in vivo Crispr-Cas9 editing offers both temporal and neuroanatomic selectivity but cannot achieve a complete knockdown. This approach based on stereotaxic delivery of the AAV-encoded guide RNAs comes with inevitable variability in the location where gene knockdown is achieved. By adjusting their original grouping of the animals based on the evaluation of the extent of kisspeptin expression in the target region, the authors obtained a much clearer and interpretable picture. Although only a few animals (n=4) displayed absent kisspeptin expression, the convergence of observations suggesting a central impairment of the reproductive axis is convincing.

In particular, the lack of activation of GnRH neurons in these mice despite a non-significant effect on the reduced LH levels in the post-hoc following a significant ANOVA (which is likely due to the limited number of concerned animals [n=4]), is convincing. Did the authors test whether LH concentrations correlated with the percentage of GnRH+ESR1 positive neurons? This could reinforce the conclusion.

Moreover, the apparent complete absence of kisspeptin expression in these 4 animals is compelling as it provides indirect confirmation of the key role of Kisspeptin neurons in this phenomenon using a different LH surge induction paradigm than Wang et al., 2019. Yet, the quality of the kisspeptin immunostaining in control animals does seem suboptimal and casts doubts about this conclusion (see the section on weaknesses for more details).

It is also interesting that a few animals with unilateral reduction in Kp expression also showed deficits in GnRH neuron activation suggesting that the impact of Kp may not be limited to the side of the brain where it is produced or the existence of a dose effect of Kp activation of GnRH neurons.

Finally, the observation that the pulsatile secretion of LH is maintained in the absence of Kp expression in the RP3V lends support to the notion that LH surge and pulsatility are regulated independently by distinct neuronal populations, a model put forward by the corresponding author a few years ago.

Weaknesses:<br /> One aspect for which I have ambiguous feelings is the minimal level of detail regarding the HPG axis and its regulation by estrogens. This limited amount of detail allows for an easy read with the well-articulated introduction quickly presenting the framework of the study. Although not presenting the axis itself nor mentioning the position of GnRH neurons in this axis or its lack of ERα expression is not detrimental to the understanding of the study, presenting at least the position of GnRH neurons in the axis and their critical role for fertility would likely broaden the impact of this work beyond a rather specialist audience.

The expression of kisspeptin constitutes a key element for the analysis and conclusion of the present work. However, the quality of the kisspeptin immunostaining seems suboptimal based on the representative images. The staining primarily consists of light punctuated structures and it is very difficult to delineate cytoplasmic immunoreactive material defining the shape of neurons in LacZ animals. For some of the cells marked by an arrow, it is also sometimes difficult to determine whether the staining for ESR1 and Kp are in the same focal plane and thus belong to the same neurons. Although this co-expression is not critical for the conclusions of the study, this begs the question of whether Kp expression was determined directly at the microscope (where the focal plan can be adjusted) or on the picture (without possible focal adjustment). Moreover, in the representative image of Kp loss, several nuclei stained for fos (black) show superimposed brown staining looking like a dense nucleus (but smaller than an actual nucleus). This suggests some sort of condensed accumulation of Kp immunoproduct in the nucleus which is not commented. Given the critical importance of this reported change in Kp expression for the interpretation of the present results, it is important to provide strong evidence of the quality/nature of this staining and its analysis which may help interpret the observed functional phenotype.

As acknowledged in the introduction, this study is not the first to use in vivo Crisp-Cas editing to demonstrate the role of kisspeptin neurons in the control of positive feedback. Although the present work achieved this indirectly by targeting VGAT neurons, I was surprised that the paper did not include more comparison of their results with those of Wang et al., 2019. In particular, why was the present approach more successful in achieving both lack of surge and complete acyclicity? Moreover, why is it that targeting ESR1 in a selected fraction of GABAergic neurons can lead to a near-complete absence of Kp expression in this region? This is briefly discussed in the penultimate paragraph but mostly focuses on the non-kisspeptinergic GABAneurons rather than those co-expressing the two markers.

Author Response

Reviewer #1 (Public Review):

Like the "preceding" co-submitted paper, this is again a very strong and interesting paper in which the authors address a question that is raised by the finding in their co-submitted paper - how does one factor induce two different fates. The authors provide an extremely satisfying answer - only one subset of the cells neighbors a source of signaling cells that trigger that subset to adopt a specific fate. The signal here is Delta and the read-out is Notch, whose intracellular domain, in conjunction with, presumably, SuH cooperates with Bsh to distinguish L4 from L5 fate (L5 is not neighbored by signal-providing cells). Like the back-to-back paper, the data is rigorous, well-presented and presents important conclusions. There's a wealth of data on the different functions of Notch (with and without Bsh). All very satisfying.

Thanks!

I have again one suggestion that the authors may want to consider discussing. I'm wondering whether the open chromatin that the author convincingly measure is the CAUSE or the CONSEQUENCE of Bsh being able to activate L4 target genes. What I mean by this is that currently the authors seem to be focused on a somewhat sequential model where Notch signaling opens chromatin and this then enables Bsh to activate a specific set of target genes. But isn't it equally possible that the combined activity of Bsh/Notch(intra)/SuH opens chromatin? That's not a semantic/minor difference, it's a fundamentally different mechanism, I would think. This mechanism also solves the conundrum of specificity - how does Notch know which genes to "open" up? It would seem more intuitive to me to think that it's working together with Bsh to open up chromatin, with chromatin accessibility than being a "mere" secondary consequence. If I'm not overlooking something fundamental here, there is actually also a way to distinguish between these models - test chromatin accessibility in a Bsh mutant. If the author's model is true, chromatin accessibility should be unchanged.

I again finish by commending the authors for this terrific piece of work.

Thanks! It is a crucial question whether Notch signaling regulates chromatin landscape independently of a primary HDTF. We will include this discussion in the text and pursue it in our next project. We think Notch signaling may regulate chromatin accessibility independently of a primary HDTF based on our observation: in larval ventral nerve cord, all motor neurons are NotchON neurons while all sensory neurons are NotchOFF neurons; NotchON neurons share similar functional properties, despite expressing distinct HDTFs, possibly due to the common chromatin landscape regulated by Notch signaling.

Reviewer #2 (Public Review):

Summary:

In this work, the authors explore how Notch activity acts together with Bsh homeodomain transcription factors to establish L4 and L5 fates in the lamina of the visual system of Drosophila. They propose a model in which differential Notch activity generates different chromatin landscapes in presumptive L4 and L5, allowing the differential binding of the primary homeodomain TF Bsh (as described in the co-submitted paper), which in turn activates downstream genes specific to either neuronal type. The requirement of Notch for L4 vs. L5 fate is well supported, and complete transformation from one cell type into the other is observed when altering Notch activity. However, the role of Notch in creating differential chromatin landscapes is not directly demonstrated. It is only based on correlation, but it remains a plausible and intriguing hypothesis.

Thanks for the positive feedback!

Strengths:

The authors are successful in characterizing the role of Notch to distinguish between L4 and L5 cell fates. They show that the Notch pathway is active in L4 but not in L5. They identify L1, the neuron adjacent to L4 as expressing the Delta ligand, therefore being the potential source for Notch activation in L4. Moreover, the manuscript shows molecular and morphological/connectivity transformations from one cell type into the other when Notch activity is manipulated.

Thanks!

Using DamID, the authors characterize the chromatin landscape of L4 and L5 neurons. They show that Bsh occupies distinct loci in each cell type. This supports their model that Bsh acts as a primary selector gene in L4/L5 that activates different target genes in L4 vs L5 based on the differential availability of open chromatin loci.

Thanks!

Overall, the manuscript presents an interesting example of how Notch activity cooperates with TF expression to generate diverging cell fates. Together with the accompanying paper, it helps thoroughly describe how lamina cell types L4 and L5 are specified and provides an interesting hypothesis for the role of Notch and Bsh in increasing neuronal diversity in the lamina during evolution.

Thanks for the positive feedback on both manuscripts.

Weaknesses:

Differential Notch activity in L4 and L5:

● The manuscript focuses its attention on describing Notch activity in L4 vs L5 neurons. However, from the data presented, it is very likely that the pool of progenitors (LPCs) is already subdivided into at least two types of progenitors that will rise to L4 and L5, respectively. Evidence to support this is the activity of E(spl)-mɣ-GFP and the Dl puncta observed in the LPC region. Discussion should naturally follow that Notch-induced differences in L4/L5 might preexist L1-expressed Dl that affect newborn L4/L5. Therefore, the differences between L4 and L5 fates might be established earlier than discussed in the paper. The authors should acknowledge this possibility and discuss it in their model.

We agree. Historically, LPCs are thought to be homogenous; our data suggests otherwise. We now emphasize this in the Discussion as requested. We are also investigating this question using single cell RNAseq on LPCs to look for molecular heterogeneities. Thanks for the great comment!

● The authors claim that Notch activation is caused by L1-expressed Delta. However, they use an LPC driver to knock down Dl. Dl-KD should be performed exclusively in L1, and the fate of L4 should be assessed.

Dl is transiently expressed in newborn L1 neurons. To knock down Dl in L1, we need to express Dl-RNAi before Dl protein is expressed in newborn L1; the only known Gal4 line expressed that early is the LPC-Gal4 that we used. There is no L1-gal4 line expressed early enough to eliminate L1 expression of Dl.

● To test whether L4 neurons are derived from NotchON LPCs, I suggest performing MARCM clones in early pupa with an E(spl)-mɣ-GFP reporter.

We agree! Whether L4 neurons are derived from NotchON LPCs is a great question. However, MARCM clones in early pupa with an E(spl)-mɣ-GFP reporter will not work because E(spl)-mɣ-GFP reporter is only expressed in LPCs but not lamina neurons. We now mention this in the Discussion.

● The expression of different Notch targets in LPCs and L4 neurons may be further explored. I suggest using different Notch-activity reporters (i.e., E(spl)-GFP reporters) to further characterize these. differences. What cause the switch in Notch target expression from LPCs to L4 neurons should be a topic of discussion.

Thanks! It is a great question why Notch induces Espl-mɣ in LPCs but Hey in new-born neurons. However, it is not the question we are tackling in this paper and it will be a great direction to pursue in future. We will add this to our Discussion.

Notch role in establishing L4 vs L5 fates:

● The authors describe that 27G05-Gal4 causes a partial Notch Gain of Function caused by its genomic location between Notch target genes. However, this is not further elaborated. The use of this driver is especially problematic when performing Notch KD, as many of the resulting neurons express Ap, and therefore have some features of L4 neurons. Therefore, Pdm3+/Ap+ cells should always be counted as intermediate L4/L5 fate (i.e., Fig3 E-J, Fig3-Sup2), irrespective of what the mechanistic explanation for Ap activation might be. It's not accurate to assume their L5 identity. In Fig4 intermediate-fate cells are correctly counted as such.

Thanks for the comment! We will annotate Pdm3/Ap+ as L4/L5 fate in the corresponding figures.

● Lines 170-173: The temporal requirement for Notch activity in L5-to-L4 transformation is not clearly delineated. In Fig4-figure supplement 1D-E, it is not stated if the shift to 29{degree sign}C is performed as in Fig4-figure supplement 1A-C.

Thank you for catching this. We will correct it in the text.

● Additionally, using the same approach, it would be interesting to explore the window of competence for Notch-induced L5-to-L4 transformation: at which point in L5 maturation can fate no longer be changed by Notch GoF?

Our data show that Bsh with Notch signaling in newborn neurons specifies L4 fate while Bsh without Notch signaling in newborn neurons specifies L5 fate. Therefore, we think the window of fate competence is during newborn neurons. We will include the data to support this.

L4-to-L3 conversion in the absence of Bsh

● Although interesting, the L4-to-L3 conversion in the absence of Bsh is never shown to be dependent on Notch activity. Importantly, L3 NotchON status is assumed based on their position next to Dl-expressing L1, but it is not empirically tested. Perhaps screening Notch target reporter expression in the lamina, as suggested above, could inform this issue.

Our data show that the L4-to-L3 conversion in the absence of Bsh and in the presence of Notch activity while the L5-to-L1 conversion in the absence of Bsh and in the absence of Notch activity. Therefore, Notch activity is necessary for the L4-to-L3 conversion. Unfortunately, currently we only have Hey as an available Notch target reporter in new-born neurons. To tackle this challenge in the future, we will profile the genome-binding targets of endogenous Notch in newborn neurons. This will identify novel genes as Notch signaling reporters in neurons for the field.

● Otherwise, the analysis of Bsh Loss of Function in L4 might be better suited to be included in the accompanying manuscript that specifically deals with the role of Bsh as a selector gene for L4 and L5.

That is an interesting suggestion, but without knowing that Bsh + Notch = L4 identity the experiment would be hard to interpret. Note that we took advantage of Notch signaling to trace the cell fate in the absence of Bsh and found the L4-to-L3 conversion (see Figure 5G-K).

Different chromatin landscape in L4 and L5 neurons

● A major concern is that, although L4 and L5 neurons are shown to present different chromatin landscapes (as expected for different neuronal types), it is not demonstrated that this is caused by Notch activity. The paper proves unambiguously that Notch activity, in concert with Bsh, causes the fate choice between L4 and L5. However, that this is caused by Notch creating a differential chromatin landscape is based only in correlation. (NotchON cells having a different profile than NotchOFF). Although the authors are careful not to claim that differential chromatin opening is caused directly by Notch, this is heavily suggested throughout the text and must be toned down.e.g.: Line 294: "With Notch signaling, L4 neurons generate distinct open chromatin landscape" and Line 298: "Our findings propose a model that the unique combination of HDTF and open chromatin landscape (e.g. by Notch signaling)" . These claims are not supported well enough, and alternative hypotheses should be provided in the discussion. An alternative hypothesis could be that LPCs are already specified towards L4 and L5 fates. In this context, different early Bsh targets in each cell type could play a pioneer role generating a differential chromatin landscape.

We agree and appreciate the comment, it is well justified. We have toned down our comments and clearly state that this is a correlation that needs to be tested for a causal relationship. Thank you for requesting it!

● The correlation between open chromatin and Bsh loci with Differentially Expressed genes is much higher for L4 than L5. It is not clear why this is the case, and should be discussed further by the authors.

We agree, and think in L5 neurons, the secondary HDTF Pdm3 also contributes to L5 specific gene transcription during synaptogenesis window, in addition to Bsh. We will include this in the text.

eLife assessment

This paper explores how Notch activity acts together with homeodomain transcription Bsh factors to establish distinct cell fates (L4 vs L5) in the visual system of Drosophila. The findings are important and have theoretical or practical implications beyond a single subfield. The methods, data and analyses are solid, and broadly support the claims with only minor weaknesses.

Reviewer #1 (Public Review):

Like the "preceding" co-submitted paper, this is again a very strong and interesting paper in which the authors address a question that is raised by the finding in their co-submitted paper - how does one factor induce two different fates. The authors provide an extremely satisfying answer - only one subset of the cells neighbors a source of signaling cells that trigger that subset to adopt a specific fate. The signal here is Delta and the read-out is Notch, whose intracellular domain, in conjunction with, presumably, SuH cooperates with Bsh to distinguish L4 from L5 fate (L5 is not neighbored by signal-providing cells). Like the back-to-back paper, the data is rigorous, well-presented and presents important conclusions. There's a wealth of data on the different functions of Notch (with and without Bsh). All very satisfying.

I have again one suggestion that the authors may want to consider discussing. I'm wondering whether the open chromatin that the author convincingly measure is the CAUSE or the CONSEQUENCE of Bsh being able to activate L4 target genes. What I mean by this is that currently the authors seem to be focused on a somewhat sequential model where Notch signaling opens chromatin and this then enables Bsh to activate a specific set of target genes. But isn't it equally possible that the combined activity of Bsh/Notch(intra)/SuH opens chromatin? That's not a semantic/minor difference, it's a fundamentally different mechanism, I would think. This mechanism also solves the conundrum of specificity - how does Notch know which genes to "open" up? It would seem more intuitive to me to think that it's working together with Bsh to open up chromatin, with chromatin accessibility than being a "mere" secondary consequence. If I'm not overlooking something fundamental here, there is actually also a way to distinguish between these models - test chromatin accessibility in a Bsh mutant. If the author's model is true, chromatin accessibility should be unchanged.<br /> I again finish by commending the authors for this terrific piece of work.

Reviewer #2 (Public Review):

Summary:

In this work, the authors explore how Notch activity acts together with Bsh homeodomain transcription factors to establish L4 and L5 fates in the lamina of the visual system of Drosophila. They propose a model in which differential Notch activity generates different chromatin landscapes in presumptive L4 and L5, allowing the differential binding of the primary homeodomain TF Bsh (as described in the co-submitted paper), which in turn activates downstream genes specific to either neuronal type. The requirement of Notch for L4 vs. L5 fate is well supported, and complete transformation from one cell type into the other is observed when altering Notch activity. However, the role of Notch in creating differential chromatin landscapes is not directly demonstrated. It is only based on correlation, but it remains a plausible and intriguing hypothesis.

Strengths:

The authors are successful in characterizing the role of Notch to distinguish between L4 and L5 cell fates. They show that the Notch pathway is active in L4 but not in L5. They identify L1, the neuron adjacent to L4 as expressing the Delta ligand, therefore being the potential source for Notch activation in L4. Moreover, the manuscript shows molecular and morphological/connectivity transformations from one cell type into the other when Notch activity is manipulated.

Using DamID, the authors characterize the chromatin landscape of L4 and L5 neurons. They show that Bsh occupies distinct loci in each cell type. This supports their model that Bsh acts as a primary selector gene in L4/L5 that activates different target genes in L4 vs L5 based on the differential availability of open chromatin loci.

Overall, the manuscript presents an interesting example of how Notch activity cooperates with TF expression to generate diverging cell fates. Together with the accompanying paper, it helps thoroughly describe how lamina cell types L4 and L5 are specified and provides an interesting hypothesis for the role of Notch and Bsh in increasing neuronal diversity in the lamina during evolution.

Weaknesses:

Differential Notch activity in L4 and L5:<br /> ● The manuscript focuses its attention on describing Notch activity in L4 vs L5 neurons. However, from the data presented, it is very likely that the pool of progenitors (LPCs) is already subdivided into at least two types of progenitors that will rise to L4 and L5, respectively. Evidence to support this is the activity of E(spl)-mɣ-GFP and the Dl puncta observed in the LPC region. Discussion should naturally follow that Notch-induced differences in L4/L5 might preexist L1-expressed Dl that affect newborn L4/L5. Therefore, the differences between L4 and L5 fates might be established earlier than discussed in the paper. The authors should acknowledge this possibility and discuss it in their model.<br /> ● The authors claim that Notch activation is caused by L1-expressed Delta. However, they use an LPC driver to knock down Dl. Dl-KD should be performed exclusively in L1, and the fate of L4 should be assessed.<br /> ● To test whether L4 neurons are derived from NotchON LPCs, I suggest performing MARCM clones in early pupa with an E(spl)-mɣ-GFP reporter.<br /> ● The expression of different Notch targets in LPCs and L4 neurons may be further explored. I suggest using different Notch-activity reporters (i.e., E(spl)-GFP reporters) to further characterize these differences. What cause the switch in Notch target expression from LPCs to L4 neurons should be a topic of discussion.

Notch role in establishing L4 vs L5 fates:<br /> ● The authors describe that 27G05-Gal4 causes a partial Notch Gain of Function caused by its genomic location between Notch target genes. However, this is not further elaborated. The use of this driver is especially problematic when performing Notch KD, as many of the resulting neurons express Ap, and therefore have some features of L4 neurons. Therefore, Pdm3+/Ap+ cells should always be counted as intermediate L4/L5 fate (i.e., Fig3 E-J, Fig3-Sup2), irrespective of what the mechanistic explanation for Ap activation might be. It's not accurate to assume their L5 identity. In Fig4 intermediate-fate cells are correctly counted as such.<br /> ● Lines 170-173: The temporal requirement for Notch activity in L5-to-L4 transformation is not clearly delineated. In Fig4-figure supplement 1D-E, it is not stated if the shift to 29{degree sign}C is performed as in Fig4-figure supplement 1A-C.<br /> ● Additionally, using the same approach, it would be interesting to explore the window of competence for Notch-induced L5-to-L4 transformation: at which point in L5 maturation can fate no longer be changed by Notch GoF?

L4-to-L3 conversion in the absence of Bsh<br /> ● Although interesting, the L4-to-L3 conversion in the absence of Bsh is never shown to be dependent on Notch activity. Importantly, L3 NotchON status is assumed based on their position next to Dl-expressing L1, but it is not empirically tested. Perhaps screening Notch target reporter expression in the lamina, as suggested above, could inform this issue.<br /> ● Otherwise, the analysis of Bsh Loss of Function in L4 might be better suited to be included in the accompanying manuscript that specifically deals with the role of Bsh as a selector gene for L4 and L5.

Different chromatin landscape in L4 and L5 neurons<br /> ● A major concern is that, although L4 and L5 neurons are shown to present different chromatin landscapes (as expected for different neuronal types), it is not demonstrated that this is caused by Notch activity. The paper proves unambiguously that Notch activity, in concert with Bsh, causes the fate choice between L4 and L5. However, that this is caused by Notch creating a differential chromatin landscape is based only in correlation (NotchON cells having a different profile than NotchOFF). Although the authors are careful not to claim that differential chromatin opening is caused directly by Notch, this is heavily suggested throughout the text and must be toned down.<br /> e.g.: Line 294: "With Notch signaling, L4 neurons generate distinct open chromatin landscape" and Line 298: "Our findings propose a model that the unique combination of HDTF and open chromatin landscape (e.g. by Notch signaling)" . These claims are not supported well enough, and alternative hypotheses should be provided in the discussion. An alternative hypothesis could be that LPCs are already specified towards L4 and L5 fates. In this context, different early Bsh targets in each cell type could play a pioneer role generating a differential chromatin landscape.

● The correlation between open chromatin and Bsh loci with Differentially Expressed genes is much higher for L4 than L5. It is not clear why this is the case, and should be discussed further by the authors.

Author Response

Reviewer #1 (Public Review):

In this very strong and interesting paper the authors present a convincing series of experiments that reveal molecular mechanism of neuronal cell type diversification in the nervous system of Drosophila. The authors show that a homeodomain transcription factor, Bsh, fulfills several critical functions - repressing an alternative fate and inducing downstream homeodomain transcription factors with whom Bsh may collaborate to induce L4 and L5 fates (the author's accompanying paper reveals how Bsh can induce two distinct fates). The authors make elegant use of powerful genetic tools and an arsenal of satisfying cell identity markers.

Thanks!

I believe that this is an important study because it provides some fundamental insights into the conservation of neuronal diversification programs. It is very satisfying to see that similar organizational principles apply in different organisms to generate cell type diversity. The authors should also be commended for contextualizing their work very well, giving a broad, scholarly background to the problem of neuronal cell type diversification.

Thanks!

My one suggestion for the authors is to perhaps address in the Discussion (or experimentally address if they wish) how they reconcile that Bsh is on the one hand: (a) continuously expressed in L4/L4, (b) binding directly to a cohort of terminal effectors that are also continuously expressed but then, on the other hand, is not required for their maintaining L4 fate? A few questions: Is Bsh only NOT required for maintaining Ap expression or is it also NOT required for maintaining other terminal markers of L4? The former could be easily explained - Bsh simply kicks of Ap, Ap then autoregulates, but Bsh and Ap then continuously activate terminal effector genes. The second scenario would require a little more complex mechanism: Bsh binding of targets (with Notch) may open chromatin, but then once that's done, Bsh is no longer needed and Ap alone can continue to express genes. I feel that the authors should be at least discussing this. The postmitotic Bsh removal experiment in which they only checked Ap and depression of other markers is a little unsatisfying without further discussion (or experiments, such as testing terminal L4 markers). I hasten to add that this comment does not take away from my overall appreciation for the depth and quality of the data and the importance of their conclusions.

Great suggestions, we will discuss these two hypotheses as requested.

Bsh initiates Ap expression in L4 neurons which then maintain Ap expression independently of Bsh expression, likely through Ap autoregulation. During the synaptogenesis window, Ap expression becomes independent from Bsh expression, but Bsh and Ap are both still required to activate the synapse recognition molecule DIP-beta. Additionally, Bsh also shows putative binding to other L4 identity genes, e.g., those required for neurotransmitter choice, and electrophysiological properties, suggesting Bsh may initiates L4 identity genes as a suite of genes. The mechanism of maintaining identity features (e.g., morphology, synaptic connectivity and functional properties) in the adult remains poorly understood. It is a great question whether primary HDTF Bsh maintains the expression of L4 identity genes in the adult. To test this, in our next project, we will specifically knock out Bsh in L4 neurons of the adult fly and examine the effect on L4 morphology, connectivity and function properties.

Reviewer #2 (Public Review):

Summary:

In this paper, the authors explore the role of the Homeodomain Transcription Factor Bsh in the specification of Lamina neuronal types in the optic lobe of Drosophila. Using the framework of terminal selector genes and compelling data, they investigate whether the same factor that establishes early cell identity is responsible for the acquisition of terminal features of the neuron (i.e., cell connectivity and synaptogenesis).

Thanks for the positive words!

The authors convincingly describe the sequential expression and activity of Bsh, termed here as 'primary HDTF', and of Ap in L4 or Pdm3 in L5 as 'secondary HDTFs' during the specification of these two neurons. The study demonstrates the requirement of Bsh to activate either Ap and Pdm3, and therefore to generate the L4 and L5 fates. Moreover, the authors show that in the absence of Bsh, L4 and L5 fates are transformed into a L1 or L3-like fates.

Thanks!

Finally, the authors used DamID and Bsh:DamID to profile the open chromatin signature and the Bsh binding sites in L4 neurons at the synaptogenesis stage. This allows the identification of putative Bsh target genes in L4, many of which were also found to be upregulated in L4 in a previous single-cell transcriptomic analysis. Among these genes, the paper focuses on Dip-β, a known regulator of L4 connectivity. They demonstrate that both Bsh and Ap are required for Dip-β, forming a feed-forward loop. Indeed, the loss of Bsh causes abnormal L4 synaptogenesis and therefore defects in several visual behaviors. The authors also propose the intriguing hypothesis that the expression of Bsh expanded the diversity of Lamina neurons from a 3 cell-type state to the current 5 cell-type state in the optic lobe.

Thanks for the excellent summary of our findings!

Strengths:

Overall, this work presents a beautiful practical example of the framework of terminal selectors: Bsh acts hierarchically with Ap or Pdm3 to establish the L4 or L5 cell fates and, at least in L4, participates in the expression of terminal features of the neuron (i.e., synaptogenesis through Dip-β regulation).

Thanks!

The hierarchical interactions among Bsh and the activation of Ap and Pdm3 expression in L4 and L5, respectively, are well established experimentally. Using different genetic drivers, the authors show a window of competence during L4 neuron specification during which Bsh activates Ap expression. Later, as the neuron matures, Ap becomes independent of Bsh. This allows the authors to propose a coherent and well-supported model in which Bsh acts as a 'primary' selector that activates the expression of L4-specific (Ap) and L5-specific (Pdm3) 'secondary' selector genes, that together establish neuronal fate.

Thanks again!

Importantly, the authors describe a striking cell fate change when Bsh is knocked down from L4/L5 progenitor cells. In such cases, L1 and L3 neurons are generated at the expense of L4 and L5. The paper demonstrates that Bsh in L4/L5 represses Zfh1, which in turn acts as the primary selector for L1/L3 fates. These results point to a model where the acquisition of Bsh during evolution might have provided the grounds for the generation of new cell types, L4 and L5, expanding lamina neuronal diversity for a more refined visual behaviors in flies. This is an intriguing and novel hypothesis that should be tested from an evo-devo standpoint, for instance by identifying a species when L4 and L5 do not exist and/or Bsh is not expressed in L neurons.

Thanks for the appreciation of our findings!

To gain insight into how Bsh regulates neuronal fate and terminal features, the authors have profiled the open chromatin landscape and Bsh binding sites in L4 neurons at mid-pupation using the DamID technique. The paper describes a number of genes that have Bsh binding peaks in their regulatory regions and that are differentially expressed in L4 neurons, based on available scRNAseq data. Although the manuscript does not explore this candidate list in depth, many of these genes belong to classes that might explain terminal features of L4 neurons, such as neurotransmitter identity, neuropeptides or cytoskeletal regulators. Interestingly, one of these upregulated genes with a Bsh peak is Dip-β, an immunoglobulin superfamily protein that has been described by previous work from the author's lab to be relevant to establish L4 proper connectivity. This work proves that Bsh and Ap work in a feed-forward loop to regulate Dip-β expression, and therefore to establish normal L4 synapses. Furthermore, Bsh loss of function in L4 causes impairs visual behaviors.

Thanks for the excellent summary of our findings.

Weaknesses:

● The last paragraph of the introduction is written using rhetorical questions and does not read well. I suggest rewriting it in a more conventional direct style to improve readability.

We agree, and will update the text as suggested.

● A significant concern is the way in which information is conveyed in the Figures. Throughout the paper, understanding of the experimental results is hindered by the lack of information in the Figure headers. Specifically, the genetic driver used for each panel should be adequately noted, together with the age of the brain and the experimental condition. For example, R27G05-Gal4 drives early expression in LPCs and L4/L5, while the 31C06-AD, 34G07-DBD Split-Gal4 combination drives expression in older L4 neurons, and the use of one or the other to drive Bsh-KD has dramatic differences in Ap expression. The indication of the driver used in each panel will facilitate the reader's grasp of the experimental results.

We agree, and will update the figure annotation.

● Bsh role in L4/L5 cell fate:

o It is not clear whether Tll+/Bsh+ LPCs are the precursors of L4/L5. Morphologically, these cells sit very close to L5, but are much more distant from L4.

Our current data show L4 and L5 neurons are generated by different LPCs. However, currently we don’t have tools to demonstrate which subset of LPCs generate which lamina neuron type. We are currently working on a followup manuscript on LPC heterogeneity, but those experiments have just barely been started.

o Somatic CRISPR knockout of Bsh seems to have a weaker phenotype than the knockdown using RNAi. However, in several experiments down the line, the authors use CRISPR-KO rather than RNAi to knock down Bsh activity: it should be explained why the authors made this decision. Alternatively, a null mutant could be used to consolidate the loss of function phenotype, although this is not strictly necessary given that the RNAi is highly efficient and almost completely abolishes Bsh protein.

The reason we chose CRISPR-KO (L4-specific Gal4, uas-Cas9, and uas-Bsh-sgRNAs) is that it effectively removed Bsh expression from majority of L4 neurons. However, it failed to knock down Bsh in L4 neurons using L4-split Gal4 and Bsh-RNAi because L4-split Gal4 expression depends on Bsh. We will include this explanation in the text.

o Line 102: Rephrase "R27G05-Gal4 is expressed in all LPCs and turned off in lamina neurons" to "is turned off as lamina neurons mature", as it is kept on for a significant amount of time after the neurons have already been specified.

Thanks; we will make that change.

o Line 121: "(a) that all known lamina neuron markers become independent of Bsh regulation in neurons" is not an accurate statement, as the markers tested were not shown to be dependent on Bsh in the first place.

Good point. We will rephrase it as “that all known lamina neuron markers are independent of Bsh regulation in neurons”.

o Lines 129-134: Make explicit that the LPC-Gal4 was used in this experiment. This is especially important here, as these results are opposite to the Bsh Loss of Function in L4 neurons described in the previous section. This will help clarify the window of competence in which Bsh establishes L4/L5 neuronal identities through ap/pdm3 expression.

Thanks! We will include Gal4 information in the text for every manipulation.

● DamID and Bsh binding profile:

○ Figure 5 - figure supplement 1C-E: The genotype of the Control in (C) has to be described within the panel. As it is, it can be confused with a wild type brain, when it is in fact a Bsh-KO mutant.

Great point! Thank you for catching this and we will update it.

○ It Is not clear how L4-specific Differentially Expressed Genes were found. Are these genes DEG between Lamina neurons types, or are they upregulated genes with respect to all neuronal clusters? If the latter is the case, it could explain the discrepancy between scRNAseq DEGs and Bsh peaks in L4 neurons.

We did not use “L4-specific Differentially Expressed Genes”. Instead, we used all genes that are significantly transcribed in L4 neurons (line 209-210).

● Dip-β regulation:

○ Line 234: It is not clear why CRISPR KO is used in this case, when Bsh-RNAi presents a stronger phenotype.

As we explained it above, the reason we chose CRISPR-KO (L4-specific Gal4, uas-Cas9, and uas-Bsh-sgRNAs) is that it effectively removed Bsh expression from majority of L4 neurons. However, it failed to knock down Bsh in L4 neurons using L4-split Gal4 and Bsh-RNAi because L4-split Gal4 expression depends on Bsh. We’ll include this explanation in the text.

○ Figure 6N-R shows results using LPC-Gal4. It is not clear why this driver was used, as it makes a less accurate comparison with the other panels in the figure, which use L4-Split-Gal4. This discrepancy should be acknowledged and explained, or the experiment repeated with L4-Split-Gal4>Ap-RNAi.

I think you mean 6J-M shows results using LPC-Gal4. We first tried L4-Split-Gal4>Ap-RNAi but it failed to knock down Ap because L4-Split-Gal4 expression depends on Ap. We will add this to the text.

○ Line 271: It is also possible that L4 activity is dispensable for motion detection and only L5 is required.

Thanks! Work from Tuthill et al, 2013 showed that L5 is not required for any motion detection. We will include this citation in the text.

● Discussion: It is necessary to de-emphasize the relevance of HDTFs, or at least acknowledge that other, non-homeodomain TFs, can act as selector genes to determine neuronal identity. By restricting the discussion to HDTFs, it is not mentioned that other classes of TFs could follow the same Primary-Secondary selector activation logic.

That is a great point, thank you! We will include this in the discussion.

eLife assessment

This paper, offering insights into the mechanisms of neuronal cell type diversification, provides important findings that have theoretical or practical implications beyond a single subfield. The data are compelling and provide evidence that features methods, data and analyses that are more rigorous than the current state-of-the-art.

Reviewer #1 (Public Review):

In this very strong and interesting paper the authors present a convincing series of experiments that reveal molecular mechanism of neuronal cell type diversification in the nervous system of Drosophila. The authors show that a homeodomain transcription factor, Bsh, fulfills several critical functions - repressing an alternative fate and inducing downstream homeodomain transcription factors with whom Bsh may collaborate to induce L4 and L5 fates (the author's accompanying paper reveals how Bsh can induce two distinct fates). The authors make elegant use of powerful genetic tools and an arsenal of satisfying cell identity markers.

I believe that this is an important study because it provides some fundamental insights into the conservation of neuronal diversification programs. It is very satisfying to see that similar organizational principles apply in different organisms to generate cell type diversity. The authors should also be commended for contextualizing their work very well, giving a broad, scholarly background to the problem of neuronal cell type diversification.

My one suggestion for the authors is to perhaps address in the Discussion (or experimentally address if they wish) how they reconcile that Bsh is on the one hand: (a) continuously expressed in L4/L4, (b) binding directly to a cohort of terminal effectors that are also continuously expressed but then, on the other hand, is not required for their maintaining L4 fate? A few questions: Is Bsh only NOT required for maintaining Ap expression or is it also NOT required for maintaining other terminal markers of L4? The former could be easily explained - Bsh simply kicks of Ap, Ap then autoregulates, but Bsh and Ap then continuously activate terminal effector genes. The second scenario would require a little more complex mechanism: Bsh binding of targets (with Notch) may open chromatin, but then once that's done, Bsh is no longer needed and Ap alone can continue to express genes. I feel that the authors should be at least discussing this. The postmitotic Bsh removal experiment in which they only checked Ap and depression of other markers is a little unsatisfying without further discussion (or experiments, such as testing terminal L4 markers). I hasten to add that this comment does not take away from my overall appreciation for the depth and quality of the data and the importance of their conclusions.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors explore the role of the Homeodomain Transcription Factor Bsh in the specification of Lamina neuronal types in the optic lobe of Drosophila. Using the framework of terminal selector genes and compelling data, they investigate whether the same factor that establishes early cell identity is responsible for the acquisition of terminal features of the neuron (i.e., cell connectivity and synaptogenesis).

The authors convincingly describe the sequential expression and activity of Bsh, termed here as 'primary HDTF', and of Ap in L4 or Pdm3 in L5 as 'secondary HDTFs' during the specification of these two neurons. The study demonstrates the requirement of Bsh to activate either Ap and Pdm3, and therefore to generate the L4 and L5 fates. Moreover, the authors show that in the absence of Bsh, L4 and L5 fates are transformed into a L1 or L3-like fates.

Finally, the authors used DamID and Bsh:DamID to profile the open chromatin signature and the Bsh binding sites in L4 neurons at the synaptogenesis stage. This allows the identification of putative Bsh target genes in L4, many of which were also found to be upregulated in L4 in a previous single-cell transcriptomic analysis. Among these genes, the paper focuses on Dip-β, a known regulator of L4 connectivity. They demonstrate that both Bsh and Ap are required for Dip-β, forming a feed-forward loop. Indeed, the loss of Bsh causes abnormal L4 synaptogenesis and therefore defects in several visual behaviors.

The authors also propose the intriguing hypothesis that the expression of Bsh expanded the diversity of Lamina neurons from a 3 cell-type state to the current 5 cell-type state in the optic lobe.

Strengths:

Overall, this work presents a beautiful practical example of the framework of terminal selectors: Bsh acts hierarchically with Ap or Pdm3 to establish the L4 or L5 cell fates and, at least in L4, participates in the expression of terminal features of the neuron (i.e., synaptogenesis through Dip-β regulation).

The hierarchical interactions among Bsh and the activation of Ap and Pdm3 expression in L4 and L5, respectively, are well established experimentally. Using different genetic drivers, the authors show a window of competence during L4 neuron specification during which Bsh activates Ap expression. Later, as the neuron matures, Ap becomes independent of Bsh. This allows the authors to propose a coherent and well-supported model in which Bsh acts as a 'primary' selector that activates the expression of L4-specific (Ap) and L5-specific (Pdm3) 'secondary' selector genes, that together establish neuronal fate.

Importantly, the authors describe a striking cell fate change when Bsh is knocked down from L4/L5 progenitor cells. In such cases, L1 and L3 neurons are generated at the expense of L4 and L5. The paper demonstrates that Bsh in L4/L5 represses Zfh1, which in turn acts as the primary selector for L1/L3 fates. These results point to a model where the acquisition of Bsh during evolution might have provided the grounds for the generation of new cell types, L4 and L5, expanding lamina neuronal diversity for a more refined visual behaviors in flies. This is an intriguing and novel hypothesis that should be tested from an evo-devo standpoint, for instance by identifying a species when L4 and L5 do not exist and/or Bsh is not expressed in L neurons.

To gain insight into how Bsh regulates neuronal fate and terminal features, the authors have profiled the open chromatin landscape and Bsh binding sites in L4 neurons at mid-pupation using the DamID technique. The paper describes a number of genes that have Bsh binding peaks in their regulatory regions and that are differentially expressed in L4 neurons, based on available scRNAseq data. Although the manuscript does not explore this candidate list in depth, many of these genes belong to classes that might explain terminal features of L4 neurons, such as neurotransmitter identity, neuropeptides or cytoskeletal regulators. Interestingly, one of these upregulated genes with a Bsh peak is Dip-β, an immunoglobulin superfamily protein that has been described by previous work from the author's lab to be relevant to establish L4 proper connectivity. This work proves that Bsh and Ap work in a feed-forward loop to regulate Dip-β expression, and therefore to establish normal L4 synapses. Furthermore, Bsh loss of function in L4 causes impairs visual behaviors.

Weaknesses:

● The last paragraph of the introduction is written using rhetorical questions and does not read well. I suggest rewriting it in a more conventional direct style to improve readability.

● A significant concern is the way in which information is conveyed in the Figures. Throughout the paper, understanding of the experimental results is hindered by the lack of information in the Figure headers. Specifically, the genetic driver used for each panel should be adequately noted, together with the age of the brain and the experimental condition. For example, R27G05-Gal4 drives early expression in LPCs and L4/L5, while the 31C06-AD, 34G07-DBD Split-Gal4 combination drives expression in older L4 neurons, and the use of one or the other to drive Bsh-KD has dramatic differences in Ap expression. The indication of the driver used in each panel will facilitate the reader's grasp of the experimental results.

● Bsh role in L4/L5 cell fate:<br /> o It is not clear whether Tll+/Bsh+ LPCs are the precursors of L4/L5. Morphologically, these cells sit very close to L5, but are much more distant from L4.<br /> o Somatic CRISPR knockout of Bsh seems to have a weaker phenotype than the knockdown using RNAi. However, in several experiments down the line, the authors use CRISPR-KO rather than RNAi to knock down Bsh activity: it should be explained why the authors made this decision. Alternatively, a null mutant could be used to consolidate the loss of function phenotype, although this is not strictly necessary given that the RNAi is highly efficient and almost completely abolishes Bsh protein.<br /> o Line 102: Rephrase "R27G05-Gal4 is expressed in all LPCs and turned off in lamina neurons" to "is turned off as lamina neurons mature", as it is kept on for a significant amount of time after the neurons have already been specified.<br /> o Line 121: "(a) that all known lamina neuron markers become independent of Bsh regulation in neurons" is not an accurate statement, as the markers tested were not shown to be dependent on Bsh in the first place.<br /> o Lines 129-134: Make explicit that the LPC-Gal4 was used in this experiment. This is especially important here, as these results are opposite to the Bsh Loss of Function in L4 neurons described in the previous section. This will help clarify the window of competence in which Bsh establishes L4/L5 neuronal identities through ap/pdm3 expression.

● DamID and Bsh binding profile:<br /> ○ Figure 5 - figure supplement 1C-E: The genotype of the Control in (C) has to be described within the panel. As it is, it can be confused with a wild type brain, when it is in fact a Bsh-KO mutant.<br /> ○ It Is not clear how L4-specific Differentially Expressed Genes were found. Are these genes DEG between Lamina neurons types, or are they upregulated genes with respect to all neuronal clusters? If the latter is the case, it could explain the discrepancy between scRNAseq DEGs and Bsh peaks in L4 neurons.

● Dip-β regulation:<br /> ○ Line 234: It is not clear why CRISPR KO is used in this case, when Bsh-RNAi presents a stronger phenotype.<br /> ○ Figure 6N-R shows results using LPC-Gal4. It is not clear why this driver was used, as it makes a less accurate comparison with the other panels in the figure, which use L4-Split-Gal4. This discrepancy should be acknowledged and explained, or the experiment repeated with L4-Split-Gal4>Ap-RNAi.<br /> ○ Line 271: It is also possible that L4 activity is dispensable for motion detection and only L5 is required.

● Discussion: It is necessary to de-emphasize the relevance of HDTFs, or at least acknowledge that other, non-homeodomain TFs, can act as selector genes to determine neuronal identity. By restricting the discussion to HDTFs, it is not mentioned that other classes of TFs could follow the same Primary-Secondary selector activation logic.

Reviewer #1 (Public Review):

Summary:<br /> This work examined transcription factor Meis2 in the development of mouse and chick DRG neurons, using a combination of techniques, such as the generation of a new conditional mutant strain of Meis2, behavioral assays, in situ hybridization, transcriptomic study, immunohistochemistry, and electrophysiological (ex vivo skin-nerve preparation) recordings. The authors found that Meis2 was selectively expressed in A fiber LTMRs and that its disruption affects the A-LTMRs' end-organ innervation, transcriptome, electrophysiological properties, and light touch-sensation.

Strengths:<br /> 1) The authors utilized a well-designed mouse genetics strategy to generate a mouse model where the Meis2 is selectively ablated from pre- and post-mitotic mouse DRG neurons. They used a combination of readouts, such as in situ hybridization, immunhistochemistry, transcriptomic analysis, skin-nerve preparation, electrophysiological recordings, and behavioral assays to determine the role of Meis2 in mouse DRG afferents.

2) They observed a similar preferential expression of Meis2 in large-diameter DRG neurons during development in chicken, suggesting evolutionarily conserved functions of this transcription factor.

3) Conducted severe behavioral assays to probe the reduction of light-touch sensitivity in mouse glabrous and hairy skin. Their behavioral findings support the idea that the function of Meis2 is essential for the development and/or maturation of LTMRs.

4) RNAseq data provide potential molecular pathways through which Meis2 regulates embryonic target-field innervation.

5) Well-performed electrophysiological study using skin-nerve preparation and recordings from saphenous and tibial nerves to investigate physiological deficits of Meis2 mutant sensory afferents.

6) Nice whole-mount IHC of the hair skin, convincingly showing morphological deficits of Meis2 mutant SA- and RA- LTMRs.

Overall, this manuscript is well-written. The experimental design and data quality are good, and the conclusion from the experimental results is logical.

Weaknesses:<br /> 1) Although the authors justify this study for the involvement of Meis2 in Autism and Autism associated disorders, no experiments really investigated Autism-like specific behavior in the Meis2 ablated mice.

2) For mechanical force sensing-related behavioral assays, the authors performed VFH and dynamic cotton swabs for the glabrous skin, and sticky tape on the back (hairy skin) for the hairy skin. A few additional experiments involving glabrous skin plantar surfaces, such as stick tape or flow texture discrimination, would make the conclusion stronger.

3) The authors considered von Frey filaments (1 and 1.4 g) as noxious mechanical stimuli (Figure 1E and statement on lines 181-183), which is questionable. Alligator clips or pinpricks are more certain to activate mechanical nociceptors.

4) There are disconnections and inconsistencies among findings from morphological characterization, physiological recordings, and behavior assays. For example, Meis2 mutant SA-LTMRs show a deficiency in Merkel cell innervation in the glabrous skin but not in hairy skin. With no clear justification, the authors pooled recordings of SA-LTMRs from both glabrous and hairy skin and found a significant increase in mean vibration threshold. Will the results be significantly different if the data are analyzed separately? In addition, whole-mount IHC of Meissner's corpuscles showed morphological changes, but electrophysiological recordings didn't find significant alternation of RAI LTMRs. What does the morphological change mean then? Since the authors found that Meis2 mice are less sensitive to a dynamic cotton swab, which is usually considered as an RA-LTMR mediated behavior, is the SAI-LTMR deficit here responsible for this behavior? Connections among results from different methods are not clear, and the inconsistency should be discussed.

Reviewer #2 (Public Review):

Summary:<br /> Desiderio and colleagues investigated the role of the TALE (three amino acid loop extension) homeodomain transcription factor Meis2 during maturation and target innervation of mechanoreceptors and their sensation to touch. They start with a series of careful in situ hybridizations to examine Meis2 transcript expression in mouse and chick DRGs of different embryonic stages. By this approach, they identify Meis2+ neurons as slowly- and rapidly adapting A-beta LTMRs, respectively. Retrograde tracing experiments in newborn mice confirmed that Meis2-expressing sensory neurons project to the skin, while unilateral limb bud ablations in chick embryos in Ovo showed that these neurons require target-derived signals for survival. The authors further generated a conditional knock-out (cKO) mouse model in which Meis2 is selectively lost in Islet1-expressing, postmitotic neurons in the DRG (IsletCre/+::Meis2flox/flox, abbreviated below as cKO). WT and Islet1Cre/+ littermates served as controls. cKO mice did not exhibit any obvious alteration in volume or cellular composition of the DRGs but showed significantly reduced sensitivity to touch stimuli and various innervation defects to different end-organ targets. RNA-sequencing experiments of E18.5 DRGs taken from WT, Islet1Cre/+, and cKO mice reveal extensive gene expression differences between cKO cells and the two controls, including synaptic proteins and components of the GABAergic signaling system. Gene expression also differed considerably between WT and heterozygous Islet1Cre/+ mice while several of the other parameters tested did not. These findings suggest that Islet1 heterozygosity affects gene expression in sensory neurons but not sensory neuron functionality. However, only some of the parameters tested were assessed for all three genotypes. Histological analysis and electrophysiological recordings shed light on the physiological defects resulting from the loss of Meis2. By immunohistochemical approaches, the authors describe distinct innervation defects in glabrous and hairy skin (reduced innervation of Merkel cells by SA1-LTMRs in glabrous but not hairy skin, reduced complexity of A-beta RA1-LTMs innervating Meissner's corpuscles in glabrous skin, reduced branching and innervation of A-betA RA1-LTMRs in hairy skin). Electrophysiological recordings from ex vivo skin nerve preparations found that several, but not all of these histological defects are matched by altered responses to external stimuli, indicating that compensation may play a considerable role in this system.

Strengths:<br /> This is a well-conducted study that combines different experimental approaches to convincingly show that the transcription factor Meis2 plays an important role in the perception of light touch. The authors describe a new mouse model for compromised touch sensation and identify a number of genes whose expression depends on Meis2 in mouse DRGs. Given that dysbalanced MEIS2 expression in humans has been linked to autism and that autism seems to involve an inappropriate response to light touch, the present study makes a novel and important link between this gene and ASD.

Weaknesses:<br /> The authors make use of different experimental approaches to investigate the role of Meis2 in touch sensation, but the results obtained by these techniques could be connected better. For instance, the authors identify several genes involved in synapse formation, synaptic transmission, neuronal projections, or axon and dendrite maturation that are up- or downregulated upon targeted Meis2 deletion, but it is unresolved whether these chances can in any way explain the histological, electrophysiological, or behavioral deficits observed in cKO animals. The use of two different controls (WT and Islet1Cre/+) is unsatisfactory and it is not clear why some parameters were studied in all three genotypes (WT, Islet1Cre/+ and cKO) and others only in WT and cKO. In addition, Meis2 mutant mice apparently are less responsive to touch, whereas in humans, mutation or genomic deletion involving the MEIS2 gene locus is associated with ASD, a condition that, if anything, is associated with an elevated sensitivity to touch. It would be interesting to know how the authors reconcile these two findings. A minor weakness, the first manuscript suffers from some ambiguities and errors, but these can be easily corrected.

Author Response

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

This important study shows that two methods of sleep induction in the fly, optogenetically activation of the dorsal fan-shaped body (which is rapidly reversible and maintains a neuronal activity signature similar to wakefulness), and Gaboxadol-induced sleep (which shuts down neuronal activity), produce distinct forms of sleep and have different effects on brain-wide neural activity. The majority of the conclusions of the paper are supported by compelling data, but the evidence supporting the claim that the two interventions trigger distinct transcriptional responses is incomplete.

Thank you for the helpful and detailed reviews. We feel that these have improved the manuscript considerably, and hopefully the additional figures in this Reply letter will help further convince our readers.

Public Review

In this study, Anthoney and coworkers continue an important, unique, and technologically innovative line of inquiry from the van Swinderen lab aimed at furthering our understanding of the different sleep stages that may exist in Drosophila. Here, they compare the physiological and transcriptional hallmarks of sleep that have been induced by two distinct means, a pharmacological block of GABA signaling and optogenetic activation of dorsal fan-shaped-body neurons. They first employ an incredibly impressive fly-on-the-ball 2-photon functional imaging setup to monitor neural activity during these interventions, and then perform bulk RNA sequencing of fly brains at different stages. These transcriptomic analyses leads them to (a) knocking out nicotinic acetyl-choline receptor subunits and (b) knocking down AkhR throughout the fly brain testing the impact of these genetic interventions on sleep behaviors in flies. Based on this work, the authors present evidence that optogenetically and pharmacologically induced sleep produces highly distinct brain-wide effects on physiology and transcription. The study is of significant interest, is easy to read, and the figures are mostly informative. However there are features of the experimental design and the interpretation of results that diminish enthusiasm.

a- Conditions under which sleep is induced for behavioral vs neural and transcriptional studies

1- There is a major conceptual concern regarding the relationships between the physiological and transcriptomic effects of optogenetic and pharmacological sleep promotion, and the effects that these manipulations have on sleep behavior. The authors show that these two means of sleep-induction produce remarkably distinct physiological and transcriptional responses, however, they also show that they produce highly similar effects on sleep behavior, causing an increase in sleep through increases in the duration of sleep bouts. If dFB neurons were promoting active sleep, the sleep it produces should be more fragmented than the sleep induced by the drug, because the latter is supposed to produce quiet sleep. Yet both manipulations seem to be biasing behavior toward quiet sleep.

This is a correct observation, which is already evident in our sleep architecture data (Figure 2E-H): chronic optogenetic sleep induction promotes longer sleep bouts that are similar in structure (bout number vs bout duration) to those produced by THIP feeding. Since our plots in Figure 2E-H follow the 5min sleep criterion cutoff, upon the Reviewer’s advice we re-analyzed our optogenetic experiments for short (1-5min) sleep. These are graphed below in Author response image 1. As can be seen, and as suspected by the Reviewer, the optogenetic manipulation does not increase the total amount of short sleep; indeed, it decreases it compared to baseline (these are for the exact same data as in Figure 2). Optogenetic sleep induction does not create a bunch of short sleep bouts.

Author response image 1.

Short sleep in optogenetic experiments. A. Average baseline (±SEM) 1-5min sleep across a day and night. B. Average (±SEM) 1-5min sleep in optogenenetically-activated flies, across a day and night.

We agree with the reviewer that this observation might seem inconsistent with the idea that optogenetic activation promotes active sleep, and that short sleep is active sleep. However, it does not necessarily follow that optogenetic activation has to produce short sleep. Indeed, we know from our brain imaging data (and the associated behavioral analysis) that active sleep will persist for as long as we induce it with red light. While we have not induced it for longer than 15 minutes (Tainton-Heap et al, Current Biology, 2021; Troup et al, J. of Neuroscience, 2023), this is already clearly longer than a <5min sleep bout. So our interpretation is that the longer sleep bouts induced by optogenetic activation are prolonged active sleep, rather than quiet sleep. In other words, this artificial sleep manipulation induces prolonged active sleep, rather than many short sleep bouts. This is of course different than what happens during spontaneous sleep. We have tried to be clearer about sleep bout durations in the revised manuscript (e.g., the new Figure 3), and we now admit early in the results (lines 376-380) that that we don’t know what optogenetic activation looks like in the fly brain beyond 15 minutes.

2- The authors show that the pharmacological block of GABA signaling and the optogenetic activation of dorsal fan-shaped-body neurons cause different responses on brain activity. Based on these recordings and the behavioral and brain transcriptomic data they then claim that these responses correspond to different sleep states and are associated with the expression and repression of a different constellation of genes. Nevertheless, neural activity in animals was recorded following short stimulations whereas behavioral and transcriptomic data were obtained following chronic stimulation. In this regard, it would be interesting to determine how the 12-hour pharmacological intervention they employed for their transcriptomic analysis changes neural activity throughout the brain - 12 hours will likely be too long for the open-cuticle preps, but an in-between time-point (e.g. 1h) would probably be equally informative.

The longest we’ve imaged brain activity for optogenetic sleep induction is 15 minutes, as discussed above. We see no changes in activity across this time, which would normally have led to a quiet sleep stage in spontaneous sleep recordings. Whole-brain imaging after 10 hours of optogenetic sleep induction (our RNA collection timepoint) is not realistic, and even 1 hour is difficult. We have however conducted overnight electrophysiological recordings (with multichannel silicon probes), where we activated the same R23E10 neurons for successive 20-minute bouts (alternating with 20min of no red light). We are preparing this work for publication (Van De Poll, et al). We see no evidence of optogenetic activation of this circuit ever producing anything resembling quiet sleep. Since we are not in a position to provide this new electrophysiological data in the current study, we are careful to clarify that we have not investigated what brain imaging looks like after chronic optogenetic activation (lines 376-380). We are showing through diverse lines of evidence that what is called sleep can look different in flies.

b- Efficiency of THIP treatment under different conditions

1- There are no data to quantify how THIP alters food consumption. It is evident that flies consume it otherwise they would not show increased sleep. However, they may consume different amounts of food overall than the minus THIP controls. This might have an influence on the animal's metabolism, which could at least explain the fact that metabolism-related genes are regulated (Figure 5). Therefore, in the current state, it is not possible to be certain that gene regulation events measured in this experiment are solely due to THIP effects on sleep.

We have two arguments against this reasonable criticism. First, as discussed above, the optogenetic flies are sleeping at least as much as the THIP-fed flies, so in principle they also might be feeding less. But we see no metabolic gene downregulation in the optogenetic dataset. We include this counterargument in the discussion (lines 752-756). Then, together with our co-author Paul Shaw we have shown that THIP-fed flies are not eating less compared to controls (Dissel et al, Current Biology, 2015), by tracking dye consumption. We show those results again below in Author response image 2 to support our reasoning that feeding is not an issue.

Author response image 2.

Flies were fed blue dye in their food while being sleep deprived (SD), or while being induced to sleep with 0.1mg/ml THIP in their food, or both. Dye consumption was measured in triplicate for pooled groups of 16 flies. Average absorbance at 625nm (±stan dev) is shown. Experiments were not significantly different (ANOVA of means).

2- A similar problem exists in the sleep deprivation experiments. If flies are snapped every 20 seconds, they may not have the freedom to consume appropriate amounts of food, and therefore their consumption of THIP or ATR may be smaller than in non-sleep deprived controls. Thus, it would be crucial to know whether the flies that are sleep-deprived (i.e. shaken every 20 seconds for 12 hours) actually consume comparable amounts of food (and therefore THIP) as those that are undisturbed. If not, then perhaps the transcriptional differences between the two groups are not sleep-specific, but instead reflect varying degrees of exposure to THIP.

Please see our response to the similar critique above, and how Figure R2 addresses this concern.

3- The authors should further discuss the slow action of THIP perfusion vs dFB activation, especially as flies only seem to fall asleep several minutes after THIP is being washed away. Is it a technical artifact? If not, it may not be unreasonable to hypothesize that THIP, at the concentration used, could prevent flies from falling asleep, and that its removal may lower the concentration to a point that allows its sleep-promoting action. The authors could easily test this by extending THIP treatment for another 4-5 minutes.

The reviewer is partially correct in suggesting a technical artifact: THIP does not get washed away immediately after 5min of perfusion. The drip system we employ means that THIP concentration will slowly increase to the maximum concentration of 0.2mg/ml, and then slowly get diluted away at a rate of 1.25ml/minute (this is all in the Methods). In a previous study (Yap et al, Nature Communications, 2017) we used this exact same perfusion procedure to test a range of THIP concentrations, and settled on 0.2mg/ml as the lowest that reliably induced quiet sleep within 5 minutes. Higher concentrations induced quiet sleep faster, so the alternate explanation proposed by the Reviewer is not supported. We feel that our previous electrophysiological study provided the necessary groundwork for using the same approach and dosage here for our whole-brain imaging readout.

c- Comments regarding the behavioral assays

1- L319-322: the authors conclude that dFB stimulation and THIP consumption have similar behavioral effects on sleep. However, this is inaccurate as in Figure S1 they explain that one increases bout number in both day and night and the other one only during the day.

We have now added a caveat about night bout architecture being different (lines 353-356). Figure S1 is now Figure 3.

2- The behavioral definitions used for active and quiet sleep do not fit well with strong evidence that deep sleep (defined by lowered metabolic rates) is probably most closely associated with bouts of inactivity that are much longer than the >5min duration used here, i.e., probably 30min and longer (Stahl et al. 2017 Sleep 40: zsx084). Given that the authors are providing evidence that quiet sleep is correlated with changes in the expression of metabolism related genes, they should at least discuss the fact that reductions in metabolism have been shown to occur after relatively long bouts of inactivity and might reconsider their behavioral sleep analysis (i.e., their criteria for sleep state) with this in mind.

Interestingly, induced sleep bout durations are on average longer for the optogenetic manipulation (40min vs 25min); this was evident in Figure S1C vs S1F (now Figure 3). So as discussed above, this provides a counterargument for sleep bout duration alone being indicative of metabolic processes associated with quiet sleep: the optogenetic dataset did not uncover metabolic-related pathways as relevant to that sleep manipulation. We refer to Stahl et al, Sleep, 2017, in our discussion (lines 748-750), making exactly this point about metabolic rates being decreased in longer sleep bouts, and flowing up with our observation that optogenetic flies sleep just as much, and their bouts are actually longer. So clearly different processes must be involved.

d- Comments regarding the recordings of neuronal activity

1- There is an additional concern regarding the proposed active and quiet sleep states that rest at the heart of this study. Here these two states in the fly are compared to the REM and NREM sleep states observed in mammals and the parallels between active fly sleep and REM and quiet fly sleep and NREM provide the framework for the study. The establishment of such parallel sleep states in the fly is highly significant and identifying the physiological and molecular correlates of distinct sleep stages in the fly is of critical importance to the field. However, the proposal that the dorsal fan shaped body (dFB) neurons promote active sleep runs counter to the prevailing model that these neurons act as a major site of sleep homeostasis. If quiet sleep were akin to NREM, wouldn't we expect the major site of sleep homeostasis in the brain to promote it? Furthermore, the authors state that the effects of dFB neuron excitation on transcription have "almost no overlap" (line 500) with the transcriptomic effects of sleep deprivation (Supplementary Table 3), which is not what would be expected if dFB neurons are tracking sleep pressure and promoting sleep, as suggested by a growing body of convergent work summarized on page four of the manuscript. Wouldn't the 10h excitation of the dFB neurons be predicted to mimic the effects of sleep deprivation if these neurons "...serve as the discharge circuit for the insect's sleep homeostat..." (line 60)? Shouldn't their prolonged excitation produce an artificial increase in sleep drive (even during sleep) that would favor deep, restorative sleep? How do the authors interpret their results with regard to the current prevailing model that dFB neurons act as a major site of sleep homeostasis? This study could be seen as evidence against it, but the authors do not discuss this in their Discussion.

These are all excellent and thoughtful points, which have made us re-think parts of our discussion. First off, the potential comparison with REM and NREM is entirely speculative, and we have tried to make that more obvious in introduction) and the discussion (e.g, see lines 43, 708, 818). The evidence that the FB neurons (and maybe others) are involved in the homeostatic regulation of sleep is well-supported in the literature, so that part of the discussion holds. However, we concede that the timing of our sleep manipulations could benefit from more explanation. We conducted these during the flies’ subjective day, after the animals had presumably had a good night’s sleep. This means that we induced either kind of sleep for 10 daytime hours, which presumably replaced whatever behavioural states would ‘naturally’ be happening during the day. Female flies sleep less during the day than at night, and we have shown in previous work that daytime sleep quality is different than night-time sleep (van Alphen et al, Journal of Neuroscience, 2013), leading us to suggest that most ‘deep’ or quiet sleep happens at night, for flies. Following this reasoning, daytime optogenetic activation might not be depriving flies of much quiet sleep, or accumulating a deep sleep drive as the Reviewer proposes. Rather, both induced sleep manipulations could be providing 10 hours of either kind of sleep that the flies don’t really ‘need’. Why did we design it this way? Firstly, we were interested in simply asking what these chronic sleep manipulations do to gene expression in rested flies, and how they might be similar or different. We focussed on daytime manipulations to avoid precisely the confound of sleep pressure, and also because we observed red-light artifacts at night for our optogenetic experiments (which we reported). Our sleep deprivation strategy was designed specifically as a control for the THIP (Gaboxadol) experiments, to control for non-sleep related effects of the drug (see below our rationale for why this was less crucial for the optogenetic experiments). In conclusion, we had a logical rationale for how the experiments were done, centred on the straightforward question of whether these two different approaches to sleep induction were having similar effects in well-rested flies. In retrospect, we were not anticipating the Reviewer’s thoughtful logic regarding the dFB’s potential role in also regulating deep sleep homeostasis. We now provide some discussion along these lines to make readers aware of this line of reasoning, as well as our rationale for why prolonged optogenetic sleep induction was not sleep-depriving (lines 768-777).

2- Regarding the physiological effects of Gaboxadol, to what extent is the quieting induced by this drug reminiscent of physiology of the brains of flies spontaneously meeting the behavioral criterion for quiet sleep? Given the relatively high dose of the drug being delivered to the de-sheathed brain in the imaging experiments (at least when compared to the dose used in the fly food), one worries that the authors may be inducing a highly abnormal brain state that might bear very little resemblance to the deeply sleeping brain under normal conditions. As the authors acknowledge, it is difficult to compare these two situations. Comparing the physiological state of brains put to sleep by Gaboxadol and brains that have spontaneously entered a deep sleep state therefore seems critical.

As discussed above, our Gaboxadol (THIP) perfusion concentration (0.2mg/ml) was the minimal dosage that effectively induced sleep within 5 minutes, based upon previously published work (Yap et al, Nature Communications, 2017). Lower concentrations were unreliable, with some never inducing sleep at all. Comparisons with feeding THIP are tenuous, and we make that clear in our discussion (lines 731-735). Nevertheless, the Reviewer makes an excellent point about comparisons with spontaneous ‘quiet’ sleep. Here, we feel well supported (please see Author response image 3 below, comparing THIP-induced sleep (this work, B) and spontaneous sleep (A) from previous study). In our previous study (Tainton-Heap et al, 2021) we showed that neural activity and connectivity decreases during spontaneous quiet sleep. This is what we also see with THIP perfusion. In contrast, in Troup et al, J. of Neuroscience (2023) we confirm that neither neural activity nor connectivity changes during optogenetic R23E10 activation, and general anesthesia – unlike THIP – does NOT produce a quiet brain state. Our finding that THIP effects are nothing like general anesthesia (at the level of brain activity levels) suggests a physiological sleep state closer to spontaneous quiet sleep. We elaborate on this important observation in our results, also pointing to crucial differences with general anesthesia (lines 411-415).

Author response image 3.

THIP-induced sleep resembles quiet spontaneous sleep. A. Calcium imaging data from spontaneously sleeping flies, taken from Tainton-Heap et al, 2021. Left, percent neurons active; right, mean degree, a measure connectivity among active neurons. Both measures decrease during later stages of sleep. B. Calcium imaging data from flies induced to sleep with 5min of 0.2mg/ml THIP perfusion (this study). Left, percent neurons active; right, mean degree. Both measures are significantly decreased, resembling the later stages of spontaneous sleep, which we have termed ‘quiet sleep. Hence THIP-induced sleep resembles quiet sleep. Note that the genetic background is different in A and B, hence the different baseline activity levels.

3- There are some issues with Figure 3, in particular 3C-D. It is not clear whether these panels show representative traces or an average, however both the baseline activity and fluorescence are different between C and D, in particular in their amplitude. Therefore, it is difficult to attribute the differences between C and D to the stimulation itself or to the previously different baseline. In addition, the fact that flies with dFB activation seem to keep a basal level of locomotor activity whereas THIP-treated ones don't is quite striking, however it is not being discussed. Finally, the authors claim that the flies eventually wake up from THIP-induced sleep (L360-361), however there are no data to support this statement.

These are representative traces, which is a way of showing the raw calcium data (Cell ID) so readers can see for themselves that one manipulation silences whereas the other does not – even though flies become inactive for both. The Y-axis scale is standard deviation of the experiment mean. Since THIP decreases neural activity, then the baseline is comparatively higher. Since optogenetic activation does not change average neural activity levels, the baseline is centered on zero. This is an outcome of our analysis method and does not reflect any ‘true’ baseline. We have now clarified this in our figure legend. We now also confess that flies rendered asleep optogenetically can be ‘twitchy’ (line 374). Finally, we show data for 3 flies that were recorded until they woke up. The rest were verified behaviorally, after the experiment. This is now explained in the Methods.

4- In Figure 4C, it is strange that the SEM is always exactly the same across the whole experiment. Readers should be aware that there might have been an issue when plotting the figure.

This is not a mistake, the standard errors are just all quite close (between 0.17 and 0.22). This is because of the way we did the analysis, asking how many flies responded to each stimulus event, with incremental levels of responsiveness. This is explained in the Methods. The figure makes the important point of sleep and recovery.

e- Comments regarding the transcript analyses

1- General comment: the title of this manuscript is inaccurate - the "transcriptome" commonly refers to the entirety of all transcripts in a cell/tissue/organ/animal (including genes that are not differentially expressed following their interventions), and it is therefore impossible to "engage two non-overlapping transcriptomes" in the same tissue. Perhaps the word "transcriptional programs" or transcriptional profiles" would be more accurate here?

We thank the Reviewer for this advice and have changed the title as proposed.

2- Given the sensitivity of transcriptomic methods, there is a significant concern that the optogenetic experiments are not as well controlled as they could be. Given the need for supplemental all-trans retinal (ATR) for functional light gating of channelrhodopsins in the fly, it is convenient to use flies with Gal4-driven opsin that have not been given supplemental ATR as a negative control, particularly as a control for the effects of light. However, there is another critical control to do here. Flies bearing the UAS-opsin responder element but lacking the GAL4 driver and that have been fed ATR are critical for confirming that the observed effects of optogenetic stimulation are indeed caused by the specific excitation of the targeted neurons and not due to leaky opsin expression, or the effect of ATR feeding under light stimulation or some combination of these factors. Given the sensitivity of transcriptomic methods, it would be good to see that the candidate transcripts identified by comparing ATR+ and ATR- R23E10GAL4/UAS-Chrimson flies are also apparent when comparing R23E10GAL4/UAS-Chrimson (ATR+) with UAS-Chrimson (ATR+) alone.

We have not done these experiments on UAS-Chrimson/+ controls. Like many others in our field, we viewed non-ATR flies as the best controls, because this involves identical genotypes. Since we were however aware that ATR feeding itself could be affect gene expression, we specifically checked for this with our early (1hour) collection timepoint. We only found 26 gene expression differences between ATR and -ATR flies at this early timepoint, compared with 277 for the 10-hour timepoint. We detail this rationale in our results, explaining why this is a convincing control for ATR feeding. If there was leaky opsin expression / activity, this would have been evident in our design. Regarding the cumulative effect of light, this would also have been accounted in our design, as only 1 hour would have elapsed in our first timepoint compared to 10 hours in our second. While the Reviewer is correct in saying that parental controls are called for in many Drosophila experiments, this becomes quickly unmanageable in transcriptomic studies, which is exactly why well-designed +ATR vs -ATR comparisons in the exact same strain are most appropriate. We feel that our 1-hr timepoint mostly addresses this concern.

3- Figures about qPCR experiments (5G and 6G) are problematic. First, whereas the authors seem satisfied with the 'good correspondence' between their RNA-seq and qPCR results, this is true for only ~9/19 genes in 5G and 2/6 genes in 6G. Whereas discrepancies are not rare between RNA-seq and qPCR, the text in L460-461 and 540-541 is misleading. In addition, it is unclear whether the n=19 in L458 refers to the number of genes tested or the number of replicates. If the qPCR includes replicates, this should be more clearly mentioned, and error bars should be added to the corresponding figures.

We consider that our qPCR validations were convincing, as they were all mostly changed in the ‘right’ direction. We agree that are some discrepancies, so have modified our language to reflect this. We have also clarified that 19 refers to the number of genes validated by qPCR in that THIP dataset. All qPCRs involved three technical replicates. We prefer to keep these histograms the way they are to convey these simple trends. For complete transparency, we now provide a supplemental Excel worksheet with all of the qPCR data, alongside corresponding RNAseq data and stats for the selected genes (Supplementary Table 9).

4- There is a lack of error bars for all their RNAseq and qPCR comparisons, which is particularly surprising because the authors went to great lengths and analyzed an applaudably large amount of independent biological replicates, yet the variability observed in the corresponding molecular data is not reported.

The genes reported in each of our datasets and associated supplemental figures and tables were all significant, as determined by criteria outlined in the Methods. However, we appreciate that readers might want to get a sense of the values and variances involved, as well as access to the entire gene datasets. We now provide all of these as additional ‘sheets’ in our existing supplemental tables (S2-S7), so this should be very easy to navigate and evaluate. In addition to the previously provided lists for significant genes, in the second Excel sheet (‘All genes’) readers will be able to see the data for all 5 replicates, for the significant genes as well as all other ~15,000 genes (listed in alphabetical order). We feel that this will be a helpful resource, because admittedly significance thresholds can still be a little arbitrary and some readers might want to look up ‘their’ genes of interest.

Comments to authors

Other comments

1- Text in L441 & 606 is misleading. According to ref 52, AkhR is involved specifically in starvation-induced sleep loss, and not in general sleep regulation.

Corrected.

2- The language used in L568-570 and 573-574 is confusing. The authors should specify that the knock down of cholinergic subunits, rather than the subunits themselves is what causes sleep to increase or decrease.

Corrected.

3- The authors' investigation of cholinergic receptor subunits function is very preliminary, and it is difficult to draw any conclusion from what is presented here. In particular, their behavioral data is difficult to reconcile with the RNA-seq data showing overexpression of both short sleep increasing and short sleep decreasing subunits. Without knowing where in the brain these subunits are required for controlling sleep, the data in Figure 7 is difficult to appreciate.

We have now conducted additional experiments where we specifically knocked down these alpha receptor subunits (all 7 of them) in the R23E10 neurons. This seemed an obvious knockdown location, to determine if any of these subunits regulated activity in the same sleep promoting neurons that were the focus of this study. We found that alpha1 knockdown in these neurons had similar sleep phenotypes, which we believe is an important result. Since this functional localisation is a logical ending for the paper, we have now made it the final figure.

Suggestions & comments

1- It would be interesting if the authors could discuss their findings that metabolism genes are downregulated in THIP flies in the context of recent work that showed upregulation of mitochondrial ROS after sleep deprivation (Kempf et al, 2019).

We now add the Kempf 2019 reference and allude to how those findings could be consistent with ours.

2- The fact that THIP-induced sleep persists long after THIP removal (Fig 3D) is very intriguing and interesting. This suggests that the drug might trigger a sleep-inducing pathway that can continue on its own without the drug, once activated.

This is correct, and in stark contrast to the optogenetic manipulation we employ, which does not appear to show such sleep inertia. We have now added a sentence highlighting this interesting difference (lines 394-396).

3- The authors identify many new genes regulated in response to specific methods for sleep induction. These are all potentially interesting candidates for further studies investigating the molecular basis of sleep. It would be interesting to know which of these genes are already known to display circadian expression patterns.

By providing all of the gene lists, these are now available to ask questions such as these. We hesitate however to delve into this domain for this work, as our main goal was to compare these two kinds of sleep in flies.

4- The brain-wide monitoring of neural activity invites a number of very exciting follow-up experiments - most importantly, it would be fascinating to establish, which neurons are active in the different phases the authors describe! Are these neurons that are involved in transmitting external visual stimuli to the central brain? Do they also project into the central complex? They could make use of the large collection of existing driver lines in the fly and they could also exploit the extraordinary knowledge of the connectome and transcriptome of the fly brain.

Thank you for sharing our enthusiasm for these likely future directions.

5- The Dalpha2,3,4,6 and 7 Knock-out strains they generate will be a useful reagent for the Drosophila neuroscience community once the efficiency/success of the knock-out has been confirmed by qPCR.

These knockout strains have all been confirmed by our co-authors Hang Luong, Trent Perry, and Philip Batterham. These knockout confirmations are outlined in publications that we reference (Perry et al, 2021).

Materials and methods:

1- This study has employed custom-built apparatus and custom-written code/scripts, but these do not appear to be available to the reader. For the sake of replicability, the authors should make these available.

The code/scripts are available via the University of Queensland research data management system as described in the Methods, and can be sent by the Lead Contact. The imaging hardware and analysis code are identical to what was described in a previous publication, and available as directed therein (Tainton-Heap et al, 2021).

2- Also, the authors should give details on the food used to rear their flies. Fly media comes in several common forms and sleep is sensitive to diet.

This has now been elaborated in the beginning of the Methods.

3- The light regime used for optogenetic excitation of dFB neurons consists of 12h of uninterrupted bright red LED light. Most optogenetic stimulations consist of pulsed high frequency flashes interlaced with pauses in illumination. Can dFB neurons be driven constitutively with 12 hours of bright light?

We showed in Tainton-Heap (2021) that 7Hz pulsed red light had exactly the same effect on R23E10/Chrimson readouts as continuous red light, which is why we opted here to provide continuous red light. That optogenetic sleep induction can be driven continuously for 12 hours is evident by our 24-hour sleep profiles. However, we agree that one could question whether sleep quality is similar after 12 hours. To address this, we did an additional experiment where we stimulated the flies hourly, to determine if their behavioural responsiveness to mechanical stimuli changed over the course of continued sleep induction, for both optogenetic and THIP-induced sleep. We present the data below in Author response image 4. As can be seen in these new analyses, while optogenetic sleep induction persists across 12 daytime hours (speed is close to zero throughout), flies do indeed become more responsive later in the day. This could have two different interpretations: either some sleep functions are being satisfied over time, or the activation regime is becoming less effective over time. Either way, these data show that at our 10-hour daytime timepoint, unstimulated flies are still largely inactive, even though their arousal thresholds might have gradually changed; so the uninterrupted red-light regime is still effective. The comparison with THIP is interesting: here there does not seem to be a change in responsiveness over time; the drug just decreases behavioral responsiveness throughout. Together, these experiments support our view that both approaches are sleep-promoting throughout the 12-hour day, although we appreciate that sleep quality is not identical.

Author response image 4.

A) The average speed of baseline (grey) and optogenetically-activated flies (green) across 24 hours. Red dots indicate vibration stimulus times. B) The average speed of control (grey) and THIP-fed flies (blue) across 24 hours. Flies are all R23E10/Chrimson. N= 87 for optogenetic, n=88 for -THIP, n=85 for +THIP.

4- The authors use the SNAP apparatus to prevent THIP-treated flies from sleeping to tease out possible sleep-independent effects. This is an excellent control. Why have the authors not done the same with the optogenetic treatment? It's surprising not to see this control given the concern the authors express (lines 501 - 502) that the dFB manipulation might be paralyzing awake flies, which certainly seems possible given the light regimes used. Why not test this directly with SNAP?

We appreciate that this may have been a valuable additional control. However, we designed this control for the THIP experiments specifically because of concerns about THIP’s (yet unknown) mechanism of action in flies. THIP is a gabaergic drug with most likely many off-target effects that have little to do with sleep, hence the need for a control where we compare to flies that ingested THIP but have been prevented from sleeping. In contrast, R23E10-driven sleep induction is exactly that, a circuit when activated that induces sleep. Whatever specific neurons might really be involved, the Gal4 circuit is sleep-inducing. This is well supported by multiple publications. The most appropriate control for assessing transcriptomic effects during optogenetic sleep here is not preventing sleep, but rather no increased sleep in flies that have not ingested ATR, and comparing that to effects of ATR alone, which is what we have done. Adding a sleep-deprivation layer onto both of these analyses may have been interesting, but a lot more analyses and not strictly required to identify relevant sleep-related genes. We have rephrased the misleading sentence about paralyzing flies, to instead clarify that lack of overlap with the SD dataset suggests that optogenetic activation is not preventing sleep functions from being engaged.

5- A pairwise comparison of ZT01 and ZT10 does not address circadian expression cycles in a meaningful way. There will be strong effects of the LD cycle here. I suggest toning this down. (Though it is gratifying to see the expected changes in the core clock genes.)

We have changed the language from ‘circadian’ to ‘light-dark’ to address this, although have kept the word ‘circadian’ when referring specifically to genes such as per, clock, timeless, etc.

6- Line 109: There is a reference missing.

We now provide the relevant reference.

Results

1- General comment regarding the figures: a general effort could be made to improve the design and quality of the figures and make them more readable. There are a lot of issues such as stretched or misaligned text, badly drawn frames, etc.

We think we know which figures this might relate to (e.g., Figures 3,4B), so we have adjusted where appropriate.

2- Instead of 'dFB-induced' (e.g., L77) it would be more accurate to use 'optogenetically-induced'

Thank you for this helpful advice. We have changed our language throughout to say ‘optognetically-induced’

3- Figure S1 should be integrated in the main figure to make the quantification more easily 4accessible.

We have integrated Figure S1 into the main figures. It is now Figure 3.

5- It would be good to include red light controls in Figure 2C, E, G.

Making Figure S1 a main figure has better highlighted the fact that we have done red light controls (‘baseline’).

6- line 313: Fig2E-H - these graphs would benefit if the authors made it more obvious where the maximum sleep amount would fall - i.e. the combination of bouts and minutes that add up to 12 hours (and therefore the entire day/night)

If a fly were to sleep uninterrupted for all 12 hours of a day or night, that would amount to a sleep bout 720 minutes long. We do not feel that identifying this maximum on these graphs would be helpful. It should be clear from the data that a floor is reached with very few sleep bouts exceeding 60 minutes in our paradigm. To help orient the reader though, we now clarify in the figure legend that the maximum is 720 minutes or 12 hours.

7- Fig. 2B, D: It was not clear why the authors took the 3-day average here. Doesn't that lead to a whole range of very different behaviors? I could, perhaps naively, imagine that a fly's behavior changes after 2 days of almost-permanent sleep?

We took the 3-day average because the effect of THIP on each successive day was not significantly different (see Author response image 5, below). Flies wake up enough to have a good feed (see Author response image 2) and then go back to sleep. Since this is however an important point raised by the reviewer, we now mention in the Methods that sleep duration was not different among the 3 averaged days and nights (lines 193-195).

Author response image 5.

Data from THIP feeding experiment (Figure 2B) in manuscript, separated into 3 successive days and nights, with THIP-fed flies (blue) compared to controls (white). Averages  SD are shown, samples sizes are the same as in Figure 2D. No THIP data was significantly different across days and nights (ANOVA of means).

8- In Figure 2C the authors compare optogenetically induced to "spontaneous sleep," which I think refers to baseline sleep before stimulation, according to the figure. I think the proper comparison would be to the red light control (ATR-); though see the comment above regarding optogenetic controls).

This information was provided in Figure S1. We now provide it as a main Figure 3, as requested above.

We also made a point about red light having an effect at night, which is why we focussed on daytime effects for our transcriptomic comparisons. We feel that the ATR-fed flies (minus red light) are an appropriate control here for optogenetically-induced sleep: same exact genotype and ATR feeding, just no optogenetic activation. We therefor would prefer to keep these graphs as they are, especially since we show -ATR data subsequently.

9- Figures 3A and 4A are redundant; Figure 3B has some active ROIs that are outside of the brain. I am not sure how this is possible?

We have removed the redundant 4A and replaced it with the THIP molecule to clearly signal what this figure is focussed on. In Figure 3B (now 4B), the brain mask is a visual estimate made from the middle of the image stack. Some neurons in other layers are outside this single-layer estimate. All neurons were all accounted for.

10- Figure 4B is confusing. It took me a while to understand and so it can do with re-drawing in a more accessible way.

We agree that this was confusing, e.g. there were too many arrows. We have redrawn and simplified (Now 5A).

11- The authors state that flies wake up from THIP-induced sleep on the ball, but in Figure 4D there appears to be fewer samples for flies who have woken up from THIP (3) compared to those observed before THIP administration. Are flies dying?

None of the flies died. Most flies were removed from imaging to confirm recovery, while 3 were left in our imaging setup to measure brain activity upon recovery. These results are in Figure 5C and now clarified in the Methods.

12- Fig5C,D: I'm surprised that by far the most significant changes (in terms of log2-FC and p-val) occur in the sleep-deprived flies? It is not clear to me what the authors mean by effects that "relate waking process"? Perhaps they could elaborate on this?

We have removed the phrase ‘relates to waking processes’. We now also remark on the high level of fold-change in many of these genes but refrain from discussing this further in the results. It is interesting though.

13- The sentence in L425-428 is unclear - it would be good to rephrase this.

We have rephrased this sentence, hopefully it’s clearer now.

14- Text in L544-545 is confusing. What do you mean by 'less clear'?

We have replaced ‘less clear’ with ‘not dominated by a single category’.

15- It is unclear what is the control in Fig 7A. It would be good to mention what strain was used.

Different knockout strains had different controls. These are identified in the figure legend and Methods.

16- L579-581: it would be helpful to include this data in a supplementary figure.

We now provide this as a supplementary figure as requested (Supplementary Figure 6).

17- There is no information about R57C10 in the methods - it would be good to explain which neurons this line labels, and why you chose it.

We now clarify in the methods that R57C10-Gal4 is a pan-neural driver, and provide a reference.

18- Table S5 - If I'm not mistaken then the first line should say 1h, not 10h.

Corrected

Joint Public Review:

Anthoney et al. provide an honorable attempt at furthering our understanding of the different sleep stages that may exist in Drosophila. The establishment of definable sleep stages/state in the fly model should be seen as a central goal in the field and this study represents an important step toward that goal. In particular, managing to draw parallels between sleep stages in flies and humans would make it relevant to use the power of fly genetics to better understand the molecular and cellular basis of these sleep stages. The authors use behavioral, physiological and transcriptomics approaches to describe the differences that exist between sleep triggered by optogenetic stimulation of dFB neurons and sleep induced by consumption of the sleep-promoting drug Gaboxadol (THIP). While there are still concerns regarding the interpretation of the major results, the authors have, in general, adequately responded to the reviewers' concerns.

The strengths of this work are:<br /> 1- The article is easy to read, and the figures are mostly informative.<br /> 2- The authors employ state-of-the-art techniques to measure neuronal activity and locomotion in a single assay.<br /> 3- The analysis of transcriptomic data is appropriate.<br /> 4- The authors identify many new genes regulated in response to specific methods for sleep induction. These are all potentially interesting candidates for further studies investigating the molecular basis of sleep. It would be interesting to know which of these genes are already known to display circadian expression patterns.

Concerns:<br /> 1- The fact that flies with dFB activation seem to keep a basal level of locomotor activity whereas THIP-treated ones don't is quite striking. Is it possible that there is an error with Figure 4C-D? Based on this, it is hard to believe that dFB stimulation and THIP consumption have similar behavioral effects on sleep.<br /> 2- The authors seem satisfied with the 'good correspondence' between their RNA-seq and qPCR results, this is true for only ~9/19 genes in Fig 6G and 2/6 genes in Fig 7G. The variability between the three biological replicates is not represented in the figure.

Comments<br /> 1- The fact that THIP-induced sleep persists long after THIP removal (Fig 3D) is intriguing. This suggests that the drug might trigger a sleep-inducing pathway that, once activated, can continue on its own without the drug.<br /> 2- The claim that induction of the two forms (active/quiet) of sleep produce distinct transcriptomic and physiological effects while producing highly similar behavioral effects makes it difficult to understand the relationships between the former changes with sleep state. In their response the authors argue that sleep behavior, as currently measured using duration of inactivity, is not necessarily expected to allow for a differentiation between active and quiet sleep. This further argues for a need for better physiological/molecular correlates of sleep state in the fly (a laudable goal of this very study). However, until clear behavioral correlates can be strongly associated with physiological/molecular correlates, we will be limited to speculation about this important issue.<br /> 3- The authors suggest that the duration of the periods of inactivity may not be particularly useful for defining sleep states/stages in the fly. If this is the case, it is certainly an important issue as this measurement is the behavioral criteria for defining sleep in the field. In this regard, it raises the question of the relationship between the active and quiet sleep states examined in this study and the growing evidence for a deep sleep state (characterized by, among other things, a lowered metabolism).<br /> 4- Although the methodological concerns regarding the dose of Gaboxadol and the controls for the optogenetic/transcriptomic experiments remain, the authors have explained the rationale for the experimental design they used.<br /> 5- Overall, the authors have managed to clearly illustrate the differences between dFB stimulation and THIP consumption on behavior, neuronal activity, and gene expression. In this regard, they have achieved what they claim in the title of the article. Overall, the results support the conclusions, however the main point to consider is that the methods employed here are artificial, and there is no guarantee at this stage that 'spontaneous' sleep has the same effects on the transcriptome than what is presented here.<br /> 5- This article represents an interesting attempt at addressing very important questions, and some of the data presented here, especially the RNA-seq, can be very useful for others. However, because of the lack of definitive conclusion about whether the results presented are applicable to 'natural' sleep, the impact of this article may remain limited beyond a relatively small field.

Comments to authors

The authors have suitably addressed all comments from this section.

Author Response

We are grateful for the constructive comments of the reviewers and for the succinct assessment of our work by the editors. Here we provide a brief summary of our response to answer the major criticism of our reviewers. We will give a detailed point-to-point response soon when we upload a revision of our paper.

1) The MATLAB code for the spatial autocorrelation analysis is now freely available at the following site: : https://github.com/dcsabaCD225/Moran_Matlab/blob/main/moran_local.m If any question arises during its implementation, please contact Csaba Dávid (david.csaba@koki.hu)

2) Concerning the computer resources and times required to perform Moran’s I image analysis, here we provide a brief description of the hardware and the calculations for images with different sizes.

Hardware used for performing the analysis:

Intel(R) Xeon(R) Silver 4112 CPU @ 2.60GHz, 2594 Mhz, 4 kernel CPU, 64GB RAM, NVIDIA GeForce GTX 1080 graphic card.

MATLAB R2021b software was used for implementation.

Computation times are shown in Author response table 1.

Author response table 1.

3) In response to the comment:

“While the method's avoidance of AI training appeals to those lacking computational know-how and shows improved accuracy over basic threshold-based techniques, there are valid concerns regarding its performance in comparison to advanced methodologies”.

Comparison of Moran’s I image analysis with AI based segmentations raises conceptual problems which will be addressed in detail in the revised version. Briefly, the basis of AI based analyses is that the ground truth is known and using a large teaching set AI learns to extract the relevant information for image segmentation. In several cases, however (like protein distribution in the membrane) the ground truth is not known and cannot be easily determined by any single observer. Defining spatial inhomogeneities in protein distribution, differentiating proteins involved vs not involved in clusters is highly subjective. Indeed, our analysis showed the 23 expert human observers varied hugely in establishing the boundaries of a protein cluster. As a consequence, establishing and using a teaching set would be highly contentious in these cases. In an average laboratory setting generating a teaching set using hundreds of images examined by two dozen people would not be impossible but not really plausible. The beauty of Moran’n I analysis is that it is able to extract the relevant signals from an image generated in different, often noisy condition using a simple algorithm that allows quantitative characterization and identification of changes in many biological and non-biological samples.

eLife assessment

The presented study introduces a valuable non-AI computational method for segmenting noisy grayscale images, particularly highlighting its applicability in identifying immunostained potassium ion channel clusters. While the method's avoidance of AI training appeals to those lacking computational know-how and shows improved accuracy over basic threshold-based techniques, there are valid concerns regarding its performance in comparison to advanced methodologies. The evidence supporting the method's efficacy is solid but incomplete, necessitating comparisons to more advanced techniques and the provision of user-friendly computational tools for a comprehensive evaluation.

Reviewer #1 (Public Review):

The study describes a new computational method for unsupervised (i.e., non-artificial intelligence) segmentation of objects in grayscale images that contain substantial noise, to differentiate object, no object, and noise. Such a problem is essential in biology because they are commonly confronted in the analysis of microscope images of biological samples and recently have been resolved by artificial intelligence, especially by deep neural networks. However, training artificial intelligence for specific sample images is a difficult task and not every biological laboratory can handle it. Therefore, the proposed method is particularly appealing to laboratories with little computational background. The method was shown to achieve better performance than a threshold-based method for artificial and natural test images. To demonstrate the usability, the authors applied the method to high-power confocal images of the thalamus for the identification and quantification of immunostained potassium ion channel clusters formed in the proximity of large axons in the thalamic neuropil and verified the results in comparison to electron micrographs. 

Strengths: <br /> The authors claim that the proposed method has higher pixel-wise accuracy than the threshold-based method when applied to gray-scale images with substantial noises.

Since the method does not use artificial intelligence, training and testing are not necessary, which would be appealing to biologists who are not familiar with machine learning technology.

The method does not require extensive tuning of adjustable parameters (trying different values of "Moran's order") given that the size of the object in question can be estimated in advance.

Weaknesses:<br /> It is understood that the strength of the method is that it does not depend on artificial intelligence and therefore the authors wanted to compare the performance with another non-AI method (i.e. the threshold-based method; TBM). However, the TBM used in this work seems too naive to be fairly compared to the expensive computation of "Moran's I" used for the proposed method. To provide convincing evidence that the proposed method advances object segmentation technology and can be used practically in various fields, it should be compared to other advanced methods, including AI-based ones, as well.

This method was claimed to be better than the TBM when the noise level was high. Related to the above, TBMs can be used in association with various denoising methods as a preprocess. It is questionable whether the claim is still valid when compared to the methods with adequate complexity used together with denoising. Consider for example, Weigert et al. (2018) https://doi.org/10.1038/s41592-018-0216-7; or Lehtinen et al (2018) https://doi.org/10.48550/arXiv.1803.04189.

The computational complexity of the method, determined by the convolution matrix size (Moran's order), linearly increases as the object size increases (Fig. S2b). Given that the convolution must be run separately for each pixel, the computation seems quite demanding for scale-up, e.g. when the method is applied for 3D image volumes. It will be helpful if the requirement for computer resources and time is provided.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by David et al. describes a novel image segmentation method, implementing Local Moran's method, which determines whether the value of a datapoint or a pixel is randomly distributed among all values, in differentiating pixel clusters from the background noise. The study includes several proof-of-concept analyses to validate the power of the new approach, revealing that implementation of Local Moran's method in image segmentation is superior to threshold-based segmentation methods commonly used in analyzing confocal images in neuroanatomical studies.

Strengths:<br /> Several proof-of-concept experiments are performed to confirm the sensitivity and validity of the proposed method. Using composed images with varying levels of background noise and analyzing them in parallel with the Local Moran's or a Threshold-Based Method (TBM), the study is able to compare these approaches directly and reveal their relative power in isolating clustered pixels.

Similarly, dual immuno-electron microscopy was used to test the biological relevance of a colocalization that was revealed by Local Moran's segmentation approach on dual-fluorescent labeled tissue using immuno-markers of the axon terminal and a membrane-protein (Figure 5). The EM revealed that the two markers were present in terminals and their post-synaptic partners, respectively. This is a strong approach to verify the validity of the new approach for determining object-based colocalization in fluorescent microscopy.

The methods section is clear in explaining the rationale and the steps of the new method (however, see the weaknesses section). Figures are appropriate and effective in illustrating the methods and the results of the study. The writing is clear; the references are appropriate and useful.

Weaknesses:<br /> While the steps of the mathematical calculations to implement Local Moran's principles for analyzing high-resolution images are clearly written, the manuscript currently does not provide a computation tool that could facilitate easy implementation of the method by other researchers. Without a user-friendly tool, such as an ImageJ plugin or a code, the use of the method developed by David et al by other investigators may remain limited.

eLife assessment

This valuable multicenter study provides solid evidence that the auditory noise emitted during online transcranial ultrasound stimulation (TUS) protocols can pose a considerable confound and is able to explain corticospinal excitability changes as measured with Motor Evoked Potentials (MEP). The findings may lay the ground for future studies optimizing protocols and control conditions to leverage transcranial ultrasound stimulation as a meaningful experimental and clinical tool. A clear strength of the study is the multitude of control conditions (i.e., control sites, acoustic masking, acoustic stimulation).

Reviewer #1 (Public Review):

Summary: The authors have used transcranial magnetic stimulation (TMS) and motor evoked potentials (MEPs) to determine whether the peripheral auditory confound arising from TUS can drive motor inhibition on its own. They gathered data from three international centers in four experiments testing:<br /> In Experiment 1 (n = 11), two different TUS durations and intensities under sound masking or without.<br /> Experiment 2 (n = 27) replicates Exp 1 with different intensities and a fixed TUS duration of 500ms.<br /> Experiment 3 ( n = 16) studied the effect of various auditory stimuli testing different duration and pitches while applying TUS in an active site, on-target or no TUS.<br /> Experiment 4 (n = 12) used an inactive control site to reproduce the sound without effective neuromodulation, while manipulating the volume of the auditory confound at different TUS intensities with and without continuous sound masking.

Strengths: This study comes from three very strong groups in noninvasive brain stimulation with long experience in neuromodulation, multimodal and electrophysiological recordings. Although complex to understand due to slightly different methodologies across centers, this study provides quantitative evidence alerting on the potential auditory confound of online US. Their results are in line with reductions seen in motor-evoked responses during online 1kHz TUS, and remarkable efforts were made to isolate peripheral confounds from actual neuromodulation factors, highlighting the confounding effect of sound itself.

Weaknesses: However, there are some points that need attention. In my view, the most important are:<br /> 1. Despite the main conclusion of the authors stating that there is no dose-response effects of TUS on corticospinal inhibition, the point estimates for change in MEP and Ipssa indicate a more complex picture. The present data and analyses cannot rule out that there is a dose-response function which cannot be fully attributed to difference in sound (since the relationship in inversed, lower intracranial Isppa leads to higher MEP decrease). These results suggest that dose-response function needs to be further studied in future studies.<br /> 2. Other methods to test or mask the auditory confound are possible (e.g., smoothed ramped US wave) which could substantially solve part of the sound issue in future studies or experiments in deaf animals etc...

Reviewer #2 (Public Review):

Summary:<br /> This study aims to test auditory confounds during transcranial ultrasound stimulation (TUS) protocols that rely on audible frequencies. In several experiments, the authors show that a commonly observed suppression of motor-evoked potentials (MEP) during TUS can be explained by acoustic stimulation. For instance, not only target TUS, but also stimulation of a control site and acoustic stimulation led to suppressed MEP.

Strengths:<br /> A clear strength of the study is the multitude of control conditions (control sites, acoustic masking, acoustic stimulation etc) that makes results very convincing.<br /> Indeed, I do not have much to criticise. The paper follows a clear structure and is easy to follow, the research question is clearly relevant, and analyses are sound. Figures are of high quality.<br /> Although auditory confounds during TUS have been demonstrated before, the thorough design of the study will lead to a strong impact in the field.

Weaknesses:<br /> I cannot see major weaknesses. A few minor ones are that (1) the overview of previous related work, and how frequent audible TUS protocols are in the field, could be a bit clearer/more detailed; (2) the acoustic control stimulus can be described in more detail; and (3) the finding that remaining motor inhibition is observed during acoustically masked trials deserves further discussion.

eLife assessment

This manuscript describes a deep mutational scanning study of the kinase domain of the MET receptor tyrosine kinase. The study yields a valuable catalog of essentially all possible deleterious mutations in this portion of the receptor., with convincing evidence. The manuscript will be of interest to researchers working in the field of receptor tyrosine kinases.

Reviewer #1 (Public Review):

Summary:<br /> This manuscript by Estevam et al. reports new insights into the regulation of the receptor tyrosine kinase MET gained from two deep mutational scanning (DMS) datasets. In this paper, the authors use a classic selection system for oncogenic kinase signaling, the murine Ba/F3 cell line, to assess the functional effects of thousands of mutations in the kinase domains of MET in two contexts: (1) fusion of the whole MET intracellular region to the dimerization domain TPR, and (2) the same fusion protein, but with exon 14, which encodes part of the juxtamembrane region of MET, skipped. Critically, exon 14 skipping yields a version of MET that is found in many cancers and has higher signaling activity than the canonical MET isoform. The authors extensively analyze their DMS data to very convincingly show that their selection assay reports on kinase activity, by illustrating that many functionally important structural components of the kinase domain are not tolerant of mutation. Then, they turn their attention to a helical region of the juxtamembrane region (αJM), immediately after exon 14, which is posited to play a regulatory role in MET. Their DMS data illustrate that the strength and mutational tolerance of interactions between αJM and the key αC helix in the kinase domain depends on the presence or absence of exon 14. They also identify residues in the N-lobe of the kinase, such as P1153, which are not conserved across tyrosine kinases but appear to be essential for MET and MET-like kinases. Finally, the authors analyze their DMS data in the context of clinically-observed mutations and drug-resistance mutations.

Overall, this manuscript is exciting because it provides new insights into MET regulation in general, as well as the role of exon 14. It also reveals ways in which the JM region of MET is different from that of many other receptor tyrosinekinases. The exon 14-skipped fusion protein DMS data is somewhat underexplored and could be discussed in greater detail, which would elevate excitement about the work. Furthermore, some of the cell biological validation experiments and the juxtaposition with clinical data are perhaps not assessed/interpreted as clearly they could be. Some constructive suggestions are given below to enhance the impact of the manuscript.

Strengths:<br /> The main strengths of this paper, also summarized above in the summary, are as follows:

1. The authors very convincingly show that Ba/F3 cells can be coupled with deep mutational scanning to examine MET mutational effects. This is most clearly shown by highlighting how all of the known kinase structure and regulatory elements are highly sensitive to mutations, in accordance with a few other DMS datasets on other kinases.

2. A highlight of this paper is the juxtaposition of two DMS datasets for two different isoforms of the MET receptor. Very few comparisons like this exist in the literature, and they show how small changes to the overall architecture of a protein can impact its regulation and mutational sensitivity.

3. Another exciting advance in this manuscript is the deep structural analysis of the MET juxtamembrane region with respect to that of other tyrosine kinases - guided by the striking effect of mutations in the juxtamembrane helical region. The authors illustrate how the JM region of MET differs from that of other tyrosine kinases.

4. Overall, this manuscript will provide a resource for interpreting clinically relevant MET mutations.

Weaknesses:<br /> 1. The manuscript is front-loaded with extensive analysis of the first DMS dataset, in which exon 14 is present, however, the discussion and analysis of the exon 14-skipped dataset is somewhat limited. In particular, a deeper discussion of the differences between the two datasets is warranted, to lay out the full landscape of mutations that have different functional consequences in the two isoforms. Rather, the authors only focus on differences in the JM region. What are the broader structural effects of exon 14 skipping across the whole kinase domain?

2. It is unclear if gain-of-function mutations can actually be detected robustly in this specific system. This isn't a problem at face value, as different selection assays have different dynamic ranges. However, the authors don't discuss the statistical significance and reproducibility of gain- vs loss-of-function mutations, and none of the gain-of-function mutations are experimentally validated (some appear to show loss-of-function in their cellular validation assay with full-length MET). The manuscript would benefit from deeper statistical analysis (and discussion in the text) of gain-of-function mutations, as well as further validation of a broad range of activity scores in a functional assay. For the latter point, one option would be to express individual clones from their library in Ba/F3 cells and blot for MET activation loop phosphorylation (which is probably a reasonable proxy for activity/activation).

3. In light of point 2, above, much of the discussion about clinically-relevant gain-of-function mutations feels a bit stretched - although this section is definitely very interesting in premise. A clearer delineation of gain-of-function, with further statistical support and ideally also some validation, would greatly strengthen the claims in this section.

Reviewer #2 (Public Review):

Summary:<br /> The authors describe a deep mutational scanning (DMS) study of the kinase domain of the c-MET receptor tyrosine kinase. The screen is conducted with a highly activated fusion oncoprotein - Tpr-MET - in which the MET kinase domain is fused to the Tpr dimerization element. The mutagenized region includes the entire kinase domain and an alpha-helix in the juxtamembrane region that is essentially part of the MET kinase domain. The DMS screen is carried out in two contexts, one containing the entire cytoplasmic region of MET, and the other with an "exon 14 deletion" which removes a large portion of the juxtamembrane region (but retains the aforementioned alpha-helix). The work provides a robust and essentially exhaustive catalog of the effect of mutations (within the kinase domain) on the ability of the Tpr-MET fusion oncoproteins to drive IL3-independent growth of Ba/F3 cells. Every residue in the kinase is mutated to every natural amino acid. Given the design of the screen, one would expect it to be a powerful tool for identifying mutations that impair catalytic activity and therefore impair IL3-independent proliferation, but not the right tool for identifying gain-of-function mutations that operate by shifting the kinase from an inactive to active state (because the Tpr-Met fusion construct is already very highly activated). This is borne out by the data, which reveal many many deleterious mutations and few "gain-of-function" mutations (which are of uncertain significance, as discussed below).

Strengths:<br /> The authors take a very scholarly and thorough approach to interpreting the effect of mutations in light of available information for the structure and regulation of MET and other kinases. They examine the effect of mutations in the so-called catalytic (C) and regulatory (R) spines, the interface between the JM alpha-helix and the C-helix, the glycine-rich loop, and other key elements of the kinase, providing a structural rationale for the deleterious effect of mutations. Comparison of the panoply of deleterious mutations in the TPR-met versus TPR- exon14del-MET DMS screens reveals an interesting difference - the exon14 deletion MET is much more tolerant of mutations in the JM alpha-helix/C-helix interface. The reason for this is unclear, however.

Weaknesses:<br /> Because the screens were conducted with highly active Tpr-MET fusions, they have limited power to reveal gain-of-function mutations. Indeed, to the extent that Tpr-MET is as active or even more active than ligand-activated WT MET, one could argue that it is "fully" activated and that any additional gain of fitness would be "super-physiologic". I would expect such mutations to be rare (assuming that they could be detected at all in the Ba/F3 proliferation assay). Consistent with this, the authors note that gain-of-function mutations are rare in their screen (as judged by being more fit than the average of synonymous mutations). In their discussion of cancer-associated mutations, they highlight several "strong GOF variants in the DMS". It is unclear what the authors mean by "strong GOF", indeed it is unclear to this reviewer whether the screen has revealed any true gain of function mutations at all. A few points in this regard:

1) more active than the average of synonymous mutations (nucleotide changes that have no effect on the sequence of the expressed protein) seems to be an awfully low bar for GOF - by that measure, several synonymous mutations would presumably be classified as GOF.

2) In the +IL3 heatmap in supplemental Figure 1A, there is as much or more "blue" indicating GOF as in the -IL3 heatmap, which could suggest that the observed level of gain in fitness is noise, not signal.

3) And finally, consistent with this interpretation, in Supplemental Figure 1C, comparing the synonymous and missense panels in the IL3 withdrawal condition suggests that the most active missense mutations (characterized here as strong GOF) are no more active than the most active synonymous mutations.

My other major concern with the work as presented is that the authors conflate "activity" and "activation" in discussing the effects of mutations. "Activation" implies a role in regulation - affecting a switch between inactive and active conformations or states - at least in this reviewer's mind. As discussed above, the screen per se does not probe activation, only activity. To the extent that the residues discussed are important for activation/regulation of the kinase, that information is coming from prior structural/functional studies of MET and other kinases, not from the DMS screen conducted here. Of course, it is appropriate and interesting for the authors to consider residues that are known to form important structural/regulatory elements, but they should be careful with the use of activity vs. activation and make it clear to the reader that the screen probes the former. One example - in the abstract, the authors rightly note that their approach has revealed a critical hydrophobic interaction between the JM segment and the C-helix, but then they go on to assert that this points to differences in the regulation of MET and other RTKs. There is no evidence that this is a regulatory interaction, as opposed to simply a structural element present in MET (and indeed the authors' examination of prior crystal structures shows that the interaction is present in both active and inactive states.

eLife assessment

This study combines CLIP, RNA-seq, and splicing assays with manipulation of RBMX and its homologs RBMY and RBMXL2 to show that the RBMX family suppresses the recognition of cryptic splicing within long exons. The study is important in that it puts forward the intriguing claim that the RBMX family is responsible for the cryptic splice site repression in ultra-long exons. The methods, data, and analyses supporting the claims are solid, broadly supporting the claims, with some weaknesses. The generalization of the findings is somewhat overstated but could be strengthened by deeper statistical integration of the RNA-seq and CLIP datasets.

Reviewer #1 (Public Review):

Summary:<br /> The article by Siachisumo, Luzzi and Aldalaquan et al. describes studies of RBMX and its role in maintaining proper splicing of ultra-long exons. They combine CLIP, RNA-seq, and individual example validations with manipulation of RBMX and its family members RBMY and RBMXL2 to show that the RBMX family plays a key role in maintaining proper splicing of these exons.

Strengths:<br /> I think one of the main strengths of the manuscript is its ability to explore a unique but interesting question (splicing of ultra-long exons), and derive a relatively simple model from the resulting genomics data. The results shown are quite clean, suggesting that RBMX plays an important role in the proper regulation of these exons. The ability of family members to rescue this phenotype (as well as only particular domains) is also quite intriguing and suggests that the mechanisms for keeping these exons properly spliced may be a quite important and highly conserved mechanism.

Weaknesses:<br /> I think my main critique is that a lot of the conclusions in the text are written with very broad and general claims, but these are often based on either a small number of examples or a non-transcriptome-wide analysis that I think would be necessary for such broad conclusions. For example, pg 5 - "The above data indicated that RBMX has a major role in repressing cryptic splicing patterns in human somatic cells." is based on seeing ~120 exons with a 2:1 ratio of exclusion to inclusion (and doesn't include analysis confirming which of these are cryptic). Similarly, on page 15, line 31 "The above experiments showed that RBMXL2 is able to globally replace RBMX activity" and Fig 5 as well - I think the 'globally' term here is a bit too much with most of the analysis derived from n=3 events.

Another weakness I think is the lack of context in the paper, where the basic description of how many 'long' and 'ultra-long' exons there are and what percent of those are bound by and/or regulated by RBMX is missing. The text (including the title, which to me is written to imply a general role for ultra-long exons') seems to imply that RBMX maintains proper splicing of ultra-long exons globally (which to me implies a significant percent of them), but I don't think the manuscript succeeds in strongly proving this global role.

Along those same lines, I think the manuscript claims that the described principles are specific to the RBMX family, but I think the lack of negative examples weakens the strength of this claim. For example, for Figure 2 (D/E/F/G), what I think would make this more powerful is a negative example - if you take CLIP + knockdown/RNA-seq data for a different splicing regulator (RBFOX2, PTBP1, etc.), do you not see this size difference? I think the analysis described here is sufficient to show that there is an overlap for RBMX, but I think this would make it much stronger in confirming it's not just a 'longer genes get more CLIP tags' artifact.

Reviewer #2 (Public Review):

Summary:<br /> One of the greatest challenges for the spliceosome is to be able to repress the many cryptic splice sites that can occur in both the intronic and exotic sequences of genes. Although many studies have focused on cryptic signals in introns (because of their common involvement in disease) the question still remained open as to the factors that repress cryptic exons in exons. Because exons are normally much shorter than introns, in many cases the problem does not exist. However, in human genes, a significant proportion of exons can be considerably longer than the average 150 nt length and this raises the question of how cryptic splicing can be prevented in long exons. To address this question, the authors have focused on the possible role played by an ancient mammalian RBD protein called RBMX. Using a combination of high-throughput and classic splicing methodologies, they have shown that there is a class of RBMX-dependent ultra-long exons connected where the RBMX, RBMXL2, and RBMY paralogs have closely related functional activity in repressing cryptic splice site selection.

Strengths:<br /> In general, the present work sheds light on what has been a rather understudied process in splicing research. The use of iCLIP and RNA-seq data has not only allowed us to identify the long exons where cryptic splicing is prevented by the RBMX proteins but has also allowed us to identify a network of genes mostly involved in genome stability and transcriptional control where these proteins seem to play a prominent role. This can therefore also shed additional information on the way splicing has shaped evolutionary processes in the mammalian lineage and will therefore be of interest to many researchers in this field.

Weaknesses:<br /> There are no major weaknesses.

Reviewer #3 (Public Review):

The manuscript by Siachisumo et al builds upon a previous publication from the same group of collaborators that showed that depletion of mouse RBMXL2 leads to a block in spermatogenesis associated with mis-splicing, particularly of large exons in genes associated with genome stability (Ehrmann et al eLife 2019). RBMXL2 is an RNA-binding protein and an autosomal retrotransposed paralog of the X-chromosomally encoded RBMX. RBMXL2 is expressed during meiosis when RBMX and the more distantly related RBMY (on the Y chromosome) are silenced. It is therefore an appealing hypothesis that RBMXL2 might provide cover for RBMX function during meiosis. To address this hypothesis the authors analysed the transcriptomic consequences of RBMX depletion by RNA-Seq in human cells (MDA-MB-231 and existing RNA-Seq data from HEK293 cells), complemented by iCLIP to analyze the binding targets of FLAG-tagged RBMX in HEK293 cells. The findings convincingly demonstrate that - like RBMXL2 - RBMX mainly acts as a splicing repressor and that it particularly acts to protect the integrity of very long ("ultra-long") exons. Upon RBMX depletion, many of these exons are shortened due to the use of cryptic 5' and/or 3' splice sites. Moreover, affected genes are particularly enriched for functions associated with genome integrity - indeed "comet assays" show that RBMX depletion leads to DNA damage defects.

The manuscrupt therefore delivers a clear affirmative answer to the question of whether the two highly related proteins have similar molecular functions, particularly with respect to suppressing cryptic splicing that affects ultra-long exons. This conclusion is reinforced by the ability of induced expression of either RBMXL2 or RBMY to fully complement the effects of RBMX knockdown upon three target events in the ETAA1, REV3L, and ATRX genes.

The manuscript also includes some experiments that address more mechanistic questions, such as the potential for RBMX to block access of spliceosome components to splice site elements and structure-function analyses of RBMX. These areas have a distinctly "preliminary" feel to them. For example, for one target (ETAA1) it is shown that CLIP tags are close to mapped branchpoints. However, no attempt is made to integrate the RNA-Seq and iCLIP data-sets to look for more generalized relationships between binding and activity. Likewise, one experiment shows that the RRM domain of RBMXL2 is not necessary for activity. Given that the RRM domain represents only ~25% of the total RBMXL2 sequence, this is a somewhat preliminary, albeit interesting, observation. Another surprising omission was that there was no global comparison of the consequences of RBMX depletion and complementation by RBMXL2, despite the fact that the relevant RNA-Seq data-sets had been generated (Figure 4 supplement 1 shows RNA-Seq IGV tracks that confirm the effects on ETAA1, REV3L and ATRX shown by RT-PCR in Figure 4).

In summary, this manuscript provides clear evidence to support the role of RBMX as a repressor of cryptic splice sites in ultra-long exons, similar to RBMXL2.

eLife assessment

This important manuscript focuses on the mechanisms by which food signals and food ingestion modulate animal foraging. The authors provide solid support for the interesting idea that chemosensory and interoceptive signals converge on transcriptional regulation of the TGF-beta ligand DAF-7 in a single pair of C. elegans chemosensory neurons to regulate behavior. Their studies implicate a conserved signaling molecule, ALK, in this regulation, suggesting a conserved link between food cues and the neuroendocrine control of foraging behavior.

Reviewer #1 (Public Review):

Summary:<br /> Here, Boor et al focus on the regulation of daf-7 transcription in the ASJ chemosensory neurons, which has previously been shown to be sensitive to a variety of external and internal signals. Interestingly, they find that soluble (but not volatile) signals released by food activate daf-7 expression in ASJ, but that this is counteracted by signals from the ASIC channels del-3 and del-7, previously shown to detect the ingestion of food in the pharynx. Importantly, the authors find that ASJ-derived daf-7 can promote exploration, suggesting a feedback loop that influences locomotor states to promote feeding behavior. They also implicate signals known to regulate exploratory behavior (the neuropeptide receptor PDFR-1 and the neuromodulator serotonin) in the regulation of daf-7 expression in ASJ. Additionally, they identify a novel role for a pathway previously implicated in C. elegans sensory behavior, HEN-1/SCD-2, in the regulation of daf-7 in ASJ, suggesting that the SCD-2 homolog ALK may have a conserved role in feeding and metabolism.

Strengths:<br /> The studies reported here, particularly the quantitation of gene expression and the careful behavioral analysis, are rigorously done and interpreted appropriately. The results suggest that, with respect to food, DAF-7 expression encodes a state of "unmet need" - the availability of nearby food to animals that are not currently eating. This is an interesting finding that reinforces and extends our understanding of the neurobiological significance of this important signaling pathway. The identification of a role for ASJ-derived daf-7 in motor behavior is a valuable advance, as is the finding that SCD-2 acts in the AIA interneurons to influence daf-7 expression in ASJ.

Weaknesses:<br /> A limitation of the work is that some mechanistic relationships between the identified signaling pathways are not carefully examined, but this provides interesting opportunities for future work. A minor weakness concerns the experiment in which daf-7 is conditionally deleted from ASJ. This is an ideal approach for probing the function of daf-7, but these experiments seem to be carried out in the well-fed, on-food condition in which control animals should express little or no daf-7 in ASJ. Thus, the experimental design does not allow an assessment of the role of daf-7 under conditions in which its expression is activated (e.g., in animals exposed to un-ingestible food). An additional minor issue concerns the interpretation of the scd-2 experiments. The authors' findings do support a role for scd-2 signaling in the activation of daf-7 expression by un-ingestible food, but the data also suggest that scd-2 signaling is not essential for this effect, as there is still an effect in scd-2 mutants (Figure 4B).

Reviewer #2 (Public Review):

Summary:<br /> In this work, Boor and colleagues explored the role of microbial food cues in the regulation of neuroendocrine-controlled foraging behavior. Consistent with previous reports, the authors find that C. elegans foraging behavior is regulated by the neuroendocrine TGFβ ligand encoded by daf-7. In addition to its known role in the neuroendocrine/sensory ASI neurons, Boot and colleagues show that daf-7 expression is dynamically regulated in the ASJ sensory neurons by microbial food cues - and that this regulation is important for exploration/exploitation balance during foraging. They identify at least two independent pathways by which microbial cues regulate daf-7 expression in ASJ: a likely gustatory pathway that promotes daf-7 expression and an opposing interoceptive pathway, also likely chemosensory in nature but which requires microbial ingestion to inhibit daf-7 expression. Two neuroendocrine pathways known to regulate foraging (serotonin and PDF-1) appear to act at least in part via daf-7 induction. They further identify a novel role for the C. elegans ALK orthologue encoded by scd-2, which acts in interneurons to regulate daf-7 expression and foraging behavior. These results together imply that distinct cues from microbial food are used to regulate the balance between exploration and exploitation via conserved signaling pathways.

Strengths:<br /> The findings that gustatory and interoceptive inputs into foraging behavior are separable and opposing are novel and interesting, which they have shown clearly in Figure 1. It is also clear from their results that removal of the interoceptive cue (via transfer to non-digestible food) results in rapid induction of daf-7::gfp in ASJ, and that ASJ plays an important role in the regulation of foraging behavior.

The role of the hen-1/scd-2 pathway in mediating the effects of ingested food is also compelling and well-interpreted. The use of precise gain-of-function alleles further supports their conclusions. This implies that important elements of this food-sensing pathway may be conserved in mammals.

Weaknesses:<br /> What is less clear to me from the work at this stage is how the gustatory input fits into this picture and to what extent can it be strongly concluded that the daf-7-regulating pathways that they have identified (del-3/7, 5-HT, PDFR-1, scd-2) act via the interoceptive pathway as opposed to the gustatory pathway. It follows from the work of the Flavell lab that del-3/7 likely acts via the interoceptive pathway in this context as well but this isn't shown directly - e.g. comparing the effects of aztreonam-treated bacteria and complete food removal to controls. The roles of 5-HT and PDFR-1 are even a bit less clear. Are the authors proposing that these are entirely parallel pathways? This could be explained in better detail.

It would also be helpful to elaborate more on why the identified transcriptional positive feedback loop is predicted to extend roaming state duration - as opposed to some other mechanism of increasing roaming such as increased probability of roaming state initiation. This doesn't seem self-evident to me. Related to this point is the somewhat confusing conclusion that the effects of tph-1 and pdfr-1 mutations on daf-7 expression are due to changes in ingestion during roaming/dwelling. From my understanding (e.g. Cermak et al., 2020), pharyngeal pumping rate does not reliably decrease during roaming - so is it clear that there are in fact lower rates of ingestion during roaming in their experiments? If so, why does increased roaming (via tph-1 mutation) result in further increases in daf-7 expression in animals fed aztreonam-treated food (Fig 3B)? Alternatively, there could be a direct signaling connection between the 5-HT/PDFR-1 pathways and daf-7 expression which could be acknowledged or explained.

Reviewer #3 (Public Review):

Summary:<br /> In this interesting study, the authors examine the function of a C. elegans neuroendocrine TGF-beta ligand DAF-7 in regulating foraging movement in response to signals of food and ingestion. Building on their previous findings that demonstrate the critical role of daf-7 in a sensory neuron ASJ in behavioral response to pathogenic P. aeruginosa PA14 bacteria and different foraging behavior between hermaphrodite and male worms, the authors show, here, that ingestion of E. coli OP50, a common food for the worms, suppresses ASJ expression of daf-7 and secreted water-soluble cues of OP50 increases it. They further showed that the level of daf-7 expression in ASJ is positively associated with a higher level of roaming/exploration movement. Furthermore, the authors identify that a C. elegans ortholog of Anaplastic Lymphoma Kinase, scd-2, functions in an interneuron AIA to regulate ASJ expression of daf-7 in response to food ingestion and related cues. These findings place the DAF-7 TGF-beta ligand in the intersection of environmental food conditions, food intake, and food-searching behavior to provide insights into how orchestrated neural functions and behaviors are generated under various internal and external conditions.

Strengths:<br /> The study addresses an important question that appeals to a wide readership. The findings are demonstrated by generally strong results from carefully designed experiments.

Weaknesses:<br /> However, a few questions remain to provide a complete picture of the regulatory pathways and some analyses need to be strengthened. Specifically,

1. The authors show that diffusible cues of bacteria OP50 increase daf-7 expression in ASJ which is suppressed by ingestible food. Their results on del-3 and del-7 suggest that NSM neuron suppresses daf-7 ASJ expression. What sensory neurons respond to bacterial diffusible cues to increase daf-7 expression of ASJ? Since ASJ is able to respond to some bacterial metabolites, does it directly regulate daf-7 expression in response to diffusible cues of OP50 or does it depend on neurotransmission for the regulation? Some level of exploration in this question would provide more insights into the regulatory network of daf-7.

2. The results including those in Figure 2 strongly support that daf-7 in ASJ is required for roaming. Meanwhile, authors also observe increased daf-7 expression in ASJ under several conditions, such as non-ingestible food. Does non-ingestible food induce more roaming? It would complete the regulatory loop by testing whether a higher (than wild type) level of daf-7 in ASJ could further increase roaming. The results in pdf-1 and scd-2 gain-of-function alleles support more ASJ leads to more roaming, but the effect of these gain-of-function alleles may not be ASJ-specific and it would be interesting to know whether ASJ-specific increase of daf-7 leads to a higher level of roaming. In my opinion, either outcome would be informative and strengthen our understanding of the critical function of daf-7 in ASJ demonstrated here.

3. The analyses in Figure 4 cannot fully support "We further observed that the magnitude of upregulation of daf-7 expression in the ASJ neurons when animals were moved from ingestible food to non-ingestible food was reduced in scd-2(syb2455) to levels only about one-fourth of those seen in wild-type animals (Figure 4D)...", because the authors tested and found the difference in daf-7 expression between ingestible and non-ingestible food conditions in both wild type and the mutant worms. The authors did not analyze whether the induction was different between wild type and mutant. Under the ingestible food condition, ASJ expression of daf-7 already looks different in scd-2(syb2455).

4. The authors used unpaired two-tailed t-tests for all the statistical analyses, including when there are multiple groups of data and more than one treatment. In their previous study Meisel et al 2014, the authors used one-way ANOVA, followed by Dunnett's or Tukey's multiple comparison test when they analyzed daf-7 expression or lawn leaving in different mutants or under different bacterial conditions. It is not clear why a two-tailed t-test was used in similar analyses in this study.

eLife assessment

This study presents potentially useful findings describing how activity in the corticotropin-releasing hormone neurons in the paraventricular nucleus of the hypothalamus modulates sevoflurane anesthesia, as well as a phenomenon the authors term a "general anesthetic stress response". The technical approaches are solid and the data presented are largely clear. However, the primary conclusion, that the PVHCRH neurons are a mechanism of sevoflurane anesthesia, is inadequately supported.

Joint Public Review:

This study describes a group of CRH-releasing neurons, located in the paraventricular nucleus of the hypothalamus, which, in mice, affects both the state of sevoflurane anesthesia and a grooming behavior observed after it. PVH-CRH neurons showed elevated calcium activity during the post-anesthesia period. Optogenetic activation of these PVH-CRH neurons during sevoflurane anesthesia shifts the EEG from burst-suppression to a seemingly activated state (an apparent arousal effect), although without a behavioral correlate. Chemogenetic activation of the PVH-CRH neurons delays sevoflurane-induced loss of righting reflex (another apparent arousal effect). On the other hand, chemogenetic inhibition of PVH-CRH neurons delays recovery of the righting reflex and decreases sevoflurane-induced stress (an apparent decrease in the arousal effect). The authors conclude that PVH-CRH neurons are a common substrate for sevoflurane-induced anesthesia and stress. The PVH-CRH neurons are related to behavioral stress responses, and the authors claim that these findings provide direct evidence for a relationship between sevoflurane anesthesia and sevoflurane-mediated stress that might exist even when there is no surgical trauma, such as an incision. In its current form, the article does not achieve its intended goal.

Strengths<br /> The manuscript uses targeted manipulation of the PVH-CRH neurons, and is technically sound. Also, the number of experiments is substantial.

Weaknesses<br /> The most significant weaknesses are a) the lack of consideration and measurement of GABAergic mechanisms of sevoflurane anesthesia, b) the failure to use another anesthetic as a control, c) a failure to document a compelling post-anesthesia stress response to sevoflurane in humans, d) limitations in the novelty of the findings. These weaknesses are related to the primary concerns described below:

Concerns about the primary conclusion, that PVH-CRH neurons mediate "the anesthetic effects and post-anesthesia stress response of sevoflurane GA".

1) Just because the activity of a given neural cell type or neural circuit alters an anesthetic's response, this does not mean that those neurons play a role in how the anesthetic creates its anesthetic state. For example, sevoflurane is commonly used in children. Its primary mechanism of action is through enhancement of GABA-mediated inhibition. Children with ADHD on Ritalin (a dopamine reuptake inhibitor) who take it on the day of surgery can often require increased doses of sevoflurane to achieve the appropriate anesthetic state. The mesocortical pathway through which Ritalin acts is not part of the mechanism of action of sevoflurane. Through this pathway, Ritalin is simply increasing cortical excitability making it more challenging for the inhibitory effects of sevoflurane at GABAergic synapses to be effective. Similarly, here, altering the activity of the PVHCRH neurons and seeing a change in anesthetic response to sevoflurane does not mean that these neurons play a role in the fundamental mechanism of this anesthetic's action. With the current data set, the primary conclusions should be tempered.

2) It is important to compare the effects of sevoflurane with at least one other inhaled ether anesthetic. Isoflurane, desflurane, and enflurane are ether anesthetics that are very similar to each other, as well as being similar to sevoflurane. It is important to distinguish whether the effects of sevoflurane pertain to other anesthetics, or, alternatively, relate to unique idiosyncratic properties of this gas that may not be a part of its anesthetic properties.

For example, one study cited by the authors (Marana et al.. 2013) concludes that there is weak evidence for differences in stress-related hormones between sevoflurane and desflurane, with lower levels of cortisol and ACTH observed during the desflurane intraoperative period. It is not clear that this difference in some stress-related hormones is modeled by post-sevoflurane excess grooming in the mice, but using desflurane as a control could help determine this.

Concerns about the clinical relevance of the experiments<br /> In anesthesiology practice, perioperative stress observed in patients is more commonly related to the trauma of the surgical intervention, with inadequate levels of antinociception or unconsciousness intraoperatively and/or poor post-operative pain control. The authors seem to be suggesting that the anesthetic itself is causing stress, but there is no evidence of this from human patients cited. We were not aware that this is a documented clinical phenomenon. It is important to know whether sevoflurane effectively produces behavioral stress in the recovery room in patients that could be related to the putative stress response (excess grooming) observed in mice. For example, in surgeries or procedures that required only a brief period of unconsciousness that could be achieved by administering sevoflurane alone (comparable to the 30 min administered to the mice), is there clinical evidence of post-operative stress?

Patients who receive sevoflurane as the primary anesthetic do not wake up more stressed than if they had had one of the other GABAergic anesthetics. If there were signs of stress upon emergence (increased heart rate, blood pressure, thrashing movements) from general anesthesia, the anesthesiologist would treat this right away. The most likely cause of post-operative stress behaviors in humans is probably inadequate anti-nociception during the procedure, which translates into inadequate post-op analgesia and likely delirium. It is the case that children receiving sevoflurane do have a higher likelihood of post-operative delirium. Perhaps the authors' studies address a mechanism for delirium associated with sevoflurane, but this is not considered. Delirium seems likely to be the closest clinical phenomenon to what was studied.

Concerns about the novelty of the findings<br /> CRH is associated with arousal in numerous studies. In fact, the authors' own work, published in eLife in 2021, showed that stimulating the hypothalamic CRH cells leads to arousal and their inhibition promotes hypersomnia. In both papers, the authors use fos expression in CRH cells during a specific event to implicate the cells, then manipulate them and measure EEG responses. In the previous work, the cells were active during wakefulness; here- they were active in the awake state that follows anesthesia (Figure 1). Thus, the findings in the current work are incremental.

The activation of CRH cells in PVN has already been shown to result in grooming by Jaideep Bains (cited as reference 58). Thus, the involvement of these cells in this behavior is expected. The authors perform elaborate manipulations of CRH cells and numerous analyses of grooming and related behaviors. For example, they compare grooming and paw licking after anesthesia with those after other stressors such as forced swim, spraying mice with water, physical attack, and restraint. However, the relevance of these behaviors to humans and generalization to other types of anesthetics is not clear.

eLife assessment

This work is valuable in exploring the role of nutritional immunity in bacteria-mediated tumor therapeutics, involving proteomics and in vivo mouse model results which provide largely solid supporting evidence of the observational claims, but is incomplete when extrapolating the mechanisms of how manipulation of iron status can affect E. coli-mediated tumor therapy. This work is of interest to a broad audience including researchers in cancer biology, cell biology, and microbiology.

Reviewer #1 (Public Review):

In this manuscript, Huang and colleagues explored the role of iron in bacterial therapy for cancer. Using proteomics, they revealed the upregulation of bacterial genes that uptake iron, and reasoned that such regulation is an adaptation to the iron-deficient tumor microenvironment. Logically, they engineered E. Coli strains with enhanced iron-uptake efficiency, and showed that these strains, together with iron scavengers, suppress tumor growth in a mouse model. Lastly, they reported the tumor suppression by IroA-E. Coli provides immunological memory via CD8+ T cells. In general, I find the findings in the manuscript novel and the evidence convincing.

1. Although the genetic and proteomic data are convincing, would it be possible to directly quantify the iron concentration in (1) E. Coli in different growth environments, and (2) tumor microenvironment? This will provide the functional consequences of upregulating genes that import iron into the bacteria.

2. Related to 1, the experiment to study the synergistic effect of CDG and VLX600 (lines 139-175) is very nice and promising, but one flaw here is a lack of the measurement of iron concentration. Therefore, a possible explanation could be that CDG acts in another manner, unrelated to iron uptake, that synergizes with VLX600's function to deplete iron from cancer cells. Here, a direct measurement of iron concentration will show the effect of CDG on iron uptake, thus complementing the missing link.

3. Lines 250-268: Although statistically significant, I would recommend the authors characterize the CD8+ T cells a little more, as the mechanism now seems quite elusive. What signals or memories do CD8+ T cells acquire after IroA-E. Coli treatment to confer their long-term immunogenicity?

4. Perhaps this goes beyond the scope of the current manuscript, but how broadly applicable is the observed iron-transport phenomenon in other tumor models? I would recommend the authors to either experimentally test it in another model or at least discuss this question.

Reviewer #2 (Public Review):

Summary:

The authors provide strong evidence that bacteria, such as E. coli, compete with tumor cells for iron resources and consequently reduce tumor growth. When sequestration between LCN2 and bacterobactin is blocked by upregulating CDG(DGC-E. coli) or salmochelin(IroA-E.coli), E. coli increase iron uptake from the tumor microenvironment (TME) and restrict iron availability for tumor cells. Long-term remission in IroA-E.coli treated mice is associated with enhanced CD8+ T cell activity. Additionally, systemic delivery of IroA-E.coli shows a synergistic effect with chemotherapy reagent oxaliplatin to reduce tumor growth.

Strengths:

It is important to identify the iron-related crosstalk between E. coli and TME. Blocking lcn2-bacterobactin sequestration by different strategies consistently reduces tumor growth.

Weaknesses:

As engineered E.coli upregulate their function to uptake iron, they may increase the likelihood of escaping from nutritional immunity (LCN2 becomes insensitive to sequester iron from the bacteria). Would this raise the chance of developing sepsis? Do authors think that it is safe to administrate these engineered bacteria in mice or humans?

Reviewer #3 (Public Review):

Summary:

Based on their observation that tumor has an iron-deficient microenvironment, and the assumption that nutritional immunity is important in bacteria-mediated tumor modulation, the authors postulate that manipulation of iron homeostasis can affect tumor growth. They show that iron chelation and engineered DGC-E. coli have synergistic effects on tumor growth suppression. Using engineered IroA-E. coli that presumably have more resistance to LCN2, they show improved tumor suppression and survival rate. They also conclude that the IroA-E. coli treated mice develop immunological memory, as they are resistant to repeat tumor injections, and these effects are mediated by CD8+ T cells. Finally, they show synergistic effects of IroA-E. coli and oxaliplatin in tumor suppression, which may have important clinical implications.

Strengths:

This paper uses straightforward in vitro and in vivo techniques to examine a specific and important question of nutritional immunity in bacteria-mediated tumor therapy. They are successful in showing that manipulation of iron regulation during nutritional immunity does affect the virulence of the bacteria, and in turn the tumor. These findings open future avenues of investigation, including the use of different bacteria, different delivery systems for therapeutics, and different tumor types.

Weaknesses:

-- There is no discussion of the cancer type and why this cancer type was chosen. Colon cancer is not one of the more prominently studied cancer types for LCN2 activity. While this is a proof-of-concept paper, there should be some recognition of the potential different effects on different tumor types. For example, this model is dependent on significant LCN production, and different tumors have variable levels of LCN expression. Would the response of the tumor depend on the role of iron in that cancer type? For example, breast cancer aggressiveness has been shown to be influenced by FPN levels and labile iron pools.<br /> -- Are the effects on tumor suppression assumed to be from E. coli virulence, i.e. Does the higher number of bacteria result in increased immune-mediated tumor suppression? Or are the effects partially from iron status in the tumor cells and the TME?<br /> -- If the effects are iron-related, could the authors provide some quantification of iron status in tumor cells and/or the TME? Could the proteomic data be queried for this data?

Author Response

Reviewer #2 (Public Review):

The authors describe the synthesis and testing of the anti-cancer activity of a new molecule CK21 against pancreatic cancer mouse models. This part of the study is very strong showing regression of pancreatic tumors at non-toxic concentrations, which is very hard to achieve for practically uncurable pancreatic cancer. Authors synthesized CK21 as an analog of a known inhibitor of RNA synthesis which is very toxic. The authors did very little attempt to understand whether the mechanism of anti-cancer efficacy of CK2 is similar to this known inhibitor of transcription or not. One cannot compare gene expression profiles between untreated and CK21-treated cells, taking into account that CK2 may inhibit the expression of all genes. The effect of CK2 on general transcription needs to be tested first, and then based on this data absolute changes in the expression of genes may be considered for the revealing of the mechanism of activity of CK21.

We also appreciated the toxicity concerns; thus, we designed the transcriptomic analysis on the human organoid cultured cells for early time points of 3, 6, 9 and 12 h, and with a CK21 concentration of 50nM, to ensure that at the time of harvest, the cells were ~100% viable. At these time points, many genes were upregulated but defined by IPA as enriched for cell death (apoptosis and necrosis), senescence and cell cycle arrest (Fig 5). This led us to hypothesize that the direct effect of CK21 on the tumor cells is the induction of apoptosis, but via multiple pathways.

Reviewer #3 (Public Review):

This manuscript describes CK21, a modified version of Triptolide, a natural compound with antcancer activities, to improve its bioavailability. The authors tested the compound in two human pancreatic cancer cell lines, in vitro and in vivo. The authors also use two human organoid lines derived from pancreatic cancer, and mouse KC and KPC cell lines. In all models, CK21 treatment induces dose-dependent cytotoxicity. In vivo, CK21 causes tumor regression. The authors perform gene expression analysis and show that treated organoids have generally lower transcription, consistent with cytotoxicity, and a reduction in the KFkB pathway activation.

Key experiments that would strengthen the current manuscript are: the inclusion of normal cell lines and organoids, too, presumably, show no cytotoxic effect. If that is the case, the authors would have the opportunity to compare responses and determine whether a tumor-specific mechanism can be defined.

Our in vivo studies suggest that CK21 is more specific to tumors, as CK21 ≤3 mg/kg treated mice were 100% viable and gained weight comparably to no treatment group (Fig.2d). Furthermore, in vitro studies with primary fibroblast cells indicate that comparable significant toxicity to CK21 after 72h culture was observed at 500 nM (Fig.s2). In contrast, CK21 induced significant toxicity in AsPC1 and Panc-1 cells at 50 nM (Fig. 1f.)

The authors observe that few gene changes - besides from overall lowering in transcription, occur upon treatment with CK21. They suggest that the drug acts through inhibition of the NFkB pathway and an increase in reactive oxygen species (ROS). However, no experiments to test whether either/both of these findings explain the cytotoxic effect (rescue experiments would be particularly valuable).

We performed a rescue study using an ROS inhibitor (acetylcysteine) but observed no significant effect (data not shown). We speculate that ROS and/or NF-B might function synergistically; additionally, it is possible that other mechanisms might be involved in the anti-tumor effects of CK21.

In the last figure, the authors text whether CK21 is immunosuppressive by testing immunity against a mis-matched tumor cell line (using KPC tumors, mixed strain, in mixed strain mice). The immunity against HLA mis-matched cells is a very strong immune reaction, and mild immune suppression might be missed, which diminishes the value of these findings.

KPC-960 tumor cells were derived from KPC (C57BL/6 background); therefore, KPC-960 tumors were HLA matched with host C57BL/6 mice. We were surprised to observe spontaneous rejection of the KPC-960 tumor line, since this contrasts with Torres et al. 2013. We speculate that this could be due to the increased number of passages resulting in antigenic drift, which may result in the accumulation of mutations that induce spontaneous rejection.

We agree that there might be mild immunosuppression that we did not detect; we have included this caveat in the discussion. KC-6141 tumor cells used as CTL targets were from KC mice (mixed background – B6.129).

Author Response

Reviewer #1:

This is a very timely paper that addresses an important and difficult-to-address question in the decision-making field - the degree to which information leakage can be strategically adapted to optimise decisions in a task-dependent fashion. The authors apply a sophisticated suite of analyses that are appropriate and yield a range of very interesting observations. The paper centres on analyses of one possible model that hinges on certain assumptions about the nature of the decision process for this task which raises questions about whether leak adjustments are the only possible explanation for the current data. I think the conclusions would be greatly strengthened if they were supported by the application and/or simulation of alternative model structures.

We thank the reviewer for this positive appraisal of our study. We now entirely agree with their central comment about whether leak adjustments are the only (or even the best) explanation for the current data. We hope that the additional modelling sections that we have discussed in response to main comment 1 above have strengthened the paper. We have responded point-by-point to their public review, as this contained their main recommendations for revision.

The behavioural trends when comparing blocks with frequent versus rare response periods seem difficult to tally with a change in the leak. […] Are there other models that could reproduce such effects? For example, could a model in which the drift rate varies between Rare and Frequent trials do a similar or better job of explaining the data?

We can see why the reviewer has advocated for a possible change of drift rate (or ‘gain’ applied to sensory evidence) between conditions to explain our behavioural findings. We found, however, that changes in drift rate could elicit qualitatively similar changes in integration kernels to changes in decision threshold:

Author response image 1.

Changes in gain applied to incoming sensory evidence (A parameter in model) have similar effects on recovered integration kernels from Ornstein-Uhlenbeck simulation as changes in decision threshold.

The likely reason for this is that the overall probability of emitting a response at any point in the continuous decision process is determined by the ratio of accumulated evidence to decision threshold. A similar logic applies to effects on reactions times and detection probability (main figure 2): increasing sensory gain/decreasing decision threshold will lead to faster reaction times and increased detection probability during response periods.

Both parameters may even have a similar effect on ‘false alarms’, because (as the reviewer notes below) false alarms in our paradigm are primarily being driven by the occurrence of stimulus changes as well as internal noise. In fact, the false alarm findings mean it is difficult to fully reconcile all of our behavioural findings in terms of changes in a single set of model parameters in the O-U process. It is possible that other changes not considered within our model (such as expectations of hazard rates of inter-response intervals leading to dynamic thresholds etc.) may have had a strong impact upon the resulting false alarm rates. A full exploration of different variations in O-U model (with varying urgency signals, hazard rates, etc.) is beyond the scope of this paper.

For this reason, we have decided in our new modelling section to focus primarily on a single, well-established model (the O-U process) and explore how changes in leak and threshold affect task performance and the resulting integration kernels. We note that this is in line with the suggestion of reviewer #2, who focussed on similar behavioural findings to reviewer #1 but suggested that we look at decision threshold rather than drift rate as our primary focus.

This ties in to a related query about the nature of the task employed by the authors. Due to the very significant volatility of the stimulus, it seems likely that the participants are not solely making judgments about the presence/absence of coherent motion but also making judgments about its duration (because strong coherent motion frequently occurs in the inter-target intervals). If that is so, then could the Rare condition equate to less evidence because there is an increased probability that an extended period of coherent motion could be an outlier generated from the noise distribution? Note that a drift rate reduction would also be expected to result in fewer hits and slower reaction times, as observed.

As mentioned above, the rare and frequent targets are indeed matched in terms of the ease with which they can be distinguished from the intervening noise intervals. To confirm this, we directly calculated the variance (across frames) of the motion coherence presented during baseline periods and response periods (until response) in all four conditions:

Author response image 2.

The average empirical standard deviation of the stimulus stream presented during each baseline period (‘baseline’) and response period (‘trial’), separated by each of the four conditions (F = frequent response periods, R = rare, L = long response periods, S = short). Data were averaged across all response/baseline periods within the stimuli presented to each participant (each dot = 1 participant). Note that the standard deviation shown here is the standard deviation of motion coherence across frames of sensory evidence. This is smaller than the standard deviation of the generative distribution of ‘step’-changes in the motion coherence (std = 0.5 for baseline and 0.3 for response periods), because motion coherence remains constant for a period after each ‘step’ occurs.

Some adjustment of the language used when discussing FAs seems merited. If I have understood correctly, the sensory samples encountered by the participants during the inter-response intervals can at times favour a particular alternative just as strongly (or more strongly) than that encountered during the response interval itself. In that sense, the responses are not necessarily real false alarms because the physical evidence itself does not distinguish the target from the non-target. I don't think this invalidates the authors' approach but I think it should be acknowledged and considered in light of the comment above regarding the nature of the decision process employed on this task.

This is a good point. We hope that the reviewer will allow us to keep the term ‘false alarms’ in the paper, as it does conveniently distinguish responses during baseline periods from those during response periods, but we have sought to clarify the point that the reviewer makes when we first introduce the term.

“Indeed, participants would occasionally make ‘false alarms’ during baseline periods in which the structure of the preceding noise stream mistakenly convinced them they were in a response period (see Figure 4, below). Indeed, this means that a ‘false alarm’ in our paradigm has a slightly different meaning than in most psychophysics experiments; rather than it referring to participants responding when a stimulus was not present, we use the term to refer to participants responding when there was no shift in the mean signal from baseline.”

And:

“The fact that evidence integration kernels naturally arise from false alarms, in the same manner as from correct responses, demonstrates that false alarms were not due to motor noise or other spurious causes. Instead, false alarms were driven by participants treating noise fluctuations during baseline periods as sensory evidence to be integrated across time, and the physical evidence preceding ‘false alarms’ need not even distinguish targets from non-targets.”

The authors report that preparatory motor activity over central electrodes reached a larger decision threshold for RARE vs. FREQUENT response periods. It is not clear what identifies this signal as reflecting motor preparation. Did the authors consider using other effectorselective EEG signatures of motor preparation such as beta-band activity which has been used elsewhere to make inferences about decision bounds? Assuming that this central ERP signal does reflect the decision bounds, the observation that it has a larger amplitude at the response on Rare trials appears to directly contradict the kernel analyses which suggest no difference in the cumulative evidence required to trigger commitment.

Thanks for this comment. First, we should simply comment that this finding emerged from an agnostic time-domain analysis of the data time-locked to button presses, in which we simply observed that the negative-going potential was greater (more negative) in RARE vs. FREQUENT trials. So it is simply the fact that it precedes each button press that we relate it to motor preparation; nonetheless, we note that (Kelly and O’Connell, 2013) found similar negative-going potentials at central sensors without applying CSD transform (as in this study). Like them, we would relate this potential to either the well-established Bereitschaftpotential or the contingent negative potential (CNV).

We agree that many other studies have focussed on beta-band activity as another measure of motor preparation, and to make inferences about decision bounds. To investigate this, we used a Morlet wavelet transform to examine the time-varying power estimate at a central frequency of 20Hz (wavelet factor 7). We repeated the convolutional GLM analysis on this time-varying power estimate.

We first examined average beta desynchonisation at a central cluster of electrodes (CPz, CP1, CP2, C1, Cz, C2) in the run-up to correct button presses during response periods. We found a reliable beta desynchonisation occurred, and, just as in the time-domain signal, this reached a greater threshold in the RARE trials than in the FREQUENT trials:

Author response image 3.

Beta desynchronisation prior to a correct response is greater over central electrodes in the RARE condition than in the FREQUENT condition.

We agree with the reviewer that this is likely indicative of a change in decision threshold between rare and frequent trials. We also note that our new computational modelling of the O-U process suggests that this in fact reconciles well with the behavioural findings (changes in integration kernels). We now mention this at the relevant point in the results section:

“As large changes in mean evidence are less frequent in the RARE condition, the increased neural response to |Devidence| may reflect the increased statistical surprise associated with the same magnitude of change in evidence in this condition. In addition, when making a correct response, preparatory motor activity over central electrodes reached a larger decision threshold for RARE vs. FREQUENT response periods (Figure 7b; p=0.041, cluster-based permutation test). We found similar effects in beta-band desynchronisation prior, averaged over the same electrodes; beta desynchronisation was greater in RARE than FREQUENT response periods. As discussed in the computational modelling section above, this is consistent with the changes in integration kernels between these conditions as it may reflect a change in decision threshold (figure 2d, 3c/d). It is also consistent with the lower detection rates and slower reaction times when response periods are RARE (figure 2 b/c).”

We did also investigate the lateralised response (left minus right beta-desynchronisation, contrasted on left minus right responses). We found, however, that we were simply unable to detect a reliable lateralised signal in either condition using these lateralised responses. We suspect that this is because we have far fewer response periods than conventional trialbased EEG experiments of decision making, and so we did not have sufficient SNR to reliably detect this signal. This is consistent with standard findings in the literature, which report that the magnitude of the lateralised signal is far smaller than the magnitude of the overall beta desynchronisation (e.g. (Doyle et al., 2005))

P11, the "absolute sensory evidence" regressor elicited a triphasic potential over centroparietal electrodes. The first two phases of this component look to have an occipital focus. The third phase has a more centroparietal focus but appears markedly more posterior than the change in evidence component. This raises the question of whether it is safe to assume that they reflect the same process.

We agree. We have now referred to this as a ‘triphasic component over occipito-parietal cortex’ rather than centroparietal electrodes.

Reviewer #2:

Overall, the authors use a clever experimental design and approach to tackle an important set of questions in the field of decision-making. The manuscript is easy to follow with clear writing. The analyses are well thought-out and generally appropriate for the questions at hand. From these analyses, the authors have a number of intriguing results. So, there is considerable potential and merit in this work. That said, I have a number of important questions and concerns that largely revolve around putting all the pieces together. I describe these below.

Thanks to the reviewer for their positive appraisal of the manuscript; we are obviously pleased that they found our work to have considerable potential and merit. We seek to address the main comments from their public review and recommendations below.

1) It is unclear to what extent the decision threshold is changing between subjects and conditions, how that might affect the empirical integration kernel, and how well these two factors can together explain the overall changes in behavior.

I would expect that less decay in RARE would have led to more false alarms, higher detection rates, and faster RTs unless the decision threshold also increased (or there was some other additional change to the decision process). The CPP for motor preparatory activity reported in Fig. 5 is also potentially consistent with a change in the decision threshold between RARE and FREQUENT. If the decision threshold is changing, how would that affect the empirical integration kernel? These are important questions on their own and also for interpreting the EEG changes.

This important comment, alongside the comments of reviewer 1 above, made us carefully consider the effects of changes in decision threshold on the evidence integration kernel via simulation. As discussed above (in response to ‘essential revisions for the authors’), we now include an entirely new section on how changes in decision threshold and leak may affect the evidence integration kernel, and be used to optimise performance across the different sensory environments. In particular, we agree with the reviewer that the motor preparatory activity that differs between RARE and FREQUENT is consistent with a change in decision threshold, and our simulations have suggested that our behavioural findings on evidence integration are also consistent with this change as well. These are detailed on pp.1-4 of the rebuttal, above.

2) The authors find an interesting difference in the CPP for the FREQUENT vs RARE conditions where they also show differences in the decay time constant from the empirical integration kernel. As mentioned above, I'm wondering what else may be different between these conditions. Do the authors have any leverage in addressing whether the decision threshold differs? What about other factors that could be important for explaining the CPP difference between conditions? Big picture, the change in CPP becomes increasingly interesting the more tightly it can be tied to a particular change in the decision process.

We fully agree with the spirit of this comment, and we’ve tried much more carefully to consider what the influences of decision threshold and leak would be on our behavioural analyses. As discussed in the response to reviewer 1, we think that the negative-going potential at the time of responses (which is greater in RARE vs. FREQUENT, main figure 7b, and mirrored by equivalent changes in beta desynchronisation, see Reviewer Response Figure 5 above) are both reflective of a change in decision threshold between RARE and FREQUENT conditions. We have tried to make this link explicit in the revised results section:

“As large changes in mean evidence are less frequent in the RARE condition, the increased neural response to |Devidence| may reflect the increased statistical surprise associated with the same magnitude of change in evidence in this condition. In addition, when making a correct response, preparatory motor activity over central electrodes reached a larger decision threshold for RARE vs. FREQUENT response periods (Figure 7b; p=0.041, cluster-based permutation test). We found similar effects in beta-band desynchronisation prior, averaged over the same electrodes; beta desynchronisation was greater in RARE than FREQUENT response periods. As discussed in the computational modelling section above, this is consistent with the changes in integration kernels between these conditions as it may reflect a change in decision threshold (figure 2d, 3c/d). It is also consistent with the lower detection rates and slower reaction times when response periods are RARE (figure 2 b/c).”

I'll note that I'm also somewhat skeptical of the statements by the authors that large shifts in evidence are less frequent in the RARE compared to FREQUENT conditions (despite the names) - a central part of their interpretation of the associated CPP change. The FREQUENT condition obviously has more frequent deviations from the baseline, but this is countered to some extent by the experimental design that has reduced the standard deviation of the coherence for these response periods. I think a calculation of overall across-time standard deviation of motion coherence between the RARE and FREQUENT conditions is needed to support these statements, and I couldn't find that calculation reported. The authors could easily do this, so I encourage them to check and report it.

See Author response image 2.

3) The wide range of decay time constants between subjects and the correlation of this with another component of the CPP is also interesting. However, in trying to interpret this change in CPP, I'm wondering what else might be changing in the inter-subject behavior. For instance, it looks like there could be up to 4 fold changes in false alarm rates. Are there other changes as well? Do these correlate with the CPP? Similar to my point above, the changes in CPP across subjects become increasingly interesting the more tightly it can be tied to a particular difference in subject behavior. So, I would encourage the authors to examine this in more depth.

Thanks for the interesting suggestion. We explored whether there might be any interindividual correlation in this measure with the false alarm rate across participants, but found that there was no such correlation. (See Author response image 4; plotting conventions are as in main figure 9).

Author response image 4.

No evidence of between-subject correlations in CPP responses and false alarm rates, in any of the four conditions.

We hope instead that the extended discussion of how the integration kernel should be interpreted (in light of computational modelling) provides at least some increased interpretability of the between-subject effects that we report in figure 9.

Reviewer #3 (Public Review):

The main strength is in the task design which is novel and provides an interesting approach to studying continuous evidence accumulation. Because of the continuous nature of the task, the authors design new ways to look at behavioral and neural traces of evidence. The reverse-correlation method looking at the average of past coherence signals enables us to characterize the changes in signal leading to a decision bound and its neural correlate. By varying the frequency and length of the so-called response period, that the participants have to identify, the method potentially offers rich opportunities to the wider community to look at various aspects of decision-making under sensory uncertainty.

We are pleased that the reviewer agrees with our general approach as a novel way of characterising various aspects of decision-making under uncertainty.

The main weaknesses that I see lie within the description and rigor of the method. The authors refer multiple times to the time constant of the exponential fit to the signal before the decision but do not provide a rigorous method for its calculation and neither a description of the goodness of the fit. The variable names seem to change throughout the text which makes the argumentation confusing to the reader. The figure captions are incomplete and lack clarity.

We apologise that some of our original submission was difficult to follow in places, and we are very grateful to the reviewer for their thorough suggestions for how this could be improved. We address these in turn below, and we hope that this answers their questions, and has also led to a significant improvement in the description and rigour of the methodology.

Author Response

Reviewer #3 (Public Review):

Dysbiosis has a substantial impact on host physiology. Using the nematode C. elegans and E.coli as a model of host-microbe interactions, Yang et al. defined a mechanism by which the host deals with gut dysbiosis to maintain fitness. They found that accumulation of E. coli in the intestine secreted indole, a tryptophan metabolite, and activated the transcription factor DAF-16. DAF-16 induced the expression of lys-7 and lys-8, which in turn limited E. coli proliferation in the gut of worms and maintained the longevity of worms. Finally, these authors demonstrated that indole-activated DAF-16 via TRPA-1 in neurons of worms.

This study revealed a new mechanism of host-microbe interaction. The concept of their work is of broad interest and the results they present are convincing. However, there are some issues that need to be addressed to support the conclusions.

Major issues

1) The authors isolated the crude extract from a high-performance liquid chromatograph (HPLC). A candidate compound was detected by activity-guided isolation and further identified as indole with mass spectrometry and NMR data. The HPLC fractionations and activity-guided isolation experiments should be described in more detail with a schematic figure to reveal how these experiments were performed and how indole was identified. Showing a chemical characterization of indole in Figure 2A is not sufficient for the evaluation of the results. Rather, a figure comparing the fraction 26th with standard indole by MS and NMR is more appealing.

We appreciate the concerns of the reviewer. Activity-guided isolation was performed as follows: The crude extract of E. coli supernatant metabolites was divided into 45 fractions according to polarity using Ultimate 3000 HPLC (Thermofisher, Waltham, MA) coupled with automated fraction collector. After freeze-drying each fraction, 1 mg of metabolites were dissolved in DMSO for DAF-16 nuclear localization assay in worms (Please see new Supplementary Table S2). The 26th fraction with DAF-16 nuclear translocation-inducing activity was then separated on silica gel column (200-300 mesh) with a continuous gradient of decreasing polarity (100%, 70%, 50%, 30%, petroleum ether/acetone) to yield four fractions (26a-d). Only the fraction of 26b could induce DAF-16 nuclear translocation. Then the fraction was further separated using a Sephadex LH-20 column to yield 32 fractions. The 26b-11th fraction with DAF-16 nuclear translocation-inducing activity contained a single compound identified by thin layer chromatography, mass spectrometry and nuclear magnetic resonance (NMR). The compound exhibited a quasimolecular ion peak at m/z 181.0782 [M+H]+ in the positive APCI-MS, and was assigned to a molecular formula of C8H7N. A comparison of these 1H NMR and 13C NMR spectra with the data reported in the literature revealed that the compound was indole (Yagudaev, 1986). The figure shows the comparison of the 26b-11 fraction with the standard indole by MS (Author response image 1).

Author response image 1.

High resolution mass spectrum of the candidate compound and indole.

2) DAF-16::GFP was mainly located in the cytoplasm of the intestine in worms expressing daf-16p::daf-16::gfp fed live E. coli OP50 on Day 1 (Figure 1A and 1B). The nuclear translocation of DAF-16 in the intestine was increased in worms fed live E. coli OP50 on Days 4 and 7, but not in age-matched WT worms fed heat-killed (HK) E. coli OP50 (Figure 1A and 1B). Since DAF-16 functions downstream of DAF-2, have the levels of DAF-2 been tested during aging on OP50 and (HK) OP50, or with and without indole supplementation?

In response to the reviewer’s suggestion, we carried out the RT-PCR experiment in 4-day-old and 7-day-old worms. It has been shown that DAF-2 initiates a kinase cascade that leads to the phosphorylation and cytoplasmic retention of DAF-16. By contrast, a reduction in the DAF-2 signaling leads to the dephosphorylation of DAF-16, allowing its nuclear translocation. In response to the reviewer’s suggestion, we tested the expression of daf-2 in 4-day-old and 7-day-old worms fed with OP50 and (HK) OP50. We found that the mRNA levels of daf-2 were significantly increased in worms on days 4 and 7 in the presence of either live or dead E. coli OP50, compared with those in worms on day 1 (Author response image 2A). In addition, supplementation with indole did not alter the mRNA levels of daf-2 in young adult worms (Author response image 2B). To conclude, the activation of DAF-16 is independent of DAF-2.

Author response image 2.

DAF-16 nuclear translocationisindependent of DAF-2.(A) The mRNA levelsof daf-2weregradually increasedin worms with age.P< 0.01;*P< 0.001; ns, not significant. (B)The mRNA levelsof daf-2were not alteredaftertreatment withindole for 24 hours.ns, not significant.

3) In lines 155-157, the author argued that the increase in the levels of indole in worms results from the intestinal accumulation of live E. coli OP50, rather than exogenous indole produced by E. coli OP50 on the NGM plates. However, the work also showed that supplementation with indole (50-200 μM) could significantly increase the indole levels in young adult worms on Day 1 (Figure 2-figure supplement 3B), which could induce nuclear translocation of DAF-16 in worms (Figure 2B). This result suggested that worms could take in indole from outside culturing environment. The concentration of indole in OP50 and (HK) OP50 could be measured.

We appreciate the concerns of the reviewer. Reviewer #2 also pointed out this problem. In this study, our data showed that the levels of indole were 30.9, 71.9, and 105.9 nmol/g dry weight in worms fed live E. coli OP50 on days 1, 4, and 7, respectively (Figure 2C). This increase in the levels of indole in worms was accompanied by an increase in CFU of live E. coli OP50 in the intestine of worms with age (Figure 2C). In addition, we determined the levels of indole in worms fed HK E. coli OP50, and found that the levels of indole were 28.2, 31.6, and 36.1 nmol/g dry weight in worms fed HK E. coli OP50 on days 1, 4, and 7, respectively (Figure 2-figure supplement 3A). It should be noted that the levels of indole in worms fed dead E. coli OP50 on day 1 were comparable of those in worms fed live E. coli OP50 on day 1 (30.9 vs 28.2 nmol/g dry weight). However, the levels of indole were not increased in worms fed HK E. coli OP50 on days 4 and 7. Furthermore, the observation that DAF-16 was retained in the cytoplasm of the intestine in worms fed live E. coli OP50 on day 1 (Figure 1A and 1B) also indicated that indole produced by E. coli OP50 on the NGM plates is not enough to induce DAF-16 nuclear translocation. By contrast, supplementation with indole (50-200 μM) significantly increased the indole levels in worms on day 1 (Figure 2-figure supplement 3B), which could induce nuclear translocation of DAF-16 in worms (Figure 2B). Thus, the increase in the levels of indole in worms with age results from intestinal accumulation of live E. coli OP50, rather than indole produced by E. coli OP50 on the NGM plates.

4) Recent work showed that the multicopy DAF-16 transgene acts differently from the single copy GFP knock in DAF-16 transgene. Which DAF-16 transgene was used in this work?

The strain we used is TJ356. Its genotype has been described as zIs356 [daf-16p::daf-16a/b::GFP+rol-6(su1006)] (Lee, Hench, & Ruvkun, 2001; Lin, Hsin, Libina, & Kenyon, 2001), from the Caenorhabditis Genetics Center (CGC).

5) In lines 190-193, the author argued that the supplementation with indole (100 M) inhibited the CFU of E. coli K-12 in WT worms, but not daf-16(mu86) mutants, on Days 4 and 7 (Figure 3H and 3I). These results suggest that endogenous indole is involved in maintaining a normal lifespan in worms. This is overstating. The data here more likely suggest that indole could inhibit the proliferation of E. coli through DAF-16.

We really appreciate this reviewer’s preciseness. In response to the reviewer’s suggestion, we had changed "...indole is involved in maintaining a normal lifespan in worms" to "...indole produced by bacteria in the gut could inhibit the proliferation of E. coli via DAF-16 in worms".

6) Sonowal (2017) reported that AHR mediates indole-promoted lifespan extension at 16 C. Yet this work argued that RNAi knockdown of ahr-1 did not affect the nuclear translocation of DAF-16 in worms fed E. coli K12 strain on Day 7 (Figure 4-figure supplement 1A) or young adult worms treated with indole (100 M) for 24 h. The difference between these two works should be discussed.

We really appreciate this reviewer’s preciseness. It has been shown that AHR-1 mediates indole-promoted lifespan extension in worms at 16 C (Sonowal et al., 2017). However, our data show that AHR-1 is not involved in activation of DAF-16 by indole-induced nuclear translocation of DAF-16 at 20 C. This means that AHR-1 and TRPA-1-lifespan extension by indole are essentially different. In our study, indole is added to NGM plates when worms reached the young adult stage. In the study by Sonowal et al., indole is supplemented at the stage of L1 larva. In addition, lifespan of C. elegans varies at different temperatures (Xiao et al., 2013). Thus, indole may promote lifespan extension via different mechanisms, which is dependent on exposure time and temperature.

7) Sonowal (2017) conducted mRNA profiling for worms growing on K12 and K12△tnaA. Is TRPA1 in their de-regulated gene list? Have other de-regulated genes been tested in this work?

We appreciate the concerns of the reviewer. We found that TRPA-1 is not included in the de-regulated gene list. Sonowal et al. focus on the gene expression profiles in worms from L1 larvae to young adults, whereas we pay attention to gene expression profiles in worms from young adults to aged worms. Thus, we did not test the de-regulated genes in their work.

8) How does indole activate TRPA1? In the absence of trpa1, what is the concentration of indole in worms? Since TRPA1 is a channel, is there any possibility that TRPA1 is involved in the transport of indole? It is really interesting and surprising that neuronal TRPA-1, but not intestinal TRPA-1, mediates the beneficial effect of indole. How does indole specifically activate TRPA-1 in neurons to preserve the longevity of worms?

We appreciate the concerns of the reviewer. TRPA1 is a nonselective cation channel permeable to Ca2+, Na+, and K+ (Zygmunt & Hogestatt, 2014). It is unlikely that TRPA1 is capable of transporting heterocyclic organic compounds, such as indole.

In response to the reviewer’s suggestion, we detected the content of indole in trpa-1(ok999) worms. We found that the levels of indole in trpa-1(ok999) worms were slightly increased in worms on days 4 and 7, compared to those in WT worms on days 4 and 7 (Author response image 3).

Recently, Ye et al. have demonstrated that indole and indole-3-carboxaldehyde (IAld) are agonists of TRPA1, which is conserved in vertebrates (Ye et al., 2021). Thus, it is mostly likely that indole acts as an agonist of TRPA-1 in C. elegans by directly binding to TRPA-1. One possibility is that activation of TRPA-1 in neurons by indole could induce a pathway that release a neurotransmitter, which in turn triggers a signaling pathway to extend lifespan of worms via activating DAF-16 in a non-cell autonomous manner. In contrast, the activation of TRPA-1 in the intestine by indole is unable to release such a neurotransmitter. Indeed, TRPA1 induces the releasing of calcitonin gene-related peptide in perivascular sensory nerves, leading to membrane hyperpolarization and arterial dilation on smooth muscle cells (Talavera et al., 2020). Moreover, the activation of TRPA1 by indole and IAld induces the secretion of the neurotransmitter serotonin in zebrafish (Ye et al., 2021).

Author response image 3.

The indole levels in trpa-1 mutants are increased on days 4 and 7, compared with those in WT worms. *P < 0.05.

9) How neuronal- and intestinal-specific knockdown of trpa-1 by RNAi was conducted? And what is the tissue-specific expression pattern of trap-1? Speculating how indole was transported to neuron cells is pretty appealing.

We appreciate the concerns of the reviewer. SID-1 is required cell-autonomously for systemic RNAi (Winston, Molodowitch, & Hunter, 2002). Thus, the sid-1 mutants are resistant to RNAi in the neuronal- and intestinal-specific RNAi strains, sid-1 was expressed under control of the neuronal-specific unc-119 and the intestinal-specific vha-6 promoters, respectively. Although it has been reported that TRPA-1 is expressed in neurons, muscles, hypodermal cells, and the intestine, Xiao et al. proved that only TRPA-1 expressed in the intestine and neurons contributes to life extension at low temperature (Xiao et al., 2013). The transporter of indole has not been identified. In Arabidopsis, ATP-binding cassette (ABC) transporter G family 37(ABCG37) has been reported to transport a range of indole derivatives (Ruzicka et al., 2010). However, all fifteen C. elegans ABC transporters share less than 30% sequence identity with ABCG37. Thus, it is impossible to determine which one is the transport channel for indole and indole derivatives in C. elegans.

10) Supplementation with indole only up-regulated the expression of lys-7 and lys-8 in worms subjected to intestinal-specific (Figure 7-figure supplement 2C), but not neuronal-specific, RNAi of trpa-1 (Figure 7-figure supplement 2D). If this is the case, should the addition of indole specifically induce the expression of lys-7p::gfp or lys-8p::gfp in neurons?

We really appreciate this reviewer’s preciseness. Indeed, lys-7 and lys-8 are expressed in both neurons and the intestine (Author response image 4A and 7B). However, the expression of lys-8p::gfp and lys-7p::gfp in neurons was not altered in worms after treatment with indole or knockdown of trpa-1 by RNAi (Author response image 4C and 4D).

Author response image 4.

The expression of LYS-7 and LYS-8 in neurons is not altered after treatment with indole or knockdown of trpa-1 by RNAi. (A and C) Representative images of lys-7p::gfp (A) and lys-8p::gfp (C). Both lys-7 and lys-8 could be expressed in neurons and the intestine. (B and D) Quantification of fluorescent intensity of lys-7p::gfp (B) and lys-8p::gfp (D) in neurons. These results are means ± SD of three independent experiments. ns, not significant.

11) The authors demonstrated that K-12△tnaA strain had undetectable tnaA mRNA or indole levels. Furthermore, the deletion of tnaA significantly inhibited the nuclear translocation of DAF-16 in worms. However, mutations in E. coli still have non-specific effects as there are several transposon insertions or polar mutations influencing downstream genes. The authors should demonstrate that only disruption of TnaA causes the failure of nuclear translocation of DAF-16.

In response to the reviewer’s suggestion, we rescued the expression of tnaA in the K-12 △tnaA strain. As expected, the indole level of from the supernatant in the K12 △tnaA::tnaA strain cultures was 34.1 μmol/L, which was comparable of that in the K12 strain cultures (42.5 μmol/L)（new Figure 2-figure supplement 4D). In addition, DAF-16 nuclear accumulation was increased in worms grown in the K12 △tnaA::tnaA strain on days 4 and 7 (new Figure 2-figure supplement 4E).

Reviewer #2 (Public Review):

This manuscript by Muñoz-Reyes et al. presents studies on the molecular mechanisms by which NCS-1 regulates Ric-8A and its interaction with Ga. They have investigated how calcium ions and phosphorylation of Ric-8A affect these interactions. They found that NCS-1 induces a conformational change in Ric-8A that prevents its phosphorylation and subsequent interaction with Ga, and this can be reversed by increasing calcium ion concentration. Using structural biology methods, they determined the interaction surfaces between NCS-1 and Ric-8A. These mechanistic analyses are needed in the field and beneficial to helping us understand specificity in the regulation of G protein signaling.

Overall, this manuscript presents an abundance of data that supports the authors' conclusions. The introduction is thorough and well-written. The structure figures are beautiful and clear - well done. Most of the biochemical and biophysical experiments are convincing. In some cases, further elaborations and explanations of data interpretation are needed. The crystallographic data is solid. However, I have major concerns with the cryo-EM data presented due to its low quality and the conclusions drawn from it.

Reviewer #3 (Public Review):

The current manuscript investigates the molecular basis of calcium-sensitive regulation of the guanine exchange factor Ric8A by the neuronal calcium sensor 1 (NCS-1). The authors provide insight into a number of aspects of this interaction, including high-resolution structures of the NCS1-Ric8A binding interface (using peptides based on Ric8A), low-resolution cryo-EM data that hints at a structural rearrangement, and a biochemical investigation of the influence of calcium binding, sodium binding, and phosphorylation on this interaction. Altogether, this manuscript provides a comprehensive set of experiments that provide insight into this important interaction. In particular, the identification of ions bound to NCS-1 using crystallography and binding assays is very nicely done and convincing. The cryo-EM data is at low resolution and provides only weak support for the proposed mechanism.

Author Response

Reviewer #1 (Public Review):

The study by Akter et al demonstrates that astrocyte-derived L-lactate plays a key role in schema memory formation and promotes mitochondrial biogenesis in the Anterior Cingulate Cortex (ACC).

The main tool used by the authors is the DREADD technology that allows to pharmacologically activate receptors in a cell-specific manner. In the study, the authors used the DREADD technique to activate appropriately transfected astrocytes, a subtype of muscarinic receptor that is not normally present in cells. This receptor being coupled to a Gi-mediated signal transduction pathway inhibiting cAMP formation, the authors could demonstrate cell-(astrocyte) specific decreases in cAMP levels that result in decreased L-lactate production by astrocytes.

Behaviorally this pharmacological manipulation results in impairments of schema memory formation and retrieval in the ACC in flavor-place paired associate paradigms. Such impairments are prevented by co-administration of L-lactate.

The authors also show that activation of Gi signaling resulting in L-lactate decreased release by astrocytes impairs mitochondrial biogenesis in neurons in an L-lactate reversible manner.

By using MCT 2 inhibitors and an NMDAR antagonist the authors conclude that the molecular mechanisms underlying the observed effects are mediated by L-lactate entering neurons through MCT2 transporters and involve NMDAR.

Overall, the article's conclusions are warranted by the experimental evidence, but some weak points could be addressed which would make the conclusions even stronger.

The number of animals in some of the experiments is on the low side (4 to 6).

In the revised manuscript, we have increased the animal numbers in two key experimental groups (hM4Di-CNO and Control groups) of behavioral experiments. Now the animal numbers in different groups are as follows:

• 15 rats in hM4Di-CNO group

o Further divided into two subgroups for probe tests (PT1-4) conducted during flavor-place paired associate training; 8 rats in the hM4Di-CNO (saline) and 7 rats in the hM4Di-CNO (CNO) subgroups receiving I.P. saline or I.P. CNO, respectively, before these PTs.

• 8 rats in the Control group

• 7 rats in the Rescue group (hM4Di-CNO+L-lactate)

• 4 rats in the Control-CNO group. Animal number in this group was not increased as it was apparent from these 4 rats that CNO alone was not impairing the PA learning and memory retrieval in these rats (AAV8-GFAP-mCherry injected). Their result was very similar to the control group. Additionally, in a previous study (Liu et al., 2022), we showed that CNO administration in the rats injected with AAV8-GFAP-mCherry into the hippocampus does not show any impairments in schema.

Also, in the newly added open field test experiments to investigate the locomotor activity as suggested by the Reviewer #2, 8 rats were used in each group.

The use of CIN to inhibit MCT2 is not optimal. Authors may want to decrease MCT2 expression by using antisense oligonucleotides.

In the revised manuscript, we have conducted the experiment using MCT2 antisense oligodeoxynucleotide (ODN) as suggested.

To test whether the L-lactate-induced neuronal mitochondrial biogenesis is dependent on MCT2, we bilaterally injected MCT2 antisense oligodeoxynucleotide (MCT2-ODN, n=8 rats, 2 nmol in 1 μl PBS per ACC) or scrambled ODN (SC-ODN, n=8 rats, 2 nmol in 1 μl PBS per ACC) into the ACC. After 11 hours, bilateral infusion of L-lactate (10 nmol, 1 μl) or ACSF (1 μl) was given into the ACC and the rats were kept in the PA event arena. After 60 mins (12 hours from MCT2-ODN or SC-ODN administration), the rats were sacrificed. As shown in Author response image 1B, SC-ODN+L-lactate group showed significantly increased relative mtDNA copy number compared to the SC-ODN+ACSF group (p<0.001, ANOVA followed by Tukey's multiple comparisons test). However, this effect was completely abolished in MCT2-ODN+L-lactate group, suggesting that MCT2 is required for the L-lactate-induced mitochondrial biogenesis in the ACC.

We have integrated this new data and results in the revised manuscript.

Author response image 1.

Mitochondrial biogenesis by L-lactate is dependent on MCT2 and NMDAR. A. Experimental design to investigate whether MCT2 and NMDAR activity are required for L-lactate-induced mitochondrial biogenesis. B and C. mtDNA copy number abundance in the ACC of different rat groups relative to nDNA. Data shown as mean ± SD (n=4 rats in each group). ***p<0.001, ANOVA followed by Tukey's multiple comparisons test.

The experiment using AVP to block NMDAR only partially supports the conclusions. Indeed, blocking NMDAR will knock down any response that involves these receptors, whether L-lactate is necessary or not.

In the current study we found that Astrocytic Gi activation in the ACC reduced L-lactate level in the ECF of ACC which was also associated with decreased PGC-1α/SIRT3/ATPB/mtDNA abundance suggesting downregulation of mitochondrial biogenesis pathway. We also found that exogenous administration of L-lactate into the ACC of astrocytic Gi-activated rats rescued this downregulation. In line with this, in a recently published study (Akter et al., 2023), we found upregulation of mitochondrial biogenesis pathway in the hippocampus neurons of exogenous L-lactate-treated anesthetized rats. Another recent study has demonstrated that exercise-induced L-lactate release from skeletal muscle or I.P. injection of L-lactate can induce hippocampal PGC-1α (which is a master regulator of mitochondrial biogenesis) expression and mitochondrial biogenesis in mice (Park et al., 2021). Together, these results provide compelling evidence that L-lactate promotes mitochondrial biogenesis.

L-lactate is known to promote expression of synaptic plasticity genes like Arc, c-Fos, and Zif268 in neurons (Yang et al., 2014). After entry into the neuronal cytoplasm, mainly through MCT2, it is converted into pyruvate by lactate dehydrogenase 1 (LDH1). This conversion also produces NADH, affecting the redox state of the neuron. NADH positively modulates the activity of NMDAR resulting in enhanced Ca2+ currents, the activation of intracellular signaling cascades, and the induction of the expression of plasticity-associated genes (Yang et al., 2014; Magistretti & Allaman, 2018). The study demonstrated that L-lactate–induced plasticity gene expression was abolished in the presence of NMDAR antagonists including D-APV (Yang et al., 2014). These results suggested that the MCT2 and NMDAR are key players in the regulation of L-lactate induced plasticity gene expression.

In the current study, we investigated whether similar mechanisms might be involved in L-lactate-induced neuronal mitochondrial biogenesis. We now used MCT2 antisense oligodeoxynucleotide to decrease the expression of MCT2 (as mentioned in the previous response and Author response image 1B) and showed that MCT2 is necessary for L-lactate-induced mitochondrial biogenesis to manifest, indicating that L-lactate’s entry into the neuron is required. As mentioned before, after entry into neuron, L-lactate is converted into pyruvate by LDH, which also produce NADH, which in turn potentiates NMDAR activity. Therefore, we investigated whether NMDAR activity is required for L-lactate-induced mitochondrial biogenesis. We used D-APV to inhibit NMDAR (Author response image 1C) and found that L-lactate does not increase mtDNA copy number abundance if D-APV is given, suggesting that NMDAR activity is required for L-lactate to promote mitochondrial biogenesis.

NMDAR serves diverse functions. Therefore, as mentioned by the reviewer, blocking NMDAR may knock down many such functions. While our current data only suggests the involvement of MCT2 and NMDAR in the upregulation of mitochondrial biogenesis by L-lactate, we have not investigated other mechanisms and pathways modulating mitochondrial biogenesis that are either dependent or independent of MCT2 and NMDAR activity. Further studies are needed in future to dissect and better understand this interesting observation. We have now clarified this in the discussion section of the manuscript.

Is inhibition of glycogenolysis involved in the observed effects mediated by Gi signaling? Indeed, L-lactate is formed both by glycolysis and glycogenolysis. The authors could test whether the glycogen metabolism-inhibiting drug DAB would mimic the effects of Gi activation.

In this study we have shown that astrocytic Gi activation in the ACC leads to a decrease in the cAMP and L-lactate. L-lactate is produced by glycogenolysis and glycolysis. cAMP in astrocytes acts as a trigger for L-lactate production (Choi et al., 2012; Horvat, Muhič, et al., 2021; Horvat, Zorec, et al., 2021; Zhou et al., 2021) by promoting glycogenolysis and glycolysis (Vardjan et al., 2018; Horvat, Muhič, et al., 2021; Horvat, Zorec, et al., 2021). Therefore, one promising explanation of reduced L-lactate level observed in our study is the reduction of L-lactate production in the astrocyte due to decreased glycogen metabolism as a result of decreased cAMP. We have now mentioned this in the discussion.

DAB is an inhibitor of glycogen phosphorylase that suppresses L-lactate production. It was shown to impair memory by decreasing L-lactate (Newman et al., 2011; Suzuki et al., 2011; Iqbal et al., 2023). As we found that the impairment in the schema memory and mitochondrial biogenesis was associated with decreased L-lactate level in the ACC and that the exogenous L-lactate administration can rescue the impairments, it is likely that DAB will mimic the effect of Gi activation in terms of schema memory and mitochondrial biogenesis. However, further study is needed to confirm this.  

Reviewer #2 (Public Review):

The manuscript of Akter et al is an important study that investigates the role of astrocytic Gi signaling in the anterior cingulate cortex in the modulation of extracellular L-lactate level and consequently impairment in flavor-place associates (PA) learning. However, whereas some of the behavioral observations and signaling mechanism data are compelling, the conclusions about the effect on memory are inadequate as they rely on an experimental design that does not allow to differentiate acute or learning effect from the effect outlasting pharmacological treatments, i.e. effect on memory retention. With the addition of a few experiments, this paper would be of interest to the larger group of researchers interested in neuron-glia interactions during complex behavior.

• Largely, I agree with the authors' conclusion that activating Gi signaling in astrocytes impairs PA learning, however, the effect on memory retrieval is not that obvious. All behavioral and molecular signaling effects described in this study are obtained with the continuous presence of CNO, therefore it is not possible to exclude the acute effect of Gi pathway activation in astrocytes. What will happen with memory on retrieval test when CNO is omitted selectively during early, middle, or late session blocks of PA learning?

We have now added 8 more rats to the hM4Di-CNO group (i.e., the group with astrocytic Gi activation) to clarify the memory retrieval. These rats underwent flavor-place paired associate (PA) training similar to the previously described rats (n=7) of this group, that is they received CNO 30 minutes before and 30 minutes after the PA training sessions (S1-2, S4-8, S10-17). However, contrasting to the previous rats of this group which received CNO before PTs (PT1, PT2, PT3), we omitted the CNO (instead administered I.P. saline) selectively on these PTs conducted at the early, middle, and late stage of PA training, as suggested by the reviewer. These newly added rats did not show memory retrieval in these PTs, suggesting that the rats were not learning the PAs from the PA training sessions. See Author response image 2C-E, where this subgroup is denoted as hM4Di-CNO (Saline).

We then continued more PA training sessions (S21 onwards, Author response image 2B) for these rats without CNO. They gradually learned the PAs. PTs (PT5, PT6, PT7; Author response image 2G-I) were done during this continuation phase of PA training; once without CNO (i.e., with I.P. saline instead), and another one with CNO. As seen in the Author response image 2H and 2I, they retrieved the memory when PT6 and PT7 were done without CNO. However, if these PTs were done with CNO, they could not retrieve the memory. Together these results suggest that ACC astrocytic Gi activation by CNO during PT can impair memory retrieval in rats which have already learned the PAs.

As shown in the Author response image 2B, we replaced two original PAs with two new PAs (NPA 9 and 10) at S34. This was followed by PT8 (S35). As seen in Author response image 2J, these rats retrieved the NPA memory if the PT is done without CNO. However, they could not retrieve the NPA memory if the PT was done with CNO. This result suggests that ACC astrocytic Gi activation by CNO during PT can impair NPA memory retrieval.

In summary, these data show that astrocytic Gi activation in the ACC can impair PA memory retrieval. We have integrated this new data and results in the revised manuscript.

Author response image 2.

A. PI (mean ± SD) during the acquisition of the six original PAs (OPAs) (S1-2, 4-8, 10-17) and new PAs (NPAs) (S19) of the control (n=8), hM4Di-CNO (n=15), and rescue (hM4Di-CNO+L-lactate) (n=7) groups. From S6 onwards, hM4Di-CNO group consistently showed lower PI compared to control. However, concurrent L-lactate administration into the ACC (rescue group) can rescue this impairment. B. PI (mean ± SD) of hM4Di-CNO group (n=8) from S21 onwards showing gradual increase in PI when CNO was withdrawn. C, D, and E. Non-rewarded PTs (PT1, PT2, and PT3 conducted on S3, S9, and S18, respectively) to test memory retrieval of OPAs for the control, hM4Di-CNO, and rescue groups. The percentage of digging time at the cued location relative to that at the non-cued locations are shown (mean ± SD). In both PT2 and PT3, the control group spent significantly more time digging the cued sand well above the chance level, indicating that the rats learned OPAs and could retrieve it. Contrasting to this, hM4Di-CNO group did not spend more time digging the cued sand well above the chance level irrespective of CNO administration before the PTs. The rescue group showed results similar to the hM4Di-CNO group if CNO is given without L-lactate. On the other hand, they showed results similar to the control group if L-lactate is concurrently given with CNO, indicating that this group learned OPAs and could retrieve it. p < 0.05, p < 0.01, p < 0.001, one-sample t-test comparing the proportion of digging time at the cued sand well with the chance level of 16.67%. F. Non-rewarded PT4 (S20) which was conducted after replacing two OPAs with two NPAs (NPA 7 & 8) in S19 for the control, hM4Di-CNO, and rescue groups. Results show that the control group spent significantly more time digging the new cued sand well above the chance level indicating that the rats learned the NPAs from S19 and could retrieve it in this PT. Contrasting to this, hM4Di-CNO group did not spend more time digging the new-cued sand well above the chance level irrespective of CNO administration before the PT. The rescue group showed results similar to the hM4Di-CNO group if CNO is given without L-lactate. On the other hand, they showed results similar to the control group if L-lactate is concurrently given with CNO indicating that this group learned NPAs from S19 and could retrieve it. p < 0.001, one-sample t-test comparing the proportion of digging time at the new cued sand well with the chance level of 16.67%. G, H, and I. Non-rewarded PTs (PT5, PT6, and PT7 conducted on S23, S27, and S33, respectively) to test memory retrieval of OPAs for the hM4Di-CNO group. In both PT6 and PT7, the rats spent significantly more time digging the cued sand well above the chance level if the tests are done without CNO, indicating that the rats learned the OPAs and could retrieve it. However, CNO prevented memory retrieval during these PTs. p < 0.001, one-sample t-test comparing the proportion of digging time at the cued sand well with the chance level of 16.67%. J. Non-rewarded PT4 (S35) which was conducted after replacing two OPAs with two NPAs (NPA 9 & 10) in S34 for the hM4Di-CNO group. Results show that the rats spent significantly more time digging the new cued sand well above the chance level if CNO was not given before the PT, indicating that the rats learned the NPAs from S34 and could retrieve it in this PT. However, if CNO is given before the PT, the retrieval is impaired. *p < 0.001, one-sample t-test comparing the proportion of digging time at the new cued sand well with the chance level of 16.67%.

• I found it truly exciting that the administration of exogenous L-lactate is capable to rescue CNO-induced PA learning impairment, when co-applied. Would it be possible that this treatment has a sensitivity to a particular stage of learning (acquisition, consolidation, or memory retrieval) when L-lactate administration would be the most efficacious?

The hM4Di-CNO group, when continued with PA training without CNO (S21-S32) (Author response image 2B), was able to learn the six original PAs (OPAs). In the PT7 done at S33 (Author response image 2I), this group of rats was able to retrieve the memory if the test was done without CNO but could not retrieve the memory if CNO was given. Similarly, the Rescue group (hM4Di-CNO+L-lactate) (Author response image 2A), which received both CNO and L-lactate during PA training sessions (S1-S17), they were able to learn the OPAs. And at PT3 done at S18 (Author response image 2E), these rats were able to retrieve the memory when the test was done with CNO+L-lactate but not if the test is done with only CNO. Together, these results clearly show that ACC astrocytic Gi activation with CNO impairs memory retrieval and exogenous L-lactate can rescue the impairment. Therefore, it can be concluded that the memory retrieval is sensitive to L-lactate.

The PA learning is hippocampus-dependent. Over the course of repeated PA training, systems consolidation occurs in the ACC, after which the already learned PA memory (schema) becomes hippocampus-independent (Tse et al., 2007; Tse et al., 2011). A higher activation (indicated by expression of c-Fos) in the hippocampus relative to the ACC during the early period of schema development, and the reverse at the late stage was observed in our previous study (Liu et al., 2022). However, rapid assimilation of new PA into the ACC requires simultaneous activation/retrieval of previous schema from ACC and hippocampus dependent new PA learning (Tse et al., 2007; Tse et al., 2011). During new PA learning, increase of c-Fos neurons in both CA1 and ACC was detected (Liu et al., 2022).

Our hM4Di-CNO group received CNO 30 mins before and after each PA training session in S1-S17 (Author response image 2A). Also, the Rescue group similarly received CNO+L-lactate before and after each PA training session in S1-S17. Therefore, while this study design allowed us to conclude that ACC astrocytic Gi activation impairs PA learning and that exogenous L-lactate can rescue the impairment, it does not allow clear differentiation of the effects of these treatments on memory acquisition and consolidation. Further studies are needed to investigate this.

• The hypothesis that observed learning impairments could be associated with diminished mitochondrial biogenesis caused by decreased l-lactate in the result of astrocytic Gi-DREADDS stimulation is very appealing, but a few key pieces of evidence are missing. So far, the hypothesis is supported by experiments demonstrating reduced expression of several components of mitochondrial membrane ATP synthase and a decrease in relative mtDNA copy numbers in ACC of rats injected with Gi-DREADDs. L-lactate injections into ACC restored and even further increased the expression of the above-mentioned markers. Co-administration of NMDAR antagonist D-APV or MCT-2 (mostly neuronal) blocker 4-CIN with L-lactate, prevented L-lactate-induced increase in relative mtDNA copy. I am wondering how the interference with mitochondrial biogenesis is affecting neuronal physiology and if it would result in impaired PA learning or schema memory.

The observation of diminished mitochondrial biogenesis in the astrocytic Gi-activated rats that showed impaired PA learning is exciting. However, our study does not provide experimental data on how mitochondrial biogenesis could be associated with impaired PA learning and schema memory. Results from several previous studies linked mitochondrial biogenesis and its regulators such as PGC-1α and SIRT3 to diverse neuronal and cognitive functions as described in the discussion section of the manuscript. In the revised manuscript, we have provided further discussion as follows to discuss potential mechanisms:

“In this study, we have demonstrated that ACC astrocytic Gi activation impairs PA learning and schema formation, PA memory retrieval, and NPA learning and retrieval by decreasing L-lactate level in the ACC. Although we have shown that these impairments are associated with diminished expression of proteins of mitochondrial biogenesis, the precise mechanisms of how astrocytic Gi activation affects neuronal functions and schema memory remain to be elucidated. We previously demonstrated that neuronal inhibition in either the hippocampus or the ACC impairs PA learning and schema formation (Hasan et al., 2019). In another recent study (Liu et al., 2022), we showed that astrocytic Gi activation in the CA1 impaired PA training-associated CA1-ACC projecting neuronal activation. Yao et al. recently showed that reduction of astrocytic lactate dehydrogenase A (an enzyme that reversibly catalyze L-lactate production from pyruvate) in the dorsomedial prefrontal cortex reduces L-lactate levels and neuronal firing frequencies, promoting depressive-like behaviors in mice (Yao et al., 2023). These impairments could be rescued by L-lactate infusion. It is possible that the impairment in PA learning and schema observed in our study might have involved a similar functional consequence of reduced neuronal activity in the ACC neurons upon astrocytic Gi activation.

Schema consolidation is associated with synaptic plasticity-related gene expression (such as Zif268, Arc) in the ACC (Tse et al., 2011). L-lactate, after entry into neurons, can be converted to pyruvate during which NADH is also produced, promoting synaptic plasticity-related gene expression by potentiating NMDA signaling in neurons (Yang et al., 2014; Margineanu et al., 2018). Furthermore, L-lactate acts as an energy substrate to fuel learning-induced de novo neuronal translation critical for long-term memory (Descalzi et al., 2019). On the other hand, mitochondria play crucial role in fueling local translation during synaptic plasticity (Rangaraju et al., 2019). Therefore, it could be hypothesized that the rescue of astrocytic Gi activation-mediated impairment of schema by exogenous L-lactate could have been mediated by facilitating synaptic plasticity-related gene expression by directly fueling the protein translation, potentiating NMDA signaling, as well as increasing mitochondrial capacity for ATP production by promoting mitochondrial biogenesis. Furthermore, the potential involvement of HCAR1, a receptor for L-lactate that may regulate neuronal activity (Bozzo et al., 2013; Tang et al., 2014; Herrera-López & Galván, 2018; Abrantes et al., 2019), cannot be excluded. Future research could explore these potential mechanisms, examining the interactions among them, and determining their relative contributions to schema. Our previous study also showed that ACC myelination is necessary for PA learning and schema formation, and that repeated PA training is associated with oligodendrogenesis in the ACC (Hasan et al., 2019). Oligodendrocytes facilitate fast, synchronized, and energy efficient transfer of information by wrapping axons in myelin sheath. Furthermore, they supply axons with glycolysis products, such as L-lactate, to offer metabolic support (Fünfschilling et al., 2012; Lee et al., 2012). The association of oligodendrogenesis and myelination with schema memory may suggest an adaptive response of oligodendrocytes to enhance metabolic support and neuronal energy efficiency during PA learning. Given the impairments in PA learning observed in the ACC astrocytic Gi-activated rats in the current study, it is reasonable to conclude that the direct metabolic support to axons provided by oligodendrocytes is not sufficient to rescue the schema impairments caused by decreased L-lactate levels upon astrocytic Gi activation. On the other hand, L-lactate was shown to be important for oligodendrogenesis and myelination (Sánchez-Abarca et al., 2001; Rinholm et al., 2011; Ichihara et al., 2017). Therefore, it is tempting to speculate that a decrease in L-lactate level may also impede oligodendrogenesis and myelination, consequently preventing the enhanced axonal support provided by oligodendrocytes and myelin during schema learning. Recently, a study has demonstrated that upon demyelination, mitochondria move from the neuronal cell body to the demyelinated axon (Licht-Mayer et al., 2020). Enhancement of this axonal response of mitochondria to demyelination, by targeting mitochondrial biogenesis and mitochondrial transport from the cell body to axon, protects acutely demyelinated axons from degeneration. Given the connection between schema and increased myelination, it remains an open question whether L-lactate-induced mitochondrial biogenesis plays a beneficial role in schema through a similar mechanism. Nevertheless, our results contribute to the mounting evidence of the glial role in cognitive functions and underscores the new paradigm in which glial cells are considered as integral players in cognitive functions alongside neurons. Disruption of neurons, myelin, or astrocytes in the ACC can disrupt PA learning and schema memory.”

Reviewer #3 (Public Review):

Akter et al. investigated how the astroglial Gi signaling pathway in the rat anterior cingulate cortex (ACC) affects cognitive functions, in particular schema memory formation. Using a stereotactic approach they intracranially introduced AAV8 vectors carrying mCherry-tagged hM4Di DREADD (Designer Receptor Exclusively Activated by Designer Drugs) under astrocyte selective GFAP promotor (AAV8-GFAP-hM4Di-mCherry) into the AAC region of the rat brain. hM4Di DREADD is a genetically modified form of the human M4 muscarinic (hM4) receptor insensitive to endogenous acetylcholine but is activated by the inert clozapine metabolite clozapine-N-oxide (CNO), triggering the Gi signaling pathway. The authors confirmed that hM4Di DREADD is selectively expressed in astrocytes after the application of the AAV8 vector by analysing the mCherry signals and immunolabeling of astrocytes and neurons in the ACC region of the rat brain. They activated hM4Di DREADD (Gi signalling) in astrocytes by intraperitoneal administration of CNO and measured cognitive functions in animals after CNO administration. Activation of Gi signaling in astrocytes by CNO application decreased paired-associate (PA) learning, schema formation, and memory retrieval in tested animals. This was associated with a decrease in cAMP in astrocytes and L-lactate in extracellular fluid as measured by immunohistochemistry in situ and in awake rats by microdialysis, respectively. Administration of exogenous L-lactate rescued the astroglial Gi-mediated deficits in PA learning, memory retrieval, and schema formation, suggesting that activation of astroglial Gi signalling downregulates L-lactate production in astrocytes and its transport to neurons affecting memory formation. Authors also show that expression level of proteins involved in mitochondrial biogenesis, which is associated with cognitive functions, is decreased in neurons, when Gi signalling is activated in astrocytes, and rescued when exogenous L-lactate is applied, suggesting the implication of astrocyte-derived L-lactate in the maintenance of mitochondrial biogenesis in neurons. The latter depended on lactate MCT2 transporter activity and glutamate NMDA receptor activity.

The paper is very well written and discussed. The conclusions of this paper are well supported by the data. Although this is a study that uses established and previously published methodologies, it provides new insights into L-lactate signalling in the brain, particularly in AAC, and further confirms the role of astroglial L-lactate in learning and memory formation. It also raises new questions about the molecular mechanisms underlying astrocyte-derived L-lactate-mediated mitochondrial biogenesis in neurons and its contribution to schema memory formation.

• The authors discuss astrocytic L-lactate signalling without considering the recently discovered L-lactate-sensitive Gs and Gi protein-coupled receptors in the brain, which are present in both astrocytes and neurons. The use of nonendogenous L-lactate receptor agonists (Compound 2, 3-chloro-5-hydroxybenzoic acid) would clarify the implication of L-lactate receptor signalling in schema memory formation.

In the revised manuscript, we have included this point in the discussion section to mention the potential role of HCAR1 in schema memory as follows:

“Schema consolidation is associated with synaptic plasticity-related gene expression (such as Zif268, Arc) in the ACC (Tse et al., 2011). L-lactate, after entry into neurons, can be converted to pyruvate during which NADH is also produced, promoting synaptic plasticity-related gene expression by potentiating NMDA signaling in neurons (Yang et al., 2014; Margineanu et al., 2018). Furthermore, L-lactate acts as an energy substrate to fuel learning-induced de novo neuronal translation critical for long-term memory (Descalzi et al., 2019). On the other hand, mitochondria play crucial role in fueling local translation during synaptic plasticity (Rangaraju et al., 2019). Therefore, it could be hypothesized that the rescue of astrocytic Gi activation-mediated impairment of schema by exogenous L-lactate could have been mediated by facilitating synaptic plasticity-related gene expression by directly fueling the protein translation, potentiating NMDA signaling, as well as increasing mitochondrial capacity for ATP production by promoting mitochondrial biogenesis. Furthermore, the potential involvement of HCAR1, a receptor for L-lactate that may regulate neuronal activity (Bozzo et al., 2013; Tang et al., 2014; Herrera-López & Galván, 2018; Abrantes et al., 2019), cannot be excluded. Future research could explore these potential mechanisms, examining the interactions among them, and determining their relative contributions to schema.”

• The use of control animals transduced with an "empty" AAV9 vector (AAV8-GFAP-mCherry) compared with animals transduced with AAV8-GFAP-hM4Di-mCherry throughout the study would strengthen the results of this study, since transfection itself, as well as overexpression of the mCherry protein, may affect cell function.

We thank the reviewer for pointing this. The schema experiment includes a control group (Control-CNO group) of rats injected with AAV8-GFAP-mCherry bilaterally into the ACC. As shown in Author response image 3, after habituation and pretraining, these rats were trained for PA learning similarly to the other groups. Before 30 mins and after 30 mins of each PA training session, they received I.P. CNO. The PA learning, schema formation, memory retrieval, NPA learning and retrieval, and latency (time needed to commence digging at the correct well) were similar to the control group of rats. This result is consistent with our previous study where rats bilaterally injected with AAV8-GFAP-mCherry into CA1 of hippocampus did not show impairments in PA learning and schema formation upon CNO treatment (Liu et al., 2022).

Author response image 3.

A. PI (mean ± SD) during the acquisition of the original six PAs (OPAs) (S1-2, 4-8, 10-17) and new PAs (NPAs) (S19) of the control (n=6) and control-CNO (n=4) groups. B. Non-rewarded PTs (PT1, PT2, and PT3 done on S3, S9, and S18, respectively) to test memory retrieval of OPAs for the control-CNO group. C. Non-rewarded PT4 (S20) which was done after replacing two OPAs with two NPAs (NPA 7 & 8) in S19 for the control-CNO group. D. Latency (in seconds) before commencing digging at the correct well for control and control-CNO groups. Data shown as mean ± SD.

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Reviewer #2 (Public Review):

The manuscript of Akter et al is an important study that investigates the role of astrocytic Gi signaling in the anterior cingulate cortex in the modulation of extracellular L-lactate level and consequently impairment in flavor-place associates (PA) learning. However, whereas some of the behavioral observations and signaling mechanism data are compelling, the conclusions about the effect on memory are inadequate as they rely on an experimental design that does not allow to differentiate acute or learning effect from the effect outlasting pharmacological treatments, i.e. effect on memory retention. With the addition of a few experiments, this paper would be of interest to the larger group of researchers interested in neuron-glia interactions during complex behavior.<br /> • Largely, I agree with the authors' conclusion that activating Gi signaling in astrocytes impairs PA learning, however, the effect on memory retrieval is not that obvious. All behavioral and molecular signaling effects described in this study are obtained with the continuous presence of CNO, therefore it is not possible to exclude the acute effect of Gi pathway activation in astrocytes. What will happen with memory on retrieval test when CNO is omitted selectively during early, middle, or late session blocks of PA learning?<br /> • I found it truly exciting that the administration of exogenous L-lactate is capable to rescue CNO-induced PA learning impairment, when co-applied. Would it be possible that this treatment has a sensitivity to a particular stage of learning (acquisition, consolidation, or memory retrieval) when L-lactate administration would be the most efficacious?<br /> • The hypothesis that observed learning impairments could be associated with diminished mitochondrial biogenesis caused by decreased l-lactate in the result of astrocytic Gi-DREADDS stimulation is very appealing, but a few key pieces of evidence are missing. So far, the hypothesis is supported by experiments demonstrating reduced expression of several components of mitochondrial membrane ATP synthase and a decrease in relative mtDNA copy numbers in ACC of rats injected with Gi-DREADDs. L-lactate injections into ACC restored and even further increased the expression of the above-mentioned markers. Co-administration of NMDAR antagonist D-APV or MCT-2 (mostly neuronal) blocker 4-CIN with L-lactate, prevented L-lactate-induced increase in relative mtDNA copy. I am wondering how the interference with mitochondrial biogenesis is affecting neuronal physiology and if it would result in impaired PA learning or schema memory.

eLife assessment

This valuable manuscript systematically addresses the role of intracellular lipid transfer proteins on cellular lipid levels. The authors have provided solid evidence on the role of ORP9 and ORP11 in sphingolipid metabolism at the Golgi complex. The data is in general convincing, except for the claim that ORP9/11 might counter-exchange phosphatidylinositol 4-phosphate and phosphatidylserine, which is not fully supported by the data presented. This article will be of broad interest to cell biologists interested in lipid metabolism and membrane biology.

Reviewer #1 (Public Review):

Summary:<br /> In this well-designed study, the authors of the manuscript have analyzed the impact of individually silencing 90 lipid transfer proteins on the overall lipid composition of a specific cell type. They confirmed some of the evidence obtained by their own and other research groups in the past, and additionally, they identified an unreported role for ORP9-ORP11 in sphingomyelin production at the trans-Golgi. As they delved into the nature of this effect, the authors discovered that ORP9 and ORP11 form a dimer through a helical region positioned between their PH and ORD domains.

Strengths:<br /> This well-designed study presents compelling new evidence regarding the role of lipid transfer proteins in controlling lipid metabolism. The discovery of ORP9 and ORP11's involvement in sphingolipid metabolism invites further investigation into the impact of the membrane environment on sphingomyelin synthase activity.

Weaknesses:<br /> There are a couple of weaknesses evident in this manuscript. Firstly, there's a lack of mechanistic understanding regarding the regulatory role of ORP9-11 in sphingomyelin synthase activity. Secondly, the broader role of hetero-dimerization of LTPs at ER-Golgi membrane contact sites is not thoroughly addressed. The emerging theme of LTP dimerization through coiled domains has been reported for proteins such as CERT, OSBP, ORP9, and ORP10. However, the specific ways in which these LTPs hetero and/or homo-dimerize and how this impacts lipid fluxes at ER-Golgi membrane contact sites remain to be fully understood.

Regardless of the unresolved points mentioned above, this manuscript presents a valuable conceptual advancement in the study of the impact of lipid transfer on overall lipid metabolism. Moreover, it encourages further exploration of the interplay among LTP actions across various cellular organelles.

Reviewer #2 (Public Review):

Summary:<br /> The authors set out to determine which lipid transfer proteins impact the lipids of Golgi apparatus, and they identified a reasonable number of "hits" where the lack of one lipid transfer protein affected a particular Golgi lipid or class of lipids. They then carried out something close to a "proof of concept" for one lipid (sphingomyelin) and two closely related lipid transfer proteins (ORP9/ORP11). They looked into that example in great detail and found a previously unknown relationship between the level of phosphatidylserine in the Golgi (presumably trans-Golgi, trans-Golgi Network) and the function of the sphingomyelin synthase enzyme. This was all convincingly done - results support their conclusions - showing that the authors achieved their aims.

Impact:<br /> There are likely to be 2 types of impact:<br /> (I) cell biology: sphoingomyelin synthase, ORP9/11 will be studied in the future in more informed ways to understand (a) the role of different Golgi lipids - this work opens that out and produces more questions than answers (b) the role of different ORPs: what distinguishes ORP11 from its paralogy ORP10?<br /> (ii) molecular biochemistry: combining knockdown miniscreen with organelle lipidomics must be time-consuming, but here it is shown to be quite a powerful way to discover new aspects of lipid-based regulation of protein function. This will be useful to others as an example, and if this kind of workflow could be automated, then the possible power of the method could be widely applied.

Strengths:<br /> Nicely controlled data;<br /> Wide-ranging lipidomics dataset with repeats and SDs - all data easily viewed.<br /> Simple take-home message that PS traffic to the TGN by ORP9/11 is required for some aspect of SMS1 function.

Weaknesses:<br /> Model and Discussion:<br /> No idea about the aspect of SMS1 function that is being affected. Even if no further experiments were carried out, the authors could discuss possibilities. One might speculate what the PS is being used for. For example, is it a co-factor for integral membrane proteins, such as flippases? Is it a co-factor for peripheral membrane proteins, such as yet more LTPs? The model could include the work of Peretti et al (2008), which linked Nir2 activity exchanging PI:PA (Yadav et al, 2015) to the eventual function of CERT. Could the PS have a role in removing/reducing DAG produced by CERT?

Reviewer #3 (Public Review):

Summary:<br /> The authors developed a lipid transfer protein knock-out library to identify lipid transfer proteins with roles in lipid homeostasis/metabolism. They investigated one of their hits, the ORP9/ORP11 dimer, which they found affects sphingomyelin synthesis. Further. they found that ORP9/11 localizes to ER-Golgi contact sites via interactions between a known FFAT motif in ORP9, which can interact with the ER protein VAP, and the PH domains of ORP9 and ORP11 that target PI4P at the Golgi. They showed defects in Golgi PI4P and PS levels when ORP9 or 11 were dysfunctional, supporting but not demonstrating that ORP9/11 might exchange PI4P and PS at these contact sites. Their in vitro data indicates that both ORP9 and 11 can transfer PS. They do not assess whether either protein can transfer PI4P (although there is a very nice recent paper by He et al et You, PMID 36853333, showing that ORP9 can transfer PI4P in vitro), and they do not assess the consequences of heterodimerization on either PS or PI4P transfer. The mechanisms by which PI4P/PS level perturbations affect sphingomyelin synthesis remain unclear.

Strengths:<br /> The authors have developed an LTP knock-out library that might generate hypotheses regarding lipid metabolism, although defects are not unexpected and mechanisms will be difficult to work out--as, in fact, evidenced by this manuscript.

The OPR9/11 localization data and imaging studies are well done; this is the first more comprehensive characterization of the ORP9/11 heterodimer, which was discovered in 2010.

Weaknesses:<br /> A major flaw is that the authors claim to but do not, in fact, provide evidence of PS/PI4P counter exchange in vitro. That the presence of PI4P on the acceptor liposomes accelerates PS transfer in the in vitro assays is not proof that there is a counter exchange. In fact, since the rate-determining step in the transfer reactions is lipid transfer between membrane and transfer protein and this depends on the association of the transfer protein with donor and/or acceptor liposomes, a more likely explanation for the more efficient transfer in the presence of PI4P is that PI4P allows for longer association of lipid transfer protein with acceptor liposomes. To show the plausibility of the counter-exchange idea as applied to the ORP9/ORP11 heterodimer, the authors would need to show that it can transfer PI4P. (The work by He et al et You, 2023, mentioned above, is a very nice study that the authors might use as a model.)

Mechanistic insights from the study are limited. How does a PI4P/PS imbalance affect sphingomyelin synthesis?

The ORP9/11 heterodimer seems to behave very much like ORP9/ORP10 heterodimer, including in its localization and dimerization mode. Is ORP9/11 just another version of 9/10? I wonder whether discussions of whether they are redundant or what their different roles are might be in order. There is little mechanistic or conceptual novelty arising from this study.

A minor point, but the statement (p2., lines 19-20) that "vesicular trafficking contributes only to a small portion of lipid trafficking" is not correct and raises eyebrows. What is more correct is that protein-mediated lipid transfer ALSO plays an important role in lipid transfer. It might even be said that LTP-mediated lipid transfer is critical in fine-tuning membrane lipid composition, including of phosphoinositides.

elife assessment:

This study presents valuable new findings on the role and mechanism of action of the poorly characterized cell surface protein TMEM263 in regulating mouse postnatal growth. The evidence supporting the whole-body growth and skeletal phenotypes, as well as the disruption of GH/insulin-like growth factor-1 (IGF-1) signaling seen in TMEM263 knockout mice, is convincing. However, additional data are needed to definitively conclude that the observed alteration of hepatic GH/IGF1 signaling is causative of the body growth and skeletal phenotypes.

Reviewer #1 (Public Review):

Summary:<br /> In this study, the authors present a new mouse model with a deletion of the Tmem263 gene, also known as C12orf23, which encodes the transmembrane protein 263.

Strengths:<br /> The study indicates that Tmem263 interacts with growth hormone, and possibly participates in controlling body growth.

Weaknesses (Major):<br /> The current study confirms previous findings using a mouse model of TMEM263 gene deletion. However, it remains descriptive and does not provide critical insights into the mechanism of action of TMEM263 protein. While the study demonstrates Tmem263-mediated reduction in GHR gene expression in the liver and subsequent growth retardation, it does not elucidate the pathway through which Tmem263 affects GHR expression.

Weaknesses (minor):<br /> 1. Since GH-resistant dwarfism is typically associated with increased body adiposity and hepatic lipid accumulation, considering the high expression levels of Tmem263 in adipose tissue (Figure 1C), it would be valuable to measure body adiposity and hepatic lipid content.

2. It would be helpful to specify the age at which the growth plate parameters were tested. Additionally, were there any differences observed between male and female mice (Figure 3 L-N)? Information on the local (growth plate) expression of IGF-1, IGF-1R, and GHR would also be beneficial.

3. Given the low levels of blood glucose and serum insulin, it would be relevant to know if the mice were challenged with ITT or GTT.

4. The liver transcriptomics data presented in the study is impressive. However, there seems to be a missed opportunity to delve into the data and identify potential factors involved in the regulation of GHR expression.

5. The effects of whole-body Tmem263 nullification on osteoblast differentiation and function, as well as on osteoclastogenesis, were not investigated in the study. Considering the potential impact on bone health, these aspects could be explored in future research.

Reviewer #2 (Public Review):

Summary: The study demonstrates that deletion of a small cytoplasmic membrane protein, Tmem263, caused severe impairment of longitudinal bone growth and that the impaired bone growth was caused by suppression of expression and/or protein levels of growth hormone receptors in the liver.

Strengths: The experimental design of the study is sound and the results are in general supportive of the conclusions.

Weaknesses: The study lacks mechanistic investigation into how the deletion of a gene corresponding to a small cytoplasmic membrane protein would lead to a substantial reduction in the gene expression of growth hormone receptor, which takes place in the nuclei. Accordingly, the manuscript is of a largely descriptive nature.

Reviewer #3 (Public Review):

Prior studies in humans and in chickens suggested that TMEM263 could play an important role in longitudinal bone growth, but a definitive assessment of the function and potential mechanism of action of this species-conserved plasma membrane protein has been lacking. Here, the authors create a TMEM263 null mouse model and convincingly show a dramatic cessation of post-natal growth, which becomes apparent by day PND21. They report proportional dwarfism, highly significant bone and related phenotypes, as well as notable alterations of hepatic GH signaling to IGF1. A large body of prior work has established an essential role for GH and its stimulation of IGF1 production in liver and other tissues in post-natal growth. On this basis, the authors conclude that the observed decrease in serum IGF1 seen in TMEM263-KO mice is causal for the growth phenotype, which seems likely. Moreover, they ascribe the low serum IGF1 to the observed decreases in hepatic GH receptor (GHR) expression and GHR/JAK2/STAT5 signaling to IGF1, which is plausible but not proven by the experiments presented.

The finding that TMEM263 is essential for normal hepatic GHR/IGF1 signaling is an important, and unexpected finding, one that is likely to stimulate further research into the underlying mechanisms of TMEM263 action, including the distinct possibility that these effects involve direct protein-protein interactions between GHR and TMEM263 on the plasma membrane of hepatocytes, and perhaps on other mouse cell types and tissues as well, where TMEM263 expression is up to 100-fold higher (Fig. 1C).

An intriguing finding of this study, which is under-emphasized and should be noted in the Abstract, is the apparent feminization of liver gene expression in male TMEM263-KO mice, where many male-biased genes are downregulated, and many female-biased genes are upregulated. Further investigation of these liver gene responses by comparison to public datasets could be very useful, as it could help determine: (1) whether the TMEM263 liver phenotype is similar to that of hypophysectomized male mouse liver, where GH and GHR/STAT5/IGF1 signaling are both totally ablated; or alternatively, (2) whether the phenotype is more similar to that of a male mouse given GH as a continuous infusion, which induces widespread feminization of gene expression in the liver, and is perhaps similar to the gene responses seen in the TMEM263-KO mice. Answering this question could provide critical insight into the mechanistic basis for the hepatic effects of TMEM263 gene KO.

One notable weakness of this study is the conclusion (in the Abstract, and elsewhere), that the low serum IGF-I "is due to a deficit in hepatic GH receptor (GHR) expression, and GH-induced JAK2/STAT5 signaling." This conclusion is speculative in the absence of any experimental assessment of the impact of TMEM 263-KO on GHR/IGF1 signaling in other tissues that contribute to systematic IGF1 production and which likely also impact bone growth. More direct evidence for the impact of hepatic IGF1 production per se in this mouse model could be obtained by liver-specific delivery into the TMEM263-KO mice of a constitutively active. STAT5 construct, which was recently reported to normalize hepatic and serum IGF1 levels in liver-specific GHR-KO mice (PMID: 35396838).

Another weakness is the experiment presented in Fig. 5E, which is presented as evidence for the proposed GH resistance of TMEM263-KO mice. This experiment has several design flaws: 1) It uses human GH, which unlike mouse GH, activates mouse prolactin receptor as well as GH receptor; 2) the dose of hGH used, 3µg GH/g BW, is 100 times higher than is required to activate liver STAT5; and 3) the experiment lacks a set of control livers, which are needed to establish the level of STAT5 tyrosine phosphorylation in the absence of exogenous GH treatment. Moreover, if the mice used in Fig. 5E are males (the sex was not specified), then high variability in the basal phospho-STAT5 levels of control livers is expected, in which case n=6 or more individual control male livers may be required.

eLife assessment

This is a confirmatory study providing useful findings bearing on the regulation of the inflammatory response of skeletal muscle macrophages by ultrasonic mechanostimulation. It provides solid data on its effect on promoting extracellular vesicle secretion and consolidates previous studies.

Reviewer #1 (Public Review):

This work reports the use of pulsed high-intensity (1-3 W/cm2) 1 MHz ultrasonic waves to stimulate the secretion of extracellular vesicles (EVs) from skeletal muscle cells (C2C12 myotubes) through the modulation of intracellular calcium, and that these, in turn, regulate the inflammatory response in macrophages.

The authors first checked that the ultrasonic irradiation did not have any adverse effect on the structure (protein content) and function (proliferation and metabolic activity) of the myotubes, and showed an up to twofold increase in EV secretion, which they attributed to an increase in Ca2+ uptake into the cell. Finally, the authors show that the myotubes exposed to the ultrasonic irradiation wherein the EV concentration was found to be elevated led to a significant decrease in expression of IL-1b and IL-6 pro-inflammatory cytokines, therefore leading the authors to assert the potential of the use of ultrasonic irradiation for promoting anti-inflammatory effects on macrophages.

While the manuscript was reasonably clearly written and the methodology and results sound, it is not clear what the real contribution of the work is. The authors' findings - that ultrasonic stimulation is capable of altering intracellular Ca2+ to effect an increase in EV secretion from cells as long as the irradiation does not affect cell viability-is well established (see, for example, Ambattu et al., Commun Biol 3, 553, 2020; Deng et al., Theranostics, 11, 9 2021; Li et al., Cell Mol Biol Lett 28, 9, 2023). Moreover, the authors' own work (Maeshige et al., Ultrasonics 110, 106243, 2021) using the exact same stimulation (including the same parameters, i.e., intensity and frequency) and cells (C2C12 skeletal myotubes) reported this. Similarly, the authors themselves reported that EV secretion from C2C12 myotubes has the ability to regulate macrophage inflammatory response (Yamaguchi et al., Front Immunol 14, 1099799, 2023). It would then stand to reason that a reasonable and logical deduction from both studies is that the ultrasonic stimulation would lead to the same attenuation of inflammatory response in macrophages through enhanced secretion of EVs from the myotubes.

The authors' claim that 'the role of Ca2+ in ultrasound-induced EV release and its intensity-dependency are still unclear', and that the aim of the present work is to clarify the mechanism, is somewhat overstated. That ultrasonic stimulation alters intracellular Ca2+ to lead to EV release, therefore establishing their interdependency and hence demonstrating the mechanism by which EV secretion is enhanced by the ultrasonic stimulation, was detailed in Ambattu et al., Commun Biol 3, 553, 2020. While this was carried out at a slightly higher frequency (10 MHz) and slightly different form of ultrasonic stimulation, the same authors have appeared to since establish that a universal mechanism of transduction across an entire range of frequencies and stimuli (Ambattu, Biophysics Rev 4, 021301, 2023).

Similarly, the anti-inflammatory effects of EVs on macrophages have also been extensively reported (Li et al., J Nanobiotechnol 20, 38, 2022; Lo Sicco et al., Stem Cells Transl Med 6, 3, 2017; Hu et al., Acta Pharma Sin B 11, 6, 2021), including that by the authors themselves in a recent study on the same C2C12 myotubes (Yamaguchi et al., Front Immunol 14, 1099799, 2023). Moreover, the authors' stated aim for the present work - clarifying the mechanism of the anti-inflammatory effects of ultrasound-induced skeletal muscle-derived EVs on macrophages - appears to be somewhat redundant given that they simply repeated the microRNA profiling study they carried out in Yamaguchi et al., Front Immunol 14, 1099799, 2023. The only difference was that a fraction of the EVs (from identical cells) that they tested were now a consequence of the ultrasound stimulation they imposed.

That the authors have found that their specific type of ultrasonic stimulation maintains this EV content (i.e., microRNA profile) is novel, although this, in itself, appears to be of little consequence to the overall objective of the work which was to show the suppression of macrophage pro-inflammatory response due to enhanced EV secretion under the ultrasonic irradiation since it was the anti-inflammatory effects were attributed to the increase in EV concentration and not their content.

Reviewer #2 (Public Review):

Summary:<br /> The authors embarked on a journey to understand the mechanisms and intensity-dependency of ultrasound (US)-induced extracellular vesicle (EV) release from myotubes and the potential anti-inflammatory effects of these EVs on macrophages. This study builds on their prior work from 2021 that initially reported US-induced EV secretion.

Strengths:<br /> 1. The finding that US-treated myotube EVs can suppress macrophage inflammatory responses is particularly intriguing, hinting at potential therapeutic avenues in inflammation modulation.

Weaknesses:<br /> 1. The exploration of output parameters for US induction appears limited, with only three different output powers (intensities) tested, thus narrowing the scope of their findings.<br /> 2. Their claim of elucidating mechanisms seems to be only partially met, with a predominant focus on the correlation between calcium responses and EV release.<br /> 3. While the intracellular calcium response is a dynamic activity, the method used to measure it could risk a loss of kinetic information.<br /> 4. The inclusion of miRNA sequencing is commendable; however, the interpretation of this data fails to draw clear conclusions, diminishing the impact of this segment.

While the authors have shown the anti-inflammatory effects of US-induced EVs on macrophages, there are gaps in the comprehensive understanding of the mechanisms underlying US-induced EV release. Certain aspects, like the calcium response and the utility of miRNA sequencing, were not fully explored to their potential. Therefore, while the study establishes some findings, it leaves other aspects only partially substantiated.

Author Response

Reviewer #2 (Public Review):

Zou et al. presented a comprehensive study where they generated single-cell RNA profiling of 138,982 cells from 13 samples of six patients including AK, squamous cell carcinoma in situ (SCCIS), cSCC, and their matched normal tissues, covering comprehensive clinical courses of cSCC. Using bioinformatics analysis, they identified keratinocytes, CAFs, immune cells, and their subpopulations. The authors further compared signatures within subpopulations of keratinocytes along with the clinical progression, especially basal cells, and identified many interesting genes. They also further validate some of the markers in an independent cohort using IHC, followed by some knockdown experiments using cSCC cell lines.

The strength of this study is the unique data set they have created, providing the community with invaluable resources to study and validate their findings. However, a lot of analyses were not robust enough to support the claims and conclusions in the paper. More clarification and cross-comparison with polished data are needed to further strengthen the study and claims.

1) Stemness markers were used. The authors used COL17A1, TP63, ITGB1, and ITGA3 to represent stemness markers. However, these were not common classic stemness markers used in cSCC. What is the source claiming these genes were stemness markers in cSCC? TP63 is a master regulator and early driver event in SCC, while COL17A1, ITGB1, and ITGA3 are all ECM genes. The authors need to use commonly well-known stem cell markers in cSCC, e.g., LGR5, to mark stem-like cells.

Thanks for raising this good point. We may not have provided a clear description of the markers COL17A1, TP63, ITGB1, and ITGA3 in the previous texts. We would like to clarify that these genes were used as the markers of epidermal stem cells in normal skin samples rather than tumor stem cells in cSCC. To avoid any possible misunderstanding, we revised the main text accordingly and added the references [4-11].

2) Cell proportion analysis. The authors used the mean proportions to compare different clinical groups for subpopulations of keratinocytes, e.g., Figure 2B, and Figure 5B. This is not robust, as no statistics can be derived from this. For example, from Fig 2A, it is clearly shown there is a high level of heterogeneity of cellular compositions for normal samples. One cannot say which group is higher or lower simply based on mean not variance as well.

We replotted the proportion analysis with statistics and presented the new graphs in Figure 2-figure supplement 1 for Figure 2B and Figure 5-figure supplement 1 for Figure 5B.

3) Basal tumour cells in SCCIS and SCC. To make the findings valid, authors need to compare these cells/populations with the keratinocyte cell populations defined by Ji et al. Cell 2020. Do basal-SCCIS-tumours cells, also in SCC samples, resemble any of the population defined in Ji et al. Ji et al. also had 10 match normal, thus the authors need to validate their findings of SCC vs normal analysis using the Ji et al. dataset.

Thanks for this valuable suggestion. We compared basal tumor cell in our study with the cell populations defined in Ji et al. Cell 2020 data using SingleCellNet [1]. The results showed that both the basal-SCCIS-tumor cells of SCCIS and basal tumor cells of cSCC in our study closely resemble the Tumor_KC_Basal subcluster defined in Ji et al’s paper (Figure 4-figure supplement 4, C and D). Tumor_KC_Basal highly expressed CCL2, CXCL14, FTH1, MT2A, which is consistent with our findings in basal tumor cells.

4) Copy number analysis. Authors used inferCNV to perform copy number analysis using scRNA-seq data and identified CNVs in subpopulations of keratinocytes in SCCIS and SCC. To ensure these CNVs were not artefacts, were some of the CNVs identified by inferCNV well-known copy number changes previously reported in cSCC?

In poorly-differentiated cSCC sample, the significant gains in chromosome 7, 9 and deletion in chromosome 10 were reported in previous study, indicating the reliability of the CNV analysis results (Figure 5-figure supplement 2) [12].

5) Pseudotime analysis lines 308-313. Not sure the pseudotime analysis added much as, as it is unclear two distinct subgroups were identified from this analysis. Suggest removing this to keep it neater

Thank you for this suggestion. We have deleted the result of pseudotime analysis.

6) Selection of candidate genes for validation using IHC and cell line work. For example, lines 205-206, lines 352-356 and lines 437-441, authors selected several genes associated with AK and SCC to further validate using IHC and cell line knockdown work. What are the criteria for selecting those genes for validation? It is unclear to readers how these were selected. It reads like a fishing experiment, then followed by a knockdown. Clear rationale/criteria need to be elaborated.

The first consideration of candidate gene selection is the fold change of expression. We have provided the statistical results of DEGs in Supplementary file 1b, 1h, 1j-1m. Then we selected top changed genes and conducted an extensive literature search on these genes. We prioritized genes that, although not directly associated with cSCC development, have a close relationship with related pathways, as determined through functional enrichment analysis. These genes were arranged for further verification experiments. We have added more details in main text and methods section.

7) TME. Compared to keratinocytes populations, the investigation of TME cells was weak. (a) can authors produce UMAP files just for T cells, DC cells, and fibroblasts separately? Figure 7B is not easy to see those subclusters. (b) similar to what was done for keratinocytes, can authors find differentially expressed clusters and genes among the different clinical groups, associated with disease progression? (c) where are the myeloid cell populations, also B cells?

Thank you for your suggestions. (a) We have added the UMAP files for T cells, DC cells and stromal cells separately in new Figure 7A. (b) We identified DEGs in TME cells among the different groups. Several key genes showed monotonically changing trends associated with disease progression. For example, with the increase of malignancy, FOS shows down-regulation while S100A8 and S100A9 monotonically increase in all three types of TME cells (Figure 7C). (c) We identified two types of myeloid cell populations, macrophage and monocyte derived DCs (MoDC). We didn’t find other myeloid cells, such as neutrophil. For B cells, there were only 28 B cells in poorly-differentiated cSCC sample, which didn’t meet the threshold for further cell-cell communication analysis.

8) Heat shock protein genes line 327-329. HSP signature was well-known to be induced via tissue dissociation and library prep during the scRNA experiment. How could the authors be sure these were not artefacts induced by the experiment? If authors regress their gene expression against HSP gene signatures, would this cluster still be identified?

Thank you for this valuable suggestion. It is important to note that the Basal-SCCIS-tumor cluster was identified through CNV analysis, rather than the HSP signature. To address this concern and further validate this result, “AddModuleScore” function in Seurat package was used to regress gene expression against HSP gene signatures for retrieved basal cells. Our result showed that Basal_SCCIS tumor population still can be identified after regression, even more clearly (Author response image 1).

Author response image 1.

The identity of Basal-SCCIS-tumor cluster considering regression against HSP signatures.

9) Cell-cell communication analysis. The authors claimed that that cell-to-cell interaction was significantly enhanced in poorly-differentiated cSCC, and multiple interaction pathways were significantly active. How was this kind of analysis carried out? How did the authors define significance? what statistical method was used? these were all unclear. Furthermore, it is difficult to judge the robustness of the cell-cell communication analysis. Were these findings also supported by another method, such as celltalker, and cellphoneDB?

To determine the significance of the increased overall cell-to-cell interaction strength between two groups, we utilized CellChat to obtain the communication strength in different samples. We combined the communication strength based on cell type pairs, where missing values were set to 0. We performed a paired Wilcoxon test to determine whether the enhancement of cell-to-cell interaction between samples was significant.

For the comparison of outgoing or incoming interaction strength of the same cell types between two groups, we first extracted the communication strength of each signal pathway contributing to outgoing or incoming strength, and then merged the strengths of signal pathways among samples, where the strength of non-shared pathways with missing value was determined to be 0. Subsequently, we performed a paired Wilcoxon test to define the significance.

For multiple groups comparisons, the Kruskal-Wallis rank sum test was first performed. If the p-value is less than 0.1, the pairwise Wilcoxon test was used for subsequent pairwise comparisons. The comparison of individual signaling pathways between groups is similar to the above. We defined p-value < 0.1 as significance threshold. We have added the significance test method in figure legend for Figure 7 and Figure 8 as well as and detailed statistical data in new Supplementary file 1q-1u.

As suggested, we also used the approach of CellPhoneDB based on CellChatDB database to verify our cell-cell communication results. There are 55-58% of the ligand-receptor interactions predicted by CellChat were also predicted by CellPhoneDB (Author response image 2). The enhancement of cell interaction through MHC-II, Laminin and TNF signaling pathways in poorly-differentiated cSCC sample compare to normal sample were consistent in both CellChat and CellPhoneDB (Figure 8C and Figure 8-figure supplement 1B).

Author response image 2.

The overlap of the predicted ligand-receptor interactions between CellChat and CellPhoneDB.

10) Statistics and significance. In general, the detail of statistics and significance was lacking throughout the paper. Authors need to specify what statistical tests were used, and the p-values. It is difficult to judge the correctness of the test, and robustness without seeing the stats.

We have included all statistics and significance values in the figure legend and supplemental tables, and described the statistical tests in the methods section. In this revision, we have added the necessary details of statistics and significance in the main text and figures.

11) Overall, this manuscript needs a lot of re-writing. A lot of discussion was also included in the results, making it really difficult to read overall. The authors should simplify the results sections, remove the discussion bits, and further highlight and streamline with the key results of this paper.

Thanks a lot for this advice. We have revised the paper thoroughly, removed discussion in results section to make the manuscript easier to read.

Author Response

Reviewer #1 (Public Review):

Zhao et al. investigated the molecular nature of the binding site for carbohydrates within the UDP-sugars known to activate the P2Y14 receptor. In order to do so, they built a molecular model of the hP2Y14, docked the corresponding agonists, and performed MD simulation on the resulting complexes. The modeling was used to identify the key molecular interactions with a cluster of charged residues in the extracellular side of the TM region of the receptor, which they show are conserved within the P2Y receptors. The binding site of the UDP region was, not surprisingly, overlapping with the analogous ADP binding site experimentally observed for the P2Y12 receptor, and consequently, the region that recognizes the sugars could be anticipated. Nevertheless, the detailed modeling and simulation work shows the consistency of this hypothesis and provides a quantification of the particular interactions involved, pinpointing specifically the residues candidate to be involved in the recognition of sugars.

It follows the characterization, by functional assays, of the effect of single-point mutations of these residues in the efficacy of the different UDP-sugars. Here the results show a tendency to correlate with the molecular models, however some of the data has very low statistical significance and consequently the interpretation and conclusions extracted from this data should be taken with caution. This pertains to the particular role of the identified residues in the binding of the different sugars, which in some cases should be taken as a suggestion rather than a proof, though the general conclusion of the identification of the binding region for the sugar, its conservation among P2Y receptors and the role of some specific residues in sugar recognition seems convincing and the data are conveniently presented.

Finally, the design of ADP-sugars that activate the P2Y12 receptor, based on the transferability of the observations with the UDP-sugars for the P2Y14 receptor, is a first indication that such a recognition is possible and should happen in an analogous binding region. However, the low potencies exhibited by the ADP-sugars, in the micromolar range, are too far from the ADP agonist and the relevance of this mechanism remains to be proved. The difference between P2Y12 and P2Y14, with the last one showing much higher potencies for UDP-sugar derivatives than P2Y12 for the corresponding ADP-sugars, remains an interesting question not explored in this manuscript.

Thanks for your valuable comments. We have revised the interpretation of the data that has relatively low statistical significance in the manuscript. The conclusions extracted from this data have also been modified as suggestions. In this work, to investigate whether sugar nucleotides can also activate human P2Y12, we tested three ADP-sugars for human P2Y12. Discovery of highly potent P2Y12 agonists requires screening of a large number of compounds. It is possible there are the other ADP-sugars, which are highly potent P2Y12 agonists. It is technically challenging to synthesize ADP-sugars. Currently, we can only obtain ADP-Glc, ADP-GlcA and ADP-Man. Once the other ADP-sugars are available for us, we will test them and try to discover highly potent agonists in the future work. The highly potent agonists will be useful chemical tools to unveil the relevance mechanism of P2Y12. To explore the nature of binding site of the P2Y12 and P2Y14, we performed more experiments of mutagenesis study and added relevant data in the revised manuscript.

Reviewer #2 (Public Review):

The manuscript employs multiple approaches, including molecular docking, molecular dynamic simulations, and functional experiments to uncover a distinct uridine diphosphate-sugar-binding site on P2Y14 - a key drug target for inflammation and immune responses. Overall, the manuscript is clearly written, and the experimental techniques are well-documented. However, it may benefit from further analysis, particularly in terms of validating the binding pose.

Thanks for your comments. We used MMPBSA to analyze the ligand-binding energy for each receptor residue using MD trajectories. To further characterize the ligand-binding pose, we calculated the percentage of occurrence of hydrogen binding between the ligand and the carbohydrate-binding site (K277, E278, R253 and K77). We also calculated the ligand RMSF and ligand RMSD to show the stability of the ligand-binding pose and the simulation convergence. These data have been included in the revised manuscript.

Author Response

Reviewer #3 (Public Review):

Seeking a selective inhibitor that precisely inhibits on-target activities and avoids side effects is a major challenge in the field of drug discovery and therapeutics. The authors proposed an alternative method that combines multiple inhibitors to maximize on-target inhibition and minimize off-target inhibition. Focusing on the kinase-inhibitor interaction dataset, the authors developed a quantitative way to measure the selectivity for mixtures of inhibitors by using the Jenson-Sahannon distance metric. The method sounds technical.

From their computation and assays, the multi-compound-multitarget scoring (MMS) method framework was validated to be able to select a combination of inhibitors that is more selective than a single highly selective inhibitor for one kinase target, or for multiple targets. The MMS method is a promising solution to reduce off-target effects and could be applicable to other inhibitor-target interactions. My suggestion is that a comparative analysis of MMS with other similar methods can be conducted to highlight the advantage of MMS over others.

We thank the reviewer for this excellent summary and their suggestions. We agree that comparing new methods to prior ones is an important step in benchmarking new approaches and methods. However, to our knowledge, no other method exists for calculating selective combinations of kinase inhibitors. We compare our JSD selectivity scoring metric to other representative target-specific and non target-specific selectivity metrics (Figure 2 Figure Supplement 2).

The paper is not well organized and not easily readable. For example, first, the captions of the figures are two long; some of these texts could be moved to methods or results sections. Second, the concept of "penalty distribution" or "penalty prior" is vital to understand the MMS method, thus, at least a brief definition and introduction should be put in the main text rather than supporting method, as well as the rationale to use it. Third, the method section can be divided into several subsections with clear organizations and connections. Fourth, what is the difference between "a less selective inhibitor profile" and "an even less selective inhibitor profile" in Figure 3? Overall, the details of the paper are difficult to understand in the current version. I suggest rewriting the paper in a more concise and logical style.

We appreciate these suggestions and have significantly edited and revised our manuscript in order to facilitate clear communication. Specifically:

1) We have added an additional description of the penalty distribution to the description of the MMS method in the main Results section of the manuscript as opposed to solely in the Materials and Methods section.

2) We have provided a high-level concise summary of the MMS method in the results section in order to help orient a reader to the method. This description follows the same order (1 to 5) as the associated Figure 2, we hope this helps more clearly communicate the method.

3) We have moved descriptive figure captions to the methods section and, in general, substantially reduce the size of figure captions.

4) We have subdivided the Materials and Methods section as suggested.

5) We now describe in our main text how the simulated profiles were generated by smoothing the PKIS2645-like profile with two restraints; non-zero activity for LS inhibitors, and similar on-target probability for PKIS2-645-like, RS, and LS inhibitors to facilitate direct comparisons. We provide a new figure to quantify the selectivity of these simulated inhibitors and their similarity with true compounds (Figure 3 Figure Supplement 1).

6) We have removed content from the introduction and results sections that was less important to communicate to a general audience in order to make the manuscript more concise. We have also removed or condensed extraneous supplemental figures that were not required to communicate the central results and findings of experiments (ex: supplemental figures for Figure 3 and Figure 4 from the prior submission).

Author Response

Joint Public Review

(1) The developed model considers the interaction of multiple signaling networks that are essential for morphogenesis and homeostasis in the intestinal tissue, as well as other elements that had been proposed as relevant in the literature. Nevertheless, the details of how these interactions are modeled couldn't be evaluated in the current revision as the model was not shared with the reviewers and it is not available yet online, nor specified in any detail in the current manuscript. Additionally, how quantitative information from Wnt and BMP signaling pathways is incorporated in a quantitative way in the model is not clear.

Model files are provided with this reply. These are ‘.jl’ files for use with Julia. The model (the files provided with this reply) will be freely publicly available through BioModels upon acceptance of this manuscript for publication.

The model includes abstracted values to reproduce Wnt and BMP signalling gradients and their effect on cell proliferation and differentiation to generate the three-dimensional crypt spatial cell distribution. To further clarify the implementation of the quantitative information from Wnt and BMP signalling pathways in the model, we have added the following paragraph in the Appendix Section 8) Cell fate: proliferation, differentiation, arrest, apoptosis

"…During this migration the Wnt content in absorptive progenitors is halved in each division and, away from Wnt sources, progressively decreases, while BMP signals increase, towards the villus. In our model, differentiation into enterocytes occurs when progenitors encounter a BMP signal level, higher that their Wnt signal content. For instance, in the ileal crypt in homeostasis this occurs approximately at cell position 16 from the crypt base, where progenitors migrating from the stem cell niche reach a reduced content of Wnt signals of about 8 a.u. On the other hand, the BMP signalling level has a maximum value of 64 at approximately cell position 23 from the crypt base, where BMP signals are generated by mature enterocytes. These BMP signals diffuse towards the crypt base and, hence, decrease exponentially to reach values of 8 a.u. at approximately position 16, which, hence, enable differentiation into enterocytes. Epithelial injuries resulting in a decreased number of enterocytes reduce BMP signal production and its diffusion range which results in the enlargement of the proliferation compartment as cells encounter the required level of BMP signals for differentiation only at higher positions in the crypt."

(2) Some conclusions by the authors are not properly justified in the text, as "Paneth cells are the main driver behind the differential mechanical environment in the niche", "Wnt-mediated feedback loop prevents the uncontrolled expansion of the niche", the specific effect of p27 in contrast with Wee1 phosphorylation over the cell cycle length, and "their recovery [absorptive progenitors] started before the end of the treatment, driven by a negative feedback loop from mature enterocytes to their progenitors".

We have reworded these statements as described below.

The paragraph “Paneth cells are the main driver behind the differential mechanical environment in the niche, where cells with longer cycles accumulate more Wnt and Notch signals. In agreement with experimental reports {Pin, 2015 #719}, in our model Paneth cells are assumed to be stiffer and larger than other epithelial cells, requiring higher forces to be displaced and generating high intercellular pressure in the region” has been modified and now reads as follows “In agreement with experimental reports {Pin, 2015 #719}, Paneth cells are assumed to be stiffer and larger than other epithelial cells, requiring higher forces to be displaced and generating high intercellular pressure in the niche. Due to this increased mechanical pressure, cells in the niche have longer division cycles and can accumulate more Wnt and Notch signals.”

The sentence “Wnt-mediated feedback loop prevents the uncontrolled expansion of the niche” has been deleted from paragraph, that now reads “To generate a niche of stable size, we implemented a negative Wnt-mediated feedback loop that resembles the reported stem cell production of RNF43/ZNRF3 ligands to increase the turnover of Wnt receptors in nearby cells {Hao, 2012 #2086;Koo, 2012 #2089;Clevers, 2013 #538;Clevers, 2013 #2098}. Similarly, in our model, a number of stem cells in excess of the homeostatic value reduces cell tethering of Wnt ligands and hence inhibits Paneth and stem cell generation (Figures 1A-B).”

Regarding the specific effect of p27 in contrast with Wee1 phosphorylation over the cell cycle length. We have simplified the text in the main manuscript that now reads “Using the model of Csikasz-Nagy et al. {Csikasz-Nagy, 2006 #1870}, we modulated the duration of G1 through the production rate of the p27 protein. The p27 protein has been reported to regulate the duration of G1 by preventing the activation of Cyclin E-Cdk2 which induces DNA replication and the beginning of S-phase {Morgan, 2007 #2073}. We, hence, hypothesized that rapid cycling absorptive progenitors located in regions of low mechanical pressure outside the stem cell niche have low levels of p27, which bring forward the start of S-phase to shorten G1 (Figures 2D). In support of this hypothesis, it has been demonstrated that p27 inhibition has no effect on the proliferation of absorptive progenitors {Zheng, 2008 #2074} (see the Appendix for a full description).

In the Appendix Section 2 we provide an extended explanation of the use of the p27 and Wee1 kinetic governing parameters to decrease the length of the cell cycle by decreasing mainly G1 but maintaining the length of S phase constant, which is as follows

"Regarding G1 phase, the p27 protein has been reported to regulate the duration of G1 by preventing the activation of Cyclin E-Cdk2 which induces DNA replication and defines the beginning of S-phase {Morgan, 2007 #2073}. We hypothesized that fast cycling cells have low levels of p27 which result in earlier DNA replication, bringing forward the start of S-phase and shortening the length of G1. In support of this hypothesis, it has been experimentally demonstrated that inhibiting p27 has no effect on the proliferation of absorptive progenitors {Zheng, 2008 #2074}. In the Csikasz-Nagy model {Csikasz-Nagy, 2006 #1870}, the duration of G1 can be modulated through the parameter V_si, which is the basal production rate of p21/p27 (in the Csikasz-Nagy model, the p21 and p27 proteins are represented by a single variable, here we refer to that model quantity as p21/p27).

Additionally, the end of S-phase is associated with the decrease of Wee1 to basal levels due to Cdc14 mediated phosphorylation of Wee1. In the Csikasz-Nagy model {Csikasz-Nagy, 2006 #1870}, this reaction is described by a Goldbeter-Koshland function, which includes the parameter KA_Wee1p to regulate the level of Cdc14 required for the phosphorylation of Wee1.

Therefore, we modified these two parameters, V_si and KA_Wee1p, to ensure that variations of the cycle duration mostly impact on G1 while the length of S phase remains constant. We assumed that the value of the two parameters scales linearly with the duration of the division cycle, t_cycle, between a lower and upper bound, which prevent aberrant behaviour of the cell cycle model in the dynamically changing conditions of the crypt."

The paragraph related to “their recovery started before the end of the treatment…” sentence has been amended in the text and now reads “Simulated proliferative absorptive progenitors were indirectly affected by stem cell ablation and their decrease was followed by a reduction in mature enterocytes. The progenitors recovered soon after treatment interruption to later reach values above baseline when responding to the negative feedback signalling from mature enterocytes (Figure 3A).”

(3) Only the results of the "main" model are shown, with no information about its sensitivity to parameter values, and how their conclusions depend on specific decisions on the model. For example, the authors said that "an optimal crypt cell composition is achieved when BMP and Wnt differentiation thresholds result in progenitors dividing approximately four times before differentiating into enterocytes", but the results of alternative scenarios are not shown.

To address this comment, we have included a new section in the Appendix, called “What-if Analysis”, and new figures (Figure S4-S8) with simulations of alternative scenarios affecting the main signalling pathways that govern crypt composition, in particular, we simulated stronger and weaker Wnt, BMP, Notch and ZNRF3/RNF43 signalling.

We attach the new section here:

"10) What-if Analysis

We investigated the effect on the simulated crypt of increasing and decreasing the strength of the main signalling pathways, Wnt, BMP and ZNRF3/RNF43 signalling, and modifying the Notch thresholds. For each alternative parameterisation, except when decreasing ZNRF3/RNF43 signalling, the simulation was run for 30 days to ensure stability was reached with the new parameter set and the final 10 days were included in the analysis. When decreasing ZNRF3/RNF43 signalling, we simulated 60 days to demonstrate the expansion of the niche and analysed the final 10 days. The reference parameter set used as baseline was the ileal mouse crypt parameter set reported in Appendix Table 1. In all cases, we only consider modifications of one signalling mechanism at a time.

To study alternative Wnt signalling scenarios, we used the WntRange parameter (Appendix Table 1), to double and halve the spreading area of Wnt signals emitted by Paneth cells while we maintained the original WntRange value for Wnt-emitting mesenchymal cells at the bottom of the crypt (Appendix Section 7.1) (Figures S4A-S4F). When WntRange was doubled, we observed increased number of stem and Paneth cells in a noticeably enlarged niche (Figures S4C-S4D), with cells choosing the stem cell fate instead of differentiating into absorptive progenitors. On the other hand, decreasing Wnt signalling, by halving WntRange in Paneth cells but maintaining its homeostatic value in mesenchymal cells, resulted in no apparent changes in the niche cell composition (Figures S4E-S4F) which resembled published experimental results of persisting functional stem cells after Paneth cell ablation {Durand, 2012 #434}.

The ZNRF3/RNF43-mediated negative feedback mechanism regulates the size of the niche by modulating Wnt signalling. We simulated increasing and decreasing the strength of the ZNRF3/RNF43, by doubling and halving, respectively, the parameter Z described in the Appendix Section 7.2 (Figures S5A-S5F). Following the increase of the intensity of ZNRF3/RNF43 signalling, we observed a decrease in the number of stem and Paneth cells together with relatively minor changes in the transit-amplifying region (Figures S5C-S5D). On the other hand, when decreasing ZNRF3/RNF43 signalling levels, the niche expanded , resulting in a crypt dominated by Paneth and stem cells (Figures S5E-S5F ) which replicates reported experimental phenotypes {Koo, 2012 #2089}.

To modify Notch signalling, we increased and decreased by 1 A.U. the Notch threshold required for lateral inhibition (Figures S6A-S6F). This Notch signalling threshold determines the number of contacting Notch-secreting cells (secretory lineage) to inhibit the differentiation of stem cells into the secretory lineage. Thus, increasing this Notch threshold enhances the production of secretory cells leading to the increase of Paneth, goblet and enteroendocrine cells (Figure S6C-S6D). Alternatively, decreasing the Notch threshold enhances differentiation into the absorptive lineage, reducing the number of Paneth and secretory cells (Figure S6E-S6F).

We modified the range of diffusion of BMP signals by doubling and halving the parameter A , (Figures S7A-S7F) which denotes the amount of diffusing BMP signals towards the base of the crypt (Appendix Section 7.4). When we increased the BMP signalling range, enterocytes differentiated at lower crypt positions effectively reducing the transit-amplifying zone (Figure S7A, Figure S7B). Decreasing BMP signalling strength by halving A resulted in the increase of proliferative absorptive progenitors, which reach higher positions in the crypt (Figure S7C-S7D). The niche was largely unaffected in both cases (Figure S7E-S7F)."

(4) Regarding the construction of the model, the authors used "counts of Ki-67 positive cells recorded by position" while the original data reported "overall cell counts per crypt and villus". Some explanation about how this conversion was made, why it is valid, as well as any potential problems, is needed. Additionally, the model is based on experiments done by others in mouse models; the similarity to the response in human intestinal crypts is not discussed.

Ki-67 immunostaining data during 5-FU treatment was derived from the same experiments. The overall cell counts per crypt and villus are published in {Jardi, 2022 #2416}. For this manuscript, we reanalysed the intestinal samples to estimate counts of cell types by position in the crypt.

We have clarified the text, which now reads …“The samples from this later study {Jardi, 2022 #2416} were analysed again to count Ki-67 positive cells at each position along the longitudinal crypt axis, for 30-50 individual hemi crypt units per tissue section per mouse as previously described {Williams, 2016 #2165}.”

We agree that the understanding of the translation of results derived from animal models into a human or clinical context is of high relevance. The mouse crypt is a model of choice to study epithelial biology and exhibits remarkable similarities with the human crypt. In our team, we are focussed on developing translational modelling strategies and have a version of the model that describes a human crypt. That model assumes mostly conserved crypt biology and structure across species and includes changes in parameter values needed to compensate reported differences in morphometrics and cell cycle duration. Due to the relevance and extent of this translational work, we chose to focus on the mouse crypt entirely in this manuscript. We think the translational modelling strategy to explore the quantitative translation between human and mouse and/or other species/settings merits a full report.

(5) The authors imply that their mathematical model of the intestinal crypt is an improvement over those already published but there is no direct comparison or review of the literature to substantiate this claim.

An extended literature review including more details of previous ABMs to enable a direct comparison with our model is now included in the manuscript and reads as follows:

“Several agent-based models (ABMs) have been proposed to describe the complexity and dynamic nature of the intestinal crypt. Early models were used as in silico platforms to study the dynamics and cellular organisation of the crypt. For instance, one of the pioneering ABMs was used to study the distribution and organisation of labelling and mitotic indices {Meineke, 2001 #326}. This model comprises a fixed ring of Paneth cells beneath a row of stem cells, which divide asymmetrically to produce a stem cell and a transit-amplifying cell that terminally differentiates after a fixed number of divisions. Some subsequent models are lattice-free, recapitulate neutral drift of equipotent stem cells and describe proliferation and cell fate regulated by a fixed Wnt signalling spatial gradient, which is defined by the distance from the crypt base, with proliferating cells progressing through discrete phases of the cell cycle and showing variable duration of the G1 phase {Pitt-Francis, 2009 #129}. Further model refinements can be seen in the model of Buske et al (2011), with stochastic cell growth and division time {Buske, 2011 #1}, Wnt levels defined by the fixed local curvature of the crypt and lateral inhibition driven by Notch signalling. Here, we present a lattice-free agent-based model that describes the spatiotemporal dynamics of single cells in the small intestinal crypt driven by the interaction of surface tethered Wnt signals, cell-cell Notch signalling, BMP diffusive signals, RNF43/ZNRF3-mediated feedback mechanisms and the cycle protein network responding to the crypt mechanical environment. We show that our computational model enables the simulation of the ablation and recovery of the stem cell niche as well as of how drug-induced molecular perturbations trigger a cascade of disruptive events spanning from the cell cycle to single cell arrest and/or apoptosis, altered cell migration and turnover and ultimately loss of epithelial integrity.”

(6) The authors claim that the simulated data and the available mouse data match up. Nevertheless, the data vs the model still appear both quantitatively and qualitatively different (as presented in Figures 2E, F, and 5C, D). This puts in doubt how much the model can actually reproduce the experimental data. In conclusion, the model would benefit from further refinement, particularly if the goal is to use the model for predicting the dynamics of oncogenic drug candidates.

To address this comment, we have made several adjustments: we refined the counting algorithm that determines cell position and improved the Ki67 and BrdU staining simulations by modifying the simulated staining criteria and adding an estimation of the experimental error to the simulated responses. A description of these changes is described in a new section in the appendix called “ABM simulation of Ki-67 and BrdU Staining”

With these changes we think we have achieved a more satisfactory agreement between observed and predicted results and updated all figures with Ki67 and BrdU staining simulated results.

Author Response

We are grateful to the editors and the reviewers for the thorough evaluation of our manuscript and their feedback, as it allows us to provide additional clarification of our findings and improve the manuscript.

In their evaluation reviewers raised a key conceptual point linked to the inhibitory mechanism that appeared to be insufficiently explained in the manuscript, leading to a misconception regarding the physiological relevance. They have also missed experimental data related to the concentrations of Aβ used and their relevance for Alzheimer’s disease (AD). We believe that our studies, although performed in vitro in model systems, provide novel conceptual framework and shed light on the unexplored mechanisms underlying AD.

We discuss these points below in a provisional response to their comments.

Reviewer #1 (Public Review):

Summary:

Human Abeta42 inhibits gamma-secretase activity in biochemical assays.

Strengths:

Determination of inhibitory concentration human Abeta42 on gamma-secretase activity in biochemical assays.

Weaknesses:

Human Abeta42 may concentrate up to microM order in endosomes.

This is correct.

If so, production of Abeta42 would be attenuated then lead to less Abeta deposition in the brain. The authors finding is interesting but does not fit the physiological condition in the brain.

We thank the reviewer for raising this key conceptual point, as this gives us the opportunity to clarify it for the future readers.

The characterized inhibitory mechanism is more complex than the reviewer’s interpretation, and a number of factors must be considered. Indeed, our data show that Aβ42 upon intracellular concentration inhibits γ-secretase activity, resulting in increased γ-secretase substrate (C-terminal fragment, CTF) levels. It is important however to highlight that this inhibition is competitive in nature, implying that it is partial, reversible, and regulated by the relative concentrations of the Aβ42 peptide (inhibitor) and the substrates. The model that we put forward is that cellular uptake and intracellular concentration of Aβ42 facilitates γ-secretase inhibition, which results in the accumulation of APP-CTFs (and γ-secretase substrates in general). However, as Aβ42 levels fall, the increased concentration of substrates shifts the equilibrium towards their processing and Aβ production. As Aβ42 concentration raises again, equilibrium is shifted back towards inhibition and so on. This inhibitory mechanism will translate into pulses of (partial) γ-secretase inhibition, which will alter γ-secretase mediated signalling (arising from increased CTF levels or decreased release of soluble intracellular domains from substrates). These alterations may affect the dynamics of systems oscillating in the brain, such as NOTCH signalling, implicated in memory formation (2), and potentially others (related to e.g. cadherins, p75 or neuregulins).

It is worth noting that oscillations in γ-secretase activity induced by treatment with a γ-secretase inhibitor (semagacestat) have been proposed to have contributed to the cognitive alterations observed in semagacestat treated patients in the failed Phase-3 IDENTITY clinical trial (2, 3); and that semagacestat, like Aβ42, acts as a high affinity competitor of substrates (Koch et al, 2023). We will include this clarification in the discussion of the revised manuscript and create an additional figure presenting the proposed mechanism.

It is not clear whether the FRET-based assay in living cells really reflect gamma-secretase activity.

The specificity of this assay is supported by the γ-secretase inhibitor treatment included as a positive control (Figure 3). In addition, the following literature supports that this assay truthfully assesses γ-secretase activity in cellular context (4-7).

Processing of APP-CTF in living cells is not only the cleavage by gamma-secretase.

This is correct, and therefore we have analysed the contribution of other APP-CTF degradation pathways by performing cycloheximide-based stability assay in the presence of γ-secretase inhibitor. Quantitative analysis of the levels of both APP-CTFs and APP-FL over the 5h time-course failed to reveal significant differences between Aβ42 treated cells and controls. As expected, Bafilomycin A1 treatment markedly prolonged the half-life of both proteins (Figure 7B & C). The lack of a significant impact of Aβ42 on the half-life of APP-CTFs under the conditions of γ-secretase inhibition is consistent with the proposed inhibitory mechanism. Finally, we note that the inhibition will not only affect APP-CTF, but also the processing of γ-secretase substrates in general.

Reviewer #2 (Public Review):

Summary:

In the current study, the authors tested the hypothesis that Aβ42 toxicity arises from its proven affinity for γ-secretases. Specifically, the increases in Aβ42, particularly in the endolysosomal compartment, promote the establishment of a product feedback inhibitory mechanism on γ-secretases, and thereby impair downstream signaling events. They showed that human Aβ42 peptides, but neither murine Aβ42 nor human Aβ17-42 (p3), inhibit γ-secretases and trigger accumulation of unprocessed substrates in neurons, including (CTFs of APP, p75 and pan-cadherin. Moreover, Aβ42 dysregulated cellular homeostasis by inducing p75-dependent neuronal death. Because γ-secretases process many other membrane proteins, including NOTCH, ERB-B2 receptor tyrosine kinase 4 (ERBB4), N-cadherin (NCAD) and p75 neurotrophin receptor (p75-NTR), revealing a broad range of downstream signaling pathways, including those critical for neuronal structure and function. Hence, they propose to identification of a selective role for the Aβ42 peptide, and raise the intriguing possibility that compromised γ-secretase activity against the CTFs of APP and/or other neuronal substrates contributes to the pathogenesis of AD. Overall, the data are not very convincing to support the main claim.

Strengths.

Different in vitro and cellular approaches are employed to test the hypothesis.

Weaknesses.

The experimental concentrations for Aβ42 peptide in the assay are too high, which are far beyond the physiological concentrations or pathological levels. The artificial observations are not supported by any in vivo experimental evidence.

It is correct that in the majority of the experiments we used low μM concentrations of Aβ42. However, we would like to note that we also performed experiments where conditioned medium collected from human APP.Swe expressing neurons was used as a source of Aβ. In these experiments total Aβ concentration was in low nM range (0.5-1 nM) (Figure 4G). Treatment with this conditioned medium led to the increase APP-CTF levels, supporting that low nM concentrations of Aβ are sufficient for partial inhibition of γ-secretase.

We would like to underline that Aβ is estimated to be present in the brain in concentration ranging from fM to mM, depending on the pool (soluble, aggregated, fibrillar, etc) that is considered (8, 9). However, it is rather the local than the global concentration of Aβ that is critical for the disease pathogenesis. In this regard, it is proposed that as AD progresses Aβ42 slowly accumulates in the endo-lysosomal system wherein it reaches μM concentrations that are required for aggregation and seeding (1, 10, 11). Our findings are consistent with the analysis showing that extracellular soluble Aβ42 peptide, at low nM concentrations, is taken up by cortical neurons and neuroblastoma (SH-SY5Y) cells, and concentrated in the endo-lysosomal system wherein effective peptide concentrations reach ~2.5 μM (1). Hence, a slow vesicular peptide accumulation and/or degradation imbalance (1, 11, 12) could lead to several order of magnitude increases in the effective concentration of Aβ42 over the span of years to decades in AD pathogenesis. We note that our experimental settings, using low μM concentrations of extracellular Aβ42 over 24h treatment, were designed to accelerate this 'peptide concentration’ process in vitro. As discussed in our report, a high μM Aβ peptide concentration in the endo-lysosomal system not only leads to aggregation but also facilitates γ-secretase inhibition. Of note, we are currently developing protocols and will undertake follow up studies to quantitatively define the Aβ concentration in synaptosomes and endosomes in AD brain, as well as in in vitro systems (i.e. cells treated with Aβ preparations obtained from AD brains).

Finally, we would like to highlight that analyses of the brains of the AD affected individuals have shown that APP-CTFs accumulate in both sporadic and genetic forms of the disease (13-15); and recently, Ferrer-Raventós et al have revealed a correlation between APP-CTFs and Aβ levels at the synapse (13).

To conclude, we would like to highlight that as clarified above, the Aβ peptide concentrations and the conditions tested fit well within pathophysiology, and that the data presented in our report collectively provide evidence in support of an Aβ42-mediated inhibitory effect on γ-secretase.

References:

1. X. Hu et al., Amyloid seeds formed by cellular uptake, concentration, and aggregation of the amyloid-beta peptide. Proc Natl Acad Sci U S A 106, 20324-20329 (2009).
2. B. De Strooper, Lessons from a failed γ-secretase Alzheimer trial. Cell 159, 721-726 (2014).
3. R. S. Doody et al., A phase 3 trial of semagacestat for treatment of Alzheimer's disease. N Engl J Med 369, 341-350 (2013).
4. M. C. Houser et al., A Novel NIR-FRET Biosensor for Reporting PS/γ-Secretase Activity in Live Cells. Sensors (Basel) 20, (2020).
5. M. C. Q. Houser et al., Limited Substrate Specificity of PS/γ-Secretase Is Supported by Novel Multiplexed FRET Analysis in Live Cells. Biosensors (Basel) 11, (2021).
6. M. Maesako et al., Visualization of PS/γ-Secretase Activity in Living Cells. iScience 23, 101139 (2020).
7. M. Maesako, M. C. Q. Houser, Y. Turchyna, M. S. Wolfe, O. Berezovska, Presenilin/γ-Secretase Activity Is Located in Acidic Compartments of Live Neurons. J Neurosci 42, 145-154 (2022).
8. B. R. Roberts et al., Biochemically-defined pools of amyloid-β in sporadic Alzheimer's disease: correlation with amyloid PET. Brain 140, 1486-1498 (2017).
9. J. A. Raskatov, What Is the "Relevant" Amyloid β42 Concentration? Chembiochem 20, 1725-1726 (2019).
10. M. P. Schützmann et al., Endo-lysosomal Aβ concentration and pH trigger formation of Aβ oligomers that potently induce Tau missorting. Nat Commun 12, 4634 (2021).
11. E. Wesén, G. D. M. Jeffries, M. Matson Dzebo, E. K. Esbjörner, Endocytic uptake of monomeric amyloid-β peptides is clathrin- and dynamin-independent and results in selective accumulation of Aβ(1-42) compared to Aβ(1-40). Sci Rep 7, 2021 (2017).
12. M. F. Knauer, B. Soreghan, D. Burdick, J. Kosmoski, C. G. Glabe, Intracellular accumulation and resistance to degradation of the Alzheimer amyloid A4/beta protein. Proc Natl Acad Sci U S A 89, 7437-7441 (1992).
13. P. Ferrer-Raventós et al., Amyloid precursor protein Neuropathol Appl Neurobiol 49, e12879 (2023).
14. M. Pera et al., Distinct patterns of APP processing in the CNS in autosomal-dominant and sporadic Alzheimer disease. Acta Neuropathol 125, 201-213 (2013).
15. L. Vaillant-Beuchot et al., Accumulation of amyloid precursor protein C-terminal fragments triggers mitochondrial structure, function, and mitophagy defects in Alzheimer's disease models and human brains. Acta Neuropathol 141, 39-65 (2021).

Author Response

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

Reviewer #1

As written in my public review I consider the science of this work to be high quality. I have some suggestions for the write-up though. As a general comment, I think that too much has been put into the appendices. In particular, the main text could contain more details about the model.

We are pleased that this Reviewer feels that our work to be of “high quality”. We value the reviewer’s insightful suggestions and comments. Following this Reviewer’s suggestion we have moved certain sections to the main text.

In what follows, we provide responses to each of the reviewer’s inquiry, and indicate the appropriate changes in the revised version.

P2 -

ϕ is introduce as packing fraction - on p3 it’s called cell density. Also it is not clear whether it is an area fraction or a cell number density. Please define properly and I would suggest sticking to one notion.

ϕ is the cell packing fraction. In two dimensions (as is the case in our simulations) it is the area fraction. However, in order to stick to one general notation (independent of dimension) we use “packing fraction” to represent how densely the cells are packed. We changed it the revised manuscript to ensure uniformity.

P3 -

“which should and should slow down the overall dynamics” Typo?

Corrected it in the revised manuscript.

“One would intuitively expect that the ϕfree should decrease with increasing cell density” Please, define ϕfree

ϕfree is defined in Eqn. 4. We ought to have defined it in the introduction.

“When ϕ exceeds ϕS, the free area ϕfree saturates because the soft cells interpenetrate each other,” I suggest clearly distinguishing between biological cells and the agents (disks) used in the simulation. Please, also clarify What interpenetration of agents corresponds to in tissues?

We have rewritten the sentence as, ”The simulations show that when..” Soft disks used in the simulations seem to be not an unrealistic model for biological cells. The small deformations noted in our model is not that different from the cells in the tissues. For visual reference, please see Author response image 1. In the left panel of the figure, a 2D snapshot of the experimental zebrafish tissue, displays the deformation of cells labeled as 1 and 2. Likewise, the right panel illustrates the extent to which such deformations are replicated in the simulation by allowing two cells to overlap (the white area in the right panel of Author response image 1 represents the interpenetration). In the revised manuscript, we have made the necessary change from “soft cells” to “soft disks.”

Author response image 1.

Snapshots of zebrafish tissue (left panel) (Ref. [14] main text) and model two dimensional tissue (right). In the right panel the white area represents the overlap and the black vertical line represents the intersection.

“The facilitation mechanism, invoked in glassy systems [22] allows large cells to move with low mobility.” What is the facilitation mechanism?

Facilitation, which is an intuitive idea, that refers to a mechanism by which cells in a in highly jammed environment can only move if the neighboring cells get out of the way. In our case (as shown in the text (Fig.3 (A) and Fig. 13 (A) & (B)) the smaller cells move faster almost independent of ϕ. When a small cell moves, it creates a void which could facilitate neighboring cells (including big ones) to move.

“η (or relaxation time)” I suggest explaining the link between η and the relaxation time.

First, in making this point on aging we only showed that the relaxation time is independent of the waiting time. In the revised manuscript we deleted η.

Although not germane to this study, in the literature on glass transition, it is not uncommon to use relaxation time τα (as a proxy of viscosity η) to describe the dynamics. The relation between τα and η is given by

where G∞ is the “infinite frequency” shear modulus, which holds in unjammed or in liquids. This relation suggests that τα is proportional to η, which is almost never satisfied in glass forming systems.

P5 - “In addition, the elastic forces characterizing cell-cell interactions are soft, which implies that the cells can penetrate with rij − (Ri + Rj) < 0 when they are jammed.” Is this about the model or the biological tissue? Presumably the former, because real cells do not penetrate each other, right? What are rij, Ri and Rj?

This is about the model. The cells are sufficiently soft that they can be deformed, which allows for modest interpenetration. Real cells exhibit similar behavior (see Fig. 1). In inset of Fig. 4 (b) rij is the center to center distance between cells with radii Ri and Rj. It is better to use the word overlap instead of penetrate, which is what we have done in the revised version.

“we simulated a highly polydisperse system (PDs) in which the cell sizes vary by a factor of ∼ 8” Is it important to have a factor 8 - the zebra fish tissue presents a factor 5 − 6?

This is an important question, which is difficult to answer using analytic theory. It does require simulations unfortunately. We do not know a priori the polysipersity value needed to observe saturation in η at high value of ϕ. However, we have shown that the a system with one type of cell (monodisperse) crystallizes. Furthermore, mixtures of two cell types do not show any saturation in η over the parameter range that we explored. A systematic simulation study is needed to explore a range of parameter values to determine the minimum PD, which would match the experimental findings.

We performed 3D simulations to figure out if much less PD would yield saturation in η. Preliminary simulations in three dimensions with a lower value of PD (11.5% with a size variations by a factor of ≈ 2 ) exhibits saturation in the relaxation time. For comparison, the value of PD in the current work is ≈ 24% with a size variation by a factor of 8.

P6 -

“which is related to the Doolittle equation [26] for fluidity ( )” what is the Doolittle equation? Is it important here? Also: “VFT equation for cells”? Is it the same as given on p.2 - so nothing special for cells - or a different one?

Historically, the Doolittle equation was proposed to describe the change in η in terms of free volume in the context polymer systems over 60 years ago. The physics in the polymers is very different from the soft models for cells considered here. Nevertheless, the equations has meaning in the context as well. The Doolittle (other names associated with similar equations are Ferry, Flory... ) equation is given by

, where A and B are constants, V is the total volume and Vhc is the hardcore volume. Essentially, is the relative free volume. It can be shown that one can arrive at the VFT equation starting from the Doolittle equation.

The VFT equation for cells is same as given in page 2, which we restate for completeness. Here, we introduce the apparent activation energy.

“The stress-stress tensor” Why not simply stress tensor?

We have corrected it.

“shows qualitatively the same behavior as the estimate of viscosity (using dimensional arguments) made in experiments.” Where is this shown?

The dependence of viscosity as a function ϕ is shown in Figure 1 (c).

P7 -

Fig 2A caption “dashed line” Maybe full line?

This should be full line. It is fixed in in the revised manuscript.

P8 -

“a puzzling finding that is also reflected” Why is it puzzling?

In figure 2 (C), it shows that the increase in the duration in the plateau of Fs(q,t) ceases when ϕ exceeds ≈ 0.90. This to us is puzzling (always a matter of perspective) because we expected that the duration of Fs(q,t) plateau to increase as a function of ϕ based on the VFT behavior for ϕ ≤ ϕS. As a result, we imagined that the relaxation time τα would continue to increase beyond ϕS. However, the simulations show that the relaxation time is essentially a constant for ϕ > 0.90, which implies that the soft disk system (our model for the tissue) is an unusual with behavior that has no counter part in the material world.

“If the VFT relation continues” –“If the VFT relation continued”

We have fixed it.

First paragraph does not seem to be coherent

What is RS (or Rs)?

RS is the radius of the small cell. In the revised manuscript we have made this clear.

P10 -

Please, define the waiting time.

The waiting time refers to the period between sample preparation and data collection either in experiments or in simulations. In an ergodic system, the properties should not depend on the waiting time provided provided it is large. In other words, after the system reaches thermal equilibrium, the waiting time tω should not have an impact on the properties of the system.

“fully jammed” Please, define.

The term “fully jammed” refers to a state in which the constituent particles in a system do not move. For example, it a hard sphere system at a packing fraction of approximately 0.84 is fully jammed, which implies there is wiggle room for a particle move without violating the excluded volume restriction. At this specific packing fraction, the hard sphere system undergoes a jamming transition, resulting in the particles becoming completely immobile. The nonconfluent tissue modeled here is not fully jammed.

P11 -

Fig.4 it is hard to see that the width of P(hij) increases with ϕ.

Please see Author response image 2 with a less number of curves for a better visualization. We have replaced this figure in the revised version.

Author response image 2.

Probability of overlap (hij) between two cells, P(hij), for various ϕ values.

“Thus, even if the cells are highly jammed at ϕ ≈ ϕS, free area is available because of an increase in the overlap between cells.” This conclusion seems premature at this point.

The Referee is correct. This is shown in Fig. 5. We amended the ends of the sentence to reflect this observation.

P12 -

“as is the case when the extent of compression increases” extent of compression = density?

This is correct. Extent of compression corresponds to the packing fraction or the density.

“This effect is expected to occur with high probability at ϕS and beyond,” Why? What is special about ϕS.

To achieve high packing fractions beyond a certain value of ϕ soft cells have, which would occur at a certain value ϕS. In the system studied here, ϕ ≈ 0.90 = ϕS. Note that ϕS could be altered by changing the system parameters.

P15 -

“local equilibrium” In a thermodynamic sense? There is also cell migration, so thermodynamic equilibrium does not seem to be appropriate.

This is an important point. The observation that equilibrium concepts hold in what is manifestly a non-equilibrium system is a surprise. It is referred in a thermodynamic sense. We agree with the reviewer because of cell division (in Ref. [14] main text), cell death, thermodynamic equilibrium does not seems to be appropriate. This is exactly the point we raise in the introduction. However, considering the timescale of cell division and death it appears that there may be a local steady state, which we we call a “local equilibrium”. As a consequence phase transition ideas and Green-Kubo relations are applicable. Indeed, a surprise in the conclusion in Ref. [14] is that in the zebrafish morphogenesis equilibrium description seems adequate.

“number of near neighbor cells that is in contact with the ith cell. The jth cell is the nearest neighbor of the ith cell, if hij > 0” A neighbour cell or the nearest neihbor?

A neighbour cell is accurate.

P16 -

“In our model there is no dynamics with only systematic forces because the temperature is zero.” What is a systematic force? I do not understand the sentence.

Systematic force between two cells is defined in Eqn. 5 in the main text. Because temperature is not a relevant variable in our model, we want to emphasize that in the absence of self propulsion, the cells would not move at all.

Reviewer #2

Major comments:

A/ Role of size polydispersity

In the text, and also in the methods (Appendix A), the authors mention that they need large polydispersity of particle sizes to explain the viscous plateau, as the dynamics of small vs large cells are ”dramatically different” (Appendix G). They simulate a system where cell sizes vary by a factor 8, mentioning this is typical in tissues, but I found this quite surprising - this would be heterogeneities in cell volume of 500, many orders of magnitude above what has been measured in tissues. As far as I’m aware, divisions are quite symmetric and synchronous in early vertebrate embryogenesis, so volume variations are expected to be very small (similarly in epithelial tissues, where jamming has been looked at extensively, I’m not aware of examples with ratio of 8 between cell diameters). One question I had is that when the authors look at ”small polydispersity”, there are 50 − 50 mixtures. Would small polydispersity with continuous distributions change this picture? Could they take their current simulations but smoothly change the ratio of polydispersity from 8 to 0 to see exactly how much they need to explain viscosity plateauing, and at which point is the transition?

We thank the reviewer for raising this important question, which was also a concern for Reviewer #1. The value of polydispersity (PD) required to observe such behavior is not known a priori even within the simple model used. We selected a PD value, with a size variation of a factor of 8, guided in part by the experiment (projection onto 2D) shown in Figure 1(B) and Figure 6(D). We also showed that the monodisperse system crystallizes, and the binary system do not show signs of saturation within the explored range of parameter space and ϕ. This suggests that a certain degree of size dispersity is necessary to obtain saturation in η.

As discussed in Appendix B, the binary system is characterized by the variables , where RB and RS represent the radii of the big and small cells, respectively, and the packing fraction ϕ. By more fully exploring the parameter space encompassing λ and ϕ than we did, it maybe possible, as the Referee suggests, that a system with two different cell sizes would yield the experimentally observed dependence of η on ϕ.

As part of an answer to the Reviewer #1 on a the same issue, we mentioned results of preliminary simulations in three dimensions with reduced levels of polydispersity, and discovered that at lower levels of polydispersity (variation in size by a factor of ≈ 2 and polydispersity value 11.50%), the relaxation time does saturate beyond a certain packing fraction (see Fig. 3). We have not established if η, the key quantity of interest, would exhibit a similar behavior in 3D.

Author response image 3.

(A) τα as a function of ϕ for 11% polydispersity with size variation by a factor of ∼ 2 in the three dimensional system. (B) Same as (A) except polydispersity value is 24% and a size variation by a factor of ∼ 8.

B/ Role of fluctuations/self-propulsion in this system, and relationship to recent findings

“A priori it is unclear why equilibrium concepts should hold in zebrafish morphogenesis, which one would expect is controlled by non-equilibrium processes such as self-propulsion, growth and cell division. ”

This is raised as a key paradox, but is not very clear to me in the context raised by the authors. In particular, they use self-propulsion as a source of activity and explain the evolution of viscosity but a facilitation process involving re-arrangements/motility. But I don’t think self-propulsion has been argued to play a role in zebrafish blastoderm - Ref 14 argues that this is effectively a zerotemperature phenomenon and that cell motility/rearrangements do not show any correlation with viscosity. So this part of the model assumption was not clear to me in relationship with the proposed experimental system. Active noise has been proposed to play key roles in other systems, including motility-driven and tension fluctuation-driven unjamming (among many others Bi et al, PRX, 2016, Mitchel et al, Nat Comm, 2020, Pinheiro et al, Nat Phys, 2022 as well as Kim & Campas, Nat Physics, 2021) - maybe this is somewhere where the author model could fit? In Kim & Campas, Nat Phys, 2021 in particular, the authors develop simulations of non-confluent tissues with noise, that seems to bear some resemblance to the model developed here, so it would be important to discuss the similarities and distinctions (usually I think polydispersity is not considered indeed). In general, the authors look here at a particle based model, but cells have adhesions with well-defined contact angles, so there is a question of the cross-over between their findings and the large body of recent literature on active foams/vertex models (which are not really discussed there).

We appreciate the lengthy comment here, and there is a lot to unpack. We also thank the referee for the references, some of which we did not know about earlier.

The primary objective of our study is to determine the simplest minimal model that would explain the experimentally observed dependence of viscosity in zebrafish blastoderm tissue as ϕ is increased beyond a certain packing fraction during morphogenesis. In Reference 14, the authors analyzed the data using the framework of rigidity percolation theory and presented evidence of a genuine equilibrium phase transition. Consequently, one would that expect zebrafish blastoderm tissue to be in equilibrium, which is surprising from many perspectives. However, since the tissue is a growing system involving numerous cell divisions and cell death, it is not immediately evident whether the assumption of equilibrium is valid. Indeed, the same problem arises when considering the glass transition where rapid cooling drives the system out of equilibrium. Nevertheless, heat capacity and η are often analyzed using the notion of equilibrium. Hence, considering this issue within the context of our research appears to be reasonable.

To the best of our knowledge, the authors in Ref. 14 did not provide an explanation for the η behavior. The focus was, which was excellent and is the basis on which we initiated this study, was on the use of rigidity percolation theory to explain the results. Indeed, they performed an experiment by mildly reducing myosin II activity, which apparently affects cell motility. The quantitative effect was not reported.

We did not impose any requirement of cell rearrangements etc in the model. There is essentially one variable, free area available, that explains the η dependence on ϕ. It is possible that one can come up with other zero temperature models that could also explain the data. To the best of our knowledge, it has not been proposed.

It would be interesting to set our model in the context of other models that the referee points out. This would be an interesting research topic to explore. The only comment we would like to make is that it is unclear how vertex model for confluent tissues could explain the viscosity data.

C/ Calculation of the effective shear viscosity

The authors calculate viscosity from a Green-Kubo relation, although it would be good to clarify at which time scale (and maybe even shear amplitude) they expect this to be valid. These kinds of model would be expected to show plastic rearrangements for large deformations for instance, could the authors simulate realistic rheological deformations (e.g. Kim & Campas, 2021 applying external shear on the simulations) to see how much this matches both their expectation and the data?

Once it is established that there is local equilibrium (as implied by the use of phase transition ideas to analyse the experimental data in Ref. 14), it is natural to use the Green-Kubo relation to calculate transport properties. Hence, for our purposes, it is valid for all time scales and amplitude. The Reviewer also wonders if the model could be used to simulate response to shear in order to probe rheological properties. There is no conceptual issue here and indeed this is an excellent suggestion that we intend to pursue in the future.

D/ Role of cell adhesion

The authors consider soft elastic disks of different sizes but unless I missed it, there is no adhesion being considered. This is expected to play a key role in jamming and multicellular mechanics, so I think the authors should either look at what this changes in their simulations, or at least discuss why they are neglecting it. One reason I’m asking is that it’s not totally clear to me that the ”free space” picture, coming from the fact that cells can interpenetrate in their model would hold in a model of deformable cells adhering to each other with constant volume (leading to more equilibration of deformations it would seem?).

The referee raises another question regarding the lack of adhesion in the simulations. As pointed out before, we were trying to create a minimal model to account for the experimental observations for η upon changing the packing fraction. Thus, we a coarse-grained model where we considered poly-disperse cells with elastic interactions which recapitulates the experimental observations. The referee is correct that adhesion plays a role in jammed systems, and examination of how it would affect is an aspect that would be interesting to consider in the future. We hasten to add that even systems without attractive adhesion-type interaction become jammed. In principle, in many-body systems, the parameter space is large and one needs to carefully determine which parameter is important for the problem at hand. Therefore, in the first pass we did not find the need to consider the role of adhesion.

Minor comments:

The writing could be condensed in some places, with some details being moved to SI (for instance, section E on ageing is very short and seem more suited for supplements, or at least not as an independent section, note that the figure numbering also jumps to Fig. 9 there, although it’s Fig. 3 just before and Fig. 9 just after - re-ordering into main and supporting figures would be clearer.

We thank the Reviewer for this recommendation. The ageing section, although is short, it does provide a line of evidence that equilibrium approaches could be valid. We have modestly expanded the section by moving Appendix D to the main text, a general suggestion made by Referee 1. We have tried to be consistent in the numbering of figures in the revision.

Reviewer #3

I am very much in favor of the manuscript in its present form - I only suggest commenting (in the manuscript) on the issue described below.

Motivated by the fact that the experimental system consists of living, motile cells the authors use an active particle model (eq. 6) with stochastic selfpropulsion as the only source for noise (zero-temperature). It would be useful to elaborate briefly how important this stochastic self-propulsion is for the emergent rheological properties of the system (as summarized above): would these properties also be present in the “passive” version of the same model at “non-vanishing” temperature, and if not, why? Or analogously in a “passive” version which is “shaken”, reminiscent of shaken granular matter? To clarify these issues would relate this study to (or discriminate it from) passive, but complex, liquids or granular matter.

We appreciate the reviewer’s positive feedback on our work. The reviewer has raised an important question concerning our model in which self-propulsion serves as the source of noise. Without self-propulsion, the system would come to a stationary state after reaching mechanical equilibrium. As mentioned in Eqn. (6) (in the main text), we can define a characteristic time . It is possible that scaling the time t by τ would not alter the results.

The second question raised by the reviewer is also important. A passive version of the model would be to consider Eq. 6 in our article, and instead of using activity use the standard stochastic force. The resulting force would be at a finite temperature,. The coefficient of noise (a diffusion term) would be related to γi through the Fluctuation dissipation theorem(FDT)). Such a system of equations cannot ne mapped to Eq. 6 in which µ and γi are independently varied. It is unlikely that such a model, incorporating a “non-vanishing” temperature, would not result in the observed dependence of η on ϕ for the following reason. The passive model represents a polydisperse system, which would form a glass with η increasing with volume fraction, following the VFT law, as has been demonstrated in the glass transition literature for harmonic glasses. The other proposal whether the shaken version version would explain the experiments is also interesting. These are worth pursuing in future studies.

eLife assessment

This fundamental study substantially advances our physical understanding of the sharp increase and saturation of the viscosity of non-confluent tissues with increasing cell density. Through the analysis of a simplified model this study provides compelling evidence that polydispersity in cell size and the softness of cells together can lead to this phenomenon. The work will be of general interest to biologists and biophysicists working on development.

Reviewer #1 (Public Review):

The authors consider data by the Heisenberg group on rheological properties of non-confluent tissue in zebrafish embryos. These data had shown a steep increase and subsequent saturation in viscosity with cell density. The authors introduce a physical agent-based model of such tissues that accounts for the dispersion in cell size and the softness of the cells. The model is inspired by previous models to study glassy dynamics and reveals essential physical features that can explain the observed behavior. It goes beyond previous studies that had analysed the observations in terms of a percolation problem. The numerics is thoroughly done and could have a deep impact on how we describe non-confluent tissues.

Reviewer #2 (Public Review):

This paper explores how minimal active matter simulations can model tissue rheology, with applications to the in vivo situation of zebrafish morphogenesis. The authors explore the idea of active noise, particle softness and size heterogeneity cooperating to give rise to surprising features of experimental tissue rheologies (in particular an increase and then a plateau in viscosity with fluid fraction). In general, the paper is interesting from a theoretical standpoint, by providing a bridge between concepts from jamming of particulate systems and experiments in developmental biology. The idea of exploring a free space picture in this context is also interesting. However, I'm still unsure right now though of how much it can be applied to the specific system that the authors refer to - which could be fixed either by doing theoretical checks or considering other experimental systems/models reported in the recent literature.

Reviewer #3 (Public Review):

The authors successfully explain the sharp rise and subsequent saturation of the viscosity in dependence of cell packing fraction in zebrafish blastoderm with the help of a 2D model of soft deformable, polydisperse and self-propelled (active) disks. The main experimental observations can be reproduced and the unusual dependence of the viscosity on packing fraction can be explained by the available free area and the emergent motility of small sized cells facilitating multi-cell rearrangement in a highly jammed environment.

The paper is very well written, the results (experimental as well as theoretical) are original and scientifically valid. This is an important contribution to understanding the rheological properties of non-confluent tissues linking equilibrium and transport properties.

Author Response

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

Thank you very much for the kind comments about our manuscript. We have improved the text to address all reviewers’ comments and suggestions. Additionally, we corrected and improved the supplementary tables.

Reviewer #1 (Public Review):

This paper provides new evidence on the relationship between genetic/chromosome divergence and capacity for asexual reproduction (via unreduced, clonal gametes) in hybrid males or females. Whereas previous studies have focussed just on the hybrid combinations that have yielded asexual lineages in nature, the authors take an experimental approach, analysing meiotic processes in F1 hybrids for combinations of species spanning different levels of divergence, whether or not they form asexual lineages in nature. As such, the findings here are a substantial advance towards understanding how new asexual lineages form.

The quality of the work is high, the analyses are sound, and the authors sensibly link their observations to the speciation continuum. I should also add that the cytogenetic work here is just beautiful!

A key finding is that the precondition for asexual reproduction - the formation of unreduced gametes - is not unusual among hybrid females, so that we have to consider other factors to explain the rarity of asexual species - a major unresolved issue in evolutionary biology. This work also highlights a previously overlooked effect of chromosome organisation on speciation.

Thank you for the nice comments about our work as well as for appreciating our cytogenetics work and figures.

Reviewer #2 (Public Review):

The authors investigate the origin of asexual reproduction through hybridization between species. In loaches, diploid, polyploid, and asexual forms have been described in natural populations. The authors experimentally cross multiple species of loaches and conduct an impressively detailed characterization of gametogenesis using molecular cytogenetics to show that although meiosis arrests early in male hybrids, a subset of cells in females undergo endoreplication before meiosis, producing diploid eggs. This only occurred in hybrids of parental species that were of intermediate divergence. This work supports an expanding view of speciation where asexuality could emerge during a narrow evolutionary window where genomic divergence between species is not too high to cause hybrid inviability, but high enough to disrupt normal meiotic processes.

Thank you.

I enjoyed reading this study and I appreciate the amount of work it takes to conduct these types of cytogenetic experiments. But, my main concern with this study is I was left wondering if the sample sizes are large enough to get a sense how variable endoreplication is in these loach species. Most of the hybrids between species are the result of crosses between 1-2 families. Within males and females, meiocyte observations are limited to a handful of pachytene and diplotene stages. I think it would be helpful to be more transparent about the sample sizes in the main text.

Thank you for raising this point. We have improved the Supplementary Tables S2 and S3 to clarify how many individuals we analyzed from each genetic family and added this information to the main text. In total we obtained 12 combinations with 19 F1 hybrid families. For the combination, C. elongatoides x C. taenia hybrids we obtained three families, for C. elongatoides x C. ohridana, C. elongatoides x C. tanaitica, C. elongatoides x C. bilineata and C. ohridana x C. bilineata, we obtained two families For the rest of the combinations of hybrids we obtained single family. From these families, 79 individuals were used for the analysis of the meiocites. Additionally, 24 parental individuals, males and females, were analysed. For the parental species, we analysed 852 cells, for hybrid males we investigated 244 cells, and 665 cells for hybrid females.

Along these lines, the authors argue against the possibility that endoreplication may be predisposed to occur at a higher rate in some species (line 291). Instead, they suggest that endoreplication is a result of perturbing the cell cycle by combining the genomes of two different species. Their main argument is based on gonocyte counts from parental females in a previous reference. It is essential to include counts from the parents used in this study to make a clear comparison with the F1s.

Thank you, we agree with your comment and included the observations of meiocytes from several parental species, i.e. C. elongatoides, C. taenia, C. pontica, C. tanaitica, and C. ohridana. Among 852 cells analyzed, we did not observe cells with duplicated genomes and abnormalities in chromosomal pairing. By contrast, among 665 pachytene cells of F1 hybrid females, we revealed altogether ~1% of endoreplicated ones. We tested these data by binomial GLM and found these differences to be significant, suggesting that sexuals, even if they may have some unnoticed duplication events, clearly have a significantly lower incidence of abnormal pachytene cells. We have now included this information in the main text.

In the discussion (lines 320-333), the authors postulate the sex-specific clonality they observe could be a result of Haldane's rule. Given these fish do not have known sex chromosomes, I do not find this argument strong. Haldane's rule refers to the exposure of recessive incompatibilities with the sex chromosomes in the hybrid heterogametic sex. This effect would therefore be limited to degenerated sex chromosomes where much of the sequence content on the Y or W has been lost. These species may have homomorphic sex chromosomes, but if this is the case, they likely are not very degenerated. Instead, it seems more plausible that the sex-specific effect the authors observe is due to intrinsic differences of spermatogenesis and oogenesis. Is there any information about sex-specific differences in the fidelity of gametogenesis from other species that would support a higher likelihood of endoreplication?

Thank you for this important question, however, we think it was a misunderstanding. We do not postulate that our observation conforms to Haldanes’ rule as, by contrast to this rule based on sex chromosomes, our previous publication demonstrated that whatever the gonadal sex differentiation is in our taxa, the ability to overcome sterility by asexual gametogenesis is always confined to female gonadal environment (or oogenesis in general), even in the transplanted spermatogonial cells (Tichopad et al. 2022). What we meant by our text is that our results do not fully conform to Haldane’s rule. We therefore reworded our text to rule out such a misconception.

Nonetheless, we note that it has been demonstrated that Haldanes’ rule is also applicable to species with little differentiated sex chromosomes (e.g. Presgraves and Orr 1998) and that recessive incompatibilities are not the only explanation as faster male theory or faster X may also apply in such cases (Dufresnes et al. 2016). Therefore, we have kept our remarks about Haldane’s rule here. Moreover, for several parental species, we preliminary found the occurrence of an XY gonadal sex differentiation system, albeit these are unpublished and need further validation.

The final thing I was left wondering about was this missing link between endoreplication and activating the embryonic development of the diploid egg. In these loach species, a sperm is required to activate egg development, but the sperm genome is discarded (line 100). What is the mechanism of this and how does it evolve concurrently during hybridization?

Thank you for the comment. There have been many speculations about why gynogens actually need sperm to activate their egg development, but to our knowledge, no explanation has been validated to date. Interestingly, a recent theoretical model by Fyon et al. BiorXiv 2023 suggested that the ability of sperm exclusion may evolve separately from the ability to produce clonal eggs. Hence, this topic is complex and remains unresolved, and we feel that it is out of the scope of the present MS. We have slightly modified the text and added 2 refs., to address your suggestion.

Reviewer #1 (Recommendations For The Authors):

The paper is well prepared - though the resolution of Fig 1 on the pdf is rather poor.

Thank you! We have now provided the high-resolution figures.

Overall, I have few suggestions for improvements:

Line 58. How does endoduplication itself "overcome accumulated incompatibilities" other than failure of synapsis? Perhaps by maintaining the F1 state, and so avoiding reduced fitness arising from recombination and disruption of coadapted gene combinations.

We have added a sentence to the main text “Premeiotic genome endoreplication thus not only ensures clonal reproduction but also allows hybrids to overcome problems in chromosome pairing that would otherwise lead to their sterility 15,17.” that we hope sufficiently addresses this issue.

Line 118 - please explain the AKD index here - as you have some in SI. Also please be clearer on how you measure genetic divergence as proportion of heterozygous SNPs - presumably this is via exon sequences from F1 females?

Please note that we have explained the AKD index in the relevant part of the Methods section already. However, we have now also added a brief explanation to the Results section, as suggested. We apologize for imprecise description of the genetic divergence measurements. As described in the Methods section, this is not measured by heterozygosity (as we wrongly stated here), but as p-distance among sequences of coding regions between parental species.

Lines 126 ff. It is unfortunate that the design of the crosses was not more balanced or extensive. Nonetheless, I do appreciate the effort involved here and think the results are solid as is.

Thank you.

Line 142. Please define PS and TB (and other acronyms) at first use.

We have added the definition for all acronyms at the first use.

Lines 192-193. What about EP and EN - as shown to have unreduced gametes in Fig. 2?

Thank you for this question. Based on analyses of the diplotene stage, we showed that EP and EN hybrids produced diploid eggs. However, in pachytene, we did not find duplicated oocytes due to the rarity of endoreplication. Similarly, the low incidence of duplicated pachytene cells was observed in natural as well as F1-hybrids in loaches and reptiles (Newton et al., 2016, Dedukh et al., 2021, 2022).

Lines 217-219. The observed correlation of chromosome divergence (AKD index) and numbers of bivalents in pachytene makes sense and is an important observation. Did this GLM simultaneously consider the effect of genetic divergence (as implied in methods)?

Thank you for this comment. We originally tested separately the fit of two models, one with AKD and the other with SNP divergence. Since the AKD model significantly outperformed the SNP-based one, we focused our interpretation on the former. However, as you suggested, we now re-calculated the model taking into account the joint effects of both predictors in a single model and indeed, this model outperformed both single predictors. In conclusion, while AKD is still the strongest single predictor for the observed amounts of bivalents, the additional effect of genetic distance still significantly improves the model fit. We have now included this result into the main text.

This finding does not alter our conclusions, it just suggests that the effect of chromosomal morphology is probably more complex, involving the role of more subtle sequence divergence or structural variants.

Line 242. The Discussion is a great read - careful interpretation and a really interesting interpretation in context of the broader literature.

Thank you for the appreciation. Your positive feedback and evaluation are highly motivating us to expand our work.

Line 396. Some references from book chapters (18, 52) are incomplete. Please fix.

We have now corrected these references accordingly.

Reviewer #2 (Recommendations For The Authors):

Transparency about meiocyte sample sizes: These counts are all in supplemental table 3. From this table, it is unclear if a majority of these meiocytes are from a single individual or from multiple males or females. Or, in the crosses where there are multiple families, are the meiocytes sampled from all families? I am trying to get a sense whether endoreplication and the fidelity of oogenesis could be influenced by genetic variants segregating within species. If the meiotcytes are only sampled from a single individual from a single cross, you may not see this variation. If this is the case, perhaps the correlation between genetic divergence and the formation of asexual clones may not be as strong. Additional replicates may not be feasible, but at a minimum I think it would be helpful to address whether endoreplication could or could not be variable and if the sample sizes are sufficient.

Thank you for raising this point. We have improved the Supplementary table to clarify how many individuals we analyzed from each family and added this information to the main text. Unfortunately, additional replicates are not feasible due to the long generation time of the fish. We otherwise agree with your comment and included this point in the Discussion.

Gonocyte counts from parental females: The authors say they "analysed hundreds of gonocytes of sexual females without a single incidence of genome endoreplication." I could not find a clear count in the references given. They note that the incidence of endoreplication was very low in pachytene cells in this study (0.7%).

Thank you, we agree with your comment and included the observations of meiocytes from several parental species, i.e. C. elongatoides, C. taenia, C. pontica, C. tanaitica, and C. ohridana. Among 852 cells analyzed, we did not observe cells with duplicated genomes and abnormalities in chromosomal pairing. By contrast, among 665 pachytenic cells of F1 hybrid females, we revealed altogether ~1% of endoreplicated ones. We tested these data by binomial GLM and found these differences to be significant, suggesting that sexuals, even if they may have some unnoticed duplication events, clearly have significantly lower incidence. of abnormal pachytene cells. We have now included this information in the main text.

They refer to supplemental table 4 (line 196), which does not exist in the supplement. The authors should report these numbers in the revised manuscript.

Thank you for pointing this out. We have corrected the name of the supplementary table, it actually is supplementary table S3.

eLife assessment

This paper provides important insights into how asexual reproduction can arise in interspecific hybrids. The evidence supporting the conclusions is compelling, with rigorous molecular cytogenetic experiments showing the production of clonal gametes is common across hybrids between closely to moderately divergent sexual species. By highlighting the potential for asexuality to evolve in hybrids during a narrow window of species divergence, this work will be of broad interest to evolutionary biologists.

Reviewer #1 (Public Review):

This paper provides new evidence on the relationship between genetic/chromosome divergence and capacity for asexual reproduction (via unreduced, clonal gametes) in hybrid males or females. Whereas previous studies have focussed just on the hybrid combinations that have yielded asexual lineages in nature, the authors take an experimental approach, analysing meiotic processes in F1 hybrids for combinations of species spanning different levels of divergence, whether or not they form asexual lineages in nature. As such, the findings here are a substantial advance towards understanding how new asexual lineages form.

The quality of the work is high, the analyses are sound, and the authors sensibly link their observations to the speciation continuum. I should also add that the cytogenetic work here is just beautiful!

A key finding is that the precondition for asexual reproduction - the formation of unreduced gametes - is not unusual among hybrid females, so that we have to consider other factors to explain the rarity of asexual species - a major unresolved issue in evolutionary biology. This work also highlights a previously overlooked effect of chromosome organisation on speciation.

Reviewer #2 (Public Review):

The authors investigate the origin of asexual reproduction through hybridization between species. In loaches, diploid, polyploid, and asexual forms have been described in natural populations. The authors experimentally cross multiple species of loaches and conduct an impressively detailed characterization of gametogenesis using molecular cytogenetics to show that although meiosis arrests early in male hybrids, a subset of cells in females undergo endoreplication before meiosis, producing diploid eggs. This only occurred in hybrids of parental species that were of intermediate divergence. This work supports an expanding view of speciation where asexuality could emerge during a narrow evolutionary window where genomic divergence between species is not too high to cause hybrid inviability, but high enough to disrupt normal meiotic processes.

I enjoyed reading this study and I was impressed by the rigorous experiments. The authors provide strong evidence that premeiotic genome endoreplication is the mechanism behind asexually-reproducing females. In addition, I found the evidence convincing that this phenomenon is a consequence of combining two divergent genomes in an F1 hybrid female. The authors did not observe a single incidence of genome duplication in any of the parental species among a large number of surveyed oocytes.

Reviewer #3 (Public Review):

Summary: This manuscript aims to unravel the mechanisms behind Aquaporin-0 (AQP0) tetramer array formation within lens membranes. The authors utilized electron crystallography and molecular dynamics (MD) simulations to shed light on the role of cholesterol in shaping the structural organization of AQP0. The evidence suggests that cholesterol not only defines the positions and orientations of associated molecules but also plays a crucial role in stabilizing AQP0 tetramer arrays. This study provides valuable insights into the potential principles driving protein clustering within lipid rafts, advancing our understanding of membrane biology.

In this review, I will focus on the MD simulations part, since this is my area of expertise. The authors conducted an impressive set of MD simulations aiming at understanding the role of cholesterol in structural organization of AQP0 arrays. These simulations clearly demonstrate the well-defined localization of cholesterol molecules around a single AQP0 tetramer, aligning with previous computational studies and the crystallographic structures presented in this manuscript. Interestingly, authors identified an unusual position for one cholesterol molecule, located near the center of the lipid bilayer, which was stabilized by the adjacent AQP0 tetramers. The authors showed that these adjacent tetramers can withstand a larger lateral detachment force when deep cholesterol molecules are present at the interface compared to scenarios with sphingomyelin (SM) molecules at the interface between two AQP0 tetramers. Authors interpret that result as evidence that deep cholesterol molecules mechanically stabilize the interface of the AQP0 tetramers. This conclusion has minor weaknesses, and the rigor of the lateral detachment simulations could be increased by establishing a reference point for the detachment force needed to separate AQP0 tetramers in a scenario without lipids at the interface between tetramers, and by increasing the number of repeats for the non-equilibrium steered MD simulations. Thermodynamic integration might be a better approach to compute the stabilization energy in the presence of cholesterol compared to the SM case.

eLife assessment

This manuscript aims to unravel the contribution of cholesterol to aquaporin-0 (AQP0) tetramer array formation within lens membranes. Convincing electron crystallography and molecular dynamics identify a specific cholesterol binding site of significance to protein clustering within lipid rafts. The important work advances our understanding of membrane biology and will be of broad interest to membrane transport biologists, biochemists, and structural biologists.

Reviewer #1 (Public Review):

Aquaporin-0 forms 2D crystals in the lens of the eye. This propensity to form 2D crystals was originally exploited to solve the structure of aquaporin-0 reconstituted in membranes. Existing structures do not explain why the proteins spontaneously form these arrays, however. In this work the authors investigate the hypothesis that the main lipids in the native membranes, sphingomyelin and cholesterol, contribute to lattice formation. By titrating the cholesterol: sphingomyelin ratio, the authors identify cholesterol binding sites of increasing stability. The authors identify a cholesterol that interacts with adjacent tetramers and is bound at an unusual membrane depth. Computational simulations suggest that this cholesterol is only stable in the context of adjacent tetramers (ie lattice formation) and that the presence of the cholesterol increases the stability of that interface. The exact mechanism is not clear, but the authors propose that the so-called "deep cholesterol" improves shape complementarity between adjacent tetramers and modulates the kinetics of protein-protein interactions. Finally, the authors provide a reasonable model for the role of cholesterol in

Strengths of this manuscript include the analysis of multiple structures determined with different lipid compositions and lipid:cholesterol ratios. For each these, multiple lipids can be modelled, giving a good sense of the lipid specificity at various favorable lipid binding positions. In addition, multiple hypotheses are tested in a very thorough computational analysis that provides the framework for interpreting the structural observations. The authors also provide a thorough scholarly discussion that connects their work with other studies of membrane protein-cholesterol interactions.

The model presented by the authors is consistent with the data described. Further testing of this model, for example by mutating the deep cholesterol binding site, would strengthen the model. However, such experiments might be challenging due to the relatively non-specific/hydrophobic nature of the deep cholesterol binding site.

Reviewer #2 (Public Review):

Summary:<br /> In the manuscript by Chiu et al., "Structure and dynamics of cholesterol-mediated aquaporin-0 arrays and implications for lipid rafts," the authors address the effect of cholesterol on array formation by AQP0. Using a combination of electron crystallography and molecular dynamics simulations, the authors show binding of a "deep" cholesterol molecule between AQP0 tetramers. Each AQP0 tetramers binds four deep cholesterols to form a crystallographic array of AQP0.

Strengths:<br /> The combined approaches of electron crystallography and MD simulations under different lipid conditions (different sphingomyelin and cholesterol concentrations) are a strength of the study. The authors provide a thorough and convincing assessment of cholesterol binding, protein-protein interactions, and array formation by AQP0. The MD simulations allow the authors to consider the propensity of cholesterol to occupy the observed binding sites in the absence of crystal contacts. The combined methods and the breadth of analyses set a high standard in the field of membrane protein structural biology.

The findings of the authors fit nicely into a growing body of literature on cholesterol binding sites that mediate membrane protein-protein interactions. Cholesterol interacts with a variety of membrane proteins via its smooth alpha face of rough beta face. AQP0 is somewhat unique in that it binds the rough face of cholesterol in a "deep" binding site that places cholesterol in the middle of the membrane bilayer. So-called "deep" cholesterol binding sites have been described for GPCRs and docking studies suggest they may exist on other ion channels and transporters. In the case of AQP0, the deep cholesterol acts as a glue that holds two tetramers together. Since each tetramer has four binding sites for deep cholesterol, the assembly and mechanical stability of an extended two-dimensional array of AQP0 tetramers is a natural consequence in lens membranes.

Weaknesses:<br /> The authors report that the findings generally apply to raft formation in membranes. However, this point is less clear as the lens membrane in which AQP0 resides is rather unique in lipid and protein content and density. Nonetheless, the authors achieve the overall goal of evaluating cholesterol binding to AQP0, and there are many valuable and informative figures in the main manuscript and supplement that provide convincing results and interpretations.

Reviewer #3 (Public Review):

Summary:<br /> This paper by Sabelo et al. describes a new pathway by which lack of IgM in the mouse lowers bronchial hyperresponsiveness (BHR) in response to metacholine in several mouse models of allergic airway inflammation in Balb/c mice and C57/Bl6 mice. Strikingly, loss of IgM does not lead to less eosinophilic airway inflammation, Th2 cytokine production or mucus metaplasia, but to a selective loss of BHR. This occurs irrespective of the dose of allergen used. This was important to address since several prior models of HDM allergy have shown that the contribution of B cells to airway inflammation and BHR is dose dependent.

After a description of the phenotype, the authors try to elucidate the mechanisms. There is no loss of B cells in these mice. However, there is a lack of class switching to IgE and IgG1, with a concomitant increase in IgD. Restoring immunoglobulins with transfer of naïve serum in IgM deficient mice leads to restoration of allergen-specific IgE and IgG1 responses, which is not really explained in the paper how this might work. There is also no restoration of IgM responses, and concomitantly, the phenotype of reduced BHR still holds when serum is given, leading authors to conclude that the mechanism is IgE and IgG1 independent. Wild type B cell transfer also does not restore IgM responses, due to lack of engraftment of the B cells. Next authors do whole lung RNA sequencing and pinpoint reduced BAIAP2L1 mRNA as the culprit of the phenotype of IgM-/- mice. However, this cannot be validated fully on protein levels and immunohistology since differences between WT and IgM KO are not statistically significant, and B cell and IgM restoration are impossible. The histology and flow cytometry seems to suggest that expression is mainly found in alpha smooth muscle positive cells, which could still be smooth muscle cells or myofibroblasts. Next therefore, the authors move to CRISPR knock down of BAIAP2L1 in a human smooth muscle cell line, and show that loss leads to less contraction of these cells in vitro in a microscopic FLECS assay, in which smooth muscle cells bind to elastomeric contractible surfaces.

Strengths:<br /> 1. There is a strong reduction in BHR in IgM-deficient mice, without alterations in B cell number, disconnected from effects on eosinophilia or Th2 cytokine production<br /> 2. BAIAP2L1 has never been linked to asthma in mice or humans

Weaknesses:

1. While the observations of reduced BHR in IgM deficient mice are strong, there is insufficient mechanistic underpinning on how loss of IgM could lead to reduced expression of BAIAP2L1. Since it is impossible to restore IgM levels by either serum or B cell transfer and since protein levels of BAIAP2L1 are not significantly reduced, there is a lack of a causal relationship that this is the explanation for the lack of BHR in IgM-deficient mice. The reader is unclear if there is a fundamental (maybe developmental) difference in non-hematopoietic cells in these IgM-deficient mice (which might have accumulated another genetic mutation over the years). In this regard, it would be important to know if littermates were newly generated, or historically bred along with the KO line.<br /> 2. There is no mention of the potential role of complement in activation of AHR, which might be altered in IgM-deficient mice<br /> 3. What is the contribution of elevated IgD in the phenotype of the IgM-deficient mice. It has been described by this group that IgD levels are clearly elevated<br /> 4. How can transfer of naïve serum in class switching deficient IgM KO mice lead to restoration of allergen specific IgE and IgG1?<br /> 5. Alpha smooth muscle antigen is also expressed by myofibroblasts. This is insufficiently worked out. The histology mentions "expression in cells in close contact with smooth muscle". This needs more detail since it is a very vague term. Is it in smooth muscle or in myofibroblasts.<br /> 6. Have polymorphisms in BAIAP2L1 ever been linked to human asthma?<br /> 7. IgM deficient patients are at increased risk for asthma. This paper suggests the opposite. So the translational potential is unclear.

eLife assessment

Studying several allergens in different mouse strains, the authors assess the role of IgM in airway inflammatory responses and show that IgM deficient mice have reduced airway hyperresponsiveness. Although the findings, based on experimental evidence from a wide range of immunological and other assays, including the expression of a protein that regulates actin in smooth cells, are interesting and useful, the study is incomplete as the data and analyses do not support their primary claims.

Reviewer #1 (Public Review):

Summary: The authors of this study sought to define a role for IgM in responses to house dust mites in the lung.

Strengths:

Unexpected observation about IgM biology<br /> Combination of experiments to elucidate function

Weaknesses:

Would love more connection to human disease

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Hadebe and colleagues describes a striking reduction in airway hyperresponsiveness in Igm-deficient mice in response to HDM, OVA and papain across the B6 and BALB-c backgrounds. The authors suggest that the deficit is not due to improper type 2 immune responses, nor an aberrant B cell response, despite a lack of class switching in these mice. Through RNA-Seq approaches, the authors identify few differences between the lungs of WT and Igm-deficient mice, but see that two genes involved in actin regulation are greatly reduced in IgM-deficient mice. The authors target these genes by CRISPR-Cas9 in in vitro assays of smooth muscle cells to show that these may regulate cell contraction. While the study is conceptually interesting, there are a number of limitations, which stop us from drawing meaningful conclusions.

Strengths:<br /> Fig. 1. The authors clearly show that IgMKO mice have striking reduced AHR in the HDM model, despite the presence of a good cellular B cell response.

Weaknesses:<br /> Fig. 2.<br /> The authors characterize the cd4 t cell response to HDM in IGMKO mice.<br /> They have restimulated medLN cells with antiCD3 for 5 days to look for IL-4 and IL-13, and find no discernible difference between WT and KO mice. The absence of PBS-treated WT and KO mice in this analysis means it is unclear if HDM-challenged mice are showing IL-4 or IL-13 levels above that seen at baseline in this assay. The choice of 5 days is strange, given that the response the authors want to see is in already primed cells. A 1-2 day assay would have been better. It is concerning that the authors state that HDM restimulation did not induce cytokine production from medLN cells, since countless studies have shown that restimulation of medLN would induce IL-13, IL-5 and IL-10 production from medLN. This indicates that the sensitization and challenge model used by the authors is not working as it should. The IL-13 staining shown in panel c is also not definitive. One should be able to optimize their assays to achieve a better level of staining, to my mind.

In d-f, the authors perform a serum transfer, but they only do this once. The half life of IgM is quite short. The authors should perform multiple naïve serum transfers to see if this is enough to induce FULL AHR.

The presence of negative values of total IgE in panel F would indicate some errors in calculation of serum IgE concentrations.

Overall, it is hard to be convinced that IgM-deficiency does not lead to a reduction in Th2 inflammation, since the assays appear suboptimal.

Fig. 3. Gene expression differences between WT and KO mice in PBS and HDM challenged settings are shown. PCA analysis does not show clear differences between all four groups, but genes are certainly up and downregulated, in particular when comparing PBS to HDM challenged mice. In both PBS and HDM challenged settings, three genes stand out as being upregulated in WT v KO mice. these are Baiap2l1, erdr1 and Chil1.

Fig. 4. The authors attempt to quantify BAIAP2L1 in mouse lungs. It is difficult to know if the antibody used really detects the correct protein. A BAIAP2L1-KO is not used as a control for staining, and I am not sure if competitive assays for BAIAP2L1 can be set up. The flow data is not convincing. The immunohistochemistry shows BAIAP2L1 (in red) in many, many cells, essentially throughout the section. There is also no discernible difference between WT and KO mice, which one might have expected based on the RNA-Seq data. So, from my perspective, it is hard to say if/where this protein is located, and whether there truly exists a difference in expression between wt and ko mice.

Fig. 5 and 6. The authors use a single cell contractility assay to measure whether BAIAP2L1 and ERDR1 impact on bronchial smooth muscle cell contractility. I am not familiar with the assay, but it looks like an interesting way of analysing contractility at the single cell level.<br /> The authors state that targeting these two genes with Cas9gRNA reduces smooth muscle cell contractility, and the data presented for contractility supports this observation. However, the efficiency of Cas9-mediated deletion is very unclear. The authors present a PCR in supp fig 9c as evidence of gene deletion, but it is entirely unclear with what efficiency the gene has been deleted. One should use sequencing to confirm deletion. Moreover, if the antibody was truly working, one should be able to use the antibody used in Fig 4 to detect BAIAP2L1 levels in these cells. The authors do not appear to have tried this.

Other impressions:<br /> The paper is lacking a link between the deficiency of IgM and the effects on smooth muscle cell contraction.<br /> The levels of IL-13 and TNF in lavage of WT and IGMKO mice could be analysed.

Moreover, what is the impact of IgM itself on smooth muscle cells? In the Fig. 7 schematic, are the authors proposing a direct role for IgM on smooth muscle cells? Does IgM in cell culture media induce contraction of SMC? This could be tested and would be interesting, to my mind.

Reviewer #2 (Public Review):

Summary:

The manuscript by Wohlwend et al. investigates the implications of inhibiting ceramide synthase Cers1 on skeletal muscle function during aging. The authors propose a role for Cers1 in muscle myogenesis and aging sarcopenia. Both pharmacological and AAV-driven genetic inhibition of Cers1 in 18-month-old mice lead to reduced C18 ceramides in skeletal muscle, exacerbating age-dependent features such as muscle atrophy, fibrosis, and center-nucleated fibers. Similarly, inhibition of the Cers1 orthologue in C. elegans reduces motility and causes alterations in muscle morphology.

Strengths:

The study is well-designed, carefully executed, and provides highly informative and novel findings that are relevant to the field.

Weaknesses:

The following points should be addressed to support the conclusions of the manuscript.

1) It would be essential to investigate whether P053 treatment of young mice induces age-dependent features besides muscle loss, such as muscle fibrosis or regeneration. This would help determine whether the exacerbation of age-dependent features solely depends on Cers1 inhibition or is associated with other factors related to age-dependent decline in cell function. Additionally, considering the reported role of Cers1 in whole-body adiposity, it is necessary to present data on mice body weight and fat mass in P053-treated aged-mice.

2) As grip and exercise performance tests evaluate muscle function across several muscles, it is not evident how intramuscular AAV-mediated Cers1 inhibition solely in the gastrocnemius muscle can have a systemic effect or impact different muscles. This point requires clarification.

3) To further substantiate the role of Cers1 in myogenesis, it would be crucial to investigate the consequences of Cers1 inhibition under conditions of muscle damage, such as cardiotoxin treatment or eccentric exercise.

4) It would be informative to determine whether the muscle defects are primarily dependent on the reduction of C18-ceramides or the compensatory increase of C24-ceramides or C24-dihydroceramides.

5) Previous studies from the research group (PMID 37118545) have shown that inhibiting the de novo sphingolipid pathway by blocking SPLC1-3 with myriocin counteracts muscle loss and that C18-ceramides increase during aging. In light of the current findings, certain issues need clarification and discussion. For instance, how would myriocin treatment, which reduces Cers1 activity because of the upstream inhibition of the pathway, have a positive effect on muscle? Additionally, it is essential to explain the association between the reduction of Cers1 gene expression with aging (Fig. 1B) and the age-dependent increase in C18-ceramides (PMID 37118545).

Addressing these points will strengthen the manuscript's conclusions and provide a more comprehensive understanding of the role of Cers1 in skeletal muscle function during aging.

eLife assessment

This solid study presents valuable insights into the role of Cers1 on skeletal muscle function during aging, although further substantiation would help to fully establish the experimental assertions. It examines an unexplored aspect of muscle biology that is a relevant opening to future studies in this area of muscle research.

Reviewer #1 (Public Review):

Summary: The authors identified that genetically and pharmacological inhibition of CERS1, an enzyme implicated in ceramides biosynthesis worsen muscle fibrosis and inflammation during aging.

Strengths: the study points out an interesting issue on excluding CERS1 inhibition as a therapeutic strategy for sarcopenia. Overall, the article it's well written and clear.

Weaknesses: Many of the experiments confirmed previous published data, which also show a decline of CERS1 in ageing and the generation and characterization of a muscle specific knockout mouse line. The mechanistic insights of how the increased amount of long ceramides (cer c24) and the decreased of shorter ones (cer c18) might influence muscle mass, force production, fibrosis and inflammation in aged mice have not been addressed.

Reviewer #3 (Public Review):

The valuable work shows some unique characteristics of long-lived PCs in comparison with bulk PCs. In particular, the authors clearly indicated the dependency of CXCR4 in PC longevity and provided a deal of resource of PC transcriptomes. Though CD93 is known as a marker for long-lived PCs, the authors can provide more data related to CD93.

Summary: Long-lived PCs are maintained with low motility and in a CXCR4-dependent manner.

Strengths: The reporter mice for fate-mapping can clearly distinguish long-lived PCs from total PCs and greatly contribute to the identification of long-lived PCs.

Weaknesses: The authors are unable to find a unique marker for long-lived PCs.

eLife assessment

Despite the importance of long-lived plasma cells (LLPCs), particularly in the vaccination field, their natures are still unclear. In this valuable manuscript, as a first step towards clarifying these natures, the authors used a solid genetic approach (time-stamping one) and successfully labelled only functional LLPCs. Although four groups have already published data by the same genetic approach, the authors' manuscript includes additional significant findings in the LLPC field.

Reviewer #1 (Public Review):

The mechanisms underlying the generation and maintenance of LLPCs have been one of the unresolved issues. Recently, four groups have independently generated new genetic tools that allow fate tracing of murine plasma cells and have addressed how LLPCs are generated or maintained in homeostatic conditions or upon antigen immunization or viral infection. Here, Jing et al. have established another, but essentially the same, PC time stamping system, and tried to address the issues above. The question is whether the findings reported here provide significant conceptual advances from what has already been published.

1) Some of the observations in this manuscript have already been made by other studies (Xu et al. 2020, Robinson et al. 2022, Liu et al. 2022, Koike et al. 2023, Robinson et al. 2023). In my opinion, however, genetic analysis of the role of CXCR4 on PC localization or survival in BM (Figure 5) was well performed and provided some new aspects which have not been addressed in previous reports. The motility of CXCR4 cKO plasma cells in BM is not shown, but it could further support the idea that reduced mobility or increased clustering is required for longevity.

2) The combination of the several surface markers shown in Figure 3&4 doesn't seem to be practically applicable to identify or gate on LLPCs, because differential expression of CD81, CXCR4, CD326, CD44, or CD48 on LLPCs vs bulk PCs was very modest. EpCAMhi/CXCR3-, Ly6Ahi/Tigit- (Liu et al. 2022), B220lo/MHC-IIlo (Koike et al. 2023), or SLAMF6lo/MHC-IIlo (Robinson et al. 2023) has been reported as markers for LLPC population. It is unclear that the combination of surface markers presented here is superior to published markers. In addition, it is unclear why the authors did not use their own gene expression data (Fig.6), instead of using public datasets, for picking up candidate markers.

Reviewer #2 (Public Review):

In this study by Jing, Fooksman, and colleagues, a Blimp1-CreERT2-based genetic tracing study is employed to label plasma cells. Over the course of several months post-tamoxifen treatment, the only remaining labeled cells are long-lived plasma cells. This system provides a way to sort live long-lived plasma cells and compare them to unlabeled plasma cells, which contain a range of short-to-long-lived cells. From this analysis, several observations are made: 1) the turnover rate of plasma cells is greater in the spleen than in the bone marrow; 2) the turnover rate is highest early in life; 3) subtle transcriptional and cell surface marker differences distinguish long- from shorter-lived plasma cells; 4) long-lived plasma cells in the bone marrow are sessile and localize in clusters with each other; 5) CXCR4 is required for plasma cell retention in these clusters and in the bone marrow; 6) Repertoire analysis hints that the selection of long-lived plasma cells is not random for any cell that lands in the bone marrow.

Strengths:

1) The genetic timestamping approach is a clever and functional way to separate plasma cells of differing longevities.

2) This approach led to the identification of several markers that could help prospective separation of long-lived plasma cells from others.

3) Functional labeling of long-lived plasma cells allowed for a higher resolution analysis of transcriptomes and motility than was previously possible.

4) The genetic system allowed for a revisitation of the importance of CXCR4 in plasma cell retention and survival.

Weaknesses:

1) Most of the labeling studies, likely for practical reasons, were done on polyclonal rather than antigen-specific plasma cells. The triggers of these responses could vary based on age at the time of exposure, anatomical sites, etc. How these differences might influence markers and transcriptomes, independently of longevity, is not completely known.

2) The fraction of long-lived plasma cells in the unlabeled fraction varies with age, potentially diluting differences between long- and short-lived plasma cells.

3) The authors suggest their data favors a model by which plasma cells compete for niche space. Yet there is no evidence presented here that these niches are limiting.

4) The functional importance of the observed transcriptome differences between long- and shorter-lived plasma cells is unknown. An assessment as to whether these differences are conserved in human long- and short-lived bone marrow plasma cells might provide circumstantial supporting evidence that these changes are important for longevity.

Author Response

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.

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 sgRNAs used to generate initial PARG KO cells in this manuscript target all three catalytically active isoforms (isoforms 1, 2 and 3), while isoforms 4 and 5 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 in Author response image 1. 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.

2) 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.

3) 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 sgRNAs used to generate PARG KO cells in this manuscript target all three catalytically active isoforms (isoforms 1, 2 and 3), while isoforms 4 and 5 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 in Author response image 2.

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 in Author response image 3. 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.

4) 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.

5) 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.

6) 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 (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?

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?

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.

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

Reviewer #2 (Public Review):

Summary:<br /> 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.

Strengths:<br /> The authors show that PARG is essential for removing ADP-ribosylation in S-phase.

Weaknesses:<br /> 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).<br /> 2) 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.<br /> 3) 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.<br /> 4) 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.<br /> 5) All colony formation assays should be quantified and sensitivity plots should be shown next to example plates.<br /> 6) Please indicate how many times each experiment was performed independently and include statistical analysis.

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.

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.

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.

Author Response

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

Reviewer #1:

1) Utilization of known AhR ligands as controls will strengthen the interpretation of the conclusions.

We agree with the reviewer that AhR ligands could be used as controls for delineating structure-activity relationships and cell context-specific effects. However, such studies are beyond the scope of the current manuscript. The AhR has many endogenous ligands, including several tryptophan derived metabolites, that have been shown to elicit different responses depending on the dose and cell type. Our unpublished data show that the expression of AhR target genes such as Cyp1a1, Cyyp2e1, and Tiparp were not modulated by I3A in RAW cells, which suggests that the observed effects may occur independent of the AhR.

Reviewer #2:

Specific comments:

1) The title is misleading "Microbially-derived indole-3-actate" suggests that this article is about the production of I3A by the gut microbiota, in fact this is a dietary supplementation article. The title needs to reflect this fact.

Our title reflects the natural source of I3A in mice. We used oral supplementation to study the effects of this metabolite. Per suggestion by the reviewer, we changed the title as follows: <br /> “Oral supplementation of gut microbial metabolite indole-3-acetate alleviates diet-induced steatosis and inflammation in mice”

2). The description of the amount of I3A in the drinking water is not properly described. The actual concentration in the drinking water should be given.

The concentration of I3A in drinking water was as follows: WD50 = 0.5mg/ml and WD100 = 1mg/ml. We added this information in the revised manuscript.

3) The serum concentration data of I3A is critical data and should be moved in Figure 1.

We have now included serum levels of I3A as part of Figure 1.

4) The authors should have determined the actual concentration of indole-3-actetate in serum by running a standard curve of I3A during the LC-MS analysis. Also, recovery and matrix effects should be determined. Without this information their data will be difficult to compare to other studies.

We agree with the reviewer that quantification of I3A in serum would be useful. However, we are unable to do so due to limited sample available as well as concerns with sample integrity after long-term storage.

5) In the data in Figure S1C, there appears to be only 2-3 mice out of nine that exhibit a difference in serum indole-3-acetate levels between the WD-50 and WD-100. Do the authors have an explanation for this small difference compared to the other endpoints assessed?

The serum I3A measurements at week 16 are a snapshot that may not reflect tissue levels due to differences in water intake, I3A metabolism in the body, and/or elimination of I3A. The other phenotypic assays are physiological measurements that reflect the result of sustained administration of I3A.

6) Since the Ah receptor may play a role in the results obtained CYP1A1 mRNA levels in the liver and intestinal tract should have been measured.

We measured alterations in Cyp1a1 mRNA in the liver and no significant change was observed in the WD50 and WD100 groups relative to controls. Also, see response to reviewer 1.

7) The main mechanistic experiment performed is shown in Figure 6 and the figure legend states that they are examining macrophages, but these are cell lines, they are macrophages models, and this should be clearly stated. The first two panels are liver data, so the title of the figure legend needs to reflect that fact.

We agree and have changed the title of Figure 6 to “I3A modulates AMPK phosphorylation and suppresses RAW 264.7 macrophage cell inflammation in an AMPK dependent manner”.

8) In Figure 6, 1 mM I3A is added to the cells, how is this very high concentration relevant to the concentrations observed in vivo? Does adding 1 mM acetate to the cell culture media lower the pH of the media and could this influence the results obtained? Would acetic acid yield the same results? Could treatment with an acid even explain in vivo results?

It is difficult to match the concentration of I3A in the in vitro experiments to liver tissue concentrations. Addition of 1 mM I3A did not lower the pH of cell culture media or reduce the viability of cultured RAW 264.7 macrophages. As I3A is not known to degrade into acetic acid and indole, we do not expect acetic acid to recapitulate the effects elicited by I3A.

Reviewer #3:

My primary concern with the manuscript is the organization and interpretation of the data. It appears that little effort was given by the authors on interpreting the data and digesting it for the reader into a coherent package. Rather, the authors have collected a vast amount of data and organized it without much thought about what the reader would take away from it. Furthermore, it seems the authors have taken this as an opportunity to overload this manuscript with data that are superfluous to the conclusions the authors draw at the end. Based on this, I think the authors need to invest more time into distilled their complex biological data into a unifying scientific interpretation for the readers that advances our understanding of I3A. My suggestions for the authors are described below.

1) The data lack a rationale behind how they are organized within the manuscript. For example, the authors will combine disparate biological pathways and lump data together without logic as in Figure 2. Why are inflammatory pathways and bile acid synthesis combined in a figure? What was the rationale?

We respectfully disagree that the data are presented without rationale. Both inflammation and bile acid dysregulation are commonly observed with NAFLD and thus are presented in two separate panels of Figure 2 (A, inflammatory cytokines, and B bile acids).

2) The authors give very little effort to performing integrative omics analysis even though multi-omics is provided. Example given, the authors provide proteomic data on the fatty acid metabolism pathway, however, no mention of this pathway within the metabolomic dataset. Vice versa, the authors provide in depth investigation in the metabolic changes within the tryptophan pathway, however, no investigation into the proteomic changes that may underlie this phenomenon. It would be recommended that the authors invest more energy into performing more in-depth analysis of their multi-omics data presented.

We attempted to co-analyze the proteomic and metabolomic data, but this analysis was not informative. Protein and metabolite abundances do not necessarily correlate, and the two types of omics data carry different observation biases. For example, label-free, untargeted proteomics data favor abundant proteins, whereas untargeted metabolomics data are influenced by concentration and ionization efficiency, among other factors. Therefore, we opted to analyze the two datasets independently, and then linked the findings from the two analyses using biological pathways as guides. For example, we describe changes in acyl-carnitine and discuss how this observation is consistent with changes in abundance of fatty acid metabolism enzymes.

3) Figures 1&2 shows that low dose treatment reduces inflammation but does not alter hepatic TG levels. This is in direct disagreement with the graphical model provided by the authors (Supp. Fig 9). In the author's model, I3A is directing hepatic lipid metabolism through modulation of macrophage inflammation. This interpretation is erroneous and needs to be reevaluated by the authors. Furthermore, the tryptophan pathway and bile acid pathways are not even represented in the model, which begs the question of why that data are included in the manuscript to begin with.

We would like to respectfully point out that Figure 1D does show a statistically significant (p < 0.05) difference in liver TG between the WD and WD100 groups. Supp. Figure S9 is meant to be a summary of the main biochemical changes elicited by I3A that we have shown in the current study (e.g., the involvement of AMPK) rather an atlas of all the changes detected in the metabolomics and proteomic data. Specifically, we have not included the tryptophan or bile acid pathways as we do not have mechanistic information on how these changes are mediated by I3A.

4) The authors switch from hepatocytes to macrophages without giving any rationale, The authors need to invest more time into describing a logical flow of thought when assembling the manuscript.

We mention the rationale for investigating the effect of I3A on macrophages in the introduction (last paragraph of the section): “In vitro, both I3A and TA attenuated the expression of inflammatory cytokines (Tnfα, Il-1β and Mcp-1) in macrophages exposed to palmitate and LPS.”. We also explain why we used an in vitro model, RAW cells, at the beginning of the corresponding Results section: “Since our previous study found that the metabolic effects of I3A in hepatocytes depend on the AhR, we tested if this was also the case in macrophages.” Moreover, the strong effects of I3A on liver inflammatory cytokines also motivates the macrophage experiments.

eLife assessment

The studies are important to the field of hepatic steatosis and inflammation. The data provided are convincing that treatment with I3A mitigated Western diet (WD)-induced hepatic steatosis, inflammation and reversed WD-induced alterations in liver bile acids and free fatty acids in mice.

Joint Public Review:

The study by Ding et al demonstrated that microbial metabolite I3A reduced western diet induced steatosis and inflammation in mice. They showed that I3A mediates its anti-inflammatory activities through AMP-activated protein kinase (AMPK)-dependent manner in macrophages. Translationally, they proposed that I3A could be a potential therapeutic molecule in preventing the progression of steatosis to NASH.

Major strengths<br /> • Authors clearly demonstrated that the Western Diet (WD)-induced steatosis and I3A treatment reduced steatosis and inflammation in pre-clinical models. Data clearly supports these statements.<br /> • I3A treatment rescued WD-altered bile acids as well as partially rescued the metabolome, proteome in the liver.<br /> • I3A treatment reduced the levels of enzymes in fatty acid transport, de novo lipogenesis and β-oxidation<br /> • I3A mediates its anti-inflammatory activities through AMP-activated protein kinase (AMPK)-dependent manner in macrophages.

Minor<br /> I agree with the authors that investigating known other AhR ligands in comparison may be beyond the scope of this study.

Author Response

We thank the Editors and the Reviewers for the time spent on our manuscript entitled “The CD4 transmembrane GGXXG and juxtamembrane (C/F)CV+C motifs mediate pMHCII-specific signaling independently of CD4-Lck interactions”. We appreciate the helpful feedback and the opportunity to participate in eLife’s new model for publishing.

We are writing to provide the following provisional author responses for posting with the first version of the reviewed preprint:

1) To address comments about the limited scope of this study and referencing of the Methods section to our prior study, we would like to note that we submitted the current study via the Research Advance mechanism. Our goal was to build upon the conclusions of our 2022 eLife publication (PMID: 35861317) and address an unresolved question from that study (as nicely summarized by Reviewer #2). In the current manuscript we present data from reductionist experiments that were designed specifically for this purpose and, as noted by the reviewers, we provide answers to the question being asked. We think that the Research Advance mechanism is an ideal opportunity to make these results available to the field given the stated purpose of such articles (for reference: “A Research Advance might use a new technique or a different experimental design to generate results that build upon the conclusions of the original research by, for example, providing new mechanistic insights or extend the pathway under investigation…”).

a. The Methods were not duplicated in this manuscript because we referenced our prior study as per instructions for the Research Advance mechanism.

2) The constituent residues of the motifs analyzed in this and our prior study were determined to be functionally significant in vivo through the computational reconstruction of CD4’s evolutionary history, which provided us with data from ~435 million years of natural experiments with CD4 in numerous jawed vertebrate species. We agree that having conditional knock-in mice of these CD4 mutants, and those characterized in our last study, would be useful for determining how these mutations impact T cell development, activation, differentiation, and effector function. Given the costs involved with making genetically engineered mouse model systems, the computational and experimental data we have generated in the current and prior study will help us prioritize next steps to dig deeper into the details of why the residues we are studying are under purifying selection (fail to propagate to progeny if mutated, meaning terminal). In short, only now, with the data in hand, can we prioritize mouse studies. We think it is important for the advancement of the field that we make these results available in a timely manner rather than waiting to report them together with the results of mouse models once generated and analyzed.

3) The reductionist experimental data presented here provide us with mechanistic insights into why the residues we are studying are functionally important. We therefore think it is of value to note that 58a-b- T cell hybridomas were used in seminal work that established a link between CD4Lck association, via motifs in the CD4 intracellular domain, and signaling output as measured by IL-2 production (Glaichenhaus, et al., 1991). Importantly, the impact of disrupting CD4-Lck interactions on proximal signaling were not interrogated until the work we describe here and in our preceding study, wherein we establish that CD4-Lck association does not regulate proximal signaling in 58a-b- T cell hybridomas. Given that this experimental system was used to help establish the dominant paradigm (i.e. the widely held view that CD4 recruits Lck to TCR-CD3 to initiate pMHCII-specific signaling), we think it is a legitimate system to directly test this model and further test core questions of CD4 function by employing more modern experimental techniques.

eLife assessment

This study provides a useful description of two evolutionary conserved motifs in CD4 that contribute to the CD4-mediated enhancement of T cell receptor (TCR) signaling, which are notably acting independently of the CD4-LCK interaction. The supporting evidence is solid and supported using a T cell hybridoma in vitro system, but remains incomplete without experiments with primary T cells or the use of in vivo models to assess the importance of the CD4 motifs in T-cell biology.

Reviewer #1 (Public Review):

Summary:

This study by Lee et al. is a direct follow-up on their previous study that described an evolutionary conservancy among placental mammals of two motifs (a transmembrane motif and a juxtamembrane palmitoylation site) in CD4, an antigen co-receptor, and showed their relevance for T-cell antigen signaling. In this study, they describe the contribution of these two motifs to the CD4-mediated antigen signaling in the absence of CD4-LCK binding. Their approach was the comparison of antigen-induced proximal TCR signaling and distal IL-2 production in 58-/- T-cell hybridoma expressing exogenous truncated version of CD4 (without the interaction with LCK), called T1 with T1 version with the mutations in either or both of the conserved motifs. They show that the T1 CD4 can support signaling to the extend similar to WT CD4, but the mutation of the conserved motifs substantially reduced the signaling. The authors conclude that the role of these motifs is independent of the LCK-binding.

Strengths:<br /> The authors convincingly show that T1 CD4, lacking the interaction with LCK supports the TCR signaling and also that the two studied motifs have a significant contribution to it.

Weaknesses:<br /> The study has several weaknesses.

1. The whole study is based on a single experimental system, genetically modified 58-/- hybridoma. It is unclear at this moment, how the molecular motifs studied here contribute to the signaling in a real T cell. The evolutionary conservancy suggests that these motifs are important for T cell biology. However, the LCK-binding motif is conserved as well (perhaps even more) and it plays a very minor role in their model. Without verifying their results in primary cells, the quantitative, but even qualitative, importance of these motifs for T-cell signaling and biology is unclear. Although the authors discuss this issue in the Discussion, it should be noted in all important parts of the manuscript, where conclusions are made (abstract, end of introduction, perhaps also in the title) that the results are coming from the hybridoma cells.

2. Many of the experiments lack the negative control. I believe that two types of negative controls should be included in all experiments. First, hybridoma cells without CD4 (or with CD4 mutant unable to bind MHCII). Second, no peptide control, i.e., activation of the hybridoma cells with the APC not loaded with the cognate peptide. These controls are required to distinguish the basal levels of phoshorylation and CD4-independent antigen-induced phosphorylation to quantify, what is the contribution of the particular motifs to the CD4-mediated support. Although these controls are included in some of the experiments, they are missing in other ones. The binding mutant appears in some FC results as a horizontal bar (without any error bar/variability), showing that CD4 does not give a huge advantage in these readouts. Why don't the authors show no peptide controls here as well? Why the primary FC data (histograms) are not shown? Why neither of these two controls is shown for the % of responders plots? Although the IL-2 production is a very robust and convincing readout, the phosphoflow is much less sensitive. It seems that the signaling is elevated only marginally. Without the mentioned controls and showing the raw data, the precise interpretation is not possible.

3. The processing of the data is not clear. Some of the figures seem to be overprocessed. For instance, I am not sure what "Normalized % responders of pCD3zeta" means (e.g., Fig. 1C and elsewhere)? Why do not the authors show the actual % of pCD3zeta+ cells including the gating strategy? Why do the authors subtract the two histograms in Fig. 2- Fig.S3? It is very unusual.

4. The manuscript lacks Materials and Methods. It only refers to the previous paper, which is very unusual. Although most of the methods are the same, they still should be mentioned here. Moreover, some of the mutants presented here were not generated in the previous study, as far as I understand. Perhaps the authors plan to include Materials and Methods during the revision...

5. Membrane rafts are a very controversial topic. I recommend the authors stick to the more consensual term "detergent resistant microdomains" in all cases/occurances.

6. Last, but not least, the mechanistic explanation (beyond the independence of LCK binding) of the role of these motifs is very unclear at the moment.

Reviewer #2 (Public Review):

Summary:<br /> The paper by Kuhn and colleagues follows upon a 2022 paper in which they identified residues in CD4 constrained by evolutionary purifying selection in placental mammals and then performed functional analyses of these conserved sequences. They showed that sequences distinct from the CXC "clamp" involved in recruitment of Lck have critical roles in TCR signaling, and these include a glycine-rich motif in the transmembrane (TM) domain and the cys-containing juxtamembrane (JM) motif that undergoes palmitoylation, both of which promote TCR signaling, and a cytoplasmic domain helical motif, also involved in Lck binding, that constrains signaling. Mutations in the transmembrane and juxtamembrane sequences led to reduced proximal signaling and IL-2 production in a hybridoma's response to antigen presentation, despite retention of abundant CD4 association with Lck in the detergent-soluble membrane fraction, presumably mislocalized outside of lipid rafts and distal to the TCR. A major conclusion of that study was that CD4 sequences required for Lck association, including the CXC "clasp" motif, are not as consequential for CD4 co-receptor function in TCR signaling as the conserved TM and JM motifs. However, the experiments did not determine whether the functions of the TM and JM motifs are dependent on the Lck-binding properties of CD4 - the mutations in those motifs could result in free Lck redistributing to associate with CD4 in signaling-incompetent membrane domains or could function independently of CD4-Lck association. The current study addresses this specific question.

Using the same model system as in the earlier paper (the entire methods section is a citation to the earlier paper), the authors show that truncation of the Lck-binding intracellular domain resulted in a moderate reduction in IL-2 response, as previously shown, but there was no apparent effect on proximal phosphorylation events (CD3z, Lck, ZAP70, PLCg1). They then evaluated a series of TM and JM motif mutations in the context of the truncated Lck-nonbinding molecule, and showed that these had substantially impaired co-receptor function in the IL-2 assay and reduced proximal signaling. The proximal signaling could be observed at high ligand density even with a MHC non-binding mutation in CD4, although there was still impaired IL-2 production. This result additionally illustrates that phosphorylation of the proximal signaling molecules is not sufficient to activate IL-2 expression in the context of antigen presentation.

Strengths:<br /> The strength of the paper is the further clear demonstration that the classical model of CD4 co-receptor function (MHCII-binding CD4 bringing Lck to the TCR complex, for phosphorylation of the CD3 chain ITAMs and of the ZAP70 kinase) is not sufficient to explain TCR activation. The data, combined with the earlier paper, further implicate the gly-rich TM sequence and the palmitoylation targets in the JM region as having critical roles in productive co-receptor-dependent TCR activation.

Weaknesses:<br /> The major weakness of the paper is the lack of mechanistic insight into how the TM and JM motifs function. The new results are largely incremental in light of the earlier paper from this group as well as other literature, cited by the authors, that implicates "free" Lck, not associated with co-receptors, as having the major role in TCR activation. It is clear that the two motifs are important for CD4 function at low pMHCII ligand density. The proposal that they modulate interactions of TCR complex with cholesterol or other membrane lipids is an interesting one, and it would be worth further exploring by employing approaches that alter membrane lipid composition. The JM sequence presumably dictates localization within the membrane, by way of palmitoylation, which may be critical to regulate avidity of the TCR:CD4 complex for pMHCII or TCR complex allosteric effects that influence the activation threshold. Experiments that explore the basis of the mutant phenotype could substantially enhance the impact of this study.

Author Response:

We would like to express our heartfelt gratitude for the reviewers’ scholarly and insightful reviews of our manuscript. The constructive comments and thought-provoking experimental proposals have been invaluable not only in improving the quality of this study but also in shaping the direction of future research. In revision, all comments will be addressed point-by-point, and the manuscript will be revised thoroughly. Here in this reply, we focus on the most critical issue regarding the source of noises during stability inference.

When faced a stack of objects, individuals are more likely to assess taller stacks of objects as being more unstable compared to shorter ones (Fig. 2b & 2d). This bias persists even when comparing single objects of different heights that share the same contact area with the supporting surface. Known as “stability inference bias,” this phenomenon challenges deterministic models with a single, fixed vector for the representation of gravity’s direction (i.e., directly downward). To reconcile this bias with deterministic models, previous studies (e.g., Allen et al., 2020; Battaglia et al., 2013; Kubricht et al., 2017) have incorporated external noises such as perceptual uncertainty and external force perturbations to increase their fit to human performance, also pointed out by Reviewer 1.

In this study, we introduced an alternative perspective through a stochastic model in which variability is instead embedded in the representation of gravity’s direction. In this framework, gravity’s direction is not a fixed vector but a distribution of possible vectors, with the vertical direction serving as the maximum likelihood. While the distinction between deterministic and stochastic models is conceptually clear, mathematically they are equivalent. In addition, our stochastic model does not negate the role of external noises in stability inference, because gravity is seldom the sole force acting upon a moving object in the physical world, as pointed out by Reviewer 1. Together, these two factors make it challenging to ascribe the source of variability to either external or internal noises (Smith & Vul, 2013). This is the major concern raised by all three reviewers.

To distinguish between the deterministic and stochastic models, we designed a series of experiments aimed at demonstrating that internal noises, rather than external noises such as perceptual uncertainty or external force perturbations, influences our inference about object stability. However, the supporting evidence was dispersed and at times implicit throughout the manuscript. In revision, we will thoroughly clarify the ambiguities. In this reply, we will consolidate and present the evidence comprehensively.

1. The examination of external noises.

1.1 External Force Perturbations. Deterministic models suggests that during object stability inference, individuals implicitly assume the presence of external forces (e.g., wind) that could destabilize stacks. While this assumption aligns with the omnipresence of such forces in natural settings, it overlooks a crucial variable: the directionality of these external forces. In psychological studies, individual differences are commonly observed, and the perceived force direction is not an exception. That is, some may assume that it comes from the left, while others from the right. In essence, if external forces were to play a significant role in stability inference, one would expect the perceived force directions to exhibit non-uniform distributions (i.e., anisotropy) in the horizontal plane within individuals and to show substantial variability between individuals.

Contrary to this expectation, our study revealed a different pattern. In the study, we specifically measured the distribution of 𝜑, the horizontal component reflecting the direction of object collapse. Our results indicated that all participants exhibited a uniform distribution of gravity’s directions in the horizontal plane (Fig. 1d right; Extended Data Fig. 2 and 3). This uniformity suggests that if external forces were a key determinant in stability inference, participants would have to assume a varying direction of external force in each trial—an assumption we consider unlikely. Instead, our RL model simulation suggests that the isotropy of 𝜑 arises from agent-environment interactions, notably in the absence of external forces (Extended Data Fig. 6).

In summary, the uniform distribution of horizontal direction component, 𝜑, observed in all participants, challenges the argument for the dominant role of external forces in stability inference. We are sorry that this aspect was not explicitly emphasized in the original text, and in revision we will explain why external forces are unlikely to substantially shape our perception of object stability.

1.2 Perceptual uncertainty. To assess the impact of perceptual uncertainty on stability inference, we examined whether the representation of gravity’s direction is cognitive impenetrable. Specifically, we posited that if noises are external (i.e., perceptual uncertainty), the inference bias should be modulated by task context; in contrast, if noises are internal, the stochastic representation of gravity’s direction will be encapsulated from the context. To test this idea, we inverted the virtual environment, making gravity appear to point upward (also see a similar idea by Reviewer 3). In this unfamiliar context, which diverges dramatically from daily experiences, one would expect heightened perceptual uncertainty, which according to deterministic models would result in a larger inference bias – manifested as an increased width of the distribution (𝜎) of gravity’s direction. Contrary to this prediction, we observed that the width of the distribution remained unchanged (Fig. 1d and 1f). Furthermore, there was a high correlation (r = 0.91) between widths in the upright and inverted conditions across participants (Extended Data Fig. 2 and 3).

In summary, this finding suggests that the manipulation of perceptual uncertainty is unable to cognitively penetrate the representation of gravity’s direction, casting doubt on its dominant role in stability inference. We are sorry that in the original text, we did not clarify the rationale for employing the approach of cognitive impenetrability. In revision, this will be clarified.

2. The origin of intrinsic noises in stability inference.

In deterministic models, either external force perturbations or perceptual uncertainty is often assumed but rarely empirically tested. Indeed, these external noises are introduced primarily to account for observed biases in stability inference. In this study, we explicitly examined the possible origin of the intrinsic noises embedded in the representation of gravity’s direction. Without assumed perceptual uncertainty and external perturbation of forces, the RL model simulation showed that the distribution could evolve naturally based mainly on the agent’s experience, as it used the mismatch between the expectation and the observed state of the stack under natural gravity to update its representation of gravity’s direction (Fig. 3a). Importantly, the width of the distribution for the agent was comparable to that of human participants as measured in the psychophysics experiments (Fig. 3b). Therefore, the experience alone may be sufficient to generate stochastic representation of gravity’s direction, obviating the need for external noises.

Taken together, these findings underscore the limitations of the combination of deterministic models and external noises in accounting for stability inference, and suggest that intrinsic noises embedded in the representation of gravity play a pivotal role in shaping our stability inference of the physical world.

3. Thought experiments.

Although the evidence shown above may provide valuable insights, our study does not definitively settle the debate between deterministic models and our proposed stochastic model. Specifically, our study only preliminarily investigates two sources of external noise, perceptual uncertainty and external force perturbations, leaving many other factors such as object mass and surface friction, unexplored (for studies on these factors, please see Hamrick et al., 2016). As such, the reviewers have proposed a series of thought experiments that warrant further investigation. Below, we enumerate some of them, followed by ours.

3.1 Experiment 1. Reviewer 3 proposed a thought experiment in which participants assess stability of a single block of varying heights. The reviewer argues that a block, regardless of its height, will remain stable on a horizontal surface unless externally disturbed. This assertion is perfectly true in the physical realm. However, in the cognitive domain, both deterministic models and our stochastic model predict differently. Take an extreme example of a standing needle: while it would remain upright in the physical world without external disturbances, both deterministic and stochastic models, which account for mental inference of physical events, will predict a likelihood of it falling, aligning with our subjective feelings. This is because in both models, noises are considered in the intuitive physics engine. In deterministic models, external force perturbations, as well as perceptual uncertainty, are assumed to be omnipresent noises in probabilistic reasoning. In our stochastic model, noises are embedded in the representation of gravity’s direction. Therefore, although this thought experiment, along with other thought experiments on object mass, surface friction (proposed by Reviewer 3), and falling trajectories behind an occlude (proposed by Reviewer 1), is insightful, but it cannot serve to differentiate deterministic and stochastic models. 3.2 Experiment 2. Reviewer 2 suggested constructing a wall on one side of the virtual scene to make it improbable that participants would infer an external force perturbation emanating from that direction. In this setting, deterministic models would predict a non-uniform distribution of the horizontal component, 𝜑, skewed away from the wall. In contrast, according to our stochastic model, the distribution of 𝜑 would remain unaffected, maintaining the uniform distribution observed in previous experiments. Extending this logic, another test scenario could contrast an indoor scene with an outdoor scene. In a confined and static indoor environment, the likelihood of external force perturbations should be much lower than in a dynamic, open outdoor setting. Here, deterministic models would predict an increase in the width of the distribution, 𝜎, in the outdoor environment, whereas our model would anticipate no such change. The underlying rationale for these experiments parallels that of our previous setup (figure 1e), where we inverted the virtual environment and reversed the direction of gravity. Indeed, they all aim to assess the extent to which manipulations of external factors can cognitively penetrate the representation of gravity’s direction.

3.3 Experiment 3: A noteworthy insight derived from our RL model simulation relates to variations in the number of blocks within the virtual worlds. Deterministic models would predict an enlarged bias in stability inference as the number of blocks increased, which is attributed to elevated levels of perceptual uncertainty and an expanded area susceptible to external force perturbations. However, the results from our RL model simulation contradict this prediction, revealing that an augmented number of blocks instead led to a narrowing of the width of the distribution. This decrease in width can be ascribed to richer information provided by a larger number of blocks for refining its representation of gravity’s direction. In line with this rationale, we propose a new experiment from the perspective of ecological psychology, which emphasizes that cognitive processes are shaped by our interactions with the environment. Specifically, we hypothesize that individuals raised in mountainous terrains may exhibit more accurate representations of gravity’s direction than those raised in flat terrains. This proposed experiment could not only help resolving the ongoing debate between two models to some extent, but also advocate future studies on intuitive physics within a more ecologically valid framework.

To conclude, both deterministic and stochastic models align closely with Bayesian principles, where stability inference is conceptualized as probabilistic reasoning. Nevertheless, the divergence between them is no trivial, as it hinges on distinct philosophical assumptions about the relationship between the inner mind and the external world. Deterministic models propose that the mind serves as a faithful reflection of the world; therefore, gravity’s direction is represented as a single, fixed vector directly downward, the same as that in the world. In these models, uncertainty for probabilistic reasoning emanates from factors external to the module of the intuitive physics engine. In contrast, our stochastic model underscores the notion that the mind is an active inference machine, continually reinterpreting inputs from outside world; therefore, the mind gains increased adaptability, allowing for a more nuanced accounting of uncertainty in the world – factors often crucial for survival. Such active inference necessitates flexible representations; accordingly, within the model of intuitive physics engine, variations are embedded into the representation of gravity’s direction. While resolving this philosophical debate is beyond the capacity of the present study, we contend that the field of intuitive physics offers a valuable lens through which to pry open the complex interplay between the mind and the world we live in.

References

• Allen, K. R., Smith, K. A., & Tenenbaum, J. B. (2020). Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning. Proceedings of the National Academy of Sciences, 117(47), 29302–29310.
• Battaglia, P. W., Hamrick, J. B., & Tenenbaum, J. B. (2013). Simulation as an engine of physical scene understanding. Proceedings of the National Academy of Sciences, 110(45), 18327–18332.
• Kubricht, J. R., Holyoak, K. J., & Lu, H. (2017). Intuitive physics: Current research and controversies. Trends in Cognitive Sciences, 21(10), 749–759.
• Smith, K. A., & Vul, E. (2013). Sources of uncertainty in intuitive physics. Topics in Cognitive Science, 5(1), 185–199.

eLife assessment

This study presents valuable findings on the organization of respiratory chain complexes in mitochondria. It provides solid evidence that respiratory supercomplex formation in the fruit fly does not impact respiratory function, suggesting the role of these complexes is structural, rather than catalytic. However, whether the conclusions extend to other species requires further evidence. This manuscript will be of broad interest to the field of mitochondrial bioenergetics.

eLife assessment

The authors discuss an effect, "diffusive lensing", by which particles would accumulate in high-viscosity regions, for instance in the intracellular medium. To obtain these results, the authors rely on agent-based simulations using custom rules performed with the Ito stochastic calculus convention. The "lensing effect" discussed is a direct consequence of the choice of the Ito convention without spurious drift which has been discussed before and is likely to be inadequate for the intracellular medium, causing the presented results to likely have little relevance for biology.

Reviewer #1 (Public Review):

The manuscript "Diffusive lensing as a mechanism of intracellular transport and compartmentalization", explores the implications of heterogeneous viscosity on the diffusive dynamics of particles. The authors analyze three different scenarios:

(i) diffusion under a gradient of viscosity,

(ii) clustering of interacting particles in a viscosity gradient, and

(iii) diffusive dynamics of non-interacting particles with circular patches of heterogeneous viscous medium.

The implications of a heterogeneous environment on phase separation and reaction kinetics in cells are under-explored. This makes the general theme of this manuscript very relevant and interesting. However, the analysis in the manuscript is not rigorous, and the claims in the abstract are not supported by the analysis in the main text.

Following are my main comments on the work presented in this manuscript:

(a) The central theme of this work is that spatially varying viscosity leads to position-dependent diffusion constant. This, for an overdamped Langevin dynamics with Gaussian white noise, leads to the well-known issue of the interpretation of the noise term. The authors use the Ito interpretation of the noise term because their system is non-equilibrium.

One of the main criticisms I have is on this central point. The issue of interpretation arises only when there are ill-posed stochastic dynamics that do not have the relevant timescales required to analyze the noise term properly. Hence, if the authors want to start with an ill-posed equation it should be mentioned at the start. At least the Langevin dynamics considered should be explicitly mentioned in the main text. Since this work claims to be relevant to biological systems, it is also of significance to highlight the motivation for using the ill-posed equation rather than a well-posed equation. The authors refer to the non-equilibrium nature of the dynamics but it is not mentioned what non-equilibrium dynamics to authors have in mind. To properly analyze an overdamped Langevin dynamics a clear source of integrated timescales must be provided. As an example, one can write the dynamics as<br /> Eq. (1) \dot x = f(x) + g(x) \eta , which is ill-defined if the noise \eta is delta correlated in time but well-defined when \eta is exponentially correlated in time. One can of course look at the limit in which the exponential correlation goes to a delta correlation which leads to Eq. (1) interpreted in Stratonovich convention. The choice to use the Ito convention for Eq. (1) in this case is not justified.

(b) Generally, the manuscript talks of viscosity gradient but the equations deal with diffusion which is a combination of viscosity, temperature, particle size, and particle-medium interaction. There is no clear motivation provided for focus on viscosity (cytoplasm as such is a complex fluid) instead of just saying position-dependent diffusion constant. Maybe authors should use viscosity only when talking of a context where the existence of a viscosity gradient is established either in a real experiment or in a thought experiment.

(c) The section "Viscophoresis drives particle accumulation" seems to not have new results. Fig. 1 verifies the numerical code used to obtain the results in the later sections. If that is the case maybe this section can be moved to supplementary or at least it should be clearly stated that this is to establish the correctness of the simulation method. It would also be nice to comment a bit more on the choice of simulation methods with changing hopping sizes instead of, for example, numerically solving stochastic ODE.

A minor comment, the statement "the physically appropriate convention to use depends upon microscopic parameters and timescale hierarchies not captured in a coarse-grained model of diffusion." is not true as is noted in the references that authors mention, a correct coarse-grained model provides a suitable convention (see also Phys. Rev. E, 70(3), 036120., Phys. Rev. E, 100(6), 062602.).

(d) The section "Interaction-mediated clustering is affected by viscophoresis" makes an interesting statement about the positioning of clusters by a viscous gradient. As a theoretical calculation, the interplay between position-dependent diffusivity and phase separation is indeed interesting, but the problem needs more analysis than that offered in this manuscript. Just a plot showing clustering with and without a gradient of diffusion does not give enough insight into the interplay between density-dependent diffusion and position-dependent diffusion. A phase plot that somehow shows the relative contribution of the two effects would have been nice. Also, it should be emphasized in the main text that the inter-particle interaction is through a density-dependent diffusion constant and not a conservative coupling by an interaction potential.

(e) The section "In silico microrheology shows that viscophoresis manifests as anomalous diffusion" the authors show that the MSD with and without spatial heterogeneity is different. This is not a surprise - as the underlying equations are different the MSD should be different. There are various analogies drawn in this section without any justification:<br /> (i) "the saturation MSD was higher than what was seen in the homogeneous diffusion scenario possibly due to particles robustly populating the bulk milieu followed by directed motion into the viscous zone (similar to that of a Brownian ratchet, (Peskin et al., 1993))."<br /> (ii) "Note that lensing may cause particle displacements to deviate from a Gaussian distribution, which could explain anomalous behaviors observed both in our simulations and in experiments in cells (Parry et al., 2014)."<br /> Since the full trajectory of the particles is available, it can be analyzed to check if this is indeed the case.

(f) The final section "In silico FRAP in a heterogeneously viscous environment ... " studies the MSD of the particles in a medium with heterogeneous viscous patches which I find the most novel section of the work. As with the section on inter-particle interaction, this needs further analysis.

To summarise, as this is a theory paper, just showing MSD or in silico FRAP data is not sufficient. Unlike experiments where one is trying to understand the systems, here one has full access to the dynamics either analytically or in simulation. So just stating that the MSD in heterogeneous and homogeneous environments are not the same is not sufficient. With further analysis, this work can be of theoretical interest. Finally, just as a matter of personal taste, I am not in favor of the analogy with optical lensing. I don't see the connection.

Reviewer #2 (Public Review):

Summary:<br /> The authors study through theory and simulations the diffusion of microscopic particles and aim to account for the effects of inhomogeneous viscosity and diffusion - in particular regarding the intracellular environment. They propose a mechanism, termed "Diffusive lensing", by which particles are attracted towards high-viscosity regions where they remain trapped. To obtain these results, the authors rely on agent-based simulations using custom rules performed with the Ito stochastic calculus convention, without spurious drift. They acknowledge the fact that this convention does not describe equilibrium systems, and that their results would not hold at equilibrium - and discard these facts by invoking the fact that cells are out-of-equilibrium. Finally, they show some applications of their findings, in particular enhanced clustering in the high-viscosity regions. The authors conclude that as inhomogeneous diffusion is ubiquitous in life, so must their mechanism be, and hence it must be important.

Strengths:<br /> The article is well-written, and clearly intelligible, its hypotheses are stated relatively clearly and the models and mathematical derivations are compatible with these hypotheses.

Weaknesses:<br /> The main problem of the paper is these hypotheses. Indeed, it all relies on the Ito interpretation of the stochastic integrals. Stochastic conventions are a notoriously tricky business, but they are both mathematically and physically well-understood and do not result in any "dilemma" [some citations in the article, such as (Lau and Lubensky) and (Volpe and Wehr), make an unambiguous resolution of these]. Conventions are not an intrinsic, fixed property of a system, but a choice of writing; however, whenever going from one to another, one must include a "spurious drift" that compensates for the effect of this change - a mathematical subtlety that is entirely omitted in the article: if the drift is zero in one convention, it will thus be non-zero in another in the presence of diffusive gradients. It is well established that for equilibrium systems obeying fluctuation-dissipation, the spurious drift vanishes in the anti-Ito stochastic convention (which is not "anticipatory", contrarily to claims in the article, are the "steps" are local and infinitesimal). This ensures that the diffusion gradients do not induce currents and probability gradients, and thus that the steady-state PDF is the Gibbs measure. This equilibrium case should be seen as the default: a thermal system NOT obeying this law should warrant a strong justification (for instance in the Volpe and Wehr review this can occur through memory effects in robotic dynamics, or through strong fluctuation-dissipation breakdown). In near-equilibrium thermal systems such as the intracellular medium (where, although out-of-equilibrium, temperature remains a relevant and mostly homogeneous quantity), deviations from this behavior must be physically justified and go to zero when going towards equilibrium.

Here, drifts are arbitrarily set to zero in the Ito convention (the exact opposite of the equilibrium anti-Ito), which is the equilibrium equivalent to adding a force (with drift $- grad D$) exactly compensating the spurious drift. If we were to interpret this as a breakdown of detailed balance with inhomogeneous temperature, the "hot" region would be effectively at 4x higher temperature than the cold region (i.e. 1200K) in Fig 1A.

It is the effects of this arbitrary force (exactly compensating the Ito spurious drift) that are studied in the article. The fact that it results in probability gradients is trivial once formulated this way (and in no way is this new - many of the references, for instance, Volpe and Wehr, mention this). Enhanced clustering is also a trivial effect of this probability gradient (the local concentration is increased by this force field, so phase separation can occur). As a side note the "neighbor sensing" scheme to describe interactions is very peculiar and not physically motivated - it violates stochastic thermodynamics laws too, as the detailed balance is apparently not respected. Finally, the "anomalous diffusion" discussion is at odds with what the literature on this subject considers anomalous (the exponent does not appear anomalous).

The authors make no further justification of their choice of convention than the fact that cells are out-of-equilibrium, leaving the feeling that this is a detail. They make mentions of systems (eg glycogen, prebiotic environment) for which (near-)equilibrium physics should mostly prevail, and of fluctuation-dissipation ("Diffusivity varies inversely with viscosity", in the introduction). Yet the "phenomenon" they discuss is entirely reliant on an undiscussed mechanism by which these assumptions would be completely violated (the citations they make for this - Gnesotto '18 and Phillips '12 - are simply discussions of the fact that cells are out-of-equilibrium, not on any consequences on the convention).

Finally, while inhomogeneous diffusion is ubiquitous, the strength of this effect in realistic conditions is not discussed (this would be a significant problem if the effect were real, which it isn't). Gravitational attraction is also an ubiquitous effect, but it is not important for intracellular compartmentalization.

To conclude, the "diffusive lensing" effect presented here is not a deep physical discovery, but a well-known effect of sticking to the wrong stochastic convention.

eLife assessment

This important study combines cellular assays and biophysical methods to identify heparan sulfate (HS) as a regulator of apoptosis. The multidisciplinary approach for investigating HS•TRAIL interactions is highly compelling, but the central hypothesis could be further strengthened from additional investigations on downstream effects. This paper is very relevant to cancer biologists and biophysicists working on TRAIL-dependent apoptotic pathways.

Reviewer #1 (Public Review):

Summary: Yin Luo and colleagues describe a new regulatory mode of TRAIL (Tumor necrosis factor (TNF)-related apoptosis-inducing ligand) -induced apoptosis of tumor cells. The work is timely because high expectations existed on the possibility to induce tumor-specific apoptosis by activation of TRAIL-receptors DR4 and DR5. So far, however, attempted TRAIL-based anti-tumor therapies failed, and several tumor types were found to resist TRAIL-induced apoptosis. The work is also important because it aims at a better mechanistic understanding of TRAIL-induced apoptosis and TRAIL resistance of some tumor types.

Strengths: The major novel finding of this study is that extracellular heparan sulfate (HS) acts as a positive regulator of TRAIL-induced tumor cell apoptosis, and that HS expression of different tumor cell lines correlates with their capacity to induce cell death. The authors first show by affinity chromatography and SPR that murine and human TRAIL bind strongly to heparin (heparin is a highly sulfated, and thus strongly negatively charged form of HS that is derived from connective tissue type mast cells), and identify three basic amino acids on the TRAIL N-terminus that are required for the interaction. Size exclusion chromatography (SEC) and multiangle light scattering (MALS) revealed that TRAIL exists as a trimer that requires a minimum heparin length of 8 sugar residues for binding, and small angle X-ray scattering (SAXS) showed that TRAIL interaction with longer oligosaccharides induced higher order multimerization of TRAIL. Consistent with these biochemical and biophysical analyses, HS on tumor cells contributes to TRAIL-binding to their cell surface and subsequent apoptosis. Minor novel findings include domain swapping observed by TRAIL trimer crystallization and different degrees of HS core protein and DR receptor expression in different tumor cell types. These findings are well supported and together with the advanced and established methodology used by the authors are the strengths of this paper. The paper will be of great interest to medical biologists studying TRAIL-resistance of tumors, to biologists interested in DR4 and DR5 receptor function and the effects of receptor internalization, and to glycobiologists aiming to understand the multiple important roles that HS plays in development and disease. The authors also raise the important point (and support it well) that routine heparin treatment of cancer patients potentially interferes with TRAIL-based therapies, providing one possible reason for their failure.

Weaknesses: Despite the importance and the clear strengths of the paper, some of its aspects could have been developed further to better support the authors claims and their hypothesis that HS downregulation may represent one strategy to explain tumor resistance to TRAIL-induced apoptosis.

1) The authors demonstrate that HS at the tumor surface promotes TRAIL binding, and that HS promotes TRAIL-induced breast cancer and myeloma cell apoptosis. These findings are based on pre-treatment with heparinase to remove cell-surface HS prior to TRAIL-treatment, or presence of soluble heparin to compete with cell-surface HS for TRAIL binding. A more direct way to establish such new HS function could have been genetic manipulation of cancer cells to overexpress HS (e.g. by Syndecan 1 core-protein transgene expression in resistant cell types, e.g. IM-9 cells) or to express less or undersulfated HS (by interfering with the biosynthetic pathway in apoptosis-prone cells, for example by targeted inactivation/RNAi of the HS co-polymerase or sulfotransferases in RPMI-8226 cells). Changed susceptibility of these cells to TRAIL-induced apoptosis would greatly support the authors claim and suggest one promising new strategy to decrease tumor resistance against TRAIL-induced apoptosis.

2) The mechanistics of TRAIL-induced, HS-modulated tumor cell apoptosis could be more clearly defined. For example, the authors demonstrate convincingly that cell surface HS is essential for TRAIL-induced apoptosis in MDA-MB-453 breast cancer cells, and later show that a tumor cell line (IM-9 cells) that expresses HS and the core protein to which HS is attached to only limited degrees is the most resistant to TRAIL-induced apoptosis. Yet, these findings suggest, but do not prove a dose-dependent role of HS in TRAIL-induced apoptosis. Indeed, the authors later report that cell surface HS promotes TRAIL-induced myeloma cell apoptosis regardless of the sensitivity levels, and that other factors - the degree of TRAIL multimerization or DR4/DR5 receptor internalization - are also important. A cartoon could be added to the final figure to sort through these possible mechanisms and their relative importance.

3) The authors show that RPMI-8226 cell-surface HS promotes DR5 internalization despite the absence of direct DR5/heparin interactions. This is important because, as the authors are certainly aware of, HS manipulation at the cell surface (for example by heparinase treatment) changes a plethora of signaling pathways that may also indirectly affect apoptosis. HS-dependent internalization of TRAIL-receptor complexes, however, provides an important direct link from HS expression to TRAIL-induced apoptosis. To further support this possible link, it would therefore be worthwhile to include the binding characteristics and HS-dependent internalization of DR4 into this study.

Reviewer #2 (Public Review):

Summary:<br /> In the manuscript by Luo et al, the authors investigated the nature and function of TRAIL-HS binding for the regulation of TRAIL-mediated apoptosis in cancer cells. The authors discovered that TRAIL binds to 12mer HS and identified the amino acid residues critical for the binding. The authors further nicely showed that 12mer HS binds to TRAIL homotrimer and larger HS can further promote the formation of larger TRAIL oligomers. Structural analyses were conducted to characterize the binding of TRAIL/HS complexes. At functional level, the authors demonstrated that HS promotes the cell surface binding of TRAIL to enhance TRAIL-mediated apoptosis in a variety of cancer cells. Moreover, the ability of TRAIL to induce apoptosis is correlated with cell surface HS level. Lastly, the authors showed that HS forms complex with TRAIL and its receptor DR5 and promotes DR5 internalization.

Strengths:<br /> Overall, this is a nicely executed study providing both mechanistic and functional insight for TRAIL-mediated apoptosis. It conducted detailed characterization on the direct binding between HS and TRAIL and provided solid evidence supporting the role of such interaction for the regulation of TRAIL-induced apoptosis. The experiments were well-designed with proper controls included. The data interpretation is accurate. The manuscript was clearly written and easy to follow by general readers.

Weaknesses:<br /> There is no major weakness identified from this study. However, the role of HS for the formation of TRAIL homotrimer needs to be further clarified. In addition, the current relationship between cell surface HS level and sensitivity to TRAIL-mediated apoptosis is still correlative, as the authors indicated. Additional evidence to support the regulatory function of HS would further strengthen the significance of the study.

eLife assessment

This valuable work by Pulfer et al. advances our understanding of spatial-temporal cell dynamics both in vivo and in vitro. The authors provide convincing evidence for their innovative deep learning-based apoptosis detection system, ADeS, that utilizes the principle of activity recognition. Nevertheless, the work is incomplete due to the authors' claim that their system is valid for non-fluorescently labeled cells, without evidence supporting this notion. After revisions, this work will be of broad interest to cell biologists and neuroscientists.

Reviewer #1 (Public Review):

Summary:<br /> Pulfer et al., describe the development and testing of a transformer-based deep learning architecture called ADeS, which the authors use to identify apoptotic events in cultured cells and live animals. The classifier is trained on large datasets and provides robust classification accuracies in test sets that are comparable to and even outperform existing deep learning architectures for apoptosis detection. Following this validation, the authors also design use cases for their technique both in vitro and in vivo, demonstrating the value of ADeS to the apoptosis research space.

Strengths:<br /> ADeS is a powerful tool in the arsenal of cell biologists interested in the spatio-temporal co-ordinates of apoptotic events in vitro, since live cell imaging typically generates densely packed fields of view that are challenging to parse by manual inspection. The authors also integrate ADeS into the analysis of data generated using different types of fluorescent markers in a variety of cell types and imaging modalities, which increases its adaptability by a larger number of researchers. ADeS is an example of the successful deployment of activity recognition (AR) in the automated bioimage analysis space, highlighting the potential benefits of AR to quantifying other intra- and intercellular processes observable using live cell imaging.

Weaknesses:<br /> A major drawback was the lack of access to the ADeS platform for the reviewers; the authors state that the code is available in the code availability section, which is missing from the current version of the manuscript. This prevented an evaluation of the usability of ADeS as a resource for other researchers. The authors also emphasize the need for label-free apoptotic cell detection in both their abstract and their introduction but have not demonstrated the performance of ADeS in a true label-free environment where the cells do not express any fluorescent markers. While Pulfer et al., provide a wealth of information about the generation and validation of their DL classifier for in vitro movies, and the utility of ADeS is obvious in identifying apoptotic events among FOVs containing ~1700 cells, the evidence is not as strong for in vivo use cases. They mention the technical challenges involved in identifying apoptotic events in vivo, and use 3D rotation to generate a larger dataset from their original acquisitions. However, it is not clear how this strategy would provide a suitable training dataset for understanding the duration of apoptotic events in vivo since the temporal information remains the same. The authors also provide examples of in vivo acquisitions in their paper, where the cell density appears to be quite low, questioning the need for automated apoptotic detection in those situations. In the use cases for in vivo apoptotic detection using ADeS (Fig 8), it appears that the location of the apoptotic event itself was obvious and did not need ADeS, as in the case of laser ablation in the spleen and the sparse distribution of GFP labeled neutrophils in the lymph nodes. Finally, the authors also mention that video quality altered the sensitivity of ADeS in vivo (Fig 6L) but fail to provide an example of ADeS implementation on a video of poor quality, which would be useful for end users to assess whether to adopt ADeS for their own live cell movies.